feat: 用户行为信誉分系统

- User 新增 credit_score 字段（0-200，默认100）
- 信誉分影响检测阈值系数：高分降低敏感度，低分提高敏感度
- 发布成功+1分，被拦截-2分；申诉通过+10分，驳回-5分
- 新增手动调整和批量重算信誉分接口
- admin-users 页面显示信誉分进度条，支持编辑调整

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

backend/app/routes/admin_routes.py

@@ -309,11 +309,17 @@ def process_appeal(post_id: int):
         row.prediction = "ham"
         row.manual_review_status = "approved_ham"
         _upsert_manual_sample(row.text, "ham", admin.id if admin else None)
+        # 申诉通过，增加用户信誉分
+        if row.author:
+            row.author.credit_score = min(200, (row.author.credit_score or 100) + 10)
     else:
         row.status = "blocked"
         row.prediction = "spam"
         row.manual_review_status = "confirmed_spam"
         _upsert_manual_sample(row.text, "spam", admin.id if admin else None)
+        # 申诉驳回，减少用户信誉分
+        if row.author:
+            row.author.credit_score = max(0, (row.author.credit_score or 100) - 5)
 
     db.session.commit()
     return ok(_serialize_post(row), "申诉处理完成")
@@ -404,6 +410,12 @@ def update_user(user_id: int):
         user.phone = (payload.get("phone") or "").strip()
     if "is_admin" in payload:
         user.is_admin = bool(payload.get("is_admin"))
+    if "credit_score" in payload:
+        try:
+            credit = int(payload.get("credit_score", 100))
+            user.credit_score = max(0, min(200, credit))
+        except Exception:
+            pass
     if payload.get("password"):
         if len(payload["password"]) < 6:
             return fail("密码至少6位", 400)
@@ -427,3 +439,73 @@ def delete_user(user_id: int):
     db.session.commit()
     return ok({}, "用户已删除")
 
+
+@admin_bp.put("/users/<int:user_id>/credit")
+@admin_required
+def update_user_credit(user_id: int):
+    """手动调整用户信誉分"""
+    user = User.query.get(user_id)
+    if not user:
+        return fail("用户不存在", 404)
+
+    payload = request.get_json(silent=True) or {}
+    try:
+        credit = int(payload.get("credit_score", user.credit_score or 100))
+        credit = max(0, min(200, credit))
+    except Exception:
+        return fail("信誉分必须是0-200之间的整数", 400)
+
+    user.credit_score = credit
+    db.session.commit()
+    return ok(user.to_dict(), "信誉分已更新")
+
+
+@admin_bp.post("/users/recalculate-credit")
+@admin_required
+def recalculate_all_credit():
+    """根据用户发布历史和申诉通过率重新计算信誉分"""
+    users = User.query.filter_by(is_admin=False).all()
+    updated_count = 0
+
+    for user in users:
+        posts = ContentPost.query.filter_by(user_id=user.id).all()
+        if not posts:
+            continue
+
+        # 计算发布成功率
+        published_count = sum(1 for p in posts if p.status == "published")
+        blocked_count = sum(1 for p in posts if p.status == "blocked")
+        total_count = len(posts)
+
+        if total_count == 0:
+            continue
+
+        publish_ratio = published_count / total_count
+
+        # 计算申诉通过率
+        appeals = [p for p in posts if p.appeal_status != "none"]
+        approved_appeals = sum(1 for p in appeals if p.appeal_status == "approved")
+        appeal_ratio = approved_appeals / len(appeals) if appeals else 0
+
+        # 基础信誉分：发布成功率贡献
+        base_score = 100
+        if publish_ratio >= 0.9:
+            base_score += 30  # 90%以上发布成功，+30
+        elif publish_ratio >= 0.7:
+            base_score += 15  # 70%以上，+15
+        elif publish_ratio < 0.5:
+            base_score -= 20  # 低于50%，-20
+
+        # 申诉通过率贡献
+        if appeal_ratio >= 0.8 and len(appeals) >= 3:
+            base_score += 20  # 80%以上申诉通过且有3次以上申诉，+20
+        elif appeal_ratio >= 0.5 and len(appeals) >= 2:
+            base_score += 10
+
+        # 限制范围
+        user.credit_score = max(0, min(200, base_score))
+        updated_count += 1
+
+    db.session.commit()
+    return ok({"updated_count": updated_count}, "信誉分批量重算完成")
+

backend/app/routes/content_routes.py

@@ -77,12 +77,22 @@ def _resolve_recipient(payload: dict, visibility: str, current_user_id: int):
     return recipient, None
 
