revert: 回退克里金多线程并发，恢复串行处理

并发实测无提速（32GB 内存下单个克里金已用 5-6GB，两个并发
竞争内存带宽导致实际耗时相同），回退为简单串行循环。

src/core/handlers/step11_map_handler.py

@@ -243,79 +243,32 @@ class Step11MapHandler(BaseStepHandler):
                 context.notify('step11_map', 'warning',
                               f'共享上下文预计算失败: {e}，回退逐个处理')
 
-        # ── 并发生成 ──
-        # 克里金插值内部为 numpy/scipy 运算（释放 GIL），
-        # 使用 ThreadPoolExecutor 并发处理多个 CSV，大幅缩短总耗时。
-        # 注意：不使用 ProcessPoolExecutor（Windows spawn 会导致死锁）。
-        _max_workers = int(config.get('kriging_workers', 2))
-        _max_workers = max(1, min(_max_workers, total, os.cpu_count() or 4))
-
+        # ── 串行生成 ──
+        # 注：克里金是内存密集型运算（32GB 下单个用 5-6GB），
+        # 多线程并发竞争内存带宽，实际无提速，因此保持串行。
         generated: List[str] = []
         errors: Dict[str, str] = {}
 
-        if _max_workers > 1 and total > 1:
-            # ★ 主线程预先设置 matplotlib Agg 后端（避免多线程竞争）
-            import matplotlib
+        context.notify('step11_map', 'info',
+                       f'串行生成 {total} 张专题图（克里金自适应分块）')
+
+        for idx, csv_p in enumerate(csv_paths):
+            percent = int(idx / total * 100)
+            context.notify('step11_map', 'info',
+                          f'专题图 [{idx+1}/{total}]: {Path(csv_p).name}')
+
+            global_event_bus.publish('ProgressUpdate', {
+                'percentage': percent,
+                'message': f'Step11: {idx+1}/{total} {Path(csv_p).stem}',
+            })
+
             try:
-                matplotlib.use('Agg', force=True)
-            except Exception:
-                pass
-
-            import concurrent.futures
-            context.notify('step11_map', 'info',
-                           f'并发生成 {total} 张专题图（{_max_workers} 线程并行）')
-
-            completed = 0
-            with concurrent.futures.ThreadPoolExecutor(
-                    max_workers=_max_workers) as executor:
-                future_map = {
-                    executor.submit(
-                        _process_one_map, csv_p, base_kwargs, output_dir
-                    ): csv_p
-                    for csv_p in csv_paths
-                }
-                for future in concurrent.futures.as_completed(future_map):
-                    csv_p = future_map[future]
-                    completed += 1
-                    try:
-                        result_path, _ = future.result()
-                        generated.append(result_path)
-                        context.notify('step11_map', 'info',
-                                       f'专题图 [{completed}/{total}] ✓: '
-                                       f'{Path(csv_p).name}')
-                    except Exception as e:
-                        errors[csv_p] = str(e)
-                        context.notify('step11_map', 'warning',
-                                       f'专题图 [{completed}/{total}] ✗: '
-                                       f'{Path(csv_p).name} — {e}')
-
-                    percent = int(completed / total * 100)
-                    global_event_bus.publish('ProgressUpdate', {
-                        'percentage': percent,
-                        'message': f'Step11: {completed}/{total} '
-                                   f'{Path(csv_p).stem}',
-                    })
-        else:
-            context.notify('step11_map', 'info',
-                           f'顺序生成 {total} 张专题图（局部 Kriging 自适应分块）')
-
-            for idx, csv_p in enumerate(csv_paths):
-                percent = int(idx / total * 100)
-                context.notify('step11_map', 'info',
-                              f'专题图 [{idx+1}/{total}]: {Path(csv_p).name}')
-
-                global_event_bus.publish('ProgressUpdate', {
-                    'percentage': percent,
-                    'message': f'Step11: {idx+1}/{total} {Path(csv_p).stem}',
-                })
-
-                try:
-                    result_path, _ = _process_one_map(csv_p, base_kwargs, output_dir)
-                    generated.append(result_path)
-                except Exception as e:
-                    errors[csv_p] = str(e)
-                    context.notify('step11_map', 'warning',
-                                  f'专题图 FAIL: {Path(csv_p).name} — {e}')
+                result_path, _ = _process_one_map(csv_p, base_kwargs, output_dir)
+                generated.append(result_path)
+            except Exception as e:
+                errors[csv_p] = str(e)
+                context.notify('step11_map', 'warning',
+                              f'专题图 FAIL: {Path(csv_p).name} — {e}')
 
         step_end_time = time.time()
         elapsed = step_end_time - step_start_time

src/gui/panels/step11_map_panel.py

@@ -7,7 +7,7 @@ Step10 面板 - 专题图生成
 import os
 import traceback
 from pathlib import Path
-from typing import List, Optional, Tuple
+from typing import List, Optional
 
 from src.gui.panels._step_path_resolver import resolve_subdir, get_step_output_path, scan_work_dir_for_input
 
@@ -100,104 +100,42 @@ class Step11MapBatchThread(QThread):
                         f"[警告] 共享上下文失败: {e}，回退逐个处理", "warning"
                     )
 
