A8多套打包+M4归档+R5多图:存量功能备份

A8 多套交付包(packaging_task.py):
- 修复交付包只打第1条混乱note的bug,按ImageCandidate.strategy分A/B/C组
- 每组生独立note_0N夹(6图+文案.txt),同seq留最新去重,老数据兼容
- task74端到端验:3套各6图,独立agent7项交叉验证全过

M4 归档(tasks.py/exports.py/前端):
- list_tasks加date_from/date_to/product_id筛选+product_name批量填(防N+1)
- 新增exports.py:产品JSON导出+标杆CSV导出(UTF-8 BOM)
- 前端HistoryFilters日期/产品筛选+产品列+打回原因红banner
- response.py加raise_param_error;独立agent验A1/A2/A9通过

R5 产品多图(product_images.py/020迁移/前端):
- product_images表+5端点(上传/列/改场景/设主图/删图)
- 生图按ROLE_SCENE_PREFERENCE选对应场景图,回落primary
- 前端ProductImageManager多图画廊

R6 账号config拆页(settings/):
- 配置页按角色拆/settings(运营+组长+admin)+/config(仅admin)
- Key只显末4位不显余额(守红线)

核销表对齐真实代码状态:D1改稿框/M7裂变/E12评图分纠偏为已完成(曾漏回写)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

backend/app/services/ai_engine/_text_prompt.py

@@ -131,6 +131,38 @@ angle（本条角度标签）/ coverTitle（封面大字≤10字）/ imageBrief
 硬性格式：只输出JSON，不要markdown代码块，字符串内用中文引号「」。"""
 
 
+# ── 第2环标杆爆款配方 → 文案 prompt（借方法层结构，禁抄竞品原话）──────────────
+_BM_DIM_LABELS: list[tuple[str, str]] = [
+    ("title_formula", "标题公式"),
+    ("opening_hook", "开篇钩子"),
+    ("content_structure", "内容结构"),
+    ("selling_point_style", "卖点表达风格"),
+    ("emotion_tone", "情绪基调"),
+    ("topic_tags", "话题标签思路"),
+]
+
+
+def _build_benchmark_block(refs: list[dict]) -> str:
+    """把标杆 8维配方渲染成 prompt 块。只借'方法结构'，硬约束禁抄竞品品牌/功效原话。"""
+    if not refs:
+        return ""
+    lines: list[str] = []
+    for i, f in enumerate(refs[:3], 1):  # 最多取3条标杆，避免prompt膨胀
+        dims = [f"{label}=「{str(f.get(key, '')).strip()}」"
+                for key, label in _BM_DIM_LABELS if str(f.get(key, "")).strip()]
+        if dims:
+            lines.append(f"  标杆{i}：" + "；".join(dims))
+    if not lines:
+        return ""
+    body = "\n".join(lines)
+    return (
+        "\n【对标爆款配方参考（只借方法层结构，绝不照抄）】\n"
+        f"{body}\n"
+        "硬性约束：仅参考上面的标题套路/开篇方式/结构节奏/情绪基调来组织本产品文案；"
+        "禁止照搬竞品品牌名、产品名、功效原话或具体数字；产品信息一律以本产品为准。"
+    )
+
+
 def build_prompt(product: dict, count: int, extra_rules: str = "") -> str:
     """
     组装文案生成 user_prompt。
@@ -153,6 +185,7 @@ def build_prompt(product: dict, count: int, extra_rules: str = "") -> str:
 
     angle_hint = f"文案角度要覆盖：{'、'.join(angles)}（每条用不同角度）。" if angles else ""
     brand_rule = f"每条正文和标题中植入品牌词「{brand_kw}」一次（自然融入，不生硬）。" if brand_kw else ""
+    benchmark_block = _build_benchmark_block(product.get("benchmark_refs") or [])
 
     lines = [
         f"产品：{name}",
@@ -161,6 +194,7 @@ def build_prompt(product: dict, count: int, extra_rules: str = "") -> str:
         angle_hint,
         brand_rule,
         custom,
+        benchmark_block,
         f"\n【Q1随机变量池·每条身份/起因/小缺点各不相同，严格按下方分配使用】",
         combos_text,
         extra_rules,

backend/app/services/ai_engine/constants.py

@@ -70,6 +70,23 @@ PAGE_ROLES = [
 ]
 PAGE_ROLE_MAP = {r["role"]: r for r in PAGE_ROLES}
 
