1. 修正实时走航数据平滑处理算法，PM2.5、PM10、VOC采用新的修正算法

feiyu02

2024-07-18 5b0d58c3f7f35f61c0a0437bac3ff708db57fe61

 src/main/kotlin/com/flightfeather/uav/repository/impl/AirDataRepositoryImpl.kt

@@ -1,12 +1,8 @@
package com.flightfeather.uav.repository.impl

import com.flightfeather.uav.common.utils.GsonUtils
import com.flightfeather.uav.domain.MyMapper
import com.flightfeather.uav.domain.entity.*
import com.flightfeather.uav.domain.mapper.RealTimeDataGridMapper
import com.flightfeather.uav.domain.mapper.RealTimeDataMapper
import com.flightfeather.uav.domain.mapper.RealTimeDataUavMapper
import com.flightfeather.uav.domain.mapper.RealTimeDataVehicleMapper
import com.flightfeather.uav.domain.mapper.*
import com.flightfeather.uav.lightshare.bean.DataVo
import com.flightfeather.uav.repository.AirDataRepository
import com.flightfeather.uav.socket.bean.AirData
@@ -19,7 +15,11 @@
import tk.mybatis.mapper.entity.Example
import java.math.BigDecimal
import java.text.SimpleDateFormat
import java.time.LocalDateTime
import java.time.ZoneId
import java.util.*
import kotlin.math.abs
import kotlin.math.sqrt

/**
 * @author riku
@@ -30,11 +30,19 @@
    private val realTimeDataMapper: RealTimeDataMapper,
    private val realTimeDataVehicleMapper: RealTimeDataVehicleMapper,
    private val realTimeDataUavMapper: RealTimeDataUavMapper,
    private val realTimeDataGridMapper: RealTimeDataGridMapper
    private val realTimeDataGridMapper: RealTimeDataGridMapper,
    private val factorCalibrationMapper: FactorCalibrationMapper
): AirDataRepository {

    // FIXME: 2021/10/25 临时车载数据，由于无人机部分监测因子数据无效，因此暂时采用车载数据作为填充
    private val tmpVehicleDataList = mutableListOf<BaseRealTimeData>()

    // 走航监测校准系数
    private val calibrationMap = mutableMapOf<String, MutableMap<Int, Float>>()
    // 走航监测校准系数更新时间
    private var cUpdateTime = LocalDateTime.now()
    // 走航监测校准系数更新时间间隔（分钟）
    private val cInterval = 5L

    override fun saveAirData(dataPackage: AirDataPackage): Int {
        val data = RealTimeData().apply {
@@ -89,6 +97,7 @@
                realTimeDataGridMapper.insert(d)
                count++
            }
            else -> Unit
        }
        return count
    }
@@ -115,6 +124,7 @@
                    realTimeDataGridMapper.insert(d)
                    count++
                }
                else -> Unit
            }
        }
        return count
@@ -128,11 +138,8 @@
                    val d = RealTimeDataVehicle()
                    dataTransform(vo, d)
                    /***************************************************************************************************/
                    // FIXME: 2021/10/27 车载监测部分因子量级调整：NO2*0.6，H2S*0.3, SO2*0.2, O3*0.5，其他要素不变
                    d.no2 = d.no2?.times(0.6f)
                    d.h2s = d.h2s?.times(0.3f)
                    d.so2 = d.so2?.times(0.2f)
                    d.o3 = d.o3?.times(0.5f)
                    // FIXME: 2021/10/27 车载监测部分因子量级调整
                    calibration(d, UWDeviceType.VEHICLE)
                    /***************************************************************************************************/
                    realTimeDataVehicleMapper.insert(d)
                    count++
@@ -165,9 +172,39 @@
                UWDeviceType.GRID -> {
                    val d = RealTimeDataGrid()
                    dataTransform(vo, d)
                    /**************************************************************************/
                    // FIXME: 2021/11/8 针对历史网格化原始数据，进行临时校准处理
//                    val dTime = LocalDateTime.ofInstant(d.dataTime?.toInstant(), ZoneId.systemDefault())
//                    // CO: 2021.8.28 17:27起 *0.25
//                    val coTime = LocalDateTime.of(2021, 8, 28, 17, 27, 0)
//                    if (dTime.isAfter(coTime)) {
//                        d.co = d.co?.times(0.25f)
//                    }
//
//                    // NO2
//                    d.no2 = d.no2?.times(0.6f)
//
//                    // O3
//                    d.o3 = abs(d.o3?.minus(d.no2?.div(2) ?: 0f) ?: 0f) * 1.5f
//
//                    // SO2: *0.2, 2021.8.29 6:00起 *0.08
//                    val so2Time = LocalDateTime.of(2021, 8, 29, 6, 0, 0)
//                    d.so2 = if (dTime.isAfter(so2Time)) {
//                        d.so2?.times(0.08f)
//                    } else {
//                        d.so2?.times(0.2f)
//                    }
//
//                    // H2S
//                    d.h2s = d.h2s?.let { sqrt(it) * 2 }
                    /**************************************************************************/
                    realTimeDataGridMapper.insert(d)
                    count++
                }
                UWDeviceType.BOAT -> {

                }
                else -> Unit
            }
        }
        return count
@@ -236,7 +273,7 @@
        }
    }

    private fun dataTransform(vo: DataVo, bean: BaseRealTimeData) {
    fun dataTransform(vo: DataVo, bean: BaseRealTimeData) {
        bean.apply {
            deviceCode = vo.deviceCode
            latitude = vo.lat?.toBigDecimal()
@@ -267,4 +304,30 @@

        }
    }

    private fun calibration(data: BaseRealTimeData, type: UWDeviceType) {
        //1. 校准系数按照一定时间间隔进行刷新
        val now = LocalDateTime.now()
        if (calibrationMap.isEmpty() || now.minusMinutes(cInterval).isAfter(cUpdateTime)) {
            cUpdateTime = now
            calibrationMap[type.value] = mutableMapOf()
            factorCalibrationMapper.selectByExample(Example(FactorCalibration::class.java).apply {
                createCriteria().andEqualTo("deviceType", type.value)
            }).forEach {
                calibrationMap[type.value]?.put(it.factorId, it.factorScale)
            }
        }
        //2. 根据校准系数计算
        calibrationMap[type.value]?.let{
            data.voc = data.voc?.times(it[FactorType.VOC.value] ?: 1f)
            data.co = data.co?.times(it[FactorType.CO.value] ?: 1f)
            data.pm25 = data.pm25?.times(it[FactorType.PM25.value] ?: 1f)
            data.pm10 = data.pm10?.times(it[FactorType.PM10.value] ?: 1f)

            data.no2 = data.no2?.times(it[FactorType.NO2.value] ?: 1f)
            data.h2s = data.h2s?.times(it[FactorType.H2S.value] ?: 1f)
            data.so2 = data.so2?.times(it[FactorType.SO2.value] ?: 1f)
            data.o3 = data.o3?.times(it[FactorType.O3.value] ?: 1f)
        }
    }
}