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/workers/packaging_task.py

@@ -42,47 +42,62 @@ def build_delivery_package(self, package_id: int) -> dict:
         settings = get_settings()
         upload_base = settings.UPLOAD_BASE_PATH.rstrip("/")
 
-        selected_text = db.query(TextCandidate).filter(
+        # A8 多套打包：按 strategy(A/B/C 三套正交叙事)分组，每套成一篇独立 note。
+        # 北哥拿到的交付包含完整 3 套（note_01/02/03），不再只打第 1 条文案。
+        selected_texts = db.query(TextCandidate).filter(
             TextCandidate.task_id == task_id, TextCandidate.is_selected == True,
-        ).first()
-        # 整套全打（倩倩姐2026-06-08拍板）：一条笔记的全部图按 seq 排序进包，
-        # 不再只打 is_selected 的封面。北哥6张标准套 seq=1 是 hook 封面，天然排第一。
-        selected_images = db.query(ImageCandidate).filter(
-            ImageCandidate.task_id == task_id,
-        ).order_by(ImageCandidate.seq).all()
-
-        if not selected_text:
+        ).order_by(TextCandidate.id).all()
+        if not selected_texts:
             raise ValueError("无已选文案，请先选择文案")
 
-        text_data = json.loads(selected_text.content or "{}")
-        images_data = []
-        for ic in selected_images:
+        # 整套全打：一套内全部图按 seq 排序进包，不只打封面。重生场景同 (strategy,seq)
+        # 可能多条(新增不删旧)，去重取最新(id最大)，避免包内重复图。
+        all_images = db.query(ImageCandidate).filter(
+            ImageCandidate.task_id == task_id,
+        ).order_by(ImageCandidate.strategy, ImageCandidate.seq).all()
+
+        def _read_image(ic) -> dict:
             img_bytes = b""
             if ic.url:
-                # url 形如 /uploads/ws/task/file.jpg，本身已含 uploads 前缀。
-                # 工作目录是 /app，直接 lstrip("/") 当相对路径读，不能再拼 upload_base(会重复 uploads/uploads)。
-                rel = ic.url.lstrip("/")
-                abs_path = rel
+                # url 已含 uploads 前缀；工作目录 /app，lstrip 当相对路径读，勿再拼 base(防 uploads/uploads)
                 try:
-                    with open(abs_path, "rb") as f:
+                    with open(ic.url.lstrip("/"), "rb") as f:
                         img_bytes = f.read()
                 except OSError as e:
-                    logger.warning("图片读取失败，跳过：%s %s", abs_path, e)
-            images_data.append({
+                    logger.warning("图片读取失败，跳过：%s %s", ic.url, e)
+            return {
                 "seq": ic.seq,
                 "role": ic.role.value if hasattr(ic.role, "value") else str(ic.role),
                 "data": img_bytes,
-            })
+            }
 
-        notes = [{
-            "title": text_data.get("title", ""),
-            "content": text_data.get("content", ""),
-            "tags": text_data.get("tags", []),
-            "images": images_data,
-            "banned_word_status": (selected_text.banned_word_status.value
-                                   if hasattr(selected_text.banned_word_status, "value")
-                                   else str(selected_text.banned_word_status)),
-        }]
+        # 按 strategy 分组（A/B/C；老数据 strategy=None 归一套，向后兼容）
+        from collections import OrderedDict
+        groups: "OrderedDict[str, dict]" = OrderedDict()
+        for ic in all_images:
+            slot = groups.setdefault(ic.strategy or "_", {})
+            prev = slot.get(ic.seq)
+            if prev is None or ic.id > prev.id:
+                slot[ic.seq] = ic  # 同 seq 留最新
+
+        notes = []
+        for idx, (_strategy, slot) in enumerate(groups.items()):
+            images_data = [_read_image(slot[k]) for k in sorted(slot)]
+            # 文案配对：选中文案数≥套数则一套一条；否则各套共用第 1 条
+            # (图均以第 1 条文案为语境生成，共用合理；多选则尊重运营按套选的文案)
+            tc = selected_texts[idx] if idx < len(selected_texts) else selected_texts[0]
+            text_data = json.loads(tc.content or "{}")
+            notes.append({
+                "title": text_data.get("title", ""),
+                "content": text_data.get("content", ""),
+                "tags": text_data.get("tags", []),
+                "images": images_data,
+                "banned_word_status": (tc.banned_word_status.value
+                                       if hasattr(tc.banned_word_status, "value")
+                                       else str(tc.banned_word_status)),
+            })
+        if not notes:
+            raise ValueError("无图片候选，无法打包")
 
         from app.services.ai_engine.package_exporter import build_delivery_package as do_build
         # 打包产物放专用目录 uploads/packages/，与图片目录 uploads/{ws}/{task}/ 分开

backend/app/workers/pipeline_io.py

@@ -148,7 +148,8 @@ def run_image_generation(db, clients, task, product_dict: dict,
                          first_copy: dict, upload_base_path: str,
                          regen_strategy: str | None = None,
                          regen_role: str | None = None,
-                         custom_prompt: str | None = None) -> int:
+                         custom_prompt: str | None = None,
+                         flywheel_fragment: str | None = None) -> int:
     """
     Step6+7+8(image): 调 generate_storyboard_images → 后处理 → 存 ImageCandidate → 推 SSE。
     返回 next_seq。
@@ -202,6 +203,23 @@ def run_image_generation(db, clients, task, product_dict: dict,
             "拒绝降级纯文生图。请确认产品已上传参考图。"
         )
 
