refactor(gui): 专题图UI美化/目录寻路/掩膜继承/隐藏冗余回归步骤

src/core/steps/__init__.py

@@ -0,0 +1,20 @@
+# -*- coding: utf-8 -*-
+"""业务步骤层模块"""
+
+from src.core.steps.water_mask_step import WaterMaskStep
+from src.core.steps.glint_detection_step import GlintDetectionStep
+from src.core.steps.glint_removal_step import GlintRemovalStep
+from src.core.steps.data_preparation_step import DataPreparationStep
+from src.core.steps.modeling_step import ModelingStep
+from src.core.steps.prediction_step import PredictionStep
+from src.core.steps.mapping_step import MappingStep
+
+__all__ = [
+    "WaterMaskStep",
+    "GlintDetectionStep",
+    "GlintRemovalStep",
+    "DataPreparationStep",
+    "ModelingStep",
+    "PredictionStep",
+    "MappingStep",
+]

src/core/steps/data_preparation_step.py

@@ -0,0 +1,184 @@
+# -*- coding: utf-8 -*-
+"""
+数据准备步骤
+
+包含 step4_process_csv, step5_extract_training_spectra, step5_5_calculate_water_quality_indices
+"""
+
+import time
+from pathlib import Path
+from typing import Optional, List, Union, Callable, Dict
+
+import pandas as pd
+import numpy as np
+
+
+class DataPreparationStep:
+    """数据准备步骤"""
+
+    # ---- Step 4: 处理CSV文件 ----
+
+    @staticmethod
+    def process_csv(
+        csv_path: str,
+        output_dir: Union[str, Path] = "./4_processed_data",
+        callback: Optional[Callable] = None,
+    ) -> str:
+        """处理CSV文件（筛选剔除异常值）"""
+        from src.preprocessing.process_water_quality_data import process_water_quality_data
+
+        output_dir = Path(output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+        output_path = str(output_dir / "processed_data.csv")
+
+        def notify(status, msg=""):
+            if callback:
+                callback("步骤4", status, msg)
+
+        print("\n" + "=" * 80)
+        print("步骤4: 处理CSV文件，筛选剔除异常值")
+        print("=" * 80)
+
+        step_start_time = time.time()
+
+        if Path(output_path).exists():
+            print(f"检测到已存在的处理后CSV文件，直接使用: {output_path}")
+            notify("skipped", f"处理后的CSV文件已设置: {output_path}")
+            return output_path
+
+        process_water_quality_data(csv_path, output_path)
+        notify("completed", f"处理后的CSV文件已保存: {output_path}")
+        return output_path
+
+    # ---- Step 5: 提取训练样本点光谱 ----
+
+    @staticmethod
+    def extract_training_spectra(
+        deglint_img_path: Optional[str] = None,
+        radius: int = 5,
+        source_epsg: int = 4326,
+        csv_path: Optional[str] = None,
+        boundary_path: Optional[str] = None,
+        glint_mask_path: Optional[str] = None,
+        water_mask_path: Optional[str] = None,
+        output_dir: Union[str, Path] = "./5_training_spectra",
+        callback: Optional[Callable] = None,
+    ) -> str:
+        """根据采样点坐标在去耀斑影像中提取平均光谱"""
+        from src.core.glint_removal.get_spectral import get_spectral_in_coor
+
+        output_dir = Path(output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+        output_path = str(output_dir / "training_spectra.csv")
+
+        def notify(status, msg=""):
+            if callback:
+                callback("步骤5", status, msg)
+
+        print("\n" + "=" * 80)
+        print("步骤5: 提取训练样本点的平均光谱")
+        print("=" * 80)
+
+        step_start_time = time.time()
+
+        if deglint_img_path is None:
+            raise ValueError("必须提供 deglint_img_path 参数")
+        if csv_path is None:
+            raise ValueError("必须提供 csv_path 参数")
+
+        if Path(output_path).exists():
+            print(f"检测到已存在的训练光谱数据文件，直接使用: {output_path}")
+            notify("skipped", f"训练光谱数据已设置: {output_path}")
+            return output_path
+
+        # 确保水体掩膜存在
+        final_boundary_path = boundary_path
+        if final_boundary_path is None and water_mask_path is not None:
+            final_boundary_path = water_mask_path
+
+        # 【新增安全防护】智能拦截矢量 .shp，强制替换为步骤 1 生成的栅格 .dat
+        if final_boundary_path and str(final_boundary_path).lower().endswith('.shp'):
+            # 向上追溯查找 1_water_mask 目录下的 dat 替身
+            possible_dat = Path(deglint_img_path).parent.parent / "1_water_mask" / "water_mask_from_shp.dat"
+            if not possible_dat.exists() and output_path:
+                possible_dat = Path(output_path).parent.parent / "1_water_mask" / "water_mask_from_shp.dat"
+
+            if possible_dat.exists():
+                print(f"💡 智能拦截：检测到输入掩膜为矢量 .shp，自动切换为已生成的栅格掩膜: {possible_dat}")
+                final_boundary_path = str(possible_dat)
+            else:
+                print(f"⚠️ 警告：检测到输入掩膜为 .shp 且未找到对应 .dat 替身，可能导致底层读取失败。")
+
+        flare_path = glint_mask_path
+        if flare_path:
+            print(f"光谱提取使用耀斑掩膜: {flare_path}")
+
+        get_spectral_in_coor(
+            deglint_img_path, csv_path, output_path,
+            radius=radius, flare_path=flare_path,
+            boundary_path=final_boundary_path, source_epsg=source_epsg
+        )
+
+        notify("completed", f"训练光谱数据已保存: {output_path}")
+        return output_path
+
+    # ---- Step 5.5: 计算水质光谱指数 ----
+
+    @staticmethod
+    def calculate_water_quality_indices(
+        training_spectra_path: Optional[str] = None,
+        formula_csv_file: Optional[str] = None,
+        formula_names: Optional[List[str]] = None,
+        output_file: Optional[str] = None,
+        enabled: bool = True,
+        output_dir: Union[str, Path] = "./6_water_quality_indices",
+        callback: Optional[Callable] = None,
+    ) -> Optional[str]:
+        """根据训练光谱计算水质光谱指数（使用 band_math 方法）"""
+        output_dir = Path(output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+        def notify(status, msg=""):
+            if callback:
+                callback("步骤5.5", status, msg)
+
+        print("\n" + "=" * 80)
+        print("步骤5.5: 计算水质光谱指数（使用band_math方法）")
+        print("=" * 80)
+
+        step_start_time = time.time()
+
+        if not enabled:
+            print("已设置跳过水质指数计算（enabled=False）。")
+            notify("skipped", "跳过水质指数计算")
+            return None
+
+        if training_spectra_path is None:
+            raise ValueError("必须提供 training_spectra_path 参数")
+        if formula_csv_file is None:
+            raise ValueError("必须提供 formula_csv_file 参数")
+
+        if output_file:
+            output_path = str(Path(output_file))
+        else:
+            output_path = str(output_dir / "water_quality_indices.csv")
+
+        if Path(output_path).exists():
+            print(f"检测到已存在的水质指数文件，直接使用: {output_path}")
+            notify("skipped", f"水质指数数据已设置: {output_path}")
+            return output_path
+
+        from src.utils.band_math import BandMathCalculator
+
+        calculator = BandMathCalculator(training_spectra_path)
+        result_df = calculator.process_formulas_from_csv(
+            formula_csv_file=formula_csv_file,
+            formula_names=formula_names,
+            output_file=output_path
+        )
+
+        if result_df is None:
+            raise ValueError("计算水质指数失败，请检查公式CSV文件格式")
+
+        notify("completed", f"水质指数已保存: {output_path}")
+        return output_path

src/core/steps/glint_detection_step.py

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+# -*- coding: utf-8 -*-
+"""
+步骤2: 耀斑区域检测
+
+支持多种检测方法: otsu, zscore, percentile, iqr, adaptive, multi_band
+"""
+
+import time
+from pathlib import Path
+from typing import Optional, List, Union
+
+
+class GlintDetectionStep:
+    """耀斑区域检测步骤"""
+
+    @staticmethod
+    def run(
+        img_path: str,
+        glint_wave: float = 750.0,
+        method: str = "otsu",
+        z_threshold: float = 2.5,
+        percentile: float = 95.0,
+        iqr_multiplier: float = 1.5,
+        window_size: int = 15,
+        multi_band_waves: Optional[List[float]] = None,
+        sub_method: str = "zscore",
+        weights: Optional[List[float]] = None,
+        max_area: Optional[int] = None,
+        buffer_size: Optional[int] = None,
+        water_mask_path: Optional[str] = None,
+        glint_dir: Union[str, Path] = "./2_glint",
+        callback: Optional[callable] = None,
+    ) -> str:
+        """
+        执行耀斑区域检测
+
+        Args:
+            img_path: 输入影像文件路径
+            glint_wave: 用于耀斑检测的波段波长（nm）
+            method: 检测方法 ('otsu' | 'zscore' | 'percentile' | 'iqr' | 'adaptive' | 'multi_band')
+            z_threshold: Z-score 方法阈值（默认 2.5）
+            percentile: 百分位数阈值（默认 95.0）
+            iqr_multiplier: IQR 倍数（默认 1.5）
+            window_size: 自适应阈值窗口大小（默认 15）
+            multi_band_waves: 多波段方法的波长列表，如 [750, 800, 850]
+            sub_method: 多波段方法的子方法（默认 'zscore'）
+            weights: 多波段方法的权重列表（None 表示等权重）
+            max_area: 最大连通域面积阈值（像素），超过则过滤
+            buffer_size: 岸边缓冲区大小（像素），用于去除岸边附近错误掩膜
+            water_mask_path: 水域掩膜文件路径（dat 格式优先）
+            glint_dir: 工作目录
+            callback: 回调函数
+
+        Returns:
+            耀斑掩膜文件路径 (.dat)
+        """
+        from src.utils.find_severe_glint_area import find_severe_glint_area
+
+        glint_dir = Path(glint_dir)
+        glint_dir.mkdir(parents=True, exist_ok=True)
+
+        def notify(status, msg=""):
+            if callback:
+                callback("步骤2", status, msg)
+
+        print("\n" + "=" * 80)
+        print("步骤2: 找到耀斑区域")
+        print("=" * 80)
+
+        step_start_time = time.time()
+
+        # 确定水体掩膜路径
+        if water_mask_path is not None and Path(water_mask_path).exists():
+            final_water_mask_path = water_mask_path
+        else:
+            final_water_mask_path = None
+
+        output_path = str(glint_dir / "severe_glint_area.dat")
+
+        # 跳过已存在的文件
+        if Path(output_path).exists():
+            print(f"检测到已存在的耀斑掩膜文件，直接使用: {output_path}")
+            notify("skipped", f"耀斑掩膜已设置: {output_path}")
+            return output_path
+
+        # 构建检测参数字典
+        kwargs = {
+            "method": method,
+            "z_threshold": z_threshold,
+            "percentile": percentile,
+            "iqr_multiplier": iqr_multiplier,
+            "window_size": window_size,
+        }
+        if method == "multi_band":
+            if multi_band_waves is not None:
+                kwargs["multi_band_waves"] = multi_band_waves
+            if sub_method is not None:
+                kwargs["sub_method"] = sub_method
+            if weights is not None:
+                kwargs["weights"] = weights
+        if max_area is not None:
+            kwargs["max_area"] = max_area
+        if buffer_size is not None:
+            kwargs["buffer_size"] = buffer_size
+
+        glint_mask_path = find_severe_glint_area(
+            img_path, final_water_mask_path, glint_wave, output_path, **kwargs
+        )
+
+        print(f"耀斑掩膜已生成: {glint_mask_path}")
+        print(f"使用检测方法: {method}")
+        notify("completed", f"耀斑掩膜已生成: {glint_mask_path}")
+        return glint_mask_path

