feat: count.

controllers/step_train.go

@@ -2,14 +2,18 @@ package controllers
 
 import (
 	"errors"
+	"fmt"
 	"github.com/gin-gonic/gin"
+	"github.com/sajari/regression"
 	"gorm.io/gorm"
 	"gorm.io/gorm/clause"
 	"hr_receiver/config"
 	"hr_receiver/models"
+	"log"
 	"math"
 	"net/http"
 	"strconv"
+	"strings"
 )
 
 type StepTrainingController struct {
@@ -53,7 +57,7 @@ func (tc *StepTrainingController) CreateTrainingRecord(c *gin.Context) {
 			if err := tx.Clauses(
 				clause.OnConflict{
 					Columns:   []clause.Column{{Name: "identifier"}}, // 指定冲突的列
-					DoUpdates: clause.Assignments(map[string]interface{}{"value": record.HeartRates[i].Value, "time": record.HeartRates[i].Time}),
+					DoUpdates: clause.Assignments(map[string]interface{}{"heart_rate_type": record.HeartRates[i].HeartRateType, "value": record.HeartRates[i].Value, "time": record.HeartRates[i].Time}),
 				},
 			).Create(&record.HeartRates[i]).Error; err != nil {
 
@@ -75,6 +79,33 @@ func (tc *StepTrainingController) CreateTrainingRecord(c *gin.Context) {
 		return nil
 	})
 
