fix(dashboard): 按有效抽奖口径修正产品动销排行
本次仅调整产品动销排行接口的后端统计逻辑，不改动活动盈亏分析页面与接口。 详细说明： - 将销量从 activity_draw_logs 总条数改为有效抽奖次数，仅统计 orders.status = 2 的抽奖记录，避免把退款、取消等无效抽奖一并计入。 - 将营收统一改为“有效抽奖次数 × 活动单抽价格(price_draw)”，不再使用总日志数乘单价，确保产品动销排行内部次数与营收口径一致。 - 将成本从 price_snapshot_cents / products.price 的近似标价口径改为 products.cost_price 成本价口径，并继续保留 drop_quantity 与倍数卡 multiplier 的影响，避免利润长期被标价成本压低。 - 将盈亏与利润率统一为 Revenue - Cost 与 Profit / Revenue，使产品动销排行内部的销量、营收、成本、盈亏逻辑自洽。 - 将贡献率从按次数占比调整为按营收占比，更符合“营收贡献率”的业务含义。 本次未处理内容： - 不修改活动盈亏分析的现有统计口径。 - 不修改前端卡片结构与活动盈亏分析页面。 已核对的业务结论： - 活动 103 的产品动销排行 4696 次是最近 30 天有效抽奖次数；活动盈亏分析 5960 次是全量历史有效抽奖次数，两边差异来自时间范围而非当前产品动销排行次数公式。
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@ -1855,18 +1855,44 @@ type productPerformanceItem struct {
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func (h *handler) OperationsProductPerformance() core.HandlerFunc {
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return func(ctx core.Context) {
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s, e := parseRange(ctx.Request().URL.Query().Get("rangeType"), "", "")
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db := h.repo.GetDbR().WithContext(ctx.RequestContext())
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// 按活动聚合抽奖数据
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type drawRow struct {
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ActivityID int64 `gorm:"column:activity_id"`
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Count int64 `gorm:"column:count"`
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TotalCost int64 `gorm:"column:total_cost"`
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type performanceRow struct {
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ActivityID int64 `gorm:"column:activity_id"`
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SalesCount int64 `gorm:"column:sales_count"`
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PaymentCount int64 `gorm:"column:payment_count"`
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GamePassCount int64 `gorm:"column:game_pass_count"`
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TotalCost int64 `gorm:"column:total_cost"`
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PriceDraw int64 `gorm:"column:price_draw"`
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RevenueCents int64 `gorm:"column:revenue_cents"`
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ContributionBase int64 `gorm:"column:contribution_base"`
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}
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var rows []drawRow
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var rows []performanceRow
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// 统计抽奖日志，按活动分组，并计算奖品成本
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if err := h.readDB.ActivityDrawLogs.WithContext(ctx.RequestContext()).UnderlyingDB().
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if err := db.Table(model.TableNameActivityDrawLogs).
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Select(`
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activity_issues.activity_id,
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SUM(CASE WHEN orders.status = 2 AND (
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orders.source_type = 4
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OR orders.order_no LIKE 'GP%'
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OR (orders.actual_amount = 0 AND COALESCE(orders.remark, '') LIKE '%use_game_pass%')
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) THEN 1 ELSE 0 END) as game_pass_count,
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SUM(CASE WHEN orders.status = 2 AND NOT (
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orders.source_type = 4
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OR orders.order_no LIKE 'GP%'
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OR (orders.actual_amount = 0 AND COALESCE(orders.remark, '') LIKE '%use_game_pass%')
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) THEN 1 ELSE 0 END) as payment_count,
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SUM(CASE WHEN orders.status = 2 THEN 1 ELSE 0 END) as sales_count,
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COALESCE(MAX(activities.price_draw), 0) as price_draw,
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SUM(CASE WHEN orders.status = 2 THEN COALESCE(activities.price_draw, 0) ELSE 0 END) as revenue_cents,
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SUM(CASE WHEN orders.status = 2 THEN COALESCE(activities.price_draw, 0) ELSE 0 END) as contribution_base,
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CAST(SUM(CASE WHEN orders.status = 2 THEN COALESCE(products.cost_price, 0) * (
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COALESCE(NULLIF(activity_reward_settings.drop_quantity, 0), 1) +
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CASE WHEN user_item_cards.used_draw_log_id = activity_draw_logs.id AND system_item_cards.effect_type = 1 AND system_item_cards.reward_multiplier_x1000 >= 2000 THEN 1 ELSE 0 END
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) ELSE 0 END) AS SIGNED) as total_cost
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`).
