iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/predict/impl/PredictModelHandlerImpl.java
@@ -142,11 +142,13 @@ result.setModelResult(modelResult); result.setPredictTime(predictTime); } catch (ModelResultErrorException ex) { ex.printStackTrace(); // ex.printStackTrace(); log.error("模型结果异常", ex); throw ex; } catch (Exception ex) { // log.error("调用发生异常,异常信息为:{0}", ex.getMessage()); ex.printStackTrace(); // ex.printStackTrace(); log.error("模型运行异常", ex); throw new ModelInvokeException(ex.getMessage()); } return result; iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/sample/PredictSampleDataConstructor.java
@@ -152,7 +152,7 @@ List<DataValueVO> predictValue = new ArrayList<>(); // double类型特殊处理 if (outResultType.equals(OutResultType.D)) { if (OutResultType.D.equals(outResultType)) { // columnItem.getStartTime()就是预测时间 String doubleData = mmItemResultJsonService.getDoubleData(outPut.getId(), columnItem.getStartTime()); if (StringUtils.isNotBlank(doubleData)) { iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/sample/SampleConstructor.java
@@ -1,13 +1,12 @@ package com.iailab.module.model.mdk.sample; import com.iailab.module.model.mdk.common.exceptions.DataAccessException; import com.iailab.module.model.mdk.common.exceptions.ModelInvokeException; import com.iailab.module.model.mdk.sample.dto.SampleData; import com.iailab.module.model.mdk.sample.dto.SampleInfo; import lombok.extern.slf4j.Slf4j; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.stereotype.Component; import java.sql.Timestamp; import java.text.MessageFormat; import java.util.Date; import java.util.List; @@ -19,6 +18,7 @@ * @Description * @createTime 2024年09月03日 */ @Slf4j @Component public class SampleConstructor { @@ -33,6 +33,7 @@ return sampleDataConstructor.prepareSampleData(sampleInfo); } catch (Exception e) { e.printStackTrace(); log.error("获取模型的算法参数异常",e); throw new ModelInvokeException(MessageFormat.format("{0},Name:{1}", ModelInvokeException.errorGetModelArithParam, itemName)); }