iailab-plat.git

Merge remote-tracking branch 'origin/master'

dengzedong

2024-12-24 aa0382e44311f9f7e62a688c8fcaa9c69a512e0f

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7fd198	1	package com.iailab.module.model.mdk.predict.impl;
潘	2
6957a3	3	import com.alibaba.fastjson.JSON;
7fd198	4	import com.alibaba.fastjson.JSONArray;
潘	5	import com.alibaba.fastjson.JSONObject;
	6	import com.iail.model.IAILModel;
4f1717	7	import com.iailab.module.model.common.enums.CommonConstant;
373ab1	8	import com.iailab.module.model.common.enums.OutResultType;
1178da	9	import com.iailab.module.model.common.exception.ModelResultErrorException;
69bd5e	10	import com.iailab.module.model.mcs.pre.entity.MmItemOutputEntity;
7fd198	11	import com.iailab.module.model.mcs.pre.entity.MmModelArithSettingsEntity;
潘	12	import com.iailab.module.model.mcs.pre.entity.MmPredictModelEntity;
7f0bcd	13	import com.iailab.module.model.mcs.pre.enums.ItemRunStatusEnum;
69bd5e	14	import com.iailab.module.model.mcs.pre.service.MmItemOutputService;
7fd198	15	import com.iailab.module.model.mcs.pre.service.MmModelArithSettingsService;
潘	16	import com.iailab.module.model.mdk.common.enums.TypeA;
	17	import com.iailab.module.model.mdk.common.exceptions.ModelInvokeException;
	18	import com.iailab.module.model.mdk.predict.PredictModelHandler;
	19	import com.iailab.module.model.mdk.sample.SampleConstructor;
	20	import com.iailab.module.model.mdk.sample.dto.SampleData;
	21	import com.iailab.module.model.mdk.vo.PredictResultVO;
45520a	22	import com.iailab.module.model.mpk.common.MdkConstant;
1a2b62	23	import com.iailab.module.model.mpk.common.utils.DllUtils;
7fd198	24	import lombok.extern.slf4j.Slf4j;
潘	25	import org.springframework.beans.factory.annotation.Autowired;
	26	import org.springframework.stereotype.Component;
	27
45520a	28	import java.util.Date;
D	29	import java.util.HashMap;
	30	import java.util.List;
	31	import java.util.Map;
7fd198	32
潘	33	/**
	34	* @author PanZhibao
	35	* @Description
	36	* @createTime 2024年09月01日
	37	*/
	38	@Slf4j
	39	@Component
	40	public class PredictModelHandlerImpl implements PredictModelHandler {
	41
	42	@Autowired
	43	private MmModelArithSettingsService mmModelArithSettingsService;
	44
	45	@Autowired
69bd5e	46	private MmItemOutputService mmItemOutputService;
7fd198	47
潘	48	@Autowired
	49	private SampleConstructor sampleConstructor;
	50
4f1717	51	/**
潘	52	* 根据模型预测,返回预测结果
	53	*
	54	* @param predictTime
	55	* @param predictModel
	56	* @return
	57	* @throws ModelInvokeException
	58	*/
7fd198	59	@Override
28c2db	60	public synchronized PredictResultVO predictByModel(Date predictTime, MmPredictModelEntity predictModel,String itemName,String itemNo) throws ModelInvokeException {
7fd198	61	PredictResultVO result = new PredictResultVO();
潘	62	if (predictModel == null) {
	63	throw new ModelInvokeException("modelEntity is null");
	64	}
	65	String modelId = predictModel.getId();
	66	try {
50084d	67	List<SampleData> sampleDataList = sampleConstructor.constructSample(TypeA.Predict.name(), modelId, predictTime, itemName);
7fd198	68	String modelPath = predictModel.getModelpath();
潘	69	if (modelPath == null) {
373ab1	70	log.info("模型路径不存在，modelId=" + modelId);
7fd198	71	return null;
