1

src/main/java/com/controller/LoadforecastController.java

@@ -1,295 +0,0 @@
-package com.controller;
-
-import com.annotation.IgnoreAuth;
-import com.annotation.SysLog;
-import com.baomidou.mybatisplus.mapper.EntityWrapper;
-import com.baomidou.mybatisplus.mapper.Wrapper;
-import com.entity.LoadForecastEntity;
-import com.entity.RainfalldataEntity;
-import com.entity.RainfalldataforecastEntity;
-import com.entity.SubLoadEntity;
-import com.entity.view.RainfalldataforecastView;
-import com.entity.view.SubLoadForecastView;
-import com.service.RainfalldataService;
-import com.service.RainfalldataforecastService;
-import com.service.SubLoadForecastService;
-import com.service.SubLoadService;
-import com.utils.DeSensUtil;
-import com.utils.MPUtil;
-import com.utils.PageUtils;
-import com.utils.R;
-import org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression;
-//import org.apache.spark.api.java.JavaSparkContext;
-import org.springframework.beans.factory.annotation.Autowired;
-import org.springframework.transaction.annotation.Transactional;
-import org.springframework.web.bind.annotation.*;
-import weka.core.*;
-
-import javax.servlet.http.HttpServletRequest;
-import java.text.DecimalFormat;
-import java.util.*;
-import java.util.stream.Stream;
-
-/**
- * 变电站负荷预测
- * 后端接口
- * @author 
- * @email 
- * @date 2025-03-17 16:24:19
- */
-@RestController
-@RequestMapping("/loadforecast")
-public class LoadforecastController {
-    @Autowired
-    private RainfalldataforecastService rainfalldataforecastService;
-    @Autowired
-    private RainfalldataService rainfalldataService;
-    @Autowired
-    private SubLoadForecastService subLoadForecastService;
-
-    @Autowired
-    private SubLoadService subLoadService;
-    /**
-     * 后台列表
-     */
-    @RequestMapping("/page")
-    public R page(@RequestParam Map<String, Object> params, LoadForecastEntity loadForecastEntity,
-                  HttpServletRequest request){
-        //设置查询条件
-        EntityWrapper<LoadForecastEntity> ew = new EntityWrapper<LoadForecastEntity>();
-        //查询结果
-		PageUtils page = subLoadForecastService.queryPage(params, MPUtil.sort(MPUtil.between(MPUtil.likeOrEq(ew, loadForecastEntity), params), params));
-        Map<String, String> deSens = new HashMap<>();
-        //给需要脱敏的字段脱敏
-        DeSensUtil.desensitize(page,deSens);
-        return R.ok().put("data", page);
-    }
-	 /**
-     * 查询
-     */
-    @RequestMapping("/query")
-    public R query(LoadForecastEntity loadForecastEntity){
-        EntityWrapper< LoadForecastEntity> ew = new EntityWrapper< LoadForecastEntity>();
- 		ew.allEq(MPUtil.allEQMapPre( loadForecastEntity, "forecast"));
-        SubLoadForecastView rainfalldataforecastView =  subLoadForecastService.selectView(ew);
-		return R.ok("查询变电站负荷预测成功").put("data", rainfalldataforecastView);
-    }
-	
-    /**
-     * 后台详情
-     */
-    @RequestMapping("/info/{id}")
-    public R info(@PathVariable("id") Long id){
-        LoadForecastEntity dataforecast = subLoadForecastService.selectById(id);
-        Map<String, String> deSens = new HashMap<>();
-        //给需要脱敏的字段脱敏
-        DeSensUtil.desensitize(dataforecast,deSens);
-        return R.ok().put("data", dataforecast);
-    }
-
-    /**
-     * 前台详情
-     */
-	@IgnoreAuth
-    @RequestMapping("/detail/{id}")
-    public R detail(@PathVariable("id") Long id){
-        LoadForecastEntity dataforecast = subLoadForecastService.selectById(id);
-        Map<String, String> deSens = new HashMap<>();
-        //给需要脱敏的字段脱敏
-        DeSensUtil.desensitize(dataforecast,deSens);
-        return R.ok().put("data", dataforecast);
-    }
-    
-
-
-
-    /**
-     * 后台保存
-     */
-    @RequestMapping("/save")
-    @SysLog("新增变电站负荷预测")
-    public R save(@RequestBody LoadForecastEntity dataforecast, HttpServletRequest request){
-        dataforecast.setUserid((Long)request.getSession().getAttribute("userId"));
-        subLoadForecastService.insert(dataforecast);
