package com.flightfeather.uav.biz.report
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import com.flightfeather.uav.biz.sourcetrace.model.PollutedClue
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import com.flightfeather.uav.domain.entity.Mission
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import com.flightfeather.uav.domain.repository.MissionRep
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import com.flightfeather.uav.lightshare.bean.AreaVo
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import com.flightfeather.uav.socket.eunm.FactorType
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import com.flightfeather.uav.socket.sender.MsgType
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import org.springframework.stereotype.Component
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import java.util.*
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import kotlin.math.round
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/**
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* 走航任务汇总
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* @date 2025/8/22
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* @author feiyu02
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*/
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class MissionSummary() {
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data class Summary(
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// 汇总周期开始时间
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val startTime: Date,
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// 汇总周期结束时间
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val endTime: Date,
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// 走航区域信息
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val area: AreaVo,
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// 走航次数
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val count: Int,
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// 总里程数（公里）
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val kilometres: Double,
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// 涉及区域
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val regionList: List<String>,
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// 各等级空气质量背景走航次数,<空气质量等级，次数，占比>
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val countByDegree: List<Triple<String, Int, Double>>,
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// 问题总数
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val probCount:Int,
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// 高风险场景总数
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val highRiskSceneCount:Int,
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// 问题按监测因子类型分布情况, <因子类型，次数，占比>
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val probByFactor:List<Triple<String, Int, Double>>
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)
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/**
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* 根据时间范围查询走航任务并生成统计结果
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* @param startTime 统计开始时间
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* @param endTime 统计结束时间
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* @param missions 走航任务列表（外部传入，避免重复查询）
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* @param clues 污染线索列表（用于问题统计）
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* @return 走航任务统计结果Summary
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*/
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fun execute(startTime: Date, endTime: Date, missions: List<Mission?>, clues: List<PollutedClue?>): Summary {
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// 1. 查询指定时间范围内的走航任务
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if (missions.isEmpty()) {
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return Summary(
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startTime = startTime,
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endTime = endTime,
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area = AreaVo(), // 空任务时返回默认区域信息
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count = 0,
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kilometres = 0.0,
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regionList = emptyList(),
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countByDegree = emptyList(),
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probCount = 0,
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highRiskSceneCount = 0,
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probByFactor = emptyList()
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)
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}
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// 2. 基础统计：总任务数、总里程
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val totalCount = missions.size
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val totalKilometres = missions.sumOf { it?.kilometres?.toDouble() ?: 0.0 }
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// 3. 区域信息：取首个任务的行政区划信息（如存在多区域可扩展为合并逻辑）
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val firstMission = missions.first()
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val area = AreaVo().apply {
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provinceCode = firstMission?.provinceCode
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provinceName = firstMission?.provinceName
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cityCode = firstMission?.cityCode
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cityName = firstMission?.cityName
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districtCode = firstMission?.districtCode
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districtName = firstMission?.districtName
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townCode = firstMission?.townCode
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townName = firstMission?.townName
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}
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// 4. 涉及区域列表：去重收集所有任务的region字段
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val regionList = missions.mapNotNull { it?.region }.distinct()
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// 5. 空气质量等级分布：按pollutionDegree分组统计次数及占比
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val degreeGroups = missions
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.filter { it?.pollutionDegree != null } // 过滤无效等级
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.groupBy { it?.pollutionDegree!! }
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.mapValues { it.value.size }
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val countByDegree = degreeGroups.map { (degree, count) ->
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Triple(degree, count, count.toDouble() / totalCount)
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}
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// 6. 问题相关统计
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val clueRes = calClue(clues)
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val probCount = clueRes.first // 需关联问题表统计
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val highRiskSceneCount = clueRes.second // 需关联场景表统计
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val probByFactor = clueRes.third
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// 7. 构建并返回统计结果
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return Summary(
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startTime = startTime,
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endTime = endTime,
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area = area,
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count = totalCount,
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kilometres = totalKilometres,
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regionList = regionList,
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countByDegree = countByDegree,
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probCount = probCount,
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highRiskSceneCount = highRiskSceneCount,
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probByFactor = probByFactor
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)
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}
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private fun calClue(clues: List<PollutedClue?>): Triple<Int, Int, List<Triple<String, Int, Double>>> {
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var probCount = 0
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var highRiskSceneCount = 0
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val probByFactorMap = mutableMapOf<FactorType, Int>()
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clues.forEach { c ->
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if (c?.msgType == MsgType.PolClue.value) {
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c.pollutedSource?.sceneList?.size?.let { s -> highRiskSceneCount += s }
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c.pollutedData?.statisticMap?.keys?.forEach { k ->
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probCount++
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if (!probByFactorMap.containsKey(k)) {
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probByFactorMap[k] = 0
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}
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probByFactorMap[k] = probByFactorMap[k]!! + 1
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}
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}
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}
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val probByFactor = probByFactorMap.entries.map {
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val per = if(probCount == 0) .0 else round(it.value.toDouble() / probCount * 100) / 100
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Triple(it.key.des, it.value, per)
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}
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return Triple(probCount, highRiskSceneCount, probByFactor)
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}
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}
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