Merge branch 'feat/h5-sgbridge' of https://gitea.shaguabijia.com/WonderableAI/shaguabijia-app-server into feat/h5-sgbridge

alembic/versions/761ef181ce7c_merge_jd_cps_order_fields_and_coupon_.py

@@ -0,0 +1,26 @@
+"""merge jd_cps_order_fields and coupon_session_origin_package heads
+
+Revision ID: 761ef181ce7c
+Revises: coupon_session_origin_package, jd_cps_order_fields
+Create Date: 2026-07-01 13:52:16.068808
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '761ef181ce7c'
+down_revision: Union[str, Sequence[str], None] = ('coupon_session_origin_package', 'jd_cps_order_fields')
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    pass
+
+
+def downgrade() -> None:
+    pass

app/admin/repositories/ad_audit.py

@@ -136,12 +136,16 @@ 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=="feed_all":所有信息流(feed/draw/NULL 都要)——业务已全切 Draw 信息流,收益报表把「Draw 信息流」
+      当作整个信息流口径(含历史误标 feed/NULL),用它避免筛选漏历史。
     - 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"
+    if scene == "feed_all":
+        return True
     return True
 
 
@@ -184,6 +188,7 @@ def _feed_rows(
                 "record_id": rec.id,
                 "user_id": rec.user_id,
                 "ad_session_id": rec.ad_session_id,
+                "trace_id": rec.trace_id,
                 "app_env": rec.app_env,
                 "our_code_id": rec.our_code_id,
                 "created_at": rec.created_at,
@@ -209,6 +214,7 @@ def _feed_rows(
                 "record_id": rec.id,
                 "user_id": rec.user_id,
                 "ad_session_id": rec.ad_session_id,
+                "trace_id": rec.trace_id,
                 "app_env": rec.app_env,
                 "our_code_id": rec.our_code_id,
                 "created_at": rec.created_at,
@@ -241,7 +247,7 @@ def audit_rows(
     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"):
+    if scene in (None, "feed", "draw", "feed_all"):
         rows.extend(_feed_rows(db, date=date, user_id=user_id, scene=scene))
     return rows
 

app/admin/repositories/ad_revenue.py

@@ -3,9 +3,11 @@
 只读。每行 = 一次广告事件(不再按用户聚合):
 - **激励视频**:一次观看 = 1 条展示(ad_ecpm)+ 1 条发奖(ad_reward),按 ad_session_id 合并成一行,
   直接给出 eCPM / 收益 + 状态 / 应发 / 实发 / 一致;点开看该条金币复算因子。
-- **信息流**:轮播每条展示各一行(impressionId 各自独立);整场发奖(ad_feed_reward,client_event_id)
-  与逐条展示无法对应,单独成「纯发奖」行。
-- 兜底:有展示无发奖(中途关 / 未达发奖)、有发奖无展示(未上报 eCPM)都各自成行。
+- **信息流(比价/领券)**:一次比价 / 一次领券 = 一条整场发奖(ad_feed_reward)一行,给出 eCPM /
+  发奖金币 + 应发 / 实发 / 一致;点开看金币复算因子。⚠️ draw 的逐条展示(ad_ecpm,impressionId 各自
+  独立、与整场发奖无公共键、无法归到「哪一次」)**不再单独占行**(2026-07 按「一次比价/领券放一块」调整)——
+  其展示数 / eCPM / 预估收益仍进全量统计(合计 / 趋势 / 分类大盘 / 穿山甲对照),只是主表不逐条铺开。
+- 兜底:激励视频有展示无发奖(中途关 / 未达发奖)、有发奖无展示(未上报 eCPM)仍各自成行。
 
 展示与收益来自 ad_ecpm_record(收益 = eCPM元 ÷ 1000);应发 / 实发金币复用金币审计逐条复算
 (ad_audit.audit_rows,与正式发奖同一公式口径,不另写公式)。合计与对账在全量上统计,
@@ -58,14 +60,6 @@ def _date_range(date_from: str, date_to: str) -> list[str]:
 _AUDIT_SCENES = {"reward_video", "feed", "draw"}
 
 
-def _event_ad_type(row: dict) -> str:
-    """纯发奖事件行的 ad_type:信息流行用 audit 带回的真实 ad_type(feed/draw),回退 feed;
-    激励视频行恒 reward_video。不再用 scene 硬映射,避免把 draw 丢成 feed。"""
-    if row["scene"] == "reward_video":
-        return "reward_video"
-    return row.get("ad_type") or "feed"
-
-
 # 发奖复算明细字段(展开下钻看「金币怎么算出来的」)——从 audit 行原样取这些 key。
 _REWARD_DETAIL_KEYS = (
     "record_id", "created_at", "status", "ecpm", "ecpm_factor", "units",
@@ -108,9 +102,14 @@ def ad_revenue_report(
     #    同时保留全量列表,未被展示合并的成「纯发奖」事件。
     reward_by_session: dict[tuple[int, str], list[dict]] = {}
     all_reward_rows: list[dict] = []
-    # 报表 ad_type 直接当 audit scene 用(取值一致);未知/无效 ad_type 不取发奖行。draw 在此被
-    # 正确传成 scene="draw",audit 会按 ad_type 筛出 Draw 发奖,不再丢成 feed。
-    audit_scene = ad_type if ad_type in _AUDIT_SCENES else None
+    # 报表 ad_type → audit scene:reward_video/feed 直传;**draw(前端「Draw 信息流」)映射成 feed_all**
+    # ——业务已全切 Draw,把「Draw 信息流」当作整个信息流口径(含历史误标 feed/NULL),否则筛选会漏历史。
+    if ad_type == "draw":
+        audit_scene = "feed_all"
+    elif ad_type in _AUDIT_SCENES:
+        audit_scene = ad_type
+    else:
+        audit_scene = None
     if ad_type is None or audit_scene is not None:
         for d in _date_range(date_from, date_to):
             for row in ad_audit.audit_rows(db, date=d, user_id=user_id, scene=audit_scene):
@@ -140,7 +139,10 @@ def ad_revenue_report(
     )
     if user_id is not None:
         stmt = stmt.where(AdEcpmRecord.user_id == user_id)
-    if ad_type is not None:
+    if ad_type == "draw":
+        # draw = 所有信息流展示(业务已全 Draw,含历史误标 feed);展示行只进统计,不占主表行
+        stmt = stmt.where(AdEcpmRecord.ad_type.in_(["draw", "feed"]))
+    elif ad_type is not None:
         stmt = stmt.where(AdEcpmRecord.ad_type == ad_type)
     for rec in db.execute(stmt).scalars():
         rwd = _pop_reward(rec.user_id, rec.ad_session_id)
@@ -165,6 +167,8 @@ def ad_revenue_report(
             ),
             "adn": rec.adn,
             "slot_id": rec.slot_id,
+            "sub_rewards": [],
+            "sub_count": 1,
         }
         if rwd is not None:
             ev.update({
@@ -184,33 +188,88 @@ def ad_revenue_report(
             })
         events.append(ev)
 
