import asyncio import concurrent.futures import json from collections import defaultdict from datetime import datetime, timedelta from typing import Any, Dict, Tuple, List from src.common.logger import get_logger from src.common.database.database import db from src.common.database.database_model import OnlineTime, LLMUsage, Messages, ActionRecords from src.manager.async_task_manager import AsyncTask from src.manager.local_store_manager import local_storage from src.config.config import global_config logger = get_logger("maibot_statistic") # 统计数据的键 TOTAL_REQ_CNT = "total_requests" TOTAL_COST = "total_cost" REQ_CNT_BY_TYPE = "requests_by_type" REQ_CNT_BY_USER = "requests_by_user" REQ_CNT_BY_MODEL = "requests_by_model" REQ_CNT_BY_MODULE = "requests_by_module" IN_TOK_BY_TYPE = "in_tokens_by_type" IN_TOK_BY_USER = "in_tokens_by_user" IN_TOK_BY_MODEL = "in_tokens_by_model" IN_TOK_BY_MODULE = "in_tokens_by_module" OUT_TOK_BY_TYPE = "out_tokens_by_type" OUT_TOK_BY_USER = "out_tokens_by_user" OUT_TOK_BY_MODEL = "out_tokens_by_model" OUT_TOK_BY_MODULE = "out_tokens_by_module" TOTAL_TOK_BY_TYPE = "tokens_by_type" TOTAL_TOK_BY_USER = "tokens_by_user" TOTAL_TOK_BY_MODEL = "tokens_by_model" TOTAL_TOK_BY_MODULE = "tokens_by_module" COST_BY_TYPE = "costs_by_type" COST_BY_USER = "costs_by_user" COST_BY_MODEL = "costs_by_model" COST_BY_MODULE = "costs_by_module" TIME_COST_BY_TYPE = "time_costs_by_type" TIME_COST_BY_USER = "time_costs_by_user" TIME_COST_BY_MODEL = "time_costs_by_model" TIME_COST_BY_MODULE = "time_costs_by_module" AVG_TIME_COST_BY_TYPE = "avg_time_costs_by_type" AVG_TIME_COST_BY_USER = "avg_time_costs_by_user" AVG_TIME_COST_BY_MODEL = "avg_time_costs_by_model" AVG_TIME_COST_BY_MODULE = "avg_time_costs_by_module" STD_TIME_COST_BY_TYPE = "std_time_costs_by_type" STD_TIME_COST_BY_USER = "std_time_costs_by_user" STD_TIME_COST_BY_MODEL = "std_time_costs_by_model" STD_TIME_COST_BY_MODULE = "std_time_costs_by_module" ONLINE_TIME = "online_time" TOTAL_MSG_CNT = "total_messages" MSG_CNT_BY_CHAT = "messages_by_chat" TOTAL_REPLY_CNT = "total_replies" class OnlineTimeRecordTask(AsyncTask): """在线时间记录任务""" def __init__(self): super().__init__(task_name="Online Time Record Task", run_interval=60) self.record_id: int | None = None # Changed to int for Peewee's default ID """记录ID""" self._init_database() # 初始化数据库 @staticmethod def _init_database(): """初始化数据库""" with db.atomic(): # Use atomic operations for schema changes OnlineTime.create_table(safe=True) # Creates table if it doesn't exist, Peewee handles indexes from model async def run(self): # sourcery skip: use-named-expression try: current_time = datetime.now() extended_end_time = current_time + timedelta(minutes=1) if self.record_id: # 如果有记录，则更新结束时间 query = OnlineTime.update(end_timestamp=extended_end_time).where(OnlineTime.id == self.record_id) # type: ignore updated_rows = query.execute() if updated_rows == 0: # Record might have been deleted or ID is stale, try to find/create self.record_id = None # Reset record_id to trigger find/create logic below if not self.record_id: # Check again if record_id was reset or initially None # 如果没有记录，检查一分钟以内是否已有记录 # Look for a record whose end_timestamp is recent enough to be considered ongoing recent_record = ( OnlineTime.select() .where(OnlineTime.end_timestamp >= (current_time - timedelta(minutes=1))) # type: ignore .order_by(OnlineTime.end_timestamp.desc()) .first() ) if recent_record: # 如果有记录，则更新结束时间 self.record_id = recent_record.id recent_record.end_timestamp = extended_end_time recent_record.save() else: # 若没有记录，则插入新的在线时间记录 new_record = OnlineTime.create( timestamp=current_time.timestamp(), # 添加此行 start_timestamp=current_time, end_timestamp=extended_end_time, duration=5, # 初始时长为5分钟 ) self.record_id = new_record.id except Exception as e: logger.error(f"在线时间记录失败，错误信息：{e}") def _format_online_time(online_seconds: int) -> str: """ 格式化在线时间 :param online_seconds: 在线时间（秒） :return: 格式化后的在线时间字符串 """ total_online_time = timedelta(seconds=online_seconds) days = total_online_time.days hours = total_online_time.seconds // 3600 minutes = (total_online_time.seconds // 60) % 60 seconds = total_online_time.seconds % 60 if days > 0: # 如果在线时间超过1天，则格式化为"X天X小时X分钟" return f"{total_online_time.days}天{hours}小时{minutes}分钟{seconds}秒" elif hours > 0: # 如果在线时间超过1小时，则格式化为"X小时X分钟X秒" return f"{hours}小时{minutes}分钟{seconds}秒" else: # 其他情况格式化为"X分钟X秒" return f"{minutes}分钟{seconds}秒" def _format_large_number(num: float | int, html: bool = False) -> str: """ 格式化大数字，使用K后缀节省空间（大于9999时） :param num: 要格式化的数字 :param html: 是否用于HTML输出（如果是，K会着色） :return: 格式化后的字符串，如 12K, 1.3K, 120K """ if num >= 10000: # 大于等于10000，使用K后缀 value = num / 1000.0 if value >= 10: number_part = str(int(value)) k_suffix = "K" else: number_part = f"{value:.1f}" k_suffix = "K" if html: # HTML输出：K着色为主题色并加粗大写 return f"{number_part}K" else: # 控制台输出：纯文本，K大写 return f"{number_part}{k_suffix}" else: # 小于10000，直接显示 if isinstance(num, float): return f"{num:.1f}" if num != int(num) else str(int(num)) else: return str(num) class StatisticOutputTask(AsyncTask): """统计输出任务""" SEP_LINE = "-" * 84 def __init__(self, record_file_path: str = "maibot_statistics.html"): # 延迟300秒启动，运行间隔300秒 super().__init__(task_name="Statistics Data Output Task", wait_before_start=0, run_interval=300) self.name_mapping: Dict[str, Tuple[str, float]] = {} """ 联系人/群聊名称映射 {聊天ID: (联系人/群聊名称, 记录时间（timestamp）)} 注：设计记录时间的目的是方便更新名称，使联系人/群聊名称保持最新 """ self.record_file_path: str = record_file_path """ 记录文件路径 """ now = datetime.now() if "deploy_time" in local_storage: # 如果存在部署时间，则使用该时间作为全量统计的起始时间 deploy_time = datetime.fromtimestamp(local_storage["deploy_time"]) # type: ignore else: # 否则，使用最大时间范围，并记录部署时间为当前时间 deploy_time = datetime(2000, 1, 1) local_storage["deploy_time"] = now.timestamp() self.stat_period: List[Tuple[str, timedelta, str]] = [ ("all_time", now - deploy_time, "自部署以来"), # 必须保留"all_time" ("last_30_days", timedelta(days=30), "近30天"), ("last_7_days", timedelta(days=7), "近7天"), ("last_3_days", timedelta(days=3), "近3天"), ("last_24_hours", timedelta(days=1), "近1天"), ("last_3_hours", timedelta(hours=3), "近3小时"), ("last_hour", timedelta(hours=1), "近1小时"), ("last_15_minutes", timedelta(minutes=15), "近15分钟"), ] """ 统计时间段 [(统计名称, 统计时间段, 统计描述), ...] """ def _statistic_console_output(self, stats: Dict[str, Any], now: datetime): """ 输出统计数据到控制台 :param stats: 统计数据 :param now: 基准当前时间 """ # 输出最近一小时的统计数据 output = [ self.SEP_LINE, f" 最近1小时的统计数据 (自{now.strftime('%Y-%m-%d %H:%M:%S')}开始，详细信息见文件：{self.record_file_path})", self.SEP_LINE, self._format_total_stat(stats["last_hour"]), "", self._format_model_classified_stat(stats["last_hour"]), "", self._format_module_classified_stat(stats["last_hour"]), "", self._format_chat_stat(stats["last_hour"]), self.SEP_LINE, "", ] logger.info("\n" + "\n".join(output)) async def run(self): try: now = datetime.now() # 使用线程池并行执行耗时操作 loop = asyncio.get_event_loop() # 在线程池中并行执行数据收集和之前的HTML生成（如果存在） with concurrent.futures.ThreadPoolExecutor() as executor: logger.info("正在收集统计数据...") # 数据收集任务 collect_task = loop.run_in_executor(executor, self._collect_all_statistics, now) # 等待数据收集完成 stats = await collect_task logger.info("统计数据收集完成") # 并行执行控制台输出和HTML报告生成 console_task = loop.run_in_executor(executor, self._statistic_console_output, stats, now) html_task = loop.run_in_executor(executor, self._generate_html_report, stats, now) # 等待两个输出任务完成 await asyncio.gather(console_task, html_task) logger.info("统计数据输出完成") except Exception as e: logger.exception(f"输出统计数据过程中发生异常，错误信息：{e}") async def run_async_background(self): """ 备选方案：完全异步后台运行统计输出 使用此方法可以让统计任务完全非阻塞 """ async def _async_collect_and_output(): try: import concurrent.futures now = datetime.now() loop = asyncio.get_event_loop() with concurrent.futures.ThreadPoolExecutor() as executor: logger.info("正在后台收集统计数据...") # 创建后台任务，不等待完成 collect_task = asyncio.create_task( loop.run_in_executor(executor, self._collect_all_statistics, now) # type: ignore ) stats = await collect_task logger.info("统计数据收集完成") # 创建并发的输出任务 output_tasks = [ asyncio.create_task(loop.run_in_executor(executor, self._statistic_console_output, stats, now)), # type: ignore asyncio.create_task(loop.run_in_executor(executor, self._generate_html_report, stats, now)), # type: ignore ] # 等待所有输出任务完成 await asyncio.gather(*output_tasks) logger.info("统计数据后台输出完成") except Exception as e: logger.exception(f"后台统计数据输出过程中发生异常：{e}") # 创建后台任务，立即返回 asyncio.create_task(_async_collect_and_output()) # -- 以下为统计数据收集方法 -- @staticmethod def _collect_model_request_for_period(collect_period: List[Tuple[str, datetime]]) -> Dict[str, Any]: """ 收集指定时间段的LLM请求统计数据 :param collect_period: 统计时间段 """ if not collect_period: return {} # 排序-按照时间段开始时间降序排列（最晚的时间段在前） collect_period.sort(key=lambda x: x[1], reverse=True) stats = { period_key: { TOTAL_REQ_CNT: 0, REQ_CNT_BY_TYPE: defaultdict(int), REQ_CNT_BY_USER: defaultdict(int), REQ_CNT_BY_MODEL: defaultdict(int), REQ_CNT_BY_MODULE: defaultdict(int), IN_TOK_BY_TYPE: defaultdict(int), IN_TOK_BY_USER: defaultdict(int), IN_TOK_BY_MODEL: defaultdict(int), IN_TOK_BY_MODULE: defaultdict(int), OUT_TOK_BY_TYPE: defaultdict(int), OUT_TOK_BY_USER: defaultdict(int), OUT_TOK_BY_MODEL: defaultdict(int), OUT_TOK_BY_MODULE: defaultdict(int), TOTAL_TOK_BY_TYPE: defaultdict(int), TOTAL_TOK_BY_USER: defaultdict(int), TOTAL_TOK_BY_MODEL: defaultdict(int), TOTAL_TOK_BY_MODULE: defaultdict(int), TOTAL_COST: 0.0, COST_BY_TYPE: defaultdict(float), COST_BY_USER: defaultdict(float), COST_BY_MODEL: defaultdict(float), COST_BY_MODULE: defaultdict(float), TIME_COST_BY_TYPE: defaultdict(list), TIME_COST_BY_USER: defaultdict(list), TIME_COST_BY_MODEL: defaultdict(list), TIME_COST_BY_MODULE: defaultdict(list), AVG_TIME_COST_BY_TYPE: defaultdict(float), AVG_TIME_COST_BY_USER: defaultdict(float), AVG_TIME_COST_BY_MODEL: defaultdict(float), AVG_TIME_COST_BY_MODULE: defaultdict(float), STD_TIME_COST_BY_TYPE: defaultdict(float), STD_TIME_COST_BY_USER: defaultdict(float), STD_TIME_COST_BY_MODEL: defaultdict(float), STD_TIME_COST_BY_MODULE: defaultdict(float), } for period_key, _ in collect_period } # 以最早的时间戳为起始时间获取记录 # Assuming LLMUsage.timestamp is a DateTimeField query_start_time = collect_period[-1][1] for record in LLMUsage.select().where(LLMUsage.timestamp >= query_start_time): # type: ignore record_timestamp = record.timestamp # This is already a datetime object for idx, (_, period_start) in enumerate(collect_period): if record_timestamp >= period_start: for period_key, _ in collect_period[idx:]: stats[period_key][TOTAL_REQ_CNT] += 1 request_type = record.request_type or "unknown" user_id = record.user_id or "unknown" # user_id is TextField, already string model_name = record.model_assign_name or record.model_name or "unknown" # 提取模块名：如果请求类型包含"."，取第一个"."之前的部分 module_name = request_type.split(".")[0] if "." in request_type else request_type stats[period_key][REQ_CNT_BY_TYPE][request_type] += 1 stats[period_key][REQ_CNT_BY_USER][user_id] += 1 stats[period_key][REQ_CNT_BY_MODEL][model_name] += 1 stats[period_key][REQ_CNT_BY_MODULE][module_name] += 1 prompt_tokens = record.prompt_tokens or 0 completion_tokens = record.completion_tokens or 0 total_tokens = prompt_tokens + completion_tokens stats[period_key][IN_TOK_BY_TYPE][request_type] += prompt_tokens stats[period_key][IN_TOK_BY_USER][user_id] += prompt_tokens stats[period_key][IN_TOK_BY_MODEL][model_name] += prompt_tokens stats[period_key][IN_TOK_BY_MODULE][module_name] += prompt_tokens stats[period_key][OUT_TOK_BY_TYPE][request_type] += completion_tokens stats[period_key][OUT_TOK_BY_USER][user_id] += completion_tokens stats[period_key][OUT_TOK_BY_MODEL][model_name] += completion_tokens stats[period_key][OUT_TOK_BY_MODULE][module_name] += completion_tokens stats[period_key][TOTAL_TOK_BY_TYPE][request_type] += total_tokens stats[period_key][TOTAL_TOK_BY_USER][user_id] += total_tokens stats[period_key][TOTAL_TOK_BY_MODEL][model_name] += total_tokens stats[period_key][TOTAL_TOK_BY_MODULE][module_name] += total_tokens cost = record.cost or 0.0 stats[period_key][TOTAL_COST] += cost stats[period_key][COST_BY_TYPE][request_type] += cost stats[period_key][COST_BY_USER][user_id] += cost stats[period_key][COST_BY_MODEL][model_name] += cost stats[period_key][COST_BY_MODULE][module_name] += cost # 收集time_cost数据 time_cost = record.time_cost or 0.0 if time_cost > 0: # 只记录有效的time_cost stats[period_key][TIME_COST_BY_TYPE][request_type].append(time_cost) stats[period_key][TIME_COST_BY_USER][user_id].append(time_cost) stats[period_key][TIME_COST_BY_MODEL][model_name].append(time_cost) stats[period_key][TIME_COST_BY_MODULE][module_name].append(time_cost) break # 计算平均耗时和标准差 for period_key in stats: for category in [REQ_CNT_BY_TYPE, REQ_CNT_BY_USER, REQ_CNT_BY_MODEL, REQ_CNT_BY_MODULE]: time_cost_key = f"time_costs_by_{category.split('_')[-1]}" avg_key = f"avg_time_costs_by_{category.split('_')[-1]}" std_key = f"std_time_costs_by_{category.split('_')[-1]}" for item_name in stats[period_key][category]: time_costs = stats[period_key][time_cost_key].get(item_name, []) if time_costs: # 计算平均耗时 avg_time_cost = sum(time_costs) / len(time_costs) stats[period_key][avg_key][item_name] = round(avg_time_cost, 3) # 计算标准差 if len(time_costs) > 1: variance = sum((x - avg_time_cost) ** 2 for x in time_costs) / len(time_costs) std_time_cost = variance**0.5 stats[period_key][std_key][item_name] = round(std_time_cost, 3) else: stats[period_key][std_key][item_name] = 0.0 else: stats[period_key][avg_key][item_name] = 0.0 stats[period_key][std_key][item_name] = 0.0 return stats @staticmethod def _collect_online_time_for_period(collect_period: List[Tuple[str, datetime]], now: datetime) -> Dict[str, Any]: """ 收集指定时间段的在线时间统计数据 :param collect_period: 统计时间段 """ if not collect_period: return {} collect_period.sort(key=lambda x: x[1], reverse=True) stats = { period_key: { ONLINE_TIME: 0.0, } for period_key, _ in collect_period } query_start_time = collect_period[-1][1] # Assuming OnlineTime.end_timestamp is a DateTimeField for record in OnlineTime.select().where(OnlineTime.end_timestamp >= query_start_time): # type: ignore # record.end_timestamp and record.start_timestamp are datetime objects record_end_timestamp = record.end_timestamp record_start_timestamp = record.start_timestamp for idx, (_, period_boundary_start) in enumerate(collect_period): if record_end_timestamp >= period_boundary_start: # Calculate effective end time for this record in relation to 'now' effective_end_time = min(record_end_timestamp, now) for period_key, current_period_start_time in collect_period[idx:]: # Determine the portion of the record that falls within this specific statistical period overlap_start = max(record_start_timestamp, current_period_start_time) overlap_end = effective_end_time # Already capped by 'now' and record's own end if overlap_end > overlap_start: stats[period_key][ONLINE_TIME] += (overlap_end - overlap_start).total_seconds() break return stats def _collect_message_count_for_period(self, collect_period: List[Tuple[str, datetime]]) -> Dict[str, Any]: """ 收集指定时间段的消息统计数据 :param collect_period: 统计时间段 """ if not collect_period: return {} collect_period.sort(key=lambda x: x[1], reverse=True) stats = { period_key: { TOTAL_MSG_CNT: 0, MSG_CNT_BY_CHAT: defaultdict(int), TOTAL_REPLY_CNT: 0, } for period_key, _ in collect_period } query_start_timestamp = collect_period[-1][1].timestamp() # Messages.time is a DoubleField (timestamp) for message in Messages.select().where(Messages.time >= query_start_timestamp): # type: ignore message_time_ts = message.time # This is a float timestamp chat_id = None chat_name = None # Logic based on Peewee model structure, aiming to replicate original intent if message.chat_info_group_id: chat_id = f"g{message.chat_info_group_id}" chat_name = message.chat_info_group_name or f"群{message.chat_info_group_id}" elif message.user_id: # Fallback to sender's info for chat_id if not a group_info based chat # This uses the message SENDER's ID as per original logic's fallback chat_id = f"u{message.user_id}" # SENDER's user_id chat_name = message.user_nickname # SENDER's nickname else: # If neither group_id nor sender_id is available for chat identification logger.warning( f"Message (PK: {message.id if hasattr(message, 'id') else 'N/A'}) lacks group_id and user_id for chat stats." ) continue if not chat_id: # Should not happen if above logic is correct continue # Update name_mapping（仅用于展示聊天名称） try: if chat_id in self.name_mapping: if chat_name != self.name_mapping[chat_id][0] and message_time_ts > self.name_mapping[chat_id][1]: self.name_mapping[chat_id] = (chat_name, message_time_ts) else: self.name_mapping[chat_id] = (chat_name, message_time_ts) except (IndexError, TypeError) as e: logger.warning(f"更新 name_mapping 时发生错误，chat_id: {chat_id}, 错误: {e}") # 重置为正确的格式 self.name_mapping[chat_id] = (chat_name, message_time_ts) for idx, (_, period_start_dt) in enumerate(collect_period): if message_time_ts >= period_start_dt.timestamp(): for period_key, _ in collect_period[idx:]: stats[period_key][TOTAL_MSG_CNT] += 1 stats[period_key][MSG_CNT_BY_CHAT][chat_id] += 1 break # 使用 ActionRecords 中的 reply 动作次数作为回复数基准 try: action_query_start_timestamp = collect_period[-1][1].timestamp() for action in ActionRecords.select().where(ActionRecords.time >= action_query_start_timestamp): # type: ignore # 仅统计已完成的 reply 动作 if action.action_name != "reply" or not action.action_done: continue action_time_ts = action.time for idx, (_, period_start_dt) in enumerate(collect_period): if action_time_ts >= period_start_dt.timestamp(): for period_key, _ in collect_period[idx:]: stats[period_key][TOTAL_REPLY_CNT] += 1 break except Exception as e: logger.warning(f"统计 reply 动作次数失败，将回复数视为 0，错误信息：{e}") return stats def _collect_all_statistics(self, now: datetime) -> Dict[str, Dict[str, Any]]: """ 收集各时间段的统计数据 :param now: 基准当前时间 """ last_all_time_stat = None try: if "last_full_statistics" in local_storage: # 如果存在上次完整统计数据，则使用该数据进行增量统计 last_stat: Dict[str, Any] = local_storage["last_full_statistics"] # 上次完整统计数据 # type: ignore # 修复 name_mapping 数据类型不匹配问题 # JSON 中存储为列表，但代码期望为元组 raw_name_mapping = last_stat["name_mapping"] self.name_mapping = {} for chat_id, value in raw_name_mapping.items(): if isinstance(value, list) and len(value) == 2: # 将列表转换为元组 self.name_mapping[chat_id] = (value[0], value[1]) elif isinstance(value, tuple) and len(value) == 2: # 已经是元组，直接使用 self.name_mapping[chat_id] = value else: # 数据格式不正确，跳过或使用默认值 logger.warning(f"name_mapping 中 chat_id {chat_id} 的数据格式不正确: {value}") continue last_all_time_stat = last_stat["stat_data"] # 上次完整统计的统计数据 last_stat_timestamp = datetime.fromtimestamp(last_stat["timestamp"]) # 上次完整统计数据的时间戳 self.stat_period = [ item for item in self.stat_period if item[0] != "all_time" ] # 删除"所有时间"的统计时段 self.stat_period.append(("all_time", now - last_stat_timestamp, "自部署以来的")) except Exception as e: logger.warning(f"加载上次完整统计数据失败，进行全量统计，错误信息：{e}") stat_start_timestamp = [(period[0], now - period[1]) for period in self.stat_period] stat = {item[0]: {} for item in self.stat_period} model_req_stat = self._collect_model_request_for_period(stat_start_timestamp) online_time_stat = self._collect_online_time_for_period(stat_start_timestamp, now) message_count_stat = self._collect_message_count_for_period(stat_start_timestamp) # 统计数据合并 # 合并三类统计数据 for period_key, _ in stat_start_timestamp: stat[period_key].update(model_req_stat[period_key]) stat[period_key].update(online_time_stat[period_key]) stat[period_key].update(message_count_stat[period_key]) if last_all_time_stat: # 若存在上次完整统计数据，则将其与当前统计数据合并 for key, val in last_all_time_stat.items(): # 确保当前统计数据中存在该key if key not in stat["all_time"]: continue if isinstance(val, dict): # 是字典类型，则进行合并 for sub_key, sub_val in val.items(): # 普通的数值或字典合并 if sub_key in stat["all_time"][key]: # 检查是否为嵌套的字典类型（如版本统计） if isinstance(sub_val, dict) and isinstance(stat["all_time"][key][sub_key], dict): # 合并嵌套字典 for nested_key, nested_val in sub_val.items(): if nested_key in stat["all_time"][key][sub_key]: stat["all_time"][key][sub_key][nested_key] += nested_val else: stat["all_time"][key][sub_key][nested_key] = nested_val else: # 普通数值累加 stat["all_time"][key][sub_key] += sub_val else: stat["all_time"][key][sub_key] = sub_val else: # 直接合并 stat["all_time"][key] += val # 更新上次完整统计数据的时间戳 # 将所有defaultdict转换为普通dict以避免类型冲突 clean_stat_data = self._convert_defaultdict_to_dict(stat["all_time"]) # 将 name_mapping 中的元组转换为列表，因为JSON不支持元组 json_safe_name_mapping = {} for chat_id, (chat_name, timestamp) in self.name_mapping.items(): json_safe_name_mapping[chat_id] = [chat_name, timestamp] local_storage["last_full_statistics"] = { "name_mapping": json_safe_name_mapping, "stat_data": clean_stat_data, "timestamp": now.timestamp(), } return stat def _convert_defaultdict_to_dict(self, data): # sourcery skip: dict-comprehension, extract-duplicate-method, inline-immediately-returned-variable, merge-duplicate-blocks """递归转换defaultdict为普通dict""" if isinstance(data, defaultdict): # 转换defaultdict为普通dict result = {} for key, value in data.items(): result[key] = self._convert_defaultdict_to_dict(value) return result elif isinstance(data, dict): # 递归处理普通dict result = {} for key, value in data.items(): result[key] = self._convert_defaultdict_to_dict(value) return result else: # 其他类型直接返回 return data # -- 以下为统计数据格式化方法 -- @staticmethod def _format_total_stat(stats: Dict[str, Any]) -> str: """ 格式化总统计数据 """ # 计算总token数（从所有模型的token数中累加） total_tokens = sum(stats[TOTAL_TOK_BY_MODEL].values()) if stats[TOTAL_TOK_BY_MODEL] else 0 # 计算花费/消息数量指标（每100条） cost_per_100_messages = (stats[TOTAL_COST] / stats[TOTAL_MSG_CNT] * 100) if stats[TOTAL_MSG_CNT] > 0 else 0.0 # 计算花费/时间指标（花费/小时） online_hours = stats[ONLINE_TIME] / 3600.0 if stats[ONLINE_TIME] > 0 else 0.0 cost_per_hour = stats[TOTAL_COST] / online_hours if online_hours > 0 else 0.0 # 计算token/时间指标（token/小时） tokens_per_hour = (total_tokens / online_hours) if online_hours > 0 else 0.0 # 计算花费/回复数量指标（每100条） total_replies = stats.get(TOTAL_REPLY_CNT, 0) cost_per_100_replies = (stats[TOTAL_COST] / total_replies * 100) if total_replies > 0 else 0.0 # 计算花费/消息数量（排除自己回复）指标（每100条） total_messages_excluding_replies = stats[TOTAL_MSG_CNT] - total_replies cost_per_100_messages_excluding_replies = ( (stats[TOTAL_COST] / total_messages_excluding_replies * 100) if total_messages_excluding_replies > 0 else 0.0 ) output = [ f"总在线时间: {_format_online_time(stats[ONLINE_TIME])}", f"总消息数: {_format_large_number(stats[TOTAL_MSG_CNT])}", f"总回复数: {_format_large_number(total_replies)}", f"总请求数: {_format_large_number(stats[TOTAL_REQ_CNT])}", f"总Token数: {_format_large_number(total_tokens)}", f"总花费: {stats[TOTAL_COST]:.2f}¥", f"花费/消息数量: {cost_per_100_messages:.4f}¥/100条" if stats[TOTAL_MSG_CNT] > 0 else "花费/消息数量: N/A", f"花费/接受消息数量: {cost_per_100_messages_excluding_replies:.4f}¥/100条" if total_messages_excluding_replies > 0 else "花费/消息数量(排除回复): N/A", f"花费/回复消息数量: {cost_per_100_replies:.4f}¥/100条" if total_replies > 0 else "花费/回复数量: N/A", f"花费/时间: {cost_per_hour:.2f}¥/小时" if online_hours > 0 else "花费/时间: N/A", f"Token/时间: {_format_large_number(tokens_per_hour)}/小时" if online_hours > 0 else "Token/时间: N/A", "", ] return "\n".join(output) @staticmethod def _format_model_classified_stat(stats: Dict[str, Any]) -> str: """ 格式化按模型分类的统计数据 """ if stats[TOTAL_REQ_CNT] <= 0: return "" data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.2f}¥ {:>10.1f} {:>10.1f} {:>12} {:>12}" total_replies = stats.get(TOTAL_REPLY_CNT, 0) output = [ "按模型分类统计:", " 模型名称 调用次数 输入Token 输出Token Token总量 累计花费 平均耗时(秒) 标准差(秒) 每次回复平均调用次数 每次回复平均Token数", ] for model_name, count in sorted(stats[REQ_CNT_BY_MODEL].items()): name = f"{model_name[:29]}..." if