2378 lines
102 KiB
Python
2378 lines
102 KiB
Python
import asyncio
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import concurrent.futures
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import json
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import os
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import glob
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from collections import defaultdict
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from datetime import datetime, timedelta
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from typing import Any, Dict, Tuple, List
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from src.common.logger import get_logger
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from src.common.database.database import db
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from src.common.database.database_model import OnlineTime, LLMUsage, Messages
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from src.manager.async_task_manager import AsyncTask
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from src.manager.local_store_manager import local_storage
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logger = get_logger("maibot_statistic")
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# 统计数据的键
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TOTAL_REQ_CNT = "total_requests"
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TOTAL_COST = "total_cost"
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REQ_CNT_BY_TYPE = "requests_by_type"
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REQ_CNT_BY_USER = "requests_by_user"
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REQ_CNT_BY_MODEL = "requests_by_model"
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REQ_CNT_BY_MODULE = "requests_by_module"
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IN_TOK_BY_TYPE = "in_tokens_by_type"
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IN_TOK_BY_USER = "in_tokens_by_user"
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IN_TOK_BY_MODEL = "in_tokens_by_model"
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IN_TOK_BY_MODULE = "in_tokens_by_module"
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OUT_TOK_BY_TYPE = "out_tokens_by_type"
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OUT_TOK_BY_USER = "out_tokens_by_user"
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OUT_TOK_BY_MODEL = "out_tokens_by_model"
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OUT_TOK_BY_MODULE = "out_tokens_by_module"
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TOTAL_TOK_BY_TYPE = "tokens_by_type"
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TOTAL_TOK_BY_USER = "tokens_by_user"
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TOTAL_TOK_BY_MODEL = "tokens_by_model"
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TOTAL_TOK_BY_MODULE = "tokens_by_module"
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COST_BY_TYPE = "costs_by_type"
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COST_BY_USER = "costs_by_user"
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COST_BY_MODEL = "costs_by_model"
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COST_BY_MODULE = "costs_by_module"
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ONLINE_TIME = "online_time"
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TOTAL_MSG_CNT = "total_messages"
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MSG_CNT_BY_CHAT = "messages_by_chat"
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# Focus统计数据的键
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FOCUS_TOTAL_CYCLES = "focus_total_cycles"
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FOCUS_AVG_TIMES_BY_STAGE = "focus_avg_times_by_stage"
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FOCUS_ACTION_RATIOS = "focus_action_ratios"
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FOCUS_CYCLE_CNT_BY_CHAT = "focus_cycle_count_by_chat"
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FOCUS_CYCLE_CNT_BY_ACTION = "focus_cycle_count_by_action"
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FOCUS_AVG_TIMES_BY_CHAT_ACTION = "focus_avg_times_by_chat_action"
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FOCUS_AVG_TIMES_BY_ACTION = "focus_avg_times_by_action"
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FOCUS_TOTAL_TIME_BY_CHAT = "focus_total_time_by_chat"
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FOCUS_TOTAL_TIME_BY_ACTION = "focus_total_time_by_action"
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FOCUS_CYCLE_CNT_BY_VERSION = "focus_cycle_count_by_version"
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FOCUS_ACTION_RATIOS_BY_VERSION = "focus_action_ratios_by_version"
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FOCUS_AVG_TIMES_BY_VERSION = "focus_avg_times_by_version"
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class OnlineTimeRecordTask(AsyncTask):
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"""在线时间记录任务"""
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def __init__(self):
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super().__init__(task_name="Online Time Record Task", run_interval=60)
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self.record_id: int | None = None # Changed to int for Peewee's default ID
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"""记录ID"""
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self._init_database() # 初始化数据库
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@staticmethod
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def _init_database():
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"""初始化数据库"""
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with db.atomic(): # Use atomic operations for schema changes
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OnlineTime.create_table(safe=True) # Creates table if it doesn't exist, Peewee handles indexes from model
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async def run(self): # sourcery skip: use-named-expression
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try:
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current_time = datetime.now()
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extended_end_time = current_time + timedelta(minutes=1)
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if self.record_id:
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# 如果有记录，则更新结束时间
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query = OnlineTime.update(end_timestamp=extended_end_time).where(OnlineTime.id == self.record_id) # type: ignore
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updated_rows = query.execute()
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if updated_rows == 0:
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# Record might have been deleted or ID is stale, try to find/create
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self.record_id = None # Reset record_id to trigger find/create logic below
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if not self.record_id: # Check again if record_id was reset or initially None
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# 如果没有记录，检查一分钟以内是否已有记录
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# Look for a record whose end_timestamp is recent enough to be considered ongoing
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recent_record = (
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OnlineTime.select()
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.where(OnlineTime.end_timestamp >= (current_time - timedelta(minutes=1))) # type: ignore
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.order_by(OnlineTime.end_timestamp.desc())
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.first()
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)
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if recent_record:
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# 如果有记录，则更新结束时间
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self.record_id = recent_record.id
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recent_record.end_timestamp = extended_end_time
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recent_record.save()
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else:
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# 若没有记录，则插入新的在线时间记录
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new_record = OnlineTime.create(
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timestamp=current_time.timestamp(), # 添加此行
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start_timestamp=current_time,
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end_timestamp=extended_end_time,
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duration=5, # 初始时长为5分钟
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)
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self.record_id = new_record.id
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except Exception as e:
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logger.error(f"在线时间记录失败，错误信息：{e}")
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def _format_online_time(online_seconds: int) -> str:
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"""
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格式化在线时间
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:param online_seconds: 在线时间（秒）
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:return: 格式化后的在线时间字符串
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"""
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total_online_time = timedelta(seconds=online_seconds)
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days = total_online_time.days
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hours = total_online_time.seconds // 3600
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minutes = (total_online_time.seconds // 60) % 60
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seconds = total_online_time.seconds % 60
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if days > 0:
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# 如果在线时间超过1天，则格式化为"X天X小时X分钟"
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return f"{total_online_time.days}天{hours}小时{minutes}分钟{seconds}秒"
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elif hours > 0:
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# 如果在线时间超过1小时，则格式化为"X小时X分钟X秒"
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return f"{hours}小时{minutes}分钟{seconds}秒"
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else:
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# 其他情况格式化为"X分钟X秒"
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return f"{minutes}分钟{seconds}秒"
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class StatisticOutputTask(AsyncTask):
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"""统计输出任务"""
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SEP_LINE = "-" * 84
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def __init__(self, record_file_path: str = "maibot_statistics.html"):
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# 延迟300秒启动，运行间隔300秒
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super().__init__(task_name="Statistics Data Output Task", wait_before_start=0, run_interval=300)
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self.name_mapping: Dict[str, Tuple[str, float]] = {}
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"""
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联系人/群聊名称映射 {聊天ID: (联系人/群聊名称, 记录时间（timestamp）)}
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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
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"""
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self.record_file_path: str = record_file_path
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"""
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记录文件路径
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"""
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now = datetime.now()
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if "deploy_time" in local_storage:
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# 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
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deploy_time = datetime.fromtimestamp(local_storage["deploy_time"]) # type: ignore
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else:
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# 否则，使用最大时间范围，并记录部署时间为当前时间
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deploy_time = datetime(2000, 1, 1)
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local_storage["deploy_time"] = now.timestamp()
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self.stat_period: List[Tuple[str, timedelta, str]] = [
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("all_time", now - deploy_time, "自部署以来"), # 必须保留"all_time"
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("last_7_days", timedelta(days=7), "最近7天"),
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("last_24_hours", timedelta(days=1), "最近24小时"),
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("last_3_hours", timedelta(hours=3), "最近3小时"),
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("last_hour", timedelta(hours=1), "最近1小时"),
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]
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"""
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统计时间段 [(统计名称, 统计时间段, 统计描述), ...]
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"""
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def _statistic_console_output(self, stats: Dict[str, Any], now: datetime):
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"""
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输出统计数据到控制台
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:param stats: 统计数据
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:param now: 基准当前时间
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"""
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# 输出最近一小时的统计数据
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output = [
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self.SEP_LINE,
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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})",
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self.SEP_LINE,
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self._format_total_stat(stats["last_hour"]),
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"",
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self._format_model_classified_stat(stats["last_hour"]),
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"",
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self._format_chat_stat(stats["last_hour"]),
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"",
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self._format_focus_stat(stats["last_hour"]),
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self.SEP_LINE,
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"",
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]
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logger.info("\n" + "\n".join(output))
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async def run(self):
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try:
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now = datetime.now()
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# 使用线程池并行执行耗时操作
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loop = asyncio.get_event_loop()
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# 在线程池中并行执行数据收集和之前的HTML生成（如果存在）
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with concurrent.futures.ThreadPoolExecutor() as executor:
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logger.info("正在收集统计数据...")
