feat: 算法升级、整车配准集成与性能分析文档

算法更新: delivery v1.2.1、平整度v1.5、测距v2.2、整车配准v1.3 适配器: vehicle-registration 新增、各算法 manifest 和 adapter 更新后端: taskCadFiles/source 字段迁移、derivedPointClouds 注册、取消任务联动前端: ElapsedTime 时长显示、两步算法流程、canRun 逻辑修复文档: 新增5份算法性能分析报告
2026-06-25 08:22:20 +00:00
parent cec0766e67
commit 29f1d45d66
266 changed files with 1311475 additions and 2846 deletions
--- a/server/_core/index.ts
+++ b/server/_core/index.ts
@@ -123,6 +123,11 @@ async function startServer() {
  // Configure body parser with larger size limit for file uploads
  app.use(express.json({ limit: "50mb" }));
  app.use(express.urlencoded({ limit: "50mb", extended: true }));
+
+  // 文件上传路由单独设置超长超时（50 GB 点云，按 100 MB/s 约需 8 分钟，留 60 分钟余量）
+  server.timeout = 0;          // 禁用 Node http.Server 全局超时
+  server.keepAliveTimeout = 0; // 长连接不超时
+  server.headersTimeout = 0;   // 读请求头不超时
  // OAuth callback under /api/oauth/callback
  registerOAuthRoutes(app);
  // 禁止代理（NPS 等）缓存 API 响应
--- a/server/algorithmJob.ts
+++ b/server/algorithmJob.ts
@@ -35,7 +35,7 @@ class ConcurrencyPool {
  private running = 0;
  private queue: PendingJob[] = [];
  private readonly limit: number;
-  private readonly activeChildren = new Set<ChildProcess>();
+  private readonly activeChildren = new Map<number, ChildProcess>(); // resultId → child

  constructor(limit: number) {
    this.limit = Math.max(1, limit);
@@ -52,6 +52,49 @@ class ConcurrencyPool {
    });
  }

+  /** 取消指定 resultId 的 job：若在队列中直接移除；若已在运行则 kill 子进程。 */
+  cancel(resultId: number): boolean {
+    // 先从队列里找（还未开始执行）
+    const idx = this.queue.findIndex((j) => j.resultId === resultId);
+    if (idx !== -1) {
+      const [job] = this.queue.splice(idx, 1);
+      job.resolve(); // 让等待方正常退出
+      return true;
+    }
+    // 再找正在运行的子进程
+    const child = this.activeChildren.get(resultId);
+    if (child) {
+      child.kill('SIGKILL');
+      return true;
+    }
+    return false;
+  }
+
+  /** 取消某个 taskId 下所有 job（删除任务时用）。返回被取消的 resultId 数组。 */
+  cancelByTask(taskId: number): number[] {
+    const cancelled: number[] = [];
+    // 队列中移除
+    const keep: PendingJob[] = [];
+    for (const job of this.queue) {
+      if (job.taskId === taskId) {
+        job.resolve();
+        cancelled.push(job.resultId);
+      } else {
+        keep.push(job);
+      }
+    }
+    this.queue = keep;
+    // 运行中 kill
+    for (const [resultId, child] of this.activeChildren) {
+      const jobTaskId = (child as ChildProcess & { _taskId?: number })._taskId;
+      if (jobTaskId === taskId) {
+        child.kill('SIGKILL');
+        cancelled.push(resultId);
+      }
+    }
+    return cancelled;
+  }
+
  private pump(): void {
    while (this.running < this.limit && this.queue.length > 0) {
      const next = this.queue.shift()!;
@@ -79,14 +122,16 @@ class ConcurrencyPool {
      stdio: ['ignore', 'inherit', 'inherit', 'ipc'],
      execArgv: isProd ? [] : ['--import', 'tsx'],
    });
-    this.activeChildren.add(child);
+    // 把 taskId 附到 child 上，cancelByTask 用
+    (child as ChildProcess & { _taskId?: number })._taskId = job.taskId;
+    this.activeChildren.set(job.resultId, child);

    return new Promise<void>((resolve) => {
      child.on('error', (err) => {
        console.error('[AlgorithmJob] fork 失败:', err);
      });
      child.on('exit', (code, signal) => {
-        this.activeChildren.delete(child);
+        this.activeChildren.delete(job.resultId);
        if (code !== 0 && code != null) {
          console.error(
            `[AlgorithmJob] 子进程退出 code=${code} signal=${signal ?? ''} pluginId=${job.pluginId} resultId=${job.resultId}`,
@@ -128,3 +173,13 @@ export function enqueueAlgorithmJob(
 export function algorithmJobStats() {
  return pool.stats;
 }
+
+/** 取消单个分析结果对应的 job（删除分析结果时调用）。 */
+export function cancelAlgorithmJob(resultId: number): boolean {
+  return pool.cancel(resultId);
+}
+
+/** 取消某检测任务下所有 job（删除任务时调用）。 */
+export function cancelAlgorithmJobsByTask(taskId: number): number[] {
+  return pool.cancelByTask(taskId);
+}
--- a/server/algorithmJobRunner.ts
+++ b/server/algorithmJobRunner.ts
@@ -321,6 +321,7 @@ export async function runAlgorithmJob(
      errorStats: result?.errorStats,
      overlay3d: result?.overlay3d,
      images: result?.images,
+      files: result?.files,
      extra: result?.extra,
      error: finalStatus === 'error'
        ? result?.message ?? `子进程退出码 ${exitCode}`
@@ -334,6 +335,35 @@ export async function runAlgorithmJob(
      resultData,
    });

+    // ── 回写配准产出点云 ─────────────────────────────────────────────────────
+    // 若 result.json 包含 derivedPointClouds，把对应 PLY 注册为任务点云，
+    // 供后续测量算法直接选用，无需用户手动上传。
+    if (finalStatus === 'completed') {
+      const derived = (result as Record<string, unknown> | null)?.derivedPointClouds;
+      if (Array.isArray(derived) && derived.length > 0) {
+        for (const item of derived) {
+          const d = item as { file?: string; name?: string };
+          if (!d.file) continue;
+          const absPath = path.join(workDir, d.file);
+          if (!fs.existsSync(absPath)) {
+            console.warn(`[AlgorithmJobRunner] derivedPointCloud not found: ${absPath}`);
+            continue;
+          }
+          try {
+            await db.createRegisteredPointcloudFile(
+              taskId,
+              d.name ?? d.file,
+              absPath,
+              resultId,
+            );
+            console.log(`[AlgorithmJobRunner] registered derived pointcloud: ${d.name ?? d.file}`);
+          } catch (err) {
+            console.error('[AlgorithmJobRunner] failed to register derived pointcloud:', err);
+          }
+        }
+      }
+    }
+
    if (finalStatus === 'completed') {
      console.log(
        `[AlgorithmJobRunner] ok pluginId=${pluginId} resultId=${resultId} taskId=${taskId}`,
--- a/server/db.ts
+++ b/server/db.ts
@@ -307,6 +307,27 @@ export async function createTaskPointcloudFile(data: InsertTaskPointcloudFile):
  return row;
 }