src/core/steps/glint_removal_step.py

@@ -0,0 +1,375 @@
+# -*- coding: utf-8 -*-
+"""
+步骤3: 去除耀斑
+
+支持多种方法: subtract_nir, regression_slope, oxygen_absorption, kutser, goodman, hedley, sugar
+
+每种方法都会：
+1. 准备水域掩膜（支持 shp 自动转 dat）
+2. 调用对应的算法类执行处理
+3. 复制 hdr 文件到输出影像
+"""
+
+import os
+import time
+from pathlib import Path
+from typing import Optional, List, Union, Callable
+
+import numpy as np
+
+
+def _safe_rename(src_bsq: str, src_hdr: str, dest_bsq: str, dest_hdr: str) -> str:
+    """将底层硬编码生成的 .bsq + .hdr 文件对重命名到用户指定的 output_path
+
+    使用 os.remove + os.rename 确保原子覆盖（不等 os.replace 的跨设备行为），
+    resolve() 断路防止同路径 self-rename 报错。
+
+    Returns:
+        dest_bsq 路径
+    """
+    src_bsq_p = Path(src_bsq)
+    src_hdr_p = Path(src_hdr)
+    dest_bsq_p = Path(dest_bsq)
+    dest_hdr_p = Path(dest_hdr)
+
+    if str(src_bsq_p.resolve()) == str(dest_bsq_p.resolve()):
+        return dest_bsq
+
+    if dest_bsq_p.exists():
+        os.remove(dest_bsq_p)
+    if dest_hdr_p.exists():
+        os.remove(dest_hdr_p)
+
+    if src_bsq_p.exists():
+        os.rename(src_bsq_p, dest_bsq_p)
+    if src_hdr_p.exists():
+        os.rename(src_hdr_p, dest_hdr_p)
+
+    return dest_bsq
+
+
+class GlintRemovalStep:
+    """去除耀斑步骤"""
+
+    @staticmethod
+    def run(
+        img_path: str,
+        method: str = "subtract_nir",
+        start_wave: Optional[float] = None,
+        end_wave: Optional[float] = None,
+        json_path: Optional[str] = None,
+        left_shoulder_wave: Optional[float] = None,
+        valley_wave: Optional[float] = None,
+        right_shoulder_wave: Optional[float] = None,
+        water_mask: Optional[Union[str, np.ndarray]] = None,
+        interpolated_img_path: Optional[str] = None,
+        interpolate_zeros: bool = False,
+        interpolation_method: str = "nearest",
+        enabled: bool = True,
+        # Kutser 参数
+        kutser_shp_path: Optional[str] = None,
+        oxy_band: int = 38,
+        lower_oxy: int = 36,
+        upper_oxy: int = 49,
+        nir_band: int = 47,
+        # Goodman 参数
+        nir_lower: int = 25,
+        nir_upper: int = 37,
+        goodman_A: float = 0.000019,
+        goodman_B: float = 0.1,
+        # Hedley 参数
+        hedley_shp_path: Optional[str] = None,
+        hedley_nir_band: int = 47,
+        # SUGAR 参数
+        sugar_bounds: Optional[List[tuple]] = None,
+        sugar_sigma: float = 1.0,
+        sugar_estimate_background: bool = True,
+        sugar_glint_mask_method: str = "cdf",
+        sugar_iter: Optional[int] = 3,
+        sugar_termination_thresh: float = 20.0,
+        # 内部工具函数
+        _get_image_geo_info=None,
+        _load_image_as_array=None,
+        _save_bands_as_image=None,
+        _copy_hdr_info=None,
+        _prepare_water_mask_for_algorithm=None,
+        _interpolate_zero_pixels_batch=None,
+        deglint_dir: Union[str, Path] = "./3_deglint",
+        water_mask_dir: Union[str, Path] = "./1_water_mask",
+        callback: Optional[Callable] = None,
+        output_path: Optional[str] = None,
+    ) -> str:
+        """
+        执行去除耀斑处理
+
+        Args:
+            img_path: 输入影像文件路径
+            method: 去耀斑方法
+            ...（其余参数同主类 step3_remove_glint）
+
+        Returns:
+            去除耀斑后的影像文件路径
+        """
+        from src.core.glint_removal.Kutser import Kutser
+        from src.core.glint_removal.Goodman import Goodman
+        from src.core.glint_removal.Hedley import Hedley
+        from src.core.glint_removal.SUGAR import SUGAR, correction_iterative
+        from src.core.utils.gdal_helper import (
+            get_image_geo_info as _default_get_geo,
+            load_image_as_array as _default_load,
+            save_bands_as_image as _default_save_bands,
+            copy_hdr_info as _default_copy_hdr,
+        )
+        from src.core.utils.mask_converter import (
+            prepare_water_mask_for_algorithm as _default_prepare,
+        )
+
+        # 使用提供的函数或默认函数
+        if _get_image_geo_info is None:
+            _get_image_geo_info = _default_get_geo
+        if _load_image_as_array is None:
+            _load_image_as_array = _default_load
+        if _save_bands_as_image is None:
+            _save_bands_as_image = _default_save_bands
+        if _copy_hdr_info is None:
+            _copy_hdr_info = _default_copy_hdr
+        if _prepare_water_mask_for_algorithm is None:
+            _prepare_water_mask_for_algorithm = _default_prepare
+
+        deglint_dir = Path(deglint_dir)
+        deglint_dir.mkdir(parents=True, exist_ok=True)
+
+        def notify(status, msg=""):
+            if callback:
+                callback("步骤3", status, msg)
+
+        print("\n" + "=" * 80)
+        print("步骤3: 去除耀斑")
+        print("=" * 80)
+
+        step_start_time = time.time()
+
+        # 方法名标准化
+        raw_method = str(method).lower()
+        if "kutser" in raw_method:
+            method = "kutser"
+        elif "goodman" in raw_method:
+            method = "goodman"
+        elif "hedley" in raw_method:
+            method = "hedley"
+        elif "sugar" in raw_method:
+            method = "sugar"
+
+        # 如果未启用，直接返回原始影像
+        if not enabled:
+            print("已设置跳过去除耀斑（enabled=False），将直接使用原始影像。")
+            notify("skipped", "跳过去耀斑，使用原始影像")
+            return img_path
+
+        # ---- 确定水域掩膜 ----
+        final_water_mask = water_mask
+        if final_water_mask is not None and str(final_water_mask).lower().endswith(".shp"):
+            # shp 自动替换为 dat
+            dat_mask = str(Path(water_mask_dir) / "water_mask_from_shp.dat")
+            if Path(dat_mask).exists():
+                print(f"检测到输入掩膜为 .shp，自动替换为栅格掩膜: {dat_mask}")
+                final_water_mask = dat_mask
+
+        if final_water_mask is None:
+            dat_mask_default = str(Path(water_mask_dir) / "water_mask_from_shp.dat")
+            if Path(dat_mask_default).exists():
+                final_water_mask = dat_mask_default
+                print(f"使用步骤1生成的水域掩膜: {final_water_mask}")
+
+        # ---- 步骤3.1: 0值像素插值 ----
+        if interpolate_zeros:
+            print("\n" + "-" * 80)
+            print("步骤3.1: 对0值像素进行插值")
+            print("-" * 80)
+            interp_start_time = time.time()
+
+            if _interpolate_zero_pixels_batch is None:
+                from src.core.algorithms.interpolation.interpolator import (
+                    interpolate_zero_pixels_batch as _interp_batch,
+                )
+                _interpolate_zero_pixels_batch = _interp_batch
+
+            interp_result, _ = _interpolate_zero_pixels_batch(
+                img_path=img_path,
+                interpolation_method=interpolation_method,
+                output_path=None,
+                water_mask=final_water_mask,
+                deglint_dir=str(deglint_dir),
+                callback_progress=lambda msg: print(f"  {msg}"),
+            )
+            img_path = interp_result
+            interp_end_time = time.time()
+            print(f"插值完成，使用插值后的影像: {img_path}")
+
+        # ---- 获取影像信息 ----
+        geotransform, projection, width, height, n_bands = _get_image_geo_info(img_path)
+        print(f"影像尺寸: {width} x {height} x {n_bands}")
+
+        mask_for_algorithm = _prepare_water_mask_for_algorithm(
+            final_water_mask, (height, width), geotransform, projection, img_path
+        )
+
+        # ==================== Kutser ====================
+        if method == "kutser":
+            print(f"使用方法: Kutser (氧吸收波段={oxy_band}, NIR波段={nir_band})")
+            hardcoded_bsq = str(deglint_dir / "deglint_kutser.bsq")
+            hardcoded_hdr = hardcoded_bsq.replace(".bsq", ".hdr")
+            # 将用户指定的 output_path 标准化为 .bsq 路径
+            if output_path:
+                final_bsq = output_path.replace('.dat', '.bsq').replace('.tif', '.bsq')
+                final_hdr = final_bsq.replace(".bsq", ".hdr")
+            else:
+                final_bsq = hardcoded_bsq
+                final_hdr = hardcoded_hdr
+
+            if Path(hardcoded_bsq).exists():
+                print(f"检测到已存在的去耀斑影像文件，直接使用: {hardcoded_bsq}")
+                notify("skipped", f"去耀斑影像已设置: {hardcoded_bsq}")
+                return _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+
+            kutser = Kutser(
+                img_path,
+                shp_path=None,
+                oxy_band=oxy_band,
+                lower_oxy=lower_oxy,
+                upper_oxy=upper_oxy,
+                NIR_band=nir_band,
+                water_mask=mask_for_algorithm,
+                output_path=hardcoded_bsq,
+            )
+            kutser.get_corrected_bands()
+
+            if Path(hardcoded_bsq).exists():
+                _copy_hdr_info(img_path, hardcoded_bsq)
+                final = _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+                notify("completed", f"去耀斑影像已生成: {final}")
+                return final
+            raise RuntimeError(f"Kutser算法未生成输出文件: {hardcoded_bsq}")
+
+        # ==================== Goodman ====================
+        elif method == "goodman":
+            print(f"使用方法: Goodman (NIR波段范围: {nir_lower}-{nir_upper})")
+            hardcoded_bsq = str(deglint_dir / "deglint_goodman.bsq")
+            hardcoded_hdr = hardcoded_bsq.replace(".bsq", ".hdr")
+            if output_path:
+                final_bsq = output_path.replace('.dat', '.bsq').replace('.tif', '.bsq')
+                final_hdr = final_bsq.replace(".bsq", ".hdr")
+            else:
+                final_bsq = hardcoded_bsq
+                final_hdr = hardcoded_hdr
+
+            if Path(hardcoded_bsq).exists():
+                print(f"检测到已存在的去耀斑影像文件，直接使用: {hardcoded_bsq}")
+                notify("skipped", f"去耀斑影像已设置: {hardcoded_bsq}")
+                return _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+
+            goodman = Goodman(
+                img_path,
+                NIR_lower=nir_lower,
+                NIR_upper=nir_upper,
+                A=goodman_A,
+                B=goodman_B,
+                water_mask=mask_for_algorithm,
+                output_path=hardcoded_bsq,
+            )
+            corrected_bands = goodman.get_corrected_bands()
+
+            if not Path(hardcoded_bsq).exists():
+                _save_bands_as_image(corrected_bands, hardcoded_bsq, geotransform, projection)
+            _copy_hdr_info(img_path, hardcoded_bsq)
+            del corrected_bands
+
+            final = _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+            notify("completed", f"去耀斑影像已生成: {final}")
+            return final
+
+        # ==================== Hedley ====================
+        elif method == "hedley":
+            print(f"使用方法: Hedley (NIR波段={hedley_nir_band})")
+            hardcoded_bsq = str(deglint_dir / "deglint_hedley.bsq")
+            hardcoded_hdr = hardcoded_bsq.replace(".bsq", ".hdr")
+            if output_path:
+                final_bsq = output_path.replace('.dat', '.bsq').replace('.tif', '.bsq')
+                final_hdr = final_bsq.replace(".bsq", ".hdr")
+            else:
+                final_bsq = hardcoded_bsq
+                final_hdr = hardcoded_hdr
+
+            if Path(hardcoded_bsq).exists():
+                print(f"检测到已存在的去耀斑影像文件，直接使用: {hardcoded_bsq}")
+                notify("skipped", f"去耀斑影像已设置: {hardcoded_bsq}")
+                return _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+
+            hedley = Hedley(
+                img_path,
+                shp_path=None,
+                NIR_band=hedley_nir_band,
+                water_mask=mask_for_algorithm,
+                output_path=hardcoded_bsq,
+            )
+            hedley.get_corrected_bands()
+
+            if Path(hardcoded_bsq).exists():
+                _copy_hdr_info(img_path, hardcoded_bsq)
+                final = _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+                notify("completed", f"去耀斑影像已生成: {final}")
+                return final
+            raise RuntimeError(f"Hedley算法未生成输出文件: {hardcoded_bsq}")
+
+        # ==================== SUGAR ====================
+        elif method == "sugar":
+            glint_method_raw = str(sugar_glint_mask_method).lower()
+            if "cdf" in glint_method_raw or "累积" in glint_method_raw:
+                sugar_glint_mask_method_fixed = "cdf"
+            elif "otsu" in glint_method_raw or "大津" in glint_method_raw:
+                sugar_glint_mask_method_fixed = "otsu"
+            else:
+                sugar_glint_mask_method_fixed = "cdf"
+
+            print(
+                f"使用方法: SUGAR (迭代次数={sugar_iter}, 掩膜方法={sugar_glint_mask_method_fixed})"
+            )
+            hardcoded_bsq = str(deglint_dir / "deglint_sugar.bsq")
+            hardcoded_hdr = hardcoded_bsq.replace(".bsq", ".hdr")
+            if output_path:
+                final_bsq = output_path.replace('.dat', '.bsq').replace('.tif', '.bsq')
+                final_hdr = final_bsq.replace(".bsq", ".hdr")
+            else:
+                final_bsq = hardcoded_bsq
+                final_hdr = hardcoded_hdr
+
+            if Path(hardcoded_bsq).exists():
+                print(f"检测到已存在的去耀斑影像文件，直接使用: {hardcoded_bsq}")
+                notify("skipped", f"去耀斑影像已设置: {hardcoded_bsq}")
+                return _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+
+            if sugar_bounds is None:
+                sugar_bounds = [(1, 2)]
+
+            correction_iterative(
+                img_path,
+                iter=sugar_iter,
+                bounds=sugar_bounds,
+                estimate_background=sugar_estimate_background,
+                glint_mask_method=sugar_glint_mask_method_fixed,
+                termination_thresh=sugar_termination_thresh,
+                water_mask=mask_for_algorithm,
+                output_path=hardcoded_bsq,
+            )
+
+            if Path(hardcoded_bsq).exists():
+                _copy_hdr_info(img_path, hardcoded_bsq)
+                final = _safe_rename(hardcoded_bsq, hardcoded_hdr, final_bsq, final_hdr)
+                notify("completed", f"去耀斑影像已生成: {final}")
+                return final
+            raise RuntimeError(f"SUGAR算法未生成输出文件: {hardcoded_bsq}")
+
+        else:
+            raise ValueError(
+                f"不支持的方法: {method}。支持的方法: kutser, goodman, hedley, sugar"
+            )

