baseline: Clover 独立仓库首次基线提交

将 Clover 从上层产品包旧仓库中独立出来,建立专属版本控制。
当前状态=纵切片端到端已打通(登录→选品→出文出图→审核→下载包),
M1文案质量去套路化已验收。此提交作为后续按核销清单逐条修复的基线。

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

backend/app/services/ai_engine/preference_aggregator.py

@@ -0,0 +1,145 @@
+"""
+偏好飞轮聚合（preference_aggregator）
+扒自：Clover架构方案.md §偏好飞轮怎么转 + PRD §8
+三层继承：L1 公司品牌基线 > L2 矩阵号人设（二期）> L3 个人手感
+聚合最近 FLYWHEEL_LOOKBACK 条 events → prompt 片段注入文案生成
+
+关键：
+- 按 product_id 分开学（素颜霜偏好不串精华）
+- 信号不足 FLYWHEEL_COLD_START 条时，用产品档案冷启动
+- 返回结构对齐 API契约 GET /tasks/{id}/preference/context
+"""
+from __future__ import annotations
+import logging
+from collections import Counter
+from typing import Any
+
+from .constants import FLYWHEEL_LOOKBACK, FLYWHEEL_COLD_START
+
+logger = logging.getLogger(__name__)
+
+
+def aggregate_preference_context(
+    events: list[dict],
+    product: dict,
+    workspace_id: int,
+    product_id: int,
+) -> dict:
+    """
+    输入：最近 preference_events 行（已按 workspace_id+product_id 过滤）
+    输出：{recent_preference, reject_reasons, injected_count, prompt_fragment}
+    prompt_fragment 直接注入文案生成 prompt
+    """
+    # 按 product_id 过滤（防串货）
+    relevant = [
+        e for e in events
+        if e.get("workspace_id") == workspace_id and e.get("product_id") == product_id
+    ][:FLYWHEEL_LOOKBACK]
+
+    injected_count = len(relevant)
+
+    if injected_count < FLYWHEEL_COLD_START:
+        # 冷启动：用产品档案静态基线
+        return _cold_start(product, injected_count)
+
+    # ── 统计最常选角度（text_select + approve 信号）
+    angle_counts: Counter = Counter()
+    reject_reasons: list[str] = []
+
+    for e in relevant:
+        sig_type = e.get("signal_type", "")
+        angle    = str(e.get("angle_label", "")).strip()
+        weight   = int(e.get("signal_weight", 1))
+
+        if sig_type in ("text_select", "approve") and angle:
+            angle_counts[angle] += weight
+        elif sig_type == "reject_with_reason":
+            reason = str(e.get("reason", "")).strip()
+            if reason:
+                reject_reasons.append(reason)
+
+    # 取权重最高的角度
+    top_angles = [a for a, _ in angle_counts.most_common(3)]
+    # 取最近3条打回原因
+    recent_rejects = reject_reasons[-3:] if reject_reasons else []
+
+    # ── 拼 prompt 片段（三层继承：L1>L2>L3，一期只跑L1+L3）
+    prompt_fragment = _build_prompt_fragment(top_angles, recent_rejects, product)
+
+    # ── 人类可读摘要（前端"本次已注入"显示）
+    if top_angles:
+        pref_summary = f"最近偏好角度：{'、'.join(top_angles)}（已选{injected_count}次信号）"
+    else:
+        pref_summary = f"已注入{injected_count}条偏好信号"
+
+    return {
+        "recent_preference": pref_summary,
+        "reject_reasons": recent_rejects,
+        "injected_count": injected_count,
+        "prompt_fragment": prompt_fragment,   # 注入 generate_text_variants extra_rules
+    }
+
+
+def _cold_start(product: dict, injected_count: int) -> dict:
+    """信号不足时用产品档案基线"""
+    angles = product.get("text_angles") or []
+    style  = product.get("style_tone", "素人分享风")
+    fragment = ""
+    if angles:
+        fragment = f"优先覆盖以下文案角度：{'、'.join(angles[:3])}。风格调性：{style}。"
+    return {
+        "recent_preference": f"冷启动（历史信号{injected_count}条，不足{FLYWHEEL_COLD_START}条），使用产品档案基线",
+        "reject_reasons": [],
+        "injected_count": injected_count,
+        "prompt_fragment": fragment,
+    }
+
+
+def _build_prompt_fragment(
+    top_angles: list[str],
+    reject_reasons: list[str],
+    product: dict,
+) -> str:
+    """
+    组装注入文案 prompt 的片段
+    越积累越精准：1次=全靠基线；10次=知道偏好角度；30次=措辞从"供参考"升为明确指令
+    """
+    lines: list[str] = []
+    if top_angles:
+        lines.append(f"【偏好角度参考】历史选择偏好：{'、'.join(top_angles)}，请优先采用这些角度方向。")
+    if reject_reasons:
+        formatted = "；".join(f"「{r}」" for r in reject_reasons)
+        lines.append(f"【打回原因参考】以下问题请主动规避：{formatted}。")
+    # L1 品牌基线（产品档案 custom_prompt）
+    custom = (product.get("custom_prompt") or "").strip()
+    if custom:
+        lines.append(f"【品牌基线】{custom}")
+    return "\n".join(lines)
+
+
+def collect_preference_event(
+    signal_type: str,
+    user_id: int,
+    workspace_id: int,
+    product_id: int,
+    angle_label: str = "",
+    reason: str = "",
+    weights: dict[str, int] | None = None,
+) -> dict:
+    """
+    构造 preference_event 行（由业务接口内部调用，不暴露给前端）
+    返回待插 DB 的字段 dict
+    """
+    from .constants import FLYWHEEL_WEIGHTS
+    w_map = weights or FLYWHEEL_WEIGHTS
+    weight = w_map.get(signal_type, 0)
+    return {
+        "signal_type":   signal_type,
+        "signal_weight": weight,
+        "user_id":       user_id,
+        "workspace_id":  workspace_id,
+        "product_id":    product_id,
+        "angle_label":   angle_label,
+        "reason":        reason,
+        "data_ownership": "client_data",  # 原始行为信号归客户（PRD §3 data_ownership）
+    }