-- Data for Name: aoi_link_datalist; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.aoi_link_datalist (aoi_aoi_id, link_datalist_id) FROM stdin;
\.


--
-- Data for Name: application_dimension; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.application_dimension (meta_application_id, dimension) FROM stdin;
1439	二维
1440	二维
1442	二维
1462	二维
2252	二维
2253	二维
2269	二维
2082	二维
2083	二维
2084	二维
2085	二维
2089	二维
2090	二维
2091	二维
2092	二维
2093	二维
2095	三维
2129	二维
2130	二维
2131	二维
2132	二维
2133	二维
2134	二维
2135	二维
2136	二维
2137	二维
2138	二维
2139	二维
2140	二维
2141	二维
2142	二维
2143	二维
2144	二维
2145	二维
2146	二维
2147	二维
2150	二维
2151	二维
2152	二维
2155	二维
2156	二维
2159	二维
2160	二维
2161	三维
2162	二维
2166	三维
2167	二维
2168	三维
2169	二维
2170	二维
2171	二维
2172	二维
2173	二维
2174	三维
2175	二维
2176	二维
2177	二维
2178	三维
2179	三维
2181	二维
2182	二维
2183	二维
2184	二维
2185	二维
2187	二维
2188	二维
2189	二维
2193	二维
2194	二维
2195	二维
2196	二维
2197	二维
2198	二维
2199	二维
2200	三维
2201	二维
2203	二维
2204	二维
2205	二维
2206	二维
2207	二维
2208	二维
2209	二维
2210	二维
2211	二维
2213	二维
2214	三维
2216	二维
2217	二维
2218	二维
2219	二维
2220	二维
2222	二维
2227	二维
2229	二维
2230	二维
2231	二维
2234	二维
2248	二维
2250	二维
2251	二维
2282	二维
2283	二维
2284	二维
2285	二维
2286	二维
2287	二维
2294	多维
2295	多维
2296	多维
2301	二维
2302	二维
2304	二维
2492	二维
2493	二维
2501	二维
2503	二维
2504	二维
2505	二维
2506	二维
2508	二维
2509	二维
2510	二维
\.


--
-- Data for Name: application_spatial_reference; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.application_spatial_reference (meta_application_id, spatial_reference) FROM stdin;
2216	投影坐标系
2217	投影坐标系
\.


--
-- Data for Name: application_spatial_resolution; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.application_spatial_resolution (meta_application_id, spatial_resolution) FROM stdin;
\.


--
-- Data for Name: application_timestep; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.application_timestep (meta_application_id, timestep) FROM stdin;
\.


--
-- Data for Name: basic_log; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.basic_log (basic_log_id) FROM stdin;
\.


--
-- Data for Name: case_reasoning_log; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.case_reasoning_log (case_reasoning_log_id) FROM stdin;
\.


--
-- Data for Name: category_data; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.category_data (meta_identification_id, category_data) FROM stdin;
1360	csv数据
1439	路网数据
1439	居民区数据
1439	设施点数据
1440	居民区数据
1440	设施点数据
1440	重力函数
1442	路网数据
1442	居民区数据
1442	设施点数据
1462	矢量点
1462	矢量面
1543	csv数据
1544	结构化事务数据
2252	矢量点
2252	矢量面
2253	矢量点
2253	矢量面
2254	图像数据
2255	图像数据
2256	遥感影像数据
2256	图像数据
2257	遥感影像数据
2257	图像数据
2258	遥感影像数据
2258	静态图像数据
2259	遥感影像数据
2259	图像数据
2260	遥感影像数据
2260	图像数据
2261	图像数据
2262	图像数据
2263	遥感影像数据
2263	图像数据
2264	图像数据
2265	图像数据
2266	图像数据
2266	遥感影像数据
2267	遥感影像数据
2267	图像数据
2268	结构化事务数据
2269	结构化事务数据
2270	结构化事务数据
2271	遥感影像数据
2271	图像数据
2272	结构化事务数据
2273	遥感影像数据
2273	图像数据
2274	结构化事务数据
2275	遥感影像数据
2275	图像数据
2276	结构化事务数据
2277	结构化事务数据
2278	数据分类
2279	结构化事务数据
2280	结构化事务数据
2281	遥感影像数据
2281	图像数据
2082	栅格数据
2083	栅格数据
2083	矢量数据
2084	矢量线
2084	矢量面
2085	矢量面
2086	矢量数据
2087	矢量数据
2088	栅格数据
2089	面数据分析
2090	矢量点
2090	矢量线
2090	矢量面
2091	矢量点
2091	矢量线
2091	矢量面
2092	矢量点
2092	矢量线
2092	矢量面
2093	点数据分析
2095	时空要素数据分析
2126	流数据分析
2127	流数据分析
2128	栅格数据
2129	矢量点数据
2130	点数据分析
2131	矢量点数据
2132	矢量点数据
2133	矢量点数据
2134	矢量点数据
2135	点数据分析
2136	矢量点数据
2137	矢量点数据
2138	点数据分析
2139	矢量点数据
2140	点数据分析
2141	点数据分析
2142	点数据分析
2143	移动轨迹数据聚类
2144	轨迹数据聚类
2145	线数据分析
2146	点数据分析
2147	面数据分析
2148	流数据分析
2149	流数据分析
2150	矢量点
2150	矢量面
2151	矢量点
2151	矢量面
2152	矢量点
2153	栅格数据
2154	栅格数据
2155	矢量点
2155	矢量面
2156	矢量点
2157	栅格数据
2158	地理空间数据
2159	路网数据
2159	居民区数据
2159	设施点数据
2160	道路网络数据
2160	居民区数据
2160	设施点数据
2161	时空数据
2162	路网数据
2162	居民区数据
2162	设施点数据
2163	表格数据
2164	表格数据
2165	表格数据
2166	矢量线数据
2167	矢量线数据
2168	矢量线数据
2169	矢量线数据
2170	矢量线数据
2171	矢量线数据
2172	矢量线数据
2173	矢量线数据
2174	矢量线数据
2175	矢量线数据
2176	流数据
2177	流数据
2178	矢量线数据
2179	矢量线数据
2180	结构化事务数据
2181	随机算法
2182	矢量点
2182	矢量面
2183	矢量点
2183	矢量面
2184	矢量点
2184	矢量面
2185	矢量点
2185	矢量面
2186	时序数据
2187	矢量点
2187	矢量面
2188	矢量点
2188	矢量面
2189	矢量点
2189	矢量面
2193	矢量点
2193	矢量线
2193	矢量面
2194	矢量点
2194	矢量线
2194	矢量面
2195	矢量点
2195	矢量线
2195	矢量面
2196	空间-专题属性数据分析
2197	轨迹数据分析
2198	面数据分析
2198	线数据分析
2199	点数据分析
2199	线数据分析
2200	点数据分析
2201	点数据分析
2202	流数据分析
2203	轨迹数据分析
2204	轨迹数据分析
2205	点数据分析
2206	二元空间点过程异常探测
2207	点数据分析
2208	矢量点数据
2209	点数据分析
2210	面数据分析
2211	点数据分析
2212	结构化事务数据
2213	点数据分析
2214	时空同现模式挖掘
2215	时空事件数据分析
2216	矢量点数据
2217	专题属性分析
2218	矢量点数据
2219	点数据分析
2220	矢量点
2220	矢量面
2221	表格数据
2222	点数据分析
2223	流数据分析
2224	流数据分析
2225	流数据分析
2226	表格数据
2227	矢量点
2227	矢量面
2229	矢量点
2229	矢量面
2230	矢量面
2231	矢量面
2232	时序数据
2234	DEM数据
2235	栅格数据
2240	DEM数据
2242	栅格数据
2243	DEM数据
2244	DEM数据
2245	DEM数据
2246	DEM数据
2247	DEM数据
2248	DEM数据
2249	栅格数据
2250	DEM
2251	DEM
2282	点数据
2283	矢量点
2283	矢量面
2284	矢量点
2284	矢量面
2285	矢量点
2285	矢量面
2286	矢量点
2286	矢量面
2287	矢量点
2287	矢量面
2294	矢量点数据
2295	矢量点数据
2296	矢量点数据
2298	点数据分析
2301	点数据分析
2302	点数据分析
2304	矢量点
2304	矢量面
2306	栅格数据
2311	点数据分析
2491	栅格DEM数据
2492	DEM数据
2493	DEM数据
2497	栅格数据
2498	栅格数据
2501	矢量数据
2503	矢量数据
2504	矢量数据
2505	矢量数据
2506	DEM数据
2508	矢量点
2508	矢量面
2509	矢量点
2509	矢量面
2510	点数据分析
\.


--
-- Data for Name: category_functional; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.category_functional (id, description, language, name, parent_category_id) FROM stdin;
modelbank	root	cn	root	\N
basic	通用基础分析主要关注地理空间对象的特征量算、时空检索、相互关系等基础分析功能，为深层次分析算法、模型及解决实际问题提供支撑。	cn	通用基础分析	modelbank
classification	时空分类是通过构建或提取分类规则，将给定地理空间对象映射到某个给定类别的模型算法。	cn	时空分类	modelbank
clustering	时空聚类是将特征相近的地理空间对象划分到不同的组的模型算法，最终分组结果使组间的差别尽可能大，组内的差别尽可能小。	cn	时空聚类	modelbank
interpolation	时空插值是通过离散的时空属性数据推测未知时间和（或）未知空间位置处属性数据的模型算法。	cn	时空插值	modelbank
network	网络分析是根据地理空间对象构建拓扑网络，以数学理论模型为基础，基于网络元素（结点、弧段）或网络关系（例如结点与弧段拓扑关系、弧段间的连通性），对网络元素规律、网络特征进行多方面的分析计算技术。	cn	网络分析	modelbank
process_simulation	过程模拟是针对应用问题及其应用场景，利用模型定量化表达地理过程、随机过程及智能优化过程的地理空间分析建模计算过程。根据功能细分为地理过程模型、随机和不确定性模拟以及智能优化等3个子类。	cn	过程模拟	modelbank
regression	时空回归分析是量化多元时空变量关系的统计分析算法，被广泛用于关系解释、归因分析和变量预测等。从时空变量关系的异质性或稳定性特征的角度，时空回归分析可细分为全局回归分析和局部回归分析。	cn	时空回归	modelbank
sampling	时空抽样是在时空和（或）空间维度上对地理空间对象（对象群体）进行抽样的方法。	cn	时空抽样	modelbank
statistics	时空统计特征描述是分析地理空间对象和对象间的统计量和统计关系的模型算法。根据功能细分为描述性统计、时空自相关、空间异质性、降维分析、关联关系挖掘和异常探测等6个子类。	cn	时空统计特征描述	modelbank
terrain	地形分析是利用数字高程模型定量计算地形信息的模型算法，地形信息主要包括关注于定量表达地形某种连续场信息的地形属性类信息（如坡度、坡向、地形湿度指数等）和关注于地形特征对象分类与识别的地形特征类信息（如河网、子流域等）。根据功能细分为地形因子提取、水文分析和视域分析等3个子类。	cn	地形分析	modelbank
feature_quantification	特征量算是计算地理空间对象几何特征和属性特征的算法。	cn	特征量算	basic
neighbor_analysis	邻域分析是以某一个地理空间对象为中心，根据规则向周围空间扩展一定的范围，以利用扩展范围内的对象进行复合运算的分析算法。	cn	邻域分析	basic
overlay	叠置分析将多个（层）对象进行交、并、补等叠置分析产生一个新的对象。	cn	叠置分析	basic
st_relation_calculation	时空关系计算是确定地理空间对象间在时空等维度上相互关系的算法。	cn	时空关系计算	basic
deep_learning	深度学习类时空分类模型算法是基于大型复杂的人工神经网络以解决分类问题的模型算法，这类模型算法通过组合低层特征形成更加抽象的高层特征，以深度挖掘同类别数据的分布式特征，并将其作为对地理空间对象进行分类的依据。根据模型算法具体实现功能的不同，涵盖地理空间的对象提取、场景分类、目标检测、语义分割等应用。	cn	深度学习	classification
ensemble_learning	集成学习是多分类器机器学习算法，通过构建并结合多个学习器来完成学习地理空间对象规律任务。	cn	集成学习	classification
machine_learning	机器学习类时空分类模型算法依托经典机器学习方法在概率论知识、统计学知识、近似理论知识和复杂算法知识的既有模型算法，从已知类别的地理空间对象中提取分类规则，实现对未知类别的地理空间对象进行类别的判别和映射。	cn	机器学习	classification
Bayesian_infrence	贝叶斯推断是一种利用概率统计知识进行分类的算法，算法以贝叶斯定理为基础，根据训练样本信息，将待分类项归类为求解在此项出现的条件下最大概率的类别。	cn	贝叶斯推断	machine_learning
geoflow_clustering	对地理流数据进行聚类的模型算法。	cn	地理流聚类	clustering
spatial_clustering	空间点过程聚类是利用空间点过程抽象地理空间对象，进而基于划分、密度、图论等视角进行地理空间对象无监督启发式聚合与区分的模型算法。	cn	空间点过程聚类	clustering
spatial_semantic_clustering	顾及专题属性的空间聚类是一类同时顾及地理空间对象专题属性相似性与空间位置邻近性的无监督启发式聚合与区分模型算法。	cn	顾及专题属性的空间聚类	clustering
st_clustering	时空点过程聚类是利用时空点过程抽象地理时空对象，进而基于时空分治或时空耦合等方式，进行地理时空对象无监督启发式聚合与区分的模型算法。	cn	时空点过程聚类	clustering
st_semantic_clustering	顾及专题属性的时空聚类是一类同时顾及地理时空对象专题属性相似性与时空坐标邻近性的无监督启发式聚合与区分模型算法。	cn	顾及专题属性的时空聚类	clustering
accessibility_analysis	通达性分析是计算给定网络结构下（如交通网络）某一地理空间对象位置到另一个位置便利程度的模型算法。	cn	通达性分析	network
community_detection	社区探测是一种网络聚类的方法。一个社区由一组连接紧密的结点组成，同时这些结点与社区外部的结点连接稀疏。而社区探测就是指在复杂网络中通过聚类算法得到这些连接紧密的社区结构。	cn	社区探测	network
network_buffer	网络缓冲区构建是计算地理空间对象在网络元素（如弧段、节点）空间内的影响或服务范围，以解决空间邻近度问题的模型算法。	cn	网络缓冲区构建	network
network_voronoi	网络Voronoi图构建是在网络中基于网络元素（如网络节点）以Voronoi多边形的结构划分地理空间的模型算法。	cn	网络Voronoi图构建	network
path_planning	路径规划是计算网络中最短路径的模型算法，最短路径可衍生为距离最短、费用最小或时间最短等一系列空间度量问题。	cn	路径规划	network
spatial_interaction_network_analysis	空间交互网络是人、商品和信息等在地点之间流动而形成的嵌入在空间中的有向流网络。空间交互网络分析是理解和预测空间交互网络中的流量分布模式的模型算法，不仅是区域科学、交通科学、经济地理学等很多领域长期以来的一个重要研究主题，在城市和交通规划、疾病传播防控、商业服务等领域也具有广泛应用价值。	cn	空间交互网络分析	network
st_prism	网络时空棱柱构造是计算在给定起止时空点下个体可达时空范围的模型算法。单个网络时空棱柱由一组起止时空锚点以及位于各个网络边的时空多边形组成。每个时空多边形表示个体的潜在活动网络边及在该网络边上活动的时间特征。	cn	网络时空棱柱构造	network
geographic_process_simulation	地理过程模型是定量化表达地理过程、现象及规律并解决实际应用问题的算法组合。	cn	地理过程模拟	process_simulation
heuristic_optimization	智能优化是针对地学应用中出现的优化问题，基于生物进化等自然现象规律进行模拟以搜索最优解的一类模型算法。	cn	智能优化	process_simulation
stochastic_uncertainty_simulation	随机和不确定性模拟是利用计算机模拟随机现象的一类模型算法。该类方法基于随机过程及其产生的不确定性，对地理现象或应用问题进行分析、理解，并得到解决方案。	cn	随机和不确定性模拟	process_simulation
global_regression	全局空间回归分析技术是指在不考虑空间位置特征的前提下对多元空间变量关系的定量分析技术，以实现全局视角下的关系解释、归因推理和变量预测等功能。	cn	全局空间回归	regression
local_regression	局部回归分析技术是指在考虑空间位置特征所蕴含的空间依赖性特征前提下的变量关系定量分析技术，以实现空间局部视角下的关系解释、归因推理和变量预测等功能。	cn	局部回归分析	regression
anomaly_detect	异常探测是识别偏离一般观测值（关系）或预期值（关系）的地理空间对象的模型算法。	cn	异常探测	statistics
association_relation_mining	关联关系挖掘是从海量复杂数据中发现多类地理事件间隐藏的前后发生、邻近出现、共同出现等带有时空关系约束的关联规则的模型算法。	cn	关联关系挖掘	statistics
descriptive_statistics	描述性统计是用统计方法描述和总结地理空间对象及其属性的基本情况的模型算法。利用描述性统计提供的统计值或图表，可以有效地提取大量数据中的代表性特征，快速表达地理空间对象的关键信息。	cn	描述性统计	statistics
dimension_reduction_analysis	降维分析是从包含大量冗余或无关变量（或称特征、属性、指标等）的高维数据中，找出数据特征和主要变量，以低维数据概括表达高维数据中的主要信息的模型算法。	cn	降维分析	statistics
geoflow_analysis	对地理流数据进行分析与预测的模型算法。	cn	地理流分析	statistics
spatial_heterogeneity	空间异质性（Spatial Heterogeneity）或空间非平稳性（Spatial Non-stationarity）是指地理现象、过程或规律在不同空间位置或区域内所呈现的不均匀性和复杂性。空间异质性描述统计是利用空间计量算法对空间异质性特征进行描述性统计，以初步理解空间数据及地理现象的算法。	cn	空间异质性	statistics
st_autocorrelation	时空自相关分析是在研究范围内定量分析地理时空对象或时空变量数据分布的相互依赖性关系或值域分布聚集性特征的模型算法。	cn	时空自相关	statistics
hydrology_analysis	水文分析是基于流域的地表形态量化其水文特征的一类模型算法，如河网提取、子流域划分等。	cn	水文分析	terrain
topographic_attribute	地形因子提取是用于提取地形属性的一类模型算法。	cn	地形因子提取	terrain
viewshed_analysis	视域分析是用于分析地形形态的可视性特征的一类模型算法，对设置雷达站、布置哨所、铺架通信线路等意义重大，主要包括视域范围分析、可视性指数计算等。	cn	视域分析	terrain
\.


--
-- Data for Name: common_log; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.common_log (common_log_id, log) FROM stdin;
\.


--
-- Data for Name: data_semantic; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.data_semantic (id, semantic) FROM stdin;
\.


--
-- Data for Name: date_category; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.date_category (data_category_id, data_path, data_semantic) FROM stdin;
faf1a4ece6e34be1b6d25c7508fc361c	/onesis/kt4/dem/slope_test.tif	slope
7c25bc46d4bc49309def5c84819b30e7	/onesis/kt4/dsm_case/xuancheng/dem_xc_900913.tif	dem
c3b869b2f0694310b625f41bf0865555	/onesis/kt4/dsm_case/heshan/dem_heshan_900913.tif	dem
35225fb6a7e54ea78919684f7476cc56	/onesis/kt/test_data/terr/dem.tif	dem
59fa81ce1a1e466bb711bfcc3bf1fe79	/onesis/kt4/basic/test_data/zw_kt5/input_big.shp	area_data
777f685f141f45098b9b5970b3c1bc0f	/onesis/kt4/application_modelling/priolo/priolo_00-15_landuse_1to2.tif	land development label
702a8781a7dd4b039413533228c4dc1b	/onesis/kt4/application_modelling/priolo/dem_priolo.tif	dem
dfd1d1eaa45b4a24979608dd2b3357ad	/onesis/kt4/application_modelling/lp/input_big.shp	area_data
18a133bb2cf5420a9c1dee6014466f60	/onesis/kt4/application_modelling/lp/grade.csv	phenology_config
ea8ec4c7da5647dbbe33e8c33aeccc88	/onesis/kt4/application_modelling/lp/zxd_config.csv	crop_confid_config
5599b67175b041bf8445ed1613e967c5	/onesis/kt4/application_modelling/lp/crop_data_n.csv	crop_data
da43b65b160c43efa907b3fc66c1f460	/onesis/kt4/application_modelling/lp/config.csv	config
d92bbb611d924dbb99160ac5ba7d4124	/onesis/kt4/application_modelling/priolo/road_Priolo.shp	road shp
\.


--
-- Data for Name: date_category_link_datalist; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.date_category_link_datalist (date_category_data_category_id, link_datalist_id) FROM stdin;
\.


--
-- Data for Name: dependency; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.dependency (id, name, version) FROM stdin;
21890	argparse4j	0.9.0
21897	argparse4j	0.9.0
21917	argparse4j	0.9.0
22095	gstat	2.0-6
23054	numpy	1.25.2
33860	MASS	\N
33877	GWmodel	2.2-9
33894	python	3.8
33895	numpy	1.24.4
33896	pillow	10.1.0
33897	setuptools	68.2.2
33898	cython	0.29.21
33899	six	1.16.0
33900	wheel	0.41.2
33901	typing_extensions	4.8.0
33902	cuda	11.0
33903	torch	1.7.0+cu110
33904	torchvision	0.8.0
33958	python	3.8
33959	pytorch	1.12.1
33960	torchvision	0.13.1
33961	numpy	1.18.5
33962	opencv-contrib-python	4.1.2.30
33963	pillow	7.2.0
33964	tqdm	4.47.0
33965	cuda	11.6
33966	cudnn	8
33988	python	3.8
33989	numpy	1.24.4
33990	opencv-contrib-python	4.9.0.80
33991	tqdm	4.66.1
33992	cuda	11.7
33993	cudnn	8
34004	python	3.8
34005	numpy	1.24.4
34006	opencv-contrib-python	4.9.0.80
34007	tqdm	4.66.1
34008	cuda	11.7
34009	cudnn	8
34031	python	3.8
34032	torch	latest
34033	torchvision	latest
34034	tensorboard	latest
34035	scipy	1.2.1
34036	numpy	1.17.0
34037	matplotlib	3.1.2
34038	opencv-python	4.1.2.30
34039	tqdm	4.60.0
34040	pillow	8.2.0
34041	h5py	2.10.0
34042	cuda	11.0
34043	cudnn	8
34097	python	3.8
34098	numpy	1.18.5
34099	opencv-contrib-python	4.1.2.30
34100	pillow	7.2.0
34101	tqdm	4.47.0
34102	cuda	10.2
34103	cudnn	8
34130	python	3.8
34131	numpy	1.18.5
34132	opencv-contrib-python	4.1.2.30
34133	pillow	7.2.0
34134	tqdm	4.47.0
34135	cuda	11.6.2
34136	cudnn	8
34144	python	3.8
34145	numpy	1.24.4
34146	opencv-python	4.8.1.78
34147	requests	2.31.0
34148	scikit-image	0.21.0
34149	cuda	11.0
34150	cudnn	8
34184	python	3.8
34185	numpy	1.18.5
34186	opencv-contrib-python	4.1.2.30
34187	pillow	7.2.0
34188	tqdm	4.47.0
34189	cuda	10.2
34190	cudnn	8
34214	python	3.8
34215	matplotlib	3.7.2
34216	pillow	10.1.0
34217	torch	2.1.0
34218	torchvision	0.16.0
34219	tqdm	4.66.1
34229	python	3.8
34230	numpy	1.24.4
34231	opencv-python	4.8.1.78
34232	requests	2.31.0
34233	scikit-image	0.21.0
34234	cuda	11.0
34235	cudnn	8
34279	python	3.7
34280	numpy	1.21.6
34281	opencv-python	4.9.0.80
34282	tqdm	4.66.2
34283	yacs	0.1.6
34284	torch	1.5.0+cu101
34285	torchvision	0.6.0+cu101
34321	python	3.8
34322	numpy	1.18.5
34323	opencv-contrib-python	4.1.2.30
34324	pillow	7.2.0
34325	tqdm	4.47.0
34326	torch	1.6.0+cpu
34327	torchvision	0.7.0+cpu
34343	scikit-learn	1.0.2
34344	pandas	1.1.3
34345	numpy	1.19.2
34363	scikit-learn	1.0.2
34364	pandas	1.1.3
34365	numpy	1.19.2
34403	scikit-learn	0.24.0
34404	pandas	1.1.5
34405	numpy	1.19.5
34425	SAGA-GIS	8.5.0
34449	scikit-learn	1.0.2
34450	pandas	1.1.3
34451	numpy	1.19.2
34472	SAGA-GIS	8.5.0
34494	scikit-learn	1.0.2
34495	pandas	1.1.3
34496	numpy	1.19.2
34540	SAGA-GIS	8.1.0
34563	scikit-learn	1.0.2
34564	pandas	1.1.3
34565	numpy	1.19.2
34579	scikit-learn	0.24.0
34580	pandas	1.1.3
34581	numpy	1.19.2
34593	scikit-learn	0.24.0
34594	pandas	1.1.5
34595	numpy	1.19.5
34603	scikit-learn	0.24.0
34604	pandas	1.1.5
34605	numpy	1.19.5
34615	scikit-learn	0.24.0
34616	pandas	1.1.5
34617	numpy	1.19.5
34628	SAGA-GIS	8.5.0
31430	\N	\N
31440	\N	\N
31446	\N	\N
31473	geopandas	0.9.0
31500	SAGA-GIS	8.1.0
31522	\N	\N
31532	\N	\N
31542	\N	\N
32428	SAGA-GIS	8.1.0
32465	scikit-learn	0.24.0
32484	numpy	1.21.2
32485	pandas	1.3.3
32486	matplotlib	3.4.3
32487	scipy	1.7.1
32512	scikit-learn	0.24.0
32541	MiniSom	2.3.0
32576	scikit-learn	0.24.0
32593	scikit-learn	0.24.0
32642	scikit-learn	0.24.0
32765	GWmodel	2.2-9
32786	GWmodel	2.2-9
32802	\N	\N
32825	GDAL	3.16
32834	python	3.11.8
32835	GDAL	3.8.4
32843	terra	1.7-71
32856	gstat	2.1-1
32872	python	3.11.8
32873	GDAL	3.8.4
32916	SAGA-GIS	8.1.0
32923	argparse4j	0.9.0
32930	argparse4j	0.9.0
32947	argparse4j	0.9.0
32956	argparse4j	0.9.0
32962	igraph	0.10.8
32968	networkx	2.5
32973	igraph	0.10.8
32976	argparse4j	0.9.0
32983	argparse4j	0.9.0
32986	argparse4j	0.9.0
32992	argparse4j	0.9.0
32998	argparse4j	0.9.0
33008	argparse4j	0.9.0
33014	argparse4j	0.9.0
33020	argparse4j	0.9.0
33022	argparse4j	0.9.0
33029	argparse4j	0.9.0
33056	GWmodel	2.2-9
33067	GWmodel	2.2-9
33074	argparse4j	0.9.0
33077	argparse4j	0.9.0
33081	numpy	1.25.2
33085	Geopandas	0.9.0
33090	gstat	2.0-6
33097	GWmodel	2.2-9
33104	GWmodel	2.2-9
33111	GWmodel	2.2-9
33119	prophet	1.0
33141	GWmodel	2.2-9
33162	GWmodel	2.3-2
33183	GWmodel	2.2-9
33240	sp	1.6-0
33249	sp	1.6-0
33259	sandwichr	3.1-0
33364	pandas	1.3.5
33389	pandas	1.3.5
33529	osgeo	3.5.1
33530	geopy	2.2.0
33531	pyproj	3.3.1
33564	GWmodel	2.2-9
33574	scikit-learn	0.24.0
33588	scikit-learn	1.0.2
33589	pandas	1.1.3
33590	numpy	1.19.2
33620	geodetector	1.0-4
33630	GWmodel	2.2-9
33652	GWmodel	2.2-9
33664	spdep	1.2-8
33675	spdep	1.2-8
33774	SAGA-GIS	8.1.0
33841	\N	\N
33852	\N	\N
34635	sf	1.0-15
34658	GWmodel	2.2-9
34681	GWmodel	2.2-9
34701	GWmodel	2.2-9
34873	pandas	1.3.3
34874	numpy	1.21.2
34875	scikit-learn	0.24.2
34899	pandas	1.3.3
34900	numpy	1.21.2
34901	scikit-learn	0.24.2
34913	pandas	1.3.3
34914	numpy	1.21.2
34915	scikit-learn	0.24.2
34992	scikit-learn	1.0.2
34993	pandas	1.1.3
34994	numpy	1.19.2
35087	fiona	1.10.1
35088	numpy	1.24.3
35089	rasterio	1.3.10
35090	scipy	1.10.1
35095	fiona	1.10.1
35096	numpy	1.24.3
35097	rasterio	1.3.10
35098	scipy	1.10.1
35155	GWmodel	2.2-9
35360	scikit-learn	1.0.2
35361	pandas	1.1.3
35362	numpy	1.19.2
39170	GWmodel	2.4-2
39220	GWmodel	2.4-2
39281	fiona	1.10.1
39282	numpy	1.24.3
39283	rasterio	1.3.10
39284	scipy	1.10.1
\.


