116 lines
5.4 KiB
Python
116 lines
5.4 KiB
Python
import influxdb_api
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import globals
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from datetime import datetime, timedelta, timezone
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import schedule
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import time
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import shutil
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from influxdb_client import InfluxDBClient, BucketsApi, WriteApi, OrganizationsApi, Point, QueryApi
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import simulation
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import influxdb_info
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# 2025/02/01
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def get_next_time() -> str:
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"""
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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'
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:return: 返回字符串格式的时间，表示下一个1分钟的时间点
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"""
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# 获取当前时间，并设定为北京时间
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now = datetime.now() # now 类型为 datetime，表示当前本地时间
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# 获取当前的分钟，并且将秒和微秒置为零
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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
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return current_time.strftime('%Y-%m-%dT%H:%M:%S+08:00')
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# 2025/02/06
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def store_realtime_SCADA_data_job() -> None:
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"""
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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
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:return: None
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"""
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# 获取当前时间并更新get_real_value_time，转换为字符串格式
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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'
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# 调用函数执行任务
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influxdb_api.store_realtime_SCADA_data_to_influxdb(get_real_value_time)
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print('{} -- Successfully store realtime SCADA data.'.format(
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datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
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# 2025/02/06
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def get_next_15minute_time() -> str:
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"""
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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'
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:return: 返回字符串格式的时间，表示下一个15分钟执行时间点
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"""
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now = datetime.now()
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# 向上舍入到下一个15分钟
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next_15minute = (now.minute // 15 + 1) * 15 - 15
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if next_15minute == 60:
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next_15minute = 0
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now = now + timedelta(hours=1)
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next_time = now.replace(minute=next_15minute, second=0, microsecond=0)
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return next_time.strftime('%Y-%m-%dT%H:%M:%S+08:00')
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# 2025/02/07
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def run_simulation_job() -> None:
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"""
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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。
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:return: None
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"""
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# 获取当前时间，并检查是否是整点15分钟
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current_time = datetime.now()
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if current_time.minute % 15 == 0:
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print(f"{current_time.strftime('%Y-%m-%d %H:%M:%S')} -- Start simulation task.")
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# 计算前，获取scada_info中的信息，按照设定的方法修改pg数据库
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simulation.query_corresponding_element_id_and_query_id("bb")
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simulation.query_corresponding_pattern_id_and_query_id('bb')
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region_result = simulation.query_non_realtime_region('bb')
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globals.source_outflow_region_id = simulation.get_source_outflow_region_id('bb', region_result)
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globals.realtime_region_pipe_flow_and_demand_id = simulation.query_realtime_region_pipe_flow_and_demand_id('bb', region_result)
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globals.pipe_flow_region_patterns = simulation.query_pipe_flow_region_patterns('bb')
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globals.non_realtime_region_patterns = simulation.query_non_realtime_region_patterns('bb', region_result)
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globals.source_outflow_region_patterns, realtime_region_pipe_flow_and_demand_patterns = simulation.get_realtime_region_patterns('bb',
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globals.source_outflow_region_id,
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globals.realtime_region_pipe_flow_and_demand_id)
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modify_pattern_start_time: str = get_next_15minute_time() # 获取下一个15分钟时间点
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# print(modify_pattern_start_time)
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simulation.run_simulation(name='bb', simulation_type="realtime",
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modify_pattern_start_time=modify_pattern_start_time)
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print('{} -- Successfully run simulation and store realtime simulation result.'.format(
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datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
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else:
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print(f"{current_time.strftime('%Y-%m-%d %H:%M:%S')} -- Skipping the simulation task.")
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# 2025/02/06
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def realtime_task() -> None:
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"""
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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。
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该任务会一直运行，定期调用store_realtime_SCADA_data_job获取SCADA数据。
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:return:
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"""
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# 等待到整分对齐
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now = datetime.now()
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wait_seconds = 60 - now.second
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time.sleep(wait_seconds)
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# 使用 .at(":00") 指定在每分钟的第0秒执行
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schedule.every(1).minute.at(":00").do(store_realtime_SCADA_data_job)
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# 每15分钟执行一次run_simulation_job
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schedule.every(1).minute.at(":00").do(run_simulation_job)
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# 持续执行任务，检查是否有待执行的任务
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while True:
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schedule.run_pending() # 执行所有待处理的定时任务
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time.sleep(1) # 暂停1秒，避免过于频繁的任务检查
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if __name__ == "__main__":
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url = influxdb_info.url
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token = influxdb_info.token
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org_name = influxdb_info.org
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client = InfluxDBClient(url=url, token=token)
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# 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
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influxdb_api.query_pg_scada_info_realtime('bb')
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# 自动执行
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realtime_task()
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