+# ── R5多图：分镜role → 优先产品图场景(scene) 偏好表 ──────────
+# 生图时按分镜 role 选该场景的产品图当参考；取不到回落主图(primary)。
+# scene 取值见 enums.ProductImageScene：primary/scene/texture/ingredient/model
+ROLE_SCENE_PREFERENCE = {
+    "hook":            ["scene", "primary"],       # 封面：真实生活场景优先
+    "pain_scene":      ["scene", "primary"],       # 痛点共鸣：使用前情境
+    "product_closeup": ["primary"],                # 单品特写：白底主图
+    "ingredient":      ["ingredient", "primary"],  # 成分拆解：成分/包装细节
+    "texture":         ["texture", "primary"],     # 质地展示：质地特写
+    "applied_proof":   ["model", "scene", "primary"],  # 上脸：上脸图/场景
+    "social_proof":    ["scene", "primary"],       # 社交背书：场景
+    "closer":          ["primary"],                # 促单收尾：主图
+    "scenario":        ["scene", "primary"],
+    "tutorial":        ["model", "scene", "primary"],
+}
+
+
 # ── 生图风格预设（扒 image.js STYLE_PROMPTS:26-29）──────────
 # 按 style 参数选小红书风格调性，注入 base_prompt 的"视觉风格"行
 STYLE_PROMPTS = {

backend/app/services/ai_engine/image_gen.py

@@ -14,13 +14,36 @@ import logging
 import os
 from typing import Any, Protocol
 
-from .constants import IMAGE_RETRY_ATTEMPTS, IMAGE_RETRY_BACKOFF_BASE, IMAGE_SIZE_DEFAULT
+from .constants import IMAGE_RETRY_ATTEMPTS, IMAGE_RETRY_BACKOFF_BASE, IMAGE_SIZE_DEFAULT, ROLE_SCENE_PREFERENCE
 from .image_scorer import score_image
 from .storyboard import plan_image_set, sanitize_text
 
 logger = logging.getLogger(__name__)
 
 
+def _pick_reference_for_role(
+    role: str,
+    images_by_scene: dict[str, list[bytes]] | None,
+    fallback: list[bytes] | None,
+) -> tuple[list[bytes] | None, str]:
+    """R5多图：按分镜 role 选该场景的产品图。取不到回落主图。
+
+    返回 (参考图bytes列表, 命中scene标签用于日志)。
+    """
+    if images_by_scene:
+        for scene in ROLE_SCENE_PREFERENCE.get(role, ["primary"]):
+            imgs = images_by_scene.get(scene)
+            if imgs:
+                return imgs, scene
+        # 偏好全落空：用任意可用图兜底（仍优先 primary）
+        if images_by_scene.get("primary"):
+            return images_by_scene["primary"], "primary"
+        for scene, imgs in images_by_scene.items():
+            if imgs:
+                return imgs, f"{scene}(兜底)"
+    return fallback, "fallback"
+
+
 class ImageClient(Protocol):
     """worker 注入的图片生成客户端协议（隔离 key 细节）"""
     async def gpt_edits(
@@ -129,12 +152,18 @@ async def generate_storyboard_images(
     strategy: str | None = None,
     target_role: str | None = None,
     custom_prompt: str | None = None,
+    images_by_scene: dict[str, list[bytes]] | None = None,
+    flywheel_fragment: str | None = None,
 ) -> list[dict]:
     """
     按 storyboard 逐张生图（asyncio.gather 并发），返回每张结果列表。
     strategy: None=默认叙事，'A'/'B'/'C'=三套正交叙事策略
     target_role: 非空时只生成该 role 那一张（R2 单张重生）
     custom_prompt: 非空时追加到每张 per_prompt 末尾（R2 人工提示词）
+    images_by_scene: R5多图，{scene: [bytes]}，按分镜 role 选对应场景图；
+                     为空则全分镜共用 reference_images（向后兼容）。
+    flywheel_fragment: R7 飞轮偏好片段（最近选图/打回真实信号聚合），注入图片
+                       排版偏好；仅影响文字角度/版式取向，绝不改瓶身（合规红线）。
     每项：{role, name, image_bytes, error}
     """
     plan = plan_image_set(note, product, image_count, analysis, strategy=strategy)
@@ -169,8 +198,20 @@ async def generate_storyboard_images(
         # R2 人工提示词：追加到末尾权重最高，但不覆盖前面合规/真实约束
         if custom_prompt:
             per_prompt += f"\n运营补充要求（在不违反上述合规与真实约束前提下尽量满足）：{sanitize_text(custom_prompt, 200)}。"
+        # R7 飞轮偏好：仅作用于文字角度/版式取向参考，绝不改瓶身（合规+真实红线）
+        if flywheel_fragment:
+            per_prompt += (
+                f"\n历史偏好参考（仅影响标题文字角度与排版取向，不得据此改动产品瓶身）："
+                f"{sanitize_text(flywheel_fragment, 300)}。"
+            )
         try:
-            img_bytes = await generate_one_image(client, per_prompt, reference_images)
+            # R5多图：按本张分镜 role 选对应场景产品图；无多图则共用 reference_images
+            ref_for_item, _scene_hit = _pick_reference_for_role(
+                item["role"], images_by_scene, reference_images
+            )
+            if images_by_scene:
+                logger.info("分镜 %s 选用产品图场景=%s", item["role"], _scene_hit)
+            img_bytes = await generate_one_image(client, per_prompt, ref_for_item)
             # 注：gpt-image-2 渲染中文偶发错别字(约1/6)。vision/OCR 文字校验闸门实测
             # 不可靠(漏报形近字+幻觉误伤品牌词),倩倩姐2026-06-16拍板先撤,纯生图,
             # 错别字作已知问题记录,后续迭代再处理。详见记忆 clover-image-text-check-shelved。

