feat: 推理端自动波长回填+linspace子采样(旧模型兼容)

src/core/prediction/inference_batch.py

@@ -375,6 +375,58 @@ class WaterQualityInference:
         print(f"  模型名称: {self.loaded_model_data['model_name']}")
         print(f"  模型类型: {type(self.loaded_model_data['model'])}")
 
+    def _auto_detect_train_wavelengths(self):
+        """自动获取训练波长：优先级 json/txt > 工作目录 CSV > None"""
+        import os as _os, json as _json, glob as _glob
+        _dir = str(self.artifacts_dir)
+        # 搜集所有可能的搜索根目录
+        _roots = [_dir, _os.path.dirname(_dir), _os.getcwd()]
+        # 找所有 work_dir 级别的父目录
+        for _r in list(_roots):
+            _p = _os.path.dirname(_r)
+            if _p and _p not in _roots:
+                _roots.append(_p)
+
+        # 优先级 1: 显式 wavelength 文件（JSON/TXT）
+        for _root in _roots:
+            for _fname in ('train_wavelengths.json', 'train_wavelengths.txt'):
+                _p = _os.path.join(_root, _fname)
+                if not _os.path.isfile(_p):
+                    continue
+                try:
+                    if _fname.endswith('.json'):
+                        wl = _json.load(open(_p))
+                        if wl:
+                            print(f"[波长回填] 来源: {_p}")
+                            return wl
+                    else:
+                        wl = [float(x) for x in open(_p).read().strip().split()]
+                        if wl:
+                            print(f"[波长回填] 来源: {_p}")
+                            return wl
+                except Exception:
+                    pass
+
+        # 优先级 2: training_spectra.csv / sampling_spectra.csv
+        for _root in _roots:
+            for _sub in ('4_sampling', '6_Spectral_Feature_Extraction', ''):
+                _d = _os.path.join(_root, _sub) if _sub else _root
+                for _pat in ('sampling_spectra.csv', 'training_spectra.csv'):
+                    _p = _os.path.join(_d, _pat)
+                    if not _os.path.isfile(_p):
+                        continue
+                    try:
+                        _df = pd.read_csv(_p, nrows=0)
+                        _wl = [float(c) for c in _df.columns
+                               if c.replace('.','').lstrip('-').isdigit()]
+                        if _wl:
+                            print(f"[波长回填] 来源: {_p} ({len(_wl)} 波长)")
+                            return _wl
+                    except Exception:
+                        pass
+
+        return None
+
     def preprocess_spectra(self, spectra: pd.DataFrame) -> np.ndarray:
         """
         对光谱数据进行预处理 + 跨传感器光谱重采样。
@@ -418,6 +470,7 @@ class WaterQualityInference:
         model = self.loaded_model_data['model']
         metadata = self.loaded_model_data.get('metadata', {})
         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)
 
         # ═══════════════════════════════════════════════════════════