+	// ====== 新增部分：启动异步回归计算 ======
+	go func() {
+		// 查询完整数据（需要关联的心率和步频数据）
+		var fullRecord models.StepTrainRecord
+		if err := tc.DB.
+			Where("train_id = ?", record.TrainId).
+			Preload("HeartRates", "heart_rate_type = ?", 1). // 只要有效心率
+			Preload("StrideFreqs", "predict_value = ?", 1).  // 只要有效步频
+			First(&fullRecord).Error; err != nil {
+			log.Printf("训练记录%d查询失败，无法计算回归: %v", record.TrainId, err)
+			return
+		}
+
+		// 检查数据是否满足计算条件
+		if len(fullRecord.HeartRates) == 0 || len(fullRecord.StrideFreqs) == 0 {
+			log.Printf("训练记录%d缺少心率或步频数据，跳过回归计算", record.TrainId)
+			return
+		}
+
+		// 计算并保存回归结果
+		if _, err := tc.GetOrCalculateRegression(fullRecord.TrainId); err != nil {
+			log.Printf("训练记录%d回归计算失败: %v", fullRecord.TrainId, err)
+		} else {
+			log.Printf("训练记录%d回归结果已保存", fullRecord.TrainId)
+		}
+	}()
+
 	if err != nil {
 		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 		return
@@ -187,3 +218,678 @@ func (tc *StepTrainingController) GetTrainingRecordByTrainId(c *gin.Context) {
 		"data":    record,
 	})
 }
+
+// 定义结构体
+type SpeedSegment struct {
+	Duration float64
+	Speed    float64
+}
+
+// 实现线性回归算法
+func performLinearRegression(averages []map[float64]float64) models.RegressionResult {
+	if len(averages) == 0 {
+		return models.RegressionResult{
+			Equation: "无数据",
+		}
+	}
+
+	// 收集数据点
+	var points []struct{ x, y float64 }
+	for _, m := range averages {
+		for x, y := range m {
+			points = append(points, struct{ x, y float64 }{x, y})
+		}
+	}
+
+	// 使用回归库计算
+	r := new(regression.Regression)
+	r.SetObserved("y")
+	r.SetVar(0, "x")
+
+	for _, p := range points {
+		r.Train(regression.DataPoint(p.y, []float64{p.x}))
+	}
+
+	if err := r.Run(); err != nil {
+		log.Printf("线性回归计算失败: %v", err)
+		return models.RegressionResult{
+			Equation: "计算失败",
+		}
+	}
+
+	// 创建结果
+	slope := r.Coeff(1)
+	intercept := r.Coeff(0)
+	r2 := r.R2
+	return models.RegressionResult{
+		RegressionType: models.LinearRegression,
+		Slope:          &slope,
+		Intercept:      &intercept,
+		RSquared:       &r2,
+		Equation:       r.Formula,
+	}
+}
+
+// 实现对数和二次回归算法
+// 对数回归算法
+func performLogarithmicRegression(averages []map[float64]float64) models.RegressionResult {
+	if len(averages) == 0 {
+		return models.RegressionResult{
+			Equation: "无数据",
+		}
+	}
+
+	// 收集数据点
+	r := new(regression.Regression)
+	r.SetObserved("y")
+	r.SetVar(0, "log(x+1)")
+
+	for _, m := range averages {
+		for speed, hr := range m {
+			logSpeed := math.Log(speed + 1)
+			r.Train(regression.DataPoint(hr, []float64{logSpeed}))
+		}
+	}
+
+	if err := r.Run(); err != nil {
+		log.Printf("对数回归计算失败: %v", err)
+		return models.RegressionResult{
+			Equation: "计算失败",
+		}
+	}
+
+	// 创建结果
+	logA := r.Coeff(1)
+	logB := r.Coeff(0)
+	r2 := r.R2
+	return models.RegressionResult{
+		RegressionType: models.LogarithmicRegression,
+		LogA:           &logA,
+		LogB:           &logB,
+		RSquared:       &r2,
+		Equation:       r.Formula,
+	}
+}
+
+// 二次回归算法
+//func performQuadraticRegression(averages []map[float64]float64) models.RegressionResult {
+//	if len(averages) == 0 {
+//		return models.RegressionResult{
+//			Equation: "无数据",
+//		}
+//	}
+//
+//	// 收集数据点
+//	r := new(regression.Regression)
+//	r.SetObserved("y")
+//	r.SetVar(0, "x")
+//	r.SetVar(1, "x²")
+//
+//	for _, m := range averages {
+//		for speed, hr := range m {
+//			speedSq := math.Pow(speed, 2)
+//			r.Train(regression.DataPoint(hr, []float64{speed, speedSq}))
+//		}
+//	}
+//
+//	if err := r.Run(); err != nil {
+//		log.Printf("二次回归计算失败: %v", err)
+//		return models.RegressionResult{
+//			Equation: "计算失败",
+//		}
+//	}
+//
+//	// 创建结果
+//	a := r.Coeff(2)
+//	b := r.Coeff(1)
+//	c := r.Coeff(0)
+//	r2 := r.R2
+//	return models.RegressionResult{
+//		RegressionType: models.QuadraticRegression,
+//		QuadraticA:     &a,
+//		QuadraticB:     &b,
+//		QuadraticC:     &c,
+//		RSquared:       &r2,
+//		Equation:       r.Formula,
+//	}
+//}
+
+func performQuadraticRegression(averages []map[float64]float64) models.RegressionResult {
+	if len(averages) == 0 {
+		return models.RegressionResult{
+			Equation: "无数据",
+		}
+	}
+
+	// 步骤1：收集所有数据点（与Flutter一致）
+	var xValues []float64
+	var yValues []float64
+	for _, m := range averages {
+		for speed, hr := range m {
+			xValues = append(xValues, speed)
+			yValues = append(yValues, hr)
+		}
+	}
+	n := float64(len(xValues))
+
+	// 步骤2：计算各项和（完全匹配Flutter的计算）
+	var sumX, sumY, sumX2, sumX3, sumX4, sumXY, sumX2Y float64
+	for i := 0; i < len(xValues); i++ {
+		x := xValues[i]
+		y := yValues[i]
+		x2 := x * x
+		x3 := x2 * x
+		x4 := x3 * x
+
+		sumX += x
+		sumY += y
+		sumX2 += x2
+		sumX3 += x3
+		sumX4 += x4
+		sumXY += x * y
+		sumX2Y += x2 * y
+	}
+
+	// 步骤3：构建正规方程矩阵（与Flutter完全一致）
+	matrix := [3][3]float64{
+		{n, sumX, sumX2},
+		{sumX, sumX2, sumX3},
+		{sumX2, sumX3, sumX4},
+	}
+	vector := []float64{sumY, sumXY, sumX2Y}
+
+	// 步骤4：计算矩阵行列式（复制Flutter的determinant3x3逻辑）
+	det := matrix[0][0]*(matrix[1][1]*matrix[2][2]-matrix[1][2]*matrix[2][1]) -
+		matrix[0][1]*(matrix[1][0]*matrix[2][2]-matrix[1][2]*matrix[2][0]) +
+		matrix[0][2]*(matrix[1][0]*matrix[2][1]-matrix[1][1]*matrix[2][0])
+
+	if det == 0 {
+		return models.RegressionResult{
+			Equation: "无法拟合",
+		}
+	}
+
+	// 步骤5：克莱姆法则求解系数（顺序与Flutter一致）
+	// 注意：最终系数顺序 a=二次项, b=一次项, c=常数项
+	c := det3x3([3][3]float64{
+		{vector[0], matrix[0][1], matrix[0][2]},
+		{vector[1], matrix[1][1], matrix[1][2]},
+		{vector[2], matrix[2][1], matrix[2][2]},
+	}) / det
+
+	b := det3x3([3][3]float64{
+		{matrix[0][0], vector[0], matrix[0][2]},
+		{matrix[1][0], vector[1], matrix[1][2]},
+		{matrix[2][0], vector[2], matrix[2][2]},
+	}) / det
+
+	a := det3x3([3][3]float64{
+		{matrix[0][0], matrix[0][1], vector[0]},
+		{matrix[1][0], matrix[1][1], vector[1]},
+		{matrix[2][0], matrix[2][1], vector[2]},
+	}) / det
+
+	// 步骤6：计算R平方（完全复制Flutter的计算逻辑）
+	var ssRes, ssTot float64
+	meanY := sumY / n
+	for i := 0; i < len(xValues); i++ {
+		x := xValues[i]
+		y := yValues[i]
+		yPred := a*x*x + b*x + c
+		ssRes += math.Pow(y-yPred, 2)
+		ssTot += math.Pow(y-meanY, 2)
+	}
+	rSquared := 0.0
+	if ssTot != 0 {
+		rSquared = 1 - ssRes/ssTot
+	}
+
+	// 步骤7：格式化公式字符串（与Flutter格式完全一致）
+	equation := formatEquation(a, b, c, rSquared)
+
+	return models.RegressionResult{
+		RegressionType: models.QuadraticRegression,
+		QuadraticA:     &a,
+		QuadraticB:     &b,
+		QuadraticC:     &c,
+		RSquared:       &rSquared,
+		Equation:       equation,
+	}
+}
+
+// 3x3行列式计算（与Flutter实现相同）
+func det3x3(m [3][3]float64) float64 {
+	return m[0][0]*(m[1][1]*m[2][2]-m[1][2]*m[2][1]) -
+		m[0][1]*(m[1][0]*m[2][2]-m[1][2]*m[2][0]) +
+		m[0][2]*(m[1][0]*m[2][1]-m[1][1]*m[2][0])
+}
+
+// 公式格式化（完全匹配Flutter格式）
+func formatEquation(a, b, c, r2 float64) string {
+	// 保留4位小数
+	aStr := fmt.Sprintf("%.4f", a)
+	bStr := fmt.Sprintf("%.4f", b)
+	cStr := fmt.Sprintf("%.4f", c)
+	r2Str := fmt.Sprintf("%.4f", r2)
+
+	builder := strings.Builder{}
+	builder.WriteString("y = ")
+
