更新工作目录子文件夹的序号

IMAGE_CONFIG.md

@@ -1,186 +0,0 @@
-# Logo和横幅图像配置说明
-
-## 功能概述
-
-本更新实现了以下需求：
-1. **Logo在菜单栏左侧** - 和菜单栏在**同一行**
-2. **菜单栏在最右侧** - 使用弹性空间布局
-3. **横幅图片撑满整个区域** - 自适应窗口宽度
-
-## 布局说明
-
-```
-┌─────────────────────────────────────┐
-│ [Logo]          文件 工具 帮助      │  ← 同一行：Logo在左，菜单在右
-├─────────────────────────────────────┤
-│      [软件名称横幅图 - 撑满宽度]    │  ← 横幅撑满整个区域
-├─────────────────────────────────────┤
-│                                      │
-│         主要内容区                   │
-│                                      │
-```
-
-## 配置步骤
-
-### 1. 准备图像文件
-
-将你的图像文件放在项目目录中。建议的目录结构：
-
-```
-fengzhuang-ui2/
-├── assets/                    # 推荐：创建资源目录
-│   ├── logo.png             # 公司logo (建议高度30像素)
-│   └── banner.png           # 软件名称横幅 (建议高度70像素)
-├── src/
-│   └── gui/
-│       └── water_quality_gui.py
-└── ...
-```
-
-### 2. 修改图像路径
-
-打开 `src/gui/water_quality_gui.py` 文件，找到以下两处代码：
-
-#### Logo路径修改（create_logo_bar 方法中，第3928行左右）
-
-```python
-# 修改前：
-logo_path = "path/to/your/logo.png"
-
-# 修改后（使用相对路径）：
-logo_path = "assets/logo.png"
-
-# 或使用绝对路径：
-logo_path = r"E:\your\full\path\to\logo.png"
-```
-
-#### 横幅路径修改（create_banner_widget 方法中，第3978行左右）
-
-```python
-# 修改前：
-banner_path = "path/to/your/banner.png"
-
-# 修改后（使用相对路径）：
-banner_path = "assets/banner.png"
-
-# 或使用绝对路径：
-banner_path = r"E:\your\full\path\to\banner.png"
-```
-
-### 3. 图像规格建议
-
-| 名称 | 建议尺寸 | 格式 | 说明 |
-|-----|--------|------|------|
-| Logo | 高度30像素 | PNG/JPG | 放在顶部Logo栏中，自动按高度缩放保持宽高比 |
-| 横幅 | 高度70像素 | PNG/JPG | 占据横幅区域，自动按高度缩放保持宽高比 |
-
-### 4. 图像加载失败处理
-
-如果图像文件未找到或加载失败，系统会自动显示占位符：
-
-- **Logo占位符**: 显示"Logo"文本，背景为浅灰色
-- **横幅占位符**: 显示"软件名称横幅"文本，背景为蓝色，字体为24号加粗
-
-### 5. 自适应缩放说明
-
-为了避免图像拉伸，代码使用了 `scaledToHeight()` 方法：
-- Logo按高度30像素缩放，自动计算宽度，保持原始宽高比
-- 横幅按高度70像素缩放，自动计算宽度，保持原始宽高比
-
-这样可以确保无论原始图像大小如何，都能自然地显示而不会出现拉伸变形。
-
-## 常见问题
-
-### Q: 如何使用项目内的图像资源？
-
-**A**: 在项目中创建 `assets` 或 `resources` 文件夹，并使用相对路径：
-
-```python
-# 假设项目结构：
-# fengzhuang-ui2/
-#   ├── assets/
-#   │   ├── logo.png
-#   │   └── banner.png
-#   └── src/gui/water_quality_gui.py
-
-# 在 water_quality_gui.py 中（第3928行）：
-logo_path = "assets/logo.png"
-
-# 在 water_quality_gui.py 中（第3978行）：
-banner_path = "assets/banner.png"
-```
-
-### Q: Logo或横幅大小不合适？
-
-**A**: 修改以下代码调整显示大小：
-
-```python
-# 在 create_logo_bar() 方法中调整Logo大小
-logo_label.setFixedSize(60, 40)  # 改为 60×40
-
-# 在 create_banner_widget() 方法中调整横幅大小
-banner_label.setMaximumHeight(100)  # 改为100像素高
-banner_label.setMinimumHeight(80)   # 改为最小80像素高
-
-# 调整缩放高度
-scaled_pixmap = logo_pixmap.scaledToHeight(35, Qt.SmoothTransformation)  # 改为35
-scaled_pixmap = banner_pixmap.scaledToHeight(85, Qt.SmoothTransformation)  # 改为85
-```
-
-### Q: 如何隐藏Logo或横幅？
-
-**A**: 在 `init_ui()` 方法中注释掉相应的创建方法：
-
-```python
-# 在 init_ui() 中
-# self.create_logo_bar()          # 注释此行隐藏Logo
-# self.create_banner_widget()     # 注释此行隐藏横幅
-```
-
-### Q: Logo显示位置不对？
-
-**A**: Logo栏是作为独立的工具栏添加在菜单栏下方，不是在菜单栏内。当前的布局顺序是：
-1. 菜单栏 (最上方)
-2. Logo栏 (菜单栏下方)
-3. 横幅区域 (Logo栏下方)
-4. 主内容区域 (最下方)
-
-### Q: 图像在高分辨率屏幕上看起来模糊？
-
-**A**: 使用 `Qt.SmoothTransformation` 可以改善图像质量。如果仍然不够清晰，可以准备高分辨率的原始图像。
-
-## 代码位置
-
-- **Logo栏创建**: `create_logo_bar()` 方法 (第3902行)
-- **横幅区域创建**: `create_banner_widget()` 方法 (第3950行)
-- **主UI初始化**: `init_ui()` 方法 (第3821行)
-
-## 支持的图像格式
-
-- PNG (推荐，支持透明背景)
-- JPG/JPEG
-- BMP
-- GIF
-- TIFF
-
-## 样式调整
-
-如需修改样式（背景色、边框等），编辑以下位置的 `setStyleSheet()` 调用：
-
-```python
-# Logo样式（第3907-3916行）
-logo_toolbar.setStyleSheet("""...""")
-
-# 占位符样式（第3931行）
-logo_label.setStyleSheet("...")
-
-# 横幅占位符样式（第3959-3965行）
-banner_label.setStyleSheet("""...""")
-```
-
-## 更新日期
-2026-03-27
-
-## 备注
-
-所有的图像路径都可以根据你的实际项目结构灵活调整。建议将图像文件与代码一起版本控制，以确保项目的可维护性。

