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3 天以前 5e0640513226d9d9f2d766c075f79832c9d290ba 
 yudao-module-bi/src/main/java/cn/iocoder/yudao/module/bi/service/decision/BiForecastServiceImpl.java


@@ -5,7 +5,11 @@


import cn.iocoder.yudao.module.bi.controller.admin.decision.vo.DecisionForecastHistorySaveReqVO;


import cn.iocoder.yudao.module.bi.controller.admin.decision.vo.DecisionForecastWorkOrderCreateReqVO;


import cn.iocoder.yudao.module.bi.dal.dataobject.decision.BiForecastDataDO;


import cn.iocoder.yudao.module.bi.dal.dataobject.decision.BiKpiDefinitionDO;


import cn.iocoder.yudao.module.bi.dal.dataobject.decision.BiKpiValueHistoryDO;


import cn.iocoder.yudao.module.bi.dal.mysql.decision.BiForecastDataMapper;


import cn.iocoder.yudao.module.bi.dal.mysql.decision.BiKpiDefinitionMapper;


import cn.iocoder.yudao.module.bi.dal.mysql.decision.BiKpiValueHistoryMapper;


import cn.iocoder.yudao.module.mes.api.workorder.MesProWorkOrderApi;


import cn.iocoder.yudao.module.mes.api.workorder.dto.MesProWorkOrderCreateReqDTO;


import jakarta.annotation.Resource;


@@ -16,6 +20,7 @@


import java.math.BigDecimal;


import java.math.RoundingMode;


import java.time.LocalDateTime;


import java.util.ArrayList;


import java.util.List;




import static cn.iocoder.yudao.framework.common.exception.util.ServiceExceptionUtil.exception;


@@ -40,6 +45,10 @@




    @Resource


    private BiForecastDataMapper forecastDataMapper;


    @Resource


    private BiKpiDefinitionMapper kpiMapper;


    @Resource


    private BiKpiValueHistoryMapper kpiValueHistoryMapper;


    @Resource


    private MesProWorkOrderApi mesProWorkOrderApi;




@@ -72,29 +81,98 @@




    @Override


    public void generateForecast(String forecastCode, LocalDateTime pointTime) {


        // 基于同预测编码的历史预测数据做简单移动平均；无历史数据时不伪造零值


        // 默认使用 SMA 模型


        generateForecast(forecastCode, "SMA", SMA_WINDOW, 7, pointTime);


    }




    @Override


    public void generateForecast(String forecastCode, String model, int window, int period,


                                 LocalDateTime pointTime) {


        // 基于同预测编码的历史预测数据做统计预测；无历史数据时不伪造零值


        List<BiForecastDataDO> history = forecastDataMapper.selectRecentByCode(forecastCode, SMA_WINDOW);


        if (CollUtil.isEmpty(history)) {


            throw exception(BI_FORECAST_NO_HISTORY);


        }


        BigDecimal sum = history.stream()


                .map(BiForecastDataDO::getForecastValue)


                .filter(value -> value != null)


                .reduce(BigDecimal.ZERO, BigDecimal::add);


        BigDecimal avg = sum.divide(BigDecimal.valueOf(history.size()), 4, RoundingMode.HALF_UP);


        BigDecimal bound = avg.multiply(new BigDecimal("0.10"));




        BiForecastDataDO forecast = BiForecastDataDO.builder()


        List<BigDecimal> values = new ArrayList<>();


        for (BiForecastDataDO h : history) {


            if (h.getForecastValue() != null) {


                values.add(h.getForecastValue());


            }


        }


        BiForecastEngine.Forecast forecast = BiForecastEngine.forecast(model, values, window, period);


        if (forecast == null) {


            throw exception(BI_FORECAST_NO_HISTORY);


        }


        BiForecastDataDO data = BiForecastDataDO.builder()


                .forecastCode(forecastCode)


                .forecastName(history.get(0).getForecastName())


                .pointTime(pointTime)


                .forecastValue(avg)


                .lowerBound(avg.subtract(bound))


                .upperBound(avg.add(bound))


                .forecastValue(forecast.value())


                .lowerBound(forecast.lowerBound())


                .upperBound(forecast.upperBound())


                .confidenceLevel(new BigDecimal("95.00"))


                .modelVersion("v1.0-sma")


                .modelVersion("v2.0-" + model.toLowerCase())


                .build();


        forecastDataMapper.insert(forecast);


        forecastDataMapper.insert(data);


    }




    @Override


    public int generateKpiForecast(String kpiCode, String model, int window, int period,


                                   int periods, int intervalMinutes) {


        // 1. 校验 KPI 定义


        BiKpiDefinitionDO kpi = kpiMapper.selectOne(BiKpiDefinitionDO::getCode, kpiCode);


        if (kpi == null) {


            throw exception(BI_FORECAST_NOT_EXISTS);


        }


        // 2. 取 KPI 历史快照（升序），无数据不预测


        List<BiKpiValueHistoryDO> historyList = kpiValueHistoryMapper.selectByKpiCodeAndTimeRange(


                kpiCode, LocalDateTime.now().minusMonths(6), LocalDateTime.now());


        if (CollUtil.isEmpty(historyList)) {


            throw exception(BI_FORECAST_NO_HISTORY);


        }


        List<BigDecimal> history = new ArrayList<>();


        LocalDateTime lastSnapshotTime = null;


        for (BiKpiValueHistoryDO h : historyList) {


            if (h.getKpiValue() != null) {


                history.add(h.getKpiValue());


                lastSnapshotTime = h.getSnapshotTime();


            }


        }


        if (history.isEmpty()) {


            throw exception(BI_FORECAST_NO_HISTORY);


        }


        // 3. 逐期滚动预测


        int targetPeriods = periods > 0 ? Math.min(periods, 90) : 30;


        int stepMinutes = intervalMinutes > 0 ? intervalMinutes : 360;


        int count = 0;


        LocalDateTime nextTime = lastSnapshotTime;


        for (int i = 0; i < targetPeriods; i++) {


            BiForecastEngine.Forecast f = BiForecastEngine.forecast(model, history, window, period);


            if (f == null) {


                break;


            }


            nextTime = nextTime.plusMinutes(stepMinutes);


            BiForecastDataDO data = BiForecastDataDO.builder()


                    .forecastCode(kpiCode)


                    .forecastName(kpi.getName() + " 预测")


                    .pointTime(nextTime)


                    .forecastValue(f.value())


                    .lowerBound(f.lowerBound())


                    .upperBound(f.upperBound())


                    .confidenceLevel(new BigDecimal("95.00"))


                    .modelVersion("v2.0-" + (model == null ? "sma" : model.toLowerCase()))


                    .dimension("kpi")


                    .dimensionValue(kpiCode)


                    .build();


            forecastDataMapper.insert(data);


            count++;


            // 将预测值追加进历史，供下一期滚动


            history.add(f.value());


            if (history.size() > 200) {


                history.remove(0);


            }


        }


        return count;


    }




    @Override