+	// 处理二次项
+	if a >= 0 {
+		builder.WriteString(aStr + " x²")
+	} else {
+		builder.WriteString("-" + strings.TrimPrefix(aStr, "-") + " x²")
+	}
+
+	// 处理一次项
+	if b >= 0 {
+		builder.WriteString(" + " + bStr + " x")
+	} else {
+		builder.WriteString(" - " + strings.TrimPrefix(bStr, "-") + " x")
+	}
+
+	// 处理常数项
+	if c >= 0 {
+		builder.WriteString(" + " + cStr)
+	} else {
+		builder.WriteString(" - " + strings.TrimPrefix(cStr, "-"))
+	}
+
+	builder.WriteString(" (R² = " + r2Str + ")")
+	return builder.String()
+}
+
+// 步频数据转换为速度段
+func convertStrideFrequencyToSegments(steps []models.StepStrideFreq) []SpeedSegment {
+	if len(steps) == 0 {
+		return []SpeedSegment{}
+	}
+
+	// 过滤零值并排序
+	validSteps := make([]models.StepStrideFreq, 0, len(steps))
+	for _, s := range steps {
+		if s.Value > 0 {
+			validSteps = append(validSteps, s)
+		}
+	}
+
+	if len(validSteps) == 0 {
+		return []SpeedSegment{}
+	}
+
+	// 按时间排序
+	for i := 0; i < len(validSteps)-1; i++ {
+		for j := i + 1; j < len(validSteps); j++ {
+			if validSteps[i].Time > validSteps[j].Time {
+				validSteps[i], validSteps[j] = validSteps[j], validSteps[i]
+			}
+		}
+	}
+
+	// 创建速度段
+	segments := make([]SpeedSegment, 0)
+	startTime := validSteps[0].Time
+	currentValue := validSteps[0].Value
+
+	for i := 1; i < len(validSteps); i++ {
+		if validSteps[i].Value != currentValue {
+			duration := float64(validSteps[i].Time-startTime) / 1000.0
+			if duration > 0 {
+				segments = append(segments, SpeedSegment{
+					Duration: duration,
+					Speed:    float64(currentValue),
+				})
+			}
+			startTime = validSteps[i].Time
+			currentValue = validSteps[i].Value
+		}
+	}
+
+	// 添加最后一个段
+	if len(validSteps) > 0 {
+		duration := float64(validSteps[len(validSteps)-1].Time-startTime) / 1000.0
+		if duration > 0 {
+			segments = append(segments, SpeedSegment{
+				Duration: duration,
+				Speed:    float64(currentValue),
+			})
+		}
+	}
+
+	return segments
+}
+
+// 计算区段平均值
+func calculateSegmentAverages(heartRates []models.StepHeartRate, segments []SpeedSegment, errorThreshold int) []map[float64]float64 {
+	currentTime := 0.0
+	results := make([]map[float64]float64, 0)
+
+	for _, seg := range segments {
+		minRequired := 60 + (60 - float64(errorThreshold))
+
+		// 跳过不满足条件的区段
+		if seg.Duration < minRequired {
+			currentTime += seg.Duration
+			continue
+		}
+
+		// 计算时间窗口
+		startSec := currentTime + 60
+		endSec := currentTime
+		if seg.Duration >= 120 {
+			endSec = currentTime + 120
+		} else {
+			endSec = currentTime + 120 - float64(errorThreshold)
+		}
+
+		// 收集该区段的心率数据
+		sum, count := 0, 0
+		for _, hr := range heartRates {
+			sec := float64(hr.Time) / 1000.0
+			if sec >= startSec && sec <= endSec {
+				sum += hr.Value
+				count++
+			}
+		}
+
+		// 计算平均值
+		if count > 0 {
+			avg := float64(sum) / float64(count)
+			results = append(results, map[float64]float64{seg.Speed: avg})
+		}
+
+		currentTime += seg.Duration
+	}
+
+	return results
+}
+
+// 计算步频区段的心率平均值
+func CalculateSegmentAveragesByRealStep(heartRates []models.StepHeartRate, steps []models.StepStrideFreq) []map[float64]float64 {
+	segments := convertStrideFrequencyToSegments(steps)
+	return calculateSegmentAverages(heartRates, segments, 15) // 默认5秒误差阈值
+}
+
+// 存储回归结果到数据库（支持多种回归类型）
+func (tc *StepTrainingController) SaveRegressionResults(trainId uint, results []models.RegressionResult) error {
+	return tc.DB.Transaction(func(tx *gorm.DB) error {
+		for i := range results {
+			results[i].TrainId = trainId
