Change bb to project_info.name

all_auto_task.py

@@ -3,6 +3,7 @@ import auto_store_non_realtime_SCADA_data
 import asyncio
 import influxdb_api
 import influxdb_info
+import project_info
 
 # 为了让多个任务并发运行，我们可以用 asyncio.to_thread 分别启动它们
 async def main():
@@ -16,8 +17,8 @@ if __name__ == "__main__":
     token = influxdb_info.token
     org_name = influxdb_info.org
 
-    influxdb_api.query_pg_scada_info_realtime('bb')
-    influxdb_api.query_pg_scada_info_non_realtime('bb')
+    influxdb_api.query_pg_scada_info_realtime(project_info.name)    
+    influxdb_api.query_pg_scada_info_non_realtime(project_info.name)
 
     # 用 asyncio 并发启动两个任务
     asyncio.run(main())

auto_realtime.py

@@ -10,6 +10,7 @@ import shutil
 from influxdb_client import InfluxDBClient, BucketsApi, WriteApi, OrganizationsApi, Point, QueryApi
 import simulation
 import influxdb_info
+import project_info
 
 def setup_logger():
     # 创建日志目录
@@ -103,19 +104,19 @@ def run_simulation_job() -> None:
     if current_time.minute % 15 == 0:
         print(f"{current_time.strftime('%Y-%m-%d %H:%M:%S')} -- Start simulation task.")
         # 计算前，获取scada_info中的信息，按照设定的方法修改pg数据库
-        simulation.query_corresponding_element_id_and_query_id("bb")
-        simulation.query_corresponding_pattern_id_and_query_id('bb')
-        region_result = simulation.query_non_realtime_region('bb')
-        globals.source_outflow_region_id = simulation.get_source_outflow_region_id('bb', region_result)
-        globals.realtime_region_pipe_flow_and_demand_id = simulation.query_realtime_region_pipe_flow_and_demand_id('bb', region_result)
-        globals.pipe_flow_region_patterns = simulation.query_pipe_flow_region_patterns('bb')
-        globals.non_realtime_region_patterns = simulation.query_non_realtime_region_patterns('bb', region_result)
-        globals.source_outflow_region_patterns, realtime_region_pipe_flow_and_demand_patterns = simulation.get_realtime_region_patterns('bb',
+        simulation.query_corresponding_element_id_and_query_id(project_info.name)
+        simulation.query_corresponding_pattern_id_and_query_id(project_info.name)
+        region_result = simulation.query_non_realtime_region(project_info.name)
+        globals.source_outflow_region_id = simulation.get_source_outflow_region_id(project_info.name, region_result)
+        globals.realtime_region_pipe_flow_and_demand_id = simulation.query_realtime_region_pipe_flow_and_demand_id(project_info.name, region_result)
+        globals.pipe_flow_region_patterns = simulation.query_pipe_flow_region_patterns(project_info.name)
+        globals.non_realtime_region_patterns = simulation.query_non_realtime_region_patterns(project_info.name, region_result)
+        globals.source_outflow_region_patterns, realtime_region_pipe_flow_and_demand_patterns = simulation.get_realtime_region_patterns(project_info.name,
                                                                                                 globals.source_outflow_region_id,
                                                                                                 globals.realtime_region_pipe_flow_and_demand_id)
         modify_pattern_start_time: str = get_next_15minute_time()  # 获取下一个15分钟时间点
         # print(modify_pattern_start_time)
-        simulation.run_simulation(name='bb', simulation_type="realtime", modify_pattern_start_time=modify_pattern_start_time)
+        simulation.run_simulation(name=project_info.name, simulation_type="realtime", modify_pattern_start_time=modify_pattern_start_time)
 
         logger.info('{} -- Successfully run simulation and store realtime simulation result.'.format(datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
     else:
@@ -150,6 +151,6 @@ if __name__ == "__main__":
 
     client = InfluxDBClient(url=url, token=token)
     # step2: 先查询pg数据库中scada_info的信息，然后存储SCADA数据到SCADA_data这个bucket里
-    influxdb_api.query_pg_scada_info_realtime('bb')
+    influxdb_api.query_pg_scada_info_realtime(project_info.name)
     # 自动执行
     realtime_task()

auto_store_non_realtime_SCADA_data.py

@@ -8,6 +8,7 @@ from logging.handlers import TimedRotatingFileHandler
 import time
 from influxdb_client import InfluxDBClient, BucketsApi, WriteApi, OrganizationsApi, Point, QueryApi
 import influxdb_info
+import project_info
 
 def setup_logger():
     # 创建日志目录
@@ -133,6 +134,6 @@ if __name__ == "__main__":
 
     client = InfluxDBClient(url=url, token=token)
     # step2: 先查询pg数据库中scada_info的信息，然后存储SCADA数据到SCADA_data这个bucket里
-    influxdb_api.query_pg_scada_info_non_realtime('bb')
+    influxdb_api.query_pg_scada_info_non_realtime(project_info.name)
     # 自动执行
     store_non_realtime_SCADA_data_task()

influxdb_api.py

@@ -17,6 +17,7 @@ import pandas as pd
 import openpyxl
 import pytz
 import influxdb_info
+import project_info
 import time_api
 
 # influxdb数据库连接信息

online_Analysis.py

@@ -705,7 +705,7 @@ def network_update(file_path: str) -> None:
         print(f"history_patterns_flows文件存在，开始处理...")
 
         # 连接到 PostgreSQL 数据库（这里是数据库 "bb"）
-        with psycopg.connect("dbname=bb host=127.0.0.1") as conn:
+        with psycopg.connect(f"dbname={project_info.name} host=127.0.0.1") as conn:
             with conn.cursor() as cur:
                 with open(csv_path, newline='', encoding='utf-8-sig') as csvfile:
                     reader = csv.DictReader(csvfile)

run_simulation.py

@@ -8,7 +8,7 @@ import json
 import pytz
 import requests
 import time
-
+import project_info
 
 url_path = 'http://10.101.15.16:9000/loong'  # 内网
 # url_path = 'http://183.64.62.100:9057/loong'  # 外网
@@ -848,7 +848,7 @@ if __name__ == '__main__':
     # run_simulation("beibeizone","2024-04-01T08:00:00Z")
     # read_inp('bb_server', 'model20_en.inp')
     run_simulation_ex(
-        name='bb', simulation_type='extended', start_datetime='2024-11-09T02:30:00Z',
+        name=project_info.name, simulation_type='extended', start_datetime='2024-11-09T02:30:00Z',
         # end_datetime='2024-05-30T16:00:00Z',
         # duration=0,
         # pump_control={'PU00006': [45, 40]}

sensitivity.py

@@ -19,7 +19,7 @@ from spopt.region import Skater
 from shapely.geometry import Point
 import geopandas as gpd
 from sklearn.metrics import pairwise_distances
-
+import project_info
 
 # 2025/03/12
 # Step1: 获取节点坐标
@@ -639,7 +639,7 @@ def get_ID(name: str, sensor_num: int, min_diameter: int) -> list[str]:
 
 
 if __name__ == '__main__':
-    sensorindex = get_ID(name='bb', sensor_num=20, min_diameter=300)
+    sensorindex = get_ID(name=project_info.name, sensor_num=20, min_diameter=300)
 
     print(sensorindex)
     # 将 sensor_coord 字典转换为 DataFrame，

sensor_placement.py

@@ -12,7 +12,7 @@ from wntr.epanet.toolkit import EpanetException
 from numpy.linalg import slogdet
 import random
 from tjnetwork import *
-
+import project_info
 
 # 2025/03/12
 # Step1: 获取节点坐标
@@ -472,7 +472,7 @@ def get_sensor_coord(name: str, sensor_num: int) -> dict[str, float]:
 
 
 if __name__ == '__main__':
-    sensor_coord = get_sensor_coord(name='bb', sensor_num=20)
+    sensor_coord = get_sensor_coord(name=project_info.name, sensor_num=20)
     print(sensor_coord)
     # '''
     # 初始测压点布置根据灵敏度来布置，计算初始情况下的校准过程的error

simulation.py

@@ -18,6 +18,7 @@ import psycopg
 import logging
 import globals
 import uuid
+import project_info
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
@@ -929,16 +930,16 @@ def run_simulation(name: str, simulation_type: str, modify_pattern_start_time: s
 
 if __name__ == "__main__":
     # 计算前，获取scada_info中的信息，按照设定的方法修改pg数据库
-    query_corresponding_element_id_and_query_id("bb")
-    query_corresponding_pattern_id_and_query_id('bb')
-    region_result = query_non_realtime_region('bb')
+    query_corresponding_element_id_and_query_id(project_info.name)
+    query_corresponding_pattern_id_and_query_id(project_info.name)
+    region_result = query_non_realtime_region(project_info.name)
 
-    globals.source_outflow_region_id = get_source_outflow_region_id('bb', region_result)
-    globals.realtime_region_pipe_flow_and_demand_id = query_realtime_region_pipe_flow_and_demand_id('bb', region_result)
-    globals.pipe_flow_region_patterns = query_pipe_flow_region_patterns('bb')
+    globals.source_outflow_region_id = get_source_outflow_region_id(project_info.name, region_result)
+    globals.realtime_region_pipe_flow_and_demand_id = query_realtime_region_pipe_flow_and_demand_id(project_info.name, region_result)
+    globals.pipe_flow_region_patterns = query_pipe_flow_region_patterns(project_info.name)
 
-    globals.non_realtime_region_patterns = query_non_realtime_region_patterns('bb', region_result)
-    globals.source_outflow_region_patterns, globals.realtime_region_pipe_flow_and_demand_patterns = get_realtime_region_patterns('bb', globals.source_outflow_region_id, globals.realtime_region_pipe_flow_and_demand_id)
+    globals.non_realtime_region_patterns = query_non_realtime_region_patterns(project_info.name, region_result)
+    globals.source_outflow_region_patterns, globals.realtime_region_pipe_flow_and_demand_patterns = get_realtime_region_patterns(project_info.name, globals.source_outflow_region_id, globals.realtime_region_pipe_flow_and_demand_id)
 
     # 基础日期和时间（日期部分保持不变）
     base_date = datetime(2025, 5, 4)
@@ -956,7 +957,7 @@ if __name__ == "__main__":
 
         # 执行函数调用
         run_simulation(
-            name='bb',
+            name=project_info.name,
             simulation_type="realtime",
             modify_pattern_start_time=iso_time
         )