feat(p3): add discovery, telegram notifications, and one-click favorites

Implement WBI video search with legacy fallback, classification scoring, candidate pool, permanent seen-aid dedupe, blacklist filtering, and batched Telegram notifications. Add write client for fav deal/sort, target-folder selection, insertion position calculation, read-back verification, fav_action locking, retry on failure, ignore and blacklist actions. Wire discovery, candidate cleanup, Telegram long polling, and new discover/candidates CLI commands into the scheduler. Add migration v3 for candidate duration and index stats.

reaction_board/classify.py

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+# -*- coding: utf-8 -*-
+"""候选分类与时长解析。
+
+规则见 project.md §3.3：分类只决定“是否通知”，不决定是否入库。
+"""
+
+from __future__ import annotations
+
+import re
+import unicodedata
+from typing import Any, Mapping
+
+_TAG_RE = re.compile(r"<[^>]+>")
+
+
+def normalize_title(text: str) -> str:
+    """全半角归一化并转小写，便于标题关键词匹配。"""
+    return unicodedata.normalize("NFKC", text or "").lower()
+
+
+def clean_title(text: str) -> str:
+    """去掉搜索结果里的 ``<em class="keyword">`` 高亮标签。"""
+    return _TAG_RE.sub("", text or "").strip()
+
+
+def parse_duration(value: Any) -> int:
+    """解析 B 站搜索结果时长。
+
+    支持 ``105:5``（分钟:秒）、``1:02:03``（时:分:秒）和整数秒。
+    """
+    if isinstance(value, bool):
+        return 0
+    if isinstance(value, int):
+        return max(0, value)
+    if not isinstance(value, str):
+        return 0
+    parts = value.strip().split(":")
+    if not parts or any(not part.isdigit() for part in parts):
+        return 0
+    numbers = [int(part) for part in parts]
+    if len(numbers) == 1:
+        return numbers[0]
+    if len(numbers) == 2:
+        return numbers[0] * 60 + numbers[1]
+    if len(numbers) == 3:
+        return numbers[0] * 3600 + numbers[1] * 60 + numbers[2]
+    return 0
+
+
+def contains_any(title: str, words: Any) -> bool:
+    normalized = normalize_title(title)
+    if not isinstance(words, (list, tuple)):
+        return False
+    return any(normalize_title(str(word)) in normalized for word in words if word)
+
+
+def passes_required_title(title: str, require_title: str) -> bool:
+    if not require_title:
+        return True
+    return normalize_title(require_title) in normalize_title(title)
+
+
+def score_item(
+    *,
+    title: str,
+    up_mid: int | None,
+    duration_seconds: int,
+    classify: Mapping[str, Any],
+) -> int:
+    """按 project.md §3.3 计算候选分数。"""
+    score = 0
+    if contains_any(title, classify.get("positive")):
+        score += 2
+    if contains_any(title, classify.get("charge")):
+        score += 2
+
+    boost = classify.get("duration_boost") or {}
+    gt_60 = int(boost.get("gt_60min", 2))
+    gt_30 = int(boost.get("gt_30min", 1))
+    if duration_seconds > 60 * 60:
+        score += gt_60
+    elif duration_seconds > 30 * 60:
+        score += gt_30
+
+    if contains_any(title, classify.get("negative")):
+        score -= 5
+    if up_mid is not None and int(up_mid) == int(classify.get("self_mid", 37754047)):
+        score -= 3
+    return score