feat: 素材选取评分注入随机噪声，降低成片查重率 (#1620)

Co-authored-by: xiaoxia <dev@xiaoxiajianji.com>
Co-committed-by: xiaoxia <dev@xiaoxiajianji.com>

.gitea/workflows/ci-base-image.yml

@@ -0,0 +1,105 @@
+name: CI Base Image Build
+
+on:
+  push:
+    branches:
+      - develop
+      - main
+    paths:
+      - 'requirements-base.txt'
+      - 'requirements-dev.txt'
+      - 'infra/docker/ci.Dockerfile'
+  workflow_dispatch:
+    inputs:
+      reason:
+        description: "触发原因"
+        required: false
+        default: "手动触发 - ci-base 镜像重建"
+
+concurrency:
+  group: ci-base-image-build
+  cancel-in-progress: false
+
+jobs:
+  build-ci-base:
+    name: Build CI Base Image
+    runs-on: runtime-builder
+    timeout-minutes: 60
+    steps:
+    - name: Checkout code
+      shell: sh
+      env:
+        GITHUB_TOKEN: ${{ github.token }}
+      run: |
+        curl -sH "Authorization: token $GITHUB_TOKEN" \
+          "${GITHUB_API_URL}/repos/${GITHUB_REPOSITORY}/raw/scripts/ci/step_checkout.sh?ref=${GITHUB_SHA}" \
+          | bash
+
+    - name: Docker login to Gitea Registry
+      shell: sh
+      env:
+        GITEA_REGISTRY_USER: xiaoxia
+        GITEA_REGISTRY_TOKEN: ${{ secrets.REGISTRY_TOKEN }}
+      run: |
+        set -eu
+        for i in 1 2 3; do
+          echo "=== Docker login 尝试 $i/3 ==="
+          if docker login git.xiaoxiajianji.com -u "${GITEA_REGISTRY_USER}" -p "${GITEA_REGISTRY_TOKEN}"; then
+            echo "✅ Docker login successful"
+            break
+          fi
+          echo "❌ Docker login 失败（尝试 $i/3），5s 后重试..."
+          sleep 5
+        done
+
+    - name: Build and push CI base image
+      shell: sh
+      run: |
+        set -eu
+        IMAGE="git.xiaoxiajianji.com/xiaoxia/xiaoxia-saas/ci-base"
+        VERSION_TAG="deps-$(date +%Y%m%d-%H%M)-${GITHUB_SHA::8}"
+
+        echo "=== Building CI base image (tags: latest, ${VERSION_TAG}) ==="
+        docker build --progress=plain \
+          -f infra/docker/ci.Dockerfile \
+          -t "${IMAGE}:latest" \
+          -t "${IMAGE}:${VERSION_TAG}" \
+          .
+        echo "✅ Image built successfully"
+
+        echo "=== Pushing ${VERSION_TAG} ==="
+        docker push "${IMAGE}:${VERSION_TAG}"
+        echo "=== Pushing latest ==="
+        docker push "${IMAGE}:latest"
+        echo "✅ Pushed to Gitea Registry"
+
+    - name: Verify image
+      shell: sh
+      run: |
+        set -eu
+        IMAGE="git.xiaoxiajianji.com/xiaoxia/xiaoxia-saas/ci-base:latest"
+        echo "=== Verifying pinned deps in fresh image ==="
+        docker run --rm "${IMAGE}" /opt/xiaoxia-ci-venv/bin/python -c \
+          "import httpcore, h2, numpy, httpx; print('VERSIONS:', httpcore.__version__, h2.__version__, numpy.__version__, httpx.__version__)"
+
+    - name: Notify result
+      if: always()
+      continue-on-error: true
+      shell: sh
+      env:
+        CI_NOTIFY_WEBHOOK: ${{ secrets.CI_NOTIFY_WEBHOOK }}
+      run: |
+        set +e
+        if [ "${{ job.status }}" = "success" ]; then
+          NOTIFY_MODE=success JOB_NAME="CI Base Image Build" python3 scripts/ci_notify.py
+        else
+          NOTIFY_MODE=failure JOB_NAME="CI Base Image Build" python3 scripts/ci_notify.py
+        fi
+
+    - name: Cleanup
+      if: always()
+      shell: sh
+      run: |
+        IMAGE="git.xiaoxiajianji.com/xiaoxia/xiaoxia-saas/ci-base"
+        docker rmi "${IMAGE}:latest" 2>/dev/null || true
+        echo "Cleanup done"

apps/api/app/api/routes/templates_editor/clips.py

@@ -23,6 +23,10 @@ import re
 from app.auth import AuthenticatedUser, get_current_user
 from app.core.storage import get_storage_service
 from app.dependencies import get_asset_repository, get_db_session
+
+# 默认转场时长（与 worker 端保持一致）
+_DEFAULT_TRANSITION_DURATION = 0.5
+
 from app.services.asset_segment_tracker import (
     REUSE_RATIO_LIMIT,
     SEGMENT_EDGE_GAP,
@@ -44,6 +48,7 @@ from packages.adapters.sqlalchemy_impl.template_repository import (
     SQLAlchemyTemplateRepository,
 )
 from packages.domain.plan_generator_utils import _calc_random_start_time
+from packages.domain.smart_match import SCORE_RANDOM_NOISE_MAX, score_asset
 from packages.shared.mediakit_client import get_mediakit_client
 
 from .dependencies import get_draft_plan_id, get_editor_services
@@ -667,10 +672,14 @@ def create_clips_from_assets_editor(
     # 2. 获取素材实际时长（去重查询）
     unique_asset_ids = list(dict.fromkeys(asset_ids))
     asset_durations: dict[str, float] = {}
+    asset_smart_scores: dict[str, float] = {}
     for asset_id in unique_asset_ids:
         asset = asset_repo.get(asset_id)
         if asset and hasattr(asset, "duration"):
             asset_durations[asset_id] = float(asset.duration or 0.0)
+            # 计算 smart_match 综合评分，用于候选排序
+            smart_score, _ = score_asset(asset)
+            asset_smart_scores[asset_id] = smart_score
 
     # 3. 在内存中计算所有片段数据（使用随机起始时间，不调用MediaKit）
     # 读取素材 metadata 中持久化的历史已用区间（跨任务/跨调用去重），
@@ -706,9 +715,25 @@ def create_clips_from_assets_editor(
     # 素材耗尽标志：某轮循环中所有素材均被跳过时为 True
     all_assets_exhausted = False
 
-    for i, (_seg_order, dur_min, dur_max) in enumerate(segments):
+    # 计算转场重叠补偿：每个 clip 需要额外增加的时长
+    # 目标：渲染后视频总时长 = 模板设定的各片段时长之和
+    # 公式：每 clip 增加 (n_segments - 1) * td / n_segments
+    n_segments = len(segments)
+    if n_segments > 1:
+        transition_compensation = (n_segments - 1) * _DEFAULT_TRANSITION_DURATION / n_segments
+    else:
+        transition_compensation = 0.0
+
+    # 打乱 segments 的处理顺序（分配素材的顺序随机化），但最终 clips_data 按原始 order 排序
+    shuffled_indices = list(range(len(segments)))
+    random.shuffle(shuffled_indices)
+
+    for idx in shuffled_indices:
+        _seg_order, dur_min, dur_max = segments[idx]
         # 在 segment 的 duration_min ~ duration_max 之间随机取值（保留一位小数）
         raw_duration = random.uniform(dur_min, dur_max)
+        # 加上转场补偿，确保最终输出时长 = 模板设定总时长
+        raw_duration += transition_compensation
 
         # 贪心分配素材：按"已使用次数"升序排列候选素材（使用最少的优先），
         # 同次数随机打散，避免"A-B-C-D"的固定组合反复出现。
@@ -719,13 +744,18 @@ def create_clips_from_assets_editor(
         clip_duration = 0.0
         start_time: float | None = None
         # 动态按使用次数排序：优先选使用最少的素材，同次数随机打散
-        asset_use_counts = {
-            aid: len(used_segments.get(aid, []))
-            for aid in asset_ids
-        }
+        asset_use_counts = {aid: len(used_segments.get(aid, [])) for aid in asset_ids}
+        # 排序键：smart_match 评分（注入随机噪声）→ 使用次数 → 纯随机。
+        # 噪声让得分接近的素材排名每次浮动，避免同一批素材反复选出相同组合，
+        # 从素材组合层面降低成片查重率；分差 > SCORE_RANDOM_NOISE_MAX 时排名稳定，
+        # 质量差距显著的素材仍保持优先级。
         sorted_candidates = sorted(
             asset_ids,
-            key=lambda aid: (asset_use_counts.get(aid, 0), random.random()),
+            key=lambda aid: (
+                -(asset_smart_scores.get(aid, 0.0) + random.uniform(0.0, SCORE_RANDOM_NOISE_MAX)),
+                asset_use_counts.get(aid, 0),
+                random.random(),
+            ),
         )
         for candidate in sorted_candidates:
             candidate_total = asset_durations.get(candidate, 0.0)
@@ -781,7 +811,7 @@ def create_clips_from_assets_editor(
 
         clips_data.append(
             {
-                "order": i,
+                "order": _seg_order,
                 "asset_id": asset_id,
                 "start_time": start_time,
                 "duration": clip_duration,
@@ -789,6 +819,9 @@ def create_clips_from_assets_editor(
             }
         )
 
+    # 按原始 segment order 排序，确保 clips_data 的 order 字段有序（0,1,2,3...）
+    clips_data.sort(key=lambda c: c["order"])
+
     # 4. 事务性替换：清空旧片段 → 创建新片段 → 标记ready（单事务，失败自动回滚）
     created_count = plan_svc.replace_all_clips_transactional(plan_id, clips_data)
 
@@ -815,15 +848,15 @@ def create_clips_from_assets_editor(
         duplicate_warning = f"查重率 {dup_rate:.1f}% 超过50%，建议更换素材或模板"
         logger.warning(
             "from-assets 成片查重率超标: plan_id=%s dup_rate=%.1f%%",
-            plan_id, dup_rate,
+            plan_id,
+            dup_rate,
         )
 
     # 7. 素材耗尽提示
     exhaustion_warning = None
     if all_assets_exhausted and created_count < len(segments):
         exhaustion_warning = (
-            "素材可切区间不足，部分片段使用了复用素材。"
-            "建议：1) 补充更多素材到素材库 2) 使用不同的素材组合生成"
+            "素材可切区间不足，部分片段使用了复用素材。" "建议：1) 补充更多素材到素材库 2) 使用不同的素材组合生成"
         )
 
     # 8. 立即返回响应
@@ -836,11 +869,58 @@ def create_clips_from_assets_editor(
     )
 
 
+def _build_scene_segments(
+    scene_changes: list[float],
+    asset_duration: float,
+) -> list[tuple[float, float]]:
+    """根据场景切换点构建镜头段列表.
+
+    Args:
+        scene_changes: 场景切换点时间戳列表（已排序，首位为 0.0）
+        asset_duration: 素材总时长
+
+    Returns:
+        镜头段列表 [(start, end), ...]
+    """
+    segments: list[tuple[float, float]] = []
+    for i, ts in enumerate(scene_changes):
+        end = scene_changes[i + 1] if i + 1 < len(scene_changes) else asset_duration
+        # 只保留有效长度的镜头段（至少 0.5 秒）
+        if end - ts >= 0.5:
+            segments.append((ts, end))
+    return segments
+
+
+def _pick_start_in_scene_segment(
+    seg_start: float,
+    seg_end: float,
+    clip_duration: float,
+) -> float | None:
+    """在镜头段内随机选取一个起始时间点.
+
+    确保 start + clip_duration <= seg_end。
+    若镜头段长度不足以容纳片段，返回 None。
+    """
+    available = seg_end - seg_start - clip_duration
+    if available < 0:
+        return None
+    max_start = seg_start + available
+    return random.uniform(seg_start, max_start)
+
+
 def _update_mediakit_recommendations_async(  # pragma: no cover
     plan_id: str,
     asset_ids: list[str],
 ) -> None:
-    """后台任务：调用 MediaKit 智能选片并更新片段的起始时间.
+    """后台任务：使用 SceneChange 智能选帧并更新片段的起始时间.
+
+    优先使用 SceneChange 策略检测视频镜头切换点，将每个素材按镜头段拆分，
+    各片段优先从不同镜头段中选取起始时间，实现「不同片段展示不同场景」的效果。
+
+    降级策略：
+    1. SceneChange 优先 → detect_scene_changes 内部已含 TimeInterval 降级
+    2. 若 detect_scene_changes 仍返回 None → 回退到旧的 analyze_videos 方式
+    3. 所有方式都失败 → 保持现有随机 start_time，不影响视频生成
 
     此函数在后台异步执行，不影响接口响应时间。
     失败时静默处理，不影响已创建的片段。
@@ -862,12 +942,6 @@ def _update_mediakit_recommendations_async(  # pragma: no cover
         asset_repo = SQLAlchemyAssetRepository(db)
         plan_svc = EditPlanService(db)
 
-        # 调用 MediaKit 获取推荐时间
-        recommendations = _get_mediakit_recommendations(asset_ids, asset_repo)
-        if not recommendations:
-            logger.info("后台任务: MediaKit 无推荐结果，跳过更新")
-            return
-
         # 查询该 plan 的所有片段（分批获取，避免硬编码 limit 截断）
         batch_size = 500
         all_clips = []
@@ -890,15 +964,16 @@ def _update_mediakit_recommendations_async(  # pragma: no cover
         unique_asset_ids = list({getattr(c, "asset_id", "") or "" for c in clips} - {""})
         assets_map: dict[str, object] = {a.id: a for a in asset_repo.find_by_ids(unique_asset_ids)}
 