-    # 3) 未被展示合并的发奖行 → 「纯发奖」事件(信息流整场发奖 / 有发奖无展示)。
-    #    收益恒 0(收益只算展示侧,避免与展示行重复计)。
+    # 3) 未被展示合并的发奖行 → 事件:
+    #    - 激励视频(reward_video):逐条成「纯发奖」事件(每次一个 ad_session_id;有发奖无展示等)。
+    #    - 信息流(feed/draw):同一次比价/领券的多条广告共享**整场 ad_session_id**(客户端整场复用),
+    #      按 (user_id, ad_session_id) 聚成**一次比价 / 一次领券**父事件;sub_rewards 为组内逐条明细,
+    #      应发/实发取组内合计;业务已全 Draw → 类型统一 "draw"。session 缺失(极少旧数据)各自单独成组。
+    feed_groups: dict[tuple[int, str], list[dict]] = {}
     for row in all_reward_rows:
         if row["record_id"] in used_reward_ids:
             continue
+        if row["scene"] == "reward_video":
+            events.append({
+                "event_key": f"rwd-{row['record_id']}",
+                "report_date": row["_report_date"],
+                "user_id": row["user_id"],
+                "ad_type": "reward_video",
+                "feed_scene": row.get("feed_scene"),
+                "app_env": row.get("app_env"),
+                "our_code_id": row.get("our_code_id"),
+                "created_at": row["created_at"],
+                "hour": _cn_hour(row["created_at"]) if by_hour else None,
+                "has_impression": False,
+                "impressions": 0,
+                "ecpm": row["ecpm"],
+                "revenue_yuan": 0.0,
+                "adn": None,
+                "slot_id": None,
+                "has_reward": True,
+                "status": row["status"],
+                "expected_coin": int(row["expected_coin"]),
+                "actual_coin": int(row["actual_coin"]),
+                "matched": bool(row["matched"]),
+                "reward_detail": _reward_detail(row),
+                "sub_rewards": [],
+                "sub_count": 1,
+            })
+        else:
+            # 聚合单位 = 一次完整比价/领券流程:优先用 trace_id(比价带 comparisonTraceId、领券带 sessionTraceId,
+            # 整个流程不变;即使中途点广告致浮层关闭重弹、ad_session_id 变了,trace_id 仍不变 → 全流程聚成一行)。
+            # 无 trace_id(历史领券未上报 / 旧数据)回退整场 ad_session_id;再无则 record_id 各自成组、不误并。
+            grp_key = row.get("trace_id") or row.get("ad_session_id") or f"_rid-{row['record_id']}"
+            feed_groups.setdefault((row["user_id"], grp_key), []).append(row)
+
+    # 信息流分组 → 「一次比价 / 一次领券」父事件(收益恒 0:收益只算展示侧,避免与展示行重复计)。
+    for (uid, grp_key), group in feed_groups.items():
+        group.sort(key=lambda r: (r["created_at"], r["record_id"]))
+        rep = group[-1]  # 代表条(最新一条):时间/场景/应用/代码位取它
+        expected_sum = sum(int(g["expected_coin"]) for g in group)
+        actual_sum = sum(int(g["actual_coin"]) for g in group)
+        # 父行 eCPM:组内各条 eCPM(分)均值(展示用,各条不同);无有效值则取代表条
+        ecpm_fens = [rewards.parse_ecpm_fen(g["ecpm"]) for g in group if g.get("ecpm")]
+        avg_ecpm = str(round(sum(ecpm_fens) / len(ecpm_fens))) if ecpm_fens else rep.get("ecpm")
+        # 主表逐行显示用:这次发奖广告的预估收益之和(发奖侧 eCPM 折算,钳顶同展示侧)。只放进
+        # row_revenue_yuan 给主表逐行展示,不进 revenue_yuan/合计/趋势——避免与展示侧 total 重复计。
+        row_revenue = round(sum(
+            min(rewards.parse_ecpm_yuan(g["ecpm"]), rewards.AD_ECPM_MAX_FEN / 100.0) / 1000.0
+            for g in group if g.get("ecpm")
+        ), 6)
         events.append({
-            "event_key": f"rwd-{row['record_id']}",
-            "report_date": row["_report_date"],
-            "user_id": row["user_id"],
-            "ad_type": _event_ad_type(row),
-            "feed_scene": row.get("feed_scene"),
-            "app_env": row.get("app_env"),
-            "our_code_id": row.get("our_code_id"),
-            "created_at": row["created_at"],
-            "hour": _cn_hour(row["created_at"]) if by_hour else None,
+            "event_key": f"feedgrp-{uid}-{grp_key}",
+            "report_date": rep["_report_date"],
+            "user_id": uid,
+            "ad_type": "draw",  # 业务已全切 Draw 信息流,聚合行统一 draw
+            "feed_scene": rep.get("feed_scene"),
+            "app_env": rep.get("app_env"),
+            "our_code_id": rep.get("our_code_id"),
+            "created_at": rep["created_at"],
+            "hour": _cn_hour(rep["created_at"]) if by_hour else None,
             "has_impression": False,
             "impressions": 0,
-            "ecpm": row["ecpm"],
+            "ecpm": avg_ecpm,
             "revenue_yuan": 0.0,
+            "row_revenue_yuan": row_revenue,
             "adn": None,
             "slot_id": None,
             "has_reward": True,
-            "status": row["status"],
-            "expected_coin": int(row["expected_coin"]),
-            "actual_coin": int(row["actual_coin"]),
-            "matched": bool(row["matched"]),
-            "reward_detail": _reward_detail(row),
+            "status": rep["status"],  # 代表状态(逐条见展开)
+            "expected_coin": expected_sum,
+            "actual_coin": actual_sum,
+            "matched": all(bool(g["matched"]) for g in group),
+            "reward_detail": None,
+            "sub_rewards": [_reward_detail(g) for g in group],
+            "sub_count": len(group),
         })
 
