__init__.py 12 KB

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  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. from common.BigDataCenterAPI import *
  4. from models import *
  5. from sqlalchemy import text
  6. from sqlalchemy import func
  7. from shapely.geometry import Polygon, MultiPolygon
  8. from shapely.ops import unary_union
  9. import json
  10. def convert_to_polygon(points):
  11. # 将点的列表转换为POLYGON格式的字符串
  12. polygon_str = "POLYGON(("
  13. for point in points:
  14. # 假设点的顺序是经度(x),纬度(y)
  15. polygon_str += f"{point['y']} {point['x']}, "
  16. # 移除最后一个逗号和空格,然后添加闭合点和结束括号
  17. polygon_str = polygon_str.rstrip(", ") + f", {points[0]['y']} {points[0]['x']}))"
  18. return polygon_str
  19. def get_town_list2(location_list:list,db):
  20. resutl = []
  21. for location in location_list:
  22. location = convert_to_polygon(location) #,geometry
  23. sql = text(f"""SELECT DISTINCT `name`,properties,pac FROM tp_geojson_data_zj WHERE ST_Intersects(geometry,ST_PolygonFromText( '{location}', 4326 ))""")
  24. # print(sql)
  25. resutl+=db.execute(sql).all()
  26. return resutl
  27. def get_village_list(location_list:list,db,pac=''):
  28. resutl = []
  29. for location in location_list:
  30. location = convert_to_polygon(location) #geometry,
  31. sql = text(f"""SELECT DISTINCT `name`,properties,pac,populationSize,GDP FROM (select * from tp_geojson_data_cj_sq {pac})A WHERE ST_Intersects(geometry,ST_PolygonFromText( '{location}', 4326 )) """)
  32. # print(sql)
  33. resutl+=db.execute(sql).all()
  34. return resutl
  35. def get_town_list(locations,):
  36. # 初始化一个空的MultiPolygon来容纳所有多边形
  37. multi_polygon = MultiPolygon()
  38. # 遍历每个位置,创建多边形并添加到multi_polygon中
  39. for location in locations:
  40. # 将边界列表转换为Polygon
  41. polygon = Polygon([(item['x'], item['y']) for item in location])
  42. multi_polygon = multi_polygon.union(polygon)
  43. # 将GeoJSON数据转换为字典
  44. with open('/home/python3/zj_geojson.json', 'r', encoding='utf-8') as file:
  45. geojson = json.load(file)
  46. # 假设GeoJSON数据是一个FeatureCollection
  47. features = geojson.get('features', [])
  48. # 初始化一个空列表来存储结果
  49. intersected_names_and_pacs = []
  50. # 遍历GeoJSON中的每个Feature,计算交集
  51. for feature in features:
  52. geom = feature['geometry']
  53. if 'coordinates' in geom:
  54. # 将GeoJSON Polygon转换为shapely Polygon
  55. if geom['type'] == 'Polygon':
  56. polygon = Polygon(geom['coordinates'][0])
  57. intersection = polygon.intersection(multi_polygon)
  58. elif geom['type'] == 'MultiPolygon':
  59. multi_polygon_feature = MultiPolygon([Polygon(coords[0]) for coords in geom['coordinates']])
  60. intersection = multi_polygon_feature.intersection(multi_polygon)
  61. else:
  62. continue # 跳过非Polygon和非MultiPolygon类型的几何对象
  63. if not intersection.is_empty:
  64. properties = feature['properties']
  65. intersected_names_and_pacs.append({
  66. "townName": properties.get('NAME', ''),
  67. "code": properties.get('PAC', ''),
  68. "populationSize": 0, # 假设值,需要从数据中获取
  69. "areaSize": round(intersection.area, 2), # 交集区域的面积
  70. "GDP": 0 # 假设值,需要从数据中获取
  71. })
  72. return intersected_names_and_pacs, len(intersected_names_and_pacs)
  73. def get_bqw_yj_quyu_data(area_code,db):
  74. sql = text(
  75. """SELECT ifnull(populationSize,0) as populationSize,
  76. ifnull(populationSize_unit,'') as populationSize_unit,
  77. ifnull(areaSize,0) as areaSize,
  78. ifnull(areaSize_unit,'')as areaSize_unit,ifnull(GDP,0) as GDP,ifnull(GDP_unit,'') as GDP_unit FROM sharedb.`bqw_yj_quyu_data` where `area_code`like :code order by area_code,year desc """).bindparams(
  79. code=f'%{area_code}%')
  80. # 执行查询
  81. result = db.execute(sql).fetchone()
  82. # 处理结果
  83. if result:
  84. return dict(result)
  85. else:
  86. return {"populationSize":0,"populationSize_unit":"","areaSize":0,"areaSize_unit":"","GDP":0,"GDP_unit":""}
  87. def get_town_village_list(locations,db):