src/core/steps/mapping_step.py

@@ -0,0 +1,109 @@
+# -*- coding: utf-8 -*-
+"""
+成图步骤
+
+包含 step9_generate_distribution_map
+"""
+
+import time
+from pathlib import Path
+from typing import Optional, Union, Callable
+
+
+class MappingStep:
+    """成图步骤"""
+
+    @staticmethod
+    def generate_distribution_map(
+        prediction_csv_path: str,
+        boundary_shp_path: str,
+        output_image_path: Optional[str] = None,
+        resolution: float = 30,
+        input_crs: str = "EPSG:32651",
+        output_crs: str = "EPSG:4326",
+        show_sample_points: bool = False,
+        base_map_tif: Optional[str] = None,
+        use_distance_diffusion: bool = True,
+        max_diffusion_distance: Optional[float] = None,
+        diffusion_power: float = 2,
+        diffusion_n_neighbors: int = 15,
+        cmap: Optional[str] = None,
+        expand_ratio: float = 0.05,
+        output_dir: Union[str, Path] = "./14_visualization",
+        callback: Optional[Callable] = None,
+    ) -> str:
+        """
+        根据采样点的坐标和反演的实测参数，通过插值方法得到水质参数可视化分布图
+
+        Args:
+            prediction_csv_path: 预测结果CSV文件路径（前两列为经纬度，第三列为预测值）
+            boundary_shp_path: 边界shapefile文件路径
+            output_image_path: 输出图片路径（如果为None，自动生成）
+            resolution: 插值网格分辨率（米）
+            input_crs: 输入坐标系
+            output_crs: 输出坐标系
+            show_sample_points: 是否在图上显示采样点
+            base_map_tif: 底图TIF路径
+            use_distance_diffusion: 是否启用距离扩散补全边界
+            max_diffusion_distance: 距离扩散最大距离（米）
+            diffusion_power: 距离扩散幂参数
+            diffusion_n_neighbors: 距离扩散最近邻数量
+            cmap: 颜色映射名称（None表示自动识别）
+            expand_ratio: 边界外扩比例（0-1之间）
+            output_dir: 输出目录
+            callback: 回调函数
+
+        Returns:
+            可视化分布图文件路径
+        """
+        from src.postprocessing.map import ContentMapper
+
+        output_dir = Path(output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+        def notify(status, msg=""):
+            if callback:
+                callback("步骤9", status, msg)
+
+        print("\n" + "=" * 80)
+        print("步骤9: 生成水质参数可视化分布图")
+        print("=" * 80)
+
+        step_start_time = time.time()
+
+        if output_image_path is None:
+            csv_name = Path(prediction_csv_path).stem
+            output_image_path = str(output_dir / f"{csv_name}_distribution.png")
+
+        if Path(output_image_path).exists():
+            print(f"检测到已存在的分布图文件，直接使用: {output_image_path}")
+            notify("skipped", f"可视化分布图已设置: {output_image_path}")
+            return output_image_path
+
+        mapper = ContentMapper(input_crs=input_crs, output_crs=output_crs)
+
+        mapper_kwargs = {
+            "resolution": resolution,
+            "show_sample_points": show_sample_points,
+            "use_distance_diffusion": use_distance_diffusion,
+            "diffusion_power": diffusion_power,
+            "diffusion_n_neighbors": diffusion_n_neighbors,
+            "expand_ratio": expand_ratio,
+        }
+
+        optional_kwargs = {
+            "base_map_tif": base_map_tif,
+            "max_diffusion_distance": max_diffusion_distance,
+            "cmap": cmap,
+        }
+        mapper_kwargs.update({k: v for k, v in optional_kwargs.items() if v is not None})
+
+        mapper.process_data(
+            csv_file=prediction_csv_path,
+            shp_file=boundary_shp_path,
+            output_file=output_image_path,
+            **mapper_kwargs,
+        )
+
+        notify("completed", f"可视化分布图已保存: {output_image_path}")
+        return output_image_path

