feat: 2026-07-12~13 全量更新 - AI对话链路改造+H5 v4/v5+坐席端v5+上下文感知诊断+知识库迭代3

## H5 员工端 v4 (2026-07-13 00:48 已部署)
- 人工按钮三态文案统一为"人工坐席"
- 按钮位置移至发送键和语音按钮上方(垂直堆叠)
- 点按钮直接调 store.shakeAgent()，删除 CallAgentModal 弹窗动画
- 截图快捷键提示改为"截图->粘贴：Alt+Shift+A-Ctrl+V ---> Ctrl+V"
- 移动端隐藏截图提示(CSS 媒体查询)
- AI转人工提示改为"已为您呼叫人工坐席，请稍等!"
- 坐席接入提示改为"坐席正在查看您的信息，请等待处理回复！"
- 删除"摇铃呼叫坐席"入口和文案
- 删除孤儿组件 MessageList.vue + shake 动画 CSS

## H5 员工端 v5 (2026-07-13 02:08 已部署)
- RightPanel v2.1：删除"软件安装"和"资源权限"标签页
- 移除标签栏，智能推荐(DynamicRecommend)直接展示
- 删除 SoftwareDownloads/ApprovalLinks 引用和相关 CSS

## AI 对话链路全栈改造 Phase 1-6 (已部署)
- Phase 1: Dify JSON输出 + 后端blocking解析 + 双WS推送 + 错误降级
- Phase 2: 关键词收窄(~25强意图词) + 两级分类Prompt + 删除前端checkApprovalIntent
- Phase 3: WS扩展(ai_thinking+dynamic_recommend) + ai_structured气泡 + RightPanel v2 + 选项回传
- Phase 4: VisionService接入 + 图片消息融合(5秒窗口) + 降级策略
- Phase 5: 坐席端ai_thinking指示器 + ai_structured/byod_card渲染 + handleNewMessage修复
- Phase 6: diagnosis_stage(6值) + response_time_ms计时 + 慢响应告警(>10s)

## 坐席端 v5 (2026-07-13 01:38 已部署)
- ai_structured/byod_card 只读渲染
- AI思考指示器 UI
- handleNewMessage 透传 msg_type/extra_data 修复
- 布局优化v2.0: QuickReplyBar L1+L2悬浮 + ReplyBox左右分区 + 右栏260/560px切换
- 键盘快捷键v2.3: 纯数字路由 + ESC分层撤销 + Shift+Space用event.code

## 上下文感知智能诊断闭环 (2026-07-12 已部署)
- 三层诊断(API→Script→AI) + 三段排队(VIP→info_locked→not locked)
- 答题插队 + 五场景关闭
- 迁移052(6表+6列) + queue_service + quiz_service + closing_service
- H5前端: QueueWaiting + RightPanel双Tab + InputBar三态 + ResolveConfirmCard
- 坐席前端: pending_close结单流程 + 信息锁定(Dify步骤完成+有效回答率≥70%)

## 知识库迭代3 (2026-07-12 已部署)
- 分诊交互(H5+坐席+Dify独立应用)
- 拓扑预览(ECharts只读)
- 代答排除(4种匹配器: keyword/regex/intent/category)
- 迁移051 + 44文件43测试通过

## 后端变更
- 6个Python文件改造(h5_ai_task.py/h5.py/ai_service.py/closing_service.py等)
- funny_phrase_service.py: shake/connected/keyword 默认文案更新
- session_service.py: 企微消息文案同步
- 新增: queue.py/quiz.py/triage.py/exclusion_rules.py 等API端点
- 新增: diagnostic.py/quiz.py/triage_session.py 等模型
- 新增: closing_service/queue_service/quiz_service/triage_service 等服务

## 文档更新
- CHANGELOG.md: 新增 [未发布] 区全部变更记录
- 项目管理主文档 v2.5: 新增v0.7.3版本 + 已完成看板 + 最近搞定
- 版本记录: 新增v0.7.3条目
- AI对话链路实施计划: Phase 1-6 全部标记✅已实施
- 新增架构图/时序图/类图(mermaid)

## 部署路径修正
- 服务器项目根路径: /opt/wecom-it-desk/
- 所有前端dist均为ro bind mount，只能在宿主机源路径操作
- 服务器nginx /h5/ 是静态文件服务(非proxy_pass)
- elFinder上传二进制不可靠(MD5不匹配)，改用base64分块上传

backend/app/services/quiz_service.py

@@ -0,0 +1,535 @@
+# =============================================================================
+# 企微IT智能服务台 — 答题与积分服务
+# =============================================================================
+# 说明：排队等待期间的答题系统，包含双模式题目选择、积分更新、插队计算
+#
+# 答题双模式（决策 D1）：
+#   模式A — 诊断题（info_locked=false）：与当前问题相关的选择题
+#           答案附加到会话上下文，供坐席接单时参考
+#   模式B — IT知识题（info_locked=true）：纯教育性质，提升IT素养
+#
+# 积分规则（决策 D2）：
+#   答对 +10分，答错不扣分，跨会话累积
+#   5级等级：0-99 IT小白 → 100-299 IT入门 → 300-599 IT达人 → 600-999 IT专家 → 1000+ IT大师
+#
+# 插队规则（决策 C4）：
+#   queue_priority = min(答题数 // 3, 2)  # 每答3题前移1位，上限2
+# =============================================================================
+
+import logging
+import random
+from typing import Any, Dict, List, Optional
+
+from sqlalchemy import func, select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.conversation import Conversation
+from app.models.quiz import (
+    EmployeePoints,
+    QuizAnswer,
+    QuizQuestion,
+)
+from app.utils.response import AppException
+
+logger = logging.getLogger(__name__)
+
+# 积分常量
+POINTS_PER_CORRECT = 10
+MAX_QUEUE_PRIORITY = 2
+QUIZ_PER_PRIORITY = 3  # 每答3题前移1位
+
+
+class QuizService:
+    """答题与积分服务。"""
+
+    # ======================================================================
+    # 获取下一道题
+    # ======================================================================
+
+    async def get_next_question(
+        self,
+        db: AsyncSession,
+        employee_id: str,
+        conversation: Optional[Conversation] = None,
+    ) -> Dict[str, Any]:
+        """获取下一道题（双模式自动选择）。
+
+        模式选择逻辑：
+        - 如果有活跃会话且 info_locked=false → 诊断题（模式A）
+        - 如果有活跃会话且 info_locked=true → IT知识题（模式B）
+        - 如果无活跃会话 → IT知识题（模式B）
+
+        Args:
+            db: 数据库会话
+            employee_id: 员工ID
+            conversation: 当前会话（可选，排队时传入）
+
+        Returns:
+            Dict: 题目数据 {question_id, type, category, question, options}
+        """
+        # 判定模式
+        use_diagnostic = (
+            conversation is not None
+            and conversation.status == "queued"
+            and not conversation.info_locked
+        )
+
+        if use_diagnostic:
+            # 模式A：诊断题 — 根据问题类别匹配
+            question = await self._get_diagnostic_question(db, employee_id, conversation)
+        else:
+            # 模式B：IT知识题
+            question = await self._get_knowledge_question(db, employee_id)
+
+        if not question:
+            return {
+                "has_question": False,
+                "message": "暂无更多题目，请稍后再试",
+            }
+
+        return {
+            "has_question": True,
+            "question_id": question.id,
+            "type": question.type,
+            "category": question.category,
+            "difficulty": question.difficulty,
+            "question": question.question,
+            "options": question.options,
+        }
+
+    async def _get_diagnostic_question(
+        self,
+        db: AsyncSession,
+        employee_id: str,
+        conversation: Conversation,
+    ) -> Optional[QuizQuestion]:
+        """获取诊断题（模式A）。
+
+        诊断题按问题类别匹配，排除已答过的题目。
+        如果没有匹配的诊断题，降级为IT知识题。
+
+        Args:
+            db: 数据库会话
+            employee_id: 员工ID
+            conversation: 当前会话
+
+        Returns:
+            QuizQuestion 或 None
+        """
+        # 查找已答过的题目ID（避免重复）
+        answered_ids_subquery = (
+            select(QuizAnswer.question_id)
+            .where(
+                QuizAnswer.employee_id == employee_id,
+                QuizAnswer.conversation_id == conversation.id,
+            )
+        )
+
+        # 查找诊断题（按问题类别匹配）
+        stmt = (
+            select(QuizQuestion)
+            .where(
+                QuizQuestion.type == "diagnostic",
+                QuizQuestion.is_active == True,  # noqa: E712
+                QuizQuestion.id.notin_(answered_ids_subquery),
+            )
+            .order_by(func.random())
+            .limit(1)
+        )
+
+        result = await db.execute(stmt)
+        question = result.scalar_one_or_none()
+
+        if question:
+            return question
+
+        # 降级：如果没有匹配的诊断题，使用IT知识题
+        logger.info("无诊断题可用，降级为IT知识题: employee=%s", employee_id)
+        return await self._get_knowledge_question(db, employee_id)
+
+    async def _get_knowledge_question(
+        self,
+        db: AsyncSession,
+        employee_id: str,
+    ) -> Optional[QuizQuestion]:
+        """获取IT知识题（模式B）。
+
+        排除已答过的题目，随机选取。
+
+        Args:
+            db: 数据库会话
+            employee_id: 员工ID
+
+        Returns:
+            QuizQuestion 或 None
+        """
+        # 查找已答过的题目ID（跨会话排除，避免重复）
+        answered_ids_subquery = (
+            select(QuizAnswer.question_id)
+            .where(QuizAnswer.employee_id == employee_id)
+        )
+
+        stmt = (
+            select(QuizQuestion)
+            .where(
+                QuizQuestion.type == "knowledge",
+                QuizQuestion.is_active == True,  # noqa: E712
+                QuizQuestion.id.notin_(answered_ids_subquery),
+            )
+            .order_by(func.random())
+            .limit(1)
+        )
+
+        result = await db.execute(stmt)
+        question = result.scalar_one_or_none()
+
+        if question:
+            return question
+
+        # 如果所有题都答完了，重置（允许重复）
+        logger.info("所有题目已答完，重置题目池: employee=%s", employee_id)
+        stmt_all = (
+            select(QuizQuestion)
+            .where(
+                QuizQuestion.type == "knowledge",
+                QuizQuestion.is_active == True,  # noqa: E712
+            )
+            .order_by(func.random())
+            .limit(1)
+        )
+        result = await db.execute(stmt_all)
+        return result.scalar_one_or_none()
+
+    # ======================================================================
+    # 提交答案
+    # ======================================================================
+
+    async def submit_answer(
+        self,
+        db: AsyncSession,
+        employee_id: str,
+        question_id: str,
+        selected_index: int,
+        conversation: Optional[Conversation] = None,
+    ) -> Dict[str, Any]:
+        """提交答案，返回正误+积分变化+插队效果+下一题。
+
+        处理流程：
+        1. 查询题目，判定正误
+        2. 记录答题（quiz_answers）
+        3. 更新积分账户（employee_points）
+        4. 如果在排队中，更新 queue_priority
+        5. 获取下一道题
+
+        Args:
+            db: 数据库会话
+            employee_id: 员工ID
+            question_id: 题目ID
+            selected_index: 员工选择的答案索引
+            conversation: 当前会话（可选）
+
+        Returns:
+            Dict: {is_correct, correct_index, explanation, points_earned,
+                   total_points, level, queue_priority_changed, next_question}
+        """
+        # 1. 查询题目
+        result = await db.execute(
+            select(QuizQuestion).where(QuizQuestion.id == question_id)
+        )
+        question = result.scalar_one_or_none()
+        if not question:
+            raise AppException(code=1004, message="题目不存在")
+
+        # 2. 判定正误
+        is_correct = selected_index == question.correct_index
+        points_earned = POINTS_PER_CORRECT if is_correct else 0
+
+        # 3. 记录答题
+        answer = QuizAnswer(
+            employee_id=employee_id,
+            conversation_id=conversation.id if conversation else None,
+            question_id=question_id,
+            selected_index=selected_index,
+            is_correct=is_correct,
+            points_earned=points_earned,
+        )
+        db.add(answer)
+
+        # 4. 更新积分账户
+        points_info = await self._update_employee_points(
+            db, employee_id, points_earned, is_correct
+        )
+
+        # 5. 如果在排队中，更新 queue_priority
+        queue_priority_changed = False
+        old_priority = 0
+        new_priority = 0
+
+        if conversation and conversation.status == "queued":
+            old_priority = conversation.queue_priority
+
+            # 计算本次会话的答题总数
+            answered_count = await db.scalar(
+                select(func.count(QuizAnswer.id)).where(
+                    QuizAnswer.employee_id == employee_id,
+                    QuizAnswer.conversation_id == conversation.id,
+                )
+            )
+            answered_count = answered_count or 0
+
+            new_priority = min(answered_count // QUIZ_PER_PRIORITY, MAX_QUEUE_PRIORITY)
+            conversation.queue_priority = new_priority
+            conversation.updated_at = __import__("datetime").datetime.now()
+
+            queue_priority_changed = new_priority > old_priority
+
+            if queue_priority_changed:
+                logger.info(
+                    "答题插队: employee=%s, answered=%d, priority %d→%d",
+                    employee_id, answered_count, old_priority, new_priority
+                )
+
+        await db.commit()
+
+        # 6. 获取下一道题
+        next_question = await self.get_next_question(db, employee_id, conversation)
+
+        # 7. 如果是诊断题且答对，将答案文本附加到会话上下文
+        if (question.type == "diagnostic" and conversation and is_correct
+                and question.options and 0 <= selected_index < len(question.options)):
+            await self._append_to_conversation_context(
+                db, conversation, question.question, question.options[selected_index]
+            )
+
+        return {
+            "is_correct": is_correct,
+            "correct_index": question.correct_index,
+            "explanation": question.explanation,
+            "points_earned": points_earned,
+            "total_points": points_info["total_points"],
+            "level": points_info["level"],
+            "answered_count": points_info["answered_count"],
+            "correct_count": points_info["correct_count"],
+            "queue_priority_changed": queue_priority_changed,
+            "old_priority": old_priority,
+            "new_priority": new_priority,
+            "next_question": next_question,
+        }
+
+    # ======================================================================
+    # 积分管理
+    # ======================================================================
+
+    async def _update_employee_points(
+        self,
+        db: AsyncSession,
+        employee_id: str,
+        points_earned: int,
+        is_correct: bool,
+    ) -> Dict[str, Any]:
+        """更新员工积分账户。
+
+        Args:
+            db: 数据库会话
+            employee_id: 员工ID
+            points_earned: 本次获得积分
+            is_correct: 是否答对
+
+        Returns:
+            Dict: 更新后的积分信息
+        """
+        result = await db.execute(
+            select(EmployeePoints).where(EmployeePoints.employee_id == employee_id)
+        )
+        points = result.scalar_one_or_none()
+
+        if points:
+            # 更新现有记录
+            points.total_points += points_earned
+            points.answered_count += 1
+            if is_correct:
+                points.correct_count += 1
+            points.level = EmployeePoints.calculate_level(points.total_points)
+        else:
+            # 首次答题，创建记录
+            points = EmployeePoints(
+                employee_id=employee_id,
+                total_points=points_earned,
+                answered_count=1,
+                correct_count=1 if is_correct else 0,
+                level=EmployeePoints.calculate_level(points_earned),
+            )
+            db.add(points)
+
+        await db.flush()
+
+        return {
+            "total_points": points.total_points,
+            "level": points.level,
+            "answered_count": points.answered_count,
+            "correct_count": points.correct_count,
+        }
+
+    async def get_employee_points(
+        self, db: AsyncSession, employee_id: str
+    ) -> Dict[str, Any]:
+        """获取员工积分信息。
+
+        Args:
+            db: 数据库会话
+            employee_id: 员工ID
+
+        Returns:
+            Dict: 积分信息
+        """
+        result = await db.execute(
+            select(EmployeePoints).where(EmployeePoints.employee_id == employee_id)
+        )
+        points = result.scalar_one_or_none()
+
+        if points:
+            return {
+                "total_points": points.total_points,
+                "level": points.level,
+                "answered_count": points.answered_count,
+                "correct_count": points.correct_count,
+                "accuracy": round(points.correct_count / max(points.answered_count, 1) * 100, 1),
+            }
+        else:
+            return {