  88. # 初始化一个空的MultiPolygon来容纳所有多边形
  89. intersected_towns = get_town_list2(locations,db)
  90. # 初始化一个空列表来存储结果
  91. intersected_names_and_pacs = []
  92. town_count = len(intersected_towns)
  93. village_count = 0
  94. for town in intersected_towns:
  95. # town_count+=1
  96. town_pac = town.pac
  97. properties = json.loads(town.properties)
  98. quyu_data = get_bqw_yj_quyu_data(town.pac,db)
  99. town_data = {
  100. "townName": town.name,
  101. "code": town.pac,
  102. "populationSize": f"{quyu_data['populationSize']}{quyu_data['populationSize_unit']}",
  103. "areaSize": f"{quyu_data['areaSize']}{quyu_data['areaSize_unit']}",
  104. "GDP": f"{quyu_data['GDP']}{quyu_data['GDP_unit']}" # 假设值,需要从数据中获取
  105. }
  106. # intersected_villages = db.query(TpCjSqGeoJSONData).filter(
  107. # func.ST_Intersects(TpCjSqGeoJSONData.geometry, multi_polygon) == True
  108. # ).filter(TpCjSqGeoJSONData.pac.like(f'{town_pac}%')).all()
  109. intersected_villages = get_village_list(locations,db,pac=f""" where pac like '{town_pac}%'""")
  110. intersected_villages_names_and_pacs = []
  111. for village in intersected_villages:
  112. quyu_data = get_bqw_yj_quyu_data(village.pac, db)
  113. # town_data['populationSize']+=village.populationSize
  114. # town_data['GDP']+=village.GDP
  115. # properties = json.loads(village.properties)
  116. village_data = {
  117. "villageName": village.name,
  118. "code": village.pac,
  119. "populationSize": f"{quyu_data['populationSize']}{quyu_data['populationSize_unit']}",#village.populationSize, # 假设值,需要从数据中获取
  120. "areaSize": f"{quyu_data['areaSize']}{quyu_data['areaSize_unit']}",#properties['GEO_AREA'], # 交集区域的面积 0,#
  121. "GDP": f"{quyu_data['GDP']}{quyu_data['GDP_unit']}"#village.GDP # 假设值,需要从数据中获取
  122. }
  123. intersected_villages_names_and_pacs.append(village_data)
  124. villageCount= len(intersected_villages_names_and_pacs)
  125. if villageCount>0:
  126. town_data['children']=intersected_villages_names_and_pacs
  127. town_data['villageCount'] =villageCount
  128. village_count += villageCount
  129. intersected_names_and_pacs.append(town_data)
  130. return intersected_names_and_pacs, town_count,village_count
  131. # import geopandas as gpd
  132. # from shapely.geometry import Polygon
  133. #
  134. #
  135. #
  136. # def get_town_list(locations):
  137. # # 读取GeoJSON文件为GeoDataFrame
  138. # gdf = gpd.read_file('zj_geojson.json')
  139. # gdf = gdf.set_crs("EPSG:4326", allow_override=True)
  140. #
  141. # # 初始化一个空的GeoDataFrame来容纳所有多边形
  142. # multi_polygon_gdf = gpd.GeoDataFrame(crs=gdf.crs)
  143. #
  144. # # 遍历每个位置,创建多边形并添加到multi_polygon_gdf中
  145. # for location in locations:
  146. # # 将边界列表转换为Polygon
  147. # polygon = Polygon([(item['x'], item['y']) for item in location])
  148. # # 将多边形添加到multi_polygon_gdf中
  149. # multi_polygon_gdf = multi_polygon_gdf.append(gpd.GeoDataFrame([1], geometry=[polygon], crs=gdf.crs))
  150. #
  151. # # 使用overlay函数来找出相交的区域
  152. # intersected = gpd.overlay(gdf, multi_polygon_gdf, how='intersection')
  153. #
  154. # # 获取相交区域的名称和PAC
  155. # intersected_names_and_pacs = [{"name": row['NAME'], "pac": row['PAC'],"populationSize":0,"areaSize":0,"GDP":0} for index, row in intersected.iterrows() if 'NAME' in row and 'PAC' in row]
  156. #
  157. # return intersected_names_and_pacs,len(intersected_names_and_pacs)
  158. def count_town_village(location_list:list,db):
  159. town_count = 0
  160. town_list = []
  161. village_count = 0
  162. village_list = []
  163. result = []
  164. url = 'https://19.15.75.180:8581/GatewayMsg/http/api/proxy/invoke'
  165. service_code= 'YZT1685418808667'
  166. service_info = db.query(OneShareApiEntity).filter(OneShareApiEntity.servercode == service_code).first()
  167. signTime = str(GetTime() // 1000)
  168. nonce = GetNonce(5)
  169. sign = GetSign(signTime, nonce, service_info.passtoken)
  170. headers = {
