Files
shaguabijia-app-server/app/admin/repositories/ad_audit.py
T
2026-06-26 23:56:29 +08:00

285 lines
12 KiB
Python

"""看广告金币审计:复算 expected_coin 并与实发对比。
只读。复用 [app.core.rewards] 的公式函数(不另写公式,避免与正式发奖口径漂移):
- 看视频:每条 granted = 1 份,第 N 份 = 该用户 granted 的 reward_video **账号累计**顺序号
(与 ad_reward.grant_ad_reward 里 `_granted_cumulative + 1` 一致;LT 因子不按天重置,
故复算时要把当日序号叠加上该用户在本日**之前**的累计已发份数)。
- 信息流:每条按 unit_count 份逐份累加,LT 序号 = 该用户 granted 份数**账号累计**
(与 ad_feed_reward._unit_reward_total 的 existing_units 一致;同样不按天重置,
复算需叠加本日之前的累计份数)。
非 granted(capped/ecpm_missing)不占用份序号、应发恒 0,据此校验闸口是否确实没发。
"""
from __future__ import annotations
from sqlalchemy import func, select
from sqlalchemy.orm import Session
from app.core import rewards
from app.models.ad_feed_reward import AdFeedRewardRecord
from app.models.ad_reward import AdRewardRecord
from app.repositories.ad_feed_reward import FEED_REWARD_UNIT_SECONDS
def _prior_granted_counts(
db: Session, *, date: str, user_id: int | None
) -> dict[int, int]:
"""各用户在 date **之前**已发奖的 reward_video 累计份数,作为当日复算的 LT 序号起点。
LT 因子改账号累计后,当日第 1 份并非全局第 1 份,需叠加历史累计。"""
stmt = (
select(AdRewardRecord.user_id, func.count())
.where(
AdRewardRecord.reward_date < date,
AdRewardRecord.reward_scene == "reward_video",
AdRewardRecord.status == "granted",
)
.group_by(AdRewardRecord.user_id)
)
if user_id is not None:
stmt = stmt.where(AdRewardRecord.user_id == user_id)
return {uid: n for uid, n in db.execute(stmt).all()}
def _reward_video_rows(
db: Session, *, date: str, user_id: int | None
) -> list[dict]:
"""看视频记录复算。按 (user_id, created_at) 升序还原账号累计第 N 份(含本日之前的累计)。"""
stmt = (
select(AdRewardRecord)
.where(
AdRewardRecord.reward_date == date,
AdRewardRecord.reward_scene == "reward_video",
)
.order_by(AdRewardRecord.user_id, AdRewardRecord.created_at, AdRewardRecord.id)
)
if user_id is not None:
stmt = stmt.where(AdRewardRecord.user_id == user_id)
# 用本日之前的累计份数做起点,当日 granted 在其上继续递增 → 与 _granted_cumulative+1 对齐
granted_n: dict[int, int] = _prior_granted_counts(db, date=date, user_id=user_id)
rows: list[dict] = []
for rec in db.execute(stmt).scalars():
if rec.status == "granted":
nth = granted_n.get(rec.user_id, 0) + 1
granted_n[rec.user_id] = nth
expected = rewards.calculate_ad_reward_coin(rec.ecpm_raw, nth)
rows.append({
"scene": "reward_video",
"record_id": rec.id,
"user_id": rec.user_id,
"ad_session_id": rec.ad_session_id,
"app_env": rec.app_env,
"our_code_id": rec.our_code_id,
"created_at": rec.created_at,
"status": rec.status,
"ecpm": rec.ecpm_raw,
"ecpm_factor": rewards.ad_ecpm_factor(rewards.parse_ecpm_yuan(rec.ecpm_raw)),
"units": 1,
"lt_index_start": nth,
"lt_index_end": nth,
"lt_factor_start": rewards.ad_lt_factor(nth),
"lt_factor_end": rewards.ad_lt_factor(nth),
"expected_coin": expected,
"actual_coin": rec.coin,
"matched": expected == rec.coin,
})
else:
# capped / ecpm_missing:不发金币,校验实发确为 0
rows.append({