data/sub/waterindex.csv

@@ -1,4 +1,4 @@
-Formula_Name,Category,Formula,Reference
+Formula_Name,Category,Formula,Reference
 BGA_Am09KBBI,Phycocyanin (BGA_PC),(w686 - w658) / (w686 + w658),"Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S.; Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery, Optics Express, 2009, 17, 11, 1-13."
 BGA_Be162B643sub629,Phycocyanin (BGA_PC),w644 - w629,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538."
 BGA_Be162B700sub601,Phycocyanin (BGA_PC),w700 - w601,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539."
@@ -12,35 +12,35 @@ BGA_Be16NDPhyI644over615,Phycocyanin (BGA_PC),(w644 - w615) / (w644 + w615),"Bec
 BGA_Be16NDPhyI644over629,Phycocyanin (BGA_PC),(w644 - w629) / (w644 + w629),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 542."
 BGA_Be16Phy2BDA644over629,Phycocyanin (BGA_PC),w644 / w629,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 545."
 BGA_Da052BDA,Phycocyanin (BGA_PC),w714 / w672,"Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672."
-BGA_Go04MCI,Phycocyanin (BGA_PC),w709 - w681 - (w753 - w681),"Gower, J.F.R.; Brown,L.; Borstad, G.A.; Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor. Can. J. Remote Sens., 2004, 30 (1), 17闁?5."
+BGA_Go04MCI,Phycocyanin (BGA_PC),w709 - w681 - (w753 - w681),"Gower, J.F.R.; Brown,L.; Borstad, G.A.; Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor. Can. J. Remote Sens., 2004, 30 (1), 17<EFBFBD><EFBFBD>?5."
 BGA_HU103BDA,Phycocyanin (BGA_PC),(((1 / w615) - (1 / w600)) - w725),"Hunter, P.D.; Tyler, A.N.; Willby, N.J.; Gilvear, D.J.; The spatial dynamics of vertical migration by Microcystis aeruginosa in a eutrophic shallow lake: A case study using high spatial resolution time-series airborne remote sensing. Limn. Oceanogr. 2008, 53, 2391-2406"
 BGA_Ku15PhyCI,Phycocyanin (BGA_PC),(-1 * (W681 - W665 - (W709 - W665))),"Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-10."
 BGA_Ku15SLH,Phycocyanin (BGA_PC),(w715 - w658) + (w715 - w658),"Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S.; Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters, Torres-Perez, J., 2015, Remote Sens. Environ., 2015, 167, 1-11."
-BGA_MI092BDA,Phycocyanin (BGA_PC),w700 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758闁?75."
-BGA_MM092BDA,Phycocyanin (BGA_PC),w724 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758闁?76."
+BGA_MI092BDA,Phycocyanin (BGA_PC),w700 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758<EFBFBD><EFBFBD>?75."
+BGA_MM092BDA,Phycocyanin (BGA_PC),w724 / w600,"Mishra, S.; Mishra, D.R.; Schluchter, W. M., A novel algorithm for predicting PC concentrations in cyanobacteria: A proximal hyperspectral remote sensing approach. Remote Sens., 2009, 1, 758<EFBFBD><EFBFBD>?76."
 BGA_MM12NDCIalt,Phycocyanin (BGA_PC),(w700 - w658) / (w700 + w658),"Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett., 2014, 9 (11), DOI:10.1088/1748-9326/9/11/114003"
 BGA_MM143BDAopt,Phycocyanin (BGA_PC),((1 / w629) - (1 / w659)) * w724,"Mishra, S.; Mishra, D.R.; A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms, Env. Res. Lett., 2014, 9 (11), DOI:10.1088/1748-9326/9/11/114004"
-BGA_SI052BDA,Phycocyanin (BGA_PC),w709 / w620,"Simis, S. G. H.; Peters, S.W. M.; Gons, H. J.; Remote sensing of the cyanobacteria pigment phycocyanin in turbid inland water. Limn. Oceanogr., 2005, 50, 237闁?45"
+BGA_SI052BDA,Phycocyanin (BGA_PC),w709 / w620,"Simis, S. G. H.; Peters, S.W. M.; Gons, H. J.; Remote sensing of the cyanobacteria pigment phycocyanin in turbid inland water. Limn. Oceanogr., 2005, 50, 237<EFBFBD><EFBFBD>?45"
 BGA_SM122BDA,Phycocyanin (BGA_PC),w709 / w600,"Mishra, S. Remote sensing of cyanobacteria in turbid productive waters, PhD Dissertation. Mississippi State University, USA. 2012."
-BGA_SY002BDA,Phycocyanin (BGA_PC),w650 / w625,"Schalles, J.; Yacobi, Y. Remote detection and seasonal patterns of phycocyanin, carotenoid and chlorophyll-a pigments in eutrophic waters. Archiv fur Hydrobiologie, Special Issues Advances in Limnology, 2000, 55,153闁?68"
+BGA_SY002BDA,Phycocyanin (BGA_PC),w650 / w625,"Schalles, J.; Yacobi, Y. Remote detection and seasonal patterns of phycocyanin, carotenoid and chlorophyll-a pigments in eutrophic waters. Archiv fur Hydrobiologie, Special Issues Advances in Limnology, 2000, 55,153<EFBFBD><EFBFBD>?68"
 BGA_Wy08CI,Phycocyanin (BGA_PC),(-1 * (W686 - W672 - (W715 - W672))),"Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., Tester, P. A., Dyble, J.; Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int. J. Remote Sens., 2008, 29, 3665-3672."
 Chl_Al10SABI,chlorophyll_a,(w857 - w644) / (w458 + w529),"Alawadi, F. Detection of surface algal blooms using the newly developed algorithm surface algal bloom index (SABI). Proc. SPIE 2010, 7825."