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# 数据收集任务
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collect_task = loop.run_in_executor(executor, self._collect_all_statistics, now)
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# 等待数据收集完成
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stats = await collect_task
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logger.info("统计数据收集完成")
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# 并行执行控制台输出和HTML报告生成
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console_task = loop.run_in_executor(executor, self._statistic_console_output, stats, now)
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html_task = loop.run_in_executor(executor, self._generate_html_report, stats, now)
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# 等待两个输出任务完成
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await asyncio.gather(console_task, html_task)
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logger.info("统计数据输出完成")
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except Exception as e:
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logger.exception(f"输出统计数据过程中发生异常，错误信息：{e}")
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async def run_async_background(self):
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"""
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备选方案：完全异步后台运行统计输出
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使用此方法可以让统计任务完全非阻塞
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"""
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async def _async_collect_and_output():
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try:
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import concurrent.futures
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now = datetime.now()
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loop = asyncio.get_event_loop()
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with concurrent.futures.ThreadPoolExecutor() as executor:
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logger.info("正在后台收集统计数据...")
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# 创建后台任务，不等待完成
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collect_task = asyncio.create_task(
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loop.run_in_executor(executor, self._collect_all_statistics, now) # type: ignore
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)
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stats = await collect_task
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logger.info("统计数据收集完成")
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# 创建并发的输出任务
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output_tasks = [
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asyncio.create_task(loop.run_in_executor(executor, self._statistic_console_output, stats, now)), # type: ignore
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asyncio.create_task(loop.run_in_executor(executor, self._generate_html_report, stats, now)), # type: ignore
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]
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# 等待所有输出任务完成
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await asyncio.gather(*output_tasks)
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logger.info("统计数据后台输出完成")
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except Exception as e:
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logger.exception(f"后台统计数据输出过程中发生异常：{e}")
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# 创建后台任务，立即返回
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asyncio.create_task(_async_collect_and_output())
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# -- 以下为统计数据收集方法 --
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@staticmethod
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def _collect_model_request_for_period(collect_period: List[Tuple[str, datetime]]) -> Dict[str, Any]:
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"""
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收集指定时间段的LLM请求统计数据
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:param collect_period: 统计时间段
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"""
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if not collect_period:
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return {}
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# 排序-按照时间段开始时间降序排列（最晚的时间段在前）
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collect_period.sort(key=lambda x: x[1], reverse=True)
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stats = {
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period_key: {
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TOTAL_REQ_CNT: 0,
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REQ_CNT_BY_TYPE: defaultdict(int),
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REQ_CNT_BY_USER: defaultdict(int),
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REQ_CNT_BY_MODEL: defaultdict(int),
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REQ_CNT_BY_MODULE: defaultdict(int),
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IN_TOK_BY_TYPE: defaultdict(int),
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IN_TOK_BY_USER: defaultdict(int),
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IN_TOK_BY_MODEL: defaultdict(int),
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IN_TOK_BY_MODULE: defaultdict(int),
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OUT_TOK_BY_TYPE: defaultdict(int),
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OUT_TOK_BY_USER: defaultdict(int),
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OUT_TOK_BY_MODEL: defaultdict(int),
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OUT_TOK_BY_MODULE: defaultdict(int),
