智慧余（总）氯
在线分析仪

# TurmCon Cl₂ 余氯趋势预测引擎
model = TurmConPredictor(
    horizon=24,              # 未来24小时预测窗口
    features=["Cl","TCl","flow","temp","pH"],
    lookback=72,              # 回溯72小时
)

pred = model.forecast(data)

print(f"T+6h 余氯: {pred[6]:.2f} mg/L")
print(f"衰减拐点: {pred.decay_knee}h")
print(f"置信区间: ±{pred.ci:.2f} mg/L")

# 余氯 3σ 异常波动实时检测
detector = ChlorineAnomalyDetector(
    method="3sigma",
    window=30,               # 滑动窗口 30 点
    threshold=3.0,           # 3倍标准差
    severity=["low","mid","high"],
)

alerts = detector.scan(chlorine_stream)

for ev in alerts:
    if ev.cl < 0.05:      # 余氯过低 → 微生物风险
        trigger("加氯不足告警", level="critical")
    elif ev.tcl > 4.0:   # 总氯过高 → 消毒副产物风险
        trigger("DBP风险告警", level="warning")

# 基于强化学习的加氯量优化策略
env = ChlorineDosingEnv(
    target_residual=0.3,    # 末梢余氯目标 ≥0.3 mg/L
    max_dosage=5.0,          # 最大投加量 5.0 mg/L
    constraints=["DBP","cost"], # 副产物+成本双约束
)

agent = PPOAgent(env, lr=1e-4)
agent.train(episodes=5000)

dose = agent.recommend(flow=1200, temp=22)
print(f"建议加氯量: {dose:.2f} mg/L, 节约: {dose.saving*100:.1f}%")