-            # ── 批量处理（多线程并发克里金插值）──
-            # 克里金内部 numpy/scipy 运算释放 GIL，ThreadPoolExecutor 真并行
-            import concurrent.futures
-            import threading
+            # ── 串行批量处理 ──
+            # 注：克里金插值是内存密集型运算（32GB 内存下单个即用 5-6GB），
+            # 多线程并发会竞争内存带宽，实际耗时反而无改善，因此保持串行。
+            for i, csv_p in enumerate(self.csv_paths):
+                if self._cancelled:
+                    self.log_message.emit("专题图批量任务已被用户取消", "warning")
+                    break
+                self.progress.emit(i + 1, n)
+                self.log_message.emit(f"专题图 [{i + 1}/{n}] {csv_p}", "info")
 
-            _all_csvs = list(self.csv_paths)  # 快照副本
-            # 过滤已存在的文件
-            _pending: List[Tuple[str, str]] = []  # [(csv_path, output_file), ...]
-            _skipped = 0
-            for csv_p in _all_csvs:
                 stem = Path(csv_p).stem
-                out_f = (
+                output_file = (
                     str(Path(self.output_dir_optional) / f'{stem}_distribution.png')
                     if self.output_dir_optional
                     else None
                 )
-                if out_f and (
-                    Path(out_f).exists()
-                    or Path(out_f).with_suffix('.tif').exists()
+                # 已存在则跳过（兼容 tif 重定向后的文件名）
+                if output_file and (
+                    Path(output_file).exists()
+                    or Path(output_file).with_suffix('.tif').exists()
                 ):
-                    _skipped += 1
-                    self.log_message.emit(f"  → 跳过（已存在）: {stem}", "info")
-                else:
-                    _pending.append((csv_p, out_f))
+                    self.log_message.emit(f"  → 跳过（已存在）", "info")
+                    continue
 
-            if _skipped > 0:
-                self.log_message.emit(
-                    f"[跳过] {_skipped} 个已存在，待处理 {len(_pending)} 个", "info")
-
-            _n_pending = len(_pending)
-            if _n_pending == 0:
-                self.log_message.emit("所有专题图均已存在，无需重新生成", "info")
-                self.finished_ok.emit(n)
-                return
-
-            _max_workers = max(1, min(2, _n_pending, os.cpu_count() or 4))
-            _completed = 0
-            _lock = threading.Lock()
-            _done = threading.Event()  # 取消信号
-
-            def _process_one(csv_path: str, output_file: str):
-                """单个 CSV → 分布图（在 ThreadPoolExecutor 线程中执行）"""
-                if _done.is_set():
-                    return None, csv_path
-                mapper.process_data(
-                    csv_file=csv_path,
-                    shp_file=boundary_shp,
-                    output_file=output_file,
-                    resolution=resolution,
-                    output_format='tif',
-                    shared_context=shared_ctx,
-                )
-                return output_file, csv_path
-
-            if _max_workers > 1 and _n_pending > 1:
-                self.log_message.emit(
-                    f"[并发] {_n_pending} 个专题图，{_max_workers} 线程并行克里金", "info")
-                with concurrent.futures.ThreadPoolExecutor(
-                        max_workers=_max_workers) as executor:
-                    _futures = {
-                        executor.submit(_process_one, csv_p, out_f): (csv_p, out_f)
-                        for csv_p, out_f in _pending
-                    }
-                    for _future in concurrent.futures.as_completed(_futures):
-                        if self._cancelled:
-                            _done.set()
-                            self.log_message.emit("专题图批量任务已被用户取消", "warning")
-                            executor.shutdown(wait=False, cancel_futures=True)
-                            break
-                        csv_p, _ = _futures[_future]
-                        try:
-                            _future.result()
-                            with _lock:
-                                _completed += 1
-                            self.progress.emit(_completed + _skipped, n)
-                            self.log_message.emit(
-                                f"专题图 [{_completed}/{_n_pending}] ✓: "
-                                f"{Path(csv_p).name}", "info")
-                        except Exception as e:
-                            with _lock:
-                                _completed += 1
-                            self.log_message.emit(
-                                f"专题图 [{_completed}/{_n_pending}] ✗: "
-                                f"{Path(csv_p).name} — {e}", "error")
-            else:
-                for csv_p, out_f in _pending:
-                    if self._cancelled:
-                        self.log_message.emit("专题图批量任务已被用户取消", "warning")
-                        break
-                    _completed += 1
-                    self.progress.emit(_completed + _skipped, n)
-                    self.log_message.emit(
-                        f"专题图 [{_completed}/{_n_pending}] {csv_p}", "info")
-                    try:
-                        _process_one(csv_p, out_f)
-                    except Exception as e:
-                        self.log_message.emit(f"  → 失败: {e}", "error")
-                        continue
+                try:
+                    mapper.process_data(
+                        csv_file=csv_p,
+                        shp_file=boundary_shp,
+                        output_file=output_file,
+                        resolution=resolution,
+                        output_format='tif',
+                        shared_context=shared_ctx,
+                    )
+                except Exception as e:
+                    self.log_message.emit(f"  → 失败: {e}", "error")
+                    continue
 
             self.finished_ok.emit(n)
         except Exception as e: