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

@@ -1,13 +1,16 @@
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;
import com.iailab.module.model.common.enums.CommonConstant;
import com.iailab.module.model.common.enums.OutResultType;
import com.iailab.module.model.common.exception.ModelResultErrorException;
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.MmPredictModelEntity;
import com.iailab.module.model.mcs.pre.enums.ItemRunStatusEnum;
import com.iailab.module.model.mcs.pre.service.MmItemOutputService;
import com.iailab.module.model.mcs.pre.service.MmModelArithSettingsService;
import com.iailab.module.model.mdk.common.enums.TypeA;
@@ -54,7 +57,7 @@
     * @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");
@@ -86,29 +89,30 @@
            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());
            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);
                throw new ModelResultErrorException("模型结果异常：" + modelResult);
            }
            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 + 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;
@@ -127,11 +131,16 @@
                        }
                        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) {