# 社会工作人员学习手册APP设计方案
## 系统架构设计
```
移动端(Flutter/React Native)
|
REST API / gRPC
|
Go核心服务(Gin/Echo) ←─── Python AI服务(FastAPI)
| (学习路径规划/智能问答)
|
Rust高性能模块(数据处理/安全)
|
PostgreSQL(知识库+用户数据)
|
Redis(缓存+实时协作)
|
MinIO(学习资源存储)
```
## 技术分工与优势
**Go负责**:
1. 核心业务逻辑(用户管理、学习进度跟踪)
2. 高并发API服务
3. 实时协作系统
4. 微服务协调
**Python负责**:
1. 智能学习路径规划
2. 自然语言处理(政策解读、案例解析)
3. 知识图谱构建
4. 数据分析与可视化
**Rust负责**:
1. 敏感数据处理(加密/脱敏)
2. 大规模数据导入导出
3. 高性能搜索与索引
4. 跨平台本地模块(Windows/macOS/Linux)
## 核心功能模块
### 1. 智能知识库(Rust + PostgreSQL)
```rust
// 社会工作知识结构
struct SocialWorkKnowledge {
id: Uuid,
title: String,
category: String, // 儿童福利/社区矫正/社会救助
tags: Vec
content: String,
references: Vec
case_studies: Vec
}
struct CaseStudy {
scenario: String,
challenge: String,
solution: String,
outcome: String,
}
```
### 2. 个性化学习路径规划(Python)
```python
# 基于用户画像的学习路径生成
def generate_learning_path(user_profile):
"""生成个性化学习路径"""
# 1. 评估用户能力
skill_gap = assess_skill_gap(user_profile)
# 2. 匹配学习资源
recommendations = recommend_resources(
user_profile.role,
skill_gap,
user_profile.learning_style
)
# 3. 生成学习计划
schedule = optimize_schedule(
recommendations,
user_profile.available_time
)
return {
"skill_gap_analysis": skill_gap,
"recommended_modules": recommendations,
"learning_schedule": schedule
}
# 能力评估算法
def assess_skill_gap(user_profile):
"""评估社会工作技能差距"""
# 使用预训练模型分析用户历史数据
from transformers import pipeline
classifier = pipeline("text-classification", model="social-work-skill-model")
# 分析案例处理记录
skill_scores = {}
for case in user_profile.case_records:
analysis = classifier(case.notes)
for skill in analysis:
skill_scores[skill['label']] = skill_scores.get(skill['label'], 0) + skill['score']
# 标准化分数
return normalize_scores(skill_scores)
```
### 3. 实时案例协作系统(Go)
```go
// 实时协作服务
func (s *CollaborationService) HandleCollaboration(stream pb.Collaboration_JoinSessionServer) error {
// 加入协作会话
req, err := stream.Recv()
sessionID := req.SessionId
userID := req.UserId
// 注册用户到会话
session := s.getSession(sessionID)
session.AddParticipant(userID, stream)
// 处理实时消息
for {
msg, err := stream.Recv()
if err != nil {
session.RemoveParticipant(userID)
return err
}
// 广播消息给所有参与者
session.Broadcast(msg)
}
}
// 案例讨论数据结构
type CaseDiscussion struct {
CaseID string
Messages []DiscussionMessage
Annotations []CaseAnnotation
Decisions []GroupDecision
}
type DiscussionMessage struct {
UserID string
Timestamp time.Time
Content string
Type string // 问题/建议/决策
}
```
### 4. 政策智能解读(Python + Rust)
```rust
// 政策文件解析器(Rust)
pub struct PolicyDocument {
title: String,
issuing_authority: String,
effective_date: String,
key_provisions: Vec
related_documents: Vec
}
impl PolicyDocument {
pub fn parse(file_path: &str) -> Result
// 使用Python NLP服务提取关键条款
let python_service = PyServiceClient::new();
let analysis = python_service.analyze_policy(file_path)?;
// 构建结构化数据
Ok(PolicyDocument {
title: analysis.title,
issuing_authority: analysis.issuing_authority,
effective_date: analysis.effective_date,
key_provisions: analysis.provisions,
related_documents: analysis.related_docs,
})
}
}
```
## 数据库设计
```sql
-- 知识库表
CREATE TABLE knowledge_base (
id UUID PRIMARY KEY,
title TEXT NOT NULL,
category VARCHAR(50) NOT NULL,
tags TEXT[],
content TEXT NOT NULL,
references TEXT[],
last_updated TIMESTAMPTZ DEFAULT NOW()
);
-- 用户学习进度表
CREATE TABLE learning_progress (
user_id UUID NOT NULL,
module_id UUID REFERENCES knowledge_base(id),
status VARCHAR(20) CHECK (status IN ('not_started', 'in_progress', 'completed')),
progress INTEGER DEFAULT 0,
last_accessed TIMESTAMPTZ,
quiz_score INTEGER,
PRIMARY KEY (user_id, module_id)
);
-- 案例协作表
CREATE TABLE case_collaborations (
session_id UUID PRIMARY KEY,
case_data JSONB NOT NULL,
participants UUID[] NOT NULL,
created_at TIMESTAMPTZ DEFAULT NOW(),