--
-- Data for Name: exec_example; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.exec_example (meta_operation_id, exec_example) FROM stdin;
1360	docker run -u 0 $(pwd)/data:/geoflow/data crazy-zlj:spark_geoflow:1.0 flow-io /data/od_flows.csv /data/gravity_res  0,1,2,3 2
1439	docker run -v $(pwd):/data CumAccess:1.0 --szNetwork/BaseStation/szHospitals.csv/Catchment
1440	docker run -v $(pwd):/data GravityAccess:1.0 --BaseStation/szHospitals.csv/beta
1442	docker run -v $(pwd):/data TwoStepsFloating:1.0 --szNetwork/BaseStation/szHospitals.csv/Catchment/beta
1462	docker run -v $PWD/data:/data sgs:1.0 /data/src/sgs.r -f /data/data/original_data.shp -n 100 -g /data/config/variogram.json -r simulated_result.shp
1543	docker run -u 0 crazy-zlj:spark_geoflow:1.0 flow-gravity /opt/spark/data/od_flows.csv /opt/spark/data/gravity_res /opt/spark/data/district_pop_wuhan.csv 1 0,1 0,1,2,3
1544	docker run -v $(pwd):/data naga-ii:1.0 -ipd /data/input.csv -mag 100  -opd /data/naga_result.csv
2252	docker run -v $PWD/discriminant_analysis_R:/ gwmodel:2.3 /scripts/discriminant.r -f /data/da-example.shp -d y -i x1,x2,x3,x4 -p /data/da-example.shp -r result.shp -h result.txt
2253	docker run -v $PWD/data:/data gwmodel:2.2 /data/scripts/gwda.r -f /data/data/gwda-example.shp -d y -i x1,x2,x3,x4 -t true -k "bisquare" -p /data/data/gwda-example.shp -r result.shp -h result.txt
2254	python resnet.py --data-dir data/flower_data --save-dir data/ckpt --image data/image/flower.jpg --img_out data/result --epochs 5 --save-every 2 --batch-size 64
2255	python DeepLabV3PLUS.py --mode train --root_path /data/deeplabv3+/data_sub/ --output_dir /data/deeplabv3+/run --epochs 5
2255	python DeepLabV3PLUS.py --mode inference --data_path /data/deeplabv3+/data_sub/val/image --save_path /data/result --model /data/deeplabv3+/run/own/checkname/model_best.pth.tar
2256	docker run densennet:1.0 -v $(pwd):/data --data_dir ./data --layers 99 --growth 12 -b 64 --reduce 1.0 --epochs 5 --result_dir ./results
2257	docker run d_linknet:1.0 -v $(pwd):/data --data_dir ./dataset/train --test_data_dir ./dataset/test --eval_data_dir ./dataset/eval --weights_path ./weights --log_path ./logs --batch_size 2 --epochs 2 --result_dir ./results
2258	python frcnn.py --mode train --data_path ./data/dataset/VOCdevkit --save_dir data/logs --unfreeze_epoch 20
2258	python frcnn.py --mode pre --model_path data/logs/best_epoch_weights.pth --img data/img/
2259	docker run --gpus all -v $(pwd):/data maskrcnn:1.0.0-gpu --data_path ./COCO2017 --img_path test.jpg --output_dir ./save_weights --epochs 3 --lr 0.004 --batch_size 1
2260	docker run -v $(pwd):/data multiple_layer_perceptron:1.0 -dr data -ne 20 -a accuracy.txt -m src -pd data/predict -rd result
2261	docker run -v $(pwd):/data yolo:1.0 --mode detect --weight src/yolov7.pt --source /data/inference/images --project /data/inference/result
2262	docker run -v $(pwd):/data retinanet_gpu:1.0 -d coco -cp ./data/coco -depth 18 -e 100 -r ./result
2263	docker run -v $(pwd):/data unet_cpu:1.0.5-gpu -max_steps /2000 [-i <png>] [-l <csv>] [-t <png>] [-r <csv>]
2264	docker run -it vgg_cpu:1.0.5-cpu /bin/bash
2265	docker run -v $(pwd):/data retinanet_gpu:1.0 -cv ./data/val_annots.csv  -cc ./data/class_list.csv -ct ./data/train_annots.csv  -depth 18 -e 100 -r ./result
2266	docker run --gpu all  -v $(pwd):/data ssd_gpu:1.0 --dataset_root E:\\datasets --train_dataset "coco_2014_train" --test_dataset "coco_2014_minival" --output_dir outputs/cocoout   --local_rank 0 --num_classes 81
2267	docker run -v $(pwd):/data unet_cpu:1.0.5-cpu -max_steps /2000 [-i <png>] [-l <csv>] [-t <png>] [-r <csv>]
2268	docker run -v $(pwd):/data Adaboost:1.0.0-skleran1.0.2 -td /data/small_abc_data.csv -tl /data/small_abc_label.csv -fd /data/small_abc_pre.csv -op result.csv
2269	docker run -v $(pwd):/data bagging:1.0-sklearn1.0.2 -I /data/test_bagging_data.csv -IT /data/test_bagging_target.csv -PRE /data/test_bagging_pre.csv
2270	docker run -v $(pwd):/data gb:1.0-sklearn0.24.0 -td /data/test_data.csv -tl /data/test_label.csv -fd /data/test_pre.csv -op result_gb.csv
2271	docker run -v $(pwd):/data sagagis:debian-8.5.0 imagery_vigra 11 [-TABLE <str>] [-FEATURES <str>] [-TRAINING <str>] [-LABEL_AS_ID <str>] [-IMPORTANCES <str>] [-RF_TREE_COUNT <num>] [-RF_TREE_SAMPLES <double>] [-RF_REPLACE <str>] [-RF_SPLIT_MIN_SIZE <num>] [-RF_NODE_FEATURES <str>] [-RF_STRATIFICATION <str>]
2271	docker run -v $(pwd):/data sagagis:debian-8.5.0 imagery_vigra 11 -TABLE /data/rf_iris_saga.csv  -FEATURES x1,x2,x3,x4 -TRAINING Class -IMPORTANCES /data/rf_result.csv
2272	docker run -v $(pwd):/data random_forest:1.0.0-sklearn1.0.2 -td /data/test_rf_data.csv -tl /data/test_rf_label.csv -fd /data/test_rf_pre.csv -op result.csv
2273	docker run -v $(pwd):/data sagagis:debian-8.5.0 imagery_opencv 6 [-FEATURES <str>] [-NORMALIZE <str>] [-CLASSES <str>] [-CLASSES_LUT <str>] [-TRAIN_AREAS <str>] [-TRAIN_CLASS <str>] [-TRAIN_BUFFER <double>] [-NEIGHBOURS <num>] [-TRAINING <str>] [-ALGORITHM <str>] [-EMAX <num>]
2274	docker run -v $(pwd):/data knn:1.0.0-sklearn1.0.2 -td /data/test_knc_data.csv -tl /data/test_knc_label.csv -fd /data/test_knc_pre.csv -op result.csv
2275	docker run -v $(pwd):/data:/data sagagis-base:alpine-8.1.0 imagery_svm 0 [-GRIDS <str>] [-CLASSES <str>] [-CLASSES_LUT <str>] [-SCALING <str>] [-MESSAGE <str>] [-MODEL_SRC <str>] [-MODEL_LOAD <str>] [-ROI <str>] [-ROI_ID <str>] [-MODEL_SAVE <str>] [-SVM_TYPE <str>] [-KERNEL_TYPE <str>] [-DEGREE <num>] [-GAMMA <double>] [-COEF0 <double>] [-COST <double>] [-NU <double>] [-EPS_SVR <double>] [-CACHE_SIZE <double>] [-EPS <double>] [-SHRINKING <str>] [-PROBABILITY <str>] [-CROSSVAL <num>]
2276	docker run -v $(pwd):/data svm:1.0.0-sklearn1.0.2 -C 1.0 -kernel rbf -train_data /data/train_data.csv -train_label /data/train_label.csv -test_data /data/-test_data.csv - test_label/data/test_label.csv -output result.csv
2277	docker run -v $(pwd):/data bnb:1.0-sklearn0.24.0 -td /data/test_bnb_data.csv -tl /data/test_bnb_label.csv -fd /data/test_bnb_pre.csv -op result.csv
2278	docker run -v $(pwd):/data ComNB:1.0-sklearn0.24.0 -td /data/test_comnb_data.csv -tl /data/test_comnb_label.csv -fd /data/test_comnb_pre.csv -op result.csv
2279	docker run -v $(pwd):/data gnb:1.0-sklearn0.24.0 -td /data/test_gnb_data.csv -tl /data/test_gnb_label.csv -fd /data/test_gnb_pre.csv -op result.csv
2280	docker run -v $(pwd):/data mnb:1.0-sklearn0.24.0 -td /data/test_rf_data.csv -tl /data/test_rf_label.csv -fd /data/test_rf_pre.csv -op result.csv
2281	docker run -v $(pwd):/data sagagis:debian-8.5.0 imagery_opencv 5 [-FEATURES <str>] [-NORMALIZE <str>] [-CLASSES <str>] [-MODEL_LOAD <str>] [-TRAIN_AREAS <str>] [-TRAIN_CLASS <str>] [-TRAIN_BUFFER <double>] [-MODEL_SAVE <str>]
2181	docker run -v $(pwd):/data las_vegas:1.0 --input /data/input.csv --output /data/output.csv
2142	docker run -v $(pwd):/data permutation_test_clustering:1.0 --input ../data/Pre.shp -field T1 --output output.csv
2082	docker run --rm -it -v $PWD/data:/data whitebox_tools:2.0.0 -r=Multiply --input1=/data/example_3857.tif --input2=/data/example_3857.tif -o=/data/Multiply_out.tif
2083	docker run -v ￥PWD/project/data:/project/data project:1.0 --file /project/data/lines.tif --epsg 4326
2084	docker run --rm -it -v $PWD/data:/data whitebox_tools:2.0.0 -r=CentroidVector -i=/data/EWHP.shp -o=/data/CentroidVector_out.shp
2085	docker run --rm -it -v $PWD/data:/data whitebox_tools:2.0.0 -r=PolygonArea -i=/data/EWHP.shp
2086	docker run -v $(pwd):/data buffer:1.0-geopandas0.9.0 --input "/data/point_test.shp" --distance 2000 --output "/data/outputfile_buffer.shp"
2086	docker run -v $(pwd):/data buffer:1.0-geopandas0.9.0 --input "/data/point_test.shp" --field "distance" --output "/data/outputfile_buffer.shp"
2087	docker run -v $(pwd):/data sagagis:base-8.1.0 shapes_tools 18 [-SHAPES <str>] [-BUFFER <str>] [-DIST_FIELD <str>] [-DIST_FIELD_DEFAULT <double>] [-DIST_SCALE <double>] [-DISSOLVE <str>] [-NZONES <num>] [-POLY_INNER <str>] [-DARC <double>]"
2087	docker run -v $(pwd):/data sagagis:base-8.1.0 shapes_tools 18 -SHAPES /data/point_test.shp -BUFFER /data/out.shp -DIST_FIELD_DEFAULT 2000
2088	docker run -v $(pwd):/data whitebox-tools:2.0.0 -r=BufferRaster -i=/data/example_3857.tif -o=/data/BufferRaster_out.tif
2089	docker run -v $(pwd):/data mathematical_morphological_analysis:1.0-geopandas --input /data/input.shp --input_radius 200 --input_type 1 --output /data/Erosion.shp
2090	docker run --rm -it -v $PWD/data:/data whitebox_tools:2.0.0 -r=Difference -input=/data/EWHP.shp --overlay=/data/londonhp.shp -o=/data/out.shp
2091	docker run --rm -it -v $PWD/data:/data whitebox_tools:2.0.0 -r=Intesect -input=/data/EWHP.shp --overlay=/data/londonhp.shp -o=/data/out.shp --snap=0.0000001
2092	docker run --rm -it -v $PWD/data:/data whitebox_tools:2.0.0 -r=Union -input=/data/EWHP.shp --overlay=/data/londonhp.shp -o=/data/Union_out.shp --snap=0.0000001
2093	docker run -v $(pwd):/data distance_calculation:1.0 --input /data/data.shp --geo_type point --id1 0 --id2 1 --dist_type euclidean --output /data/output.csv
2095	docker run -v $(pwd):/data st_topology_analysis:1.0-geopandas --shp_path ../data/shapefile.shp  --output ../output
2095	docker run -v $(pwd):/data st_topology_analysis:1.0-geopandas --shp_path ../data/shapefile1.shp  --output /..output
2143	docker run -v $(pwd):/data moving_cluster:1.0 --input ../data/matrix.csv --theta 0.6 --output /data/output.json
2126	docker run -u 0 $(pwd)/data:/geoflow/data crazy-zlj:spark-geoflow:2.0 flow-idw /data/od_flows.csv /data/gravity_res /data/district_pop_wuhan.csv 1 0,1 0,1,2,3
2127	docker run -u 0 $(pwd)/data:/geoflow/data crazy-zlj:spark-geoflow:2.0 flow-idw /data/od_flows.csv /data/gravity_res /data/district_pop_wuhan.csv 1 0,1 0,1,2,3
2128	docker run -v $(pwd):/data sagagis:base-8.1.0 imagery_classification 1 [-GRIDS <str>] [-CLUSTER <str>] [-STATISTICS <str>] [-METHOD <str>] [-NCLUSTER <num>] [-MAXITER <num>] [-NORMALISE <str>] [-INITIALIZE <str>] [-OLDVERSION <str>] [-UPDATEVIEW <str>]
2129	docker run -v $(pwd):/data ascdt:1.0 --input /data/data.csv --output /data/clustering_result.json
2129	docker run -v $(pwd):/data ascdt:1.0 --input /data/data.csv --beta 1.5 --output /data/clustering_result.json
2130	docker run -v $(pwd):/data automatic_clustering_via_boundary_extraction:1.0 --input /data/data.csv --output /data/clustering_result.json
2131	docker run -v $(pwd):/data birch_clustering:1.0-sklearn0.24.0 --input /data/data.csv  --x_field x --y_field y --output /data/clustering_result.csv
2131	docker run -v $(pwd):/data birch_clustering:1.0-sklearn0.24.0 --input /data/data.csv --x_field x --y_field y  --threshold 0.1 --branching_factor False --output /data/clustering_result.csv
2132	docker run -v $(pwd):/data cure_clustering:1.0 --input /data/data.csv --output /data/clustering_result.csv
2132	docker run -v $(pwd):/data cure_clustering:1.0 --input /data/data.csv --num_representatives 5 --alpha 0.2 --distance_threshold 2.0 --output /data/clustering_result.csv
2133	docker run -v $(pwd):/data dbscan_clustering:1.0-sklearn0.24.0 --input /data/data.csv --output /data/clustering_result.csv
2133	docker run -v $(pwd):/data dbscan_clustering:1.0-sklearn0.24.0 --input /data/data.csv --eps 1 --min_pts 5 --output /data/clustering_result.csv
2133	docker run -v $(pwd):/data dbscan_clustering:1.0-sklearn0.24.0 --input /data/data.csv --eps 1 --min_pts 5 --metric cityblock --algorithm kd_tree --leaf_size 25 --output /data/clustering_result.csv
2134	docker run -v $(pwd):/data som:1.0-MiniSom2.3.0 -i /data/data.csv -xnum 10 -ynum 10 -o /data/analysisresult.json
2135	docker run -v $(pwd):/data expectation_maximization_clustering:1.0  --input /data/data.csv -x_field x -y_field y -cluster_num 2 --epsilon 1e-5 --max_iter 100 --output output.csv
2135	docker run -v $(pwd):/data expectation_maximization_clustering:1.0  --input /data/data.csv -x_field x -y_field y -cluster_num 2 --output output.csv
2136	docker run -v $(pwd):/data k_means_clustering:1.0-sklearn0.24.0 --input /data/data.csv --x_field x --y_field y --output /data/clustering_result.csv
2136	docker run -v $(pwd):/data k_means_clustering:1.0-sklearn0.24.0 --input /data/data.csv --x_field x --y_field y --n_clusters 3 --init  random --output /data/clustering_result.csv
2137	docker run -v $(pwd):/data mean_shift_clustering:1.0-sklearn0.24.0 --input /data/data.csv  --x_field x --y_field y --output /data/clustering_result.csv
2137	docker run -v $(pwd):/data mean_shift_clustering:1.0-sklearn0.24.0 --input /data/data.csv --x_field x --y_field y  --bandwidth 2 --cluster_all False --output /data/clustering_result.csv
2138	docker run -v $(pwd):/data mstcluster:1.0 --input /data/data.csv --x_field x --y_field y --cutoff 0.6 --output /data/res.json
2139	docker run -v $(pwd):/data optics_clustering:1.0-sklearn0.24.0 --input /data/data.csv  --x_field x --y_field y --output /data/clustering_result.csv
2139	docker run -v $(pwd):/data optics_clustering:1.0-sklearn0.24.0 --input /data/data.csv --x_field x --y_field y  --min_samples 5 --max_eps 5 --output /data/clustering_result.csv
2140	docker run -v $(pwd):/data functional_semantic_clustering:1.0 --input /data/building_pt_prj.shp --input /data/attrs.csv --output /data/mst_result.shp
2141	docker run -v $(pwd):/data graph_density_hybrid_spatial_clustering:1.0 --input ../data/rain.shp -field 1998年 --output output.csv
2141	docker run -v $(pwd):/data graph_density_hybrid_spatial_clustering:1.0 --input ../data/rain.shp -field 1998年 --output output.csv
2142	docker run -v $(pwd):/data permutation_test_clustering:1.0 --input ../data/Pre.shp -field T1 --output output.csv
2143	docker run -v $(pwd):/data moving_cluster:1.0 --input ../data/matrix.csv --output /data/output.json
2144	docker run -v $(pwd):/data snn_flow_clustering:1.0 --input ../data/flow_data.csv --output /data/Cluster_flow.csv --cluster /data/flow_data_cluster.csv
2144	docker run -v $(pwd):/data snn_flow_clustering:1.0 --input ../data/flow_data.csv --alpha 0.05 --output /data/Cluster_flow.csv --cluster /data/flow_data_cluster.csv
2145	docker run -v $(pwd):/data trajectory_clustering:1.0 --input /data/data.csv --cluster_output /data/clustering_result.csv --representative_output /data/representative_result.csv
2145	docker run -v $(pwd):/data trajectory_clustering:1.0 --input /data/data.csv --beta 1.5 --cluster_output/data/clustering_result.csv --representative_output /data/representative_result.csv
2146	docker run -v $(pwd):/data st_dbscan:1.0 --input /data/data.csv --x_field x --y_field y --t_field t --esp1 0.5 --esp2 10 --min_samples 6 --output /data/res.json
2147	docker run -v $(pwd):/data st_general_clustering_framework:1.0 --input_file_path /data/input.csv --start_time_index 6 --end_time_index 45 --k 2 --l 2 --dist_threshold 1 --connect_threshold 1 --lat LATITUDE --lon LONGITUDE --point_id OBJECTID --output_file_path /data/st_clusters_result.csv
2148	docker run -u 0 $(pwd)/data:/geoflow/data crazy-zlj:spark_geoflow:1.0 flow-nn /data/od_flows.csv /data/gravity_res /data/district_pop_wuhan.csv 1,2,3,4,5 1,2,3,4
2149	docker run -u 0 $(pwd)/data:/geoflow/data crazy-zlj:spark_geoflow:1.0 flow-idw /data/od_flows.csv /data/gravity_res /data/district_pop_wuhan.csv 1 0,1 0,1,2,3
2150	docker run -v $PWD/gtwr-pred:/data gwmodel:2.3 /scripts/gtwr_predict.r -f /data/gtwr-example.shp -p /data/gtwr-pre.shp -d y -i x1,x2,x3 -t t -l 0.1 -k "bisquare" -e true -b 30 -r result.shp -h result.txt
2151	docker run -v $PWD/data:/data gwmodel:2.2 /scripts/gwrp.r -f /data/londonhp.shp -d PURCHASE -i FLOORSZ -k "bisquare" -t true -s true -a CV -b 2000 -r result.shp
2152	docker run --rm -it -v $PWD/data:/data whitebox_tools:1.1 -r=IdwInterpolation -i=/data/points.shp --field=z -o=/data/IdwInterpolation_out.tif --weight=2.0 --radius=4.0 --min_points=3 --cell_size=1.0
2153	docker run -it -v $(pwd)/data:/data ipsm:1.0 -np 4 ipsm -inlayer /data/plan_30m.tif -inlayer /data/slope_30m.tif -sample /data/samples.csv -target org -simithred 0.5 -predmap /data/pred.tif -uncmap /data/uncer.tif
2154	docker run -it -v $(pwd)/data:/data krig_map:0.3 -inlayer /data/plan_30m.tif -sample /data/samples.csv -predmap /data/krig.tif -target sand
2155	docker run -v $PWD/linear_interpolation:/data r-interpolation:1.0 /scripts/linear_interpolation.r -f /data/points.shp -d z -r result.tif
2156	docker run -v $PWD/kriging:/data r-interpolation:1.0 /data/scripts/ordinary_kriging_r.r -f /data/data/points.shp -d z -i 0.1 -r result.tif
2157	docker run -it -v (pwd)/data:/data rf_map:1.0 -inlayer /data/plan_30m.tif -inlayer /data/slope_30m.tif -sample /data/samples.csv -predmap /data/rf3.tif -target sand
2158	docker run -v $(pwd)/data:/data saga-gis:base-8.1.0 grid_spline 4 [-SHAPES <str>] [-FIELD <str>] [-TARGET_DEFINITION <str>] [-TARGET_USER_SIZE <double>] [-TARGET_USER_XMIN <double>] [-TARGET_USER_XMAX <double>] [-TARGET_USER_YMIN <double>] [-TARGET_USER_YMAX <double>] [-TARGET_USER_COLS <num>] [-TARGET_USER_ROWS <num>] [-TARGET_USER_FITS <str>] [-TARGET_TEMPLATE <str>] [-TARGET_OUT_GRID <str>] [-METHOD <str>] [-EPSILON <double>] [-LEVEL_MAX <num>]
2158	docker run -v $(pwd)/data:/data saga-gis:base-8.1.0  grid_spline 4 -SHAPES /data/point.shp -TARGET_OUT_GRID /data/out.grd -TARGET_USER_SIZE 2
2159	docker run -v $(pwd):/data cumaccess:1.0
2160	docker run -v $(pwd):/data gravityaccess:1.0
2161	docker run -v $(pwd):/data individual_accessibility:1.0 --poiFile /data/poi_wuhan_wgs84.csv --userInfoFile /data/userInfo.csv --trajFile /data/trajectory.csv --minX 12656431.323155899 --minY 3499600.399795701 --maxX 12810119.457423057 --maxY 3680020.728794876 --dx 500 --dy 500 --output /data/result.csv
2162	docker run -v $(pwd):/data twostepsfloating:1.0
2163	docker run -it -v $(pwd)/data:/data infomap:1.0 --graph_data /data/2.csv --output_path /data/
2164	docker run -it -v $(pwd)/data:/data cpm:1.0 --graph_data /data/1.csv --output_path /data/
2165	docker run -it -v $(pwd)/data:/data dcd:1.0 --folder_path /data/staticComm/ --node_path /data/node.csv --output_path /data/
2166	docker run -v $(pwd):/data
2167	docker run -v $(pwd):/data buffer:1.0 --input /data/network.csv --originId 18 --radius 200 --output /data/output.csv
2168	docker run -v $(pwd):/data vn:1.0 --input /data/network.csv --originId 18 --radius 200 --output /data/output.csv
2169	docker run -v $(pwd):/data astar:1.0 --networkFile /data/xiamenNetworkLink2.csv --originNodeId 398 --destinationNodeId 667
2170	docker run -v $(pwd):/data dijkstra:1.0 --networkFile /data/xiamenNetworkLink2.csv --originNodeId 398 --destinationNodeId 667
2171	docker run -v $(pwd):/data dspp:1.0 --networkFile /data/STDNetwork.csv --originId 1 --destinationId 937 --timeInterval 300 --departureTime 28800
2172	docker run -v $(pwd):/data kshortestpath:1.0 --networkFile /data/NetworkHK1031.csv --originNodeId 398 --destinationNodeId 667
2173	docker run -v $(pwd):/data k_shortest_path_lpa:1.0
2174	docker run -v $(pwd):/data ptsp:1.0 --input /data/network.csv --originId 18 --radius 200 --output /data/output.csv
2175	docker run -v $(pwd):/data trsp:1.0 --networkFile /data/TurnNetworkLink.csv --turnFile /data/TurnTable.csv --originId 10000 --destinationId 30000 originNodeId:10000 destinationNodeId: 30000
2176	docker run -v $PWD/geographically_weighted_interaction_model_R:/ gwmodel:2.3 /scripts/gwInteract.r -f /data/sub_select.csv -d count -i x1,x2,x3 -u wgslat_x -v wgslng_x -x wgslat_y -y wgslng_y -k "bisquare" -t true -s false -b 30 -r result.shp -h result.txt
2177	docker run -v $PWD/spatial_interaction_model_R:/ gwmodel:2.3 /scripts/spInteract.r -f /data/sub_select.csv -d count -i x1,x2,x3 -u wgslat_x -v wgslng_x -x wgslat_y -y wgslng_y -r result.csv -h result.txt
2178	docker run -v $(pwd):/data spacetimeprism:1.0 --input /data/network.csv --originId 18 --radius 200 --output /data/output.csv
2179	docker run -v $(pwd):/data spacetimeprism:1.0 --input /data/network.csv --output /data/output.csv
2180	docker run -v $(pwd):/data nsga-ii:1.0 -ipd /data/input.csv -mag 100  -opd /data/naga_result.csv
2182	docker run -v $PWD/data:/data montecarlo:1.0 /data/src/montecarlo.py --input /data/data/original_data.shp --num_simulations 100 --output /data/results/simulated_result.shp
2183	docker run -v $PWD/data:/data gwmodel:2.2 /data/scripts/linear_regression.r -f /data/data/lm-example.shp -d y -i x1,x2,x3 -r result.shp -h result.txt
2184	docker run -v $PWD/data:/data gwmodel:2.2 /data/scripts/logistic_regression.r -f /data/data/logistic.shp -d y -i x1,x2,x3 -r result.shp -h result.txt
2185	docker run -v $PWD/data:/data gwmodel:2.2 /data/scripts/poisson_regression.r -f /data/data/poisson.shp -d y -i x1,x2 -r result.shp -h result.txt
2186	docker run -v $(pwd):/prophet/data prophet:1.0-R -a data/example_wp_log_peyton_manning.csv -q 365 -s data/result
2187	docker run -v $PWD/data:/data gwmodel:2.2 /data/scripts/gtwr.r -f /data/data/gtwr-example.shp -d y -i x1,x2,x3 -t t -l 0.1 -k "bisquare" -p true -s true -a CV -r result.shp -h result.txt
2188	docker run -v $PWD/data:/data gwmodel:2.3 /scripts/gwbr.r -f /data/londonhp.shp -d TYPEDETCH -i FLOORSZ,UNEMPLOY -k "gaussian" -t true -s true -a CV -r result.shp -h result.txt
2189	docker run -v $PWD/data:/data gwmodel:2.2 /scripts/gwpr.r -f /data/londonhp.shp -d PURCHASE -i FLOORSZ -k "bisquare" -t true -s true -a CV -r result.shp -h result.txt
2193	docker run -v $PWD/data:/data r-sampling:1.0 /data/src/random_sampling.r -f /data/data/sample.shp -n 100 -r result.shp
2194	docker run -v $PWD/data:/data r-sampling:1.0 /data/src/regular_sampling.r -f /data/data/sample.shp -n 100 -r result.shp -x 300 -y 500
2195	docker run -v $PWD/data:/data r-sampling:1.0 /data/src/sandwich_sampling.r -f /data/data/sandwich_sample.shp -s /data/data/sandwich_ssh.shp -p /data/data/sandwich_report.shp -v value -i Id -r result.shp -k 10
2196	docker run -v $(pwd):/data detecting_semantic_anomaly_by_clustering:1.0 --input ../data/sg1.csv --output ../data/output.csv
2196	docker run -v $(pwd):/data detecting_semantic_anomaly_by_clustering:1.0 --input ../data/sg1.csv --beta 3.1 --output ../data/output.csv
2197	docker run -v $(pwd):/data density_based_trajectory_anomaly_detection:1.0 --input ../data/data.csv --eps 0.11 --MinTrs 10 --output output.csv
2197	docker run -v $(pwd):/data density_based_trajectory_anomaly_detection:1.0 --input ../data/data.csv  --output output.csv
2198	docker run -v $(pwd):/data detecting_anomalous_spatial_interaction_patterns:1.0-geopandas --input_shp /data/wuhan.shp --shp_id ID --shp_rk rk --shp_rkmd rkmd --shp_pmd pmd --shp_poi poi --input_od_csv /data/whlZhenshi.csv --yc_radio_threshold 0.8 --output_shp /data/zf_network.shp
2199	docker run -v $(pwd):/data detecting_flow_anomaly_by_dynamic_neighborhood:1.0 --distance_dataset ../data/PEMS04/distance.csv --traffic_flow_dataset ../data/PEMS04/pems04.csv --time_interval 300 --times 288 --roads 5 -k 0.5 --result_table_path ../data/PEMS04/result_table.html --result_csv_path ../data/PEMS04/result.csv
2200	docker run -v $(pwd):/data st_anomaly_detection_unified_framework:1.0 --input_data ../data/testshp3.shp --kmeansValue 5 --timeSpan 5 --output /output
2200	docker run -v $(pwd):/data st_anomaly_detection_unified_framework:1.0 --input_data ../data/testshp3.shp --kmeansValue 6 --timeSpan 5 --Attribute_Start_number 5 --output /output
2201	docker run -v $(pwd):/data graph_based_appraoch_for_cross_outliers:1.0 --targetinput ../data/DSCtargetdata.csv --targetinputxfield POINT_X --targetinputyfield POINT_Y --referenceinput ../data/DSCreferencedata.csv --referenceinputxfield POINT_X --referenceinputyfield POINT_Y --beta 2 --output ../data/res.json
2202	docker run -v $(pwd):/data flow_adjacency_based_anomaly_detection:1.0 --dataPath_1 ../data/test1.csv --dataPath_2 ../data/test2.csv --a 0.05 --output ../data/output
2202	docker run -v $(pwd):/data flow_adjacency_based_anomaly_detection:1.0 --dataPath_1 ../data/test1.csv --dataPath_2 ../data/test2.csv --a 0.1 --output ../data/output
2203	docker run -v $(pwd):/data isolation_based_anomalous_trajectory:1.0 --all_csv_file ../data/shenzhen/all_trace.csv --test_csv_file ../data/shenzhen/test_trace.csv -all_track_id_field Id -all_x_field x -all_y_field y -test_track_id_field Id -test_x_field x -test_y_field y -test_track_id 1  --output output.txt
2203	docker run -v $(pwd):/data isolation_based_anomalous_trajectory:1.0 --all_csv_file ../data/shenzhen/all_trace.csv --test_csv_file ../data/shenzhen/test_trace.csv -all_track_id_field Id -all_x_field x -all_y_field y -test_track_id_field Id -test_x_field x -test_y_field y -test_track_id 1 --tile_size (10,12) -trials_num 100 -sub_sample_size 200 --output output.txt
2204	docker run -v $(pwd):/data isolation_based_online_anomalous_trajectory:1.0 --all_csv_file ../data/shenzhen/all_trace.csv --test_csv_file ../data/shenzhen/test_trace.csv -all_track_id_field Id -all_x_field x -all_y_field y -test_track_id_field Id -test_x_field x -test_y_field y -test_track_id 1 --tile_size "(10,12)" -threshold 0.05 --output output.txt
2204	docker run -v $(pwd):/data isolation_based_online_anomalous_trajectory:1.0 --all_csv_file ../data/shenzhen/all_trace.csv --test_csv_file ../data/shenzhen/test_trace.csv -all_track_id_field Id -all_x_field x -all_y_field y -test_track_id_field Id -test_x_field x -test_y_field y -test_track_id 1  --output output.txt
2205	docker run -v $(pwd):/data local_outlier_factor:1.0 --input /data/data.csv --k 6 --lof_threshold 0.6 --output /data/res.json
2206	docker run -v $(pwd):/data statistical_test_method_for_cross_outliers:1.0 --data_path, ../data/all.shp, --buffer_distance, 150, --alpha, 0.01, --beta, 0.05, --p, 3, --interval,10,--output, result.shp
2207	docker run -v $(pwd):/data  regression_based_semantic_anomaly_detection:1.0-mgwr --shp_input /data/input.shp --alpha 3 --attr_input /data/attrs.csv --output /data/result.shp
2208	docker run -v $(pwd):/data spatial_anomaly_points_and_regions_detection:1.0 --input /data/data.csv --output /data/clustering_result.json
2209	docker run -v $(pwd):/data speod:1.0 --input /data/data.csv --x_field x --y_field y --output /data/res.json
2210	docker run -v $(pwd):/data cascading_st_pattern_mining:1.0 --input /data/Crime_data.shp --outputshp /data/result1.shp --outputtxt /data/result1.txt --Cascade_participation_Index 0.15 --Time_radius 700 --Space_radius 400
2211	docker run -v $(pwd):/data sequential_association_pattern_mining:1.0 --input /data/data.csv --width 1 --min_density_ratio 1.2 --outputpath /data/res.csv
2212	docker run -v $(pwd)/data:/data item_association_pattern_mining:1.0-apriori --input /data/data.csv --output /data/apriori_result.txt
2212	docker run -v $(pwd)/data:/data item_association_pattern_mining:1.0-apriori --input /data/data.csv --minconfidence 0.1 --minsupport 0.1 --output /data/apriori_result.txt
2284	docker run -v $PWD:/data r-spatialreg:1.0 /data/scripts/join.r -d /data/data/datap.shp -t /data/data/join.csv -i id -j code -o /data/data/re.shp
2213	docker run -v $(pwd):/data spatial_co_location_pattern_mining:1.0 --targetinput /data/a.csv  --referenceinput /data/b.csv  --targetinputxfield x --targetinputyfield y --referenceinputxfield x --referenceinputyfield y  --knum 5  --output result.json
2214	docker run -v $(pwd):/data st_co_occurrence_pattern_mining:1.0 --input /data/input.csv --spatial_thresold 200 --time_thresold 1 --n_sets 10 --output /data/result.csv
2215	docker run -v $(pwd):/data st_teleconnection_mining:1.0 --data_path_1 ../data/pre_test.csv --data_path_2 ../data/tem.csv --sub_events_path ../data/SOI_test.csv --output /data/output.json
2215	docker run -v $(pwd):/data st_teleconnection_mining:1.0 --data_path_1 ../data/pre_test.csv --data_path_2 ../data/tem.csv --sub_events_path ../data/SOI_test.csv --k 21 --output /data/output.json
2216	docker run -v $(pwd):/data anni:1.0 -i /data/data.csv --x_field x --y_field y -o /data/analysis_result.json
2217	docker run -v $(pwd):/data fds:1.0 --input data.csv --field x --output analysis_result.json
2218	docker run -v $(pwd):/data kernel_density_estimation:1.0-gdal3.5.1 --input /data/data.shp --output /data/kde_result.tif
2218	docker run -v $(pwd):/data kernel_density_estimation:1.0-gdal3.5.1 --input /data/data.shp --population pop --cellsize 30 --searchradius 30 --output /data/kde_result.tif
2219	docker run -v $(pwd):/data ripleyk:1.0 --input /data/data.csv --distincrement 100 --distbandnum 20 --begindist 20 --output /data/res.csv
2220	docker run -v $PWD/data:/data gwmodel:2.2 /data/scripts/gwpca.r -f /data/data/gwpca-example.shp -d y -i x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13 -t true -k "bisquare" -c 5 -r result.shp -h result.txt
2221	docker run -v $(pwd)/data:/data pca:1.0-sklearn0.24.0 --input /data/data.csv --output /data/pca_result.csv
2221	docker run -v $(pwd)/data:/data pca:1.0-sklearn0.24.0 --input /data/data.csv --components 5 --whiten true --svd_solver arpack --output /data/pca_result.csv
2222	docker run -v $(pwd)/data:/data tsne:1.0.0-sklearn1.0.2 -input_x /data/input.csv -output /data/output.csv
2223	docker run -u 0 $(pwd)/data:/geoflow/data crazy-zlj:spark_geoflow:1.0 flow-gearyc /data/phone_flows.csv /data/gearyc 0,1,2,3,4
2224	docker run -u 0 $(pwd)/data:/geoflow/data crazy-zlj:spark_geoflow:1.0 flow-gravity /data/od_flows.csv /data/gravity_res /data/district_pop_wuhan.csv 1 0,1 0,1,2,3
2225	docker run -u 0 $(pwd)/data:/geoflow/data crazy-zlj:spark-geoflow:2.0 flow-idw /data/od_flows.csv /data/gravity_res /data/district_pop_wuhan.csv 1 0,1 0,1,2,3
2226	docker run -v $(pwd)/data:/geodetector/data -v $(pwd)/logs:/geodetector/logs geodetector:1.0-R -f data/input_data.csv -d incidence -i watershed,soiltype,elevation -r data/result
2227	docker run -v $PWD/data:/data /scripts/gwss.r -f /data/gwss-example.shp -v PurPrice,FlrArea -k bisquare -t true -b 50000 -r result.shp
2229	docker run -v $PWD/data:/data /scripts/gwvc.r -f /data/gwvc-example.shp -v PurPrice,FlrArea -k bisquare -t true -b 50000 -r result.shp
2230	docker run -v $PWD:/data r-spatialreg:1.0 /data/scripts/global_sac.r -f /data/data/Mississippi.shp -v HR60 -m moran -a queen -w W -r result.txt -p res.pdf
2231	docker run -v $PWD:/data r-spatialreg:1.0 /data/scripts/lisa.r -f /data/data/Mississippi.shp -v HR60 -m moran -a queen -w W -r result.shp
2232	docker run -v $(pwd)/data:/ts_autocorrelation/data -v $(pwd)/logs:/ts_autocorrelation/logs ts_autocorrelation:1.0-R -i data/input_data.csv -r data/result
2234	docker run --rm -it -v $PWD/data:/data whitebox_tools:2.0.0 -r=D8FlowAccumulation -i=/data/FillDepressions_out.tif -o=/data/D8FlowAccumulation_out.tif
2240	docker run --rm -it -v $PWD\\test_data\\:/data whitebox_tools:1.1  -r=StreamPowerIndex --sca=/data/sca.tif --slope=/data/slope.tif -o=/data/output.tif
2242	docker run -v $(pwd):/data sagagis:base-8.1.0 ta_morphometry 1 -ELEVATION <str> [-RESULT <str>] [-METHOD <str>] [-NEIGHBOURS <str>]
2242	docker run -v $(pwd):/data sagagis:base-8.1.0 shapes_tools 18 -SHAPES /data/point_test.shp -BUFFER /data/out.shp -DIST_FIELD_DEFAULT 2000
2243	docker run -v $(pwd)/data:/data crazyzlj/taudem_ext:alpine-openmpi-gdal-latest [-np <nproc>] slope -fel /data/demfel.tif -plan /data/plan_curvature.tif
2244	docker run -it -v C:\\Users\\38646\\Desktop\\data\\:/data crazyzlj/whitebox_tools:1.1  -r=ProfileCurvature --dem=/data/example_3857.tif -o=/data/output.tif
2245	docker run -it -v C:\\Users\\38646\\Desktop\\data\\:/data crazyzlj/whitebox_tools:1.1  -r=TangentialCurvature --dem=/data/example_3857.tif -o=/data/output.tif
2246	docker run -v $(pwd)/data:/data gdal:3.16 gdaldem roughness_gdal dem_meixi.tif dem_meixi_rough.tif
2247	docker run -v $(pwd)/data:/data crazyzlj/taudem_ext:alpine-openmpi-gdal-latest [-np <nproc>] slope -fel /data/demfel.tif -p /data/demp.tif -sd8 /data/demsd8.tif
2248	docker run --rm -it -v $PWD\\test_data\\:/data whitebox_tools:1.1 -r=Slope -i=/data/example_3857.tif -o=/data/slope_out.tif --units=radians
2249	docker run -v $(pwd)/data:/data crazyzlj/taudem_ext:alpine-openmpi-gdal-latest [-np <nproc>] twi -slp data/slope.tif -ad8 /data/demad8.tif -twi /data/twi.tif
2250	docker run -v $PWD/data:/data registry.cn-hangzhou.aliyuncs.com/onesis-geomodels/whitebox-tools:2.0.0 -r=Viewshed --dem='dem.tif' --stations='stations.shp' --output=output.tif --height=10.0
2251	docker run --rm -it -v $PWD/data:/data registry.cn-hangzhou.aliyuncs.com/onesis-geomodels/whitebox-tools:2.0.0 -r=VisibilityIndex --dem='dem.tif' --output=output.tif --height=10.0 --res_factor=4
2282	docker run -v $PWD/csv2point:/data gwmodel:2.3 /data/csv2point.r -f /data/data/testdata.csv -a lon -b lat -c  epsg:4326 -r points.shp
2283	docker run -v $PWD:/data r-spatialreg:1.0 /data/scripts/near_dist.r -d /data/data/datap.shp -t /data/data/targetp.shp -i x1 -o /data/data/re.csv
2285	docker run -v $PWD/data:/data gwmodel:2.2 /scripts/GWR_VarSelect.r -f /data/VarSelect.shp -d y -i x1,x2,x3,x4,x5,x6,x7 -k "bisquare" -a CV  -r result.pdf -o result.txt
2286	docker run -v $PWD/data:/data gwmodel:2.4-txt /scripts/gwr_txtvar.r -f /data/gwr-select.shp -d y -i /data/order.txt -k "bisquare" -t true -s true -a CV  -r result.shp -h result.txt
2287	docker run -v $PWD/data:/data gwmodel:2.4-txt /scripts/mgwr_txtvar.r -f /data/gwr-select.shp -d y -i /data/order.txt -k "bisquare" -t true -s true -a CV  -r result.shp -h result.txt
2294	docker run -v $(pwd):/data air_quality_prediction:1.0 --train_data_folder /data/data/ --pred_data_path /data/data.csv --variable u10 v10 t2m sp tp AQI PM10 NO2 CO O3 SO2 --air_quality PM2.5 --geo_coord x y --output_best_arg /data/best_arg.json
2295	docker run -v $(pwd):/data mode2_clusters:1.0 --train_data_folder /data/data/ --input_best_arg /data/best_arg.json --variable u10 v10 t2m sp tp AQI PM10 NO2 CO O3 SO2 --air_quality PM2.5 --geo_coord x y --X 2 --Y 2 --N_epoch 100 --k 100 --sigma 1.5 --output_cluster /data/clusters.csv --output_iVariables /data/iVariables.json
2296	docker run -v $(pwd):/data mode3_multiple_liner_predication:1.0 --iVariables /data/iVariables.json --air_quality PM2.5 --geo_coord x y --cluster_result /data/cluster_result.csv --pred_data_path /data/data.csv --output_pred /data/pred_result.csv
2298	docker run -v $(pwd):/data random_forest:1.0.0-sklearn1.0.2 -td /data/test_rf_data.csv -tl /data/training_label.csv -fd /data/test_rf_pre.csv -op result.csv
2301	python3 ./src/EuclideanDistance.py -sfp ./data/road.shp -width 2091  -height 2858 -op ./road_distance.tif
2302	python3 ./src/EuclideanDistance.py -sfp ./data/waterways.shp  -width 2091  -height 2858 -op ./waterways_distance.tif
2304	docker run -v $PWD/data:/data /scripts/gwcorr.r -f /data/gwcorr-example.shp -v PurPrice,FlrArea -k bisquare -t true -b 50000 -r result.shp
2306	docker run -v $(pwd)/data:/data crazyzlj/taudem_ext:alpine-openmpi-gdal-latest [-np <nproc>] threshold  -ssa /data/demad8.tif -src /data/demsrc.tif -thresh 100.0
2311	docker run -v $(pwd):/data random_forest_probability:1.0.2-sklearn1.0.2  -asfm  /data/aspect_Priolo.tif  -demfm  /data/dem_priolo.tif  -popufm  /data/popu-Priolo.tif -railwayfm /data/railways_Priolo_distance.tif -sfm /data/slope_priolo.tif -rfm /data/road_Priolo_distance.tif  -tfm  /data/temp_priolo.tif  -wfm  /data/waterways_Priolo_distance.tif  -tl /data/priolo_00-15_landuse_1to2.tif -op /data/out/
2491	docker run -v $(pwd)/data:/data crazyzlj/taudem_ext:alpine-openmpi-gdal-latest [-np <nproc>] pitremove -z /data/dem_meixi.tif -fel /data/dem_fel.tif
2492	docker run --rm -it -v $PWD\\test_data\\:/data whitebox_tools:1.1 -r=BreachDepressionsLeastCost -i=/data/example_3857.tif -o=/data/BreachDepressionsLeastCost_out.tif --dist=1000 --max_cost=100.0
2493	docker run --rm -it -v D:\\whitebox_tools\\test_data\\:/data whitebox_tools:1.1 -r=FillDepressions -i=/data/example_3857.tif -o=/data/FillDepressions_out.tif
2497	docker run -v $(pwd)/data:/data crazyzlj/taudem_ext:alpine-openmpi-gdal-latest [-np <nproc>] aread8 -p /data/d8flowdir.tif -ad8 /data/demad8.tif
2501	docker run -v ￥PWD/project/data:/project/data project:1.0 --file /project/data/lines.tif --epsg 4326
2503	docker run -v ￥PWD/project/data:/project/data project:1.0 --file /project/data/lines.tif --epsg 4326
2504	docker run -v ￥PWD/project/data:/project/data project:1.0 --file /project/data/lines.tif --epsg 4326
2505	docker run -v ￥PWD/project/data:/project/data project:1.0 --file /project/data/lines.tif --epsg 4326
2506	docker run --rm -it -v $PWD\\test_data\\:/data whitebox_tools:1.1 -r=Aspect -i=/data/example_3857.tif -o=/data/aspect_out.tif
2508	docker run -v $PWD/data:/data gwmodel:2.4-new /scripts/gwr_new.r -f /data/gwr-select.shp -d y -i /data/order.txt -k "bisquare" -t true -s true -a CV  -r result.shp -h result.txt
2509	docker run -v $PWD/data:/data gwmodel:2.4-new /scripts/mgwr_new.r -f /data/gwr-select.shp -d y -i /data/order.txt -k "bisquare" -t true -s true -a CV  -r result.shp -h result.txt
2510	python3 ./src/EuclideanDistance.py -sfp ./data/waterways.shp  -width 94  -height 69 -op ./waterways_distance.tif
\.


--
-- Data for Name: integrated_software; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.integrated_software (id, license, name, version) FROM stdin;
33876	GPL-2	GWmodel	2.2-9
34342	BSD-3-Clause	scikit-learn	1.0.2
34362	BSD-3-Clause	scikit-learn	1.0.2
34402	BSD-3-Clause	scikit-learn	0.24.0
34424	GNU General Public	SAGA-GIS	8.5.0
34448	BSD-3-Clause	scikit-learn	1.0.2
34471	GNU General Public	SAGA-GIS	8.5.0
34493	BSD-3-Clause	scikit-learn	1.0.2
34539	GNU General Public	SAGA-GIS	8.1.0
34578	BSD	scikit-learn	0.24.0
34592	BSD-3-Clause	scikit-learn	0.24.0
34602	BSD-3-Clause	scikit-learn	0.24.0
34614	BSD-3-Clause	scikit-learn	0.24.0
34627	GNU General Public	SAGA-GIS	8.5.0
31429	MIT	whitebox_tools	2.0.0
31439	MIT	whitebox_tools	2.0.0
31445	MIT	whitebox_tools	2.0.0
31472	BSD	geopandas	0.9.0
31499	GNU General Public	SAGA-GIS	8.1.0
31509	MIT	whitebox_tools	2.0
31521	MIT	whitebox_tools	2.0.0
31531	MIT	whitebox_tools	2.0.0
31541	MIT	whitebox_tools	2.0.0
31558	\N	geopandas	0.14.0
32427	GNU General Public	SAGA-GIS	8.1.0
32464	BSD	scikit-learn	0.24.0
32480	BSD	numpy	1.21.2
32481	BSD	pandas	1.3.3
32482	BSD	matplotlib	3.4.3
32483	BSD	scipy	1.7.1
32511	BSD	scikit-learn	0.24.0
32540	MIT	MiniSom	2.3.0
32557	\N	scipy	1.10.1
32575	BSD	scikit-learn	0.24.0
32592	BSD	scikit-learn	0.24.0
32641	BSD	scikit-learn	0.24.0
32660	\N	pandas	2.1.1
32672	\N	pandas	2.1.1
32764	GPL-2	GWmodel	2.2-9
32785	GPL-2	GWmodel	2.2-9
32801	MIT	whitebox_tools	2.0.0
32824	库许可证	库名	库版本
32833	BSD-3	PyKrige	1.7.2
32871	BSD-3	PyKrige	1.7.2
32915	GNU General Public	SAGA-GIS	8.1.0
32961	GPL-2.0	igraph	0.10.8
32967	BSD	networkx	2.5
32972	GPL-2.0	igraph	0.10.8
33055	GPL-2	GWmodel	2.2-9
33084	MIT	Geopandas	0.9.0
33140	GPL-2	GWmodel	2.2-9
33161	GPL-2	GWmodel	2.2-9
33182	GPL-2	GWmodel	2.2-9
33266	\N	numpy	1.24.3
33273	\N	scipy	1.10.1
33289	\N	geopandas	0.14.0
33310	\N	numpy	1.26.2
33337	BSD	scikit-learn	0.24.0
33363	MIT	pandas	1.3.5
33388	MIT	pandas	1.3.5
33415	\N	Numpy	1.24.3
33502	\N	Numpy	1.24.3
33526	BSD-3	osgeo	3.5.1
33527	MIT	geopy	2.2.0
33528	MIT	pyproj	3.3.1
33563	GPL-2	GWmodel	2.2-9
33573	BSD	scikit-learn	0.24.0
33587	BSD-3-Clause	scikit-learn	1.0.2
33629	GPL-2	GWmodel	2.2-9
33651	GPL-2	GWmodel	2.2-9
33696	MIT	whitebox_tools	2.0.0
33704	GPLv3license	taudem_ext	TauDEM5.3
33756	The MIT License (MIT)	whitebox-tools	2.1.0
33773	GNU General Public	SAGA-GIS	8.1.0
33782	GPLv3license	taudem_ext	TauDEM5.3
33790	The MIT License (MIT)	whitebox-tools	2.1.0
33798	The MIT License (MIT)	whitebox-tools	2.1.0
33805	MIT	gdal	3.16
33813	GPLv3license	taudem_ext	TauDEM5.3
33822	MIT	whitebox_tools	2.1
33831	GPLv3license	taudem_ext	TauDEM5.3
33840	MIT	whitebox_tools	2.0.0
33851	MIT	whitebox_tools	2.0.0
34657	GPL-2	GWmodel	2.2-9
34680	GPL-2	GWmodel	2.2-9
34700	GPL-2	GWmodel	2.2-9
34870	BSD	pandas	1.3.3
34871	BSD	numpy	1.21.2
34872	BSD	scikit-learn	0.24.2
34896	BSD	pandas	1.3.3
34897	BSD	numpy	1.21.2
34898	BSD	scikit-learn	0.24.2
34910	BSD	pandas	1.3.3
34911	BSD	numpy	1.21.2
34912	BSD	scikit-learn	0.24.2
34991	BSD-3-Clause	scikit-learn	1.0.2
35154	GPL-2	GWmodel	2.2-9
35235	GPLv3license	taudem_ext	TauDEM5.3
35359	BSD-3-Clause	scikit-learn	1.0.2
38256	GPLv3license	TauDEM_ext	TauDEM5.3
38271	MIT	whitebox_tools	2.1
38282	MIT	whitebox_tools	2.1
38407	GPLv3license	taudem_ext	TauDEM5.3
38421	GPLv3license	taudem_ext	TauDEM5.3
39097	MIT	whitebox_tools	2.1
39169	GPL-2	GWmodel	2.4-2
39219	GPL-2	GWmodel	2.4-2
\.


