diff --git a/src/core/prediction/inference_batch.py b/src/core/prediction/inference_batch.py
index 3072d25..4767d22 100644
--- a/src/core/prediction/inference_batch.py
+++ b/src/core/prediction/inference_batch.py
@@ -475,6 +475,21 @@ class WaterQualityInference:
 
         model = self.loaded_model_data['model']
         metadata = self.loaded_model_data.get('metadata', {})
+
+        # ═══════════════════════════════════════════════════════════
+        # ★ DualStream_MNF 快速通道：Pipeline 已内置 MNF + 物理指数
+        #   无需手动重采样或 WQI 补齐，直接走 Pipeline 的 preproc 步骤
+        # ═══════════════════════════════════════════════════════════
+        if actual_preprocess_method == "DualStream_MNF" and isinstance(model, Pipeline):
+            print("[DualStream_MNF] 推理直通 Pipeline（MNF+物理指数已内置）")
+            # Pipeline 的 imputer + preproc 步骤会处理一切
+            spectra_processed = model.named_steps['imputer'].transform(spectra.values)
+            spectra_processed = model.named_steps['preproc'].transform(spectra_processed)
+            spectra_processed = np.nan_to_num(spectra_processed, nan=0.0, posinf=0.0, neginf=0.0)
+            print(f"[DualStream_MNF] 预处理完成: {spectra_processed.shape}")
+            print(f"[特征对齐] 最终输入维度: {spectra_processed.shape}")
+            return spectra_processed
+
         train_wavelengths = metadata.get('train_wavelengths', None)
         # 旧模型无 train_wavelengths → 不做波长匹配，走下方分支 B 的 linspace 路径
         train_columns = metadata.get('train_columns', None)