backend/app/services/ai_engine/preference_aggregator.py

@@ -52,7 +52,8 @@ def aggregate_preference_context(
         weight   = int(e.get("signal_weight", 1))
 
         # text_edit(改稿)是最强真实信号,角度按权重计入(倩倩姐2026-06-16拍板)
-        if sig_type in ("text_select", "approve", "text_edit") and angle:
+        # image_select(选图)按套别叙事角度计入,让选图偏好真正闭环回生图(R7断点2)
+        if sig_type in ("text_select", "approve", "text_edit", "image_select") and angle:
             angle_counts[angle] += weight
         elif sig_type == "reject_with_reason":
             reason = str(e.get("reason") or "").strip()

backend/app/services/fission_image_retry.py

@@ -60,6 +60,19 @@ def retry_fission_note_images(
         except Exception as e:  # noqa: BLE001
             logger.warning("裂变补图参考图读取失败，无参考图模式: %s", e)
 
+    # R5多图：补图也按场景分组，重生失败分镜时选对应场景图
+    images_by_scene: dict[str, list[bytes]] = {}
+    for _im in (product.get("images") or []):
+        _p = _resolve_image_path(_im.get("path", ""))
+        if _p and os.path.isfile(_p):
+            try:
+                with open(_p, "rb") as _f:
+                    images_by_scene.setdefault(_im.get("scene") or "primary", []).append(_f.read())
+            except Exception:  # noqa: BLE001
+                pass
+    if reference_images and not images_by_scene.get("primary"):
+        images_by_scene.setdefault("primary", []).extend(reference_images)
+
     upload_base = get_settings().UPLOAD_ABS_ROOT
     img_dir = os.path.join(upload_base, str(ft.workspace_id), f"fission_{ft.id}", str(fn.seq))
     os.makedirs(img_dir, exist_ok=True)
@@ -73,6 +86,7 @@ def retry_fission_note_images(
                 client=clients, note=note, product=product,
                 image_count=image_count, reference_images=reference_images,
                 target_role=role,
+                images_by_scene=images_by_scene or None,
             ))
         except Exception as exc:  # noqa: BLE001
             logger.error("裂变补图 seq=%s role=%s 仍失败: %s", fn.seq, role, exc)

backend/app/services/fission_images.py

@@ -39,6 +39,19 @@ def generate_fission_images(
         except Exception as e:  # noqa: BLE001
             logger.warning("裂变参考图读取失败，无参考图模式: %s", e)
 
+    # R5多图：裂变同样按场景分组，生图按分镜 role 选对应图
+    images_by_scene: dict[str, list[bytes]] = {}
+    for _im in (product.get("images") or []):
+        _p = _resolve_image_path(_im.get("path", ""))
+        if _p and os.path.isfile(_p):
+            try:
+                with open(_p, "rb") as _f:
+                    images_by_scene.setdefault(_im.get("scene") or "primary", []).append(_f.read())
+            except Exception:  # noqa: BLE001
+                pass
+    if reference_images and not images_by_scene.get("primary"):
+        images_by_scene.setdefault("primary", []).extend(reference_images)
+
     upload_base = get_settings().UPLOAD_ABS_ROOT
     for nid in note_ids:
         fn = db.query(FissionNote).filter(FissionNote.id == nid).first()
@@ -52,6 +65,7 @@ def generate_fission_images(
             results = asyncio.run(generate_storyboard_images(
                 client=clients, note=note, product=product,
                 image_count=image_count, reference_images=reference_images,
+                images_by_scene=images_by_scene or None,
             ))
         except Exception as exc:  # noqa: BLE001
             logger.error("裂变套 seq=%s 生图失败: %s", fn.seq, exc)

backend/app/services/flywheel_service.py

@@ -8,10 +8,10 @@ preference_aggregator：查最近50条 → 最常选角度 + 打回原因近3条
 import logging
 from typing import Any
 
-from sqlalchemy import desc, func
+from sqlalchemy import desc
 from sqlalchemy.orm import Session
 
-from app.constants.enums import SIGNAL_WEIGHTS, DataOwnership, SignalType
+from app.constants.enums import SIGNAL_WEIGHTS, DataOwnership
 from app.middleware.workspace_guard import CurrentUser
 from app.models.flywheel import PreferenceEvent
 from app.models.task import GenerationTask
@@ -20,8 +20,6 @@ logger = logging.getLogger(__name__)
 
 # 实时聚合窗口：最近50条事件
 _AGGREGATION_WINDOW = 50
-# 冷启动阈值：不足5条信号用产品档案冷启动
-_COLD_START_THRESHOLD = 5
 
 
 def record_signal(
@@ -32,12 +30,14 @@ def record_signal(
     candidate_id: int | None = None,
     angle_label: str | None = None,
     reason: str | None = None,
+    signal_meta: str | None = None,
 ) -> None:
     """
     写入飞轮信号。
     workspace_id + product_id 都必须有（基石C + 按产品分开学）。
     signal_weight 用枚举默认值，北哥可校准。
     data_ownership 默认 client_data（选择行为归客户）。
+    signal_meta：JSON 扩展（如选图存 strategy），不参与角度聚合主逻辑。
     """
     weight = SIGNAL_WEIGHTS.get(signal_type, 0)
     event = PreferenceEvent(
@@ -50,6 +50,7 @@ def record_signal(
         candidate_id=candidate_id,
         angle_label=angle_label,
         reason=reason,
+        signal_meta=signal_meta,
         data_ownership=DataOwnership.CLIENT_DATA,
     )
     try:
@@ -69,14 +70,17 @@ def record_signal(
 
 
 def get_preference_context(
-    db: Session, workspace_id: int, product_id: int
+    db: Session, workspace_id: int, product_id: int,
+    product_dict: dict[str, Any] | None = None,
 ) -> dict[str, Any]:
     """
-    实时聚合偏好上下文（最近50条 events）。
-    返回：recent_preference摘要 + reject_reasons近3条 + injected_count。
-    不足5条 → 冷启动提示（产品档案兜底，由 AIE prompt 层读 products.custom_prompt）。
+    实时聚合偏好上下文（最近50条 events）供前端展示。
+    R7断点3：统一口径——委托给生产链同一个 aggregate_preference_context（权重口径），
+    消除"前端展示按次数/生成按权重"的口径分裂（说一套做一套）。
     按 workspace_id + product_id 严格过滤（不串数据，基石C）。
     """
+    from app.services.ai_engine.preference_aggregator import aggregate_preference_context
+
     recent = (
         db.query(PreferenceEvent)
         .filter(
@@ -87,35 +91,18 @@ def get_preference_context(
         .limit(_AGGREGATION_WINDOW)
         .all()
     )
-
-    if len(recent) < _COLD_START_THRESHOLD:
-        return {
-            "recent_preference": "信号不足，使用产品档案基线（冷启动）",
-            "reject_reasons": [],
-            "injected_count": len(recent),
-        }
-
-    # 统计最常被选中的角度（text_edit 改稿=最强真实信号,按权重计入,倩倩姐2026-06-16）
-    angle_counts: dict[str, int] = {}
-    for ev in recent:
-        if ev.signal_type in (SignalType.TEXT_SELECT, SignalType.APPROVE, SignalType.TEXT_EDIT) and ev.angle_label:
-            angle_counts[ev.angle_label] = angle_counts.get(ev.angle_label, 0) + 1
-
-    top_angles = sorted(angle_counts.items(), key=lambda x: x[1], reverse=True)[:3]
-    if top_angles:
-        pref_desc = "；".join(f"{a}（已选{c}次）" for a, c in top_angles)
-        preference_summary = f"最近偏好：{pref_desc}"
-    else:
-        preference_summary = "暂无明显角度偏好"
-
-    # 取最近3条打回原因原文（不做 AI 归纳，契约§3）
-    reject_reasons = [
-        ev.reason for ev in recent
-        if ev.signal_type == SignalType.REJECT_WITH_REASON and ev.reason
-    ][:3]
-
+    events_dicts = [
+        {"signal_type": e.signal_type, "workspace_id": e.workspace_id,
+         "product_id": e.product_id, "angle_label": e.angle_label or "",
+         "signal_weight": e.signal_weight, "reason": e.reason or ""}
+        for e in recent
+    ]
+    ctx = aggregate_preference_context(
+        events_dicts, product_dict or {}, workspace_id, product_id
+    )
+    # 前端展示不需要 prompt_fragment（注入用），剥掉只回摘要+原因+计数
     return {
-        "recent_preference": preference_summary,
-        "reject_reasons": reject_reasons,
-        "injected_count": len(recent),
+        "recent_preference": ctx.get("recent_preference", ""),
+        "reject_reasons": ctx.get("reject_reasons", []),
+        "injected_count": ctx.get("injected_count", 0),
     }

backend/app/services/task_service.py

@@ -30,6 +30,28 @@ def _check_user_has_key(db: Session, user_id: int, workspace_id: int) -> None:
         raise_business("尚未配置 API Key，请先在设置中录入")
 
 
+def _check_concurrency_limit(db: Session, user_id: int, workspace_id: int) -> None:
+    """
+    校验该用户未完成任务数未超并发上限（红线=5,可配置)。
+    只算 pending/generating(真占 worker),挑选/审核态不计。超限引导稍后再试。
+    """
+    from app.core.config import get_settings
+    from app.constants.enums import TaskStatus
+
+    limit = get_settings().MAX_CONCURRENT_TASKS_PER_USER
+    running = (
+        db.query(GenerationTask)
+        .filter(
+            GenerationTask.operator_id == user_id,
+            GenerationTask.workspace_id == workspace_id,
+            GenerationTask.status.in_([TaskStatus.PENDING.value, TaskStatus.GENERATING.value]),
+        )
+        .count()
+    )
+    if running >= limit:
+        raise_business(f"您有 {running} 个任务正在生成，已达并发上限 {limit} 个，请等待完成后再发起")
+
+
 def create_generation_task(
     db: Session,
     current_user: CurrentUser,
@@ -42,6 +64,9 @@ def create_generation_task(
     if body.track == "ai":
         # 轨A：先检查有没有 key
         _check_user_has_key(db, current_user.user_id, current_user.workspace_id)
+        # 并发上限：只算正在消耗生成资源的任务(pending/generating)，
+        # 已生成完等挑选/审核的不占 worker。红线=每用户5个(可配置)。
+        _check_concurrency_limit(db, current_user.user_id, current_user.workspace_id)
 
     # 禁降级铁律：本次产品入镜(need_product_image=True)时，产品必须已上传参考图，
     # 否则拒绝建任务（不允许降级纯文生图，防产品包装跑偏/过抽检失败）。
@@ -57,6 +82,11 @@ def create_generation_task(
         if not (product.image_path or "").strip():
             raise_business("该产品未上传参考图，无法生成产品入镜内容；请先到产品库上传产品图，或关闭「产品入镜」开关")
 
+    # 第2环：关联标杆笔记ID存库（JSON list）。pipeline 据此读 features_json 注入文案 prompt。
+    import json
+    _bids = getattr(body, "benchmark_ids", None) or []
+    benchmark_ids_json = json.dumps([int(i) for i in _bids]) if _bids else None
+
     task = GenerationTask(
         workspace_id=current_user.workspace_id,
         product_id=body.product_id,
@@ -66,6 +96,7 @@ def create_generation_task(
         image_count=body.image_count,
         track=body.track,
         need_product_image=need_img,
+        benchmark_ids=benchmark_ids_json,
         status="pending",
     )
     db.add(task)