-        return R.ok().put("data",dataforecast.getId());
-    }
-    
-    /**
-     * 前台保存
-     */
-    @SysLog("新增变电站负荷预测")
-    @RequestMapping("/add")
-    public R add(@RequestBody LoadForecastEntity dataforecast, HttpServletRequest request){
-        subLoadForecastService.insert(dataforecast);
-        return R.ok().put("data",dataforecast.getId());
-    }
-
-
-
-
-
-    /**
-     * 修改
-     */
-    @RequestMapping("/update")
-    @Transactional
-    @SysLog("修改变电站负荷预测")
-    public R update(@RequestBody LoadForecastEntity dataforecast, HttpServletRequest request){
-        //全部更新
-        subLoadForecastService.updateById(dataforecast);
-        return R.ok();
-    }
-
-    /**
-     * 删除
-     */
-    @RequestMapping("/delete")
-    @SysLog("删除变电站负荷预测")
-    public R delete(@RequestBody Long[] ids){
-        subLoadForecastService.deleteBatchIds(Arrays.asList(ids));
-        return R.ok();
-    }
-
-    /**
-    * 预测算法
-    */
-    @RequestMapping("/forecast")
-    public R forecast(@RequestBody Map<String, Object> params) throws Exception {
-        // 特征值
-        String[] eigenValueArr = "main_capacity,peak_load,base_load,load_rate,electric_heating_load,users_num,pf_users_num,join_electric_heating_num".split(",");
-        // 目标值
-        String[] targetValueArr = "load_rate".split(",");
-        String[] sqlSelectArr = Stream.of(eigenValueArr, targetValueArr).flatMap(Arrays::stream).toArray(String[]::new);
-        // 模型训练
-        Wrapper<SubLoadEntity> wrapper = new EntityWrapper<>();
-        wrapper.setSqlSelect(sqlSelectArr);
-        for (String arr : sqlSelectArr) {
-            wrapper.isNotNull(arr).ne(arr, "");
-        }
-        // 从数据库获取需要元数据
-        List<Map<String,Object>> list =subLoadService.selectMaps(wrapper);
-        // 模型训练
-        Instances instances = createInstances(list, sqlSelectArr);
-        // 创建一个map来存储预测结果
-        Map<String, Object> forecastRes = forecastRes(instances, params, eigenValueArr, targetValueArr);
-
-        // 更新数据库或其他操作
-        EntityWrapper<LoadForecastEntity> ew = new EntityWrapper<>();
-        ew.eq("id", params.get("id"));
-        for (Map.Entry<String, Object> entry : forecastRes.entrySet()) {
-            String updateSet = entry.getKey() + "='" + entry.getValue().toString() + "'";
-            subLoadForecastService.updateForSet(updateSet, ew);
-        }
-        params.putAll(forecastRes);
-        return R.ok(params);
-    }
-
-    /**
-     * 预测结果
-     * @param instances
-     * @param params
-     * @param eigenValueArr
-     * @param targetValueArr
-     * @return
-     * @throws Exception
-     */
-    private Map<String, Object> forecastRes(Instances instances, Map<String, Object> params, String[] eigenValueArr, String[] targetValueArr) throws Exception {
-        Map<String, Object> forecastRes = new HashMap<>();
-        DecimalFormat df = new DecimalFormat("#.00");
-
-        for (String targetValue : targetValueArr) {
-            // 创建线性回归模型
-            OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
-
-            // 准备数据
-            double[][] x = new double[instances.numInstances()][eigenValueArr.length];
-            double[] y = new double[instances.numInstances()];
-
-            for (int i = 0; i < instances.numInstances(); i++) {
-                Instance instance = instances.instance(i);
-                for (int j = 0; j < eigenValueArr.length; j++) {
-                    x[i][j] = instance.value(instances.attribute(eigenValueArr[j]));
-                }
-                y[i] = instance.value(instances.attribute(targetValue));
-            }
-
-            // 训练模型
-            regression.newSampleData(y, x);
-
-            // 创建新的样本数据
-            double[] sampleData = new double[eigenValueArr.length];
-            for (int i = 0; i < eigenValueArr.length; i++) {
-                Attribute attribute = instances.attribute(eigenValueArr[i]);
-                if (attribute.isNominal()) {
-                    sampleData[i] = attribute.indexOfValue(params.get(eigenValueArr[i]).toString());
