运行耗时(ms)

 iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/api/MdkApiImpl.java

@@ -100,11 +100,9 @@
                Map<String, PredictResultVO> predictResultMap = predictModuleHandler.predict(predictItemList, reqDTO.getPredictTime(), intervalTime);
                // 更新Module时间
                dmModuleService.updatePredictTime(module.getId(), reqDTO.getPredictTime());

                if (reqDTO.getIsResult() == null || !reqDTO.getIsResult()) {
                    return resp;
                }

                for (Map.Entry<String, PredictResultVO> entry : predictResultMap.entrySet()) {
                    MdkPredictItemRespDTO itemResp = new MdkPredictItemRespDTO();
                    itemResp.setItemId(entry.getKey());

 iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/common/enums/CommonConstant.java

@@ -22,7 +22,6 @@

    String MDK_STATUS_100 = "100";


    String RANGE_H = "RANGE_H";

    String RANGE_L = "RANGE_L";
@@ -43,4 +42,13 @@

    // 趋势预测曲线类型，0：展示T+N,1：展示T+L,
    String LINE_TYPE = "LINE_TYPE";

    // 模型输出预测值
    String OUT_PREDICT_VALUES = "predictValues";

    // 模型输出优化值
    String OUT_OPT_VALUES = "optValues";

    // 模型输出调整值
    String OUT_ADJUST_VALUES = "adjustValues";
}

 iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/predict/PredictModuleHandler.java

@@ -58,7 +58,7 @@
            try {
                mmItemStatusService.recordStatus(predictItem.getId(), ItemRunStatusEnum.PROCESSING, totalDur, predictTime);
                PredictItemHandler predictItemHandler = predictItemFactory.create(predictItem.getId());
                Instant start = Instant.now();
                long start = System.currentTimeMillis();
                try {
                    // 预测项开始预测
                    predictResult = predictItemHandler.predict(predictTime, predictItem);
@@ -68,9 +68,9 @@
                    mmItemStatusService.recordStatus(predictItem.getId(), ItemRunStatusEnum.FAIL, totalDur, predictTime);
                    continue;
                }
                Instant end = Instant.now();
                Long drtPre = Duration.between(start, end).getSeconds();
                log.info(MessageFormat.format("预测项：{0}，预测时间：{1}秒", predictItem.getItemName(), drtPre));
                long end = System.currentTimeMillis();
                Long drtPre = end - start;
                log.info(MessageFormat.format("预测项：{0}，预测时间：{1}ms", predictItem.getItemName(), drtPre));
                totalDur = totalDur + drtPre;

                predictResult.setGranularity(predictItem.getGranularity());
@@ -80,9 +80,9 @@

                // 保存预测结果
                predictResultHandler.savePredictResult(predictResult);
                Instant endSave = Instant.now();
                Long drtSave = Duration.between(end, endSave).getSeconds();
                log.info(MessageFormat.format("预测项：{0}，保存时间：{1}秒", predictItem.getItemName(),
                long endSave = System.currentTimeMillis();
                Long drtSave = endSave - end;
                log.info(MessageFormat.format("预测项：{0}，保存时间：{1}ms", predictItem.getItemName(),
                        drtSave));
                totalDur = totalDur + drtSave;
                mmItemStatusService.recordStatus(predictItem.getId(), ItemRunStatusEnum.SUCCESS, totalDur, predictTime);