203 lines
No EOL
7.6 KiB
Python
203 lines
No EOL
7.6 KiB
Python
import logging
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import time
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from collections import deque, defaultdict
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from dataclasses import dataclass
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from aiogram import BaseMiddleware, Router
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from aiogram.types import Message
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from typing import Dict, Callable, Awaitable, Any
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router = Router()
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# 根据某只狐狐自己发的计算公式试图重构了这部分
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# 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
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# AI 太好用了你们知道吗.jpg
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# 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
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@dataclass
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class UserMetrics:
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"""用户指标数据类"""
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cai_count: int = 0 # 卖菜度发言数
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xm_count: int = 0 # 羡慕度发言数
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nsfw_count: int = 0 # NSFW度发言数
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antisocial_count: int = 0 # 反社会度发言数
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total_count: int = 0 # 总发言数
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neutral_count: int = 0 # 中性发言数（用于水群频率）
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class RikkiMiddleware(BaseMiddleware):
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def __init__(self, target_user_id: str = "5545347637"):
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# 存储每个用户的触发几率，初始值在40-50%之间
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self.user_probabilities: Dict[str, float] = {}
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# 触发关键词
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self.xm_keywords = ["啃", "羡慕", "xm", "xmsl", "羡慕死了", "我菜"]
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self.cai_keywords = ["菜", "菜了", "菜死了", "我菜", "废物"]
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self.nsfw_keywords = ["kig", "电你", "被电", "⚡", "tk", "打", "🌿我"]
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self.target_user_id = target_user_id
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# 权重配置
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self.weights = {
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'w_v': 20, # 卖菜度权重
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'w_m': 15, # 羡慕度权重
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'w_n': 30, # NSFW度权重
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'w_a': 30, # 反社会度权重
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'lambda': 1, # 水群缓冲系数
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}
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# 放大系数
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self.amplifiers = {
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'alpha_v': 2.5, # 卖菜度放大系数
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'beta_m': 2.0, # 羡慕度放大系数
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'gamma_n': 5.0, # NSFW度放大系数
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'alpha_a': 5.0, # 反社会度放大系数
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}
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# 用户数据存储
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self.user_metrics: Dict[str, UserMetrics] = defaultdict(UserMetrics)
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self.user_message_times: Dict[str, deque] = defaultdict(lambda: deque())
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self.has_sent_warning: Dict[str, bool] = defaultdict(bool)
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def record_message(self, user_id: str) -> None:
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"""记录用户发送消息的时间"""
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current_time = time.time()
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user_messages = self.user_message_times[user_id]
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# 添加当前时间戳
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user_messages.append(current_time)
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# 清理24小时前的记录
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cutoff_time = current_time - 24 * 60 * 60 # 24小时前
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while user_messages and user_messages[0] < cutoff_time:
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user_messages.popleft()
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def get_24h_message_count(self, user_id: str) -> int:
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"""获取用户24小时内的发言次数"""
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current_time = time.time()
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cutoff_time = current_time - 24 * 60 * 60
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# 清理过期记录
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user_messages = self.user_message_times[user_id]
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while user_messages and user_messages[0] < cutoff_time:
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user_messages.popleft()
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return len(user_messages)
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def classify_message(self, message: str, user_id: str) -> None:
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"""分析消息内容并更新用户指标"""
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metrics = self.user_metrics[user_id]
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metrics.total_count += 1
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message_lower = message.lower()
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classified = False
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# 检查各类关键词
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for keyword in self.cai_keywords:
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if keyword in message_lower:
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metrics.cai_count += 1
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classified = True
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break
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for keyword in self.xm_keywords:
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if keyword in message_lower:
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metrics.xm_count += 1
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classified = True
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break
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for keyword in self.nsfw_keywords:
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if keyword in message_lower:
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metrics.nsfw_count += 1
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classified = True
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break
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# 如果没有匹配任何特征关键词，算作中性发言
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if not classified:
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metrics.neutral_count += 1
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def calculate_qianda_score(self, user_id: str) -> float:
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"""计算用户欠打度"""
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if user_id != self.target_user_id:
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return 0.0
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metrics = self.user_metrics[user_id]
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if metrics.total_count == 0:
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return 0.0
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# 计算各项指标 [0,1]
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v = min(1, self.amplifiers['alpha_v'] * (metrics.cai_count / metrics.total_count))
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m = min(1, self.amplifiers['beta_m'] * (metrics.xm_count / metrics.total_count))
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n = min(1, self.amplifiers['gamma_n'] * (metrics.nsfw_count / metrics.total_count))
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a = min(1, self.amplifiers['alpha_a'] * (metrics.antisocial_count / metrics.total_count))
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# 计算水群频率缓冲因子 W
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# N 是24小时内中性发言次数，K设为7
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k = 7
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import math
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neutral_24h = min(metrics.neutral_count, self.get_24h_message_count(user_id))
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w = min(max(0, 1 - math.log(neutral_24h + 1) / k), 1)
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# 应用欠打度公式
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base_score = (
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v * self.weights['w_v'] +
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m * self.weights['w_m'] +
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n * self.weights['w_n'] +
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a * self.weights['w_a']
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)
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water_factor = 1 + self.weights['lambda'] * (1 - w)
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final_score = min(100, base_score * water_factor)
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return final_score
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async def __call__(
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self,
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handler: Callable[[Message, Dict[str, Any]], Awaitable[Any]],
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event: Message,
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data: Dict[str, Any]) -> Any:
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"""
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处理消息的主要方法
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"""
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if not event.text:
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return await handler(event, data)
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user_id = str(event.from_user.id)
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if event.chat.type in ['group', 'supergroup'] and user_id == self.target_user_id:
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self.classify_message(event.text, user_id)
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score = self.calculate_qianda_score(user_id)
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logging.debug(f"当前欠打度: {score:.2f}%")
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# 警告阈值：80%
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if score >= 80.0 and not self.has_sent_warning[user_id]:
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self.has_sent_warning[user_id] = True
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await event.reply("泥欠打了")
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# 检查是否触发
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if score >= 100.0:
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# 重置数据
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self.user_metrics[user_id] = UserMetrics()
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self.has_sent_warning[user_id] = False
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await event.reply("/打")
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if event.text and event.text.startswith('/打') and event.reply_to_message and str(
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event.reply_to_message.from_user.id) == self.target_user_id:
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# 增加反社会度作为惩罚
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metrics = self.user_metrics[self.target_user_id]
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metrics.antisocial_count += 2 # 被打时额外增加反社会度
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score = self.calculate_qianda_score(user_id)
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logging.debug("当前欠打的几率是{}".format(score))
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return await handler(event, data)
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def get_user_status(self, user_id: str) -> str:
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"""获取用户当前几率状态（用于调试）"""
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score = self.calculate_qianda_score(user_id)
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metrics = self.user_metrics[user_id]
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return (f"欠打度: {score:.2f}%\n"
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f"卖菜: {metrics.cai_count}, 羡慕: {metrics.xm_count}, "
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f"NSFW: {metrics.nsfw_count},"
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f"反社会: {metrics.antisocial_count}, 中性: {metrics.neutral_count}\n"
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f"总发言: {metrics.total_count}") |