79 lines
3.0 KiB
Python
79 lines
3.0 KiB
Python
from datetime import datetime
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from typing import Any
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from fastapi import APIRouter, Depends, HTTPException, Body
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from pydantic import BaseModel, Field
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from app.auth.keycloak_dependencies import get_current_keycloak_username
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from app.services.burst_detection import (
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run_burst_detection,
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)
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router = APIRouter()
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class BurstDetectionRequest(BaseModel):
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"""爆管检测请求模型"""
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network: str = Field(..., description="管网名称（或数据库名称）")
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observed_pressure_data: (
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dict[str, list[Any]] | list[dict[str, Any]] | list[list[Any]] | None
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) = Field(
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default=None,
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description=(
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"压力观测数据。支持列式字典 {sensor_id: [values,...]}、"
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"逐时刻对象数组 [{sensor_id: value,...}, ...]、"
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"或二维数组 [[t1_s1, t1_s2], [t2_s1, t2_s2], ...]。"
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),
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)
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points_per_day: int = Field(1440, description="每天的数据点数")
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mu: int = Field(100, description="异常值检测的参数")
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iforest_params: dict[str, Any] | None = Field(None, description="隔离森林算法参数")
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target_time: datetime | None = Field(
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None,
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description="目标侦测时刻；为空时自动使用最近一个完整的监测时刻",
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)
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sampling_interval_minutes: int | None = Field(
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None,
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ge=1,
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le=1440,
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description="采样间隔（分钟）；为空时根据压力 SCADA 传输频率自动推断",
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)
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scada_start: datetime | None = Field(None, description="SCADA数据起始时间")
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scada_end: datetime | None = Field(None, description="SCADA数据结束时间")
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sensor_nodes: list[str] | None = Field(None, description="传感器节点列表")
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scheme_name: str | None = Field(None, description="方案名称")
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data_source: str = Field("monitoring", description="数据来源：monitoring（监测）或simulation（模拟）")
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simulation_scheme_name: str | None = Field(None, description="模拟方案名称")
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simulation_scheme_type: str | None = Field(None, description="模拟方案类型")
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@router.post(
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"/burst-detections",
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summary="执行爆管检测",
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description="基于压力观测数据和其他参数执行爆管检测分析"
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)
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async def detect_burst(
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data: BurstDetectionRequest = Body(..., description="爆管检测请求数据"),
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username: str = Depends(get_current_keycloak_username),
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) -> dict[str, Any]:
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"""
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执行爆管检测分析。
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使用异常检测算法（隔离森林）识别压力时间序列中的异常，
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将其作为潜在的爆管事件。
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Args:
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data: 包含管网名称（或数据库名称）、压力数据及相关参数的请求体
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username: 当前认证用户名
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Returns:
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包含检测结果的字典
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Raises:
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HTTPException: 当处理过程中发生错误时
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"""
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try:
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return run_burst_detection(**data.model_dump(), username=username)
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except Exception as exc:
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raise HTTPException(status_code=400, detail=str(exc))
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