src/core/steps/modeling_step.py

@@ -0,0 +1,497 @@
+# -*- coding: utf-8 -*-
+"""
+建模步骤
+
+包含 step6_train_models, step6_5_non_empirical_modeling, step6_75_custom_regression
+"""
+
+import time
+import json
+from pathlib import Path
+from typing import Optional, List, Union, Callable, Dict
+
+import pandas as pd
+import numpy as np
+
+
+# ============================================================
+# 汉化 -> 英文 反向映射字典（UI 复选框显示文本 -> 底层算法键名）
+# ============================================================
+
+# 模型名称：中文 (缩写) -> 英文键名
+MODEL_NAME_MAP = {
+    "多元线性回归 (MLR)": "LinearRegression",
+    "岭回归 (Ridge)": "Ridge",
+    "套索回归 (Lasso)": "Lasso",
+    "弹性网络 (ElasticNet)": "ElasticNet",
+    "偏最小二乘 (PLSR)": "PLS",
+    "决策树 (CART)": "DecisionTree",
+    "随机森林 (RF)": "RF",
+    "极端随机树 (ET)": "ExtraTrees",
+    "极值梯度提升 (XGBoost)": "XGBoost",
+    "轻量梯度提升 (LightGBM)": "LightGBM",
+    "类别梯度提升 (CatBoost)": "CatBoost",
+    "梯度提升树 (GBDT)": "GradientBoosting",
+    "自适应提升 (AdaBoost)": "AdaBoost",
+    "支持向量回归 (SVR)": "SVR",
+    "K近邻回归 (KNN)": "KNN",
+    "多层感知机 (BP神经网络)": "MLP",
+}
+
+# 预处理方法：各种可能的中文变体 -> 标准键名
+PREPROC_NAME_MAP = {
+    # 无处理
+    "无 (None)": "None",
+    "None": "None",
+    # MMS
+    "最小-最大归一化 (MMS)": "MMS",
+    "MMS": "MMS",
+    # SS
+    "标度化 (SS)": "SS",
+    "SS": "SS",
+    # SNV
+    "标准正态变换 (SNV)": "SNV",
+    "SNV": "SNV",
+    # MA
+    "移动平均 (MA)": "MA",
+    "MA": "MA",
+    # SG
+    "Savitzky-Golay (SG)": "SG",
+    "SG": "SG",
+    # MSC
+    "多元散射校正 (MSC)": "MSC",
+    "MSC": "MSC",
+    # D1
+    "一阶导数 (D1)": "D1",
+    "D1": "D1",
+    # D2
+    "二阶导数 (D2)": "D2",
+    "D2": "D2",
+    # DT
+    "去趋势 (DT)": "DT",
+    "DT": "DT",
+    # CT
+    "中心化 (CT)": "CT",
+    "CT": "CT",
+}
+
+# 数据划分方法：各种可能的中文变体 -> 标准键名
+SPLIT_NAME_MAP = {
+    "SPXY 算法 (考量X-Y空间)": "spxy",
+    "spxy": "spxy",
+    "KS 算法 (考量X空间)": "ks",
+    "ks": "ks",
+    "随机划分 (Random)": "random",
+    "random": "random",
+}
+
+
+def _normalize_model_names(model_names: List[str]) -> List[str]:
+    """清洗模型名称列表：将汉化显示文本还原为英文键名"""
+    result = []
+    for name in model_names:
+        if name in MODEL_NAME_MAP:
+            result.append(MODEL_NAME_MAP[name])
+        else:
+            # 已经是英文键名，直接保留
+            result.append(name)
+    return result
+
+
+def _normalize_preprocessing_methods(methods: List[str]) -> List[str]:
+    """清洗预处理方法列表：将汉化显示文本还原为标准键名"""
+    result = []
+    for method in methods:
+        if method in PREPROC_NAME_MAP:
+            result.append(PREPROC_NAME_MAP[method])
+        else:
+            # 已经是标准键名，直接保留
+            result.append(method)
+    return result
+
+
+def _normalize_split_methods(methods: List[str]) -> List[str]:
+    """清洗数据划分方法列表：将汉化显示文本还原为标准键名"""
+    result = []
+    for method in methods:
+        if method in SPLIT_NAME_MAP:
+            result.append(SPLIT_NAME_MAP[method])
+        else:
+            # 已经是标准键名，直接保留
+            result.append(method)
+    return result
+
+
+class ModelingStep:
+    """建模步骤"""
+
+    # ---- Step 6: 训练机器学习模型 ----
+
+    @staticmethod
+    def train_models(
+        feature_start_column: str = "374.285004",
+        preprocessing_methods: Optional[List[str]] = None,
+        model_names: Optional[List[str]] = None,
+        split_methods: Optional[List[str]] = None,
+        cv_folds: int = 5,
+        training_csv_path: Optional[str] = None,
+        output_dir: Union[str, Path] = "./7_Supervised_Model_Training",
+        callback: Optional[Callable] = None,
+        _report_generator=None,
+    ) -> str:
+        """使用采样点光谱和实测值建立机器学习模型"""
+        from src.core.modeling.modeling_batch import WaterQualityModelingBatch
+
+        output_dir = Path(output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+        def notify(status, msg=""):
+            if callback:
+                callback("步骤6", status, msg)
+
+        print("\n" + "=" * 80)
+        print("步骤6: 训练机器学习模型")
+        print("=" * 80)
+
+        step_start_time = time.time()
+
+        if training_csv_path is None:
+            raise ValueError("必须提供 training_csv_path 参数")
+
+        # 检查模型目录是否已有模型
+        if output_dir.exists() and any(output_dir.iterdir()):
+            has_models = False
+            for item in output_dir.iterdir():
+                if item.is_dir():
+                    model_files = (
+                        list(item.glob("*.pkl"))
+                        + list(item.glob("*.joblib"))
+                        + list(item.glob("*.h5"))
+                    )
+                    if model_files:
+                        has_models = True
+                        break
+            if has_models:
+                print(f"检测到已存在的模型文件，直接使用: {output_dir}")
+                notify("skipped", f"模型目录已设置: {output_dir}")
+                return str(output_dir)
+
+        if preprocessing_methods is None:
+            preprocessing_methods = ["None", "MMS", "SS", "SNV", "MA", "SG", "MSC", "D1", "D2", "DT", "CT"]
+        if model_names is None:
+            model_names = ["SVR", "RF", "Ridge", "Lasso"]
+        if split_methods is None:
+            split_methods = ["spxy", "ks", "random"]
+
+        # ---- 汉化清洗：将 UI 传来的中文/混合名称转换为底层英文键名 ----
+        preprocessing_methods = _normalize_preprocessing_methods(preprocessing_methods)
+        model_names = _normalize_model_names(model_names)
+        split_methods = _normalize_split_methods(split_methods)
+
+        print(f"[参数清洗] 预处理方法: {preprocessing_methods}")
+        print(f"[参数清洗] 模型名称: {model_names}")
+        print(f"[参数清洗] 划分方法: {split_methods}")
+
+        modeler = WaterQualityModelingBatch(str(output_dir))
+        modeler.train_models_batch(
+            csv_path=training_csv_path,
+            feature_start_column=feature_start_column,
+            preprocessing_methods=preprocessing_methods,
+            model_names=model_names,
+            split_methods=split_methods,
+            cv_folds=cv_folds,
+        )
+
+        print(f"模型训练完成，结果保存在: {output_dir}")
+
+        if _report_generator is not None:
+            try:
+                summary_path = _report_generator.generate_training_summary(str(output_dir))
+                print(f"训练摘要报告已生成: {summary_path}")
+            except Exception as e:
+                print(f"生成训练摘要报告时出错: {e}")
+
+        notify("completed", f"模型训练完成: {output_dir}")
+        return str(output_dir)
+
+    # ---- Step 6.5: 非经验统计回归模型训练 ----
+
+    @staticmethod
+    def train_non_empirical_models(
+        csv_path: Optional[str] = None,
+        preprocessing_methods: Optional[List[str]] = None,
+        algorithms: Optional[List[str]] = None,
+        value_cols: Union[int, Dict[str, int]] = 0,
+        spectral_start_col: int = 1,
+        spectral_end_col: Optional[int] = None,
+        window: int = 5,
+        output_dir: Optional[str] = None,
+        enabled: bool = True,
+        callback: Optional[Callable] = None,
+    ) -> Dict[str, str]:
+        """非经验统计回归模型训练"""
+        def notify(status, msg=""):
+            if callback:
+                callback("步骤6.5", status, msg)
+
+        print("\n" + "=" * 80)
+        print("步骤6.5: 非经验统计回归模型训练")
+        print("=" * 80)
+
+        step_start_time = time.time()
+
+        if not enabled:
+            print("已设置跳过非经验模型训练（enabled=False）。")
+            notify("skipped", "跳过的经验模型训练")
+            return {}
+
+        if csv_path is None:
+            raise ValueError("必须提供 csv_path 参数")
+
+        if output_dir is not None:
+            non_empirical_dir = Path(output_dir)
+        else:
+            non_empirical_dir = Path.cwd() / "8_Regression_Modeling"
+        non_empirical_dir.mkdir(parents=True, exist_ok=True)
+
+        if preprocessing_methods is None:
+            preprocessing_methods = ["None"]
+        if algorithms is None:
+            algorithms = ["chl_a", "nh3", "mno4", "tn", "tp", "tss"]
+
+        if isinstance(value_cols, int):
+            value_cols_dict = {algorithm: value_cols for algorithm in algorithms}
+        elif isinstance(value_cols, dict):
+            value_cols_dict = value_cols
+        else:
+            raise ValueError("value_cols 参数必须是整数或字典")
+
+        if spectral_end_col is None:
+            df = pd.read_csv(csv_path)
+            spectral_end_col = len(df.columns) - 1
+
+        all_model_results = {}
+
+        for preprocess in preprocessing_methods:
+            preprocess_dir = non_empirical_dir / preprocess
+            preprocess_dir.mkdir(parents=True, exist_ok=True)
+
+            processed_csv_path = _apply_preprocessing_internal(
+                csv_path, preprocess, preprocess_dir, spectral_start_col
