# riskmonitor-backend **Repository Path**: personalJavaCode/riskmonitor-backend ## Basic Information - **Project Name**: riskmonitor-backend - **Description**: 数字人民币风险监控与评估后端服务 - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-07-28 - **Last Updated**: 2025-08-10 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # riskmonitor-backend ## 介绍 数字人民币风险监控与评估后端服务 ## 软件架构 ### 一、大模型能力集成架构 graph TD A[交易数据流] --> B[数据预处理] B --> C[规则引擎初筛] C -->|高风险| D[立即拦截] C -->|待分析| E[特征工程] E --> F[大模型分析] F --> G[风险评分] G --> H[决策引擎] H -->|低风险| I[放行] H -->|中风险| J[二次验证] H -->|高风险| K[拦截] I & J & K --> L[用户画像更新] L --> M[模型在线学习] ### 二、核心AI模型设计 1. 用户画像模型 (User Profiling Model) import pandas as pd from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler class UserProfiler: def __init__(self, n_clusters=5): self.model = KMeans(n_clusters=n_clusters, random_state=42) self.scaler = StandardScaler() def fit(self, transactions_df): """训练用户分群模型""" # 特征工程：用户行为特征提取 user_features = self._extract_features(transactions_df) # 数据标准化 scaled_features = self.scaler.fit_transform(user_features) # 训练K-Means模型 self.model.fit(scaled_features) user_features['cluster'] = self.model.labels_ return user_features def predict(self, new_transaction): """预测新交易所属的用户分群""" user_id = new_transaction['user_id'] # 获取该用户历史特征 (简化处理) user_history = self._get_user_history(user_id) # 合并新交易更新特征 updated_features = self._update_features(user_history, new_transaction) # 标准化特征并预测 scaled_features = self.scaler.transform([updated_features]) return self.model.predict(scaled_features)[0] def _extract_features(self, df): """从交易数据中提取用户特征""" features = df.groupby('user_id').agg( avg_amount=('amount', 'mean'), freq_day=('is_day', 'sum'), freq_night=('is_night', 'sum'), device_count=('device_id', 'nunique'), location_variance=('location', 'var') ).reset_index() return features.fillna(0) 2. 交易风险评分模型 (Risk Scoring Model) import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, LSTM, Embedding class RiskModel(tf.keras.Model): def __init__(self, num_features, num_time_steps=10): super().__init__() self.lstm = LSTM(64, input_shape=(num_time_steps, num_features)) self.dense1 = Dense(32, activation='relu') self.dense2 = Dense(1, activation='sigmoid') def call(self, inputs): x = self.lstm(inputs) x = self.dense1(x) return self.dense2(x) #### 模型训练流程 def train_risk_model(X_train, y_train): model = RiskModel(num_features=X_train.shape[2]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # 训练模型 history = model.fit( X_train, y_train, epochs=50, batch_size=64, validation_split=0.2, verbose=1 ) return model ### 三、实时风险分析流程 class RealTimeRiskAnalyzer: def __init__(self, rule_engine, ai_model): self.rule_engine = rule_engine self.ai_model = ai_model self.user_profiles = {} # 实时用户画像缓存 def process_transaction(self, transaction): # 规则引擎初筛 rule_result = self.rule_engine.apply(transaction) if rule_result == 'BLOCK': return {'action': 'BLOCK', 'reason': '规则命中'} # 获取/更新用户画像 user_id = transaction['user_id'] if user_id not in self.user_profiles: self.user_profiles[user_id] = self._init_profile(user_id) updated_profile = self._update_profile(user_id, transaction) # 提取风险特征 features = self.extract_features(transaction, updated_profile) # 大模型风险评估 risk_score = self.ai_model.predict([features])[0] # 决策逻辑 if risk_score < 0.3: action = 'PASS' elif risk_score < 0.7: action = 'VERIFY' else: action = 'BLOCK' return { 'action': action, 'risk_score': float(risk_score), 'features': features } ### 四、混合决策引擎实现 class HybridDecisionEngine: def __init__(self, rule_set, ai_model, threshold=0.65): self.rule_set = rule_set self.ai_model = ai_model self.threshold = threshold def decide(self, transaction): # 规则优先级处理 for rule in self.rule_set.get_ordered_rules(): if rule.matches(transaction): return rule.action # AI模型评估 features = extract_features(transaction) risk_score = self.ai_model.predict(features) # 模型置信度检查 if risk_score > self.threshold: return 'BLOCK' elif risk_score > (self.threshold - 0.2): return 'VERIFY' else: return 'PASS' ### 五、持续学习机制 class OnlineLearner: def __init__(self, base_model, buffer_size=1000): self.base_model = base_model self.buffer = [] self.buffer_size = buffer_size def add_feedback(self, transaction, actual_risk): """添加人工审核反馈""" # 特征提取 features = extract_features(transaction) self.buffer.append((features, actual_risk)) # 当缓冲区满时触发增量学习 if len(self.buffer) >= self.buffer_size: self.retrain() def retrain(self): """增量训练模型""" X = [item[0] for item in self.buffer] y = [item[1] for item in self.buffer] # 增量训练 self.base_model.partial_fit(X, y) # 清空缓冲区 self.buffer.clear() ### 六、部署架构建议 ![alt text](image.png) ### 七、关键技术栈 ​数据处理​： Apache Flink (实时流处理) Redis (特征缓存) ​模型服务​： TensorFlow Serving ONNX Runtime (高性能推理) ​特征存储​： Apache Cassandra (时序特征) Neo4j (交易图谱) ​监控体系​： Prometheus (指标监控) ELK (日志分析) ### 八、实施路线图 ​阶段一：基础能力建设​ 搭建实时特征计算平台 训练用户画像聚类模型 部署规则引擎+AI模型的混合决策服务 ​阶段二：模型迭代优化​ 引入图神经网络(GNN)分析交易网络 增加NLP模块分析交易备注 实现模型在线学习机制 ​阶段三：全流程智能化​ 自动生成可解释性报告 基于强化学习的策略优化 智能风险事件溯源 ## AI大模型技术选型 ![alt text](局部截取_20250729_212558.png) ## 部署架构 ![alt text](局部截取_20250729_213218.png) ## 安装教程 软件安装 pip install scikit-learn -i https://pypi.tuna.tsinghua.edu.cn/simple pip install numpy -i https://pypi.tuna.tsinghua.edu.cn/simple pip install pandas -i https://pypi.tuna.tsinghua.edu.cn/simple pip install joblib -i https://pypi.tuna.tsinghua.edu.cn/simple pip install geopy -i https://pypi.tuna.tsinghua.edu.cn/simple #### 使用说明 1. xxxx 2. xxxx 3. xxxx #### 参与贡献 1. Fork 本仓库 2. 新建 Feat_xxx 分支 3. 提交代码 4. 新建 Pull Request #### 特技 1. 使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md 2. Gitee 官方博客 [blog.gitee.com](https://blog.gitee.com) 3. 你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目 4. [GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目 5. Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help) 6. Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)