新增 Pinterest 参考模式：独立于 Google Trends 的完整链路（12国种子词池 / LLM搜索词json_schema+防重复+已用词限100 / 并发爬图 / 多模态分析→原创简报 / 生图带爬取图参考图生图 / UI流程选择）

graph/pinterest.py

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+"""Pinterest 参考模式共享辅助：种子词加载、已用搜索词持久化、搜索词全局过滤。
+
+独立于 Google Trends 采集链路，供 pinterest_search / scrape / analyze 节点复用。
+"""
+import json
+import random
+import re
+from pathlib import Path
+from typing import Any, Dict, List
+
+from graph.paths import project_root, runtime_root
+
+# 不适合 T 恤印花的类目关键词（复用 product_batch 的兜底清单）
+_UNSUITABLE = re.compile(
+    r"\b(nails?|manicure|pedicure|recipes?|cooking|lottery|jackpot|results?|score|scores?|"
+    r"fixtures?|forecast|weather|temperature|map|directions?|parking|opening hours?|"
+    r"prices?|price|reviews?|jobs?|salary|mortgage|council tax|election|referendum|"
+    r"stock market|exchange rate|gas prices?)\b",
+    re.IGNORECASE,
+)
+
+
+def pinterest_seed_path(country: str) -> Path:
+    for root in (runtime_root(), project_root()):
+        p = root / "configs" / "pinterest" / f"{country}.yaml"
+        if p.exists():
+            return p
+    return Path("configs") / "pinterest" / f"{country}.yaml"
+
+
+def load_pinterest_seeds(country: str) -> List[str]:
+    """读国家 Pinterest 种子词池（configs/pinterest/<CC>.yaml 的 seeds）。"""
+    try:
+        import yaml
+        p = pinterest_seed_path(country)
+        if not p.exists():
+            print(f"[pinterest] 未找到种子词配置: {p}")
+            return []
+        data = yaml.safe_load(p.read_text(encoding="utf-8")) or {}
+        seeds = [str(s).strip() for s in (data.get("seeds") or []) if str(s).strip()]
+        return seeds
+    except Exception as e:  # noqa: BLE001
+        print(f"[pinterest] 种子词加载失败: {e}")
+        return []
+
+
+def sample_seeds(country: str, n: int) -> List[str]:
+    """从国家种子池随机抽取 n 个种子词（不足则全取）。"""
+    seeds = load_pinterest_seeds(country)
+    if not seeds:
+        return []
+    if len(seeds) <= n:
+        return list(seeds)
+    return random.sample(seeds, n)
+
+
+def used_terms_path(output_dir: str, country: str) -> Path:
+    return Path(output_dir) / "pinterest_ref" / country / "used_search_terms.json"
+
+
+def load_used_terms(output_dir: str, country: str) -> List[str]:
+    """读已用搜索词（跨多次运行持久化，供动态注入防重复）。"""
+    try:
+        p = used_terms_path(output_dir, country)
+        if p.exists():
+            data = json.loads(p.read_text(encoding="utf-8"))
+            return [str(t).strip() for t in (data.get("terms") or []) if str(t).strip()]
+    except Exception as e:  # noqa: BLE001
+        print(f"[pinterest] 已用搜索词读取失败: {e}")
+    return []
+
+
+def save_used_terms(output_dir: str, country: str, terms: List[str]) -> None:
+    """持久化已用搜索词（去重保序）。"""
+    try:
+        p = used_terms_path(output_dir, country)
+        p.parent.mkdir(parents=True, exist_ok=True)
+        seen, out = set(), []
+        for t in terms:
+            k = t.strip().lower()
+            if k and k not in seen:
+                seen.add(k)
+                out.append(t.strip())
+        p.write_text(json.dumps({"terms": out}, ensure_ascii=False, indent=2), encoding="utf-8")
+    except Exception as e:  # noqa: BLE001
+        print(f"[pinterest] 已用搜索词保存失败: {e}")
+
+
+def filter_search_terms(terms: List[str], used: List[str], blacklist: List[str]) -> List[str]:
+    """全局搜索词过滤：剔除已用、黑名单、不适合 T 恤类目、去重（大小写不敏感）。"""
+    used_set = {str(u).strip().lower() for u in used if str(u).strip()}
+    black = [str(b).strip().lower() for b in (blacklist or []) if str(b).strip()]
+    seen, out = set(), []
+    for t in terms:
+        s = str(t).strip()
+        low = s.lower()
+        if not s or low in seen or low in used_set:
+            continue
+        if any(b and b in low for b in black):
+            continue
+        if _UNSUITABLE.search(low):
+            continue
+        seen.add(low)
+        out.append(s)
+    return out
+
+
+def merge_used(existing: List[str], new_terms: List[str]) -> List[str]:
+    """合并已用搜索词（新词追加到末尾，去重保序）。"""
+    seen, out = set(), []
+    for t in list(existing) + list(new_terms):
+        k = str(t).strip().lower()
+        if k and k not in seen:
+            seen.add(k)
+            out.append(str(t).strip())
+    return out