潘	72	}
	73	IAILModel newModelBean = composeNewModelBean(predictModel);
	74	HashMap<String, Object> settings = getPredictSettingsByModelId(modelId);
45520a	75	// 校验setting必须有pyFile，否则可能导致程序崩溃
D	76	if (!settings.containsKey(MdkConstant.PY_FILE_KEY)) {
	77	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！");
	78	}
	79
7fd198	80	if (settings == null) {
潘	81	log.error("模型setting不存在，modelId=" + modelId);
	82	return null;
	83	}
	84	int portLength = sampleDataList.size();
	85	Object[] param2Values = new Object[portLength + 2];
	86	for (int i = 0; i < portLength; i++) {
4f1717	87	param2Values[i] = sampleDataList.get(i).getMatrix();
7fd198	88	}
潘	89	param2Values[portLength] = newModelBean.getDataMap().get("models");
4f1717	90	param2Values[portLength + 1] = settings;
7fd198	91
595ccf	92	log.info("####################### 预测模型 "+ "【itemId:" + predictModel.getItemid() + ",itemName：" + itemName + ",itemNo：" + itemNo + "】 ##########################");
b82ba2	93	// JSONObject jsonObjNewModelBean = new JSONObject();
D	94	// jsonObjNewModelBean.put("newModelBean", newModelBean);
	95	// log.info(String.valueOf(jsonObjNewModelBean));
	96	// JSONObject jsonObjParam2Values = new JSONObject();
	97	// jsonObjParam2Values.put("param2Values", param2Values);
6957a3	98	log.info("参数： " + JSON.toJSONString(param2Values));
7fd198	99
潘	100	//IAILMDK.run
1a2b62	101	HashMap<String, Object> modelResult = DllUtils.run(newModelBean, param2Values, predictModel.getMpkprojectid());
4f1717	102	if (!modelResult.containsKey(CommonConstant.MDK_STATUS_CODE) \|\| !modelResult.containsKey(CommonConstant.MDK_RESULT) \|\|
潘	103	!modelResult.get(CommonConstant.MDK_STATUS_CODE).toString().equals(CommonConstant.MDK_STATUS_100)) {
1178da	104	throw new ModelResultErrorException("模型结果异常：" + modelResult);
b2aca2	105	}
4f1717	106	modelResult = (HashMap<String, Object>) modelResult.get(CommonConstant.MDK_RESULT);
7fd198	107	//打印结果
潘	108	JSONObject jsonObjResult = new JSONObject();
1a2b62	109	jsonObjResult.put("result", modelResult);
595ccf	110	log.info("预测模型计算完成：modelId=" + modelId + ",modelName=" + predictModel.getMethodname() + "，modelResult=" + String.valueOf(jsonObjResult));
7fd198	111
373ab1	112	List<MmItemOutputEntity> itemOutputList = mmItemOutputService.getByItemid(predictModel.getItemid());
a6e46f	113	Map<MmItemOutputEntity, double[]> predictMatrixs = new HashMap<>();
D	114	Map<MmItemOutputEntity, Double> predictDoubleValues = new HashMap<>();
373ab1	115	for (MmItemOutputEntity output : itemOutputList) {
潘	116	if (!modelResult.containsKey(output.getResultstr())) {
	117	continue;
	118	}
	119	OutResultType outResultType = OutResultType.getEumByCode(output.getResultType());
	120	switch (outResultType) {
	121	case D1:
	122	double[] temp1 = (double[]) modelResult.get(output.getResultstr());
	123	predictMatrixs.put(output, temp1);
	124	break;
	125	case D2:
	126	double[][] temp2 = (double[][]) modelResult.get(output.getResultstr());
	127	double[] tempColumn = new double[temp2.length];
	128	for (int i = 0; i < tempColumn.length; i++) {
	129	tempColumn[i] = temp2[i][output.getResultIndex()];
69bd5e	130	}
373ab1	131	predictMatrixs.put(output, tempColumn);
潘	132	break;
a6e46f	133	case D:
D	134	Double temp3 = (Double) modelResult.get(output.getResultstr());
	135	predictDoubleValues.put(output, temp3);
	136	break;
373ab1	137	default:
潘	138	break;
1a2b62	139	}
D	140	}
69bd5e	141	result.setPredictMatrixs(predictMatrixs);
a6e46f	142	result.setPredictDoubleValues(predictDoubleValues);
1a2b62	143	result.setModelResult(modelResult);
7fd198	144	result.setPredictTime(predictTime);
50084d	145	} catch (ModelResultErrorException ex) {
7fd198	146	ex.printStackTrace();
50084d	147	throw ex;
D	148	} catch (Exception ex) {
c9dd12	149	// log.error("调用发生异常，异常信息为:{0}", ex.getMessage());
D	150	ex.printStackTrace();
ead005	151	throw new ModelInvokeException(ex.getMessage());
7fd198	152	}
潘	153	return result;
	154	}
	155
	156	/**
	157	* 构造IAILMDK.run()方法的newModelBean参数
	158	*
	159	* @param predictModel
	160	* @return
	161	*/
	162	private IAILModel composeNewModelBean(MmPredictModelEntity predictModel) {
	163	IAILModel newModelBean = new IAILModel();
	164	newModelBean.setClassName(predictModel.getClassname().trim());
	165	newModelBean.setMethodName(predictModel.getMethodname().trim());
	166	//构造参数类型
	167	String[] paArStr = predictModel.getModelparamstructure().trim().split(",");
	168	Class<?>[] paramsArray = new Class[paArStr.length];
	169	for (int i = 0; i < paArStr.length; i++) {
	170	if ("[[D".equals(paArStr[i])) {
	171	paramsArray[i] = double[][].class;
	172	} else if ("Map".equals(paArStr[i]) \|\| "java.util.HashMap".equals(paArStr[i])) {
	173	paramsArray[i] = HashMap.class;
	174	}
	175	}
	176	newModelBean.setParamsArray(paramsArray);
	177	HashMap<String, Object> dataMap = new HashMap<>();
	178	HashMap<String, String> models = new HashMap<>(1);
b2aca2	179	models.put("model_path", predictModel.getModelpath());
7fd198	180	dataMap.put("models", models);
潘	181	newModelBean.setDataMap(dataMap);
	182	return newModelBean;
	183	}
	184
	185	/**
	186	* 根据模型id获取参数map
	187	*
	188	* @param modelId
	189	* @return
	190	*/
	191	private HashMap<String, Object> getPredictSettingsByModelId(String modelId) {
	192	List<MmModelArithSettingsEntity> list = mmModelArithSettingsService.getByModelId(modelId);
	193	HashMap<String, Object> result = new HashMap<>();
	194	for (MmModelArithSettingsEntity entry : list) {
	195	String valueType = entry.getValuetype().trim(); //去除两端空格
	196	if ("int".equals(valueType)) {
	197	int value = Integer.parseInt(entry.getValue());
	198	result.put(entry.getKey(), value);
	199	} else if ("double".equals(valueType)) {
	200	double value = Double.parseDouble(entry.getValue());
	201	result.put(entry.getKey(), value);
	202	} else if ("string".equals(valueType)) {
	203	String value = entry.getValue();
	204	result.put(entry.getKey(), value);
	205	} else if ("decimalArray".equals(valueType)) {
	206	JSONArray valueArray = JSONArray.parseArray(entry.getValue());
	207	double[] value = new double[valueArray.size()];
	208	for (int i = 0; i < valueArray.size(); i++) {
	209	value[i] = Double.parseDouble(valueArray.get(i).toString());
	210	}
	211	result.put(entry.getKey(), value);
	212	} else if ("decimal".equals(valueType)) {
	213	double value = Double.parseDouble(entry.getValue());
	214	result.put(entry.getKey(), value);
	215	}
	216	}
	217	return result;
	218	}
	219	}