+			// 使用复合唯一约束确保每种回归类型只存储一条记录
+			err := tx.Clauses(clause.OnConflict{
+				Columns: []clause.Column{
+					{Name: "id"},
+				},
+				DoUpdates: clause.Assignments(map[string]interface{}{
+					"equation":    results[i].Equation,
+					"slope":       results[i].Slope,
+					"intercept":   results[i].Intercept,
+					"log_a":       results[i].LogA,
+					"log_b":       results[i].LogB,
+					"quadratic_a": results[i].QuadraticA,
+					"quadratic_b": results[i].QuadraticB,
+					"quadratic_c": results[i].QuadraticC,
+					"r_squared":   results[i].RSquared,
+					"updated_at":  gorm.Expr("CURRENT_TIMESTAMP"),
+				}),
+			}).Create(&results[i]).Error
+
+			if err != nil {
+				return err
+			}
+		}
+		return nil
+	})
+}
+
+// 获取或计算回归结果（返回多种回归类型列表）
+func (tc *StepTrainingController) GetOrCalculateRegression(trainId uint) ([]models.RegressionResult, error) {
+	// 尝试从数据库获取所有类型的回归结果
+	var results []models.RegressionResult
+	err := tc.DB.Where("train_id = ?", trainId).Find(&results).Error
+
+	// 如果已存在三种类型的结果，直接返回
+	if err == nil && len(results) >= 3 {
+		return results, nil
+	}
+
+	// 查询训练记录及相关数据
+	var record models.StepTrainRecord
+	if err := tc.DB.
+		Where("train_id = ?", uint(trainId)).
+		Preload("HeartRates", "heart_rate_type = ?", 1).
+		Preload("StrideFreqs", "predict_value = ?", 1).
+		First(&record).Error; err != nil {
+		return nil, err
+	}
+
+	// 计算心率平均值
+	averages := CalculateSegmentAveragesByRealStep(record.HeartRates, record.StrideFreqs)
+	if len(averages) == 0 {
+		return nil, errors.New("无足够数据进行回归计算")
+	}
+
+	// 创建三种回归类型的结果
+	results = make([]models.RegressionResult, 3)
+
+	// 线性回归
+	linearRes := performLinearRegression(averages)
+	results[0] = models.RegressionResult{
+		RegressionType: models.LinearRegression,
+		TrainId:        trainId,
+		Equation:       linearRes.Equation,
+		Slope:          linearRes.Slope,
+		Intercept:      linearRes.Intercept,
+		RSquared:       linearRes.RSquared,
+	}
+
+	// 对数回归
+	logRes := performLogarithmicRegression(averages)
+	results[1] = models.RegressionResult{
+		RegressionType: models.LogarithmicRegression,
+		TrainId:        trainId,
+		Equation:       logRes.Equation,
+		LogA:           logRes.LogA,
+		LogB:           logRes.LogB,
+		RSquared:       logRes.RSquared,
+	}
+
+	// 二次回归
+	quadRes := performQuadraticRegression(averages)
+	results[2] = models.RegressionResult{
+		RegressionType: models.QuadraticRegression,
+		TrainId:        trainId,
+		Equation:       quadRes.Equation,
+		QuadraticA:     quadRes.QuadraticA,
+		QuadraticB:     quadRes.QuadraticB,
+		QuadraticC:     quadRes.QuadraticC,
+		RSquared:       quadRes.RSquared,
+	}
+
+	// 批量保存结果到数据库
+	if err := tc.SaveRegressionResults(trainId, results); err != nil {
+		log.Printf("保存回归结果失败: %v", err)
+		return nil, err
+	}
+
+	return results, nil
+}
+
+// 新增接口：获取回归结果
+func (tc *StepTrainingController) GetRegressionResult(c *gin.Context) {
+	trainIdStr := c.Param("trainId")
+	tid, err := strconv.ParseUint(trainIdStr, 10, 32)
+	if err != nil {
+		c.JSON(http.StatusBadRequest, gin.H{"error": "无效的训练ID"})
+		return
+	}
+
+	result, err := tc.GetOrCalculateRegression(uint(tid))
+	if err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+		return
+	}
+
+	c.JSON(http.StatusOK, gin.H{
+		"message": "获取成功",
+		"data":    result,
+	})
+}
+
+func (tc *StepTrainingController) GetTrainingRank(c *gin.Context) {
+	// 参数解析
+	trainIdStr := c.Param("trainId")
+	regressionTypeStr := c.Query("type")