ended_at TIMESTAMPTZ
);
```
## 前端界面设计
```dart
// Flutter 学习界面
class LearningPathScreen extends StatelessWidget {
@override
Widget build(BuildContext context) {
return Scaffold(
appBar: AppBar(title: Text('我的学习路径')),
body: FutureBuilder
future: fetchLearningPath(),
builder: (context, snapshot) {
if (snapshot.connectionState == ConnectionState.waiting) {
return Center(child: CircularProgressIndicator());
}
if (snapshot.hasError) {
return Center(child: Text('加载失败'));
}
final path = snapshot.data!;
return Column(
children: [
// 技能差距分析
SkillGapRadarChart(skills: path.skillGapAnalysis),
// 学习模块列表
Expanded(
child: ListView.builder(
itemCount: path.recommendedModules.length,
itemBuilder: (context, index) {
return LearningModuleCard(
module: path.recommendedModules[index],
onTap: () => _openModule(path.recommendedModules[index]),
);
},
),
),
// 学习计划时间表
ScheduleCalendar(schedule: path.learningSchedule),
],
);
},
),
);
}
}
// 案例协作界面
class CaseCollaborationScreen extends StatefulWidget {
final String caseId;
CaseCollaborationScreen({required this.caseId});
@override
_CaseCollaborationScreenState createState() => _CaseCollaborationScreenState();
}
class _CaseCollaborationScreenState extends State
final CollaborationClient _client = CollaborationClient();
List
@override
void initState() {
super.initState();
_client.joinSession(widget.caseId);
_client.messages.listen((msg) {
setState(() => messages.add(msg));
});
}
@override
Widget build(BuildContext context) {
return Scaffold(
appBar: AppBar(title: Text('案例协作')),
body: Column(
children: [
// 案例详情
Expanded(
child: CaseDetailsView(caseId: widget.caseId),
),
// 协作讨论区
CollaborationPanel(
messages: messages,
onSend: (content) {
_client.sendMessage(content);
},
onAnnotate: (annotation) {
_client.addAnnotation(annotation);
},
),
],
),
);
}
}
```
## 关键技术实现
### 1. 三语言协同工作流
```mermaid
sequenceDiagram
participant Flutter as 移动端
participant Go as Go服务
participant Python as Python AI
participant Rust as Rust引擎
Flutter->>Go: 请求学习路径规划
Go->>Python: 用户数据分析请求
Python->>Python: 能力评估+资源匹配
Python->>Go: 返回学习路径
Go->>Rust: 请求加密存储进度
Rust->>Go: 返回存储结果
Go->>Flutter: 返回学习路径
```
### 2. 知识图谱构建(Python)
```python
# 社会工作知识图谱构建
def build_knowledge_graph():
"""构建社会工作知识图谱"""
# 1. 数据收集
policies = load_policy_documents()
cases = load_case_studies()
guidelines = load_guidelines()
# 2. 实体识别
ner_model = load_ner_model("social-work-ner")
entities = {}
for doc in policies + cases + guidelines:
entities.update(extract_entities(ner_model, doc.text))
# 3. 关系抽取
relation_model = load_relation_model("social-work-relations")
relations = []
for doc in policies + cases + guidelines:
relations.extend(extract_relations(relation_model, doc.text))
# 4. 构建图谱
graph = KnowledgeGraph()
for entity in entities.values():
graph.add_node(entity)
for rel in relations:
graph.add_edge(rel.source, rel.target, rel.type)
return graph
# 图谱查询
def query_graph(graph, question):
"""自然语言查询知识图谱"""
# 解析问题
parsed = parse_question(question)
# 图谱查询
if parsed.query_type == "concept_relation":
return graph.find_relations(parsed.concept, parsed.relation)
elif parsed.query_type == "case_example":
return graph.find_case_studies(parsed.scenario)
```
### 3. 敏感数据处理(Rust)
```rust
// 案主数据加密处理
pub struct ClientData {
id: Uuid,
name: String,
contact: String,
case_details: String,
}
impl ClientData {
pub fn encrypt(&self, key: &[u8]) -> Result
let name_cipher = sm4::encrypt(&self.name.as_bytes(), key)?;
let contact_cipher = sm4::encrypt(&self.contact.as_bytes(), key)?;
let details_cipher = sm4::encrypt(&self.case_details.as_bytes(), key)?;
Ok(EncryptedData {
id: self.id,
name: name_cipher,
contact: contact_cipher,
case_details: details_cipher,
})
}
