Merge branch 'dev' of http://120.55.44.4:10080/yanzheng/DualFlow into dev

models/uf_rl/rl_model/DQN/dqn_model/dqn_statebuilder.py

@@ -1,4 +1,4 @@
-from typing import Dict
+from typing import Dict, Optional
 from dataclasses import replace
 
 import numpy as np
@@ -62,15 +62,14 @@ class DQNStateBuilder:
     # ======================================================================
     # 对外主接口
     # ======================================================================
-
     def build_from_csv_pair(
-        self,
-        unit_name,
-        uf_state_default,
-        state_bounds,
-        prev_cycle_csv: str,
-        init_cycle_csv: str,
-        predict_cycle_csv: str,
+            self,
+            unit_name,
+            uf_state_default,
+            state_bounds,
+            prev_cycle_csv: str,
+            init_cycle_csv: str,
+            predict_cycle_csv: Optional[str] = None,
     ) -> UFState:
         """
         使用【上一完整化学周期 CSV】+【当前周期初始 CSV】+ 【当前周期预测 CSV】构建 UFState
@@ -78,11 +77,26 @@ class DQNStateBuilder:
 
         df_prev = pd.read_csv(prev_cycle_csv)
         df_init = pd.read_csv(init_cycle_csv)
-        df_predict = pd.read_csv(predict_cycle_csv)
+
+        # predict_csv 允许为空
+        df_predict = None
+        if predict_cycle_csv is not None:
+            df_predict = pd.read_csv(predict_cycle_csv)
 
         # 分别处理两个 CSV
-        prev_features = self._analyze_previous_cycle_csv(df_prev, unit_name, uf_state_default, state_bounds)
-        init_features = self._analyze_init_cycle_csv(df_init, unit_name, uf_state_default, state_bounds)
+        prev_features = self._analyze_previous_cycle_csv(
+            df_prev,
+            unit_name,
+            uf_state_default,
+            state_bounds
+        )
+
+        init_features = self._analyze_init_cycle_csv(
+            df_init,
+            unit_name,
+            uf_state_default,
+            state_bounds
+        )
 
         # 化学清洗去除阻力（上一周期末 - 当前初始）
         ceb_removal = max(
@@ -90,16 +104,20 @@ class DQNStateBuilder:
             0.0
         )
 
-        # 使用df_predict修正 nuk
-        corrected_nuk = self._correct_nuk_with_predict(
-            df_predict=df_predict,
-            unit_name=unit_name,
-            R_start=init_features["R_start"],
-            q_mean=init_features["q_mean"],
-            temp_celsius=init_features["temp_mean"],
-            base_nuk=prev_features["nuK"],
-            uf_state_default=uf_state_default,
-        )
+        # 默认直接使用上一周期 nuK
+        corrected_nuk = prev_features["nuK"]
+
+        # 如果提供了 predict_csv，则进行在线修正
+        if df_predict is not None:
+            corrected_nuk = self._correct_nuk_with_predict(
+                df_predict=df_predict,
+                unit_name=unit_name,
+                R_start=init_features["R_start"],
+                q_mean=init_features["q_mean"],
+                temp_celsius=init_features["temp_mean"],
+                base_nuk=prev_features["nuK"],
+                uf_state_default=uf_state_default,
+            )
 
         # 构建 UFState
         current_state = replace(
@@ -107,8 +125,8 @@ class DQNStateBuilder:
             TMP=init_features["tmp_mean"],
             q_UF=init_features["q_mean"],
             temp=init_features["temp_mean"],
-            R = init_features["R_start"],
-            nuK = corrected_nuk,
+            R=init_features["R_start"],
+            nuK=corrected_nuk,
             slope=prev_features["slope"],
             power=prev_features["power"],
             ceb_removal=ceb_removal,
@@ -192,6 +210,7 @@ class DQNStateBuilder:
         # -------- 5️⃣ 短期污染拟合（nuK）--------
         st_fitter = ShortTermCycleFoulingFitter(unit_name)
         nuK, st_r2 = st_fitter.fit_cycle(stable_segments)
+
         if (
                 pd.isna(nuK)
                 or pd.isna(st_r2)
@@ -200,6 +219,14 @@ class DQNStateBuilder:
         ):
             nuK = uf_state_default.nuK
 
+        nuK = float(
+            np.clip(
+                nuK,
+                state_bounds.nuK_min,
+                state_bounds.nuK_max,
+            )
+        )
+
         # -------- 6️⃣ 长期不可逆污染拟合 --------
         lt_fitter = LongTermFoulingFitter(unit_name)
         slope, power, lt_r2 = lt_fitter.fit_cycle(stable_segments)