一、系统概述与设计理念
1.1 工单系统核心价值
微爱帮作为特殊群体通信服务平台，工单系统不仅是问题处理工具，更是情感连接 与信任建立的关键环节。系统设计遵循以下原则：
┌─────────────────────────────────────────────┐
│ 微爱帮工单系统设计理念 │
├─────────────────────────────────────────────┤
│ ❶ 情感优先：识别用户情绪，提供情感支持 │
│ ❷ 高效闭环：快速响应，确保问题彻底解决 │
│ ❸ 隐私保护：敏感信息脱敏，保障用户隐私 │
│ ❹ 智能路由：自动分类，精准分配处理人员 │
│ ❺ 持续改进：数据分析驱动服务优化 │
└─────────────────────────────────────────────┘1.2 系统架构概览
┌─────────────────────────────────────────────────────┐
│ 用户交互层 │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ 微信小程序│ │ 网页端 │ │ 客服端 │ │
│ └──────────┘ └──────────┘ └──────────┘ │
└─────────────────────┬───────────────────────────────┘
┌─────────────────────▼───────────────────────────────┐
│ API网关层 │
│ 负载均衡 / 鉴权 / 限流 / 监控 │
└─────────────────────┬───────────────────────────────┘
┌─────────────────────▼───────────────────────────────┐
│ 业务服务层 │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ 工单核心 │ │ 智能分配 │ │ 消息通知 │ │
│ │ 服务 │ │ 服务 │ │ 服务 │ │
│ └──────────┘ └──────────┘ └──────────┘ │
└─────────────────────┬───────────────────────────────┘
┌─────────────────────▼───────────────────────────────┐
│ 数据处理层 │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ 工单存储 │ │ 知识库 │ │ 分析引擎 │ │
│ │ (MySQL) │ │ (ES) │ │ (Spark) │ │
│ └──────────┘ └──────────┘ └──────────┘ │
└─────────────────────────────────────────────────────┘二、核心功能模块设计
2.1 工单数据结构与状态机
# app/models/ticket.py
from datetime import datetime
from enum import Enum
from typing import Optional, List, Dict, Any
from pydantic import BaseModel, Field
import hashlib
class TicketPriority(Enum):
"""工单优先级"""
LOW = "low" # 低：一般咨询
MEDIUM = "medium" # 中：功能问题
HIGH = "high" # 高：投诉建议
URGENT = "urgent" # 紧急：服务中断
CRITICAL = "critical" # 严重：司法/安全相关
class TicketStatus(Enum):
"""工单状态"""
DRAFT = "draft" # 草稿
SUBMITTED = "submitted" # 已提交
ACKNOWLEDGED = "acknowledged" # 已确认
IN_PROGRESS = "in_progress" # 处理中
PENDING = "pending" # 等待回复
RESOLVED = "resolved" # 已解决
CLOSED = "closed" # 已关闭
REOPENED = "reopened" # 重新打开
class TicketCategory(Enum):
"""工单分类"""
ACCOUNT = "account" # 账户问题
PAYMENT = "payment" # 支付问题
LETTER = "letter" # 信件问题
DELIVERY = "delivery" # 投递问题
CONTENT = "content" # 内容审核
PRIVACY = "privacy" # 隐私问题
COMPLAINT = "complaint" # 投诉建议
OTHER = "other" # 其他
class UserEmotion(Enum):
"""用户情绪分析"""
CALM = "calm" # 平静
ANXIOUS = "anxious" # 焦虑
ANGRY = "angry" # 愤怒
FRUSTRATED = "frustrated" # 沮丧
URGENT = "urgent" # 急切
GRATEFUL = "grateful" # 感激
class TicketBase(BaseModel):
"""工单基础模型"""
user_id: str = Field(..., description="用户ID")
category: TicketCategory = Field(..., description="工单分类")
priority: TicketPriority = Field(default=TicketPriority.MEDIUM)
title: str = Field(..., min_length=5, max_length=200)
description: str = Field(..., min_length=10, max_length=5000)
attachments: List[str] = Field(default_factory=list)
metadata: Dict[str, Any] = Field(default_factory=dict)
class TicketCreate(TicketBase):
"""创建工单请求"""
contact_preference: str = Field(default="in_app", description="联系偏好")
expected_resolution_time: Optional[int] = Field(None, description="期望解决时间(小时)")
class Ticket(TicketBase):
"""工单完整模型"""
ticket_id: str = Field(..., description="工单唯一ID")
status: TicketStatus = Field(default=TicketStatus.SUBMITTED)
emotion_score: Optional[Dict[str, float]] = Field(None, description="情绪分析得分")
assigned_to: Optional[str] = Field(None, description="分配给")
assignee_role: Optional[str] = Field(None, description="处理人角色")
source: str = Field(default="wechat_mini_program", description="来源渠道")
ip_address: Optional[str] = Field(None, description="用户IP")
user_agent: Optional[str] = Field(None, description="用户代理")
# 时间戳
created_at: datetime = Field(default_factory=datetime.now)
updated_at: datetime = Field(default_factory=datetime.now)
acknowledged_at: Optional[datetime] = None
first_response_at: Optional[datetime] = None
resolved_at: Optional[datetime] = None
closed_at: Optional[datetime] = None
# 性能指标
first_response_time: Optional[int] = Field(None, description="首次响应时间(分钟)")
resolution_time: Optional[int] = Field(None, description="解决时间(分钟)")
satisfaction_score: Optional[int] = Field(None, ge=1, le=5, description="满意度评分")
# 关联数据
related_letter_ids: List[str] = Field(default_factory=list)
related_order_ids: List[str] = Field(default_factory=list)
class Config:
orm_mode = True
class TicketUpdate(BaseModel):
"""工单更新模型"""
status: Optional[TicketStatus] = None
priority: Optional[TicketPriority] = None
assigned_to: Optional[str] = None
add_attachments: Optional[List[str]] = None
metadata: Optional[Dict[str, Any]] = None
class TicketInteraction(BaseModel):
"""工单交互记录"""
interaction_id: str
ticket_id: str
user_id: str
user_type: str = Field(..., description="user/customer_service/system")
content: str
content_type: str = Field(default="text", description="text/image/voice/system_action")
attachments: List[str] = Field(default_factory=list)
internal_note: bool = Field(default=False, description="是否为内部备注")
created_at: datetime = Field(default_factory=datetime.now)
metadata: Dict[str, Any] = Field(default_factory=dict)
class TicketEscalation(BaseModel):
"""工单升级记录"""
escalation_id: str
ticket_id: str
from_priority: TicketPriority
to_priority: TicketPriority
reason: str
escalated_by: str
escalated_at: datetime = Field(default_factory=datetime.now)
notes: Optional[str] = None2.2 智能工单处理引擎
# app/services/ticket_engine.py
import asyncio
import re
from typing import Dict, List, Optional, Tuple, Any
from datetime import datetime, timedelta
import jieba
import jieba.analyse
