fix: 修复工作目录与步骤名不对应、回归预测虚数报错、模型加载及预处理名称转换问题，重构可视化并修正勾选联动

src/postprocessing/visualization_reports.py

@@ -281,11 +281,13 @@ class WaterQualityVisualization:
     def plot_statistical_charts(self, csv_path: str, parameter_columns: List[str],
                                output_dir: Optional[str] = None) -> Dict[str, str]:
         """
-        绘制统计图表：箱线图、直方图、相关性热力图
+        绘制统计图表：**只针对水质参数列**（数值型，排除波长列）
+        - 水质参数列（如浓度、含量等数值型参数）使用箱线图/直方图/相关性热力图
+        - 排除光谱波长列（虽然也是数值型，但不是水质参数）
         
         Args:
             csv_path: CSV文件路径
-            parameter_columns: 参数列名列表
+            parameter_columns: **水质参数**列名列表（数值型，已排除波长列）
             output_dir: 输出目录
             
         Returns:
@@ -301,12 +303,16 @@ class WaterQualityVisualization:
         
         output_paths = {}
         
+        # 水质参数统计图表（针对数值型参数，排除波长列）
+        # 假设传入的 parameter_columns 已经是过滤后的水质参数列
+        numeric_cols = [col for col in parameter_columns if col in df.columns and pd.api.types.is_numeric_dtype(df[col])]
+        
         # 1. 箱线图
-        if len(parameter_columns) > 0:
+        if len(numeric_cols) > 0:
             fig, ax = plt.subplots(figsize=(12, 6))
-            data_for_boxplot = [df[col].dropna() for col in parameter_columns if col in df.columns]
+            data_for_boxplot = [df[col].dropna() for col in numeric_cols]
             if data_for_boxplot:
-                ax.boxplot(data_for_boxplot, labels=[col for col in parameter_columns if col in df.columns])
+                ax.boxplot(data_for_boxplot, labels=numeric_cols)
                 ax.set_ylabel('数值', fontsize=12, fontweight='bold')
                 ax.set_title('水质参数箱线图', fontsize=14, fontweight='bold')
                 ax.grid(True, alpha=0.3, axis='y')
@@ -318,51 +324,51 @@ class WaterQualityVisualization:
                 plt.close()
                 output_paths['boxplot'] = str(boxplot_path)
         
-        # 2. 直方图
-        for col in parameter_columns:
-            if col not in df.columns:
-                continue
+        # 2. 直方图 (每个水质参数列)
+        for col in numeric_cols:
             fig, ax = plt.subplots(figsize=(10, 6))
             data = df[col].dropna()
-            ax.hist(data, bins=30, edgecolor='black', alpha=0.7, color='skyblue')
-            ax.set_xlabel(f'{col} 数值', fontsize=12, fontweight='bold')
-            ax.set_ylabel('频数', fontsize=12, fontweight='bold')
-            ax.set_title(f'{col} 分布直方图', fontsize=14, fontweight='bold')
-            ax.grid(True, alpha=0.3, axis='y')
-            
-            # 添加统计信息
-            mean_val = data.mean()
-            std_val = data.std()
-            ax.axvline(mean_val, color='red', linestyle='--', linewidth=2, label=f'均值: {mean_val:.4f}')
-            ax.legend()
-            
-            plt.tight_layout()
-            
-            safe_name = "".join(c for c in col if c.isalnum() or c in ('-', '_', '.'))
-            hist_path = output_dir / f"{safe_name}_histogram.png"
-            plt.savefig(hist_path, dpi=300, bbox_inches='tight')
-            plt.close()
-            output_paths[f'histogram_{col}'] = str(hist_path)
-        
-        # 3. 相关性热力图
-        if len(parameter_columns) >= 2:
-            valid_cols = [col for col in parameter_columns if col in df.columns]
-            if len(valid_cols) >= 2:
-                corr_matrix = df[valid_cols].corr()
+            if len(data) > 1:
+                ax.hist(data, bins=30, edgecolor='black', alpha=0.7, color='skyblue')
+                ax.set_xlabel(f'{col} 数值', fontsize=12, fontweight='bold')
+                ax.set_ylabel('频数', fontsize=12, fontweight='bold')
+                ax.set_title(f'{col} 分布直方图', fontsize=14, fontweight='bold')
+                ax.grid(True, alpha=0.3, axis='y')
+                
+                # 添加统计信息
+                mean_val = data.mean()
+                std_val = data.std() if len(data) > 1 else 0
+                ax.axvline(mean_val, color='red', linestyle='--', linewidth=2, label=f'均值: {mean_val:.4f}')
+                ax.legend()
                 
-                fig, ax = plt.subplots(figsize=(10, 8))
-                sns.heatmap(corr_matrix, annot=True, fmt='.3f', cmap='coolwarm', 
-                           center=0, square=True, linewidths=1, cbar_kws={"shrink": 0.8},
-                           ax=ax, vmin=-1, vmax=1)
-                ax.set_title('水质参数相关性热力图', fontsize=14, fontweight='bold')
                 plt.tight_layout()
                 
-                heatmap_path = output_dir / "correlation_heatmap.png"
-                plt.savefig(heatmap_path, dpi=300, bbox_inches='tight')
+                safe_name = "".join(c for c in col if c.isalnum() or c in ('-', '_', '.'))
+                hist_path = output_dir / f"{safe_name}_histogram.png"
+                plt.savefig(hist_path, dpi=300, bbox_inches='tight')
                 plt.close()
-                output_paths['heatmap'] = str(heatmap_path)
+                output_paths[f'histogram_{col}'] = str(hist_path)
         
-        print(f"统计图表已保存到: {output_dir}")
+        # 3. 相关性热力图
+        if len(numeric_cols) >= 2:
+            corr_matrix = df[numeric_cols].corr()
+            
+            fig, ax = plt.subplots(figsize=(10, 8))
+            sns.heatmap(corr_matrix, annot=True, fmt='.3f', cmap='coolwarm', 
+                       center=0, square=True, linewidths=1, cbar_kws={"shrink": 0.8},
+                       ax=ax, vmin=-1, vmax=1)
+            ax.set_title('水质参数相关性热力图', fontsize=14, fontweight='bold')
+            plt.tight_layout()
+            
+            heatmap_path = output_dir / "correlation_heatmap.png"
+            plt.savefig(heatmap_path, dpi=300, bbox_inches='tight')
+            plt.close()
+            output_paths['heatmap'] = str(heatmap_path)
+        
+        if not output_paths:
+            print("警告: 没有生成任何统计图表（可能无合适的水质参数列）")
+        else:
+            print(f"统计图表已保存到: {output_dir}，共 {len(output_paths)} 个文件")
         return output_paths
     
     def plot_distribution_map_enhanced(self, prediction_csv_path: str,