feat: mock data.

controllers/statistics.go

@@ -7,6 +7,7 @@ import (
 	"net/http"
 	"strconv"
 	"strings"
+	"time"
 
 	"github.com/gin-gonic/gin"
 	"gorm.io/gorm"
@@ -110,16 +111,19 @@ func (sc *StatisticsController) DeleteAIAnalysisRecord(c *gin.Context) {
 // --- 统计接口 ---
 
 type regionStatisticsItem struct {
-	RegionID              uint32  `json:"regionId"`
+	RegionID              uint32     `json:"regionId"`
-	Count                 int64   `json:"count"`
+	KindergartenName      string     `json:"kindergartenName"`
-	TotalInputTokens      int64   `json:"totalInputTokens"`
+	Count                 int64      `json:"count"`
-	TotalOutputTokens     int64   `json:"totalOutputTokens"`
+	TotalInputTokens      int64      `json:"totalInputTokens"`
-	TotalInputSizeBytes   int64   `json:"totalInputSizeBytes"`
+	TotalOutputTokens     int64      `json:"totalOutputTokens"`
-	TotalOutputSizeBytes  int64   `json:"totalOutputSizeBytes"`
+	TotalInputSizeBytes   int64      `json:"totalInputSizeBytes"`
-	TotalDurationMs       int64   `json:"totalDurationMs"`
+	TotalOutputSizeBytes  int64      `json:"totalOutputSizeBytes"`
-	AvgDurationMs         float64 `json:"avgDurationMs"`
+	TotalDurationMs       int64      `json:"totalDurationMs"`
-	TotalOriginalFileSize int64   `json:"totalOriginalFileSize"`
+	AvgDurationMs         float64    `json:"avgDurationMs"`
-	TotalCompressedSize   int64   `json:"totalCompressedSize"`
+	TotalOriginalFileSize int64      `json:"totalOriginalFileSize"`
+	TotalCompressedSize   int64      `json:"totalCompressedSize"`
+	FirstUsedAt           *time.Time `json:"firstUsedAt"`
+	LastUsedAt            *time.Time `json:"lastUsedAt"`
 }
 
 func (sc *StatisticsController) StatisticsByRegion(c *gin.Context) {
@@ -154,6 +158,8 @@ func (sc *StatisticsController) StatisticsByRegion(c *gin.Context) {
 		TotalDurationMs       int64
 		TotalOriginalFileSize int64
 		TotalCompressedSize   int64
+		FirstUsedAt           *time.Time
+		LastUsedAt            *time.Time
 	}
 
 	var rawResults []rawStats
@@ -166,7 +172,9 @@ func (sc *StatisticsController) StatisticsByRegion(c *gin.Context) {
 		COALESCE(SUM(output_size_bytes), 0) as total_output_size_bytes,
 		COALESCE(SUM(duration_ms), 0) as total_duration_ms,
 		COALESCE(SUM(original_file_size), 0) as total_original_file_size,
-		COALESCE(SUM(compressed_content_size), 0) as total_compressed_size
+		COALESCE(SUM(compressed_content_size), 0) as total_compressed_size,
+		MIN(created_at) as first_used_at,
+		MAX(created_at) as last_used_at
 	`).Group("region_id").Scan(&rawResults).Error
 
 	if err != nil {
@@ -174,6 +182,23 @@ func (sc *StatisticsController) StatisticsByRegion(c *gin.Context) {
 		return
 	}
 
+	// 收集所有 regionId 查询幼儿园名称
+	regionIDs := make([]uint32, 0, len(rawResults))
+	for _, r := range rawResults {
+		if r.RegionID != nil && *r.RegionID > 0 {
+			regionIDs = append(regionIDs, *r.RegionID)
+		}
+	}
+	kindergartenMap := make(map[uint32]string)
+	if len(regionIDs) > 0 {
+		var kindergartens []models.Kindergarten
+		if err := sc.DB.Where("region_id IN ?", regionIDs).Find(&kindergartens).Error; err == nil {
+			for _, k := range kindergartens {
+				kindergartenMap[k.RegionID] = k.Name
+			}
+		}
+	}
+
 	overall := regionStatisticsItem{}
 	regions := make(map[string]regionStatisticsItem, len(rawResults))
 
@@ -186,8 +211,13 @@ func (sc *StatisticsController) StatisticsByRegion(c *gin.Context) {
 		if r.Count > 0 {
 			avgDuration = float64(r.TotalDurationMs) / float64(r.Count)
 		}
+		kgName := ""
+		if regionID > 0 {
+			kgName = kindergartenMap[regionID]
+		}
 		item := regionStatisticsItem{
 			RegionID:              regionID,
+			KindergartenName:      kgName,
 			Count:                 r.Count,
 			TotalInputTokens:      r.TotalInputTokens,
 			TotalOutputTokens:     r.TotalOutputTokens,
@@ -197,6 +227,8 @@ func (sc *StatisticsController) StatisticsByRegion(c *gin.Context) {
 			AvgDurationMs:         avgDuration,
 			TotalOriginalFileSize: r.TotalOriginalFileSize,
 			TotalCompressedSize:   r.TotalCompressedSize,
+			FirstUsedAt:           r.FirstUsedAt,
+			LastUsedAt:            r.LastUsedAt,
 		}
 
 		regions[strconv.FormatUint(uint64(regionID), 10)] = item
@@ -209,6 +241,17 @@ func (sc *StatisticsController) StatisticsByRegion(c *gin.Context) {
 		overall.TotalDurationMs += r.TotalDurationMs
 		overall.TotalOriginalFileSize += r.TotalOriginalFileSize
 		overall.TotalCompressedSize += r.TotalCompressedSize
+
+		if r.FirstUsedAt != nil {
+			if overall.FirstUsedAt == nil || r.FirstUsedAt.Before(*overall.FirstUsedAt) {
+				overall.FirstUsedAt = r.FirstUsedAt
+			}
+		}
+		if r.LastUsedAt != nil {
+			if overall.LastUsedAt == nil || r.LastUsedAt.After(*overall.LastUsedAt) {
+				overall.LastUsedAt = r.LastUsedAt
+			}
+		}
 	}
 
 	if overall.Count > 0 {

mockdata/main.go

@@ -0,0 +1,110 @@
+package main
+
+import (
+	"fmt"
+	"math/rand"
+	"time"
+
+	"hr_receiver/config"
+	"hr_receiver/models"
+)
+
+func main() {
+	config.InitConfig()
+	config.ConnectDB()
+
+	// 生成100条测试数据
+	count := 100
+	records := make([]models.AIAnalysisRecord, 0, count)
+
+	for i := 0; i < count; i++ {
+		records = append(records, generateRecord())
+	}
+
+	if err := config.DB.CreateInBatches(records, 50).Error; err != nil {
+		panic("failed to insert mock data: " + err.Error())
+	}
+
+	fmt.Printf("成功插入 %d 条 AI 分析记录\n", count)
+}
+
+func generateRecord() models.AIAnalysisRecord {
+	// regionID 为 1 或 3
+	regionID := uint32(1)
+	if rand.Intn(2) == 1 {
+		regionID = 3
+	}
+
+	// sourceType: upload 或 cloud
+	sourceType := "upload"
+	if rand.Intn(2) == 1 {
+		sourceType = "cloud"
+	}
+
+	// docx 教案原始文件大小: 50KB ~ 500KB
+	docxSize := int64(rand.Intn(451*1024) + 50*1024)
+
+	// 心率 csv 原始文件大小: 约 80KB (70KB ~ 90KB)
+	csvSize := int64(rand.Intn(20*1024) + 70*1024)
+
+	// 步数 csv 原始文件大小: 约 20KB ~ 40KB (heart_rate_with_steps 时才有)
+	var stepCsvSize int64
+	analysisType := analysisType()
+	if analysisType == "heart_rate_with_steps" {
+		stepCsvSize = int64(rand.Intn(20*1024) + 20*1024)
+	}
+
+	originalFileSize := docxSize + csvSize + stepCsvSize
+
+	// 压缩后内容大小: csv 每4行保留1行，大约压缩为 25% + 表头；docx 提取文本后大约 30%~60%
+	compressedDocx := int64(float64(docxSize) * (0.3 + rand.Float64()*0.3))
+	compressedCsv := int64(float64(csvSize) * (0.22 + rand.Float64()*0.08)) // ~22%-30%
+	var compressedStepCsv int64
+	if stepCsvSize > 0 {
+		compressedStepCsv = int64(float64(stepCsvSize) * (0.22 + rand.Float64()*0.08))
+	}
+	compressedContentSize := compressedDocx + compressedCsv + compressedStepCsv
+
+	// prompt 大小 = 压缩后内容 + 提示词模板 (~1.5KB)
+	promptTemplateSize := 1500 + rand.Intn(500)
+	inputSizeBytes := int(compressedContentSize) + promptTemplateSize
+
+	// AI 输出大小: 3KB ~ 25KB (分析报告)
+	outputSizeBytes := rand.Intn(22*1024) + 3*1024
+
+	// token 估算: 中文混合场景，平均约 3.5 字节/token
+	inputTokens := inputSizeBytes / (3 + rand.Intn(2))
+	outputTokens := outputSizeBytes / (3 + rand.Intn(2))
+
+	// 分析时长: 主要和输出 token 数量相关，1分钟以内
+	// 基础延迟 500ms + 每token约 15~40ms
+	tokenLatency := int64(15 + rand.Intn(26))
+	durationMs := 500 + int64(outputTokens)*tokenLatency
+	if durationMs > 60000 {
+		durationMs = 60000 - int64(rand.Intn(5000))
+	}
+
+	// 上传时间: 最近 90 天内随机
+	uploadTime := time.Now().Add(-time.Duration(rand.Intn(90*24)) * time.Hour).Add(-time.Duration(rand.Intn(60)) * time.Minute).UnixMilli()
+
+	return models.AIAnalysisRecord{
+		RegionID:              &regionID,
+		SourceType:            sourceType,
+		InputTokens:           inputTokens,
+		OutputTokens:          outputTokens,
+		InputSizeBytes:        inputSizeBytes,
+		OutputSizeBytes:       outputSizeBytes,
+		DurationMs:            durationMs,
+		OriginalFileSize:      originalFileSize,
+		CompressedContentSize: compressedContentSize,
+		UploadTime:            uploadTime,
+	}
+}
+
+func analysisType() string {
+	// 约 30% 的带步数分析
+	if rand.Intn(100) < 30 {
+		return "heart_rate_with_steps"
+	}
+	return "heart_rate_only"
+}