【Flink银行反欺诈系统设计方案】2.风控规则表设计与Flink CEP结合

Flink CEP与风控规则表结合的银行反欺诈系统

1. 实现思路

规则加载：

使用Flink的JDBC Source定期从risk_rules表中加载规则。

将规则广播到所有Flink任务中。

动态模式构建：

根据规则表中的条件动态构建Flink CEP的模式。

将交易数据流与规则广播流结合，实现动态规则匹配。

规则匹配：

使用Flink CEP对交易数据进行模式匹配。

如果匹配成功，生成风控结果并输出。

2. 表设计

2.1 风控规则表（risk_rules）

字段名类型说明
rule_id BIGINT 规则ID（主键）
rule_name VARCHAR 规则名称
rule_condition VARCHAR 规则条件（如：amount > 10000）
rule_action VARCHAR 规则动作（如：告警、拦截）
priority INT 规则优先级
is_active BOOLEAN 是否启用
create_time TIMESTAMP 创建时间
update_time TIMESTAMP 更新时间

2.2 交易数据表（transaction_data）

字段名类型说明
transaction_id VARCHAR 交易ID（主键）
user_id VARCHAR 用户ID
amount DECIMAL 交易金额
timestamp TIMESTAMP 交易时间

3. 代码实现

3.1 定义POJO

java
复制
// 交易数据POJO

public class Transaction {private String transactionId;private String userId;private Double amount;private Long timestamp;// getters and setters
}// 风控规则POJO
public class RiskRule {private Long ruleId;private String ruleName;private String ruleCondition; // 规则条件（如：amount > 10000）private String ruleAction;    // 规则动作（如：告警、拦截）private Integer priority;     // 规则优先级private Boolean isActive;     // 是否启用// getters and setters
}// 风控结果POJO
public class RiskResult {private String userId;private List<String> transactionIds;private String riskLevel;private String actionTaken;private Long createTime;// getters and setters
}
## 3.2 规则加载与动态模式构建
java
```c
public class FraudDetectionCEPWithRules {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();// 交易数据流DataStream<Transaction> transactionStream = env.addSource(transactionSource).assignTimestampsAndWatermarks(WatermarkStrategy.<Transaction>forBoundedOutOfOrderness(Duration.ofSeconds(5)).withTimestampAssigner((event, timestamp) -> event.getTimestamp()));// 规则数据流（从JDBC加载）DataStream<RiskRule> ruleStream = env.addSource(JdbcSource.buildJdbcSource().setQuery("SELECT * FROM risk_rules WHERE is_active = true").setRowTypeInfo(RiskRule.getTypeInfo()));// 广播规则流BroadcastStream<RiskRule> broadcastRuleStream = ruleStream.broadcast(RuleDescriptor.of());// 连接交易数据流和规则广播流DataStream<RiskResult> riskResultStream = transactionStream.connect(broadcastRuleStream).process(new DynamicPatternProcessFunction());// 输出结果riskResultStream.addSink(new AlertSink());env.execute("Fraud Detection with Flink CEP and Dynamic Rules");}
}

3.3 动态模式匹配逻辑

public class DynamicPatternProcessFunction extends BroadcastProcessFunction<Transaction, RiskRule, RiskResult> {private transient MapState<Long, Pattern<Transaction, ?>> patternState;@Overridepublic void open(Configuration parameters) {// 初始化模式状态MapStateDescriptor<Long, Pattern<Transaction, ?>> patternDescriptor = new MapStateDescriptor<>("patternState", Types.LONG, Types.POJO(Pattern.class));patternState = getRuntimeContext().getMapState(patternDescriptor);}@Overridepublic void processElement(Transaction transaction,ReadOnlyContext ctx,Collector<RiskResult> out) throws Exception {// 遍历所有规则模式for (Map.Entry<Long, Pattern<Transaction, ?>> entry : patternState.entries()) {Long ruleId = entry.getKey();Pattern<Transaction, ?> pattern = entry.getValue();// 使用Flink CEP进行模式匹配PatternStream<Transaction> patternStream = CEP.pattern(transactionStream.keyBy(Transaction::getUserId), pattern);// 处理匹配结果DataStream<RiskResult> resultStream = patternStream.process(new PatternProcessFunction<Transaction, RiskResult>() {@Overridepublic void processMatch(Map<String, List<Transaction>> match,Context ctx,Collector<RiskResult> out) throws Exception {RiskResult result = new RiskResult();result.setUserId(match.get("first").get(0).getUserId());result.setTransactionIds(match.values().stream().flatMap(List::stream).map(Transaction::getTransactionId).collect(Collectors.toList()));result.setRiskLevel("HIGH");result.setActionTaken("ALERT");result.setCreateTime(System.currentTimeMillis());out.collect(result);}});// 输出结果resultStream.addSink(new AlertSink());}}@Overridepublic void processBroadcastElement(RiskRule rule,Context ctx,Collector<RiskResult> out) throws Exception {// 动态构建模式Pattern<Transaction, ?> pattern = Pattern.<Transaction>begin("first").where(new SimpleCondition<Transaction>() {@Overridepublic boolean filter(Transaction transaction) {return evaluateCondition(transaction, rule.getRuleCondition());}}).next("second").where(new SimpleCondition<Transaction>() {@Overridepublic boolean filter(Transaction transaction) {return evaluateCondition(transaction, rule.getRuleCondition());}}).next("third").where(new SimpleCondition<Transaction>() {@Overridepublic boolean filter(Transaction transaction) {return evaluateCondition(transaction, rule.getRuleCondition());}}).within(Time.minutes(10));// 更新模式状态patternState.put(rule.getRuleId(), pattern);}// 规则条件评估private boolean evaluateCondition(Transaction transaction, String condition) {if ("amount > 10000".equals(condition)) {return transaction.getAmount() > 10000;}// 其他条件return false;}
}