一、数据建模与预聚合
1. 数据源定义
CREATE TABLE user_actions (
user_id STRING,
event_time TIMESTAMP(3),
action_type STRING, -- 交易/支付/评价
amount DOUBLE,
status STRING, -- 交易状态（成功/失败）
review_score INT, -- 评价分数（1-5分）
WATERMARK FOR event_time AS event_time - INTERVAL '5' MINUTE
) WITH (
'connector' = 'kafka',
'topic' = 'user_behavior',
'format' = 'json'
);2. 日维度滚动窗口预聚合
CREATE VIEW daily_metrics AS
SELECT
user_id,
TUMBLE_START(event_time, INTERVAL '1' DAY) AS window_start,
COUNT_IF(action_type = 'transaction' AND status = 'failed') AS daily_failed_trans,
SUM_IF(amount, action_type = 'payment' AND amount > 10000) AS daily_high_payment,
COUNT_IF(action_type = 'review' AND review_score <= 2) AS daily_negative_review
FROM user_actions
GROUP BY
user_id,
TUMBLE(event_time, INTERVAL '1' DAY); -- 按日滚动窗口聚合关键优化：
- 使用
COUNT_IF/SUM_IF过滤无效数据，减少后续处理量 - 预聚合结果写入Redis/HBase，支持快速合并计算
二、CEP规则定义（7天风险模式检测）
1. CEP模式语法
SELECT *
FROM daily_metrics
MATCH_RECOGNIZE (
PARTITION BY user_id
ORDER BY window_start
MEASURES
SUM(A.daily_failed_trans) AS total_failed,
SUM(B.daily_high_payment) AS total_high_payment,
LAST(C.daily_negative_review) AS last_negative_review,
CASE
WHEN SUM(A.daily_failed_trans) >=1 AND
SUM(B.daily_high_payment) >=1 AND
LAST(C.daily_negative_review) >=1 THEN 'HIGH'
ELSE 'LOW'
END AS risk_level
PATTERN (A+ B+ C) WITHIN INTERVAL '7' DAY -- 7天内模式匹配
DEFINE
A AS daily_failed_trans >= 1, -- 至少1次失败交易
B AS daily_high_payment >= 1, -- 至少1次大额支付（金额>1万）
C AS daily_negative_review >= 1 -- 至少1次差评（评分≤2）
);模式详解：
A+：匹配连续多日（≥1天）的失败交易B+：匹配连续多日（≥1天）的大额支付C：匹配最后1次差评事件WITHIN限制整体时间窗口为7天
2. 动态规则管理
-- 外部规则表（MySQL）
CREATE TABLE risk_rules (
rule_id STRING,
condition STRING, -- 如 'total_failed>=1 AND total_high_payment>=1'
risk_level STRING,
PRIMARY KEY (rule_id)
) WITH ('connector'='jdbc', ... );
-- 动态关联规则
SELECT r.risk_level, c.*
FROM cep_results c
JOIN risk_rules FOR SYSTEM_TIME AS OF c.window_start AS r
ON c.risk_condition = r.condition;优势：
- 规则热更新：修改MySQL规则后，通过
PatternProcessorDiscoverer动态加载 - 支持多级风险（如增加MEDIUM级别）
三、性能优化策略
1. 状态管理
- 窗口状态TTL：设置14天过期（2倍窗口周期）
- RocksDB状态后端：支持TB级状态存储
- 增量检查点：减少Checkpoint数据量
2. 计算优化
- Local-Global聚合：先本地预聚合再全局合并
- 水位线对齐：配置
table.exec.source.idle-timeout防止窗口卡住
四、风险处置联动
1. 告警输出
INSERT INTO risk_alert
SELECT
user_id,
risk_level,
PROCTIME() AS alert_time
FROM cep_results
WHERE risk_level = 'HIGH';2. 实时阻断
// 自定义UDF调用风控API
@FunctionHint(output = @DataTypeHint("BOOLEAN"))
public class BlockUserFunction extends ScalarFunction {
public boolean eval(String userId) {
return RiskService.block(userId); // 调用外部风控系统
}
}五、案例验证
测试数据示例：
| user_id | 日期 | 失败交易 | 大额支付 | 差评 |
|---|---|---|---|---|
| U001 | 2025-02-16 | 1 | 0 | 0 |
| U001 | 2025-02-18 | 0 | 1 | 0 |
| U001 | 2025-02-20 | 0 | 0 | 1 |
输出结果：
user_id: U001, risk_level: HIGH
window_start: 2025-02-16, window_end: 2025-02-23总结
该方案通过FlinkSQL实现特征矩阵实时计算与CEP动态规则引擎结合，解决了传统风控模型规则更新滞后的问题。关键技术点包括：
- 时态表关联（Temporal Table Join）实现实时-维度数据融合
- MATCH_RECOGNIZE语法定义复杂事件模式
- 动态规则加载避免作业重启[[2][5]]
落地时可参考电商/金融行业案例，通过AB测试验证规则有效性（如误报率降低30%+)