-Chl_Am092Bsub,chlorophyll_a,w681 - w665,"Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S. Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery. Opt. Express 2009, 17, 9126闁?144."
+Chl_Am092Bsub,chlorophyll_a,w681 - w665,"Amin, R.; Zhou, J.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S. Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery. Opt. Express 2009, 17, 9126<EFBFBD><EFBFBD>?144."
 Chl_Be16FLHblue,chlorophyll_a,w529 - (w644 + (w458 - w644)),"Beck, R.A. and 22 others; Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations, Remote Sens. Environ., 2016, 178, 15-30."
 Chl_Be16FLHviolet,chlorophyll_a,w529 - (w644 + (w429 - w644)),"Beck, R.A. and 22 others; Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations, Remote Sens. Environ., 2016, 178, 15-30."
 Chl_Be16NDTIblue,chlorophyll_a,(w658 - w458) / (w658 + w458),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 543."
 Chl_Be16NDTIviolet,chlorophyll_a,(w658 - w444) / (w658 + w444),"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 544."
 Chl_De933BDA,chlorophyll_a,w600 - w648 - w625,"Dekker, A.; Detection of the optical water quality parameters for eutrophic waters by high resolution remote sensing, Ph.D. thesis, 1993, Free University, Amsterdam."
 Chl_Gi033BDA,chlorophyll_a,((1 / w672) - (1 / w715)) * w757,"Gitelson, A.A.; U. Gritz, and M. N. Merzlyak.; Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Phys. 2003, 160, 271-282."
-Chl_Kn07KIVU,chlorophyll_a,(w458 - w644) / w529,"Kneubuhler, M.; Frank T.; Kellenberger, T.W; Pasche N.; Schmid M.; Mapping chlorophyll-a in Lake Kivu with remote sensing methods. 2007, Proceedings of the Envisat Symposium 2007, Montreux, Switzerland 23闁?7 April 2007 (ESA SP-636, July 2007)."
+Chl_Kn07KIVU,chlorophyll_a,(w458 - w644) / w529,"Kneubuhler, M.; Frank T.; Kellenberger, T.W; Pasche N.; Schmid M.; Mapping chlorophyll-a in Lake Kivu with remote sensing methods. 2007, Proceedings of the Envisat Symposium 2007, Montreux, Switzerland 23<EFBFBD><EFBFBD>?7 April 2007 (ESA SP-636, July 2007)."
 Chl_MM12NDCI,chlorophyll_a,(w715 - w686) / (w715 + w686),"Mishra, S.; and Mishra, D.R. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters, Remote Sens. Environ., 2012, 117, 394-406"
 Chl_Zh10FLH,chlorophyll_a,w686 - (w715 + (w672 - w751)),"Zhao, D.Z.; Xing, X.G.; Liu, Y.G.; Yang, J.H.; Wang, L. The relation of chlorophyll-a concentration with the reflectance peak near 700 nm in algae-dominated waters and sensitivity of fluorescence algorithms for detecting algal bloom. Int. J. Remote Sens. 2010, 31, 39-48"
 Turb_Be16GreenPlusRedBothOverViolet,Turbidity,(w558 + w658) / w444,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538"
 Turb_Be16RedOverViolet,Turbidity,w658 / w444,"Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 539"
-Turb_Bow06RedOverGreen,Turbidity,w658 / w558,"Bowers, D. G., and C. E. Binding. 2006. 闁炽儲缈籬e Optical Properties of Mineral Suspended Particles: A Review and Synthesis.闁?Estuarine Coastal and Shelf Science 67 (1闁?): 219闁?30. doi:10.1016/j.ecss.2005.11.010"
+Turb_Bow06RedOverGreen,Turbidity,w658 / w558,"Bowers, D. G., and C. E. Binding. 2006. 闁炽儲缈籬e Optical Properties of Mineral Suspended Particles: A Review and Synthesis.<EFBFBD><EFBFBD>?Estuarine Coastal and Shelf Science 67 (1<EFBFBD><EFBFBD>?): 219<EFBFBD><EFBFBD>?30. doi:10.1016/j.ecss.2005.11.010"
 Turb_Chip09NIROverGreen,Turbidity,w857 / w558,"Chipman, J. W.; Olmanson, L.G.; Gitelson, A.A.; Remote sensing methods for lake management: A guide for resource managers and decision-makers. 2009."
 Turb_Dox02NIRoverRed,Turbidity,w857 / w658,"Doxaran, D., Froidefond, J.-M.; Castaing, P. ; A reflectance band ratio used to estimate suspended matter concentrations in sediment-dominated coastal waters, Remote Sens., 2002, 23, 5079-5085"
 Turb_Frohn09GreenPlusRedBothOverBlue,Turbidity,(w558 + w658) / w458,"Frohn, R. C., & Autrey, B. C. (2009). Water quality assessment in the Ohio River using new indices for turbidity and chlorophyll-a with Landsat-7 Imagery. Draft Internal Report, US Environmental Protection Agency."
-Turb_Harr92NIR,Turbidity,w857,"Schiebe F.R., Harrington J.A., Ritchie J.C. Remote-Sensing of Suspended Sediments闁炽儲鏁刪e Lake Chicot, Arkansas Project. Int. J. Remote Sens. 1992;13:1487闁?509"
+Turb_Harr92NIR,Turbidity,w857,"Schiebe F.R., Harrington J.A., Ritchie J.C. Remote-Sensing of Suspended Sediments闁炽儲鏁刪e Lake Chicot, Arkansas Project. Int. J. Remote Sens. 1992;13:1487<EFBFBD><EFBFBD>?509"
 Turb_Lath91RedOverBlue,Turbidity,w658 / w458,"Lathrop, R. G., Jr., T. M. Lillesand, and B. S. Yandell, 1991. Testing the utility of simple multi-date Thematic Mapper calibration algorithms for monitoring turbid inland waters. International Journal of Remote Sensing"
 Turb_Moore80Red,Turbidity,w658,"Moore, G.K., Satellite remote sensing of water turbidity, Hydrological Sciences, 1980, 25, 4, 407-422"

