package com.flightfeather.uav.biz.sourcetrace.model
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import com.flightfeather.uav.common.utils.MapUtil
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import com.flightfeather.uav.domain.entity.SceneInfo
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import com.flightfeather.uav.domain.repository.SceneInfoRep
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import com.flightfeather.uav.lightshare.bean.AreaVo
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import com.flightfeather.uav.lightshare.bean.SceneInfoVo
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import com.flightfeather.uav.lightshare.eunm.SceneType
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import com.flightfeather.uav.socket.eunm.FactorType
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import org.springframework.beans.BeanUtils
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import org.springframework.web.context.ContextLoader
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import kotlin.math.round
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/**
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* 污染来源
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* 系统内部的污染场景、电子地图搜索得到的实际路段路口等标志信息
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* @date 2025/5/27
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* @author feiyu02
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*/
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class PollutedSource {
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/**
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* 溯源清单显示与临近监测站点的距离（国控、市控、网格化监测点）
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*
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*/
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// 溯源企业
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var sceneList: List<SceneInfoVo?>? = null
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// 溯源推理结论
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var conclusion: String? = null
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fun searchScenes(pollutedArea: PollutedArea, pollutedData: PollutedData) {
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ContextLoader.getCurrentWebApplicationContext()?.getBean(SceneInfoRep::class.java)?.run {
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searchScenes(pollutedArea, this, pollutedData)
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}
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}
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/**
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* 查找系统内部溯源范围内的污染企业
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*/
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fun searchScenes(pollutedArea: PollutedArea, sceneInfoRep: SceneInfoRep, pollutedData: PollutedData) {
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// Fixme 2025.5.14: 污染源的坐标是高德地图坐标系（火星坐标系），而走航数据是WGS84坐标系
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// 按照区域检索内部污染源信息
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var result = mutableListOf<SceneInfo>()
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// 1. 首先按照四至范围从数据库初步筛选污染源，此处的区域坐标已转换为火星坐标系
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val polygonTmp = pollutedArea.polygon!!
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val fb = MapUtil.calFourBoundaries(polygonTmp)
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val sceneList = sceneInfoRep.findByCoordinateRange(fb)
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// 2. 再精确判断是否在反向溯源区域多边形内部
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sceneList.forEach {
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val point = it!!.longitude.toDouble() to it.latitude.toDouble()
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if (MapUtil.isPointInPolygon(point, polygonTmp)) {
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result.add(it)
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}
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}
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val closePolygonTmp = pollutedArea.closePolygon!!
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val closeFb = MapUtil.calFourBoundaries(closePolygonTmp)
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val closeSceneList = sceneInfoRep.findByCoordinateRange(closeFb)
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// 2. 再精确判断是否在反向溯源区域多边形内部
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closeSceneList.forEach {
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val point = it!!.longitude.toDouble() to it.latitude.toDouble()
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if (MapUtil.isPointInPolygon(point, closePolygonTmp)) {
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result.add(it)
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}
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}
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// 根据污染因子的量级，计算主要的污染场景类型，筛选结果
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val mainSceneType = calSceneType(pollutedData)
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if (mainSceneType != null) {
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this.conclusion = mainSceneType.first
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result = result.filter {
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val r = mainSceneType.second.find { s->
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s.value == it.typeId.toInt()
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}
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r != null
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}.toMutableList()
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}
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this.sceneList = findClosestStation(sceneInfoRep, result)
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}
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/**
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* 计算可能的相关污染场景类型以及推理结论
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*/
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@Throws(Exception::class)
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private fun calSceneType(pollutedData: PollutedData): Pair<String, List<SceneType>>? {
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when (pollutedData.selectedFactor?.main) {
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// 氮氧化合物，一般由于机动车尾气，同步计算CO
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FactorType.NO2 -> {
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val coAvg = round(pollutedData.dataList.map { it.co!! }.average()) / 1000
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return "氮氧化合物偏高，CO的量级为${coAvg}mg/m³，一般由于机动车尾气造成，污染源以汽修、加油站为主" to
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listOf(SceneType.TYPE6, SceneType.TYPE10, SceneType.TYPE17)
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}
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FactorType.CO -> return null
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FactorType.H2S -> return null
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FactorType.SO2 -> return null
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FactorType.O3 -> return null
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// a) pm2.5、pm10特别高，两者在各情况下同步展示，pm2.5占pm10的比重变化，比重越高，越有可能是餐饮
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// b) pm10特别高、pm2.5较高，大颗粒扬尘污染，只展示pm10，pm2.5占pm10的比重变化，工地为主
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FactorType.PM25,
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FactorType.PM10,
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-> {
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val pm25Avg = round(pollutedData.dataList.map { it.pm25!! }.average() * 10) / 10
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val pm10Avg = round(pollutedData.dataList.map { it.pm10!! }.average() * 10) / 10
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// 计算异常数据的pm2.5占pm10比重的均值
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val percentageAvg = pollutedData.dataList.map {
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it.pm25!! / it.pm10!!
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}.average()
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val str =
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"PM2.5量级为${pm25Avg}μg/m³，PM10量级为${pm25Avg}μg/m³，PM2.5占PM10的比重为${round(percentageAvg * 100)}%"
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return if (percentageAvg > 0.666) {
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"${str}，比重较大，污染源以餐饮为主，工地次之" to
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listOf(SceneType.TYPE1, SceneType.TYPE2, SceneType.TYPE3, SceneType.TYPE14, SceneType.TYPE5)
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} else if (percentageAvg < 0.333) {
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"${str}，比重较小，属于大颗粒扬尘污染，污染源以工地为主" to
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listOf(SceneType.TYPE1, SceneType.TYPE2, SceneType.TYPE3, SceneType.TYPE14, SceneType.TYPE5)
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} else {
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"${str}，污染源以餐饮、工地为主" to
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listOf(SceneType.TYPE1, SceneType.TYPE2, SceneType.TYPE3, SceneType.TYPE14, SceneType.TYPE5)
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}
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}
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// c) VOC较高，同比计算pm2.5的量级，可能存在同步偏高（汽修、加油站）, 同步计算O3是否有高值
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// d) VOC较高，处于加油站（车辆拥堵情况），CO一般较高, 同步计算O3是否有高值
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FactorType.VOC -> {
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val pm25Avg = round(pollutedData.dataList.map { it.pm25!! }.average() * 10) / 10
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val coAvg = round(pollutedData.dataList.map { it.co!! }.average()) / 1000
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val o3Avg = round(pollutedData.dataList.map { it.o3!! }.average() * 10) / 10
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return "VOC偏高，同时PM2.5量级为${pm25Avg}μg/m³，CO量级为${coAvg}mg/m³，O3量级为${o3Avg}μg/m³，污染源以汽修、加油站为主" to
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listOf(SceneType.TYPE6, SceneType.TYPE17, SceneType.TYPE12)
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}
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else -> return null
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}
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}
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/**
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* 计算最近的监测站点
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*/
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private fun findClosestStation(sceneInfoRep: SceneInfoRep, sceneList: List<SceneInfo>): List<SceneInfoVo> {
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val res1 = sceneInfoRep.findByArea(AreaVo().apply {
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sceneTypeId = SceneType.TYPE19.value.toString()
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})
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val res2 = sceneInfoRep.findByArea(AreaVo().apply {
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sceneTypeId = SceneType.TYPE20.value.toString()
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})
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val res = res1.toMutableList().apply { addAll(res2) }
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return sceneList.map {
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var minLen = -1.0
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var selectedRes: SceneInfo? = null
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res.forEach { r ->
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val dis = MapUtil.getDistance(
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it.longitude.toDouble(),
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it.latitude.toDouble(),
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r!!.longitude.toDouble(),
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r.latitude.toDouble()
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)
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if (minLen < 0 || dis < minLen) {
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minLen = dis
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selectedRes = r
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}
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}
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val vo = SceneInfoVo()
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BeanUtils.copyProperties(it, vo)
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vo.closestStation = selectedRes
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vo.length = minLen
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return@map vo
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}
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}
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}
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