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
import redis
import json
class IntelligentTicketEngine:
"""智能工单处理引擎"""
def __init__(self):
self.redis_client = redis.Redis(
host=config('redis.host'),
port=config('redis.port'),
decode_responses=True
)
# 加载关键词库
self.keyword_patterns = self._load_keyword_patterns()
# 初始化情感分析模型（简化版）
self.sentiment_keywords = {
'positive': ['感谢', '满意', '很好', '帮助', '谢谢', '不错'],
'negative': ['生气', '愤怒', '失望', '投诉', '垃圾', '骗人', '投诉'],
'urgent': ['紧急', '马上', '立刻', '赶紧', '快', '急'],
'legal': ['举报', '法律', '诉讼', '公安局', '司法', '监管']
}
# 工单路由规则
self.routing_rules = self._load_routing_rules()
async def process_new_ticket(self, ticket_data: Dict[str, Any]) -> Dict[str, Any]:
"""处理新工单 - 智能分析与路由"""
# 1. 情感分析
emotion_analysis = await self.analyze_emotion(
ticket_data['title'] + " " + ticket_data['description']
)
# 2. 自动分类
auto_category = await self.auto_categorize(ticket_data)
# 3. 优先级评估
auto_priority = await self.evaluate_priority(ticket_data, emotion_analysis)
# 4. 检测紧急关键词
urgent_keywords = self.detect_urgent_keywords(ticket_data)
# 5. 相似工单搜索
similar_tickets = await self.find_similar_tickets(ticket_data)
# 6. 智能分配处理人
assignee_recommendation = await self.recommend_assignee(
auto_category, auto_priority, emotion_analysis
)
# 7. 生成处理建议
handling_suggestions = await self.generate_handling_suggestions(
ticket_data, emotion_analysis, similar_tickets
)
# 8. 风险评估
risk_assessment = await self.assess_risk(ticket_data, emotion_analysis)
# 9. 构建增强工单
enhanced_ticket = {
**ticket_data,
'auto_analysis': {
'emotion': emotion_analysis,
'category': auto_category.value,
'priority': auto_priority.value,
'urgent_keywords': urgent_keywords,
'similar_ticket_count': len(similar_tickets),
'assignee_recommendation': assignee_recommendation,
'handling_suggestions': handling_suggestions,
'risk_level': risk_assessment['level'],
'risk_reasons': risk_assessment['reasons'],
'estimated_resolution_time': self.estimate_resolution_time(
auto_category, auto_priority, emotion_analysis
)
},
'metadata': {
'processed_by_ai': True,
'ai_processing_time': datetime.now().isoformat(),
'ai_version': '1.2.0'
}
}
# 10. 触发自动化响应
if auto_priority in [TicketPriority.HIGH, TicketPriority.URGENT]:
await self.send_immediate_acknowledgment(ticket_data['user_id'], ticket_data['ticket_id'])
# 11. 记录分析结果
await self.record_analysis_result(ticket_data['ticket_id'], enhanced_ticket['auto_analysis'])
return enhanced_ticket
async def analyze_emotion(self, text: str) -> Dict[str, Any]:
"""分析用户情绪"""
# 使用jieba分词
words = jieba.lcut(text)
# 情感得分计算
emotion_scores = {
'calm': 0.0,
'anxious': 0.0,
'angry': 0.0,
'frustrated': 0.0,
'urgent': 0.0,
'grateful': 0.0
}
# 关键词匹配
for word in words:
if word in self.sentiment_keywords['positive']:
emotion_scores['grateful'] += 0.3
emotion_scores['calm'] += 0.1
elif word in self.sentiment_keywords['negative']:
emotion_scores['angry'] += 0.4
emotion_scores['frustrated'] += 0.3
elif word in self.sentiment_keywords['urgent']:
emotion_scores['urgent'] += 0.5
emotion_scores['anxious'] += 0.2
elif word in self.sentiment_keywords['legal']:
emotion_scores['angry'] += 0.6
emotion_scores['urgent'] += 0.4
# 标点符号分析
exclamation_count = text.count('!') + text.count('！')
question_count = text.count('?') + text.count('？')
if exclamation_count > 3:
emotion_scores['angry'] += 0.2 * min(exclamation_count, 10)
emotion_scores['urgent'] += 0.1 * min(exclamation_count, 10)
# 文本长度分析（长文本可能表示沮丧）
if len(text) > 500:
emotion_scores['frustrated'] += 0.2
# 归一化得分
total = sum(emotion_scores.values())
if total > 0:
for key in emotion_scores:
emotion_scores[key] = round(emotion_scores[key] / total, 2)
# 确定主要情绪
primary_emotion = max(emotion_scores.items(), key=lambda x: x[1])
return {
'scores': emotion_scores,
'primary': {
'emotion': primary_emotion[0],
'score': primary_emotion[1]
},
'indicators': {
'exclamation_count': exclamation_count,
'question_count': question_count,
'text_length': len(text),
'negative_word_count': sum(1 for w in words if w in self.sentiment_keywords['negative'])
}
}
async def auto_categorize(self, ticket_data: Dict[str, Any]) -> TicketCategory:
"""自动分类工单"""
text = f"{ticket_data['title']} {ticket_data['description']}".lower()
# 关键词匹配
category_keywords = {
TicketCategory.ACCOUNT: ['账号', '登录', '密码', '注册', '认证', '实名'],
TicketCategory.PAYMENT: ['支付', '退款', '扣款', '费用', '钱', '支付宝', '微信支付'],
TicketCategory.LETTER: ['信件', '写信', '收信', '信纸', '信封', '内容', '字数'],
TicketCategory.DELIVERY: ['投递', '快递', '邮寄', '物流', '送达', '运输', '时间', '延迟'],
TicketCategory.CONTENT: ['审核', '敏感', '违禁', '屏蔽', '删除', '内容审核'],
TicketCategory.PRIVACY: ['隐私', '信息', '泄露', '安全', '数据', '个人'],
TicketCategory.COMPLAINT: ['投诉', '举报', '不满', '建议', '意见', '差评'],
TicketCategory.OTHER: [] # 默认分类
}
# 计算每个分类的匹配度
category_scores = {}
for category, keywords in category_keywords.items():
score = 0
for keyword in keywords:
if keyword in text:
score += 1
category_scores[category] = score
# 选择最高分分类
best_category = max(category_scores.items(), key=lambda x: x[1])
# 如果最高分是0，使用默认分类
if best_category[1] == 0:
return TicketCategory.OTHER
else:
return best_category[0]
async def evaluate_priority(self, ticket_data: Dict[str, Any], emotion_analysis: Dict[str, Any]) -> TicketPriority:
"""评估工单优先级"""
base_priority = TicketPriority.MEDIUM
# 情感影响
primary_emotion = emotion_analysis['primary']['emotion']
emotion_score = emotion_analysis['primary']['score']
if primary_emotion in ['angry', 'urgent'] and emotion_score > 0.6:
base_priority = TicketPriority.HIGH
# 关键词影响
urgent_keywords = self.detect_urgent_keywords(ticket_data)
if urgent_keywords:
base_priority = TicketPriority.URGENT
# 法律相关关键词
if any(keyword in ticket_data['description'] for keyword in self.sentiment_keywords['legal']):
base_priority = TicketPriority.CRITICAL
# 历史用户价值（简化实现）
user_value = await self.get_user_value_score(ticket_data['user_id'])
if user_value > 80: # 高价值用户
if base_priority.value < TicketPriority.HIGH.value:
base_priority = TicketPriority.HIGH
return base_priority
async def recommend_assignee(self, category: TicketCategory, priority: TicketPriority,
emotion_analysis: Dict[str, Any]) -> Dict[str, Any]:
"""推荐处理人"""
# 从Redis获取在线客服
online_agents = self.redis_client.smembers('customer_service:online')
if not online_agents:
return {
'type': 'system',
'assignee_id': 'system',
'reason': '无在线客服，进入排队队列'
}
# 获取客服技能标签
agent_skills = {}
for agent_id in online_agents:
skills = self.redis_client.hgetall(f'agent:skills:{agent_id}')
agent_skills[agent_id] = skills
# 根据分类和情绪选择客服
category_skill_map = {
TicketCategory.ACCOUNT: 'account_management',
TicketCategory.PAYMENT: 'payment_support',
TicketCategory.LETTER: 'letter_specialist',
TicketCategory.DELIVERY: 'delivery_expert',
TicketCategory.CONTENT: 'content_moderator',
TicketCategory.PRIVACY: 'privacy_specialist',
TicketCategory.COMPLAINT: 'complaint_handler',
TicketCategory.OTHER: 'general_support'
}
required_skill = category_skill_map.get(category, 'general_support')
# 情绪特殊要求
if emotion_analysis['primary']['emotion'] in ['angry', 'frustrated']:
required_skill += '+anger_management'
# 寻找匹配的客服
suitable_agents = []
for agent_id, skills in agent_skills.items():
if required_skill in skills:
# 计算工作负载
workload = int(self.redis_client.get(f'agent:workload:{agent_id}') or 0)
suitable_agents.append({
'agent_id': agent_id,
'skills': skills,
'workload': workload,
'match_score': self.calculate_match_score(skills, required_skill, priority)
})
# 按匹配度排序
if suitable_agents:
suitable_agents.sort(key=lambda x: x['match_score'], reverse=True)
best_agent = suitable_agents[0]
return {
'type': 'agent',
'assignee_id': best_agent['agent_id'],
'match_score': best_agent['match_score'],
'reason': f'技能匹配: {required_skill}, 工作负载: {best_agent["workload"]}'
}
else:
# 没有匹配的客服，选择通用客服
return {
'type': 'agent',
'assignee_id': list(online_agents)[0],
'reason': '无专业技能匹配，分配至通用客服'
}
def detect_urgent_keywords(self, ticket_data: Dict[str, Any]) -> List[str]:
"""检测紧急关键词"""
text = f"{ticket_data['title']} {ticket_data['description']}"
urgent_patterns = [
r'紧急[!！]?',
r'立刻?马上',
r'现在就要',
r'立即处理',
r'救命',
r'出大事了',
r'要死了',
r'活不下去了',
r'自杀',
r'自残',
r'越狱',
r'逃跑'
]
found_keywords = []
for pattern in urgent_patterns:
if re.search(pattern, text):
found_keywords.append(pattern)
return found_keywords
async def generate_handling_suggestions(self, ticket_data: Dict[str, Any],
emotion_analysis: Dict[str, Any],
similar_tickets: List[Dict[str, Any]]) -> List[str]:
"""生成处理建议"""
suggestions = []
# 基于情绪的建议
primary_emotion = emotion_analysis['primary']['emotion']
if primary_emotion == 'angry':
suggestions.append("用户情绪愤怒，建议使用安抚话术，表达理解和歉意")
suggestions.append("优先处理，避免情绪升级")
elif primary_emotion == 'anxious':
suggestions.append("用户表现焦虑，需要提供明确的时间承诺和跟进计划")
elif primary_emotion == 'urgent':
suggestions.append("用户表达急迫需求，建议立即响应并设定明确的解决时间")
# 基于内容的建议
if '监狱' in ticket_data['description'] and '管理' in ticket_data['description']:
suggestions.append("涉及监狱管理问题，需谨慎处理，必要时转交法务部门")
if any(word in ticket_data['description'] for word in ['隐私', '泄露', '安全']):
suggestions.append("涉及隐私安全问题，需启动安全响应流程")
# 基于相似工单的建议
if similar_tickets:
resolved_count = sum(1 for t in similar_tickets if t.get('status') == 'resolved')
if resolved_count > 0:
suggestions.append(f"找到{len(similar_tickets)}个相似工单，其中{resolved_count}个已解决，可参考历史解决方案")
return suggestions
async def assess_risk(self, ticket_data: Dict[str, Any], emotion_analysis: Dict[str, Any]) -> Dict[str, Any]:
"""风险评估"""
risk_score = 0
risk_reasons = []
# 情绪风险
if emotion_analysis['primary']['emotion'] in ['angry', 'frustrated']:
risk_score += 40
risk_reasons.append(f"用户情绪{emotion_analysis['primary']['emotion']}，风险较高")
# 法律关键词风险
legal_keywords = self.sentiment_keywords['legal']
if any(keyword in ticket_data['description'] for keyword in legal_keywords):
risk_score += 60
risk_reasons.append("涉及法律词汇，存在诉讼风险")
# 紧急关键词风险
urgent_keywords = self.detect_urgent_keywords(ticket_data)
if urgent_keywords:
risk_score += 30
risk_reasons.append(f"包含紧急关键词: {', '.join(urgent_keywords[:3])}")
# 历史投诉记录
user_complaint_history = await self.get_user_complaint_history(ticket_data['user_id'])
if user_complaint_history > 3:
risk_score += 20
risk_reasons.append(f"用户历史投诉次数: {user_complaint_history}")
# 确定风险等级
if risk_score >= 80:
risk_level = "critical"
elif risk_score >= 60:
risk_level = "high"
elif risk_score >= 40:
risk_level = "medium"
else:
risk_level = "low"
return {
'score': risk_score,
'level': risk_level,
'reasons': risk_reasons
}三、投诉处理与升级机制
3.1 投诉处理工作流
python
# app/services/complaint_handler.py
class ComplaintHandler:
"""投诉处理专用处理器"""
COMPLAINT_RESOLUTION_FLOW = {
"acknowledgment": {
"time_limit": 30, # 分钟
"actions": ["immediate_response", "emotion_acknowledgment", "apology_if_needed"]
},
"investigation": {
"time_limit": 240, # 分钟
"actions": ["gather_facts", "contact_related_parties", "review_evidence"]
},
"resolution_proposal": {
"time_limit": 60, # 分钟
"actions": ["propose_solution", "seek_user_agreement", "document_agreement"]
},
"implementation": {
"time_limit": 1440, # 24小时
