333 lines
12 KiB
Python
333 lines
12 KiB
Python
from typing import List, Optional, Any
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from datetime import datetime
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from psycopg import AsyncConnection
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import pandas as pd
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import api_ex
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from postgresql.scada_info import ScadaRepository as PostgreScadaRepository
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from timescaledb.schemas.realtime import RealtimeRepository
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from timescaledb.schemas.scheme import SchemeRepository
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from timescaledb.schemas.scada import ScadaRepository
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class CompositeQueries:
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"""
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复合查询类，提供跨表查询功能
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"""
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@staticmethod
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async def get_scada_associated_realtime_simulation_data(
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timescale_conn: AsyncConnection,
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postgres_conn: AsyncConnection,
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device_ids: List[str],
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start_time: datetime,
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end_time: datetime,
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) -> List[Optional[Any]]:
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"""
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获取 SCADA 关联的 link/node 模拟值
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根据传入的 SCADA device_ids，找到关联的 link/node，
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并根据对应的 type，查询对应的模拟数据
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Args:
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timescale_conn: TimescaleDB 异步连接
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postgres_conn: PostgreSQL 异步连接
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device_ids: SCADA 设备ID列表
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start_time: 开始时间
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end_time: 结束时间
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field: 要查询的字段名
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Returns:
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模拟数据值列表，如果没有找到则对应位置返回 None
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Raises:
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ValueError: 当 SCADA 设备未找到或字段无效时
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"""
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results = []
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# 1. 查询所有 SCADA 信息
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scada_infos = await PostgreScadaRepository.get_scadas(postgres_conn)
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for device_id in device_ids:
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# 2. 根据 device_id 找到对应的 SCADA 信息
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target_scada = None
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for scada in scada_infos:
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if scada["id"] == device_id:
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target_scada = scada
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break
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if not target_scada:
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raise ValueError(f"SCADA device {device_id} not found")
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# 3. 根据 type 和 associated_element_id 查询对应的模拟数据
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element_id = target_scada["associated_element_id"]
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scada_type = target_scada["type"]
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if scada_type.lower() == "pipe_flow":
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# 查询 link 模拟数据
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res = await RealtimeRepository.get_link_field_by_time_range(
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timescale_conn, start_time, end_time, element_id, "flow"
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)
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elif scada_type.lower() == "pressure":
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# 查询 node 模拟数据
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res = await RealtimeRepository.get_node_field_by_time_range(
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timescale_conn, start_time, end_time, element_id, "pressure"
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)
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else:
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raise ValueError(f"Unknown SCADA type: {scada_type}")
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results.append(res)
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return results
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@staticmethod
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async def get_scada_associated_scheme_simulation_data(
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timescale_conn: AsyncConnection,
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postgres_conn: AsyncConnection,
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device_ids: List[str],
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start_time: datetime,
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end_time: datetime,
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scheme_type: str,
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scheme_name: str,
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) -> List[Optional[Any]]:
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"""
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获取 SCADA 关联的 link/node 模拟值
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根据传入的 SCADA device_ids，找到关联的 link/node，
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并根据对应的 type，查询对应的模拟数据
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Args:
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timescale_conn: TimescaleDB 异步连接
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postgres_conn: PostgreSQL 异步连接
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device_ids: SCADA 设备ID列表
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start_time: 开始时间
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end_time: 结束时间
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field: 要查询的字段名
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Returns:
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模拟数据值列表，如果没有找到则对应位置返回 None
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Raises:
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ValueError: 当 SCADA 设备未找到或字段无效时
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"""
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results = []
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# 1. 查询所有 SCADA 信息
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scada_infos = await PostgreScadaRepository.get_scadas(postgres_conn)
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for device_id in device_ids:
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# 2. 根据 device_id 找到对应的 SCADA 信息
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target_scada = None
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for scada in scada_infos:
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if scada["id"] == device_id:
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target_scada = scada
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break
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if not target_scada:
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raise ValueError(f"SCADA device {device_id} not found")
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# 3. 根据 type 和 associated_element_id 查询对应的模拟数据
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element_id = target_scada["associated_element_id"]
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scada_type = target_scada["type"]
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if scada_type.lower() == "pipe_flow":
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# 查询 link 模拟数据
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res = await SchemeRepository.get_link_field_by_scheme_and_time_range(
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timescale_conn,
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scheme_type,
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scheme_name,
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start_time,
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end_time,
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element_id,
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"flow",
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)
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elif scada_type.lower() == "pressure":
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# 查询 node 模拟数据
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res = await SchemeRepository.get_node_field_by_scheme_and_time_range(
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timescale_conn,
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scheme_type,
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scheme_name,
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start_time,
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end_time,
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element_id,
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"pressure",
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)
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else:
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raise ValueError(f"Unknown SCADA type: {scada_type}")
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results.append(res)
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return results
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@staticmethod
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async def get_element_associated_scada_data(
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timescale_conn: AsyncConnection,
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postgres_conn: AsyncConnection,
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element_id: str,
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start_time: datetime,
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end_time: datetime,
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use_cleaned: bool = False,
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) -> Optional[Any]:
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"""
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获取 link/node 关联的 SCADA 监测值
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根据传入的 link/node id，匹配 SCADA 信息，
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如果存在关联的 SCADA device_id，获取实际的监测数据
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Args:
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timescale_conn: TimescaleDB 异步连接
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postgres_conn: PostgreSQL 异步连接
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element_id: link 或 node 的 ID
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start_time: 开始时间
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end_time: 结束时间
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use_cleaned: 是否使用清洗后的数据 (True: "cleaned_value", False: "monitored_value")
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Returns:
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SCADA 监测数据值，如果没有找到则返回 None
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Raises:
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ValueError: 当元素类型无效时
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"""
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# 1. 查询所有 SCADA 信息
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scada_infos = await PostgreScadaRepository.get_scadas(postgres_conn)
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# 2. 根据 element_type 和 element_id 找到关联的 SCADA 设备
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associated_scada = None
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for scada in scada_infos:
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if scada["associated_element_id"] == element_id:
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associated_scada = scada
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break
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if not associated_scada:
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# 没有找到关联的 SCADA 设备
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return None
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# 3. 通过 SCADA device_id 获取监测数据
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device_id = associated_scada["id"]
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# 根据 use_cleaned 参数选择字段
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data_field = "cleaned_value" if use_cleaned else "monitored_value"
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return await ScadaRepository.get_scada_field_by_id_time_range(
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timescale_conn, device_id, start_time, end_time, data_field
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)
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@staticmethod
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async def clean_scada_data(
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timescale_conn: AsyncConnection,
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postgres_conn: AsyncConnection,
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device_ids: List[str],
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start_time: datetime,
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end_time: datetime,
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) -> str:
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"""
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清洗 SCADA 数据
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根据 device_ids 查询 monitored_value，清洗后更新 cleaned_value
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Args:
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timescale_conn: TimescaleDB 连接
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postgres_conn: PostgreSQL 连接
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device_ids: 设备 ID 列表
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start_time: 开始时间
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end_time: 结束时间
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Returns:
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"success" 或错误信息
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"""
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try:
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# 获取所有 SCADA 信息
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scada_infos = await PostgreScadaRepository.get_scadas(postgres_conn)
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# 将列表转换为字典，以 device_id 为键
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scada_device_info_dict = {info["id"]: info for info in scada_infos}
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# 按设备类型分组设备
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type_groups = {}
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for device_id in device_ids:
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device_info = scada_device_info_dict.get(device_id, {})
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device_type = device_info.get("type", "unknown")
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if device_type not in type_groups:
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type_groups[device_type] = []
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type_groups[device_type].append(device_id)
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# 批量处理每种类型的设备
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for device_type, ids in type_groups.items():
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if device_type not in ["pressure", "pipe_flow"]:
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continue # 跳过未知类型
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# 查询 monitored_value 数据
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data = await ScadaRepository.get_scada_field_by_id_time_range(
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timescale_conn, ids, start_time, end_time, "monitored_value"
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)
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if not data:
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continue
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# 将嵌套字典转换为 DataFrame，使用 time 作为索引
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# data 格式: {device_id: [{"time": "...", "value": ...}, ...]}
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all_records = []
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for device_id, records in data.items():
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for record in records:
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all_records.append(
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{
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"time": record["time"],
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"device_id": device_id,
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"value": record["value"],
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}
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)
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if not all_records:
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continue
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# 创建 DataFrame 并透视，使 device_id 成为列
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df_long = pd.DataFrame(all_records)
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df = df_long.pivot(index="time", columns="device_id", values="value")
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# 确保所有请求的设备都在列中（即使没有数据）
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for device_id in ids:
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if device_id not in df.columns:
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df[device_id] = None
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# 只保留请求的设备列
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df = df[ids]
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# 重置索引，将 time 变为普通列
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df = df.reset_index()
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# 移除 time 列，准备输入给清洗方法
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value_df = df.drop(columns=["time"])
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# 调用清洗方法
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if device_type == "pressure":
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cleaned_dict = api_ex.Pdataclean.clean_pressure_data_dict_km(
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value_df.to_dict(orient="list")
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)
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elif device_type == "pipe_flow":
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cleaned_dict = api_ex.Fdataclean.clean_flow_data_dict(
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value_df.to_dict(orient="list")
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)
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else:
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continue
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# 将字典转换为 DataFrame（字典键为设备ID，值为值列表）
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cleaned_value_df = pd.DataFrame(cleaned_dict)
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# 添加 time 列到首列
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cleaned_df = pd.concat([df["time"], cleaned_value_df], axis=1)
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# 将清洗后的数据写回数据库
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for device_id in ids:
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if device_id in cleaned_df.columns:
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cleaned_values = cleaned_df[device_id].tolist()
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time_values = cleaned_df["time"].tolist()
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for i, time_str in enumerate(time_values):
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# time_str 已经是 ISO 格式字符串
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time_dt = datetime.fromisoformat(time_str)
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value = cleaned_values[i]
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await ScadaRepository.update_scada_field(
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timescale_conn,
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time_dt,
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device_id,
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"cleaned_value",
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value,
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)
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return "success"
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except Exception as e:
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return f"error: {str(e)}"
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