"scene": "reward_video",
"record_id": rec.id,
"user_id": rec.user_id,
"ad_session_id": rec.ad_session_id,
"app_env": rec.app_env,
"our_code_id": rec.our_code_id,
"created_at": rec.created_at,
"status": rec.status,
"ecpm": rec.ecpm_raw,
"ecpm_factor": None,
"units": 1,
"lt_index_start": None,
"lt_index_end": None,
"lt_factor_start": None,
"lt_factor_end": None,
"expected_coin": 0,
"actual_coin": rec.coin,
"matched": rec.coin == 0,
})
return rows
def _feed_prior_granted_units(
db: Session, *, date: str, user_id: int | None
) -> dict[int, int]:
"""各用户在 date **之前** granted 的信息流份数累计,作为当日复算的 LT 序号起点。"""
stmt = (
select(
AdFeedRewardRecord.user_id,
func.coalesce(func.sum(AdFeedRewardRecord.unit_count), 0),
)
.where(
AdFeedRewardRecord.reward_date < date,
AdFeedRewardRecord.status == "granted",
)
.group_by(AdFeedRewardRecord.user_id)
)
if user_id is not None:
stmt = stmt.where(AdFeedRewardRecord.user_id == user_id)
return {uid: int(n) for uid, n in db.execute(stmt).all()}
def _feed_scene_matches(rec: AdFeedRewardRecord, scene: str | None) -> bool:
"""该信息流记录是否落入请求的展示筛选 scene。
- scene=="feed":ad_type in ("feed", NULL)(旧数据 NULL 视为 feed,向后兼容)
- scene=="draw":ad_type=="draw"
- scene 为 None:不筛(两类都要)。
"""
if scene == "feed":
return rec.ad_type in (None, "feed")
if scene == "draw":
return rec.ad_type == "draw"
return True
def _feed_rows(
db: Session, *, date: str, user_id: int | None, scene: str | None = None
) -> list[dict]:
"""信息流记录复算。granted 记录逐份累加,LT 序号沿用账号累计份数(含本日之前)。
**关键:LT 因子账号累计按全表 unit 累计(feed+draw 共享同一发奖池/上限),不按 ad_type 拆分**——
故无论 scene 怎么筛展示,这里都遍历当日**全部**信息流记录维持 granted_units 累加;scene 只决定
哪些行被**留下展示**(由 _feed_scene_matches 判断),不影响累计基线,保证复算序号与正式发奖一致。
"""
stmt = (
select(AdFeedRewardRecord)
.where(AdFeedRewardRecord.reward_date == date)
.order_by(AdFeedRewardRecord.user_id, AdFeedRewardRecord.created_at, AdFeedRewardRecord.id)
)
if user_id is not None:
stmt = stmt.where(AdFeedRewardRecord.user_id == user_id)
# 本日之前的累计份数做起点,与 _unit_reward_total 的 existing_units(累计)对齐
granted_units: dict[int, int] = _feed_prior_granted_units(db, date=date, user_id=user_id)
rows: list[dict] = []
for rec in db.execute(stmt).scalars():
keep = _feed_scene_matches(rec, scene) # 累计照常推进,这里只决定是否展示本行
if rec.status == "granted":
existing = granted_units.get(rec.user_id, 0)
units = rec.unit_count
granted_units[rec.user_id] = existing + units
if not keep:
continue
expected = sum(
rewards.calculate_ad_reward_coin(rec.ecpm_raw, existing + offset)
for offset in range(1, units + 1)
)
start = existing + 1 if units > 0 else None
end = existing + units if units > 0 else None
rows.append({
"scene": "feed",
"ad_type": rec.ad_type or "feed",
"feed_scene": rec.feed_scene,
"record_id": rec.id,
"user_id": rec.user_id,
"ad_session_id": rec.ad_session_id,
"app_env": rec.app_env,
"our_code_id": rec.our_code_id,
"created_at": rec.created_at,
"status": rec.status,
"ecpm": rec.ecpm_raw,
"ecpm_factor": rewards.ad_ecpm_factor(rewards.parse_ecpm_yuan(rec.ecpm_raw)),
"units": units,
"lt_index_start": start,
"lt_index_end": end,
"lt_factor_start": rewards.ad_lt_factor(start) if start else None,
"lt_factor_end": rewards.ad_lt_factor(end) if end else None,
"expected_coin": expected,
"actual_coin": rec.coin,
"matched": expected == rec.coin,
})
else:
if not keep:
continue
rows.append({
"scene": "feed",
"ad_type": rec.ad_type or "feed",
"feed_scene": rec.feed_scene,
"record_id": rec.id,
"user_id": rec.user_id,
"ad_session_id": rec.ad_session_id,
"app_env": rec.app_env,
"our_code_id": rec.our_code_id,
"created_at": rec.created_at,
"status": rec.status,
"ecpm": rec.ecpm_raw,
"ecpm_factor": None,
"units": rec.unit_count,
"lt_index_start": None,
"lt_index_end": None,
"lt_factor_start": None,
"lt_factor_end": None,
"expected_coin": 0,
"actual_coin": rec.coin,
"matched": rec.coin == 0,
})
return rows
def audit_rows(
db: Session, *, date: str, user_id: int | None, scene: str | None = None
) -> list[dict]:
"""当日逐条发奖复算行(未排序)。scene: None=两类 / "reward_video" / "feed" / "draw"
"feed""draw" 都查 ad_feed_reward_record(同一发奖表),按 ad_type 区分:feed 含历史 NULL,
draw 仅 ad_type=="draw"。信息流行额外带 `ad_type`/`feed_scene`,供收益报表区分比价/领券 Draw 收益。
每行含 `app_env`/`our_code_id`/`expected_coin`/`actual_coin` 等,供金币审计逐条对账,
也供广告收益报表把「应发/实发」按 用户×类型×应用×代码位 聚合(见 ad_revenue,复用同一复算口径)。
**LT 因子账号累计仍按全表 unit 累计(feed+draw 共享),scene 只筛展示,不拆累计。**
"""
rows: list[dict] = []
if scene in (None, "reward_video"):
rows.extend(_reward_video_rows(db, date=date, user_id=user_id))
if scene in (None, "feed", "draw"):
rows.extend(_feed_rows(db, date=date, user_id=user_id, scene=scene))
return rows
def ad_coin_audit(
db: Session,
*,
date: str,
user_id: int | None,
scene: str | None,
limit: int,
only_mismatch: bool = False,
) -> dict:
"""当日发奖复算。返回 {total, mismatch_count, truncated, items}。
scene: None=两类都要 / "reward_video" / "feed";only_mismatch=True 只展示不一致(✗)行。
关键:`total` 与 `mismatch_count` 在**全量**(截断前)上统计,故对账数字始终可信,不受 limit
影响;`items` 才是展示集(only_mismatch 时只取 ✗ 行)按 created_at 倒序截断到 limit。
份序号在全天数据上已算好,limit 只影响展示条数、不影响 expected 复算正确性。
"""
rows = audit_rows(db, date=date, user_id=user_id, scene=scene)
rows.sort(key=lambda r: (r["created_at"], r["record_id"]), reverse=True)
total = len(rows)
mismatch_count = sum(1 for r in rows if not r["matched"])
display = [r for r in rows if not r["matched"]] if only_mismatch else rows
return {
"total": total,
"mismatch_count": mismatch_count,
"truncated": len(display) > limit,
"items": display[:limit],
}
def formula_snapshot() -> dict:
"""当前公式参数快照(给前端展示规则参照)。直接读 rewards 常量,与发奖同源。"""
return {
"coin_per_yuan": rewards.COIN_PER_YUAN,
"feed_unit_seconds": FEED_REWARD_UNIT_SECONDS,
"ecpm_factor_tiers": [list(t) for t in rewards.AD_ECPM_FACTOR_TABLE],
"lt_factor_tiers": [list(t) for t in rewards.AD_LT_FACTOR_TABLE],
}