Add more code from WMH

This commit is contained in:
DingZQ
2025-02-08 20:11:27 +08:00
parent f6f37d012b
commit e360540989
6 changed files with 382 additions and 61 deletions

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auto_realtime.py Normal file
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import influxdb_api
import globals
from datetime import datetime, timedelta, timezone
import schedule
import time
from influxdb_client import InfluxDBClient, BucketsApi, WriteApi, OrganizationsApi, Point, QueryApi
import simulation
# 2025/02/01
def get_next_time() -> str:
"""
获取下一个1分钟时间点返回格式为字符串'YYYY-MM-DDTHH:MM:00+08:00'
:return: 返回字符串格式的时间表示下一个1分钟的时间点
"""
# 获取当前时间,并设定为北京时间
now = datetime.now() # now 类型为 datetime表示当前本地时间
# 获取当前的分钟,并且将秒和微秒置为零
current_time = now.replace(second=0, microsecond=0) # current_time 类型为 datetime时间的秒和微秒部分被清除
return current_time.strftime('%Y-%m-%dT%H:%M:%S+08:00')
# 2025/02/06
def store_realtime_SCADA_data_job() -> None:
"""
定义的任务1每分钟执行1次每次执行时更新get_real_value_time并调用store_realtime_SCADA_data_to_influxdb函数
:return: None
"""
# 获取当前时间并更新get_real_value_time转换为字符串格式
get_real_value_time: str = get_next_time() # get_real_value_time 类型为 str格式为'2025-02-01T18:45:00+08:00'
# 调用函数执行任务
influxdb_api.store_realtime_SCADA_data_to_influxdb(get_real_value_time)
print('{} -- Successfully store realtime SCADA data.'.format(
datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
# 2025/02/06
def get_next_15minute_time() -> str:
"""
获取下一个15分钟的时间点返回格式为字符串'YYYY-MM-DDTHH:MM:00+08:00'
:return: 返回字符串格式的时间表示下一个15分钟执行时间点
"""
now = datetime.now()
# 向上舍入到下一个15分钟
next_15minute = (now.minute // 15 + 1) * 15 - 15
if next_15minute == 60:
next_15minute = 0
now = now + timedelta(hours=1)
next_time = now.replace(minute=next_15minute, second=0, microsecond=0)
return next_time.strftime('%Y-%m-%dT%H:%M:%S+08:00')
# 2025/02/07
def run_simulation_job() -> None:
"""
定义的任务3每15分钟执行一次在store_realtime_SCADA_data_to_influxdb之后执行run_simulation。
:return: None
"""
# 获取当前时间并检查是否是整点15分钟
current_time = datetime.now()
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',
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)
print('{} -- Successfully run simulation and store realtime simulation result.'.format(
datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
else:
print(f"{current_time.strftime('%Y-%m-%d %H:%M:%S')} -- Skipping the simulation task.")
# 2025/02/06
def realtime_task() -> None:
"""
定时执行任务1和使用schedule库每1分钟执行一次store_realtime_SCADA_data_job函数。
该任务会一直运行定期调用store_realtime_SCADA_data_job获取SCADA数据。
:return:
"""
# 等待到整分对齐
now = datetime.now()
wait_seconds = 60 - now.second
time.sleep(wait_seconds)
# 使用 .at(":00") 指定在每分钟的第0秒执行
schedule.every(1).minute.at(":00").do(store_realtime_SCADA_data_job)
# 每15分钟执行一次run_simulation_job
schedule.every(1).minute.at(":00").do(run_simulation_job)
# 持续执行任务,检查是否有待执行的任务
while True:
schedule.run_pending() # 执行所有待处理的定时任务
time.sleep(1) # 暂停1秒避免过于频繁的任务检查
if __name__ == "__main__":
url = "http://localhost:8086" # 替换为你的InfluxDB实例地址
token = "Z4UZj9HuLwLlwoApywvT2nGVP3bwLy18y-sJQ7enzZlJd8YMzMWbBA6F-q4gBiZ-7-IqdxR5aR9LvicKiSNmnA==" # 替换为你的InfluxDB Token
org_name = "beibei" # 替换为你的Organization名称
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')
# 自动执行
realtime_task()