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构建并部署到测试环境（无 SSH） / build-and-deploy (push) Successful in 9m27s
- 每卡 Redis 采集队列（默认 N=3、跨天清空、回退容差 1 MB），攒满且不下降才写最后一个点 - 测试环境全量切换日快照计量，下线灰度名单、切换水位与卡表基线扣费；全部卡置待建起点 - 读数区分没给值与 0；有套餐的卡读数 0 也写快照与日用量 0 行；重置日比较基准为 0 照常防旧值 - 新增采集记录（按月分区保留 90 天）、人工冲正流水与后台接口、采集告警 - 扣减按采集时刻有效套餐查找，只对生效中套餐做状态迁移，查不到套餐按成功跳过 - 换货新卡（新设备下的卡）打待建起点；采集间隔上限只校验启用中的轮询配置 Change: add-card-traffic-queue-and-observed-deduction Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
51 lines
1.6 KiB
Go
51 lines
1.6 KiB
Go
package cardflow
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import (
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"math"
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"time"
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)
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// 队列写入判断结果，与采集记录的写入判断取值一致。
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const (
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QueueWrite = "written"
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QueueNotFull = "queue_not_full"
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QueueSlopeNegative = "slope_negative"
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QueueOutOfOrder = "out_of_order"
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)
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// QueuePoint 是采集队列中的一个点：采集时间与运营商原始读数。
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type QueuePoint struct {
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ObservedAt time.Time `json:"observed_at"`
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ReadingMB float64 `json:"reading_mb"`
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}
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// JudgeQueue 判断本次新点能否写入：新点在队尾、队列满 size 个点、相邻两点后一个 ≥ 前一个 − 回退容差。
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// 不满足时返回不写原因；满足时写入的是队列最后一个点（即本次新点）。
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func JudgeQueue(points []QueuePoint, newPointIsTail bool, size int, dropToleranceMB float64) string {
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if !newPointIsTail {
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return QueueOutOfOrder
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}
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if len(points) < size {
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return QueueNotFull
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}
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for i := 1; i < len(points); i++ {
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if points[i].ReadingMB < points[i-1].ReadingMB-dropToleranceMB {
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return QueueSlopeNegative
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}
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}
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return QueueWrite
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}
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// MaxCollectionInterval 返回启用中轮询配置流量采集间隔的上限：留 2 次冗余，
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// 跨天清空开启时为 24 小时 ÷ (N+2)，关闭时为最长点龄 ÷ (N+2)；最长点龄为 0（不限）时不设上限，返回 0。
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func MaxCollectionInterval(size int, clearOnNewDay bool, maxPointAgeHours int, spare int) time.Duration {
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window := 24 * time.Hour
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if !clearOnNewDay {
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if maxPointAgeHours <= 0 {
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return 0
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}
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window = time.Duration(maxPointAgeHours) * time.Hour
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}
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return time.Duration(math.Floor(float64(window) / float64(size+spare)))
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}
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