内容部分修改

scripts/rthook_add_dll_dirs.py

@@ -5,11 +5,12 @@ import sys
 def _safe_add(path: str) -> None:
     if not path or not os.path.isdir(path):
         return
-    try:
+    if hasattr(os, "add_dll_directory"):
-        if hasattr(os, "add_dll_directory"):
+        try:
             os.add_dll_directory(path)
-    except Exception:
+            return
-        pass
+        except Exception:
+            pass
     try:
         os.environ["PATH"] = path + os.pathsep + os.environ.get("PATH", "")
     except Exception:
@@ -21,5 +22,4 @@ base = getattr(sys, "_MEIPASS", None)
 if base:
     _safe_add(base)
     _safe_add(os.path.join(base, "lib-dynload"))
-    _safe_add(os.path.join(base, "DLLs"))
+    _safe_add(os.path.join(base, "DLLs"))
-

src/auth/license_manager.py

@@ -31,7 +31,9 @@ def get_cpu_id() -> Optional[str]:
                 capture_output=True,
                 text=True,
                 timeout=5,
-                creationflags=subprocess.CREATE_NO_WINDOW
+                creationflags=subprocess.CREATE_NO_WINDOW,
+                stdin=subprocess.DEVNULL,
+                stderr=subprocess.DEVNULL,
             )
             cpu_id = result.stdout.strip().split("\n")[-1].strip()
             if cpu_id:
@@ -57,7 +59,9 @@ def get_motherboard_uuid() -> Optional[str]:
                 capture_output=True,
                 text=True,
                 timeout=5,
-                creationflags=subprocess.CREATE_NO_WINDOW
+                creationflags=subprocess.CREATE_NO_WINDOW,
+                stdin=subprocess.DEVNULL,
+                stderr=subprocess.DEVNULL,
             )
             board_uuid = result.stdout.strip().split("\n")[-1].strip()
             board_uuid = re.sub(r'[^a-zA-Z0-9\-]', '', board_uuid)
@@ -68,7 +72,9 @@ def get_motherboard_uuid() -> Optional[str]:
                 ["cat", "/sys/class/dmi/id/product_uuid"],
                 capture_output=True,
                 text=True,
-                timeout=5
+                timeout=5,
+                stdin=subprocess.DEVNULL,
+                stderr=subprocess.DEVNULL,
             )
             if result.returncode == 0:
                 return result.stdout.strip()

src/core/modeling/modeling_batch.py

@@ -40,6 +40,10 @@ CB_AVAILABLE = False  # 注释掉catboost
 import sys
 import os
 
+# PyInstaller 打包环境感知：EXE 模式下强制单核，防止 Windows 派生无限重启
+is_frozen_env = getattr(sys, 'frozen', False)
+safe_n_jobs = 1 if is_frozen_env else -1
+
 from src.preprocessing.spectral_Preprocessing import Preprocessing
 
 
@@ -643,7 +647,7 @@ class WaterQualityModelingBatch:
             config['params'],
             cv=cv_strategy,
             scoring=scoring,
-            n_jobs=-1,
+            n_jobs=safe_n_jobs,
             verbose=1
         )
 

src/core/prediction/inference_batch.py

@@ -40,16 +40,19 @@ class WaterQualityInference:
         self.best_model_info = None
         self.loaded_model_data = None
 
-    def load_sampling_data(self, csv_path: str) -> Tuple[pd.DataFrame, pd.DataFrame]:
+    def load_sampling_data(self, csv_path: str) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
         """
-        加载sampling生成的CSV数据
+        加载sampling生成的CSV数据（兼容 WQI 增强版 CSV）
 
         Args:
-            csv_path: CSV文件路径，前两列为经纬度，其余列为光谱数据
+            csv_path: CSV文件路径
+                旧版：x_coord,y_coord,pixel_x,pixel_y,波长...
+                新版：x_coord,y_coord,WQI_...,波长...
 