"actions": ["execute_solution", "update_status", "notify_user"]
},
"follow_up": {
"time_limit": 10080, # 7天
"actions": ["satisfaction_survey", "preventive_measures", "case_close"]
}
}
async def handle_complaint(self, ticket: Ticket) -> Dict[str, Any]:
"""处理投诉工单"""
# 1. 启动投诉处理流程
process_id = await self.initiate_complaint_process(ticket)
# 2. 分配专业投诉处理员
handler = await self.assign_complaint_specialist(ticket)
# 3. 发送正式投诉受理通知
await self.send_complaint_acknowledgment(ticket, handler)
# 4. 调查阶段
investigation_result = await self.investigate_complaint(ticket)
# 5. 制定解决方案
resolution_plan = await self.create_resolution_plan(ticket, investigation_result)
# 6. 与用户协商
user_agreement = await self.negotiate_with_user(ticket, resolution_plan)
# 7. 执行解决方案
if user_agreement:
execution_result = await self.execute_resolution(ticket, resolution_plan)
else:
# 进入升级流程
execution_result = await self.escalate_complaint(ticket)
# 8. 跟进与关闭
follow_up_result = await self.follow_up_and_close(ticket, execution_result)
return {
"process_id": process_id,
"handler": handler,
"investigation_result": investigation_result,
"resolution_plan": resolution_plan,
"user_agreement": user_agreement,
"execution_result": execution_result,
"follow_up_result": follow_up_result,
"total_time_minutes": self.calculate_process_time(process_id)
}
async def investigate_complaint(self, ticket: Ticket) -> Dict[str, Any]:
"""投诉调查"""
investigation = {
"ticket_id": ticket.ticket_id,
"investigator": ticket.assigned_to,
"start_time": datetime.now(),
"steps": [],
"evidence": [],
"findings": []
}
# 1. 收集相关数据
related_data = await self.collect_related_data(ticket)
investigation["steps"].append({
"action": "collect_related_data",
"result": f"收集到{len(related_data)}条相关记录"
})
# 2. 分析信件历史
if ticket.related_letter_ids:
letter_analysis = await self.analyze_letter_history(ticket.related_letter_ids)
investigation["findings"].append({
"type": "letter_history",
"data": letter_analysis
})
# 3. 联系相关部门
departments = self.identify_related_departments(ticket)
for dept in departments:
dept_response = await self.contact_department(dept, ticket)
investigation["steps"].append({
"action": f"contact_{dept}",
"result": dept_response.get("status", "unknown")
})
# 4. 验证用户声称
user_claims = self.extract_user_claims(ticket.description)
verified_claims = []
for claim in user_claims:
verification = await self.verify_claim(claim, ticket)
investigation["findings"].append({
"claim": claim,
"verified": verification["verified"],
"evidence": verification.get("evidence")
})
if verification["verified"]:
verified_claims.append(claim)
# 5. 确定责任方
responsible_party = await self.identify_responsible_party(ticket, investigation["findings"])
investigation["end_time"] = datetime.now()
investigation["responsible_party"] = responsible_party
investigation["verified_claims"] = verified_claims
# 保存调查记录
await self.save_investigation_record(investigation)
return investigation
async def create_resolution_plan(self, ticket: Ticket, investigation: Dict[str, Any]) -> Dict[str, Any]:
"""制定解决方案"""
# 根据调查结果制定方案
resolution_types = {
"apology_only": {"score": 10, "actions": ["formal_apology", "explanation"]},
"compensation_small": {"score": 30, "actions": ["apology", "small_compensation"]},
"compensation_medium": {"score": 50, "actions": ["apology", "medium_compensation", "service_credit"]},
"compensation_full": {"score": 70, "actions": ["full_refund", "apology", "service_improvement"]},
"service_recovery": {"score": 90, "actions": ["problem_fix", "compensation", "preventive_measures"]}
}
# 评估投诉严重性
severity_score = self.calculate_complaint_severity(ticket, investigation)
# 选择合适的解决方案
selected_resolution = None
for res_type, res_info in resolution_types.items():
if severity_score >= res_info["score"]:
selected_resolution = res_type
if not selected_resolution:
selected_resolution = "apology_only"
# 生成具体方案
resolution_plan = {
"ticket_id": ticket.ticket_id,
"resolution_type": selected_resolution,
"severity_score": severity_score,
"proposed_actions": resolution_types[selected_resolution]["actions"],
"timeline": self.create_resolution_timeline(selected_resolution),
"compensation_details": self.calculate_compensation(ticket, selected_resolution),
"preventive_measures": self.suggest_preventive_measures(investigation),
"approval_required": severity_score > 50
}
# 如果需要审批，发送给上级
if resolution_plan["approval_required"]:
await self.request_approval(resolution_plan, ticket.assigned_to)
return resolution_plan
async def negotiate_with_user(self, ticket: Ticket, resolution_plan: Dict[str, Any]) -> bool:
"""与用户协商解决方案"""
# 1. 准备协商消息
negotiation_message = self.prepare_negotiation_message(ticket, resolution_plan)
# 2. 发送给用户
message_id = await self.send_negotiation_message(ticket.user_id, negotiation_message)
# 3. 等待用户回复（最长24小时）
user_response = await self.wait_for_user_response(ticket.user_id, message_id, timeout_hours=24)
if not user_response:
# 用户未回复，默认接受
return True
# 4. 分析用户回复
response_analysis = await self.analyze_user_response(user_response)
if response_analysis["agreement"]:
return True
else:
# 用户不接受，记录反对原因
await self.record_user_objections(ticket, response_analysis["objections"])
# 检查是否需要调整方案
if response_analysis["suggested_changes"]:
adjusted_plan = await self.adjust_resolution_plan(
resolution_plan,
response_analysis["suggested_changes"]
)
# 再次协商
return await self.negotiate_with_user(ticket, adjusted_plan)
else:
# 用户完全拒绝3.2 工单升级机制
# app/services/ticket_escalation.py
class TicketEscalationManager:
"""工单升级管理器"""
ESCALATION_LEVELS = {
1: {
"name": "一线客服",
"time_limit": 120, # 2小时
"actions": ["initial_response", "basic_troubleshooting"]
},
2: {
"name": "高级客服",
"time_limit": 240, # 4小时
"actions": ["advanced_troubleshooting", "issue_investigation"]
},
3: {
"name": "专家团队",
"time_limit": 480, # 8小时
"actions": ["root_cause_analysis", "solution_design"]
},
4: {
"name": "管理层",
"time_limit": 1440, # 24小时
"actions": ["strategic_decision", "compensation_approval"]
},
5: {
"name": "执行层",
"time_limit": 2880, # 48小时
"actions": ["executive_intervention", "policy_change"]
}
}
ESCALATION_TRIGGERS = {
"time_based": {
"level1_to_2": 7200, # 2小时无响应
"level2_to_3": 14400, # 4小时未解决
"level3_to_4": 28800, # 8小时未解决
"level4_to_5": 86400 # 24小时未解决
},
"priority_based": {
TicketPriority.URGENT: 3, # 紧急工单直接到3级
TicketPriority.CRITICAL: 4 # 严重工单直接到4级
},
"emotion_based": {
"angry_escalation": 2, # 愤怒用户升一级
"legal_threat": 4 # 法律威胁到4级
},
"issue_based": {
"privacy_breach": 4,
"financial_loss": 3,
"service_outage": 3,
"security_issue": 4
}
}
async def check_escalation_need(self, ticket: Ticket) -> Optional[Dict[str, Any]]:
"""检查是否需要升级"""
escalation_reasons = []
suggested_level = ticket.metadata.get('escalation_level', 1)
# 1. 检查时间触发
time_escalation = await self.check_time_based_escalation(ticket)
if time_escalation:
escalation_reasons.append(time_escalation["reason"])
suggested_level = max(suggested_level, time_escalation["suggested_level"])
# 2. 检查用户情绪触发
emotion_escalation = await self.check_emotion_based_escalation(ticket)
if emotion_escalation:
escalation_reasons.append(emotion_escalation["reason"])
suggested_level = max(suggested_level, emotion_escalation["suggested_level"])
# 3. 检查问题严重性触发
issue_escalation = await self.check_issue_based_escalation(ticket)
if issue_escalation:
escalation_reasons.append(issue_escalation["reason"])
suggested_level = max(suggested_level, issue_escalation["suggested_level"])
# 4. 检查用户价值触发（VIP用户）
user_value_escalation = await self.check_user_value_escalation(ticket)
if user_value_escalation:
escalation_reasons.append(user_value_escalation["reason"])
suggested_level = max(suggested_level, user_value_escalation["suggested_level"])
# 5. 检查重复投诉触发
repeat_escalation = await self.check_repeat_complaint_escalation(ticket)
if repeat_escalation:
escalation_reasons.append(repeat_escalation["reason"])
suggested_level = max(suggested_level, repeat_escalation["suggested_level"])
# 判断是否需要升级
current_level = ticket.metadata.get('escalation_level', 1)
if suggested_level > current_level and escalation_reasons:
return {
"ticket_id": ticket.ticket_id,
"current_level": current_level,
"suggested_level": suggested_level,
"reasons": escalation_reasons,
"escalation_required": True
}
return None
async def escalate_ticket(self, ticket: Ticket, escalation_info: Dict[str, Any]) -> Dict[str, Any]:
"""执行工单升级"""
current_level = escalation_info["current_level"]
target_level = escalation_info["suggested_level"]
# 1. 创建升级记录
escalation_record = {
"escalation_id": f"ESC-{ticket.ticket_id}-{datetime.now().strftime('%Y%m%d%H%M%S')}",
"ticket_id": ticket.ticket_id,
"from_level": current_level,
"to_level": target_level,
"reasons": escalation_info["reasons"],
"escalated_by": "system",
"escalated_at": datetime.now(),
"previous_handler": ticket.assigned_to
}
# 2. 寻找目标级别处理人
target_handler = await self.find_handler_for_level(target_level, ticket)
# 3. 更新工单
ticket.assigned_to = target_handler["agent_id"]
ticket.assignee_role = target_handler["role"]
ticket.metadata["escalation_level"] = target_level
ticket.metadata["escalation_history"] = ticket.metadata.get("escalation_history", []) + [escalation_record]
# 4. 发送升级通知
notifications_sent = await self.send_escalation_notifications(
ticket, escalation_record, target_handler
)
# 5. 应用升级后的SLA
new_sla = self.ESCALATION_LEVELS[target_level]
ticket.metadata["current_sla"] = new_sla
# 6. 记录升级操作
await self.log_escalation_action(escalation_record)
# 7. 更新升级后的处理计划
await self.update_handling_plan_after_escalation(ticket, target_level)
return {
"success": True,
"escalation_id": escalation_record["escalation_id"],
"new_handler": target_handler,
"new_sla": new_sla,
"notifications_sent": notifications_sent,
"timestamp": datetime.now().isoformat()
}
async def find_handler_for_level(self, level: int, ticket: Ticket) -> Dict[str, Any]:
"""寻找适合级别的处理人"""
level_info = self.ESCALATION_LEVELS.get(level, self.ESCALATION_LEVELS[1])
# 从Redis获取该级别可用人员
level_key = f"support_agents:level_{level}"
available_agents = self.redis_client.smembers(level_key)
if not available_agents:
# 没有该级别人员，尝试上一级
if level > 1:
return await self.find_handler_for_level(level - 1, ticket)
else:
# 回到一线客服
return {
"agent_id": "system",
"role": "system",
"level": 1,
"reason": "无可用处理人，系统托管"
}
# 根据工单特点选择最合适的处理人
suitable_agent = await self.select_most_suitable_agent(
list(available_agents), ticket, level
)
return {
"agent_id": suitable_agent["id"],
"role": suitable_agent["role"],
"level": level,
"name": suitable_agent.get("name", "Unknown"),
"specialties": suitable_agent.get("specialties", []),
"reason": suitable_agent.get("selection_reason", "自动分配")
}四、API接口设计
4.1 工单相关API
# app/api/tickets.py
from fastapi import APIRouter, Depends, HTTPException, status
from typing import List, Optional
from app.models.ticket import Ticket, TicketCreate, TicketUpdate, TicketStatus
from app.services.ticket_engine import IntelligentTicketEngine
from app.services.complaint_handler import ComplaintHandler
router = APIRouter(prefix="/api/v1/tickets", tags=["tickets"])
@router.post("/", response_model=Ticket, status_code=status.HTTP_201_CREATED)
async def create_ticket(
ticket_create: TicketCreate,
current_user: dict = Depends(get_current_user),
ticket_engine: IntelligentTicketEngine = Depends(get_ticket_engine)
):
"""
创建新工单
"""
try:
# 1. 基础数据验证
if len(ticket_create.description) < 10:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="问题描述至少需要10个字符"
)
# 2. 检查用户是否有未解决的相似工单
similar_active = await check_similar_active_tickets(
current_user["user_id"],
ticket_create.title,
ticket_create.description
)
if similar_active:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"您已有相似问题正在处理中，工单ID: {similar_active[0]['ticket_id']}"
)
# 3. 生成工单ID
ticket_id = generate_ticket_id(current_user["user_id"])
# 4. 构建工单对象
ticket_data = {
"ticket_id": ticket_id,
"user_id": current_user["user_id"],
**ticket_create.dict(),
"source": "api",
"ip_address": request.client.host,
"user_agent": request.headers.get("user-agent")
}
# 5. 智能分析
enhanced_ticket = await ticket_engine.process_new_ticket(ticket_data)
# 6. 保存到数据库
ticket = await save_ticket_to_db(enhanced_ticket)
# 7. 发送创建通知
await send_ticket_creation_notifications(ticket)
# 8. 记录用户行为
await log_user_action(
current_user["user_id"],
"create_ticket",
{"ticket_id": ticket_id, "category": ticket_create.category.value}
)
return ticket
except HTTPException:
raise
except Exception as e:
logger.error(f"创建工单失败: {str(e)}", exc_info=True)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail="工单创建失败，请稍后重试"
)
@router.get("/{ticket_id}", response_model=Ticket)
async def get_ticket(
ticket_id: str,
current_user: dict = Depends(get_current_user),
check_permission: bool = Depends(check_ticket_permission)
):
"""
获取工单详情
"""
ticket = await get_ticket_from_db(ticket_id)
if not ticket:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail="工单不存在"
)
# 检查权限
if not await check_permission(ticket, current_user):
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="无权访问此工单"
)
# 脱敏处理（如果是客服查看）
if current_user.get("role") == "customer_service":
ticket = sanitize_ticket_for_agent(ticket)
return ticket
@router.post("/{ticket_id}/interactions")
async def add_ticket_interaction(
ticket_id: str,
interaction_data: dict,
current_user: dict = Depends(get_current_user)
):
"""
添加工单交互记录
"""
# 1. 验证工单状态
ticket = await get_ticket_from_db(ticket_id)
if not ticket:
raise HTTPException(status_code=404, detail="工单不存在")
if ticket.status in [TicketStatus.CLOSED, TicketStatus.RESOLVED]:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="工单已关闭，无法添加交互"
)
# 2. 创建交互记录
interaction_id = generate_interaction_id(ticket_id)
interaction = {
"interaction_id": interaction_id,
"ticket_id": ticket_id,
"user_id": current_user["user_id"],
"user_type": current_user.get("role", "user"),
"content": interaction_data.get("content"),
"content_type": interaction_data.get("content_type", "text"),
"attachments": interaction_data.get("attachments", []),
"internal_note": interaction_data.get("internal_note", False),
"created_at": datetime.now()
}
# 3. 保存交互
await save_interaction_to_db(interaction)
# 4. 更新工单状态
if current_user.get("role") == "customer_service":
# 客服回复，更新首次响应时间
if not ticket.first_response_at:
ticket.first_response_at = datetime.now()
ticket.first_response_time = calculate_response_time(
ticket.created_at, ticket.first_response_at
)
# 更新工单状态
if ticket.status == TicketStatus.SUBMITTED:
ticket.status = TicketStatus.IN_PROGRESS
# 5. 发送通知
if not interaction["internal_note"]:
recipient = ticket.user_id if current_user.get("role") == "customer_service" else "customer_service"
await send_interaction_notification(interaction, recipient)
# 6. 触发自动响应（如果是用户回复）
if current_user.get("role") == "user":
await trigger_auto_response(ticket, interaction)
return {"success": True, "interaction_id": interaction_id}
@router.post("/{ticket_id}/escalate")
async def escalate_ticket(
ticket_id: str,
escalation_reason: dict,
current_user: dict = Depends(get_current_user),
escalation_manager: TicketEscalationManager = Depends(get_escalation_manager)
):
"""
手动升级工单
"""
# 权限检查：只有客服或管理员可以手动升级
if current_user.get("role") not in ["customer_service", "admin", "supervisor"]:
raise HTTPException(status_code=403, detail="无权升级工单")
ticket = await get_ticket_from_db(ticket_id)
if not ticket:
raise HTTPException(status_code=404, detail="工单不存在")
# 检查是否需要审批
current_level = ticket.metadata.get("escalation_level", 1)
target_level = escalation_reason.get("target_level", current_level + 1)
if target_level >= 4 and current_user.get("role") != "admin":
# 需要管理员审批
approval_required = True
else:
approval_required = False
if approval_required:
# 创建审批请求
approval_request = await create_escalation_approval_request(
ticket, current_user, escalation_reason, target_level
)
return {
"status": "pending_approval",
"approval_id": approval_request["approval_id"],
"message": "升级需要管理员审批，已提交审批请求"
}
else:
# 直接执行升级
escalation_info = {
"ticket_id": ticket_id,
"current_level": current_level,
"suggested_level": target_level,
"reasons": [escalation_reason.get("reason", "手动升级")]
}
result = await escalation_manager.escalate_ticket(ticket, escalation_info)
return {
"status": "escalated",
"escalation_id": result["escalation_id"],
"new_handler": result["new_handler"],
"new_sla": result["new_sla"]
}
@router.get("/user/{user_id}/history")
async def get_user_ticket_history(
user_id: str,
page: int = 1,
limit: int = 20,
status_filter: Optional[str] = None,
current_user: dict = Depends(get_current_user)
):
"""
获取用户工单历史
"""
# 权限检查：只能查看自己的工单或客服查看用户工单
if current_user["user_id"] != user_id and current_user.get("role") != "customer_service":
raise HTTPException(status_code=403, detail="无权查看该用户工单")
# 构建查询条件
query_filters = {"user_id": user_id}
if status_filter:
query_filters["status"] = status_filter
# 分页查询
tickets, total = await get_tickets_by_filters(
query_filters,
skip=(page - 1) * limit,
limit=limit,
sort_by="-created_at"
)
# 脱敏处理
if current_user.get("role") == "customer_service":
tickets = [sanitize_ticket_for_agent(t) for t in tickets]
return {
"tickets": tickets,
"pagination": {
"page": page,
"limit": limit,
"total": total,
"pages": (total + limit - 1) // limit
}
}五、监控与分析系统
5.1 工单数据监控
# app/services/ticket_analytics.py
class TicketAnalyticsService:
"""工单数据分析服务"""
async def generate_daily_report(self, date: datetime = None) -> Dict[str, Any]:
"""生成日报"""
if date is None:
date = datetime.now()
start_date = date.replace(hour=0, minute=0, second=0, microsecond=0)
end_date = start_date + timedelta(days=1)
# 1. 基础统计
stats = await self.get_daily_stats(start_date, end_date)
# 2. 分类分析
category_analysis = await self.analyze_by_category(start_date, end_date)
# 3. 满意度分析
satisfaction_analysis = await self.analyze_satisfaction(start_date, end_date)
# 4. 响应时间分析
response_time_analysis = await self.analyze_response_times(start_date, end_date)
# 5. 客服绩效
agent_performance = await self.analyze_agent_performance(start_date, end_date)
# 6. 问题趋势
issue_trends = await self.identify_issue_trends(start_date, end_date)
# 7. 投诉分析
complaint_analysis = await self.analyze_complaints(start_date, end_date)
# 8. 生成洞察
insights = await self.generate_insights(
stats, category_analysis, satisfaction_analysis, issue_trends
)
report = {
"report_date": start_date.strftime("%Y-%m-%d"),
"generated_at": datetime.now().isoformat(),
"summary": {
"total_tickets": stats["total"],
"resolved_tickets": stats["resolved"],
"avg_response_time": response_time_analysis["average"],
"avg_resolution_time": response_time_analysis["avg_resolution"],
"satisfaction_rate": satisfaction_analysis["average_score"]
},
"detailed_analysis": {
"by_category": category_analysis,
"by_priority": await self.analyze_by_priority(start_date, end_date),
"by_channel": await self.analyze_by_channel(start_date, end_date),
"by_time": await self.analyze_by_time_of_day(start_date, end_date)
},
"performance_metrics": {
"agents": agent_performance,
"sla_compliance": await self.calculate_sla_compliance(start_date, end_date),
"escalation_rate": await self.calculate_escalation_rate(start_date, end_date)
},
"complaint_analysis": complaint_analysis,
"trends_and_patterns": issue_trends,
"insights_and_recommendations": insights,
"anomalies": await self.detect_anomalies(start_date, end_date)
}
# 保存报告
await self.save_report(report)
return report
async def analyze_complaints(self, start_date: datetime, end_date: datetime) -> Dict[str, Any]:
"""投诉深度分析"""
# 获取投诉工单
complaint_tickets = await self.get_tickets_by_category(
start_date, end_date, "complaint"
)
analysis = {
"total_complaints": len(complaint_tickets),
"resolved_complaints": sum(1 for t in complaint_tickets if t.status == "resolved"),
"open_complaints": sum(1 for t in complaint_tickets if t.status not in ["resolved", "closed"]),
"by_reason": await self.categorize_complaint_reasons(complaint_tickets),
"by_severity": await self.analyze_complaint_severity(complaint_tickets),
"resolution_time": await self.analyze_complaint_resolution_time(complaint_tickets),
"repeat_complaints": await self.identify_repeat_complainers(complaint_tickets),
"compensation_given": await self.analyze_compensation_cases(complaint_tickets),
"root_causes": await self.identify_root_causes(complaint_tickets),
"preventive_actions": await self.suggest_preventive_actions(complaint_tickets)
}
return analysis
async def generate_insights(self, stats: Dict, category_analysis: Dict,
satisfaction_analysis: Dict, issue_trends: Dict) -> List[Dict[str, Any]]:
"""生成业务洞察"""
insights = []
# 1. 问题集中领域洞察
top_category = max(category_analysis.items(), key=lambda x: x[1]["count"])
if top_category[1]["count"] > stats["total"] * 0.3: # 占比超过30%
insights.append({
"type": "concentration_risk",
"severity": "high",
"message": f"{top_category[0]}类问题占比过高 ({top_category[1]['percentage']}%)",
"recommendation": "需要专项优化该领域服务流程",
"impact": "可能影响整体用户满意度"
})
# 2. 满意度下降洞察
if satisfaction_analysis.get("trend") == "decreasing":
insights.append({
"type": "satisfaction_decline",
"severity": "medium",
"message": "用户满意度呈下降趋势",
"recommendation": "深入分析最近解决的工单，找出满意度下降原因",
"impact": "长期可能影响用户留存"
})
# 3. 响应时间洞察
if stats.get("avg_response_time") > 60: # 超过1小时
insights.append({
"type": "slow_response",
"severity": "high",
"message": f"平均响应时间较长 ({stats['avg_response_time']}分钟)",
"recommendation": "优化客服排班或引入自动响应机制",
"impact": "影响用户即时服务体验"
})
# 4. 投诉趋势洞察
if issue_trends.get("complaint_trend") == "increasing":
insights.append({
"type": "complaint_increase",
"severity": "critical",
"message": "投诉量持续上升，需要立即关注",
"recommendation": "召开专项会议分析投诉原因，制定改进措施",
"impact": "可能引发大规模用户流失或品牌危机"
})
# 5. 重复问题洞察
recurring_issues = issue_trends.get("recurring_issues", [])
if recurring_issues:
insights.append({
"type": "recurring_problems",
"severity": "medium",
"message": f"发现{len(recurring_issues)}个重复出现的问题",
"recommendation": "建立问题知识库，优化解决方案复用",
"data": recurring_issues[:5] # 前5个重复问题
})
return insights六、部署与运维
6.1 环境配置
# docker-compose.ticket.yml
version: '3.8'
services:
ticket-api:
build:
context: .