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Joins("JOIN activity_issues ON activity_issues.id = activity_draw_logs.issue_id").
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Joins("JOIN activities ON activities.id = activity_issues.activity_id").
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Joins("LEFT JOIN activity_reward_settings ON activity_reward_settings.id = activity_draw_logs.reward_id").
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Joins("LEFT JOIN products ON products.id = activity_reward_settings.product_id").
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Joins("LEFT JOIN orders ON orders.id = activity_draw_logs.order_id").
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@ -1874,41 +1900,33 @@ func (h *handler) OperationsProductPerformance() core.HandlerFunc {
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Joins("LEFT JOIN system_item_cards ON system_item_cards.id = user_item_cards.card_id").
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Where("activity_draw_logs.created_at >= ?", s).
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Where("activity_draw_logs.created_at <= ?", e).
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Select(
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"activity_issues.activity_id",
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"COUNT(activity_draw_logs.id) as count",
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"CAST(SUM(IF(activity_draw_logs.is_winner = 1, COALESCE(activity_reward_settings.price_snapshot_cents, products.price, 0) * GREATEST(COALESCE(system_item_cards.reward_multiplier_x1000, 1000), 1000) / 1000, 0)) AS SIGNED) as total_cost",
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).
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Group("activity_issues.activity_id").
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Order("count DESC").
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Order("sales_count DESC").
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Limit(10).
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Scan(&rows).Error; err != nil {
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h.logger.Error(fmt.Sprintf("OperationsProductPerformance draw cost stats error: %v", err))
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h.logger.Error(fmt.Sprintf("OperationsProductPerformance stats error: %v", err))
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}
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// 获取活动详情（名称和单价）
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activityIDs := make([]int64, len(rows))
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for i, r := range rows {
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activityIDs[i] = r.ActivityID
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}
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type actInfo struct {
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Name string
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PriceDraw int64
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Name string
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}
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actMap := make(map[int64]actInfo)
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if len(activityIDs) > 0 {
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acts, _ := h.readDB.Activities.WithContext(ctx.RequestContext()).ReadDB().
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Where(h.readDB.Activities.ID.In(activityIDs...)).Find()
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for _, a := range acts {
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actMap[a.ID] = actInfo{Name: a.Name, PriceDraw: a.PriceDraw}
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actMap[a.ID] = actInfo{Name: a.Name}
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}
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}
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// 计算总数用于贡献率
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var totalCount int64
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var totalRevenueCents int64
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for _, r := range rows {
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totalCount += r.Count
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totalRevenueCents += r.ContributionBase
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}
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out := make([]productPerformanceItem, len(rows))
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@ -1916,30 +1934,29 @@ func (h *handler) OperationsProductPerformance() core.HandlerFunc {
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info := actMap[r.ActivityID]
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var contribution float64
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if totalCount > 0 {
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contribution = float64(r.Count) / float64(totalCount) * 100
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if totalRevenueCents > 0 {
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contribution = float64(r.ContributionBase) / float64(totalRevenueCents) * 100
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}
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// 周转率简化计算
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days := e.Sub(s).Hours() / 24
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if days < 1 {
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days = 1
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}
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turnover := float64(r.Count) / days * 7
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turnover := float64(r.SalesCount) / days * 7
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profitCents := r.RevenueCents - r.TotalCost
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out[i] = productPerformanceItem{
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ID: r.ActivityID,
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SeriesName: info.Name,
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SalesCount: r.Count,
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Amount: (r.Count * info.PriceDraw) / 100, // 转换为元
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Profit: (r.Count*info.PriceDraw - r.TotalCost) / 100,
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SalesCount: r.SalesCount,
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Amount: r.RevenueCents / 100,
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Profit: profitCents / 100,
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ProfitRate: 0,
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ContributionRate: float64(int(contribution*10)) / 10.0,
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InventoryTurnover: float64(int(turnover*10)) / 10.0,
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}
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if r.Count > 0 && info.PriceDraw > 0 {
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revenue := r.Count * info.PriceDraw
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pr := float64(revenue-r.TotalCost) / float64(revenue) * 100
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if r.RevenueCents > 0 {
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pr := float64(profitCents) / float64(r.RevenueCents) * 100
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out[i].ProfitRate = float64(int(pr*10)) / 10.0
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}
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}
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