-        # 按 asset_id 预分组片段时间段（消除 O(N^2) 嵌套循环）
-        clips_by_asset: dict[str, list[tuple[str, float, float]]] = defaultdict(list)
+        # 按 asset_id 预分组片段对象（按 order 排序，保证按模板顺序分配镜头段）
+        clips_by_asset: dict[str, list] = defaultdict(list)
         for clip in clips:
             aid = getattr(clip, "asset_id", "") or ""
-            if aid and clip.start_time is not None:
-                clips_by_asset[aid].append((clip.id, clip.start_time, clip.start_time + clip.duration))
+            if aid:
+                clips_by_asset[aid].append(clip)
+        for aid in clips_by_asset:
+            clips_by_asset[aid].sort(key=lambda c: c.order)
 
-        # 读取素材全部历史已用区间（跨任务/跨 plan 持久化记录）：
-        # MediaKit 挪点必须与随机选片一样避让历史区间，否则会把片段挪回已用过的画面
+        # 读取素材全部历史已用区间（跨任务/跨 plan 持久化记录）
         historical_segments = get_used_segments(db, unique_asset_ids)
 
         # 已更新的片段ID（用于排除已移动的旧时间段）
@@ -907,16 +982,22 @@ def _update_mediakit_recommendations_async(  # pragma: no cover
         updated_segments: dict[str, list[tuple[float, float]]] = {}
         updated_count = 0
 
-        # 遍历片段，按 asset_id 匹配推荐时间
-        for clip in clips:
-            asset_id = getattr(clip, "asset_id", "") or ""
-            if not asset_id or asset_id not in recommendations:
+        # 尝试获取存储服务（用于生成视频 URL）
+        try:
+            storage = get_storage_service()
+        except Exception:
+            logger.warning("后台任务: 获取存储服务失败，跳过 SceneChange 更新")
+            return
+
+        # 获取 MediaKit 客户端
+        client = get_mediakit_client()
+
+        # 对每个素材，检测场景切换点并分配镜头段
+        for asset_id in unique_asset_ids:
+            asset_clips = clips_by_asset.get(asset_id, [])
+            if not asset_clips:
                 continue
 
-            recommended_start = recommendations[asset_id]
-            clip_duration = clip.duration
-
-            # 从预加载字典获取素材（O(1) 查找）
             asset = assets_map.get(asset_id)
             if not asset:
                 continue
@@ -924,89 +1005,147 @@ def _update_mediakit_recommendations_async(  # pragma: no cover
             if asset_total <= 0:
                 continue
 
-            # 推荐时间 + 片段时长不能超过素材总时长
-            if recommended_start + clip_duration > asset_total:
-                logger.info(
-                    "后台任务: 推荐时间越界，跳过: asset_id=%s recommended=%.2f duration=%.1f total=%.1f",
-                    asset_id,
-                    recommended_start,
-                    clip_duration,
-                    asset_total,
-                )
-                continue
-
-            # 构建排除当前片段及已更新片段后的占用列表（O(M)，M=同素材片段数）
-            other_segments: list[tuple[float, float]] = [
-                (cs, ce)
-                for cid, cs, ce in clips_by_asset.get(asset_id, [])
-                if cid != clip.id and cid not in updated_clip_ids
-            ]
-            other_segments.extend(updated_segments.get(asset_id, []))
-
-            # 并入该素材全部历史已用区间（含其他 plan/其他任务），set 去重：
-            # 本 plan 片段创建时已写入历史记录
-            # 并入该素材全部历史已用区间（含其他 plan/其他任务）。
-            # set 去重前先归一化精度（round 3 位），避免浮点尾差导致逻辑相同的
-            # 区间（如 1.0 与 1.0000000001）被误判为不同区间
-            def _norm(segs):
-                return {(round(float(a), 3), round(float(b), 3)) for a, b in segs}
-
-            other_segments = list(_norm(other_segments) | _norm(historical_segments.get(asset_id, [])))
-
-            # 检查推荐时间是否与同 plan 片段或历史已用区间冲突（含 0.3s 边缘间隙）：
-            # 冲突时放弃该推荐、保留原随机起点（不硬挪到已用过的画面）
-            if _recommended_time_conflicts(recommended_start, clip_duration, other_segments):
-                logger.info(
-                    "后台任务: 推荐时间与同片/历史区间冲突，保留原起点: asset_id=%s recommended=%.2f",
-                    asset_id,
-                    recommended_start,
-                )
-                continue
-
-            # 逐个更新并捕获异常（单点失败不影响其他片段）
-            try:
-                old_start = clip.start_time
-                old_end = old_start + clip_duration
-                # MediaKit 移动片段起点 + 同步素材 metadata 区间记录放在同一事务：
-                # 删旧区间记录（按 plan_id + 旧 start 匹配，兼容无 plan_id 的旧数据）、
-                # 写新区间，最后统一 commit；任一步失败整体 rollback，
-                # 保证 clip.start_time 与 metadata.used_time_ranges 不出现不一致。
-                plan_svc.update_clip(clip.id, start_time=recommended_start)
+            # 获取素材视频 URL
+            video_url: str | None = None
+            storage_key = getattr(asset, "storage_key", None) or ""
+            mime = getattr(asset, "mime_type", "") or ""
+            if storage_key and mime.startswith("video/"):
                 try:
-                    if remove_used_segment(db, asset_id, old_start, old_end, plan_id=plan_id):
-                        record_used_segments(
-                            db,
-                            asset_id,
-                            recommended_start,
-                            recommended_start + clip_duration,
-                            plan_id,
-                        )
-                except Exception as me:
-                    logger.warning(
-                        "后台任务: 同步素材区间记录失败，回滚本次片段更新: clip_id=%s error=%s",
-                        clip.id,
-                        me,
+                    video_url = storage.get_download_url(storage_key)
+                except Exception as e:
+                    logger.warning("后台任务: 获取素材URL失败: asset_id=%s error=%s", asset_id, e)
+
+            # 构建该素材的占用区间列表（排除已更新片段）
+            def _get_other_segments(asset_id_inner, clip_id_inner):
+                segs: list[tuple[float, float]] = []
+                for c in clips_by_asset.get(asset_id_inner, []):
+                    cid = c.id
+                    if cid != clip_id_inner and cid not in updated_clip_ids:
+                        segs.append((c.start_time, c.start_time + c.duration))
+                segs.extend(updated_segments.get(asset_id_inner, []))
+
+                # 并入历史已用区间
+                def _norm(segs_in):
+                    return {(round(float(a), 3), round(float(b), 3)) for a, b in segs_in}
+
+                return list(_norm(segs) | _norm(historical_segments.get(asset_id_inner, [])))
+
+            # 优先使用 SceneChange 策略
+            scene_segments: list[tuple[float, float]] = []
+            if client.is_available and video_url:
+                scene_changes = client.detect_scene_changes(video_url)
+                if scene_changes is not None:
+                    scene_segments = _build_scene_segments(scene_changes, asset_total)
+                    logger.info(
+                        "后台任务: 素材场景检测完成: asset_id=%s scenes=%d",
+                        asset_id,
+                        len(scene_segments),
                     )
-                    db.rollback()
-                    continue
-                db.commit()
-                updated_count += 1
-                updated_clip_ids.add(clip.id)
-            except Exception as ue:
-                logger.warning("后台任务: 单个片段更新失败: clip_id=%s error=%s", clip.id, ue)
-                try:
-                    db.rollback()
-                except Exception:
-                    pass
+
+            # SceneChange 未获得有效结果 → 尝试 analyze_videos 作为 fallback
+            if not scene_segments and video_url:
+                fallback_recs = _get_mediakit_recommendations([asset_id], asset_repo)
+                if fallback_recs and asset_id in fallback_recs:
+                    # analyze_videos 只返回单个推荐点，转为单镜头段
+                    rec_start = fallback_recs[asset_id]
+                    scene_segments = [(rec_start, asset_total)]
+                    logger.info(
+                        "后台任务: 使用 analyze_videos fallback: asset_id=%s start=%.2f",
+                        asset_id,
+                        rec_start,
+                    )
+
+            if not scene_segments:
+                # 所有方式都失败 → 保持现有随机 start_time
+                logger.info(
+                    "后台任务: SceneChange 与 analyze_videos 均无结果，保持随机起点: asset_id=%s",
+                    asset_id,
+                )
                 continue
 
-            updated_segments.setdefault(asset_id, []).append((recommended_start, recommended_start + clip_duration))
-            logger.info(
-                "后台任务: 更新片段起始时间: clip_id=%s asset_id=%s start_time=%.2f",
-                clip.id,
-                asset_id,
-                recommended_start,
-            )
+            # 为每个片段分配不同的镜头段
+            scene_segments_pool = list(scene_segments)  # 可消费的镜头段池
+            for clip in asset_clips:
+                clip_duration = clip.duration
+                recommended_start: float | None = None
+
+                # 从镜头段池中依次尝试，选一个不冲突的
+                for seg_idx, (seg_start, seg_end) in enumerate(scene_segments_pool):
+                    candidate_start = _pick_start_in_scene_segment(seg_start, seg_end, clip_duration)
+                    if candidate_start is None:
+                        continue  # 镜头段太短，跳过
+
+                    # 检查越界
+                    if candidate_start + clip_duration > asset_total:
+                        continue
+
+                    # 检查与已用区间冲突
+                    other_segs = _get_other_segments(asset_id, clip.id)
+                    if _recommended_time_conflicts(candidate_start, clip_duration, other_segs):
+                        continue
+
+                    recommended_start = candidate_start
+                    # 消费该镜头段（从池中移除，下一个片段用不同镜头段）
+                    scene_segments_pool.pop(seg_idx)
+                    break
+
+                if recommended_start is None:
+                    # 镜头段用完或都冲突 → 尝试 _calc_random_start_time 兜底
+                    used_segs_for_calc: dict[str, list[tuple[float, float]]] = {
+                        asset_id: _get_other_segments(asset_id, clip.id)
+                    }
+                    fallback_start = _calc_random_start_time(
+                        asset_id,
+                        clip_duration,
+                        {asset_id: asset_total},
+                        used_segs_for_calc,
+                    )
+                    if fallback_start is None:
+                        continue  # 完全无法分配，保持原起点
+                    recommended_start = fallback_start
+
+                # 更新片段起始时间
+                try:
+                    old_start = clip.start_time
+                    old_end = old_start + clip_duration
+
+                    plan_svc.update_clip(clip.id, start_time=recommended_start)
+                    try:
+                        if remove_used_segment(db, asset_id, old_start, old_end, plan_id=plan_id):
+                            record_used_segments(
+                                db,
+                                asset_id,
+                                recommended_start,
+                                recommended_start + clip_duration,
+                                plan_id,
+                            )
+                    except Exception as me:
+                        logger.warning(
+                            "后台任务: 同步素材区间记录失败，回滚本次片段更新: clip_id=%s error=%s",
+                            clip.id,
+                            me,
+                        )
+                        db.rollback()
+                        continue
+                    db.commit()
+                    updated_count += 1
+                    updated_clip_ids.add(clip.id)
+                    updated_segments.setdefault(asset_id, []).append(
+                        (recommended_start, recommended_start + clip_duration)
+                    )
+                    logger.info(
+                        "后台任务: 更新片段起始时间(场景选帧): clip_id=%s asset_id=%s start_time=%.2f",
+                        clip.id,
+                        asset_id,
+                        recommended_start,
+                    )
+                except Exception as ue:
+                    logger.warning("后台任务: 单个片段更新失败: clip_id=%s error=%s", clip.id, ue)
+                    try:
+                        db.rollback()
+                    except Exception:
+                        pass
+                    continue
 
         logger.info("后台任务完成: plan_id=%s 成功更新 %d 个片段", plan_id, updated_count)
 

apps/api/app/services/plan_generator_service.py

@@ -13,6 +13,7 @@
 from __future__ import annotations
 
 import logging
+import random
 from typing import Any, List
 
 from sqlalchemy.orm import Session
@@ -32,6 +33,7 @@ from packages.domain.plan_generator_utils import (
     generate_default_clips,
     map_clip_types_for_mode,
 )
+from packages.domain.smart_match import SCORE_RANDOM_NOISE_MAX, score_asset
 from packages.domain.template_clip_config import TemplateClipConfig
 
 logger = logging.getLogger(__name__)
@@ -218,8 +220,13 @@ class PlanGeneratorService:
     ) -> None:
         """按 editing_mode 将素材分配到 clips（就地修改，未持久化）.
 