pub fn anonymize(&self) -> AnonymizedData {
AnonymizedData {
age_group: self.calculate_age_group(),
case_category: self.classify_case(),
location: self.generalize_location(3), // 三级泛化
outcome: self.outcome.clone(),
}
}
}
```
### 4. 实时协作白板(Go + WebRTC)
```go
// WebRTC白板协作
type WhiteboardServer struct {
rooms map[string]*WhiteboardRoom
}
func (ws *WhiteboardServer) HandleWhiteboard(stream pb.Whiteboard_StreamActionsServer) error {
// 加入白板房间
req, err := stream.Recv()
roomID := req.RoomId
userID := req.UserId
room := ws.getOrCreateRoom(roomID)
client := NewWhiteboardClient(userID, stream)
room.AddClient(client)
// 处理白板动作
for {
action, err := stream.Recv()
if err != nil {
room.RemoveClient(userID)
return err
}
// 广播动作给房间内其他用户
room.Broadcast(action, userID)
}
}
// 白板数据结构
type WhiteboardAction struct {
Type string // draw/erase/move
Tool string // pen/highlight/text
Color string
Points []Point
Text string
UserID string
Timestamp time.Time
}
```
## 特色功能
### 1. 智能场景模拟器
```python
# 社工场景模拟
class ScenarioSimulator:
def __init__(self, knowledge_graph):
self.graph = knowledge_graph
def generate_scenario(self, category):
"""生成随机社工场景"""
# 从知识图谱中抽取要素
client_profile = self.graph.random_node("ClientProfile")
problem = self.graph.random_related_node(client_profile, "has_problem")
constraints = self.graph.random_node("Constraint", limit=2)
return {
"client": client_profile.attributes,
"presenting_problem": problem.attributes,
"constraints": [c.attributes for c in constraints],
"resources_available": self.graph.random_node("CommunityResource").attributes
}
def evaluate_response(self, scenario, user_actions):
"""评估用户响应"""
# 使用AI模型评估响应质量
evaluation_model = load_evaluation_model("scenario-response")
return evaluation_model.predict({
"scenario": scenario,
"actions": user_actions
})
```
### 2. 政策更新追踪
```rust
// 政策更新监测
pub struct PolicyTracker {
sources: Vec
}
impl PolicyTracker {
pub fn new(sources: Vec
PolicyTracker { sources }
}
pub fn monitor(&self) -> Receiver
let (tx, rx) = channel();
for source in &self.sources {
let source_url = source.clone();
let tx_clone = tx.clone();
thread::spawn(move || {
loop {
if let Ok(updates) = check_source(&source_url) {
for update in updates {
tx_clone.send(update).unwrap();
}
}
thread::sleep(Duration::from_secs(3600)); // 每小时检查一次
}
});
}
rx
}
}
fn check_source(url: &str) -> Result
// 爬取政策网站并检测更新
// ...
}
```
### 3. 督导反馈系统
```go
// 督导反馈处理
type SupervisionFeedback struct {
SupervisorID string
WorkerID string
SessionDate time.Time
Strengths []string
AreasForImprovement []string
ActionPlan string
Resources []string
}
func (s *SupervisionService) GenerateActionPlan(feedback SupervisionFeedback) (ActionPlan, error) {
// 使用AI生成个性化改进计划
aiResponse, err := aiClient.GenerateActionPlan(feedback)
if err != nil {
return ActionPlan{}, err
}
// 匹配学习资源
resources := matchResources(aiResponse.SkillsNeeded)
return ActionPlan{
WorkerID: feedback.WorkerID,
SupervisorID: feedback.SupervisorID,
Goals: aiResponse.Goals,
Timeline: aiResponse.Timeline,
Resources: resources,
Checkpoints: generateCheckpoints(aiResponse.Timeline),
}, nil
}
```
## 部署方案
1. **核心服务**:
- Go服务编译为Docker容器
- Kubernetes集群部署,自动扩缩容
2. **AI服务**:
- Python服务使用FastAPI部署
- Triton Inference Server托管NLP模型
3. **本地模块**:
- Rust编译为跨平台本地库
- Windows/macOS/Linux兼容版本
4. **数据存储**:
- PostgreSQL分片集群
- Redis集群管理实时状态
- MinIO存储学习资源文件
5. **安全合规**:
- 通过等保三级认证
- 数据本地化存储选项
- 国密算法加密敏感数据
## 开发计划
| 阶段 | 时间 | 重点任务 |
|------|------|----------|
| 核心架构 | 1.5个月 | 三语言通信框架、知识库构建、用户系统 |
| 学习系统 | 2个月 | 学习路径算法、知识图谱、测试评估 |
| 协作功能 | 1个月 | 实时协作、案例模拟、督导系统 |
| 安全加固 | 0.5个月 | 数据加密、权限控制、合规审计 |
| 测试部署 | 1个月 | 社工机构试点、用户反馈迭代 |
本方案结合了Go的高并发服务能力、Python的AI优势以及Rust的安全高性能特性,为社工人员提供全方位的学习支持。系统不仅包含知识学习和技能训练,还提供案例协作和督导支持功能,帮助社工在实际工作中不断提升专业能力。