+            )
+
+            for algorithm in algorithms:
+                algorithm_value_col = value_cols_dict[algorithm]
+                print(f"\n训练 {preprocess} + {algorithm} 模型 (实测值列: {algorithm_value_col})...")
+
+                model_outpath = str(preprocess_dir / f"{preprocess}_{algorithm}.json")
+
+                if Path(model_outpath).exists():
+                    print(f"检测到已存在的模型文件，直接使用: {model_outpath}")
+                    all_model_results[f"{preprocess}_{algorithm}"] = model_outpath
+                    continue
+
+                try:
+                    from src.core.non_empirical_model_correction import run_model_correction
+                    run_model_correction(
+                        algorithm=algorithm,
+                        csv_file=processed_csv_path if Path(processed_csv_path).exists() else csv_path,
+                        value_col=algorithm_value_col,
+                        spectral_start=spectral_start_col,
+                        spectral_end=spectral_end_col,
+                        model_info_outpath=model_outpath,
+                        window=window,
+                    )
+                    all_model_results[f"{preprocess}_{algorithm}"] = model_outpath
+                    print(f"模型训练完成: {model_outpath}")
+                except Exception as e:
+                    print(f"训练 {preprocess}_{algorithm} 模型时出错: {e}")
+                    continue
+
+        summary_path = _generate_non_empirical_summary(all_model_results, non_empirical_dir)
+        notify("completed", f"非经验模型训练完成: {non_empirical_dir}")
+        return all_model_results
+
+    # ---- Step 6.75: 自定义回归分析 ----
+
+    @staticmethod
+    def custom_regression(
+        csv_path: Optional[str] = None,
+        x_columns: Optional[Union[str, List[str]]] = None,
+        y_columns: Optional[Union[str, List[str]]] = None,
+        methods: Union[str, List[str]] = "all",
+        output_dir: Optional[str] = None,
+        enabled: bool = True,
+        callback: Optional[Callable] = None,
+        work_dir: Union[str, Path] = "./work_dir",
+    ) -> Optional[str]:
+        """使用自定义回归方法分析指标与目标参数之间的关系"""
+        def notify(status, msg=""):
+            if callback:
+                callback("步骤6.75", status, msg)
+
+        print("\n" + "=" * 80)
+        print("步骤6.75: 自定义回归分析")
+        print("=" * 80)
+
+        step_start_time = time.time()
+
+        if not enabled:
+            print("已设置跳过自定义回归分析（enabled=False）。")
+            notify("skipped", "跳过自定义回归分析")
+            return None
+
+        if csv_path is None:
+            raise ValueError("必须提供 csv_path 参数")
+        if y_columns is None:
+            raise ValueError("必须指定 y_columns")
+        if x_columns is None:
+            raise ValueError("必须指定 x_columns")
+
+        if isinstance(x_columns, str):
+            x_columns = [x_columns]
+        if isinstance(y_columns, str):
+            y_columns = [y_columns]
+
+        df = pd.read_csv(csv_path)
+        missing_x = [col for col in x_columns if col not in df.columns]
+        missing_y = [col for col in y_columns if col not in df.columns]
+        if missing_x:
+            raise ValueError(f"自变量列不存在: {missing_x}")
+        if missing_y:
+            raise ValueError(f"因变量列不存在: {missing_y}")
+
+        if output_dir is None:
+            custom_regression_dir = Path(work_dir) / "9_Custom_Regression_Modeling"
+        else:
+            custom_regression_dir = Path(work_dir) / output_dir
+        custom_regression_dir.mkdir(parents=True, exist_ok=True)
+
+        from src.core.modeling.regression import SingleVariableRegressionAnalysis
+        analyzer = SingleVariableRegressionAnalysis()
+        analyzer.batch_single_variable_regression(
+            data=df,
+            x_columns=x_columns,
+            y_columns=y_columns,
+            methods=methods,
+            output_dir=str(custom_regression_dir),
+        )
+
+        notify("completed", f"自定义回归结果已保存到目录: {custom_regression_dir}")
+        return str(custom_regression_dir)
+
+
+# ============================================================
+# 内部辅助函数（供 ModelingStep 内部使用）
+# ============================================================
+
+def _apply_preprocessing_internal(
+    csv_path: str,
+    preprocess_method: str,
+    output_dir: Path,
+    spectral_start_col: int = 4,
+) -> str:
+    """应用预处理到CSV数据（内部函数）"""
+    raw_p = str(preprocess_method).lower()
+    if raw_p == "none" or "无" in raw_p or "跳过" in raw_p:
+        preprocess_method = "None"
+    elif raw_p == "mms" or "minmax" in raw_p or "最大最小" in raw_p:
+        preprocess_method = "MMS"
+    elif raw_p == "ss" or "标准" in raw_p or "标准化" in raw_p:
+        preprocess_method = "SS"
+    elif raw_p == "snv" or "标准正态" in raw_p:
+        preprocess_method = "SNV"
+    elif raw_p == "ma" or "移动" in raw_p:
+        preprocess_method = "MA"
+    elif raw_p == "sg" or "savitzky" in raw_p or "平滑" in raw_p:
+        preprocess_method = "SG"
+    elif raw_p == "msc" or "多元散射" in raw_p:
+        preprocess_method = "MSC"
+    elif raw_p in ("d1", "d2", "dt"):
+        preprocess_method = {"d1": "D1", "d2": "D2", "dt": "DT"}.get(raw_p, raw_p.upper())
+    elif raw_p == "ct" or "去趋势" in raw_p:
+        preprocess_method = "CT"
+
+    if preprocess_method == "None":
+        return csv_path
+
+    output_filename = f"preprocessed_{preprocess_method}.csv"
+    output_path = str(output_dir / output_filename)
+
+    if Path(output_path).exists():
+        print(f"检测到已存在的预处理文件，直接使用: {output_path}")
+        return output_path
+
+    df = pd.read_csv(csv_path)
+    non_spectral_cols = df.iloc[:, :spectral_start_col]
+    spectral_data = df.iloc[:, spectral_start_col:]
+
+    from src.preprocessing.spectral_Preprocessing import Preprocessing
+
+    save_path = None
+    if preprocess_method == "SS":
+        models_dir = output_dir.parent.parent / "7_Supervised_Model_Training"
+        models_dir.mkdir(parents=True, exist_ok=True)
+        save_path = str(models_dir / "scaler_params.pkl")
+        print(f"SS预处理: scaler模型将保存到 {save_path}")
+
+    processed_spectral = Preprocessing(preprocess_method, spectral_data, save_path=save_path)
+
+    if isinstance(processed_spectral, pd.DataFrame):
+        processed_df = pd.concat([non_spectral_cols, processed_spectral], axis=1)
+    else:
+        processed_spectral_df = pd.DataFrame(
+            processed_spectral, columns=spectral_data.columns, index=spectral_data.index
+        )
+        processed_df = pd.concat([non_spectral_cols, processed_spectral_df], axis=1)
+
+    processed_df.to_csv(output_path, index=False)
+    print(f"预处理完成: {output_path}")
+    return output_path
+
+
+def _generate_non_empirical_summary(model_results: Dict[str, str], output_dir: Path) -> str:
+    """生成非经验模型训练结果汇总CSV"""
+    summary_path = str(output_dir / "non_empirical_models_summary.csv")
+    summary_data = []
+
+    for model_key, model_path in model_results.items():
+        try:
+            parts = model_key.split("_")
+            preprocess_method = parts[0]
+            algorithm_name = "_".join(parts[1:]) if len(parts) > 2 else parts[1]
+
+            with open(model_path, "r", encoding="utf-8") as f:
+                model_info = json.load(f)
+
+            accuracy_list = model_info.get("accuracy", [])
+            summary_row = {
+                "Preprocessing Method": preprocess_method,
+                "Algorithm Name": algorithm_name,
+                "Model Type": model_info.get("model_type", ""),
+                "Coefficient Count": len(model_info.get("model_info", [])),
+                "Average Accuracy(%)": np.mean(accuracy_list) if accuracy_list else 0,
+                "Min Accuracy(%)": np.min(accuracy_list) if accuracy_list else 0,
+                "Max Accuracy(%)": np.max(accuracy_list) if accuracy_list else 0,
+                "Sample Count": len(model_info.get("long", [])),
+                "Model File": model_path,
+            }
+
+            coefficients = model_info.get("model_info", [])
+            for i, coeff in enumerate(coefficients[:5]):
+                summary_row[f"系数_{i+1}"] = coeff
+
+            summary_data.append(summary_row)
+        except Exception as e:
+            print(f"读取模型文件 {model_path} 时出错: {e}")
+            continue
+
+    if summary_data:
+        df_summary = pd.DataFrame(summary_data)
+        df_summary.to_csv(summary_path, index=False, encoding="utf-8-sig")
+        print(f"汇总文件已生成: {summary_path}")
+    else:
+        print("警告: 没有有效的模型数据可汇总")
+        summary_path = ""
+
+    return summary_path