dockerfile: Dockerfile.ticket
image: weiaibang/ticket-api:latest
container_name: ticket-api
environment:
- NODE_ENV=production
- REDIS_HOST=redis-ticket
- MYSQL_HOST=mysql-ticket
- ELASTICSEARCH_HOST=elasticsearch
- KAFKA_HOST=kafka
ports:
- "8001:8000"
depends_on:
- redis-ticket
- mysql-ticket
networks:
- ticket-network
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
interval: 30s
timeout: 10s
retries: 3
ticket-worker:
build:
context: .
dockerfile: Dockerfile.ticket-worker
image: weiaibang/ticket-worker:latest
container_name: ticket-worker
environment:
- NODE_ENV=production
- REDIS_HOST=redis-ticket
- MYSQL_HOST=mysql-ticket
depends_on:
- redis-ticket
- mysql-ticket
networks:
- ticket-network
deploy:
replicas: 3
redis-ticket:
image: redis:6-alpine
container_name: redis-ticket
ports:
- "6379:6379"
volumes:
- redis-ticket-data:/data
command: redis-server --appendonly yes
networks:
- ticket-network
mysql-ticket:
image: mysql:8.0
container_name: mysql-ticket
environment:
MYSQL_ROOT_PASSWORD: ${MYSQL_ROOT_PASSWORD}
MYSQL_DATABASE: ticket_system
MYSQL_USER: ticket_user
MYSQL_PASSWORD: ${MYSQL_PASSWORD}
ports:
- "3307:3306"
volumes:
- mysql-ticket-data:/var/lib/mysql
- ./sql/init.sql:/docker-entrypoint-initdb.d/init.sql
networks:
- ticket-network
ticket-monitor:
image: prom/prometheus:latest
container_name: ticket-monitor
volumes:
- ./prometheus/ticket.yml:/etc/prometheus/prometheus.yml
- prometheus-ticket-data:/prometheus
ports:
- "9091:9090"
networks:
- ticket-network
networks:
ticket-network:
driver: bridge
volumes:
redis-ticket-data:
mysql-ticket-data:
prometheus-ticket-data:6.2 性能监控配置
# prometheus/ticket.yml
global:
scrape_interval: 15s
evaluation_interval: 15s
rule_files:
- "ticket_alerts.yml"
scrape_configs:
- job_name: 'ticket-api'
static_configs:
- targets: ['ticket-api:8000']
metrics_path: '/metrics'
- job_name: 'ticket-redis'
static_configs:
- targets: ['redis-ticket:6379']
- job_name: 'ticket-mysql'
static_configs:
- targets: ['mysql-ticket:3306']
- job_name: 'ticket-worker'
static_configs:
- targets: ['ticket-worker:8000']
# 告警规则
# ticket_alerts.yml
groups:
- name: ticket_system_alerts
rules:
- alert: HighTicketVolume
expr: rate(tickets_created_total[5m]) > 10
for: 5m
labels:
severity: warning
annotations:
summary: "工单创建量过高"
description: "过去5分钟工单创建速率超过10/分钟"
- alert: SlowResponseTime
expr: histogram_quantile(0.95, rate(ticket_response_time_seconds_bucket[5m])) > 300
for: 10m
labels:
severity: critical
annotations:
summary: "工单响应时间过长"
description: "95%的工单响应时间超过5分钟"
- alert: HighComplaintRate
expr: rate(tickets_created_total{category="complaint"}[1h]) / rate(tickets_created_total[1h]) > 0.2
for: 1h
labels:
severity: critical
annotations:
summary: "投诉率过高"
description: "投诉工单占比超过20%"
- alert: LowSatisfactionScore
expr: avg_over_time(ticket_satisfaction_score[1h]) < 3
for: 2h
labels:
severity: warning
annotations:
summary: "用户满意度过低"
description: "过去1小时平均满意度低于3分"七、总结
微爱帮工单系统通过以下技术创新，实现了高效、智能的投诉处理：
7.1 核心优势
-
情感智能分析：识别用户情绪状态，提供个性化响应策略
-
智能路由分配：基于技能、工作负载和工单特性自动分配
-
动态升级机制：多维度触发条件，确保重要问题及时升级
-
全面数据监控：实时分析工单数据，提供业务洞察
-
安全隐私保护：敏感信息脱敏，符合等保3.0要求
7.2 关键性能指标
-
平均响应时间：< 30分钟（紧急工单<15分钟）
-
首次解决率：> 85%
-
用户满意度：> 4.5/5.0
-
系统可用性：> 99.95%
-
投诉解决率：> 95%
7.3 特殊场景优化
针对监狱通信场景的特殊性，系统额外提供：
-
司法相关工单：特殊处理流程，法律顾问介入
-
情绪危机干预：识别高风险情绪，启动心理支持
-
隐私保护强化：敏感信息多重加密，访问严格审计
-
跨部门协同：与监狱管理部门协同处理机制
该系统不仅解决了微爱帮平台的投诉处理需求，更为特殊群体提供了有温度、有效率的问题解决通道，是平台服务质量的重要保障。