--
-- Data for Name: keywords; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.keywords (meta_identification_id, keywords) FROM stdin;
1360	地理流
1439	可达性
1439	累积机会法
1440	可达性
1440	重力法
1442	可达性
1442	两步移动搜索法
1462	序贯高斯模拟
1462	空间数据模拟
1543	地理流
1544	多目标优化
2252	空间异质性
2252	判别分析
2253	地理加权
2253	空间异质性
2253	判别分析
2254	图像分类
2254	对象识别
2255	语义分割
2255	建筑提取
2256	语义分割
2256	图像分类
2256	对象识别
2257	语义分割
2257	图像分类
2257	对象识别
2258	目标检测
2258	实时对象识别
2258	深度学习
2258	计算机视觉
2259	目标检测
2259	图像分类
2259	实例分割
2260	图像分类
2260	对象识别
2261	目标检测
2261	对象识别
2262	图像分类
2262	目标检测
2262	对象识别
2263	语义分割
2263	图像分类
2263	对象识别
2264	语义分割
2264	图像分类
2264	对象识别
2265	图像分类
2265	目标检测
2265	对象识别
2266	目标检测
2266	对象识别
2266	边界框预测
2267	语义分割
2267	图像分类
2267	对象识别
2268	监督分类
2269	监督分类
2270	数据分类
2270	集成学习
2270	梯度提升决策树
2270	弱分类器
2271	监督分类
2271	集成学习
2272	监督分类
2272	集成学习
2273	监督分类
2274	监督分类
2275	监督学习
2275	广义线性分类器
2276	监督学习
2276	广义线性分类器
2277	二元分类
2277	条件独立性假设
2277	空间分类
2277	机器学习
2278	数据分类
2278	机器学习
2278	朴素贝叶斯分类器
2279	数据分类
2279	机器学习
2279	朴素贝叶斯分类器
2279	高斯函数
2280	数据分类
2280	机器学习
2280	朴素贝叶斯分类器
2280	多项式模型
2281	监督学习
2281	概率分布
2281	朴素贝叶斯分类器
2082	代数运算
2083	投影转换
2084	几何中心
2085	表面积
2086	邻域分析
2086	缓冲区
2087	邻域分析
2087	缓冲区
2088	邻域分析
2088	栅格数据分析
2088	缓冲区
2089	数学形态学
2089	膨胀
2089	腐蚀
2089	开运算
2089	闭运算
2090	叠置
2091	叠置
2092	叠置
2093	距离计算
2093	几何对象
2095	拓扑关系
2126	地理流
2126	聚类
2127	地理流
2127	Kmeans
2127	聚类
2128	图像分割
2129	三角网约束
2129	异常长边
2129	子图分割
2130	基于图论的聚类算法
2130	异常长边
2130	子图分割
2131	层次聚类
2131	大规模数据集
2132	基于代表点的空间聚类
2132	收缩因子
2133	基于密度的空间聚类
2133	噪声
2134	无监督神经网络
2134	自组织神经网络
2135	高斯函数
2135	混合模型
2136	基于划分的空间聚类
2137	局部密度极值
2138	最小支持树
2138	异常长边
2139	层次聚类
2139	密度自适应
2140	空间分布模式
2140	功能语义特征
2140	最小生成树
2140	地图综合
2141	空间聚类算法
2141	非空间属性
2141	Delaunay三角网
2141	密度
2142	时空数据挖掘
2142	分布模式
2142	时空聚类
2142	随机重排
2142	聚类显著性
2142	时空簇
2143	移动聚类
2143	移动轨迹
2144	聚类
2144	移动轨迹
2145	拆分-聚合框架
2145	轨迹聚类
2145	MDL准则
2145	基于密度聚类
2146	聚集分布模式
2146	时空聚类
2146	密度可达
2147	时空聚类
2147	聚类分析
2148	地理流
2148	最邻近插值
2148	插值
2149	地理流
2149	IDW
2149	插值
2150	空间预测
2150	空间异质性
2151	空间预测
2151	空间异质性
2152	反距离加权
2152	距离衰减参数
2153	土壤属性空间推测
2153	基于样点个体代表性
2153	地理学第三定律
2154	克里金
2154	土壤属性空间推测
2155	线性插值
2156	变异函数
2156	无偏估计
2157	随机森林
2157	土壤属性空间推测
2158	连续
2158	光滑
2158	插值法
2158	边界条件
2159	可达性
2159	累积机会法
2160	可达性
2160	重力法
2161	活动空间
2161	可达性
2162	可达性
2162	两步移动搜索法
2163	社区发现
2164	社区发现
2165	社区发现
2166	公交网络
2166	缓冲区
2167	路网距离
2167	缓冲区
2168	最短路径
2168	Voronoi图
2169	最短路径
2169	路径规划
2170	最短路径
2170	路径规划
2171	最短路径
2171	时变道路网络
2172	最短路径
2172	路径规划
2173	最短路径
2173	路径规划
2174	公交网络
2174	最短路径
2175	最短路径
2175	路网转向
2176	地理加权
2176	空间交互
2176	流数据
2177	空间回归
2177	空间交互
2177	流数据
2178	最短路径
2178	时空棱柱
2179	公交网络
2179	时空棱柱
2180	多目标优化
2182	序贯高斯模拟
2182	空间数据模拟
2183	线性回归
2184	逻辑回归
2185	泊松回归
2186	时间序列
2186	预测
2187	时空回归
2187	空间异质性
2187	时间非平稳性
2188	空间回归
2188	空间异质性
2188	逻辑回归
2189	空间回归
2189	空间异质性
2189	泊松回归
2193	随机抽样
2194	规则抽样
2195	三明治抽样
2195	分层抽样
2195	空间异质性
2196	无监督
2196	Delaunay三角网
2196	异常检测
2197	无监督
2197	Hausdroff邻域
2198	空间交互模式
2198	异常检测
2199	动态邻域
2199	流量异常检测
2200	异常探测
2201	空间异常模式
2201	参考点
2201	图划分
2202	流空间
2202	异常探测
2203	基于孤立性的异常检测
2203	多次采样
2204	基于孤立森林的异常检测
2204	隔离
2205	空间异常模式
2205	k邻域
2205	局部可达密度
2206	异常探测
2207	空间异常检测
2207	地理加权回归
2207	Delaunay三角网
2207	自适应带宽
2208	三角网约束
2208	异常长边
2208	异常探测
2208	专题属性
2209	层次约束
2209	三角网
2209	自适应
2209	离群点
2210	时空级联模式
2210	模式挖掘
2211	时序关联模式挖掘
2211	多事件序列关联规则挖掘
2212	关联模式挖掘
2213	空间同位模式
2214	时空数据挖掘;
2214	同现模式;
2214	显著性检验;
2214	点过程模型
2215	时空遥相关
2215	SNN聚类
2216	最近邻距离
2217	频率分布
2217	聚集趋势
2217	离散趋势
2218	核函数
2218	密度分析
2219	聚集分布模式
2219	多尺度
2220	主成分分析
2220	空间异质性
2220	时间回归
2221	降维分析
2222	空间点
2222	降维分析
2223	地理流
2223	热点分析
2224	地理流
2224	预测
2225	地理流
2225	I-index
2226	空间分异性
2226	地理探测器
2227	相关性分析
2227	空间异质性
2227	空间非平稳性
2229	空间异质性
2229	空间非平稳性
2230	空间自相关
2231	局部
2231	空间自相关
2232	时间序列
2232	自相关
2234	地形分析
2234	水文分析
2234	单流向
2235	地形分析
2235	单流向
2240	地形分析
2240	水文分析
2242	邻域分析
2242	地形分析
2243	地形分析
2243	水文分析
2244	地形分析
2244	水文分析
2245	地形分析
2245	水文分析
2246	地形分析
2247	地形分析
2247	水文分析
2247	单流向
2248	地形分析
2248	水文分析
2249	地形分析
2249	水文分析
2249	单流向
2250	可见区域
2251	可见性
2251	可视性指数
2282	数据转换
2283	距离计算
2284	连接
2285	变量优选
2285	智能化
2286	空间回归
2286	空间异质性
2286	空间非平稳性
2287	空间回归
2287	空间异质性
2287	空间非平稳性
2294	空气质量
2294	多元线性回归
2295	空气质量
2295	聚类分区
2296	空气质量
2296	多元线性回归
2298	空间分类
2298	随机森林分类
2301	距离计算
2301	几何对象
2302	距离计算
2302	几何对象
2304	相关性分析
2304	空间异质性
2304	空间非平稳性
2306	地形分析
2306	水文分析
2306	单流向
2311	空间分类
2311	随机森林分类
2491	地形分析
2491	水文分析
2491	单流向
2492	地形分析
2492	水文分析
2493	地形分析
2493	水文分析
2497	地形分析
2497	水文分析
2497	单流向
2498	地形分析
2498	水文分析
2498	单流向
2501	test
2503	物候
2503	数据连接
2504	物候
2504	置信度
2505	物候
2506	地形分析
2506	水文分析
2508	空间回归
2508	空间异质性
2508	空间非平稳性
2509	多尺度回归
2509	空间回归
2509	空间异质性
2510	距离计算
2510	几何对象
\.


--
-- Data for Name: link_data; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.link_data (id, aoi_id, data_category_id) FROM stdin;
f181d9cc61c34e6b926aafb6ee218564	e90216d682a74f59915ec2b6fa2f87c5	faf1a4ece6e34be1b6d25c7508fc361c
6be3e36f67c141e3966a5326c478f7b6	fb21777daf544ce5b7056587048162b3	7c25bc46d4bc49309def5c84819b30e7
a8303ecabadb4679bf9a0299d0389c91	023e11aea6634504abf05e22838922a6	c3b869b2f0694310b625f41bf0865555
48fd3bcdd6764c82b9b008335031e171	ce84481419ec4842af842e5d81cd585c	702a8781a7dd4b039413533228c4dc1b
598f71f32b9b4e4199f4c88776c1c29b	ce84481419ec4842af842e5d81cd585c	777f685f141f45098b9b5970b3c1bc0f
e84cc56c6fa54e15b705c3646e11fc09	ce84481419ec4842af842e5d81cd585c	d92bbb611d924dbb99160ac5ba7d4124
aea82a8bd5694923a3cb0c5e7a694c2e	6b1a93045bb6402e994cfa7e5263d9ed	da43b65b160c43efa907b3fc66c1f460
b8642760dde4413c9d31e435ce1263a9	6b1a93045bb6402e994cfa7e5263d9ed	ea8ec4c7da5647dbbe33e8c33aeccc88
7f0f83a71a584a659b5cbc5f5c4e2762	6b1a93045bb6402e994cfa7e5263d9ed	5599b67175b041bf8445ed1613e967c5
fd1d95e2f189446593d4ca2dd39a275f	6b1a93045bb6402e994cfa7e5263d9ed	ea8ec4c7da5647dbbe33e8c33aeccc88
e29b78ce14cf413eb4fa830e702b0d91	6b1a93045bb6402e994cfa7e5263d9ed	dfd1d1eaa45b4a24979608dd2b3357ad
9fa6cd59520b49bbbf94c9b0f63ee7e2	6b1a93045bb6402e994cfa7e5263d9ed	18a133bb2cf5420a9c1dee6014466f60
\.


--
-- Data for Name: link_models; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.link_models (id) FROM stdin;
20368
22091
23045
33853
33861
33878
34044
34137
34151
34191
34220
34236
34286
31423
31431
31434
31441
31515
31523
31533
32384
32395
32724
32732
32741
32766
32787
32803
32826
32836
32844
32857
33030
33057
33086
33091
33098
33105
33112
33120
33142
33163
33236
33241
33250
33547
33596
33605
33642
33653
33665
33676
33688
33697
33748
33775
33806
33823
33832
33842
34629
34636
34641
34647
34659
34682
35144
35226
38398
38408
38530
39069
39076
39083
39140
39198
\.


--
-- Data for Name: link_models_alternative; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.link_models_alternative (link_models_id, alternative) FROM stdin;
33878	resnet_pytorch
34044	FasterRCNN
34137	pytorch-retinanet
34151	U-Net_CPU_pytorch
34191	vgg_cpu_pytorch
34220	pytorch-retinanet
34236	SSD_GPU_pytorch
34286	U-Net_GPU_pytorch
\.


--
-- Data for Name: link_models_preorder; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.link_models_preorder (link_models_id, preorder) FROM stdin;
33688	BreachDepressionsLeastCost_whiteboxtools
33697	pitRemove
33748	D8FlowAccumulation
33748	Slope
33775	pitRemove
33806	pitRemove
33823	slope_d8
33823	AreaD8
35226	AreaD8
38398	d8flowdir
38408	d8flowdir
38408	rasterstreamgrid_extraction_taudem
\.


--
-- Data for Name: map_layer; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.map_layer (id, boundary, is_deleted, layer_name, owner_path, svc_id, svc_name, svc_path) FROM stdin;
75553b766ec94c2091a64b1351430073	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[116.4023093263838,25.63563320734702],[116.50889495493364,25.636030408386947],[116.50864889867641,25.696079526673756],[116.40200987504826,25.695681262234064],[116.4023093263838,25.63563320734702]]]}	t	slope_25	/onesis/kt4/test_data/interp/slope_25.tif	29491	slope_25	/onesis/kt4/test_data/interp/slope_25.tif
0a0848889fa34188a834db6d2f5e347d	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[118.49507609668993,25.89088083465758],[118.90558387492864,25.89088083465758],[118.90558387492864,26.26774978106839],[118.49507609668993,26.26774978106839],[118.49507609668993,25.89088083465758]]]}	t	dem_meixi_fel	/onesis/kt4/dem/dem_meixi_fel.tif	29399	dem_meixi_fel	/onesis/kt4/dem/dem_meixi_fel.tif
499d0d5176ef45e8b647f107f98589bf	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[118.49507609668993,25.89088083465758],[118.90558387492864,25.89088083465758],[118.90558387492864,26.26774978106839],[118.49507609668993,26.26774978106839],[118.49507609668993,25.89088083465758]]]}	t	dem_meixi	/onesis/kt4/test_data/terr/terr/dem_meixi.tif	29497	dem_meixi	/onesis/kt4/test_data/terr/terr/dem_meixi.tif
5ba7859217864ae195f820b6fbbc6aba	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[116.4023093263838,25.63563320734702],[116.50889495493364,25.636030408386947],[116.50864889867641,25.696079526673756],[116.40200987504826,25.695681262234064],[116.4023093263838,25.63563320734702]]]}	t	1	/onesis/kt4/job_results/1.tif	29494	1	/onesis/kt4/job_results/1.tif
1da85fc5831448eeb3867a4717b542d1	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[116.4023093263838,25.63563320734702],[116.50889495493364,25.636030408386947],[116.50864889867641,25.696079526673756],[116.40200987504826,25.695681262234064],[116.4023093263838,25.63563320734702]]]}	t	slope_25	/onesis/kt4/test_data/interp/slope_25.tif	29491	slope_25	/onesis/kt4/test_data/interp/slope_25.tif
0bb45ce0d6d14b4a8d3942d478893ffa	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[118.49507609668993,25.89088083465758],[118.90558387492864,25.89088083465758],[118.90558387492864,26.26774978106839],[118.49507609668993,26.26774978106839],[118.49507609668993,25.89088083465758]]]}	t	slope	/onesis/kt4/test_data/terr/terr/result/slope.tif	29480	slope	/onesis/kt4/test_data/terr/terr/result/slope.tif
62b8651811f447058d761d51d8f37eb4	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[118.49507609668993,25.89088083465758],[118.90558387492864,25.89088083465758],[118.90558387492864,26.26774978106839],[118.49507609668993,26.26774978106839],[118.49507609668993,25.89088083465758]]]}	t	acc_d8	/onesis/kt4/test_data/terr/terr/result/acc_d8.tif	29478	acc_d8	/onesis/kt4/test_data/terr/terr/result/acc_d8.tif
16c5427d167f474f8af66bf789937215	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[118.49507609668993,25.89088083465758],[118.90558387492864,25.89088083465758],[118.90558387492864,26.26774978106839],[118.49507609668993,26.26774978106839],[118.49507609668993,25.89088083465758]]]}	t	acc_d8	/onesis/kt4/test_data/terr/terr/result/acc_d8.tif	29478	acc_d8	/onesis/kt4/test_data/terr/terr/result/acc_d8.tif
8b9f4c3773a2491eb995b4ffa1b515a2	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[118.49507609668993,25.89088083465758],[118.90558387492864,25.89088083465758],[118.90558387492864,26.26774978106839],[118.49507609668993,26.26774978106839],[118.49507609668993,25.89088083465758]]]}	t	acc_d8	/onesis/kt4/test_data/terr/terr/acc_d8.tif	29475	acc_d8	/onesis/kt4/test_data/terr/terr/acc_d8.tif
36facc8bb2394fc894099cf774757dc4	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[118.49507609668993,25.89088083465758],[118.90558387492864,25.89088083465758],[118.90558387492864,26.26774978106839],[118.49507609668993,26.26774978106839],[118.49507609668993,25.89088083465758]]]}	t	acc_d8	/onesis/kt4/test_data/terr/terr/acc_d8.tif	29475	acc_d8	/onesis/kt4/test_data/terr/terr/acc_d8.tif
b2e03c6b5031444b97ba7f3666c43db5	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[118.49507609668993,25.89088083465758],[118.90558387492864,25.89088083465758],[118.90558387492864,26.26774978106839],[118.49507609668993,26.26774978106839],[118.49507609668993,25.89088083465758]]]}	t	acc_d8	/onesis/kt4/test_data/terr/terr/acc_d8.tif	29475	acc_d8	/onesis/kt4/test_data/terr/terr/acc_d8.tif
01232ea4858544bd98b1bd83f9ac6fe7	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[118.49507609668993,25.89088083465758],[118.90558387492864,25.89088083465758],[118.90558387492864,26.26774978106839],[118.49507609668993,26.26774978106839],[118.49507609668993,25.89088083465758]]]}	t	dem_meixi	/onesis/kt4/test_data/terr/terr/dem_meixi.tif	29497	dem_meixi	/onesis/kt4/test_data/terr/terr/dem_meixi.tif
6862fdfc0fa14ecdbf0aa6d76caeaa14	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[118.49507609668993,25.89088083465758],[118.90558387492864,25.89088083465758],[118.90558387492864,26.26774978106839],[118.49507609668993,26.26774978106839],[118.49507609668993,25.89088083465758]]]}	t	slope.tif	/onesis/kt4/dem/slope.tif	29408	slope	/onesis/kt4/dem/slope.tif
7bcdd4697e654aa182beb1e165591831	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[118.49507609668993,25.89088083465758],[118.90558387492864,25.89088083465758],[118.90558387492864,26.26774978106839],[118.49507609668993,26.26774978106839],[118.49507609668993,25.89088083465758]]]}	t	dem_meixi	/onesis/kt4/test_data/terr/terr/dem_meixi.tif	29497	dem_meixi	/onesis/kt4/test_data/terr/terr/dem_meixi.tif
70d0de2a118c4eec9fcb36392db5a7d7	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[118.49507609668993,25.89088083465758],[118.90558387492864,25.89088083465758],[118.90558387492864,26.26774978106839],[118.49507609668993,26.26774978106839],[118.49507609668993,25.89088083465758]]]}	t	ad81843304357119000576	/onesis/kt4/dem/ad81843304357119000576.tif	29508	ad81843304357119000576	/onesis/kt4/dem/ad81843304357119000576.tif
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\.


--
-- Data for Name: map_service; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.map_service (id, boundary, owner_path, svc_name, svc_path) FROM stdin;
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30245	null	/onesis/kt4/job_results/air_prediction_result_1204.csv	air_prediction_result_1204	/kt4/job_results/air_prediction_result_1204.csv
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30248	null	/onesis/kt4/test_data/regr/_whhp/res/neardist_12041418.csv	neardist_12041418	/kt4/test_data/regr/_whhp/res/neardist_12041418.csv
30249	{"type":"Polygon","crs":{"type":"name","properties":{"name":"EPSG:4326"}},"coordinates":[[[118.49507609668993,25.89088083465758],[118.90558387492864,25.89088083465758],[118.90558387492864,26.26774978106839],[118.49507609668993,26.26774978106839],[118.49507609668993,25.89088083465758]]]}	/onesis/kt4/test_data/terr/result/slope_dir.tif	slope_dir	/kt4/test_data/terr/result/slope_dir.tif
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30253	null	/onesis/kt4/job_results/air-wl-1206.csv	air-wl-1206	/kt4/job_results/air-wl-1206.csv
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30267	null	/onesis/kt4/job_results/air_wl_1211.csv	air_wl_1211	/kt4/job_results/air_wl_1211.csv
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\.


--
-- Data for Name: meta_application; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.meta_application (id, domain, mechanism, objective, link_models_id) FROM stdin;
1360	地理流	\N	预测未知流的流量	20368
1439	可达性	使用累积机会法计算可达性。从居民区出发，计算搜索范围内途径格网包含的所有设施点数量。	计算搜索范围内能够到达的城市设施数	\N
1440	可达性	使用重力法计算可达性	计算搜索范围内能够到达的城市设施数	\N
1442	可达性	使用两步移动搜索法计算可达性	计算两步移动搜索范围内能够到达的城市设施数	\N
1462	空间数据模拟	基于高斯随机场的序贯模拟方法生成空间数据。	生成符合指定统计特性和变异结构的空间数据，用于进一步的分析和决策支持。	22091
1543	地理流	\N	估计流量	23045
1544	结构化事务数据的多目标优化	\N	\N	\N
2252	判别分析	按照一定的判别准则,建立判别函数,用研究对象的大量资料确定判别函数中的待定系数,并计算判别指标。	求解判别系数，进而计算对象的判别指标，判断其类别归属。	33853
2253	局部尺度下的判别分析	在研究区域中抽样回归分析点,针对每个位置分别采用加权线性最小二乘方法进行回归模型解算,得到与特定时间、空间位置一一对应的回归系数,量化反映研究区域内多元变量关系的时空异质性特征。	是融合地理加权建模思想,通过构建地理位置相关的判别函数,在局部尺度下求解关于空间位置的判别系数,进而计算地理空间对象的判别指标,判断其类别归属,提升分类精度。	33861
2254	图像分类	对输入影像数据集使用编码器进行深度特征提取，再使用解码器从特征中获取各区域块的空间和语义信息。	依据先验信息对数据进行分类	33878
2255	语义分割	网络由不同模块组成，输入数据经过模块计算后得到图像每个像素所属类别，最后输出图像分割结果。	依据先验信息对影像数据进行图像分割	\N
2256	图像语义分割	对输入影像数据集使用编码器进行深度特征提取，再使用解码器从特征中获取各区域块的空间和语义信息。	依据先验信息对影像数据分割成具有一定语义含义的区域块，并识别出每个区域块的语义类别。	\N
2257	图像语义分割	对输入影像数据集使用编码器进行深度特征提取，再使用解码器从特征中获取各区域块的空间和语义信息。	依据先验信息对影像数据分割成具有一定语义含义的区域块，并识别出每个区域块的语义类别。	\N
2258	目标识别	\N	依据先验信息对影像数据进行识别，生成预测框	\N
2259	图像目标检测与分割	使用区域建议网络生成候选区域，并通过卷积网络对这些区域进行分类和边界框回归，同时生成像素级的分割掩码。	使用Mask R-CNN模型对图像进行目标检测与实例分割，识别并标注图像中的目标。	34044
2260	图像分类	网络由不同层次的结点集合组成，输入数据经过每个隐藏层计算后得到数据预测为每个类别的概率，并依据概率做出图像分类。	依据先验信息对影像数据进行分类	\N
2261	目标检测	YOLO将图像划分为网格，每个网格负责预测包含在其范围内的目标。对于每个网格，模型生成多个边界框和相应的置信度分数，以及每个边界框对应的类别概率。通过非极大值抑制（NMS），去除冗余的边界框，最终输出检测到的目标及其位置。	依据先验信息对影像数据指定目标的检测	\N
2262	图像目标检测	对输入影像数据集使用编码器进行深度特征提取，再使用解码器从特征中获取目标的类别标签、位置信息（边界框坐标）以及置信度分数。	依据先验信息处理影像数据，输出目标的类别标签、位置信息（边界框坐标）以及置信度分数。	34137
2263	图像语义分割	对输入影像数据集使用编码器进行深度特征提取，再使用解码器从特征中获取各区域块的空间和语义信息。	依据先验信息对影像数据分割成具有一定语义含义的区域块，并识别出每个区域块的语义类别。	34151
2264	图像分类	对输入影像数据集使用编码器进行深度特征提取，再使用解码器从特征中获取各区域块的空间和语义信息。	根据图像信息中所反映的不同特征，把不同类别的图像进行区分	34191
2265	图像目标检测	对输入影像数据集使用编码器进行深度特征提取，再使用解码器从特征中获取目标的类别标签、位置信息（边界框坐标）以及置信度分数。	依据先验信息处理影像数据，输出目标的类别标签、位置信息（边界框坐标）以及置信度分数。	34220
2266	目标检测	对输入图像数据使用编码器进行特征提取，再使用检测器从特征中识别并定位对象。	依据先验信息对图像数据进行检测，识别并定位图像中的对象。	34236
2267	图像语义分割	对输入影像数据集使用编码器进行深度特征提取，再使用解码器从特征中获取各区域块的空间和语义信息。	依据先验信息对影像数据分割成具有一定语义含义的区域块，并识别出每个区域块的语义类别。	34286
2268	结构化事务数据分类	Adaboost是一种基于迭代的集成学习算法，用反复调整的数据来训练一系列的弱学习器，由这些弱学习器的预测结果通过加权投票(或加权求和)的方式组合, 产生最终的预测结果。	根据先验信息对待预测数据进行分类型预测	\N
2269	结构化事务数据分类	根据训练样本集，通过随机采样策略分别训练几个不同的模型，然后通过集合策略让所有模型表决测试样例的输出。	对输入数据进行监督分类	\N
2270	结构化事务数据分类	梯度提升决策树算法在训练基分类器时采用串行的方式，各个基分类器之间有依赖，它的基本思路是将基分类器层层叠加，每一层在训练的时候，让新的预测器对前一个预测器的残差进行拟合。预测时，根据各层分类器的结果的加权得到最终结果	基于梯度提升决策树的学习算法在表格类数据挖掘、预测方面具有巨大的优势，在贷款风险、疾病预测、入侵检测等领域具有广泛的应用	\N
2271	格网数据分类	收集所有局决策树分类投票结果后，将投票次数最多的类别指定为最终的输出	根据先验信息对待预测数据进行分类型预测	\N
2272	结构化事务数据分类	收集所有决策树分类投票结果后，将投票次数最多的类别指定为最终的输出	根据先验信息对待预测数据进行分类型预测	\N
2273	格网数据分类	在定类决策上依据最邻近的k个样本的类别来决定待分样本所属的类别	给定先验信息，在特征空间中根据某种距离度量找出训练集中与其最靠近的k个训练样本，基于这些样本信息进行类别预测	\N
2162	可达性	使用两步移动搜索法计算可达性	计算两步移动搜索范围内能够到达的城市设施数	\N
2274	结构化事务数据分类	在定类决策上依据最邻近的k个样本的类别来决定待分样本所属的类别	给定先验信息，在特征空间中根据某种距离度量找出训练集中与其最靠近的k个训练样本，基于这些样本信息进行类别预测	\N
2275	格网数据分类	求解能够正确划分训练数据集并且在特征空间中几何间隔最大的分离超平面	根据先验信息对待预测数据进行分类型预测	\N
2276	结构化事务数据分类	求解能够正确划分训练数据集并且在特征空间中几何间隔最大的分离超平面	根据先验信息对待预测数据进行分类型预测	\N
2277	结构化事务数据分类	假设特征的分布是符合伯努利分布，那么就可以通过该分布函数计算出任意特征值所对应的概率，以此进行空间数据的分类通过贝叶斯准则计算出各个类别的概率进行决策，以此进行空间数据的分类。	根据先验信息对带预测数据进行分类型预测。	\N
2278	数据分类	对一批样本进行增量拟合,方法需要对数据集的不同块连续调用多次，以实现核外学习或在线学习。当整个数据集太大而无法立即放入内存拟合时，这个功能特别有用。	一种基于概率方法的机器学习的分类技术	\N
2279	数据类别预测	假设特征的分布是符合高斯分布，那么就可以通过该分布函数计算出任意特征值所对应的概率，以此进行空间数据的分类	根据先验信息对待预测数据进行类别预测	\N
2280	结构化事务数据分类	基于多项式模型的朴素贝叶斯分类器，主要适用于离散特征的概率计算，多用于文档分类，它可以计算出一篇文档为某些类别的概率，最大概率的类型就是该文档的类别。	根据先验信息对待预测数据进行类别预测	\N
2281	格网数据分类	对于给定的训练集，首先基于特征条件独立假设学习输入输出的联合概率分布（朴素贝叶斯法这种通过学习得到模型的机制，显然属于生成模型）；然后基于此模型，对给定的输入x，利用贝叶斯定理求出后验概率最大的输出y。	根据先验信息对待预测数据进行类别预测	\N
2082	栅格数据地理代数运算	输入数据为地理空间对象或对象集，参数包括加、减、乘、除等各类代数运算符及地理分析函数，输出结果为新对象。	对栅格数据进行简单代数运算。	31423
2083	数据转换	栅格或者矢量数据转换为目标坐标系	数据投影转换	31431
2084	矢量数据几何中心量算	几何中心由构成地理空间对象的特征点空间位置的算术平均值或加权平均值计算。输入为地理空间对象，输出为对象对应的几何中心点坐标，通常适用于以矢量数据形式表达的地理空间对象。	用于计算矢量数据的几何中心。	31434
2085	矢量数据表面积量算	表面积由构成地理空间面对象空间坐标按照多边形面积公式计算。输入为地理空间面对象，输出为对象对应的几何面积。	用于计算矢量数据的表面积。	31441
2086	矢量数据缓冲区分析	遍历计算输入要素中每个折点的缓冲区偏移，创建输出缓冲区要素	提取矢量对象的邻域范围	\N
2087	矢量数据缓冲区分析	遍历计算输入要素中每个折点的多层缓冲区偏移，创建输出多层缓冲区要素	提取矢量对象的邻域范围	\N
2088	栅格数据缓冲区分析	计算每个像元与输入栅格单元之间的欧氏距离，输出距离小于设定阈值的栅格单元作为缓冲区	确定栅格像元指定距离内的目标区域。	\N
2089	数学形态学分析	数学形态学分析是一种以形态结构元素为基础对地理空间对象数据进行处理的邻域分析算法，如腐蚀、膨胀、开运算和闭运算等功能。腐蚀是利用某一结构元素对数据进行消除边界点，使边界向内部收缩的过程;膨胀是将某一结构元素合并到数据中，使边界向外部扩张的过程;开运算是先腐蚀再膨胀的过程;闭运算是先膨胀再腐蚀的过程。	对于输入的面要素、结构元素半径，实现膨胀、腐蚀、开运算和闭运算	\N
2090	矢量数据叠置分析	将移除输入和叠加矢量文件之间的所有重叠要素或重叠要素的一部分，仅输出两个输入中一个输入中出现的要素，但不会同时输出这两个输入中出现的要素。	通常用于空间矢量对象间叠置分析（补）操作。	31515
2091	矢量数据叠置分析	相交矢量叠加操作的结果包括两个输入图层中出现的所有要素部分，不包括所有其他部分。	通常用于空间矢量对象间的叠置分析（交）操作。	31523
2092	矢量数据叠置分析	在矢量图层重叠处分割矢量图层，从而创建一个包含输入图层和叠加图层中所有部分的图层。	通常用于空间矢量对象间的叠置分析（并）操作。	31533
2093	用于计算两个几何对象之间的距离	根据用户指定的距离类型，计算两个几何对象之间的距离	计算两个几何对象之间的距离	\N
2095	时空拓扑关系分析	\N	\N	\N
2126	地理流分析	\N	本算法旨在计算地理流数据聚类后各个类别的中心流以及最接近中心流的实际流	32384
2127	地理流分析	\N	该方法使用Kmeans方法对地理流数据进行聚类。	32395
2128	图像分割	将多通道栅格数据转为多维特征数据，应用Kmeans对其进行划分	将图像中特征较为相似的像素点进行聚类	\N
2129	空间点聚类	对于空间点对象建立三角网，通过删除三角网中的异常长边和“颈”、“链”边，得到的每个子图的空间点对象集合即为空间簇	在局部密度差异较大的空间点数据中发现任意形状的空间簇	\N
2130	空间点对象聚类	对于空间点对象建立三角网，通过删除三角网中的异常长边，得到的每个子图的空间点对象集合，即空间簇	空间对象簇不同密度差异较大的空间数据库中发现任意形状的空间簇	\N
2131	空间点聚类	综合了层次算法和迭代的重定位方法，将点与点之间的聚类，转换成为点与簇中心之间的聚类	超大规模数据集聚类	\N
2163	社区发现	使用infomap算法进行社区发现	对网络数据进行不重叠社区发现	\N
2132	空间点聚类	通过随机抽样，然后在抽样数据上进行层次聚类，找到每个簇的代表点，然后将代表点向簇中心收缩，最后根据最近的代表点合并簇	在空间点数据中发现簇	\N
2166	网络缓冲区构建算法	使用最短路径算法构建缓冲区	构建公共交通网络中的缓冲区	\N
2167	网络缓冲区构建算法	选中任意一条路径，使用最短路径算法搜索其给定路网距离内的所有路径	给定一定的路网距离，为选定的路径构建缓冲区	\N
2168	道路网络Voronoi图构建	使用最短路径算法构建道路网络Voronoi图	道路网络Voronoi图构建	\N
2133	空间点聚类	将空间点对象邻域半径内邻居数量定义为密度，任选一个未被访问的空间点对象，若其密度大于指定阈值，则与其邻域对象密度相连为一个簇，迭代访问簇内其他未被访问对象，重复上述过程直到所有空间点对象均被访问完毕，最终提取所有密度相连的簇	在具有噪声对象的空间点数据中发现任意形状的高密度空间簇	\N
2134	空间点聚类	将输入的空间点数据降维表示成网络中的活动点，然后对于活动点进行竞争、协作和适应这三个学习过程，最后得到的活动点代表输入空间点的聚类结果	通过生成一个低维的映射网络对于空间点数据进行聚类	\N
2135	空间点对象聚类	通过采用高斯混合模型，反复迭代估计概率分布的参数和数据点的分配	适用于各种数据类型和复杂度的聚类	\N
2136	空间点聚类	随机选取k个空间点对象作为初始簇中心，通过“距簇中心最近”原则将所有地理空间对象分配给最近的簇，据此更新各簇中心位置，迭代上述过程直到满足终止条件	对空间点数据进行聚类划分	\N
2137	空间点聚类	搜索点沿着空间点密度增加的方向“漂移”到局部密度极大点，反复迭代搜索特征空间中样本点最密集的区域	在分布均匀的点群中找到显著聚集的点簇	\N
2138	空间聚集模式	基于图论的聚类算法	该算法首先对所有的数据点建立生成最小生成树（MST），然后逐步删除最小生成树（MST）中具有最大长度的那些边,从而形成更多的聚类。	\N
2139	空间点聚类	在不同层次的邻域点密度下，对未被访问的核心点寻找密度可达的其他核心点，与其邻域内的点聚为一个簇	克服DBSCAN算法中全局参数难以适应密度不均匀情况下的聚类	\N
2140	基于最小生成树的地理对象非空间属性与空间属性聚类方法	该方法首先地理对象的空间位置邻近性约束进行聚类,获得地理对象的空间分布模式和它们之间的空间邻近关系;然后,根据其功能语义相似性约束进行分割,获得初步聚类结果;最后,考虑簇内相似性与簇间差异性进行整体优化,获得最终聚类结果.	根据地理对象的空间分布模式和空间邻近关系,顾及地理对象的功能语义进行聚类	\N
2141	顾及非空间属性的空间点对象聚类	该方法首先借助附加约束的Delaunay三角网来建立空间实体之间的邻接关系，然后对基于密度的聚类方法进行改进，顾及空间邻近与非空间属性相似性进行聚类	顾及空间邻近与非空间属性，从空间数据库中发现任意形状的空间簇	\N
2142	基于快速重排检验的适应性聚类	首先通过重排检验发现时空数据集中的均质子区域；进而，采用均方误差准则合并均质子区域内的时空实体生成时空簇，并通过簇内重排检验自动识别聚类合并的终止条件.	融合时空邻近与专题属性相似的时空聚类	\N
2143	移动聚类	\N	这种算法适用于许多大型时空数据集。	\N
2144	移动聚类	\N	这种算法适用于许多大型时空数据集。	\N
2145	用于轨迹聚类的分区-分组框架	算法包括两个阶段:分区和分组。对于第一阶段，使用最小描述长度(MDL)原则的形式化轨迹划分算法。第二阶段，利用基于密度的线段聚类算法实现对轨迹的聚类。	轨迹分区为一组线段，然后将相似的线段归为一个群组，从轨迹数据库中发现共同的子轨迹。	\N
2146	时空聚集分布模式	扩展传统DBSCAN邻域的定义,将时间邻域纳入邻域的定义之中,使得DBSCAN能在时空点数据中应用	基于时间距离和空间距离的邻域半径参数共同定义密度聚类中的核点概念；另外，该算法通过为每个聚类分配一个密度因子解决DBSCAN算法无法适应地理空间对象不同密度分布中探测簇的局限性。	\N
2147	时空聚类分析	\N	对于输入的面要素、结构元素半径，实现时空聚类分析	\N
2148	地理流	\N	对未知流的属性进行插值	32724
2149	地理流	\N	该方法使用现有样本流数据对未知流的属性进行插值。	32732
2150	考虑空间异质性特征的指定时间的空间变量预测。	利用时空地理加权回归分析模型在预测点位置进行求解,通过具有自变量观测值的样本与对应位置的参数估计进行变量预测。	通过时空地理加权回归分析模型和样本空间数据进行对应时刻的空间位置上的因变量值预测,可用于多元变量背景下的时空变量预测。	32741
2151	考虑空间异质性特征的空间变量预测。	在带宽优选的基础上,将地理加权回归分析模型在预测点位置进行求解,通过具有自变量观测值的样本与对应位置的参数估计进行变量预测。	通过地理加权回归分析模型和样本空间数据进行对应空间位置上的因变量值预测,可用于多元变量背景下的空间变量预测。	32766
2152	矢量点数据插值	将已知点到预测点的距离带入反距离权重计算方程，获得各已知点相对于预测点的权重，再对已知点进行加权计算便可得到预测点的值。	通常用于以矢量点数据形式表达的地理空间对象插值。	32787
2153	土壤属性空间推测	\N	基于样点个体代表性的方法推测土壤属性的空间分布	32803
2154	土壤属性空间推测	克里金插值方法基于空间自相关性，通过半方差函数量化样点间的变异性，利用最优线性无偏估计法对未知点进行插值预测，实现高精度空间推测。	克里金插值方法推测土壤属性的空间分布	32826
2155	矢量点数据线性插值	按照已知点到预测点的距离,线性计算预测点的值。	以矢量点数据形式表达的地理空间对象插值。	32836
2156	普通克里金插值	基于变异函数理论和结构分析,通过区域化变量的空间观测值来构建相应的变异函数模型,利用克里金插值法进行内插。	矢量点数据的地理空间对象普通克里金插值。	32844
2157	土壤属性空间推测	\N	克里金插值方法推测土壤属性的空间分布	32857
2158	地理空间数据分析	使用 B 样条细化将这些函数的总和减少到一个等效的 B 样条函数，从而实现性能提升。	散布数据的网格化，提升性能提升。	\N
2159	可达性	使用累积机会法计算可达性。从居民区出发，计算搜索范围内途径格网包含的所有设施点数量。	计算搜索范围内能够到达的城市设施数	\N
2160	可达性	使用重力法计算可达性	计算距离衰减范围内能够到达的城市设施数	\N
2161	通达性分析	基于时间地理学构建个人的活动空间，然后基于累计机会法度量个人可达性	实现个体的可达性度量	\N
2164	社区发现	使用cpm算法进行社区发现	对网络数据进行重叠社区发现	\N
2165	社区发现	基于网络快照模型进行社区发现	对网络数据进行时变社区发现	\N
2169	路径规划	使用启发式搜索解决有权图的单源最短路径问题	为道路网络中的点寻找最短路径，从而实现路径规划	\N
2170	路径规划	使用广度优先搜索的方法解决有权图的单源最短路径问题	为道路网络中的点寻找最短路径，从而实现路径规划	\N
2171	路径规划	使用广度优先搜索的方法解决时变道路网络中的最短路径问题	为时变道路网络中的点寻找最短路径，从而实现路径规划	\N
2172	路径规划	使用启发式搜索解决有权图的单源最短路径问题	为道路网络中的点寻找最短路径，从而实现路径规划	\N
2173	路径规划	使用启发式搜索解决有权图的单源最短路径问题	为道路网络中的点寻找最短路径，从而实现路径规划	\N
2174	公交网络中的最短路径计算	公交网络中的最短路径计算	公交网络中的最短路径计算	\N
2175	路径规划	使用Dijkstra算法解决转向道路网络中的最短路径问题	为转向道路网络中的点寻找最短路径，从而实现路径规划	\N
2176	地理加权空间交互模型	在空间交互网络建模的基础上，针对每个位置分别采用加权线性最小二乘方法进行回归模型解算，得到与空间位置一一对应的回归系数，量化反映研究区域在O端模式、D端模式和OD流模式的多元变量关系和空间异质性特征。	在空间交互网络建模的基础上，采用地理加权回归分析技术进行求解多元变量模型，并提供O端模式、D端模式和OD流模式分别定义不同的模型求解算法，从不同角度理解空间交互网络的复杂性和非均衡性特征。	33030
2177	空间交互模型	在研究的流数据中考虑与出发地和目的地的空间语义与属性要素，利用重力模型和线性回归方法，量化分析空间交互量与多元要素特征间的关系。	通过空间交互网络的定量分析模型,量化分析空间交互量与多元要素特征间的关系。	33057
2178	网络时空棱柱构造	使用最短路径算法构建时空棱柱	构建个体在路网中的时空棱柱	\N
2179	网络时空棱柱构造	使用最短路径算法构建时空棱柱	构建个体在路网中的时空棱柱	\N
2180	结构化事务数据的多目标优化	\N	\N	\N
2181	遥感分析	\N	随机算法	\N
2182	空间数据模拟	基于高斯随机场的序贯模拟方法生成空间数据。	生成符合指定统计特性和变异结构的空间数据，用于进一步的分析和决策支持。	33086
2183	全局线性回归分析	在残差项服从均值为0的正态分布情况下,对多元变量采用线性最小二乘方法进行模型求解,全局描述变量关系。	在全局视角下,对多元变量进行线性回归,描述变量关系	33091
2184	全局逻辑回归分析	针对因变量为分类或定性变量的场景的一种广义线性回归分析模型。	针对因变量为分类或定性变量的线性回归	33098
2185	全局泊松回归分析	针对因变量服从泊松分布的场景的一种广义线性回归分析模型。	针对因变量服从泊松分布的全局回归	33105
2186	时间序列预测	首先将时间序列分解成趋势项，周期项，节假日项和误差项，然后通过机器学习算法stan拟合模型，累加得到时间序列的预测值	根据输入的历史时间序列数据，预测时间序列未来的趋势	33112
2231	局部空间自相关	利用整个研究区域内地理空间对象的邻近性，分析其非空间属性空间聚集分布模式的算法。该算法按照某种距离度量方式，量化地理空间对象非空间属性值与经距离加权的非空间属性值之间的相关性。	利用每个地理空间对象与其他地理空间对象的空间邻近性，分析非空间属性相对于每个地理空间对象的聚集分布模式。	33665
2187	局部尺度下的时空回归分析	在研究区域中抽样回归分析点,针对每个位置分别采用加权线性最小二乘方法进行回归模型解算,得到与特定时间、空间位置一一对应的回归系数,量化反映研究区域内多元变量关系的时空异质性特征。	通过计算时空距离对回归分析模型进行关于空间和时间维度的局部尺度求解,同时解释多元变量关系在时间和空间上所呈现的异质性和非平稳性特征,可用于关系解释和归因分析等应用。	33120
2188	局部尺度下的空间回归分析	在研究区域中抽样回归分析点，针对每个位置分别采用加权线性最小二乘方法进行回归模型解算，得到与空间位置一一对应的空间回归系数，量化反映研究区域内多元变量关系的空间异质性特征。	通过在研究区域中抽样回归分析点，在每个位置上采用迭代法对局部逻辑回归分析模型进行求解，得到与空间位置一一对应的系数估计，量化反映服从二项分布的空间数据关系的异质性或非平稳性特征。	33142
2189	局部尺度下的空间回归分析	在研究区域中抽样回归分析点，针对每个位置分别采用加权线性最小二乘方法进行回归模型解算，得到与空间位置一一对应的空间回归系数，量化反映研究区域内多元变量关系的空间异质性特征。	通过在研究区域中抽样回归分析点，在每个位置上采用迭代法对局部泊松回归分析模型进行求解，得到与空间位置一一对应的系数估计，量化反映服从泊松分布的空间数据关系的异质性或非平稳性特征。	33163
2213	多尺度同位模式挖掘	多尺度同位模式挖掘	多尺度同位模式挖掘	\N
2193	随机抽样	从目标总体中随机抽取样本点。	从目标总体中随机抽取一部分作为样本，每个样本单元被抽中的概率相等，样本的每个单元完全独立	33236
2194	规则抽样	从目标总体中规则地抽取样本点。	对目标总体进行排列，并按相同的所设定间隔进行采样。	33241
2195	三明治抽样	利用抽样对象在空间上变异特征,考虑数据相关性的特点,根据抽样对象特征进行分层抽样。	根据抽样对象在空间上的空间分层异质性进行抽样。	33250
2196	空间-专题属性异常检测	首先采用约束 Delaunay 三角剖分来自适应地构建空间邻域关系。在此基础上，构建了一种基于高斯函数的考虑非空间属性值变化的空间异常程度测量方法，以确定空间异常对象。 以识别出的空间异常物体为种子，进一步采用多向优化方法统计发现非空间属性值偏差较大的异常区域。	这种算法适用于许多异常检测应用，特别是定量评估识别任意形状的异常区域方面。	\N
2197	空间轨迹异常检测	通过将轨迹数据划分为内点（正常点）和异常点，对于密集的轨迹区域将内点分组，而对于稀疏的轨迹区域将异常点标记出来，从而检测出异常轨迹。	这种算法适用于许多异常检测应用，特别是那些需要考虑轨迹密度的应用场景。	\N
2198	空间交互流异常检测	\N	对于输入的地理空间数据，实现异常空间交互模式的检测	\N
2199	流量异常检测	\N	对于输入的交通流量数据，实现动态邻域检测流量异常	\N
2200	时空序列异常探测	\N	\N	\N
2201	空间异常模式	该算法利用Delaunay三角网构建目标点对象和参考点对象之间的空间邻接关系，通过边长分布特征自适应地构建层次性的全局边长约束和局部边长约束，量化参考点对目标点的空间分布依赖关系，最后通过顾及距离目标点的空间距离和依赖关系的局部差异性，基于三阶边长约束对目标点和参考点的二元依赖关系进行分割，实现二元空间异常探测，同时计算空间二元异常值。	将与参考点分布不一致的目标点视为异常点	\N
2202	流空间异常探测	\N	\N	\N
2203	异常检测、数据挖掘	准备轨迹数据，将轨迹点映射到一个网格上。这个网格可以看作是地理空间的离散化表示，用于更方便地处理轨迹数据。从所有轨迹中随机选择一些子样本轨迹，这些子样本轨迹将用于后续的计算。针对每个轨迹点，在子样本轨迹中查找是否存在相同的点。如果存在，将该轨迹点标记为非异常，否则标记为异常。通过统计被标记为异常的轨迹点数量，计算轨迹的异常分数。根据异常分数，可以设定一个阈值来判定轨迹是否异常。如果异常分数高于阈值，则认为该轨迹是异常的，否则是正常的。	异常轨迹检测算法适用于识别移动对象轨迹数据中的异常行为。它的主要应用领域包括：交通堵塞、车辆违规行驶等	\N
2204	轨迹异常检测、数据挖掘	准备轨迹数据，将轨迹点映射到一个网格上。这个网格可以看作是地理空间的离散化表示，用于更方便地处理轨迹数据。算法会根据网格中的历史轨迹数据集中的子轨迹，筛选掉与之不一致的历史轨迹。换句话说，它会删除那些与工作窗口中的子轨迹不相符的历史轨迹。只要工作窗口中子轨迹的支持度高于某个阈值（通常用符号θ表示），一旦子轨迹的支持度下降到低于阈值θ，则说明这个是异常轨迹点，将其保存到异常轨迹窗口中。不断重复上述过程	异常轨迹检测算法适用于识别移动对象轨迹数据中的异常行为。它的主要应用领域包括：交通堵塞、车辆违规行驶等	\N
2205	空间异常模式	通过度量地理空间点对象在其k邻域内与其它地理空间点对象间的可达距离表征该对象的局部可达密度，将局部异常度定义为地理空间点对象局部可达密度与其空间邻近对象的局部可达密度均值之比，基于此将局部异常度较大的地理空间点对象识别为一元空间点过程异常	将空间局部密度较高的点识别为异常点	\N
2232	时间序列数据基础统计分析	ACF描述了一个观测值和另一个观测值之间的自相关，包括直接和间接的相关性信息。PACF只描述观测值和其k阶滞后项的直接关系，调整了其他较短滞后项(<k)的影响	描述序列的当前值与其过去值之间的相关程度	33676
2206	空间-专题属性异常检测	该算法将空间点对象分为基本实体和参考实体，利用交叉K函数检验两类点对象共现的显著性，将基本实体共现强度定义为缓冲区邻域中的参考实体的个数。进一步利用层次约束的Delaunay三角剖分为基本实体，构建稳健合理的空间邻域关系。在此基础上，基于α-shape算法构建支撑域，利用空间随机过程为基础构建零模型，利用蒙特卡洛模拟检验空间交叉异常的显著性。	这种算法适用于考虑多类空间点的共现来统计识别显着的异常值的异常探测应用。	\N
2207	基于自适应回归的空间-专题属性异常探测	该方法构建不规则三角网来获取各个区域之间稳定合理的地理邻近关系,根据空间邻近关系获取自适应回归带宽,并纳入地理加权回归模型进行计算,最后根据回归残差项的统计分布性质开展空间异常度量与分析	顾及空间邻近与非空间属性，从空间数据库中发现专题属性异常的点	\N
2208	空间点聚类	对于空间点对象建立三角网，通过删除三角网中的异常长边和“颈”、“链”边，得到的每个子图的空间点对象集合即为空间簇	在局部密度差异较大的空间点数据中发现任意形状的空间簇	\N
2209	空间离群点	基于层次约束图的地理空间离群点对象自适应探测算法	基于层次约束图的地理空间离群点对象自适应探测算法	\N
2210	时空模式挖掘	\N	对于输入的地理空间数据，实现时空级联模式的挖掘	\N
2211	时序关联模式挖掘	若某前件在感兴趣后件邻域内发生概率远大于其在整体序列中平均发生概率，则认为该前件与感兴趣后件之间存在较强的时序关联关系	该算法指定任一感兴趣事件作为时序关联规则约束后件，提取时间影响域内前置事件及其局部分布特征，同时将整体时间序列中各前件独立分布特征作为全局特征，分析其局部分布特征和全局分布特征的差异	\N
2212	非时空频繁事务发掘	逐层搜索满足支持度和置信度的基础频繁项集，通过不断迭代连接策略和剪枝策略以组成高级频繁项集，得到的有效频繁项集即为关联规则。	挖掘非时空事务数据库中频繁共现事件间的关联关系	\N
2214	时空同位模式挖掘	地理现象在特定时间的特定地点是否发生，可以建模为布尔特征(对象类型)和实例(对象)。给定这样的数据集，不同布尔特征的子集(其实例通常在空间和时间上紧密接近地同时出现)被识别为时空共现模式(stcop)。STCOP检测是时空关联规则挖掘的主要任务之一，有助于识别涉及空间和时间的不同特征之间的潜在相互作用。时空同位在时空格局分析的其他任务中也发挥着关键作用	对于给定的时空点集，挖掘不同类别点集之间的具有统计显著性的同位模式	\N
2215	时空遥相关挖掘	\N	这种算法适用于许多遥相关应用，能够发现时空数据之间的遥相关模式。	\N
2216	空间点对象聚集程度度量	根据每个地理空间对象与其最近邻对象之间的平均距离计算最近邻指数。	研究空间点的聚集程度	\N
2217	专题属性值分布统计分析	将一组专题属性值划分到若干区间范围内，统计各区间的频数	对专题属性值进行频率统计并反映专题属性值的聚集程度	\N
2218	空间点密度分析	将研究区域划分为若干栅格单元，根据落入每个单元周围邻域内的点要素的数量，并根据核函数赋给不同权值，求和计算得到单元的点密度值	研究空间点密度分布	\N
2219	聚集分布模式	利用地理空间对象不同邻域分析半径范围内邻居对象的点密度度量地理空间对象的聚集分布模式	利用地理空间对象不同邻域分析半径范围内邻居对象的点密度度量地理空间对象的聚集分布模式	\N
2220	地理加权视角下的主成分分析	通过计算地理空间对象的属性关于空间位置的方差-协方差矩阵并进行正交分解，得到对应的特征值和特征向量，从而识别各个主成分载荷在精细空间尺度下的变化，以更好地体现地理空间对象属性的空间异质性特征。	通过关于位置的局部加权回归分析模型求解，以随着空间位置不同，时间点差异而变化的参数估计结果，量化反映时空数据关系中的异质性或非平稳性特征	33547
2221	降维分析	使用数据的奇异值分解将其投影到较低维空间的线性降维。	对高维数据进行降维	\N
2222	空间点数据降维	该算法将样本间的距离转换为联合概率分布来表达点与点之间的相似度，并在低维空间中构建这些点的概率分布，使相似的样本由邻近的点建模、不相似的样本由远点建模，以达到降维的目的。	高维点数据	\N
2223	地理流	\N	OD流热点分析	\N
2224	地理流	\N	预测未知流的流量	33596
2225	地理流分析	\N	本算法旨在计算地点的I指数，可用于量化城市地点的不可替代性。	33605
2226	空间分异性	假设研究区分为若干子区域，如果子区域的方差之和小于区域总方差，则存在空间分异性；如果两变量的空间分布趋于一致，则两者存在统计关联性	探测空间分异性，以及揭示其背后驱动因子	\N
2227	精细尺度下的探索性数据分析	对特定的空间位置进行局部尺度下均值和偏度的计算，以量化描述在不同尺度（带宽）条件下单个空间变量的空间异质性特征	针对预设的空间位置点进行局部尺度的均值和偏度计算，以此量化表征空间变量之间的异质性特征，可用于探索性数据分析、单变量空间插值等应用。	\N
2229	精细尺度下的探索性数据分析	地理加权方差-协方差矩阵是利用求解关于位置的方差-协方差矩阵的思路，描述局部尺度下多元空间变量差异特征的分析方法。	利用求解关于位置的方差-协方差矩阵的思路，描述局部尺度下空间变量差异特征，可用于局部方差分析、地理加权相关性分析和地理加权主成分分析等算法。	33642
2230	全局空间自相关	利用整个研究区域内地理空间对象的邻近性，分析其非空间属性空间聚集分布模式的算法。该算法按照某种距离度量方式，量化地理空间对象非空间属性值与经距离加权的非空间属性值之间的相关性。	通过关于位置的局部加权回归分析模型求解，以随着空间位置不同而变化的参数估计结果，量化反映空间数据关系中的异质性或非平稳性特征	33653
2234	水文分析数据预处理	该算法根据单流向算法计算的水流方向及分配，确定每一个像元上水流的累积量。	通常用于生成流量累积网格（即集水区）。	33688
2235	水文分析数据预处理	流向算法是计算地表水流方向的算法。D8算法是最常用的单流向算法，该算法将流向确定为中心计算单元到所在邻域窗口（如3×3）中的最大坡降方向。	计算生成流向	33697
2240	地形因子提取	\N	水流强度指数	33748
2242	地形分析	将相邻单元斜坡方向的偏差与中心单元方向的偏差取平均值，再减去 90 度，即可得到该指数。	表示地形栅格与邻域栅格的坡向的一致性	\N
2243	地形因子提取	过地形表面上某点得水平面沿水平方向切地形表面所得的曲线在该点的曲率值，即该点所在的地面等高线的弯曲程度	计算生成平面曲率	33775
2244	地形因子提取	此工具可计算剖面（或垂直）曲率，或沿流线的斜率变化率，从数字高程模型 （DEM） 获取。它是具有共同切线的正态截面的曲率在表面上给定点处具有坡度线的线。此变量具有无界可以采用正值或负值的范围。该指数的正值表示流动加速度，而负曲线曲率值表示流量减速。	垂直曲率	\N
2245	地形因子提取	此工具计算切向（或水平）曲率，即倾斜度的曲率垂直于流动方向和表面的平面。或者它可以被描述为在给定处与等高线相切的正态截面的曲率地形表面的点。此变量具有无限范围，可以是阳性或阴性。正值表示流动发散，而负切线表示曲率值表示流动收敛。因此，切向曲率类似于平面曲率。	算切向（或水平）曲率	\N
2246	地形因子提取	地形起伏度（粗糙度）是定量表达中心像元与周围像元之间的最大差异	计算生成地形起伏度	\N
2247	地形因子提取	坡度算法计算地表坡面空间倾斜程度，坡向算法计算坡面空间朝向。例如最大下坡法，将最大下坡坡度取值为中心，在邻域窗口中计算高程差与格网间距比值的最大值，此时坡度方向与正北方向之间的夹角为最大下坡坡向。	计算生成坡度	33806
2248	地形因子提取	对于投影坐标系中的 DEM，使用 Florinsky （2016） 描述的三阶二元泰勒多项式方法。基于每个像元周围 5x5 邻域内高程的多项式拟合，此方法被认为比其他方法更能抵抗异常值高程（噪声）。对于地理坐标系中的 DEM（即角度单位），该工具使用 3x3 多项式拟合方法进行。	通常用于计算输入数字高程模型（DEM）中每个格网像元的坡度梯度（即以度、弧度或百分比为单位的坡度陡度）。	\N
2249	水文分析数据预处理	算法将地形湿度指数确定为单位等高线上汇流面积与对应坡度比降之间比值的自然对数。	计算地形湿度指	33823
2250	视域范围分析算法	输出图像指示每个格网像元中可见的工作站数。查看高度的单位与 DEM 的高程相同，表示高于地面高程的高度，从该高度计算视域。	根据输入DEM定义的地形,从一个位置(即观测站)或一组位置计算即可见区域。	33832
2251	可视性指数	视域是根据分辨率因子为 DEM 中的格网像元子集计算的。可见性指数值(0.0-1.0)表示每个像元可见的测试桩号的比例(由分辨率因子确定)	根据输入数字高程模型(DEM)的地形计算景观可见性的度量	33842
2282	数据转换	csv文件输入x，y和坐标系，转换成shp	点数据csv转换成shp	34629
2283	点距离计算	计算所有输入数据与所有邻近要素之间的距离。在距离矩阵中选取最小值，获得最邻近的点距离。	确定一个事物与另一个事物之间的距离，计算了两点之间的直线距离或欧氏距离。	34636
2284	数据连接	将输入矢量与目标数据按照相对应的字段进行一对一连接。	将输入矢量与目标数据按照相对应的字段进行一对一连接。	34641
2285	基于地理加权回归的模型智能化选择	在研究区域中由回归分析点针对每个变量分别解算,根据AICc或CV来优选变量	基于地理加权回归的模型智能化选择	34647
2286	局部尺度下的空间回归分析	在研究区域中抽样回归分析点，针对每个位置分别采用加权线性最小二乘方法进行回归模型解算，得到与空间位置一一对应的空间回归系数，量化反映研究区域内多元变量关系的空间异质性特征。	通过关于位置的局部加权回归分析模型求解，以随着空间位置不同而变化的参数估计结果，量化反映空间数据关系中的异质性或非平稳性特征	34659
2287	局部尺度下的空间回归分析	在研究区域中抽样回归分析点，针对每个位置分别采用加权线性最小二乘方法进行回归模型解算，得到与空间位置一一对应的空间回归系数，量化反映研究区域内多元变量关系的空间异质性特征。	通过关于位置的局部加权回归分析模型求解，分别采用对应的不同带宽或距离度量进行估计，量化反映空间数据关系中的异质性或非平稳性特征	34682
2294	相关性分析与特征选择	使用多元线性回归模型进行预测	智能识别并选择与空气质量最密切相关的气象特征,并输出最佳的权重大小。	\N
2295	Geo-SOM时空聚类与分区	使用GeoSOM算法进行聚类分区	利用GeoSOM算法实现空气站点的时空聚类与分区。通过整合地理空间信息与时间序列数据，模型有效捕捉空气质量监测站点的空间和时序特征。。	\N
2296	空气质量多元线性回归预测	使用多元线性回归模型进行预测	通过与对应空气质量指标的强相关变量数据以及聚类分区进行多元线性回归预测空气质量。	\N
2298	空间数据分类	该算法收集所有决策树分类投票结果后，将投票次数最多的类别指定为最终的输出	根据先验信息对带预测数据进行分类型预测	\N
2301	欧式距离分析	通过输入的图片尺寸来自动计算分辨率大小，对线状要素进行欧氏距离分析	对于线要素进行欧式距离分析	\N
2302	欧式距离分析	通过输入的图片尺寸来自动计算分辨率大小，对线状要素进行欧氏距离分析	对于线要素进行欧式距离分析	\N
2304	精细尺度下的探索性数据分析	地理加权相关性分析在充分考虑空间变量异质性或非平稳性特征的基础上，针对预设的空间位置点进行局部尺度的相关系数计算，以此量化表征空间变量之间的局部密切程度特征	对空间位置点进行局部尺度的相关系数计算，量化表征空间变量之间的局部密切程度特征。	35144
2306	水文分析数据预处理	该算法基于汇流累积量，根据选定的阈值确定河网。	计算生成栅格河网数据	35226
2311	空间数据分类	该算法收集所有决策树分类投票结果后，将投票次数最多的类别计算概率进行输出	根据先验信息对带预测数据进行分类型预测	\N
2491	水文分析数据预处理	识别DEM中的所有凹坑，并将它们的高度提高到边缘附近的最低倾点	调整洼地区域高程值	\N
2492	水文分析数据预处理	使用成本最低的路径分析来识别将坑像元（即没有较低相邻点的网格像元）连接到某个远处较低像元的填洼通道。在填洼之前，为了最大限度地减少填洼通道的深度，所有坑像元都上升到最低邻居的高程减去一个小的高度值。在这里，填洼路径的成本取决于通过周围地形切割破坏通道所需的高程降低量。	通常用于调整洼地区域高程值。	\N
2493	水文分析数据预处理	首先识别单像元凹坑（即没有较低相邻像元的内部网格像元）来运行。然后从最高到最低访问每个坑单元，并启动优先区域生长操作。确定了从坑像元开始并根据洪水顺序生长的单调增加高程区域。一旦先前未访问过且海拔低于其发现邻居细胞的像元被识别，发现邻居被标记为出口（溢出点），并记录出口高程。然后，该算法回填标记的区域，将输出DEM中的高程提高到出口的高程。一旦每个凹坑像元完成此过程（注意嵌套的凹坑像元通常由先前的凹坑解决），填充坑的平坦区域将被选择性地处理与远离出口的小斜率梯度（注意，每个凹陷可能存在多个出口单元）。	通常可用于填充数字高程模型 （DEM） 中的所有洼地并处理平整区域。	\N
2497	水文分析数据预处理	该算法根据单流向算法计算的水流方向及分配，确定每一个像元上水流的累积量。	计算生成汇流累积量	38398
2498	水文分析数据预处理	该函数计算从每个网格单元向下游移动的距离，直到遇到所定义的河网栅格单元为止.	计算每个栅格到最近河栅格点的距离	38408
2501	test	\N	test	38530
2503	物候	对地块矢量与物候信息进行数据连接	物候数据连接	39069
2504	物候	对地块矢量与作物置信度信息进行数据连接	物候数据连接	39076
2505	物候	将一个shp的zw_1，zw_2和zw_3初始化为0，然后根据输入的面积阈值threshold和操作索引zw将一些更高置信度的地块的zw_1，zw_2和zw_3进行赋值，被赋值的这些地块的面积会达到threshold，但会略微小于threshold。	物候数据连接	39083
2506	地形因子提取	对于投影坐标系中的 DEM，使用 Florinsky （2016） 描述的三阶二元泰勒多项式方法。基于每个像元周围 5x5 邻域内高程的多项式拟合，此方法被认为比其他方法更能抵抗异常值高程（噪声）。对于地理坐标系中的 DEM（即角度单位），该工具使用 3x3 多项式拟合方法进行。	通常用于计算输入数字高程模型 （DEM） 中每个格网像元的坡度宽高（即从北顺时针方向表示的坡度方向）。	\N
2508	局部尺度下的空间回归分析	在研究区域中抽样回归分析点，针对每个位置分别采用加权线性最小二乘方法进行回归模型解算，得到与空间位置一一对应的空间回归系数，量化反映研究区域内多元变量关系的空间异质性特征。	通过关于位置的局部加权回归分析模型求解，以随着空间位置不同而变化的参数估计结果，量化反映空间数据关系中的异质性或非平稳性特征	39140
2509	多尺度的空间回归分析	对多元地理加权回归模型中不同参数分别采用对应的不同带宽或距离度量进行估计,采用后向迭代对整个模型进行求解,以准确呈现不同参数估计对应的异质性尺度特征。	为空间范围内每个点处的不同参数,分别采用对应的不同带宽或距离度量进行估计,准确量化空间数据的异质性特征	39198
2510	欧式距离分析	通过输入的图片尺寸来自动计算分辨率大小，对线状要素进行欧氏距离分析	对于线要素进行欧式距离分析	\N
\.


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-- Data for Name: meta_identification; Type: TABLE DATA; Schema: public; Owner: onesis
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COPY public.meta_identification (id, category_application, description, docker_image_name, docker_run_prefix, model_name, semantic) FROM stdin;
1360	DIAGNOSE	本算法旨在使用介入机会模型预测未知流的流量。	spark-geoflow:1.0	\N	流的介入机会模型	\N
1439	PRESCRIBE	从居民区出发，根据搜索范围，计算到达终点途径格网内包含的所有设施点数量。	CumAccess:1.0	\N	基于累积机会法的可达性计算	Accessibility measures based on CUM
1440	PRESCRIBE	从居民区出发，根据重力衰减函数计算到达终点途径的格网，计算所有格网包含的设施点数量。	CumAccess:1.0	\N	基于重力法的可达性计算	Accessibility measures based on CUM
1442	PRESCRIBE	两步移动搜索法通过以设施点和居民区为基础进行二次搜索，计算供需比并加总得到可达性。该方法首先计算设施点服务范围内包含的居民区，得到设施点供需比，之后计算居民区服务范围内包含的设施点供需比之和，为两步移动搜索法的可达性值。	TwoStepsFloating:1.0	\N	基于两步移动搜索法的可达性计算	Accessibility measures based on TwoStepsFloating
1462	PREDICT	该模型用于进行序贯高斯模拟，以生成符合给定统计特性的空间数据。	sgs:1.0	/src/sgs.r	序贯高斯模拟	sequential_gaussian_simulation
1543	DIAGNOSE	本算法旨在使用辐射模型公式估计流量test3。	spark-geoflow:1.0	\N	流的辐射模型	\N
1544	PREDICT	在基本遗传算法的基础上，对选择再生方法进行改进:将每个个体按照它们的支配与非支配关系进行分层，再做选择操作，从而使得该算法在多目标优化方面得到非常满意的结果。	naga-ii:1.0.0	\N	NSGA_II（CSV）	带有精英保留策略的快速非支配多目标优化算法
2252	DIAGNOSE	判别分析，计算了位置级概率及其相关熵，对研究对象进行判别分析。	gwmodel:2.3	\N	判别分析	discriminant_analysis
2253	DIAGNOSE	地理加权判别分析,计算了位置级概率及其相关熵,利用地理加权的思想计算判别系数,对研究对象进行判别分析。	gwmodel:2.3	\N	地理加权判别分析	discriminant_analysis
2254	PREDICT	以监督学习方式对图像进行分类	resnet:1.0	\N	残差卷积网络（GPU）	Residual Convolutional Neural Network
2255	PREDICT	通过深度学习的方法对图像中的目标进行语义分割	deeplabv3plus:1.0	\N	DEEPLABV3PLUS	DEEPLABV3PLUS
2256	PREDICT	把网络中前面的层与后面的层进行连接，让前面的层的输出成为后面的层的输入，解决梯度消失和参数过多问题，实现图像分类	densennet:1.0	\N	densenet卷积网络	Densely Connected Convolutional Networks
2257	PREDICT	以监督学习方式对图像进行语义分割，实现对图像中对象的识别与分类	d_linknet:1.0	\N	D_LinkNet（GPU）	LinkNet With Pretrained Encoder and Dilated Convolution
2258	PREDICT	通过监督学习的方式对图像进行深度分析，实现对图像中多个对象的准确识别与定位。	frcnn:1.0	\N	深度卷积网络	Deep Convolutional Neural Network
2259	PREDICT	用于图像的目标检测和实例分割的深度学习模型	maskrcnn:1.0.0-gpu	\N	Mask R-CNN	Mask Regional Convolutional Neural Network
2260	PREDICT	以监督学习方式对图像进行语义分类	multiple_layer_perceptron:1.0	\N	多层感知机	Multiple Layer Perceptron Classification
2261	PREDICT	通过深度学习的方法对图像中的目标进行检测	yolo:1.0	\N	YOLO	YOLO
2262	PREDICT	以监督学习方式对图像进行目标检测，实现对图像中对象的识别与定位	retinanet_coco:1.0	\N	视网膜网络(COCO)	RetinaNet
2263	PREDICT	以监督学习方式对图像进行语义分割，实现对图像中对象的识别与分类	unet_gpu:1.0.6-gpu	\N	U型卷积网络（GPU）	U-shaped convolutional network
2264	PREDICT	是一类包含卷积计算且具有深度结构的前馈神经网络	vgg_cpu:1.0.5-cpu	\N	vgg_cpu	u-shaped convolutional network
2265	PREDICT	以监督学习方式对图像进行目标检测，实现对图像中对象的识别与定位	retinanet_csv:1.0	\N	视网膜网络(CSV)	RetinaNet
2266	PREDICT	以监督学习方式进行目标检测，实现对图像中对象的识别与定位	ssd_gpu:1.0	\N	单步多框检测器（SSD）	Single Shot MultiBox Detector
2267	PREDICT	以监督学习方式对图像进行语义分割，实现对图像中对象的识别与分类	unet_cpu:1.0.5-cpu	\N	U型卷积网络（CPU）	U-shaped convolutional network
2268	PREDICT	Adaboost是用反复调整的数据来训练一系列的弱学习器，由这些弱学习器的预测结果通过加权投票(或加权求和)的方式组合, 产生最终的预测结果。	adaboost:1.0.0-skleran1.0.2	\N	自适应集成学习算法	Adaboost
2269	PREDICT	基于自主采样法对多组弱学习器进行并行式的集成学习， 产生最终的预测结果。	bagging:1.0-sklearn1.0.2	\N	引导聚集	Bagging
2270	PREDICT	梯度提升算法根据当前模型损失函数的负梯度信息来训练新加入的弱分类器，然后将训练好的弱分类器以累加的形式结合到现有模型中	gb:1.0-sklearn0.24.0	\N	梯度提升	Gradient Boosting
2271	PREDICT	基于ViGrA（Vision with Generic Algorithms），收集所有分类投票结果后，将投票次数最多的类别指定为最终的输出	sagagis:debian-8.5.0	\N	随机森林算法（ViGrA）	random forest
2272	PREDICT	该算法收集所有分类投票结果后，将投票次数最多的类别指定为最终的输出	random_forest:1.0.0-sklearn1.0.2	\N	随机森林算法（点数据-CSV）	random forest
2273	PREDICT	通过某种距离度量找出训练集中与其特征空间中最邻近最靠近的k个训练样本，然后基于这k个邻居的信息进行预测。	sagagis:debian-8.5.0	\N	k近邻分类器（栅格-TIF）	K-Nearest Neighbor
2274	PREDICT	通过某种距离度量找出训练集中与其特征空间中最邻近最靠近的k个训练样本，然后基于这k个邻居的信息进行预测	knn:1.0.0-sklearn1.0.2	\N	k近邻分类器（点数据-CSV）	K-Nearest Neighbor
2275	PREDICT	一类对数据进行二元分类的含核函数的广义线性分类器，其决策边界是对学习样本求解的最大分类超平面，通过多类泛化推广为多类分类器。	sagagis-base:alpine-8.1.0	\N	支持向量机（栅格-SAGAGRID）	support vector machine
2276	PREDICT	一类对数据进行二元分类的含核函数的广义线性分类器，其决策边界是对学习样本求解的最大分类超平面，通过多类泛化推广为多类分类器。	svm:1.0.0-sklearn1.0.2	\N	支持向量机（点数据-CSV）	support vector machine
2277	PREDICT	与多项式朴素贝叶斯一样，此分类器适用于离散数据。不同之处在于，虽然多项式朴素贝叶斯处理发生计数，但伯努利朴素贝叶斯是为二元/布尔特征设计的。	bnb:1.0-sklearn0.24.0	\N	伯努利朴素贝叶斯分类	Bernoulli Normal Bayes
2278	PREDICT	补充朴素贝叶斯分类器(ComNB)旨在纠正标准多项朴素贝叶斯分类器所做的“严重假设”。它特别适合用于不平衡的数据集。	comnb:1.0-sklearn0.24.0	\N	补充朴素贝叶斯分类器	classification
2279	PREDICT	高斯朴素贝叶斯是一种基于概率方法和高斯分布的机器学习的分类技术。	gnb:1.0-sklearn0.24.0	\N	高斯朴素贝叶斯分类	Gaussian Naive Bayes Classification
2280	PREDICT	该算法基于原始的贝叶斯理论，假设概率分布服从一个简单多项式分布的机器学习的分类技术	mnb:1.0-sklearn0.24.0	\N	多项式分布朴素贝叶斯分类	Multinomial Naive Bayes Classification
2281	PREDICT	SAGA-GIS基于概率方法和正态分布的机器学习分类算法，应用于遥感影像的要素分类。	sagagis:debian-8.5.0	\N	正态朴素贝叶斯分类（影像）	Normal Bayes Classification
2163	DIAGNOSE	基于infomap算法将网络划分为多个不重叠的社区	infomap:1.0	\N	非重叠社区探测	non-overlapping community detection
2082	BASIC	对单个或多个栅格数据执行加减乘除等简单代数运算。	whitebox-tools:2.0.0	\N	简单栅格地图代数	map algebra
2083	BASIC	为数据定义地理坐标系或投影坐标系	project:1.0	\N	投影转换	data projection
2084	BASIC	在特定空间参考下计算矢量地理空间对象的几何中心。	whitebox-tools:2.0.0	\N	几何中心量算	geometric center identification
2085	BASIC	在特定空间参考下计算地理空间面对象的曲面体表面积。	whitebox-tools:2.0.0	\N	表面积量算	surface area calculation
2086	DIAGNOSE	使用固定或动态距离为输入要素中的所有要素创建缓冲区	buffer:1.0-geopandas0.9.0	\N	缓冲区分析（basic）	buffer
2087	DIAGNOSE	使用固定或动态距离间距为输入要素中的所有要素创建多层环状缓冲区	sagagis:base-8.1.0	\N	缓冲区分析（buffer ring）	buffer
2088	DIAGNOSE	用于生成栅格数据集指定距离下的缓冲区。	whitebox-tools:2.0.0	\N	缓冲区分析（栅格-GeoTIFF）	buffer
2089	DIAGNOSE	数学形态学分析是一种以形态结构元素为基础，对地理空间对象数据进行处理的邻域分析算法，包括腐蚀、膨胀、开闭运算等功能。	mathematical_morphological_analysis:1.0-geopandas	\N	数学形态学分析	Mathematical Morphological Analysis
2090	BASIC	将多个对象图层进行叠置并进行求补操作，产生一个新的对象。	whitebox-tools:2.0.0	\N	叠置分析（补）	overlay_difference
2091	BASIC	将多个对象图层进行叠置并进行求交操作，产生一个新的对象。	whitebox-tools:2.0.0	\N	叠置分析（交）	overlay_intersect
2092	BASIC	将多个图层对象进行叠置并进行求并操作，产生一个新的对象。	whitebox-tools:2.0.0	\N	叠置分析（并）	overlay_union
2093	BASIC	计算两个几何对象之间的距离	distance_calculation:1.0	\N	距离计算	distance calculation
2095	DIAGNOSE	时空拓扑关系分析是分析多个地理空间对象间是否存在邻接、关联、包含等空间拓扑关系，以及前序、后序、包含等时间拓扑关系的时空关系计算算法	st_topology_analysis:1.0-geopandas	\N	时空拓扑关系分析模型	Spatio-temporal topology analysis
2126	DIAGNOSE	本算法旨在计算地理流数据聚类后各个类别的中心流以及最接近中心流的实际流	spark-geoflow:2.0	\N	地理流的聚类中心计算	flow cluster center calculation
2127	DIAGNOSE	该方法读取流数据,使用Kmeans方法对流进行聚类。	spark-geoflow:2.0	\N	地理流的Kmeans聚类	\N
2128	DIAGNOSE	将图像像素值视为特征值，对其进行kmeans聚类	sagagis:base-8.1.0	\N	KMeans聚类（栅格-SAGAGRID）	imagery classification
2129	DIAGNOSE	基于Delaunay三角网进行多层长边删除的空间点聚类方法。	ascdt:1.0	\N	ASCDT聚类	ascdt clustering
2130	DESCRIBE	AUTOCLUST（Automatic Clustering via Boundary Extraction）算法是一种经典的基于Delaunay三角网的空间聚类方法。	automatic_clustering_via_boundary_extraction:1.0	\N	AUTOCLUST聚类	point clustering
2131	DIAGNOSE	基于聚类特征树的快速聚类算法，利用有限的内存资源完成大数据集聚类计算。	birch_clustering:1.0-sklearn0.24.0	\N	Birch聚类	birch clustering
2164	DIAGNOSE	基于cpm算法将网络划分为多个重叠的社区	cpm:1.0	\N	重叠社区探测	overlapping community detection
2132	DIAGNOSE	CURE（Clustering Using Representatives）是一种针对大型地理空间对象集的高效层次聚类算法，基于代表点和收缩因子的聚类算法，将空间点对象无监督地划分为若干簇。	cure_clustering:1.0	\N	CURE聚类	cure clustering
2133	DIAGNOSE	基于密度可达的典型聚类算法，将空间点对象无监督地划分为若干高密度的空间簇和噪声。	dbscan_clustering:1.0-sklearn0.24.0	\N	DBSCAN聚类（矢量点-CSV）	dbscan clustering
2134	DIAGNOSE	基于自组织神经网络节点竞争学习策略的聚类算法	som:1.0-MiniSom2.3.0	\N	SOM聚类	self organizing map clustering
2135	DIAGNOSE	基于EM实现对空间点进行聚类，将空间点对象无监督地划分为若干具有相似特征的集群。	expectation_maximization_clustering:1.0	\N	EM聚类	point clustering
2136	DIAGNOSE	基于划分的典型聚类算法，依据“距簇中心最近”原则将所有地理空间对象分配给最近的簇。	k_means_clustering:1.0-sklearn0.24.0	\N	KMeans聚类（矢量点-CSV）	k-means clustering
2137	DIAGNOSE	基于局部密度的聚类算法，假设不同簇的概率密度分布不同，根据密度增大的最快方向聚类	mean_shift_clustering:1.0-sklearn0.24.0	\N	MeanShift聚类	meanshift clustering
2138	DESCRIBE	MST聚类算法（Minimum Spanning Tree）是一种基于图论的聚类算法，该算法不需要进行一些相似度的计算, 就能把聚类问题映射为图论中的一个组合优化问题。	mst_clustering:1.0	\N	MST聚类	point clustering
2139	DIAGNOSE	DBSCAN聚类的改进算法，有效地解决了密度不同导致的聚类效果差的问题	optics_clustering:1.0-sklearn0.24.0	\N	OPTICS聚类	optics clustering
2140	DESCRIBE	顾及功能语义特征的地理空间对象聚类是一种能够识别空间邻近且功能语义一致的地理空间对象的模型算法。	functional_semantic_clustering:1.0	\N	顾及功能语义特征的地理空间对象聚类	point clustering
2141	DIAGNOSE	HGDSC聚类是一种能够发现任意形状和密度、且专题属性相似的空间簇的算法。	graph_density_hybrid_spatial_clustering:1.0	\N	HGDSC聚类	Hybrid Spatial Clustering method based on Graph theory and Spatial Density
2304	DIAGNOSE	地理加权相关性分析在充分考虑空间变量异质性的基础上，进行局部尺度的相关性分析。	gwmodel:2.4	\N	地理加权相关性分析	exploratory data analysis
2142	DIAGNOSE	基于快速重排检验的适应性聚类是一种融合时空邻近性与专题属性相似的地理空间对象聚类方法。	permutation_test_clustering:1.0	\N	基于快速重排检验的适应性聚类	point clustering
2232	DESCRIBE	度量当前时间序列与其k阶滞后项时间序列的相关程度	ts_autocorrelation:1.0-R	\N	时序自相关	time series autocorrelation
2143	DIAGNOSE	Moving Cluster算法是一种挖掘地理时空对象聚集簇的模型算法，可以发现一组移动对象在一定时间内紧密移动的行为模式	moving_cluster:1.0	\N	Moving Cluster聚类	Spatial-temporal Moving Cluster
2144	DIAGNOSE	SNN flow聚类(Shared Nearest-Neighbor-based Clustering for Inhomogeneous OD Flows)是一种基于共享最近邻的网络约束内时空对象聚类方法。	snn_flow_clustering:1.0	\N	SNN_Flow聚类	SNN Flow Clustering
2145	DIAGNOSE	基于分割和聚合策略的地理时空对象移动轨迹聚类算法。	trajectory_clustering:1.0	\N	TRACLUS聚类	trajectory clustering
2146	DIAGNOSE	利用地理空间对象不同邻域分析半径范围内邻居对象的点密度度量地理空间对象的聚集分布模式	st_dbscan:1.0	\N	ST-DBSCAN聚类	spatiotemporal point clustering
2147	DIAGNOSE	时空聚类统一框架是一个综合顾及时空相关性、时空异质性以及时空耦合问题提出的统一的地理时空对象聚类分析框架。	st_general_clustering_framework:1.0	\N	时空聚类统一框架	Spatio-Temporal Clustering Framework
2148	DIAGNOSE	本算法旨在使用历史样本流量数据估计引力模型的参数，带入引力模型公式预测未知流的流量属性。	spark-geoflow:2.0	\N	地理流的最邻近插值	\N
2149	DIAGNOSE	该方法使用现有样本流数据对未知流的属性进行插值。	spark-geoflow:2.0	\N	地理流的IDW插值	\N
2150	PREDICT	时空加权回归分析预测模型通过对特定时刻，对空间范围内每个预测点处的线性回归方程进行局部加权求解,通过输入具有自变量观测值样本值的空间数据作为预测点,进行对应空间位置上的因变量值进行预测。	gwmodel:2.3	\N	时空加权回归分析预测模型	Spatial prediction
2151	PREDICT	地理加权回归分析预测模型通过对特定空间范围内每个预测点处的线性回归方程进行局部加权求解,通过输入具有自变量观测值样本值的空间数据作为预测点,在带宽优选的基础上进行对应空间位置上的因变量值进行预测。	gwmodel:2.3	\N	地理加权回归分析预测模型	Spatial prediction
2152	DIAGNOSE	反距离加权插值以预测点邻域范围内的已知点观测值及已知点到预测点的距离为依据，估计预测点属性值的插值算法。	whitebox-tools:2.0.0	\N	反距离加权插值	Inverse Distance Weighting
2153	PREDICT	基于样点个体代表性的地理空间特征空间推测，多用于土壤属性的空间推测	ipsm:1.0	\N	基于样点个体代表性的地理空间特征空间推测(iPSM)	基于样点个体代表性的地理属性空间推测
2154	PREDICT	使用克里金插值方法推测地理变量空间分布信息，多用于土壤属性的空间推测	kriging_map:1.0	\N	克里金插值制图	克里金插值制图
2155	DIAGNOSE	线性插值,R语言实现,以预测点邻域范围内的已知点观测值及已知点到预测点的距离,线性估计预测点属性值的插值算法。	r-interpolation:1.0	\N	线性插值	Spatial interpolation
2156	DIAGNOSE	克里金插值是一种基于变异函数理论和结构分析,对区域化变量进行无偏最优估计的插值算法。	r-interpolation:1.0	\N	普通克里金插值	ordinary kriging interpolation
2157	PREDICT	使用随机森林方法推测地理变量空间分布信息，多用于土壤属性的空间推测	randomforest_map:1.0	\N	随机森林预测制图	随机森林预测制图
2158	DIAGNOSE	依据B-样条曲线对随机过程/随机场进行空间建模和预测（插值）。	sagagis:base-8.1.0	\N	样条插值	spline interpolation
2159	PRESCRIBE	从居民区出发，根据搜索范围，计算到达终点途径格网内包含的所有设施点数量。	cumaccess:1.0	\N	基于累积机会法的可达性计算	Accessibility measures based on CUM
2160	PRESCRIBE	从居民区出发，根据重力衰减函数计算到达终点途径的格网，计算所有格网包含的设施点数量。	gravityaccess:1.0	\N	基于重力法的可达性计算	Accessibility measures based on Gravity
2161	PRESCRIBE	基于时间地理实体构建个体的活动空间，然后计算其可达性	individual_accessibility:1.0	\N	个人可达性度量方法	Personal accessibility measurement methods
2162	PRESCRIBE	两步移动搜索法通过以设施点和居民区为基础进行二次搜索，计算供需比并加总得到可达性。该方法首先计算设施点服务范围内包含的居民区，得到设施点供需比，之后计算居民区服务范围内包含的设施点供需比之和，为两步移动搜索法的可达性值。	twostepsfloating:1.0	\N	基于两步移动搜索法的可达性计算	Accessibility measures based on TwoStepsFloating
2165	DIAGNOSE	基于网络快照将网络划分为多个社区	dcd:1.0	\N	时变稳定社区探测	time-varying community detection
2166	PRESCRIBE	基于最短路径算法，构建公交网络中的缓冲区	buffer_transit:1.0	\N	公交网络缓冲区构造算法	Algorithm for constructing buffer in transit network
2167	PRESCRIBE	构建道路网路的缓冲区，找出给定路径一定路网距离内的所有路径	buffer_network:1.0	\N	道路网络缓冲区构建算法	Algorithm for constructing buffer zones in road networks
2168	PRESCRIBE	基于最短路径算法，构建路网中的时Voronoi图	vn:1.0	\N	道路网络Voronoi图构建算法	Road Network Voronoi Diagram Construction Algorithm
2169	PRESCRIBE	基于aStar算法进行路径规划，为道路网络中的点寻找最短路径	astar:1.0	\N	基于aStar算法的路径规划	path planning based on aStar algorithm
2170	PRESCRIBE	基于dijkstra算法进行路径规划，为道路网络中的点寻找最短路径	dijkstra:1.0	\N	基于dijkstra算法的路径规划	path planning based on dijkstra algorithm
2171	PRESCRIBE	基于dijkstra算法进行路径规划，为时变道路网络中的点寻找最短路径	dspp:1.0	\N	时变最短路径算法	shortest path algorithm in deterministic discrete-time dynamic networks
2172	PRESCRIBE	解决寻找给定起点和终点之间的 K 条最短路径的经典问题	kshortestpath:1.0	\N	经典的K最短路径规划算法	classical K shortest path planning algorithm
2173	PRESCRIBE	基于aStar算法进行路径规划，为道路网络中的点寻找最短路径	k_shortest_path_lpa:1.0	\N	基于重优化技术的K最短路径规划算法	K shortest path planning algorithm based on reoptimization technology
2174	PRESCRIBE	计算公交网络中的最短路径	ptsp:1.0	\N	公共交通网络中的最短路径算法	Shortest Path Algorithms in Public Transportation Networks
2175	PRESCRIBE	考虑了路网中的转向问题，为道路网络中的点寻找最短路径	trsp:1.0	\N	路网转向约束的最短路径算法	The Shortest Path Algorithm for Road Networks with Turning Constraints
2176	DIAGNOSE	在空间交互网络建模的基础上，采用地理加权回归分析技术进行求解多元变量模型，并提供O端模式、D端模式和OD流模式分别定义不同的模型求解算法，从不同角度理解空间交互网络的复杂性和非均衡性特征。	gwmodel:2.3	\N	地理加权空间交互模型	network analysis
2177	DIAGNOSE	研究空间交互网络（如O-D网络）的定量分析模型,通过考虑与出发地（Origin）和目的地（Destination）的相关空间语义与属性要素,量化分析空间交互量与多元要素特征间的关系。	gwmodel:2.3	\N	空间交互模型	network analysis
2178	PRESCRIBE	基于最短路径算法，构建路网中的时空棱柱	spacetimeprism:1.0	\N	道路网络空间中的时空棱柱构造算法	Algorithm for constructing space time prisms in network space
2179	PRESCRIBE	基于最短路径算法，构建公交网络中的时空棱柱	spacetimeprism_transit:1.0	\N	公共交通网络中的时空棱柱构造算法	Algorithm for constructing space time prisms in transit network
2180	PREDICT	在基本遗传算法的基础上，对选择再生方法进行改进:将每个个体按照它们的支配与非支配关系进行分层，再做选择操作，从而使得该算法在多目标优化方面得到非常满意的结果。	nsga-ii:1.0.0	\N	NSGA_II（CSV）	带有精英保留策略的快速非支配多目标优化算法
2181	DESCRIBE	使用拉斯维加斯算法模型进行随机算法分析。	las_vegas:1.0	\N	拉斯维加斯模型	遥感分析
2182	PREDICT	该模型用于进行序贯高斯模拟，以生成符合给定统计特性的空间数据。	montecarlo:1.0	\N	序贯高斯模拟	sequential_gaussian_simulation
2183	DIAGNOSE	在残差项服从均值为0的正态分布情况下,对多元变量采用线性最小二乘方法进行模型求解,全局描述变量关系。	r-spatialreg:1.0	\N	线性回归	global regression
2184	DIAGNOSE	针对因变量为分类或定性变量的场景的一种广义线性回归分析模型。	r-spatialreg:1.0	\N	逻辑回归	global regression
2185	DIAGNOSE	针对因变量服从泊松分布的场景的一种广义线性回归分析模型。	r-spatialreg:1.0	\N	泊松回归	global regression
2186	PREDICT	基于可加模型拟合年度、周度、日度的季节性周期变化及节假日效应的非线性趋势，实现单变量时间序列的预测	prophet:1.0-R	\N	Prophet预测	time series forecasting
2187	DIAGNOSE	通过计算时空距离,建立研究对象每个点处的局部回归方程,量化反映时空数据关系中的异质性或非平稳性特征。	gwmodel:2.3	\N	时空地理加权回归	extended gwr
2188	DIAGNOSE	通过建立空间范围内每个点处的局部逻辑回归方程，来探索研究对象在某一尺度下的空间变化及相关驱动因素。	gwmodel:2.3	\N	地理加权逻辑回归分析	extended gwr
2189	DIAGNOSE	通过建立空间范围内每个点处的局部泊松回归方程，来探索研究对象在某一尺度下的空间变化及相关驱动因素。	gwmodel:2.3	\N	地理加权泊松回归分析	extended gwr
2193	DIAGNOSE	对空间的点、线、面数据进行随机抽样，每个样本单元被抽中的概率相等。	r-sampling:1.0	\N	随机抽样	sampling
2194	DIAGNOSE	对空间的点、线、面数据进行排列，并按相同的所设定间隔进行采样。	r-sampling:1.0	\N	规则抽样	sampling
2195	DIAGNOSE	利用抽样对象在空间上变异特征,考虑数据相关性的特点,根据抽样对象特征进行分层抽样。	r-sampling:1.0	\N	三明治抽样	sampling
2196	DIAGNOSE	基于聚类策略的空间-专题属性异常探测旨在利用稳健的、顾及非空间专题属性的空间聚类方法探测空间显著分离或专题属性差异大的小簇或噪声，间接实现空间-专题属性异常对象集合。	detecting_semantic_anomaly_by_clustering:1.0	\N	基于聚类策略的空间-专题属性异常探测	Spatial-thematic properties
2197	DESCRIBE	一种基于密度的无监督异常检测算法（DB-TRAD）来检测异常轨迹。	density_based_trajectory_anomaly_detection:1.0	\N	基于时空聚类策略的轨迹异常探测	trajectories anomaly detection
2219	DESCRIBE	利用地理空间对象不同邻域分析半径范围内邻居对象的点密度度量地理空间对象的聚集分布模式	ripley_k:1.0	\N	Ripley’s K指数	Ripley’s K indicator
2198	DESCRIBE	流空间交互关系约束下的异常探测旨在发现偏离理想规划状态下最优供需匹配关系的异常交互现象，如人群过度聚集于某处承载力有限的城市基础设施等情况。	detecting_anomalous_spatial_interaction_patterns:1.0-geopandas	\N	流交互关系约束下的流空间异常探测	Detecting Anomalous Spatial Interaction Patterns
2199	DESCRIBE	动态邻域框架下的时空序列异常探测是面对交通流等具有时变专题属性(速度、密度、流量等)的地理空间对象发现全局和局部时变属性异常的异常空间实体。	detecting_flow_anomaly_by_dynamic_neighborhood:1.0	\N	动态邻域框架下的时空序列异常探测	Detecting Flow Anomaly by Dynamic Neighborhood
2200	DIAGNOSE	时空一体化框架下的时空序列异常探测是一种顾及地理时空相关性与异质性的时空异常地理对象探测算法。	st_anomaly_detection_unified_framework:1.0	\N	时空一体化框架下的时空序列异常探测	anomaly detection
2201	DESCRIBE	基于层次约束图的二元空间点过程异常探测算法是一种基于图划分的二元地理空间点对象（目标点对象与参考点对象）异常分布模式自适应挖掘模型。	graph_based_appraoch_for_cross_outliers:1.0	\N	基于层次约束图的二元空间点过程异常探测	outlier detection
2202	DIAGNOSE	邻近关系约束下的流空间异常探测是一种探测地理流协变量相似、流过程或态势偏离的算法	flow_adjacency_based_anomaly_detection:1.0	\N	流邻近关系约束下的流空间异常探测	Slow space anomaly detection
2203	DIAGNOSE	iBAT是一种基于孤立性的异常检测算法，通过多次采样与比较，识别出测试轨迹是否异常。	isolation_based_anomalous_trajectory:1.0	\N	基于隔离树模型的轨迹异常探测	anomaly trajectory detection
2204	DIAGNOSE	iBOAT基于孤立森林（Isolation Forest）的概念，通过将轨迹点隔离在一个树结构中，来识别不符合正常轨迹模式的异常轨迹。	isolation_based_online_anomalous_trajectory:1.0	\N	基于频繁序列挖掘的轨迹异常探测	anomaly trajectory detection
2205	DIAGNOSE	通过度量地理空间点对象在其k邻域内与其它地理空间点对象间的可达距离表征该对象的局部可达密度，将局部异常度定义为地理空间点对象局部可达密度与其空间邻近对象的局部可达密度均值之比，基于此将局部异常度较大的地理空间点对象识别为一元空间点过程异常	local_outlier_factor:1.0	\N	基于局部密度度量的一元空间点过程异常探测	outlier detection
2234	PRESCRIBE	汇流累积量算法是用于定量指示地表水流受地形控制下的空间分配的算法。该算法根据流向算法计算的水流方向及分配，确定每一个像元上水流的累积量。	whitebox-tools:2.0.0	\N	汇流累积量_D8（单位汇水面积）	FlowAccumulation
2235	DESCRIBE	流向算法是计算地表水流方向的算法。根据流向下游相邻计算单元(如栅格DEM中的栅格单元)是否唯一,本算法为单流向D8算法。	taudem_ext:5.3.9	\N	流向(D8)	D8FlowDirection
2206	DESCRIBE	基于统计检验的二元空间点过程异常探测算法是一种发现两元地理空间点对象空间分布及其依赖关系显著异常的非参数统计检验方法。该算法将空间点对象分为基本实体和参考实体，利用交叉K函数检验两类点对象共现的显著性，将基本实体共现强度定义为缓冲区邻域中的参考实体的个数。进一步利用层次约束的Delaunay三角剖分为基本实体，构建稳健合理的空间邻域关系。在此基础上，基于α-shape算法构建支撑域，利用空间随机过程为基础构建零模型，利用蒙特卡洛模拟检验空间交叉异常的显著性。最后，通过生存距离对空间交叉异常模式稳定性进行评价分析。	statistical_test_method_for_cross_outliers:1.0	\N	基于统计检验的二元空间点过程异常探测	Binary space point process anomaly detection algorithm based on statistical test
2207	DESCRIBE	基于自适应回归的空间-专题属性异常探测旨在发现地理空间变量间相关关系表现异常的区域。该算法通过构建层次性约束图模型表达地理空间单元间稳健、合理的邻近关系，根据空间邻近关系自适应确定回归带宽，利用地理加权回归建模空间变量依赖关系，最后基于回归残差项分布特征计算空间-专题属性异常度，最终得到专题属性相对于空间位置表现异常的地理空间对象	regression_based_semantic_anomaly_detection:1.0-mgwr	\N	基于自适应回归的空间-专题属性异常探测	regression_based_semantic_anomaly_detection
2208	DIAGNOSE	SAPRD（Spatial Anomaly Points and Regions Detection）算法利用多层次边长约束从全局到局部逐步发现空间显著分离或专题属性显著偏离邻居对象属性的地理空间异常对象集合。	spatial_anomaly_points_and_regions_detection:1.0	\N	基于层次约束图的空间-专题属性异常探测	Spatial Anomaly Points and Regions Detection
2209	DIAGNOSE	基于层次约束图的地理空间离群点对象自适应探测算法	spatial_point_event_outlier_detection:1.0	\N	基于层次约束图的一元空间点过程异常探测	outlier detection
2210	DIAGNOSE	时空级联模式挖掘旨在发现严格按照时间次序在邻近时间范围内依次频繁出现于邻近空间位置的地理时空对象集合。	cascading_st_pattern_mining:1.0	\N	时空级联模式挖掘	Cascading Spatio-Temporal Pattern Mining
2211	DESCRIBE	发现不同地理空间对象在邻近时间域内相继发生、相互依赖的关联关系挖掘算法	sequential_association_pattern_mining:1.0	\N	时序关联模式挖掘	Density Ratio Based Association Rules Mining for Multiple Sequences
2212	DIAGNOSE	面向事务型数据关联模式挖掘的基于逐层搜索策略的经典关联算法	item_association_pattern_mining:1.0-apriori	\N	事务关联模式挖掘	item association pattern mining
2213	DESCRIBE	空间同位模式挖掘是发现频繁同时出现于邻近空间位置的地理空间对象集合的关联关系挖掘算法。其中，常用的基于点过程分解的多尺度空间同位模式挖掘方法是一种可以有效挖掘不同观测尺度下空间同位模式及其地理空间对象分布的算法。	spatial_co_location_pattern_mining:1.0	\N	空间同位模式挖掘	Spatial isotopic mode mining
2214	DESCRIBE	时空同现模式挖掘是发现邻近空间位置以及时间范围内同时出现的地理时空对象的关联关系挖掘算法，如犯罪学中不同类型犯罪案件间的时空诱导机制。该算法通过构建概率密度函数方式分解其中一类地理时空对象的条件分布过程，在不同时空地理对象独立分布的零假设下，通过显著性检验对时空同现模式的流行度进行评估。	st_co_occurrence_pattern_mining:1.0	\N	时空同现模式挖掘	A statistical method for detecting spatiotemporal co-occurrence patterns
2215	DESCRIBE	时空遥相关模式挖掘旨在发现大研究范围下具有宽松时空约束、显著频繁共同发生的地理时空对象集合，例如北大西洋异常低温事件和玻利维亚北部洪水事件之间的远距离关联。	st_teleconnection_mining:1.0	\N	时空遥相关挖掘	Spatial-temporal telecorrelation mining
2216	DESCRIBE	基于最近邻地理空间对象的平均距离度量空间对象聚集程度的描述性算法	anni:1.0	\N	平均最近邻指数	average nearest neighbor index
2217	DESCRIBE	基于等距区间的专题属性频率分布统计	fds:2.0	\N	频率分布统计	frequency distribution statistic
2218	DESCRIBE	反映地理空间对象密度在连续空间内变化的估计算法。	kernel_density_estimation:1.0-gdal3.5.1	\N	核密度分析	kernel density estimation
2220	DIAGNOSE	通过引入空间位置变量，计算空间距离，进行局部尺度下数据降维与理解的主成分分析算法。	gwmodel:2.4	\N	地理加权主成分分析	dimension_reduction_analysis
2221	DESCRIBE	基于奇异值分解的降维算法	pca:1.0-sklearn0.24.0	\N	主成分分析	principal component analysis
2222	DESCRIBE	将数据从高维数据转到低维数据且保持其在高维空间里所携带的信息	tsne:1.0.0-sklearn1.0.2	\N	分布式随机近邻嵌入	point dimensionality reduction
2223	DIAGNOSE	该方法旨在计算OD流的局部Getis-Ord GI*统计值，进行热点分析。	spark-geoflow:2.0	\N	地理流的Geary'C指数分析	\N
2224	DIAGNOSE	本算法旨在使用历史样本流量数据估计引力模型的参数，带入引力模型公式预测未知流的流量属性。	spark-geoflow:2.0	\N	地理流的重力模型	\N
2225	DIAGNOSE	本算法旨在计算地点的I指数，该指数同时表达了某地的流的数目和流的长度，可用于量化城市地点的不可替代性。借鉴了文献计量学中H-index的思想, 某地点的I-index为 i 表示最多有 i 条长度分别至少为 i * alpha 米的流到达该地点。	spark-geoflow:2.0	\N	地理流的I指数	\N
2226	DESCRIBE	基于“如果某个自变量对某个因变量有重要影响，那么自变量和因变量的空间分布应该具有相似性”的假设，构造统计量以度量空间分异性，探测解释因子、分析变量之间的交互关系	geodetector:1.0-R	\N	地理探测器	spatial stratified heterogeneity
2227	DIAGNOSE	地理加权平均数对特定的空间位置进行局部尺度下均值和偏度的计算，探索性分析和量化描述在不同尺度（带宽）条件下单个空间变量的空间异质性特征。	gwmodel:2.3	\N	地理加权平均数	exploratory data analysis
2229	DIAGNOSE	地理加权方差-协方差矩阵是利用求解关于位置的方差-协方差矩阵的思路，描述局部尺度下空间变量差异特征的分析方法。	gwmodel:2.3	\N	地理加权方差-协方差矩阵	exploratory data analysis
2230	DESCRIBE	利用整个研究区域内地理空间对象的邻近性，分析其非空间属性的空间聚集分布模式。	r-spatialreg:1.0	\N	全局空间自相关	spatial autocorrelation
2231	DESCRIBE	利用每个地理空间对象与其他地理空间对象的空间邻近性，分析非空间属性相对于每个地理空间对象的聚集分布模式。	r-spatialreg:1.0	\N	局部空间自相关	Local Indicators of Spatial Association
2240	PRESCRIBE	基于水流强度完全受上游集水区面积所决定的假设来计算水流功率	whitebox-tools:2.0.0	\N	水流功率指数	\N
2242	DIAGNOSE	将地形条件表达为一组汇聚区（通道）和发散区（山脊），表示地形栅格与邻域栅格的坡向的一致性	sagagis:base-8.1.0	\N	地形汇聚指数	convergence_index
2243	PRESCRIBE	过地形表面上某点得水平面沿水平方向切地形表面所得的曲线在该点的曲率值，即该点所在的地面等高线的弯曲程度	taudem_ext:5.3.9	\N	平面曲率	plan curvature
2244	PRESCRIBE	计算局部地形在截面处的形状特征以及凹凸变化程度的算法	whitebox-tools:2.0.0	\N	剖面曲率	curvature
2245	PRESCRIBE	计算局部地形在截面处的形状特征以及凹凸变化程度的算法	whitebox-tools:2.0.0	\N	切向曲率	curvature
2246	DESCRIBE	定量表达地表的起伏程度	gdal:3.16	\N	地形起伏度	roughness
2247	PRESCRIBE	计算栅格8邻域中最大下坡方向的地表坡面空间倾斜程度	taudem_ext:5.3.9	\N	坡度（最大下坡）	Slope
2248	PRESCRIBE	计算地表坡面空间倾斜程度。	whitebox-tools:2.0.0	\N	坡度（三阶二元泰勒多项式）	slope
2249	PRESCRIBE	利用单位等高线上的汇流面积和坡度定量计算土壤干湿状况,值越大说明土壤越湿	taudem_ext:5.3.9	\N	地形湿度指数	topographic wetness index
2250	DESCRIBE	用于根据输入DEM定义的地形,从一个位置(即观测站)或一组位置计算即可见区域	whitebox-tools:2.0.0	\N	视域范围分析算法	viewshed
2251	DESCRIBE	用于根据输入数字高程模型(DEM)的地形计算景观可见性的度量	whitebox-tools:2.0.0	\N	可视性指数	visibility
2282	DESCRIBE	基于sf包,点数据csv转换成shp	gwmodel:2.4	\N	点数据csv转换成shp	exploratory data analysis
2283	DESCRIBE	计算输入数据与所有邻近要素之间的最邻近的距离。	r-spatialreg:1.1	\N	最近距离计算	exploratory data analysis
2284	DESCRIBE	将输入矢量与目标数据按照相对应的字段进行一对一连接。	r-spatialreg:1.1	\N	数据连接	exploratory data analysis
2285	DESCRIBE	基于地理加权回归的模型智能化选择,在进行地理加权回归之前为进入回归模型的变量进行优选	gwmodel:2.2	\N	地理加权回归变量优选	standard gwr
2286	DIAGNOSE	通过建立空间范围内每个点处的局部回归方程，量化反映空间数据关系中的异质性或非平稳性特征。	gwmodel:2.4-txt	\N	地理加权回归(txt输入自变量)	local regression
2287	DIAGNOSE	通过建立空间范围内每个点处的局部回归方程，量化反映空间数据关系中的异质性或非平稳性特征。	gwmodel:2.4-txt	\N	多尺度地理加权回归(txt输入自变量)	local regression
2294	PREDICT	该模型通过深入分析数据集中的变量相关性，智能识别并选择与空气质量最密切相关的气象特征,并输出最佳的权重大小。	csu_workflow_mode1_argument_select:1.0	\N	相关性分析与特征选择	mode1_argument_select
2295	PREDICT	利用GeoSOM算法实现空气站点的时空聚类与分区。通过整合地理空间信息与时间序列数据，模型有效捕捉空气质量监测站点的空间和时序特征。GeoSOM算法通过自组织特征映射（SOM）技术，结合地理约束，精准识别站点之间的相似性和差异性，实现对复杂环境变量的高效聚类与分区。	csu_workflow_mode2_clusters:3.0	\N	Geo-SOM时空聚类与分区	mode2_clusters
2296	PREDICT	该模型实现了空气质量预测的多元线性回归，通过与对应空气质量指标的强相关变量数据以及聚类分区结果进行多元线性回归预测空气质量。	csu_workflow_mode3_multiple_liner_predication:1.0	\N	空气质量多元线性回归预测	mode3_multiple_liner_predication
2298	PREDICT	该算法收集所有分类投票结果后，将投票次数最多的类别指定为最终的输出	land_development_potential:1.0.2-sklearn	\N	土地利用类型发展潜力（随机森林算法）	land development potential
2301	BASIC	基于欧氏距离对矢量数据进行分析	distance_analysis:1.0-scipy	\N	欧氏距离分析(道路)	Euclidean distance analysis (road)
2302	BASIC	基于欧氏距离对矢量数据进行分析	distance_analysis:1.0-scipy	\N	欧氏距离分析(水流)	Euclidean distance analysis (water)
2306	PRESCRIBE	将汇流累积量按照确定的阈值进行栅格河网的提取	taudem_ext:5.3.9	\N	河网提取（栅格）	stream raster grid extraction
2311	PREDICT	该算法收集所有分类投票结果后，将投票次数最多的类别计算概率进行输出	random_forest_probability:1.0.2-sklearn1.0.2	\N	面向土地利用类型扩张的随机森林	random forest probability
2491	DIAGNOSE	调整洼地区域高程值，使模拟过程中洼地内水流能顺畅向低处流动的算法	taudem_ext:5.3.9	\N	填洼（坡度抬升填平）	pit_removing
2492	DIAGNOSE	填洼算法是一种调整洼地区域高程值，使模拟过程中洼地内水流能顺畅向低处流动的算法。	whitebox-tools:2.0.0	\N	填洼（降低边缘洼地高度）	pit_removing
2493	DIAGNOSE	填洼算法是一种调整洼地区域高程值，使模拟过程中洼地内水流能顺畅向低处流动的算法。	whitebox-tools:2.0.0	\N	填洼（填平）	pit_removing
2498	PRESCRIBE	计算每个栅格到最近河栅格点的距离	taudem_ext:5.3.9	\N	至河流的距离计算	distance to stream grid
2497	PRESCRIBE	汇流累积量算法是用于定量指示地表水流受地形控制下的空间分配的算法	taudem_ext:5.3.9	\N	汇流累积量_D8（栅格）	FlowAccumulation
2501	BASIC	test	gagis-service:1.0	\N	两条线的夹角	line angle
2503	BASIC	根据物候的配置文件和所处的区域，进行赋值物候信息。	add_phenology:1.0	\N	种植条件图谱构建	add phenology
2504	BASIC	根据统计的各项作物百分比，给地块矢量添加物候置信度。	add_crop_confid:1.0	\N	种植信息推测	add crop confidence
2505	BASIC	作物地块面积统计	speculation_area_confirm:1.0	\N	种植类型计算	speculation area confirm
2506	PRESCRIBE	计算坡面空间朝向。	whitebox-tools:2.0.0	\N	坡向（三阶二元泰勒多项式）	aspect
2508	DIAGNOSE	通过建立空间范围内每个点处的局部回归方程，量化反映空间数据关系中的异质性或非平稳性特征。	gwmodel:2.4-new	\N	地理加权回归	standard gwr
2509	DIAGNOSE	为空间范围内每个点处的不同参数,分别采用对应的不同带宽或距离度量进行估计,准确量化空间数据的异质性特征。	gwmodel:2.4-new	\N	多尺度地理加权回归	extended gwr
2510	BASIC	基于欧氏距离对矢量数据进行分析	distance_analysis:1.0-scipy	\N	欧氏距离分析	Euclidean distance analysis
\.


--
-- Data for Name: meta_identification_docker_run_prefix; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.meta_identification_docker_run_prefix (meta_identification_id, docker_run_prefix) FROM stdin;
2150	/scripts/gtwr_predict.r
2151	/scripts/gwrp.r
2155	/scripts/linear_interpolation.r
2156	/scripts/ordinary_kriging_r.r
2176	/scripts/gwInteract.r
2177	/scripts/spInteract.r
2183	/scripts/linear_regression.r
2184	/scripts/logistic_regression.r
2185	/scripts/poisson_regression.r
2187	/scripts/gtwr.r
2188	/scripts/gwbr.r
2189	/scripts/gwpr.r
2193	/src/random_sampling.r
2194	/src/regular_sampling.r
2195	/src/sandwich_sampling.r
2220	/scripts/gwpca.r
2227	/scripts/gwss.r
2229	/scripts/gwvc.r
2230	/scripts/global_sac.r
2231	/scripts/lisa.r
2252	/scripts/discriminant.r
2253	/scripts/gwda.r
2271	imagery_vigra
2271	11
2273	imagery_opencv
2273	6
2275	imagery_svm
2275	0
2281	imagery_opencv
2281	5
2282	/scripts/csv2point.r
2283	/scripts/near_dist.r
2284	/scripts/join.r
2285	/scripts/GWR_VarSelect.r
2286	/scripts/gwr_txtvar.r
2287	/scripts/mgwr_txtvar.r
2304	/scripts/gwcorr.r
2508	/scripts/gwr_new.r
2509	/scripts/mgwr_new.r
\.


--
-- Data for Name: meta_operation; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.meta_operation (id, license, tips, usage) FROM stdin;
1360	\N	\N	使用方法描述
1439	\N	\N	该算法需要四个参数，道路网络矢量数据(network_data)，居民区数据(basestation_data)，设施点数据(POI_data)，搜索范围(Catchment)
1440	\N	\N	该算法需要三个参数，居民区数据(basestation_data)，设施点数据(POI_data)，重力参数(beta)
1442	\N	\N	该算法需要五个参数，道路网络矢量数据(network_data)，居民区数据(basestation_data)，设施点数据(POI_data)，搜索范围(Catchment)，距离衰减函数(beta)
1462	GPL-2;GPL-3	\N	运行模型时需要指定必选参数，包括输入数据、模拟次数、变异函数模型以及输出数据。
1543	\N	\N	使用方法描述
1544	\N	\N	在命令行中用代码指定输入数据、最大迭代次数和输出参数，完成计算。
2252	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据，可选参数指定预测数据。
2253	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据,可选参数核函数、带宽类型、带宽优选,选择带宽优选后可指定带宽优选方法,若不选择带宽优选则需指定带宽大小。
2254	If you use this implementation in you work, please don't forget to mention the author, Yerlan Idelbayev. ''' '''Copyright (c) 2018, Yerlan Idelbayev 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.\n2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.'''	\N	在命令行中用代码指定最大步数,图像路径，标签路径,测试数据路径,训练结果路径路径,完成算法运行。
2255	BSD-3-Clause;MIT;HPND	\N	在命令行中用代码指定训练数据路径，待预测数据路径，训练轮次，训练结果模型路径，分类结果路径，完成算法运行。
2256	BSD-3-Clause;MIT;HPND	\N	在命令行中用代码指定图像路径,批量大小,训练周期，模型总层数，训练结果路径路径,完成算法运行。
2257	BSD-3-Clause;MIT;HPND	\N	在命令行中用代码指定训练图像路径，测试图像路径，验证集图像和标签数据路径，训练日志文件，预训练模型路径，批量大小以及训练次数，完成算法运行。
2258	BSD-3-Clause;MIT;HPND	\N	在命令行中用代码指定,图像路径，标签路径,测试数据路径,训练结果路径路径,完成算法运行。
2259	\N	\N	在命令行中通过指定参数运行模型训练、测试和预测任务。
2260	BSD-3-Clause;MIT;HPND	\N	在命令行中用代码指定训练数据路径，待预测数据路径，训练轮次，训练结果模型路径，分类结果路径，完成算法运行。
2261	BSD-3-Clause;MIT;HPND	\N	在命令行中用代码指定训练数据路径，待预测数据路径，训练轮次，训练结果模型路径，分类结果路径，完成算法运行。
2262	Apache License	\N	在命令行中用代码指定图像数据类型,图像路径,网络深度,数据遍历次数,训练结果路径路径,完成算法运行。
2263	BSD-3-Clause;MIT;HPND	\N	在命令行中用代码指定最大步数,图像路径，标签路径,测试数据路径,训练结果路径路径,完成算法运行。
2264	BSD-3-Clause;MIT;HPND	\N	在命令行中用代码指定最大步数,图像路径，标签路径,测试数据路径,训练结果路径路径,完成算法运行。
2265	Apache License	\N	在命令行中用代码指定图像数据类型,图像路径,网络深度,数据遍历次数,训练结果路径路径,完成算法运行。
2266	\N	\N	在命令行中用代码指定最大步数, 图像路径，标签路径, 测试数据路径, 训练结果路径路径, 完成算法运行。
2267	BSD-3-Clause;MIT;HPND	\N	在命令行中用代码指定最大步数,图像路径，标签路径,测试数据路径,训练结果路径路径,完成算法运行。
2268	BSD-3-Clause;PSF-2.0;GPL-2.0	导入数据第一行为表头，会忽略读取。	须指定训练数据、训练标签、待预测的数据及输出路径，其他参数均可默认。
2269	BSD-3-Clause;PSF-2.0;GPL-2.0	导入数据第一行为表头，会忽略读取。	须指定训练数据、训练标签、待预测的数据及输出路径，其他参数均可默认
2270	sklearn-BSD	\N	须指定训练数据、训练标签、待预测的数据及输出路径，而后进行其他参数的调整以提高分类器的正确率
2271	SAGA-GIS-GNU General Public	\N	该算法须指定输入图像文件、训练区、训练标签字段、预测结果存放字段及输出路径，其他参数均可默认
2272	BSD-3-Clause;PSF-2.0;GPL-2.0	导入数据第一行为表头，会忽略读取。	须指定训练数据、训练标签、待预测的数据及输出路径，其他参数均可默认
2273	SAGA-GIS-GNU General Public	\N	该算法须指定训练区、训练标签字段、待预测的数据及输出路径，其他参数均可默认
2274	BSD-3-Clause;PSF-2.0;GPL-2.0	导入数据第一行为表头，会忽略读取。	须指定训练数据、训练标签、待预测的数据及输出路径，其他参数均可默认
2275	SAGA-GIS-GNU General Public	\N	该算法需要需要输入网格数据与训练区图形文件以完成图像网格的要素分类
2276	BSD-3-Clause;PSF-2.0;GPL-2.0	\N	在命令行中用代码指定正则化参数，内核类型，输入训练数据，输入训练标签，输入预测数据，输入预测标签，输出预测结果，完成算法运行
2277	BSD	导入数据第一行为表头，会忽略读取。	须指定训练数据、训练标签、待预测的数据及输出路径，其他参数均可默认，训练集质量直接关系分类器正确率。
2278	BSD-3-Clause;PSF-2.0;GPL-2.0	导入数据第一行为表头，会忽略读取。	须指定训练数据、训练标签、待预测的数据及输出路径，其他参数均可默认，训练集质量直接关系分类器正确率。
2279	BSD-3-Clause;PSF-2.0;GPL-2.0	导入数据第一行为表头，会忽略读取。	须指定训练数据、训练标签、待预测的数据及输出路径，其他参数均可默认，训练集质量直接关系分类器正确率
2280	BSD-3-Clause;PSF-2.0;GPL-2.0	导入数据第一行为表头，会忽略读取。	须指定训练数据、训练标签、待预测的数据及输出路径，其他参数均可默认，训练集质量直接关系分类器正确率。
2281	SAGA-GIS-GNU General Public	\N	在命令行中用代码指定待分类数据，归一化，输出分类结果，读取模型路径，输入训练区，训练区分类标签，训练区缓冲区大小，模型存储路径,完成算法运行
2152	MIT	\N	模型参数包括必选参数输入数据、字段和输出数据，像元大小和基础栅格二选一。
2082	MIT	\N	模型参数包括必选参数输入数据，运算方式和输出数据。
2083	\N	\N	点数据csv转换成shp
2084	MIT	\N	模型参数包括必选参数输入数据和输出数据。
2085	MIT	\N	模型参数包括必选参数输入数据和输出数据。
2086	geopandas-BSD	\N	该算法使用缓冲区距离(distance)或缓冲区字段(field)为输入图层中的所有要素计算缓冲区。
2087	SAGA-GIS-GNU General Public	\N	该算法使用缓冲区距离(distance)或缓冲区字段(field)为输入图层中的所有要素计算缓冲区。
2088	MIT	\N	用户必须指定输入和输出图像名称、所需的缓冲区大小，以及（可选）是否以栅格单元格（即标志）。如果未指定该标志，则将使用栅格坐标参考系的线性单位。
2089	\N	\N	该算法需要四个参数,输入面shapefile的路径,结构元素半径radius,运算类型type，结果shapefile路径
2090	MIT	\N	必须指定输入和叠加矢量文件的名称以及输出矢量文件名。该工具对矢量点、线或面进行操作，但输入文件和叠加文件必须包含相同的 ShapeType。
2091	MIT	\N	模型参数包括必选参数输入数据、叠置数据、输出数据。
2092	MIT	\N	必须指定输入和叠加矢量文件的名称以及输出矢量文件名。该工具对矢量点、线或面进行操作，但输入文件和叠加文件必须包含相同的 ShapeType。
2093	\N	\N	该算法需要五个参数，分别是输入文件、几何类型、第一个几何对象的ID、第二个几何对象的ID和距离类型。
2095	\N	\N	该算法需要两个参数，空间要素数据(shp_path)，时空拓扑关系分析结果(output)
2126	\N	\N	使用方法描述
2127	\N	\N	使用方法描述
2128	SAGA-GIS-GNU General Public	每个输入影像必须具有相同的行数和列数以及相同的空间范围，因为分析是逐个像素地执行的。任何输入图像中的 NoData 值都将导致从分析中删除相应的像素。	此工具可用于对两个或多个输入执行 k 均值聚类操作。该工具会创建两个输出，包括分类影像和统计数据。
2129	\N	\N	该算法需要三个参数,X坐标列名,Y坐标列名,识别局部长边的敏感度。
2130	\N	\N	该算法需要两个参数,X坐标列名,Y坐标列名
2131	sklearn-BSD	\N	该算法需要三个参数，X坐标列，Y坐标列，聚类数量。
2132	BSD	\N	该算法需要五个参数，X坐标列，Y坐标列，簇代表点的数量，代表点的收缩因子，自适应聚类的距离阈值。
2133	sklearn-BSD	\N	该算法需要四个参数，X坐标列，Y坐标列，聚类点之间允许的最大距离，生成簇的最小点数。
2134	MiniSom-MIT	\N	该算法需要四个参数,X坐标列名(x_field),Y坐标列名(y_field),X方向神经元个数(xnum),Y方向神经元个数(ynum)。
2135	\N	\N	该算法需要五个参数，X坐标列(x field)，Y坐标列(y field)，聚类数目(num_clusters)，收敛阈值(epsilon)，最大迭代次数(max_iter)
2136	sklearn-BSD	\N	该算法需要三个参数，X坐标列(x field)，Y坐标列(y field)，聚类数量(number of clusters)。
2137	sklearn-BSD	\N	该算法需要三个参数，X坐标列(x field)，Y坐标列(y field)，带宽(bandwidth)。
2138	\N	\N	该算法需要三个参数,X坐标列名,Y坐标列名,截止比例
2139	sklearn-BSD	\N	该算法需要四个参数，X坐标列(x field)，Y坐标列(y field)，最小邻居数(minimum number of neighbors)，最大邻域半径(maximum neighborhood radius)。
2140	\N	\N	该算法需要3个参数,点shapefile路径,属性csv路径,结果shapefile保存路径。
2141	\N	\N	该算法需要三个参数,输入文件路径与名称,attr非空间属性,输出文件路径与名称。
2142	\N	\N	该算法需要三个参数,输入文件路径与名称,attr非空间属性,输出文件路径与名称。
2143	\N	\N	该算法需要5个参数，输入数据(input_data)，邻域半径(eps)，最小邻近点数(min_samples)， 完整性阈值(theta)，输出数据(output_data)
2144	\N	\N	该算法需要7个参数，蒙特卡洛模拟次数(rep)，显著性水平(alpha)，计算流的邻居数(num_neighbors)，构建网络节点距离矩阵的阈值(k_value)，输入数据(input)，输出数据(output)，聚类结果(cluster)
2145	\N	\N	该算法需要一个参数,coord坐标串列名。
2146	\N	\N	该算法需要六个参数,X坐标列名,Y坐标列名,T坐标列名,空间邻域半径,时间邻域半径,邻域内最小样本数
2147	\N	\N	该算法需要十个参数,输入面csv的路径,开始时间索引,结束时间索引,参数k,距离阈值,连接阈值,纬度列名,经度列名,点ID列名,结果csv路径
2148	\N	\N	使用方法描述
2149	\N	\N	使用方法描述
2150	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、时间变量、输出数据,可选参数lambda值、核函数、带宽类型、带宽优选,选择带宽优选后可指定带宽优选方法,若不选择带宽优选则需指定带宽大小。
2151	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据,可选参数核函数、带宽类型、带宽优选,选择带宽优选后可指定带宽优选方法,若不选择带宽优选则需指定带宽大小。
2153	\N	\N	该算法需要用户选择待推测的土壤属性，选择至少一个相关的环境变量因子，从而推测研究区的土壤属性空间分布特征。
2154	\N	\N	该算法需要用户选择待推测的土壤属性，提供目标制图区域的参考栅格数据，从而推测研究区的土壤属性空间分布特征。
2155	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、插值属性、输出数据,可选参数包括区域像素大小,最近的点数。
2156	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、插值字段和输出数据,可选参数包括区域面数据,像素大小,可选变异函数的参数包括抽样宽度,偏基台值,块金值,变程等。输出结果包括插值结果和变异函数的图片。
2157	\N	\N	该算法需要用户选择待推测的土壤属性，提供至少一个与土壤属性协同变化的环境变量数据，从而推测研究区的土壤属性空间分布特征。
2158	SAGA-GIS-GNU General Public	\N	该算法利用从粗到细的控制晶格层次结构来生成一系列双三次B样条函数，其总和接近所需的插值函数。通过使用 B 样条细化将这些函数的总和减少到一个等效的 B 样条函数。
2159	\N	\N	该算法需要四个参数，道路网络矢量数据，居民区数据，设施点数据，搜索范围。
2160	\N	\N	该算法需要三个参数，居民区数据，设施点数据，重力参数
2161	\N	\N	该算法需要三个参数，用户信息数据(userFile)，用户轨迹数据(TrajFile)，POI数据(poiFile)。
2162	\N	\N	该算法需要五个参数，道路网络矢量数据，居民区数据，设施点数据，搜索范围，距离衰减函数
2163	GPL-2.0	\N	该算法需要3个参数,使用infomap算法进行社区发现
2164	BSD	\N	该算法需要3个参数，使用cpm算法进行社区发现
2165	GPL-2.0	\N	该算法需要3个参数进行社区发现
2166	\N	\N	该算法需要5个文件。
2167	\N	\N	该算法需要三个参数，道路网络矢量数据(pFileInput)，路径ID(originId)，路网距离(radius)。
2168	\N	\N	该算法需要两个参数，道路网络矢量数据(network_data)，生成器(Generators)。
2169	\N	\N	该算法需要三个参数，道路网络矢量数据(network_data)，起点ID(originNodeId)，终点ID(destinationNodeId)。
2170	\N	\N	该算法需要三个参数，道路网络矢量数据(network_data)，起点ID(originNodeId)，终点ID(destinationNodeId)。
2171	\N	\N	该算法需要三个参数，时变道路网络矢量数据(network_data)，起点ID(originId)，终点ID(destinationId)。
2172	\N	\N	该算法需要三个参数，道路网络矢量数据(network_data)，起点ID(originNodeId)，终点ID(destinationNodeId)。
2173	\N	\N	该算法需要三个参数，道路网络矢量数据(network_data)，起点ID(originNodeId)，终点ID(destinationNodeId)。
2174	\N	\N	该算法需要十个参数，公交路线数据(RoadDataFile)，公交行程数据（TripFile）,公交站点时刻信息（StopTimeFile）,公交站点信息（StopFile）,公交换乘信息（TransferFile），起始点ID(startstopID)，起始时刻（starttime）,终止点ID(endstopID)，终止时刻（endtime）。
2175	\N	\N	该算法需要四个参数，道路网络矢量数据(network_data)，路网转向数据(turnFile)，起点ID(originId)，终点ID(destinationId)。
2176	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据、出发地和目的地两者的经纬度，可选参数核函数、带宽类型、带宽优选，选择带宽优选后可指定带宽优选方法，若不选择带宽优选则需指定带宽大小。
2177	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据，出发地和目的地两者的经纬度。
2178	\N	\N	该算法需要三个参数，道路网络矢量数据(network_data)，起始点(startpoint)，终止点(endpoint)，最小停留时间(stoptime)。
2179	\N	\N	该算法需要5个参数
2180	\N	\N	在命令行中用代码指定输入数据、最大迭代次数和输出参数，完成计算。
2181	\N	\N	该算法通过 Flask 提供 API 接口，支持遥感数据的上传和坡度分析。
2182	GPL-2;GPL-3	\N	运行模型时需要指定必选参数，包括输入数据、模拟次数、变异函数模型以及输出数据。
2183	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据。
2184	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据。
2185	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据。
2186	MIT	\N	通过输入历史时间序列数据，并指定期望预测的期数，得到时间序列未来的趋势
2187	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、时间变量、输出数据,可选参数lambda值、核函数、带宽类型、带宽优选,选择带宽优选后可指定带宽优选方法,若不选择带宽优选则需指定带宽大小。
2188	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据，可选参数核函数、带宽类型、带宽优选，选择带宽优选后可指定带宽优选方法，选择带宽类型后可指定带宽大小。
2189	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据，可选参数核函数、带宽类型、带宽优选，选择带宽优选后可指定带宽优选方法，选择带宽类型后可指定带宽大小。
2193	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、抽样点数量、输出数据。
2194	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、抽样点数量、输出数据，可选参数包括：x和y方向的格网距离（同时），规则抽样的方法（可选规则网格和六边形网格抽样）。
2195	GPL-2;GPL-3	\N	模型参数包括必选参数：样本点数据、SSH层数据、输出层数据、结果空间数据,可选参数为：进行交叉验证折数。
2196	\N	\N	该算法需要三个参数，空间-专题属性数据(input_data)，beta值(beta)，蒙特卡罗模拟数(num_monte)
2197	\N	\N	该算法需要三个参数，空间轨迹数据(input_data)，邻域半径(eps)，最小邻近轨迹数(MinTrs)
2198	\N	\N	该算法需要九个参数，输入shapefile的路径，shapefile中的ID字段名，shapefile中的rk字段名，shapefile中的rkmd字段名，shapefile中的pmd字段名，shapefile中的poi字段名，输入OD矩阵csv文件路径，异常比例阈值，输出shapefile路径
2199	\N	\N	该算法需要八个参数,距离数据集路径,交通流量数据集路径,时间间隔,时间次数,道路数量,参数k,结果表格路径,结果csv文件路径
2200	\N	\N	该算法需要五个参数，空间点数据(input_data)，聚类中心数(kmeansValue)，时间序列天数(timeSpan)，首天数据的列序号(Attribute_Start_number)，异常探测结果(output)
2201	\N	\N	该算法需要五个参数,目标点X坐标列名,目标点Y坐标列名,参考点X坐标列名,参考点Y坐标列名,控制因子
2202	\N	\N	该算法需要五个参数，人群移动数据(dataPath_1)，数据(dataPath_2)，显著性水平阈值(a)，全局异常探测/局部异常探测(GorP)，异常探测结果(output)。
2203	\N	\N	该算法需要以下参数:历史轨迹集X坐标列(all_x_field)，历史轨迹集Y坐标列(all_y_field)，历史轨迹集id列(all_track_id_field)。测试轨迹X坐标列(test_x_field)，测试轨迹Y坐标列(test_y_field)，测试轨迹id列(test_track_id_field)，测试轨迹id值（test_track_id）
2286	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据，可选参数核函数、带宽类型、带宽优选，选择带宽优选后可指定带宽优选方法，若不选择带宽优选则需指定带宽大小。
2204	\N	\N	该算法需要以下参数:历史轨迹集X坐标列(all_x_field)，历史轨迹集Y坐标列(all_y_field)，历史轨迹集id列(all_track_id_field)。测试轨迹X坐标列(test_x_field)，测试轨迹Y坐标列(test_y_field)，测试轨迹id列(test_track_id_field)，测试轨迹id值（test_track_id）
2205	\N	\N	该算法需要五个参数,X坐标列名,Y坐标列名,k邻域大小，局部密度阈值
2206	\N	\N	该算法需要七个参数，空间点数据(input)，缓冲距离(buffer_distance)，alpha值(alpha)， beta值(beta)，显著性水平阈值(p)，缓冲区间隔数(interval)，异常探测结果(output)。
2207	\N	\N	该算法需要四个参数,输入点shapefile的路径,三角网边长约束参数alpha,attr非空间属性，结果shapefile路径
2208	\N	\N	该算法需要三个参数,X坐标列名,Y坐标列名,识别局部长边的敏感度。
2209	\N	\N	该算法需要两个参数,X坐标列名,Y坐标列名
2210	\N	\N	该算法需要六个参数,输入shp的路径,输出shapefile路径,输出文本文件路径,级联参与指数,时间半径,空间半径
2211	\N	\N	该算法需要四个参数,输入数据,滑动窗口宽度,最小密度比阈值,输出路径
2212	MIT	\N	该算法使用最小支持度(minsupport)与最小置信度(minconfidence)挖掘频繁共现于同一时间或空间的事件间的关联关系。
2213	\N	\N	该算法需要五个参数,目标点数据的x坐标，目标点数据的y坐标,参考点数据的x坐标，参考点数据的y坐标, k值
2214	\N	\N	该算法需要四个参数,输入时空点csvfile的路径,判定时空同位的距离阈值spatial_thresold与时间阈值time_thresold，模拟数据集数目n_sets，结果csvfile路径
2215	\N	\N	该算法需要11个参数，数据列表(data_path_list)，子事件路径(sub_events_path)，k值(k)，邻域半径(eps)， 最小邻近点数(min_pts)，聚类中心数目(n_cluster)，窗口大小(window_size)，最小发生次数(min_sup)， 最小置信度(conf_threshold)，事件限制(rule_filtering)，时空遥相关挖掘结果(output)
2216	\N	\N	该算法需要两个参数,X坐标列名(x_field),Y坐标列名(y_field)。
2217	\N	\N	该算法需要两个参数,专题属性列名(field),分组数(boxsnum)。
2218	osgeo-BSD-3;geopy-MIT;pyproj-MIT	\N	该算法使用像元大小(cellsize)或计算密度的搜索半径(searchradius)使用核函数将输入数据中的所有要素拟合为光滑锥状表面。搜索半径参数值越大，生成的密度栅格越平滑且概化程度越高。值越小，生成的栅格所显示的信息越详细。
2219	\N	\N	该算法需要五个参数,X坐标列名,Y坐标列名,分析半径初始距离,分析半径距离增量,分析半径距离增长次数
2220	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据，可选参数包括保留的主成分个数、核函数、带宽类型、带宽优选，选择带宽优选后可指定带宽优选方法，若不选择带宽优选则需指定带宽大小。
2221	sklearn-BSD	\N	该算法通过保留的特征数(components)使用数据的奇异值分解将其投影到较低维空间的线性降维。
2222	BSD-3-Clause;PSF-2.0;GPL-2.0	导入数据第一行为表头，会忽略读取。	须指定训练数据、训练标签、待预测的数据及输出路径，其他参数均可默认
2223	\N	\N	使用方法描述
2224	\N	\N	使用方法描述
2225	\N	\N	使用方法描述
2226	GPL-2;GPL-3	\N	通过指定输入的表格数据，因变量和自变量，计算得到地理探测因子的结果
2227	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、带宽大小、输出数据，可选参数核函数、带宽类型。
2229	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、带宽大小、输出数据，可选参数核函数、带宽类型。
2230	GPL-2;GPL-3	\N	输入参数包括空间距离度量方式、属性值字段、计算方法和权重标准等，输出数据为对应的全局空间自相关指数及其辅助信息。
2231	GPL-2;GPL-3	\N	输入参数包括空间距离度量方式、属性值字段、计算方法和权重标准等，输出数据为对应的局部空间自相关指数等。
2232	\N	\N	通过指定输入的时间序列数据，最大滞后次数(可选)，即可得到序列自相关系数和偏自相关系数的值
2234	MIT	请注意，此模型不会填充 DEM 中的 NoData 区域。建议在应用之前处理此类区域。	用户必须指定输入 DEM 文件的名称、输出的栅格文件 ，可选参数为输出类型、日志、剪辑、输入栅格数据格式和输入D8指针样式。
2235	\N	\N	使用默认方法d8flowdir，线程数默认为2，可通过-np 指定，（目前使用默认值，不暴露给前端），输入数据在-fel后指定已填洼dem，输出数据在-p后指定输出流向栅格(-sd8可选填输出坡度)
2240	\N	\N	用户必须指定输入单位汇水面积，坡度和输出栅格的名称。可通过设置exponent参数控制集水区面积与流量的关系，其默认值为1.0。
2242	SAGA-GIS-GNU General Public	\N	该算法使用缓冲区距离(distance)或缓冲区字段(field)为输入图层中的所有要素计算缓冲区。
2243	\N	\N	用户输入填洼后DEM，计算输出曲率
2244	\N	\N	用户必须指定输入DEM和输出光栅图像的名称。仅当DEM中的垂直和水平单位不相同时，Z转换因子才重要。在这种情况下，算法会将DEM中的每个高程乘以Z转换因子。
2245	\N	\N	用户必须指定输入数字高程模型 （DEM）的名称和输出栅格。
2246	\N	\N	用户输入填洼后DEM，计算输出坡度
2247	\N	\N	用户输入填洼后DEM，计算输出坡度
2248	MIT	\N	用户必须指定输入 DEM和输出栅格图像的名称。仅当 DEM 中的垂直和水平单位不相同时，Z 转换因子才重要。在这种情况下，算法会将 DEM 中的每个高程乘以 Z 转换因子。
2249	\N	\N	用户输入流域内坡度和汇流累积量，输出该流域范围地形湿度指数
2250	MIT	\N	模型参数包括必选参数输入DEM,输入站点矢量数据、高度和输出栅格数据。
2251	MIT	\N	模型参数包括必选参数输入DEM,高度、分辨率和输出栅格数据。
2282	GPL-2	\N	点数据csv转换成shp
2283	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、目标数据、输出数据，可选参数为保留的id变量属性名。
2284	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、目标数据、连接的字段、输出数据。
2285	GPL-2;GPL-3	\N	基于地理加权回归的模型智能化选择
2287	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据，可选参数核函数、带宽类型、带宽优选，选择带宽优选后可指定带宽优选方法，若不选择带宽优选则需指定带宽大小。
2294	BSD	\N	该算法需要五个参数，历史空气质量数据文件夹，空气质量自变量，待预测的空气质量指标，气象站点地理坐标，最佳参数输出，最优自变量和权重结果。
2295	BSD	\N	该算法需要十二个参数，训练数据文件夹，最佳参数输入，变量，空气质量指标，地理坐标，聚类行数，聚类列数，GeoSOM算法迭代次数，地理邻域大小参数，初始邻域大小阈值，区域结果输出，独立变量输出。
2296	BSD	\N	该算法需要六个参数，变量输入，空气质量指标，地理坐标，聚类结果输入，预测数据路径，预测结果输出。
2298	BSD-3-Clause;PSF-2.0;GPL-2.0	\N	须指定训练数据、训练标签、待预测的数据及输出路径，其他参数均可默认
2301	\N	\N	通过指明输入的shp文件路径，tif图的大小，和输出文件路径来进行欧式距离分析
2302	\N	\N	通过指明输入的shp文件路径，tif图的大小，和输出文件路径来进行欧式距离分析
2304	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、带宽大小、输出数据，可选参数核函数、带宽类型。
2306	\N	\N	用户输入汇流累积量以及提取河网的阈值，输出栅格的河网结果
2311	BSD-3-Clause;PSF-2.0;GPL-2.0	\N	须指定至少三个训练样本数据、一个训练标签和一个输出路径，其他参数均可默认
2491	\N	\N	用户指定待填洼DEM，输出结果为填洼后DEM
2492	MIT	请注意，此模型不会填充 DEM 中的 NoData 区域。建议在应用之前处理此类区域。	用户必须指定输入 DEM 文件的名称、输出违规的 DEM 文件 、最大搜索窗口半径 ，可选参数为最大填洼成本和平面高度增量值等。
2493	MIT	用户可以选择指定用于求解平整的高程增量的大小，尽管最好不要指定此可选值，而是让算法自己确定最合适的值。此外，此模型不会填充 DEM 中的 NoData 区域。建议在应用之前处理此类区域。	用户必须指定输入 DEM 文件的名称、输出填洼的 DEM 文件 ，可选参数为修整平面、平整区域增量和最大深度。
2497	\N	\N	用户输入数流向，输出汇流累积量
2498	\N	\N	使用默认方法d8hdisttostrm，线程数默认为2，可通过-np 指定，（目前使用默认值，不暴露给前端）。
2501	\N	\N	test
2503	\N	\N	为地块添加物候信息
2504	\N	\N	为地块添加作物置信度信息
2505	\N	\N	统计作物地块面积
2506	MIT	\N	用户必须指定输入DEM和输出光栅图像的名称。仅当DEM中的垂直和水平单位不相同时，Z转换因子才重要。在这种情况下，算法会将DEM中的每个高程乘以Z转换因子。
2508	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据，可选参数核函数、带宽类型、带宽优选，选择带宽优选后可指定带宽优选方法，若不选择带宽优选则需指定带宽大小。
2509	GPL-2;GPL-3	\N	模型参数包括必选参数输入数据、因变量、自变量、输出数据，可选参数核函数、带宽类型。
2510	\N	\N	通过指明输入的shp文件路径，tif图的大小，和输出文件路径来进行欧式距离分析
\.


--
-- Data for Name: meta_parameter; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.meta_parameter (id) FROM stdin;
1360
1439
1440
1442
1462
1543
1544
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2095
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2229
2230
2231
2232
2234
2235
2240
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2282
2283
2284
2285
2286
2287
2294
2295
2296
2298
2301
2302
2304
2306
2311
2491
2492
2493
2497
2498
2501
2503
2504
2505
2506
2508
2509
2510
\.


--
-- Data for Name: meta_parameter_group_relations; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.meta_parameter_group_relations (meta_parameter_id, group_relations_id) FROM stdin;
\.


--
-- Data for Name: meta_parameter_groups; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.meta_parameter_groups (meta_parameter_id, groups_id) FROM stdin;
2086	31447
2086	31448
2087	31474
2129	32429
2130	32440
2131	32449
2133	32488
2133	32489
2134	32513
2135	32542
2136	32558
2137	32577
2138	32594
2139	32605
2139	32606
2139	32607
2145	32673
2146	32680
2147	32698
2151	32767
2151	32768
2153	32804
2154	32827
2157	32858
2158	32874
2176	33031
2176	33032
2188	33143
2188	33144
2189	33164
2189	33165
2201	33311
2201	33312
2203	33338
2203	33339
2204	33365
2204	33366
2205	33390
2208	33421
2209	33432
2216	33503
2218	33517
2219	33532
2226	33613
2273	34452
2275	34497
2276	34541
2286	34660
2286	34661
2508	39141
2508	39142
\.


--
-- Data for Name: meta_parameter_parameters; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.meta_parameter_parameters (meta_parameter_id, parameters_id) FROM stdin;
1360	20370
1360	20371
1360	20372
1360	20373
1360	20374
1439	21883
1439	21885
1439	21887
1439	21889
1440	21892
1440	21894
1440	21896
1442	21908
1442	21910
1442	21912
1442	21914
1442	21916
1462	22092
1462	22093
1462	22094
1543	23047
1543	23048
1543	23049
1543	23050
1544	23051
1544	23052
1544	23053
2252	33854
2252	33855
2252	33856
2252	33857
2252	33858
2252	33859
2253	33862
2253	33863
2253	33864
2253	33866
2253	33868
2253	33870
2253	33871
2253	33873
2253	33874
2253	33875
2254	33879
2254	33880
2254	33881
2254	33883
2254	33885
2254	33887
2254	33889
2254	33891
2254	33893
2255	33906
2255	33908
2255	33910
2255	33912
2255	33914
2255	33916
2255	33918
2255	33919
2255	33921
2255	33923
2255	33925
2255	33927
2255	33929
2255	33931
2255	33933
2255	33935
2255	33937
2255	33939
2255	33941
2255	33943
2255	33945
2255	33947
2255	33949
2255	33951
2255	33953
2255	33955
2255	33957
2256	33967
2256	33968
2256	33969
2256	33971
2256	33973
2256	33975
2256	33977
2256	33979
2256	33981
2256	33983
2256	33985
2256	33987
2257	33994
2257	33995
2257	33996
2257	33997
2257	33998
2257	33999
2257	34001
2257	34003
2258	34010
2258	34011
2258	34012
2258	34014
2258	34016
2258	34018
2258	34020
2258	34022
2258	34024
2258	34026
2258	34028
2258	34030
2259	34046
2259	34048
2259	34050
2259	34052
2259	34054
2259	34056
2259	34058
2259	34060
2259	34062
2259	34064
2259	34066
2259	34068
2259	34070
2259	34072
2259	34074
2260	34075
2260	34077
2260	34079
2260	34081
2260	34083
2260	34085
2260	34087
2260	34088
2260	34090
2260	34092
2260	34093
2260	34094
2260	34095
2260	34096
2261	34105
2261	34107
2261	34108
2261	34110
2261	34112
2261	34114
2261	34115
2261	34116
2261	34117
2261	34118
2261	34119
2261	34120
2261	34121
2261	34122
2261	34124
2261	34125
2261	34127
2261	34128
2261	34129
2262	34138
2262	34139
2262	34141
2262	34143
2263	34152
2263	34153
2263	34154
2263	34155
2263	34156
2263	34157
2263	34159
2263	34161
2263	34163
2263	34165
2263	34167
2263	34169
2263	34171
2263	34173
2263	34175
2263	34177
2263	34179
2263	34181
2263	34183
2264	34193
2264	34195
2264	34197
2264	34199
2264	34201
2264	34203
2264	34205
2264	34207
2264	34209
2264	34211
2264	34213
2265	34221
2265	34222
2265	34223
2265	34224
2265	34226
2265	34228
2266	34238
2266	34240
2266	34242
2266	34244
2266	34246
2266	34248
2266	34250
2266	34252
2266	34254
2266	34256
2266	34258
2266	34260
2266	34262
2266	34264
2266	34266
2266	34268
2266	34270
2266	34272
2266	34274
2266	34276
2266	34278
2267	34287
2267	34288
2267	34289
2267	34290
2267	34291
2267	34292
2267	34293
2267	34294
2267	34296
2267	34298
2267	34300
2267	34302
2267	34304
2267	34306
2267	34308
2267	34310
2267	34312
2267	34314
2267	34316
2267	34318
2267	34320
2268	34328
2268	34329
2268	34330
2268	34331
2268	34333
2268	34335
2268	34337
2268	34339
2268	34341
2269	34346
2269	34347
2269	34348
2269	34349
2269	34351
2269	34353
2269	34355
2269	34357
2269	34359
2269	34360
2269	34361
2270	34366
2270	34367
2270	34368
2270	34369
2270	34371
2270	34373
2270	34375
2270	34377
2270	34379
2270	34381
2270	34383
2270	34385
2270	34387
2270	34389
2270	34391
2270	34393
2270	34395
2270	34397
2270	34399
2270	34401
2271	34406
2271	34407
2271	34408
2271	34410
2271	34411
2271	34413
2271	34415
2271	34417
2271	34419
2271	34421
2271	34423
2272	34426
2272	34427
2272	34428
2272	34429
2272	34431
2272	34433
2272	34435
2272	34437
2272	34439
2272	34441
2272	34443
2272	34445
2272	34447
2273	34453
2273	34455
2273	34456
2273	34457
2273	34458
2273	34459
2273	34461
2273	34463
2273	34465
2273	34467
2273	34469
2274	34473
2274	34474
2274	34475
2274	34476
2274	34478
2274	34480
2274	34482
2274	34484
2274	34486
2274	34488
2274	34490
2274	34492
2275	34498
2275	34499
2275	34500
2275	34502
2275	34504
2275	34506
2275	34507
2275	34509
2275	34510
2275	34511
2275	34514
2275	34516
2275	34518
2275	34520
2275	34522
2275	34524
2275	34526
2275	34528
2275	34530
2275	34532
2275	34534
2275	34536
2275	34538
2276	34542
2276	34543
2276	34544
2276	34545
2276	34547
2276	34549
2276	34551
2276	34553
2276	34555
2276	34557
2276	34559
2276	34561
2276	34562
2277	34566
2277	34567
2277	34568
2277	34569
2277	34571
2277	34573
2277	34575
2277	34577
2278	34582
2278	34583
2278	34584
2278	34585
2278	34587
2278	34589
2278	34591
2279	34596
2279	34597
2279	34598
2279	34599
2279	34601
2280	34606
2280	34607
2280	34608
2280	34609
2280	34611
2280	34613
2281	34618
2281	34620
2281	34621
2281	34622
2281	34623
2281	34624
2281	34626
2082	31425
2082	31426
2082	31427
2082	31428
2083	31432
2083	31433
2084	31436
2084	31437
2084	31438
2085	31443
2085	31444
2086	31449
2086	31451
2086	31453
2086	31455
2086	31459
2086	31461
2086	31463
2086	31465
2086	31468
2086	31470
2086	31471
2087	31476
2087	31478
2087	31479
2087	31481
2087	31482
2087	31486
2087	31489
2087	31491
2087	31493
2087	31495
2087	31497
2087	31498
2088	31502
2088	31503
2088	31505
2088	31507
2088	31508
2089	31510
2089	31511
2089	31513
2089	31514
2090	31517
2090	31518
2090	31519
2090	31520
2091	31525
2091	31526
2091	31527
2091	31529
2091	31530
2092	31535
2092	31536
2092	31537
2092	31539
2092	31540
2093	31543
2093	31545
2093	31546
2093	31547
2093	31549
2093	31550
2095	31556
2095	31557
2126	32386
2126	32387
2126	32388
2126	32389
2126	32390
2126	32391
2126	32392
2126	32393
2126	32394
2127	32397
2127	32398
2127	32399
2127	32400
2127	32401
2127	32402
2127	32403
2127	32404
2127	32405
2128	32407
2128	32409
2128	32410
2128	32411
2128	32412
2128	32414
2128	32416
2128	32418
2128	32420
2128	32422
2128	32424
2128	32426
2129	32430
2129	32432
2129	32435
2129	32438
2129	32439
2130	32441
2130	32443
2130	32446
2130	32448
2131	32450
2131	32451
2131	32453
2131	32456
2131	32458
2131	32460
2131	32462
2131	32463
2132	32466
2132	32468
2132	32471
2132	32474
2132	32476
2132	32478
2132	32479
2133	32490
2133	32492
2133	32495
2133	32498
2133	32500
2133	32502
2133	32504
2133	32506
2133	32509
2133	32510
2134	32514
2134	32516
2134	32519
2134	32522
2134	32524
2134	32526
2134	32528
2134	32530
2134	32532
2134	32534
2134	32536
2134	32538
2134	32539
2135	32543
2135	32545
2135	32548
2135	32551
2135	32553
2135	32555
2135	32556
2136	32559
2136	32560
2136	32562
2136	32565
2136	32567
2136	32569
2136	32571
2136	32573
2136	32574
2137	32578
2137	32579
2137	32581
2137	32584
2137	32586
2137	32588
2137	32590
2137	32591
2138	32595
2138	32597
2138	32600
2138	32603
2138	32604
2139	32608
2139	32609
2139	32611
2139	32614
2139	32616
2139	32618
2139	32620
2139	32622
2139	32625
2139	32628
2139	32631
2139	32634
2139	32636
2139	32639
2139	32640
2140	32643
2140	32644
2140	32645
2141	32646
2141	32647
2141	32648
2142	32649
2142	32650
2142	32651
2143	32652
2143	32654
2143	32656
2143	32658
2143	32659
2144	32662
2144	32664
2144	32666
2144	32668
2144	32669
2144	32670
2144	32671
2145	32674
2145	32676
2145	32678
2145	32679
2146	32681
2146	32683
2146	32686
2146	32689
2146	32692
2146	32694
2146	32696
2146	32697
2147	32699
2147	32701
2147	32704
2147	32707
2147	32709
2147	32711
2147	32713
2147	32715
2147	32718
2147	32721
2147	32723
2148	32726
2148	32727
2148	32728
2148	32729
2148	32730
2148	32731
2149	32734
2149	32735
2149	32736
2149	32737
2149	32738
2149	32739
2149	32740
2150	32742
2150	32743
2150	32744
2150	32745
2150	32746
2150	32747
2150	32750
2150	32752
2150	32754
2150	32756
2150	32758
2150	32760
2150	32762
2150	32763
2151	32769
2151	32770
2151	32771
2151	32772
2151	32774
2151	32776
2151	32778
2151	32780
2151	32782
2151	32784
2152	32789
2152	32790
2152	32791
2152	32793
2152	32795
2152	32796
2152	32797
2152	32798
2152	32799
2152	32800
2153	32806
2153	32808
2153	32809
2153	32810
2153	32812
2153	32813
2153	32814
2153	32815
2153	32816
2153	32817
2153	32818
2153	32819
2153	32821
2153	32822
2153	32823
2154	32828
2154	32829
2154	32831
2154	32832
2155	32837
2155	32838
2155	32839
2155	32840
2155	32841
2155	32842
2156	32845
2156	32846
2156	32847
2156	32848
2156	32850
2156	32851
2156	32852
2156	32853
2156	32854
2156	32855
2157	32859
2157	32860
2157	32862
2157	32863
2157	32864
2157	32865
2157	32866
2157	32867
2157	32868
2157	32869
2157	32870
2158	32876
2158	32878
2158	32879
2158	32880
2158	32881
2158	32883
2158	32885
2158	32888
2158	32891
2158	32894
2158	32897
2158	32900
2158	32903
2158	32906
2158	32909
2158	32911
2158	32913
2158	32914
2159	32917
2159	32918
2159	32919
2159	32921
2159	32922
2160	32924
2160	32925
2160	32926
2160	32928
2160	32929
2161	32931
2161	32932
2161	32933
2161	32935
2161	32937
2161	32939
2161	32941
2161	32943
2161	32945
2161	32946
2162	32948
2162	32949
2162	32950
2162	32952
2162	32954
2162	32955
2163	32957
2163	32959
2163	32960
2164	32963
2164	32965
2164	32966
2165	32969
2165	32970
2165	32971
2166	32974
2166	32975
2167	32977
2167	32979
2167	32981
2167	32982
2168	32984
2168	32985
2169	32987
2169	32989
2169	32991
2170	32993
2170	32995
2170	32997
2171	32999
2171	33001
2171	33003
2171	33005
2171	33007
2172	33009
2172	33011
2172	33013
2173	33015
2173	33017
2173	33019
2174	33021
2175	33023
2175	33024
2175	33026
2175	33028
2176	33033
2176	33034
2176	33035
2176	33036
2176	33037
2176	33038
2176	33039
2176	33041
2176	33043
2176	33045
2176	33047
2176	33049
2176	33052
2176	33053
2176	33054
2177	33058
2177	33059
2177	33060
2177	33061
2177	33062
2177	33063
2177	33064
2177	33065
2177	33066
2178	33068
2178	33070
2178	33072
2178	33073
2179	33075
2179	33076
2180	33078
2180	33079
2180	33080
2181	33082
2181	33083
2182	33087
2182	33088
2182	33089
2183	33092
2183	33093
2183	33094
2183	33095
2183	33096
2184	33099
2184	33100
2184	33101
2184	33102
2184	33103
2185	33106
2185	33107
2185	33108
2185	33109
2185	33110
2186	33113
2186	33115
2186	33117
2186	33118
2187	33121
2187	33122
2187	33123
2187	33124
2187	33126
2187	33128
2187	33130
2187	33132
2187	33134
2187	33136
2187	33138
2187	33139
2188	33145
2188	33146
2188	33147
2188	33149
2188	33151
2188	33153
2188	33155
2188	33157
2188	33159
2188	33160
2189	33166
2189	33167
2189	33168
2189	33170
2189	33172
2189	33174
2189	33176
2189	33178
2189	33180
2189	33181
2193	33237
2193	33238
2193	33239
2194	33242
2194	33243
2194	33244
2194	33245
2194	33247
2194	33248
2195	33251
2195	33252
2195	33253
2195	33254
2195	33255
2195	33257
2195	33258
2196	33260
2196	33262
2196	33264
2196	33265
2197	33267
2197	33269
2197	33271
2197	33272
2198	33274
2198	33276
2198	33278
2198	33280
2198	33282
2198	33284
2198	33285
2198	33287
2198	33288
2199	33290
2199	33291
2199	33293
2199	33295
2199	33297
2199	33299
2199	33300
2199	33301
2200	33302
2200	33304
2200	33306
2200	33308
2200	33309
2201	33313
2201	33315
2201	33318
2201	33320
2201	33322
2201	33325
2201	33328
2201	33329
2202	33330
2202	33331
2202	33333
2202	33335
2202	33336
2203	33340
2203	33341
2203	33343
2203	33345
2203	33347
2203	33348
2203	33350
2203	33352
2203	33354
2203	33357
2203	33359
2203	33361
2203	33362
2204	33367
2204	33368
2204	33370
2204	33372
2204	33374
2204	33375
2204	33377
2204	33379
2204	33381
2204	33384
2204	33386
2204	33387
2205	33391
2205	33393
2205	33396
2205	33399
2205	33401
2205	33402
2206	33403
2206	33405
2206	33407
2206	33409
2206	33411
2206	33413
2206	33414
2207	33416
2207	33418
2207	33419
2207	33420
2208	33422
2208	33424
2208	33427
2208	33429
2208	33431
2209	33433
2209	33435
2209	33438
2209	33440
2210	33441
2210	33442
2210	33443
2210	33445
2210	33447
2210	33449
2211	33450
2211	33452
2211	33454
2211	33455
2212	33456
2212	33458
2212	33460
2212	33461
2213	33462
2213	33463
2213	33465
2213	33467
2213	33468
2213	33470
2213	33473
2213	33474
2214	33475
2214	33477
2214	33479
2214	33481
2214	33482
2215	33483
2215	33484
2215	33485
2215	33487
2215	33489
2215	33491
2215	33493
2215	33495
2215	33497
2215	33499
2215	33500
2215	33501
2216	33504
2216	33506
2216	33509
2216	33511
2217	33512
2217	33513
2217	33515
2217	33516
2218	33518
2218	33519
2218	33522
2218	33524
2218	33525
2219	33533
2219	33535
2219	33538
2219	33541
2219	33543
2219	33545
2219	33546
2220	33548
2220	33549
2220	33550
2220	33552
2220	33554
2220	33556
2220	33557
2220	33560
2220	33561
2220	33562
2221	33565
2221	33567
2221	33569
2221	33571
2221	33572
2222	33575
2222	33577
2222	33579
2222	33581
2222	33583
2222	33585
2222	33586
2223	33592
2223	33593
2223	33594
2223	33595
2224	33598
2224	33599
2224	33600
2224	33601
2224	33602
2224	33603
2224	33604
2225	33607
2225	33608
2225	33609
2225	33610
2225	33611
2225	33612
2226	33614
2226	33615
2226	33617
2226	33619
2227	33621
2227	33622
2227	33624
2227	33626
2227	33627
2227	33628
2229	33643
2229	33644
2229	33646
2229	33648
2229	33649
2229	33650
2230	33654
2230	33655
2230	33657
2230	33659
2230	33661
2230	33662
2230	33663
2231	33666
2231	33667
2231	33669
2231	33671
2231	33673
2231	33674
2232	33677
2232	33678
2232	33679
2234	33690
2234	33691
2234	33692
2234	33694
2234	33695
2235	33699
2235	33701
2235	33702
2235	33703
2240	33750
2240	33751
2240	33752
2240	33753
2240	33755
2242	33764
2242	33766
2242	33768
2242	33770
2242	33771
2242	33772
2243	33777
2243	33779
2243	33780
2243	33781
2244	33784
2244	33785
2244	33786
2244	33788
2244	33789
2245	33792
2245	33793
2245	33794
2245	33796
2245	33797
2246	33800
2246	33802
2246	33803
2246	33804
2247	33808
2247	33810
2247	33811
2247	33812
2248	33815
2248	33816
2248	33818
2248	33820
2248	33821
2249	33825
2249	33827
2249	33828
2249	33829
2249	33830
2250	33834
2250	33835
2250	33836
2250	33838
2250	33839
2251	33844
2251	33845
2251	33847
2251	33849
2251	33850
2282	34630
2282	34631
2282	34632
2282	34633
2282	34634
2283	34637
2283	34638
2283	34639
2283	34640
2284	34642
2284	34643
2284	34644
2284	34645
2284	34646
2285	34648
2285	34649
2285	34650
2285	34652
2285	34654
2285	34655
2285	34656
2286	34662
2286	34663
2286	34664
2286	34666
2286	34668
2286	34670
2286	34672
2286	34674
2286	34676
2286	34678
2286	34679
2287	34683
2287	34684
2287	34685
2287	34687
2287	34689
2287	34691
2287	34693
2287	34695
2287	34697
2287	34698
2287	34699
2294	34862
2294	34864
2294	34866
2294	34868
2294	34869
2295	34876
2295	34877
2295	34879
2295	34881
2295	34883
2295	34885
2295	34887
2295	34889
2295	34891
2295	34893
2295	34894
2295	34895
2296	34902
2296	34904
2296	34906
2296	34907
2296	34908
2296	34909
2298	34967
2298	34968
2298	34969
2298	34970
2298	34971
2298	34972
2298	34974
2298	34976
2298	34978
2298	34980
2298	34982
2298	34984
2298	34986
2298	34988
2298	34990
2301	35083
2301	35084
2301	35085
2301	35086
2302	35091
2302	35092
2302	35093
2302	35094
2304	35145
2304	35146
2304	35148
2304	35150
2304	35152
2304	35153
2306	35228
2306	35230
2306	35231
2306	35232
2306	35234
2311	35329
2311	35330
2311	35331
2311	35332
2311	35333
2311	35334
2311	35335
2311	35336
2311	35337
2311	35338
2311	35340
2311	35342
2311	35344
2311	35346
2311	35348
2311	35350
2311	35352
2311	35354
2311	35356
2311	35358
2491	38251
2491	38253
2491	38254
2491	38255
2492	38258
2492	38259
2492	38261
2492	38263
2492	38265
2492	38267
2492	38269
2492	38270
2493	38273
2493	38274
2493	38276
2493	38278
2493	38280
2493	38281
2497	38400
2497	38402
2497	38403
2497	38405
2497	38406
2498	38410
2498	38412
2498	38413
2498	38414
2498	38415
2498	38417
2498	38419
2498	38420
2501	38532
2501	38534
2501	38535
2503	39070
2503	39071
2503	39073
2503	39074
2503	39075
2504	39077
2504	39078
2504	39079
2504	39081
2504	39082
2505	39085
2505	39086
2505	39087
2505	39088
2505	39089
2505	39090
2506	39092
2506	39093
2506	39095
2506	39096
2508	39143
2508	39144
2508	39145
2508	39146
2508	39148
2508	39151
2508	39153
2508	39155
2508	39157
2508	39160
2508	39163
2508	39165
2508	39167
2508	39168
2509	39199
2509	39200
2509	39201
2509	39202
2509	39204
2509	39207
2509	39209
2509	39211
2509	39213
2509	39215
2509	39217
2509	39218
2510	39275
2510	39277
2510	39279
2510	39280
\.


--
-- Data for Name: meta_source; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.meta_source (id, contact, maintainer, organization, repository) FROM stdin;
1360	fanxc@lreis.ac.cn	Xingchen Fan	Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences	\N
1439	9027575@qq.com	Zhangjinmeng && Zhongyuxiang	Wuhan University	\N
1440	9027575@qq.com	Zhangjinmeng && Zhongyuxiang	Wuhan University	\N
1442	9027575@qq.com	Zhangjinmeng && Zhongyuxiang	Wuhan University	\N
1462	2108160224007@stu.bucea.edu.cn	Chen Jian	Beijing University of Civil Engineering and Architecture	\N
1543	fanxc@lreis.ac.cn	Xingchen Fan	Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences	\N
1544	2108160224011@stu.bucea.edu.cn	Han Li	Beijing University of Civil Engineering and Architecture	\N
2252	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2253	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2254	2108160223006@stu.bucea.edu.cn	HaiPeng Yang	Beijing University of Civil Engineering and Architecture	\N
2255	1035305483@qq.com	Shan Huang	Beijing University of Civil Engineering and Architecture	\N
2256	wjd1009@163.com	Ding Wenjing	Beijing University of Civil Engineering and Architecture	\N
2257	wjd1009@163.com	Wenjing Ding	Beijing University of Civil Engineering and Architecture	\N
2258	2108160223006@stu.bucea.edu.cn	Haipeng Yang	Beijing University of Civil Engineering and Architecture	\N
2259	liubingsai2023@163.com	Bingsai Liu	Beijing University of Civil Engineering and Architecture	\N
2260	1035305483@qq.com	Shan Huang	Beijing University of Civil Engineering and Architecture	\N
2261	1035305483@qq.com	Shan Huang	Beijing University of Civil Engineering and Architecture	\N
2262	386469007@qq.com	Xuandi Liu	Beijing University of Civil Engineering and Architecture	\N
2263	2108570020097@stu.bucea.edu.cn	Tianming Zhao	Beijing University of Civil Engineering and Architecture	\N
2264	liubingsai2023@163.com	bingsai liu	beijing university of civil engineering and architecture	\N
2265	386469007@qq.com	Xuandi Liu	Beijing University of Civil Engineering and Architecture	\N
2266	lxd386469007@163.com	XuanDi Liu	Beijing University of Civil Engineering and Architecture	\N
2267	2108570020097@stu.bucea.edu.cn	Tianming Zhao	Beijing University of Civil Engineering and Architecture	\N
2268	southerndog@foxmail.com	Xian Cao	Beijing University of Civil Engineering and Architecture	\N
2269	2108570021077@stu.bucea.edu.cn	Biao liu	Beijing University of Civil Engineering and Architecture	\N
2270	huangshan3mt@foxmail.com	Shan Huang	Beijing University of Civil Engineering and Architecture	\N
2271	huangshan3mt@foxmail.com	Shan Huang	Beijing University of Civil Engineering and Architecture	\N
2272	southerndog@foxmail.com	Xian Cao	Beijing University of Civil Engineering and Architecture	\N
2273	huangshan3mt@foxmail.com	Shan Huang	Beijing University of Civil Engineering and Architecture	\N
2274	southerndog@foxmail.com	Xian Cao	Beijing University of Civil Engineering and Architecture	\N
2275	huangshan3mt@foxmail.com	Shan Huang	Beijing University of Civil Engineering and Architecture	\N
2276	2108570020097@stu.bucea.edu.cn	Tianming Zhao	Beijing University of Civil Engineering and Architecture	\N
2277	lxd386469007@163.com	XuanDi Liu	Beijing University of Civil Engineering and Architecture	\N
2278	14159wangshuo@sina.com	Wang Shuo	Beijing University of Civil Engineering and Architecture	\N
2279	huangshan3mt@foxmail.com	Shan Huang	Beijing University of Civil Engineering and Architecture	\N
2280	huangshan3mt@foxmail.com	Shan Huang	Beijing University of Civil Engineering and Architecture	\N
2281	huangshan3mt@foxmail.com	Shan Huang	Beijing University of Civil Engineering and Architecture	\N
2082	1049662535@qq.com	Haotian Zhang	Wuhan University	https://github.com/jblindsay/whitebox-tools
2083	fanxc@lreis.ac.cn	Xingchen Fan	Institute of Geographic Sciences and Natural Resources Research	\N
2084	1049662535@qq.com	Haotian Zhang	Wuhan University	https://github.com/jblindsay/whitebox-tools
2085	1049662535@qq.com	Haotian Zhang	Wuhan University	https://github.com/jblindsay/whitebox-tools
2086	286381830@qq.com	Minghui Li	Central South University	\N
2087	286381830@qq.com	Minghui Li	Central South University	\N
2088	1049662535@qq.com	Haotian Zhang	Wuhan University	https://github.com/jblindsay/whitebox-tools
2089	235011045@csu.edu.cn	Zhilong Liu	Central South University	\N
2090	1049662535@qq.com	Haotian Zhang	Wuhan University	https://github.com/jblindsay/whitebox-tools
2091	1049662535@qq.com	Haotian Zhang	Wuhan University	https://github.com/jblindsay/whitebox-tools
2092	1049662535@qq.com	Haotian Zhang	Wuhan University	https://github.com/jblindsay/whitebox-tools
2093	liuyixun@github.com	liuyixun	GitHub	\N
2095	1749405717@qq.com	Shiyi Li	Central South University	\N
2126	fanxc@lreis.ac.cn	Xingchen Fan	Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences	\N
2127	fanxc@lreis.ac.cn	Xingchen Fan	Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences	\N
2128	huangshan3mt@foxmail.com	Shan Huang	Beijing University of Civil Engineering and Architecture	\N
2129	2247486718@qq.com	Nanwei Cheng	Central South University	\N
2130	2247486718@qq.com	Nanwei Cheng	Central South University	\N
2131	286381830@qq.com	Minghui Li	Central South University	\N
2132	1907269689@qq.com	Yixun Liu	Central South University	\N
2133	286381830@qq.com	Minghui Li	Central South University	\N
2134	2247486718@qq.com	Nanwei Cheng	Central South University	\N
2135	1749405717@qq.com	Shiyi Li	Central South University	\N
2136	286381830@qq.com	Minghui Li	Central South University	\N
2137	286381830@qq.com	Minghui Li	Central South University	\N
2138	2247486718@qq.com	Nanwei Cheng	Central South University	\N
2139	286381830@qq.com	Minghui Li	Central South University	\N
2140	235011045@csu.edu.cn	Zhilong Liu	Central South University	\N
2141	235011045@csu.edu.cn	Zhilong Liu	Central South University	\N
2142	235011045@csu.edu.cn	Zhilong Liu	Central South University	\N
2143	wenhaochen@csu.edu.cn	Wenhao Chen	Central South University	\N
2144	8211190114@csu.edu.cn	Liu Yixun	Central South University	\N
2145	235011045@csu.edu.cn	Zhilong Liu	Central South University	\N
2146	2247486718@qq.com	Nanwei Cheng	Central South University	\N
2147	235011045@csu.edu.cn	Zhilong Liu	Central South University	\N
2148	fanxc@lreis.ac.cn	Xingchen Fan	Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences	\N
2149	fanxc@lreis.ac.cn	Xingchen Fan	Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences	\N
2150	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2151	229857471@qq.com	Tianyang Xia	Wuhan University	https://github.com/GWmodel-Lab
2152	1049662535@qq.com	Haotian Zhang	Wuhan University	https://github.com/jblindsay/whitebox-tools
2153	联系方式	维护者	组织名称	代码仓库地址
2154	zhaofh@lreis.ac.cn	赵芳鹤	igsnrr	代码仓库地址
2155	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://rspatial.github.io/terra/
2156	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/r-spatial/gstat/
2157	zhaofh@lreis.ac.cn	赵芳鹤	igsnrr	代码仓库地址
2158	lxd386469007@163.com	Xuandi Liu	Beijing University of Civil Engineering and Architecture	\N
2159	9027575@qq.com	Zhangjinmeng && Zhongyuxiang	Wuhan University	\N
2160	9027575@qq.com	Zhangjinmeng && Zhongyuxiang	Wuhan University	\N
2161	9027575@qq.com	Zhangyu && Zhongyuxiang	Wuhan University	\N
2162	9027575@qq.com	Zhangjinmeng && Zhongyuxiang	Wuhan University	\N
2163	9027575@qq.com	Zhaoyuhui Hulingjie	Wuhan University	\N
2164	9027575@qq.com	Zhaoyuhui Hulingjie	Wuhan University	\N
2165	9027575@qq.com	Zhaoyuhui Hulingjie	Wuhan University	\N
2166	9027575@qq.com	Zhangyu && Zhongyuxiang	Wuhan University	\N
2167	9027575@qq.com	Zhangyu && Zhongyuxiang	Wuhan University	\N
2168	9027575@qq.com	Zhangchao	Wuhan University	\N
2169	9027575@qq.com	Huanghuihuang && Zhongyuxiang	Wuhan University	\N
2170	9027575@qq.com	Huanghuihuang && Zhongyuxiang	Wuhan University	\N
2171	9027575@qq.com	Zhangyu && Zhongyuxiang	Wuhan University	\N
2172	9027575@qq.com	Huanghuihuang && Zhongyuxiang	Wuhan University	\N
2173	9027575@qq.com	Huanghuihuang && Zhongyuxiang	Wuhan University	\N
2174	9027575@qq.com	Zhangchao	Wuhan University	\N
2175	9027575@qq.com	Huanghuihuang && Zhongyuxiang	Wuhan University	\N
2176	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2177	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	\N
2178	9027575@qq.com	Zhangyu && Zhongyuxiang	Wuhan University	\N
2179	9027575@qq.com	Zhangyu && Zhongyuxiang	Wuhan University	\N
2180	2108160224011@stu.bucea.edu.cn	Han Li	Beijing University of Civil Engineering and Architecture	\N
2181	1325423527@qq.com	Jingye Yang	GitHub	https://github.com/las-vegas/remote_sensing_model
2182	2108160224007@stu.bucea.edu.cn	Chen Jian	Beijing University of Civil Engineering and Architecture	\N
2183	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2184	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2185	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2186	lybnju@163.com	Yuanbo Luo	Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences	\N
2187	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2188	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2189	229857471@qq.com	Tianyang Xia	Wuhan University	https://github.com/GWmodel-Lab
2193	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/edzer/sp/
2194	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/edzer/sp/
2195	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/linyuehzzz/sandwich_spatial_interpolator/tree/master/r/sandwichr
2196	wenhaochen@csu.edu.cn	Wenhao Chen	Central South University	\N
2197	1749405717@qq.com	Shiyi Li	Central South University	\N
2198	235011043@csu.edu.cn	liuyixun	Central South University	\N
2199	your.email@example.com	Your Name	Your Organization	\N
2200	1749405717@qq.com	Shiyi Li	Central South University	\N
2201	2247486718@qq.com	Nanwei Cheng	Central South University	\N
2202	1749405717@qq.com	Shiyi Li	Central South University	\N
2203	1907269689@qq.com	Yixun Liu	Central South University	\N
2204	1907269689@qq.com	Yixun Liu	Central South University	\N
2205	2247486718@qq.com	Nanwei Cheng	Central South University	\N
2206	wenhaochen@csu.edu.cn	Wenhao Chen	Central South University	\N
2207	235011045@csu.edu.cn	Zhilong Liu	Central South University	\N
2208	2247486718@qq.com	Nanwei Cheng	Central South University	\N
2209	2247486718@qq.com	Nanwei Cheng	Central South University	\N
2210	235011043@csu.edu.cn	YiXun Liu	Central South University	\N
2211	2247486718@qq.com	Nanwei Cheng	Central South University	\N
2212	286381830@qq.com	Minghui Li	Central South University	\N
2213	2247486718@qq.com	Nanwei Cheng	Central South University	\N
2214	235011045@csu.edu.cn	Zhilong Liu	Central South University	\N
2215	wenhaochen@csu.edu.cn	Wenhao Chen	Central South University	\N
2216	2247486718@qq.com	Nanwei Cheng	Central South University	\N
2217	2247486718@qq.com	Nanwei Cheng	Central South University	\N
2218	286381830@qq.com	Minghui Li	Central South University	\N
2219	2247486718@qq.com	Nanwei Cheng	Central South University	\N
2220	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2221	286381830@qq.com	Minghui Li	Central South University	\N
2222	2108570021077@stu.bucea.edu.cn	Biao liu	Beijing University of Civil Engineering and Architecture	\N
2223	fanxc@lreis.ac.cn	Xingchen Fan	Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences	\N
2224	fanxc@lreis.ac.cn	Xingchen Fan	Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences	\N
2225	fanxc@lreis.ac.cn	Xingchen Fan	Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences	\N
2226	lybnju@163.com	Yuanbo Luo	Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences	\N
2227	229857471@qq.com	Tianyang Xia	Wuhan University	https://github.com/GWmodel-Lab
2229	229857471@qq.com	Tianyang Xia	Wuhan University	https://github.com/GWmodel-Lab
2230	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/r-spatial/spdep
2231	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/r-spatial/spdep
2232	lybnju@163.com	Yuanbo Luo	Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences	\N
2234	1049662535@qq.com	Haotian Zhang	Wuhan University	https://github.com/jblindsay/whitebox-tools
2235	chenzy@lreis.ac.cn	Zi-Yue Chen	Institute of Geographic Sciences and Natural Resources Research	https://github.com/lreis2415/TauDEM_ext
2240	fanxingchen22@mails.ucas.ac.cn	Xingchen Fan	Institute of Geographic Sciences and Natural Resources Research	https://www.whiteboxgeo.com/manual/wbt_book/available_tools/geomorphometric_analysis.html#StreamPowerIndex
2242	286381830@qq.com	Minghui Li	Central South University	\N
2243	chenzy@lreis.ac.cn	Zi-Yue Chen	Institute of Geographic Sciences and Natural Resources Research	https://github.com/lreis2415/TauDEM_ext
2244	lxd386469007@163.com	Xuandi Liu	Beijing University of Civil Engineering and Architecture	https://www.whiteboxgeo.com/manual/wbt_book/available_tools/geomorphometric_analysis.html#ProfileCurvature
2245	lxd386469007@163.com	Xuandi Liu	Beijing University of Civil Engineering and Architecture	https://github.com/jblindsay/whitebox-tools/blob/master/whitebox-tools-app/src/tools/terrain_analysis/tan_curvature.rs
2246	chenzy@lreis.ac.cn	Zi-Yue Chen	Institute of Geographic Sciences and Natural Resources Research	https://github.com/OSGeo/gdal/tree/master/docker
2247	chenzy@lreis.ac.cn	Zi-Yue Chen	Institute of Geographic Sciences and Natural Resources Research	https://github.com/lreis2415/TauDEM_ext
2248	1049662535@qq.com	Haotian Zhang	Wuhan University	https://github.com/jblindsay/whitebox-tools
2249	chenzy@lreis.ac.cn	Zi-Yue Chen	Institute of Geographic Sciences and Natural Resources Research	https://github.com/lreis2415/TauDEM_ext
2250	1049662535@qq.com	Guangyu Ou	Wuhan University	https://github.com/jblindsay/whitebox-tools
2251	1049662535@qq.com	Guangyu Ou	Wuhan University	https://github.com/jblindsay/whitebox-tools
2282	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://r-spatial.github.io/sf
2283	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2284	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2285	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2286	229857471@qq.com	Tianyang Xia	Wuhan University	https://github.com/GWmodel-Lab
2287	229857471@qq.com	Tianyang Xia	Wuhan University	https://github.com/GWmodel-Lab
2294	1907269689@qq.com	Yixun Liu	Central South University	\N
2295	1907269689@qq.com	Yixun Liu	Central South University	\N
2296	1907269689@qq.com	Yixun Liu	Central South University	\N
2298	southerndog@foxmail.com	Xian Cao	Beijing University of Civil Engineering and Architecture	\N
2301	2108160224011@stu.bucea.edu.cn	Han Li	Beijing University of Civil Engineering and Architecture	\N
2302	2108160224011@stu.bucea.edu.cn	Han Li	Beijing University of Civil Engineering and Architecture	\N
2304	229857471@qq.com	Tianyang Xia	Wuhan University	https://github.com/GWmodel-Lab
2306	chenzy@lreis.ac.cn	Zi-Yue Chen	Institute of Geographic Sciences and Natural Resources Research	https://github.com/lreis2415/TauDEM_ext
2311	southerndog@foxmail.com	Xian Cao	Beijing University of Civil Engineering and Architecture	\N
2491	chenzy@lreis.ac.cn	Zi-Yue Chen	Institute of Geographic Sciences and Natural Resources Research	https://github.com/lreis2415/TauDEM_ext
2492	1049662535@qq.com	Haotian Zhang	Wuhan University	https://github.com/jblindsay/whitebox-tools
2493	1049662535@qq.com	Haotian Zhang	Wuhan University	https://github.com/jblindsay/whitebox-tools
2497	chenzy@lreis.ac.cn	Zi-Yue Chen	Institute of Geographic Sciences and Natural Resources Research	https://github.com/lreis2415/TauDEM_ext
2498	chenzy@lreis.ac.cn	Zi-Yue Chen	Institute of Geographic Sciences and Natural Resources Research	https://github.com/lreis2415/TauDEM_ext
2501	fanxc@lreis.ac.cn	Xingchen Fan	Institute of Geographic Sciences and Natural Resources Research	\N
2503	fanxc@lreis.ac.cn	Xingchen Fan	Institute of Geographic Sciences and Natural Resources Research	\N
2504	fanxc@lreis.ac.cn	Xingchen Fan	Institute of Geographic Sciences and Natural Resources Research	\N
2505	fanxc@lreis.ac.cn	Xingchen Fan	Institute of Geographic Sciences and Natural Resources Research	\N
2506	1049662535@qq.com	Haotian Zhang	Wuhan University	https://github.com/jblindsay/whitebox-tools
2508	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2509	g_ruoyu@qq.com	Guangyu Ou	Wuhan University	https://github.com/GWmodel-Lab
2510	2108160224011@stu.bucea.edu.cn	Han Li	Beijing University of Civil Engineering and Architecture	\N
\.


--
-- Data for Name: meta_source_integrated_softwares; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.meta_source_integrated_softwares (meta_source_id, integrated_softwares_id) FROM stdin;
2253	33876
2268	34342
2269	34362
2270	34402
2271	34424
2272	34448
2273	34471
2274	34493
2275	34539
2277	34578
2278	34592
2279	34602
2280	34614
2281	34627
2082	31429
2084	31439
2085	31445
2086	31472
2087	31499
2088	31509
2090	31521
2091	31531
2092	31541
2095	31558
2128	32427
2131	32464
2132	32480
2132	32481
2132	32482
2132	32483
2133	32511
2134	32540
2135	32557
2136	32575
2137	32592
2139	32641
2143	32660
2144	32672
2150	32764
2151	32785
2152	32801
2153	32824
2154	32833
2157	32871
2158	32915
2163	32961
2164	32967
2165	32972
2176	33055
2181	33084
2187	33140
2188	33161
2189	33182
2196	33266
2197	33273
2198	33289
2200	33310
2202	33337
2203	33363
2204	33388
2206	33415
2215	33502
2218	33526
2218	33527
2218	33528
2220	33563
2221	33573
2222	33587
2227	33629
2229	33651
2234	33696
2235	33704
2240	33756
2242	33773
2243	33782
2244	33790
2245	33798
2246	33805
2247	33813
2248	33822
2249	33831
2250	33840
2251	33851
2285	34657
2286	34680
2287	34700
2294	34870
2294	34871
2294	34872
2295	34896
2295	34897
2295	34898
2296	34910
2296	34911
2296	34912
2298	34991
2304	35154
2306	35235
2311	35359
2491	38256
2492	38271
2493	38282
2497	38407
2498	38421
2506	39097
2508	39169
2509	39219
\.


--
-- Data for Name: meta_technique; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.meta_technique (id, hardware, language, os, parallel) FROM stdin;
1360	\N	scala	windows;linux	\N
1439	\N	Java	Linux;Windows	\N
1440	\N	Java	Linux;Windows	\N
1442	\N	Java	Linux;Windows	\N
1462	\N	R	Linux;Windows	\N
1543	\N	scala	windows;linux	\N
1544	cpu	Python	windows;linux	\N
2252	\N	R;C++	Linux;Windows	unsupported
2253	\N	R;C++	Linux;Windows	unsupported
2254	GPU	Python	Linux	\N
2255	GPU	Python	Linux;Windows	\N
2256	GPU	Python	Linux;Windows	\N
2257	GPU	Python	Linux;Windows	\N
2258	GPU	Python	Linux;Windows	\N
2259	GPU	Python	Linux	\N
2260	GPU	Python	Linux;Windows	\N
2261	GPU	Python	Linux;Windows	\N
2262	GPU	Python	Linux;Windows	\N
2263	GPU	Python	Linux;Windows	\N
2264	CPU	Python	Linux;Windows	\N
2265	GPU	Python	Linux;Windows	\N
2266	GPU	Python	Linux;Windows	\N
2267	CPU	Python	Linux;Windows	\N
2268	cpu	python	windows;linux	multithreading
2269	cpu	python	linux;windows	multithreading
2270	CPU	python	Windows;Linux;MacOS	multiprocessor
2271	\N	C++	Linux;Windows	\N
2272	cpu	python	windows;linux	multithreading
2273	\N	Python	Linux;Windows	\N
2274	cpu	python	windows;linux	multithreading
2275	\N	Python	Linux;Windows	\N
2276	cpu	Python	windows;linux	multithreading
2277	CPU	python	Windows;Linux	multiprocessor
2278	CPU	python	Windows;Linux;MacOS	multiprocessor
2279	CPU	python	Windows;Linux;MacOS	multiprocessor
2280	CPU	python	Windows;Linux;MacOS	multiprocessor
2281	\N	Python	Linux;Windows	\N
2082	\N	Rust	Linux;Windows	\N
2083	\N	Python	Linux;Windows	\N
2084	\N	Rust	Linux;Windows	\N
2085	\N	Rust	Linux;Windows	\N
2086	\N	Python	Linux;Windows	\N
2087	\N	C++	Linux;Windows	\N
2088	\N	Rust	Linux;Windows	\N
2089	\N	Python	Linux;Windows	\N
2090	\N	Rust	Linux;Windows	\N
2091	\N	Rust	Linux;Windows	\N
2092	\N	Rust	Linux;Windows	\N
2093	\N	Python	Linux;Windows	\N
2095	\N	Python	Linux;Windows	\N
2126	\N	scala	windows;linux	\N
2127	\N	scala	windows;linux	\N
2128	\N	C++	Linux;Windows	\N
2129	\N	Python	Linux;Windows	\N
2130	\N	Python	Linux;Windows	\N
2131	\N	Python	Linux;Windows	\N
2132	CPU	Python	Linux;Windows	multiprocessing
2133	CPU	Python	Linux;Windows	multiprocessing
2134	\N	Python	Linux;Windows	\N
2135	\N	Python	Linux;Windows	\N
2136	\N	Python	Linux;Windows	\N
2137	\N	Python	Linux;Windows	multiprocessing
2138	\N	Python	Linux;Windows	\N
2139	\N	Python	Linux;Windows	multiprocessing
2140	\N	Python	Linux;Windows	\N
2141	\N	Python	Linux;Windows	\N
2142	\N	Python	Linux;Windows	\N
2143	\N	Python	Linux;Windows	\N
2144	\N	Python	Linux;Windows	\N
2145	\N	Python	Linux;Windows	\N
2146	\N	Python	Linux;Windows	\N
2147	\N	Python	Linux;Windows	\N
2148	\N	scala	windows;linux	\N
2149	\N	scala	windows;linux	\N
2150	\N	R;C++	Linux;Windows	\N
2151	\N	R;C++	Linux;Windows	\N
2152	\N	Rust	Linux;Windows	\N
2153	CPU;GPU	C++	Windows;Linux;MacOS	multiprocessor
2154	CPU	python	Windows;Linux;MacOS	\N
2155	\N	R	Linux;Windows	\N
2156	\N	R	Linux;Windows	\N
2157	CPU	python	Windows;Linux;MacOS	\N
2158	\N	python	Windows;Linux	\N
2159	\N	Java	Linux;Windows	\N
2160	\N	Java	Linux;Windows	\N
2161	\N	Python	Linux;Windows	\N
2162	\N	Java	Linux;Windows	\N
2163	\N	Python	Linux;Windows	\N
2164	\N	Python	Linux;Windows	\N
2165	\N	Python	Linux;Windows	\N
2166	\N	python	Linux;Windows	\N
2167	\N	Python	Linux;Windows	\N
2168	\N	python	Linux;Windows	\N
2169	\N	Java	Linux;Windows	\N
2170	\N	Java	Linux;Windows	\N
2171	\N	Java	Linux;Windows	\N
2172	\N	Java	Linux;Windows	\N
2173	\N	Java	Linux;Windows	\N
2174	\N	python	Linux;Windows	\N
2175	\N	Java	Linux;Windows	\N
2176	\N	R;C++	Linux;Windows	OpenMP
2177	\N	R;C++	Linux;Windows	\N
2178	\N	python	Linux;Windows	\N
2179	\N	python	Linux;Windows	\N
2180	cpu	Python	windows;linux	\N
2181	\N	Python	Linux;Windows	\N
2182	\N	R	Linux;Windows	\N
2183	\N	R;C++	Linux;Windows	unsupported
2184	\N	R;C++	Linux;Windows	unsupported
2185	\N	R;C++	Linux;Windows	unsupported
2186	CPU	R	Windows;Linux	\N
2187	\N	R;C++	Linux;Windows	unsupported
2188	\N	R;C++	Linux;Windows	\N
2189	\N	R;C++	Linux;Windows	\N
2193	\N	R	Linux;Windows	\N
2194	\N	R	Linux;Windows	\N
2195	\N	R	Linux;Windows	\N
2196	\N	Python	Linux;Windows	\N
2197	\N	Python	Linux;Windows	\N
2198	\N	Python	Linux;Windows	\N
2199	\N	Python	Linux;Windows	\N
2200	\N	Python	Linux;Windows	\N
2201	\N	Python	Linux;Windows	\N
2202	\N	Python	Linux;Windows	\N
2203	\N	Python	Linux;Windows	\N
2204	\N	Python	Linux;Windows	\N
2205	\N	Python	Linux;Windows	\N
2206	\N	Python	Linux;Windows	\N
2207	\N	Python	Linux;Windows	\N
2208	\N	Python	Linux;Windows	\N
2209	\N	Python	Linux;Windows	\N
2210	\N	Python	Linux;Windows	\N
2211	\N	Python	Linux;Windows	\N
2212	\N	Python	Linux;Windows	\N
2213	\N	Python	Linux;Windows	\N
2214	\N	Python	Linux;Windows	\N
2215	\N	Python	Linux;Windows	\N
2216	\N	Python	Linux;Windows	\N
2217	\N	Python	Linux;Windows	\N
2218	\N	Python	Linux;Windows	\N
2219	\N	Python	Linux;Windows	\N
2220	\N	R;C++	Linux;Windows	unsupported
2221	\N	Python	Linux;Windows	\N
2222	cpu	python	linux;windows	multithreading
2223	\N	scala	windows;linux	\N
2224	\N	scala	windows;linux	\N
2225	\N	scala	windows;linux	\N
2226	CPU	R	Windows;Linux	\N
2227	\N	R	Linux;Windows	\N
2229	\N	R	Linux;Windows	\N
2230	\N	R	Linux;Windows	\N
2231	\N	R	Linux;Windows	\N
2232	CPU	R	Windows;Linux	\N
2234	\N	Rust	Linux;Windows	\N
2235	CPU	C++	Windows;Linux;MacOS	MPI
2240	\N	Rust	Linux;Windows	\N
2242	\N	C++	Linux;Windows	\N
2243	CPU	C++	Windows;Linux;MacOS	MPI
2244	\N	Rust	Linux;Windows	\N
2245	\N	Rust	Linux;Windows	\N
2246	CPU	C++	Windows;Linux;MacOS	\N
2247	CPU	C++	Windows;Linux;MacOS	MPI
2248	\N	Rust	Linux;Windows	\N
2249	CPU	C++	Windows;Linux;MacOS	MPI
2250	\N	Rust	Linux;Windows	\N
2251	\N	Rust	Linux;Windows	\N
2282	\N	R	Linux;Windows	\N
2283	\N	R;C++	Linux;Windows	unsupported
2284	\N	R;C++	Linux;Windows	unsupported
2285	\N	R;C++	Linux;Windows	unsupported
2286	\N	R;C++	Linux;Windows	OpenMP
2287	\N	R;C++	Linux;Windows	OpenMP
2294	\N	Python	Linux;Windows	\N
2295	\N	Python	Linux;Windows	\N
2296	\N	Python	Linux;Windows	\N
2298	CPU	python	Windows;Linux	multithreading
2301	CPU	python	Windows;Linux;MacOS	\N
2302	CPU	python	Windows;Linux;MacOS	\N
2304	\N	R	Linux;Windows	\N
2306	CPU	C++	Windows;Linux;MacOS	MPI
2311	CPU	python	Windows;Linux	multithreading
2491	CPU	C++	Windows;Linux;MacOS	MPI
2492	\N	Rust	Linux;Windows	\N
2493	\N	Rust	Linux;Windows	\N
2497	CPU	C++	Windows;Linux;MacOS	MPI
2498	CPU	C++	Windows;Linux;MacOS	MPI
2501	\N	JavaScript	Linux;Windows	\N
2503	\N	Python	Linux;Windows	\N
2504	\N	Python	Linux;Windows	\N
2505	\N	Python	Linux;Windows	\N
2506	\N	Rust	Linux;Windows	\N
2508	\N	R;C++	Linux;Windows	OpenMP
2509	\N	R;C++	Linux;Windows	OpenMP
2510	CPU	python	Windows;Linux;MacOS	\N
\.


--
-- Data for Name: meta_technique_dependencies; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.meta_technique_dependencies (meta_technique_id, dependencies_id) FROM stdin;
1439	21890
1440	21897
1442	21917
1462	22095
1544	23054
2252	33860
2253	33877
2254	33894
2254	33895
2254	33896
2254	33897
2254	33898
2254	33899
2254	33900
2254	33901
2254	33902
2254	33903
2254	33904
2255	33958
2255	33959
2255	33960
2255	33961
2255	33962
2255	33963
2255	33964
2255	33965
2255	33966
2256	33988
2256	33989
2256	33990
2256	33991
2256	33992
2256	33993
2257	34004
2257	34005
2257	34006
2257	34007
2257	34008
2257	34009
2258	34031
2258	34032
2258	34033
2258	34034
2258	34035
2258	34036
2258	34037
2258	34038
2258	34039
2258	34040
2258	34041
2258	34042
2258	34043
2260	34097
2260	34098
2260	34099
2260	34100
2260	34101
2260	34102
2260	34103
2261	34130
2261	34131
2261	34132
2261	34133
2261	34134
2261	34135
2261	34136
2262	34144
2262	34145
2262	34146
2262	34147
2262	34148
2262	34149
2262	34150
2263	34184
2263	34185
2263	34186
2263	34187
2263	34188
2263	34189
2263	34190
2264	34214
2264	34215
2264	34216
2264	34217
2264	34218
2264	34219
2265	34229
2265	34230
2265	34231
2265	34232
2265	34233
2265	34234
2265	34235
2266	34279
2266	34280
2266	34281
2266	34282
2266	34283
2266	34284
2266	34285
2267	34321
2267	34322
2267	34323
2267	34324
2267	34325
2267	34326
2267	34327
2268	34343
2268	34344
2268	34345
2269	34363
2269	34364
2269	34365
2270	34403
2270	34404
2270	34405
2271	34425
2272	34449
2272	34450
2272	34451
2273	34472
2274	34494
2274	34495
2274	34496
2275	34540
2276	34563
2276	34564
2276	34565
2277	34579
2277	34580
2277	34581
2278	34593
2278	34594
2278	34595
2279	34603
2279	34604
2279	34605
2280	34615
2280	34616
2280	34617
2281	34628
2082	31430
2084	31440
2085	31446
2086	31473
2087	31500
2090	31522
2091	31532
2092	31542
2128	32428
2131	32465
2132	32484
2132	32485
2132	32486
2132	32487
2133	32512
2134	32541
2136	32576
2137	32593
2139	32642
2150	32765
2151	32786
2152	32802
2153	32825
2154	32834
2154	32835
2155	32843
2156	32856
2157	32872
2157	32873
2158	32916
2159	32923
2160	32930
2161	32947
2162	32956
2163	32962
2164	32968
2165	32973
2166	32976
2167	32983
2168	32986
2169	32992
2170	32998
2171	33008
2172	33014
2173	33020
2174	33022
2175	33029
2176	33056
2177	33067
2178	33074
2179	33077
2180	33081
2181	33085
2182	33090
2183	33097
2184	33104
2185	33111
2186	33119
2187	33141
2188	33162
2189	33183
2193	33240
2194	33249
2195	33259
2203	33364
2204	33389
2218	33529
2218	33530
2218	33531
2220	33564
2221	33574
2222	33588
2222	33589
2222	33590
2226	33620
2227	33630
2229	33652
2230	33664
2231	33675
2242	33774
2250	33841
2251	33852
2282	34635
2285	34658
2286	34681
2287	34701
2294	34873
2294	34874
2294	34875
2295	34899
2295	34900
2295	34901
2296	34913
2296	34914
2296	34915
2298	34992
2298	34993
2298	34994
2301	35087
2301	35088
2301	35089
2301	35090
2302	35095
2302	35096
2302	35097
2302	35098
2304	35155
2311	35360
2311	35361
2311	35362
2508	39170
2509	39220
2510	39281
2510	39282
2510	39283
2510	39284
\.


--
-- Data for Name: model_app; Type: TABLE DATA; Schema: public; Owner: onesis
--

COPY public.model_app (id, bpmn, create_time, deleted, name, params, user_id, status, ml_modeling_project_id, ml_modeling_id) FROM stdin;
f420c9b3-2c12-445e-8c87-f62e94f9e9bf	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1712541852145" name="业务流程_1712541852145" isExecutable="true"><bpmn:serviceTask id="Activity_1dvgmq9" name="流向(D8)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="eb43c54f-a81c-3c0f-9d7d-69f305c7fd5e" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1pimux6</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_1k7a1qx" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_1pimux6</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_1pimux6" sourceRef="Activity_1k7a1qx" targetRef="Activity_1dvgmq9" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1712541852145"><bpmndi:BPMNShape id="Activity_1dvgmq9_di" bpmnElement="Activity_1dvgmq9"><dc:Bounds x="180" y="150" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1k7a1qx_di" bpmnElement="Activity_1k7a1qx"><dc:Bounds x="30" y="150" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_1pimux6_di" bpmnElement="Flow_1pimux6"><di:waypoint x="130" y="190" /><di:waypoint x="180" y="190" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-09-19 12:33:04.987666	f	流向	\N	02228d0a-d4ed-45de-8c55-020badfde43b	2	\N	\N
d7db1cec-634a-4068-bf93-e43a8c84f96c	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1726231089646" name="业务流程_1726231089646" isExecutable="true"><bpmn:serviceTask id="Activity_1roqg4d" name="水流功率指数"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="e4d5b0ab-6d31-3fae-9c44-817b0a0b4df7" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0rn8oqf</bpmn:incoming><bpmn:incoming>Flow_0iutreo</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_0qrau0a" name="汇流累积量_D8（栅格）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="6665e8c4-ef1d-3c7a-9bfc-35ad56239a8c" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0l603md</bpmn:incoming><bpmn:outgoing>Flow_0rn8oqf</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0rn8oqf" sourceRef="Activity_0qrau0a" targetRef="Activity_1roqg4d" /><bpmn:serviceTask id="Activity_0g96nzb" name="坡度（最大下坡）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="d3639683-4569-3091-84dd-3bf440f04df0" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_16grid1</bpmn:incoming><bpmn:outgoing>Flow_0iutreo</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0iutreo" sourceRef="Activity_0g96nzb" targetRef="Activity_1roqg4d" /><bpmn:serviceTask id="Activity_0bqxduu" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_16grid1</bpmn:outgoing><bpmn:outgoing>Flow_0kv64o1</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_16grid1" sourceRef="Activity_0bqxduu" targetRef="Activity_0g96nzb" /><bpmn:serviceTask id="Activity_1fao8nn" name="流向(D8)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="eb43c54f-a81c-3c0f-9d7d-69f305c7fd5e" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0kv64o1</bpmn:incoming><bpmn:outgoing>Flow_0l603md</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0l603md" sourceRef="Activity_1fao8nn" targetRef="Activity_0qrau0a" /><bpmn:sequenceFlow id="Flow_0kv64o1" sourceRef="Activity_0bqxduu" targetRef="Activity_1fao8nn" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1726231089646"><bpmndi:BPMNShape id="Activity_1roqg4d_di" bpmnElement="Activity_1roqg4d"><dc:Bounds x="410" y="200" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0qrau0a_di" bpmnElement="Activity_0qrau0a"><dc:Bounds x="260" y="280" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0g96nzb_di" bpmnElement="Activity_0g96nzb"><dc:Bounds x="260" y="120" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0bqxduu_di" bpmnElement="Activity_0bqxduu"><dc:Bounds x="110" y="120" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1fao8nn_di" bpmnElement="Activity_1fao8nn"><dc:Bounds x="110" y="280" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_0rn8oqf_di" bpmnElement="Flow_0rn8oqf"><di:waypoint x="360" y="320" /><di:waypoint x="390" y="320" /><di:waypoint x="390" y="240" /><di:waypoint x="410" y="240" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0iutreo_di" bpmnElement="Flow_0iutreo"><di:waypoint x="360" y="160" /><di:waypoint x="390" y="160" /><di:waypoint x="390" y="240" /><di:waypoint x="410" y="240" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_16grid1_di" bpmnElement="Flow_16grid1"><di:waypoint x="210" y="160" /><di:waypoint x="260" y="160" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0l603md_di" bpmnElement="Flow_0l603md"><di:waypoint x="210" y="320" /><di:waypoint x="260" y="320" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0kv64o1_di" bpmnElement="Flow_0kv64o1"><di:waypoint x="160" y="200" /><di:waypoint x="160" y="280" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-09-19 12:33:35.504672	f	水流功率指数	\N	02228d0a-d4ed-45de-8c55-020badfde43b	2	\N	\N
aa11650f-932e-4fad-83b2-97daf41e74d3	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1726231089646" name="业务流程_1726231089646" isExecutable="true"><bpmn:serviceTask id="Activity_09pnpwm" name="汇流累积量_D8（栅格）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="6665e8c4-ef1d-3c7a-9bfc-35ad56239a8c" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_15p54df</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_0c20ccx" name="流向(D8)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="eb43c54f-a81c-3c0f-9d7d-69f305c7fd5e" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1c115gn</bpmn:incoming><bpmn:outgoing>Flow_15p54df</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_15p54df" sourceRef="Activity_0c20ccx" targetRef="Activity_09pnpwm" /><bpmn:serviceTask id="Activity_1n6c2yg" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_1c115gn</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_1c115gn" sourceRef="Activity_1n6c2yg" targetRef="Activity_0c20ccx" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1726231089646"><bpmndi:BPMNShape id="Activity_09pnpwm_di" bpmnElement="Activity_09pnpwm"><dc:Bounds x="640" y="320" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0c20ccx_di" bpmnElement="Activity_0c20ccx"><dc:Bounds x="490" y="320" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1n6c2yg_di" bpmnElement="Activity_1n6c2yg"><dc:Bounds x="340" y="320" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_15p54df_di" bpmnElement="Flow_15p54df"><di:waypoint x="590" y="360" /><di:waypoint x="640" y="360" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1c115gn_di" bpmnElement="Flow_1c115gn"><di:waypoint x="440" y="360" /><di:waypoint x="490" y="360" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-09-19 12:33:24.798792	f	汇流累积量	\N	02228d0a-d4ed-45de-8c55-020badfde43b	2	\N	\N
793a2db5-f637-4680-860a-15d3151ac367	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1726909060175" name="业务流程_1726909060175" isExecutable="true"><bpmn:serviceTask id="Activity_0oqong4" name="坡向（三阶二元泰勒多项式）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="b69e1bf4-06cf-3d70-be83-81a23ad61ab1" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1d0jo7c</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_0o43bp8" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_1d0jo7c</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_1d0jo7c" sourceRef="Activity_0o43bp8" targetRef="Activity_0oqong4" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1726909060175"><bpmndi:BPMNShape id="Activity_0oqong4_di" bpmnElement="Activity_0oqong4"><dc:Bounds x="-1510" y="-1020" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0o43bp8_di" bpmnElement="Activity_0o43bp8"><dc:Bounds x="-1660" y="-1020" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_1d0jo7c_di" bpmnElement="Flow_1d0jo7c"><di:waypoint x="-1560" y="-980" /><di:waypoint x="-1510" y="-980" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-09-21 17:03:07.738127	f	坡向	\N	02228d0a-d4ed-45de-8c55-020badfde43b	2	\N	\N
240bb6e2-a581-42c2-9c45-c5bab63f20ec	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1726231089646" name="业务流程_1726231089646" isExecutable="true"><bpmn:serviceTask id="Activity_0jezkgy" name="基于样点个体代表性的地理空间特征空间推测(iPSM)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="7d9c8b88-f377-3870-81a6-ac7db3b9956a" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0xf8y4q</bpmn:incoming><bpmn:incoming>Flow_0a8rzvj</bpmn:incoming><bpmn:incoming>Flow_0n7kigv</bpmn:incoming><bpmn:incoming>Flow_0pdzdhn</bpmn:incoming><bpmn:incoming>Flow_13uza01</bpmn:incoming><bpmn:incoming>Flow_01288vx</bpmn:incoming><bpmn:incoming>Flow_0x1fc2f</bpmn:incoming><bpmn:incoming>Flow_18muiwq</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_00ytjwm" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_02pxrxe</bpmn:outgoing><bpmn:outgoing>Flow_0p1y2th</bpmn:outgoing><bpmn:outgoing>Flow_0spjeey</bpmn:outgoing><bpmn:outgoing>Flow_1694qcb</bpmn:outgoing><bpmn:outgoing>Flow_0d40510</bpmn:outgoing><bpmn:outgoing>Flow_1qo9rzn</bpmn:outgoing><bpmn:outgoing>Flow_18muiwq</bpmn:outgoing></bpmn:serviceTask><bpmn:serviceTask id="Activity_0vkxouk" name="流向(D8)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="eb43c54f-a81c-3c0f-9d7d-69f305c7fd5e" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0d40510</bpmn:incoming><bpmn:outgoing>Flow_18uclk9</bpmn:outgoing></bpmn:serviceTask><bpmn:serviceTask id="Activity_1v77ogo" name="坡度（最大下坡）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="d3639683-4569-3091-84dd-3bf440f04df0" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1qo9rzn</bpmn:incoming><bpmn:outgoing>Flow_167fddy</bpmn:outgoing><bpmn:outgoing>Flow_1cbnddx</bpmn:outgoing><bpmn:outgoing>Flow_0x1fc2f</bpmn:outgoing></bpmn:serviceTask><bpmn:serviceTask id="Activity_0zijgmj" name="剖面曲率"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="9db5af17-3f21-3265-924b-afa40e7ddf8a" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1694qcb</bpmn:incoming><bpmn:outgoing>Flow_0a8rzvj</bpmn:outgoing></bpmn:serviceTask><bpmn:serviceTask id="Activity_13xa6pu" name="平面曲率"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="d8bcff1d-3318-35bf-ae69-540b08d4c3ec" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0spjeey</bpmn:incoming><bpmn:outgoing>Flow_0pdzdhn</bpmn:outgoing></bpmn:serviceTask><bpmn:serviceTask id="Activity_115ldy4" 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targetRef="Activity_183x33c" /><bpmn:serviceTask id="Activity_0qb4okn" name="河网提取（栅格）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="19a662d0-b39e-35cb-a063-0e1c13ee9e6b" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1ld8e7n</bpmn:incoming><bpmn:outgoing>Flow_0ndkgw9</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0ndkgw9" sourceRef="Activity_0qb4okn" targetRef="Activity_183x33c" /><bpmn:serviceTask id="Activity_1agqo9c" name="汇流累积量_D8（栅格）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="6665e8c4-ef1d-3c7a-9bfc-35ad56239a8c" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1qxjgsm</bpmn:incoming><bpmn:outgoing>Flow_1ld8e7n</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_1ld8e7n" sourceRef="Activity_1agqo9c" targetRef="Activity_0qb4okn" /><bpmn:sequenceFlow id="Flow_1qxjgsm" sourceRef="Activity_09f6xcy" targetRef="Activity_1agqo9c" /><bpmn:sequenceFlow id="Flow_10uepsb" sourceRef="Activity_0k46jrr" targetRef="Activity_09f6xcy" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1736317199230"><bpmndi:BPMNShape id="Activity_1lv0n5q_di" bpmnElement="Activity_1lv0n5q"><dc:Bounds x="350" y="260" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_183x33c_di" bpmnElement="Activity_183x33c"><dc:Bounds x="200" y="420" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0an9m4m_di" bpmnElement="Activity_0an9m4m"><dc:Bounds x="200" y="260" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0luadoc_di" bpmnElement="Activity_0luadoc"><dc:Bounds x="200" y="100" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1agqo9c_di" bpmnElement="Activity_1agqo9c"><dc:Bounds x="-100" y="420" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0k46jrr_di" bpmnElement="Activity_0k46jrr"><dc:Bounds x="-100" y="100" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0qb4okn_di" bpmnElement="Activity_0qb4okn"><dc:Bounds x="50" y="420" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_09f6xcy_di" bpmnElement="Activity_09f6xcy"><dc:Bounds x="-100" y="230" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_1mzp5xs_di" bpmnElement="Flow_1mzp5xs"><di:waypoint x="300" y="460" /><di:waypoint x="330" y="460" /><di:waypoint x="330" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1tcc5x6_di" bpmnElement="Flow_1tcc5x6"><di:waypoint x="300" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1p9stwe_di" bpmnElement="Flow_1p9stwe"><di:waypoint x="300" y="140" /><di:waypoint x="330" y="140" /><di:waypoint x="330" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1sdr299_di" bpmnElement="Flow_1sdr299"><di:waypoint x="0" y="140" /><di:waypoint x="200" y="140" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1vk5wss_di" bpmnElement="Flow_1vk5wss"><di:waypoint x="0" y="140" /><di:waypoint x="180" y="140" /><di:waypoint x="180" y="300" /><di:waypoint x="200" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1ne7tby_di" bpmnElement="Flow_1ne7tby"><di:waypoint x="0" y="140" /><di:waypoint x="180" y="140" /><di:waypoint x="180" y="460" /><di:waypoint x="200" y="460" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1pmqewz_di" bpmnElement="Flow_1pmqewz"><di:waypoint x="0" y="270" /><di:waypoint x="180" y="270" /><di:waypoint x="180" y="460" /><di:waypoint x="200" y="460" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0ndkgw9_di" bpmnElement="Flow_0ndkgw9"><di:waypoint x="150" y="460" /><di:waypoint x="200" y="460" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1ld8e7n_di" bpmnElement="Flow_1ld8e7n"><di:waypoint x="0" y="460" /><di:waypoint x="50" y="460" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1qxjgsm_di" bpmnElement="Flow_1qxjgsm"><di:waypoint x="-50" y="310" /><di:waypoint x="-50" y="420" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_10uepsb_di" bpmnElement="Flow_10uepsb"><di:waypoint x="-50" y="180" /><di:waypoint x="-50" y="230" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2025-01-08 14:23:42.911186	f	土地利用类型发展潜力预测	\N	02228d0a-d4ed-45de-8c55-020badfde43b	0	\N	\N
e943bb0e-ee28-4fed-9c77-c4cac561d766	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1736326306155" name="业务流程_1736326306155" isExecutable="true"><bpmn:serviceTask id="Activity_129etca" name="土壤属性空间推测(iPSM)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="7d9c8b88-f377-3870-81a6-ac7db3b9956a" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0bp5vpy</bpmn:incoming><bpmn:incoming>Flow_05z4nqa</bpmn:incoming><bpmn:incoming>Flow_049doag</bpmn:incoming><bpmn:incoming>Flow_12xi40t</bpmn:incoming><bpmn:incoming>Flow_062y0g8</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_1n2ebok" name="地形湿度指数"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="da1949e3-a546-335b-8507-c1231c35e2a8" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1rxc06y</bpmn:incoming><bpmn:incoming>Flow_08vy6jp</bpmn:incoming><bpmn:outgoing>Flow_0bp5vpy</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0bp5vpy" sourceRef="Activity_1n2ebok" targetRef="Activity_129etca" /><bpmn:serviceTask id="Activity_08stl19" name="坡度（最大下坡）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="d3639683-4569-3091-84dd-3bf440f04df0" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0zsv2zx</bpmn:incoming><bpmn:outgoing>Flow_05z4nqa</bpmn:outgoing><bpmn:outgoing>Flow_08vy6jp</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_05z4nqa" sourceRef="Activity_08stl19" targetRef="Activity_129etca" /><bpmn:serviceTask id="Activity_1lx5r0y" name="平面曲率"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="d8bcff1d-3318-35bf-ae69-540b08d4c3ec" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1j6ke3i</bpmn:incoming><bpmn:outgoing>Flow_049doag</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_049doag" sourceRef="Activity_1lx5r0y" targetRef="Activity_129etca" /><bpmn:serviceTask id="Activity_15kazc1" name="剖面曲率"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="9db5af17-3f21-3265-924b-afa40e7ddf8a" 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sourceRef="Activity_16drhby" targetRef="Activity_1lx5r0y" /><bpmn:sequenceFlow id="Flow_0zsv2zx" sourceRef="Activity_16drhby" targetRef="Activity_08stl19" /><bpmn:serviceTask id="Activity_00pmwip" name="汇流累积量_D8（栅格）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="6665e8c4-ef1d-3c7a-9bfc-35ad56239a8c" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1ozrabc</bpmn:incoming><bpmn:outgoing>Flow_1rxc06y</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_1rxc06y" sourceRef="Activity_00pmwip" targetRef="Activity_1n2ebok" /><bpmn:sequenceFlow id="Flow_08vy6jp" sourceRef="Activity_08stl19" targetRef="Activity_1n2ebok" /><bpmn:serviceTask id="Activity_0lqedf9" name="流向(D8)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="eb43c54f-a81c-3c0f-9d7d-69f305c7fd5e" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0ahh3vs</bpmn:incoming><bpmn:outgoing>Flow_1ozrabc</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_1ozrabc" sourceRef="Activity_0lqedf9" targetRef="Activity_00pmwip" /><bpmn:sequenceFlow id="Flow_0ahh3vs" sourceRef="Activity_16drhby" targetRef="Activity_0lqedf9" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1736326306155"><bpmndi:BPMNShape id="Activity_129etca_di" bpmnElement="Activity_129etca"><dc:Bounds x="350" y="260" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1n2ebok_di" bpmnElement="Activity_1n2ebok"><dc:Bounds x="200" y="580" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_08stl19_di" bpmnElement="Activity_08stl19"><dc:Bounds x="200" y="420" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1lx5r0y_di" bpmnElement="Activity_1lx5r0y"><dc:Bounds x="200" y="260" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_15kazc1_di" bpmnElement="Activity_15kazc1"><dc:Bounds x="200" y="100" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_16drhby_di" bpmnElement="Activity_16drhby"><dc:Bounds x="200" y="-60" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_00pmwip_di" bpmnElement="Activity_00pmwip"><dc:Bounds x="50" y="660" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0lqedf9_di" bpmnElement="Activity_0lqedf9"><dc:Bounds x="-100" y="660" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_0bp5vpy_di" bpmnElement="Flow_0bp5vpy"><di:waypoint x="300" y="620" /><di:waypoint x="330" y="620" /><di:waypoint x="330" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_05z4nqa_di" bpmnElement="Flow_05z4nqa"><di:waypoint x="300" y="460" /><di:waypoint x="330" y="460" /><di:waypoint x="330" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_049doag_di" bpmnElement="Flow_049doag"><di:waypoint x="300" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_12xi40t_di" bpmnElement="Flow_12xi40t"><di:waypoint x="300" y="140" /><di:waypoint x="330" y="140" /><di:waypoint x="330" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_062y0g8_di" bpmnElement="Flow_062y0g8"><di:waypoint x="300" y="-20" /><di:waypoint x="330" y="-20" /><di:waypoint x="330" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1h7633l_di" bpmnElement="Flow_1h7633l"><di:waypoint x="250" y="20" /><di:waypoint x="250" y="100" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1j6ke3i_di" bpmnElement="Flow_1j6ke3i"><di:waypoint x="250" y="20" /><di:waypoint x="250" y="260" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0zsv2zx_di" bpmnElement="Flow_0zsv2zx"><di:waypoint x="250" y="20" /><di:waypoint x="250" y="420" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1rxc06y_di" bpmnElement="Flow_1rxc06y"><di:waypoint x="150" y="700" /><di:waypoint x="180" y="700" /><di:waypoint x="180" y="620" /><di:waypoint x="200" y="620" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_08vy6jp_di" bpmnElement="Flow_08vy6jp"><di:waypoint x="250" y="500" /><di:waypoint x="250" y="580" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1ozrabc_di" bpmnElement="Flow_1ozrabc"><di:waypoint x="0" y="700" /><di:waypoint x="50" y="700" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0ahh3vs_di" bpmnElement="Flow_0ahh3vs"><di:waypoint x="200" y="-20" /><di:waypoint x="100" y="-20" /><di:waypoint x="100" y="700" /><di:waypoint x="0" y="700" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2025-01-08 16:52:05.866969	f	土壤属性空间推测-鹤山-01	\N	02228d0a-d4ed-45de-8c55-020badfde43b	1	\N	\N
aa193480-e216-4870-944a-cb43525b3fea	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1728358211941" name="业务流程_1728358211941" isExecutable="true"><bpmn:serviceTask id="Activity_0tvl2ug" name="地形湿度指数"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="da1949e3-a546-335b-8507-c1231c35e2a8" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0g1hioc</bpmn:incoming><bpmn:incoming>Flow_0nekyr4</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_1f4xyik" name="汇流累积量_D8（栅格）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="6665e8c4-ef1d-3c7a-9bfc-35ad56239a8c" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_02v7h4g</bpmn:incoming><bpmn:outgoing>Flow_0g1hioc</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0g1hioc" sourceRef="Activity_1f4xyik" targetRef="Activity_0tvl2ug" /><bpmn:serviceTask id="Activity_1bl74cq" name="坡度（最大下坡）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="d3639683-4569-3091-84dd-3bf440f04df0" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_01d9a5e</bpmn:incoming><bpmn:outgoing>Flow_0nekyr4</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0nekyr4" sourceRef="Activity_1bl74cq" targetRef="Activity_0tvl2ug" /><bpmn:serviceTask id="Activity_06t9w7s" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_01d9a5e</bpmn:outgoing><bpmn:outgoing>Flow_0ykbk09</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_01d9a5e" sourceRef="Activity_06t9w7s" targetRef="Activity_1bl74cq" /><bpmn:serviceTask id="Activity_1bl6n5z" name="流向(D8)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="eb43c54f-a81c-3c0f-9d7d-69f305c7fd5e" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0ykbk09</bpmn:incoming><bpmn:outgoing>Flow_02v7h4g</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_02v7h4g" sourceRef="Activity_1bl6n5z" targetRef="Activity_1f4xyik" /><bpmn:sequenceFlow id="Flow_0ykbk09" sourceRef="Activity_06t9w7s" targetRef="Activity_1bl6n5z" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1728358211941"><bpmndi:BPMNShape id="Activity_0tvl2ug_di" bpmnElement="Activity_0tvl2ug"><dc:Bounds x="320" y="110" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1f4xyik_di" bpmnElement="Activity_1f4xyik"><dc:Bounds x="170" y="190" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1bl74cq_di" bpmnElement="Activity_1bl74cq"><dc:Bounds x="170" y="30" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_06t9w7s_di" bpmnElement="Activity_06t9w7s"><dc:Bounds x="20" y="30" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1bl6n5z_di" bpmnElement="Activity_1bl6n5z"><dc:Bounds x="20" y="190" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_0g1hioc_di" bpmnElement="Flow_0g1hioc"><di:waypoint x="270" y="230" /><di:waypoint x="300" y="230" /><di:waypoint x="300" y="150" /><di:waypoint x="320" y="150" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0nekyr4_di" bpmnElement="Flow_0nekyr4"><di:waypoint x="270" y="70" /><di:waypoint x="300" y="70" /><di:waypoint x="300" y="150" /><di:waypoint x="320" y="150" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_01d9a5e_di" bpmnElement="Flow_01d9a5e"><di:waypoint x="120" y="70" /><di:waypoint x="170" y="70" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_02v7h4g_di" bpmnElement="Flow_02v7h4g"><di:waypoint x="120" y="230" /><di:waypoint x="170" y="230" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0ykbk09_di" bpmnElement="Flow_0ykbk09"><di:waypoint x="70" y="110" /><di:waypoint x="70" y="190" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-10-08 11:53:32.605764	f	地形湿度指数-115255	\N	02228d0a-d4ed-45de-8c55-020badfde43b	0	\N	\N
f0502f16-3013-4c83-b905-12c60227cf25	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1728358211941" name="业务流程_1728358211941" isExecutable="true"><bpmn:serviceTask id="Activity_0tvl2ug" name="地形湿度指数"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="da1949e3-a546-335b-8507-c1231c35e2a8" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0g1hioc</bpmn:incoming><bpmn:incoming>Flow_0nekyr4</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_1f4xyik" name="汇流累积量_D8（栅格）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="6665e8c4-ef1d-3c7a-9bfc-35ad56239a8c" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_02v7h4g</bpmn:incoming><bpmn:outgoing>Flow_0g1hioc</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0g1hioc" sourceRef="Activity_1f4xyik" targetRef="Activity_0tvl2ug" /><bpmn:serviceTask id="Activity_1bl74cq" name="坡度（最大下坡）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="d3639683-4569-3091-84dd-3bf440f04df0" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_01d9a5e</bpmn:incoming><bpmn:outgoing>Flow_0nekyr4</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0nekyr4" sourceRef="Activity_1bl74cq" targetRef="Activity_0tvl2ug" /><bpmn:serviceTask id="Activity_06t9w7s" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_01d9a5e</bpmn:outgoing><bpmn:outgoing>Flow_0ykbk09</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_01d9a5e" sourceRef="Activity_06t9w7s" targetRef="Activity_1bl74cq" /><bpmn:serviceTask id="Activity_1bl6n5z" name="流向(D8)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="eb43c54f-a81c-3c0f-9d7d-69f305c7fd5e" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0ykbk09</bpmn:incoming><bpmn:outgoing>Flow_02v7h4g</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_02v7h4g" sourceRef="Activity_1bl6n5z" targetRef="Activity_1f4xyik" /><bpmn:sequenceFlow id="Flow_0ykbk09" sourceRef="Activity_06t9w7s" targetRef="Activity_1bl6n5z" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1728358211941"><bpmndi:BPMNShape id="Activity_0tvl2ug_di" bpmnElement="Activity_0tvl2ug"><dc:Bounds x="320" y="110" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1f4xyik_di" bpmnElement="Activity_1f4xyik"><dc:Bounds x="170" y="190" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1bl74cq_di" bpmnElement="Activity_1bl74cq"><dc:Bounds x="170" y="30" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_06t9w7s_di" bpmnElement="Activity_06t9w7s"><dc:Bounds x="20" y="30" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1bl6n5z_di" bpmnElement="Activity_1bl6n5z"><dc:Bounds x="20" y="190" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_0g1hioc_di" bpmnElement="Flow_0g1hioc"><di:waypoint x="270" y="230" /><di:waypoint x="300" y="230" /><di:waypoint x="300" y="150" /><di:waypoint x="320" y="150" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0nekyr4_di" bpmnElement="Flow_0nekyr4"><di:waypoint x="270" y="70" /><di:waypoint x="300" y="70" /><di:waypoint x="300" y="150" /><di:waypoint x="320" y="150" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_01d9a5e_di" bpmnElement="Flow_01d9a5e"><di:waypoint x="120" y="70" /><di:waypoint x="170" y="70" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_02v7h4g_di" bpmnElement="Flow_02v7h4g"><di:waypoint x="120" y="230" /><di:waypoint x="170" y="230" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0ykbk09_di" bpmnElement="Flow_0ykbk09"><di:waypoint x="70" y="110" /><di:waypoint x="70" y="190" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-10-08 12:09:17.493629	f	地形湿度指数-120904	\N	02228d0a-d4ed-45de-8c55-020badfde43b	0	\N	\N
d0c4b07b-57d1-4cfc-8fd7-4745ba55fc33	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1728305378051" name="业务流程_1728305378051" isExecutable="true"><bpmn:serviceTask id="Activity_0tvcfan" name="坡向（三阶二元泰勒多项式）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="b69e1bf4-06cf-3d70-be83-81a23ad61ab1" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1tqddtu</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_0jv27do" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_1tqddtu</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_1tqddtu" sourceRef="Activity_0jv27do" targetRef="Activity_0tvcfan" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1728305378051"><bpmndi:BPMNShape id="Activity_0tvcfan_di" bpmnElement="Activity_0tvcfan"><dc:Bounds x="640" y="240" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0jv27do_di" bpmnElement="Activity_0jv27do"><dc:Bounds x="490" y="240" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_1tqddtu_di" bpmnElement="Flow_1tqddtu"><di:waypoint x="590" y="280" /><di:waypoint x="640" y="280" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-10-07 21:06:13.301178	f	坡向107	\N	02228d0a-d4ed-45de-8c55-020badfde43b	1	\N	\N
be5638ea-e32e-4691-bb07-b2ebe2b6c14a	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1728358211941" name="业务流程_1728358211941" isExecutable="true"><bpmn:serviceTask id="Activity_0an93as" name="流向(D8)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="eb43c54f-a81c-3c0f-9d7d-69f305c7fd5e" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1u9cnr1</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_0kwjdg5" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_1u9cnr1</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_1u9cnr1" sourceRef="Activity_0kwjdg5" targetRef="Activity_0an93as" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1728358211941"><bpmndi:BPMNShape id="Activity_0an93as_di" bpmnElement="Activity_0an93as"><dc:Bounds x="430" y="110" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0kwjdg5_di" bpmnElement="Activity_0kwjdg5"><dc:Bounds x="280" y="110" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_1u9cnr1_di" bpmnElement="Flow_1u9cnr1"><di:waypoint x="380" y="150" /><di:waypoint x="430" y="150" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-10-08 11:52:17.386585	f	流向(D8)-115137	\N	02228d0a-d4ed-45de-8c55-020badfde43b	0	\N	\N
cb8653bf-b2e7-4334-9db4-e9f1f63bf69e	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1728375068892" name="业务流程_1728375068892" isExecutable="true"><bpmn:serviceTask id="Activity_1b3uecj" name="汇流累积量_D8（单位汇水面积）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="9d1209e6-dec8-3157-8542-73662b5e1bb1" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0nll2iw</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_104hgt7" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_0nll2iw</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0nll2iw" sourceRef="Activity_104hgt7" targetRef="Activity_1b3uecj" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1728375068892"><bpmndi:BPMNShape id="Activity_1b3uecj_di" bpmnElement="Activity_1b3uecj"><dc:Bounds x="410" y="240" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_104hgt7_di" bpmnElement="Activity_104hgt7"><dc:Bounds x="0" y="220" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_0nll2iw_di" bpmnElement="Flow_0nll2iw"><di:waypoint x="100" y="260" /><di:waypoint x="255" y="260" /><di:waypoint x="255" y="280" /><di:waypoint x="410" y="280" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-10-08 16:29:33.976175	f	填洼（降低边缘洼地高度）-162924	\N	02228d0a-d4ed-45de-8c55-020badfde43b	0	\N	\N
4a09e7d7-8d0e-4509-8413-5ea897c0ae46	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1728375068892" name="业务流程_1728375068892" isExecutable="true"><bpmn:serviceTask id="Activity_18r72fd" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_1spdtth</bpmn:outgoing></bpmn:serviceTask><bpmn:serviceTask id="Activity_0iqsdah" name="汇流累积量_D8（单位汇水面积）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="9d1209e6-dec8-3157-8542-73662b5e1bb1" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1spdtth</bpmn:incoming></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_1spdtth" sourceRef="Activity_18r72fd" targetRef="Activity_0iqsdah" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1728375068892"><bpmndi:BPMNShape id="Activity_18r72fd_di" bpmnElement="Activity_18r72fd"><dc:Bounds x="80" y="220" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0iqsdah_di" bpmnElement="Activity_0iqsdah"><dc:Bounds x="60" y="340" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_1spdtth_di" bpmnElement="Flow_1spdtth"><di:waypoint x="130" y="300" /><di:waypoint x="130" y="320" /><di:waypoint x="110" y="320" /><di:waypoint x="110" y="340" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-10-08 16:32:41.583521	f	填洼（坡度抬升填平）-163211	\N	02228d0a-d4ed-45de-8c55-020badfde43b	0	\N	\N
7b74b5c6-a381-441b-bd07-3f63d7a295aa	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1728305378051" name="业务流程_1728305378051" isExecutable="true"><bpmn:serviceTask id="Activity_0u298l8" name="地形湿度指数"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="da1949e3-a546-335b-8507-c1231c35e2a8" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1gm13ds</bpmn:incoming><bpmn:incoming>Flow_0u36hqs</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_08zvyey" name="汇流累积量_D8（栅格）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="6665e8c4-ef1d-3c7a-9bfc-35ad56239a8c" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1yb4yd5</bpmn:incoming><bpmn:outgoing>Flow_1gm13ds</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_1gm13ds" sourceRef="Activity_08zvyey" targetRef="Activity_0u298l8" /><bpmn:serviceTask id="Activity_0wymsbj" name="坡度（最大下坡）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="d3639683-4569-3091-84dd-3bf440f04df0" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_15ivqh4</bpmn:incoming><bpmn:outgoing>Flow_0u36hqs</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0u36hqs" sourceRef="Activity_0wymsbj" targetRef="Activity_0u298l8" /><bpmn:serviceTask id="Activity_0mdeem4" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_15ivqh4</bpmn:outgoing><bpmn:outgoing>Flow_06d2n0j</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_15ivqh4" sourceRef="Activity_0mdeem4" targetRef="Activity_0wymsbj" /><bpmn:serviceTask id="Activity_0q8run6" name="流向(D8)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="eb43c54f-a81c-3c0f-9d7d-69f305c7fd5e" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_06d2n0j</bpmn:incoming><bpmn:outgoing>Flow_1yb4yd5</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_1yb4yd5" sourceRef="Activity_0q8run6" targetRef="Activity_08zvyey" /><bpmn:sequenceFlow id="Flow_06d2n0j" sourceRef="Activity_0mdeem4" targetRef="Activity_0q8run6" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1728305378051"><bpmndi:BPMNShape id="Activity_0u298l8_di" bpmnElement="Activity_0u298l8"><dc:Bounds x="400" y="220" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_08zvyey_di" bpmnElement="Activity_08zvyey"><dc:Bounds x="250" y="300" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0wymsbj_di" bpmnElement="Activity_0wymsbj"><dc:Bounds x="250" y="140" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0mdeem4_di" bpmnElement="Activity_0mdeem4"><dc:Bounds x="100" y="140" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0q8run6_di" bpmnElement="Activity_0q8run6"><dc:Bounds x="100" y="300" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_1gm13ds_di" bpmnElement="Flow_1gm13ds"><di:waypoint x="350" y="340" /><di:waypoint x="380" y="340" /><di:waypoint x="380" y="260" /><di:waypoint x="400" y="260" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0u36hqs_di" bpmnElement="Flow_0u36hqs"><di:waypoint x="350" y="180" /><di:waypoint x="380" y="180" /><di:waypoint x="380" y="260" /><di:waypoint x="400" y="260" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_15ivqh4_di" bpmnElement="Flow_15ivqh4"><di:waypoint x="200" y="180" /><di:waypoint x="250" y="180" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1yb4yd5_di" bpmnElement="Flow_1yb4yd5"><di:waypoint x="200" y="340" /><di:waypoint x="250" y="340" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_06d2n0j_di" bpmnElement="Flow_06d2n0j"><di:waypoint x="150" y="220" /><di:waypoint x="150" y="300" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-10-07 22:55:43.000532	f	地形湿度指数107	\N	02228d0a-d4ed-45de-8c55-020badfde43b	2	\N	\N
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/></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0rekrk4_di" bpmnElement="Flow_0rekrk4"><di:waypoint x="250" y="-220" /><di:waypoint x="250" y="-140" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_13w2d40_di" bpmnElement="Flow_13w2d40"><di:waypoint x="150" y="140" /><di:waypoint x="180" y="140" /><di:waypoint x="180" y="60" /><di:waypoint x="200" y="60" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_16y5duq_di" bpmnElement="Flow_16y5duq"><di:waypoint x="250" y="660" /><di:waypoint x="250" y="100" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0l6ft7r_di" bpmnElement="Flow_0l6ft7r"><di:waypoint x="0" y="140" /><di:waypoint x="50" y="140" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0kie1q7_di" bpmnElement="Flow_0kie1q7"><di:waypoint x="200" y="-260" /><di:waypoint x="-50" y="-260" /><di:waypoint x="-50" y="100" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1rtax61_di" bpmnElement="Flow_1rtax61"><di:waypoint x="250" y="-220" /><di:waypoint x="250" y="180" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1ff1sgv_di" bpmnElement="Flow_1ff1sgv"><di:waypoint x="250" y="-220" /><di:waypoint x="250" y="340" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0tqfyfp_di" bpmnElement="Flow_0tqfyfp"><di:waypoint x="250" y="-220" /><di:waypoint x="250" y="500" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_17494f2_di" bpmnElement="Flow_17494f2"><di:waypoint x="250" y="-220" /><di:waypoint x="250" y="660" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1iae122_di" bpmnElement="Flow_1iae122"><di:waypoint x="150" y="140" /><di:waypoint x="180" y="140" /><di:waypoint x="180" y="860" /><di:waypoint x="200" y="860" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1d2p6p2_di" bpmnElement="Flow_1d2p6p2"><di:waypoint x="250" y="740" /><di:waypoint x="250" y="820" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-10-08 09:53:06.474293	f	土壤属性空间推测(iPSM)-094956	\N	02228d0a-d4ed-45de-8c55-020badfde43b	2	\N	\N
60f1740f-aa51-4cd4-aad5-61aed93a2519	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1728358211941" name="业务流程_1728358211941" isExecutable="true"><bpmn:serviceTask id="Activity_1lv4rzj" name="流向(D8)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="eb43c54f-a81c-3c0f-9d7d-69f305c7fd5e" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_154mhtv</bpmn:incoming><bpmn:outgoing>Flow_13rl04y</bpmn:outgoing></bpmn:serviceTask><bpmn:serviceTask id="Activity_0nxxr1n" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_154mhtv</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_154mhtv" sourceRef="Activity_0nxxr1n" targetRef="Activity_1lv4rzj" /><bpmn:serviceTask id="Activity_0dehhg8" name="汇流累积量_D8（栅格）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="6665e8c4-ef1d-3c7a-9bfc-35ad56239a8c" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_13rl04y</bpmn:incoming></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_13rl04y" sourceRef="Activity_1lv4rzj" targetRef="Activity_0dehhg8" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1728358211941"><bpmndi:BPMNShape id="Activity_1lv4rzj_di" bpmnElement="Activity_1lv4rzj"><dc:Bounds x="190" y="100" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0nxxr1n_di" bpmnElement="Activity_0nxxr1n"><dc:Bounds x="40" y="100" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0dehhg8_di" bpmnElement="Activity_0dehhg8"><dc:Bounds x="140" y="270" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_154mhtv_di" bpmnElement="Flow_154mhtv"><di:waypoint x="140" y="140" /><di:waypoint x="190" y="140" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_13rl04y_di" bpmnElement="Flow_13rl04y"><di:waypoint x="240" y="180" /><di:waypoint x="240" y="225" /><di:waypoint x="190" y="225" /><di:waypoint x="190" y="270" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-10-08 11:43:11.784934	f	流向(D8)-1	\N	02228d0a-d4ed-45de-8c55-020badfde43b	0	\N	\N
01100980-e6e6-40ff-92fd-e1e168e44e53	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1728358211941" name="业务流程_1728358211941" isExecutable="true"><bpmn:serviceTask id="Activity_1lv4rzj" name="流向(D8)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="eb43c54f-a81c-3c0f-9d7d-69f305c7fd5e" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_154mhtv</bpmn:incoming><bpmn:outgoing>Flow_13rl04y</bpmn:outgoing></bpmn:serviceTask><bpmn:serviceTask id="Activity_0nxxr1n" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_154mhtv</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_154mhtv" sourceRef="Activity_0nxxr1n" targetRef="Activity_1lv4rzj" /><bpmn:serviceTask id="Activity_0dehhg8" name="汇流累积量_D8（栅格）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="6665e8c4-ef1d-3c7a-9bfc-35ad56239a8c" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_13rl04y</bpmn:incoming></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_13rl04y" sourceRef="Activity_1lv4rzj" targetRef="Activity_0dehhg8" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1728358211941"><bpmndi:BPMNShape id="Activity_1lv4rzj_di" bpmnElement="Activity_1lv4rzj"><dc:Bounds x="190" y="100" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0nxxr1n_di" bpmnElement="Activity_0nxxr1n"><dc:Bounds x="40" y="100" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0dehhg8_di" bpmnElement="Activity_0dehhg8"><dc:Bounds x="140" y="270" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_154mhtv_di" bpmnElement="Flow_154mhtv"><di:waypoint x="140" y="140" /><di:waypoint x="190" y="140" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_13rl04y_di" bpmnElement="Flow_13rl04y"><di:waypoint x="240" y="180" /><di:waypoint x="240" y="225" /><di:waypoint x="190" y="225" /><di:waypoint x="190" y="270" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-10-08 11:48:08.376042	f	流向(D8)-114720	\N	02228d0a-d4ed-45de-8c55-020badfde43b	0	\N	\N
718a5d39-5cd1-43ee-a41c-6341b80611ba	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1728375068892" name="业务流程_1728375068892" isExecutable="true"><bpmn:serviceTask id="Activity_0pj2j9e" name="汇流累积量_D8（单位汇水面积）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="9d1209e6-dec8-3157-8542-73662b5e1bb1" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_19pcijv</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_05f3vxd" name="填洼（降低边缘洼地高度）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="d398ef98-1395-3a6a-bab3-e2e7301cc13b" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_19pcijv</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_19pcijv" sourceRef="Activity_05f3vxd" targetRef="Activity_0pj2j9e" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1728375068892"><bpmndi:BPMNShape id="Activity_0pj2j9e_di" bpmnElement="Activity_0pj2j9e"><dc:Bounds x="650" y="350" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_05f3vxd_di" bpmnElement="Activity_05f3vxd"><dc:Bounds x="90" y="300" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_19pcijv_di" bpmnElement="Flow_19pcijv"><di:waypoint x="190" y="340" /><di:waypoint x="420" y="340" /><di:waypoint x="420" y="390" /><di:waypoint x="650" y="390" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-10-08 16:27:40.387187	f	汇流累积量_D8（单位汇水面积）-162726	\N	02228d0a-d4ed-45de-8c55-020badfde43b	0	\N	\N
5a53260e-3643-4c49-b736-be107f404cc5	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1735496608616" name="业务流程_1735496608616" isExecutable="true"><bpmn:serviceTask id="Activity_0gteszr" name="土壤属性空间推测(iPSM)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="7d9c8b88-f377-3870-81a6-ac7db3b9956a" 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/><di:waypoint x="250" y="420" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_10vs65d_di" bpmnElement="Flow_10vs65d"><di:waypoint x="150" y="700" /><di:waypoint x="180" y="700" /><di:waypoint x="180" y="620" /><di:waypoint x="200" y="620" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_08lom6x_di" bpmnElement="Flow_08lom6x"><di:waypoint x="250" y="500" /><di:waypoint x="250" y="580" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0dyzf28_di" bpmnElement="Flow_0dyzf28"><di:waypoint x="0" y="700" /><di:waypoint x="50" y="700" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1i5ulns_di" bpmnElement="Flow_1i5ulns"><di:waypoint x="200" y="-20" /><di:waypoint x="100" y="-20" /><di:waypoint x="100" y="700" /><di:waypoint x="0" y="700" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-12-30 02:24:05.053681	f	土壤属性空间推测(iPSM)-OK	\N	02228d0a-d4ed-45de-8c55-020badfde43b	0	\N	\N
19190fd7-0352-4d41-badf-1387bec45fab	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1735526976413" name="业务流程_1735526976413" isExecutable="true"><bpmn:serviceTask id="Activity_07jl5zd" name="土壤属性空间推测(iPSM)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="7d9c8b88-f377-3870-81a6-ac7db3b9956a" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0fijvwq</bpmn:incoming><bpmn:incoming>Flow_0wzb72n</bpmn:incoming><bpmn:incoming>Flow_1nybm1t</bpmn:incoming><bpmn:incoming>Flow_1m8l424</bpmn:incoming><bpmn:incoming>Flow_1311j1c</bpmn:incoming><bpmn:incoming>Flow_0s5kgzs</bpmn:incoming><bpmn:incoming>Flow_1es3f25</bpmn:incoming><bpmn:incoming>Flow_1oglim8</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_114zcbk" name="地形湿度指数"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="da1949e3-a546-335b-8507-c1231c35e2a8" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1p58xln</bpmn:incoming><bpmn:incoming>Flow_0ls86ed</bpmn:incoming><bpmn:outgoing>Flow_0fijvwq</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0fijvwq" sourceRef="Activity_114zcbk" targetRef="Activity_07jl5zd" /><bpmn:serviceTask id="Activity_0d9e8fz" name="坡度（最大下坡）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="d3639683-4569-3091-84dd-3bf440f04df0" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1y9bter</bpmn:incoming><bpmn:outgoing>Flow_0wzb72n</bpmn:outgoing><bpmn:outgoing>Flow_0kuoo9c</bpmn:outgoing><bpmn:outgoing>Flow_0ls86ed</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0wzb72n" sourceRef="Activity_0d9e8fz" targetRef="Activity_07jl5zd" /><bpmn:serviceTask id="Activity_0p36gfn" name="坡向（三阶二元泰勒多项式）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="b69e1bf4-06cf-3d70-be83-81a23ad61ab1" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_05j7tns</bpmn:incoming><bpmn:outgoing>Flow_1nybm1t</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_1nybm1t" sourceRef="Activity_0p36gfn" targetRef="Activity_07jl5zd" /><bpmn:serviceTask 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/></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1x0h733</bpmn:incoming><bpmn:outgoing>Flow_0o74wag</bpmn:outgoing><bpmn:outgoing>Flow_1p58xln</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0o74wag" sourceRef="Activity_09che7l" targetRef="Activity_0rllisp" /><bpmn:sequenceFlow id="Flow_0kuoo9c" sourceRef="Activity_0d9e8fz" targetRef="Activity_0rllisp" /><bpmn:serviceTask id="Activity_1wxutxg" name="流向(D8)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="eb43c54f-a81c-3c0f-9d7d-69f305c7fd5e" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_19pkotc</bpmn:incoming><bpmn:outgoing>Flow_1x0h733</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_1x0h733" sourceRef="Activity_1wxutxg" targetRef="Activity_09che7l" /><bpmn:sequenceFlow id="Flow_19pkotc" sourceRef="Activity_047djgb" targetRef="Activity_1wxutxg" /><bpmn:sequenceFlow id="Flow_06k0thg" 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id="Activity_0d9e8fz_di" bpmnElement="Activity_0d9e8fz"><dc:Bounds x="200" y="660" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0p36gfn_di" bpmnElement="Activity_0p36gfn"><dc:Bounds x="200" y="500" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_186gexh_di" bpmnElement="Activity_186gexh"><dc:Bounds x="200" y="340" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0w1eyzf_di" bpmnElement="Activity_0w1eyzf"><dc:Bounds x="200" y="180" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0rllisp_di" bpmnElement="Activity_0rllisp"><dc:Bounds x="200" y="20" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_14kixh7_di" bpmnElement="Activity_14kixh7"><dc:Bounds x="200" y="-140" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_047djgb_di" bpmnElement="Activity_047djgb"><dc:Bounds x="200" y="-300" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_09che7l_di" bpmnElement="Activity_09che7l"><dc:Bounds x="50" y="100" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1wxutxg_di" bpmnElement="Activity_1wxutxg"><dc:Bounds x="-100" y="100" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_0fijvwq_di" bpmnElement="Flow_0fijvwq"><di:waypoint x="300" y="860" /><di:waypoint x="330" y="860" /><di:waypoint x="330" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0wzb72n_di" bpmnElement="Flow_0wzb72n"><di:waypoint x="300" y="700" /><di:waypoint x="330" y="700" /><di:waypoint x="330" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1nybm1t_di" bpmnElement="Flow_1nybm1t"><di:waypoint x="300" y="540" /><di:waypoint x="330" y="540" /><di:waypoint x="330" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1m8l424_di" 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/></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_06lr6r9_di" bpmnElement="Flow_06lr6r9"><di:waypoint x="250" y="-220" /><di:waypoint x="250" y="-140" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0o74wag_di" bpmnElement="Flow_0o74wag"><di:waypoint x="150" y="140" /><di:waypoint x="180" y="140" /><di:waypoint x="180" y="60" /><di:waypoint x="200" y="60" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0kuoo9c_di" bpmnElement="Flow_0kuoo9c"><di:waypoint x="250" y="660" /><di:waypoint x="250" y="100" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1x0h733_di" bpmnElement="Flow_1x0h733"><di:waypoint x="0" y="140" /><di:waypoint x="50" y="140" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_19pkotc_di" bpmnElement="Flow_19pkotc"><di:waypoint x="200" y="-260" /><di:waypoint x="100" y="-260" /><di:waypoint x="100" y="140" /><di:waypoint x="0" y="140" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_06k0thg_di" bpmnElement="Flow_06k0thg"><di:waypoint x="250" y="-220" /><di:waypoint x="250" y="180" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_04sv6u0_di" bpmnElement="Flow_04sv6u0"><di:waypoint x="250" y="-220" /><di:waypoint x="250" y="340" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_05j7tns_di" bpmnElement="Flow_05j7tns"><di:waypoint x="250" y="-220" /><di:waypoint x="250" y="500" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1y9bter_di" bpmnElement="Flow_1y9bter"><di:waypoint x="250" y="-220" /><di:waypoint x="250" y="660" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1p58xln_di" bpmnElement="Flow_1p58xln"><di:waypoint x="150" y="140" /><di:waypoint x="180" y="140" /><di:waypoint x="180" y="860" /><di:waypoint x="200" y="860" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0ls86ed_di" bpmnElement="Flow_0ls86ed"><di:waypoint x="250" y="740" /><di:waypoint x="250" y="820" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-12-30 10:50:18.252205	f	土壤属性空间推测(iPSM)-testxc	\N	02228d0a-d4ed-45de-8c55-020badfde43b	1	\N	\N
59b30e6e-4bcc-49f2-8bb5-de8cbc132934	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1735527136904" name="业务流程_1735527136904" isExecutable="true"><bpmn:serviceTask id="Activity_0qpet27" name="土壤属性空间推测(iPSM)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="7d9c8b88-f377-3870-81a6-ac7db3b9956a" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0a444my</bpmn:incoming><bpmn:incoming>Flow_04u3k8n</bpmn:incoming><bpmn:incoming>Flow_1sn4wrw</bpmn:incoming><bpmn:incoming>Flow_1wnp94t</bpmn:incoming><bpmn:incoming>Flow_1oxl0yr</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_1nf3poq" name="地形湿度指数"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="da1949e3-a546-335b-8507-c1231c35e2a8" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0002pu0</bpmn:incoming><bpmn:incoming>Flow_022z9u3</bpmn:incoming><bpmn:outgoing>Flow_0a444my</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0a444my" sourceRef="Activity_1nf3poq" targetRef="Activity_0qpet27" /><bpmn:serviceTask id="Activity_1dc930i" name="坡度（最大下坡）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="d3639683-4569-3091-84dd-3bf440f04df0" 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f614900b-1b68-48c3-8ab5-b2ff530e4aee	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1733234734223" name="业务流程_1733234734223" isExecutable="true"><bpmn:serviceTask id="Activity_0afc5j2" name="点数据csv转换成shp"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="0a6495c7-e59d-3cd4-94c6-c060e262e98b" 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84f19fff-0282-49ee-a418-fca3c2b3998c	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1735110581261" name="业务流程_1735110581261" isExecutable="true"><bpmn:serviceTask id="Activity_0mmo67s" name="至河流的距离计算"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="99792477-6354-3e47-8097-07adbb5e11cb" 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d61845e0-4587-4a8c-9c72-bd81c272d465	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1735109802989" name="业务流程_1735109802989" isExecutable="true"><bpmn:serviceTask id="Activity_067vpla" name="地块面积赋值"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="3f83271b-4116-359e-9bd1-a4744506f0cd" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0lg918b</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_1fmdszi" name="添加物候置信度"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="b3e67d4d-3b66-3f96-9875-cd036de39e95" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_08jvqkh</bpmn:incoming><bpmn:outgoing>Flow_0lg918b</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0lg918b" sourceRef="Activity_1fmdszi" targetRef="Activity_067vpla" /><bpmn:serviceTask id="Activity_17hv6ho" name="添加物候信息"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="70050be4-e818-32e1-a631-c99d0d94d5de" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_08jvqkh</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_08jvqkh" sourceRef="Activity_17hv6ho" targetRef="Activity_1fmdszi" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1735109802989"><bpmndi:BPMNShape id="Activity_17hv6ho_di" bpmnElement="Activity_17hv6ho"><dc:Bounds x="120" y="140" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1fmdszi_di" bpmnElement="Activity_1fmdszi"><dc:Bounds x="350" y="140" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_067vpla_di" bpmnElement="Activity_067vpla"><dc:Bounds x="590" y="140" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_0lg918b_di" bpmnElement="Flow_0lg918b"><di:waypoint x="450" y="180" /><di:waypoint x="590" y="180" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_08jvqkh_di" bpmnElement="Flow_08jvqkh"><di:waypoint x="220" y="180" /><di:waypoint x="350" y="180" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-12-25 15:15:56.465929	f	地块面积赋值	\N	02228d0a-d4ed-45de-8c55-020badfde43b	1	\N	\N
eff9e412-0b30-4b7d-872b-3aeacf00f7d8	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1735109802989" name="业务流程_1735109802989" isExecutable="true"><bpmn:serviceTask id="Activity_067vpla" name="地块面积赋值"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelId" value="3f83271b-4116-359e-9bd1-a4744506f0cd" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0lg918b</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_1fmdszi" name="添加物候置信度"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="b3e67d4d-3b66-3f96-9875-cd036de39e95" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_08jvqkh</bpmn:incoming><bpmn:outgoing>Flow_0lg918b</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0lg918b" sourceRef="Activity_1fmdszi" targetRef="Activity_067vpla" /><bpmn:serviceTask id="Activity_17hv6ho" name="添加物候信息"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="70050be4-e818-32e1-a631-c99d0d94d5de" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_08jvqkh</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_08jvqkh" sourceRef="Activity_17hv6ho" targetRef="Activity_1fmdszi" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1735109802989"><bpmndi:BPMNShape id="Activity_067vpla_di" bpmnElement="Activity_067vpla"><dc:Bounds x="590" y="140" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1fmdszi_di" bpmnElement="Activity_1fmdszi"><dc:Bounds x="350" y="140" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_17hv6ho_di" bpmnElement="Activity_17hv6ho"><dc:Bounds x="120" y="140" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_0lg918b_di" bpmnElement="Flow_0lg918b"><di:waypoint x="450" y="180" /><di:waypoint x="590" y="180" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_08jvqkh_di" bpmnElement="Flow_08jvqkh"><di:waypoint x="220" y="180" /><di:waypoint x="350" y="180" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-12-26 10:43:16.241721	f	地块面积赋值-1226	\N	02228d0a-d4ed-45de-8c55-020badfde43b	1	\N	\N
a8a07de4-e9cb-48c8-9af5-d852b6b889bb	<?xml version="1.0" encoding="UTF-8"?>\n<bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" xmlns:bpmndi="http://www.omg.org/spec/BPMN/20100524/DI" xmlns:dc="http://www.omg.org/spec/DD/20100524/DC" xmlns:zeebe="http://camunda.org/schema/zeebe/1.0" xmlns:di="http://www.omg.org/spec/DD/20100524/DI" xmlns:modeler="http://camunda.org/schema/modeler/1.0" id="Definitions_0rbx7f1" targetNamespace="http://bpmn.io/schema/bpmn" exporter="Camunda Modeler" exporterVersion="5.9.0" modeler:executionPlatform="Camunda Cloud" modeler:executionPlatformVersion="8.1.0"><bpmn:process id="Process_1735495053787" name="业务流程_1735495053787" isExecutable="true"><bpmn:serviceTask id="Activity_0clnwpq" name="土壤属性空间推测(iPSM)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="7d9c8b88-f377-3870-81a6-ac7db3b9956a" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_0d4uddt</bpmn:incoming><bpmn:incoming>Flow_04rslqi</bpmn:incoming><bpmn:incoming>Flow_11hsutl</bpmn:incoming><bpmn:incoming>Flow_0effn0p</bpmn:incoming><bpmn:incoming>Flow_04jllu7</bpmn:incoming></bpmn:serviceTask><bpmn:serviceTask id="Activity_0p27jxp" name="地形湿度指数"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="da1949e3-a546-335b-8507-c1231c35e2a8" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_008wgln</bpmn:incoming><bpmn:incoming>Flow_0jv76di</bpmn:incoming><bpmn:outgoing>Flow_0d4uddt</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0d4uddt" sourceRef="Activity_0p27jxp" targetRef="Activity_0clnwpq" /><bpmn:serviceTask id="Activity_1s3o5ql" name="坡度（最大下坡）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="d3639683-4569-3091-84dd-3bf440f04df0" 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/></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_1m8allw</bpmn:incoming><bpmn:outgoing>Flow_0effn0p</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_0effn0p" sourceRef="Activity_1f4dlw4" targetRef="Activity_0clnwpq" /><bpmn:serviceTask id="Activity_1bz3vde" name="填洼（坡度抬升填平）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="cc241952-4ff2-36f3-ba31-d23ccd3919df" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:outgoing>Flow_04jllu7</bpmn:outgoing><bpmn:outgoing>Flow_1m8allw</bpmn:outgoing><bpmn:outgoing>Flow_0w7h4gx</bpmn:outgoing><bpmn:outgoing>Flow_0iz47y1</bpmn:outgoing><bpmn:outgoing>Flow_17ojarn</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_04jllu7" sourceRef="Activity_1bz3vde" targetRef="Activity_0clnwpq" /><bpmn:sequenceFlow id="Flow_1m8allw" sourceRef="Activity_1bz3vde" targetRef="Activity_1f4dlw4" /><bpmn:sequenceFlow id="Flow_0w7h4gx" sourceRef="Activity_1bz3vde" targetRef="Activity_0zqw8ng" /><bpmn:sequenceFlow id="Flow_0iz47y1" sourceRef="Activity_1bz3vde" targetRef="Activity_1s3o5ql" /><bpmn:serviceTask id="Activity_0j4no8y" name="汇流累积量_D8（栅格）"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="6665e8c4-ef1d-3c7a-9bfc-35ad56239a8c" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_18ipd8s</bpmn:incoming><bpmn:outgoing>Flow_008wgln</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_008wgln" sourceRef="Activity_0j4no8y" targetRef="Activity_0p27jxp" /><bpmn:sequenceFlow id="Flow_0jv76di" sourceRef="Activity_1s3o5ql" targetRef="Activity_0p27jxp" /><bpmn:serviceTask id="Activity_1y9yl7c" name="流向(D8)"><bpmn:extensionElements><zeebe:taskDefinition type="GeoModel" /><zeebe:taskHeaders><zeebe:header key="modelID" value="eb43c54f-a81c-3c0f-9d7d-69f305c7fd5e" /></zeebe:taskHeaders></bpmn:extensionElements><bpmn:incoming>Flow_17ojarn</bpmn:incoming><bpmn:outgoing>Flow_18ipd8s</bpmn:outgoing></bpmn:serviceTask><bpmn:sequenceFlow id="Flow_18ipd8s" sourceRef="Activity_1y9yl7c" targetRef="Activity_0j4no8y" /><bpmn:sequenceFlow id="Flow_17ojarn" sourceRef="Activity_1bz3vde" targetRef="Activity_1y9yl7c" /></bpmn:process><bpmndi:BPMNDiagram id="BPMNDiagram_1"><bpmndi:BPMNPlane id="BPMNPlane_1" bpmnElement="Process_1735495053787"><bpmndi:BPMNShape id="Activity_0clnwpq_di" bpmnElement="Activity_0clnwpq"><dc:Bounds x="350" y="260" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0p27jxp_di" bpmnElement="Activity_0p27jxp"><dc:Bounds x="200" y="580" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1s3o5ql_di" bpmnElement="Activity_1s3o5ql"><dc:Bounds x="200" y="420" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0zqw8ng_di" bpmnElement="Activity_0zqw8ng"><dc:Bounds x="200" y="260" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1f4dlw4_di" bpmnElement="Activity_1f4dlw4"><dc:Bounds x="200" y="100" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1bz3vde_di" bpmnElement="Activity_1bz3vde"><dc:Bounds x="200" y="-60" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_0j4no8y_di" bpmnElement="Activity_0j4no8y"><dc:Bounds x="50" y="660" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNShape id="Activity_1y9yl7c_di" bpmnElement="Activity_1y9yl7c"><dc:Bounds x="-100" y="660" width="100" height="80" /></bpmndi:BPMNShape><bpmndi:BPMNEdge id="Flow_0d4uddt_di" bpmnElement="Flow_0d4uddt"><di:waypoint x="300" y="620" /><di:waypoint x="330" y="620" /><di:waypoint x="330" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_04rslqi_di" bpmnElement="Flow_04rslqi"><di:waypoint x="300" y="460" /><di:waypoint x="330" y="460" /><di:waypoint x="330" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_11hsutl_di" bpmnElement="Flow_11hsutl"><di:waypoint x="300" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0effn0p_di" bpmnElement="Flow_0effn0p"><di:waypoint x="300" y="140" /><di:waypoint x="330" y="140" /><di:waypoint x="330" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_04jllu7_di" bpmnElement="Flow_04jllu7"><di:waypoint x="300" y="-20" /><di:waypoint x="330" y="-20" /><di:waypoint x="330" y="300" /><di:waypoint x="350" y="300" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_1m8allw_di" bpmnElement="Flow_1m8allw"><di:waypoint x="250" y="20" /><di:waypoint x="250" y="100" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0w7h4gx_di" bpmnElement="Flow_0w7h4gx"><di:waypoint x="250" y="20" /><di:waypoint x="250" y="260" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0iz47y1_di" bpmnElement="Flow_0iz47y1"><di:waypoint x="250" y="20" /><di:waypoint x="250" y="420" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_008wgln_di" bpmnElement="Flow_008wgln"><di:waypoint x="150" y="700" /><di:waypoint x="180" y="700" /><di:waypoint x="180" y="620" /><di:waypoint x="200" y="620" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_0jv76di_di" bpmnElement="Flow_0jv76di"><di:waypoint x="250" y="500" /><di:waypoint x="250" y="580" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_18ipd8s_di" bpmnElement="Flow_18ipd8s"><di:waypoint x="0" y="700" /><di:waypoint x="50" y="700" /></bpmndi:BPMNEdge><bpmndi:BPMNEdge id="Flow_17ojarn_di" bpmnElement="Flow_17ojarn"><di:waypoint x="200" y="-20" /><di:waypoint x="100" y="-20" /><di:waypoint x="100" y="700" /><di:waypoint x="0" y="700" /></bpmndi:BPMNEdge></bpmndi:BPMNPlane></bpmndi:BPMNDiagram></bpmn:definitions>	2024-12-30 01:59:37.643707	f	土壤属性空间推测(iPSM)-test	\N	02228d0a-d4ed-45de-8c55-020badfde43b	0	\N	\N