src/core/steps/prediction_step.py

@@ -0,0 +1,350 @@
+# -*- coding: utf-8 -*-
+"""
+预测步骤
+
+包含 step7_generate_sampling_points, step8_predict_water_quality,
+step8_5_predict_with_non_empirical_models, step8_75_predict_with_custom_regression
+"""
+
+import time
+from pathlib import Path
+from typing import Optional, List, Union, Callable, Dict
+
+
+class PredictionStep:
+    """预测步骤"""
+
+    # ---- Step 7: 生成采样点并提取光谱 ----
+
+    @staticmethod
+    def generate_sampling_points(
+        deglint_img_path: Optional[str] = None,
+        interval: int = 50,
+        sample_radius: int = 5,
+        chunk_size: int = 1000,
+        water_mask_path: Optional[str] = None,
+        glint_mask_path: Optional[str] = None,
+        output_dir: Union[str, Path] = "./10_sampling",
+        callback: Optional[Callable] = None,
+    ) -> str:
+        """生成水域掩膜内且耀斑掩膜外的采样点，统计平均光谱"""
+        from pathlib import Path
+        from src.utils.sampling import get_spectral_sampling_points_chunked
+
+        output_dir = Path(output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+        output_path = str(output_dir / "sampling_spectra.csv")
+
+        def notify(status, msg=""):
+            if callback:
+                callback("步骤7", status, msg)
+
+        print("\n" + "=" * 80)
+        print("步骤7: 生成预测采样点并提取光谱")
+        print("=" * 80)
+
+        step_start_time = time.time()
+
+        if deglint_img_path is None:
+            raise ValueError("必须提供 deglint_img_path 参数")
+
+        # 1. 初始归一化与安全转换
+        original_path = Path(deglint_img_path)
+        final_deglint_path = original_path
+
+        # 2. 智能回溯探测：如果当前路径不存在，或者后缀是前端死板的 .dat
+        if not final_deglint_path.exists() or final_deglint_path.suffix.lower() == '.dat':
+            print(f"🔍 智能探测：输入去耀斑路径不存在或为 .dat 占位符 ({final_deglint_path})，正在向上搜索真实产物...")
+
+            # 定位到预期的 3_deglint 根目录
+            possible_dir = original_path.parent
+            if possible_dir.name != '3_deglint' and Path(output_path).parent.parent.exists():
+                possible_dir = Path(output_path).parent.parent / "3_deglint"
+
+            if possible_dir.exists():
+                # 搜寻该目录下所有真实存在的 .bsq 文件（接管 goodman/sugar/kutser/hedley 的硬编码产物）
+                existing_bsqs = list(possible_dir.glob("*.bsq"))
+                if existing_bsqs:
+                    final_deglint_path = existing_bsqs[0]
+                    print(f"💡 智能拦截成功：自动寻回底层真实去耀斑影像: {final_deglint_path}")
+                else:
+                    final_deglint_path = original_path.with_suffix('.bsq')
+            else:
+                final_deglint_path = original_path.with_suffix('.bsq')
+
+        deglint_img_str = str(final_deglint_path)
+
+        if Path(output_path).exists():
+            print(f"检测到已存在的采样点光谱数据文件，直接使用: {output_path}")
+            notify("skipped", f"采样点光谱数据已设置: {output_path}")
+            return output_path
+
+        glint_mask_to_use = glint_mask_path
+        if glint_mask_to_use is None:
+            print("未检测到耀斑掩膜，将在采样点生成时不做耀斑区域剔除。")
+
+        # 传递极度安全的 deglint_img_str 进底层
+        get_spectral_sampling_points_chunked(
+            deglint_img_str, water_mask_path, glint_mask_to_use,
+            output_path, interval, sample_radius, chunk_size
+        )
+
+        notify("completed", f"采样点光谱数据已保存: {output_path}")
+        return output_path
+
+    # ---- Step 8: 机器学习模型预测水质参数 ----
+
+    @staticmethod
+    def predict_water_quality(
+        sampling_csv_path: str,
+        models_dir: Optional[str] = None,
+        metric: str = "test_r2",
+        prediction_column: str = "prediction",
+        output_dir: Union[str, Path] = "./11_12_13_predictions/Machine_Learning_Prediction",
+        callback: Optional[Callable] = None,
+        _report_generator=None,
+    ) -> Dict[str, str]:
+        """将训练好的最佳机器学习模型应用到采样点光谱上，预测水质参数"""
+        from src.core.prediction.inference_batch import WaterQualityInference
+
+        def notify(status, msg=""):
+            if callback:
+                callback("步骤8", status, msg)
+
+        print("\n" + "=" * 80)
+        print("步骤8: 预测水质参数")
+        print("=" * 80)
+
+        step_start_time = time.time()
+
+        if models_dir is None:
+            raise ValueError("必须提供 models_dir 参数")
+
+        ml_prediction_dir = Path(output_dir)
+        ml_prediction_dir.mkdir(parents=True, exist_ok=True)
+
+        prediction_files = {}
+        if ml_prediction_dir.exists():
+            csv_files = list(ml_prediction_dir.glob("*.csv"))
+            for csv_file in csv_files:
+                file_stem = csv_file.stem
+                if "_prediction" in file_stem:
+                    target_name = file_stem.replace("_prediction", "")
+                elif "_pred" in file_stem:
+                    target_name = file_stem.replace("_pred", "")
+                else:
+                    target_name = file_stem
+                prediction_files[target_name] = str(csv_file)
+
+        # 检查是否所有目标参数都有预测文件
+        if prediction_files:
+            models_path_obj = Path(models_dir)
+            if models_path_obj.exists():
+                target_folders = [d.name for d in models_path_obj.iterdir() if d.is_dir()]
+                missing_targets = [t for t in target_folders if t not in prediction_files]
+                if not missing_targets:
+                    print(f"检测到已存在的预测结果文件，直接使用: {ml_prediction_dir}")
+                    notify("skipped", f"预测结果已设置: {ml_prediction_dir}")
+                    return prediction_files
+                else:
+                    print(f"检测到部分预测结果文件，缺少: {missing_targets}，将继续生成...")
+
+        inferencer = WaterQualityInference(models_dir)
+        all_results = inferencer.batch_inference_multi_models(
+            models_root_dir=models_dir,
+            sampling_csv_path=sampling_csv_path,
+            output_dir=str(ml_prediction_dir),
+            metric=metric,
+            prediction_column=prediction_column,
+            output_format="csv",
+        )
+
+        for target_name, result in all_results.items():
+            if result.get("status") == "success":
+                prediction_files[target_name] = result["output_file"]
+
+        print(f"预测完成，结果保存在: {ml_prediction_dir}")
+
+        if _report_generator is not None:
+            try:
+                report_path = _report_generator.generate_prediction_report(prediction_files)
+                print(f"预测结果报告已生成: {report_path}")
+            except Exception as e:
+                print(f"生成预测结果报告时出错: {e}")
+
+        notify("completed", f"预测完成: {ml_prediction_dir}")
+        return prediction_files
+
+    # ---- Step 8.5: 非经验模型预测 ----
+
+    @staticmethod
+    def predict_with_non_empirical_models(
+        sampling_csv_path: str,
+        non_empirical_models_dir: Optional[str] = None,
+        output_dir: Optional[str] = None,
+        metric: str = "Average Accuracy(%)",
+        prediction_column: str = "prediction",
+        enabled: bool = True,
+        callback: Optional[Callable] = None,
+        work_dir: Union[str, Path] = "./work_dir",
+    ) -> Dict[str, str]:
+        """使用非经验统计回归模型进行参数预测"""
+        def notify(status, msg=""):
+            if callback:
+                callback("步骤8.5", status, msg)
+
+        print("\n" + "=" * 80)
+        print("步骤8.5: 使用非经验模型进行参数预测")
+        print("=" * 80)
+
+        step_start_time = time.time()
+
+        if not enabled:
+            print("已设置跳过非经验模型预测（enabled=False）。")
+            notify("skipped", "跳过非经验模型预测")
+            return {}
+
+        if non_empirical_models_dir is not None:
+            final_models_dir = non_empirical_models_dir
+        else:
+            default_models_dir = str(Path(work_dir) / "8_Regression_Modeling")
+            if Path(default_models_dir).exists():
+                final_models_dir = default_models_dir
+            else:
+                raise ValueError("请先执行步骤6.5: 非经验模型训练，或提供 non_empirical_models_dir 参数")
+
+        if output_dir is not None:
+            non_empirical_prediction_dir = Path(output_dir)
+        else:
+            non_empirical_prediction_dir = Path(work_dir) / "11_12_13_predictions" / "Non_Empirical_Prediction"
+        non_empirical_prediction_dir.mkdir(parents=True, exist_ok=True)
+
+        prediction_files = {}
+        summary_path = Path(final_models_dir) / "non_empirical_models_summary.csv"
+        if not summary_path.exists():
+            raise ValueError(f"未找到非经验模型汇总文件: {summary_path}")
+
+        import pandas as pd
+        df_summary = pd.read_csv(summary_path)
+
+        best_models = {}
+        for algorithm in df_summary["Algorithm Name"].unique():
+            algorithm_df = df_summary[df_summary["Algorithm Name"] == algorithm]
+            if metric in algorithm_df.columns:
+                best_model_row = algorithm_df.nlargest(1, metric)
+            else:
+                best_model_row = algorithm_df.iloc[[0]]
+
+            best_model_path = best_model_row["Model File"].values[0]
+            best_preprocess = best_model_row["Preprocessing Method"].values[0]
+            best_accuracy = best_model_row[metric].values[0] if metric in best_model_row.columns else "N/A"
+
+            best_models[algorithm] = {
+                "model_path": best_model_path,
+                "preprocess_method": best_preprocess,
+                "accuracy": best_accuracy,
+            }
+            print(f"算法 {algorithm}: 选择 {best_preprocess} (准确率: {best_accuracy})")
+
+        pd.read_csv(sampling_csv_path)  # just to validate
+
+        for algorithm, model_info in best_models.items():
+            print(f"\n使用 {algorithm} 算法进行预测...")
+            output_path = str(non_empirical_prediction_dir / f"non_empirical_{algorithm}_{prediction_column}.csv")
+
+            if Path(output_path).exists():
+                print(f"检测到已存在的预测结果文件，直接使用: {output_path}")
+                prediction_files[algorithm] = output_path
+                continue
+
+            try:
+                from src.core.non_empirical_retrieval import non_empirical_retrieval
+                non_empirical_retrieval(
+                    algorithm=algorithm,
+                    model_info_path=model_info["model_path"],
+                    coor_spectral_path=sampling_csv_path,
+                    output_path=output_path,
+                    wave_radius=5,
+                )
+                prediction_files[algorithm] = output_path
+                print(f"预测完成: {output_path}")
+            except Exception as e:
+                print(f"使用 {algorithm} 算法预测时出错: {e}")
+                continue
+
+        notify("completed", f"非经验模型预测完成: {non_empirical_prediction_dir}")
+        return prediction_files
+
+    # ---- Step 8.75: 自定义回归模型预测 ----
+
+    @staticmethod
+    def predict_with_custom_regression(
+        sampling_csv_path: str,
+        custom_regression_dir: Optional[str] = None,
+        formula_csv_path: Optional[str] = None,
+        coordinate_columns: Optional[List[str]] = None,
+        output_dir: Optional[str] = None,
+        filename_prefix: str = "custom_regression_prediction",
+        enabled: bool = True,
+        callback: Optional[Callable] = None,
+        work_dir: Union[str, Path] = "./work_dir",
+    ) -> Dict[str, str]:
+        """使用自定义回归模型进行参数预测"""
+        def notify(status, msg=""):
+            if callback:
+                callback("步骤8.75", status, msg)
+
+        print("\n" + "=" * 80)
+        print("步骤8.75: 使用自定义回归模型进行参数预测")
+        print("=" * 80)
+
+        step_start_time = time.time()
+
+        if not enabled:
+            print("已设置跳过自定义回归模型预测（enabled=False）。")
+            notify("skipped", "跳过自定义回归预测")
+            return {}
+
+        if not Path(sampling_csv_path).exists():
+            raise FileNotFoundError(f"采样点CSV文件不存在: {sampling_csv_path}")
+
+        if custom_regression_dir is not None:
+            final_regression_dir = custom_regression_dir
+        else:
+            final_regression_dir = str(Path(work_dir) / "9_Custom_Regression_Modeling")
+            if not Path(final_regression_dir).exists():
+                raise ValueError(
+                    "请先执行步骤6.75: 自定义回归分析，或提供 custom_regression_dir 参数"
+                )
+
+        if output_dir is None:
+            custom_regression_prediction_dir = Path(work_dir) / "11_12_13_predictions" / "Custom_Regression_Prediction"
+            custom_regression_prediction_dir.mkdir(parents=True, exist_ok=True)
+            prediction_output_dir = str(custom_regression_prediction_dir)
+        else:
+            prediction_output_dir = output_dir
+
+        from src.core.prediction.custom_regression_prediction import CustomRegressionPredictor
+
+        predictor = CustomRegressionPredictor(
+            regression_csv_dir=final_regression_dir,
+            formula_csv_path=formula_csv_path,
+        )
+
+        print(f"开始使用自定义回归模块进行批量预测...")
+        print(f"  采样点数据: {sampling_csv_path}")
+        print(f"  回归模型目录: {final_regression_dir}")
+        print(f"  输出目录: {prediction_output_dir}")
+
+        saved_files = predictor.run_batch_prediction(
+            input_csv_path=sampling_csv_path,
+            coordinate_columns=coordinate_columns,
+            filename_prefix=filename_prefix,
+        )
+
+        print(f"自定义回归预测完成，生成 {len(saved_files)} 个预测文件:")
+        for param_name, filepath in saved_files.items():
+            print(f"  {param_name}: {filepath}")
+
+        notify("completed", f"自定义回归预测完成: {len(saved_files)} 个文件")
+        return saved_files

