194 lines
7.7 KiB
Python
194 lines
7.7 KiB
Python
import re
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import random
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from datetime import datetime, time, timedelta
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import toml
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import os
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from loguru import logger
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class RoomState:
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"""每个群的独立状态"""
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def __init__(self):
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self.participation_score = 0.0
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self.last_active_time = datetime.now()
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self.last_bot_reply_time = None # 上次机器人回复的时间
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class InterventionBot:
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def __init__(self, config_path=None):
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# 加载配置
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self.config = {}
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if config_path and os.path.exists(config_path):
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self.config = toml.load(config_path)
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# 从配置中获取关键词
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keywords = self.config.get("Keywords", {})
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self.emojis = keywords.get("emojis", [])
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self.hot_topics = keywords.get("hot_topics", [])
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self.fish_keywords = keywords.get("fish_keywords", [])
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self.tech_keywords = keywords.get("tech_keywords", [])
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self.news_keywords = keywords.get("news_keywords",[])
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# 拟人化配置
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hl_config = self.config.get("HumanLike", {})
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self.max_energy = hl_config.get("max_energy", 100.0)
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self.energy_recovery_rate = hl_config.get("energy_recovery_per_minute", 1.0)
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self.energy_cost = hl_config.get("energy_cost_per_reply", 15.0)
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self.participation_inc = hl_config.get("participation_increase_per_msg", 5.0)
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self.topic_bonus = hl_config.get("topic_match_bonus", 15.0)
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self.participation_threshold = hl_config.get("participation_threshold", 20.0)
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self.participation_drop = hl_config.get("participation_drop_factor", 0.8)
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self.base_prob = hl_config.get("base_reply_probability", 0.6)
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# 机器人全局状态
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self.current_energy = self.max_energy
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self.last_energy_update_time = datetime.now()
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# 群组状态 {room_id: RoomState}
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self.room_states = {}
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# 辅助功能：早晨时间窗口
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time_window = self.config.get("TimeWindow", {})
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self.morning_window = (
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time(time_window.get("morning_start_hour", 8), time_window.get("morning_start_minute", 0)),
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time(time_window.get("morning_end_hour", 8), time_window.get("morning_end_minute", 30))
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)
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def _get_room_state(self, room_id):
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if room_id not in self.room_states:
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self.room_states[room_id] = RoomState()
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return self.room_states[room_id]
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def _update_energy(self):
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"""更新全局体力值"""
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now = datetime.now()
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minutes_passed = (now - self.last_energy_update_time).total_seconds() / 60.0
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recovered = minutes_passed * self.energy_recovery_rate
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self.current_energy = min(self.max_energy, self.current_energy + recovered)
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self.last_energy_update_time = now
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logger.debug(f"[Energy] Recovered {recovered:.2f}, Current: {self.current_energy:.2f}")
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def detect_topic(self, message):
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if not isinstance(message, str):
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return None
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message_lower = message.lower()
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if any(keyword in message_lower for keyword in self.fish_keywords):
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return "fish"
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if any(keyword in message_lower for keyword in self.tech_keywords):
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return "tech"
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if any(keyword in message_lower for keyword in self.news_keywords):
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return "news"
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if any(keyword in message_lower for keyword in self.hot_topics):
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return "hot_topic"
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return None
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def is_morning_window(self, timestamp):
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try:
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# 简化时间处理，这里假设timestamp通常是当前时间附近
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now = datetime.now()
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return self.morning_window[0] <= now.time() <= self.morning_window[1]
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except:
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return False
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def calculate_participation_boost(self, message, messages, is_at=False):
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"""计算这条消息带来的参与度提升"""
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if is_at:
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return 100.0 # 被AT直接拉满
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boost = self.participation_inc
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# 话题加成
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topic = self.detect_topic(message)
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if topic:
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boost += self.topic_bonus
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# 关键词加成 (早安/表情等)
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if any(e in message for e in self.emojis):
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boost += 5
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if "签到" in message or "早" in message:
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if self.is_morning_window(None):
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boost += 20 # 早上问好权重高
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return boost
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def should_intervene(self, room_id, timestamp, message, messages, chat_log, is_at=False):
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"""
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核心判定逻辑
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:param room_id: 群ID
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:param timestamp: 消息时间
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:param message: 当前消息内容
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:param messages: 最近消息列表（文本）
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:param chat_log: 完整聊天记录对象
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:param is_at: 是否被AT
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"""
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self._update_energy()
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state = self._get_room_state(room_id)
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# 1. 增加参与度（Listening）
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boost = self.calculate_participation_boost(message, messages, is_at)
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# 连续对话奖励：如果机器人在最近 2 分钟内回复过，说明可能在对话中，参与度增加翻倍
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if state.last_bot_reply_time:
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time_since_last_reply = (datetime.now() - state.last_bot_reply_time).total_seconds()
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if time_since_last_reply < 120: # 2分钟内
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boost *= 2.0
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logger.debug(f"[{room_id}] 连续对话奖励触发 (上次回复 {int(time_since_last_reply)}s 前)")
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state.participation_score += boost
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state.last_active_time = datetime.now()
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logger.debug(f"[{room_id}] 收到消息: '{message}' | 参与度+{boost} -> {state.participation_score:.2f} | 体力: {self.current_energy:.2f}")
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# 2. 检查阈值
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if state.participation_score < self.participation_threshold:
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return False
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# 3. 检查体力
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if self.current_energy < self.energy_cost:
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logger.debug(f"[{room_id}] 体力不足 ({self.current_energy:.2f} < {self.energy_cost})，跳过")
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return False
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# 4. 概率判定
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# 参与度越高，概率越高；体力越高，概率越高
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# 归一化因子
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participation_factor = min(state.participation_score / 100.0, 1.5) # 上限1.5倍
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energy_factor = self.current_energy / self.max_energy
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final_prob = self.base_prob * participation_factor * energy_factor
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# 被AT必然回复
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if is_at:
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final_prob = 1.0
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# 随机判定
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rand_val = random.random()
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should_reply = rand_val < final_prob
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logger.debug(f"[{room_id}] 判定: Prob={final_prob:.2f} (Base={self.base_prob} * Part={participation_factor:.2f} * Energy={energy_factor:.2f}) vs Rand={rand_val:.2f} -> {should_reply}")
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if should_reply:
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# 扣除消耗
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self.current_energy -= self.energy_cost
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# 更新状态
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state.last_bot_reply_time = datetime.now()
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# 降低参与度（满足了表达欲）
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# 改为减法，保留部分参与度以便连续对话
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# 如果是高参与度（>50），减去 30；否则减半
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if state.participation_score > 50:
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state.participation_score = max(0, state.participation_score - 40)
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else:
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state.participation_score *= 0.5
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return True
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return False
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def rule_high_reply_rate(self, timestamp, chat_log):
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# 保留这个方法以兼容 main.py 的调用，或者在 main.py 中移除
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# 这里我们可以简单的返回 False，因为新的逻辑已经包含了频率控制（通过体力值）
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return False
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