len(model_name) > 32 else model_name in_tokens = stats[IN_TOK_BY_MODEL][model_name] out_tokens = stats[OUT_TOK_BY_MODEL][model_name] tokens = stats[TOTAL_TOK_BY_MODEL][model_name] cost = stats[COST_BY_MODEL][model_name] avg_time_cost = stats[AVG_TIME_COST_BY_MODEL][model_name] std_time_cost = stats[STD_TIME_COST_BY_MODEL][model_name] # 计算每次回复平均值 avg_count_per_reply = count / total_replies if total_replies > 0 else 0.0 avg_tokens_per_reply = tokens / total_replies if total_replies > 0 else 0.0 # 格式化大数字 formatted_count = _format_large_number(count) formatted_in_tokens = _format_large_number(in_tokens) formatted_out_tokens = _format_large_number(out_tokens) formatted_tokens = _format_large_number(tokens) formatted_avg_count = _format_large_number(avg_count_per_reply) if total_replies > 0 else "N/A" formatted_avg_tokens = _format_large_number(avg_tokens_per_reply) if total_replies > 0 else "N/A" output.append( data_fmt.format( name, formatted_count, formatted_in_tokens, formatted_out_tokens, formatted_tokens, cost, avg_time_cost, std_time_cost, formatted_avg_count, formatted_avg_tokens, ) ) output.append("") return "\n".join(output) @staticmethod def _format_module_classified_stat(stats: Dict[str, Any]) -> str: """ 格式化按模块分类的统计数据 """ if stats[TOTAL_REQ_CNT] <= 0: return "" data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.2f}¥ {:>10.1f} {:>10.1f} {:>12} {:>12}" total_replies = stats.get(TOTAL_REPLY_CNT, 0) output = [ "按模块分类统计:", " 模块名称 调用次数 输入Token 输出Token Token总量 累计花费 平均耗时(秒) 标准差(秒) 每次回复平均调用次数 每次回复平均Token数", ] for module_name, count in sorted(stats[REQ_CNT_BY_MODULE].items()): name = f"{module_name[:29]}..." if len(module_name) > 32 else module_name in_tokens = stats[IN_TOK_BY_MODULE][module_name] out_tokens = stats[OUT_TOK_BY_MODULE][module_name] tokens = stats[TOTAL_TOK_BY_MODULE][module_name] cost = stats[COST_BY_MODULE][module_name] avg_time_cost = stats[AVG_TIME_COST_BY_MODULE][module_name] std_time_cost = stats[STD_TIME_COST_BY_MODULE][module_name] # 计算每次回复平均值 avg_count_per_reply = count / total_replies if total_replies > 0 else 0.0 avg_tokens_per_reply = tokens / total_replies if total_replies > 0 else 0.0 # 格式化大数字 formatted_count = _format_large_number(count) formatted_in_tokens = _format_large_number(in_tokens) formatted_out_tokens = _format_large_number(out_tokens) formatted_tokens = _format_large_number(tokens) formatted_avg_count = _format_large_number(avg_count_per_reply) if total_replies > 0 else "N/A" formatted_avg_tokens = _format_large_number(avg_tokens_per_reply) if total_replies > 0 else "N/A" output.append( data_fmt.format( name, formatted_count, formatted_in_tokens, formatted_out_tokens, formatted_tokens, cost, avg_time_cost, std_time_cost, formatted_avg_count, formatted_avg_tokens, ) ) output.append("") return "\n".join(output) def _format_chat_stat(self, stats: Dict[str, Any]) -> str: """ 格式化聊天统计数据 """ if stats[TOTAL_MSG_CNT] <= 0: return "" output = ["聊天消息统计:", " 联系人/群组名称 消息数量"] for chat_id, count in sorted(stats[MSG_CNT_BY_CHAT].items()): try: chat_name = self.name_mapping.get(chat_id, ("未知聊天", 0))[0] formatted_count = _format_large_number(count) output.append(f"{chat_name[:32]:<32} {formatted_count:>10}") except (IndexError, TypeError) as e: logger.warning(f"格式化聊天统计时发生错误，chat_id: {chat_id}, 错误: {e}") formatted_count = _format_large_number(count) output.append(f"{'未知聊天':<32} {formatted_count:>10}") output.append("") return "\n".join(output) def _get_chat_display_name_from_id(self, chat_id: str) -> str: """从chat_id获取显示名称""" try: # 首先尝试从chat_stream获取真实群组名称 from src.chat.message_receive.chat_stream import get_chat_manager chat_manager = get_chat_manager() if chat_id in chat_manager.streams: stream = chat_manager.streams[chat_id] if stream.group_info and hasattr(stream.group_info, "group_name"): group_name = stream.group_info.group_name if group_name and group_name.strip(): return group_name.strip() elif stream.user_info and hasattr(stream.user_info, "user_nickname"): user_name = stream.user_info.user_nickname if user_name and user_name.strip(): return user_name.strip() # 如果从chat_stream获取失败，尝试解析chat_id格式 if chat_id.startswith("g"): return f"群聊{chat_id[1:]}" elif chat_id.startswith("u"): return f"用户{chat_id[1:]}" else: return chat_id except Exception as e: logger.warning(f"获取聊天显示名称失败: {e}") return chat_id # 移除_generate_versions_tab方法 def _generate_html_report(self, stat: dict[str, Any], now: datetime): """ 生成HTML格式的统计报告 :param stat: 统计数据 :param now: 基准当前时间 :return: HTML格式的统计报告 """ # 移除版本对比内容相关tab和内容 tab_list = [ f'' for period in self.stat_period ] tab_list.append('') tab_list.append('') def _format_stat_data(stat_data: dict[str, Any], div_id: str, start_time: datetime) -> str: """ 格式化一个时间段的统计数据到html div块 :param stat_data: 统计数据 :param div_id: div的ID :param start_time: 统计时间段开始时间 """ # format总在线时间 # 按模型分类统计 total_replies = stat_data.get(TOTAL_REPLY_CNT, 0) model_rows = "\n".join( [ f"" f"{model_name}" f"{_format_large_number(count, html=True)}" f"{_format_large_number(stat_data[IN_TOK_BY_MODEL][model_name], html=True)}" f"{_format_large_number(stat_data[OUT_TOK_BY_MODEL][model_name], html=True)}" f"{_format_large_number(stat_data[TOTAL_TOK_BY_MODEL][model_name], html=True)}" f"{stat_data[COST_BY_MODEL][model_name]:.2f} ¥" f"{stat_data[AVG_TIME_COST_BY_MODEL][model_name]:.1f} 秒" f"{stat_data[STD_TIME_COST_BY_MODEL][model_name]:.1f} 秒" f"{_format_large_number(count / total_replies, html=True) if total_replies > 0 else 'N/A'}" f"{_format_large_number(stat_data[TOTAL_TOK_BY_MODEL][model_name] / total_replies, html=True) if total_replies > 0 else 'N/A'}" f"" for model_name, count in sorted(stat_data[REQ_CNT_BY_MODEL].items()) ] if stat_data[REQ_CNT_BY_MODEL] else ["暂无数据"] ) # 按请求类型分类统计 type_rows = "\n".join( [ f"" f"{req_type}" f"{_format_large_number(count, html=True)}" f"{_format_large_number(stat_data[IN_TOK_BY_TYPE][req_type], html=True)}" f"{_format_large_number(stat_data[OUT_TOK_BY_TYPE][req_type], html=True)}" f"{_format_large_number(stat_data[TOTAL_TOK_BY_TYPE][req_type], html=True)}" f"{stat_data[COST_BY_TYPE][req_type]:.2f} ¥" f"{stat_data[AVG_TIME_COST_BY_TYPE][req_type]:.1f} 秒" f"{stat_data[STD_TIME_COST_BY_TYPE][req_type]:.1f} 秒" f"{_format_large_number(count / total_replies, html=True) if total_replies > 0 else 'N/A'}" f"{_format_large_number(stat_data[TOTAL_TOK_BY_TYPE][req_type] / total_replies, html=True) if total_replies > 0 else 'N/A'}" f"" for req_type, count in sorted(stat_data[REQ_CNT_BY_TYPE].items()) ] if stat_data[REQ_CNT_BY_TYPE] else ["暂无数据"] ) # 按模块分类统计 module_rows = "\n".join( [ f"" f"{module_name}" f"{_format_large_number(count, html=True)}" f"{_format_large_number(stat_data[IN_TOK_BY_MODULE][module_name], html=True)}" f"{_format_large_number(stat_data[OUT_TOK_BY_MODULE][module_name], html=True)}" f"{_format_large_number(stat_data[TOTAL_TOK_BY_MODULE][module_name], html=True)}" f"{stat_data[COST_BY_MODULE][module_name]:.2f} ¥" f"{stat_data[AVG_TIME_COST_BY_MODULE][module_name]:.1f} 秒" f"{stat_data[STD_TIME_COST_BY_MODULE][module_name]:.1f} 秒" f"{_format_large_number(count / total_replies, html=True) if total_replies > 0 else 'N/A'}" f"{_format_large_number(stat_data[TOTAL_TOK_BY_MODULE][module_name] / total_replies, html=True) if total_replies > 0 else 'N/A'}" f"" for module_name, count in sorted(stat_data[REQ_CNT_BY_MODULE].items()) ] if stat_data[REQ_CNT_BY_MODULE] else ["暂无数据"] ) # 聊天消息统计 chat_rows = [] for chat_id, count in sorted(stat_data[MSG_CNT_BY_CHAT].items()): try: chat_name = self.name_mapping.get(chat_id, ("未知聊天", 0))[0] chat_rows.append(f"{chat_name}{_format_large_number(count, html=True)}") except (IndexError, TypeError) as e: logger.warning(f"生成HTML聊天统计时发生错误，chat_id: {chat_id}, 错误: {e}") chat_rows.append(f"未知聊天{_format_large_number(count, html=True)}") chat_rows_html = ( "\n".join(chat_rows) if chat_rows else "暂无数据" ) # 生成HTML return f"""

统计时段: {start_time.strftime("%Y-%m-%d %H:%M:%S")} ~ {now.strftime("%Y-%m-%d %H:%M:%S")}

总在线时间
{_format_online_time(stat_data[ONLINE_TIME])}
总消息数
{_format_large_number(stat_data[TOTAL_MSG_CNT], html=True)}
总回复数
{_format_large_number(stat_data.get(TOTAL_REPLY_CNT, 0), html=True)}
总请求数
{_format_large_number(stat_data[TOTAL_REQ_CNT], html=True)}
总Token数
{_format_large_number(sum(stat_data[TOTAL_TOK_BY_MODEL].values()) if stat_data[TOTAL_TOK_BY_MODEL] else 0, html=True)}
总花费
{stat_data[TOTAL_COST]:.2f} ¥
花费/消息数量
{(stat_data[TOTAL_COST] / stat_data[TOTAL_MSG_CNT] * 100 if stat_data[TOTAL_MSG_CNT] > 0 else 0.0):.4f} ¥/100条
花费/消息数量(排除回复)
{(stat_data[TOTAL_COST] / (stat_data[TOTAL_MSG_CNT] - stat_data.get(TOTAL_REPLY_CNT, 0)) * 100 if (stat_data[TOTAL_MSG_CNT] - stat_data.get(TOTAL_REPLY_CNT, 0)) > 0 else 0.0):.4f} ¥/100条
花费/回复数量
{(stat_data[TOTAL_COST] / stat_data.get(TOTAL_REPLY_CNT, 0) * 100 if stat_data.get(TOTAL_REPLY_CNT, 0) > 0 else 0.0):.4f} ¥/100条
花费/时间
{(stat_data[TOTAL_COST] / (stat_data[ONLINE_TIME] / 3600.0) if stat_data[ONLINE_TIME] > 0 else 0.0):.2f} ¥/小时
Token/时间
{_format_large_number(sum(stat_data[TOTAL_TOK_BY_MODEL].values()) / (stat_data[ONLINE_TIME] / 3600.0) if stat_data[ONLINE_TIME] > 0 and stat_data[TOTAL_TOK_BY_MODEL] else 0.0, html=True)}/小时

按模型分类统计

{model_rows}
模型名称调用次数输入Token输出TokenToken总量累计花费平均耗时(秒)标准差(秒)每次回复平均调用次数每次回复平均Token数

按模块分类统计

{module_rows}
模块名称调用次数输入Token输出TokenToken总量累计花费平均耗时(秒)标准差(秒)每次回复平均调用次数每次回复平均Token数

按请求类型分类统计

{type_rows}
请求类型调用次数输入Token输出TokenToken总量累计花费平均耗时(秒)标准差(秒)每次回复平均调用次数每次回复平均Token数

聊天消息统计

{chat_rows_html}
联系人/群组名称消息数量

数据分布图表

模型花费分布

模块花费分布

请求类型花费分布

聊天消息分布

""" tab_content_list = [ _format_stat_data(stat[period[0]], period[0], now - period[1]) for period in self.stat_period if period[0] != "all_time" ] tab_content_list.append( _format_stat_data(stat["all_time"], "all_time", datetime.fromtimestamp(local_storage["deploy_time"])) # type: ignore ) # 不再添加版本对比内容 # 添加图表内容 chart_data = self._generate_chart_data(stat) tab_content_list.append(self._generate_chart_tab(chart_data)) # 添加指标趋势图表 metrics_data = self._generate_metrics_data(now) tab_content_list.append(self._generate_metrics_tab(metrics_data)) joined_tab_list = "\n".join(tab_list) joined_tab_content = "\n".join(tab_content_list) html_template = ( """ MaiBot运行统计报告 """ + f"""

