fix: 公式报告 4 项修复 — 通用章节+AI接口+图片路径+标题数字

修复一: _generate_formula_report 封面后恢复
  _add_company_description_page/doc
  _add_data_acquisition_section/doc
  _add_data_processing_section/doc

修复二: _call_minimax_text / _call_minimax_vision
  URL 自动补齐 /chat/completions 后缀

修复三: _call_minimax_vision
  MIME 类型动态检测 (.png→image/png, 其他→image/jpeg)

修复四: _add_hyperspectral_images_section
  航线图搜索: 多路径 + rglob 递归查找
  去掉所有硬编码编号 (3.1/图3-2/3-3/3-4/3-5/3-6)

src/postprocessing/report_word.py

@@ -368,6 +368,8 @@ class WaterQualityReportGenerator:
             return "（Minimax API Key 未配置，请设置 MINIMAX_API_KEY 环境变量）"
 
         url = self.minimax_base_url
+        if not url.endswith("/chat/completions"):
+            url = f"{url.rstrip('/')}/chat/completions"
 
         payload: Dict[str, Any] = {
             "model": self.minimax_text_model,
@@ -433,6 +435,12 @@ class WaterQualityReportGenerator:
             return f"（读取图片失败：{e}）"
 
         url = self.minimax_base_url
+        if not url.endswith("/chat/completions"):
+            url = f"{url.rstrip('/')}/chat/completions"
+
+        # 动态 MIME 类型：PNG 和 JPEG 分别处理，避免 API 400
+        ext = image_path.suffix.lower()
+        mime_type = "image/png" if ext == ".png" else "image/jpeg"
 
         payload: Dict[str, Any] = {
             "model": self.minimax_vision_model,
@@ -444,7 +452,7 @@ class WaterQualityReportGenerator:
                         {"type": "text", "text": user_prompt},
                         {
                             "type": "image_url",
-                            "image_url": {"url": f"data:image/jpeg;base64,{img_b64}"},
+                            "image_url": {"url": f"data:{mime_type};base64,{img_b64}"},
                         },
                     ],
                 }
@@ -961,6 +969,11 @@ class WaterQualityReportGenerator:
         try: progress.update(1)
         except Exception: pass
 
+        # ── 通用章节：单位介绍 / 数据采集 / 工作流程 ──
+        self._add_company_description_page(doc)
+        self._add_data_acquisition_section(doc)
+        self._add_data_processing_section(doc)
+
         # ── 1. 项目背景 ──
         h1 = doc.add_heading("1 项目背景", level=1)
         self._style_heading(h1, 1)
@@ -1422,29 +1435,33 @@ class WaterQualityReportGenerator:
 
     def _add_hyperspectral_images_section(self, doc):
         """添加高光谱图像、耀斑区域和去耀斑图像展示"""
-        h = doc.add_heading("3.1 高光谱图像处理过程", level=2)
+        h = doc.add_heading("高光谱图像处理过程", level=2)
         self._style_heading(h, level=2)
 
         work_dir_path = self.work_dir
         vis_dir = self.visualization_dir
 
-        # 0. 航线规划图
-        flight_path_img_path = work_dir_path / "12_visualization" / "flight_paths"
+        # 0. 航线规划图（鲁棒搜索：多路径 + rglob）
         h3 = doc.add_heading("航线规划：", level=3)
         self._style_heading(h3, level=3)
 
-        # 查找航线图文件
         flight_map_files = []
-        if flight_path_img_path.exists():
-            flight_map_files = list(flight_path_img_path.glob("*.png")) + list(flight_path_img_path.glob("*.jpg"))
+        flight_search_dirs = [
+            work_dir_path / "12_visualization" / "flight_paths",
+            vis_dir / "flight_paths",
+            vis_dir,
+        ]
+        for search_dir in flight_search_dirs:
+            if search_dir.exists():
+                flight_map_files = list(search_dir.rglob("*.png")) + list(search_dir.rglob("*.jpg"))
+                if flight_map_files:
+                    break
 
