package cn.flightfeather.supervision.business.autooutput.dataanalysis
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import cn.flightfeather.supervision.business.autooutput.AopOutput
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import cn.flightfeather.supervision.business.autooutput.datasource.AopDataConfig
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import cn.flightfeather.supervision.business.autooutput.datasource.AopDataSource
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import cn.flightfeather.supervision.business.autooutput.datasource.AopDbMapper
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import cn.flightfeather.supervision.business.autooutput.datasource.AopSceneTypeCheck
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import cn.flightfeather.supervision.domain.ds1.entity.DustDataResult
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import java.time.LocalDateTime
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import java.time.ZoneId
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import java.util.*
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import kotlin.math.round
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/**
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* 监测数据分析入库
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* 根据每月为一个周期，统计当月的超标次数、均值、最大最小值、超区均值百分比、数据个数、有效率等数据
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*/
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abstract class AopDataAnalysis<T>(
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private val aopDbMapper: AopDbMapper,
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private val aopSceneTypeCheck: AopSceneTypeCheck,
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private val aopOutput: AopOutput,
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) {
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/**
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* 统计数据 临时存储结构
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*/
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inner class TempResult(
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var total: Double = .0,
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var count: Int = 0,
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){
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var avg: Double = -1.0
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get() {
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if (field == -1.0) {
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field = avg()
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}
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return field
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}
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private fun avg(): Double {
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return if (count == 0) {
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.0
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} else {
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round((total / count) * 1000) / 1000
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}
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}
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}
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//数据源
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private var source: AopDataSource? = null
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fun setResource(topTaskGuid: String?, sceneType: Int, year: Int, month: Int) {
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source = AopDataSource(aopDbMapper, aopSceneTypeCheck)
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val st = LocalDateTime.of(year, month, 1, 0, 0, 0)
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val et = st.plusMonths(1).minusSeconds(1)
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val sTime = Date.from(st.atZone(ZoneId.systemDefault()).toInstant())
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val eTime = Date.from(et.atZone(ZoneId.systemDefault()).toInstant())
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val config = AopDataConfig(topTaskGuid = topTaskGuid, sceneType = sceneType, startTime = sTime, endTime = eTime)
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source?.setResource(config)
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}
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/**
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* 执行统计逻辑
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*/
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fun execute(avg: Double? = null) {
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//计算区均值
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val dAvg = avg ?: districtAvg(source)
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println("均值：${dAvg}")
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//循环处理每个场景
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source?.loop { _, evaluationScene ->
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//获取原始监测数据
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val dataList = fetchDataResources(evaluationScene)
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//计算统计结果
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var tempExceedTimes = TempResult()
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var tempAvg = TempResult()
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var tempMax = TempResult()
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var tempMin = TempResult()
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dataList.forEach {
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// 监测点有多台设备时，每台设备单独统计，取均值最高的为统计结果
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val _tempExceedTimes = TempResult()
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val _tempAvg = TempResult()
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val _tempMax = TempResult()
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val _tempMin = TempResult()
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it.forEach { t ->
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t?.let {
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exceedTimes(t, _tempExceedTimes)
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avg(t, _tempAvg)
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max(t, _tempMax)
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min(t, _tempMin)
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}
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}
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// 保留均值最高的一台设备
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if (_tempAvg.avg > tempAvg.avg) {
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tempExceedTimes = _tempExceedTimes
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tempAvg = _tempAvg
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tempMax = _tempMax
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tempMin = _tempMin
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}
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}
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val dustDataResult = DustDataResult().apply {
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drSceneId = evaluationScene.scene.value?.guid
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drSceneName = evaluationScene.scene.value?.name
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drTime = source?.config?.startTime
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drExceedTimes = tempExceedTimes.count
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drAvg = tempAvg.avg
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drMax = tempMax.total
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drMin = tempMin.total
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drOverAvgPer = overAvgRate(drAvg, dAvg)
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drDataNum = count(dataList)
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drEffectiveRate = effectiveRate(dataList, evaluationScene)
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}
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//更新入库
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aopOutput.toDbDataResult(dustDataResult)
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}
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}
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/**
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* 计算区均值
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* 当前为范围内全部监测点的均值
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*/
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abstract fun districtAvg(source: AopDataSource?): Double?
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/**
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* 获取原始监测数据
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*/
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abstract fun fetchDataResources(evaluationScene: AopDataSource.EvaluationScene): List<List<T?>>
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/**
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* 超标次数
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*/
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abstract fun exceedTimes(data: T, t: TempResult)
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/**
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* 月均值
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*/
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abstract fun avg(data: T, t: TempResult)
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/**
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* 最大值
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*/
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abstract fun max(data: T, t: TempResult)
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/**
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* 最小值
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*/
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abstract fun min(data: T, t: TempResult)
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/**
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* 超月均值百分比
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*/
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abstract fun overAvgRate(avg: Double, dAvg: Double?): Double?
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/**
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* 数据个数
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*/
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open fun count(dataList: List<List<T?>>): Int {
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var count = 0
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dataList.forEach {
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count += it.size
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}
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return count
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}
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/**
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* 有效率
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*/
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abstract fun effectiveRate(dataList: List<List<T?>>, evaluationScene: AopDataSource.EvaluationScene): Double
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}
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