-                } else {
-                    sampleData[i] = Double.parseDouble(params.get(eigenValueArr[i]).toString());
-                }
-            }
-
-            // 进行预测
-            double predictedValue = regression.estimateRegressionParameters()[0]; // 截距
-            for (int i = 1; i <= eigenValueArr.length; i++) {
-                predictedValue += regression.estimateRegressionParameters()[i] * sampleData[i - 1];
-            }
-
-            // 添加预测结果
-            forecastRes.put(targetValue, df.format(predictedValue));
-        }
-    return forecastRes;
-}
-
-    /**
-    * 训练模型
-    */
-    private Instances createInstances(List<Map<String, Object>> dataList, String[] attrs) throws Exception {
-        // 遍历数据集以确定每个属性的类型
-        Map<String, Set<Object>> uniqueValuesPerAttribute = new HashMap<>();
-        for (Map<String, Object> data : dataList) {
-            for (String attr : attrs) {
-                uniqueValuesPerAttribute.computeIfAbsent(attr, k -> new HashSet<>()).add(data.get(attr));
-            }
-        }
-        FastVector attributes = new FastVector();
-        for (String attr : attrs) {
-            Set<Object> uniqueValues = uniqueValuesPerAttribute.get(attr);
-            if (isNominal(uniqueValues)) {
-                // 如果是分类属性，创建分类属性
-                FastVector nominalValues = new FastVector();
-                uniqueValues.forEach(value -> nominalValues.addElement(value));
-                nominalValues.add("unknown");
-                attributes.addElement(new Attribute(attr, nominalValues));
-            } else {
-                attributes.addElement(new Attribute(attr));
-            }
-        }
-
-        // 创建 Instances 对象
-        Instances dataset = new Instances("dataset-name", attributes, dataList.size());
-        dataset.setClassIndex(dataset.numAttributes() - 1); // 设置类属性索引
-        // 填充数据
-        for (Map<String, Object> data : dataList) {
-            double[] instanceValue = new double[dataset.numAttributes()];
-            int i = 0;
-            for (String attr : attrs) {
-                Attribute attribute = dataset.attribute(attr);
-                if (attribute.isNominal()) {
-                    instanceValue[i++] = attribute.indexOfValue(data.get(attr).toString());
-                } else {
-                    instanceValue[i++] = Double.parseDouble(data.get(attr).toString());
-                }
-            }
-            Instance instance = new DenseInstance(1.0, instanceValue);
-            dataset.add(instance);
-        }
-        return dataset;
-    }
-
-    /**
-    * 检测数据类型是否为字符串
-    */
-    private boolean isNominal(Set<Object> values) {
-        // 检查是否所有的值都是字符串，并且数量是否超过某个阈值（例如，如果超过50%的值是唯一的，则可能是分类属性）
-        return values.stream().allMatch(v -> v instanceof String);
-    }
-}

src/main/java/com/service/predict/LinearRegressionPredictionService.java

@@ -94,7 +94,7 @@ public class LinearRegressionPredictionService {
         }
         List<VoltageHistData>  historicalData=voltageLoadDataService.selectListViewByDate(predictConf.getStartTime(),substation.getRegion(),substation.getSubstation(),predictConf.getMinTemperatures(),predictConf.getMaxTemperatures(),predictConf.getDimension());
         if(historicalData==null || historicalData.size()<5){
-            response.setMessage(substation.getSubstation()+"无足够的数据，该变电站构建训练模型失败");
+            response.setMessage("警告,"+substation.getSubstation()+"无足够的数据，该变电站构建训练模型失败");
             response.setSuccess(false);
             return response;
         }

src/main/resources/mapper/VoltageLoadDataDao.xml

@@ -47,7 +47,7 @@
 			</if>
 		</where>
 		GROUP BY voltageload.substation,voltageload.record_date
-		 order by 	voltageload.substation  LIMIT 1000) a  where a.temperatures is not null
+		 order by 	voltageload.substation ,voltageload.record_date desc LIMIT 1000) a  where a.temperatures is not null
 	</select>
 	
 	<select id="selectView"