feat: Step1~Step14 面板单步按钮 EventBus 解耦 + Handler 补全（Step8~Step14）+ 旧上帝类删除

- 9 个面板（step1~step6/step8_ml_train/step8_qaa/step9_ml_predict/step10）单步执行按钮从 parent 链上溯改为 global_event_bus.publish('RequestRunSingleStep')

- PipelineExecutor 新增 _on_request_run_single_step 订阅

- 新增 Handler: step8_ml_train / step9_ml_predict / step10_qaa_inversion / step11_concentration / step12_kriging / step13_visualization / step14_report

- 删除旧 water_quality_inversion_pipeline_GUI.py（上帝类已肢解完毕）

src/gui/panels/step12_viz_panel.py

@@ -32,12 +32,7 @@ from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
 from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
 from matplotlib.figure import Figure
 
-# Pipeline 可用性（与 core/worker_thread.py 保持一致）
-try:
-    from src.core.water_quality_inversion_pipeline_GUI import WaterQualityInversionPipeline
-    PIPELINE_AVAILABLE = True
-except ImportError:
-    PIPELINE_AVAILABLE = False
+PIPELINE_AVAILABLE = True
 
 
 def _viz_training_spectra_csv_path(work_path: Path) -> Path:
@@ -208,7 +203,7 @@ class VisualizationWorkerThread(QThread):
                     {"task": "statistics", "output_paths": output_paths}
                 )
             elif self.task == "scatter":
-                from src.core.water_quality_inversion_pipeline_GUI import WaterQualityInversionPipeline
+                from src.core.visualization.scatter_plot import generate_model_scatter_plots
 
                 training_csv_path = (self.extra.get("training_csv_path") or "").strip()
                 models_dir = (self.extra.get("models_dir") or "").strip()
@@ -218,10 +213,9 @@ class VisualizationWorkerThread(QThread):
                 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 7_Supervised_Model_Training 下的参数子文件夹。")
                     return
-                pipeline = WaterQualityInversionPipeline(work_dir=str(wp))
-                scatter_paths = pipeline.generate_model_scatter_plots(
-                    training_csv_path=training_csv_path,
+                scatter_paths = generate_model_scatter_plots(
                     models_dir=models_dir,
+                    training_csv_path=training_csv_path,
                 )
                 self.finished_ok.emit({"task": "scatter", "scatter_paths": scatter_paths or {}})
             elif self.task == "generate_all_selected":
@@ -235,11 +229,10 @@ class VisualizationWorkerThread(QThread):
                     if training_csv.is_file():
                         models_dir = wp / "7_Supervised_Model_Training"
                         if models_dir.is_dir() and any(d.is_dir() for d in models_dir.iterdir()):
-                            from src.core.water_quality_inversion_pipeline_GUI import WaterQualityInversionPipeline
-                            pipeline = WaterQualityInversionPipeline(work_dir=str(wp))
-                            scatter_paths = pipeline.generate_model_scatter_plots(
-                                training_csv_path=str(training_csv),
+                            from src.core.visualization.scatter_plot import generate_model_scatter_plots
+                            scatter_paths = generate_model_scatter_plots(
                                 models_dir=str(models_dir),
+                                training_csv_path=str(training_csv),
                             )
                             count = len(scatter_paths) if scatter_paths else 0
                             parts.append(f"散点图: {count} 个")