+    # R5多图：按场景分组加载产品图，生图按分镜 role 选对应场景图
+    images_by_scene: dict[str, list[bytes]] = {}
+    for _im in (product_dict.get("images") or []):
+        _p = _resolve_image_path(_im.get("path", ""))
+        _scene = _im.get("scene") or "primary"
+        if _p and os.path.isfile(_p):
+            try:
+                with open(_p, "rb") as _f:
+                    images_by_scene.setdefault(_scene, []).append(_f.read())
+            except Exception as _e:
+                logger.warning("产品图(scene=%s)读取失败，跳过：%s %s", _scene, _p, _e)
+    # 主图始终保底进 primary（多图表为空或主图未入表时仍可用）
+    if reference_images and not images_by_scene.get("primary"):
+        images_by_scene.setdefault("primary", []).extend(reference_images)
+    if images_by_scene:
+        logger.info("R5多图已加载：%s", {k: len(v) for k, v in images_by_scene.items()})
+
     seq = seq_start
     # R2: 限定重生套别(regen_strategy)则只跑该套，否则全量 A/B/C 三套正交叙事
     _strategies = (regen_strategy,) if regen_strategy else ("A", "B", "C")
@@ -227,6 +245,8 @@ def run_image_generation(db, clients, task, product_dict: dict,
                 strategy=strategy,
                 target_role=regen_role,
                 custom_prompt=custom_prompt,
+                images_by_scene=images_by_scene or None,
+                flywheel_fragment=flywheel_fragment,
             ))
         except Exception as exc:
             img_success = False

backend/app/workers/pipeline_steps.py

@@ -58,6 +58,7 @@ def build_clients_and_clear_key(plain_key: str):
 def build_product_dict(product) -> dict:
     """把 ORM product 转成 AI 引擎所需的 dict（不含任何 key）。"""
     return {
+        "id": product.id,
         "name": product.name,
         "category": product.category or "通用好物",
         "selling_points": json.loads(product.selling_points or "[]"),
@@ -66,10 +67,52 @@ def build_product_dict(product) -> dict:
         "custom_prompt": product.custom_prompt or "",
         "brand_keyword": product.brand_keyword or "",       # S3: 品牌词透传进生成prompt(每条植入)
         "target_audience": product.target_audience or "",   # 012: 人群透传进storyboard/文案prompt
-        "image_path": product.image_path or "",             # 产品参考图路径（前端上传后填入）
+        "image_path": product.image_path or "",             # 产品参考图路径（主图，向后兼容）
+        # R5多图：每张产品图 {path, scene}，生图按分镜role选对应场景图
+        "images": [
+            {"path": im.path, "scene": im.scene}
+            for im in (getattr(product, "images", None) or [])
+        ],
+        # 第2环标杆配方，默认空；走 AI 主链时由 load_benchmark_features 覆盖填充
+        "benchmark_refs": [],
     }
 
 
+def load_benchmark_features(db, task, workspace_id: int) -> list[dict]:
+    """
+    第2环→第5环接线：读 task.benchmark_ids → 查 analyze_status=done 的标杆 features_json。
+    返回 8维配方 dict 列表（供 build_prompt 借方法层结构，禁抄竞品品牌/功效原话）。
+    未选/未分析完/解析失败都安全返空，绝不阻断生成。
+    """
+    from app.models.product import BenchmarkNote
+
+    raw_ids = getattr(task, "benchmark_ids", None)
+    if not raw_ids:
+        return []
+    try:
+        ids = [int(i) for i in json.loads(raw_ids)]
+    except Exception:
+        return []
+    if not ids:
+        return []
+
+    rows = db.query(BenchmarkNote).filter(
+        BenchmarkNote.id.in_(ids),
+        BenchmarkNote.workspace_id == workspace_id,
+        BenchmarkNote.analyze_status == "done",
+    ).all()
+
+    feats: list[dict] = []
+    for b in rows:
+        if not b.features_json:
+            continue
+        try:
+            feats.append(json.loads(b.features_json))
+        except Exception:
+            logger.warning("标杆 features_json 解析失败 id=%s", b.id)
+    return feats
+
+
 def load_flywheel_context(db, workspace_id: int, product_id: int, product_dict: dict) -> tuple[str, dict]:
     """
     查最近50条飞轮事件，聚合偏好上下文。

backend/app/workers/tasks.py

@@ -106,6 +106,7 @@ def run_generation_pipeline(self, task_id: int, regen_strategy: str | None = Non
             build_clients_and_clear_key,
             build_product_dict,
             load_flywheel_context,
+            load_benchmark_features,
         )
         from app.workers.pipeline_io import run_text_generation, run_image_generation
         from app.core.config import get_settings
@@ -135,6 +136,8 @@ def run_generation_pipeline(self, task_id: int, regen_strategy: str | None = Non
         }, seq)
 
         product_dict = build_product_dict(product)
+        # 第2环爆款配方接进文案链：选中并分析完的标杆 8维配方注入 build_prompt（借方法层结构）
+        product_dict["benchmark_refs"] = load_benchmark_features(db, task, workspace_id)
         flywheel_fragment, flywheel_ctx = load_flywheel_context(db, workspace_id, task.product_id, product_dict)
 
         if flywheel_fragment:
@@ -192,6 +195,7 @@ def run_generation_pipeline(self, task_id: int, regen_strategy: str | None = Non
             first_copy, settings.UPLOAD_BASE_PATH,
             regen_strategy=regen_strategy, regen_role=regen_role,
             custom_prompt=custom_prompt,
+            flywheel_fragment=flywheel_fragment,
         )
 
         # 最终状态 + task_done