初始提交：添加分时处理
This commit is contained in:
liuzhiming
parent
600242462e
commit
186a3db27c
@ -3,7 +3,43 @@ package com.southern.power.grid.dao;
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import com.baomidou.mybatisplus.core.mapper.BaseMapper;
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import com.southern.power.grid.entity.RegionalWeatherData;
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import org.apache.ibatis.annotations.Mapper;
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import org.apache.ibatis.annotations.Param;
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import java.util.List;
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import java.util.Map;
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@Mapper
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public interface RegionalWeatherDataMapper extends BaseMapper<RegionalWeatherData> {
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/**
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* 插入一条区域气象数据记录
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* @param record 实体对象
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* @return 影响行数
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*/
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int insert(RegionalWeatherData record);
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/**
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* 根据主键ID查询
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* @param id 主键ID
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* @return 实体对象
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*/
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RegionalWeatherData selectByPrimaryKey(Long id);
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/**
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* 根据地区编码和资料时次查询
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* @param orgCode 地区编码
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* @param dataTime 资料时次
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* @return 实体列表
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*/
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List<RegionalWeatherData> selectByOrgCodeAndDataTime(@Param("orgCode") String orgCode, @Param("dataTime") String dataTime);
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/**
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* 根据条件动态查询
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* @param params 查询参数Map，支持：
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* orgCode（精确匹配）
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* dataTime（精确匹配）
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* startCreateTime（创建时间起始，格式 yyyy-MM-dd HH:mm:ss）
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* endCreateTime（创建时间结束）
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* @return 实体列表
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*/
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List<RegionalWeatherData> selectByCondition(Map<String, Object> params);
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}
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@ -1,25 +0,0 @@
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package com.southern.power.grid.entity;
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import lombok.Data;
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import java.time.LocalDateTime;
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@Data
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public class ExcelHourlyOutageRowDTO {
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/** 资料时次，对应分时表的 data_time */
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private LocalDateTime dataTime;
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/** Excel 中体现的南网省/地市局/区县局（用于映射） */
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private String nwProvince;
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private String nwCity;
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private String nwDistrict;
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/** 停电影响用户数等业务字段（示例） */
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private Integer userCount;
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private Integer outageState;
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private Integer outageType;
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// ... 根据你的 Excel 列补全
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// getter/setter
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}
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@ -1,18 +0,0 @@
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package com.southern.power.grid.entity;
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import lombok.Data;
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import java.util.List;
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/**
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* 分时统计结果DTO
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*/
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@Data
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public class HourlyStatisticDTO {
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private List<String> timePoints; // 时间点列表，格式：yyyy-MM-dd HH:00:00
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private List<Long> userCounts; // 停电影响用户数
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private List<Double> precipitations; // 小时降水量
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private List<Double> avgTemperatures; // 平均气温
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private List<Double> maxTemperatures; // 最高气温
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private List<Double> minTemperatures; // 最低气温
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private List<Double> maxWindSpeeds; // 极大风速
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}
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@ -1,30 +0,0 @@
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package com.southern.power.grid.entity;
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import lombok.Data;
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import java.time.LocalDateTime;
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/**
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* 站点气象数据
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*
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* @author: LIUZHIMING
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* @date: 2026/3/17
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**/
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@Data
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public class WeatherAreaDataDTO {
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String districtCode;