docs/项目交接文档.md

@@ -0,0 +1,175 @@
+# 水质参数反演分析系统 — 项目交接文档
+
+> 文档用途：帮助新负责人快速理解仓库结构、运行方式、核心逻辑与维护要点。  
+> 适用仓库：本地工程 **fengzhuang-ui2V3**（水质 GUI + 反演流水线）。
+
+---
+
+## 1. 项目概述
+
+本系统为 **基于遥感影像与机器学习的水质反演桌面应用**：水域掩膜、耀斑检测与去除、CSV/光谱处理、监督与非经验建模、采样与预测、空间制图与报告生成。  
+
+- **图形界面**：PyQt5，主入口 `src/gui/water_quality_gui.py`。  
+- **业务编排**：GUI 使用 `src/core/water_quality_inversion_pipeline_GUI.py` 中的 `WaterQualityInversionPipeline`；命令行/无 GUI 场景可参考 `src/core/water_quality_inversion_pipeline.py`（子目录命名已与 GUI 管线对齐，但 **不含** 步骤 5.5 / 6.5 / 6.75 等扩展目录的完整定义，以 GUI 管线为准）。  
+- **打包**：PyInstaller，`scripts/water_quality_gui_file.spec`，单文件 exe，`console=False`。
+
+---
+
+## 2. 目录结构（精简）
+
+```
+项目根/
+├── src/
+│   ├── gui/                    # PyQt5 主界面、步骤面板、工作线程
+│   ├── core/                   # 反演流水线、去耀斑、建模、预测、非经验反演
+│   ├── preprocessing/          # 光谱预处理、水质数据加工
+│   ├── postprocessing/         # 制图、可视化、Word 报告、航迹等
+│   ├── visualization/        # 可视化包初始化（具体实现多在 postprocessing）
+│   └── utils/                  # 水域提取、指数、采样、工具函数
+├── data/                       # 图标、公式子数据等静态资源
+├── scripts/                    # PyInstaller spec、构建产物常出现在 scripts/build|dist
+├── docs/                       # 说明文档（含本文）
+├── requirements*.txt         # pip/conda 依赖清单
+├── README.md / README-conda.md # 安装与 conda 说明
+└── README_SAMPLING_MAP.md      # 采样地图相关说明（若仍写旧目录名，需与下文「工作目录」对照更新）
+```
+
+---
+
+## 3. 运行方式
+
+### 3.1 开发运行 GUI
+
+在项目根目录（或已将 `src` 加入 `PYTHONPATH`）下：
+
+```bash
+python src/gui/water_quality_gui.py
+```
+
+依赖见 `requirements.txt`；地理栈（GDAL/rasterio 等）建议在 **Conda** 环境按 `README-conda.md` / `requirements-conda.txt` 安装。
+
+### 3.2 打包 Windows exe
+
+在已激活的构建环境中执行（路径按本机调整）：
+
+```bash
+pyinstaller scripts/water_quality_gui_file.spec
+```
+
+产物一般为 `scripts/dist/water_quality_gui.exe`（以 spec 中 `name` 为准）。spec 中已收集 rasterio 动态库、conda `Library/bin` 下 DLL、`data/icons` 等。
+
+### 3.3 多进程说明（Windows / 打包）
+
+- 若使用 **sklearn `GridSearchCV(n_jobs=-1)`** 等并行，joblib 会起子进程；**冻结 exe 时可能出现控制台窗口闪烁**。缓解思路：线程后端 `parallel_backend("threading")`、或 `n_jobs=1`、或模型内部 `n_jobs` 并行（需改 `modeling_batch.py`，交接时按需评估）。  
+- 入口脚本建议在 `if __name__ == "__main__":` 内、启动 GUI 前调用 `multiprocessing.freeze_support()`，以符合 PyInstaller + multiprocessing 规范（是否已加请以当前 `water_quality_gui.py` 为准）。
+
+---
+
+## 4. GUI 与流水线步骤对应关系
+
+界面上的「步骤」通过 `WorkerThread.run_single_step` 映射到管线方法（配置键名与面板一致，如 `step6`、`step5_5`）：
+
+| 配置键 / 逻辑步骤 | 管线方法名 |
+|-------------------|------------|
+| step1 | `step1_generate_water_mask` |
+| step2 | `step2_find_glint_area` |
+| step3 | `step3_remove_glint` |
+| step4 | `step4_process_csv` |
+| step5 | `step5_extract_training_spectra` |
+| step5_5 | `step5_5_calculate_water_quality_indices` |
+| step6 | `step6_train_models` |
+| step6_5 | `step6_5_non_empirical_modeling` |
+| step6_75 | `step6_75_custom_regression` |
+| step7 | `step7_generate_sampling_points` |
+| step8 | `step8_predict_water_quality` |
+| step8_5 | `step8_5_predict_with_non_empirical_models` |
+| step8_75 | `step8_75_predict_with_custom_regression` |
+| step9 | `step9_generate_distribution_map` |
+
+单步运行时会注入 `skip_dependency_check=True`，便于独立调试；全流程运行仍应保证输入/前置步骤产物存在。
+
+---
+
+## 5. 工作目录（work_dir）子文件夹命名
+
+所有中间结果默认写在用户选择的 **工作目录** 下。当前代码中约定如下（**若磁盘上仍为旧文件夹名，需手动改名或重新跑流程**）：
+
+| 子目录名 | 含义 |
+|----------|------|
+| `1_water_mask` | 水域掩膜 |
+| `2_glint` | 耀斑相关 |
+| `3_deglint` | 去耀斑影像 |
+| `4_processed_data` | 处理后数据 |
+| `5_training_spectra` | 训练光谱等 |
+| `6_water_quality_indices` | 水质指数（原 `5_5_water_quality_indices`） |
+| `7_models` | 监督学习模型（原 `6_models`） |
+| `8_non_empirical_models` | 非经验模型（原 `6_5_non_empirical_models`） |
+| `9_custom_regression` | 自定义回归结果（原 `6_75_custom_regression`） |
+| `10_sampling` | 采样点/光谱（原 `7_sampling`） |
+| `11_12_13_predictions` | 预测 CSV 等（原 `8_predictions`） |
+| `14_visualization` | 图表、分布图、预览等（原 `9_visualization`） |
+| `reports` | 报告输出（原 `10_reports`） |
+
+**定义位置**：`water_quality_inversion_pipeline_GUI.py` 构造函数中对 `self.*_dir` 的赋值；GUI 中大量路径拼接需与此一致（如 `14_visualization`、`7_models`）。
+
+---
+
+## 6. 核心模块说明
+
+| 模块路径 | 职责 |
+|----------|------|
+| `src/gui/water_quality_gui.py` | 主窗口、各步骤面板、线程 Worker、图像查看、配置收集与单步/全流程触发 |
+| `src/core/water_quality_inversion_pipeline_GUI.py` | GUI 用流水线：步骤实现、目录管理、与可视化/报告类协作 |
+| `src/core/water_quality_inversion_pipeline.py` | 无扩展步骤目录时的管线实现，供脚本或非 GUI 参考 |
+| `src/core/modeling/modeling_batch.py` | 批量训练、`GridSearchCV`、模型落盘（joblib） |
+| `src/core/prediction/inference_batch.py` | 批量预测与模型加载 |
+| `src/core/glint_removal/*.py` | Goodman、Hedley、Kutser、SUGAR 等去耀斑算法 |
+| `src/postprocessing/visualization_reports.py` | 水质可视化封装（输出目录常指向 `14_visualization`） |
+| `src/postprocessing/report_word.py` | Word 报告；默认从 `work_dir/14_visualization` 等读图 |
+| `src/postprocessing/map.py` | 分布图、插值相关（内含开发者本地示例路径，勿当生产配置） |
+
+---
+
+## 7. 依赖与环境
+
+- **Python**：3.8+（README 声明；实际环境以 `requirements-py310.txt` 等为准）。  
+- **关键库**：PyQt5、numpy、pandas、scipy、scikit-learn、joblib、matplotlib、rasterio/geopandas（视功能启用）、opencv-python、Pillow 等。  
+- **Conda**： GDAL 等二进制依赖推荐用 conda-forge，见 `README-conda.md`。
+
+---
+
+## 8. 维护与排错提示
+
+1. **资源路径（打包后图标不显示）**：冻结运行时勿依赖 `Path(__file__)` 指向源码树；应使用 `sys._MEIPASS` 或项目内统一的 `get_icon_path()` 类逻辑。  
+2. **tqdm / 无控制台**：打包 GUI 时若 `sys.stdout` 为 `None`，tqdm 可能报错；`Goodman.py` 等处已对 frozen GUI 做了 `disable` 处理，新增进度条时注意同样处理。  
+3. **配置与 YAML**：全流程配置由 GUI 各面板 `get_config()` 汇总；改字段名需同步管线 `**config['stepX']` 形参。  
+4. **文档滞后**：`README_SAMPLING_MAP.md` 等若仍出现 `9_visualization` 等旧目录名，应以本文第 5 节为准或更新该文档。
+
+---
+
+## 9. 交接检查清单
+
+- [ ] 已能在本机用 `python src/gui/water_quality_gui.py` 启动 GUI 并完成一次最小流程（或关键单步）。  
+- [ ] 已确认 Conda/pip 环境与 `README-conda.md`、`requirements.txt` 一致。  
+- [ ] 已阅读 `scripts/water_quality_gui_file.spec`，知悉打包入口与 `datas` 列表。  
+- [ ] 已知悉 `work_dir` 子目录当前命名，并能区分「配置键 step6」与「文件夹 `7_models`」。  
+- [ ] 已记录客户/内部使用的 **工作目录样例** 与 **典型输入数据格式**（CSV 列、影像格式）。  
+- [ ] Git 远程、发布分支、Issue/需求跟踪方式已交接。  
+- [ ] 许可证与第三方库合规（MIT 与 README 声明等）已确认。
+
+---
+
+## 10. 文档修订记录
+
+| 日期 | 说明 |
+|------|------|
+| 2026-04-09 | 初版：目录结构、步骤映射、工作目录新命名、打包与多进程注意事项 |
+
+---
+
+**联系人（请交接时填写）**  
+
+| 角色 | 姓名 | 联系方式 |
+|------|------|----------|
+| 移交人 | | |
+| 接收人 | | |

src/core/non_empirical_retrieval.py

@@ -246,8 +246,8 @@ def non_empirical_retrieval(algorithm, model_info_path, coor_spectral_path, outp
 
 if __name__ == "__main__":
     algorithm= "chl_a"
-    model_info_path= r"E:\code\WQ\pipeline_result\work_dir\5_training_spectra\6_5_non_empirical_models\SS\SS_chl_a.json"
-    coor_spectral_path= r"E:\code\WQ\pipeline_result\work_dir\7_sampling\sampling_spectra.csv"
-    output_path= r"E:\code\WQ\pipeline_result\work_dir\8_predictions\SS_chl_a.csv"
+    model_info_path= r"E:\code\WQ\pipeline_result\work_dir\5_training_spectra\8_non_empirical_models\SS\SS_chl_a.json"
+    coor_spectral_path= r"E:\code\WQ\pipeline_result\work_dir\10_sampling\sampling_spectra.csv"
+    output_path= r"E:\code\WQ\pipeline_result\work_dir\11_12_13_predictions\SS_chl_a.csv"
     wave_radius=5.0
     non_empirical_retrieval(algorithm, model_info_path, coor_spectral_path, output_path, wave_radius)

src/core/water_quality_inversion_pipeline.py

@@ -95,11 +95,11 @@ class WaterQualityInversionPipeline:
         self.deglint_dir = self.work_dir / "3_deglint"
         self.processed_data_dir = self.work_dir / "4_processed_data"
         self.training_spectra_dir = self.work_dir / "5_training_spectra"
-        self.models_dir = self.work_dir / "6_models"
-        self.sampling_dir = self.work_dir / "7_sampling"
-        self.prediction_dir = self.work_dir / "8_predictions"
-        self.visualization_dir = self.work_dir / "9_visualization"
-        self.reports_dir = self.work_dir / "10_reports"
+        self.models_dir = self.work_dir / "7_models"
+        self.sampling_dir = self.work_dir / "10_sampling"
+        self.prediction_dir = self.work_dir / "11_12_13_predictions"
+        self.visualization_dir = self.work_dir / "14_visualization"
+        self.reports_dir = self.work_dir / "reports"
         
         # 创建所有子目录
         for dir_path in [self.water_mask_dir, self.glint_dir, self.deglint_dir,

src/core/water_quality_inversion_pipeline_GUI.py

@@ -105,14 +105,14 @@ class WaterQualityInversionPipeline:
         self.deglint_dir = self.work_dir / "3_deglint"
         self.processed_data_dir = self.work_dir / "4_processed_data"
         self.training_spectra_dir = self.work_dir / "5_training_spectra"
-        self.indices_dir = self.work_dir / "5_5_water_quality_indices"
-        self.models_dir = self.work_dir / "6_models"
-        self.non_empirical_models_dir = self.work_dir / "6_5_non_empirical_models"
-        self.custom_regression_dir = self.work_dir / "6_75_custom_regression"
-        self.sampling_dir = self.work_dir / "7_sampling"
-        self.prediction_dir = self.work_dir / "8_predictions"
-        self.visualization_dir = self.work_dir / "9_visualization"
-        self.reports_dir = self.work_dir / "10_reports"
+        self.indices_dir = self.work_dir / "6_water_quality_indices"
+        self.models_dir = self.work_dir / "7_models"
+        self.non_empirical_models_dir = self.work_dir / "8_non_empirical_models"
+        self.custom_regression_dir = self.work_dir / "9_custom_regression"
+        self.sampling_dir = self.work_dir / "10_sampling"
+        self.prediction_dir = self.work_dir / "11_12_13_predictions"
+        self.visualization_dir = self.work_dir / "14_visualization"
+        self.reports_dir = self.work_dir / "reports"
         
         # 创建所有子目录
         for dir_path in [self.water_mask_dir, self.glint_dir, self.deglint_dir,
@@ -3379,10 +3379,10 @@ class WaterQualityInversionPipeline:
             else:
                 # 如果output_dir为空，使用工作目录
                 if hasattr(self, 'work_dir') and self.work_dir is not None:
-                    non_empirical_dir = Path(self.work_dir) / "6_5_non_empirical_models"
+                    non_empirical_dir = Path(self.work_dir) / "8_non_empirical_models"
                 else:
                     # 如果没有工作目录，使用当前目录
-                    non_empirical_dir = Path.cwd() / "6_5_non_empirical_models"
+                    non_empirical_dir = Path.cwd() / "8_non_empirical_models"
             non_empirical_dir.mkdir(parents=True, exist_ok=True)
             
             # 设置默认参数
@@ -3712,7 +3712,7 @@ class WaterQualityInversionPipeline:
             if non_empirical_models_dir is not None:
                 final_models_dir = non_empirical_models_dir
             else:
-                default_models_dir = str(self.work_dir / "6_5_non_empirical_models")
+                default_models_dir = str(self.work_dir / "8_non_empirical_models")
                 if Path(default_models_dir).exists():
                     final_models_dir = default_models_dir
                 else:

src/gui/water_quality_gui.py

@@ -536,7 +536,7 @@ class VisualizationWorkerThread(QThread):
             wp = Path(self.work_dir)
             if self.task == "mask_glint":
                 from src.postprocessing.visualization_reports import WaterQualityVisualization
-                viz = WaterQualityVisualization(output_dir=str(wp / "9_visualization"))
+                viz = WaterQualityVisualization(output_dir=str(wp / "14_visualization"))
                 preview_paths = viz.generate_glint_deglint_previews(
                     work_dir=str(wp),
                     output_subdir="glint_deglint_previews",
@@ -572,7 +572,7 @@ class VisualizationWorkerThread(QThread):
                 csv_path = str(csv_files[0])
                 from src.postprocessing.point_map import SamplingPointMap
                 map_generator = SamplingPointMap(
-                    output_dir=str(wp / "9_visualization" / "sampling_maps"),
+                    output_dir=str(wp / "14_visualization" / "sampling_maps"),
                     fast_mode=True,
                 )
                 map_path = map_generator.create_sampling_point_map(
@@ -597,7 +597,7 @@ class VisualizationWorkerThread(QThread):
                 )
             elif self.task == "spectrum":
                 from src.postprocessing.visualization_reports import WaterQualityVisualization
-                viz = WaterQualityVisualization(output_dir=str(wp / "9_visualization"))
+                viz = WaterQualityVisualization(output_dir=str(wp / "14_visualization"))
                 csv_file = self.extra.get("csv_path")
                 wl = self.extra.get("wavelength_start_column", "UTM_Y")
                 n_groups = int(self.extra.get("n_groups", 5))
@@ -641,7 +641,7 @@ class VisualizationWorkerThread(QThread):
                     )
             elif self.task == "statistics":
                 from src.postprocessing.visualization_reports import WaterQualityVisualization
-                viz = WaterQualityVisualization(output_dir=str(wp / "9_visualization"))
+                viz = WaterQualityVisualization(output_dir=str(wp / "14_visualization"))
                 csv_file = self.extra.get("csv_path")
                 param_cols = self.extra.get("param_cols") or []
                 output_paths = viz.plot_statistical_charts(
@@ -660,7 +660,7 @@ class VisualizationWorkerThread(QThread):
                     self.failed.emit("训练光谱 CSV 无效或不存在，请确认已选择步骤5输出的文件。")
                     return
                 if not models_dir or not Path(models_dir).is_dir():
-                    self.failed.emit("模型目录无效或不存在，请确认步骤6已生成 6_models 下的参数子文件夹。")
+                    self.failed.emit("模型目录无效或不存在，请确认步骤6已生成 7_models 下的参数子文件夹。")
                     return
                 pipeline = WaterQualityInversionPipeline(work_dir=str(wp))
                 scatter_paths = pipeline.generate_model_scatter_plots(
@@ -670,7 +670,7 @@ class VisualizationWorkerThread(QThread):
                 self.finished_ok.emit({"task": "scatter", "scatter_paths": scatter_paths or {}})
             elif self.task == "generate_all_selected":
                 from src.postprocessing.visualization_reports import WaterQualityVisualization
-                viz = WaterQualityVisualization(output_dir=str(wp / "9_visualization"))
+                viz = WaterQualityVisualization(output_dir=str(wp / "14_visualization"))
                 parts = []
                 if self.extra.get("gen_mask_glint"):
                     preview_paths = viz.generate_glint_deglint_previews(
@@ -703,7 +703,7 @@ class VisualizationWorkerThread(QThread):
                             csv_path = str(csv_files[0])
                             from src.postprocessing.point_map import SamplingPointMap
                             map_generator = SamplingPointMap(
-                                output_dir=str(wp / "9_visualization" / "sampling_maps"),
+                                output_dir=str(wp / "14_visualization" / "sampling_maps"),
                                 fast_mode=True,
                             )
                             map_path = map_generator.create_sampling_point_map(
@@ -2699,12 +2699,12 @@ class Step9Panel(QWidget):
         params_group.setLayout(params_layout)
         layout.addWidget(params_group)
 
-        # 输出目录（可选）：在此目录下生成「CSV文件名_distribution.png」；留空则用工作目录/9_visualization
+        # 输出目录（可选）：在此目录下生成「CSV文件名_distribution.png」；留空则用工作目录/14_visualization
         self.output_dir = FileSelectWidget(
             "输出分布图目录:",
             "Directories;;All Files (*.*)"
         )
-        self.output_dir.line_edit.setPlaceholderText("留空→工作目录/9_visualization")
+        self.output_dir.line_edit.setPlaceholderText("留空→工作目录/14_visualization")
         # 修改浏览按钮为选择目录
         self.output_dir.browse_btn.clicked.disconnect()
         self.output_dir.browse_btn.clicked.connect(self.browse_output_dir)
@@ -3404,10 +3404,10 @@ class ImageCategoryTree(QTreeWidget):
         if not work_path.exists():
             return
         
-        # 查找所有图像文件：9_visualization 为主，同时扫描步骤产出目录（如 1_water_mask 下的预览/叠置图）
+        # 查找所有图像文件：14_visualization 为主，同时扫描步骤产出目录（如 1_water_mask 下的预览/叠置图）
         image_extensions = ['*.png', '*.jpg', '*.jpeg', '*.tif', '*.tiff', '*.bmp']
         scan_roots: List[Path] = []
-        _viz = work_path / "9_visualization"
+        _viz = work_path / "14_visualization"
         if _viz.is_dir():
             scan_roots.append(_viz)
         _wm = work_path / "1_water_mask"
@@ -3761,7 +3761,7 @@ class VisualizationPanel(QWidget):
                     self,
                     "成功",
                     f"掩膜和耀斑缩略图生成完成，共 {cnt} 个预览图。\n"
-                    f"保存位置: 9_visualization/glint_deglint_previews/",
+                    f"保存位置: 14_visualization/glint_deglint_previews/",
                 )
             else:
                 QMessageBox.warning(
@@ -3776,7 +3776,7 @@ class VisualizationPanel(QWidget):
                 "成功",
                 "采样点地图生成完成。\n"
                 f"输出: {Path(map_path).name if map_path else ''}\n"
-                "路径: 9_visualization/sampling_maps/",
+                "路径: 14_visualization/sampling_maps/",
             )
             if map_path:
                 self.show_chart_viewer(map_path, "采样点分布图")
@@ -3787,7 +3787,7 @@ class VisualizationPanel(QWidget):
                 errs = payload.get("errors") or []
                 msg = (
                     f"已为 {len(ok_paths)} 个水质参数生成光谱对比图。\n"
-                    f"保存目录: 工作目录/9_visualization/"
+                    f"保存目录: 工作目录/14_visualization/"
                 )
                 if errs:
                     msg += f"\n\n以下列未生成或出错 ({len(errs)} 项，详见日志):\n"
@@ -3818,14 +3818,14 @@ class VisualizationPanel(QWidget):
                     self,
                     "成功",
                     f"已生成 {len(ok_paths)} 个模型评估散点图。\n"
-                    f"保存位置: 9_visualization/scatter_plots/",
+                    f"保存位置: 14_visualization/scatter_plots/",
                 )
                 self.show_chart_viewer(ok_paths[0], "模型评估散点图")
             else:
                 QMessageBox.warning(
                     self,
                     "提示",
-                    "未生成任何散点图。请确认 6_models 下已有各参数子目录及模型文件，"
+                    "未生成任何散点图。请确认 7_models 下已有各参数子目录及模型文件，"
                     "且训练 CSV 与建模时一致。",
                 )
         elif t == "generate_all_selected":
@@ -3935,7 +3935,7 @@ class VisualizationPanel(QWidget):
         
         self.gen_scatter_btn = QPushButton("📊 散点图")
         self.gen_scatter_btn.setToolTip(
-            "基于工作目录下 5_training_spectra/training_spectra.csv 与 6_models 生成模型评估散点图"
+            "基于工作目录下 5_training_spectra/training_spectra.csv 与 7_models 生成模型评估散点图"
         )
         self.gen_scatter_btn.clicked.connect(lambda: self.generate_chart('scatter'))
         specific_layout.addWidget(self.gen_scatter_btn)
@@ -4000,7 +4000,7 @@ class VisualizationPanel(QWidget):
         self.image_tree.scan_directory(str(work_path))
         
         # 如果有图像，自动选择第一个
-        viz_dir = work_path / "9_visualization"
+        viz_dir = work_path / "14_visualization"
         if viz_dir.exists():
             image_files = list(viz_dir.glob("**/*.png")) + list(viz_dir.glob("**/*.jpg"))
             if image_files:
@@ -4082,7 +4082,7 @@ class VisualizationPanel(QWidget):
                     )
                     return
                 training_csv = training_spectra_csv
-                models_dir = work_path / "6_models"
+                models_dir = work_path / "7_models"
                 if not models_dir.is_dir() or not any(
                     d.is_dir() for d in models_dir.iterdir()
                 ):
@@ -4188,9 +4188,9 @@ class VisualizationPanel(QWidget):
             return
         
         work_path = Path(self.work_dir)
-        viz_dir = work_path / "9_visualization"
-        viz_dir2 = work_path / "9_visualization/boxplots"
-        viz_dir3 = work_path / "9_visualization/scatter_plots"
+        viz_dir = work_path / "14_visualization"
+        viz_dir2 = work_path / "14_visualization/boxplots"
+        viz_dir3 = work_path / "14_visualization/scatter_plots"
         if not viz_dir.exists():
             QMessageBox.warning(self, "警告", 
                 f"可视化目录不存在:\n{viz_dir}\n\n请先生成图表。")
@@ -4338,7 +4338,7 @@ class ReportGenerationPanel(QWidget):
         layout.setSpacing(10)
 
         intro = QLabel(
-            "根据工作目录下的可视化结果（9_visualization 等）生成 Word 分析报告。"
+            "根据工作目录下的可视化结果（14_visualization 等）生成 Word 分析报告。"
             "需已存在可视化图表；AI 分析通过 Ollama /api/chat 调用本地或远程服务。"
         )
         intro.setWordWrap(True)
@@ -4350,7 +4350,7 @@ class ReportGenerationPanel(QWidget):
 
         wd_row = QHBoxLayout()
         self.work_dir_edit = QLineEdit()
-        self.work_dir_edit.setPlaceholderText("选择流程工作目录（含 9_visualization）…")
+        self.work_dir_edit.setPlaceholderText("选择流程工作目录（含 14_visualization）…")
         wd_browse = QPushButton("浏览…")
         wd_browse.clicked.connect(self.browse_work_dir)
         sync_btn = QPushButton("同步主窗口工作目录")
@@ -4362,7 +4362,7 @@ class ReportGenerationPanel(QWidget):
 
         out_row = QHBoxLayout()
         self.output_dir_edit = QLineEdit()
-        self.output_dir_edit.setPlaceholderText("留空则保存到 工作目录/9_visualization")
+        self.output_dir_edit.setPlaceholderText("留空则保存到 工作目录/14_visualization")
         out_browse = QPushButton("浏览…")
         out_browse.clicked.connect(self.browse_output_dir)
         out_row.addWidget(self.output_dir_edit, 1)
@@ -4494,7 +4494,7 @@ class ReportGenerationPanel(QWidget):
         if not wd or not os.path.isdir(wd):
             QMessageBox.warning(self, "提示", "请选择有效的工作目录。")
             return
-        viz = Path(wd) / "9_visualization"
+        viz = Path(wd) / "14_visualization"
         if not viz.is_dir():
             QMessageBox.warning(
                 self,
@@ -4755,7 +4755,7 @@ class Step6_5Panel(QWidget):
             "输出模型目录:",
             "Directories;;All Files (*.*)"
         )
-        self.output_dir.line_edit.setPlaceholderText("6_5_non_empirical_models")
+        self.output_dir.line_edit.setPlaceholderText("8_non_empirical_models")
         # 修改浏览按钮为选择目录
         self.output_dir.browse_btn.clicked.disconnect()
         self.output_dir.browse_btn.clicked.connect(self.browse_output_dir)
@@ -4825,9 +4825,9 @@ class Step6_5Panel(QWidget):
             # 如果output_dir为空，使用工作目录或当前目录
             main_window = self.parent().window()
             if hasattr(main_window, 'work_dir') and main_window.work_dir:
-                output_dir = str(Path(main_window.work_dir) / "6_5_non_empirical_models")
+                output_dir = str(Path(main_window.work_dir) / "8_non_empirical_models")
             else:
-                output_dir = str(Path.cwd() / "6_5_non_empirical_models")
+                output_dir = str(Path.cwd() / "8_non_empirical_models")
         config['output_dir'] = output_dir
 
         # 添加训练数据路径（用于独立运行）
@@ -5044,7 +5044,7 @@ class Step6_75Panel(QWidget):
         output_layout = QFormLayout()
 
         self.output_dir = QLineEdit()
-        self.output_dir.setText("6_75_custom_regression")
+        self.output_dir.setText("9_custom_regression")
         output_layout.addRow("输出目录名:", self.output_dir)
 
         output_group.setLayout(output_layout)
@@ -5202,7 +5202,7 @@ class Step6_75Panel(QWidget):
                 checkbox.setChecked(method in selected_methods)
 
         if 'output_dir' in config:
-            self.output_dir.setText(config['output_dir'] or "6_75_custom_regression")
+            self.output_dir.setText(config['output_dir'] or "9_custom_regression")
         if 'enabled' in config:
             self.enable_checkbox.setChecked(config['enabled'])
 

src/postprocessing/flight_path.py

@@ -534,7 +534,7 @@ if __name__ == "__main__":
     print("  )")
 
 
-    visualizer = FlightPathVisualizer(output_dir=r"E:\code\WQ\pipeline_result\work_dir\9_visualization\flight_maps")
+    visualizer = FlightPathVisualizer(output_dir=r"E:\code\WQ\pipeline_result\work_dir\14_visualization\flight_maps")
     # 生成飞行轨迹图
     map_path = visualizer.create_flight_path_map(
         gps_folder=r"D:\BaiduNetdiskDownload\20250902\gps",           # GPS文件夹路径

src/postprocessing/map.py

@@ -2130,9 +2130,9 @@ def main():
     mapper = ContentMapper()
 
     # 示例1：处理单个文件
-    csv_file =  r"E:\code\WQ\pipeline_result\tests1\8_predictions\BGA.csv"  # 采样点的预测值
+    csv_file =  r"E:\code\WQ\pipeline_result\tests1\11_12_13_predictions\BGA.csv"  # 采样点的预测值
     shp_file = r"D:\BaiduNetdiskDownload\yaobao\roi\roi.shp" # 水体边界shapefile路径
-    output_file = r"E:\code\WQ\pipeline_result\work_dir\8_predictions\BGA.png"   # 输出图片路径
+    output_file = r"E:\code\WQ\pipeline_result\work_dir\11_12_13_predictions\BGA.png"   # 输出图片路径
     # 
     mapper.process_data(
         csv_file=csv_file,

src/postprocessing/report_word.py

@@ -79,7 +79,7 @@ class WaterQualityReportGenerator:
             self.work_dir = Path(work_dir)
         
         # 基于工作目录设置各子目录
-        self.visualization_dir = self.work_dir / "9_visualization"
+        self.visualization_dir = self.work_dir / "14_visualization"
         
         # 设置报告保存位置：默认为可视化目录（visualization_dir）
         self._output_dir_is_default = output_dir is None
@@ -585,12 +585,12 @@ class WaterQualityReportGenerator:
                        output_path: Optional[str] = None) -> str:
         """
         生成 Word 报告 - 所有数据均来自工作目录（work_dir）
-        可视化图片、统计数据等均从 work_dir/9_visualization 和 work_dir/4_processed_data 中读取
+        可视化图片、统计数据等均从 work_dir/14_visualization 和 work_dir/4_processed_data 中读取
         """
         # 设置工作目录（整个流程的核心）
         if work_dir is not None:
             self.work_dir = Path(work_dir)
-            self.visualization_dir = self.work_dir / "9_visualization"
+            self.visualization_dir = self.work_dir / "14_visualization"
             if getattr(self, "_output_dir_is_default", False):
                 self.output_dir = self.visualization_dir
                 self.output_dir.mkdir(parents=True, exist_ok=True)
@@ -1050,7 +1050,7 @@ class WaterQualityReportGenerator:
         vis_dir = self.visualization_dir
 
         # 0. 航线规划图
-        flight_path_img_path = work_dir_path / "9_visualization" / "flight_maps"
+        flight_path_img_path = work_dir_path / "14_visualization" / "flight_maps"
         h3 = doc.add_heading("航线规划：", level=3)
         self._style_heading(h3, level=3)
 

src/postprocessing/visualization_reports.py

@@ -423,7 +423,7 @@ class WaterQualityVisualization:
         - 2_glint文件夹：单波段二值耀斑掩膜，使用红色高亮显示
         - 3_deglint文件夹：多波段去耀斑影像，使用RGB合成显示
         - 自动识别文件类型并应用相应的可视化方案
-        - 输出保存至9_visualization/glint_deglint_previews/
+        - 输出保存至14_visualization/glint_deglint_previews/
 
         Args:
             work_dir: 工作目录路径
@@ -713,7 +713,7 @@ class WaterQualityVisualization:
             from src.postprocessing.point_map import SamplingPointMap
 
             # 如果没有提供路径，自动查找
-            work_dir = self.output_dir.parent  # 9_visualization的父目录就是工作目录
+            work_dir = self.output_dir.parent  # 14_visualization的父目录就是工作目录
 
             if hyperspectral_path is None:
                 # 查找高光谱影像