     # 「场景」作为全局筛选(与 user_id/ad_type 一致):同时作用于明细、合计与 daily/hourly 趋势。
@@ -331,9 +390,20 @@ def ad_revenue_report(
     is_today = date_from == date_to == rewards.cn_today().isoformat()
     dau = admin_stats.today_dau(db) if is_today else None
 
+    # 主表「逐行」= 单次广告行为(2026-07 按「一次比价/领券放一块」聚合):激励视频 = 一次观看一行(展示+发奖
+    # 按 ad_session_id 合并);一次比价 / 一次领券 = 该次整场多条广告按 ad_session_id 聚成一行(展开看逐条)。
+    # 信息流(draw/feed)的逐条展示(ad_ecpm,impressionId 各自独立、与整场发奖无公共键)不再单独占行
+    # ——其展示数 / eCPM / 预估收益已计入上面的全量统计(total_*、daily / hourly、type_stats、穿山甲对照),
+    # 只是主表不逐条铺开;逐条明细在父行展开里看(sub_rewards)。合计 / 趋势 / 分类大盘均基于全量 events,
+    # 不受此过滤影响;total / 分页只作用于主表行。
+    main_rows = [
+        e for e in events
+        if not (e["ad_type"] in ("draw", "feed") and e["has_impression"] and not e["has_reward"])
+    ]
+
     return {
-        "total": len(events),
-        "truncated": len(events) > offset + limit,
+        "total": len(main_rows),
+        "truncated": len(main_rows) > offset + limit,
         "total_impressions": total_impressions,
         "total_revenue_yuan": total_revenue_yuan,
         # 穿山甲后台收益合计(元):预估 revenue + 收益Api;非全量视图(带 user/类型/场景过滤)或无数据为 None。
@@ -347,5 +417,5 @@ def ad_revenue_report(
         "hourly": hourly,
         "type_stats": type_stats,
         "dau": dau,
-        "items": events[offset:offset + limit],
+        "items": main_rows[offset:offset + limit],
     }

app/admin/repositories/queries.py

@@ -806,6 +806,12 @@ def get_user_overview(db: Session, user_id: int) -> dict | None:
     }
 
 
+def _as_utc_naive(value: datetime) -> datetime:
+    """窗口入参 → UTC naive(= _as_utc 去时区),与库里按 naive UTC 存取的 created_at 同口径比较。
+    历史遗留:_window_conds 一直引用本函数却未定义(自定义区间会 NameError),此处补上。"""
+    return _as_utc(value).replace(tzinfo=None)
+
+
 def _window_conds(col, date_from: datetime | None, date_to: datetime | None) -> list:
     """把 [date_from, date_to] 转成对 col(created_at)的过滤条件;都为 None = 全量(注册至今)。"""
     conds = []
@@ -895,6 +901,13 @@ def user_reward_stats(
     }
 
 
+def _cn_wall_to_utc(dt: datetime) -> datetime:
+    """coin_transaction 存的是北京 wall-clock(naive,见 wallet.grant_coins「存北京 wall-clock」),转成 UTC naive,
+    与广告表(func.now() UTC)统一 —— 让本函数按同一绝对时刻排序、且前端 apiTime(把无时区时间当 UTC 再 +8 展示)
+    口径一致;否则签到会比实际多显示 8 小时(北京时间又被 +8)。"""
+    return dt.replace(tzinfo=rewards.CN_TZ).astimezone(timezone.utc).replace(tzinfo=None)
+
+
 def user_coin_records(
     db: Session,
     user_id: int,
@@ -915,6 +928,9 @@ def user_coin_records(
     offset = max(cursor or 0, 0)
     fetch = offset + limit + 1
     rows: list[dict] = []
+    # coin_transaction 存北京 wall-clock(其余表存 UTC);签到窗口边界 +8h 对齐北京,过滤/计数才不偏移 8 小时
+    signin_from = date_from + timedelta(hours=8) if date_from is not None else None
+    signin_to = date_to + timedelta(hours=8) if date_to is not None else None
 
     for rec in db.execute(
         select(AdRewardRecord)
@@ -958,7 +974,7 @@ def user_coin_records(
         .where(
             CoinTransaction.user_id == user_id,
             CoinTransaction.biz_type == "signin",
-            *_window_conds(CoinTransaction.created_at, date_from, date_to),
+            *_window_conds(CoinTransaction.created_at, signin_from, signin_to),
         )
         .order_by(CoinTransaction.created_at.desc())
         .limit(fetch)
@@ -966,7 +982,8 @@ def user_coin_records(
         rows.append({
             "source": "signin",
             "source_label": "签到",
-            "created_at": rec.created_at,
+            # 北京 wall-clock → UTC,与广告记录统一(前端 apiTime 会 +8 回北京展示,不然签到会多 8 小时)
+            "created_at": _cn_wall_to_utc(rec.created_at),
             "ecpm": None,
             "coin": rec.amount,
         })
@@ -992,7 +1009,7 @@ def user_coin_records(
         + _count(
             CoinTransaction, CoinTransaction.user_id == user_id,
             CoinTransaction.biz_type == "signin",
-            *_window_conds(CoinTransaction.created_at, date_from, date_to),
+            *_window_conds(CoinTransaction.created_at, signin_from, signin_to),
         )
     )
     return rows[offset:offset + limit], (offset + limit if has_more else None), total

app/admin/schemas/ad_revenue.py

@@ -94,6 +94,11 @@ class AdRevenueRow(BaseModel):
     impressions: int = Field(..., description="本行展示条数:有展示=1 / 纯发奖=0(供日汇总、趋势图复用)")
     ecpm: str | None = Field(None, description="eCPM 原始值(分/千次);展示行取展示值,纯发奖行取发奖采用值")
     revenue_yuan: float = Field(..., description="本次展示预估收益(元)= eCPM元 ÷ 1000;纯发奖行=0")
+    row_revenue_yuan: float | None = Field(
+        None,
+        description="主表逐行展示用的预估收益(元):一次比价/领券聚合行=该次发奖广告 eCPM 折算之和;"
+        "其它行为空(前端回退取 revenue_yuan)。不进合计/趋势,避免与展示侧重复计",
+    )
     adn: str | None = Field(None, description="实际填充 ADN 子渠道(pangle/gdt…);纯发奖行为空")
     slot_id: str | None = Field(None, description="底层 mediation rit(非我们配置的广告位 ID);纯发奖行为空")
     # ── 发奖侧 ──
@@ -106,6 +111,15 @@ class AdRevenueRow(BaseModel):
         None,
         description="发奖复算明细(eCPM/因子1/份数/LT/因子2/应发/实发/一致);点行展开下钻用,纯展示为空",
     )
+    sub_rewards: list[AdRevenueRecord] = Field(
+        default_factory=list,
+        description="一次比价/领券聚合行的组内逐条发奖明细(同一整场 ad_session_id 的多条广告);"
+        "点行展开渲染多行。激励视频/纯展示行为空(单条看 reward_detail)",
+    )
+    sub_count: int = Field(
+        1,
+        description="本行聚合的发奖条数:一次比价/领券=该次广告条数(≥1);激励视频/纯展示=1",
+    )
 
 
 class AdRevenueReportOut(BaseModel):

app/admin/schemas/user.py

@@ -17,6 +17,7 @@ class AdminUserListItem(BaseModel):
     status: str
     debug_trace_enabled: bool = False
     wechat_openid: str | None = None
+    wechat_nickname: str | None = None
     created_at: datetime
     last_login_at: datetime
 

h5/mine/index.html

@@ -9,6 +9,17 @@
 <link rel="stylesheet" href="../shared/fonts.css">
 <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, viewport-fit=cover">
 <title>我的 · 傻瓜比价</title>
+<!-- App/预览:首帧前就撑满 .device + 白底,避免原型黑 body 闪一下(原逻辑在 body 中段 JS 才注入,太晚)。
+     仅 App(SGBridgeNative)或 ?wv=1 预览生效;浏览器裸开仍保留黑底手机壳。与 body 中段注入等价、只是提前。 -->
+<script>
+(function () {
+  if (window.SGBridgeNative || /[?&]wv=1/.test(location.search)) {
+    var s = document.createElement('style');
+    s.textContent = 'html,body{background-color:#fff!important}.device{position:fixed!important;top:0!important;left:0!important;right:0!important;bottom:0!important;width:auto!important;height:auto!important;border-radius:0!important;box-shadow:none!important}.tab-bar{display:none!important}.prof-statusbar{display:none!important}.variant-panel{display:none!important}#debugBack{display:none!important}';
+    (document.head || document.documentElement).appendChild(s);
+  }
+})();
+</script>
 <style>
 * { margin: 0; padding: 0; box-sizing: border-box; -webkit-tap-highlight-color: transparent; }
 html { background: #000; }
@@ -3562,6 +3573,31 @@ TODO: 补充 @owner / @from / @to / @notes（参照 FLOW.md）
  </div>
  </div>
  </div><!-- end profileLogged -->
+ <!-- 首帧前定登录态:getAuthState() 是同步桥,已登录就先隐藏未登录态、显示已登录态,避免"未登录/立即登录"闪一下
+      (原逻辑到 DOMContentLoaded 的 loadMineData 才判,太晚)。loadMineData 随后填真实数据。 -->
+ <script>
+ (function () {
+   try {
+     var raw = window.SGBridgeNative && window.SGBridgeNative.getAuthState();
+     var auth = raw ? JSON.parse(raw) : null;
+     if (auth && auth.loggedIn) {
+       document.getElementById('profileUnlogged').style.display = 'none';
+       document.getElementById('profileLogged').style.display = 'block';
+       // 昵称/头像 getAuthState 首帧就有(与 loadMineData 同源),直接填,避免 mock 名/头像闪。
+       var pn = document.getElementById('profName');
+       if (pn && auth.nickname) { pn.textContent = auth.nickname; pn.setAttribute('data-fullname', auth.nickname); }
+       if (auth.avatarUrl && /^https?:/.test(auth.avatarUrl)) {
+         var av = document.querySelector('#profileLogged .prof-avatar');
+         if (av) av.innerHTML = '<img src="' + auth.avatarUrl + '" style="width:100%;height:100%;border-radius:50%;object-fit:cover" onerror="this.style.display=\'none\'">';
+       }
+       // 统计数据要走接口:先清掉静态 mock 数值(省钱额 + 领券/比价/金币),等 loadMineData 填真值,绝不闪 mock。
+       var amt = document.querySelector('#profSavingsCard .prof-amount');
+       if (amt) amt.innerHTML = '';
+       document.querySelectorAll('#profSavingsCard .prof-stat-num').forEach(function (n) { n.innerHTML = ''; });
+     }
+   } catch (e) {}
+ })();
+ </script>
  <div class="tab-bar">
  <div class="tab-item" onclick="go('home')"><span class="tab-icon"><svg class="i-outline" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="#1a1a1a" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M3 12l9-8 9 8"></path><path d="M5 10v9a1 1 0 001 1h3v-5h6v5h3a1 1 0 001-1v-9"></path></svg><svg class="i-filled" width="24" height="24" viewBox="0 0 24 24" fill="none"><path d="M3 12l9-8 9 8" stroke="#1a1a1a" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path><path d="M5 10v9a1 1 0 001 1h3v-5h6v5h3a1 1 0 001-1v-9" fill="#FFD600" stroke="#1a1a1a" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></svg></span><span class="tab-label">首页</span></div>
  <div class="tab-item" id="homeTabWelfare" onclick="go('welfare')"><span class="welfare-tab-dot" aria-hidden="true"></span><span class="tab-icon" id="homeTabWelfareIcon"><svg class="i-outline" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="#1a1a1a" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><rect x="3" y="8" width="18" height="4" rx="1"></rect><path d="M12 8v12"></path><path d="M5 12v7a1 1 0 001 1h12a1 1 0 001-1v-7"></path><path d="M7.5 8C6 8 5 6.5 6 5.5S9 4 9.5 5.5c.4 1.2 2.5 2.5 2.5 2.5s2.1-1.3 2.5-2.5C15 4 17 4.5 18 5.5s-1 2.5-2.5 2.5"></path></svg><svg class="i-filled" width="24" height="24" viewBox="0 0 24 24" fill="none"><rect x="3" y="8" width="18" height="4" rx="1" fill="#FFD600" stroke="#1a1a1a" stroke-width="2"></rect><path d="M5 12v7a1 1 0 001 1h12a1 1 0 001-1v-7" fill="#FFD600" stroke="#1a1a1a" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path><path d="M12 8v12" stroke="#1a1a1a" stroke-width="2"></path><path d="M7.5 8C6 8 5 6.5 6 5.5S9 4 9.5 5.5c.4 1.2 2.5 2.5 2.5 2.5s2.1-1.3 2.5-2.5C15 4 17 4.5 18 5.5s-1 2.5-2.5 2.5" stroke="#1a1a1a" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></svg></span><span class="tab-label">福利</span></div>

h5/records/index.html

@@ -302,8 +302,8 @@ body.transitioning-tab { /* no animation */ }
 /* 反馈低价 · 6 套 FAB 样式 + 单选模式 + 反馈抽屉 */
 /* ======================================== */
 
-/* —— 反馈低价 CTA —— 顶部右侧，位于搜索图标下方 */
-.fb-fab { position: absolute; top: 96px; right: 16px; bottom: auto; height: 30px; padding: 0 12px; background: #FFFCE5; color: var(--color-on-primary); border-radius: 999px; font-size: 12px; font-weight: 700; font-family: inherit; display: flex; align-items: center; justify-content: center; border: 1px solid var(--color-primary); cursor: pointer; z-index: 4; transition: transform .14s ease, background .14s ease; white-space: nowrap; }
+/* —— 反馈低价 CTA —— 顶部右侧，与筛选行(全部记录/已下单)同高 */
+.fb-fab { position: absolute; top: 52px; right: 16px; bottom: auto; height: 30px; padding: 0 12px; background: #FFFCE5; color: var(--color-on-primary); border-radius: 999px; font-size: 12px; font-weight: 700; font-family: inherit; display: flex; align-items: center; justify-content: center; border: 1px solid var(--color-primary); cursor: pointer; z-index: 4; transition: transform .14s ease, background .14s ease; white-space: nowrap; }
 .fb-fab:active { transform: scale(.96); }
 .fb-fab-badge { display: none; }
 .device.cta-outline .fb-fab { background: #FFFFFF; border-color: #FFD600; box-shadow: none; }