feat: 看板优化——中文标题+Token消耗+时间段筛选+品牌名统一

- 学员端：LoginPanel/App.vue 描述文案优化，去掉生硬表述
- 后台品牌统一：index.html/App.vue 标题改为'大本营千问千答'
- 看板tab：Dashboard -> 数据看板
- 看板新增：输入Token、输出Token、总Token 统计
- 看板支持按时间段筛选：后端API增加start/end参数，前端加日期选择器
- stats-grid布局从5列调整为4列，响应式改为2列

ai_knowledge_base_v2/apps/backend/app/api/admin_dashboard.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
-from fastapi import APIRouter, Depends
+from datetime import datetime
+
+from fastapi import APIRouter, Depends, Query
 from sqlalchemy.orm import Session
 
 from app.core.database import get_db
@@ -15,8 +17,16 @@ router = APIRouter()
 
 @router.get("/dashboard")
 def dashboard(
+    start: str = Query(default="", description="开始日期 YYYY-MM-DD"),
+    end: str = Query(default="", description="结束日期 YYYY-MM-DD"),
     db: Session = Depends(get_db),
     current_admin: Admin = Depends(get_current_admin),
 ) -> dict:
-    stats = AdminDashboardService.stats(db)
+    start_dt = datetime.strptime(start, "%Y-%m-%d") if start else None
+    if end:
+        end_dt = datetime.strptime(end, "%Y-%m-%d")
+        end_dt = end_dt.replace(hour=23, minute=59, second=59)
+    else:
+        end_dt = None
+    stats = AdminDashboardService.stats(db, start_dt, end_dt)
     return api_success(DashboardStats.model_validate(stats).model_dump())

ai_knowledge_base_v2/apps/backend/app/schemas/admin.py

@@ -31,6 +31,9 @@ class DashboardStats(BaseModel):
     messageCount: int
     aiRequestCount: int
     knowledgeCount: int
+    inputToken: int
+    outputToken: int
+    totalToken: int
 
 
 class AdminUserUpdateRequest(BaseModel):

ai_knowledge_base_v2/apps/backend/app/services/admin_service.py

@@ -60,13 +60,35 @@ class AdminAuthService:
 
 class AdminDashboardService:
     @staticmethod
-    def stats(db: Session) -> dict:
+    def stats(db: Session, start: datetime | None = None, end: datetime | None = None) -> dict:
+        from sqlalchemy import and_
+
+        # 基础表查询条件（时间范围）
+        user_filter = [User.is_deleted == 0]
+        session_filter = [ChatSession.is_deleted == 0]
+        msg_filter = []
+        ai_filter = []
+
+        if start:
+            user_filter.append(User.created_at >= start)
+            session_filter.append(ChatSession.created_at >= start)
+            msg_filter.append(ChatMessage.created_at >= start)
+            ai_filter.append(AiRequestLog.created_at >= start)
+        if end:
+            user_filter.append(User.created_at <= end)
+            session_filter.append(ChatSession.created_at <= end)
+            msg_filter.append(ChatMessage.created_at <= end)
+            ai_filter.append(AiRequestLog.created_at <= end)
+
         return {
-            "userCount": db.scalar(select(func.count(User.id)).where(User.is_deleted == 0)) or 0,
-            "sessionCount": db.scalar(select(func.count(ChatSession.id)).where(ChatSession.is_deleted == 0)) or 0,
-            "messageCount": db.scalar(select(func.count(ChatMessage.id))) or 0,
-            "aiRequestCount": db.scalar(select(func.count(AiRequestLog.id))) or 0,
+            "userCount": db.scalar(select(func.count(User.id)).where(and_(*user_filter))) or 0,
+            "sessionCount": db.scalar(select(func.count(ChatSession.id)).where(and_(*session_filter))) or 0,
+            "messageCount": db.scalar(select(func.count(ChatMessage.id)).where(and_(*msg_filter))) or 0,
+            "aiRequestCount": db.scalar(select(func.count(AiRequestLog.id)).where(and_(*ai_filter))) or 0,
             "knowledgeCount": db.scalar(select(func.count(Knowledge.id))) or 0,
+            "inputToken": db.scalar(select(func.coalesce(func.sum(AiRequestLog.input_token), 0)).where(and_(*ai_filter))) or 0,
+            "outputToken": db.scalar(select(func.coalesce(func.sum(AiRequestLog.output_token), 0)).where(and_(*ai_filter))) or 0,
+            "totalToken": db.scalar(select(func.coalesce(func.sum(AiRequestLog.total_token), 0)).where(and_(*ai_filter))) or 0,
         }