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- import psycopg2
- import PGDB_Read
- import requests
- import json
- from writeDataToRealtimeData import upsert_realtime_data
- def calc_water_quality():
- nm_values = (
- 'lfgawqyq_plc3_Tag17',
- 'lfgawqyq_plc3_Tag22',
- 'lfgawqyq_plc3_Tag20',
- 'lfgawqyq_plc10_Tag45',
- 'lfgawqyq_plc10_Tag43',
- 'lfgawqyq_plc10_Tag52',
- 'lfgawqyq_plc10_Tag50',
- 'lfgawqyq_plc10_Tag51',
- 'lfgawqyq_plc10_Tag53',
- )
- # 连接到数据库
- with open('db_params.json', 'r') as f:
- db_params = json.load(f)
- db_params = db_params['test']
- # 建立连接
- conn = psycopg2.connect(**db_params)
- cursor = conn.cursor()
- json_data = PGDB_Read.query_database(cursor, nm_values)
- print(json_data)
- # 目标 URL
- url = 'http://localhost:8087/water_quality'
- # 发送 POST 请求
- response = requests.post(url, data=json_data, headers={'Content-Type': 'application/json'})
- # 打印响应内容
- result = response.json()
- write_data_list = [
- 'lfgawqyq_plc11_Tag23',
- 'lfgawqyq_plc11_Tag124',
- 'lfgawqyq_plc11_Tag25',
- 'lfgawqyq_plc11_Tag122',
- 'lfgawqyq_xn_cscod',
- 'lfgawqyq_xn_cstn',
- ]
- # 使用字典推导式将标签和值对应起来
- data_mapping = {tag: value for tag, value in zip(write_data_list, result)}
- # 打印结果字典
- upsert_realtime_data(cursor, data_mapping)
- # 关闭游标和连接
- cursor.close()
- conn.close()
- return json.dumps(data_mapping)
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