MaiBot运行统计报告

统计截止时间: {now.strftime("%Y-%m-%d %H:%M:%S")}
{joined_tab_list}
{joined_tab_content}
Made with ❤️ by MaiBot • 本页会定期自动覆盖生成
""" + """ """ ) with open(self.record_file_path, "w", encoding="utf-8") as f: f.write(html_template) def _generate_chart_data(self, stat: dict[str, Any]) -> dict: """生成图表数据""" now = datetime.now() chart_data = {} # 支持多个时间范围 time_ranges = [ ("6h", 6, 10), # 6小时，10分钟间隔 ("12h", 12, 15), # 12小时，15分钟间隔 ("24h", 24, 15), # 24小时，15分钟间隔 ("48h", 48, 30), # 48小时，30分钟间隔 ] for range_key, hours, interval_minutes in time_ranges: range_data = self._collect_interval_data(now, hours, interval_minutes) chart_data[range_key] = range_data return chart_data def _collect_interval_data(self, now: datetime, hours: int, interval_minutes: int) -> dict: """收集指定时间范围内每个间隔的数据""" # 生成时间点 start_time = now - timedelta(hours=hours) time_points = [] current_time = start_time while current_time <= now: time_points.append(current_time) current_time += timedelta(minutes=interval_minutes) # 初始化数据结构 total_cost_data = [0] * len(time_points) cost_by_model = {} cost_by_module = {} message_by_chat = {} time_labels = [t.strftime("%H:%M") for t in time_points] interval_seconds = interval_minutes * 60 # 查询LLM使用记录 query_start_time = start_time for record in LLMUsage.select().where(LLMUsage.timestamp >= query_start_time): # type: ignore record_time = record.timestamp # 找到对应的时间间隔索引 time_diff = (record_time - start_time).total_seconds() interval_index = int(time_diff // interval_seconds) if 0 <= interval_index < len(time_points): # 累加总花费数据 cost = record.cost or 0.0 total_cost_data[interval_index] += cost # type: ignore # 累加按模型分类的花费 model_name = record.model_assign_name or record.model_name or "unknown" if model_name not in cost_by_model: cost_by_model[model_name] = [0] * len(time_points) cost_by_model[model_name][interval_index] += cost # 累加按模块分类的花费 request_type = record.request_type or "unknown" module_name = request_type.split(".")[0] if "." in request_type else request_type if module_name not in cost_by_module: cost_by_module[module_name] = [0] * len(time_points) cost_by_module[module_name][interval_index] += cost # 查询消息记录 query_start_timestamp = start_time.timestamp() for message in Messages.select().where(Messages.time >= query_start_timestamp): # type: ignore message_time_ts = message.time # 找到对应的时间间隔索引 time_diff = message_time_ts - query_start_timestamp interval_index = int(time_diff // interval_seconds) if 0 <= interval_index < len(time_points): # 确定聊天流名称 chat_name = None if message.chat_info_group_id: chat_name = message.chat_info_group_name or f"群{message.chat_info_group_id}" elif message.user_id: chat_name = message.user_nickname or f"用户{message.user_id}" else: continue if not chat_name: continue # 累加消息数 if chat_name not in message_by_chat: message_by_chat[chat_name] = [0] * len(time_points) message_by_chat[chat_name][interval_index] += 1 return { "time_labels": time_labels, "total_cost_data": total_cost_data, "cost_by_model": cost_by_model, "cost_by_module": cost_by_module, "message_by_chat": message_by_chat, } def _generate_chart_tab(self, chart_data: dict) -> str: # sourcery skip: extract-duplicate-method, move-assign-in-block """生成图表选项卡HTML内容""" # 生成不同颜色的调色板 colors = [ "#8b5cf6", "#9f8efb", "#b5a6ff", "#c7bbff", "#d9ceff", "#a78bfa", "#9073d8", "#bfaefc", "#cabdfd", "#e6e0ff", ] # 默认使用24小时数据生成数据集 default_data = chart_data["24h"] # 为每个模型生成数据集 model_datasets = [] for i, (model_name, cost_data) in enumerate(default_data["cost_by_model"].items()): color = colors[i % len(colors)] model_datasets.append(f"""{{ label: '{model_name}', data: {cost_data}, borderColor: '{color}', backgroundColor: '{color}20', tension: 0.4, fill: false }}""") ",\n ".join(model_datasets) # 为每个模块生成数据集 module_datasets = [] for i, (module_name, cost_data) in enumerate(default_data["cost_by_module"].items()): color = colors[i % len(colors)] module_datasets.append(f"""{{ label: '{module_name}', data: {cost_data}, borderColor: '{color}', backgroundColor: '{color}20', tension: 0.4, fill: false }}""") ",\n ".join(module_datasets) # 为每个聊天流生成消息数据集 message_datasets = [] for i, (chat_name, message_data) in enumerate(default_data["message_by_chat"].items()): color = colors[i % len(colors)] message_datasets.append(f"""{{ label: '{chat_name}', data: {message_data}, borderColor: '{color}', backgroundColor: '{color}20', tension: 0.4, fill: false }}""") ",\n ".join(message_datasets) return f"""