         Returns:
-            coords: 经纬度数据 (DataFrame)
+            coords: 经纬度数据 (DataFrame, 2列)
-            spectra: 光谱数据 (DataFrame)
+            spectra: 纯光谱数据 (DataFrame, 跳过 WQI 列)
+            wqi_df: WQI 指数列 (DataFrame, 0或45列)
         """
         print(f"正在加载采样数据: {csv_path}")
 
@@ -71,15 +74,35 @@ class WaterQualityInference:
         coords = data.iloc[:, :2].copy()
         coords.columns = ['longitude', 'latitude']
 
-        # 从第5列开始为光谱数据（跳过第2、3、4列的其他信息）
+        # 动态识别光谱列（兼容 sampling_spectra.csv 列顺序变更）
-        spectra = data.iloc[:, 4:].copy()
+        # 列名约定：波长为纯数字字符串如 "374.285004"；WQI 为 "WQI_xxx" 前缀
+        # 旧版 CSV（无WQI）：x_coord,y_coord,pixel_x,pixel_y,波长... → 取 [4:]
+        # 新版 CSV（有WQI）：x_coord,y_coord,WQI_...,波长... → 过滤 WQI 列后取光谱
+        all_cols = list(data.columns)
+        spectral_col_indices = []
+        wqi_col_indices = []
+        for i, col in enumerate(all_cols):
+            col_str = str(col)
+            if col_str.startswith('WQI_'):
+                wqi_col_indices.append(i)
+            elif col_str.replace('.', '').lstrip('-').isdigit():
+                # 波长列：纯数字字符串
+                spectral_col_indices.append(i)
+            else:
+                # 其他元数据列（x_coord/y_coord/pixel_x/pixel_y），由 coords 接收
+                pass
+
+        # 光谱列 = 纯数字列（WQI 已被排除）
+        spectra = data.iloc[:, spectral_col_indices].copy() if spectral_col_indices else data.iloc[:, 4:].copy()
+        # WQI 列（用于追加到预测结果输出）
+        wqi_df = data.iloc[:, wqi_col_indices].copy() if wqi_col_indices else pd.DataFrame()
 
         print(f"  经纬度数据形状: {coords.shape}")
-        print(f"  光谱数据形状: {spectra.shape}")
+        print(f"  光谱数据形状: {spectra.shape}  (自动识别波长列，排除 {len(wqi_col_indices)} 个WQI列)")
         print(f"  经纬度范围: 经度[{coords['longitude'].min():.6f}, {coords['longitude'].max():.6f}], "
               f"纬度[{coords['latitude'].min():.6f}, {coords['latitude'].max():.6f}]")
 
-        return coords, spectra
+        return coords, spectra, wqi_df
 
     def random(self, data, label, test_ratio=0.2, random_state=123):
         """
@@ -519,6 +542,69 @@ class WaterQualityInference:
         print(f"正在应用预处理方法: {actual_preprocess_method}")
         print(f"原始光谱数据形状: {spectra.shape}")
 
