feat: Dashboard数据关联修复 + 通知模板优化 + 工单创建页布局

1. Dashboard: stats新增queryStats查询统计, trend API真实工单趋势数据
2. Layout铃铛: 显示真实待审批工单数量
3. 通知模板优化:
   - 创建工单通知: 含申请人+目标实例+库+SQL摘要
   - 审批通过/拒绝通知: 含审批人+目标实例
   - 执行完成/失败通知: 含执行人+目标实例+库
4. 飞书卡片颜色: 创建=turquoise, 审批=blue, 成功=green, 失败=red
5. 钉钉消息: 添加状态emoji标签(🆕✅❌⚠️)
6. TicketCreatePage: 底部操作栏(SQL检测+提交审批)
7. ticket_executor: 移除aiomysql不支持的read_timeout/write_timeout

Tests: 1057 passed, 17 pre-existing failures (notification 35/35 passed)

backend/app/api/v1/endpoints/dashboard.py

@@ -1,6 +1,6 @@
 from datetime import datetime, timedelta, timezone
-from fastapi import APIRouter, Depends
-from sqlalchemy import select, func
+from fastapi import APIRouter, Depends, Query
+from sqlalchemy import select, func, case, extract
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.core.database import get_db
@@ -9,7 +9,8 @@ from app.models.user import User
 from app.models.ticket import Ticket
 from app.models.instance import Instance
 from app.models.audit_log import AuditLog
-from app.schemas.dashboard import DashboardStats
+from app.models.query_history import QueryHistory
+from app.schemas.dashboard import DashboardStats, TrendData, TrendPoint
 
 router = APIRouter(prefix="/dashboard", tags=["仪表盘"])
 
@@ -44,12 +45,41 @@ async def get_stats(
     )).scalar() or 0
 
     queries_today = (await db.execute(
-        select(func.count(AuditLog.id)).where(
-            AuditLog.action == "QUERY",
-            AuditLog.created_at >= today_start,
+        select(func.count(QueryHistory.id)).where(
+            QueryHistory.created_at >= today_start,
         )
     )).scalar() or 0
 
+    # --- 查询统计 ---
+    qh_today = select(QueryHistory).where(QueryHistory.created_at >= today_start)
+    total_q = (await db.execute(
+        select(func.count(QueryHistory.id))
+    )).scalar() or 0
+    avg_time = (await db.execute(
+        select(func.avg(QueryHistory.execution_time_ms))
+    )).scalar() or 0
+    slow_count = (await db.execute(
+        select(func.count(QueryHistory.id)).where(QueryHistory.execution_time_ms > 1000)
+    )).scalar() or 0
+
+    # 按数据库类型分布
+    type_rows = (await db.execute(
+        select(QueryHistory.db_type, func.count(QueryHistory.id).label('cnt'))
+        .group_by(QueryHistory.db_type)
+    )).all()
+    type_distribution = [
+        {"label": row[0] or "unknown", "count": row[1]}
+        for row in type_rows
+    ]
+
+    from app.schemas.dashboard import QueryStatsData
+    query_stats = QueryStatsData(
+        total=total_q,
+        avg_time_ms=int(avg_time),
+        slow_count=slow_count,
+        type_distribution=type_distribution,
+    )
+
     return DashboardStats(
         ticket_total=ticket_total,
         ticket_pending=ticket_pending,
@@ -59,4 +89,45 @@ async def get_stats(
         instance_online=instance_online,
         user_total=user_total,
         queries_today=queries_today,
+        query_stats=query_stats,
     )
+
+
+@router.get("/trend", response_model=TrendData)
+async def get_trend(
+    days: int = Query(7, ge=1, le=90),
+    db: AsyncSession = Depends(get_db),
+    current_user: User = Depends(get_current_user),
+):
+    """工单趋势数据 — 按天聚合最近 N 天的提交/成功/失败数"""
+    now = datetime.now(timezone.utc)
+    start = now - timedelta(days=days)
+
+    # 按天聚合工单数
+    rows = (await db.execute(
+        select(
+            func.date(Ticket.create_time).label('d'),
+            func.count(Ticket.id).label('total'),
+            func.sum(case((Ticket.status.in_(['completed', 'success']), 1), else_=0)).label('success'),
+            func.sum(case((Ticket.status.in_(['failed', 'rejected']), 1), else_=0)).label('failed'),
+        )
+        .where(Ticket.create_time >= start)
+        .group_by(func.date(Ticket.create_time))
+        .order_by(func.date(Ticket.create_time))
+    )).all()
+
+    # 构建日期映射
+    data_map = {}
+    for row in rows:
+        d = str(row[0])
+        data_map[d] = {"submitted": row[1], "success": int(row[2] or 0), "failed": int(row[3] or 0)}
+
+    # 填充缺失日期
+    items = []
+    for i in range(days):
+        dt = (start + timedelta(days=i)).strftime('%Y-%m-%d')
+        d = data_map.get(dt, {"submitted": 0, "success": 0, "failed": 0})
+        label = dt[5:]  # MM-DD
+        items.append(TrendPoint(date=label, **d))
+
+    return TrendData(items=items)