hr_data_analyzer/mockdata/main.go

package main

import (
	"encoding/json"
	"fmt"
	"math/rand"
	"time"

	"hr_receiver/config"
	"hr_receiver/models"
)

const (
	aiAnalysisRecordCount      = 100
	trainingSessionRecordCount = 80
	insertBatchSize            = 50
)

var (
	regionIDs   = []uint32{1, 3}
	sourceTypes = []string{"upload", "cloud"}
	appNames    = []string{"HeartRate Teacher", "HeartRate Console", "HeartRate Admin"}
)

func main() {
	rng := rand.New(rand.NewSource(time.Now().UnixNano()))

	config.InitConfig()
	config.ConnectDB()

	//aiRecords := generateAIAnalysisRecords(rng, aiAnalysisRecordCount)
	//if err := config.DB.CreateInBatches(aiRecords, insertBatchSize).Error; err != nil {
	//	panic("failed to insert AI analysis mock data: " + err.Error())
	//}

	trainingRecords := generateTrainingSessionRecords(rng, trainingSessionRecordCount)
	if err := config.DB.CreateInBatches(trainingRecords, insertBatchSize).Error; err != nil {
		panic("failed to insert training session mock data: " + err.Error())
	}

	//fmt.Printf("成功插入 %d 条 AI 分析记录\n", len(aiRecords))
	fmt.Printf("成功插入 %d 条训练会话记录\n", len(trainingRecords))
}

func generateAIAnalysisRecords(rng *rand.Rand, count int) []models.AIAnalysisRecord {
	records := make([]models.AIAnalysisRecord, 0, count)
	for i := 0; i < count; i++ {
		records = append(records, generateAIAnalysisRecord(rng))
	}
	return records
}

func generateAIAnalysisRecord(rng *rand.Rand) models.AIAnalysisRecord {
	regionID := pickRegionID(rng)
	sourceType := pickOne(rng, sourceTypes)

	docxSize := int64(rng.Intn(451*1024) + 50*1024)
	csvSize := int64(rng.Intn(20*1024) + 70*1024)

	analysisType := randomAnalysisType(rng)
	var stepCSVSize int64
	if analysisType == "heart_rate_with_steps" {
		stepCSVSize = int64(rng.Intn(20*1024) + 20*1024)
	}

	originalFileSize := docxSize + csvSize + stepCSVSize

	compressedDocx := int64(float64(docxSize) * (0.3 + rng.Float64()*0.3))
	compressedCSV := int64(float64(csvSize) * (0.22 + rng.Float64()*0.08))
	var compressedStepCSV int64
	if stepCSVSize > 0 {
		compressedStepCSV = int64(float64(stepCSVSize) * (0.22 + rng.Float64()*0.08))
	}
	compressedContentSize := compressedDocx + compressedCSV + compressedStepCSV

	promptTemplateSize := 1500 + rng.Intn(500)
	inputSizeBytes := int(compressedContentSize) + promptTemplateSize
	outputSizeBytes := rng.Intn(22*1024) + 3*1024

	inputTokens := inputSizeBytes / (3 + rng.Intn(2))
	outputTokens := outputSizeBytes / (3 + rng.Intn(2))

	tokenLatency := int64(15 + rng.Intn(26))
	durationMs := 500 + int64(outputTokens)*tokenLatency
	if durationMs > 60000 {
		durationMs = 60000 - int64(rng.Intn(5000))
	}

	uploadTime := randomRecentMillis(rng, 90)
	totalCost, costJSON := buildMockCost(inputTokens, outputTokens)

	return models.AIAnalysisRecord{
		RegionID:              &regionID,
		SourceType:            sourceType,
		AnalysisType:          analysisType,
		AnalysisResult:        "mock analysis result",
		CostJSON:              costJSON,
		TotalCost:             totalCost,
		InputTokens:           inputTokens,
		OutputTokens:          outputTokens,
		InputSizeBytes:        inputSizeBytes,
		OutputSizeBytes:       outputSizeBytes,
		DurationMs:            durationMs,
		OriginalFileSize:      originalFileSize,
		CompressedContentSize: compressedContentSize,
		UploadTime:            uploadTime,
	}
}

func generateTrainingSessionRecords(rng *rand.Rand, count int) []models.MqttTrainingSessionRecord {
	records := make([]models.MqttTrainingSessionRecord, 0, count)
	sessionCount := count / 2
	if sessionCount == 0 {
		sessionCount = 1
	}

	for i := 0; i < sessionCount; i++ {
		sessionRecords := generateTrainingSessionPair(rng, i)
		records = append(records, sessionRecords...)
		if len(records) >= count {
			break
		}
	}

	if len(records) > count {
		records = records[:count]
	}
	return records
}

func generateTrainingSessionPair(rng *rand.Rand, index int) []models.MqttTrainingSessionRecord {
	regionID := pickRegionID(rng)
	appName := pickOne(rng, appNames)
	testID := fmt.Sprintf("mock-test-%04d", index+1)
	startedAt := randomRecentMillis(rng, 60)
	publishedAt := startedAt + int64(rng.Intn(30_000))

	startRecord := models.MqttTrainingSessionRecord{
		Identifier:  fmt.Sprintf("mock-heartrate-%d-%s", regionID, testID),
		Topic:       fmt.Sprintf("/whgw/v2/region/test/%d", regionID),
		TestID:      testID,
		EventType:   "start_test",
		RegionID:    regionID,
		FlavorType:  "heartrate",
		RawFlavor:   "heartrate",
		AppName:     appName,
		StartedAt:   int64Ptr(startedAt),
		PublishedAt: publishedAt,
		ReceivedAt:  publishedAt + int64(rng.Intn(3000)),
		RawPayload:  buildTrainingPayloadJSON(testID, regionID, appName, "start_test", publishedAt),
	}

	records := []models.MqttTrainingSessionRecord{startRecord}

	// 保留少量“开始未结束”的样本，用于测试进行中统计。
	if rng.Intn(100) < 15 {
		return records
	}

	durationMs := int64((5+rng.Intn(41))*60*1000 + rng.Intn(60_000))
	endedAt := startedAt + durationMs
	stopPublishedAt := endedAt + int64(rng.Intn(20_000))
	stopRecord := models.MqttTrainingSessionRecord{
		Identifier:  fmt.Sprintf("mock-heartrate-%d-%s-stop", regionID, testID),
		Topic:       fmt.Sprintf("/whgw/v2/region/test/%d", regionID),
		TestID:      testID,
		EventType:   "stop_test",
		RegionID:    regionID,
		FlavorType:  "heartrate",
		RawFlavor:   "heartrate",
		AppName:     appName,
		StartedAt:   int64Ptr(startedAt),
		EndedAt:     int64Ptr(endedAt),
		PublishedAt: stopPublishedAt,
		ReceivedAt:  stopPublishedAt + int64(rng.Intn(3000)),
		RawPayload:  buildTrainingPayloadJSON(testID, regionID, appName, "stop_test", stopPublishedAt),
	}

	return append(records, stopRecord)
}

func buildMockCost(inputTokens, outputTokens int) (float64, string) {
	inputCost := float64(inputTokens) * 0.000002
	outputCost := float64(outputTokens) * 0.000008
	totalCost := inputCost + outputCost

	payload := map[string]float64{
		"inputCost":  inputCost,
		"outputCost": outputCost,
	}
	data, _ := json.Marshal(payload)
	return totalCost, string(data)
}

func buildTrainingPayloadJSON(testID string, regionID uint32, appName, eventType string, publishedAt int64) string {
	payload := map[string]string{
		"appName":   appName,
		"eventType": eventType,
		"flavor":    "heartrate",
		"regionId":  fmt.Sprintf("%d", regionID),
		"testId":    testID,
		"timestamp": time.UnixMilli(publishedAt).UTC().Format("2006-01-02T15:04:05.000Z"),
		"type":      "mqtt_test",
	}
	data, _ := json.Marshal(payload)
	return string(data)
}

func randomAnalysisType(rng *rand.Rand) string {
	if rng.Intn(100) < 30 {
		return "heart_rate_with_steps"
	}
	return "heart_rate_only"
}

func randomRecentMillis(rng *rand.Rand, days int) int64 {
	return time.Now().
		Add(-time.Duration(rng.Intn(days*24)) * time.Hour).
		Add(-time.Duration(rng.Intn(60)) * time.Minute).
		UnixMilli()
}

func pickRegionID(rng *rand.Rand) uint32 {
	return regionIDs[rng.Intn(len(regionIDs))]
}

func pickOne(rng *rand.Rand, values []string) string {
	return values[rng.Intn(len(values))]
}

func int64Ptr(v int64) *int64 {
	return &v
}