+                "total_points": 0,
+                "level": "IT小白",
+                "answered_count": 0,
+                "correct_count": 0,
+                "accuracy": 0,
+            }
+
+    # ======================================================================
+    # 答题历史
+    # ======================================================================
+
+    async def get_quiz_history(
+        self,
+        db: AsyncSession,
+        employee_id: str,
+        page: int = 1,
+        page_size: int = 20,
+    ) -> Dict[str, Any]:
+        """获取答题历史记录。
+
+        Args:
+            db: 数据库会话
+            employee_id: 员工ID
+            page: 页码
+            page_size: 每页数量
+
+        Returns:
+            Dict: {total, items, points}
+        """
+        # 统计总数
+        total = await db.scalar(
+            select(func.count(QuizAnswer.id)).where(
+                QuizAnswer.employee_id == employee_id
+            )
+        )
+        total = total or 0
+
+        # 分页查询
+        offset = (page - 1) * page_size
+        stmt = (
+            select(QuizAnswer, QuizQuestion)
+            .join(QuizQuestion, QuizAnswer.question_id == QuizQuestion.id)
+            .where(QuizAnswer.employee_id == employee_id)
+            .order_by(QuizAnswer.created_at.desc())
+            .offset(offset)
+            .limit(page_size)
+        )
+
+        result = await db.execute(stmt)
+        rows = result.all()
+
+        items = []
+        for answer, question in rows:
+            items.append({
+                "answer_id": answer.id,
+                "question_text": question.question,
+                "options": question.options,
+                "selected_index": answer.selected_index,
+                "correct_index": question.correct_index,
+                "is_correct": answer.is_correct,
+                "points_earned": answer.points_earned,
+                "category": question.category,
+                "type": question.type,
+                "created_at": answer.created_at.isoformat() if answer.created_at else None,
+            })
+
+        # 积分信息
+        points_info = await self.get_employee_points(db, employee_id)
+
+        return {
+            "total": total,
+            "page": page,
+            "page_size": page_size,
+            "items": items,
+            "points": points_info,
+        }
+
+    # ======================================================================
+    # 诊断题答案附加到会话上下文
+    # ======================================================================
+
+    async def _append_to_conversation_context(
+        self,
+        db: AsyncSession,
+        conversation: Conversation,
+        question_text: str,
+        answer_text: str,
+    ) -> None:
+        """将诊断题答案附加到会话上下文（供坐席接单时参考）。
+
+        这相当于员工在排队期间做了自助信息补充。
+        答案通过 WS 推送给坐席端，坐席接单时能看到。
+
+        Args:
+            db: 数据库会话
+            conversation: 当前会话
+            question_text: 题目文本
+            answer_text: 员工选择的答案文本
+        """
+        try:
+            from app.services.ws_manager import manager as ws_manager
+
+            # 通过WS推送给坐席端
+            ws_data = {
+                "type": "quiz_context_collected",
+                "data": {
+                    "conversation_id": conversation.id,
+                    "employee_id": conversation.employee_id,
+                    "question": question_text,
+                    "answer": answer_text,
+                    "timestamp": __import__("datetime").datetime.now().isoformat(),
+                },
+            }
+
+            # 推送给坐席端（如果有分配的坐席）
+            if conversation.assigned_agent_id:
+                await ws_manager.send_to_agent(conversation.assigned_agent_id, ws_data)
+
+            logger.info(
+                "诊断题答案附加到上下文: conv=%s, Q=%s, A=%s",
+                conversation.id, question_text[:50], answer_text[:50]
+            )
+        except Exception as e:
+            logger.warning("WS推送诊断题答案失败: %s", e)
+
+
+# 单例
+_quiz_service: Optional[QuizService] = None
+
+
+def get_quiz_service() -> QuizService:
+    """获取 QuizService 单例。"""
+    global _quiz_service
+    if _quiz_service is None:
+        _quiz_service = QuizService()
+    return _quiz_service