mirror of https://github.com/Mai-with-u/MaiBot.git
feat: 更新年度报告数据模型，添加深夜回复和最喜欢的回复统计
墨梓柒
parent
71a85667e3
commit
c3da65c259
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@ -54,7 +54,6 @@ class SocialNetworkData(BaseModel):
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"""社交网络数据"""
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total_groups: int = Field(0, description="加入的群组总数")
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new_friends_count: int = Field(0, description="今年新认识的朋友数")
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top_groups: List[Dict[str, Any]] = Field(default_factory=list, description="话痨群组TOP3")
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top_users: List[Dict[str, Any]] = Field(default_factory=list, description="互动最多的用户TOP3")
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at_count: int = Field(0, description="被@次数")
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@ -71,6 +70,7 @@ class BrainPowerData(BaseModel):
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favorite_model: Optional[str] = Field(None, description="最爱用的模型")
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favorite_model_count: int = Field(0, description="最爱模型的调用次数")
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model_distribution: List[Dict[str, Any]] = Field(default_factory=list, description="模型使用分布")
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top_reply_models: List[Dict[str, Any]] = Field(default_factory=list, description="最喜欢的回复模型TOP5")
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most_expensive_cost: float = Field(0.0, description="最昂贵的一次思考花费")
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most_expensive_time: Optional[str] = Field(None, description="最昂贵思考的时间")
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top_token_consumers: List[Dict[str, Any]] = Field(default_factory=list, description="烧钱大户TOP3")
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@ -89,13 +89,15 @@ class ExpressionVibeData(BaseModel):
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"""个性与表达数据"""
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top_emoji: Optional[Dict[str, Any]] = Field(None, description="表情包之王")
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top_emojis: List[Dict[str, Any]] = Field(default_factory=list, description="TOP5表情包")
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top_expressions: List[Dict[str, Any]] = Field(default_factory=list, description="最常用的表达风格")
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top_emojis: List[Dict[str, Any]] = Field(default_factory=list, description="TOP3表情包")
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top_expressions: List[Dict[str, Any]] = Field(default_factory=list, description="印象最深刻的表达风格")
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rejected_expression_count: int = Field(0, description="被拒绝的表达次数")
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checked_expression_count: int = Field(0, description="已检查的表达次数")
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total_expressions: int = Field(0, description="表达总数")
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action_types: List[Dict[str, Any]] = Field(default_factory=list, description="动作类型分布")
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image_processed_count: int = Field(0, description="处理的图片数量")
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late_night_reply: Optional[Dict[str, Any]] = Field(None, description="深夜还在回复")
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favorite_reply: Optional[Dict[str, Any]] = Field(None, description="最喜欢的回复")
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class AchievementData(BaseModel):
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@ -231,25 +233,19 @@ async def get_time_footprint(year: int = 2025) -> TimeFootprintData:
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async def get_social_network(year: int = 2025) -> SocialNetworkData:
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"""获取社交网络数据"""
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from src.config.config import global_config
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data = SocialNetworkData()
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start_ts, end_ts = get_year_time_range(year)
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# 获取 bot 自身的 QQ 账号，用于过滤
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bot_qq = str(global_config.bot.qq_account or "")
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try:
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# 1. 加入的群组总数
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data.total_groups = ChatStreams.select().where(ChatStreams.group_id.is_null(False)).count()
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# 2. 今年新认识的朋友数
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data.new_friends_count = (
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PersonInfo.select()
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.where(
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(PersonInfo.know_times.is_null(False))
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& (PersonInfo.know_times >= start_ts)
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& (PersonInfo.know_times <= end_ts)
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)
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.count()
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)
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# 3. 话痨群组 TOP3
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# 2. 话痨群组 TOP3
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top_groups_query = (
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Messages.select(
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Messages.chat_info_group_id,
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@ -274,7 +270,7 @@ async def get_social_network(year: int = 2025) -> SocialNetworkData:
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for row in top_groups_query.dicts()
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]
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# 4. 互动最多的用户 TOP3
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# 3. 互动最多的用户 TOP3（过滤 bot 自身）
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top_users_query = (
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Messages.select(
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Messages.user_id,
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@ -285,6 +281,7 @@ async def get_social_network(year: int = 2025) -> SocialNetworkData:
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(Messages.time >= start_ts)
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& (Messages.time <= end_ts)
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& (Messages.user_id.is_null(False))
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& (Messages.user_id != bot_qq) # 过滤 bot 自身
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)
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.group_by(Messages.user_id)
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.order_by(fn.COUNT(Messages.id).desc())
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@ -299,7 +296,7 @@ async def get_social_network(year: int = 2025) -> SocialNetworkData:
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for row in top_users_query.dicts()
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]
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# 5. 被@次数
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# 4. 被@次数
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data.at_count = (
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Messages.select()
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.where(
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@ -310,7 +307,7 @@ async def get_social_network(year: int = 2025) -> SocialNetworkData:
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.count()
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)
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# 6. 被提及次数
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# 5. 被提及次数
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data.mentioned_count = (
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Messages.select()
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.where(
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@ -321,15 +318,17 @@ async def get_social_network(year: int = 2025) -> SocialNetworkData:
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.count()
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)
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# 7. 最长情陪伴的用户
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# 找出跨度时间最长的用户
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# 6. 最长情陪伴的用户（过滤 bot 自身）
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companion_query = (
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ChatStreams.select(
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ChatStreams.user_id,
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ChatStreams.user_nickname,
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(ChatStreams.last_active_time - ChatStreams.create_time).alias("duration"),
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)
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.where(ChatStreams.user_id.is_null(False))
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.where(
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(ChatStreams.user_id.is_null(False))
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& (ChatStreams.user_id != bot_qq) # 过滤 bot 自身
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)
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.order_by((ChatStreams.last_active_time - ChatStreams.create_time).desc())
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.limit(1)
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)
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@ -403,14 +402,19 @@ async def get_brain_power(year: int = 2025) -> BrainPowerData:
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data.most_expensive_cost = round(expensive_result.cost or 0, 4)
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data.most_expensive_time = expensive_result.timestamp.strftime("%Y-%m-%d %H:%M:%S")
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# 4. 烧钱大户 TOP3 (按用户)
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# 4. 烧钱大户 TOP3 (按用户，过滤 system)
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consumer_query = (
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LLMUsage.select(
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LLMUsage.user_id,
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fn.COALESCE(fn.SUM(LLMUsage.cost), 0).alias("cost"),
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fn.COALESCE(fn.SUM(LLMUsage.total_tokens), 0).alias("tokens"),
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)
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.where((LLMUsage.timestamp >= start_dt) & (LLMUsage.timestamp <= end_dt))
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.where(
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(LLMUsage.timestamp >= start_dt)
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& (LLMUsage.timestamp <= end_dt)
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& (LLMUsage.user_id != "system") # 过滤 system 用户
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& (LLMUsage.user_id.is_null(False))
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)
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.group_by(LLMUsage.user_id)
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.order_by(fn.SUM(LLMUsage.cost).desc())
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.limit(3)
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@ -424,7 +428,32 @@ async def get_brain_power(year: int = 2025) -> BrainPowerData:
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for row in consumer_query.dicts()
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]
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# 5. 高冷指数 (沉默率) - 基于 ActionRecords
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# 5. 最喜欢的回复模型 TOP5（按模型的回复次数统计，只统计 replyer 调用）
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# 假设 replyer 调用有特定的 model_assign_name 格式或可以通过某种方式识别
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reply_model_query = (
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LLMUsage.select(
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fn.COALESCE(LLMUsage.model_assign_name, LLMUsage.model_name).alias("model"),
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fn.COUNT(LLMUsage.id).alias("count"),
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)
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.where(
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(LLMUsage.timestamp >= start_dt)
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& (LLMUsage.timestamp <= end_dt)
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& (
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LLMUsage.model_assign_name.contains("replyer")
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| LLMUsage.model_assign_name.contains("回复")
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| LLMUsage.model_assign_name.is_null(True) # 包含没有 assign_name 的情况
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)
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)
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.group_by(fn.COALESCE(LLMUsage.model_assign_name, LLMUsage.model_name))
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.order_by(fn.COUNT(LLMUsage.id).desc())
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.limit(5)
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)
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data.top_reply_models = [
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{"model": row["model"], "count": row["count"]}
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for row in reply_model_query.dicts()
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]
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# 6. 高冷指数 (沉默率) - 基于 ActionRecords
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total_actions = ActionRecords.select().where(
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(ActionRecords.time >= start_ts) & (ActionRecords.time <= end_ts)
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).count()
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@ -586,7 +615,12 @@ async def get_expression_vibe(year: int = 2025) -> ExpressionVibeData:
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.count()
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)
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# 6. 动作类型分布 (非 reply 动作)
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# 6. 动作类型分布 (过滤无意义的动作)
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# 过滤掉: no_reply_until_call, make_question, no_action, wait, complete_talk, listening, block_and_ignore
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excluded_actions = [
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"reply", "no_reply", "no_reply_until_call", "make_question",
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"no_action", "wait", "complete_talk", "listening", "block_and_ignore"
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]
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action_query = (
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ActionRecords.select(
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ActionRecords.action_name,
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@ -595,8 +629,7 @@ async def get_expression_vibe(year: int = 2025) -> ExpressionVibeData:
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.where(
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(ActionRecords.time >= start_ts)
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& (ActionRecords.time <= end_ts)
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& (ActionRecords.action_name != "reply")
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& (ActionRecords.action_name != "no_reply")
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& (ActionRecords.action_name.not_in(excluded_actions))
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)
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.group_by(ActionRecords.action_name)
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.order_by(fn.COUNT(ActionRecords.id).desc())
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@ -618,6 +651,96 @@ async def get_expression_vibe(year: int = 2025) -> ExpressionVibeData:
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.count()
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)
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# 8. 深夜还在回复 (0-6点最晚的10条消息中随机抽取一条)
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import random
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late_night_messages = list(
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Messages.select(
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Messages.time,
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Messages.processed_plain_text,
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Messages.display_message,
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)
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.where(
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(Messages.time >= start_ts)
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& (Messages.time <= end_ts)
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& (Messages.is_self == True) # bot 发送的消息
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)
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.order_by(Messages.time.desc())
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)
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# 筛选出0-6点的消息
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late_night_filtered = []
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for msg in late_night_messages:
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msg_dt = datetime.fromtimestamp(msg.time)
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hour = msg_dt.hour
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if 0 <= hour < 6: # 0点到6点
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late_night_filtered.append({
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"time": msg.time,
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"hour": hour,
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"minute": msg_dt.minute,
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"content": msg.processed_plain_text or msg.display_message or "",
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"datetime_str": msg_dt.strftime("%H:%M"),
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})
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if len(late_night_filtered) >= 10:
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break
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if late_night_filtered:
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selected = random.choice(late_night_filtered)
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content = selected["content"][:50] + "..." if len(selected["content"]) > 50 else selected["content"]
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data.late_night_reply = {
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"time": selected["datetime_str"],
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"content": content,
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}
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# 9. 最喜欢的回复（按 action_data 统计回复内容出现次数）
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from collections import Counter
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import json as json_lib
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reply_records = (
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ActionRecords.select(ActionRecords.action_data)
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.where(
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(ActionRecords.time >= start_ts)
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& (ActionRecords.time <= end_ts)
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& (ActionRecords.action_name == "reply")
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& (ActionRecords.action_data.is_null(False))
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& (ActionRecords.action_data != "")
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)
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)
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reply_contents = []
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for record in reply_records:
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try:
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# action_data 可能是 JSON 格式
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action_data = record.action_data
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if action_data:
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# 尝试解析 JSON
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try:
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parsed = json_lib.loads(action_data)
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if isinstance(parsed, dict) and "content" in parsed:
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content = parsed["content"]
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elif isinstance(parsed, str):
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content = parsed
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else:
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content = str(parsed)
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except (json_lib.JSONDecodeError, TypeError):
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content = action_data
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# 只统计有意义的回复（长度大于2）
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if content and len(content) > 2:
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reply_contents.append(content)
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except Exception:
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continue
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if reply_contents:
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content_counter = Counter(reply_contents)
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most_common = content_counter.most_common(1)
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if most_common:
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fav_content, fav_count = most_common[0]
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# 截断过长的内容
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display_content = fav_content[:50] + "..." if len(fav_content) > 50 else fav_content
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data.favorite_reply = {
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"content": display_content,
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"count": fav_count,
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}
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except Exception as e:
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logger.error(f"获取个性与表达数据失败: {e}")
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