1. 新增临时网格化秒级值表，用于数据校准处理

feiyu02

2021-11-22 15925907ba4b16d6fe3a5210186e0ce3cd19efa5

 src/main/kotlin/com/flightfeather/uav/lightshare/service/impl/RealTimeDataServiceImpl.kt

@@ -1,5 +1,6 @@
package com.flightfeather.uav.lightshare.service.impl

import com.flightfeather.uav.common.scaleMap
import com.flightfeather.uav.common.utils.DateUtil
import com.flightfeather.uav.common.utils.ExcelUtil
import com.flightfeather.uav.common.utils.FileExchange
@@ -15,16 +16,19 @@
import com.flightfeather.uav.socket.eunm.UWDeviceType
import com.github.pagehelper.PageHelper
import org.apache.poi.xssf.streaming.SXSSFWorkbook
import org.springframework.beans.BeanUtils
import org.springframework.stereotype.Service
import org.springframework.web.multipart.MultipartFile
import tk.mybatis.mapper.entity.Example
import java.io.ByteArrayInputStream
import java.text.SimpleDateFormat
import java.time.LocalDateTime
import java.time.ZoneId
import java.time.format.DateTimeFormatter
import java.util.*
import javax.servlet.http.HttpServletResponse
import javax.swing.text.DateFormatter
import kotlin.math.sqrt

@Service
class RealTimeDataServiceImpl(
@@ -33,6 +37,7 @@
    private val realTimeDataVehicleMapper: RealTimeDataVehicleMapper,
    private val realTimeDataUavMapper: RealTimeDataUavMapper,
    private val realTimeDataGridMapper: RealTimeDataGridMapper,
    private val realTimeDataGridOptMapper: RealTimeDataGridOptMapper,
    private val realTimeDataGridMinMapper: RealTimeDataGridMinMapper
) : RealTimeDataService {

@@ -227,7 +232,7 @@
        return contents
    }

    private fun getOriginData(deviceCode: String?, startTime: String?, endTime: String?, page: Int?, perPage: Int?): BaseResponse<List<DataVo>> {
    override fun getOriginData(deviceCode: String?, startTime: String?, endTime: String?, page: Int?, perPage: Int?): BaseResponse<List<DataVo>> {
        val _perPage = perPage ?: 60
        val _page = page ?: 1
        val sTime = startTime?.let { dateFormatter.parse(it) }
@@ -287,7 +292,7 @@
        var count = 0

        val minFormatter = SimpleDateFormat("yyyy-MM-dd HH:mm")
        val averageUtil = AverageUtil<RealTimeDataGrid, RealTimeDataGridMin>({d ->
        val averageUtil = AverageUtil<RealTimeDataGridOpt, RealTimeDataGridMin>({d ->
            minFormatter.format(d.dataTime)
        },{list ->
            list.avg()
@@ -296,8 +301,8 @@
        while (total == -1 || page <= total) {
            println("------均值计算start------")

            val p = PageHelper.startPage<RealTimeDataGrid>(page, 50000)
            val res = realTimeDataGridMapper.selectByExample(Example(RealTimeDataGrid::class.java).apply {
            val p = PageHelper.startPage<RealTimeDataGridOpt>(page, 50000)
            val res = realTimeDataGridOptMapper.selectByExample(Example(RealTimeDataGridOpt::class.java).apply {
                createCriteria().andBetween("dataTime", "2021-06-01 00:00:00", "2021-11-05 00:00:00")
            })

@@ -319,6 +324,58 @@
    }

    override fun dataCalibration(): BaseResponse<String> {
        return BaseResponse(true)
        var page = 1
        var total = -1
        var count = 0
        val sT = "2021-06-19 00:00:00"
        val eT = "2021-10-21 00:00:00"

        println("------数据优化start------")
        while (total == -1 || page <= total) {
            // 预处理后的网格化数据
            val p = PageHelper.startPage<RealTimeDataGrid>(page, 50000)
            val dataList1 = realTimeDataGridMapper.selectByExample(Example(RealTimeDataGrid::class.java).apply {
                createCriteria().andBetween("dataTime", sT, eT)
            })
            // 原始数据（H2S）
            val res = getOriginData("0d0000000001", sT, eT, page, 50000)
            val dataList2 = res.data ?: emptyList()

            total = p.pages

            if (page == 1) {
                println("总页数：$total")
            }
            println("当前页数：$page")

            for (i in dataList1.indices) {
                if (i >= dataList2.size) break
                val d1 = dataList1[i]
                val d2 = dataList2[i]
                // 判断小时，获取对应时段的优化系数
                val h = LocalDateTime.ofInstant(d1.dataTime?.toInstant(), ZoneId.systemDefault()).hour
                val scaleList = scaleMap[h] ?: continue
                d1.apply {
                    // 各预处理后的因子（除H2S）乘系数
                    no2 = no2?.times(scaleList[0])
                    co = co?.times(scaleList[1])
                    so2 = so2?.times(scaleList[3])
                    o3 = o3?.times(scaleList[4])
                    voc = voc?.times(scaleList[7])

                    // 单独把原始的H2S乘以系数
                    h2s = d2.values?.get(2)?.factorData?.toFloat()?.times(scaleList[2])
                    h2s = h2s?.let { sqrt(it) * 2 }
                }

                // 更新秒级值
                val opt = RealTimeDataGridOpt()
                BeanUtils.copyProperties(d1, opt)
                count += realTimeDataGridOptMapper.insert(opt)
            }
            page++
        }

        return BaseResponse(count > 0, data = "插入数据: ${count}条")
    }
}