From 85eff4d751aa52dd80f81711410dc4be2adcf0e9 Mon Sep 17 00:00:00 2001
From: dengzedong <dengzedong@email>
Date: 星期三, 12 三月 2025 14:48:44 +0800
Subject: [PATCH] 删除多余方法

---
 iailab-module-model/iailab-module-model-biz/src/main/java/com/iailab/module/model/mdk/predict/impl/PredictModelHandlerImpl.java |   26 ++++++++++++--------------
 1 files changed, 12 insertions(+), 14 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 466ee10..9c7bd75 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
@@ -3,6 +3,7 @@
 import com.alibaba.fastjson.JSON;
 import com.alibaba.fastjson.JSONArray;
 import com.iail.model.IAILModel;
+import com.iailab.module.model.mdk.vo.StAdjustDeviationDTO;
 import com.iailab.module.model.enums.CommonConstant;
 import com.iailab.module.model.common.enums.OutResultType;
 import com.iailab.module.model.common.exception.ModelResultErrorException;
@@ -55,7 +56,7 @@
      * @throws ModelInvokeException
      */
     @Override
-    public synchronized PredictResultVO predictByModel(Date predictTime, MmPredictModelEntity predictModel,String itemName,String itemNo) throws ModelInvokeException {
+    public PredictResultVO predictByModel(Date predictTime, MmPredictModelEntity predictModel, String itemName, String itemNo) throws ModelInvokeException {
         PredictResultVO result = new PredictResultVO();
         if (predictModel == null) {
             throw new ModelInvokeException("modelEntity is null");
@@ -72,7 +73,7 @@
             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!");
+                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) {
@@ -87,18 +88,13 @@
             param2Values[portLength] = newModelBean.getDataMap().get("models");
             param2Values[portLength + 1] = settings;
 
-            log.info("####################### 预测模型 "+ "【itemId:" + predictModel.getItemid() + ",itemName:" + itemName + ",itemNo:" + itemNo + "】 ##########################");
-//            JSONObject jsonObjNewModelBean = new JSONObject();
-//            jsonObjNewModelBean.put("newModelBean", newModelBean);
-//            log.info(String.valueOf(jsonObjNewModelBean));
-//            JSONObject jsonObjParam2Values = new JSONObject();
-//            jsonObjParam2Values.put("param2Values", param2Values);
+            log.info("####################### 预测模型 " + "【itemId:" + predictModel.getItemid() + ",itemName:" + itemName + ",itemNo:" + itemNo + "】 ##########################");
             log.info("参数: " + JSON.toJSONString(param2Values));
 
             //IAILMDK.run
             HashMap<String, Object> modelResult = DllUtils.run(newModelBean, param2Values, predictModel.getMpkprojectid());
             //打印结果
-            log.info("预测模型计算完成:modelId=" + modelId + ",modelName=" + predictModel.getMethodname() + ",modelResult=" + JSON.toJSONString(modelResult));
+            log.info("预测模型计算完成:modelId=" + modelId + ",modelName=" + predictModel.getModelname() + ",modelResult=" + JSON.toJSONString(modelResult));
             //判断模型结果
             if (!modelResult.containsKey(CommonConstant.MDK_STATUS_CODE) || !modelResult.containsKey(CommonConstant.MDK_RESULT) ||
                     !modelResult.get(CommonConstant.MDK_STATUS_CODE).toString().equals(CommonConstant.MDK_STATUS_100)) {
@@ -157,18 +153,19 @@
      * @param predictModel
      * @param itemName
      * @param itemNo
+     * @param deviationList
      * @return
      * @throws ModelInvokeException
      */
     @Override
-    public synchronized PredictResultVO predictByModel(Date predictTime, MmPredictModelEntity predictModel,String itemName,String itemNo, double[][] deviation) throws ModelInvokeException {
+    public PredictResultVO predictByModel(Date predictTime, MmPredictModelEntity predictModel, String itemName, String itemNo, List<StAdjustDeviationDTO> deviationList) 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, itemName, new HashMap<>());
+            List<SampleData> sampleDataList = sampleConstructor.constructSample(TypeA.Predict.name(), modelId, predictTime, itemName, new HashMap<>(), deviationList);
             String modelPath = predictModel.getModelpath();
             if (modelPath == null) {
                 log.info("模型路径不存在,modelId=" + modelId);
@@ -178,7 +175,7 @@
             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!");
+                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) {
@@ -193,13 +190,13 @@
             param2Values[portLength] = newModelBean.getDataMap().get("models");
             param2Values[portLength + 1] = settings;
 
-            log.info("####################### 模拟调整 "+ "【itemId:" + predictModel.getItemid() + ",itemName:" + itemName + ",itemNo:" + itemNo + "】 ##########################");
+            log.info("####################### 模拟调整 " + "【itemId:" + predictModel.getItemid() + ",itemName:" + itemName + ",itemNo:" + itemNo + "】 ##########################");
             log.info("参数: " + JSON.toJSONString(param2Values));
 
             //IAILMDK.run
             HashMap<String, Object> modelResult = DllUtils.run(newModelBean, param2Values, predictModel.getMpkprojectid());
             //打印结果
-            log.info("预测模型计算完成:modelId=" + modelId + ",modelName=" + predictModel.getMethodname() + ",modelResult=" + JSON.toJSONString(modelResult));
+            log.info("预测模型计算完成:modelId=" + modelId + ",modelName=" + predictModel.getModelname() + ",modelResult=" + JSON.toJSONString(modelResult));
             //判断模型结果
             if (!modelResult.containsKey(CommonConstant.MDK_STATUS_CODE) || !modelResult.containsKey(CommonConstant.MDK_RESULT) ||
                     !modelResult.get(CommonConstant.MDK_STATUS_CODE).toString().equals(CommonConstant.MDK_STATUS_100)) {
@@ -247,6 +244,7 @@
         }
         return result;
     }
+
     /**
      * 构造IAILMDK.run()方法的newModelBean参数
      *

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