         if flight_map_files:
-            # 使用最新的航线图文件
             latest_flight_map = max(flight_map_files, key=lambda p: p.stat().st_mtime)
-            success = self._add_image_with_caption(doc, str(latest_flight_map), "图3-1 航线规划", width=Inches(5.5))
-            
+            success = self._add_image_with_caption(doc, str(latest_flight_map), "航线规划", width=Inches(5.5))
+
             if success:
-                # AI 分析航线规划图
                 flight_analysis = self._analyze_flight_path_image(str(latest_flight_map))
                 self._add_ai_analysis_paragraph(doc, flight_analysis)
         else:
@@ -1455,7 +1472,7 @@ class WaterQualityReportGenerator:
         h3 = doc.add_heading("高光谱原始影像：", level=3)
         self._style_heading(h3, level=3)
         if hyperspectral_img_path.exists():
-            self._add_image_with_caption(doc, str(hyperspectral_img_path), "图3-2 高光谱原始影像", width=Inches(5.5))
+            self._add_image_with_caption(doc, str(hyperspectral_img_path), "高光谱原始影像", width=Inches(5.5))
         else:
             doc.add_paragraph("[高光谱原始影像 - 文件未找到]")
 
@@ -1465,7 +1482,7 @@ class WaterQualityReportGenerator:
         self._style_heading(h3, level=3)
         if water_mask_overlay_path.exists():
             success = self._add_image_with_caption(doc, str(water_mask_overlay_path), 
-                                                    "图3-3 水体区域识别（蓝色半透明区域为水域）", 
+                                                    "水体区域识别（蓝色半透明区域为水域）", 
                                                     width=Inches(5.5))
             if success:
                 water_analysis = self._analyze_water_mask_overlay(str(water_mask_overlay_path))
@@ -1480,13 +1497,13 @@ class WaterQualityReportGenerator:
         h3 = doc.add_heading("耀斑区域识别结果：", level=3)
         self._style_heading(h3, level=3)
         if glint_img_path.exists():
-            self._add_image_with_caption(doc, str(glint_img_path), "图3-4 耀斑区域识别结果", width=Inches(5.5))
+            self._add_image_with_caption(doc, str(glint_img_path), "耀斑区域识别结果", width=Inches(5.5))
         else:
             # 尝试查找其他可能的耀斑预览图
             glint_files = list(vis_dir.glob("glint_deglint_previews/*glint*.png"))
             if glint_files:
                 glint_img_path = glint_files[0]
-                self._add_image_with_caption(doc, str(glint_img_path), "图3-4 耀斑区域识别结果", width=Inches(5.5))
+                self._add_image_with_caption(doc, str(glint_img_path), "耀斑区域识别结果", width=Inches(5.5))
             else:
                 doc.add_paragraph("[耀斑区域识别结果 - 文件未找到]")
 
@@ -1497,13 +1514,13 @@ class WaterQualityReportGenerator:
         h3 = doc.add_heading("去除耀斑后的影像：", level=3)
         self._style_heading(h3, level=3)
         if deglint_img_path.exists():
-            self._add_image_with_caption(doc, str(deglint_img_path), "图3-5 去除耀斑后的高光谱影像", width=Inches(5.5))
+            self._add_image_with_caption(doc, str(deglint_img_path), "去除耀斑后的高光谱影像", width=Inches(5.5))
         else:
             # 尝试查找其他去耀斑预览图
             deglint_files = list(vis_dir.glob("glint_deglint_previews/*deglint*.png"))
             if deglint_files:
                 deglint_img_path = deglint_files[0]
-                self._add_image_with_caption(doc, str(deglint_img_path), "图3-5 去除耀斑后的影像", width=Inches(5.5))
+                self._add_image_with_caption(doc, str(deglint_img_path), "去除耀斑后的影像", width=Inches(5.5))
             else:
                 doc.add_paragraph("[去除耀斑后的影像 - 文件未找到]")
 
@@ -1531,7 +1548,7 @@ class WaterQualityReportGenerator:
         if sampling_map_files:
             # 使用最新的采样点分布图文件
             latest_sampling_map = max(sampling_map_files, key=lambda p: p.stat().st_mtime)
-            success = self._add_image_with_caption(doc, str(latest_sampling_map), "图3-6 采样点分布图", width=Inches(5.5))
+            success = self._add_image_with_caption(doc, str(latest_sampling_map), "采样点分布图", width=Inches(5.5))
             
             if success:
                 # AI 分析采样点分布图