1. 新增网格化数据优化逻辑

riku

2021-11-17 b7ad984df43f95251375984516e7b0760dcd15cd

 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
@@ -15,6 +16,7 @@
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
@@ -34,6 +36,7 @@
    private val realTimeDataVehicleMapper: RealTimeDataVehicleMapper,
    private val realTimeDataUavMapper: RealTimeDataUavMapper,
    private val realTimeDataGridMapper: RealTimeDataGridMapper,
    private val realTimeDataGridOptMapper: RealTimeDataGridOptMapper,
    private val realTimeDataGridMinMapper: RealTimeDataGridMinMapper
) : RealTimeDataService {

@@ -323,14 +326,18 @@
        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", "2021-06-19 00:00:00", "2021-10-21 00:00:00")
                createCriteria().andBetween("dataTime", sT, eT)
            })
            val res = getOriginData("0d0000000001", "2021-06-19 00:00:00", "2021-10-21 00:00:00", page, 50000)
            // 原始数据（H2S）
            val res = getOriginData("0d0000000001", sT, eT, page, 50000)
            val dataList2 = res.data ?: emptyList()

            total = p.pages
@@ -344,16 +351,26 @@
                if (i >= dataList2.size) break
                val d1 = dataList1[i]
                val d2 = dataList2[i]
                // 判断小时
                // 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 scale =
                // 各预处理后的因子（除H2S）乘系数
                // 单独把原始的H2S乘以系数
                // 更新秒级值
            }
//            val result = epwDataPrep.mDataPrep2(dataList)
//            count += airDataRepository.savePrepData2(result)
                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])
                }

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