feat: mock data.

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"
+}