diff --git a/ML/nmWTAI-ML/scripts/generate_autofit_neighborhood_dataset.py b/ML/nmWTAI-ML/scripts/generate_autofit_neighborhood_dataset.py
index 0ef5b73..210862b 100644
--- a/ML/nmWTAI-ML/scripts/generate_autofit_neighborhood_dataset.py
+++ b/ML/nmWTAI-ML/scripts/generate_autofit_neighborhood_dataset.py
@@ -337,8 +337,8 @@ def build_schedule_vector(cfg: Config, schedule: Schedule) -> np.ndarray:
     """把 Schedule 编码成正演代理模型可直接接收的流量制度特征向量。"""
     enc = encode_schedule_to_timegrid(
         cfg,
-        sectionIndex=int(schedule.sectionIndex),
-        timeQ=schedule.timeQ,
+        section_index=int(schedule.sectionIndex),
+        time_q=schedule.timeQ,
         q=schedule.q,
         n_sections=len(schedule.timeQ),
     )
diff --git a/ML/nmWTAI-ML/src/data/schedule_features.py b/ML/nmWTAI-ML/src/data/schedule_features.py
index 87e9055..9e0eadf 100644
--- a/ML/nmWTAI-ML/src/data/schedule_features.py
+++ b/ML/nmWTAI-ML/src/data/schedule_features.py
@@ -69,8 +69,8 @@ def build_schedule_base_vector(cfg: Config, schedule: Schedule) -> np.ndarray:
     """构造基础流量制度向量，通常包含时间网格上的流量和关井信息。"""
     enc = encode_schedule_to_timegrid(
         cfg,
-        sectionIndex=int(schedule.sectionIndex),
-        timeQ=schedule.timeQ,
+        section_index=int(schedule.sectionIndex),
+        time_q=schedule.timeQ,
         q=schedule.q,
         n_sections=len(schedule.timeQ),
     )