+/** 把配准算法产出的点云注册为任务点云（source='registered'）。
+ *  filePath 必须是绝对路径，文件必须已存在于磁盘。
+ */
+export async function createRegisteredPointcloudFile(
+  taskId: number,
+  name: string,
+  filePath: string,
+  sourceResultId: number,
+): Promise<TaskPointcloudFile> {
+  const fs = await import('node:fs');
+  const fileSize = fs.existsSync(filePath) ? fs.statSync(filePath).size : 0;
+  return createTaskPointcloudFile({
+    taskId,
+    name,
+    filePath,
+    fileSize,
+    source: 'registered',
+    sourceResultId,
+  } as InsertTaskPointcloudFile);
+}
+
 export async function listTaskPointcloudFiles(taskId: number): Promise<TaskPointcloudFile[]> {
  const db = await getDb();
  if (!db) return [];
--- a/server/fileRoutes.ts
+++ b/server/fileRoutes.ts
@@ -43,7 +43,7 @@ const diskStorage = multer.diskStorage({

 const diskUpload = multer({
  storage: diskStorage,
-  limits: { fileSize: 5 * 1024 * 1024 * 1024 }, // 5 GB
+  limits: { fileSize: 50 * 1024 * 1024 * 1024 }, // 50 GB
 });

 // ─── 工具函数 ─────────────────────────────────────────────────────────────────
--- a/server/plugins/adapters/_plugin_io.py
+++ b/server/plugins/adapters/_plugin_io.py
@@ -43,19 +43,49 @@ def emit_progress(percent: float, message: str = "") -> None:
 def write_result(out_dir: str | os.PathLike, payload: dict[str, Any]) -> Path:
    """写 result.json 到 outDir，并强制 schemaVersion / status 字段存在。
    NaN/Infinity 会被替换为 null，确保输出合法 JSON。
+
+    自动发现 outDir 下的 Excel 产物（*.xlsx / *.xls），以相对路径写入
+    payload["files"]，前端据此展示下载入口（适配器也可自行预置 files）。
    """
    import math

    def _sanitize(obj: Any) -> Any:
+        # numpy 标量 → Python 原生类型
+        try:
+            import numpy as np  # type: ignore
+            if isinstance(obj, np.ndarray):
+                return _sanitize(obj.tolist())
+            if isinstance(obj, (np.floating,)):
+                f = float(obj)
+                return None if (math.isnan(f) or math.isinf(f)) else f
+            if isinstance(obj, (np.integer,)):
+                return int(obj)
+            if isinstance(obj, (np.bool_,)):
+                return bool(obj)
+        except ImportError:
+            pass
        if isinstance(obj, float) and (math.isnan(obj) or math.isinf(obj)):
            return None
        if isinstance(obj, dict):
            return {k: _sanitize(v) for k, v in obj.items()}
        if isinstance(obj, list):
            return [_sanitize(v) for v in obj]
+        if isinstance(obj, tuple):
+            return [_sanitize(v) for v in obj]
        return obj

-    out_path = Path(out_dir) / "result.json"
+    out_dir = Path(out_dir)
+
+    excel_files: list[str] = list(payload.get("files") or [])
+    for pattern in ("*.xlsx", "*.xls"):
+        for f in sorted(out_dir.rglob(pattern)):
+            rel = str(f.relative_to(out_dir))
+            if rel not in excel_files:
+                excel_files.append(rel)
+    if excel_files:
+        payload["files"] = excel_files
+
+    out_path = out_dir / "result.json"
    payload.setdefault("schemaVersion", 1)
    payload.setdefault("status", "completed")
    out_path.write_text(json.dumps(_sanitize(payload), ensure_ascii=False, indent=2), encoding="utf-8")
--- a/server/plugins/adapters/coupler_panel_run.py
+++ b/server/plugins/adapters/coupler_panel_run.py
@@ -1,8 +1,13 @@
-"""coupler-panel 算法适配器 (v1.2)。
+"""coupler-panel 算法适配器 (v1.2.1)。
+
+v1.2.1 变更：
+  - 算法包升级至 delivery_v1.2.1
+  - 新增项点 Excel 报表（write_inspection_report）：
+    项点名称/理论尺寸/公差要求/测量结果/误差结果/测量结论
+  - PYTHONPATH 指向 delivery_v1.2.1/coupler_panel_release

 v1.2 变更：
-  - 算法包升级至 delivery_v1.2（新增 autotune 自动调参与 layer_select 分层选点）
-  - PYTHONPATH 指向 delivery_v1.2/coupler_panel_release
+  - 新增 autotune 自动调参与 layer_select 分层选点

 v1.1 变更：
  - 入口从 compute_coupler_measurement → run_coupler_measurement_pipeline
@@ -20,7 +25,6 @@ from pathlib import Path
 sys.path.insert(0, str(Path(__file__).parent))
 from _plugin_io import (
    emit_progress,
-    ensure_ascii_ply,
    first_pointcloud,
    load_input,
    write_error_result,
@@ -46,23 +50,45 @@ def main() -> int:
    out_dir.mkdir(parents=True, exist_ok=True)

    try:
-        emit_progress(0.05, "加载 coupler_panel v1.2 算法包")
+        emit_progress(0.05, "加载 coupler_panel v1.2.1 算法包")
        from coupler_panel.pipeline import run_coupler_measurement_pipeline  # type: ignore

        pc_path = first_pointcloud(payload)
        emit_progress(0.15, f"读取点云：{os.path.basename(pc_path)}")

-        # 若是二进制 PLY，先转成 ASCII PLY（算法内置 reader 不依赖 open3d）
-        pc_path = ensure_ascii_ply(pc_path, out_dir / "_ply_cache")
-
        profile = str(config.get("profile") or "auto")
        right_axis_sign = int(config.get("rightAxisSign") or 1)

        # 可选 CAD STP 路径（前端传入 cadPath 时使用）
        cad_stp_path = inputs.get("cadPath") or None

-        # 可选 ROI JSON 路径（前端传入 roiJson 时使用）
+        # ROI 裁剪：优先用 roiJson 文件，其次用前端填的 6 个坐标参数
        roi_json = inputs.get("roiJson") or None
+        roi_spec = None
+        if not roi_json:
+            def _f(key: str) -> float | None:
+                v = config.get(key)
+                try:
+                    return float(v) if v is not None and str(v).strip() != "" else None
+                except (TypeError, ValueError):
+                    return None
+
+            x_min, x_max = _f("roiXMin"), _f("roiXMax")
+            y_min, y_max = _f("roiYMin"), _f("roiYMax")
+            z_min, z_max = _f("roiZMin"), _f("roiZMax")
+            # 至少有一对轴的范围被填写时才构建 ROI
+            if any(v is not None for v in (x_min, x_max, y_min, y_max, z_min, z_max)):
+                from coupler_panel.roi import ROISpec  # type: ignore
+
+                roi_spec = ROISpec(
+                    x_min=x_min if x_min is not None else float("-inf"),
+                    x_max=x_max if x_max is not None else float("inf"),
+                    y_min=y_min if y_min is not None else float("-inf"),
+                    y_max=y_max if y_max is not None else float("inf"),
+                    z_min=z_min if z_min is not None else float("-inf"),
+                    z_max=z_max if z_max is not None else float("inf"),
+                )
+                emit_progress(0.20, f"ROI 裁剪：x[{x_min},{x_max}] y[{y_min},{y_max}] z[{z_min},{z_max}]")

        emit_progress(0.25, "调用 run_coupler_measurement_pipeline")

@@ -74,7 +100,8 @@ def main() -> int:
            out_dir=str(out_dir),
            profile=profile,
            cad_stp_path=cad_stp_path,
-            roi_json=roi_json,
+            roi_spec=roi_spec,     # 优先用坐标参数构建的 ROISpec
+            roi_json=roi_json,     # 备选：直接传 ROI JSON 文件
            progress=_on_progress,
        )

@@ -83,6 +110,41 @@ def main() -> int:
        # right_axis_sign 影响 theta_right_deg 的符号约定
        theta_right = float(result.theta_right_deg) * right_axis_sign
        theta_ref = float(result.theta_ref_deg)
+
+        # ── 项点 Excel 报表（v1.2.1 新增）────────────────────────────────────
+        # 用未截断的拟合平面法向量算原始角度（避免微小角被钳为 0），
+        # 公差按斜率形式给出：参考面 0.8/375mm，纵向中心 1/700mm。
+        # 产物 xlsx 由 write_result 自动发现并写入 files 字段。
+        try:
+            import math
+
+            from coupler_panel.reporting import InspectionItem, write_inspection_report  # type: ignore
+
+            raw_right, raw_ref = theta_right, theta_ref
+            report_dict = result.report if isinstance(result.report, dict) else {}
+            normal = (report_dict.get("plane") or {}).get("normal")
+            if normal and len(normal) == 3:
+                nx, ny, nz = (float(v) for v in normal)
+                raw_right = math.degrees(math.atan2(ny, nx)) * right_axis_sign
+                raw_ref = math.degrees(math.atan2(nz, nx))
+
+            item_ref = InspectionItem(
+                name="车钩面板距参考面的右偏转角（头车一位端处车钩）",
+                theta_deg=raw_ref,
+                baseline_mm=375.0,
+                tol_value_mm=0.8,
+            )
+            item_long = InspectionItem(
+                name="车钩面板距纵向中心的右偏转角(头车一位端处车钩)",
+                theta_deg=raw_right,
+                baseline_mm=700.0,
+                tol_value_mm=1.0,
+            )
+            write_inspection_report(item_ref, out_dir / "report_ref_plane.xlsx")
+            write_inspection_report(item_long, out_dir / "report_longitudinal.xlsx")
+        except Exception:
+            # Excel 报表失败不影响主结果
+            traceback.print_exc()
        dx_y = float(result.dx_y_max_mm)
        dx_z = float(result.dx_z_max_mm)
        D_y = float(result.D_y_max_mm)
--- a/server/plugins/adapters/flatness_suite_run.py
+++ b/server/plugins/adapters/flatness_suite_run.py
@@ -1,10 +1,10 @@
-"""flatness-suite 算法适配器 (v1.2)。
+"""flatness-suite 算法适配器 (v1.5)。

-v1.2 变更：
-  - 算法目录从 alg/平整度、平面度、轮廓测量/ → alg/point_porj/
-  - 入口从独立脚本（importlib 加载）→ MeasurementPipeline 类
-  - run_plane / run_curvature / run_contour 统一通过 MeasurementPipeline 调用
-  - 点云路径通过 config["paths"]["pointcloud"] 注入，不再依赖 open3d 直接读取
+v1.5 变更：
+  - 算法目录从 alg/point_porj → alg/平整度、平面度、轮廓测量v1.5
+  - 新增 car_part_v1/ 多车型 STEP CAD 模板
+  - run_contour 新增 Excel 报表输出（excel_output_path）
+  - pipeline 新增 result_template_path / combined_context_pointcloud_path 参数

 约定：
  - module=all：plane + curvature（contour 需要 CAD STL，缺失时跳过）
@@ -25,7 +25,6 @@ from typing import Any
 sys.path.insert(0, str(Path(__file__).parent))
 from _plugin_io import (
    emit_progress,
-    ensure_ascii_ply,
    first_pointcloud,
    load_input,
    write_error_result,
@@ -41,8 +40,9 @@ def _safe_float(v: object) -> float | None:


 def _find_alg_dir(alg_root: str) -> Path:
-    """定位 point_porj 目录，支持多种可能的路径。"""
+    """定位平整度算法目录，支持多个历史版本路径，优先使用最新版。"""
    candidates = [
+        Path(alg_root) / "平整度、平面度、轮廓测量v1.5",
        Path(alg_root) / "point_porj",
        Path(alg_root) / "平整度、平面度、轮廓测量v1.2" / "point_porj",
        Path(alg_root) / "平整度、平面度、轮廓测量" / "point_porj",
@@ -51,9 +51,9 @@ def _find_alg_dir(alg_root: str) -> Path:
        if c.is_dir() and (c / "pipeline.py").exists():
            return c
    raise RuntimeError(
-        f"找不到 point_porj 算法目录，已尝试：\n"
+        f"找不到平整度算法目录，已尝试：\n"
        + "\n".join(f"  {c}" for c in candidates)
-        + "\n请确认 alg/ 下已解压 point_porj/。"
+        + "\n请确认 alg/ 下已解压 平整度、平面度、轮廓测量v1.5/。"
    )


@@ -105,8 +105,47 @@ def main() -> int:
        pc_path_raw = first_pointcloud(payload)
        emit_progress(0.10, f"读取点云：{os.path.basename(pc_path_raw)}")

-        # 二进制 PLY → ASCII PLY（算法内置 reader 不依赖 open3d）
-        pc_path = ensure_ascii_ply(pc_path_raw, out_dir / "_ply_cache")
+        # ── ROI 裁剪（整车点云场景下必须填写，否则算法极慢）──────────────────────
+        def _f(key: str) -> float | None:
+            v = config.get(key)
+            try:
+                return float(v) if v is not None and str(v).strip() != "" else None
+            except (TypeError, ValueError):
+                return None
+
+        x_min, x_max = _f("roiXMin"), _f("roiXMax")
+        y_min, y_max = _f("roiYMin"), _f("roiYMax")
+        z_min, z_max = _f("roiZMin"), _f("roiZMax")
+        has_roi = any(v is not None for v in (x_min, x_max, y_min, y_max, z_min, z_max))
+
+        if has_roi:
+            emit_progress(0.12, "ROI 裁剪中…")
+            import numpy as np
+            import open3d as o3d  # type: ignore
+
+            pcd = o3d.io.read_point_cloud(pc_path_raw)
+            pts = np.asarray(pcd.points)
+            mask = np.ones(len(pts), dtype=bool)
+            if x_min is not None: mask &= pts[:, 0] >= x_min
+            if x_max is not None: mask &= pts[:, 0] <= x_max
+            if y_min is not None: mask &= pts[:, 1] >= y_min
+            if y_max is not None: mask &= pts[:, 1] <= y_max
+            if z_min is not None: mask &= pts[:, 2] >= z_min
+            if z_max is not None: mask &= pts[:, 2] <= z_max
+            n_total, n_crop = len(pts), int(mask.sum())
+            if n_crop == 0:
+                raise RuntimeError(
+                    f"ROI 裁剪后无点（原始 {n_total} 点），请检查坐标范围是否正确。"
+                )
+            cropped = pcd.select_by_index(np.where(mask)[0])
+            roi_ply = out_dir / "_roi_cropped.ply"
+            # 二进制写出，算法用 o3d 读所以支持二进制，比 ASCII 快很多
+            o3d.io.write_point_cloud(str(roi_ply), cropped, write_ascii=False)
+            pc_path_raw = str(roi_ply)
+            emit_progress(0.14, f"ROI 裁剪完成：{n_total} → {n_crop} 点")
+
+        # v1.5 用 o3d.io.read_point_cloud 读取，直接支持二进制 PLY，无需转 ASCII
+        pc_path = pc_path_raw

        module = str(config.get("module") or "all")
        emit_progress(0.15, f"初始化 MeasurementPipeline（module={module}）")
--- a/server/plugins/adapters/train_measurement_run.py
+++ b/server/plugins/adapters/train_measurement_run.py
@@ -1,17 +1,18 @@
-"""train-measurement 算法适配器 (v1.1)。
+"""train-measurement 算法适配器 (v2.2)。

-读 input.json → 调 TaskManager（来自 alg/点云测距v1.1）→ 写统一 result.json。
+读 input.json → 调 TaskManager（来自 alg/train_measurement_system_v2.2）→ 写统一 result.json。

-v1.1 变更：
-  - 路径从 260505列车点云测距v1 → 点云测距v1.1
-  - 算法包现在是可安装的 Python 包（有 __init__.py），PYTHONPATH 指向 src/
-  - TRAIN_MEAS_ROOT 指向 train_measurement_system/（用于定位 config/measurements.yaml）
-  - TaskManager.run_all_tasks() 返回结构不变，data 字段含 mean_distance / std_distance 等
+v2.2 变更：
+  - 算法路径从 alg/点云测距v1.1 → alg/train_measurement_system_v2.2
+  - 新增孔中心拟合（hole_center_fitter）、测量日志、性能日志
+  - 内置配置文件改为 config/measurements_0618.yaml
+  - TRAIN_MEAS_ROOT 指向 train_measurement_system_v2.2/（用于定位 config/）
+  - 算法内置 _read_binary_ply，直接支持二进制 PLY，无需 ASCII 转换

 约定：
  - 第一个点云路径作为 pointcloud_file 注入到测量配置 YAML 中；
  - 若用户在 UI 选择「自定义 YAML」并提供 customYaml，则使用之；
-    否则使用算法仓库自带的 config/measurements.yaml 作为模板。
+    否则使用算法仓库自带的 config/measurements_0618.yaml 作为模板。
  - 所有任务结果合成成 measurements[] 列表，附带可视化 PLY 路径到 extra。
 """

@@ -25,7 +26,6 @@ from pathlib import Path
 sys.path.insert(0, str(Path(__file__).parent))
 from _plugin_io import (
    emit_progress,
-    ensure_ascii_ply,
    first_pointcloud,
    load_input,
    write_error_result,
@@ -41,14 +41,14 @@ def main() -> int:
    out_dir.mkdir(parents=True, exist_ok=True)

    try:
-        emit_progress(0.05, "加载 train_measurement v1.1 算法包")
+        emit_progress(0.05, "加载 train_measurement v2.2 算法包")

-        # TRAIN_MEAS_ROOT 由 manifest envExtra 注入，指向 train_measurement_system/
+        # TRAIN_MEAS_ROOT 由 manifest envExtra 注入，指向 train_measurement_system_v2.2/
        train_root = Path(os.environ.get("TRAIN_MEAS_ROOT") or "")
        if not train_root.exists():
            # 回退：从 ALG_ROOT 推导
            alg_root = Path(os.environ.get("ALG_ROOT") or "/alg")
-            train_root = alg_root / "点云测距v1.1" / "train_measurement_system"
+            train_root = alg_root / "train_measurement_system_v2.2" / "train_measurement_system_v2.2"

        if not train_root.exists():
            raise RuntimeError(
@@ -61,9 +61,6 @@ def main() -> int:
        pc_path = first_pointcloud(payload)
        emit_progress(0.15, f"读取点云：{os.path.basename(pc_path)}")

-        # 若是二进制 PLY，先转成 ASCII PLY（算法内置 reader 不依赖 open3d）
-        pc_path = ensure_ascii_ply(pc_path, out_dir / "_ply_cache")
-
        # ── 准备 YAML 配置 ────────────────────────────────────────────────────
        config_preset = str(config.get("configPreset") or "builtin")
        custom_yaml_str = str(config.get("customYaml") or "").strip()
@@ -81,8 +78,8 @@ def main() -> int:
                # 已经是 YAML 格式，直接写盘
                yaml_path.write_text(custom_yaml_str, encoding="utf-8")
        else:
-            # 使用算法仓库内置的 config/measurements.yaml
-            yaml_path = train_root / "config" / "measurements.yaml"
+            # 使用算法仓库内置的 config/measurements_0618.yaml
+            yaml_path = train_root / "config" / "measurements_0618.yaml"
            if not yaml_path.exists():
                raise RuntimeError(f"内置配置文件不存在：{yaml_path}")

@@ -92,6 +89,8 @@ def main() -> int:
        tm = TaskManager()
        # 先注入点云路径，让 load_config 跳过 YAML 里的 pointcloud_file（开发测试路径）
        tm.pointcloud_path_override = pc_path
+        # 把输出目录重定向到 outDir，避免写到只读的 /alg 目录
+        tm.output_dir_override = str(out_dir)
        if not tm.load_config(str(yaml_path)):
            raise RuntimeError(f"TaskManager 加载配置失败：{yaml_path}")

--- a/server/plugins/adapters/underframe_deflection_run.py
+++ b/server/plugins/adapters/underframe_deflection_run.py
@@ -1,8 +1,12 @@
-"""underframe-deflection 算法适配器 (v1.2)。
+"""underframe-deflection 算法适配器 (v1.2.1)。
+
+v1.2.1 变更：
+  - 算法包升级至 delivery_v1.2.1（单梁结果新增项点检验字段：公差/测量/误差/结论）
+  - report.xlsx 等 Excel 产物通过 result.json 的 files 字段暴露给前端下载
+  - PYTHONPATH 指向 delivery_v1.2.1

 v1.2 变更：
-  - 算法包升级至 delivery_v1.2（新增 single_beam 单梁管线）
-  - PYTHONPATH 指向 delivery_v1.2
+  - 新增 single_beam 单梁管线

 v1.1 变更：
  - 入口改为 run_measurement_pipeline（返回 dict，同时落盘 report.json / xlsx / PLY）
@@ -21,7 +25,6 @@ from pathlib import Path
 sys.path.insert(0, str(Path(__file__).parent))
 from _plugin_io import (
    emit_progress,
-    ensure_ascii_ply,
    first_pointcloud,
    load_input,
    write_error_result,
@@ -54,15 +57,12 @@ def main() -> int:
    out_dir.mkdir(parents=True, exist_ok=True)

    try:
-        emit_progress(0.05, "加载 underframe_deflection v1.2 算法包")
+        emit_progress(0.05, "加载 underframe_deflection v1.2.1 算法包")
        from underframe_deflection.pipeline import run_measurement_pipeline  # type: ignore

        pc_path = first_pointcloud(payload)
        emit_progress(0.15, f"读取点云：{os.path.basename(pc_path)}")

-        # 若是二进制 PLY，先转成 ASCII PLY（算法内置 reader 不依赖 open3d）
-        pc_path = ensure_ascii_ply(pc_path, out_dir / "_ply_cache")
-
        profile = str(config.get("profile") or "auto")
        skip_registration = bool(config.get("skipRegistration") or False)
        enable_bilateral = bool(config.get("enableBilateral") if "enableBilateral" in config else True)
--- a/server/plugins/adapters/vehicle_registration_run.py
+++ b/server/plugins/adapters/vehicle_registration_run.py
@@ -0,0 +1,181 @@
+"""vehicle-registration 整车配准适配器 (v1.3)。
+
+调用 alg/整车配准v1.3 的 translate_align_by_front_tail：
+  - 输入：整车点云 PLY + 标准 STEP CAD
+  - 原理：车头/车尾各采样少量点算质心差，融合成整体平移矩阵
+  - v1.3 新增：ICP 精配准、CAD 采样缓存、配准误差统计（RMSE/p95/p99）
+  - 输出：
+      aligned_pointcloud_translation_only.ply  配准后的完整点云
+      T_translation_only.txt                   4x4 平移矩阵
+
+result.json 额外约定 derivedPointClouds 字段：runner 会把列出的 PLY
+回写为该任务的新点云记录（source='registered'），供后续测量算法选用。
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+import traceback
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent))
+from _plugin_io import (
+    emit_progress,
+    first_pointcloud,
+    load_input,
+    write_error_result,
+    write_result,
+)
+
+
+def main() -> int:
+    payload = load_input(sys.argv)
+    inputs = payload.get("inputs") or {}
+    config = inputs.get("config") or {}
+    out_dir = Path(payload["outDir"])
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    try:
+        emit_progress(0.05, "加载整车配准算法包 v1.3")
+        from translate_align_pointcloud_to_cad import translate_align_by_front_tail  # type: ignore
+
+        pc_path = first_pointcloud(payload)
+        cad_path = inputs.get("cadPath")
+        if not cad_path:
+            raise RuntimeError("整车配准需要 STEP CAD 文件，请在任务下上传并选中 CAD。")
+
+        axis = str(config.get("axis") or "x")
+        crop_ratio = float(config.get("cropRatio") or 0.01)
+        mesh_deflection = float(config.get("meshDeflection") or 0.1)
+        cad_sample_points = int(config.get("cadSamplePoints") or 30000)
+        local_sample_points = int(config.get("localSamplePoints") or 1000)
+        scale_pointcloud = float(config.get("scalePointcloud") or 1.0)
+
+        # v1.3 新增参数
+        use_icp = bool(config.get("useIcp") if config.get("useIcp") is not None else True)
+        icp_sample_points = int(config.get("icpSamplePoints") or 200000)
+        compute_error_stats = bool(config.get("computeErrorStats") if config.get("computeErrorStats") is not None else True)
+        use_cad_cache = bool(config.get("useCadCache") if config.get("useCadCache") is not None else True)
+
+        emit_progress(
+            0.15,
+            f"开始配准：{os.path.basename(pc_path)} ← {os.path.basename(str(cad_path))}"
+            + (" + ICP" if use_icp else ""),
+        )
+
+        result = translate_align_by_front_tail(
+            cad_path=str(cad_path),
+            pointcloud_path=pc_path,
+            output_dir=str(out_dir),
+            axis=axis,
+            crop_ratio=crop_ratio,
+            mesh_deflection=mesh_deflection,
+            cad_sample_points=cad_sample_points,
+            local_sample_points=local_sample_points,
+            scale_pointcloud=scale_pointcloud,
+            visualize=False,
+            use_cad_cache=use_cad_cache,
+            use_icp=use_icp,
+            icp_sample_points=icp_sample_points,
+            compute_error_stats=compute_error_stats,
+            save_debug_samples=False,
+        )
+
+        emit_progress(0.92, "整理结果")
+
+        t_final = [float(v) for v in result["translation"]]
+        ft_diff = float(result["front_tail_translation_difference"])
+        elapsed = float(result.get("elapsed_seconds") or 0)
+
+        measurements = [
+            {"name": "最终平移量 Tx", "value": t_final[0], "unit": "mm", "status": "info"},
+            {"name": "最终平移量 Ty", "value": t_final[1], "unit": "mm", "status": "info"},
+            {"name": "最终平移量 Tz", "value": t_final[2], "unit": "mm", "status": "info"},
+            {
+                "name": "前后端平移差",
+                "value": ft_diff,
+                "unit": "mm",
+                "tolerance": 50.0,
+                "status": "pass" if ft_diff <= 50.0 else "warning",
+            },
+        ]
+
+        # ICP 结果指标
+        icp_stats = result.get("icp_stats")
+        if icp_stats:
+            measurements.append({
+                "name": "ICP fitness",
+                "value": float(icp_stats.get("fitness", 0)),
+                "unit": "",
+                "status": "pass" if float(icp_stats.get("fitness", 0)) >= 0.9 else "warning",
+            })
+            measurements.append({
+                "name": "ICP inlier RMSE",
+                "value": float(icp_stats.get("inlier_rmse", 0)),
+                "unit": "mm",
+                "status": "info",
+            })
+
+        # 配准误差统计
+        error_stats = result.get("error_stats")
+        if error_stats:
+            rmse = float(error_stats.get("rmse", 0))
+            p95 = float(error_stats.get("p95", 0))
+            measurements += [
+                {"name": "配准 RMSE", "value": rmse, "unit": "mm",
+                 "status": "pass" if rmse <= 5.0 else "warning"},
+                {"name": "配准误差 p95", "value": p95, "unit": "mm", "status": "info"},
+                {"name": "配准误差 p99", "value": float(error_stats.get("p99", 0)), "unit": "mm", "status": "info"},
+            ]
+
+        aligned_name = Path(result["aligned_path"]).name
+        src_stem = Path(pc_path).stem
+
+        # 下载文件：矩阵 + 误差统计（如有）
+        files = [Path(result["matrix_path"]).name]
+        error_stats_path = result.get("error_stats_path")
+        if error_stats_path and Path(error_stats_path).exists():
+            files.append(Path(error_stats_path).name)
+
+        payload_out = {
+            "schemaVersion": 1,
+            "status": "completed",
+            "message": (
+                f"整车配准完成（{'平移 + ICP' if use_icp else '仅平移'}，耗时 {elapsed:.1f}s）。"
+                "配准后的点云已加入任务点云列表，请在后续算法中选用。"
+            ),
+            "measurements": measurements,
+            "overlay3d": {"kind": "none"},
+            "files": files,
+            "derivedPointClouds": [
+                {"file": aligned_name, "name": f"{src_stem}_已配准.ply"}
+            ],
+            "extra": {
+                "initialTranslation": [float(v) for v in result.get("initial_translation", result["translation"])],
+                "finalTranslation": t_final,
+                "tFront": [float(v) for v in result["t_front"]],
+                "tTail": [float(v) for v in result["t_tail"]],
+                "matrix": [[float(v) for v in row] for row in result["T_final"]],
+                "icpStats": icp_stats,
+                "elapsedSeconds": elapsed,
+                "sourcePointCloud": os.path.basename(pc_path),
+                "cad": os.path.basename(str(cad_path)),
+            },
+        }
+        write_result(out_dir, payload_out)
+        emit_progress(1.0, "完成")
+        return 0
+
+    except Exception as exc:
+        traceback.print_exc()
+        write_error_result(
+            out_dir,
+            f"整车配准失败：{exc}",
+            extra={"traceback": traceback.format_exc()},
+        )
+        return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())
--- a/server/plugins/manifests/coupler-panel.json
+++ b/server/plugins/manifests/coupler-panel.json
@@ -2,8 +2,8 @@
  "id": "coupler-panel",
  "name": "车钩面板检测",
  "shortName": "coupler-panel",
-  "description": "基于点云的车钩面板偏转角与最大偏差测量（v1.2，来自 alg/delivery_v1.2/coupler_panel_release）。输出 θ_right / θ_ref 偏转角、dx_y / dx_z 最大 x 偏差及 D_y / D_z 面板偏差。v1.2 新增自动调参（autotune）与分层选点（layer_select）。",
-  "version": "1.2.0",
+  "description": "基于点云的车钩面板偏转角与最大偏差测量（v1.2.1，来自 alg/delivery_v1.2.1/coupler_panel_release）。输出 θ_right / θ_ref 偏转角、dx_y / dx_z 最大 x 偏差及 D_y / D_z 面板偏差。v1.2.1 新增项点检验 Excel 报表（项点名称/理论尺寸/公差要求/测量结果/误差结果/测量结论）。",
+  "version": "1.2.1",
  "schemaVersion": 1,
  "category": "几何测量",
  "requiresPointCloud": true,
@@ -32,7 +32,16 @@
        { "value": "-1", "label": "-1（车体右 = -y）" }
      ],
      "description": "若车体「右」方向为 +y 保持 +1；若为 -y 改为 -1。"
-    }
+    },
+    {
+      "name": "roiXMin", "label": "ROI X 最小值 (mm)", "type": "number", "default": null,
+      "description": "车钩面板局部 ROI 裁剪范围（相对于配准后 CAD 坐标系）。整车点云时必填，否则算法处理全部点云会极慢。留空 = 不裁剪。"
+    },
+    { "name": "roiXMax", "label": "ROI X 最大值 (mm)", "type": "number", "default": null },
+    { "name": "roiYMin", "label": "ROI Y 最小值 (mm)", "type": "number", "default": null },
+    { "name": "roiYMax", "label": "ROI Y 最大值 (mm)", "type": "number", "default": null },
+    { "name": "roiZMin", "label": "ROI Z 最小值 (mm)", "type": "number", "default": null },
+    { "name": "roiZMax", "label": "ROI Z 最大值 (mm)", "type": "number", "default": null }
  ],
  "runtime": {
    "kind": "process",
@@ -42,8 +51,8 @@
      "{inputJson}"
    ],
    "envExtra": {
-      "PYTHONPATH": "{algRoot}/delivery_v1.2/coupler_panel_release"
+      "PYTHONPATH": "{algRoot}/delivery_v1.2.1/coupler_panel_release"
    },
-    "timeoutMs": 1200000
+    "timeoutMs": 5400000
  }
 }
--- a/server/plugins/manifests/flatness-suite.json
+++ b/server/plugins/manifests/flatness-suite.json
@@ -2,8 +2,8 @@
  "id": "flatness-suite",
  "name": "平整度 / 平面度 / 轮廓测量",
  "shortName": "flatness-suite",
-  "description": "平整度 / 平面度 / 轮廓 / 曲率 套件（直接调用 alg/平整度、平面度、轮廓测量/ 下的脚本：Plane_Estimation / Contour_Flatness / Curvature_Estimation）。运行需要 numpy + open3d；contour 子模块还需要 torch + trimesh + STL CAD。任意子模块缺依赖时自动跳过并在 message 里说明。",
-  "version": "1.0.0",
+  "description": "平整度 / 平面度 / 轮廓 / 曲率 套件（v1.5，来自 alg/平整度、平面度、轮廓测量v1.5）。v1.5 新增多车型 STEP CAD 模板（car_part_v1）与 contour Excel 报表。任意子模块缺依赖时自动跳过并在 message 里说明。",
+  "version": "1.5.0",
  "schemaVersion": 1,
  "category": "几何测量",
  "requiresPointCloud": true,
@@ -30,7 +30,16 @@
      "max": 10.0,
      "step": 0.1,
      "description": "平面拟合的距离阈值（同时也作为曲率搜索半径的基准 ×2）。"
-    }
+    },
+    {
+      "name": "roiXMin", "label": "ROI X 最小值 (mm)", "type": "number", "default": null,
+      "description": "待测区域裁剪范围（配准后 CAD 坐标系，mm）。整车点云时必填以加速计算，留空 = 不裁剪。"
+    },
+    { "name": "roiXMax", "label": "ROI X 最大值 (mm)", "type": "number", "default": null },
+    { "name": "roiYMin", "label": "ROI Y 最小值 (mm)", "type": "number", "default": null },
+    { "name": "roiYMax", "label": "ROI Y 最大值 (mm)", "type": "number", "default": null },
+    { "name": "roiZMin", "label": "ROI Z 最小值 (mm)", "type": "number", "default": null },
+    { "name": "roiZMax", "label": "ROI Z 最大值 (mm)", "type": "number", "default": null }
  ],
  "runtime": {
    "kind": "process",
@@ -42,6 +51,6 @@
    "envExtra": {
      "ALG_ROOT": "{algRoot}"
    },
-    "timeoutMs": 1200000
+    "timeoutMs": 5400000
  }
 }
--- a/server/plugins/manifests/train-measurement.json
+++ b/server/plugins/manifests/train-measurement.json
@@ -2,8 +2,8 @@
  "id": "train-measurement",
  "name": "列车点云测距",
  "shortName": "train-measurement",
-  "description": "基于配置 YAML 驱动的列车点云批量测距套件（v1.1）。支持「平面到平面 / 点到平面 / 点到点」三类任务，可在 3D 视图中拖拽 ROI 包围盒可视化配置。",
-  "version": "1.1.0",
+  "description": "基于配置 YAML 驱动的列车点云批量测距套件（v2.2）。v2.2 新增孔中心拟合（hole_center_fitter）、测量日志（measurement_logger）、性能日志（performance_logger）。支持「平面到平面 / 点到平面 / 点到点 / 带孔」四类任务，可在 3D 视图中拖拽 ROI 包围盒可视化配置。",
+  "version": "2.2.0",
  "schemaVersion": 1,
  "category": "尺寸测量",
  "requiresPointCloud": true,
@@ -37,9 +37,9 @@
      "{inputJson}"
    ],
    "envExtra": {
-      "PYTHONPATH": "{algRoot}/点云测距v1.1/train_measurement_system/src",
-      "TRAIN_MEAS_ROOT": "{algRoot}/点云测距v1.1/train_measurement_system"
+      "PYTHONPATH": "{algRoot}/train_measurement_system_v2.2/train_measurement_system_v2.2/src",
+      "TRAIN_MEAS_ROOT": "{algRoot}/train_measurement_system_v2.2/train_measurement_system_v2.2"
    },
-    "timeoutMs": 1200000
+    "timeoutMs": 5400000
  }
 }
--- a/server/plugins/manifests/underframe-deflection.json
+++ b/server/plugins/manifests/underframe-deflection.json
@@ -2,8 +2,8 @@
  "id": "underframe-deflection",
  "name": "构架挠度测量",
  "shortName": "underframe-deflection",
-  "description": "基于点云的高铁底架纵梁挠度测量（v1.2，来自 alg/delivery_v1.2/underframe_deflection）。支持 CAD 基准配准与 ROI 裁剪，输出左右梁挠度、配准 RMS 及挠度曲线 PLY 叠加层。v1.2 新增单梁管线（single_beam）支持。",
-  "version": "1.2.0",
+  "description": "基于点云的高铁底架纵梁挠度测量（v1.2.1，来自 alg/delivery_v1.2.1/underframe_deflection）。支持 CAD 基准配准与 ROI 裁剪，输出左右梁挠度、配准 RMS 及挠度曲线 PLY 叠加层。v1.2.1 新增项点检验字段（公差/测量/误差/结论）与 Excel 报表导出。",
+  "version": "1.2.1",
  "schemaVersion": 1,
  "category": "几何测量",
  "requiresPointCloud": true,
@@ -54,8 +54,8 @@
      "{inputJson}"
    ],
    "envExtra": {
-      "PYTHONPATH": "{algRoot}/delivery_v1.2"
+      "PYTHONPATH": "{algRoot}/delivery_v1.2.1"
    },
-    "timeoutMs": 1200000
+    "timeoutMs": 5400000
  }
 }
--- a/server/plugins/manifests/vehicle-registration.json
+++ b/server/plugins/manifests/vehicle-registration.json
@@ -0,0 +1,105 @@
+{
+  "id": "vehicle-registration",
+  "name": "整车配准",
+  "shortName": "vehicle-registration",
+  "description": "将整车扫描点云与标准 STEP CAD 进行配准（v1.3）。v1.3 新增 ICP 精配准、CAD 采样缓存加速、配准误差统计（RMSE/p95）。配准完成后，对齐点云自动加入任务点云列表，供后续测量算法选用。",
+  "version": "1.3.0",
+  "schemaVersion": 1,
+  "category": "配准",
+  "requiresPointCloud": true,
+  "requiresCad": true,
+  "inputs": [
+    {
+      "name": "axis",
+      "label": "车辆长度轴",
+      "type": "select",
+      "default": "x",
+      "options": [
+        { "value": "x", "label": "X 轴" },
+        { "value": "y", "label": "Y 轴" },
+        { "value": "z", "label": "Z 轴" }
+      ],
+      "description": "扫描坐标系中车辆纵向（车头→车尾）对应的轴。"
+    },
+    {
+      "name": "cropRatio",
+      "label": "车头/车尾截取比例",
+      "type": "number",
+      "default": 0.01,
+      "min": 0.005,
+      "max": 0.15,
+      "step": 0.005,
+      "description": "从点云两端各取此比例的长度范围用于质心估算。数据干净时 0.01 即可；噪声多时可适当加大。"
+    },
+    {
+      "name": "scalePointcloud",
+      "label": "点云缩放系数",
+      "type": "number",
+      "default": 1.0,
+      "min": 0.001,
+      "max": 1000.0,
+      "step": 0.001,
+      "description": "点云单位与 CAD 单位不一致时使用。例如点云为米、CAD 为毫米，填 1000。"
+    },
+    {
+      "name": "cadSamplePoints",
+      "label": "CAD 采样点数",
+      "type": "integer",
+      "default": 30000,
+      "min": 5000,
+      "max": 500000,
+      "description": "从 CAD mesh 均匀采样的点数。点数越多精度越高，耗时也越长。"
+    },
+    {
+      "name": "localSamplePoints",
+      "label": "端部局部采样点数",
+      "type": "integer",
+      "default": 1000,
+      "min": 100,
+      "max": 20000,
+      "description": "车头/车尾区域用于质心计算的最远点采样数。"
+    },
+    {
+      "name": "useIcp",
+      "label": "ICP 精配准",
+      "type": "boolean",
+      "default": true,
+      "description": "平移对齐后再做一轮 ICP 精配准（v1.3 新增）。通常可进一步减小配准误差，耗时约增加 30–60 秒。"
+    },
+    {
+      "name": "icpSamplePoints",
+      "label": "ICP 采样点数",
+      "type": "integer",
+      "default": 200000,
+      "min": 10000,
+      "max": 1000000,
+      "description": "ICP 配准时使用的点数。越多精度越高，耗时也越长。"
+    },
+    {
+      "name": "computeErrorStats",
+      "label": "计算配准误差统计",
+      "type": "boolean",
+      "default": true,
+      "description": "配准后抽样计算点云到 CAD 的距离误差（RMSE / p95 / p99），用于评估配准质量。"
+    },
+    {
+      "name": "useCadCache",
+      "label": "缓存 CAD 采样",
+      "type": "boolean",
+      "default": true,
+      "description": "缓存 CAD 三角化采样结果，同一 CAD 文件第二次配准时跳过耗时的 STEP 解析，大幅加速。"
+    }
+  ],
+  "runtime": {
+    "kind": "process",
+    "cmd": [
+      "{pythonBin}",
+      "{adapterDir}/vehicle_registration_run.py",
+      "{inputJson}"
+    ],
+    "envExtra": {
+      "PYTHONPATH": "{algRoot}/整车配准v1.3"
+    },
+    "timeoutMs": 1800000
+  }
+}
--- a/server/plugins/types.ts
+++ b/server/plugins/types.ts
@@ -154,6 +154,12 @@ export interface PluginResult {
  /** 相对 outDir 的图片文件名（如 plot.png 或 images/foo.png）。
   *  前端通过 /api/files/read/analysis/:taskId/:resultId/:filename 读取 */
  images?: string[];
+  /** 相对 outDir 的可下载产物（如 report.xlsx）。
+   *  前端在结果面板展示下载入口。 */
+  files?: string[];
+  /** 配准算法专用：产出的衍生点云，runner 自动回写为任务点云记录。
+   *  file 相对 outDir；name 是在任务点云列表中显示的名称。 */
+  derivedPointClouds?: { file: string; name: string }[];
  /** 算法专属字段；前端折叠在「原始字段」里展示 */
  extra?: Record<string, unknown>;
 }
--- a/server/routers.ts
+++ b/server/routers.ts
@@ -12,7 +12,7 @@ import { enqueueDeviationHeatmapJob } from "./deviationHeatmapJob.js";
 import { enqueueSmoothHeatmapJob } from "./smoothHeatmapJob.js";
 import { stepParseCacheDir } from "./stepParse.js";
 import { listPlugins, getPlugin } from "./plugins/registry.js";
-import { enqueueAlgorithmJob, algorithmJobStats } from "./algorithmJob.js";
+import { enqueueAlgorithmJob, algorithmJobStats, cancelAlgorithmJob, cancelAlgorithmJobsByTask } from "./algorithmJob.js";

 export const appRouter = router({
  system: systemRouter,
@@ -214,6 +214,11 @@ export const appRouter = router({
      .input(z.object({ id: z.number() }))
      .mutation(async ({ input }) => {
        await db.deleteInspectionTask(input.id);
+        // 取消该任务下所有正在运行或排队的算法 job
+        const cancelled = cancelAlgorithmJobsByTask(input.id);
+        if (cancelled.length > 0) {
+          console.log(`[routers] 删除任务 ${input.id}，取消 ${cancelled.length} 个算法 job:`, cancelled);
+        }
        return { success: true };
      }),
  }),
@@ -398,6 +403,8 @@ export const appRouter = router({
    delete: protectedProcedure
      .input(z.object({ id: z.number() }))
      .mutation(async ({ input }) => {
+        // 先取消正在运行或排队的 job，再删数据库记录
+        cancelAlgorithmJob(input.id);
        await db.deleteTaskAnalysisResult(input.id);
        return { success: true };
      }),