-        委托给 plan_generator_utils.distribute_assets 纯函数。
+        先用 smart_match 评分对素材排序（高分优先），再委托给
+        plan_generator_utils.distribute_assets 纯函数完成分配。
         """
+        # 用 smart_match 评分排序素材：高分（质量好/时长合适/新鲜/未使用）优先
+        if self._asset_repo and not random_selection:
+            asset_ids = self._sort_assets_by_smart_score(asset_ids)
+
         distribute_assets(
             clips,
             asset_ids,
@@ -228,6 +235,29 @@ class PlanGeneratorService:
             asset_durations=asset_durations,
         )
 
+    def _sort_assets_by_smart_score(self, asset_ids: List[str]) -> List[str]:
+        """按 smart_match 综合评分降序排列素材 ID（注入随机噪声）。
+
+        评分高的素材（质量好、时长合适、新鲜、使用次数少）倾向排在前面；
+        排序时给每个素材的得分注入 0~SCORE_RANDOM_NOISE_MAX 的随机噪声，
+        使得分接近的素材排名每次浮动，避免一键生成反复选出相同素材组合，
+        从素材组合层面降低成片查重率。分差大于噪声上限时排名保持稳定。
+        """
+        scored: list[tuple[str, float]] = []
+        for asset_id in asset_ids:
+            asset = self._asset_repo.get(asset_id)
+            if asset:
+                score, _ = score_asset(asset)
+                scored.append((asset_id, score))
+            else:
+                scored.append((asset_id, 0.0))
+        # 评分 + 随机噪声后按降序排列
+        scored.sort(
+            key=lambda x: x[1] + random.uniform(0.0, SCORE_RANDOM_NOISE_MAX),
+            reverse=True,
+        )
+        return [aid for aid, _ in scored]
+
     def _fetch_asset_durations(self, asset_ids: List[str]) -> dict[str, float]:
         """从数据库获取素材时长信息.
 

apps/web/src/pages/generate/components/title/TitlePresetsGrid.tsx

@@ -87,7 +87,6 @@ const TitlePresetsGrid: React.FC<TitlePresetsGridProps> = ({
             key={p.key}
             className={`xx-title-preset-card${isActive ? " active" : ""}`}
             onClick={() => onApply(p.key)}
-            title={p.label}
           >
             <span className="xx-title-preset-preview-text" style={{ position: "relative" }}>
               {/* 底层：描边轮廓 */}

apps/web/src/pages/generate/constants.ts

@@ -91,8 +91,6 @@ export const TITLE_PRESETS = [
     style: { size: 32, color: "#d4a843", bold: true, italic: false, stroke: false, shadow: true },
     previewStyle: {
       color: "#d4a843",
-      _strokeColor: "#1a1000",
-      _strokeWidth: 1.5,
       fontWeight: 700,
       fontSize: "32px",
       textShadow: "1px 1px 3px rgba(0,0,0,0.8)",
@@ -104,8 +102,6 @@ export const TITLE_PRESETS = [
     style: { size: 24, color: "#333333", bold: false, italic: false, stroke: false, shadow: false },
     previewStyle: {
       color: "#e8e8e8",
-      _strokeColor: "#cccccc",
-      _strokeWidth: 1,
       fontWeight: 400,
       fontSize: "32px",
     },
@@ -129,8 +125,6 @@ export const TITLE_PRESETS = [
     style: { size: 24, color: "#1a1a1a", bold: false, italic: false, stroke: false, shadow: false },
     previewStyle: {
       color: "#e0e0e0",
-      _strokeColor: "#bbbbbb",
-      _strokeWidth: 1,
       fontWeight: 400,
       fontSize: "32px",
     },
@@ -141,8 +135,6 @@ export const TITLE_PRESETS = [
     style: { size: 28, color: "#e8d5b7", bold: false, italic: false, stroke: false, shadow: true },
     previewStyle: {
       color: "#e8d5b7",
-      _strokeColor: "#3a2a1a",
-      _strokeWidth: 1.5,
       fontWeight: 400,
       fontSize: "32px",
       textShadow: "2px 2px 6px rgba(0,0,0,0.7)",
@@ -154,8 +146,6 @@ export const TITLE_PRESETS = [
     style: { size: 32, color: "#00e5ff", bold: true, italic: false, stroke: false, shadow: true },
     previewStyle: {
       color: "#00e5ff",
-      _strokeColor: "#00e5ff",
-      _strokeWidth: 1,
       fontWeight: 700,
       fontSize: "32px",
       textShadow: "0 0 4px #00e5ff, 0 0 8px #00e5ff, 0 0 16px rgba(0,229,255,0.5)",
@@ -167,13 +157,203 @@ export const TITLE_PRESETS = [
     style: { size: 28, color: "#333333", bold: false, italic: false, stroke: false, shadow: true },
     previewStyle: {
       color: "#e0e0e0",
-      _strokeColor: "#cccccc",
-      _strokeWidth: 1,
       fontWeight: 400,
       fontSize: "32px",
       textShadow: "1px 1px 2px rgba(0,0,0,0.3)",
     },
   },
+  {
+    key: "outline_yellow",
+    label: "黄色描边",
+    style: { size: 28, color: "#ffd54f", bold: true, italic: false, stroke: true, shadow: false },
+    previewStyle: {
+      color: "#ffd54f",
+      _strokeColor: "#000000",
+      _strokeWidth: 2,
+      fontWeight: 700,
+      fontSize: "32px",
+    },
+  },
+  {
+    key: "outline_pink",
+    label: "粉色描边",
+    style: { size: 28, color: "#ff80ab", bold: true, italic: false, stroke: true, shadow: false },
+    previewStyle: {
+      color: "#ff80ab",
+      _strokeColor: "#000000",
+      _strokeWidth: 2,
+      fontWeight: 700,
+      fontSize: "32px",
+    },
+  },
+  {
+    key: "outline_blue",
+    label: "蓝色描边",
+    style: { size: 28, color: "#82b1ff", bold: true, italic: false, stroke: true, shadow: false },
+    previewStyle: {
+      color: "#82b1ff",
+      _strokeColor: "#000000",
+      _strokeWidth: 2,
+      fontWeight: 700,
+      fontSize: "32px",
+    },
+  },
+  {
+    key: "outline_green",
+    label: "绿色描边",
+    style: { size: 28, color: "#69f0ae", bold: true, italic: false, stroke: true, shadow: false },
+    previewStyle: {
+      color: "#69f0ae",
+      _strokeColor: "#000000",
+      _strokeWidth: 2,
+      fontWeight: 700,
+      fontSize: "32px",
+    },
+  },
+  {
+    key: "outline_gray",
+    label: "灰色描边",
+    style: { size: 28, color: "#bdbdbd", bold: true, italic: false, stroke: true, shadow: false },
+    previewStyle: {
+      color: "#bdbdbd",
+      _strokeColor: "#000000",
+      _strokeWidth: 2,
+      fontWeight: 700,
+      fontSize: "32px",
+    },
+  },
+  {
+    key: "bg_white",
+    label: "白底黑字",
+    style: { size: 28, color: "#1a1a1a", bold: true, italic: false, stroke: false, shadow: false },
+    previewStyle: {
+      color: "#1a1a1a",
+      fontWeight: 700,
+      fontSize: "32px",
+      background: "#ffffff",
+      borderRadius: "4px",
+      padding: "2px 6px",
+    },
+  },
+  {
+    key: "bg_yellow",
+    label: "黄底黑字",
+    style: { size: 28, color: "#1a1a1a", bold: true, italic: false, stroke: false, shadow: false },
+    previewStyle: {
+      color: "#1a1a1a",
+      fontWeight: 700,
+      fontSize: "32px",
+      background: "#ffd54f",
+      borderRadius: "4px",
+      padding: "2px 6px",
+    },
+  },
+  {
+    key: "bg_pink",
+    label: "粉底黑字",
+    style: { size: 28, color: "#1a1a1a", bold: true, italic: false, stroke: false, shadow: false },
+    previewStyle: {
+      color: "#1a1a1a",
+      fontWeight: 700,
+      fontSize: "32px",
+      background: "#ff80ab",
+      borderRadius: "4px",
+      padding: "2px 6px",
+    },
+  },
+  {
+    key: "bg_red",
+    label: "红底白字",
+    style: { size: 28, color: "#ffffff", bold: true, italic: false, stroke: false, shadow: false },
+    previewStyle: {
+      color: "#ffffff",
+      fontWeight: 700,
+      fontSize: "32px",
+      background: "#ef5350",
+      borderRadius: "4px",
+      padding: "2px 6px",
+    },
+  },
+  {
+    key: "neon_orange",
+    label: "橙色发光",
+    style: { size: 32, color: "#ff9100", bold: true, italic: false, stroke: false, shadow: true },
+    previewStyle: {
+      color: "#ff9100",
+      fontWeight: 700,
+      fontSize: "32px",
+      textShadow: "0 0 4px #ff9100, 0 0 8px #ff9100, 0 0 16px rgba(255,145,0,0.5)",
+    },
+  },
+  {
+    key: "neon_purple",
+    label: "紫色发光",
+    style: { size: 32, color: "#d500f9", bold: true, italic: false, stroke: false, shadow: true },
+    previewStyle: {
+      color: "#d500f9",
+      fontWeight: 700,
+      fontSize: "32px",
+      textShadow: "0 0 4px #d500f9, 0 0 8px #d500f9, 0 0 16px rgba(213,0,249,0.5)",
+    },
+  },
+  {
+    key: "bordered_white",
+    label: "白字绿框",
+    style: { size: 28, color: "#ffffff", bold: true, italic: false, stroke: false, shadow: false },
+    previewStyle: {
+      color: "#ffffff",
+      fontWeight: 700,
+      fontSize: "32px",
+      background: "#1a1a1a",
+      border: "2px solid #69f0ae",
+      borderRadius: "4px",
+      padding: "2px 6px",
+    },
+  },
+  {
+    key: "gradient_warm",
+    label: "暖色渐变",
+    style: { size: 32, color: "#ff6d00", bold: true, italic: false, stroke: false, shadow: true },
+    previewStyle: {
+      color: "#ff6d00",
+      fontWeight: 700,
+      fontSize: "32px",
+      textShadow: "0 0 6px rgba(255,109,0,0.6), 1px 1px 2px rgba(0,0,0,0.5)",
+    },
+  },
+  {
+    key: "gradient_cool",
+    label: "冷色渐变",
+    style: { size: 32, color: "#00b0ff", bold: true, italic: false, stroke: false, shadow: true },
+    previewStyle: {
+      color: "#00b0ff",
+      fontWeight: 700,
+      fontSize: "32px",
+      textShadow: "0 0 6px rgba(0,176,255,0.6), 1px 1px 2px rgba(0,0,0,0.5)",
+    },
+  },
+  {
+    key: "shadow_deep",
+    label: "深影白字",
+    style: { size: 28, color: "#ffffff", bold: true, italic: false, stroke: false, shadow: true },
+    previewStyle: {
+      color: "#ffffff",
+      fontWeight: 700,
+      fontSize: "32px",
+      textShadow: "2px 2px 4px rgba(0,0,0,0.8), 0 0 8px rgba(0,0,0,0.4)",
+    },
+  },
+  {
+    key: "soft_gold",
+    label: "柔光金",
+    style: { size: 28, color: "#ffd54f", bold: true, italic: false, stroke: false, shadow: true },
+    previewStyle: {
+      color: "#ffd54f",
+      fontWeight: 700,
+      fontSize: "32px",
+      textShadow: "0 0 6px rgba(255,213,79,0.5), 1px 1px 2px rgba(0,0,0,0.4)",
+    },
+  },
 ]
 
 /* ── 封面模式 ── */

apps/web/src/pages/generate/generate.css

@@ -1733,18 +1733,18 @@
 /* 标题预设卡片网格 */
 .xx-title-presets-grid {
   display: grid;
-  grid-template-columns: repeat(8, 1fr);
-  gap: 1.5px;
+  grid-template-columns: repeat(6, 52px);
+  gap: 1px;
 }
 
 .xx-title-preset-card {
   display: flex;
   align-items: center;
   justify-content: center;
-  width: 48px;
-  height: 48px;
+  width: 52px;
+  height: 52px;
   padding: 0;
-  background: #3a3a3a;
+  background: #404040;
   border: 2px solid transparent;
   border-radius: 8px;
   cursor: pointer;
@@ -1753,12 +1753,12 @@
 
 .xx-title-preset-card:hover {
   border-color: #666;
-  background: #4a4a4a;
+  background: #4d4d4d;
 }
 
 .xx-title-preset-card.active {
   border-color: #409eff;
-  background: #4a4a4a;
+  background: #4d4d4d;
 }
 
 .xx-title-preset-preview-text {

apps/web/src/pages/products/components/ProductCard.tsx

@@ -221,7 +221,7 @@ export const ProductCard: React.FC<ProductCardProps> = ({
               product.duplicateRate > 0 ? ` ${dupClass}` : ""
             }`}
           >
-            查重率：{product.duplicateRate > 0 ? `${product.duplicateRate.toFixed(1)}%` : "-"}
+            查重率：{product.duplicateRate != null ? `${product.duplicateRate.toFixed(1)}%` : "-"}
           </span>
         </div>
       </div>

apps/worker/video_processing/unified_render_service.py

@@ -200,6 +200,22 @@ class UnifiedRenderService:
 
         # 3. 计算视频总时长（用于字幕显示时长）
         video_duration = self._estimate_total_duration(layers)
+        # Debug: 输出各图层时长明细
+        for layer in layers:
+            layer_total = sum(UnifiedRenderService._clip_adjusted_duration(c) for c in layer.clips)
+            clip_details = [
+                f"{c.clip_id}(dur={c.duration:.3f},actual={c.actual_duration:.3f},speed={getattr(c, 'playback_speed', 1.0):.4f})"
+                for c in layer.clips
+            ]
+            logger.info(
+                "[debug] layer=%s clips=%d total=%.3f transition_duration=%.3f details=%s",
+                layer.role,
+                len(layer.clips),
+                layer_total,
+                self.transition_duration,
+                ", ".join(clip_details),
+            )
+        logger.info("[debug] estimated video_duration=%.3f", video_duration)
 
         # 3.5 TTS 配音生成（如果配置了）
         self._maybe_add_voiceover_layer(layers, video_duration=video_duration)
@@ -1422,6 +1438,7 @@ class UnifiedRenderService:
             if trim_segments and len(trim_segments) > 1:
                 # 多段裁剪：展开为多个 clip
                 resolved_segments = TrimEngine.resolve_segments(trim_segments, actual_duration)
+                configured_speed = getattr(clip, "playback_speed", 1.0) or 1.0
                 for i, seg in enumerate(resolved_segments):
                     # 每个段生成一个独立的 ResolvedClip
                     seg_clip_id = f"{clip.id}_seg_{seg.segment_id}"
@@ -1429,6 +1446,19 @@ class UnifiedRenderService:
                     seg_start = seg.trim.start_time
                     seg_duration = seg.trim.duration
 
+                    # 多段裁剪：如果段的时长超过素材实际时长，减速补偿
+                    seg_speed = configured_speed
+                    if actual_duration > 0 and seg_duration > actual_duration + 0.05:
+                        seg_speed = max(0.25, round(configured_speed * actual_duration / seg_duration, 4))
+                        logger.info(
+                            "[debug] multi-seg clip=%s seg=%s duration=%.3f actual=%.3f → speed=%.4f",
+                            clip.id,
+                            seg.segment_id,
+                            seg_duration,
+                            actual_duration,
+                            seg_speed,
+                        )
+
                     rc = ResolvedClip(
                         clip_id=seg_clip_id,
                         asset_id=asset_id,
@@ -1439,7 +1469,7 @@ class UnifiedRenderService:
                         duration=seg_duration,
                         transition_effect=clip.transition_effect or "cut",
                         transition_duration=getattr(clip, "transition_duration", 0.0) or 0.0,
-                        playback_speed=getattr(clip, "playback_speed", 1.0) or 1.0,
+                        playback_speed=seg_speed,
                         config={**clip_config, "_segment_id": seg.segment_id},
                         actual_duration=actual_duration,
                         trim_config=seg.trim,
@@ -1461,6 +1491,7 @@ class UnifiedRenderService:
             effective_trim: TrimConfig | None = None
             final_start = clip.start_time
             final_duration = clip.duration
+            configured_speed = getattr(clip, "playback_speed", 1.0) or 1.0
 
             if trim_config is not None and actual_duration > 0:
                 effective_trim = trim_config.validate_and_resolve(actual_duration)
@@ -1474,6 +1505,25 @@ class UnifiedRenderService:
                     final_start = 0.0
                     final_duration = actual_duration
 
+            # 素材实际时长不足以覆盖配置的时长时，降低播放速度来补偿
+            # 例如：配置4s但素材只有3s → speed=0.75x，用满3s素材达到4s输出
+            if actual_duration > 0 and final_duration > actual_duration + 0.05:
+                compensated_speed = actual_duration / final_duration
+                # 保留用户设置的速度（如果已减速则叠加）
+                final_speed = configured_speed * compensated_speed
+                # 下限 0.25x
+                final_speed = max(0.25, round(final_speed, 4))
+                logger.info(
+                    "[debug] clip=%s duration=%.3f actual=%.3f → 减速补偿 speed=%.4f (configured=%.3f)",
+                    clip.id,
+                    final_duration,
+                    actual_duration,
+                    final_speed,
+                    configured_speed,
+                )
+            else:
+                final_speed = configured_speed
+
             rc = ResolvedClip(
                 clip_id=clip.id,
                 asset_id=asset_id,
@@ -1484,13 +1534,25 @@ class UnifiedRenderService:
                 duration=final_duration,
                 transition_effect=clip.transition_effect or "cut",
                 transition_duration=getattr(clip, "transition_duration", 0.0) or 0.0,
-                playback_speed=getattr(clip, "playback_speed", 1.0) or 1.0,
+                playback_speed=final_speed,
                 config=clip_config,
                 actual_duration=actual_duration,
                 trim_config=effective_trim,
             )
             resolved.append(rc)
 
+            # Debug日志：记录每个clip的时长信息
+            eff_dur = _clip_effective_duration_pure(final_duration, actual_duration)
+            logger.info(
+                "[debug] resolved clip=%s duration=%.3f actual=%.3f effective=%.3f speed=%.4f start=%.3f",
+                clip.id,
+                final_duration,
+                actual_duration,
+                eff_dur,
+                final_speed,
+                final_start,
+            )
+
         # 按 order 排序
         resolved.sort(key=lambda c: c.order)
         return resolved
@@ -1683,7 +1745,7 @@ class UnifiedRenderService:
                         if d > 0:
                             layer_dur = d
                             break
-                    xfade_filter, _ = self._transition_engine.build_xfade_chain(
+                    xfade_filter, xfade_estimated_dur = self._transition_engine.build_xfade_chain(
                         clip_durations=layer_durations,
                         clip_video_labels=layer_labels,
                         transitions=layer_transitions,
@@ -1692,6 +1754,13 @@ class UnifiedRenderService:
                     )
                     if xfade_filter:
                         filter_parts.append(xfade_filter)
+                    logger.info(
+                        "[unified-render] layer=%s xfade: clips=%d durations=%s estimated_dur=%.3f",
+                        layer.role,
+                        len(layer_labels),
+                        [round(d, 3) for d in layer_durations],
+                        xfade_estimated_dur,
+                    )
                 layer_output_labels[layer.role] = out_label
 
         # Step 3: 合成各层
@@ -1915,8 +1984,14 @@ class UnifiedRenderService:
     def _clip_effective_duration(clip: ResolvedClip) -> float:
         """计算 clip 的有效时长（原速 trim 后时长）。
 
+        如果 playback_speed < 1（为补偿素材不足而减速），返回配置的 duration，
+        而非 min(duration, actual_duration)。
         实际实现移至 packages.domain.render_layer_utils.clip_effective_duration。
         """
+        speed = getattr(clip, "playback_speed", 1.0) or 1.0
+        # 减速场景：duration 已通过降低 playback_speed 补偿，返回配置的 duration
+        if speed < 1.0 - 1e-6 and clip.duration > 0:
+            return clip.duration
         return _clip_effective_duration_pure(clip.duration, clip.actual_duration)
 
     # ── 画中画（PiP）相关方法 ──────────────────────────────────────────────────

packages/domain/smart_match.py

@@ -14,6 +14,13 @@ from dataclasses import dataclass, field
 from datetime import datetime, timezone
 from typing import Any
 
+# 素材选取排序时注入的随机噪声上限（分）。
+# score_asset 综合得分范围为 0-100，噪声 0~20 意味着：
+# - 素材间得分差距 > 20 分时，排名不受影响（质量差异显著的素材保持稳定优先级）
+# - 得分接近（差距 <= 20 分）的素材排名会随机浮动，使每次生成选出的素材组合不同，
+#   从素材组合层面降低成片重复率；排名靠后的低分素材也有机会入选。
+SCORE_RANDOM_NOISE_MAX = 20.0
+
 
 @dataclass
 class SmartMatchResult:

packages/domain/xfade_builder.py

@@ -150,8 +150,11 @@ def build_xfade_filter_chain(
         else:
             first_input_dur = cumulative - total_transition
 
-        # 原始 offset 计算
-        offset = max(0.0, cumulative - transition_duration * i)
+        # 正确的 offset 计算：offset 应相对于累积输出时长
+        # offset = 累积输出中，转场开始的时间点
+        # = first_input_dur - transition_duration
+        # 这样每个转场之间的"纯内容"时长等于原始 clip 时长
+        offset = max(0.0, first_input_dur - transition_duration)
 
         # 安全钳制：offset + td 不能超过第一个输入的时长
         available = max(0.0, first_input_dur - offset)

packages/shared/mediakit_client.py

@@ -119,6 +119,70 @@ class MediaKitClient:
 
         return None
 
+    def detect_scene_changes(
+        self,
+        video_url: str,
+        max_frames: int = 20,
+        poll_interval: float = 2.0,
+        max_poll_attempts: int = 30,
+    ) -> Optional[List[float]]:
+        """检测视频场景切换点，返回时间戳列表.
+
+        降级策略：
+        1. 先尝试 SceneChange 策略
+        2. SceneChange 失败（OOM等）→ 退回 TimeInterval（5秒间隔）
+        3. MediaKit 不可用 → 返回 None
+
+        Returns:
+            场景切换点时间戳列表，如 [0.0, 3.2, 7.8, 12.5]
+            失败返回 None
+        """
+        if not self.is_available:
+            logger.warning("MediaKit 未配置，跳过场景检测")
+            return None
+
+        # 策略1：尝试 SceneChange
+        frames = self.extract_frames(
+            video_url=video_url,
+            strategy="SceneChange",
+            max_frames=max_frames,
+            poll_interval=poll_interval,
+            max_poll_attempts=max_poll_attempts,
+        )
+
+        # 策略2：SceneChange 失败 → 退回 TimeInterval（5秒间隔）
+        if frames is None:
+            logger.info("SceneChange 策略失败，降级为 TimeInterval（5秒间隔）")
+            # 估算帧数：假设视频最长60秒，每5秒一帧
+            ti_max_frames = max(max_frames, 12)
+            frames = self.extract_frames(
+                video_url=video_url,
+                strategy="TimeInterval",
+                max_frames=ti_max_frames,
+                poll_interval=poll_interval,
+                max_poll_attempts=max_poll_attempts,
+            )
+
+        if frames is None:
+            return None
+
+        # 从帧列表中提取 timestamp，排序
+        timestamps = sorted({float(f.get("timestamp", 0.0)) for f in frames if "timestamp" in f})
+
+        if not timestamps:
+            return None
+
+        # 始终在列表开头加 0.0（素材起始点）
+        if timestamps[0] != 0.0:
+            timestamps.insert(0, 0.0)
+
+        logger.info(
+            "场景检测完成: video_url=%s scene_changes=%s",
+            video_url[:80],
+            timestamps,
+        )
+        return timestamps
+
     def _submit_extract_task(
         self,
         video_url: str,

requirements-base.txt

@@ -24,8 +24,10 @@ celery==5.4.0
 # 对象存储
 oss2==2.18.4
 
-# HTTP 客户端
+# HTTP 客户端（pin 间接依赖防止版本漂移）
 httpx==0.27.2
+httpcore==1.0.7
+h2==4.1.0
 
 # Prometheus monitoring
 prometheus-client==0.21.1

requirements-dev.txt

@@ -15,5 +15,6 @@ pytest-xdist==3.6.1
 diff-cover==8.0.3
 
 # 资产质量评分依赖（与 requirements-worker.txt 保持一致）
+numpy==1.26.4
 scipy==1.13.1
 Pillow==10.4.0

scripts/ci/run_unit_tests.sh

@@ -1,77 +1,136 @@
 #!/bin/bash
 # CI Unit Tests Job 主脚本
-# 包含：依赖安装、增量测试选择、覆盖率测试、diff覆盖率门禁
+# 包含：依赖缓存、增量测试选择、覆盖率测试、diff覆盖率门禁
 set -eu
 
 JOB_NAME="${1:-Unit Tests}"
 
 echo "=== CI Unit Tests 开始 ==="
 
+# --- 依赖缓存检查 ---
+# 如果 requirements 文件未变化且依赖已安装，跳过 pip install（持久 runner 优化）
+REQ_HASH_FILE="/tmp/.ci_unit_tests_req_hash"
+CURRENT_REQ_HASH=""
+if [ -f requirements-base.txt ] && [ -f requirements.txt ] && [ -f requirements-dev.txt ]; then
+  CURRENT_REQ_HASH=$(cat requirements-base.txt requirements.txt requirements-dev.txt | md5sum | cut -d' ' -f1)
+fi
+
+SKIP_PIP_INSTALL=false
+if [ -n "$CURRENT_REQ_HASH" ] && [ -f "$REQ_HASH_FILE" ]; then
+  CACHED_HASH=$(cat "$REQ_HASH_FILE")
+  if [ "$CACHED_HASH" = "$CURRENT_REQ_HASH" ]; then
+    # 验证关键包是否还在
+    if python3 -c "import pytest; import celery" 2>/dev/null; then
+      echo "✅ 依赖无变化 (hash=$CURRENT_REQ_HASH)，跳过 pip install"
+      SKIP_PIP_INSTALL=true
+    else
+      echo "⚠️ 依赖 hash 匹配但关键包缺失，重新安装"
+    fi
+  fi
+fi
+
 # --- 安装依赖 ---
-echo ""
-echo "=== 安装 Python 依赖 ==="
-# pip install 带重试（网络不稳定时自动重试）
-for i in 1 2 3; do
-  python3 -m pip install -q -r requirements-base.txt && break
-  echo "pip install requirements-base.txt 失败，重试 $i/3..."
-  [ $i -eq 3 ] && exit 1
-  sleep 5
-done
-for i in 1 2 3; do
-  python3 -m pip install -q -r requirements.txt && break
-  echo "pip install requirements.txt 失败，重试 $i/3..."
-  [ $i -eq 3 ] && exit 1
-  sleep 5
-done
-for i in 1 2 3; do
-  python3 -m pip install -q -r requirements-dev.txt && break
-  echo "pip install requirements-dev.txt 失败，重试 $i/3..."
-  [ $i -eq 3 ] && exit 1
-  sleep 5
-done
+if [ "$SKIP_PIP_INSTALL" = "false" ]; then
+  echo ""
+  echo "=== 安装 Python 依赖 ==="
+  # pip install 带重试（网络不稳定时自动重试），合并为一次调用减少开销
+  for i in 1 2 3; do
+    python3 -m pip install -q -r requirements-base.txt -r requirements.txt -r requirements-dev.txt && break
+    echo "pip install 失败，重试 $i/3..."
+    [ $i -eq 3 ] && exit 1
+    sleep 5
+  done
+  # 保存 hash 标记
+  if [ -n "$CURRENT_REQ_HASH" ]; then
+    echo "$CURRENT_REQ_HASH" > "$REQ_HASH_FILE"
+  fi
+fi
 pytest --version
 
 # 双保险：确保numpy已安装
 echo "=== 验证 numpy 安装 ==="
 SKIP_NUMPY_TESTS=0
-python3 -m pip install numpy==1.26.4 || {
-  echo "❌ numpy 首次安装失败，尝试不使用缓存重新安装..."
-  python3 -m pip install --no-cache-dir numpy==1.26.4 || {
-    echo "⚠️ numpy 安装失败，跳过需要 numpy 的测试"
-    SKIP_NUMPY_TESTS=1
+if python3 -c "import numpy; assert numpy.__version__ == '1.26.4'" 2>/dev/null; then
+  echo "✅ numpy 1.26.4 已就绪（缓存命中）"
+else
+  echo "需要安装 numpy 1.26.4..."
+  python3 -m pip install numpy==1.26.4 || {
+    echo "❌ numpy 首次安装失败，尝试不使用缓存重新安装..."
+    python3 -m pip install --no-cache-dir numpy==1.26.4 || {
+      echo "⚠️ numpy 安装失败，跳过需要 numpy 的测试"
+      SKIP_NUMPY_TESTS=1
+    }
   }
-}
+fi
 if [ "$SKIP_NUMPY_TESTS" = "0" ]; then
-  python3 -c "import numpy; print(f'✅ numpy {numpy.__version__} 安装成功')" || {
+  python3 -c "import numpy; print(f'✅ numpy {numpy.__version__} 就绪')" || {
     echo "⚠️ numpy 导入失败，跳过需要 numpy 的测试"
     SKIP_NUMPY_TESTS=1
   }
 fi
 
-# --- 增量测试选择（仅PR） ---
+# --- 增量测试选择（PR + push 均支持） ---
 UNIT_TEST_MODE="full"
 SELECTED_TEST_FILES="tests/unit"
 
-if [ "${GITHUB_EVENT_NAME:-}" = "pull_request" ] && [ -n "${GITHUB_TOKEN:-}" ]; then
+IS_PULL_REQUEST=false
+IS_PUSH=false
+[ "${GITHUB_EVENT_NAME:-}" = "pull_request" ] && IS_PULL_REQUEST=true
+[ "${GITHUB_EVENT_NAME:-}" = "push" ] && IS_PUSH=true
+
+if ($IS_PULL_REQUEST || $IS_PUSH) && [ -n "${GITHUB_TOKEN:-}" ]; then
   echo ""
   echo "=== 增量测试选择 ==="
-  PR_NUMBER=$(echo "$GITHUB_REF" | sed 's|refs/pull/||; s|/.*||')
-  API_URL="${GITHUB_API_URL}/repos/${GITHUB_REPOSITORY}/pulls/${PR_NUMBER}/files?limit=300"
-  CHANGED_FILES=$(curl -s -H "Authorization: token ${GITHUB_TOKEN}" "$API_URL" | python3 -c "import sys,json; [print(f['filename']) for f in json.load(sys.stdin) if f['status'] != 'removed']")
+
+  CHANGED_FILES=""
+
+  if $IS_PULL_REQUEST; then
+    PR_NUMBER=$(echo "$GITHUB_REF" | sed 's|refs/pull/||; s|/.*||')
+    API_URL="${GITHUB_API_URL}/repos/${GITHUB_REPOSITORY}/pulls/${PR_NUMBER}/files?limit=300"
+    CHANGED_FILES=$(curl -s -H "Authorization: token ${GITHUB_TOKEN}" "$API_URL" \
+      | python3 -c "import sys,json; [print(f['filename']) for f in json.load(sys.stdin) if f['status'] != 'removed']")
+  elif $IS_PUSH && [ -n "${GITHUB_SHA:-}" ]; then
+    # Push 事件：通过 GitHub API 获取本次 push 改动的文件
+    API_URL="${GITHUB_API_URL}/repos/${GITHUB_REPOSITORY}/commits/${GITHUB_SHA}"
+    RESPONSE=$(curl -s -H "Authorization: token ${GITHUB_TOKEN}" \
+      -H "Accept: application/vnd.github.v3.diff" "$API_URL" 2>/dev/null || echo "")
+
+    if [ -n "$RESPONSE" ]; then
+      CHANGED_FILES=$(echo "$RESPONSE" | grep '^diff --git' | sed 's|diff --git a/\(.*\) b/.*|\1|' || echo "")
+    fi
+
+    # 备用方案：获取 previous commit SHA 再查 API
+    if [ -z "$CHANGED_FILES" ]; then
+      PREV_SHA=$(curl -s -H "Authorization: token ${GITHUB_TOKEN}" \
+        "${GITHUB_API_URL}/repos/${GITHUB_REPOSITORY}/commits?sha=${GITHUB_SHA}&per_page=2" \
+        | python3 -c "import sys,json; commits=json.load(sys.stdin); print(commits[1]['sha'] if len(commits)>1 else '')" 2>/dev/null || echo "")
+      if [ -n "$PREV_SHA" ]; then
+        COMPARE_URL="${GITHUB_API_URL}/repos/${GITHUB_REPOSITORY}/compare/${PREV_SHA}...${GITHUB_SHA}"
+        CHANGED_FILES=$(curl -s -H "Authorization: token ${GITHUB_TOKEN}" "$COMPARE_URL" \
+          | python3 -c "import sys,json; data=json.load(sys.stdin); [print(f['filename']) for f in data.get('files',[]) if f['status'] != 'removed']" 2>/dev/null || echo "")
+      fi
+    fi
+  fi
+
   echo "改动文件数: $(echo "$CHANGED_FILES" | grep -c . || echo 0)"
-  set +e
-  CHANGED_FILES="$CHANGED_FILES" \
-  SELECTED_TESTS_OUTPUT=/tmp/selected_tests.txt \
-  python3 scripts/ci/select_unit_tests.py
-  SELECT_EXIT=$?
-  set -e
-  if [ $SELECT_EXIT -eq 0 ]; then
-    UNIT_TEST_MODE="incremental"
-    TEST_FILES=$(cat /tmp/selected_tests.txt | tr '\n' ' ')
-    SELECTED_TEST_FILES="$TEST_FILES"
-    echo "增量模式: $(cat /tmp/selected_tests.txt | wc -l) 个测试文件"
+
+  if [ -n "$CHANGED_FILES" ]; then
+    set +e
+    CHANGED_FILES="$CHANGED_FILES" \
+    SELECTED_TESTS_OUTPUT=/tmp/selected_tests.txt \
+    python3 scripts/ci/select_unit_tests.py
+    SELECT_EXIT=$?
+    set -e
+    if [ $SELECT_EXIT -eq 0 ]; then
+      UNIT_TEST_MODE="incremental"
+      TEST_FILES=$(cat /tmp/selected_tests.txt | tr '\n' ' ')
+      SELECTED_TEST_FILES="$TEST_FILES"
+      echo "增量模式: $(cat /tmp/selected_tests.txt | wc -l) 个测试文件"
+    else
+      echo "全量模式（增量选择失败）"
+    fi
   else
-    echo "全量模式"
+    echo "无法获取改动文件列表，使用全量模式"
   fi
 fi
 
@@ -116,7 +175,7 @@ if [ "${GITHUB_EVENT_NAME:-}" = "pull_request" ] && [ -n "${GITHUB_TOKEN:-}" ];
 
   PR_CODE_DIR="/tmp/pr-code-$$"
   mkdir -p "$PR_CODE_DIR"
-  # 备份PR代码（含coverage.xml，diff-cover需要用到
+  # 备份PR代码（含coverage.xml，diff-cover需要用到）
   find . -maxdepth 1 -mindepth 1 ! -name 'diff_coverage.html' -exec cp -r {} "$PR_CODE_DIR/" \;
   rm -rf .git
   git init > /dev/null 2>&1

scripts/ci_staging_deploy.sh

@@ -397,7 +397,7 @@ fi
 echo "Stopping old containers..."
 # 优雅关闭：先 stop（发 SIGTERM，等待），再 rm
 # Worker 需要更长时间（视频任务最长可能5分钟）
-docker stop -t 300 xiaoxia-worker-staging 2>/dev/null || true
+docker stop -t 120 xiaoxia-worker-staging 2>/dev/null || true
 docker stop -t 30 xiaoxia-api-staging 2>/dev/null || true
 docker stop -t 10 xiaoxia-web-staging 2>/dev/null || true
 docker rm xiaoxia-worker-staging xiaoxia-api-staging xiaoxia-web-staging 2>/dev/null || true

scripts/ci_staging_healthcheck.sh

@@ -187,7 +187,7 @@ health_check() {
             return 0
         fi
 
-        sleep 5
+        sleep 3
     done
 
     # 超时了

tests/unit/test_asset_availability.py

@@ -197,14 +197,14 @@ class TestComputeAssetAvailability:
 
     def test_large_gap_remains_usable(self):
         """区间之间留有 ≥3s 空闲段（扩边后仍 ≥3s）→ usable=True。"""
-        # [0,2] 扩边到 [0,2.3]，[5.3,10] 扩边前为 [5,10] 扩边起 4.7；空闲 [2.3,4.7]=2.4s <3
-        # 改用更大间隙：[0,2] 与 [6,10]，扩边后空闲 [2.3,5.7]=3.4s ≥3
+        # [0,2] 扩边到 [0,3.5]，[9,10] 扩边到 [7.5,10]；空闲 [3.5,7.5]=4.0s >=3
+        # 使用 [0,2] 与 [9,10]，扩边后空闲 [3.5,7.5]=4.0s ≥3 → usable
         info = compute_asset_availability(
             _make_asset(
                 duration=10.0,
                 ranges=[
                     _range(0.0, 2.0, use_count=MAX_RANGE_USE_COUNT),
-                    _range(6.0, 10.0, use_count=MAX_RANGE_USE_COUNT),
+                    _range(9.0, 10.0, use_count=MAX_RANGE_USE_COUNT),
                 ],
             )
         )
@@ -236,8 +236,8 @@ class TestComputeAssetAvailability:
         assert info["usable"] is True
 
     def test_segment_edge_gap_constant(self):
-        """边缘间隙常量为 0.3s（与 MediaKit 冲突检测同口径）。"""
-        assert SEGMENT_EDGE_GAP == 0.3
+        """边缘间隙常量为 1.5s（与 MediaKit 冲突检测同口径）。"""
+        assert SEGMENT_EDGE_GAP == 1.5
 
     def test_domain_entity_metadata_dict_form(self):
         """领域实体形态（metadata 为 dict，无 classification_result）也能读到区间。

tests/unit/test_duration_compensation.py

@@ -0,0 +1,97 @@
+"""Tests for duration compensation when source video is shorter than configured duration."""
+
+from pathlib import Path
+
+import pytest
+
+from apps.worker.video_processing.unified_render_service import ResolvedClip, UnifiedRenderService
+
+
+class TestClipEffectiveDurationWithSpeedCompensation:
+    """Test _clip_effective_duration handles speed < 1 correctly."""
+
+    def test_normal_speed_returns_min(self):
+        """When speed=1.0, effective_duration = min(duration, actual_duration)."""
+        clip = ResolvedClip(
+            clip_id="c1",
+            asset_id="a1",
+            local_path=Path("/tmp/fake.mp4"),
+            clip_type="main",
+            order=0,
+            start_time=0.0,
+            duration=4.0,
+            actual_duration=3.0,  # shorter than configured
+            playback_speed=1.0,
+        )
+        # Without speed compensation, effective = min(4, 3) = 3
+        assert UnifiedRenderService._clip_effective_duration(clip) == 3.0
+
+    def test_compensated_speed_returns_configured_duration(self):
+        """When speed < 1 (compensated), effective_duration = configured duration."""
+        clip = ResolvedClip(
+            clip_id="c1",
+            asset_id="a1",
+            local_path=Path("/tmp/fake.mp4"),
+            clip_type="main",
+            order=0,
+            start_time=0.0,
+            duration=4.0,
+            actual_duration=3.0,  # shorter than configured
+            playback_speed=0.75,  # compensated: 3/4 = 0.75
+        )
+        # With speed compensation, effective = configured duration = 4.0
+        assert UnifiedRenderService._clip_effective_duration(clip) == 4.0
+
+    def test_zero_actual_duration_returns_configured(self):
+        """When actual_duration=0, effective_duration = configured duration."""
+        clip = ResolvedClip(
+            clip_id="c1",
+            asset_id="a1",
+            local_path=Path("/tmp/fake.mp4"),
+            clip_type="main",
+            order=0,
+            start_time=0.0,
+            duration=4.0,
+            actual_duration=0.0,
+            playback_speed=1.0,
+        )
+        assert UnifiedRenderService._clip_effective_duration(clip) == 4.0
+
+    def test_compensated_speed_with_actual_zero(self):
+        """When speed < 1 and actual=0, still returns configured duration."""
+        clip = ResolvedClip(
+            clip_id="c1",
+            asset_id="a1",
+            local_path=Path("/tmp/fake.mp4"),
+            clip_type="main",
+            order=0,
+            start_time=0.0,
+            duration=4.0,
+            actual_duration=0.0,
+            playback_speed=0.5,
+        )
+        assert UnifiedRenderService._clip_effective_duration(clip) == 4.0
+
+
+class TestClipAdjustedDurationWithSpeedCompensation:
+    """Test _clip_adjusted_duration accounts for compensated speed."""
+
+    def test_adjusted_duration_with_compensation(self):
+        """Adjusted duration = min(duration, actual) / speed.
+        With compensation: min(4,3)/0.75 = 3/0.75 = 4.0
+        This equals the configured duration, which is the goal.
+        """
+        clip = ResolvedClip(
+            clip_id="c1",
+            asset_id="a1",
+            local_path=Path("/tmp/fake.mp4"),
+            clip_type="main",
+            order=0,
+            start_time=0.0,
+            duration=4.0,
+            actual_duration=3.0,
+            playback_speed=0.75,  # compensated
+        )
+        adjusted = UnifiedRenderService._clip_adjusted_duration(clip)
+        # min(4,3)/0.75 = 3/0.75 = 4.0 (matches configured duration)
+        assert abs(adjusted - 4.0) < 0.01

tests/unit/test_editor_clips_random_start.py

@@ -38,6 +38,28 @@ def _segments(count: int, dur_min: float = 3.0, dur_max: float = 5.0):
     return [(i, dur_min, dur_max) for i in range(count)]
 
 
+def _patch_zero_noise():
+    """消除 clips.py 排序随机噪声，用于确定性断言（如均衡分配）。
+
+    排序噪声（random.uniform(0, SCORE_RANDOM_NOISE_MAX)）返回 0；
+    其他 uniform 调用（片段时长随机）委托给独立 Random 实例，行为不变。
+    """
+    import random as _stdlib_random
+
+    from app.api.routes.templates_editor import clips as clips_module
+
+    from packages.domain.smart_match import SCORE_RANDOM_NOISE_MAX
+
+    _fallback = _stdlib_random.Random()
+
+    def _fake_uniform(a, b):
+        if b == SCORE_RANDOM_NOISE_MAX:
+            return 0.0
+        return _fallback.uniform(a, b)
+
+    return patch.object(clips_module.random, "uniform", _fake_uniform)
+
+
 def _patch_segments(segments=None):
     return patch(
         "app.api.routes.templates_editor.clips._get_template_segments",
@@ -58,6 +80,10 @@ def _make_mock_asset(asset_id, duration):
     asset = MagicMock()
     asset.id = asset_id
     asset.duration = duration
+    # score_asset 所需的属性（避免 MagicMock 导致类型比较错误）
+    asset.quality_score = None
+    asset.created_at = None
+    asset.metadata = {}
     return asset
 
 
@@ -126,7 +152,8 @@ class TestEditorClipsBySegments:
 
         body = ClipsFromAssetsRequest(asset_ids=["a1", "a2"], required_clips_count=2)
 
-        with _patch_segments(DEFAULT_SEGMENTS):
+        # 均衡分配由 use_count 贪心保证，消除排序噪声后确定性断言
+        with _patch_zero_noise(), _patch_segments(DEFAULT_SEGMENTS):
             result = create_clips_from_assets_editor(
                 template_id="tpl-001",
                 body=body,
@@ -414,8 +441,13 @@ class TestEditorClipsDurationAndStartTime:
 
         assert mock_calc.call_count == 2
         clips_data = _get_clips_data_from_call(mock_plan_svc)
-        assert clips_data[0]["start_time"] == 12.5
-        assert clips_data[1]["start_time"] == 18.0
+        # clips_data 按 order 排序，但分配顺序因 shuffle 而随机，
+        # 因此只验证两个 start_time 值都存在
+        start_times = {c["start_time"] for c in clips_data}
+        assert start_times == {12.5, 18.0}
+        # 验证 order 仍然有序
+        orders = [c["order"] for c in clips_data]
+        assert orders == sorted(orders)
 
     @patch("app.api.routes.templates_editor.clips.get_storage_service")
     def test_asset_durations_deduped(self, mock_storage):
@@ -632,7 +664,8 @@ class TestReuseRatioGate:
         clips_data = _get_clips_data_from_call(mock_plan_svc)
         assert len(clips_data) == 13
         # 1 个复用片段，占比 1/13 ≈ 7.7% ≤ 15%
-        assert reused.get("a1", 0.0) == 5.0
+        # 转场补偿: raw_duration = 5.0 + (13-1)*0.5/13 ≈ 5.462 → round(5.462,1) = 5.5
+        assert abs(reused.get("a1", 0.0) - 5.5) < 0.1
 
     @patch("app.api.routes.templates_editor.clips.get_storage_service")
     def test_reuse_ratio_exceeded_returns_400(self, mock_storage):
@@ -785,7 +818,8 @@ class TestClipsFromAssetsInvalidIds:
 
         body = ClipsFromAssetsRequest(asset_ids=["a1", None, "", "a2"])  # type: ignore[list-item]
 
-        with _patch_segments(_segments(2, dur_min=3.0, dur_max=5.0)):
+        # 消除排序噪声，确定性断言两条合法素材各被使用
+        with _patch_zero_noise(), _patch_segments(_segments(2, dur_min=3.0, dur_max=5.0)):
             result = create_clips_from_assets_editor(
                 template_id="tpl-001",
                 body=body,

tests/unit/test_mediakit_conflicts.py

@@ -32,16 +32,16 @@ class TestRecommendedTimeConflicts:
         assert _recommended_time_conflicts(10.0, 5.0, [(10.0, 15.0)]) is True
 
     def test_touching_endpoint_conflicts_due_to_edge_gap(self):
-        """首尾紧贴（推荐 15 开始，已用 [10,15]）：0.3s 扩边内 → 冲突。"""
+        """首尾紧贴（推荐 15 开始，已用 [10,15]）：1.5s 扩边内 → 冲突。"""
         assert _recommended_time_conflicts(15.0, 5.0, [(10.0, 15.0)]) is True
 
     def test_gap_within_edge_gap_conflicts(self):
-        """间隔 0.2s（< 0.3s 边缘间隙）→ 冲突。"""
+        """间隔 0.2s（< 1.5s 边缘间隙）→ 冲突。"""
         assert _recommended_time_conflicts(15.2, 5.0, [(10.0, 15.0)]) is True
 
     def test_gap_beyond_edge_gap_no_conflict(self):
-        """间隔 0.5s（> 0.3s 边缘间隙）→ 不冲突。"""
-        assert _recommended_time_conflicts(15.5, 5.0, [(10.0, 15.0)]) is False
+        """间隔 2.0s（> 1.5s 边缘间隙）→ 不冲突。"""
+        assert _recommended_time_conflicts(17.0, 5.0, [(10.0, 15.0)]) is False
 
     def test_far_apart_no_conflict(self):
         """相隔很远 → 不冲突。"""
@@ -54,7 +54,9 @@ class TestRecommendedTimeConflicts:
         """多个已用区间，任一冲突即返回 True。"""
         used = [(0.0, 5.0), (10.0, 15.0), (20.0, 25.0)]
         assert _recommended_time_conflicts(12.0, 2.0, used) is True
-        assert _recommended_time_conflicts(6.0, 2.0, used) is False
+        assert (
+            _recommended_time_conflicts(6.5, 2.0, used) is False
+        )  # range [6.5,8.5], just outside all expanded used ranges
 
     def test_custom_edge_gap(self):
         """edge_gap 可配置：gap=0 时紧贴不冲突（端点相接不算重叠）。"""
@@ -64,5 +66,5 @@ class TestRecommendedTimeConflicts:
         assert _recommended_time_conflicts(15.5, 5.0, [(10.0, 15.0)], edge_gap=1.0) is True
 
     def test_default_edge_gap_constant(self):
-        """默认边缘间隙常量为 0.3s（配置常量）。"""
-        assert SEGMENT_EDGE_GAP == 0.3
+        """默认边缘间隙常量为 1.5s（配置常量）。"""
+        assert SEGMENT_EDGE_GAP == 1.5

tests/unit/test_mediakit_smart_clips.py

@@ -306,6 +306,27 @@ class TestGetTemplateSegments:
 # ── from-assets 端点集成测试 ────────────────────────────────────────────────
 
 
+def _patch_zero_noise():
+    """消除 clips.py 排序随机噪声（SCORE_RANDOM_NOISE_MAX 噪声返回 0）。
+
+    用于均衡分配等确定性断言；其他 uniform 调用（片段时长随机）行为不变。
+    """
+    import random as _stdlib_random
+
+    from app.api.routes.templates_editor import clips as clips_module
+
+    from packages.domain.smart_match import SCORE_RANDOM_NOISE_MAX
+
+    _fallback = _stdlib_random.Random()
+
+    def _fake_uniform(a, b):
+        if b == SCORE_RANDOM_NOISE_MAX:
+            return 0.0
+        return _fallback.uniform(a, b)
+
+    return patch.object(clips_module.random, "uniform", _fake_uniform)
+
+
 def _make_auth_user():
     auth = MagicMock()
     auth.user.id = "user-001"
@@ -347,6 +368,10 @@ def _make_rich_asset(asset_id, duration, storage_key="v.mp4", mime="video/mp4"):
     asset.duration = duration
     asset.storage_key = storage_key
     asset.mime_type = mime
+    # score_asset 所需的属性（避免 MagicMock 导致类型比较错误）
+    asset.quality_score = None
+    asset.created_at = None
+    asset.metadata = {}
     return asset
 
 
@@ -442,8 +467,12 @@ class TestFromAssetsByTemplateSegments:
         )
 
         clips_data = _get_clips_data(mock_plan_svc)
-        assert 3.0 <= clips_data[0]["duration"] <= 5.0
-        assert 4.0 <= clips_data[1]["duration"] <= 8.0
+        # PR #1614 转场补偿：2 个片段时每 clip 时长 +(2-1)*0.5/2=0.25s
+        # （xfade 重叠在渲染时扣除，故 clip 时长 = segment 随机时长 + 补偿），
+        # 断言上界需计入补偿与一位小数舍入余量
+        comp = (2 - 1) * 0.5 / 2
+        assert 3.0 + comp - 0.1 <= clips_data[0]["duration"] <= 5.0 + comp + 0.1
+        assert 4.0 + comp - 0.1 <= clips_data[1]["duration"] <= 8.0 + comp + 0.1
 
     def test_assets_balanced_assignment(self):
         """素材按使用次数贪心分配（使用少的优先），保证均衡使用。"""
@@ -462,16 +491,18 @@ class TestFromAssetsByTemplateSegments:
         mock_asset_repo.get.side_effect = get_asset
 
         body = ClipsFromAssetsRequest(asset_ids=["a1", "a2"])
-        create_clips_from_assets_editor(
-            template_id="tmpl-1",
-            body=body,
-            background_tasks=MagicMock(),
-            plan_id="plan-1",
-            services=(mock_tpl_svc, mock_plan_svc),
-            asset_repo=mock_asset_repo,
-            db=MagicMock(),
-            current_user=_make_auth_user(),
-        )
+        # 消除排序噪声，确定性断言贪心均衡分配
+        with _patch_zero_noise():
+            create_clips_from_assets_editor(
+                template_id="tmpl-1",
+                body=body,
+                background_tasks=MagicMock(),
+                plan_id="plan-1",
+                services=(mock_tpl_svc, mock_plan_svc),
+                asset_repo=mock_asset_repo,
+                db=MagicMock(),
+                current_user=_make_auth_user(),
+            )
 
         clips_data = _get_clips_data(mock_plan_svc)
         asset_ids = [c["asset_id"] for c in clips_data]

tests/unit/test_plan_generator.py

@@ -908,6 +908,10 @@ class TestAssetDurationsAlwaysFetched:
         def fake_get(asset_id):
             mock_asset = MagicMock()
             mock_asset.duration = 30.0  # 每个素材 30 秒
+            # score_asset 所需的属性
+            mock_asset.quality_score = None
+            mock_asset.created_at = None
+            mock_asset.metadata = {}
             return mock_asset
 
         asset_repo.get = MagicMock(side_effect=fake_get)

tests/unit/test_scene_change_shuffle.py

@@ -0,0 +1,257 @@
+"""Tests for scene-change smart frame selection + random shuffle of segment processing."""
+
+from __future__ import annotations
+
+import random
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from apps.api.app.api.routes.templates_editor.clips import (
+    _build_scene_segments,
+    _pick_start_in_scene_segment,
+)
+from packages.shared.mediakit_client import MediaKitClient
+
+# ── Part 1: Random shuffle tests ──────────────────────────────────────────
+
+
+class TestRandomShuffle:
+    """验证 segments 处理顺序随机打乱逻辑."""
+
+    def test_same_segments_produce_different_asset_orders(self):
+        """同一批 segments 多次处理，asset 分配顺序有变化.
+
+        模拟打乱后的处理顺序，验证多次运行中 asset_id 分配顺序
+        存在差异（概率性验证，运行 50 次应该至少出现 2 种排列）。
+        """
+        segments = [(0, 3.0, 5.0), (1, 4.0, 6.0), (2, 3.0, 5.0), (3, 4.0, 6.0)]
+        asset_ids = ["A", "B", "C", "D"]
+
+        observed_orders: list[tuple] = set()
+
+        for _ in range(50):
+            shuffled_indices = list(range(len(segments)))
+            random.shuffle(shuffled_indices)
+            order_tuple = tuple(shuffled_indices)
+            observed_orders.add(order_tuple)
+
+        # 50 次打乱，4! = 24 种排列，应出现多种不同排列
+        assert len(observed_orders) > 1, "打乱应该产生多种不同顺序"
+
+    def test_clips_data_order_always_sorted(self):
+        """clips_data 按 order 排序后始终有序.
+
+        模拟打乱处理后 clips_data 按 order 排序，验证最终 order 为 [0,1,2,3]。
+        """
+        segments = [(0, 3.0, 5.0), (1, 4.0, 6.0), (2, 3.0, 5.0), (3, 4.0, 6.0)]
+
+        for _ in range(20):
+            shuffled_indices = list(range(len(segments)))
+            random.shuffle(shuffled_indices)
+
+            # 模拟构建 clips_data（用 _seg_order 作为 order）
+            clips_data = []
+            for idx in shuffled_indices:
+                seg_order, _, _ = segments[idx]
+                clips_data.append({"order": seg_order, "asset_id": f"asset_{idx}"})
+
+            # 按 order 排序
+            clips_data.sort(key=lambda c: c["order"])
+
+            # 验证 order 始终有序
+            orders = [c["order"] for c in clips_data]
+            assert orders == [0, 1, 2, 3], f"排序后 order 应为 [0,1,2,3]，实际为 {orders}"
+
+
+# ── Part 2: detect_scene_changes tests ────────────────────────────────────
+
+
+class TestDetectSceneChanges:
+    """验证 MediaKitClient.detect_scene_changes 方法."""
+
+    def _make_client(self) -> MediaKitClient:
+        """创建一个可用的 MediaKitClient（mock 配置）."""
+        with patch("packages.shared.mediakit_client.get_shared_settings") as mock_settings:
+            mock_settings.return_value.mediakit_api_key = "test-key"
+            mock_settings.return_value.mediakit_base_url = "http://test"
+            mock_settings.return_value.mediakit_timeout = 30
+            client = MediaKitClient()
+        return client
+
+    def test_scene_change_success(self):
+        """SceneChange 策略成功返回时间戳列表."""
+        client = self._make_client()
+
+        mock_frames = [
+            {"image_url": "url1", "timestamp": 0.0},
+            {"image_url": "url2", "timestamp": 3.2},
+            {"image_url": "url3", "timestamp": 7.8},
+            {"image_url": "url4", "timestamp": 12.5},
+        ]
+
+        with patch.object(client, "extract_frames", return_value=mock_frames):
+            result = client.detect_scene_changes("https://example.com/video.mp4")
+
+        assert result is not None
+        assert result[0] == 0.0  # 始终以 0.0 开头
+        assert 3.2 in result
+        assert 7.8 in result
+        assert 12.5 in result
+        assert result == sorted(result)  # 应已排序
+
+    def test_scene_change_fallback_to_time_interval(self):
+        """SceneChange 失败降级到 TimeInterval 策略."""
+        client = self._make_client()
+
+        # 第一次调用（SceneChange）返回 None，第二次（TimeInterval）返回结果
+        fallback_frames = [
+            {"image_url": "url1", "timestamp": 0.0},
+            {"image_url": "url2", "timestamp": 5.0},
+            {"image_url": "url3", "timestamp": 10.0},
+        ]
+
+        call_count = 0
+
+        def side_effect(*args, **kwargs):
+            nonlocal call_count
+            call_count += 1
+            if call_count == 1:
+                # 第一次 SceneChange 失败
+                return None
+            else:
+                # 第二次 TimeInterval 成功
+                assert kwargs.get("strategy") == "TimeInterval"
+                return fallback_frames
+
+        with patch.object(client, "extract_frames", side_effect=side_effect):
+            result = client.detect_scene_changes("https://example.com/video.mp4")
+
+        assert result is not None
+        assert result[0] == 0.0
+        assert 5.0 in result
+        assert 10.0 in result
+
+    def test_mediakit_not_available_returns_none(self):
+        """MediaKit 不可用时返回 None."""
+        with patch("packages.shared.mediakit_client.get_shared_settings") as mock_settings:
+            mock_settings.return_value.mediakit_api_key = ""  # 未配置
+            mock_settings.return_value.mediakit_base_url = "http://test"
+            mock_settings.return_value.mediakit_timeout = 30
+            client = MediaKitClient()
+
+        result = client.detect_scene_changes("https://example.com/video.mp4")
+        assert result is None
+
+    def test_both_strategies_fail_returns_none(self):
+        """SceneChange 和 TimeInterval 都失败时返回 None."""
+        client = self._make_client()
+
+        with patch.object(client, "extract_frames", return_value=None):
+            result = client.detect_scene_changes("https://example.com/video.mp4")
+
+        assert result is None
+
+    def test_prepends_zero_if_not_present(self):
+        """若帧列表中不包含 0.0，自动在开头添加."""
+        client = self._make_client()
+
+        # 帧列表中没有 timestamp=0.0
+        mock_frames = [
+            {"image_url": "url1", "timestamp": 2.0},
+            {"image_url": "url2", "timestamp": 5.5},
+        ]
+
+        with patch.object(client, "extract_frames", return_value=mock_frames):
+            result = client.detect_scene_changes("https://example.com/video.mp4")
+
+        assert result is not None
+        assert result[0] == 0.0
+        assert 2.0 in result
+        assert 5.5 in result
+
+
+# ── Part 2.2: Scene segment building and assignment ───────────────────────
+
+
+class TestSceneSegments:
+    """验证镜头段构建和分配逻辑."""
+
+    def test_build_scene_segments(self):
+        """从场景切换点正确构建镜头段."""
+        scene_changes = [0.0, 3.2, 7.8, 12.5]
+        asset_duration = 15.0
+
+        segments = _build_scene_segments(scene_changes, asset_duration)
+
+        assert len(segments) == 4
+        assert segments[0] == (0.0, 3.2)
+        assert segments[1] == (3.2, 7.8)
+        assert segments[2] == (7.8, 12.5)
+        assert segments[3] == (12.5, 15.0)
+
+    def test_build_scene_segments_filters_short(self):
+        """过滤掉过短的镜头段（< 0.5秒）."""
+        scene_changes = [0.0, 0.1, 5.0, 5.3, 10.0]
+        asset_duration = 12.0
+
+        segments = _build_scene_segments(scene_changes, asset_duration)
+
+        # (0.0, 0.1) 长度 0.1 < 0.5 → 过滤
+        # (0.1, 5.0) → 保留
+        # (5.0, 5.3) 长度 0.3 < 0.5 → 过滤
+        # (5.3, 10.0) → 保留
+        # (10.0, 12.0) → 保留
+        assert len(segments) == 3
+        assert segments[0] == (0.1, 5.0)
+        assert segments[1] == (5.3, 10.0)
+        assert segments[2] == (10.0, 12.0)
+
+    def test_pick_start_in_segment(self):
+        """在镜头段内随机选取起始时间."""
+        seg_start = 3.0
+        seg_end = 8.0
+        clip_duration = 2.0
+
+        starts = set()
+        for _ in range(100):
+            start = _pick_start_in_scene_segment(seg_start, seg_end, clip_duration)
+            assert start is not None
+            assert seg_start <= start <= seg_end - clip_duration
+            starts.add(round(start, 2))
+
+        # 应该有多个不同的起始时间
+        assert len(starts) > 1
+
+    def test_pick_start_segment_too_short(self):
+        """镜头段太短无法容纳片段时返回 None."""
+        result = _pick_start_in_scene_segment(0.0, 1.0, 2.0)
+        assert result is None
+
+    def test_different_clips_from_different_scenes(self):
+        """不同片段应来自不同的镜头段（模拟分配逻辑）."""
+        scene_changes = [0.0, 5.0, 10.0, 15.0]
+        asset_duration = 18.0
+        clip_duration = 3.0
+
+        segments = _build_scene_segments(scene_changes, asset_duration)
+        assert len(segments) == 4  # (0,5), (5,10), (10,15), (15,18)
+
+        # 模拟 3 个片段从不同镜头段取点
+        scene_pool = list(segments)
+        assigned_starts = []
+
+        for _ in range(3):
+            if not scene_pool:
+                break
+            seg_start, seg_end = scene_pool.pop(0)
+            start = _pick_start_in_scene_segment(seg_start, seg_end, clip_duration)
+            assert start is not None
+            assigned_starts.append(start)
+
+        # 3 个片段分别从 3 个不同镜头段中选取
+        assert len(assigned_starts) == 3
+        # 第一个来自 [0, 2]，第二个来自 [5, 7]，第三个来自 [10, 12]
+        assert 0.0 <= assigned_starts[0] <= 2.0
+        assert 5.0 <= assigned_starts[1] <= 7.0
+        assert 10.0 <= assigned_starts[2] <= 12.0

tests/unit/test_smart_match_integration.py

@@ -0,0 +1,475 @@
+"""测试 smart_match 评分集成到素材选取路径。
+
+验证：
+- 使用次数多的素材评分低于使用次数少的（unused 维度降权生效）
+- from-assets 路径中 sorted_candidates 按 smart_match 评分排序
+- 一键生成路径中 _sort_assets_by_smart_score 按评分降序
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+from unittest.mock import MagicMock, patch
+
+os.environ.setdefault("JWT_SECRET_KEY", "unit-test-secret-key-for-testing")
+os.environ.setdefault("DATABASE_URL", "sqlite:///test.db")
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "apps" / "api"))
+
+import pytest
+
+from packages.domain.smart_match import SCORE_RANDOM_NOISE_MAX, score_asset, smart_select_assets
+
+# ── 辅助工厂 ──────────────────────────────────────────────────────────────────
+
+
+@dataclass
+class FakeAsset:
+    """Minimal Asset-like object."""
+
+    id: str
+    quality_score: float | None = 70.0
+    duration: float = 15.0
+    created_at: datetime | None = None
+    metadata: dict[str, Any] = field(default_factory=dict)
+    status: str = "ready"
+    file_type: str = "video"
+
+
+def _asset_with_use_count(asset_id: str, use_count: int) -> FakeAsset:
+    """创建指定使用次数的素材，其他维度保持一致。"""
+    return FakeAsset(
+        id=asset_id,
+        quality_score=70.0,
+        duration=15.0,  # 最优区间 5-30s
+        created_at=datetime(2026, 1, 1, tzinfo=timezone.utc),
+        metadata={"generation_use_count": use_count},
+    )
+
+
+# ── score_asset 单元测试：unused 维度降权 ─────────────────────────────────────
+
+
+class TestScoreAssetUnusedDiminsh:
+    """验证 unused 维度：使用次数越多，评分越低。"""
+
+    def test_unused_scores_higher_than_used(self):
+        """use_count=0 的素材评分高于 use_count>0 的。"""
+        fresh = _asset_with_use_count("fresh", 0)
+        used = _asset_with_use_count("used", 1)
+        fresh_score, _ = score_asset(fresh)
+        used_score, _ = score_asset(used)
+        assert fresh_score > used_score
+
+    def test_high_use_count_scores_lower_than_low(self):
+        """use_count=5 的素材评分低于 use_count=1 的。"""
+        low_use = _asset_with_use_count("low", 1)
+        high_use = _asset_with_use_count("high", 5)
+        low_score, _ = score_asset(low_use)
+        high_score, _ = score_asset(high_use)
+        assert low_score > high_score
+
+    def test_unused_breakdown_values(self):
+        """验证 unused 维度的具体分值。"""
+        fresh = _asset_with_use_count("fresh", 0)
+        low = _asset_with_use_count("low", 2)
+        high = _asset_with_use_count("high", 10)
+
+        _, fresh_bd = score_asset(fresh)
+        _, low_bd = score_asset(low)
+        _, high_bd = score_asset(high)
+
+        # use_count=0 → unused_score=100 → component=10.0
+        assert fresh_bd["unused"] == 10.0
+        # use_count=2 → unused_score=70 → component=7.0
+        assert low_bd["unused"] == 7.0
+        # use_count=10 → unused_score=30 → component=3.0
+        assert high_bd["unused"] == 3.0
+
+    def test_monotonically_decreasing_scores(self):
+        """使用次数递增时，总评分单调不增。"""
+        scores = []
+        for count in [0, 1, 2, 3, 5, 10, 50]:
+            a = _asset_with_use_count(f"a{count}", count)
+            s, _ = score_asset(a)
+            scores.append(s)
+        # 验证非递增
+        for i in range(len(scores) - 1):
+            assert (
+                scores[i] >= scores[i + 1]
+            ), f"use_count 递增时评分应不增: scores[{i}]={scores[i]} < scores[{i+1}]={scores[i+1]}"
+
+
+# ── smart_select_assets 排序测试 ─────────────────────────────────────────────
+
+
+class TestSmartSelectAssetsOrdering:
+    """验证 smart_select_assets 返回结果按评分降序。"""
+
+    def test_less_used_assets_ranked_higher(self):
+        """使用次数少的素材在结果中排名更高。"""
+        assets = [
+            _asset_with_use_count("heavily_used", 10),
+            _asset_with_use_count("never_used", 0),
+            _asset_with_use_count("lightly_used", 2),
+        ]
+        results = smart_select_assets(assets)
+        ids = [r.asset.id for r in results]
+        # never_used 排第一，heavily_used 排最后
+        assert ids[0] == "never_used"
+        assert ids[-1] == "heavily_used"
+
+    def test_same_quality_different_use_count(self):
+        """质量相同时，使用次数少的排名更高。"""
+        assets = [
+            _asset_with_use_count("used_5", 5),
+            _asset_with_use_count("used_0", 0),
+        ]
+        results = smart_select_assets(assets)
+        assert results[0].asset.id == "used_0"
+        assert results[1].asset.id == "used_5"
+
+
+# ── from-assets 路径集成测试 ─────────────────────────────────────────────────
+
+
+def _make_mock_asset_for_clips(aid, duration, use_count=0):
+    """创建带 score_asset 所需属性的 mock 素材。"""
+    asset = MagicMock()
+    asset.id = aid
+    asset.duration = duration
+    asset.quality_score = None
+    asset.created_at = None
+    asset.metadata = {"generation_use_count": use_count}
+    return asset
+
+
+def _make_auth_user():
+    auth = MagicMock()
+    auth.user.id = "user-001"
+    auth.user.email = "test@example.com"
+    auth.user.display_name = "测试用户"
+    auth.user_id = "user-001"
+    return auth
+
+
+def _make_zero_noise_patcher(module):
+    """构造 patch(module.random.uniform)：噪声调用(上界=SCORE_RANDOM_NOISE_MAX)返回 0。
+
+    其他 uniform 调用（如片段时长随机）委托给一个独立的 Random 实例，
+    避免递归回已 patch 的全局函数。
+    """
+    import random as _stdlib_random
+
+    _fallback = _stdlib_random.Random()
+
+    def _fake_uniform(a, b):
+        if b == SCORE_RANDOM_NOISE_MAX:
+            return 0.0
+        return _fallback.uniform(a, b)
+
+    return patch.object(module.random, "uniform", _fake_uniform)
+
+
+def _patch_zero_noise_clips():
+    """消除 clips.py 排序噪声，其他 uniform 调用不受影响。"""
+    from app.api.routes.templates_editor import clips as clips_module
+
+    return _make_zero_noise_patcher(clips_module)
+
+
+def _patch_zero_noise_plan_service():
+    """消除 plan_generator_service.py 排序噪声，其他 uniform 调用不受影响。"""
+    from app.services import plan_generator_service as svc_module
+
+    return _make_zero_noise_patcher(svc_module)
+
+
+class TestFromAssetsSmartMatchIntegration:
+    """验证 clips.py 中 sorted_candidates 使用 smart_match 评分。"""
+
+    def test_sorted_candidates_prefers_high_score_low_use_count(self):
+        """在 from-assets 路径中，smart_match 分高且使用次数少的素材排在前面。"""
+        from app.api.routes.templates_editor.clips import create_clips_from_assets_editor
+        from app.api.routes.templates_editor.schemas import ClipsFromAssetsRequest
+
+        mock_asset_repo = MagicMock()
+
+        def _get_asset(aid):
+            use_count = {"a_heavy": 10, "a_fresh": 0}[aid]
+            return _make_mock_asset_for_clips(aid, 30.0, use_count)
+
+        mock_asset_repo.get = MagicMock(side_effect=_get_asset)
+
+        mock_plan_svc = MagicMock()
+        mock_plan_svc.replace_all_clips_transactional = MagicMock(return_value=2)
+
+        segments = [(0, 3.0, 5.0), (1, 3.0, 5.0)]
+
+        with (
+            _patch_zero_noise_clips(),
+            patch(
+                "app.api.routes.templates_editor.clips._get_template_segments",
+                return_value=segments,
+            ),
+            patch(
+                "app.api.routes.templates_editor.clips.get_used_segments",
+                return_value={},
+            ),
+            patch(
+                "app.api.routes.templates_editor.clips.record_used_segments",
+                return_value=None,
+            ),
+        ):
+            body = ClipsFromAssetsRequest(
+                asset_ids=["a_heavy", "a_fresh"],
+                required_clips_count=2,
+            )
+            create_clips_from_assets_editor(
+                template_id="tmpl-1",
+                body=body,
+                background_tasks=MagicMock(),
+                plan_id="test-plan-001",
+                services=(MagicMock(), mock_plan_svc),
+                asset_repo=mock_asset_repo,
+                db=MagicMock(),
+                current_user=_make_auth_user(),
+            )
+
+        # 验证 replace_all_clips_transactional 被调用
+        assert mock_plan_svc.replace_all_clips_transactional.called
+        call_args = mock_plan_svc.replace_all_clips_transactional.call_args
+        clips_data = call_args.args[1]
+
+        # 第一个片段应该分配给 a_fresh（smart_match 分更高）
+        first_clip_asset = clips_data[0]["asset_id"]
+        assert (
+            first_clip_asset == "a_fresh"
+        ), f"第一个片段应分配给 smart_match 分更高的 a_fresh，实际是 {first_clip_asset}"
+
+    def test_score_noise_causes_varied_selection(self):
+        """得分接近（差距 < SCORE_RANDOM_NOISE_MAX）的素材，多次生成的素材组合应有变化。
+
+        两条同等质量/时长/新鲜度的素材（use_count 相同），smart_match 得分一致，
+        噪声让两者的相对排名随机浮动，多次调用首个片段的素材分布应两者都出现。
+        """
+        from app.api.routes.templates_editor.clips import create_clips_from_assets_editor
+        from app.api.routes.templates_editor.schemas import ClipsFromAssetsRequest
+
+        def _get_asset(aid):
+            return _make_mock_asset_for_clips(aid, 30.0, 0)
+
+        mock_asset_repo = MagicMock()
+        mock_asset_repo.get = MagicMock(side_effect=_get_asset)
+
+        segments = [(0, 3.0, 5.0), (1, 3.0, 5.0)]
+
+        first_assets: set[str] = set()
+        for _ in range(30):
+            mock_plan_svc = MagicMock()
+            mock_plan_svc.replace_all_clips_transactional = MagicMock(return_value=2)
+            with (
+                patch(
+                    "app.api.routes.templates_editor.clips._get_template_segments",
+                    return_value=segments,
+                ),
+                patch(
+                    "app.api.routes.templates_editor.clips.get_used_segments",
+                    return_value={},
+                ),
+                patch(
+                    "app.api.routes.templates_editor.clips.record_used_segments",
+                    return_value=None,
+                ),
+            ):
+                body = ClipsFromAssetsRequest(
+                    asset_ids=["a_x", "a_y"],
+                    required_clips_count=2,
+                )
+                create_clips_from_assets_editor(
+                    template_id="tmpl-1",
+                    body=body,
+                    background_tasks=MagicMock(),
+                    plan_id=f"plan-noise-{len(first_assets)}-{_}",
+                    services=(MagicMock(), mock_plan_svc),
+                    asset_repo=mock_asset_repo,
+                    db=MagicMock(),
+                    current_user=_make_auth_user(),
+                )
+            clips_data = mock_plan_svc.replace_all_clips_transactional.call_args.args[1]
+            first_assets.add(clips_data[0]["asset_id"])
+
+        assert first_assets == {
+            "a_x",
+            "a_y",
+        }, f"噪声应使两条等分素材的排名浮动，30 次调用首个片段应覆盖两者，实际 {first_assets}"
+
+
+# ── 一键生成路径集成测试 ─────────────────────────────────────────────────────
+
+
+class TestPlanGeneratorSmartMatchIntegration:
+    """验证 PlanGeneratorService._sort_assets_by_smart_score 排序正确。"""
+
+    def test_sort_assets_by_smart_score_descending(self):
+        """_sort_assets_by_smart_score 返回按评分降序排列的素材 ID。"""
+        from app.services.plan_generator_service import PlanGeneratorService
+
+        mock_asset_repo = MagicMock()
+
+        def _get_asset(aid):
+            use_count = {"high_use": 10, "low_use": 0, "mid_use": 3}[aid]
+            asset = MagicMock()
+            asset.id = aid
+            asset.duration = 15.0
+            asset.quality_score = None
+            asset.created_at = None
+            asset.metadata = {"generation_use_count": use_count}
+            return asset
+
+        mock_asset_repo.get = MagicMock(side_effect=_get_asset)
+
+        db = MagicMock()
+        svc = PlanGeneratorService(db, asset_repo=mock_asset_repo)
+
+        with _patch_zero_noise_plan_service():
+            sorted_ids = svc._sort_assets_by_smart_score(["high_use", "low_use", "mid_use"])
+
+        # low_use (0次) 应排第一，high_use (10次) 应排最后
+        assert sorted_ids[0] == "low_use"
+        assert sorted_ids[-1] == "high_use"
+        assert sorted_ids[1] == "mid_use"
+
+    def test_distribute_assets_uses_smart_score_ordering(self):
+        """_distribute_assets 在非随机模式下按 smart_match 评分排序素材。"""
+        from app.services.plan_generator_service import PlanGeneratorService
+
+        from packages.domain.edit_plan_clip import EditPlanClip
+        from packages.domain.editing_mode import EditingMode
+
+        mock_asset_repo = MagicMock()
+
+        def _get_asset(aid):
+            use_count = {"old_asset": 10, "new_asset": 0}[aid]
+            asset = MagicMock()
+            asset.id = aid
+            asset.duration = 30.0
+            asset.quality_score = None
+            asset.created_at = None
+            asset.metadata = {"generation_use_count": use_count}
+            return asset
+
+        mock_asset_repo.get = MagicMock(side_effect=_get_asset)
+
+        db = MagicMock()
+        svc = PlanGeneratorService(db, asset_repo=mock_asset_repo)
+
+        # 创建 2 个 main clips（需要提供 id 参数）
+        clips = [
+            EditPlanClip(id="c1", plan_id="p1", clip_type="main", duration=5.0, order=0),
+            EditPlanClip(id="c2", plan_id="p1", clip_type="main", duration=5.0, order=1),
+        ]
+
+        with (
+            _patch_zero_noise_plan_service(),
+            patch("app.services.plan_generator_service.distribute_assets") as mock_dist,
+        ):
+            svc._distribute_assets(
+                clips,
+                ["old_asset", "new_asset"],
+                EditingMode.ONE_TAKE.value,
+                random_selection=False,
+            )
+            # 验证传给 distribute_assets 的 asset_ids 按 smart_match 排序
+            call_args = mock_dist.call_args
+            passed_ids = call_args.args[1]
+            # new_asset (0次使用) 应排在 old_asset (10次使用) 前面
+            assert passed_ids[0] == "new_asset"
+            assert passed_ids[1] == "old_asset"
+
+    def test_random_selection_skips_smart_score_sort(self):
+        """random_selection=True 时不执行 smart_match 排序。"""
+        from app.services.plan_generator_service import PlanGeneratorService
+
+        from packages.domain.edit_plan_clip import EditPlanClip
+        from packages.domain.editing_mode import EditingMode
+
+        mock_asset_repo = MagicMock()
+        db = MagicMock()
+        svc = PlanGeneratorService(db, asset_repo=mock_asset_repo)
+
+        clips = [
+            EditPlanClip(id="c1", plan_id="p1", clip_type="main", duration=5.0, order=0),
+        ]
+
+        with patch("app.services.plan_generator_service.distribute_assets") as mock_dist:
+            svc._distribute_assets(
+                clips,
+                ["a1", "a2"],
+                EditingMode.ONE_TAKE.value,
+                random_selection=True,
+            )
+            # random_selection=True 时不应调用 asset_repo.get（不执行排序）
+            mock_asset_repo.get.assert_not_called()
+
+
+class TestPlanGeneratorScoreNoise:
+    """验证一键生成路径的评分排序注入了随机噪声。"""
+
+    def test_equal_scores_produce_varied_order(self):
+        """两条 smart_match 得分相同的素材，多次排序的首位应覆盖两者。"""
+        from app.services.plan_generator_service import PlanGeneratorService
+
+        def _get_asset(aid):
+            asset = MagicMock()
+            asset.id = aid
+            asset.duration = 15.0
+            asset.quality_score = None
+            asset.created_at = None
+            asset.metadata = {"generation_use_count": 0}
+            return asset
+
+        mock_asset_repo = MagicMock()
+        mock_asset_repo.get = MagicMock(side_effect=_get_asset)
+        svc = PlanGeneratorService(MagicMock(), asset_repo=mock_asset_repo)
+
+        first_ids: set[str] = set()
+        for _ in range(30):
+            order = svc._sort_assets_by_smart_score(["equal_a", "equal_b"])
+            first_ids.add(order[0])
+
+        assert first_ids == {
+            "equal_a",
+            "equal_b",
+        }, f"噪声应使等分素材排名浮动，30 次排序首位应覆盖两者，实际 {first_ids}"
+
+    def test_large_score_gap_not_flipped(self):
+        """得分差距远大于噪声上限时，低分素材不会因噪声超过高分素材。
+
+        quality 100 vs 0 → quality 维度差距 40 分 > 噪声上限 20，
+        其余维度完全一致，50 次排序高质量素材必须始终排第一。
+        """
+        from app.services.plan_generator_service import PlanGeneratorService
+
+        def _get_asset(aid):
+            quality = {"top": 100.0, "bad": 0.0}[aid]
+            asset = MagicMock()
+            asset.id = aid
+            asset.duration = 15.0
+            asset.quality_score = quality
+            asset.created_at = None
+            asset.metadata = {"generation_use_count": 0}
+            return asset
+
+        mock_asset_repo = MagicMock()
+        mock_asset_repo.get = MagicMock(side_effect=_get_asset)
+        svc = PlanGeneratorService(MagicMock(), asset_repo=mock_asset_repo)
+
+        for _ in range(50):
+            order = svc._sort_assets_by_smart_score(["top", "bad"])
+            assert order[0] == "top", f"质量差距 40 分 > 噪声上限，top 应始终排第一，实际 {order}"