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

@@ -1,5 +1,6 @@
package com.iailab.module.model.mdk.predict.impl;

import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONArray;
import com.alibaba.fastjson.JSONObject;
import com.iail.model.IAILModel;
@@ -16,12 +17,16 @@
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.*;
import java.util.Date;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

/**
 * @author PanZhibao
@@ -50,7 +55,7 @@
     * @throws ModelInvokeException
     */
    @Override
    public synchronized PredictResultVO predictByModel(Date predictTime, MmPredictModelEntity predictModel) throws ModelInvokeException {
    public synchronized PredictResultVO predictByModel(Date predictTime, MmPredictModelEntity predictModel,String itemName) throws ModelInvokeException {
        PredictResultVO result = new PredictResultVO();
        if (predictModel == null) {
            throw new ModelInvokeException("modelEntity is null");
@@ -65,6 +70,11 @@
            }
            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;
@@ -77,13 +87,13 @@
            param2Values[portLength] = newModelBean.getDataMap().get("models");
            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() + ",itemName" + itemName + "】 ##########################");
//            JSONObject jsonObjNewModelBean = new JSONObject();
//            jsonObjNewModelBean.put("newModelBean", newModelBean);
//            log.info(String.valueOf(jsonObjNewModelBean));
//            JSONObject jsonObjParam2Values = new JSONObject();
//            jsonObjParam2Values.put("param2Values", param2Values);
            log.info("参数： " + JSON.toJSONString(param2Values));

            //IAILMDK.run
            HashMap<String, Object> modelResult = DllUtils.run(newModelBean, param2Values, predictModel.getMpkprojectid());
@@ -93,13 +103,14 @@
            }
            modelResult = (HashMap<String, Object>) modelResult.get(CommonConstant.MDK_RESULT);
            //打印结果
            log.info("模型计算完成：modelId=" + modelId + modelResult);
            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));

            List<MmItemOutputEntity> itemOutputList = mmItemOutputService.getByItemid(predictModel.getItemid());
            Map<MmItemOutputEntity, double[]> predictMatrixs = new HashMap<>(itemOutputList.size());
            Map<MmItemOutputEntity, double[]> predictMatrixs = new HashMap<>();
            Map<MmItemOutputEntity, Double> predictDoubleValues = new HashMap<>();
            for (MmItemOutputEntity output : itemOutputList) {
                if (!modelResult.containsKey(output.getResultstr())) {
                    continue;
@@ -118,11 +129,16 @@
                        }
                        predictMatrixs.put(output, tempColumn);
                        break;
                    case D:
                        Double temp3 = (Double) modelResult.get(output.getResultstr());
                        predictDoubleValues.put(output, temp3);
                        break;
                    default:
                        break;
                }
            }
            result.setPredictMatrixs(predictMatrixs);
            result.setPredictDoubleValues(predictDoubleValues);
            result.setModelResult(modelResult);
            result.setPredictTime(predictTime);
        } catch (Exception ex) {