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private LocalDateTime dataTime;
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// 平均气温
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private String temperature;
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// 小时最高气温
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private String hourlyMaxTemperature;
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// 小时最低气温
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private String hourlyMinTemperature;
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// 小时降雨量
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private Double hourlyPrecipitation;
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// 日累计降雨量
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private String dailyPrecipitation;
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// 小时内极大风速
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private String extremeWindSpeedHourly;
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}
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@ -1,15 +1,14 @@
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package com.southern.power.grid.service.impl;
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import com.baomidou.mybatisplus.core.toolkit.StringUtils;
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import com.southern.power.grid.dao.DnerHourlyPowerOutageEventMapper;
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import com.southern.power.grid.dao.NwSiteAreaConfigurationMapper;
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import com.southern.power.grid.dao.RegionalWeatherDataMapper;
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import com.southern.power.grid.dao.WeatherSiteAreaConfigurationMapper;
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import com.southern.power.grid.entity.DataExcelEntity;
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import com.southern.power.grid.entity.DnerHourlyPowerOutageEvent;
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import com.southern.power.grid.entity.NwSiteAreaConfiguration;
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import com.southern.power.grid.entity.WeatherDataDTO;
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import com.southern.power.grid.entity.RegionalWeatherData;
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import com.southern.power.grid.entity.WeatherSiteAreaConfiguration;
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import com.southern.power.grid.service.WeatherDataService;
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import lombok.extern.slf4j.Slf4j;
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import org.springframework.stereotype.Service;
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@ -17,6 +16,7 @@ import org.springframework.stereotype.Service;
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import javax.annotation.Resource;
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import java.time.LocalDateTime;
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import java.time.format.DateTimeFormatter;
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import java.time.format.DateTimeParseException;
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import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.List;
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@ -32,11 +32,15 @@ public class HourlyOutageExcelProcessService {
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private WeatherSiteAreaConfigurationMapper weatherSiteAreaConfigurationMapper;
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@Resource
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private WeatherDataService weatherDataService;
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private RegionalWeatherDataMapper regionalWeatherDataMapper;
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@Resource
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private DnerHourlyPowerOutageEventMapper dnerHourlyPowerOutageEventMapper;
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private static final Float lengthOutage = 60F;
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// 定义时间格式器（建议定义为静态常量，避免重复创建）
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private static final DateTimeFormatter DATA_TIME_FORMATTER = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss");
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/**
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* 南网区域配置
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* key = nw_province + "|" + nw_city + "|" + nw_district ；value = district_code
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@ -61,10 +65,16 @@ public class HourlyOutageExcelProcessService {
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// 2. 加载气象区划配置表
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loadWeatherAreaConfig();
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List<DnerHourlyPowerOutageEvent> toInsert = new ArrayList<>();
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// 使用 Map 存储待插入实体，Key 为 districtCode + "|" + dataTime
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Map<String, DnerHourlyPowerOutageEvent> toInsertMap = new HashMap<>();
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// 3 & 4. 循环处理 Excel 行
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for (DataExcelEntity row : excelRows) {
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if(row.getLengthOutage()<lengthOutage) {
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log.info("lengthOutage is less than 60! {}, {}, {}", row.getProvince(), row.getCity(), row.getDistrict());
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continue;
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}
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// 3.1 根据南网省/市/区县 → district_code
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String districtCode = findDistrictCode(row.getProvince(), row.getCity(), row.getDistrict());
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if (districtCode == null) {
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@ -72,39 +82,51 @@ public class HourlyOutageExcelProcessService {
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log.info("districtCode is null! {}, {}, {}", row.getProvince(), row.getCity(), row.getDistrict());
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continue;
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}
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// 3.2 district_code → station_id
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List<WeatherSiteAreaConfiguration> weatherAreas = weatherAreaMap.get(districtCode);
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if (weatherAreas == null) {
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// 找不到对应气象站
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log.info("weatherAreas is null! {}", districtCode);
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continue;
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}
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List<String> stationIds = new ArrayList<>();
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for (WeatherSiteAreaConfiguration weatherArea : weatherAreas) {
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String stationId = weatherArea.getStationId();
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if (StringUtils.isNotEmpty(stationId) && !stationIds.contains(stationId)) {
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stationIds.add(stationId);
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}
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}
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// 3.3 通过 stationId + 时间，统一取8天气象数据（内部做缓存）
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LocalDateTime dataTime = row.getStartTime();
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List<WeatherDataDTO> weatherData = weatherDataService.getWeatherData(stationIds, dataTime);
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// 3.2 获取区域时段内气象数据
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List<RegionalWeatherData> regionalWeatherDataList =
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regionalWeatherDataMapper.selectByOrgCodeAndDataTime(districtCode, DB_DATETIME_STR.format(row.getStartTime()));
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// 3.4 组装分时停电事件实体
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for (WeatherDataDTO weatherDataDTO : weatherData) {
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DnerHourlyPowerOutageEvent entity = buildHourlyEvent(row, districtCode, weatherDataDTO);
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toInsert.add(entity);
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for (RegionalWeatherData regionalWeatherData : regionalWeatherDataList) {
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LocalDateTime dataDateTime;
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try {
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dataDateTime = LocalDateTime.parse(regionalWeatherData.getDataTime(), DATA_TIME_FORMATTER);
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} catch (DateTimeParseException e) {
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log.warn("解析 dataTime 失败: {}", regionalWeatherData.getDataTime());
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continue;
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}
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if (row.getStartTime().isBefore(dataDateTime) && dataDateTime.isBefore(row.getEndTime())) {
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String key = districtCode + "|" + regionalWeatherData.getDataTime();
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// 检查 Map 中是否已存在相同组合键
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DnerHourlyPowerOutageEvent existing = toInsertMap.get(key);
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if (existing == null) {
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// 不存在，创建新实体并放入 Map
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DnerHourlyPowerOutageEvent entity = buildHourlyEvent(row, districtCode, regionalWeatherData);
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toInsertMap.put(key, entity);
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} else {
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// 已存在，执行累加逻辑
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accumulateEvent(existing, row);
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}
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}
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}
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}
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if (!toInsert.isEmpty()) {
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// 4. 批量入库
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if (!toInsertMap.isEmpty()) {
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// 将 Map 中的 values 转为 List 进行批量插入
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List<DnerHourlyPowerOutageEvent> toInsert = new ArrayList<>(toInsertMap.values());
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dnerHourlyPowerOutageEventMapper.batchInsert(toInsert);
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}
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}
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/**
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* 累加已存在实体的字段
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* @param existing 已存在的实体
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* @param row 当前 Excel 行数据
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*/
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private void accumulateEvent(DnerHourlyPowerOutageEvent existing, DataExcelEntity row) {
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existing.setUserCount(row.getUserCount() + existing.getUserCount());
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}
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/**
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* 加载南网区划配置
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* **/
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@ -155,31 +177,31 @@ public class HourlyOutageExcelProcessService {
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private DnerHourlyPowerOutageEvent buildHourlyEvent(DataExcelEntity row,
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String districtCode,
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WeatherDataDTO weatherData) {
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DnerHourlyPowerOutageEvent e = new DnerHourlyPowerOutageEvent();
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e.setOrgCode(districtCode);
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RegionalWeatherData regionalWeatherData) {
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DnerHourlyPowerOutageEvent event = new DnerHourlyPowerOutageEvent();
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event.setOrgCode(districtCode);
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// 时间转成字符串，入库到 data_time
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if (row.getStartTime() != null) {
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e.setDataTime(row.getStartTime().format(DB_DATETIME_STR));
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event.setDataTime(regionalWeatherData.getDataTime());
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}
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// Excel 本身的业务字段
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e.setUserCount(row.getUserCount());
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e.setOutageState(row.getOutageState());
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e.setOutageType(row.getOutageType());
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event.setUserCount(row.getUserCount());
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event.setOutageState(row.getOutageState());
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event.setOutageType(row.getOutageType());
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// 气象字段（如不存在数据可以为 null）
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if (weatherData != null) {
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e.setTemperature(weatherData.getTemperature());
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e.setHourlyMaxTemperature(weatherData.getHourlyMaxTemperature());
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e.setHourlyMinTemperature(weatherData.getHourlyMinTemperature());
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// e.setHourlyPrecipitation(weatherData.getHourlyPrecipitation());
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e.setDailyPrecipitation(weatherData.getDailyPrecipitation());
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e.setExtremeWindSpeedHourly(weatherData.getExtremeWindSpeedHourly());
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if (regionalWeatherData != null) {
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event.setTemperature(regionalWeatherData.getTemperature());
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event.setHourlyMaxTemperature(regionalWeatherData.getHourlyMaxTemperature());
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event.setHourlyMinTemperature(regionalWeatherData.getHourlyMinTemperature());
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event.setHourlyPrecipitation(regionalWeatherData.getHourlyPrecipitation());
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event.setDailyPrecipitation(regionalWeatherData.getDailyPrecipitation());
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event.setExtremeWindSpeedHourly(regionalWeatherData.getExtremeWindSpeedHourly());
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}
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// create_by/update_by 可根据当前登录用户等进行填充
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return e;
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return event;
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}
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}
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@ -1,411 +0,0 @@
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package com.southern.power.grid.service.impl;
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import com.southern.power.grid.dao.DnerHourlyPowerOutageEventMapper;
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import com.southern.power.grid.dao.NwSiteAreaConfigurationMapper;
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import com.southern.power.grid.dao.WeatherSiteAreaConfigurationMapper;
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import com.southern.power.grid.entity.DnerHourlyPowerOutageEvent;
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import com.southern.power.grid.entity.HourlyStatisticDTO;
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import com.southern.power.grid.entity.NwSiteAreaConfiguration;
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import com.southern.power.grid.entity.WeatherDataDTO;
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import com.southern.power.grid.entity.WeatherSiteAreaConfiguration;
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import com.southern.power.grid.service.WeatherDataService;
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import com.southern.power.grid.utils.TimeUtil;
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import org.springframework.stereotype.Service;
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import org.springframework.util.CollectionUtils;
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import javax.annotation.Resource;
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import java.time.LocalDateTime;
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import java.time.format.DateTimeFormatter;
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import java.util.*;
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import java.util.function.Function;
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import java.util.stream.Collectors;
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@Service
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public class HourlyStatisticService {
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@Resource
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private NwSiteAreaConfigurationMapper nwSiteAreaConfigurationMapper;
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@Resource
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private WeatherSiteAreaConfigurationMapper weatherSiteAreaConfigurationMapper;
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@Resource
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private WeatherDataService weatherDataService;
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@Resource
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private DnerHourlyPowerOutageEventMapper dnerHourlyPowerOutageEventMapper;
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private static final DateTimeFormatter TIME_POINT_FORMATTER = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:00:00");
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/**
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* 获取分时统计全量数据（包含所有指标）
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* @param areaType 区域类型：province-省，city-市，county-县
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* @param areaCode 区域编码（省/市/县编码，县为org_code）
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* @param startTime 开始时间（包含）
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* @param endTime 结束时间（包含）
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* @return 分时统计结果
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*/
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public HourlyStatisticDTO getHourlyStatistics(String areaType, String areaCode,
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LocalDateTime startTime, LocalDateTime endTime) {
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// 1. 生成时间点列表（整点）
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List<LocalDateTime> timePoints = generateTimePoints(startTime, endTime);
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if (timePoints.isEmpty()) {
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return new HourlyStatisticDTO();
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}
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// 2. 根据区域类型获取需要统计的区县编码列表
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List<String> districtCodes = getDistrictCodesByArea(areaType, areaCode);
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if (districtCodes.isEmpty()) {
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return new HourlyStatisticDTO();
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}
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// 3. 分别计算各指标
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HourlyStatisticDTO result = new HourlyStatisticDTO();
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result.setTimePoints(timePoints.stream().map(t -> t.format(TIME_POINT_FORMATTER)).collect(Collectors.toList()));
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// 3.1 停电用户数（直接查分时停电事件表）
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Map<LocalDateTime, Long> userCountMap = calculateUserCount(districtCodes, timePoints);
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result.setUserCounts(fillList(timePoints, userCountMap, 0L));
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// 3.2 降水量（需要多站点平均）
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Map<LocalDateTime, Double> precipMap = calculatePrecipitation(districtCodes, timePoints);
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result.setPrecipitations(fillList(timePoints, precipMap, 0.0));
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// 3.3 气温（平均/最高/最低）
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TemperatureResult tempResult = calculateTemperature(districtCodes, timePoints);
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result.setAvgTemperatures(fillList(timePoints, tempResult.getAvgMap(), 0.0));
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result.setMaxTemperatures(fillList(timePoints, tempResult.getMaxMap(), 0.0));
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result.setMinTemperatures(fillList(timePoints, tempResult.getMinMap(), 0.0));
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// 3.4 风速（极大值）
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Map<LocalDateTime, Double> windMap = calculateWindSpeed(districtCodes, timePoints);
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result.setMaxWindSpeeds(fillList(timePoints, windMap, 0.0));
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return result;
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}
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/**
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* 生成整点时间序列（从startTime的整点开始，到endTime的整点结束，包含首尾）
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*/
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private List<LocalDateTime> generateTimePoints(LocalDateTime start, LocalDateTime end) {
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List<LocalDateTime> points = new ArrayList<>();
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LocalDateTime current = start.withMinute(0).withSecond(0).withNano(0);
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LocalDateTime endPoint = end.withMinute(0).withSecond(0).withNano(0);
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while (!current.isAfter(endPoint)) {
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points.add(current);
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current = current.plusHours(1);
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}
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return points;
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}
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/**
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* 根据区域类型获取所有需要统计的区县编码列表
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*/
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private List<String> getDistrictCodesByArea(String areaType, String areaCode) {
|
||||
if ("county".equalsIgnoreCase(areaType)) {
|
||||
return Collections.singletonList(areaCode);
|
||||
}
|
||||
|
||||
// 查询南网配置表，根据省/市编码获取所有区县编码
|
||||
List<NwSiteAreaConfiguration> list;
|
||||
if ("province".equalsIgnoreCase(areaType)) {
|
||||
list = new ArrayList<>();// nwSiteAreaConfigurationMapper.selectByProvince(areaCode);
|
||||
} else if ("city".equalsIgnoreCase(areaType)) {
|
||||
list = new ArrayList<>(); // nwSiteAreaConfigurationMapper.selectByCity(areaCode);
|
||||
} else {
|
||||
return Collections.emptyList();
|
||||
}
|
||||
|
||||
if (CollectionUtils.isEmpty(list)) {
|
||||
return Collections.emptyList();
|
||||
}
|
||||
return list.stream()
|
||||
.map(NwSiteAreaConfiguration::getDistrictCode)
|
||||
.filter(Objects::nonNull)
|
||||
.distinct()
|
||||
.collect(Collectors.toList());
|
||||
}
|
||||
|
||||
// ===================== 停电用户数统计 =====================
|
||||
private Map<LocalDateTime, Long> calculateUserCount(List<String> districtCodes, List<LocalDateTime> timePoints) {
|
||||
// 查询所有区县在时间范围内的停电事件记录
|
||||
List<DnerHourlyPowerOutageEvent> events = dnerHourlyPowerOutageEventMapper.selectByDistrictsAndTime(
|
||||
districtCodes, timePoints.get(0), timePoints.get(timePoints.size() - 1).plusHours(1));
|
||||
|
||||
// 按区县和整点时刻聚合user_count
|
||||
Map<LocalDateTime, Long> result = new HashMap<>();
|
||||
for (LocalDateTime tp : timePoints) {
|
||||
result.put(tp, 0L);
|
||||
}
|
||||
|
||||
if (CollectionUtils.isEmpty(events)) {
|
||||
return result;
|
||||
}
|
||||
|
||||
// 注意：data_time是停电开始时间，需按照“停电开始时间 > 整点时刻”的规则计入该整点
|
||||
// 但我们表中没有结束时间，无法精确统计“正在停电中”，此处采用简化规则：按开始时间所在小时计入
|
||||
// 若未来表添加end_time字段，可修改为更精确的逻辑
|
||||
for (DnerHourlyPowerOutageEvent event : events) {
|
||||
if (event.getDataTime() == null) continue;
|
||||
LocalDateTime start = TimeUtil.parseToLocalDateTime(event.getDataTime()); // 需要工具类解析字符串
|
||||
if (start == null) continue;
|
||||
|
||||
// 找到大于start的最小整点（即start所在的下一个整点）
|
||||
LocalDateTime targetHour = start.withMinute(0).withSecond(0).withNano(0).plusHours(1);
|
||||
if (result.containsKey(targetHour)) {
|
||||
result.merge(targetHour, event.getUserCount() == null ? 0L : event.getUserCount().longValue(), Long::sum);
|
||||
}
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
// ===================== 降水量统计（多站平均） =====================
|
||||
private Map<LocalDateTime, Double> calculatePrecipitation(List<String> districtCodes, List<LocalDateTime> timePoints) {
|
||||
// 1. 获取所有区县关联的气象站ID
|
||||
List<String> stationIds = getStationIdsByDistricts(districtCodes);
|
||||
if (stationIds.isEmpty()) {
|
||||
return emptyMap(timePoints);
|
||||
}
|
||||
|
||||
// 2. 批量获取所有站点的气象数据（按小时）
|
||||
Map<String, Map<LocalDateTime, WeatherDataDTO>> stationDataMap = batchFetchWeatherData(stationIds, timePoints);
|
||||
|
||||
// 3. 按小时计算各县平均降水量
|
||||
// 由于降水量县级规则是“国家站+区域站平均”，但我们有所有站的数据，需要先计算各县的平均值，再汇总
|
||||
// 简化：直接对所有站的数据按小时取平均（因为最终市级还需要再平均，最终结果等价于所有站的平均）
|
||||
// 但需求要求县级统计时是县级内平均，市级统计时是各县平均再平均，因此必须按县分组先计算平均值
|
||||
// 此处我们实现为：先按县分组，每个小时取该县所有站的降水量平均值，得到各县小时降水量；然后所有县的小时降水量再平均（市级）或直接使用（县级）
|
||||
// 但本方法返回值是总体结果，需要在调用前已经确定是县级还是市级，但调用时districtCodes已经是一个集合，方法需要返回这个集合整体的平均降水量。
|
||||
// 因此：对每个小时，计算所有区县在该小时的平均降水量（即先计算每个县内所有站的平均，再对这些县的平均取平均）
|
||||
return calculateAverageByDistrict(stationDataMap, districtCodes, timePoints,
|
||||
WeatherDataDTO::getHourlyPrecipitation);
|
||||
}
|
||||
|
||||
// ===================== 气温统计 =====================
|
||||
private TemperatureResult calculateTemperature(List<String> districtCodes, List<LocalDateTime> timePoints) {
|
||||
// 1. 获取所有区县关联的气象站ID
|
||||
List<String> stationIds = getStationIdsByDistricts(districtCodes);
|
||||
if (stationIds.isEmpty()) {
|
||||
return new TemperatureResult(emptyMap(timePoints), emptyMap(timePoints), emptyMap(timePoints));
|
||||
}
|
||||
|
||||
// 2. 获取所有站点的气象数据
|
||||
Map<String, Map<LocalDateTime, WeatherDataDTO>> stationDataMap = batchFetchWeatherData(stationIds, timePoints);
|
||||
|
||||
// 3. 分别计算平均、最高、最低
|
||||
Map<LocalDateTime, Double> avgMap = calculateAverageByDistrict(stationDataMap, districtCodes, timePoints,
|
||||
d -> parseDouble(d.getTemperature()));
|
||||
Map<LocalDateTime, Double> maxMap = calculateExtremeByDistrict(stationDataMap, districtCodes, timePoints,
|
||||
d -> parseDouble(d.getHourlyMaxTemperature()), true);
|
||||
Map<LocalDateTime, Double> minMap = calculateExtremeByDistrict(stationDataMap, districtCodes, timePoints,
|
||||
d -> parseDouble(d.getHourlyMinTemperature()), false);
|
||||
|
||||
return new TemperatureResult(avgMap, maxMap, minMap);
|
||||
}
|
||||
|
||||
// ===================== 风速统计（极大值） =====================
|
||||
private Map<LocalDateTime, Double> calculateWindSpeed(List<String> districtCodes, List<LocalDateTime> timePoints) {
|
||||
List<String> stationIds = getStationIdsByDistricts(districtCodes);
|
||||
if (stationIds.isEmpty()) {
|
||||
return emptyMap(timePoints);
|
||||
}
|
||||
|
||||
Map<String, Map<LocalDateTime, WeatherDataDTO>> stationDataMap = batchFetchWeatherData(stationIds, timePoints);
|
||||
|
||||
// 风速规则：先取各县内所有站的最大值，再取所有县的最大值（市级）
|
||||
return calculateExtremeByDistrict(stationDataMap, districtCodes, timePoints,
|
||||
d -> parseDouble(d.getExtremeWindSpeedHourly()), true);
|
||||
}
|
||||
|
||||
// ===================== 通用辅助方法 =====================
|
||||
|
||||
/**
|
||||
* 根据区县编码列表获取所有气象站ID（去重）
|
||||
*/
|
||||
private List<String> getStationIdsByDistricts(List<String> districtCodes) {
|
||||
if (CollectionUtils.isEmpty(districtCodes)) {
|
||||
return Collections.emptyList();
|
||||
}
|
||||
List<WeatherSiteAreaConfiguration> configs = new ArrayList<>(); // new weatherSiteAreaConfigurationMapper.selectByDistrictCodes(districtCodes);
|
||||
if (CollectionUtils.isEmpty(configs)) {
|
||||
return Collections.emptyList();
|
||||
}
|
||||
return configs.stream()
|
||||
.map(WeatherSiteAreaConfiguration::getStationId)
|
||||
.filter(Objects::nonNull)
|
||||
.distinct()
|
||||
.collect(Collectors.toList());
|
||||
}
|
||||
|
||||
/**
|
||||
* 批量获取多个站点在多个时间点的气象数据
|
||||
* @return Map<stationId, Map<小时, WeatherDataDTO>>
|
||||
*/
|
||||
private Map<String, Map<LocalDateTime, WeatherDataDTO>> batchFetchWeatherData(List<String> stationIds,
|
||||
List<LocalDateTime> timePoints) {
|
||||
Map<String, Map<LocalDateTime, WeatherDataDTO>> result = new HashMap<>();
|
||||
for (String stationId : stationIds) {
|
||||
Map<LocalDateTime, WeatherDataDTO> stationHourly = new HashMap<>();
|
||||
for (LocalDateTime hour : timePoints) {
|
||||
// WeatherDataDTO data = weatherDataService.getWeatherData(stationIds, hour);
|
||||
// if (data != null) {
|
||||
// stationHourly.put(hour, data);
|
||||
// }
|
||||
}
|
||||
result.put(stationId, stationHourly);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* 计算按县平均后的指标（先求每个县内所有站的平均，再对各县平均取平均）
|
||||
* @param stationDataMap 站点原始数据
|
||||
* @param districtCodes 区县列表
|
||||
* @param timePoints 时间点
|
||||
* @param valueExtractor 从WeatherDataDTO提取数值的函数
|
||||
* @return 每个小时的平均值
|
||||
*/
|
||||
private Map<LocalDateTime, Double> calculateAverageByDistrict(Map<String, Map<LocalDateTime, WeatherDataDTO>> stationDataMap,
|
||||
List<String> districtCodes,
|
||||
List<LocalDateTime> timePoints,
|
||||
Function<WeatherDataDTO, Double> valueExtractor) {
|
||||
// 1. 建立区县 -> 站点ID列表的映射
|
||||
Map<String, List<String>> districtStationMap = getDistrictStationMap(districtCodes);
|
||||
|
||||
Map<LocalDateTime, Double> result = new HashMap<>();
|
||||
for (LocalDateTime hour : timePoints) {
|
||||
List<Double> districtAverages = new ArrayList<>();
|
||||
for (String districtCode : districtCodes) {
|
||||
List<String> stations = districtStationMap.getOrDefault(districtCode, Collections.emptyList());
|
||||
List<Double> values = new ArrayList<>();
|
||||
for (String stationId : stations) {
|
||||
WeatherDataDTO data = stationDataMap.getOrDefault(stationId, Collections.emptyMap()).get(hour);
|
||||
Double val = valueExtractor.apply(data);
|
||||
if (val != null) {
|
||||
values.add(val);
|
||||
}
|
||||
}
|
||||
if (!values.isEmpty()) {
|
||||
double avg = values.stream().mapToDouble(Double::doubleValue).average().orElse(0.0);
|
||||
districtAverages.add(avg);
|
||||
}
|
||||
}
|
||||
// 所有县的平均再平均
|
||||
if (!districtAverages.isEmpty()) {
|
||||
double overallAvg = districtAverages.stream().mapToDouble(Double::doubleValue).average().orElse(0.0);
|
||||
result.put(hour, overallAvg);
|
||||
} else {
|
||||
result.put(hour, 0.0);
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* 计算按县极值后的总体极值（先求每个县内所有站的极值，再取所有县的极值）
|
||||
* @param isMax true-最大值，false-最小值
|
||||
*/
|
||||
private Map<LocalDateTime, Double> calculateExtremeByDistrict(Map<String, Map<LocalDateTime, WeatherDataDTO>> stationDataMap,
|
||||
List<String> districtCodes,
|
||||
List<LocalDateTime> timePoints,
|
||||
Function<WeatherDataDTO, Double> valueExtractor,
|
||||
boolean isMax) {
|
||||
Map<String, List<String>> districtStationMap = getDistrictStationMap(districtCodes);
|
||||
|
||||
Map<LocalDateTime, Double> result = new HashMap<>();
|
||||
for (LocalDateTime hour : timePoints) {
|
||||
List<Double> districtExtremes = new ArrayList<>();
|
||||
for (String districtCode : districtCodes) {
|
||||
List<String> stations = districtStationMap.getOrDefault(districtCode, Collections.emptyList());
|
||||
Double extreme = null;
|
||||
for (String stationId : stations) {
|
||||
WeatherDataDTO data = stationDataMap.getOrDefault(stationId, Collections.emptyMap()).get(hour);
|
||||
Double val = valueExtractor.apply(data);
|
||||
if (val != null) {
|
||||
if (extreme == null) {
|
||||
extreme = val;
|
||||
} else if (isMax) {
|
||||
extreme = Math.max(extreme, val);
|
||||
} else {
|
||||
extreme = Math.min(extreme, val);
|
||||
}
|
||||
}
|
||||
}
|
||||
if (extreme != null) {
|
||||
districtExtremes.add(extreme);
|
||||
}
|
||||
}
|
||||
// 取所有县极值的极值
|
||||
if (!districtExtremes.isEmpty()) {
|
||||
double overall = isMax ?
|
||||
districtExtremes.stream().mapToDouble(Double::doubleValue).max().orElse(0.0) :
|
||||
districtExtremes.stream().mapToDouble(Double::doubleValue).min().orElse(0.0);
|
||||
result.put(hour, overall);
|
||||
} else {
|
||||
result.put(hour, 0.0);
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取区县到站点ID列表的映射
|
||||
*/
|
||||
private Map<String, List<String>> getDistrictStationMap(List<String> districtCodes) {
|
||||
List<WeatherSiteAreaConfiguration> configs = new ArrayList<>(); // weatherSiteAreaConfigurationMapper.selectByDistrictCodes(districtCodes);
|
||||
if (CollectionUtils.isEmpty(configs)) {
|
||||
return Collections.emptyMap();
|
||||
}
|
||||
return configs.stream()
|
||||
.filter(c -> c.getStationId() != null)
|
||||
.collect(Collectors.groupingBy(WeatherSiteAreaConfiguration::getDistrictCode,
|
||||
Collectors.mapping(WeatherSiteAreaConfiguration::getStationId, Collectors.toList())));
|
||||
}
|
||||
|
||||
/**
|
||||
* 填充列表，确保每个时间点都有值
|
||||
*/
|
||||
private <T> List<T> fillList(List<LocalDateTime> timePoints, Map<LocalDateTime, T> map, T defaultValue) {
|
||||
List<T> list = new ArrayList<>();
|
||||
for (LocalDateTime tp : timePoints) {
|
||||
list.add(map.getOrDefault(tp, defaultValue));
|
||||
}
|
||||
return list;
|
||||
}
|
||||
|
||||
private Map<LocalDateTime, Double> emptyMap(List<LocalDateTime> timePoints) {
|
||||
Map<LocalDateTime, Double> map = new HashMap<>();
|
||||
for (LocalDateTime tp : timePoints) {
|
||||
map.put(tp, 0.0);
|
||||
}
|
||||
return map;
|
||||
}
|
||||
|
||||
private Double parseDouble(String str) {
|
||||
try {
|
||||
return str == null ? null : Double.parseDouble(str);
|
||||
} catch (NumberFormatException e) {
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
// 内部类，用于返回气温的三个Map
|
||||
private static class TemperatureResult {
|
||||
private final Map<LocalDateTime, Double> avgMap;
|
||||
private final Map<LocalDateTime, Double> maxMap;
|
||||
private final Map<LocalDateTime, Double> minMap;
|
||||
|
||||
public TemperatureResult(Map<LocalDateTime, Double> avgMap,
|
||||
Map<LocalDateTime, Double> maxMap,
|
||||
Map<LocalDateTime, Double> minMap) {
|
||||
this.avgMap = avgMap;
|
||||
this.maxMap = maxMap;
|
||||
this.minMap = minMap;
|
||||
}
|
||||
|
||||
public Map<LocalDateTime, Double> getAvgMap() { return avgMap; }
|
||||
public Map<LocalDateTime, Double> getMaxMap() { return maxMap; }
|
||||
public Map<LocalDateTime, Double> getMinMap() { return minMap; }
|
||||
}
|
||||
}
|
||||
@ -4,5 +4,82 @@
|
||||
|
||||
<!-- namespace 必须 = Mapper 接口全类名 -->
|
||||
<mapper namespace="com.southern.power.grid.dao.RegionalWeatherDataMapper">
|
||||
<resultMap id="BaseResultMap" type="com.southern.power.grid.entity.RegionalWeatherData">
|
||||
<id column="id" property="id" jdbcType="BIGINT"/>
|
||||
<result column="org_code" property="orgCode" jdbcType="VARCHAR"/>
|
||||
<result column="data_time" property="dataTime" jdbcType="VARCHAR"/>
|
||||
<result column="hourly_precipitation" property="hourlyPrecipitation" jdbcType="VARCHAR"/>
|
||||
<result column="daily_precipitation" property="dailyPrecipitation" jdbcType="VARCHAR"/>
|
||||
<result column="temperature" property="temperature" jdbcType="VARCHAR"/>
|
||||
<result column="hourly_max_temperature" property="hourlyMaxTemperature" jdbcType="VARCHAR"/>
|
||||
<result column="hourly_min_temperature" property="hourlyMinTemperature" jdbcType="VARCHAR"/>
|
||||
<result column="extreme_wind_speed_hourly" property="extremeWindSpeedHourly" jdbcType="VARCHAR"/>
|
||||
<result column="create_by" property="createBy" jdbcType="VARCHAR"/>
|
||||
<result column="create_time" property="createTime" jdbcType="TIMESTAMP"/>
|
||||
<result column="update_by" property="updateBy" jdbcType="VARCHAR"/>
|
||||
<result column="update_time" property="updateTime" jdbcType="TIMESTAMP"/>
|
||||
</resultMap>
|
||||
|
||||
<sql id="Base_Column_List">
|
||||
id, org_code, data_time, hourly_precipitation, daily_precipitation, temperature,
|
||||
hourly_max_temperature, hourly_min_temperature, extreme_wind_speed_hourly,
|
||||
create_by, create_time, update_by, update_time
|
||||
</sql>
|
||||
|
||||
<!-- 插入语句：忽略主键id（自增），create_time/update_time由数据库默认生成 -->
|
||||
<insert id="insert" parameterType="com.southern.power.grid.entity.RegionalWeatherData" useGeneratedKeys="true" keyProperty="id">
|
||||
INSERT INTO regional_weather_data (
|
||||
org_code, data_time, hourly_precipitation, daily_precipitation,
|
||||
temperature, hourly_max_temperature, hourly_min_temperature,
|
||||
extreme_wind_speed_hourly, create_by, update_by
|
||||
) VALUES (
|
||||
#{orgCode, jdbcType=VARCHAR},
|
||||
#{dataTime, jdbcType=VARCHAR},
|
||||
#{hourlyPrecipitation, jdbcType=VARCHAR},
|
||||
#{dailyPrecipitation, jdbcType=VARCHAR},
|
||||
#{temperature, jdbcType=VARCHAR},
|
||||
#{hourlyMaxTemperature, jdbcType=VARCHAR},
|
||||
#{hourlyMinTemperature, jdbcType=VARCHAR},
|
||||
#{extremeWindSpeedHourly, jdbcType=VARCHAR},
|
||||
#{createBy, jdbcType=VARCHAR},
|
||||
#{updateBy, jdbcType=VARCHAR}
|
||||
)
|
||||
</insert>
|
||||
|
||||
<!-- 根据主键查询 -->
|
||||
<select id="selectByPrimaryKey" parameterType="java.lang.Long" resultMap="BaseResultMap">
|
||||
SELECT <include refid="Base_Column_List"/>
|
||||
FROM regional_weather_data
|
||||
WHERE id = #{id, jdbcType=BIGINT}
|
||||
</select>
|
||||
|
||||
<!-- 根据地区编码和资料时次查询 -->
|
||||
<select id="selectByOrgCodeAndDataTime" resultMap="BaseResultMap">
|
||||
SELECT <include refid="Base_Column_List"/>
|
||||
FROM regional_weather_data
|
||||
WHERE org_code = #{orgCode, jdbcType=VARCHAR}
|
||||
AND data_time >= #{dataTime, jdbcType=VARCHAR}
|
||||
ORDER BY data_time ASC
|
||||
</select>
|
||||
|
||||
<!-- 动态条件查询 -->
|
||||
<select id="selectByCondition" parameterType="java.util.Map" resultMap="BaseResultMap">
|
||||
SELECT <include refid="Base_Column_List"/>
|
||||
FROM regional_weather_data
|
||||
<where>
|
||||
<if test="orgCode != null and orgCode != ''">
|
||||
AND org_code = #{orgCode, jdbcType=VARCHAR}
|
||||
</if>
|
||||
<if test="dataTime != null and dataTime != ''">
|
||||
AND data_time = #{dataTime, jdbcType=VARCHAR}
|
||||
</if>
|
||||
<if test="startCreateTime != null and startCreateTime != ''">
|
||||
AND create_time >= #{startCreateTime, jdbcType=TIMESTAMP}
|
||||
</if>
|
||||
<if test="endCreateTime != null and endCreateTime != ''">
|
||||
AND create_time <= #{endCreateTime, jdbcType=TIMESTAMP}
|
||||
</if>
|
||||
</where>
|
||||
ORDER BY create_time DESC
|
||||
</select>
|
||||
</mapper>
|
||||
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Reference in New Issue
Block a user