diff --git a/influxdb_api(2).py b/influxdb_api(2).py deleted file mode 100644 index e30c710..0000000 --- a/influxdb_api(2).py +++ /dev/null @@ -1,2865 +0,0 @@ -from influxdb_client import InfluxDBClient, BucketsApi, WriteApi, OrganizationsApi, Point, QueryApi, WriteOptions -from typing import List, Dict -from datetime import datetime, timedelta, timezone -from influxdb_client.client.write_api import SYNCHRONOUS, ASYNCHRONOUS -from dateutil import parser -import get_realValue -import get_data -import psycopg -import time -import simulation -from tjnetwork import * -import schedule -import threading -import globals -import csv -import pandas as pd -import openpyxl -import pytz - - -# influxdb数据库连接信息 -url = "http://localhost:8086" # 替换为你的InfluxDB实例地址 -token = "MhJDl7odKW-y6wNXXUhUMRJ9oPzOvEe52E4NYD5GXtAAMV7BoHMFdet6HqUOt4DjZ-syKjwGao_k0ZIcgrGAPA==" # 替换为你的InfluxDB Token -org_name = "beibei" # 替换为你的Organization名称 - - -def query_pg_scada_info_realtime(name: str) -> None: - """ - 查询pg数据库中，scada_info中，属于realtime的数据 - :param name: 数据库名称 - :return: - """ - # 连接数据库 - conn_string = f"dbname={name} host=127.0.0.1" - try: - with psycopg.connect(conn_string) as conn: - with conn.cursor() as cur: - # 查询 transmission_mode 为 'realtime' 的记录 - cur.execute(""" - SELECT type, api_query_id - FROM scada_info - WHERE transmission_mode = 'realtime'; - """) - records = cur.fetchall() - # 清空全局列表 - globals.reservoir_liquid_level_realtime_ids.clear() - globals.tank_liquid_level_realtime_ids.clear() - globals.fixed_pump_realtime_ids.clear() - globals.variable_pump_realtime_ids.clear() - globals.source_outflow_realtime_ids.clear() - globals.pipe_flow_realtime_ids.clear() - globals.pressure_realtime_ids.clear() - globals.demand_realtime_ids.clear() - globals.quality_realtime_ids.clear() - # 根据 type 分类存储 api_query_id - for record in records: - record_type, api_query_id = record - if api_query_id is not None: # 确保 api_query_id 不为空 - if record_type == "reservoir_liquid_level": - globals.reservoir_liquid_level_realtime_ids.append(api_query_id) - elif record_type == "tank_liquid_level": - globals.tank_liquid_level_realtime_ids.append(api_query_id) - elif record_type == "fixed_pump": - globals.fixed_pump_realtime_ids.append(api_query_id) - elif record_type == "variable_pump": - globals.variable_pump_realtime_ids.append(api_query_id) - elif record_type == "source_outflow": - globals.source_outflow_realtime_ids.append(api_query_id) - elif record_type == "pipe_flow": - globals.pipe_flow_realtime_ids.append(api_query_id) - elif record_type == "pressure": - globals.pressure_realtime_ids.append(api_query_id) - elif record_type == "demand": - globals.demand_realtime_ids.append(api_query_id) - elif record_type == "quality": - globals.quality_realtime_ids.append(api_query_id) - # 打印结果，方便调试 - # print("Query completed. Results:") - # print("Reservoir Liquid Level IDs:", globals.reservoir_liquid_level_realtime_ids) - # print("Tank Liquid Level IDs:", globals.tank_liquid_level_realtime_ids) - # print("Fixed Pump IDs:", globals.fixed_pump_realtime_ids) - # print("Variable Pump IDs:", globals.variable_pump_realtime_ids) - # print("Source Outflow IDs:", globals.source_outflow_realtime_ids) - # print("Pipe Flow IDs:", globals.pipe_flow_realtime_ids) - # print("Pressure IDs:", globals.pressure_realtime_ids) - # print("Demand IDs:", globals.demand_realtime_ids) - # print("Quality IDs:", globals.quality_realtime_ids) - except Exception as e: - print(f"查询时发生错误：{e}") - - -def query_pg_scada_info_non_realtime(name: str) -> None: - """ - 查询pg数据库中，scada_info中，属于non_realtime的数据，以及这些数据transmission_frequency的最大值 - :param name: 数据库名称 - :return: - """ - # 重新打开数据库 - if is_project_open(name): - close_project(name) - open_project(name) - dic_time = get_time(name) - globals.hydraulic_timestep = dic_time['HYDRAULIC TIMESTEP'] - close_project(name) - # 连接数据库 - conn_string = f"dbname={name} host=127.0.0.1" - try: - with psycopg.connect(conn_string) as conn: - with conn.cursor() as cur: - # 查询 transmission_mode 为 'non_realtime' 的记录 - cur.execute(""" - SELECT type, api_query_id, transmission_frequency - FROM scada_info - WHERE transmission_mode = 'non_realtime'; - """) - records = cur.fetchall() - # 清空全局列表 - globals.reservoir_liquid_level_non_realtime_ids.clear() - globals.fixed_pump_non_realtime_ids.clear() - globals.variable_pump_non_realtime_ids.clear() - globals.source_outflow_non_realtime_ids.clear() - globals.pipe_flow_non_realtime_ids.clear() - globals.pressure_non_realtime_ids.clear() - globals.demand_non_realtime_ids.clear() - globals.quality_non_realtime_ids.clear() - # 用于计算 transmission_frequency 最大值 - transmission_frequencies = [] - # 根据 type 分类存储 api_query_id - for record in records: - record_type, api_query_id, freq = record - if api_query_id is not None: # 确保 api_query_id 不为空 - if record_type == "reservoir_liquid_level": - globals.reservoir_liquid_level_non_realtime_ids.append(api_query_id) - elif record_type == "fixed_pump": - globals.fixed_pump_non_realtime_ids.append(api_query_id) - elif record_type == "variable_pump": - globals.variable_pump_non_realtime_ids.append(api_query_id) - elif record_type == "source_outflow": - globals.source_outflow_non_realtime_ids.append(api_query_id) - elif record_type == "pipe_flow": - globals.pipe_flow_non_realtime_ids.append(api_query_id) - elif record_type == "pressure": - globals.pressure_non_realtime_ids.append(api_query_id) - elif record_type == "demand": - globals.demand_non_realtime_ids.append(api_query_id) - elif record_type == "quality": - globals.quality_non_realtime_ids.append(api_query_id) - # 收集 transmission_frequency，用于计算最大值 - if freq is not None: - transmission_frequencies.append(freq) - # 计算 transmission_frequency 最大值 - globals.transmission_frequency = max(transmission_frequencies) if transmission_frequencies else None - # 打印结果，方便调试 - # print("Query completed. Results:") - # print("Reservoir Liquid Level Non-Realtime IDs:", globals.reservoir_liquid_level_non_realtime_ids) - # print("Fixed Pump Non-Realtime IDs:", globals.fixed_pump_non_realtime_ids) - # print("Variable Pump Non-Realtime IDs:", globals.variable_pump_non_realtime_ids) - # print("Source Outflow Non-Realtime IDs:", globals.source_outflow_non_realtime_ids) - # print("Pipe Flow Non-Realtime IDs:", globals.pipe_flow_non_realtime_ids) - # print("Pressure Non-Realtime IDs:", globals.pressure_non_realtime_ids) - # print("Demand Non-Realtime IDs:", globals.demand_non_realtime_ids) - # print("Quality Non-Realtime IDs:", globals.quality_non_realtime_ids) - # print("Maximum Transmission Frequency:", globals.transmission_frequency) - # print("Hydraulic Timestep:", globals.hydraulic_timestep) - except Exception as e: - print(f"查询时发生错误：{e}") - - -# 2025/03/23 -def get_new_client() -> InfluxDBClient: - """每次调用返回一个新的 InfluxDBClient 实例。""" - return InfluxDBClient(url=url, token=token, org=org_name) - - -# 2025/02/01 -def delete_buckets(org_name: str) -> None: - """ - 删除InfluxDB中指定organization下的所有buckets。 - :param org_name: InfluxDB中organization的名称。 - :return: None - """ - client = get_new_client() - # 定义需要删除的 bucket 名称列表 - buckets_to_delete = ['SCADA_data', 'realtime_simulation_result', 'scheme_simulation_result'] - buckets_api = client.buckets_api() - buckets_obj = buckets_api.find_buckets(org=org_name) - # 确保 buckets_obj 拥有 buckets 属性 - if hasattr(buckets_obj, 'buckets'): - for bucket in buckets_obj.buckets: - if bucket.name in buckets_to_delete: # 只删除特定名称的 bucket - try: - buckets_api.delete_bucket(bucket) - print(f"Bucket {bucket.name} has been deleted successfully.") - except Exception as e: - print(f"Failed to delete bucket {bucket.name}: {e}") - else: - print(f"Skipping bucket {bucket.name}. Not in the deletion list.") - else: - print("未找到 buckets 属性，无法迭代 buckets。") - client.close() - - -# 2025/02/01 -def create_and_initialize_buckets(org_name: str) -> None: - """ - 初始化influxdb的三个数据存储库，分别为SCADA_data、realtime_simulation_result、scheme_simulation_result - :param org_name: InfluxDB中organization的名称 - :return: - """ - client = get_new_client() - # 先删除原有的，然后再进行初始化 - delete_buckets(org_name) - bucket_api = BucketsApi(client) - write_api = client.write_api() - org_api = OrganizationsApi(client) - # 获取 org_id - org = next((o for o in org_api.find_organizations() if o.name == org_name), None) - if not org: - raise ValueError(f"Organization '{org_name}' not found.") - org_id = org.id - print(f"Using Organization ID: {org_id}") - # 定义 Buckets 信息 - buckets = [ - {"name": "SCADA_data", "retention_rules": []}, - {"name": "realtime_simulation_result", "retention_rules": []}, - {"name": "scheme_simulation_result", "retention_rules": []} - ] - # 创建一个临时存储点数据的列表 - points_to_write = [] - # 创建 Buckets 并初始化数据 - for bucket in buckets: - # 创建 Bucket - created_bucket = bucket_api.create_bucket( - bucket_name=bucket["name"], - retention_rules=bucket["retention_rules"], - org_id=org_id - ) - print(f"Bucket '{bucket['name']}' created with ID: {created_bucket.id}") - # 根据 Bucket 初始化数据 - if bucket["name"] == "SCADA_data": - point = Point("SCADA") \ - .tag("date", None) \ - .tag("description", None) \ - .tag("device_ID", None) \ - .field("monitored_value", 0.0) \ - .field("datacleaning_value", 0.0) \ - .field("simulation_value", None) \ - .time("2024-11-21T00:00:00Z", write_precision='s') - points_to_write.append(point) - # write_api.write(bucket="SCADA_data", org=org_name, record=point) - # print("Initialized SCADA_data with default structure.") - elif bucket["name"] == "realtime_simulation_result": # realtime_simulation_result - link_point = Point("link") \ - .tag("date", None) \ - .tag("ID", None) \ - .field("flow", 0.0) \ - .field("leakage", 0.0) \ - .field("velocity", 0.0) \ - .field("headloss", 0.0) \ - .field("status", None) \ - .field("setting", 0.0) \ - .field("quality", 0.0) \ - .field("reaction", 0.0) \ - .field("friction", 0.0) \ - .time("2024-11-21T00:00:00Z", write_precision='s') - points_to_write.append(link_point) - node_point = Point("node") \ - .tag("date", None) \ - .tag("ID", None) \ - .field("head", 0.0) \ - .field("pressure", 0.0) \ - .field("actualdemand", 0.0) \ - .field("demanddeficit", 0.0) \ - .field("totalExternalOutflow", 0.0) \ - .field("quality", 0.0) \ - .time("2024-11-21T00:00:00Z", write_precision='s') - points_to_write.append(node_point) - # write_api.write(bucket="realtime_simulation_result", org=org_name, record=link_point) - # write_api.write(bucket="realtime_simulation_result", org=org_name, record=node_point) - # print("Initialized realtime_simulation_result with default structure.") - elif bucket["name"] == "scheme_simulation_result": - link_point = Point("link") \ - .tag("date", None) \ - .tag("ID", None) \ - .tag("scheme_Type", None) \ - .tag("scheme_Name", None) \ - .field("flow", 0.0) \ - .field("leakage", 0.0) \ - .field("velocity", 0.0) \ - .field("headloss", 0.0) \ - .field("status", None) \ - .field("setting", 0.0) \ - .field("quality", 0.0) \ - .time("2024-11-21T00:00:00Z", write_precision='s') - points_to_write.append(link_point) - node_point = Point("node") \ - .tag("date", None) \ - .tag("ID", None) \ - .tag("scheme_Type", None) \ - .tag("scheme_Name", None) \ - .field("head", 0.0) \ - .field("pressure", 0.0) \ - .field("actualdemand", 0.0) \ - .field("demanddeficit", 0.0) \ - .field("totalExternalOutflow", 0.0) \ - .field("quality", 0.0) \ - .time("2024-11-21T00:00:00Z", write_precision='s') - points_to_write.append(node_point) - SCADA_point = Point("SCADA") \ - .tag("date", None) \ - .tag("description", None) \ - .tag("device_ID", None) \ - .tag("scheme_Type", None) \ - .tag("scheme_Name", None) \ - .field("monitored_value", 0.0) \ - .field("datacleaning_value", 0.0) \ - .field("scheme_simulation_value", None) \ - .time("2024-11-21T00:00:00Z", write_precision='s') - points_to_write.append(SCADA_point) - # write_api.write(bucket="scheme_simulation_result", org=org_name, record=link_point) - # write_api.write(bucket="scheme_simulation_result", org=org_name, record=node_point) - # write_api.write(bucket="scheme_simulation_result", org=org_name, record=SCADA_point) - # print("Initialized scheme_simulation_result with default structure.") - # 批量写入数据 - if points_to_write: - write_api.write(bucket=bucket, org=org_name, record=points_to_write) - write_api.flush() # 刷新缓存一次 - print("All buckets created and initialized successfully.") - client.close() - - -def store_realtime_SCADA_data_to_influxdb(get_real_value_time: str, bucket: str = "SCADA_data") -> None: - """ - 将SCADA数据通过数据接口导入数据库 - :param get_real_value_time: 获取数据的时间，格式如'2024-11-25T09:00:00+08:00' - :param bucket: (str): InfluxDB 的 bucket 名称，默认值为 "SCADA_data"。 - :return: - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - write_options = WriteOptions( - jitter_interval=200, # 添加抖动以避免同时写入 - max_retry_delay=30000 # 最大重试延迟（毫秒） - ) - write_api = client.write_api(write_options=write_options) - # 创建一个临时存储点数据的列表 - points_to_write = [] - - try_count = 0 - reservoir_liquid_level_realtime_data_list = [] - tank_liquid_level_realtime_data_list = [] - fixed_pump_realtime_data_list =[] - variable_pump_realtime_data_list =[] - source_outflow_realtime_data_list = [] - pipe_flow_realtime_data_list = [] - pressure_realtime_data_list =[] - demand_realtime_data_list = [] - quality_realtime_data_list = [] - while try_count <= 5: # 尝试6次 ******* - try: - try_count += 1 - if globals.reservoir_liquid_level_realtime_ids: - # print(globals.reservoir_liquid_level_realtime_ids) - reservoir_liquid_level_realtime_data_list = get_realValue.get_realValue( - ids=','.join(globals.reservoir_liquid_level_realtime_ids)) - # print(reservoir_liquid_level_realtime_data_list) - if globals.tank_liquid_level_realtime_ids: - tank_liquid_level_realtime_data_list = get_realValue.get_realValue(ids=','.join(globals.tank_liquid_level_realtime_ids)) - if globals.fixed_pump_realtime_ids: - fixed_pump_realtime_data_list = get_realValue.get_realValue(ids=','.join(globals.fixed_pump_realtime_ids)) - if globals.variable_pump_realtime_ids: - variable_pump_realtime_data_list = get_realValue.get_realValue(ids=','.join(globals.variable_pump_realtime_ids)) - if globals.source_outflow_realtime_ids: - source_outflow_realtime_data_list = get_realValue.get_realValue(ids=','.join(globals.source_outflow_realtime_ids)) - if globals.pipe_flow_realtime_ids: - pipe_flow_realtime_data_list = get_realValue.get_realValue(ids=','.join(globals.pipe_flow_realtime_ids)) - if globals.pressure_realtime_ids: - pressure_realtime_data_list = get_realValue.get_realValue(ids=','.join(globals.pressure_realtime_ids)) - if globals.demand_realtime_ids: - demand_realtime_data_list = get_realValue.get_realValue(ids=','.join(globals.demand_realtime_ids)) - if globals.quality_realtime_ids: - quality_realtime_data_list = get_realValue.get_realValue(ids=','.join(globals.quality_realtime_ids)) - except Exception as e: - print(e) - time.sleep(10) - else: - try_count = 100 - # 写入数据 - if reservoir_liquid_level_realtime_data_list: - for data in reservoir_liquid_level_realtime_data_list: - # 将 data['time'] 和 get_realValue_time 转换为 datetime 对象 - data_time = datetime.fromisoformat(data['time']) - get_real_value_time_dt = datetime.fromisoformat(get_real_value_time).replace(tzinfo=None) - # 将获取的时间转换为 UTC 时间 - get_real_value_time_utc = get_real_value_time_dt.astimezone(timezone.utc) - # 计算时间差（绝对值） - time_difference = abs((data_time - get_real_value_time_dt).total_seconds()) - # 判断时间差是否超过3分钟 - if time_difference > 60: # 超过1分钟 - monitored_value = None - else: # 小于等于3分钟 - monitored_value = data['monitored_value'] - # 创建Point对象 - point = ( - Point('reservoir_liquid_level_realtime') - .tag("date", datetime.fromisoformat(get_real_value_time).strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", monitored_value) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(get_real_value_time_utc, write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - # write_api.flush() - if tank_liquid_level_realtime_data_list: - for data in tank_liquid_level_realtime_data_list: - # 将 data['time'] 和 get_realValue_time 转换为 datetime 对象 - data_time = datetime.fromisoformat(data['time']) - get_real_value_time_dt = datetime.fromisoformat(get_real_value_time).replace(tzinfo=None) - # 将获取的时间转换为 UTC 时间 - get_real_value_time_utc = get_real_value_time_dt.astimezone(timezone.utc) - # 计算时间差（绝对值） - time_difference = abs((data_time - get_real_value_time_dt).total_seconds()) - # 判断时间差是否超过1分钟 - if time_difference > 60: # 超过1分钟 - monitored_value = None - else: # 小于等于3分钟 - monitored_value = data['monitored_value'] - # 创建Point对象 - point = ( - Point('tank_liquid_level_realtime') - .tag("date", datetime.fromisoformat(get_real_value_time).strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", (monitored_value)) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(get_real_value_time_utc, write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - # write_api.flush() - if fixed_pump_realtime_data_list: - for data in fixed_pump_realtime_data_list: - # 将 data['time'] 和 get_realValue_time 转换为 datetime 对象 - data_time = datetime.fromisoformat(data['time']) - get_real_value_time_dt = datetime.fromisoformat(get_real_value_time).replace(tzinfo=None) - # 将获取的时间转换为 UTC 时间 - get_real_value_time_utc = get_real_value_time_dt.astimezone(timezone.utc) - # 计算时间差（绝对值） - time_difference = abs((data_time - get_real_value_time_dt).total_seconds()) - # 判断时间差是否超过1分钟 - if time_difference > 60: # 超过1分钟 - monitored_value = None - else: # 小于等于3分钟 - monitored_value = data['monitored_value'] - # 创建Point对象 - point = ( - Point('fixed_pump_realtime') - .tag("date", datetime.fromisoformat(get_real_value_time).strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", monitored_value) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(get_real_value_time_utc, write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - # write_api.flush() - if variable_pump_realtime_data_list: - for data in variable_pump_realtime_data_list: - # 将 data['time'] 和 get_realValue_time 转换为 datetime 对象 - data_time = datetime.fromisoformat(data['time']) - get_real_value_time_dt = datetime.fromisoformat(get_real_value_time).replace(tzinfo=None) - # 将获取的时间转换为 UTC 时间 - get_real_value_time_utc = get_real_value_time_dt.astimezone(timezone.utc) - # 计算时间差（绝对值） - time_difference = abs((data_time - get_real_value_time_dt).total_seconds()) - # 判断时间差是否超过1分钟 - if time_difference > 60: # 超过1分钟 - monitored_value = None - else: # 小于等于3分钟 - monitored_value = data['monitored_value'] - # 创建Point对象 - point = ( - Point('variable_pump_realtime') - .tag("date", datetime.fromisoformat(get_real_value_time).strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", monitored_value) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(get_real_value_time_utc, write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - # write_api.flush() - if source_outflow_realtime_data_list: - for data in source_outflow_realtime_data_list: - # 将 data['time'] 和 get_realValue_time 转换为 datetime 对象 - data_time = datetime.fromisoformat(data['time']) - get_real_value_time_dt = datetime.fromisoformat(get_real_value_time).replace(tzinfo=None) - # 将获取的时间转换为 UTC 时间 - get_real_value_time_utc = get_real_value_time_dt.astimezone(timezone.utc) - # 计算时间差（绝对值） - time_difference = abs((data_time - get_real_value_time_dt).total_seconds()) - # 判断时间差是否超过1分钟 - if time_difference > 60: # 超过1分钟 - monitored_value = None - else: # 小于等于3分钟 - monitored_value = data['monitored_value'] - # 创建Point对象 - point = ( - Point('source_outflow_realtime') - .tag("date", datetime.fromisoformat(get_real_value_time).strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", monitored_value) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(get_real_value_time_utc, write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - # write_api.flush() - if pipe_flow_realtime_data_list: - for data in pipe_flow_realtime_data_list: - # 将 data['time'] 和 get_realValue_time 转换为 datetime 对象 - data_time = datetime.fromisoformat(data['time']) - get_real_value_time_dt = datetime.fromisoformat(get_real_value_time).replace(tzinfo=None) - # 将获取的时间转换为 UTC 时间 - get_real_value_time_utc = get_real_value_time_dt.astimezone(timezone.utc) - # 计算时间差（绝对值） - time_difference = abs((data_time - get_real_value_time_dt).total_seconds()) - # 判断时间差是否超过1分钟 - if time_difference > 60: # 超过1分钟 - monitored_value = None - else: # 小于等于3分钟 - monitored_value = data['monitored_value'] - # 创建Point对象 - point = ( - Point('pipe_flow_realtime') - .tag("date", datetime.fromisoformat(get_real_value_time).strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", monitored_value) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(get_real_value_time_utc, write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - # write_api.flush() - if pressure_realtime_data_list: - for data in pressure_realtime_data_list: - # 将 data['time'] 和 get_realValue_time 转换为 datetime 对象 - data_time = datetime.fromisoformat(data['time']) - get_real_value_time_dt = datetime.fromisoformat(get_real_value_time).replace(tzinfo=None) - # 将获取的时间转换为 UTC 时间 - get_real_value_time_utc = get_real_value_time_dt.astimezone(timezone.utc) - # 计算时间差（绝对值） - time_difference = abs((data_time - get_real_value_time_dt).total_seconds()) - # 判断时间差是否超过1分钟 - if time_difference > 60: # 超过1分钟 - monitored_value = None - else: # 小于等于3分钟 - monitored_value = data['monitored_value'] - # 创建Point对象 - point = ( - Point('pressure_realtime') - .tag("date", datetime.fromisoformat(get_real_value_time).strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", monitored_value) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(get_real_value_time_utc, write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - # write_api.flush() - if demand_realtime_data_list: - for data in demand_realtime_data_list: - # 将 data['time'] 和 get_realValue_time 转换为 datetime 对象 - data_time = datetime.fromisoformat(data['time']) - get_real_value_time_dt = datetime.fromisoformat(get_real_value_time).replace(tzinfo=None) - # 将获取的时间转换为 UTC 时间 - get_real_value_time_utc = get_real_value_time_dt.astimezone(timezone.utc) - # 计算时间差（绝对值） - time_difference = abs((data_time - get_real_value_time_dt).total_seconds()) - # 判断时间差是否超过1分钟 - if time_difference > 60: # 超过1分钟 - monitored_value = None - else: # 小于等于3分钟 - monitored_value = data['monitored_value'] - # 创建Point对象 - point = ( - Point('demand_realtime') - .tag("date", datetime.fromisoformat(get_real_value_time).strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", monitored_value) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(get_real_value_time_utc, write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - # write_api.flush() - if quality_realtime_data_list: - for data in quality_realtime_data_list: - # 将 data['time'] 和 get_realValue_time 转换为 datetime 对象 - data_time = datetime.fromisoformat(data['time']) - get_real_value_time_dt = datetime.fromisoformat(get_real_value_time).replace(tzinfo=None) - # 将获取的时间转换为 UTC 时间 - get_real_value_time_utc = get_real_value_time_dt.astimezone(timezone.utc) - # 计算时间差（绝对值） - time_difference = abs((data_time - get_real_value_time_dt).total_seconds()) - # 判断时间差是否超过1分钟 - if time_difference > 60: # 超过1分钟 - monitored_value = None - else: # 小于等于3分钟 - monitored_value = data['monitored_value'] - # 创建Point对象 - point = ( - Point('quality_realtime') - .tag("date", datetime.fromisoformat(get_real_value_time).strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", monitored_value) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(get_real_value_time_utc, write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - # write_api.flush() - # 批量写入数据 - if points_to_write: - write_api.write(bucket=bucket, org=org_name, record=points_to_write) - write_api.flush() # 刷新缓存一次 - client.close() - - -def convert_time_format(original_time: str) -> str: - """ - 格式转换，将“2024-04-13T08:00:00+08:00"转为“2024-04-13 08:00:00” - :param original_time: str， “2024-04-13T08:00:00+08:00"格式的时间 - :return: str，“2024-04-13 08:00:00”格式的时间 - """ - new_time = original_time.replace('T', ' ') - new_time = new_time.replace('+08:00', '') - return new_time - - -# 2025/01/10 -def store_non_realtime_SCADA_data_to_influxdb(get_history_data_end_time: str, bucket: str = "SCADA_data") -> None: - """ - 获取某段时间内传回的scada数据 - :param get_history_data_end_time: 获取历史数据的终止时间时间，格式如'2024-11-25T09:00:00+08:00' - :param bucket: (str): InfluxDB 的 bucket 名称，默认值为 "SCADA_data"。 - :return: - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - write_options = WriteOptions( - jitter_interval=200, # 添加抖动以避免同时写入 - max_retry_delay=30000 # 最大重试延迟（毫秒） - ) - write_api = client.write_api(write_options=write_options) - # 创建一个临时存储点数据的列表 - points_to_write = [] - - # 将end_date字符串转换为datetime对象 - end_date_dt = datetime.strptime(convert_time_format(get_history_data_end_time), '%Y-%m-%d %H:%M:%S') - end_date = end_date_dt.strftime('%Y-%m-%d %H:%M:%S') - # 将transmission_frequency字符串转换为timedelta对象 - transmission_frequency_dt = datetime.strptime(globals.transmission_frequency, '%H:%M:%S') - datetime(1900, 1, 1) - get_history_data_start_time = end_date_dt - transmission_frequency_dt - begin_date = get_history_data_start_time.strftime('%Y-%m-%d %H:%M:%S') - # print(begin_date) - # print(end_date) - reservoir_liquid_level_non_realtime_data_list = [] - tank_liquid_level_non_realtime_data_list = [] - fixed_pump_non_realtime_data_list = [] - variable_pump_non_realtime_data_list = [] - source_outflow_non_realtime_data_list = [] - pipe_flow_non_realtime_data_list = [] - pressure_non_realtime_data_list = [] - demand_non_realtime_data_list = [] - quality_non_realtime_data_list = [] - try_count = 0 - while try_count < 5: - try: - try_count += 1 - # reservoir_liquid_level_non_realtime_data_list = get_data.get_history_data( - # ids=','.join(reservoir_liquid_level_non_realtime_ids), begin_date=begin_date, end_date=end_date, downsample='1m') - if globals.reservoir_liquid_level_non_realtime_ids: - reservoir_liquid_level_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.reservoir_liquid_level_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.tank_liquid_level_non_realtime_ids: - tank_liquid_level_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.tank_liquid_level_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.fixed_pump_non_realtime_ids: - fixed_pump_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.fixed_pump_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.variable_pump_non_realtime_ids: - variable_pump_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.variable_pump_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.source_outflow_non_realtime_ids: - source_outflow_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.source_outflow_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.pipe_flow_non_realtime_ids: - pipe_flow_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.pipe_flow_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - # print(pipe_flow_non_realtime_data_list) - if globals.pressure_non_realtime_ids: - pressure_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.pressure_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - # print(pressure_non_realtime_data_list) - if globals.demand_non_realtime_ids: - demand_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.demand_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.quality_non_realtime_ids: - quality_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.quality_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - except Exception as e: - print(f"Attempt {try_count} failed with error: {e}") - if try_count < 5: - print("Retrying in 10 seconds...") - time.sleep(10) - else: - print("Max retries reached. Exiting.") - else: - print("Data fetched successfully.") - break # 成功后退出循环 - if reservoir_liquid_level_non_realtime_data_list: - for data in reservoir_liquid_level_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('reservoir_liquid_level_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if tank_liquid_level_non_realtime_data_list: - for data in tank_liquid_level_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('tank_liquid_level_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if fixed_pump_non_realtime_data_list: - for data in fixed_pump_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('fixed_pump_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if variable_pump_non_realtime_data_list: - for data in variable_pump_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('variable_pump_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if source_outflow_non_realtime_data_list: - for data in source_outflow_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('source_outflow_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if pipe_flow_non_realtime_data_list: - for data in pipe_flow_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('pipe_flow_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if pressure_non_realtime_data_list: - for data in pressure_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('pressure_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if demand_non_realtime_data_list: - for data in demand_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('demand_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if quality_non_realtime_data_list: - for data in quality_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('quality_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - # 批量写入数据 - if points_to_write: - write_api.write(bucket=bucket, org=org_name, record=points_to_write) - write_api.flush() # 刷新缓存一次 - client.close() - - -# 2025/03/01 -def download_history_data_manually(begin_time: str, end_time: str, bucket: str = "SCADA_data") -> None: - """ - 获取某个时间段内所有SCADA设备的历史数据，非实时执行，手动补充数据版 - :param begin_time: 获取历史数据的开始时间，格式如'2024-11-25T09:00:00+08:00' - :param end_time: 获取历史数据的结束时间，格式如'2024-11-25T09:00:00+08:00' - :param bucket: InfluxDB 的 bucket 名称，默认值为 "SCADA_data" - :return: - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - write_options = WriteOptions( - jitter_interval=200, # 添加抖动以避免同时写入 - max_retry_delay=30000 # 最大重试延迟（毫秒） - ) - write_api = client.write_api(write_options=write_options) - # 创建一个临时存储点数据的列表 - points_to_write = [] - - begin_date = convert_time_format(begin_time) - end_date = convert_time_format(end_time) - - reservoir_liquid_level_realtime_data_list = [] - tank_liquid_level_realtime_data_list = [] - fixed_pump_realtime_data_list =[] - variable_pump_realtime_data_list =[] - source_outflow_realtime_data_list = [] - pipe_flow_realtime_data_list = [] - pressure_realtime_data_list =[] - demand_realtime_data_list = [] - quality_realtime_data_list = [] - - reservoir_liquid_level_non_realtime_data_list = [] - tank_liquid_level_non_realtime_data_list = [] - fixed_pump_non_realtime_data_list = [] - variable_pump_non_realtime_data_list = [] - source_outflow_non_realtime_data_list = [] - pipe_flow_non_realtime_data_list = [] - pressure_non_realtime_data_list = [] - demand_non_realtime_data_list = [] - quality_non_realtime_data_list = [] - - try_count = 0 - while try_count < 5: - try: - try_count += 1 - if globals.reservoir_liquid_level_realtime_ids: - reservoir_liquid_level_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.reservoir_liquid_level_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.tank_liquid_level_realtime_ids: - tank_liquid_level_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.tank_liquid_level_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.fixed_pump_realtime_ids: - fixed_pump_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.fixed_pump_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.variable_pump_realtime_ids: - variable_pump_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.variable_pump_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.source_outflow_realtime_ids: - source_outflow_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.source_outflow_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.pipe_flow_realtime_ids: - pipe_flow_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.pipe_flow_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.pressure_realtime_ids: - pressure_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.pressure_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.demand_realtime_ids: - demand_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.demand_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.quality_realtime_ids: - quality_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.quality_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - # reservoir_liquid_level_non_realtime_data_list = get_data.get_history_data( - # ids=','.join(reservoir_liquid_level_non_realtime_ids), begin_date=begin_date, end_date=end_date, downsample='1m') - if globals.reservoir_liquid_level_non_realtime_ids: - reservoir_liquid_level_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.reservoir_liquid_level_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.tank_liquid_level_non_realtime_ids: - tank_liquid_level_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.tank_liquid_level_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.fixed_pump_non_realtime_ids: - fixed_pump_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.fixed_pump_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.variable_pump_non_realtime_ids: - variable_pump_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.variable_pump_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.source_outflow_non_realtime_ids: - source_outflow_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.source_outflow_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.pipe_flow_non_realtime_ids: - pipe_flow_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.pipe_flow_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - # print(pipe_flow_non_realtime_data_list) - if globals.pressure_non_realtime_ids: - pressure_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.pressure_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - # print(pressure_non_realtime_data_list) - if globals.demand_non_realtime_ids: - demand_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.demand_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - if globals.quality_non_realtime_ids: - quality_non_realtime_data_list = get_data.get_history_data( - ids=','.join(globals.quality_non_realtime_ids), - begin_date=begin_date, end_date=end_date, - downsample='1m') - except Exception as e: - print(f"Attempt {try_count} failed with error: {e}") - if try_count < 5: - print("Retrying in 10 seconds...") - time.sleep(10) - else: - print("Max retries reached. Exiting.") - else: - print("Data fetched successfully.") - break # 成功后退出循环 - - if reservoir_liquid_level_realtime_data_list: - for data in reservoir_liquid_level_realtime_data_list: - # 创建Point对象 - point = ( - Point('reservoir_liquid_level_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if tank_liquid_level_realtime_data_list: - for data in tank_liquid_level_realtime_data_list: - # 创建Point对象 - point = ( - Point('tank_liquid_level_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if fixed_pump_realtime_data_list: - for data in fixed_pump_realtime_data_list: - # 创建Point对象 - point = ( - Point('fixed_pump_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if variable_pump_realtime_data_list: - for data in variable_pump_realtime_data_list: - # 创建Point对象 - point = ( - Point('variable_pump_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if source_outflow_realtime_data_list: - for data in source_outflow_realtime_data_list: - # 创建Point对象 - point = ( - Point('source_outflow_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if pipe_flow_realtime_data_list: - for data in pipe_flow_realtime_data_list: - # 创建Point对象 - point = ( - Point('pipe_flow_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if pressure_realtime_data_list: - for data in pressure_realtime_data_list: - # 创建Point对象 - point = ( - Point('pressure_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if demand_realtime_data_list: - for data in demand_realtime_data_list: - # 创建Point对象 - point = ( - Point('demand_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if quality_realtime_data_list: - for data in quality_realtime_data_list: - # 创建Point对象 - point = ( - Point('quality_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if reservoir_liquid_level_non_realtime_data_list: - for data in reservoir_liquid_level_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('reservoir_liquid_level_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if tank_liquid_level_non_realtime_data_list: - for data in tank_liquid_level_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('tank_liquid_level_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if fixed_pump_non_realtime_data_list: - for data in fixed_pump_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('fixed_pump_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if variable_pump_non_realtime_data_list: - for data in variable_pump_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('variable_pump_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if source_outflow_non_realtime_data_list: - for data in source_outflow_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('source_outflow_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if pipe_flow_non_realtime_data_list: - for data in pipe_flow_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('pipe_flow_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if pressure_non_realtime_data_list: - for data in pressure_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('pressure_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if demand_non_realtime_data_list: - for data in demand_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('demand_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - if quality_non_realtime_data_list: - for data in quality_non_realtime_data_list: - # 创建Point对象 - point = ( - Point('quality_non_realtime') - .tag("date", data['time'].strftime('%Y-%m-%d')) - .tag("description", data['description']) - .tag("device_ID", data['device_ID']) - .field("monitored_value", data['monitored_value']) - .field("datacleaning_value", None) - .field("simulation_value", None) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - # 批量写入数据 - if points_to_write: - write_api.write(bucket=bucket, org=org_name, record=points_to_write) - write_api.flush() # 刷新缓存一次 - client.close() - - -def query_SCADA_data_by_device_ID_and_time(query_ids_list: List[str], query_time: str, bucket: str="SCADA_data") -> Dict[str, float]: - """ - 根据SCADA设备的ID和时间查询值 - :param query_ids_list: SCADA设备ID的列表 - :param query_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 - :param bucket: InfluxDB 的 bucket 名称，默认值为 "SCADA_data"。 - :return: - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 将北京时间转换为 UTC 时间 - beijing_time = datetime.fromisoformat(query_time) - utc_time = beijing_time.astimezone(timezone.utc) - utc_start_time = utc_time - timedelta(seconds=1) - utc_stop_time = utc_time + timedelta(seconds=1) - # 构建查询字典 - SCADA_result_dict = {} - for device_id in query_ids_list: - # 构建 Flux 查询语句 - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()}) - |> filter(fn: (r) => r["device_ID"] == "{device_id}" and r["_field"] == "monitored_value") - ''' - # 执行查询 - try: - result = query_api.query(flux_query) - # 从查询结果中提取 monitored_value - if result: - # 假设返回的结果为一行数据 - for table in result: - for record in table.records: - # 获取字段 "_value" 即为 monitored_value - monitored_value = record.get_value() - SCADA_result_dict[device_id] = monitored_value - else: - # 如果没有结果，默认设置为 None 或其他值 - SCADA_result_dict[device_id] = None - except Exception as e: - print(f"Error querying InfluxDB for device ID {device_id}: {e}") - SCADA_result_dict[device_id] = None - client.close() - return SCADA_result_dict - -# 2025/03/14 -def query_SCADA_data_by_device_ID_and_timerange(query_ids_list: List[str], start_time: str, end_time: str, bucket: str="SCADA_data"): - """ - 查询指定时间范围内，多个SCADA设备的数据，用于漏损定位 - :param query_ids_list: SCADA设备ID的列表 - :param start_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 - :param end_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 - :param bucket: InfluxDB 的 bucket 名称，默认值为 "SCADA_data"。 - :return: - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 将北京时间转换为 UTC 时间 - beijing_start_time = datetime.fromisoformat(start_time) - utc_start_time = beijing_start_time.astimezone(timezone.utc) - timedelta(seconds=1) - print(utc_start_time) - beijing_end_time = datetime.fromisoformat(end_time) - utc_end_time = beijing_end_time.astimezone(timezone.utc) + timedelta(seconds=1) - print(utc_end_time) - SCADA_dict = {} - for device_id in query_ids_list: - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: {utc_start_time.isoformat()}, stop: {utc_end_time.isoformat()}) - |> filter(fn: (r) => r["_measurement"] == "SCADA_data" and r["device_ID"] = {device_id} and r["_field"] == "monitored_value") - |> pivot(rowKey: ["_time"], columnKey: ["device_ID"], valueColumn: "_value") - |> sort(columns: ["_time"]) - ''' - # 执行查询，返回一个 FluxTable 列表 - tables = query_api.query(flux_query) - records_list = [] - for table in tables: - for record in table.records: - # 获取记录的时间和监测值 - records_list.append({ - "time": record["_time"], - "value": record["_value"] - }) - SCADA_dict[device_id] = records_list - client.close() - return SCADA_dict - - -# 2025/02/01 -def store_realtime_simulation_result_to_influxdb(node_result_list: List[Dict[str, any]], link_result_list: List[Dict[str, any]], - result_start_time: str, bucket: str = "realtime_simulation_result"): - """ - 将实时模拟计算结果数据存储到 InfluxDB 的realtime_simulation_result这个bucket中。 - :param node_result_list: (List[Dict[str, any]]): 包含节点和结果数据的字典列表。 - :param link_result_list: (List[Dict[str, any]]): 包含连接和结果数据的字典列表。 - :param result_start_time: (str): 计算结果的模拟开始时间。 - :param bucket: (str): InfluxDB 的 bucket 名称，默认值为 "realtime_simulation_result"。 - :return: - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - # 开始写入数据 - try: - write_options = WriteOptions( - jitter_interval=200, # 添加抖动以避免同时写入 - max_retry_delay=30000 # 最大重试延迟（毫秒） - ) - write_api = client.write_api(write_options=write_options) - # 创建一个临时存储点数据的列表 - points_to_write = [] - date_str = result_start_time.split('T')[0] - time_beijing = datetime.strptime(result_start_time, '%Y-%m-%dT%H:%M:%S%z').isoformat() - for result in node_result_list: - # 提取节点信息和结果数据 - node_id = result.get('node') - data_list = result.get('result', []) - for data in data_list: - # 构建 Point 数据，多个 field 存在于一个数据点中 - node_point = Point("node") \ - .tag("date", date_str) \ - .tag("ID", node_id) \ - .field("head", data.get('head', 0.0)) \ - .field("pressure", data.get('pressure', 0.0)) \ - .field("actualdemand", data.get('demand', 0.0)) \ - .field("demanddeficit", None) \ - .field("totalExternalOutflow", None) \ - .field("quality", data.get('quality', 0.0)) \ - .time(time_beijing, write_precision='s') - points_to_write.append(node_point) - # 写入数据到 InfluxDB，多个 field 在同一个 point 中 - # write_api.write(bucket=bucket, org=org_name, record=node_point) - # write_api.flush() - # print(f"成功将 {len(node_result_list)} 条node数据写入 InfluxDB。") - for result in link_result_list: - link_id = result.get('link') - data_list = result.get('result', []) - for data in data_list: - link_point = Point("link") \ - .tag("date", date_str) \ - .tag("ID", link_id) \ - .field("flow", data.get('flow', 0.0)) \ - .field("velocity", data.get('velocity', 0.0)) \ - .field("headloss", data.get('headloss', 0.0)) \ - .field("quality", data.get('quality', 0.0)) \ - .field("status", data.get('status', "UNKNOWN")) \ - .field("setting", data.get('setting', 0.0)) \ - .field("reaction", data.get('reaction', 0.0)) \ - .field("friction", data.get('friction', 0.0)) \ - .time(time_beijing, write_precision='s') - points_to_write.append(link_point) - # write_api.write(bucket=bucket, org=org_name, record=link_point) - # write_api.flush() - # print(f"成功将 {len(link_result_list)} 条link数据写入 InfluxDB。") - # 批量写入数据 - if points_to_write: - write_api.write(bucket=bucket, org=org_name, record=points_to_write) - write_api.flush() # 刷新缓存一次 - except Exception as e: - raise RuntimeError(f"数据写入 InfluxDB 时发生错误: {e}") - client.close() - - -# 2025/02/01 -def query_latest_record_by_ID(ID: str, type: str, bucket: str="realtime_simulation_result") -> dict: - """ - 查询指定ID的最新的一条记录 - :param ID: (str): 要查询的 ID。 - :param type: (str): "node"或“link” - :param bucket: (str): 数据存储的 bucket 名称。 - :return: dict: 最新记录的数据，如果没有找到则返回 None。 - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - if type == "node": - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: -1d, stop: now()) // 查找最近七天的记录 - |> filter(fn: (r) => r["_measurement"] == "node" and r["ID"] == "{ID}") - |> pivot( - rowKey:["_time"], - columnKey:["_field"], - valueColumn:"_value" - ) - |> group() // 将所有数据聚合到同一个 group - |> sort(columns: ["_time"], desc: true) - |> limit(n: 1) - ''' - tables = query_api.query(flux_query) - # 解析查询结果 - for table in tables: - for record in table.records: - return { - "time": record["_time"], - "ID": ID, - "head": record["head"], - "pressure": record["pressure"], - "actualdemand": record["actualdemand"], - # "demanddeficit": record["demanddeficit"], - # "totalExternalOutflow": record["totalExternalOutflow"], - "quality": record["quality"] - } - elif type == "link": - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: -1d, stop: now()) // 查找最近七天的记录 - |> filter(fn: (r) => r["_measurement"] == "link" and r["ID"] == "{ID}") - |> pivot( - rowKey:["_time"], - columnKey:["_field"], - valueColumn:"_value" - ) - |> group() // 将所有数据聚合到同一个 group - |> sort(columns: ["_time"], desc: true) - |> limit(n: 1) - ''' - tables = query_api.query(flux_query) - # 解析查询结果 - for table in tables: - for record in table.records: - return { - "time": record["_time"], - "ID": ID, - "flow": record["flow"], - "velocity": record["velocity"], - "headloss": record["headloss"], - "quality": record["quality"], - "status": record["status"], - "setting": record["setting"], - "reaction": record["reaction"], - "friction": record["friction"] - } - client.close() - return None # 如果没有找到记录 - - -# 2025/02/01 -def query_all_record_by_time(query_time: str, bucket: str="realtime_simulation_result") -> tuple: - """ - 查询指定北京时间的所有记录，包括 'node' 和 'link' measurement，分别以指定格式返回。 - :param query_time: (str): 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 - :param bucket: (str): 数据存储的 bucket 名称。 - :return: dict: tuple: (node_records, link_records) - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 将北京时间转换为 UTC 时间 - beijing_time = datetime.fromisoformat(query_time) - utc_time = beijing_time.astimezone(timezone.utc) - utc_start_time = utc_time - timedelta(seconds=1) - utc_stop_time = utc_time + timedelta(seconds=1) - # 构建 Flux 查询语句 - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()}) - |> filter(fn: (r) => r["_measurement"] == "node" or r["_measurement"] == "link") - |> pivot( - rowKey:["_time"], - columnKey:["_field"], - valueColumn:"_value" - ) - ''' - # 执行查询 - tables = query_api.query(flux_query) - node_records = [] - link_records = [] - # 解析查询结果 - for table in tables: - for record in table.records: - # print(record.values) # 打印完整记录内容 - measurement = record["_measurement"] - # 处理 node 数据 - if measurement == "node": - node_records.append({ - "time": record["_time"], - "ID": record["ID"], - "head": record["head"], - "pressure": record["pressure"], - "actualdemand": record["actualdemand"], - "quality": record["quality"] - }) - # 处理 link 数据 - elif measurement == "link": - link_records.append({ - "time": record["_time"], - "ID": record["ID"], - "flow": record["flow"], - "velocity": record["velocity"], - "headloss": record["headloss"], - "quality": record["quality"], - "status": record["status"], - "setting": record["setting"], - "reaction": record["reaction"], - "friction": record["friction"] - }) - client.close() - return node_records, link_records - - -# 2025/03/03 -def query_all_record_by_time_property(query_time: str, type: str, property: str, bucket: str="realtime_simulation_result") -> list: - """ - 查询指定北京时间的所有记录，查询 'node' 或 'link' 的某一属性值，以指定格式返回。 - :param query_time: (str): 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 - :param type: (str): 查询的类型（决定 measurement） - :param property: (str): 查询的字段名称（field） - :param bucket: (str): 数据存储的 bucket 名称。 - :return: list(dict): result_records - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 确定 measurement - if type == "node": - measurement = "node" - elif type == "link": - measurement = "link" - else: - raise ValueError(f"不支持的类型: {type}") - # 将北京时间转换为 UTC 时间 - beijing_time = datetime.fromisoformat(query_time) - utc_time = beijing_time.astimezone(timezone.utc) - utc_start_time = utc_time - timedelta(seconds=1) - utc_stop_time = utc_time + timedelta(seconds=1) - # 构建 Flux 查询语句 - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()}) - |> filter(fn: (r) => r["_measurement"] == "{measurement}" and r["_field"] == "{property}") - ''' - # 执行查询 - tables = query_api.query(flux_query) - result_records = [] - # 解析查询结果 - for table in tables: - for record in table.records: - # print(record.values) # 打印完整记录内容 - result_records.append({ - "ID": record["ID"], - "value": record["_value"] - }) - client.close() - return result_records - - -# 2025/02/21 -def query_all_record_by_date(query_date: str, bucket: str="realtime_simulation_result") -> tuple: - """ - 查询指定日期的所有记录，包括‘node’和‘link’，分别以指定的格式返回 - :param query_date: 输入的日期，格式为‘2025-02-14’ - :param bucket: 数据存储的bucket名称 - :return: dict: tuple: (node_records, link_records) - """ - client = get_new_client() - # 记录开始时间 - time_cost_start = time.perf_counter() - print('{} -- Hydraulic simulation started.'.format( - datetime.now(pytz.timezone('Asia/Shanghai')).strftime('%Y-%m-%d %H:%M:%S'))) - - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 将 start_date 的北京时间转换为 UTC 时间 - start_time = (datetime.strptime(query_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat() - stop_time = datetime.strptime(query_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat() - # 构建 Flux 查询语句 - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: {start_time}, stop: {stop_time}) - |> filter(fn: (r) => r["_measurement"] == "node" or r["_measurement"] == "link" and r["date"] == "{query_date}") - |> pivot( - rowKey:["_time"], - columnKey:["_field"], - valueColumn:"_value" - ) - ''' - # 执行查询 - tables = query_api.query(flux_query) - node_records = [] - link_records = [] - # 解析查询结果 - for table in tables: - for record in table.records: - # print(record.values) # 打印完整记录内容 - measurement = record["_measurement"] - # 处理 node 数据 - if measurement == "node": - node_records.append({ - "time": record["_time"], - "ID": record["ID"], - "head": record["head"], - "pressure": record["pressure"], - "actualdemand": record["actualdemand"], - "quality": record["quality"] - }) - # 处理 link 数据 - elif measurement == "link": - link_records.append({ - "time": record["_time"], - "ID": record["ID"], - "flow": record["flow"], - "velocity": record["velocity"], - "headloss": record["headloss"], - "quality": record["quality"], - "status": record["status"], - "setting": record["setting"], - "reaction": record["reaction"], - "friction": record["friction"] - }) - time_cost_end = time.perf_counter() - print('{} -- Hydraulic simulation finished, cost time: {:.2f} s.'.format( - datetime.now(pytz.timezone('Asia/Shanghai')).strftime('%Y-%m-%d %H:%M:%S'), - time_cost_end - time_cost_start)) - client.close() - return node_records, link_records - - -# 2025/02/21 -def query_all_record_by_date_property(query_date: str, type: str, property: str, bucket: str="realtime_simulation_result") -> list: - """ - 查询指定日期的‘node’或‘link’的某一属性值的所有记录，以指定的格式返回 - :param query_date: 输入的日期，格式为‘2025-02-14’ - :param type: (str): 查询的类型（决定 measurement） - :param property: (str): 查询的字段名称（field） - :param bucket: 数据存储的bucket名称 - :return: list(dict): result_records - """ - client = get_new_client() - # 记录开始时间 - time_cost_start = time.perf_counter() - print('{} -- Hydraulic simulation started.'.format( - datetime.now(pytz.timezone('Asia/Shanghai')).strftime('%Y-%m-%d %H:%M:%S'))) - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 确定 measurement - if type == "node": - measurement = "node" - elif type == "link": - measurement = "link" - else: - raise ValueError(f"不支持的类型: {type}") - # 将 start_date 的北京时间转换为 UTC 时间 - start_time = (datetime.strptime(query_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat() - stop_time = datetime.strptime(query_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat() - # 构建 Flux 查询语句 - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: {start_time}, stop: {stop_time}) - |> filter(fn: (r) => r["_measurement"] == "{measurement}" and r["date"] == "{query_date}" and r["_field"] == "{property}") - ''' - # 执行查询 - tables = query_api.query(flux_query) - result_records = [] - # 解析查询结果 - for table in tables: - for record in table.records: - # print(record.values) # 打印完整记录内容 - result_records.append({ - "ID": record["ID"], - "time": record["_time"], - "value": record["_value"] - }) - time_cost_end = time.perf_counter() - print('{} -- Hydraulic simulation finished, cost time: {:.2f} s.'.format( - datetime.now(pytz.timezone('Asia/Shanghai')).strftime('%Y-%m-%d %H:%M:%S'), - time_cost_end - time_cost_start)) - client.close() - return result_records - - -# 2025/02/01 -def query_curve_by_ID_property_daterange(ID: str, type: str, property: str, start_date: str, end_date: str, bucket: str="realtime_simulation_result") -> list: - """ - 根据 type 查询对应的 measurement，根据 ID 和 date 查询对应的 tag，根据 property 查询对应的 field。 - :param ID: (str): 要查询的 ID（tag） - :param type: (str): 查询的类型（决定 measurement） - :param property: (str): 查询的字段名称（field） - :param start_date: (str): 查询的开始日期，格式为 'YYYY-MM-DD' - :param end_date: (str): 查询的结束日期，格式为 'YYYY-MM-DD' - :param bucket: (str): 数据存储的 bucket 名称，默认值为 "realtime_simulation_result" - :return: 查询结果的列表 - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 确定 measurement - if type == "node": - measurement = "node" - elif type == "link": - measurement = "link" - else: - raise ValueError(f"不支持的类型: {type}") - # 解析日期范围（当天的 UTC 开始和结束时间） - # previous_day = datetime.strptime(start_date, "%Y-%m-%d") - timedelta(days=1) - # start_time = previous_day.isoformat() + "T16:00:00Z" - # stop_time = datetime.strptime(end_date, "%Y-%m-%d").isoformat() + "T15:59:59Z" - # 将 start_date 的北京时间转换为 UTC 时间范围 - start_time = (datetime.strptime(start_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat() - stop_time = datetime.strptime(end_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat() - # 构建 Flux 查询语句 - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: {start_time}, stop: {stop_time}) - |> filter(fn: (r) => r["_measurement"] == "{measurement}" and r["ID"] == "{ID}" and r["_field"] == "{property}") - ''' - # 执行查询 - tables = query_api.query(flux_query) - # 解析查询结果 - results = [] - for table in tables: - for record in table.records: - results.append({ - "time": record["_time"], - "value": record["_value"] - }) - client.close() - return results - - -# 2025/02/13 -def store_scheme_simulation_result_to_influxdb(node_result_list: List[Dict[str, any]], link_result_list: List[Dict[str, any]], - scheme_start_time: str, num_periods: int = 1, scheme_Type: str = None, scheme_Name: str = None, - bucket: str = "scheme_simulation_result"): - """ - 将方案模拟计算结果存入 InfluxuDb 的scheme_simulation_result这个bucket中。 - :param node_result_list: (List[Dict[str, any]]): 包含节点和结果数据的字典列表。 - :param link_result_list: (List[Dict[str, any]]): 包含连接和结果数据的字典列表。 - :param scheme_start_time: (str): 方案模拟开始时间。 - :param num_periods: (int): 方案模拟的周期数 - :param scheme_Type: (str): 方案类型 - :param scheme_Name: (str): 方案名称 - :param bucket: (str): InfluxDB 的 bucket 名称，默认值为 "scheme_simulation_result"。 - :return: - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - try: - write_options = WriteOptions( - jitter_interval=200, # 添加抖动以避免同时写入 - max_retry_delay=30000 # 最大重试延迟（毫秒） - ) - write_api = client.write_api(write_options=write_options) - # 创建一个临时存储点数据的列表 - points_to_write = [] - date_str = scheme_start_time.split('T')[0] - time_beijing = datetime.strptime(scheme_start_time, '%Y-%m-%dT%H:%M:%S%z') - timestep_parts = globals.hydraulic_timestep.split(':') - timestep = timedelta(hours=int(timestep_parts[0]), minutes=int(timestep_parts[1]), seconds=int(timestep_parts[2])) - for node_result in node_result_list: - # 提取节点信息和数据结果 - node_id = node_result.get('node') - # 从period 0 到 period num_period - 1 - for period_index in range(num_periods): - scheme_time = (time_beijing + (timestep * period_index)).isoformat() - data_list = [node_result.get('result', [])[period_index]] - for data in data_list: - # 构建 Point 数据，多个 field 存在于一个数据点中 - node_point = Point("node") \ - .tag("date", date_str) \ - .tag("ID", node_id) \ - .tag("scheme_Type", scheme_Type) \ - .tag("scheme_Name", scheme_Name) \ - .field("head", data.get('head', 0.0)) \ - .field("pressure", data.get('pressure', 0.0)) \ - .field("actualdemand", data.get('demand', 0.0)) \ - .field("demanddeficit", None) \ - .field("totalExternalOutflow", None) \ - .field("quality", data.get('quality', 0.0)) \ - .time(scheme_time, write_precision='s') - points_to_write.append(node_point) - # 写入数据到 InfluxDB，多个 field 在同一个 point 中 - # write_api.write(bucket=bucket, org=org_name, record=node_point) - # write_api.flush() - for link_result in link_result_list: - link_id = link_result.get('link') - for period_index in range(num_periods): - scheme_time = (time_beijing + (timestep * period_index)).isoformat() - data_list = [link_result.get('result', [])[period_index]] - for data in data_list: - link_point = Point("link") \ - .tag("date", date_str) \ - .tag("ID", link_id) \ - .tag("scheme_Type", scheme_Type) \ - .tag("scheme_Name", scheme_Name) \ - .field("flow", data.get('flow', 0.0)) \ - .field("velocity", data.get('velocity', 0.0)) \ - .field("headloss", data.get('headloss', 0.0)) \ - .field("quality", data.get('quality', 0.0)) \ - .field("status", data.get('status', "UNKNOWN")) \ - .field("setting", data.get('setting', 0.0)) \ - .field("reaction", data.get('reaction', 0.0)) \ - .field("friction", data.get('friction', 0.0)) \ - .time(scheme_time, write_precision='s') - points_to_write.append(link_point) - # write_api.write(bucket=bucket, org=org_name, record=link_point) - # write_api.flush() - # 批量写入数据 - if points_to_write: - write_api.write(bucket=bucket, org=org_name, record=points_to_write) - write_api.flush() # 刷新缓存一次 - except Exception as e: - raise RuntimeError(f"数据写入 InfluxDB 时发生错误: {e}") - client.close() - - -# 2025/03/12 -def query_corresponding_query_id_and_element_id(name: str) -> None: - """ - 查询scada_info这张表中，api_query_id与associated_element_id的对应关系，用于下一步fill_scheme_simulation_result_to_SCADA - :param name: 数据库名称 - :return: - """ - # 连接数据库 - conn_string = f"dbname={name} host=127.0.0.1" - try: - with psycopg.connect(conn_string) as conn: - with conn.cursor() as cur: - # 查询 transmission_mode 为 'realtime' 的记录 - cur.execute(""" - SELECT type, associated_element_id, api_query_id - FROM scada_info - WHERE type IN ('source_outflow', 'pipe_flow', 'demand', 'pressure', 'quality'); - """) - records = cur.fetchall() - # 遍历查询结果，根据 type 分类存入对应的字典 - for record in records: - record_type, associated_element_id, api_query_id = record - if record_type == "source_outflow": - globals.scheme_source_outflow_ids[api_query_id] = associated_element_id - elif record_type == "pipe_flow": - globals.scheme_pipe_flow_ids[api_query_id] = associated_element_id - elif record_type == "pressure": - globals.scheme_pressure_ids[api_query_id] = associated_element_id - elif record_type == "demand": - globals.scheme_demand_ids[api_query_id] = associated_element_id - elif record_type == "quality": - globals.scheme_quality_ids[api_query_id] = associated_element_id - # 如果需要调试，可以打印该字典 - # print("scheme_source_outflow_ids:", globals.scheme_source_outflow_ids) - # print("scheme_pipe_flow_ids:", globals.scheme_pipe_flow_ids) - # print("scheme_pressure_ids:", globals.scheme_pressure_ids) - # print("scheme_demand_ids:", globals.scheme_demand_ids) - # print("scheme_quality_ids:", globals.scheme_quality_ids) - except psycopg.Error as e: - print(f"数据库连接或查询出错: {e}") - - -# 2025/03/22 -# def auto_get_burst_flow(): - - -# 2025/03/22 -# def manually_get_burst_flow(): - - -# 2025/03/11 -def fill_scheme_simulation_result_to_SCADA(scheme_Type: str = None, scheme_Name: str = None, query_date: str = None, - bucket: str = "scheme_simulation_result"): - """ - :param scheme_Type: 方案类型 - :param scheme_Name: 方案名称 - :param query_date: 查询日期，格式为 'YYYY-MM-DD' - :param bucket: InfluxDB 的 bucket 名称，默认值为 "scheme_simulation_result" - :return: - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - write_options = WriteOptions( - jitter_interval=200, # 添加抖动以避免同时写入 - max_retry_delay=30000 # 最大重试延迟（毫秒） - ) - write_api = client.write_api(write_options=write_options) - # 创建一个临时存储点数据的列表 - points_to_write = [] - # 查找associated_element_id的对应值 - for key, value in globals.scheme_source_outflow_ids.items(): - scheme_source_outflow_result = (query_scheme_curve_by_ID_property(scheme_Type=scheme_Type, scheme_Name=scheme_Name, - query_date=query_date, ID=value, type='link', property='flow')) - # print(f"Key: {key}, Query result: {scheme_source_outflow_result}") # 调试输出 - for data in scheme_source_outflow_result: - point = ( - Point('scheme_source_outflow') - .tag("date", query_date) - .tag("device_ID", key) - .tag("scheme_Type", scheme_Type) - .tag("scheme_Name", scheme_Name) - .field("monitored_value", data['value']) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - - for key, value in globals.scheme_pipe_flow_ids.items(): - scheme_pipe_flow_result = (query_scheme_curve_by_ID_property(scheme_Type=scheme_Type, scheme_Name=scheme_Name, - query_date=query_date, ID=value, type='link', property='flow')) - for data in scheme_pipe_flow_result: - point = ( - Point('scheme_pipe_flow') - .tag("date", query_date) - .tag("device_ID", key) - .tag("scheme_Type", scheme_Type) - .tag("scheme_Name", scheme_Name) - .field("monitored_value", data['value']) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - - for key, value in globals.scheme_pressure_ids.items(): - scheme_pressure_result = (query_scheme_curve_by_ID_property(scheme_Type=scheme_Type, scheme_Name=scheme_Name, - query_date=query_date, ID=value, type='node', property='pressure')) - for data in scheme_pressure_result: - point = ( - Point('scheme_pressure') - .tag("date", query_date) - .tag("device_ID", key) - .tag("scheme_Type", scheme_Type) - .tag("scheme_Name", scheme_Name) - .field("monitored_value", data['value']) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - - for key, value in globals.scheme_demand_ids.items(): - scheme_demand_result = (query_scheme_curve_by_ID_property(scheme_Type=scheme_Type, scheme_Name=scheme_Name, - query_date=query_date, ID=value, type='node', property='actualdemand')) - for data in scheme_demand_result: - point = ( - Point('scheme_demand') - .tag("date", query_date) - .tag("device_ID", key) - .tag("scheme_Type", scheme_Type) - .tag("scheme_Name", scheme_Name) - .field("monitored_value", data['value']) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - - for key, value in globals.scheme_quality_ids.items(): - scheme_quality_result = (query_scheme_curve_by_ID_property(scheme_Type=scheme_Type, scheme_Name=scheme_Name, - query_date=query_date, ID=value, type='node', property='quality')) - for data in scheme_quality_result: - point = ( - Point('scheme_quality') - .tag("date", query_date) - .tag("device_ID", key) - .tag("scheme_Type", scheme_Type) - .tag("scheme_Name", scheme_Name) - .field("monitored_value", data['value']) - .time(data['time'], write_precision='s') - ) - points_to_write.append(point) - # write_api.write(bucket=bucket, org=org_name, record=point) - # 批量写入数据 - if points_to_write: - write_api.write(bucket=bucket, org=org_name, record=points_to_write) - write_api.flush() # 刷新缓存一次 - client.close() - - -# 2025/02/15 -def query_SCADA_data_curve(api_query_id: str, start_date: str, end_date: str, bucket: str="SCADA_data") -> list: - """ - 根据SCADA设备的api_query_id和时间范围，查询得到曲线，查到的数据为0时区时间 - :param api_query_id: SCADA设备的api_query_id - :param start_date: 查询开始的时间，格式为 'YYYY-MM-DD' - :param end_date: 查询结束的时间，格式为 'YYYY-MM-DD' - :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data" - :return: 查询结果的列表 - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 将 start_date 的北京时间转换为 UTC 时间范围 - start_time = (datetime.strptime(start_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat() - stop_time = datetime.strptime(end_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat() - # 构建 Flux 查询语句 - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: {start_time}, stop: {stop_time}) - |> filter(fn: (r) => r["device_ID"] == "{api_query_id}") - ''' - # 执行查询 - tables = query_api.query(flux_query) - # 解析查询结果 - results = [] - for table in tables: - for record in table.records: - results.append({ - "time": record["_time"], - "value": record["_value"] - }) - client.close() - return results - - -# 2025/02/18 -def query_scheme_all_record_by_time(scheme_Type: str, scheme_Name: str, query_time: str, bucket: str="scheme_simulation_result") -> tuple: - """ - 查询指定方案某一时刻的所有记录，包括‘node'和‘link’，分别以指定格式返回。 - :param scheme_Type: 方案类型 - :param scheme_Name: 方案名称 - :param query_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 - :param bucket: 数据存储的 bucket 名称。 - :return: dict: tuple: (node_records, link_records) - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 将北京时间转换为 UTC 时间 - beijing_time = datetime.fromisoformat(query_time) - utc_time = beijing_time.astimezone(timezone.utc) - utc_start_time = utc_time - timedelta(seconds=1) - utc_stop_time = utc_time + timedelta(seconds=1) - # 构建 Flux 查询语句 - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()}) - |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}" and r["_measurement"] == "node" or r["_measurement"] == "link") - |> pivot( - rowKey:["_time"], - columnKey:["_field"], - valueColumn:"_value" - ) - ''' - # 执行查询 - tables = query_api.query(flux_query) - node_records = [] - link_records = [] - # 解析查询结果 - for table in tables: - for record in table.records: - # print(record.values) # 打印完整记录内容 - measurement = record["_measurement"] - # 处理 node 数据 - if measurement == "node": - node_records.append({ - "time": record["_time"], - "ID": record["ID"], - "head": record["head"], - "pressure": record["pressure"], - "actualdemand": record["actualdemand"], - "quality": record["quality"] - }) - # 处理 link 数据 - elif measurement == "link": - link_records.append({ - "time": record["_time"], - "ID": record["ID"], - "flow": record["flow"], - "velocity": record["velocity"], - "headloss": record["headloss"], - "quality": record["quality"], - "status": record["status"], - "setting": record["setting"], - "reaction": record["reaction"], - "friction": record["friction"] - }) - client.close() - return node_records, link_records - - -# 2025/03/04 -def query_scheme_all_record_by_time_property(scheme_Type: str, scheme_Name: str, query_time: str, type: str, property: str, - bucket: str="scheme_simulation_result") -> list: - """ - 查询指定方案某一时刻‘node'或‘link’某一属性值，以指定格式返回。 - :param scheme_Type: 方案类型 - :param scheme_Name: 方案名称 - :param query_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 - :param type: 查询的类型（决定 measurement） - :param property: 查询的字段名称（field） - :param bucket: 数据存储的 bucket 名称。 - :return: dict: tuple: (node_records, link_records) - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 确定 measurement - if type == "node": - measurement = "node" - elif type == "link": - measurement = "link" - else: - raise ValueError(f"不支持的类型: {type}") - # 将北京时间转换为 UTC 时间 - beijing_time = datetime.fromisoformat(query_time) - utc_time = beijing_time.astimezone(timezone.utc) - utc_start_time = utc_time - timedelta(seconds=1) - utc_stop_time = utc_time + timedelta(seconds=1) - # 构建 Flux 查询语句 - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()}) - |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}" and r["_measurement"] == "{measurement}" and r["_field"] == "{property}") - ''' - # 执行查询 - tables = query_api.query(flux_query) - result_records = [] - # 解析查询结果 - for table in tables: - for record in table.records: - result_records.append({ - "ID": record["ID"], - "value": record["_value"] - }) - client.close() - return result_records - - -# 2025/02/19 -def query_scheme_curve_by_ID_property(scheme_Type: str, scheme_Name: str, query_date: str, ID: str, type: str, property: str, - bucket: str="scheme_simulation_result") -> list: - """ - 根据scheme_Type和scheme_Name,查询该模拟方案中，某一node或link的某一属性值的所有时间的结果 - :param scheme_Type: 方案类型 - :param scheme_Name: 方案名称 - :param query_date: 查询日期，格式为 'YYYY-MM-DD' - :param ID: 元素的ID - :param type: 元素的类型，node或link - :param property: 元素的属性值 - :param bucket: 数据存储的 bucket 名称，默认值为 "scheme_simulation_result" - :return: 查询结果的列表 - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 确定 measurement - if type == "node": - measurement = "node" - elif type == "link": - measurement = "link" - else: - raise ValueError(f"不支持的类型: {type}") - start_time = (datetime.strptime(query_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat() - stop_time = datetime.strptime(query_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat() - - # 构建 Flux 查询语句 - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: {start_time}, stop: {stop_time}) - |> filter(fn: (r) => r["_measurement"] == "{measurement}" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}" and r["ID"] == "{ID}" and r["_field"] == "{property}") - ''' - # 执行查询 - tables = query_api.query(flux_query) - # 解析查询结果 - results = [] - for table in tables: - for record in table.records: - results.append({ - "time": record["_time"], - "value": record["_value"] - }) - client.close() - return results - - -# 2025/02/21 -def query_scheme_all_record(scheme_Type: str, scheme_Name: str, query_date: str, bucket: str="scheme_simulation_result") -> tuple: - """ - 查询指定方案的所有记录，包括‘node'和‘link’，分别以指定格式返回。 - :param scheme_Type: 方案类型 - :param scheme_Name: 方案名称 - :param query_date: 查询日期，格式为 'YYYY-MM-DD' - :param bucket: 数据存储的 bucket 名称。 - :return: dict: tuple: (node_records, link_records) - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - start_time = (datetime.strptime(query_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat() - stop_time = datetime.strptime(query_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat() - # 构建 Flux 查询语句 - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: {start_time}, stop: {stop_time}) - |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}" and r["_measurement"] == "node" or r["_measurement"] == "link") - |> pivot( - rowKey:["_time"], - columnKey:["_field"], - valueColumn:"_value" - ) - ''' - # 执行查询 - tables = query_api.query(flux_query) - node_records = [] - link_records = [] - # 解析查询结果 - for table in tables: - for record in table.records: - # print(record.values) # 打印完整记录内容 - measurement = record["_measurement"] - # 处理 node 数据 - if measurement == "node": - node_records.append({ - "time": record["_time"], - "ID": record["ID"], - "head": record["head"], - "pressure": record["pressure"], - "actualdemand": record["actualdemand"], - "quality": record["quality"] - }) - # 处理 link 数据 - elif measurement == "link": - link_records.append({ - "time": record["_time"], - "ID": record["ID"], - "flow": record["flow"], - "velocity": record["velocity"], - "headloss": record["headloss"], - "quality": record["quality"], - "status": record["status"], - "setting": record["setting"], - "reaction": record["reaction"], - "friction": record["friction"] - }) - client.close() - return node_records, link_records - - -# 2025/03/04 -def query_scheme_all_record_property(scheme_Type: str, scheme_Name: str, query_date: str, type: str, property: str, - bucket: str="scheme_simulation_result") -> list: - """ - 查询指定方案的‘node'或‘link’的某一属性值，以指定格式返回。 - :param scheme_Type: 方案类型 - :param scheme_Name: 方案名称 - :param query_date: 查询日期，格式为 'YYYY-MM-DD' - :param type: 查询的类型（决定 measurement） - :param property: 查询的字段名称（field） - :param bucket: 数据存储的 bucket 名称。 - :return: dict: tuple: (node_records, link_records) - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 确定 measurement - if type == "node": - measurement = "node" - elif type == "link": - measurement = "link" - else: - raise ValueError(f"不支持的类型: {type}") - start_time = (datetime.strptime(query_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat() - stop_time = datetime.strptime(query_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat() - # 构建 Flux 查询语句 - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: {start_time}, stop: {stop_time}) - |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}" and r["date"] == "{query_date}" and r["_measurement"] == "{measurement}" and r["_field"] == "{property}") - ''' - # 执行查询 - tables = query_api.query(flux_query) - result_records = [] - # 解析查询结果 - for table in tables: - for record in table.records: - result_records.append({ - "time": record["_time"], - "ID": record["ID"], - "value": record["_value"] - }) - client.close() - return result_records - - -# 2025/02/16 -def export_SCADA_data_to_csv(start_date: str, end_date: str, bucket: str="SCADA_data") -> None: - """ - 导出influxdb中SCADA_data这个bucket的数据到csv中 - :param start_date: 查询开始的时间，格式为 'YYYY-MM-DD' - :param end_date: 查询结束的时间，格式为 'YYYY-MM-DD' - :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data" - :return: - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 将 start_date 的北京时间转换为 UTC 时间范围 - start_time = (datetime.strptime(start_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat() - stop_time = datetime.strptime(end_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat() - # 构建 Flux 查询语句 - flux_query = f''' - from(bucket: "{bucket}") - |> range(start: {start_time}, stop: {stop_time}) - ''' - # 执行查询 - tables = query_api.query(flux_query) - # 存储查询结果 - rows = [] - for table in tables: - for record in table.records: - row = { - 'time': record.get_time(), - 'measurement': record.get_measurement(), - 'date': record.values.get('date', None), - 'description': record.values.get('description', None), - 'device_ID': record.values.get('device_ID', None), - 'monitored_value': record.get_value() if record.get_field() == 'monitored_value' else None, - 'datacleaning_value': record.get_value() if record.get_field() == 'datacleaning_value' else None, - 'simulation_value': record.get_value() if record.get_field() == 'simulation_value' else None, - } - rows.append(row) - # 动态生成 CSV 文件名 - csv_filename = f"SCADA_data_{start_date}至{end_date}.csv" - # 写入到 CSV 文件 - with open(csv_filename, mode='w', newline='') as file: - writer = csv.DictWriter(file, fieldnames=['time', 'measurement', 'date', 'description', 'device_ID', 'monitored_value', 'datacleaning_value', 'simulation_value']) - writer.writeheader() - writer.writerows(rows) - print(f"Data exported to {csv_filename} successfully.") - client.close() - - -# 2025/02/17 -def export_realtime_simulation_result_to_csv(start_date: str, end_date: str, bucket: str="realtime_simulation_result") -> None: - """ - 导出influxdb中realtime_simulation_result这个bucket的数据到csv中 - :param start_date: 查询开始的时间，格式为 'YYYY-MM-DD' - :param end_date: 查询结束的时间，格式为 'YYYY-MM-DD' - :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data" - :return: - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 将 start_date 的北京时间转换为 UTC 时间范围 - start_time = (datetime.strptime(start_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat() - stop_time = datetime.strptime(end_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat() - # 构建 Flux 查询语句，查询指定时间范围内的数据 - flux_query_link = f''' - from(bucket: "{bucket}") - |> range(start: {start_time}, stop: {stop_time}) - |> filter(fn: (r) => r["_measurement"] == "link") - ''' - # 执行查询 - link_tables = query_api.query(flux_query_link) - # 存储link类的数据 - link_rows = [] - link_data = {} - for table in link_tables: - for record in table.records: - key = (record.get_time(), record.values.get('ID', None)) - if key not in link_data: - link_data[key] = {} - field = record.get_field() - link_data[key][field] = record.get_value() - link_data[key]['measurement'] = record.get_measurement() - link_data[key]['date'] = record.values.get('date', None) - # 构建 Flux 查询语句，查询指定时间范围内的数据 - flux_query_node = f''' - from(bucket: "{bucket}") - |> range(start: {start_time}, stop: {stop_time}) - |> filter(fn: (r) => r["_measurement"] == "node") - ''' - # 执行查询 - node_tables = query_api.query(flux_query_node) - # 存储node类的数据 - node_rows = [] - node_data = {} - for table in node_tables: - for record in table.records: - key = (record.get_time(), record.values.get('ID', None)) - if key not in node_data: - node_data[key] = {} - field = record.get_field() - node_data[key][field] = record.get_value() - node_data[key]['measurement'] = record.get_measurement() - node_data[key]['date'] = record.values.get('date', None) - - for key in set(link_data.keys()): - row = {'time': key[0], "ID": key[1]} - row.update(link_data.get(key, {})) - link_rows.append(row) - for key in set(node_data.keys()): - row = {'time': key[0], "ID": key[1]} - row.update(node_data.get(key, {})) - node_rows.append(row) - # 动态生成 CSV 文件名 - csv_filename_link = f"realtime_simulation_link_result_{start_date}至{end_date}.csv" - csv_filename_node = f"realtime_simulation_node_result_{start_date}至{end_date}.csv" - # 写入到 CSV 文件 - with open(csv_filename_link, mode='w', newline='') as file: - writer = csv.DictWriter(file, fieldnames=['time', 'measurement', 'date', 'ID', 'flow', 'leakage', 'velocity', 'headloss', 'status', 'setting', 'quality', 'friction', 'reaction']) - writer.writeheader() - writer.writerows(link_rows) - with