src/core/steps/water_mask_step.py

@@ -0,0 +1,148 @@
+# -*- coding: utf-8 -*-
+"""
+步骤1: 水域掩膜生成
+
+支持三种方式:
+1. 基于 shp 文件栅格化
+2. 使用现有栅格格式掩膜文件 (.dat/.tif)
+3. 基于 NDWI 从影像自动生成水体掩膜
+"""
+
+import os
+import time
+from pathlib import Path
+from typing import Optional, List, Callable, Union
+import numpy as np
+
+
+class WaterMaskStep:
+    """水域掩膜生成步骤"""
+
+    @staticmethod
+    def run(
+        mask_path: Optional[str] = None,
+        img_path: Optional[str] = None,
+        ndwi_threshold: float = 0.4,
+        use_ndwi: bool = False,
+        generate_png: bool = True,
+        output_path: Optional[str] = None,
+        water_mask_dir: Union[str, Path] = "./1_water_mask",
+        callback: Optional[Callable] = None,
+    ) -> str:
+        """
+        执行水域掩膜生成
+
+        Args:
+            mask_path: 水体掩膜文件路径，支持 .shp（需 img_path）或 .dat/.tif（直接使用）
+            img_path: 输入影像文件路径（当 mask_path 为 shp 或 use_ndwi=True 时必须提供）
+            ndwi_threshold: NDWI 阈值（use_ndwi=True 时使用）
+            use_ndwi: 是否使用 NDWI 方法从影像生成水体掩膜
+            generate_png: 是否生成 PNG 预览图（默认 True）
+            output_path: 指定输出掩膜文件的保存路径（可选）
+            water_mask_dir: 工作目录
+            callback: 回调函数，签名为 callback(step, status, message)
+
+        Returns:
+            dat 格式的水域掩膜文件路径
+        """
+        from src.utils.extract_water_area import rasterize_shp, ndwi
+        from src.core.utils.preview_generator import (
+            generate_image_preview,
+            generate_water_mask_overlay,
+        )
+
+        water_mask_dir = Path(water_mask_dir)
+        water_mask_dir.mkdir(parents=True, exist_ok=True)
+
+        def notify(status, msg=""):
+            if callback:
+                callback("步骤1", status, msg)
+
+        print("\n" + "=" * 80)
+        print("步骤1: 生成或设置水域mask")
+        print("=" * 80)
+
+        step_start_time = time.time()
+
+        # 生成影像预览图
+        if generate_png and img_path is not None and Path(img_path).exists():
+            preview_path = str(water_mask_dir / "hsi_preview.png")
+            generate_image_preview(
+                img_path=img_path,
+                output_path=preview_path,
+                title="影像预览: RGB波段(基于波长)"
+            )
+
+        # ---- NDWI 方法 ----
+        if use_ndwi:
+            if img_path is None:
+                raise ValueError("当 use_ndwi=True 时，必须提供 img_path 参数")
+            if not Path(img_path).exists():
+                raise ValueError(f"影像文件不存在: {img_path}")
+
+            print(f"使用NDWI方法从影像生成水体掩膜，阈值={ndwi_threshold}...")
+
+            ndwi_output_path = output_path or str(water_mask_dir / "water_mask_from_ndwi.dat")
+            os.makedirs(Path(ndwi_output_path).parent, exist_ok=True)
+
+            if Path(ndwi_output_path).exists():
+                print(f"检测到已存在的NDWI掩膜文件，直接使用: {ndwi_output_path}")
+                notify("skipped", f"水域掩膜已设置: {ndwi_output_path}")
+                return ndwi_output_path
+
+            ndwi(img_path, ndwi_threshold, ndwi_output_path)
+
+            if generate_png:
+                overlay_path = water_mask_dir / "water_mask_overlay.png"
+                generate_water_mask_overlay(
+                    img_path=img_path, mask_path=ndwi_output_path, output_path=str(overlay_path)
+                )
+
+            notify("completed", f"NDWI水体掩膜已生成: {ndwi_output_path}")
+            return ndwi_output_path
+
+        # ---- 必须提供 mask_path ----
+        if mask_path is None:
+            raise ValueError("必须提供 mask_path 参数或设置 use_ndwi=True")
+        if not Path(mask_path).exists():
+            raise ValueError(f"文件不存在: {mask_path}")
+
+        file_ext = Path(mask_path).suffix.lower()
+
+        # ---- SHP 栅格化 ----
+        if file_ext == ".shp":
+            if img_path is None:
+                raise ValueError("当 mask_path 为 shp 格式时，必须提供 img_path 参数")
+
+            print(f"检测到shp格式的水体掩膜，正在转换为dat格式...")
+
+            shp_output_path = output_path or str(water_mask_dir / "water_mask_from_shp.dat")
+            os.makedirs(Path(shp_output_path).parent, exist_ok=True)
+
+            if Path(shp_output_path).exists():
+                print(f"检测到已存在的栅格化掩膜文件，直接使用: {shp_output_path}")
+                notify("skipped", f"水域掩膜已设置: {shp_output_path}")
+                if generate_png:
+                    overlay_path = water_mask_dir / "water_mask_overlay.png"
+                    if not overlay_path.exists():
+                        generate_water_mask_overlay(img_path, shp_output_path, str(overlay_path))
+                return shp_output_path
+
+            safe_mask_path = os.path.abspath(mask_path).replace("\\", "/")
+            rasterize_shp(safe_mask_path, shp_output_path, img_path)
+
+            if generate_png:
+                overlay_path = water_mask_dir / "water_mask_overlay.png"
+                generate_water_mask_overlay(img_path, shp_output_path, str(overlay_path))
+
+            notify("completed", f"dat格式水域掩膜已生成: {shp_output_path}")
+            return shp_output_path
+
+        # ---- 栅格格式直接使用 ----
+        print(f"检测到栅格格式的水体掩膜，直接使用: {mask_path}")
+        if generate_png and img_path is not None and Path(img_path).exists():
+            overlay_path = water_mask_dir / "water_mask_overlay.png"
+            generate_water_mask_overlay(img_path, mask_path, str(overlay_path))
+
+        notify("completed", f"水域掩膜已设置: {mask_path}")
+        return mask_path

src/gui/panels/step3_panel.py

@@ -209,7 +209,7 @@ class Step3Panel(QWidget):
             "输出影像:",
             "Image Files (*.bsq *.dat *.tif);;All Files (*.*)"
         )
-        self.output_file.line_edit.setPlaceholderText("deglint_image.dat")
+        self.output_file.line_edit.setPlaceholderText("deglint_image.bsq")
         layout.addWidget(self.output_file)
 
         # 启用步骤
@@ -301,7 +301,7 @@ class Step3Panel(QWidget):
         if self.work_dir:
             output_dir = os.path.join(self.work_dir, "3_deglint")
             os.makedirs(output_dir, exist_ok=True)
-            default_output_path = os.path.join(output_dir, "deglint_image.dat").replace('\\', '/')
+            default_output_path = os.path.join(output_dir, "deglint_image.bsq").replace('\\', '/')
             self.output_file.set_path(default_output_path)
         else:
             self.output_file.set_path("")

src/gui/panels/step6_panel.py

@@ -17,6 +17,57 @@ from src.gui.components.custom_widgets import FileSelectWidget
 from src.gui.styles import ModernStylesheet
 
 
+# ============================================================
+# 中文映射表（内部键名 -> 显示文本）
+# ============================================================
+
+# 预处理方法：内部键 -> 显示文本
+PREPROC_CHINESE = {
+    'None': '无 (None)',
+    'MMS': '最小-最大归一化 (MMS)',
+    'SS': '标度化 (SS)',
+    'SNV': '标准正态变换 (SNV)',
+    'MA': '移动平均 (MA)',
+    'SG': 'Savitzky-Golay (SG)',
+    'MSC': '多元散射校正 (MSC)',
+    'D1': '一阶导数 (D1)',
+    'D2': '二阶导数 (D2)',
+    'DT': '去趋势 (DT)',
+    'CT': '中心化 (CT)',
+}
+
+# 模型类型：内部键 -> 显示文本
+MODEL_CHINESE = {
+    # 线性模型
+    'LinearRegression': '多元线性回归 (MLR)',
+    'Ridge': '岭回归 (Ridge)',
+    'Lasso': '套索回归 (Lasso)',
+    'ElasticNet': '弹性网络 (ElasticNet)',
+    'PLS': '偏最小二乘 (PLSR)',
+    # 树模型
+    'DecisionTree': '决策树 (CART)',
+    'RF': '随机森林 (RF)',
+    'ExtraTrees': '极端随机树 (ET)',
+    'XGBoost': '极值梯度提升 (XGBoost)',
+    'LightGBM': '轻量梯度提升 (LightGBM)',
+    'CatBoost': '类别梯度提升 (CatBoost)',
+    # 集成学习
+    'GradientBoosting': '梯度提升树 (GBDT)',
+    'AdaBoost': '自适应提升 (AdaBoost)',
+    # 其他模型
+    'SVR': '支持向量回归 (SVR)',
+    'KNN': 'K近邻回归 (KNN)',
+    'MLP': '多层感知机 (BP神经网络)',
+}
+
+# 数据划分方法：内部键 -> 显示文本
+SPLIT_CHINESE = {
+    'spxy': 'SPXY 算法 (考量X-Y空间)',
+    'ks': 'KS 算法 (考量X空间)',
+    'random': '随机划分 (Random)',
+}
+
+
 class Step6Panel(QWidget):
     """步骤6：机器学习建模"""
     def __init__(self, parent=None):
@@ -54,7 +105,7 @@ class Step6Panel(QWidget):
 
         # 启用步骤
         self.enable_checkbox = QCheckBox("启用此步骤")
-        self.enable_checkbox.setChecked(True)
+        self.enable_checkbox.setChecked(False)
         layout.addWidget(self.enable_checkbox)
 
         # 独立运行按钮
@@ -95,8 +146,8 @@ class Step6Panel(QWidget):
         preproc_methods = ['None', 'MMS', 'SS', 'SNV', 'MA', 'SG', 'MSC', 'D1', 'D2', 'DT', 'CT']
 
         for i, method in enumerate(preproc_methods):
-            checkbox = QCheckBox(method)
-            checkbox.setChecked(True)
+            checkbox = QCheckBox(PREPROC_CHINESE.get(method, method))
+            checkbox.setChecked(False)
             self.preproc_checkboxes[method] = checkbox
             preproc_grid.addWidget(checkbox, i // 4, i % 4)
 
@@ -122,10 +173,10 @@ class Step6Panel(QWidget):
         self.model_checkboxes = {}
 
         model_groups = [
-            ("线性模型", ['LinearRegression', 'Ridge', 'Lasso', 'ElasticNet', 'PLS']),
-            ("树模型", ['DecisionTree', 'RF', 'ExtraTrees', 'XGBoost', 'LightGBM', 'CatBoost']),
-            ("集成学习", ['GradientBoosting', 'AdaBoost']),
-            ("其他模型", ['SVR', 'KNN', 'MLP'])
+            ("【线性模型】", ['LinearRegression', 'Ridge', 'Lasso', 'ElasticNet', 'PLS']),
+            ("【树模型】", ['DecisionTree', 'RF', 'ExtraTrees', 'XGBoost', 'LightGBM', 'CatBoost']),
+            ("【集成学习】", ['GradientBoosting', 'AdaBoost']),
+            ("【其他模型】", ['SVR', 'KNN', 'MLP'])
         ]
 
         row = 0
@@ -140,8 +191,8 @@ class Step6Panel(QWidget):
             row += 1
 
             for i, model in enumerate(models):
-                checkbox = QCheckBox(model)
-                checkbox.setChecked(model in ['SVR', 'RF', 'Ridge', 'Lasso'])
+                checkbox = QCheckBox(MODEL_CHINESE.get(model, model))
+                checkbox.setChecked(False)
                 self.model_checkboxes[model] = checkbox
                 model_grid.addWidget(checkbox, row, i % 4)
                 if (i + 1) % 4 == 0:
@@ -172,8 +223,8 @@ class Step6Panel(QWidget):
         split_methods = ['spxy', 'ks', 'random']
 
         for i, method in enumerate(split_methods):
-            checkbox = QCheckBox(method)
-            checkbox.setChecked(True)
+            checkbox = QCheckBox(SPLIT_CHINESE.get(method, method))
+            checkbox.setChecked(False)
             self.split_checkboxes[method] = checkbox
             split_grid.addWidget(checkbox, 0, i)
 

src/gui/panels/step9_panel.py

@@ -109,7 +109,7 @@ class Step9Panel(QWidget):
         mode_row.addStretch()
         layout.addLayout(mode_row)
 
-        # ---------- RadioButton 美化样式（选中状态更醒目） ----------
+        # ---------- RadioButton 美化样式（选中状态为方形实心块，贴合主界面风格） ----------
         radio_style = """
             QRadioButton {
                 font-size: 14px;
@@ -117,21 +117,16 @@ class Step9Panel(QWidget):
                 color: #333333;
             }
             QRadioButton::indicator {
-                width: 18px;
-                height: 18px;
+                width: 16px;
+                height: 16px;
                 border: 2px solid #999999;
-                border-radius: 9px;
+                border-radius: 3px;
                 background-color: white;
             }
             QRadioButton::indicator:checked {
                 border: 2px solid #0078d4;
-                background-color: qradialgradient(
-                    cx:0.5, cy:0.5, radius:0.5,
-                    fx:0.5, fy:0.5,
-                    stop:0 #0078d4,
-                    stop:0.6 white,
-                    stop:1.0 white
-                );
+                background-color: #0078d4;
+                image: none;
             }
             QRadioButton::indicator:hover {
                 border: 2px solid #005a9e;
@@ -353,7 +348,7 @@ class Step9Panel(QWidget):
             if not main_window:
                 return
 
-            # 1. 尝试从 Step8 界面读取机器学习预测输出目录（优先）
+            # 1. 尝试从 Step8 界面读取机器学习预测输出目录（最优先）
             pred_dir = None
             if hasattr(main_window, 'step8_panel'):
                 step8_widget = getattr(main_window.step8_panel, 'output_file', None)
@@ -367,7 +362,10 @@ class Step9Panel(QWidget):
                     # 若为相对路径，使用 work_dir 合成为绝对路径
                     if not os.path.isabs(step8_output):
                         step8_output = os.path.join(self.work_dir or '', step8_output).replace('\\', '/')
-                    pred_dir = str(Path(step8_output).parent)
+                    # 提取父目录后追加 Machine_Learning_Prediction（最底层真实子目录）
+                    base_pred_dir = str(Path(step8_output).parent)
+                    ml_pred_dir = Path(base_pred_dir) / "Machine_Learning_Prediction"
+                    pred_dir = str(ml_pred_dir) if ml_pred_dir.exists() else base_pred_dir
 
             # 2. 备选：从 Step8.5 界面读取非经验预测输出目录
             if not pred_dir and hasattr(main_window, 'step8_5_panel'):
@@ -411,6 +409,14 @@ class Step9Panel(QWidget):
                 existing_out = self.output_dir.get_path()
                 if not existing_out or not existing_out.strip():
                     self.output_dir.set_path(output_dir)
+
+            # 5. 自动继承步骤1的水域掩膜作为边界文件
+            if self.work_dir:
+                default_mask = Path(self.work_dir) / "1_water_mask" / "water_mask_from_shp.dat"
+                if default_mask.exists():
+                    existing_boundary = (self.boundary_file.get_path() or "").strip()
+                    if not existing_boundary:
+                        self.boundary_file.set_path(str(default_mask))
         except Exception as e:
             import traceback
             print(f"【{self.__class__.__name__}】自动填充失败，跳过: {e}")

src/gui/water_quality_gui.py

@@ -1825,6 +1825,11 @@ class WaterQualityGUI(QMainWindow):
             for step_id, step_display in steps:
                 item = QListWidgetItem(f"  └─ {step_display}")
                 item.setData(Qt.UserRole, step_id)
+
+                # 隐藏4个冗余回归步骤（树节点）
+                if step_id in ("step6_5", "step6_75", "step8_5", "step8_75"):
+                    item.setHidden(True)
+
                 self.step_name_map[step_display] = step_id
                 
                 # 设置步骤项的样式
@@ -1905,9 +1910,11 @@ class WaterQualityGUI(QMainWindow):
 
         self.step6_5_panel = Step6_5Panel()
         self.step_stack.addTab(self.create_scroll_area(self.step6_5_panel), QIcon(self.get_icon_path("6.png")), "回归建模")
+        self.step_stack.tabBar().setTabVisible(7, False)  # 隐藏回归建模 Tab
 
         self.step6_75_panel = Step6_75Panel()
         self.step_stack.addTab(self.create_scroll_area(self.step6_75_panel), QIcon(self.get_icon_path("6.png")), "自定义回归建模")
+        self.step_stack.tabBar().setTabVisible(8, False)  # 隐藏自定义回归建模 Tab
 
         self.step7_panel = Step7Panel()
         self.step_stack.addTab(self.create_scroll_area(self.step7_panel), QIcon(self.get_icon_path("7.png")), "采样点布设")
@@ -1917,9 +1924,11 @@ class WaterQualityGUI(QMainWindow):
 
         self.step8_5_panel = Step8_5Panel()
         self.step_stack.addTab(self.create_scroll_area(self.step8_5_panel), QIcon(self.get_icon_path("8.png")), "回归预测")
+        self.step_stack.tabBar().setTabVisible(11, False)  # 隐藏回归预测 Tab
 
         self.step8_75_panel = Step8_75Panel()
         self.step_stack.addTab(self.create_scroll_area(self.step8_75_panel), QIcon(self.get_icon_path("8.png")), "自定义回归预测")
+        self.step_stack.tabBar().setTabVisible(12, False)  # 隐藏自定义回归预测 Tab
 
         self.step9_panel = Step9Panel()
         self.step_stack.addTab(self.create_scroll_area(self.step9_panel), QIcon(self.get_icon_path("10.png")), "专题图生成")

src/postprocessing/visualization_reports.py

@@ -1003,67 +1003,84 @@ class ReportGenerator:
         Returns:
             保存的文件路径
         """
-        from modeling_batch import WaterQualityModelingBatch
-        
+        from src.core.modeling.modeling_batch import WaterQualityModelingBatch
+        import joblib
+
         modeler = WaterQualityModelingBatch(models_dir)
-        
-        # 需要先加载训练结果
-        # 这里假设results已经存储在modeler中，或者需要从保存的文件中读取
-        # 由于modeling_batch.py的结构，我们需要另一种方式来获取所有结果
-        
-        # 尝试遍历模型目录，查找所有保存的结果
         models_path = Path(models_dir)
         all_results = []
-        
-        # 遍历所有目标参数文件夹
-        for target_folder in models_path.iterdir():
-            if not target_folder.is_dir():
-                continue
-            
-            target_name = target_folder.name
-            
-            # 查找所有模型文件
-            for model_file in target_folder.rglob("*.pkl"):
-                # 从文件名提取信息（假设格式为：{preprocess}_{model}_{split}.pkl）
-                model_info = {
-                    'target': target_name,
-                    'model_file': str(model_file),
-                    'preprocess': 'Unknown',
-                    'model': 'Unknown',
-                    'split_method': 'Unknown'
+
+        # 递归扫描 *.joblib 和 *.pkl，兼容 artifacts_dir/target_name/ 的所有子目录层级
+        model_files = list(models_path.rglob("*.joblib")) + list(models_path.rglob("*.pkl"))
+
+        for model_file in model_files:
+            # 目标参数取直系父目录名（符合 artifacts_dir/target_name/ 结构）
+            target_name = model_file.parent.name
+            stem = model_file.stem
+
+            # 文件名格式：{safe_target}_{preprocess}_{model_name}.joblib
+            # 使用 split('_', 2) 最多切 3 段，目标 1 段、预处理 1 段、模型 1 段
+            parts = stem.split('_', 2)
+            preprocess = parts[1] if len(parts) > 1 else 'Unknown'
+            model_name_str = parts[2] if len(parts) > 2 else stem
+
+            # 尝试从 joblib/pkl 读取元数据，提取性能指标
+            metrics = {}
+            try:
+                data = joblib.load(model_file)
+                metadata = data.get('metadata', {})
+                metrics = {
+                    'train_r2': metadata.get('train_r2', 'N/A'),
+                    'test_r2': metadata.get('test_r2', 'N/A'),
+                    'test_rmse': metadata.get('test_rmse', 'N/A'),
+                    'train_rmse': metadata.get('train_rmse', 'N/A'),
+                    'train_mae': metadata.get('train_mae', 'N/A'),
+                    'test_mae': metadata.get('test_mae', 'N/A'),
+                    'cv_mean': metadata.get('cv_mean', 'N/A'),
                 }
-                
-                # 尝试从文件名解析
-                parts = model_file.stem.split('_')
-                if len(parts) >= 3:
-                    model_info['preprocess'] = parts[0]
-                    model_info['model'] = parts[1]
-                    model_info['split_method'] = parts[2]
-                
-                all_results.append(model_info)
-        
-        # 如果有训练结果数据，使用实际指标
-        # 否则创建一个基本的摘要
+            except Exception:
+                pass  # 加载失败时 metrics 保持为空字典，摘要中该列为 N/A
+
+            all_results.append({
+                'target': target_name,
+                'model_file': str(model_file),
+                'preprocess': preprocess,
+                'model': model_name_str,
+                **metrics,
+            })
+
         summary_data = []
         for result in all_results:
             summary_data.append({
                 '目标参数': result['target'],
                 '预处理方法': result['preprocess'],
                 '模型名称': result['model'],
-                '划分方法': result['split_method'],
-                '模型文件': result['model_file']
+                '模型文件': result['model_file'],
+                '训练集R²': result.get('train_r2', 'N/A'),
+                '测试集R²': result.get('test_r2', 'N/A'),
+                '测试集RMSE': result.get('test_rmse', 'N/A'),
+                '训练集RMSE': result.get('train_rmse', 'N/A'),
+                '训练集MAE': result.get('train_mae', 'N/A'),
+                '测试集MAE': result.get('test_mae', 'N/A'),
+                'CV均值': result.get('cv_mean', 'N/A'),
             })
-        
+
         if not summary_data:
             print("警告：未找到模型文件，生成空摘要")
             summary_data = [{
                 '目标参数': 'No Data',
                 '预处理方法': 'N/A',
                 '模型名称': 'N/A',
-                '划分方法': 'N/A',
-                '模型文件': 'N/A'
+                '模型文件': 'N/A',
+                '训练集R²': 'N/A',
+                '测试集R²': 'N/A',
+                '测试集RMSE': 'N/A',
+                '训练集RMSE': 'N/A',
+                '训练集MAE': 'N/A',
+                '测试集MAE': 'N/A',
+                'CV均值': 'N/A',
             }]
-        
+
         df_summary = pd.DataFrame(summary_data)
         
         if output_path is None:

src/utils/band_math.py

@@ -96,8 +96,14 @@ class BandMathCalculator:
 
             print(f"计算表达式: {calc_expression}")
 
-            # 安全地计算表达式
-            result = eval(calc_expression)
+            # 【新增安全防护】引入 numpy 命名空间，让 eval 引擎安全识别 nan 与 inf
+            import numpy as np
+            try:
+                # 即使 calc_expression 含有纯字符 nan，也能被 np.nan 安全接管
+                result = eval(calc_expression, {"__builtins__": None}, {"nan": np.nan, "inf": np.inf, "np": np})
+            except Exception as e:
+                print(f"⚠️ 警告：公式计算异常 ({e})，该点赋值为 nan")
+                result = np.nan
 
             # 返回结果
             if var_name: