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优化预报接口、优化配置参数功能

master
chenxiwang 2024-08-06 14:59:15 +08:00
parent 6e0b120ec6
commit bae7a0ea9f
3 changed files with 17 additions and 14 deletions
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2
src/main/java/com/gunshi/project/xyt/model/ForecastUseparam.java
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@ -79,7 +79,7 @@ public class ForecastUseparam extends GenericPageParams implements Serializable
/**
* 参数描述
*/
@TableField(value="param_remarks")
@TableField(value="param_desc")
@Schema(description="参数描述")
@Size(max = 255,message = "参数描述最大长度要小于 255")
private String paramDesc;

27
src/main/java/com/gunshi/project/xyt/service/ForecastResultsService.java
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@ -14,8 +14,8 @@ import com.gunshi.project.xyt.grb.RainGrib2Layer;
import com.gunshi.project.xyt.mapper.ForecastResultsMapper;
import com.gunshi.project.xyt.model.AttResBase;
import com.gunshi.project.xyt.model.ForecastPa;
import com.gunshi.project.xyt.model.ForecastTask;
import com.gunshi.project.xyt.model.ForecastResults;
import com.gunshi.project.xyt.model.ForecastTask;
import com.gunshi.project.xyt.model.ForecastU;
import com.gunshi.project.xyt.model.ForecastUseparam;
import com.gunshi.project.xyt.model.StPptnR;
@ -145,7 +145,7 @@ public class ForecastResultsService extends ServiceImpl<ForecastResultsMapper, F
pptnRAllList.addAll(pptnRExistedList);
pptnRAllList.addAll(pptnRFutureList);
// 获取配置参数
List<ForecastUseparam> paramList = forecastUseparamService.list();
List<ForecastUseparam> paramList = forecastUseparamService.list(new QueryWrapper<ForecastUseparam>().isNotNull("param_code").isNotNull("param_value"));
List<ForecastU> uList = forecastUService.list();
if (CollectionUtils.isEmpty(paramList) || CollectionUtils.isEmpty(uList) || CollectionUtils.isEmpty(pptnRAllList)) {
return voList;
@ -181,6 +181,9 @@ public class ForecastResultsService extends ServiceImpl<ForecastResultsMapper, F
AttResBase attResBase = attResBaseService.getOne(new QueryWrapper<>());
// 泄流量
List<StZqrlB> stZqrlBList = stZqrlBService.list(new QueryWrapper<StZqrlB>().eq("stcd", "716153201"));
// 水位历史数据
List<StRsvrR> rsvrRRealList = stRsvrRService.list(new QueryWrapper<StRsvrR>().eq("stcd", "716153201").ge("tm", startTime).le("tm", endTime));
List<StRsvrR> resultList = reorganizeRsvrRData(rsvrRRealList, dt);
List<Date[]> periods = splitByDay8To8(startTime, endTime);
for (Date[] period : periods) {
// 根据每段时间的开始时间，如果在08点前，则采用前一天的pa值计算
@ -195,10 +198,10 @@ public class ForecastResultsService extends ServiceImpl<ForecastResultsMapper, F
// 筛选时间段内的降雨数据。第一个条件：时间大于等开始时间；第二个条件：时间小于等于结束时间（不包前但包后，比如从8点开始，就要拆下一个时间：9点的数据）
List<StPptnR> filterList = pptnRAllList.stream().filter(e -> e.getTm().compareTo(period[0]) >= 0).filter(e -> e.getTm().compareTo(period[1]) <= 0).collect(Collectors.toList());
// 从第二条数据开始
for (int i = 1; i <= filterList.size(); i++) {
for (int i = 1; i < filterList.size(); i++) {
StPptnR stPptnRLast = filterList.get(i - 1);
StPptnR stPptnR = filterList.get(i);
String drp = stPptnR.getDrp();
StPptnR stPptnRLast = filterList.get(i - 1);
// 两条数据的小时差
double diffHours = dateHourDifference(stPptnRLast.getTm(), stPptnR.getTm());
// 两条数据间需要增补几条
@ -209,13 +212,12 @@ public class ForecastResultsService extends ServiceImpl<ForecastResultsMapper, F
}
}
double[] PList = pResultList.stream().mapToDouble(Double::parseDouble).toArray();
// 水位历史数据
List<StRsvrR> rsvrRRealList = stRsvrRService.list(new QueryWrapper<StRsvrR>().eq("stcd", "716153201").ge("tm", period[0]).le("tm", period[1]));
rsvrRRealList = reorganizeRsvrRData(rsvrRRealList, dt);
// 预测执行
List<FloodAlgorithemVo> forecastVoList = RrainfallForecast.getData(sdf.format(period[0]), forecastPa.getK().doubleValue(), forecastPa.getPa0().doubleValue(), Wm, forecastPa.getPt0().doubleValue(), H1, dt,
forecastPa.getPa().doubleValue(), PList, u, "716153201");
if (CollectionUtils.isNotEmpty(forecastVoList)) {
// 筛选同时段的真实水位数据
List<StRsvrR> realRsvrList = resultList.stream().filter(item -> item.getTm().compareTo(period[0]) >=0 && item.getTm().compareTo(period[1]) <=0).collect(Collectors.toList());
forecastVoList = forecastVoList.subList(0, PList.length);
for (int j = 0; j < forecastVoList.size(); j++) {
FloodAlgorithemVo floodAlgorithemVo = forecastVoList.get(j);
@ -226,8 +228,8 @@ public class ForecastResultsService extends ServiceImpl<ForecastResultsMapper, F
resultVo.setYcCkQValue(floodAlgorithemVo.getCq());// 预测出库流量
resultVo.setYcSwHValue(floodAlgorithemVo.getKh());// 预测水库水位
H1 = resultVo.getYcSwHValue().doubleValue();// 先以预测水位作为下一次预测段起始值
if (j < rsvrRRealList.size()) {
BigDecimal realSwHValue = new BigDecimal(rsvrRRealList.get(j).getRz());
if (j < realRsvrList.size()) {
BigDecimal realSwHValue = new BigDecimal(realRsvrList.get(j).getRz());
resultVo.setRealSwHValue(realSwHValue);// 真实水库水位
H1 = realSwHValue.doubleValue();// 如果有真实水位，将最后一条的真实水位作为下一次预测段的初始水位
// 真实出库流量=真实水库水位与泄流量曲线差值法
@ -334,13 +336,14 @@ public class ForecastResultsService extends ServiceImpl<ForecastResultsMapper, F
// 两条数据的小时差
double diffHours = dateHourDifference(stRsvrR.getTm(), stRsvrRNext.getTm());
int floorNum = (int) Math.floor(diffHours / dt);
BigDecimal meanDifference = new BigDecimal(stRsvrRNext.getRz()).subtract(new BigDecimal(drp)).divide(new BigDecimal(floorNum), BigDecimal.ROUND_HALF_UP, 2);
BigDecimal meanDifference = new BigDecimal(stRsvrRNext.getRz()).subtract(new BigDecimal(drp)).divide(new BigDecimal(floorNum), BigDecimal.ROUND_HALF_UP, 3);
for (int j = 1; j < floorNum; j++) {
// 增补出的数据
StRsvrR suppleStRsvrR = new StRsvrR();
BigDecimal add = new BigDecimal(drp).add(meanDifference.multiply(new BigDecimal(j))).setScale(2, BigDecimal.ROUND_HALF_UP);
BigDecimal add = new BigDecimal(drp).add(meanDifference.multiply(new BigDecimal(j))).setScale(3, BigDecimal.ROUND_HALF_UP);
try {
suppleStRsvrR.setTm(sdf.parse(RunoffService.calc(sdf.format(tm), hours, minutes, true)));
tm = sdf.parse(RunoffService.calc(sdf.format(tm), hours, minutes, true));
suppleStRsvrR.setTm(tm);
} catch (ParseException e) {
throw new RuntimeException(e);
}

2
src/main/java/com/gunshi/project/xyt/timetask/PaDataTask.java
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@ -71,7 +71,7 @@ public class PaDataTask {
calendar.setTime(new Date());
Date nowTime = calendar.getTime();
// 获取配置参数
List<ForecastUseparam> paramList = forecastUseparamService.list();
List<ForecastUseparam> paramList = forecastUseparamService.list(new QueryWrapper<ForecastUseparam>().isNotNull("param_code").isNotNull("param_value"));
List<ForecastK> kList = forecastKService.list();
// 获取最新pa
ForecastPa forecastPaLast = forecastPaService.getOne(new QueryWrapper<ForecastPa>().orderBy(true, false, "tm").last("limit 1"));