package com.flightfeather.uav.lightshare.service.impl
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import com.flightfeather.uav.common.GridLat
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import com.flightfeather.uav.common.GridLng
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import com.flightfeather.uav.common.utils.MapUtil
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import com.flightfeather.uav.lightshare.bean.BaseResponse
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import com.flightfeather.uav.lightshare.bean.GridVo
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import com.flightfeather.uav.lightshare.service.EPWModelService
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import com.flightfeather.uav.lightshare.service.RealTimeDataService
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import com.flightfeather.uav.model.epw.EPWGridModel
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import com.flightfeather.uav.model.epw.EPWModel
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import com.flightfeather.uav.socket.eunm.UWDeviceType
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import org.springframework.stereotype.Service
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import kotlin.math.PI
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@Service
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class EPWModelServiceImpl(
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private val realTimeDataService: RealTimeDataService,
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) : EPWModelService {
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val epwModel = EPWGridModel()
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override fun getEpwModelResult(deviceCode: String, startTime: String, endTime: String, len: Double): BaseResponse<List<GridVo>> {
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if (deviceCode.length < 2) return BaseResponse(false, "设备编号格式错误")
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val type = deviceCode.substring(0, 2)
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// 确定数据源类型，区分为‘定点监测数据’和‘移动监测数据两种’
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val gridType = when (type) {
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UWDeviceType.UAV.value -> '0'
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UWDeviceType.VEHICLE.value -> '0'
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UWDeviceType.GRID.value -> '1'
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UWDeviceType.BOAT.value -> 'f'
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else -> 'f'
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}
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if (gridType == 'f') return BaseResponse(false)
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val points = mutableListOf<GridVo>()
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// 根据不同类型，确定不同的网格生成方式，得出网格中心点集合(网格默认采用正方形)
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// 走航监测
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if (gridType == '0') {
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// TODO: 2021/12/6 走航监测网格点生成
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}
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// 定点监测
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else if (gridType == '1') {
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// FIXME: 2021/12/6 此处为了测试暂时将站点经纬度写死，后续通过数据库配置获取
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val center = Pair(121.235813, 30.835898)
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// a.确定网格长度对应的坐标差值
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val p1 = MapUtil.getPointByLen(center, len, PI / 2)//正东方向（90°）的坐标点
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val p2 = MapUtil.getPointByLen(center, len, PI)//正南方向（180°）的坐标点
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val dx = p1.first - center.first
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val dy = center.second - p2.second
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// b.确定单边有多少个网格(规定监测点在中心网格的中点上，因此单边网格数一定为奇数)
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val totalLen = 2000 // 网格范围，边长为20千米的正方形
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val gridNum = ((totalLen / 2 / len).toInt() - 1) * 2 + 1
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// c.确定左上角网格左下和右上的两个对角点坐标
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//中心点坐标
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val g1CenterLng = center.first - (gridNum - 1) / 2 * dx//经度减小
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val g1CenterLat = center.second + (gridNum - 1) / 2 * dy//纬度增加
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//左下坐标
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val g1LB = Pair(g1CenterLng - dx / 2, g1CenterLat - dy / 2)
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//右上坐标
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val g1RT = Pair(g1CenterLng + dx / 2, g1CenterLat + dy / 2)
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// d.得出所有网格的两个对角点坐标
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for (x in 0 until gridNum) {
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for (y in 0 until gridNum) {
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points.add(GridVo("$x-$y").apply {
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this.lb = Pair(g1LB.first + dx * x, g1LB.second - dy * y)
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this.rt = Pair(g1RT.first + dx * x, g1RT.second - dy * y)
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this.ciLongitude = (lb!!.first + dx / 2).toBigDecimal()
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this.ciLatitude = (lb!!.second + dy / 2).toBigDecimal()
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})
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}
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}
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}
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// 计算各中心点污染风险权重结果并赋予对应影响等级
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var page = 1
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var totalPage = -1
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while (totalPage == -1 || page <= totalPage) {
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realTimeDataService.getSecondData(deviceCode, startTime, endTime, 0, page, 5000).apply {
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if (totalPage == -1) {
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totalPage = head?.totalPage ?: 0
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}
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val dataList = data ?: emptyList()
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// FIXME: 2021/7/13 此处为了测试暂时将站点经纬度写死，后续通过数据库配置获取
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dataList.forEach {
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if (it.lng == 0.0 && it.lat == 0.0) {
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it.lng = GridLng
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it.lat = GridLat
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}
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}
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epwModel.execute(dataList, points, true)
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page++
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}
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}
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val r = epwModel.outputResult()
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val max = mutableMapOf<String, Double>()//记录每种监测因子的最大值
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// val min = mutableMapOf<String, Double>()//记录每种监测因子的最小值
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//为每个网格赋值权重结果并且筛选各监测因子的最大最小值
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points.forEach {
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val key = "${it.sourceName};${it.index}"
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val d = r[key]
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d?.forEach { (t, u) ->
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it.result[t] = u["综合(${t})"]?.average ?: .0
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//筛选最大值
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if (!max.containsKey(t)) {
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max[t] = it.result[t]!!
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} else {
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if (max[t]!! < it.result[t]!!) {
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max[t] = it.result[t]!!
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}
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}
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// //筛选最小值
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// if (!min.containsKey(t)) {
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// min[t] = it.result[t]!!
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// } else {
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// if (min[t]!! > it.result[t]!!) {
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// min[t] = it.result[t]!!
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// }
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// }
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}
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}
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// 根据最大最小值，计算每个网格的各监测因子的影响等级(0->2)(影响大->小)
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points.forEach {
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it.result.forEach{ (k, v) ->
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max[k]?.let {m ->
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val level = when (v / m) {
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in 0.6666..1.0 -> 0
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in 0.3333..0.6665 -> 1
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in .0..0.3332 -> 2
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else -> 2
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}
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it.level[k] = level
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}
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}
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}
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return BaseResponse(true, data = points)
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}
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}
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