From b82ba2a29aa9ee181c707677157d8057fff68450 Mon Sep 17 00:00:00 2001
From: dengzedong <dengzedong@email>
Date: 星期一, 16 十二月 2024 13:50:33 +0800
Subject: [PATCH] 预测项数据图表查询bug,不能从缓存中查item,要最新的运行时间

---
 iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/predict/impl/PredictModelHandlerImpl.java |   95 ++++++++++++++++++++++++++++++-----------------
 1 files changed, 60 insertions(+), 35 deletions(-)

diff --git a/iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/predict/impl/PredictModelHandlerImpl.java b/iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/predict/impl/PredictModelHandlerImpl.java
index ef75d02..88cefdd 100644
--- a/iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/predict/impl/PredictModelHandlerImpl.java
+++ b/iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/predict/impl/PredictModelHandlerImpl.java
@@ -2,28 +2,30 @@
 
 import com.alibaba.fastjson.JSONArray;
 import com.alibaba.fastjson.JSONObject;
-import com.iail.IAILMDK;
 import com.iail.model.IAILModel;
+import com.iailab.module.model.common.enums.CommonConstant;
+import com.iailab.module.model.common.enums.OutResultType;
+import com.iailab.module.model.mcs.pre.entity.MmItemOutputEntity;
 import com.iailab.module.model.mcs.pre.entity.MmModelArithSettingsEntity;
-import com.iailab.module.model.mcs.pre.entity.MmModelResultstrEntity;
 import com.iailab.module.model.mcs.pre.entity.MmPredictModelEntity;
+import com.iailab.module.model.mcs.pre.service.MmItemOutputService;
 import com.iailab.module.model.mcs.pre.service.MmModelArithSettingsService;
-import com.iailab.module.model.mcs.pre.service.MmModelResultstrService;
 import com.iailab.module.model.mdk.common.enums.TypeA;
 import com.iailab.module.model.mdk.common.exceptions.ModelInvokeException;
 import com.iailab.module.model.mdk.predict.PredictModelHandler;
 import com.iailab.module.model.mdk.sample.SampleConstructor;
 import com.iailab.module.model.mdk.sample.dto.SampleData;
 import com.iailab.module.model.mdk.vo.PredictResultVO;
+import com.iailab.module.model.mpk.common.MdkConstant;
 import com.iailab.module.model.mpk.common.utils.DllUtils;
 import lombok.extern.slf4j.Slf4j;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.stereotype.Component;
 
-import java.util.ArrayList;
 import java.util.Date;
 import java.util.HashMap;
 import java.util.List;
+import java.util.Map;
 
 /**
  * @author PanZhibao
@@ -38,28 +40,40 @@
     private MmModelArithSettingsService mmModelArithSettingsService;
 
     @Autowired
-    private MmModelResultstrService mmModelResultstrService;
+    private MmItemOutputService mmItemOutputService;
 
     @Autowired
     private SampleConstructor sampleConstructor;
 
+    /**
+     * 根据模型预测,返回预测结果
+     *
+     * @param predictTime
+     * @param predictModel
+     * @return
+     * @throws ModelInvokeException
+     */
     @Override
-    public PredictResultVO predictByModel(Date predictTime, MmPredictModelEntity predictModel) throws ModelInvokeException {
+    public synchronized PredictResultVO predictByModel(Date predictTime, MmPredictModelEntity predictModel) throws ModelInvokeException {
         PredictResultVO result = new PredictResultVO();
         if (predictModel == null) {
             throw new ModelInvokeException("modelEntity is null");
         }
         String modelId = predictModel.getId();
-
         try {
             List<SampleData> sampleDataList = sampleConstructor.constructSample(TypeA.Predict.name(), modelId, predictTime);
             String modelPath = predictModel.getModelpath();
             if (modelPath == null) {
-                System.out.println("模型路径不存在,modelId=" + modelId);
+                log.info("模型路径不存在,modelId=" + modelId);
                 return null;
             }
             IAILModel newModelBean = composeNewModelBean(predictModel);
             HashMap<String, Object> settings = getPredictSettingsByModelId(modelId);
+            // 校验setting必须有pyFile,否则可能导致程序崩溃
+            if (!settings.containsKey(MdkConstant.PY_FILE_KEY)) {
+                throw new RuntimeException("模型设置参数缺少必要信息【" + MdkConstant.PY_FILE_KEY +  "】,请重新上传模型!");
+            }
+
             if (settings == null) {
                 log.error("模型setting不存在,modelId=" + modelId);
                 return null;
@@ -67,52 +81,63 @@
             int portLength = sampleDataList.size();
             Object[] param2Values = new Object[portLength + 2];
             for (int i = 0; i < portLength; i++) {
-                param2Values[i]=sampleDataList.get(i).getMatrix();
+                param2Values[i] = sampleDataList.get(i).getMatrix();
             }
             param2Values[portLength] = newModelBean.getDataMap().get("models");
-            param2Values[portLength+1] = settings;
+            param2Values[portLength + 1] = settings;
 