  171. # 'Content-Type': 'application/json',
  172. 'x-tif-signature': sign,
  173. 'x-tif-timestamp': signTime,
  174. 'x-tif-nonce': nonce,
  175. 'x-tif-paasid': service_info.passid,
  176. 'x-tif-serviceId': service_code
  177. }
  178. response = requests.post(url=url, headers=headers, json=location_list, verify=False)
  179. if response.status_code==200:
  180. data_list = response.json()['data']
  181. for data in data_list:
  182. township = data['townshipCode']
  183. if township not in town_list:
  184. town_count+=1
  185. town_list.append(township)
  186. # result.append({'township':data['township'],"townshipCode":data['townshipCode'],"villages":[]})
  187. result.append({'township':data['township'],"townshipCode":data['townshipCode'],"village":'-',"villageCode":'-',"populationSize":0,"areaSize":0,"GDP":0})
  188. village = data['villageCode']
  189. if village not in village_list:
  190. village_count+=1
  191. village_list.append(village)
  192. # for town in result:
  193. # if town['townshipCode']==data['townshipCode']:
  194. # town["villages"].append({'village': data['village'], "villageCode": data['villageCode']})
  195. result.append({'township':data['township'],"townshipCode":data['townshipCode'],'village': data['village'], "villageCode": data['villageCode'],"populationSize":0,"areaSize":0,"GDP":0})
  196. return result,town_count,village_count
  197. def count_emergency_expert(location_list:list,db):
  198. location = convert_to_polygon(location_list)
  199. sql = text(f"""SELECT * FROM emergency_expert WHERE ST_Contains(ST_PolygonFromText( '{location}', 4326 ),ST_PointFromText(CONCAT('POINT(', latitude, ' ', longitude, ')'), 4326))""")
  200. return len(db.execute(sql).all())
  201. def count_emergency_management(location_list: list, db):
  202. location = convert_to_polygon(location_list)
  203. sql = text(f"""SELECT DISTINCT management_unit FROM `rescue_materia` WHERE ST_Contains(ST_PolygonFromText( '{location}', 4326 ),ST_PointFromText(CONCAT('POINT(', latitude, ' ', longitude, ')'), 4326))""")
  204. return len(db.execute(sql).all())
  205. def get_hospital_list(location_list:list,db):
  206. resutl = []
  207. for location in location_list:
  208. location = convert_to_polygon(location)
  209. sql = text(f"""SELECT hospital_name as `name`,longitude,latitude,6 AS `dataType` FROM mid_hospital WHERE ST_Contains(ST_PolygonFromText( '{location}', 4326 ),ST_PointFromText(CONCAT('POINT(', latitude, ' ', longitude, ')'), 4326))""")
  210. resutl+=db.execute(sql).all()
  211. return resutl
  212. def get_emergency_shelter_list(location_list:list,db):
  213. resutl = []
  214. for location in location_list:
  215. location = convert_to_polygon(location)
  216. sql = text(f"""SELECT shelter_name as `name`,lng as longitude,lat as latitude,3 AS `dataType` FROM mid_emergency_shelter WHERE ST_Contains(ST_PolygonFromText( '{location}', 4326 ),ST_PointFromText(CONCAT('POINT(', lat, ' ', lng, ')'), 4326))""")
  217. resutl+=db.execute(sql).all()
  218. return resutl
  219. def get_waterlogged_roads_list(location_list:list,db):
  220. resutl = []
  221. for location in location_list:
  222. location = convert_to_polygon(location)
  223. sql = text(f"""SELECT flood_name as `name`,lng as longitude,lat as latitude,4 AS `dataType` FROM mid_waterlogged_roads WHERE ST_Contains(ST_PolygonFromText( '{location}', 4326 ),ST_PointFromText(CONCAT('POINT(', lat, ' ', lng, ')'), 4326))""")
  224. resutl+=db.execute(sql).all()
  225. return resutl
  226. def get_point_list(location_list:list,db):
  227. resutl = []
  228. for location in location_list:
  229. location = convert_to_polygon(location)
  230. sql = text(f"""SELECT `id` ,`name`, longitude, latitude, `dataType` FROM point_data WHERE ST_Contains(ST_PolygonFromText( '{location}', 4326 ),ST_PointFromText(CONCAT('POINT(', latitude, ' ', longitude, ')'), 4326))""")
  231. resutl+=db.execute(sql).all()
  232. return resutl