From efdc380e66bbc3112eb87d7522f0a37d082082e1 Mon Sep 17 00:00:00 2001
From: dengzedong <dengzedong@email>
Date: 星期四, 26 十二月 2024 09:42:07 +0800
Subject: [PATCH] double类型特殊处理 异常处理
---
iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/sample/PredictSampleDataConstructor.java | 78 +++++++++++++++++++++++++++++++++-----
1 files changed, 67 insertions(+), 11 deletions(-)
diff --git a/iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/sample/PredictSampleDataConstructor.java b/iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/sample/PredictSampleDataConstructor.java
index dc0ff35..e4539bf 100644
--- a/iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/sample/PredictSampleDataConstructor.java
+++ b/iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/sample/PredictSampleDataConstructor.java
@@ -1,11 +1,17 @@
package com.iailab.module.model.mdk.sample;
+import com.iailab.module.data.api.plan.PlanItemApi;
+import com.iailab.module.data.api.plan.dto.ApiPlanItemDTO;
import com.iailab.module.data.api.point.DataPointApi;
import com.iailab.module.data.api.point.dto.ApiPointDTO;
import com.iailab.module.data.api.point.dto.ApiPointValueDTO;
import com.iailab.module.data.api.point.dto.ApiPointValueQueryDTO;
+import com.iailab.module.data.common.ApiDataQueryDTO;
+import com.iailab.module.data.common.ApiDataValueDTO;
+import com.iailab.module.model.common.enums.OutResultType;
import com.iailab.module.model.mcs.pre.entity.MmItemOutputEntity;
import com.iailab.module.model.mcs.pre.service.MmItemOutputService;
+import com.iailab.module.model.mcs.pre.service.MmItemResultJsonService;
import com.iailab.module.model.mcs.pre.service.MmItemResultService;
import com.iailab.module.model.mcs.pre.service.MmItemTypeService;
import com.iailab.module.model.mdk.common.enums.ModelParamType;
@@ -14,18 +20,21 @@
import com.iailab.module.model.mdk.sample.dto.SampleData;
import com.iailab.module.model.mdk.sample.dto.SampleInfo;
import com.iailab.module.model.mdk.vo.DataValueVO;
+import lombok.extern.slf4j.Slf4j;
+import org.apache.commons.lang3.StringUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Component;
+import org.springframework.util.CollectionUtils;
-import java.math.BigDecimal;
import java.util.*;
import java.util.stream.Collectors;
/**
* 预测样本数据构造
*/
+@Slf4j
@Component
public class PredictSampleDataConstructor extends SampleDataConstructor {
@@ -35,7 +44,13 @@
private DataPointApi dataPointApi;
@Autowired
+ private PlanItemApi planItemApi;
+
+ @Autowired
private MmItemResultService mmItemResultService;
+
+ @Autowired
+ private MmItemResultJsonService mmItemResultJsonService;
@Autowired
private MmItemTypeService mmItemTypeService;
@@ -51,8 +66,10 @@
* @return
*/
@Override
- public List<SampleData> prepareSampleData(SampleInfo sampleInfo) {
+ public List<SampleData> prepareSampleData(SampleInfo sampleInfo) throws Exception {
List<SampleData> sampleDataList = new ArrayList<>();
+ Map<String, ApiPointDTO> pointMap = sampleInfo.getPointMap();
+ Map<String, ApiPlanItemDTO> planMap = sampleInfo.getPlanMap();
//对每个爪分别进行计算
for (ColumnItemPort entry : sampleInfo.getColumnInfo()) {
//先依据爪内数据项的modelParamOrder进行排序——重写comparator匿名函数
@@ -74,11 +91,11 @@
//对每一项依次进行数据查询,然后将查询出的值赋给matrix对应的位置
for (int i = 0; i < entry.getColumnItemList().size(); i++) {
try {
- List<DataValueVO> dataEntityList = getData(entry.getColumnItemList().get(i));
+ List<DataValueVO> dataEntityList = getData(entry.getColumnItemList().get(i),pointMap,planMap);
//补全数据
ColumnItem columnItem = entry.getColumnItemList().get(i);
dataEntityList = super.completionData(matrix.length, dataEntityList, columnItem.startTime, columnItem.endTime,
- columnItem.paramId, columnItem.getParamType());
+ columnItem.paramId, columnItem.getParamType(),pointMap,planMap);
/** 如果数据取不满，把缺失的数据点放在后面 */
if (dataEntityList != null && dataEntityList.size() != 0) {
@@ -89,6 +106,7 @@
}
} catch (Exception e) {
e.printStackTrace();
+ throw e;
}
}
SampleData sampleData = new SampleData();
@@ -102,20 +120,24 @@
* getData
*
* @param columnItem
+ * @param pointMap
+ * @param planMap
* @return
* @throws Exception
*/
- private List<DataValueVO> getData(ColumnItem columnItem) throws Exception {
+ private List<DataValueVO> getData(ColumnItem columnItem, Map<String, ApiPointDTO> pointMap, Map<String, ApiPlanItemDTO> planMap) throws Exception {
List<DataValueVO> dataList = new ArrayList<>();
String paramType = columnItem.getParamType();
switch (ModelParamType.getEumByCode(paramType)) {
case DATAPOINT:
- ApiPointDTO point = dataPointApi.getInfoById(columnItem.getParamId());
ApiPointValueQueryDTO queryDto = new ApiPointValueQueryDTO();
- queryDto.setPointNo(point.getPointNo());
+ queryDto.setPointNo(pointMap.get(columnItem.getParamId()).getPointNo());
queryDto.setStart(columnItem.getStartTime());
queryDto.setEnd(columnItem.getEndTime());
List<ApiPointValueDTO> pointValueList = dataPointApi.queryPointHistoryValue(queryDto);
+ if (CollectionUtils.isEmpty(pointValueList)) {
+ break;
+ }
dataList = pointValueList.stream().map(t -> {
DataValueVO vo = new DataValueVO();
vo.setDataTime(t.getT());
@@ -126,15 +148,49 @@
case NORMALITEM:
case MERGEITEM:
MmItemOutputEntity outPut = mmItemOutputService.getOutPutById(columnItem.getParamId());
- dataList = mmItemResultService.getPredictValue(outPut.getId(),
- columnItem.getStartTime(), columnItem.getEndTime());
- if (dataList == null) {
- throw new Exception("没有预测值");
+ OutResultType outResultType = OutResultType.getEumByCode(outPut.getResultType());
+ List<DataValueVO> predictValue = new ArrayList<>();
+
+ // double类型特殊处理
+ 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)) {
+ DataValueVO dataValueVO = new DataValueVO();
+ dataValueVO.setDataTime(columnItem.getStartTime());
+ dataValueVO.setDataValue(Double.valueOf(doubleData));
+ predictValue.add(dataValueVO);
+ }
+ } else {
+ predictValue = mmItemResultService.getPredictValue(outPut.getId(), columnItem.getStartTime(), columnItem.getEndTime());
}
+
+ if (CollectionUtils.isEmpty(predictValue)) {
+ break;
+ }
+ dataList = predictValue;
break;
+ case PLAN:
+ ApiDataQueryDTO queryPlanItemDto = new ApiDataQueryDTO();
+ queryPlanItemDto.setItemNo(planMap.get(columnItem.getParamId()).getItemNo());
+ queryPlanItemDto.setStart(columnItem.getStartTime());
+ queryPlanItemDto.setEnd(columnItem.getEndTime());
+ List<ApiDataValueDTO> planValueList = planItemApi.queryPlanItemHistoryValue(queryPlanItemDto);
+ if (CollectionUtils.isEmpty(planValueList)) {
+ break;
+ }
+ dataList = planValueList.stream().map(t -> {
+ DataValueVO vo = new DataValueVO();
+ vo.setDataTime(t.getDataTime());
+ vo.setDataValue(t.getDataValue());
+ return vo;
+ }).collect(Collectors.toList());
default:
break;
}
+ // 避免生产环境日志过多，分级打印
+ log.debug("数据获取,columnItem:" + columnItem + ",dataList：" + dataList);
+ log.info("数据获取,columnItem:" + columnItem + ",dataListLength：" + dataList.size());
return dataList;
}
}
--
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