数据图表

""" def _generate_metrics_data(self, now: datetime) -> dict: """生成指标趋势数据""" metrics_data = {} # 24小时尺度：1小时为单位 metrics_data["24h"] = self._collect_metrics_interval_data(now, hours=24, interval_hours=1) # 7天尺度：1天为单位 metrics_data["7d"] = self._collect_metrics_interval_data(now, hours=24 * 7, interval_hours=24) # 30天尺度：1天为单位 metrics_data["30d"] = self._collect_metrics_interval_data(now, hours=24 * 30, interval_hours=24) return metrics_data def _collect_metrics_interval_data(self, now: datetime, hours: int, interval_hours: int) -> dict: """收集指定时间范围内每个间隔的指标数据""" start_time = now - timedelta(hours=hours) time_points = [] current_time = start_time # 生成时间点 while current_time <= now: time_points.append(current_time) current_time += timedelta(hours=interval_hours) # 初始化数据结构 cost_per_100_messages = [0.0] * len(time_points) # 花费/消息数量（每100条） cost_per_hour = [0.0] * len(time_points) # 花费/时间（每小时） tokens_per_hour = [0.0] * len(time_points) # Token/时间（每小时） cost_per_100_replies = [0.0] * len(time_points) # 花费/回复数量（每100条） # 每个时间点的累计数据 total_costs = [0.0] * len(time_points) total_tokens = [0] * len(time_points) total_messages = [0] * len(time_points) total_replies = [0] * len(time_points) total_online_hours = [0.0] * len(time_points) # 获取bot的QQ账号 bot_qq_account = ( str(global_config.bot.qq_account) if hasattr(global_config, "bot") and hasattr(global_config.bot, "qq_account") else "" ) interval_seconds = interval_hours * 3600 # 查询LLM使用记录 query_start_time = start_time for record in LLMUsage.select().where(LLMUsage.timestamp >= query_start_time): # type: ignore record_time = record.timestamp # 找到对应的时间间隔索引 time_diff = (record_time - start_time).total_seconds() interval_index = int(time_diff // interval_seconds) if 0 <= interval_index < len(time_points): cost = record.cost or 0.0 prompt_tokens = record.prompt_tokens or 0 completion_tokens = record.completion_tokens or 0 total_token = prompt_tokens + completion_tokens total_costs[interval_index] += cost total_tokens[interval_index] += total_token # 查询消息记录 query_start_timestamp = start_time.timestamp() for message in Messages.select().where(Messages.time >= query_start_timestamp): # type: ignore message_time_ts = message.time time_diff = message_time_ts - query_start_timestamp interval_index = int(time_diff // interval_seconds) if 0 <= interval_index < len(time_points): total_messages[interval_index] += 1 # 检查是否是bot发送的消息（回复） if bot_qq_account and message.user_id == bot_qq_account: total_replies[interval_index] += 1 # 查询在线时间记录 for record in OnlineTime.select().where(OnlineTime.end_timestamp >= start_time): # type: ignore record_start = record.start_timestamp record_end = record.end_timestamp # 找到记录覆盖的所有时间间隔 for idx, time_point in enumerate(time_points): interval_start = time_point interval_end = time_point + timedelta(hours=interval_hours) # 计算重叠部分 overlap_start = max(record_start, interval_start) overlap_end = min(record_end, interval_end) if overlap_end > overlap_start: overlap_hours = (overlap_end - overlap_start).total_seconds() / 3600.0 total_online_hours[idx] += overlap_hours # 计算指标 for idx in range(len(time_points)): # 花费/消息数量（每100条） if total_messages[idx] > 0: cost_per_100_messages[idx] = total_costs[idx] / total_messages[idx] * 100 # 花费/时间（每小时） if total_online_hours[idx] > 0: cost_per_hour[idx] = total_costs[idx] / total_online_hours[idx] # Token/时间（每小时） if total_online_hours[idx] > 0: tokens_per_hour[idx] = total_tokens[idx] / total_online_hours[idx] # 花费/回复数量（每100条） if total_replies[idx] > 0: cost_per_100_replies[idx] = total_costs[idx] / total_replies[idx] * 100 # 生成时间标签 if interval_hours == 1: time_labels = [t.strftime("%H:%M") for t in time_points] else: time_labels = [t.strftime("%m-%d") for t in time_points] return { "time_labels": time_labels, "cost_per_100_messages": cost_per_100_messages, "cost_per_hour": cost_per_hour, "tokens_per_hour": tokens_per_hour, "cost_per_100_replies": cost_per_100_replies, } def _generate_metrics_tab(self, metrics_data: dict) -> str: """生成指标趋势图表选项卡HTML内容""" colors = { "cost_per_100_messages": "#8b5cf6", "cost_per_hour": "#9f8efb", "tokens_per_hour": "#c7bbff", "cost_per_100_replies": "#d9ceff", } return f"""

指标趋势图表

""" class AsyncStatisticOutputTask(AsyncTask): """完全异步的统计输出任务 - 更高性能版本""" def __init__(self, record_file_path: str = "maibot_statistics.html"): # 延迟0秒启动，运行间隔300秒 super().__init__(task_name="Async Statistics Data Output Task", wait_before_start=0, run_interval=300) # 直接复用 StatisticOutputTask 的初始化逻辑 temp_stat_task = StatisticOutputTask(record_file_path) self.name_mapping = temp_stat_task.name_mapping self.record_file_path = temp_stat_task.record_file_path self.stat_period = temp_stat_task.stat_period async def run(self): """完全异步执行统计任务""" async def _async_collect_and_output(): try: now = datetime.now() loop = asyncio.get_event_loop() with concurrent.futures.ThreadPoolExecutor() as executor: logger.info("正在后台收集统计数据...") # 数据收集任务 collect_task = asyncio.create_task( loop.run_in_executor(executor, self._collect_all_statistics, now) # type: ignore ) stats = await collect_task logger.info("统计数据收集完成") # 创建并发的输出任务 output_tasks = [ asyncio.create_task(loop.run_in_executor(executor, self._statistic_console_output, stats, now)), # type: ignore asyncio.create_task(loop.run_in_executor(executor, self._generate_html_report, stats, now)), # type: ignore ] # 等待所有输出任务完成 await asyncio.gather(*output_tasks) logger.info("统计数据后台输出完成") except Exception as e: logger.exception(f"后台统计数据输出过程中发生异常：{e}") # 创建后台任务，立即返回 asyncio.create_task(_async_collect_and_output()) # 复用 StatisticOutputTask 的所有方法 def _collect_all_statistics(self, now: datetime): return StatisticOutputTask._collect_all_statistics(self, now) # type: ignore def _statistic_console_output(self, stats: Dict[str, Any], now: datetime): return StatisticOutputTask._statistic_console_output(self, stats, now) # type: ignore def _generate_html_report(self, stats: dict[str, Any], now: datetime): return StatisticOutputTask._generate_html_report(self, stats, now) # type: ignore # 其他需要的方法也可以类似复用... @staticmethod def _collect_model_request_for_period(collect_period: List[Tuple[str, datetime]]) -> Dict[str, Any]: return StatisticOutputTask._collect_model_request_for_period(collect_period) @staticmethod def _collect_online_time_for_period(collect_period: List[Tuple[str, datetime]], now: datetime) -> Dict[str, Any]: return StatisticOutputTask._collect_online_time_for_period(collect_period, now) def _collect_message_count_for_period(self, collect_period: List[Tuple[str, datetime]]) -> Dict[str, Any]: return StatisticOutputTask._collect_message_count_for_period(self, collect_period) # type: ignore @staticmethod def _format_total_stat(stats: Dict[str, Any]) -> str: return StatisticOutputTask._format_total_stat(stats) @staticmethod def _format_model_classified_stat(stats: Dict[str, Any]) -> str: return StatisticOutputTask._format_model_classified_stat(stats) def _format_chat_stat(self, stats: Dict[str, Any]) -> str: return StatisticOutputTask._format_chat_stat(self, stats) # type: ignore def _generate_chart_data(self, stat: dict[str, Any]) -> dict: return StatisticOutputTask._generate_chart_data(self, stat) # type: ignore def _collect_interval_data(self, now: datetime, hours: int, interval_minutes: int) -> dict: return StatisticOutputTask._collect_interval_data(self, now, hours, interval_minutes) # type: ignore def _generate_chart_tab(self, chart_data: dict) -> str: return StatisticOutputTask._generate_chart_tab(self, chart_data) # type: ignore def _generate_metrics_data(self, now: datetime) -> dict: return StatisticOutputTask._generate_metrics_data(self, now) # type: ignore def _collect_metrics_interval_data(self, now: datetime, hours: int, interval_hours: int) -> dict: return StatisticOutputTask._collect_metrics_interval_data(self, now, hours, interval_hours) # type: ignore def _generate_metrics_tab(self, metrics_data: dict) -> str: return StatisticOutputTask._generate_metrics_tab(self, metrics_data) # type: ignore def _get_chat_display_name_from_id(self, chat_id: str) -> str: return StatisticOutputTask._get_chat_display_name_from_id(self, chat_id) # type: ignore def _convert_defaultdict_to_dict(self, data): return StatisticOutputTask._convert_defaultdict_to_dict(self, data) # type: ignore