-            log.info("#######################预测模型 " + predictModel.getItemid() + " ##########################");
-            JSONObject jsonObjNewModelBean = new JSONObject();
-            jsonObjNewModelBean.put("newModelBean", newModelBean);
-            log.info(String.valueOf(jsonObjNewModelBean));
-            JSONObject jsonObjParam2Values = new JSONObject();
-            jsonObjParam2Values.put("param2Values", param2Values);
-            log.info(String.valueOf(jsonObjParam2Values));
+            log.info("####################### 预测模型 "+ "【itemId:" + predictModel.getItemid() + ",modelName" + predictModel.getMethodname() + "】 ##########################");
+//            JSONObject jsonObjNewModelBean = new JSONObject();
+//            jsonObjNewModelBean.put("newModelBean", newModelBean);
+//            log.info(String.valueOf(jsonObjNewModelBean));
+//            JSONObject jsonObjParam2Values = new JSONObject();
+//            jsonObjParam2Values.put("param2Values", param2Values);
+//            log.info(String.valueOf(jsonObjParam2Values));
 
             //IAILMDK.run
-//            HashMap<String, Object> modelResult = IAILMDK.run(newModelBean, param2Values);
             HashMap<String, Object> modelResult = DllUtils.run(newModelBean, param2Values, predictModel.getMpkprojectid());
-            if(!modelResult.containsKey("status_code") || !modelResult.containsKey("result") || Integer.parseInt(modelResult.get("status_code").toString()) != 100) {
+            if (!modelResult.containsKey(CommonConstant.MDK_STATUS_CODE) || !modelResult.containsKey(CommonConstant.MDK_RESULT) ||
+                    !modelResult.get(CommonConstant.MDK_STATUS_CODE).toString().equals(CommonConstant.MDK_STATUS_100)) {
                 throw new RuntimeException("模型结果异常:" + modelResult);
             }
-
-            modelResult = (HashMap<String, Object>) modelResult.get("result");
+            modelResult = (HashMap<String, Object>) modelResult.get(CommonConstant.MDK_RESULT);
             //打印结果
+            log.info("预测模型计算完成:modelId=" + modelId + ",modelName" + predictModel.getMethodname());
             JSONObject jsonObjResult = new JSONObject();
             jsonObjResult.put("result", modelResult);
             log.info(String.valueOf(jsonObjResult));
 
-            MmModelResultstrEntity modelResultstr = mmModelResultstrService.getInfo(predictModel.getResultstrid());
-            log.info("模型计算完成:modelId=" + modelId + result);
-            if (modelResult.containsKey(modelResultstr.getResultstr())) {
-                Double[][] temp = (Double[][]) modelResult.get(modelResultstr.getResultstr());
-                double[][] temp1 = new double[temp.length][temp[0].length];
-                for (int i = 0; i < temp.length; i++) {
-                    for (int j = 0; j < temp[i].length; j++) {
-                        temp1[i][j] = temp[i][j].doubleValue();
-                    }
+            List<MmItemOutputEntity> itemOutputList = mmItemOutputService.getByItemid(predictModel.getItemid());
+            Map<MmItemOutputEntity, double[]> predictMatrixs = new HashMap<>(itemOutputList.size());
+            for (MmItemOutputEntity output : itemOutputList) {
+                if (!modelResult.containsKey(output.getResultstr())) {
+                    continue;
                 }
-                result.setPredictMatrix(temp1);
+                OutResultType outResultType = OutResultType.getEumByCode(output.getResultType());
+                switch (outResultType) {
+                    case D1:
+                        double[] temp1 = (double[]) modelResult.get(output.getResultstr());
+                        predictMatrixs.put(output, temp1);
+                        break;
+                    case D2:
+                        double[][] temp2 = (double[][]) modelResult.get(output.getResultstr());
+                        double[] tempColumn = new double[temp2.length];
+                        for (int i = 0; i < tempColumn.length; i++) {
+                            tempColumn[i] = temp2[i][output.getResultIndex()];
+                        }
+                        predictMatrixs.put(output, tempColumn);
+                        break;
+                    default:
+                        break;
+                }
             }
+            result.setPredictMatrixs(predictMatrixs);
             result.setModelResult(modelResult);
             result.setPredictTime(predictTime);
         } catch (Exception ex) {
-            log.error("IAILModel对象构造失败,modelId=" + modelId);
-            log.error(ex.getMessage());
-            log.error("调用发生异常,异常信息为:{}" , ex);
+            log.error("调用发生异常,异常信息为:{}", ex);
             ex.printStackTrace();
-
+            throw new ModelInvokeException(ex.getMessage());
         }
         return result;
     }

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