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()