+	regressionType, err := strconv.Atoi(regressionTypeStr)
+	if err != nil {
+		c.JSON(http.StatusBadRequest, gin.H{"error": "参数type必须为整数"})
+		return
+	}
+
+	// 验证回归类型
+	regType := models.RegressionType(regressionType)
+	if regType != models.LinearRegression && regType != models.QuadraticRegression {
+		c.JSON(http.StatusBadRequest, gin.H{"error": "无效的回归类型，必须是'linear'或'quadratic'"})
+		return
+	}
+
+	// 转换训练ID
+	tid, err := strconv.ParseUint(trainIdStr, 10, 64)
+	if err != nil {
+		c.JSON(http.StatusBadRequest, gin.H{"error": "无效的训练ID"})
+		return
+	}
+	trainId := uint(tid)
+
+	// 确保回归结果存在
+	if _, err := tc.GetOrCalculateRegression(trainId); err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": "获取回归结果失败：" + err.Error()})
+		return
+	}
+	// 获取指定训练的基准值
+	var baseValue float64
+	baseQuery := tc.DB.Model(&models.RegressionResult{}).
+		Select(getValueColumn(regType)).
+		Where("train_id = ?", trainId)
+
+	if err := baseQuery.Row().Scan(&baseValue); err != nil {
+		if errors.Is(err, gorm.ErrRecordNotFound) {
+			c.JSON(http.StatusNotFound, gin.H{"error": "指定的训练数据不存在"})
+			return
+		}
+		c.JSON(http.StatusInternalServerError, gin.H{"error": "获取基准数据失败"})
+		return
+	}
+
+	// 在数据库中进行排名计算
+	var rank struct {
+		BetterCount int64
+		Total       int64
+	}
+
+	// 动态生成比较条件
+	betterCondition := fmt.Sprintf("%s %s ?",
+		getValueColumn(regType),
+		getComparisonOperator(regType))
+
+	totalQuery := tc.DB.Model(&models.RegressionResult{}).
+		Where(getTypeCondition(regType))
+
+	if err := totalQuery.Count(&rank.Total).Error; err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": "统计总数失败"})
+		return
+	}
+
+	if err := tc.DB.Model(&models.RegressionResult{}).
+		Where(getTypeCondition(regType)).
+		Where(betterCondition, baseValue).
+		Count(&rank.BetterCount).Error; err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": "计算排名失败"})
+		return
+	}
+
+	// 计算实际排名 (并列排名)
+	currentRank := rank.BetterCount + 1
+
+	// 返回响应
+	c.JSON(http.StatusOK, gin.H{
+		"message": "排名查询成功",
+		"data": gin.H{
+			"trainId": trainId,
+			"type":    regressionType,
+			"rank":    currentRank,
+			"total":   rank.Total,
+		},
+	})
+}
+
+// 辅助函数：获取排序字段名
+func getValueColumn(regType models.RegressionType) string {
+	switch regType {
+	case models.LinearRegression:
+		return "slope"
+	case models.QuadraticRegression:
+		return "ABS(quadratic_a)" // 计算绝对值
+	default:
+		return ""
+	}
+}
+
+// 辅助函数：获取比较操作符
+func getComparisonOperator(regType models.RegressionType) string {
+	switch regType {
+	case models.LinearRegression:
+		return "<" // 线性回归：值越小越好
+	case models.QuadraticRegression:
+		return ">" // 二次回归：绝对值越大越好
+	default:
+		return ""
+	}
+}
+
+// 辅助函数：获取类型条件
+func getTypeCondition(regType models.RegressionType) string {
+	switch regType {
+	case models.LinearRegression:
+		return "slope IS NOT NULL"
+	case models.QuadraticRegression:
+		return "quadratic_a IS NOT NULL AND quadratic_a < 0" // 确保是负值
+	default:
+		return ""
+	}
+}
+
+// 辅助函数：比较浮点指针（用于线性回归）
+func compareFloatPtr(a, b *float64, ascending bool) bool {
+	if a == nil && b == nil {
+		return false
+	}
+	if a == nil {
+		return false // 空值排最后
+	}
+	if b == nil {
+		return true // 非空值排前
+	}
+
+	if ascending {
+		return *a < *b
+	}
+	return *a > *b
+}
+
+// 辅助函数：比较二次项系数（用于二次回归）
+func compareQuadraticA(a, b *float64) bool {
+	if a == nil && b == nil {
+		return false
+	}
+	if a == nil {
+		return false
+	}
+	if b == nil {
+		return true
+	}
+
+	// 比较绝对值（a和b都是负值，所以取绝对值后大的排前面）
+	return math.Abs(*a) > math.Abs(*b)
+}

go.mod

@@ -5,6 +5,7 @@ go 1.23.3
 require (
 	github.com/gin-gonic/gin v1.10.0
 	github.com/golang-jwt/jwt/v5 v5.2.1
+	github.com/sajari/regression v1.0.1
 	github.com/spf13/viper v1.20.0
 	gonum.org/v1/gonum v0.16.0
 	gorm.io/driver/postgres v1.5.11

go.sum

@@ -75,6 +75,8 @@ github.com/rogpeppe/go-internal v1.13.1 h1:KvO1DLK/DRN07sQ1LQKScxyZJuNnedQ5/wKSR
 github.com/rogpeppe/go-internal v1.13.1/go.mod h1:uMEvuHeurkdAXX61udpOXGD/AzZDWNMNyH2VO9fmH0o=
 github.com/sagikazarmark/locafero v0.7.0 h1:5MqpDsTGNDhY8sGp0Aowyf0qKsPrhewaLSsFaodPcyo=
 github.com/sagikazarmark/locafero v0.7.0/go.mod h1:2za3Cg5rMaTMoG/2Ulr9AwtFaIppKXTRYnozin4aB5k=
+github.com/sajari/regression v1.0.1 h1:iTVc6ZACGCkoXC+8NdqH5tIreslDTT/bXxT6OmHR5PE=
+github.com/sajari/regression v1.0.1/go.mod h1:NeG/XTW1lYfGY7YV/Z0nYDV/RGh3wxwd1yW46835flM=
 github.com/sourcegraph/conc v0.3.0 h1:OQTbbt6P72L20UqAkXXuLOj79LfEanQ+YQFNpLA9ySo=
 github.com/sourcegraph/conc v0.3.0/go.mod h1:Sdozi7LEKbFPqYX2/J+iBAM6HpqSLTASQIKqDmF7Mt0=
 github.com/spf13/afero v1.12.0 h1:UcOPyRBYczmFn6yvphxkn9ZEOY65cpwGKb5mL36mrqs=

main.go

@@ -22,7 +22,9 @@ func main() {
 		&models.BeltAnalysis{},
 		&models.StepTrainRecord{},
 		&models.StepHeartRate{},
-		&models.StepStrideFreq{})
+		&models.StepStrideFreq{},
+		&models.RegressionResult{},
+	)
 
 	// 启动服务
 	r := routes.SetupRouter()

models/step_train.go

@@ -7,6 +7,7 @@ type StepStrideFreq struct {
 	TrainId      uint   `gorm:"column:train_id; index" json:"trainId"` // 外键关联训练记录[4](@ref)
 	Time         int64  `gorm:"type:bigint" json:"time"`               // 保持与前端一致的毫秒时间戳[3](@ref)
 	Value        int    `gorm:"type:int" json:"value"`
+	Count        int    `gorm:"type:int" json:"count"`
 	PredictValue int    `gorm:"type:int" json:"predictValue"`
 	Identifier   string `gorm:"uniqueIndex;type:varchar(255)" json:"identifier"`
 }
@@ -28,7 +29,7 @@ type StepTrainRecord struct {
 	StartTime    int64            `gorm:"type:bigint" json:"time"`    // 开始时间戳
 	EndTime      int64            `gorm:"type:bigint" json:"endTime"` // 结束时间戳[3](@ref)
 	Name         string           `gorm:"size:100" json:"name"`
-	RunType      string           `gorm:"size:100" json:"RunType"`
+	RunType      string           `gorm:"size:100" json:"runType"`
 	MaxHeartRate int              `gorm:"type:int" json:"maxHeartRate"`
 	Duration     int              `gorm:"type:int" json:"duration"` // 持续时间(秒)
 	DeadZone     int              `gorm:"type:int" json:"deadZone"`
@@ -36,3 +37,33 @@ type StepTrainRecord struct {
 	HeartRates   []StepHeartRate  `gorm:"foreignKey:TrainId;references:TrainId" json:"heartRates"`
 	StrideFreqs  []StepStrideFreq `gorm:"foreignKey:TrainId;references:TrainId" json:"strideFreqs"`
 }
+
+type RegressionType int
+
+const (
+	LinearRegression RegressionType = iota + 1
+	LogarithmicRegression
+	QuadraticRegression
+)
+
+type RegressionResult struct {
+	gorm.Model
+	RegressionType RegressionType `gorm:"column:regression_type;index" json:"regressionType"` // 训练记录ID
+	TrainId        uint           `gorm:"column:train_id;index" json:"trainId"`               // 训练记录ID
+	Equation       string         `gorm:"type:text" json:"equation"`                          // 回归方程
+
+	// 线性回归系数
+	Slope     *float64 `gorm:"column:slope" json:"slope"`
+	Intercept *float64 `gorm:"column:intercept" json:"intercept"`
+
+	// 对数回归系数
+	LogA *float64 `gorm:"column:log_a" json:"logA"`
+	LogB *float64 `gorm:"column:log_b" json:"logB"`
+
+	// 二次回归系数
+	QuadraticA *float64 `gorm:"column:quadratic_a" json:"quadraticA"`
+	QuadraticB *float64 `gorm:"column:quadratic_b" json:"quadraticB"`
+	QuadraticC *float64 `gorm:"column:quadratic_c" json:"quadraticC"`
+
+	RSquared *float64 `gorm:"column:r_squared" json:"rSquared"` // R平方值
+}

routes/routes.go

@@ -27,6 +27,7 @@ func SetupRouter() *gin.Engine {
 			steps.POST("", stepTrainController.CreateTrainingRecord)
 			steps.GET("train-records", stepTrainController.GetTrainingRecords)
 			steps.GET("train-data/:trainId", stepTrainController.GetTrainingRecordByTrainId)
+			steps.GET("train-rank/:trainId", stepTrainController.GetTrainingRank)
 			// 可扩展其他路由：GET, PUT, DELETE等
 		}
 		auth := v1.Group("/auth")