 
-def _predict_and_decide(text: str) -> tuple[dict, float, bool]:
+def _predict_and_decide(text: str, user_credit: int = 100) -> tuple[dict, float, bool]:
+    """根据用户信誉分调整阈值系数。信誉分越高，阈值越高（降低敏感度）"""
     clf = _ensure_ready()
     result = clf.predict(text)
-    threshold = float(_get_config().spam_threshold)
-    blocked = float(result["spam_probability"]) >= threshold
-    return result, threshold, blocked
+    base_threshold = float(_get_config().spam_threshold)
+
+    # 信誉分影响阈值系数：credit 0-200，默认100
+    # credit > 100：阈值提高（降低敏感度，减少误判）
+    # credit < 100：阈值降低（提高敏感度，加强拦截）
+    # 系数范围：0.85 - 1.15
+    credit_factor = 1.0 + (user_credit - 100) * 0.0015  # 每10分变化1.5%
+    credit_factor = max(0.85, min(1.15, credit_factor))
+
+    adjusted_threshold = base_threshold * credit_factor
+    blocked = float(result["spam_probability"]) >= adjusted_threshold
+    return result, adjusted_threshold, blocked
 
 
 @content_bp.post("/publish")
@@ -103,7 +113,7 @@ def publish_text():
     if err:
         return fail(err, 400)
 
-    result, threshold, blocked = _predict_and_decide(text)
+    result, threshold, blocked = _predict_and_decide(text, user.credit_score or 100)
 
     post = ContentPost(
         user_id=user.id,
@@ -134,6 +144,13 @@ def publish_text():
 
     db.session.add(post)
     db.session.add(detect_log)
+
+    # 发布成功（未被拦截），小幅增加信誉分；被拦截则小幅减少
+    if not blocked:
+        user.credit_score = min(200, (user.credit_score or 100) + 1)
+    else:
+        user.credit_score = max(0, (user.credit_score or 100) - 2)
+
     db.session.commit()
 
     feedback = "发布成功" if not blocked else "疑似垃圾信息，系统已拦截，可提交申诉"
@@ -171,7 +188,7 @@ def edit_post(post_id: int):
     if err:
         return fail(err, 400)
 
-    result, threshold, blocked = _predict_and_decide(text)
+    result, threshold, blocked = _predict_and_decide(text, user.credit_score or 100)
 
     post.text = result["text"]
     post.visibility = visibility

backend/app/routes/spam_routes.py

@@ -32,6 +32,15 @@ def _threshold() -> float:
     return float(row.spam_threshold) if row else 0.75
 
 
+def _adjusted_threshold(user_credit: int = 100) -> float:
+    """根据用户信誉分调整阈值"""
+    base_threshold = _threshold()
+    # 系数范围：0.85 - 1.15
+    credit_factor = 1.0 + (user_credit - 100) * 0.0015
+    credit_factor = max(0.85, min(1.15, credit_factor))
+    return base_threshold * credit_factor
+
+
 @spam_bp.post("/predict")
 @jwt_required()
 def predict_one():
@@ -46,7 +55,7 @@ def predict_one():
 
     clf = _ensure_ready()
     result = clf.predict(text)
-    threshold = _threshold()
+    threshold = _adjusted_threshold(user.credit_score or 100)
     blocked = float(result["spam_probability"]) >= threshold
 
     row = SpamPredictionLog(
@@ -82,7 +91,7 @@ def predict_batch():
     clf = _ensure_ready()
     rows = []
     results = []
-    threshold = _threshold()
+    threshold = _adjusted_threshold(user.credit_score or 100)
 
     for text in items:
         content = (text or "").strip()