feat: more accurate formula provided the fox itself

adapters/tg.py

@@ -25,6 +25,7 @@ from core.actions import handle_actions, handle_reverse_actions
 from core.stats import handle_stats_command
 from core.middleware.stats import MessageStatsMiddleware
 from core.unpin import handle_unpin_channel_message
+from core.rikki_hit import handle_query_hit_command
 
 TOKEN = getenv("BOT_TOKEN")
 
@@ -52,6 +53,7 @@ class TelegramAdapter:
         router.message(Command('about'))(handle_about_command)
         router.message(Command('ping'))(handle_ping_command)
         router.message(Command('tips'))(handle_tips_command)
+        router.message(Command('query_hit'))(handle_query_hit_command)
         # bitflip 模块
         router.message(Command('bitflip'))(handle_bitflip_command)
         # promote 模块

core/middleware/rikki.py

@@ -1,94 +1,203 @@
 import logging
-import random
+import time
+from collections import deque, defaultdict
+from dataclasses import dataclass
+
 from aiogram import BaseMiddleware, Router
 from aiogram.types import Message
-from typing import Dict, Optional, Callable, Awaitable, Any
+from typing import Dict, Callable, Awaitable, Any
 
 router = Router()
 
+# 根据某只狐狐自己发的计算公式试图重构了这部分
+# 这个代码的目的是计算用户的欠打度，因为他的公式是用 AI 生成的，所以我也用 AI 生成了这个代码
+# AI 太好用了你们知道吗.jpg
+# 然后限于条件（比如我没有模型可以拿来分类）对公式进行了一部分精简，比如去除了发情度和理性度的计算，加大了对于别人 /打 的惩罚力度
+@dataclass
+class UserMetrics:
+    """用户指标数据类"""
+    cai_count: int = 0  # 卖菜度发言数
+    xm_count: int = 0   # 羡慕度发言数
+    nsfw_count: int = 0 # NSFW度发言数
+    antisocial_count: int = 0 # 反社会度发言数
+    total_count: int = 0 # 总发言数
+    neutral_count: int = 0 # 中性发言数（用于水群频率）
+
+
 class RikkiMiddleware(BaseMiddleware):
     def __init__(self, target_user_id: str = "5545347637"):
         # 存储每个用户的触发几率，初始值在40-50%之间
         self.user_probabilities: Dict[str, float] = {}
         # 触发关键词
-        self.trigger_plus_keywords = ["啃","羡慕", "xm", "xmsl", "羡慕死了", "我菜"]
+        self.xm_keywords = ["啃", "羡慕", "xm", "xmsl", "羡慕死了", "我菜"]
-        self.trigger_plusplus_keywords = ["kig","电你","被电","⚡","tk","打"]
+        self.cai_keywords = ["菜", "菜了", "菜死了", "我菜", "废物"]
+        self.nsfw_keywords = ["kig", "电你", "被电", "⚡", "tk", "打", "🌿我"]
+
         self.target_user_id = target_user_id
 
-    def get_user_probability(self, user_id: str) -> float:
+        # 权重配置
-        """获取用户当前触发几率，如果不存在则初始化"""
+        self.weights = {
-        if user_id != self.target_user_id:
+            'w_v': 20,  # 卖菜度权重
-            return 0.0 # 非目标用户返回默认值
+            'w_m': 15,  # 羡慕度权重
-        if user_id not in self.user_probabilities:
+            'w_n': 30,  # NSFW度权重
-            self.user_probabilities[user_id] = random.uniform(10.0, 40.0)
+            'w_a': 30,  # 反社会度权重
-        return self.user_probabilities[user_id]
+            'lambda': 1,  # 水群缓冲系数
+        }
 
-    def update_probability(self, user_id: str, message: str, hit_by_others = False) -> float:
+        # 放大系数
-        """更新用户触发几率"""
+        self.amplifiers = {
-        if user_id != self.target_user_id:
+            'alpha_v': 2.5,  # 卖菜度放大系数
-            return 0.0 # 非目标用户不更新
+            'beta_m': 2.0,  # 羡慕度放大系数
-        current_prob = self.get_user_probability(user_id)
+            'gamma_n': 5.0,  # NSFW度放大系数
+            'alpha_a': 5.0,  # 反社会度放大系数
+        }
 
-        # 基础减少0.1%
+        # 用户数据存储
-        current_prob -= 0.1
+        self.user_metrics: Dict[str, UserMetrics] = defaultdict(UserMetrics)
+        self.user_message_times: Dict[str, deque] = defaultdict(lambda: deque())
+        self.has_sent_warning: Dict[str, bool] = defaultdict(bool)
 
-        # 检查是否包含触发关键词
+
-        for keyword in self.trigger_plus_keywords:
+    def record_message(self, user_id: str) -> None:
-            if keyword in message:
+        """记录用户发送消息的时间"""
-                current_prob *= 1.14
+        current_time = time.time()
+        user_messages = self.user_message_times[user_id]
+
+        # 添加当前时间戳
+        user_messages.append(current_time)
+
+        # 清理24小时前的记录
+        cutoff_time = current_time - 24 * 60 * 60  # 24小时前
+        while user_messages and user_messages[0] < cutoff_time:
+            user_messages.popleft()
+
+    def get_24h_message_count(self, user_id: str) -> int:
+        """获取用户24小时内的发言次数"""
+        current_time = time.time()
+        cutoff_time = current_time - 24 * 60 * 60
+
+        # 清理过期记录
+        user_messages = self.user_message_times[user_id]
+        while user_messages and user_messages[0] < cutoff_time:
+            user_messages.popleft()
+
+        return len(user_messages)
+
+    def classify_message(self, message: str, user_id: str) -> None:
+        """分析消息内容并更新用户指标"""
+        metrics = self.user_metrics[user_id]
+        metrics.total_count += 1
+
+        message_lower = message.lower()
+        classified = False
+
+        # 检查各类关键词
+        for keyword in self.cai_keywords:
+            if keyword in message_lower:
+                metrics.cai_count += 1
+                classified = True
                 break
-        for keyword in self.trigger_plusplus_keywords:
+
-            if keyword in message:
+        for keyword in self.xm_keywords:
-                current_prob *= 1.91
+            if keyword in message_lower:
+                metrics.xm_count += 1
+                classified = True
                 break
-        if hit_by_others:
-            # 如果被其他人触发，增加额外几率
-            current_prob += 20.0
-        self.user_probabilities[user_id] = current_prob
-        return current_prob
 
-    def should_trigger(self, user_id: str) -> bool:
+        for keyword in self.nsfw_keywords:
-        """检查是否应该触发回复"""
+            if keyword in message_lower:
+                metrics.nsfw_count += 1
+                classified = True
+                break
+
+        # 如果没有匹配任何特征关键词，算作中性发言
+        if not classified:
+            metrics.neutral_count += 1
+
+    def calculate_qianda_score(self, user_id: str) -> float:
+        """计算用户欠打度"""
         if user_id != self.target_user_id:
-            return False  # 非目标用户不触发
+            return 0.0
-        probability = self.get_user_probability(user_id)
+
-        return probability >= 100.0
+        metrics = self.user_metrics[user_id]
+
+        if metrics.total_count == 0:
+            return 0.0
+
+        # 计算各项指标 [0,1]
+        v = min(1, self.amplifiers['alpha_v'] * (metrics.cai_count / metrics.total_count))
+        m = min(1, self.amplifiers['beta_m'] * (metrics.xm_count / metrics.total_count))
+        n = min(1, self.amplifiers['gamma_n'] * (metrics.nsfw_count / metrics.total_count))
+        a = min(1, self.amplifiers['alpha_a'] * (metrics.antisocial_count / metrics.total_count))
+
+        # 计算水群频率缓冲因子 W
+        # N 是24小时内中性发言次数，K设为7
+        k = 7
+        import math
+        neutral_24h = min(metrics.neutral_count, self.get_24h_message_count(user_id))
+        w = min(max(0, 1 - math.log(neutral_24h + 1) / k), 1)
+
+        # 应用欠打度公式
+        base_score = (
+                v * self.weights['w_v'] +
+                m * self.weights['w_m'] +
+                n * self.weights['w_n'] +
+                a * self.weights['w_a']
+        )
+
+        water_factor = 1 + self.weights['lambda'] * (1 - w)
+
+        final_score = min(100, base_score * water_factor)
+
+        return final_score
 
-    def reset_probability(self, user_id: str):
-        """重置用户触发几率到初始随机值"""
-        self.user_probabilities[user_id] = random.uniform(40.0, 50.0)
 
     async def __call__(
             self,
             handler: Callable[[Message, Dict[str, Any]], Awaitable[Any]],
             event: Message,
-            data: Dict[str, Any]
+            data: Dict[str, Any]) -> Any:
-    ) -> Any:
         """
         处理消息的主要方法
         """
         if not event.text:
             return await handler(event, data)
+
         user_id = str(event.from_user.id)
         if event.chat.type in ['group', 'supergroup'] and user_id == self.target_user_id:
-            # 更新几率
+            self.classify_message(event.text, user_id)
-            self.update_probability(user_id, event.text)
+            score = self.calculate_qianda_score(user_id)
-            logging.debug("当前欠打的几率是{}".format(self.get_user_probability(user_id)))
+            logging.debug(f"当前欠打度: {score:.2f}%")
 
-        if event.text and event.text.startswith('/打') and event.reply_to_message and str(event.reply_to_message.from_user.id) == self.target_user_id:
+            # 警告阈值：80%
-            self.update_probability(user_id, event.text, hit_by_others=True)
+            if score >= 80.0 and not self.has_sent_warning[user_id]:
-            logging.debug("当前欠打的几率是{}".format(self.get_user_probability(user_id)))
+                self.has_sent_warning[user_id] = True
-        if self.get_user_probability(user_id) >= 80.0:
+                await event.reply("泥欠打了")
-            await event.reply("泥欠打了")
+
+            # 检查是否触发
+            if score >= 100.0:
+                # 重置数据
+                self.user_metrics[user_id] = UserMetrics()
+                self.has_sent_warning[user_id] = False
+                await event.reply("/打")
+
+        if event.text and event.text.startswith('/打') and event.reply_to_message and str(
+                event.reply_to_message.from_user.id) == self.target_user_id:
+            # 增加反社会度作为惩罚
+            metrics = self.user_metrics[self.target_user_id]
+            metrics.antisocial_count += 2  # 被打时额外增加反社会度
+            score = self.calculate_qianda_score(user_id)
+            logging.debug("当前欠打的几率是{}".format(score))
 
-        # 检查是否触发
-        if self.should_trigger(user_id):
-            # 重置几率
-            self.reset_probability(user_id)
-            await event.reply("/打")
         return await handler(event, data)
 
-    def get_probability_status(self, user_id: str) -> str:
+    def get_user_status(self, user_id: str) -> str:
         """获取用户当前几率状态（用于调试）"""
-        prob = self.get_user_probability(user_id)
+        score = self.calculate_qianda_score(user_id)
-        return f"当前触发几率: {prob:.2f}%"
+        metrics = self.user_metrics[user_id]
+
+        return (f"欠打度: {score:.2f}%\n"
+                f"卖菜: {metrics.cai_count}, 羡慕: {metrics.xm_count}, "
+                f"NSFW: {metrics.nsfw_count},"
+                f"反社会: {metrics.antisocial_count}, 中性: {metrics.neutral_count}\n"
+                f"总发言: {metrics.total_count}")

core/rikki_hit.py

@@ -0,0 +1,6 @@
+from aiogram.types import Message
+from core.middleware.rikki import RikkiMiddleware
+
+async def handle_query_hit_command(message: Message) -> None:
+    hit_status = RikkiMiddleware().get_user_status("5545347637")
+    await message.reply(hit_status)