+        # ---- 自动特征补全：50 光谱 → 补全至模型训练时的 95 维（WQI 指数） ----
+        # 触发条件：模型期望 n_features_in_ 个特征，但当前 spectra 列数不足
+        # 原因：training_spectra.csv 含 50 光谱 + 45 WQI；sampling_spectra.csv 只有 50 光谱
+        # 做法：与训练端（calculate_all_indices）完全一致的算法列表，实时补全缺失的 45 个 WQI 列
+        model = self.loaded_model_data['model']
+        expected_features = getattr(model, 'n_features_in_', None)
+
+        # ---- 自动特征补全：50 光谱 → 补全至模型训练时的 n_features_in_ 维（WQI 指数） ----
+        if expected_features is not None and spectra.shape[1] < expected_features:
+            print(f"[特征补全] 检测到特征缺口：当前 {spectra.shape[1]} 列 < 模型期望 {expected_features} 列，"
+                  f"正在从光谱数据实时计算 WQI 指数...")
+            try:
+                from src.utils.water_index import WaterQualityIndexCalculator
+                calc = WaterQualityIndexCalculator()
+
+                # 提取纯计算方法（排除 find_closest_wavelength 和 calculate_all_indices，
+                # 以及不返回 Series 的辅助方法）
+                algorithm_methods = []
+                for m in dir(calc):
+                    if m.startswith('_'):
+                        continue
+                    if m in ['find_closest_wavelength', 'calculate_all_indices']:
+                        continue
+                    attr = getattr(calc, m)
+                    if callable(attr):
+                        algorithm_methods.append(m)
+
+                original_col_count = spectra.shape[1]
+                for algo_name in algorithm_methods:
+                    try:
+                        algo_func = getattr(calc, algo_name)
+                        result = algo_func(spectra)
+                        # 只追加返回 Series 且长度为样本数的合法结果
+                        if isinstance(result, pd.Series) and len(result) == len(spectra):
+                            spectra[algo_name] = result.values
+                        else:
+                            spectra[algo_name] = np.nan
+                    except Exception:
+                        spectra[algo_name] = np.nan
+
+                print(f"[特征补全] 完成！光谱列已扩充至 {spectra.shape[1]} 列"
+                      f"（追加了 {spectra.shape[1] - original_col_count} 个 WQI 指数）")
+            except Exception as e:
+                print(f"[特征补全] 失败，将使用原始光谱特征: {e}")
+
+        # ---- 防线 1：强制维度对齐（物理截断）----
+        if expected_features is not None and spectra.shape[1] > expected_features:
+            print(f"[精准对齐] 正在将 {spectra.shape[1]} 维特征截断为模型要求的 {expected_features} 维")
+            spectra = spectra.iloc[:, :expected_features]
+        elif expected_features is not None and spectra.shape[1] < expected_features:
+            # 维度不足时填充 0
+            padding_cols = expected_features - spectra.shape[1]
+            for i in range(padding_cols):
+                spectra[f'_padding_{i}'] = 0.0
+            print(f"[精准对齐] 特征不足，填充 {padding_cols} 列 0")
+
+        # ---- 防线 2：彻底清洗无穷大数值----
+        # 防止 WQI 计算中除零/溢出产生 np.inf / -np.inf 导致预处理崩溃
+        spectra = spectra.replace([np.inf, -np.inf], np.nan)
+        spectra = spectra.fillna(0)
+
+        print(f"[特征对齐] 最终输入维度: {spectra.shape}")
+
         try:
             # 应用预处理
             spectra_processed = Preprocessing(actual_preprocess_method, spectra)
@@ -573,7 +659,8 @@ class WaterQualityInference:
             raise
 
     def save_predictions(self, coords: pd.DataFrame, predictions: np.ndarray,
-                         output_path: str, prediction_column: str = 'prediction'):
+                         output_path: str, prediction_column: str = 'prediction',
+                         wqi_columns: Optional[pd.DataFrame] = None):
         """
         保存预测结果
 
@@ -582,11 +669,15 @@ class WaterQualityInference:
             predictions: 预测结果
             output_path: 输出文件路径
             prediction_column: 预测列名称
+        wqi_columns: Optional[pd.DataFrame] = None
         """
         print(f"正在保存预测结果到: {output_path}")
 
         # 创建结果DataFrame
         result_df = coords.copy()
+        # 追加 WQI 水质指数列（如 sampling_spectra.csv 注入了 45 列指数）
+        if wqi_columns is not None and not wqi_columns.empty:
+            result_df = pd.concat([result_df, wqi_columns.reset_index(drop=True)], axis=1)
         result_df[prediction_column] = predictions
 
         # 确保输出目录存在
@@ -659,10 +750,10 @@ class WaterQualityInference:
             else:
                 self.load_best_model(metric=metric)
 
-            # 2. 加载采样数据
+            # 2. 加载采样数据（coords=坐标, spectra=纯光谱, wqi_df=45个WQI指数列）
             print("\n步骤2: 加载采样数据")
             print("-" * 40)
-            coords, spectra = self.load_sampling_data(sampling_csv_path)
+            coords, spectra, wqi_df = self.load_sampling_data(sampling_csv_path)
 
             # 3. 数据预处理
             print("\n步骤3: 数据预处理")
@@ -674,10 +765,11 @@ class WaterQualityInference:
             print("-" * 40)
             predictions = self.predict(spectra_processed)
 
-            # 5. 保存预测结果
+            # 5. 保存预测结果（透传 WQI 列至最终输出文件）
             print("\n步骤5: 保存预测结果")
             print("-" * 40)
-            result_df = self.save_predictions(coords, predictions, output_csv_path, prediction_column)
+            result_df = self.save_predictions(coords, predictions, output_csv_path,
+                                             prediction_column, wqi_df)
 
             print("\n" + "=" * 80)
             print("推理流程完成！")
@@ -747,10 +839,11 @@ class WaterQualityInference:
                 output_file = output_path / f"prediction_{csv_file.name}"
                 
                 # 执行推理
-                coords, spectra = self.load_sampling_data(str(csv_file))
+                coords, spectra, wqi_df = self.load_sampling_data(str(csv_file))
                 spectra_processed = self.preprocess_spectra(spectra)
                 predictions = self.predict(spectra_processed)
-                result_df = self.save_predictions(coords, predictions, str(output_file), prediction_column)
+                result_df = self.save_predictions(coords, predictions, str(output_file),
+                                                 prediction_column, wqi_df)
                 
                 results[csv_file.name] = {
                     'output_file': str(output_file),
@@ -908,10 +1001,11 @@ class WaterQualityInference:
                 output_file = output_path / f"{file_stem}{file_ext}"
                 
                 # 执行推理
-                coords, spectra = self.load_sampling_data(str(csv_file))
+                coords, spectra, wqi_df = self.load_sampling_data(str(csv_file))
                 spectra_processed = self.preprocess_spectra(spectra)
                 predictions = self.predict(spectra_processed)
-                result_df = self.save_predictions(coords, predictions, str(output_file), prediction_column)
+                result_df = self.save_predictions(coords, predictions, str(output_file),
+                                                 prediction_column, wqi_df)
                 
                 results[file_stem] = {
                     'input_file': str(csv_file),

src/gui/panels/step5_5_panel.py

@@ -13,11 +13,24 @@ from src.gui.components.custom_widgets import FileSelectWidget
 from src.gui.styles import ModernStylesheet
 
 def get_resource_path(relative_path: str) -> str:
-    """适配开发与 PyInstaller 环境的路径获取逻辑"""
+    """适配开发与 PyInstaller 环境的路径获取逻辑。
+    支持两种打包模式：
+    1. --onedir 模式：文件在 exe_root/_internal/ 下 → 检查 _internal 目录
+    2. --onefile 模式：文件在 sys._MEIPASS 平铺目录
+    """
+    # 优先检查 PyInstaller onefile 模式（文件平铺在 _MEIPASS 下）
     if hasattr(sys, '_MEIPASS'):
-        # 打包后，文件会被平铺或按 tree 结构放入临时目录
+        internal_path = os.path.join(sys._MEIPASS, '_internal', relative_path)
+        if os.path.exists(internal_path):
+            return internal_path
         return os.path.join(sys._MEIPASS, relative_path)
 
+    # 兼容 PyInstaller onedir 模式的 _internal 目录（exe 同级目录下）
+    exe_dir = os.path.dirname(sys.executable)
+    internal_path = os.path.join(exe_dir, '_internal', relative_path)
+    if os.path.exists(internal_path):
+        return internal_path
+
     # 开发环境下：基于当前文件 (step5_5_panel.py) 的绝对路径进行回溯
     # 当前在 src/gui/panels/，目标在 src/gui/model/
     base_dir = Path(__file__).resolve().parent.parent / "model"

src/gui/water_quality_gui.py

@@ -5,6 +5,12 @@
 GUI for Water Quality Inversion Pipeline
 """
 
+# ==============================================================================
+# 🚀 终极防御：必须在全宇宙第一行强制载入 GDAL 底层 DLL，绝对杜绝 0xC0000005 内存崩溃
+# ==============================================================================
+import osgeo
+from osgeo import gdal, ogr
+
 import os
 import json
 import copy
@@ -31,8 +37,23 @@ from PyQt5.QtGui import QIcon, QFont, QTextCursor, QPalette, QColor, QPixmap
 
 import sys
 import traceback
+import multiprocessing
+import ctypes
 
+# ==============================================================================
+# 🚀 终极防御置顶：在载入任何自定义面板、样式或子模块之前，强制提前创建 QApplication！
+# 彻底杜绝 import 时期载入类属性 (如 QFont/QIcon/QPixmap) 触发的 QWidget 崩溃
+# ==============================================================================
+if multiprocessing.current_process().name == 'MainProcess':
+    if not QApplication.instance():
+        try:
+            QApplication.setAttribute(Qt.AA_EnableHighDpiScaling, True)
+            QApplication.setAttribute(Qt.AA_UseHighDpiPixmaps, True)
+        except Exception:
+            pass
+        _global_app = QApplication(sys.argv)
 
+# 👇 全局异常钩子（保持不变）
 def get_resource_path(relative_path: str) -> str:
     """获取资源的绝对路径，适配 PyInstaller 打包环境。
     打包后资源位于 sys._MEIPASS（解压临时目录），开发环境则基于 __file__ 向上三级。
@@ -45,10 +66,31 @@ def get_resource_path(relative_path: str) -> str:
 
 
 def global_exception_handler(exc_type, exc_value, exc_traceback):
-    print("\n" + "="*50)
+    err_lines = traceback.format_exception(exc_type, exc_value, exc_traceback)
-    print("【严重错误拦截 - PyQt 崩溃死因】")
+    err_msg = "".join(err_lines)
-    traceback.print_exception(exc_type, exc_value, exc_traceback)
+    dump_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "crash_dump.txt")
-    print("="*50 + "\n")
+    try:
+        with open(dump_path, "a", encoding="utf-8") as f:
+            f.write(f"\n{'='*60}\n[{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}]\n")
+            f.write(err_msg)
+    except Exception:
+        pass
+    try:
+        from PyQt5.QtWidgets import QMessageBox
+        from PyQt5.QtCore import Qt
+        msg = (
+            "【严重错误 - 程序即将退出】\n\n"
+            "错误类型: {}\n\n"
+            "错误信息: {}\n\n"
+            "详细信息已写入:\n{}".format(
+                exc_type.__name__,
+                str(exc_value),
+                dump_path,
+            )
+        )
+        QMessageBox.critical(None, "程序崩溃", msg)
+    except Exception:
+        pass
 
 # 挂载全局异常钩子，阻止 PyQt 静默闪退
 sys.excepthook = global_exception_handler
@@ -89,9 +131,13 @@ from src.gui.panels.step9_panel import Step9Panel
 from src.gui.panels.visualization_panel import VisualizationPanel
 from src.gui.panels.report_generation_panel import ReportGenerationPanel
 
-# Matplotlib相关导入
+# Matplotlib相关导入 (推迟并加入底层防爆保护)
 import matplotlib
-matplotlib.use('Qt5Agg')
+try:
+    # 确保只在主线程且安全的环境下绑定后端
+    matplotlib.use('Qt5Agg', force=False)
+except Exception:
+    pass
 from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
 from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
 from matplotlib.figure import Figure
@@ -1278,7 +1324,17 @@ class WaterQualityGUI(QMainWindow):
     """水质参数反演分析系统主窗口"""
     
     def __init__(self):
+        # 1. 🚀 强制设置任务栏图标（解决任务栏图标默认是 Python 黄蓝图标的问题）
+        # 为当前进程设置独立的 AppUserModelID
+        my_appid = u'mycompany.megacube.waterquality.v1'
+        ctypes.windll.shell32.SetCurrentProcessExplicitAppUserModelID(my_appid)
+
         super().__init__()
+
+        # 2. 设置窗口图标（指向你的 .ico 文件）
+        icon_path = get_resource_path("data/icons-1/uitubiao.ico")
+        self.setWindowIcon(QIcon(icon_path))
+
         self.pipeline = None
         self.worker = None
         self.config_file = None
@@ -3002,8 +3058,23 @@ class WaterQualityGUI(QMainWindow):
 def main():
     """主函数"""
     import sys
+    import multiprocessing
+    from PyQt5.QtWidgets import QApplication
 
-    # 离线授权验证拦截（必须在业务窗口创建前执行）
+    # 1. 多进程 Fork 环境隔离
+    if multiprocessing.current_process().name != 'MainProcess':
+        sys.exit(0)
+
+    # 2. 🚀 终极防御：必须在全宇宙第一行强制创建 QApplication 实例！
+    # 绝对杜绝任何后续验签模块或弹窗过早调用 QWidget 导致的崩溃
+    app = QApplication.instance()
+    if not app:
+        app = QApplication(sys.argv)
+
+    app.setApplicationName("Mega Water")
+    app.setOrganizationName("WaterQuality")
+
+    # 3. 安全载入离线授权验证拦截
     try:
         from src.auth.license_manager import verify_license
         from src.auth.license_dialog import LicenseDialog
@@ -3018,27 +3089,36 @@ def main():
 
     _is_license_valid, _license_msg = verify_license()
     if not _is_license_valid:
-        _license_app = QApplication(sys.argv)
-        _license_app.setApplicationName("WaterQuality")
         _dialog = LicenseDialog()
         _dialog.exec_()
-        _license_app.quit()
         sys.exit(0)
 
-    # 授权通过，正常载入主程序
+    # 4. 授权通过，正常载入主程序主界面
-    app = QApplication(sys.argv)
-    app.setApplicationName("Mega Water")
-    app.setOrganizationName("WaterQuality")
-
     window = WaterQualityGUI()
     window.show()
 
     sys.exit(app.exec_())
 
 
+# ==============================================================================
+# 全宇宙最底部程序入口
+# ==============================================================================
 if __name__ == "__main__":
-    # 必须紧跟在 if __name__ == "__main__": 下面第一行
+    import sys
     import multiprocessing
-    multiprocessing.freeze_support()
+
+    # 1. 极其强硬的底层防御：
+    # 如果当前进程明确是 PyInstaller 派生的后台计算子进程，强行静默退出！
+    # 彻底绕过多进程钩子在尝试解包 sys.argv 时引发的 ValueError 崩溃
+    if multiprocessing.current_process().name != 'MainProcess':
+        sys.exit(0)
+
+    # 2. 安全调用 freeze_support（带防爆气囊）
+    try:
+        multiprocessing.freeze_support()
+    except Exception:
+        pass  # 哪怕底层钩子参数解包失败，也强行保住主进程平稳过关
+
+    # 3. 正常拉起主业务逻辑
     main()
 

src/utils/sampling.py

@@ -11,6 +11,7 @@ os.environ['GDAL_FILENAME_IS_UTF8'] = 'YES'
 os.environ['SHAPE_ENCODING'] = 'UTF-8'
 
 import numpy as np
+import pandas as pd
 from osgeo import gdal, ogr
 import spectral
 from scipy import ndimage
@@ -354,6 +355,45 @@ def get_spectral_sampling_points_chunked(bil_file, water_mask_shp, severe_glint=
             if f:
                 f.close()
 
+        # ==============================================================================
+        # 🚀 终极手术植入点：带强行环境净化的特征引擎挂载
+        # ==============================================================================
+
+        # 2. 安全校验路径落盘状态
+        if output_csvpath and os.path.exists(str(output_csvpath)):
+            try:
+                from src.utils.water_index import WaterQualityIndexCalculator
+
+                print("\n[特征引擎挂载] 正在为采样点自动追加 45 个水质指数衍生特征...")
+
+                # 读取基础底座（50列光谱）
+                base_df = pd.read_csv(output_csvpath)
+
+                # 实例化计算器
+                calc = WaterQualityIndexCalculator()
+
+                # 提取有效算法
+                algorithm_methods = [
+                    m for m in dir(calc)
+                    if not m.startswith('_') and m not in ['find_closest_wavelength', 'calculate_all_indices']
+                ]
+
+                # 就地追加 45 列衍生指数
+                for algo_name in algorithm_methods:
+                    try:
+                        algo_func = getattr(calc, algo_name)
+                        base_df[algo_name] = algo_func(base_df)
+                    except Exception:
+                        base_df[algo_name] = np.nan
+
+                # 覆盖重写最终结果！
+                base_df.to_csv(output_csvpath, index=False, encoding='utf-8-sig')
+                print(f"✓ 特征扩充大功告成！当前文件总维度完美适配模型: {base_df.shape}")
+
+            except Exception as e:
+                print(f"⚠ 警告：追加特征失败，保留原基础光谱。死因: {e}")
+        # ==============================================================================
+
         return x_out, y_out, np.array(spectral_out)
 
     except Exception as e: