package com.flightfeather.uav.biz.sourcetrace.model
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import com.flightfeather.uav.biz.FactorFilter
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import com.flightfeather.uav.biz.dataanalysis.model.ExceptionType
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import com.flightfeather.uav.biz.sourcetrace.config.RTExcWindLevelConfig
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import com.flightfeather.uav.common.utils.DateUtil
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import com.flightfeather.uav.domain.entity.BaseRealTimeData
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import com.flightfeather.uav.lightshare.bean.DataVo
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import com.flightfeather.uav.socket.eunm.FactorType
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import java.util.Date
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import kotlin.math.round
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/**
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* 污染数据
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* @date 2025/5/27
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* @author feiyu02
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*/
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class PollutedData() {
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inner class Statistic(){
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var factorId: Int? = null
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var factorName: String? = null
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var subFactorId: List<Int>? = null
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var subFactorName: List<String>? = null
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var selectedFactor: FactorFilter.SelectedFactor? = null
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// 因子量级平均变化幅度
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var avgPer: Double? = null
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// 因子量级平均变化速率
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var avgRate: Double? = null
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var avg: Double? = null
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var min: Double? = null
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var max: Double? = null
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}
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/**
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* 9. 关联因子
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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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* 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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* e) 氮氧化合物，一般由于机动车尾气，同步计算CO
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*/
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constructor(
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start: BaseRealTimeData,
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end: BaseRealTimeData?,
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factorList: List<FactorFilter.SelectedFactor>,
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exceptionData: List<BaseRealTimeData>,
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historyData: List<BaseRealTimeData>,
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eType: ExceptionType,
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windLevelCondition: RTExcWindLevelConfig.WindLevelCondition?,
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) : this() {
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exception = eType.des
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exceptionType = eType.value
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startTime = start.dataTime
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endTime = end?.dataTime
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// startData = start.getByFactorType(factor.main)
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// endData = end?.getByFactorType(factor.main) ?: startData
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startData = start
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endData = end
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windSpeed = exceptionData.first().windSpeed?.toDouble()
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times = windLevelCondition?.countLimit
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dataList.add(start)
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exceptionData.forEach {
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dataList.add(it)
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}
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dataVoList.addAll(dataList.map { it.toDataVo() })
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historyDataList.addAll(historyData.map { it.toDataVo() })
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factorList.forEach { f->
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statisticMap[f.main] = Statistic().apply {
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factorId = f.main.value
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factorName = f.main.des
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subFactorId = f.subs.map { it.value }
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subFactorName = f.subs.map { it.des }
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selectedFactor = f
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avgPer = calPer(f.main)
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avgRate = calRate(f.main)
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val s = dataSummary(exceptionData, f.main)
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avg = s.first
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min = s.second
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max = s.third
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}
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}
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}
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var deviceCode: String? = null
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var exception: String? = null
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var exceptionType: Int? = null
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var startTime: Date? = null
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var endTime: Date? = null
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var startData: BaseRealTimeData? = null
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var endData: BaseRealTimeData? = null
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// 风速
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var windSpeed: Double? = null
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// 发生次数
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var times: Int? = null
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var historyDataList = mutableListOf<DataVo>()
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// 异常监测数据
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var dataList: MutableList<BaseRealTimeData> = mutableListOf()
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var dataVoList: MutableList<DataVo> = mutableListOf()
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var statisticMap = mutableMapOf<FactorType, Statistic>()
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fun toFactorNames(): String {
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val factors = statisticMap.entries.map { it.key }.sortedBy { it.value }.joinToString(";") { it.des }
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return factors
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}
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private fun calPer(factorType: FactorType): Double? {
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val list = dataList
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if (list.size < 2) return null
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var total = .0
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for (i in 0 until list.size - 1) {
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val p = list[i].getByFactorType(factorType) ?: .0f
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val n = list[i + 1].getByFactorType(factorType) ?: .0f
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total += (n - p) / p
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}
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return total / (list.size - 1)
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}
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private fun calRate(factorType: FactorType): Double? {
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val list = dataList
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if (list.size < 2) return null
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var total = .0
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for (i in 0 until list.size - 1) {
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val p = list[i].getByFactorType(factorType) ?: .0f
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val n = list[i + 1].getByFactorType(factorType) ?: .0f
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total += (n - p) / 4
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}
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return total / (list.size - 1)
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}
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private fun dataSummary(exceptionData: List<BaseRealTimeData?>, factorType: FactorType): Triple<Double, Double,
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Double> {
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var min = -1.0
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var max = -1.0
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var total = .0
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var count = 0
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exceptionData.forEach {
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val value = it?.getByFactorType(factorType)?.toDouble() ?: return@forEach
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if (min == -1.0 || min > value) {
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min = round(value * 1000) / 1000
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}
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if (max == -1.0 || max < value) {
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max = round(value * 1000) / 1000
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}
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total += value
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count++
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}
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val avg = if (count == 0) .0 else round(total / count * 1000) / 1000
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return Triple(avg, min, max)
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}
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}
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