震惊！用 Redis+AI 模型实现秒级实时风控，这波操作太秀了

用户行为 → 实时采集 → Redis 集群 → 特征工程 → AI 模型 → 决策引擎 → 业务系统
1.

// 伪代码：用户下单行为埋点
  void onOrderSubmit(User user, Order order) {
      // 采集基础信息
      String deviceId = user.getDeviceId();
      String ip = user.getIp();
      long timestamp = System.currentTimeMillis();


      // 写入 Redis Stream
      redis.xadd("user_events:" + deviceId, "*", "type", "order", "amount", order.getAmount());
  }
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.

# 示例：计算最近 5 分钟订单量
  def calculate_recent_orders(device_id):
      # 获取最近 5 分钟的事件
      events = redis.xrange("user_events:" + device_id, "-", "+")


      # 过滤出订单事件
      orders = [e for e in events if e['type'] == 'order']


      # 按时间倒序排序
      orders.sort(key=lambda x: x['timestamp'], reverse=True)


      # 取最近 5 分钟的订单
      recent_orders = [o for o in orders if o['timestamp'] > (now - 300000)]


      return len(recent_orders)
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.

import xgboost as xgb
  from sklearn.model_selection import train_test_split
  # 加载历史数据
  data = pd.read_csv('risk_data.csv')
  X = data.drop('label', axis=1)
  y = data['label']
  # 划分训练集和测试集
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
  # 训练 XGBoost 模型
  model = xgb.XGBClassifier(objective='binary:logistic', learning_rate=0.1, max_depth=3)
  model.fit(X_train, y_train)
  # 模型评估
  accuracy = model.score(X_test, y_test)
  print(f"Model accuracy: {accuracy}")
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.

# 保存模型到 Redis
  import redisai as rai
  r = rai.Client()
  r.modelset("risk_model", "TF", "CPU", model_bytes)
1.
2.
3.
4.

// 从 Redis 获取用户特征
  Map<String, String> features = redis.hgetall("user_features:" + userId);
1.
2.

# 加载模型并进行预测
  import numpy as np
  input_data = np.array([[float(features['order_count']), 
                          float(features['ip_blacklist_score'])]])
  result = r.modelrun("risk_model", inputs=[input_data])
  probability = result[0][0]
1.
2.
3.
4.
5.
6.

// 根据模型输出决定是否拦截
  if (probability > 0.9) {
      // 高风险：拦截交易
      return new RiskResult(true, "高风险交易");
  } else if (probability > 0.7) {
      // 中风险：二次验证
      return new RiskResult(true, "需要短信验证");
  } else {
      // 低风险：正常放行
      return new RiskResult(false, "交易正常");
  }
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.

# 计算过去 1 小时的独立设备数
def get_unique_devices():
    return redis.pfcount("devices:" + now.hour)
1.
2.
3.

# 设备指纹与 IP 关联分析
def device_ip_correlation(device_id, ip):
    return redis.hget("ip_device_map", ip) == device_id
1.
2.
3.

# 示例：将 Keras 模型转换为 TensorFlow Lite
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
with open("model.tflite", "wb") as f:
    f.write(tflite_model)
1.
2.
3.
4.
5.

# 每小时重新训练模型
schedule.every().hour.do(retrain_model)
1.
2.

// Java 示例：批量推理
try (RedisPipeline pipeline = redis.pipelined()) {
    for (User user : users) {
        pipeline.hgetall("user_features:" + user.getId());
    }
    List<Object> results = pipeline.syncAndReturnAll();
}
1.
2.
3.
4.
5.
6.
7.

# 缓存高置信度的结果
def cache_decision(user_id, result):
    if result.confidence > 0.95:
        redis.setex("cache:" + user_id, 3600, result)
1.
2.
3.
4.