open(csv_filename_node, mode='w', newline='') as file: - writer = csv.DictWriter(file, fieldnames=['time', 'measurement', 'date', 'ID', 'head', 'pressure', 'actualdemand', - 'demanddeficit', 'totalExternalOutflow', 'quality']) - writer.writeheader() - writer.writerows(node_rows) - print(f"Data exported to {csv_filename_link} and {csv_filename_node} successfully.") - client.close() - - -# 2025/02/18 -def export_scheme_simulation_result_to_csv_time(start_date: str, end_date: str, bucket: str="scheme_simulation_result") -> None: - """ - 导出influxdb中scheme_simulation_result这个bucket的数据到csv中 - :param start_date: 查询开始的时间，格式为 'YYYY-MM-DD' - :param end_date: 查询结束的时间，格式为 'YYYY-MM-DD' - :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data" - :return: - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - # 将 start_date 的北京时间转换为 UTC 时间范围 - start_time = (datetime.strptime(start_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat() - stop_time = datetime.strptime(end_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat() - # 构建 Flux 查询语句，查询指定时间范围内的数据 - flux_query_link = f''' - from(bucket: "{bucket}") - |> range(start: {start_time}, stop: {stop_time}) - |> filter(fn: (r) => r["_measurement"] == "link") - ''' - # 执行查询 - link_tables = query_api.query(flux_query_link) - # 存储link类的数据 - link_rows = [] - link_data = {} - for table in link_tables: - for record in table.records: - key = (record.get_time(), record.values.get('ID', None)) - if key not in link_data: - link_data[key] = {} - field = record.get_field() - link_data[key][field] = record.get_value() - link_data[key]['measurement'] = record.get_measurement() - link_data[key]['date'] = record.values.get('date', None) - link_data[key]['scheme_Type'] = record.values.get('scheme_Type', None) - link_data[key]['scheme_Name'] = record.values.get('scheme_Name', None) - # 构建 Flux 查询语句，查询指定时间范围内的数据 - flux_query_node = f''' - from(bucket: "{bucket}") - |> range(start: {start_time}, stop: {stop_time}) - |> filter(fn: (r) => r["_measurement"] == "node") - ''' - # 执行查询 - node_tables = query_api.query(flux_query_node) - # 存储node类的数据 - node_rows = [] - node_data = {} - for table in node_tables: - for record in table.records: - key = (record.get_time(), record.values.get('ID', None)) - if key not in node_data: - node_data[key] = {} - field = record.get_field() - node_data[key][field] = record.get_value() - node_data[key]['measurement'] = record.get_measurement() - node_data[key]['date'] = record.values.get('date', None) - node_data[key]['scheme_Type'] = record.values.get('scheme_Type', None) - node_data[key]['scheme_Name'] = record.values.get('scheme_Name', None) - for key in set(link_data.keys()): - row = {'time': key[0], "ID": key[1]} - row.update(link_data.get(key, {})) - link_rows.append(row) - for key in set(node_data.keys()): - row = {'time': key[0], "ID": key[1]} - row.update(node_data.get(key, {})) - node_rows.append(row) - # 动态生成 CSV 文件名 - csv_filename_link = f"scheme_simulation_link_result_{start_date}至{end_date}.csv" - csv_filename_node = f"scheme_simulation_node_result_{start_date}至{end_date}.csv" - # 写入到 CSV 文件 - with open(csv_filename_link, mode='w', newline='') as file: - writer = csv.DictWriter(file, fieldnames=['time', 'measurement', 'date', 'scheme_Type', 'scheme_Name', 'ID', 'flow', 'leakage', 'velocity', 'headloss', 'status', 'setting', 'quality', 'friction', 'reaction']) - writer.writeheader() - writer.writerows(link_rows) - with open(csv_filename_node, mode='w', newline='') as file: - writer = csv.DictWriter(file, fieldnames=['time', 'measurement', 'date', 'scheme_Type', 'scheme_Name', 'ID', 'head', 'pressure', 'actualdemand', - 'demanddeficit', 'totalExternalOutflow', 'quality']) - writer.writeheader() - writer.writerows(node_rows) - print(f"Data exported to {csv_filename_link} and {csv_filename_node} successfully.") - client.close() - - -# 2025/02/18 -def export_scheme_simulation_result_to_csv_scheme(scheme_Type: str, scheme_Name: str, query_date: str, bucket: str="scheme_simulation_result") -> None: - """ - 导出influxdb中scheme_simulation_result这个bucket的数据到csv中 - :param scheme_Type: 查询的方案类型 - :param scheme_Name: 查询的方案名 - :param query_date: 查询日期，格式为 'YYYY-MM-DD' - :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data" - :return: - """ - client = get_new_client() - if not client.ping(): - print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) - - query_api = client.query_api() - start_time = (datetime.strptime(query_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat() - stop_time = datetime.strptime(query_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat() - # 构建 Flux 查询语句，查询指定时间范围内的数据 - flux_query_link = f''' - from(bucket: "{bucket}") - |> range(start: {start_time}, stop: {stop_time}) - |> filter(fn: (r) => r["_measurement"] == "link" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}") - ''' - # 执行查询 - link_tables = query_api.query(flux_query_link) - # 存储link类的数据 - link_rows = [] - link_data = {} - for table in link_tables: - for record in table.records: - key = (record.get_time(), record.values.get('ID', None)) - if key not in link_data: - link_data[key] = {} - field = record.get_field() - link_data[key][field] = record.get_value() - link_data[key]['measurement'] = record.get_measurement() - link_data[key]['date'] = record.values.get('date', None) - link_data[key]['scheme_Type'] = record.values.get('scheme_Type', None) - link_data[key]['scheme_Name'] = record.values.get('scheme_Name', None) - # 构建 Flux 查询语句，查询指定时间范围内的数据 - flux_query_node = f''' - from(bucket: "{bucket}") - |> range(start: {start_time}, stop: {stop_time}) - |> filter(fn: (r) => r["_measurement"] == "node" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}") - ''' - # 执行查询 - node_tables = query_api.query(flux_query_node) - # 存储node类的数据 - node_rows = [] - node_data = {} - for table in node_tables: - for record in table.records: - key = (record.get_time(), record.values.get('ID', None)) - if key not in node_data: - node_data[key] = {} - field = record.get_field() - node_data[key][field] = record.get_value() - node_data[key]['measurement'] = record.get_measurement() - node_data[key]['date'] = record.values.get('date', None) - node_data[key]['scheme_Type'] = record.values.get('scheme_Type', None) - node_data[key]['scheme_Name'] = record.values.get('scheme_Name', None) - for key in set(link_data.keys()): - row = {'time': key[0], "ID": key[1]} - row.update(link_data.get(key, {})) - link_rows.append(row) - for key in set(node_data.keys()): - row = {'time': key[0], "ID": key[1]} - row.update(node_data.get(key, {})) - node_rows.append(row) - # 动态生成 CSV 文件名 - csv_filename_link = f"scheme_simulation_link_result_{scheme_Name}_of_{scheme_Type}.csv" - csv_filename_node = f"scheme_simulation_node_result_{scheme_Name}_of_{scheme_Type}.csv" - # 写入到 CSV 文件 - with open(csv_filename_link, mode='w', newline='') as file: - writer = csv.DictWriter(file, fieldnames=['time', 'measurement', 'date', 'scheme_Type', 'scheme_Name', 'ID', 'flow', 'leakage', 'velocity', 'headloss', 'status', 'setting', 'quality', 'friction', 'reaction']) - writer.writeheader() - writer.writerows(link_rows) - with open(csv_filename_node, mode='w', newline='') as file: - writer = csv.DictWriter(file, fieldnames=['time', 'measurement', 'date', 'scheme_Type', 'scheme_Name', 'ID', 'head', 'pressure', 'actualdemand', - 'demanddeficit', 'totalExternalOutflow', 'quality']) - writer.writeheader() - writer.writerows(node_rows) - print(f"Data exported to {csv_filename_link} and {csv_filename_node} successfully.") - client.close() - - -# 示例调用 -if __name__ == "__main__": - url = "http://localhost:8086" # 替换为你的InfluxDB实例地址 - token = "MhJDl7odKW-y6wNXXUhUMRJ9oPzOvEe52E4NYD5GXtAAMV7BoHMFdet6HqUOt4DjZ-syKjwGao_k0ZIcgrGAPA==" # 替换为你的InfluxDB Token - org_name = "beibei" # 替换为你的Organization名称 - - # step1: 检查连接状态，初始化influxdb的buckets - # try: - # # delete_buckets(org_name) - # create_and_initialize_buckets(org_name) - # except Exception as e: - # print(f"连接失败: {e}") - - - # 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 - query_pg_scada_info_realtime('bb') - query_pg_scada_info_non_realtime('bb') - - query_corresponding_query_id_and_element_id('bb') - - - - # 手动执行存储测试 - # 示例1：store_realtime_SCADA_data_to_influxdb - # store_realtime_SCADA_data_to_influxdb(get_real_value_time='2025-03-16T11:13:00+08:00') - - # 示例2：store_non_realtime_SCADA_data_to_influxdb - # store_non_realtime_SCADA_data_to_influxdb(get_history_data_end_time='2025-03-08T12:00:00+08:00') - - # 示例3：download_history_data_manually - # download_history_data_manually(begin_time='2025-03-21T00:00:00+08:00', end_time='2025-03-22T00:00:00+08:00') - - # step3: 查询测试示例 - - # 示例1：query_latest_record_by_ID - # bucket_name = "realtime_simulation_result" # 数据存储的 bucket 名称 - # node_id = "ZBBDTZDP000022" # 查询的节点 ID - # link_id = "ZBBGXSZW000002" - # - # latest_record = query_latest_record_by_ID(ID=node_id, type="node", bucket=bucket_name) - # # # latest_record = query_latest_record_by_ID(ID=link_id, type="link", bucket=bucket_name) - # # - # if latest_record: - # print("最新记录：", latest_record) - # else: - # print("未找到符合条件的记录。") - - # 示例2：query_all_record_by_time - # node_records, link_records = query_all_record_by_time(query_time="2025-02-14T10:30:00+08:00") - # print("Node 数据：", node_records) - # print("Link 数据：", link_records) - - # 示例3：query_curve_by_ID_property_daterange - # curve_result = query_curve_by_ID_property_daterange(ID=node_id, type="node", property="head", - # start_date="2024-11-25", end_date="2024-11-25") - # print(curve_result) - - # 示例4：query_SCADA_data_by_device_ID_and_time - # SCADA_result_dict = query_SCADA_data_by_device_ID_and_time(globals.fixed_pump_realtime_ids, query_time='2025-03-09T23:45:00+08:00') - # print(SCADA_result_dict) - - # 示例5：query_SCADA_data_curve - # SCADA_result = query_SCADA_data_curve(api_query_id='9519', start_date='2025-03-08', end_date='2025-03-08') - # print(SCADA_result) - - # 示例6：export_SCADA_data_to_csv - # export_SCADA_data_to_csv(start_date='2025-02-13', end_date='2025-02-15') - - # 示例7：export_realtime_simulation_result_to_csv - # export_realtime_simulation_result_to_csv(start_date='2025-02-13', end_date='2025-02-15') - - # 示例8：export_scheme_simulation_result_to_csv_time - # export_scheme_simulation_result_to_csv_time(start_date='2025-02-13', end_date='2025-02-15') - - # 示例9：export_scheme_simulation_result_to_csv_scheme - # export_scheme_simulation_result_to_csv_scheme(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_date='2025-03-10') - - # 示例10：query_scheme_all_record_by_time - # node_records, link_records = query_scheme_all_record_by_time(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_time="2025-02-14T10:30:00+08:00") - # print("Node 数据：", node_records) - # print("Link 数据：", link_records) - - # 示例11：query_scheme_curve_by_ID_property - # curve_result = query_scheme_curve_by_ID_property(scheme_Type='burst_Analysis', scheme_Name='scheme1', ID='ZBBDTZDP000022', - # type='node', property='head') - # print(curve_result) - - # 示例12：query_all_record_by_date - # node_records, link_records = query_all_record_by_date(query_date='2025-02-27') - # print("Node 数据：", node_records) - # print("Link 数据：", link_records) - - # 示例13：query_scheme_all_record - # node_records, link_records = query_scheme_all_record(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_date='2025-03-10') - # print("Node 数据：", node_records) - # print("Link 数据：", link_records) - - # 示例14：query_all_record_by_time_property - # result_records = query_all_record_by_time_property(query_time='2025-02-25T23:45:00+08:00', type='node', property='head') - # print(result_records) - - # 示例15：query_all_record_by_date_property - # result_records = query_all_record_by_date_property(query_date='2025-02-14', type='node', property='head') - # print(result_records) - - # 示例16：query_scheme_all_record_by_time_property - # result_records = query_scheme_all_record_by_time_property(scheme_Type='burst_Analysis', scheme_Name='scheme1', - # query_time='2025-02-14T10:30:00+08:00', type='node', property='head') - # print(result_records) - - # 示例17：query_scheme_all_record_property - # result_records = query_scheme_all_record_property(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_date='2025-03-10', type='node', property='head') - # print(result_records) - - # 示例18：fill_scheme_simulation_result_to_SCADA - # fill_scheme_simulation_result_to_SCADA(scheme_Type='burst_Analysis', scheme_Name='burst_scheme', query_date='2025-03-10') - - # 示例19：query_SCADA_data_by_device_ID_and_timerange - # result = query_SCADA_data_by_device_ID_and_timerange(query_ids_list=globals.fixed_pump_realtime_ids, start_time='2025-03-09T12:00:00+08:00', - # end_time='2025-03-09T12:10:00+08:00') - # print(result) - - - diff --git a/influxdb_api.py b/influxdb_api.py index 0a2507e..e7107de 100644 --- a/influxdb_api.py +++ b/influxdb_api.py @@ -168,14 +168,20 @@ def query_pg_scada_info_non_realtime(name: str) -> None: print(f"查询时发生错误：{e}") +# 2025/03/23 +def get_new_client() -> InfluxDBClient: + """每次调用返回一个新的 InfluxDBClient 实例。""" + return InfluxDBClient(url=url, token=token, org=org_name) + + # 2025/02/01 -def delete_buckets(client: InfluxDBClient, org_name: str) -> None: +def delete_buckets(org_name: str) -> None: """ 删除InfluxDB中指定organization下的所有buckets。 - :param client: (InfluxDBClient): 已初始化的 InfluxDBClient 实例。 :param org_name: InfluxDB中organization的名称。 :return: None """ + client = get_new_client() # 定义需要删除的 bucket 名称列表 buckets_to_delete = ['SCADA_data', 'realtime_simulation_result', 'scheme_simulation_result'] buckets_api = client.buckets_api() @@ -193,18 +199,19 @@ def delete_buckets(client: InfluxDBClient, org_name: str) -> None: print(f"Skipping bucket {bucket.name}. Not in the deletion list.") else: print("未找到 buckets 属性，无法迭代 buckets。") + client.close() # 2025/02/01 -def create_and_initialize_buckets(client: InfluxDBClient, org_name: str) -> None: +def create_and_initialize_buckets(org_name: str) -> None: """ 初始化influxdb的三个数据存储库，分别为SCADA_data、realtime_simulation_result、scheme_simulation_result - :param client: (InfluxDBClient): 已初始化的 InfluxDBClient 实例。 :param org_name: InfluxDB中organization的名称 :return: """ + client = get_new_client() # 先删除原有的，然后再进行初始化 - delete_buckets(client, org_name) + delete_buckets(org_name) bucket_api = BucketsApi(client) write_api = client.write_api() org_api = OrganizationsApi(client) @@ -321,16 +328,17 @@ def create_and_initialize_buckets(client: InfluxDBClient, org_name: str) -> None write_api.write(bucket=bucket, org=org_name, record=points_to_write) write_api.flush() # 刷新缓存一次 print("All buckets created and initialized successfully.") + client.close() -def store_realtime_SCADA_data_to_influxdb(get_real_value_time: str, bucket: str = "SCADA_data", client: InfluxDBClient = client) -> None: +def store_realtime_SCADA_data_to_influxdb(get_real_value_time: str, bucket: str = "SCADA_data") -> None: """ 将SCADA数据通过数据接口导入数据库 :param get_real_value_time: 获取数据的时间，格式如'2024-11-25T09:00:00+08:00' :param bucket: (str): InfluxDB 的 bucket 名称，默认值为 "SCADA_data"。 - :param client: (InfluxDBClient): 已初始化的 InfluxDBClient 实例。 :return: """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -638,6 +646,7 @@ def store_realtime_SCADA_data_to_influxdb(get_real_value_time: str, bucket: str if points_to_write: write_api.write(bucket=bucket, org=org_name, record=points_to_write) write_api.flush() # 刷新缓存一次 + client.close() def convert_time_format(original_time: str) -> str: @@ -652,14 +661,14 @@ def convert_time_format(original_time: str) -> str: # 2025/01/10 -def store_non_realtime_SCADA_data_to_influxdb(get_history_data_end_time: str, bucket: str = "SCADA_data", client: InfluxDBClient = client) -> None: +def store_non_realtime_SCADA_data_to_influxdb(get_history_data_end_time: str, bucket: str = "SCADA_data") -> None: """ 获取某段时间内传回的scada数据 :param get_history_data_end_time: 获取历史数据的终止时间时间，格式如'2024-11-25T09:00:00+08:00' :param bucket: (str): InfluxDB 的 bucket 名称，默认值为 "SCADA_data"。 - :param client: (InfluxDBClient): 已初始化的 InfluxDBClient 实例。 :return: """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -891,19 +900,19 @@ def store_non_realtime_SCADA_data_to_influxdb(get_history_data_end_time: str, bu if points_to_write: write_api.write(bucket=bucket, org=org_name, record=points_to_write) write_api.flush() # 刷新缓存一次 + client.close() # 2025/03/01 -def download_history_data_manually(begin_time: str, end_time: str, bucket: str = "SCADA_data", - client: InfluxDBClient = client) -> None: +def download_history_data_manually(begin_time: str, end_time: str, bucket: str = "SCADA_data") -> None: """ 获取某个时间段内所有SCADA设备的历史数据，非实时执行，手动补充数据版 :param begin_time: 获取历史数据的开始时间，格式如'2024-11-25T09:00:00+08:00' :param end_time: 获取历史数据的结束时间，格式如'2024-11-25T09:00:00+08:00' :param bucket: InfluxDB 的 bucket 名称，默认值为 "SCADA_data" - :param client: 已初始化的 InfluxDBClient 实例 :return: """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -1384,15 +1393,15 @@ def query_all_SCADA_records_by_date(query_date: str, bucket: str="SCADA_data", c return SCADA_results -def query_SCADA_data_by_device_ID_and_time(query_ids_list: List[str], query_time: str, bucket: str="SCADA_data", client: InfluxDBClient=client) -> Dict[str, float]: +def query_SCADA_data_by_device_ID_and_time(query_ids_list: List[str], query_time: str, bucket: str="SCADA_data") -> Dict[str, float]: """ 根据SCADA设备的ID和时间查询值 :param query_ids_list: SCADA设备ID的列表 :param query_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 :param bucket: InfluxDB 的 bucket 名称，默认值为 "SCADA_data"。 - :param client: 已初始化的 InfluxDBClient 实例。 :return: """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -1428,20 +1437,20 @@ def query_SCADA_data_by_device_ID_and_time(query_ids_list: List[str], query_time except Exception as e: print(f"Error querying InfluxDB for device ID {device_id}: {e}") SCADA_result_dict[device_id] = None + client.close() return SCADA_result_dict # 2025/03/14 -def query_SCADA_data_by_device_ID_and_timerange(query_ids_list: List[str], start_time: str, end_time: str, - bucket: str="SCADA_data", client: InfluxDBClient=client): +def query_SCADA_data_by_device_ID_and_timerange(query_ids_list: List[str], start_time: str, end_time: str, bucket: str="SCADA_data"): """ 查询指定时间范围内，多个SCADA设备的数据，用于漏损定位 :param query_ids_list: SCADA设备ID的列表 :param start_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 :param end_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 :param bucket: InfluxDB 的 bucket 名称，默认值为 "SCADA_data"。 - :param client: 已初始化的 InfluxDBClient 实例。 :return: """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -1453,14 +1462,14 @@ def query_SCADA_data_by_device_ID_and_timerange(query_ids_list: List[str], start beijing_end_time = datetime.fromisoformat(end_time) utc_end_time = beijing_end_time.astimezone(timezone.utc) + timedelta(seconds=1) print(utc_end_time) - - SCADA_dict = {} for device_id in query_ids_list: flux_query = f''' from(bucket: "{bucket}") |> range(start: {utc_start_time.isoformat()}, stop: {utc_end_time.isoformat()}) - |> filter(fn: (r) => r["device_ID"] == "{device_id}" and r["_field"] == "monitored_value") + |> filter(fn: (r) => r["_measurement"] == "SCADA_data" and r["device_ID"] = {device_id} and r["_field"] == "monitored_value") + |> pivot(rowKey: ["_time"], columnKey: ["device_ID"], valueColumn: "_value") + |> sort(columns: ["_time"]) ''' # 执行查询，返回一个 FluxTable 列表 tables = query_api.query(flux_query) @@ -1470,10 +1479,10 @@ def query_SCADA_data_by_device_ID_and_timerange(query_ids_list: List[str], start # 获取记录的时间和监测值 records_list.append({ "time": record["_time"], - "value": record.get_value() + "value": record["_value"] }) SCADA_dict[device_id] = records_list - + client.close() return SCADA_dict @@ -1494,17 +1503,16 @@ def query_SCADA_data_by_device_ID_and_date(query_ids_list: List[str], query_date # 2025/02/01 def store_realtime_simulation_result_to_influxdb(node_result_list: List[Dict[str, any]], link_result_list: List[Dict[str, any]], - result_start_time: str, - bucket: str = "realtime_simulation_result", client: InfluxDBClient = client): + result_start_time: str, bucket: str = "realtime_simulation_result"): """ 将实时模拟计算结果数据存储到 InfluxDB 的realtime_simulation_result这个bucket中。 :param node_result_list: (List[Dict[str, any]]): 包含节点和结果数据的字典列表。 :param link_result_list: (List[Dict[str, any]]): 包含连接和结果数据的字典列表。 :param result_start_time: (str): 计算结果的模拟开始时间。 :param bucket: (str): InfluxDB 的 bucket 名称，默认值为 "realtime_simulation_result"。 - :param client: (InfluxDBClient): 已初始化的 InfluxDBClient 实例。 :return: """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -1539,7 +1547,7 @@ def store_realtime_simulation_result_to_influxdb(node_result_list: List[Dict[str # 写入数据到 InfluxDB，多个 field 在同一个 point 中 # write_api.write(bucket=bucket, org=org_name, record=node_point) # write_api.flush() - print(f"成功将 {len(node_result_list)} 条node数据写入 InfluxDB。") + # print(f"成功将 {len(node_result_list)} 条node数据写入 InfluxDB。") for result in link_result_list: link_id = result.get('link') data_list = result.get('result', []) @@ -1566,18 +1574,19 @@ def store_realtime_simulation_result_to_influxdb(node_result_list: List[Dict[str write_api.flush() # 刷新缓存一次 except Exception as e: raise RuntimeError(f"数据写入 InfluxDB 时发生错误: {e}") + client.close() # 2025/02/01 -def query_latest_record_by_ID(ID: str, type: str, bucket: str="realtime_simulation_result", client: InfluxDBClient=client) -> dict: +def query_latest_record_by_ID(ID: str, type: str, bucket: str="realtime_simulation_result") -> dict: """ 查询指定ID的最新的一条记录 :param ID: (str): 要查询的 ID。 :param type: (str): "node"或“link” :param bucket: (str): 数据存储的 bucket 名称。 - :param client: (InfluxDBClient): 已初始化的 InfluxDB 客户端实例。 :return: dict: 最新记录的数据，如果没有找到则返回 None。 """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -1668,14 +1677,14 @@ def query_latest_record_by_ID(ID: str, type: str, bucket: str="realtime_simulati # 2025/02/01 -def query_all_record_by_time(query_time: str, bucket: str="realtime_simulation_result", client: InfluxDBClient=client) -> tuple: +def query_all_record_by_time(query_time: str, bucket: str="realtime_simulation_result") -> tuple: """ 查询指定北京时间的所有记录，包括 'node' 和 'link' measurement，分别以指定格式返回。 :param query_time: (str): 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 :param bucket: (str): 数据存储的 bucket 名称。 - :param client: (InfluxDBClient): 已初始化的 InfluxDBClient 实例。 :return: dict: tuple: (node_records, link_records) """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -1729,21 +1738,21 @@ def query_all_record_by_time(query_time: str, bucket: str="realtime_simulation_r "reaction": record["reaction"], "friction": record["friction"] }) + client.close() return node_records, link_records # 2025/03/03 -def query_all_record_by_time_property(query_time: str, type: str, property: str, bucket: str="realtime_simulation_result", - client: InfluxDBClient=client) -> list: +def query_all_record_by_time_property(query_time: str, type: str, property: str, bucket: str="realtime_simulation_result") -> list: """ 查询指定北京时间的所有记录，查询 'node' 或 'link' 的某一属性值，以指定格式返回。 :param query_time: (str): 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 :param type: (str): 查询的类型（决定 measurement） :param property: (str): 查询的字段名称（field） :param bucket: (str): 数据存储的 bucket 名称。 - :param client: (InfluxDBClient): 已初始化的 InfluxDBClient 实例。 :return: list(dict): result_records """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -1777,18 +1786,19 @@ def query_all_record_by_time_property(query_time: str, type: str, property: str, "ID": record["ID"], "value": record["_value"] }) + client.close() return result_records # 2025/02/21 -def query_all_record_by_date(query_date: str, bucket: str="realtime_simulation_result", client: InfluxDBClient=client) -> tuple: +def query_all_record_by_date(query_date: str, bucket: str="realtime_simulation_result") -> tuple: """ 查询指定日期的所有记录，包括‘node’和‘link’，分别以指定的格式返回 :param query_date: 输入的日期，格式为‘2025-02-14’ :param bucket: 数据存储的bucket名称 - :param client: 已初始化的InfluxDBClient 实例。 :return: dict: tuple: (node_records, link_records) """ + client = get_new_client() # 记录开始时间 time_cost_start = time.perf_counter() print('{} -- Hydraulic simulation started.'.format( @@ -1849,6 +1859,7 @@ def query_all_record_by_date(query_date: str, bucket: str="realtime_simulation_r print('{} -- Hydraulic simulation finished, cost time: {:.2f} s.'.format( datetime.now(pytz.timezone('Asia/Shanghai')).strftime('%Y-%m-%d %H:%M:%S'), time_cost_end - time_cost_start)) + client.close() return node_records, link_records # 2025/03/15 DingZQ @@ -1936,19 +1947,17 @@ def query_all_records_by_date_with_type(query_date: str, query_type: str, bucket time_cost_end - time_cost_start)) return result_records - # 2025/02/21 -def query_all_record_by_date_property(query_date: str, type: str, property: str, - bucket: str="realtime_simulation_result", client: InfluxDBClient=client) -> list: +def query_all_record_by_date_property(query_date: str, type: str, property: str, bucket: str="realtime_simulation_result") -> list: """ 查询指定日期的‘node’或‘link’的某一属性值的所有记录，以指定的格式返回 :param query_date: 输入的日期，格式为‘2025-02-14’ :param type: (str): 查询的类型（决定 measurement） :param property: (str): 查询的字段名称（field） :param bucket: 数据存储的bucket名称 - :param client: 已初始化的InfluxDBClient 实例。 :return: list(dict): result_records """ + client = get_new_client() # 记录开始时间 time_cost_start = time.perf_counter() print('{} -- Hydraulic simulation started.'.format( @@ -1989,11 +1998,12 @@ def query_all_record_by_date_property(query_date: str, type: str, property: str, print('{} -- Hydraulic simulation finished, cost time: {:.2f} s.'.format( datetime.now(pytz.timezone('Asia/Shanghai')).strftime('%Y-%m-%d %H:%M:%S'), time_cost_end - time_cost_start)) + client.close() return result_records # 2025/02/01 -def query_curve_by_ID_property_daterange(ID: str, type: str, property: str, start_date: str, end_date: str, bucket: str="realtime_simulation_result", client: InfluxDBClient=client) -> list: +def query_curve_by_ID_property_daterange(ID: str, type: str, property: str, start_date: str, end_date: str, bucket: str="realtime_simulation_result") -> list: """ 根据 type 查询对应的 measurement，根据 ID 和 date 查询对应的 tag，根据 property 查询对应的 field。 :param ID: (str): 要查询的 ID（tag） @@ -2002,9 +2012,9 @@ def query_curve_by_ID_property_daterange(ID: str, type: str, property: str, star :param start_date: (str): 查询的开始日期，格式为 'YYYY-MM-DD' :param end_date: (str): 查询的结束日期，格式为 'YYYY-MM-DD' :param bucket: (str): 数据存储的 bucket 名称，默认值为 "realtime_simulation_result" - :param client: (InfluxDBClient): 已初始化的 InfluxDBClient 实例 :return: 查询结果的列表 """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -2039,13 +2049,14 @@ def query_curve_by_ID_property_daterange(ID: str, type: str, property: str, star "time": record["_time"], "value": record["_value"] }) + client.close() return results # 2025/02/13 def store_scheme_simulation_result_to_influxdb(node_result_list: List[Dict[str, any]], link_result_list: List[Dict[str, any]], scheme_start_time: str, num_periods: int = 1, scheme_Type: str = None, scheme_Name: str = None, - bucket: str = "scheme_simulation_result", client: InfluxDBClient = client): + bucket: str = "scheme_simulation_result"): """ 将方案模拟计算结果存入 InfluxuDb 的scheme_simulation_result这个bucket中。 :param node_result_list: (List[Dict[str, any]]): 包含节点和结果数据的字典列表。 @@ -2055,9 +2066,9 @@ def store_scheme_simulation_result_to_influxdb(node_result_list: List[Dict[str, :param scheme_Type: (str): 方案类型 :param scheme_Name: (str): 方案名称 :param bucket: (str): InfluxDB 的 bucket 名称，默认值为 "scheme_simulation_result"。 - :param client: (InfluxDBClient): 已初始化的 InfluxDBClient 实例。 :return: """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -2127,6 +2138,7 @@ def store_scheme_simulation_result_to_influxdb(node_result_list: List[Dict[str, write_api.flush() # 刷新缓存一次 except Exception as e: raise RuntimeError(f"数据写入 InfluxDB 时发生错误: {e}") + client.close() # 2025/03/12 @@ -2171,17 +2183,25 @@ def query_corresponding_query_id_and_element_id(name: str) -> None: print(f"数据库连接或查询出错: {e}") +# 2025/03/22 +# def auto_get_burst_flow(): + + +# 2025/03/22 +# def manually_get_burst_flow(): + + # 2025/03/11 def fill_scheme_simulation_result_to_SCADA(scheme_Type: str = None, scheme_Name: str = None, query_date: str = None, - bucket: str = "scheme_simulation_result", client: InfluxDBClient = client): + bucket: str = "scheme_simulation_result"): """ :param scheme_Type: 方案类型 :param scheme_Name: 方案名称 :param query_date: 查询日期，格式为 'YYYY-MM-DD' :param bucket: InfluxDB 的 bucket 名称，默认值为 "scheme_simulation_result" - :param client: 已初始化的 InfluxDBClient 实例 :return: """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -2277,19 +2297,20 @@ def fill_scheme_simulation_result_to_SCADA(scheme_Type: str = None, scheme_Name: if points_to_write: write_api.write(bucket=bucket, org=org_name, record=points_to_write) write_api.flush() # 刷新缓存一次 + client.close() # 2025/02/15 -def query_SCADA_data_curve(api_query_id: str, start_date: str, end_date: str, bucket: str="SCADA_data", client: InfluxDBClient=client) -> list: +def query_SCADA_data_curve(api_query_id: str, start_date: str, end_date: str, bucket: str="SCADA_data") -> list: """ 根据SCADA设备的api_query_id和时间范围，查询得到曲线，查到的数据为0时区时间 :param api_query_id: SCADA设备的api_query_id :param start_date: 查询开始的时间，格式为 'YYYY-MM-DD' :param end_date: 查询结束的时间，格式为 'YYYY-MM-DD' :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data" - :param client: 已初始化的 InfluxDBClient 实例 :return: 查询结果的列表 """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -2313,21 +2334,21 @@ def query_SCADA_data_curve(api_query_id: str, start_date: str, end_date: str, bu "time": record["_time"], "value": record["_value"] }) + client.close() return results # 2025/02/18 -def query_scheme_all_record_by_time(scheme_Type: str, scheme_Name: str, query_time: str, - bucket: str="scheme_simulation_result", client: InfluxDBClient=client) -> tuple: +def query_scheme_all_record_by_time(scheme_Type: str, scheme_Name: str, query_time: str, bucket: str="scheme_simulation_result") -> tuple: """ 查询指定方案某一时刻的所有记录，包括‘node'和‘link’，分别以指定格式返回。 :param scheme_Type: 方案类型 :param scheme_Name: 方案名称 :param query_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 :param bucket: 数据存储的 bucket 名称。 - :param client: 已初始化的 InfluxDBClient 实例。 :return: dict: tuple: (node_records, link_records) """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -2381,12 +2402,13 @@ def query_scheme_all_record_by_time(scheme_Type: str, scheme_Name: str, query_ti "reaction": record["reaction"], "friction": record["friction"] }) + client.close() return node_records, link_records # 2025/03/04 def query_scheme_all_record_by_time_property(scheme_Type: str, scheme_Name: str, query_time: str, type: str, property: str, - bucket: str="scheme_simulation_result", client: InfluxDBClient=client) -> list: + bucket: str="scheme_simulation_result") -> list: """ 查询指定方案某一时刻‘node'或‘link’某一属性值，以指定格式返回。 :param scheme_Type: 方案类型 @@ -2395,9 +2417,9 @@ def query_scheme_all_record_by_time_property(scheme_Type: str, scheme_Name: str, :param type: 查询的类型（决定 measurement） :param property: 查询的字段名称（field） :param bucket: 数据存储的 bucket 名称。 - :param client: 已初始化的 InfluxDBClient 实例。 :return: dict: tuple: (node_records, link_records) """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -2430,12 +2452,13 @@ def query_scheme_all_record_by_time_property(scheme_Type: str, scheme_Name: str, "ID": record["ID"], "value": record["_value"] }) + client.close() return result_records # 2025/02/19 def query_scheme_curve_by_ID_property(scheme_Type: str, scheme_Name: str, query_date: str, ID: str, type: str, property: str, - bucket: str="scheme_simulation_result", client: InfluxDBClient=client) -> list: + bucket: str="scheme_simulation_result") -> list: """ 根据scheme_Type和scheme_Name,查询该模拟方案中，某一node或link的某一属性值的所有时间的结果 :param scheme_Type: 方案类型 @@ -2445,9 +2468,9 @@ def query_scheme_curve_by_ID_property(scheme_Type: str, scheme_Name: str, query_ :param type: 元素的类型，node或link :param property: 元素的属性值 :param bucket: 数据存储的 bucket 名称，默认值为 "scheme_simulation_result" - :param client: 已初始化的 InfluxDBClient 实例 :return: 查询结果的列表 """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -2478,21 +2501,21 @@ def query_scheme_curve_by_ID_property(scheme_Type: str, scheme_Name: str, query_ "time": record["_time"], "value": record["_value"] }) + client.close() return results # 2025/02/21 -def query_scheme_all_record(scheme_Type: str, scheme_Name: str, query_date: str, bucket: str="scheme_simulation_result", - client: InfluxDBClient=client) -> tuple: +def query_scheme_all_record(scheme_Type: str, scheme_Name: str, query_date: str, bucket: str="scheme_simulation_result") -> tuple: """ 查询指定方案的所有记录，包括‘node'和‘link’，分别以指定格式返回。 :param scheme_Type: 方案类型 :param scheme_Name: 方案名称 :param query_date: 查询日期，格式为 'YYYY-MM-DD' :param bucket: 数据存储的 bucket 名称。 - :param client: 已初始化的 InfluxDBClient 实例。 :return: dict: tuple: (node_records, link_records) """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -2543,14 +2566,13 @@ def query_scheme_all_record(scheme_Type: str, scheme_Name: str, query_date: str, "reaction": record["reaction"], "friction": record["friction"] }) - + client.close() return node_records, link_records # 2025/03/04 -# burst_Analysis def query_scheme_all_record_property(scheme_Type: str, scheme_Name: str, query_date: str, type: str, property: str, - bucket: str="scheme_simulation_result", client: InfluxDBClient=client) -> list: + bucket: str="scheme_simulation_result") -> list: """ 查询指定方案的‘node'或‘link’的某一属性值，以指定格式返回。 :param scheme_Type: 方案类型 @@ -2559,9 +2581,9 @@ def query_scheme_all_record_property(scheme_Type: str, scheme_Name: str, query_d :param type: 查询的类型（决定 measurement） :param property: 查询的字段名称（field） :param bucket: 数据存储的 bucket 名称。 - :param client: 已初始化的 InfluxDBClient 实例。 :return: dict: tuple: (node_records, link_records) """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -2592,19 +2614,20 @@ def query_scheme_all_record_property(scheme_Type: str, scheme_Name: str, query_d "ID": record["ID"], "value": record["_value"] }) + client.close() return result_records # 2025/02/16 -def export_SCADA_data_to_csv(start_date: str, end_date: str, bucket: str="SCADA_data", client: InfluxDBClient=client) -> None: +def export_SCADA_data_to_csv(start_date: str, end_date: str, bucket: str="SCADA_data") -> None: """ 导出influxdb中SCADA_data这个bucket的数据到csv中 :param start_date: 查询开始的时间，格式为 'YYYY-MM-DD' :param end_date: 查询结束的时间，格式为 'YYYY-MM-DD' :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data" - :param client: 已初始化的 InfluxDBClient 实例 :return: """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -2642,18 +2665,19 @@ def export_SCADA_data_to_csv(start_date: str, end_date: str, bucket: str="SCADA_ writer.writeheader() writer.writerows(rows) print(f"Data exported to {csv_filename} successfully.") + client.close() # 2025/02/17 -def export_realtime_simulation_result_to_csv(start_date: str, end_date: str, bucket: str="realtime_simulation_result", client: InfluxDBClient=client) -> None: +def export_realtime_simulation_result_to_csv(start_date: str, end_date: str, bucket: str="realtime_simulation_result") -> None: """ 导出influxdb中realtime_simulation_result这个bucket的数据到csv中 :param start_date: 查询开始的时间，格式为 'YYYY-MM-DD' :param end_date: 查询结束的时间，格式为 'YYYY-MM-DD' :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data" - :param client: 已初始化的 InfluxDBClient 实例 :return: """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -2724,18 +2748,19 @@ def export_realtime_simulation_result_to_csv(start_date: str, end_date: str, buc writer.writeheader() writer.writerows(node_rows) print(f"Data exported to {csv_filename_link} and {csv_filename_node} successfully.") + client.close() # 2025/02/18 -def export_scheme_simulation_result_to_csv_time(start_date: str, end_date: str, bucket: str="scheme_simulation_result", client: InfluxDBClient=client) -> None: +def export_scheme_simulation_result_to_csv_time(start_date: str, end_date: str, bucket: str="scheme_simulation_result") -> None: """ 导出influxdb中scheme_simulation_result这个bucket的数据到csv中 :param start_date: 查询开始的时间，格式为 'YYYY-MM-DD' :param end_date: 查询结束的时间，格式为 'YYYY-MM-DD' :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data" - :param client: 已初始化的 InfluxDBClient 实例 :return: """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -2809,20 +2834,20 @@ def export_scheme_simulation_result_to_csv_time(start_date: str, end_date: str, writer.writeheader() writer.writerows(node_rows) print(f"Data exported to {csv_filename_link} and {csv_filename_node} successfully.") + client.close() # 2025/02/18 -def export_scheme_simulation_result_to_csv_scheme(scheme_Type: str, scheme_Name: str, query_date: str, - bucket: str="scheme_simulation_result", client: InfluxDBClient=client) -> None: +def export_scheme_simulation_result_to_csv_scheme(scheme_Type: str, scheme_Name: str, query_date: str, bucket: str="scheme_simulation_result") -> None: """ 导出influxdb中scheme_simulation_result这个bucket的数据到csv中 :param scheme_Type: 查询的方案类型 :param scheme_Name: 查询的方案名 :param query_date: 查询日期，格式为 'YYYY-MM-DD' :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data" - :param client: 已初始化的 InfluxDBClient 实例 :return: """ + client = get_new_client() if not client.ping(): print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) @@ -2895,6 +2920,7 @@ def export_scheme_simulation_result_to_csv_scheme(scheme_Type: str, scheme_Name: writer.writeheader() writer.writerows(node_rows) print(f"Data exported to {csv_filename_link} and {csv_filename_node} successfully.") + client.close() # 示例调用 @@ -2904,15 +2930,13 @@ if __name__ == "__main__": org_name = influxdb_info.org client = InfluxDBClient(url=url, token=token) - # step1: 检查连接状态，初始化influxdb的buckets # try: - # # delete_buckets(client, org_name) - # create_and_initialize_buckets(client, org_name) + # # delete_buckets(org_name) + # create_and_initialize_buckets(org_name) # except Exception as e: # print(f"连接失败: {e}") - # finally: - # client.close() + # 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 query_pg_scada_info_realtime('bb') @@ -2930,98 +2954,98 @@ if __name__ == "__main__": # store_non_realtime_SCADA_data_to_influxdb(get_history_data_end_time='2025-03-08T12:00:00+08:00') # 示例3：download_history_data_manually - # download_history_data_manually(begin_time='2025-03-04T00:00:00+08:00', end_time='2025-03-10T00:00:00+08:00') + # download_history_data_manually(begin_time='2025-03-21T00:00:00+08:00', end_time='2025-03-22T00:00:00+08:00') # step3: 查询测试示例 with InfluxDBClient(url=url, token=token, org=org_name) as client: - # # 示例1：query_latest_record_by_ID - # bucket_name = "realtime_simulation_result" # 数据存储的 bucket 名称 - # node_id = "ZBBDTZDP000022" # 查询的节点 ID - # link_id = "ZBBGXSZW000002" + # 示例1：query_latest_record_by_ID + # bucket_name = "realtime_simulation_result" # 数据存储的 bucket 名称 + # node_id = "ZBBDTZDP000022" # 查询的节点 ID + # link_id = "ZBBGXSZW000002" # - # latest_record = query_latest_record_by_ID(ID=node_id, type="node", bucket=bucket_name, client=client) - # # # latest_record = query_latest_record_by_ID(ID=link_id, type="link", bucket=bucket_name, client=client) - # # - # if latest_record: - # print("最新记录：", latest_record) - # else: - # print("未找到符合条件的记录。") + # latest_record = query_latest_record_by_ID(ID=node_id, type="node", bucket=bucket_name) + # # # latest_record = query_latest_record_by_ID(ID=link_id, type="link", bucket=bucket_name) + # # + # if latest_record: + # print("最新记录：", latest_record) + # else: + # print("未找到符合条件的记录。") - # 示例2：query_all_record_by_time - # node_records, link_records = query_all_record_by_time(query_time="2025-02-14T10:30:00+08:00") - # print("Node 数据：", node_records) - # print("Link 数据：", link_records) + # 示例2：query_all_record_by_time + # node_records, link_records = query_all_record_by_time(query_time="2025-02-14T10:30:00+08:00") + # print("Node 数据：", node_records) + # print("Link 数据：", link_records) - # 示例3：query_curve_by_ID_property_daterange - # curve_result = query_curve_by_ID_property_daterange(ID=node_id, type="node", property="head", - # start_date="2024-11-25", end_date="2024-11-25") - # print(curve_result) + # 示例3：query_curve_by_ID_property_daterange + # curve_result = query_curve_by_ID_property_daterange(ID=node_id, type="node", property="head", + # start_date="2024-11-25", end_date="2024-11-25") + # print(curve_result) - # 示例4：query_SCADA_data_by_device_ID_and_time - # SCADA_result_dict = query_SCADA_data_by_device_ID_and_time(globals.fixed_pump_realtime_ids, query_time='2025-03-09T23:45:00+08:00') - # print(SCADA_result_dict) + # 示例4：query_SCADA_data_by_device_ID_and_time + # SCADA_result_dict = query_SCADA_data_by_device_ID_and_time(globals.fixed_pump_realtime_ids, query_time='2025-03-09T23:45:00+08:00') + # print(SCADA_result_dict) - # 示例5：query_SCADA_data_curve - # SCADA_result = query_SCADA_data_curve(api_query_id='9519', start_date='2025-03-08', end_date='2025-03-08') - # print(SCADA_result) + # 示例5：query_SCADA_data_curve + # SCADA_result = query_SCADA_data_curve(api_query_id='9519', start_date='2025-03-08', end_date='2025-03-08') + # print(SCADA_result) - # 示例6：export_SCADA_data_to_csv - # export_SCADA_data_to_csv(start_date='2025-02-13', end_date='2025-02-15') + # 示例6：export_SCADA_data_to_csv + # export_SCADA_data_to_csv(start_date='2025-02-13', end_date='2025-02-15') - # 示例7：export_realtime_simulation_result_to_csv - # export_realtime_simulation_result_to_csv(start_date='2025-02-13', end_date='2025-02-15') + # 示例7：export_realtime_simulation_result_to_csv + # export_realtime_simulation_result_to_csv(start_date='2025-02-13', end_date='2025-02-15') - # 示例8：export_scheme_simulation_result_to_csv_time - # export_scheme_simulation_result_to_csv_time(start_date='2025-02-13', end_date='2025-02-15') + # 示例8：export_scheme_simulation_result_to_csv_time + # export_scheme_simulation_result_to_csv_time(start_date='2025-02-13', end_date='2025-02-15') - # 示例9：export_scheme_simulation_result_to_csv_scheme - # export_scheme_simulation_result_to_csv_scheme(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_date='2025-03-10') + # 示例9：export_scheme_simulation_result_to_csv_scheme + # export_scheme_simulation_result_to_csv_scheme(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_date='2025-03-10') - # 示例10：query_scheme_all_record_by_time - # node_records, link_records = query_scheme_all_record_by_time(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_time="2025-02-14T10:30:00+08:00") - # print("Node 数据：", node_records) - # print("Link 数据：", link_records) + # 示例10：query_scheme_all_record_by_time + # node_records, link_records = query_scheme_all_record_by_time(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_time="2025-02-14T10:30:00+08:00") + # print("Node 数据：", node_records) + # print("Link 数据：", link_records) - # 示例11：query_scheme_curve_by_ID_property - # curve_result = query_scheme_curve_by_ID_property(scheme_Type='burst_Analysis', scheme_Name='scheme1', ID='ZBBDTZDP000022', - # type='node', property='head') - # print(curve_result) + # 示例11：query_scheme_curve_by_ID_property + # curve_result = query_scheme_curve_by_ID_property(scheme_Type='burst_Analysis', scheme_Name='scheme1', ID='ZBBDTZDP000022', + # type='node', property='head') + # print(curve_result) - # 示例12：query_all_record_by_date - node_records, link_records = query_all_record_by_date(query_date='2025-02-27') - # print("Node 数据：", node_records) - # print("Link 数据：", link_records) + # 示例12：query_all_record_by_date + # node_records, link_records = query_all_record_by_date(query_date='2025-02-27') + # print("Node 数据：", node_records) + # print("Link 数据：", link_records) - # 示例13：query_scheme_all_record - # node_records, link_records = query_scheme_all_record(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_date='2025-03-10') - # print("Node 数据：", node_records) - # print("Link 数据：", link_records) + # 示例13：query_scheme_all_record + # node_records, link_records = query_scheme_all_record(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_date='2025-03-10') + # print("Node 数据：", node_records) + # print("Link 数据：", link_records) - # 示例14：query_all_record_by_time_property - # result_records = query_all_record_by_time_property(query_time='2025-02-25T23:45:00+08:00', type='node', property='head') - # print(result_records) + # 示例14：query_all_record_by_time_property + # result_records = query_all_record_by_time_property(query_time='2025-02-25T23:45:00+08:00', type='node', property='head') + # print(result_records) - # 示例15：query_all_record_by_date_property - # result_records = query_all_record_by_date_property(query_date='2025-02-14', type='node', property='head') - # print(result_records) + # 示例15：query_all_record_by_date_property + # result_records = query_all_record_by_date_property(query_date='2025-02-14', type='node', property='head') + # print(result_records) - # 示例16：query_scheme_all_record_by_time_property - # result_records = query_scheme_all_record_by_time_property(scheme_Type='burst_Analysis', scheme_Name='scheme1', - # query_time='2025-02-14T10:30:00+08:00', type='node', property='head') - # print(result_records) + # 示例16：query_scheme_all_record_by_time_property + # result_records = query_scheme_all_record_by_time_property(scheme_Type='burst_Analysis', scheme_Name='scheme1', + # query_time='2025-02-14T10:30:00+08:00', type='node', property='head') + # print(result_records) - # 示例17：query_scheme_all_record_property - # result_records = query_scheme_all_record_property(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_date='2025-03-10', type='node', property='head') - # print(result_records) + # 示例17：query_scheme_all_record_property + # result_records = query_scheme_all_record_property(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_date='2025-03-10', type='node', property='head') + # print(result_records) - # 示例18：fill_scheme_simulation_result_to_SCADA - # fill_scheme_simulation_result_to_SCADA(scheme_Type='burst_Analysis', scheme_Name='burst_scheme', query_date='2025-03-10') + # 示例18：fill_scheme_simulation_result_to_SCADA + # fill_scheme_simulation_result_to_SCADA(scheme_Type='burst_Analysis', scheme_Name='burst_scheme', query_date='2025-03-10') - # 示例19：query_SCADA_data_by_device_ID_and_timerange - # result = query_SCADA_data_by_device_ID_and_timerange(query_ids_list=globals.fixed_pump_realtime_ids, start_time='2025-03-09T12:00:00+08:00', - # end_time='2025-03-09T12:10:00+08:00') - # print(result) + # 示例19：query_SCADA_data_by_device_ID_and_timerange + # result = query_SCADA_data_by_device_ID_and_timerange(query_ids_list=globals.fixed_pump_realtime_ids, start_time='2025-03-09T12:00:00+08:00', + # end_time='2025-03-09T12:10:00+08:00') + # print(result)