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TOTAL_TOK_BY_TYPE: defaultdict(int),
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TOTAL_TOK_BY_USER: defaultdict(int),
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TOTAL_TOK_BY_MODEL: defaultdict(int),
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TOTAL_TOK_BY_MODULE: defaultdict(int),
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TOTAL_COST: 0.0,
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COST_BY_TYPE: defaultdict(float),
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COST_BY_USER: defaultdict(float),
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COST_BY_MODEL: defaultdict(float),
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COST_BY_MODULE: defaultdict(float),
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}
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for period_key, _ in collect_period
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}
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# 以最早的时间戳为起始时间获取记录
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# Assuming LLMUsage.timestamp is a DateTimeField
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query_start_time = collect_period[-1][1]
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for record in LLMUsage.select().where(LLMUsage.timestamp >= query_start_time): # type: ignore
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record_timestamp = record.timestamp # This is already a datetime object
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for idx, (_, period_start) in enumerate(collect_period):
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if record_timestamp >= period_start:
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for period_key, _ in collect_period[idx:]:
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stats[period_key][TOTAL_REQ_CNT] += 1
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request_type = record.request_type or "unknown"
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user_id = record.user_id or "unknown" # user_id is TextField, already string
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model_name = record.model_name or "unknown"
|
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# 提取模块名：如果请求类型包含"."，取第一个"."之前的部分
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module_name = request_type.split(".")[0] if "." in request_type else request_type
|
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stats[period_key][REQ_CNT_BY_TYPE][request_type] += 1
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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
|
||
break
|
||
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),
|
||
}
|
||
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
|
||
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)
|
||
|
||
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
|
||
return stats
|
||
|
||
def _collect_focus_statistics_for_period(self, collect_period: List[Tuple[str, datetime]]) -> Dict[str, Any]:
|
||
"""
|
||
收集指定时间段的Focus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: {
|
||
FOCUS_TOTAL_CYCLES: 0,
|
||
FOCUS_AVG_TIMES_BY_STAGE: defaultdict(list),
|
||
FOCUS_ACTION_RATIOS: defaultdict(int),
|
||
FOCUS_CYCLE_CNT_BY_CHAT: defaultdict(int),
|
||
FOCUS_CYCLE_CNT_BY_ACTION: defaultdict(int),
|
||
FOCUS_AVG_TIMES_BY_CHAT_ACTION: defaultdict(lambda: defaultdict(list)),
|
||
FOCUS_AVG_TIMES_BY_ACTION: defaultdict(lambda: defaultdict(list)),
|
||
"focus_exec_times_by_chat_action": defaultdict(lambda: defaultdict(list)),
|
||
FOCUS_TOTAL_TIME_BY_CHAT: defaultdict(float),
|
||
FOCUS_TOTAL_TIME_BY_ACTION: defaultdict(float),
|
||
FOCUS_CYCLE_CNT_BY_VERSION: defaultdict(int),
|
||
FOCUS_ACTION_RATIOS_BY_VERSION: defaultdict(lambda: defaultdict(int)),
|
||
FOCUS_AVG_TIMES_BY_VERSION: defaultdict(lambda: defaultdict(list)),
|
||
"focus_exec_times_by_version_action": defaultdict(lambda: defaultdict(list)),
|
||
"focus_action_ratios_by_chat": defaultdict(lambda: defaultdict(int)),
|
||
}
|
||
for period_key, _ in collect_period
|
||
}
|
||
|
||
# 获取 log/hfc_loop 目录下的所有 json 文件
|
||
log_dir = "log/hfc_loop"
|
||
if not os.path.exists(log_dir):
|
||
logger.warning(f"Focus log directory {log_dir} does not exist")
|
||
return stats
|
||
|
||
json_files = glob.glob(os.path.join(log_dir, "*.json"))
|
||
query_start_time = collect_period[-1][1]
|
||
|
||
for json_file in json_files:
|
||
try:
|
||
# 从文件名解析时间戳 (格式: hash_version_date_time.json)
|
||
filename = os.path.basename(json_file)
|
||
name_parts = filename.replace(".json", "").split("_")
|
||
if len(name_parts) >= 4:
|
||
date_str = name_parts[-2] # YYYYMMDD
|
||
time_str = name_parts[-1] # HHMMSS
|
||
file_time_str = f"{date_str}_{time_str}"
|
||
file_time = datetime.strptime(file_time_str, "%Y%m%d_%H%M%S")
|
||
|
||
# 如果文件时间在查询范围内，则处理该文件
|
||
if file_time >= query_start_time:
|
||
with open(json_file, "r", encoding="utf-8") as f:
|
||
cycles_data = json.load(f)
|
||
self._process_focus_file_data(cycles_data, stats, collect_period, file_time)
|
||
except Exception as e:
|
||
logger.warning(f"Failed to process focus file {json_file}: {e}")
|
||
continue
|
||
|
||
# 计算平均值
|
||
self._calculate_focus_averages(stats)
|
||
return stats
|
||
|
||
def _process_focus_file_data(
|
||
self,
|
||
cycles_data: List[Dict],
|
||
stats: Dict[str, Any],
|
||
collect_period: List[Tuple[str, datetime]],
|
||
file_time: datetime,
|
||
):
|
||
"""
|
||
处理单个focus文件的数据
|
||
"""
|
||
for cycle_data in cycles_data:
|
||
try:
|
||
# 解析时间戳
|
||
timestamp_str = cycle_data.get("timestamp", "")
|
||
if timestamp_str:
|
||
cycle_time = datetime.fromisoformat(timestamp_str.replace("Z", "+00:00"))
|
||
else:
|
||
cycle_time = file_time # 使用文件时间作为后备
|
||
|
||
chat_id = cycle_data.get("chat_id", "unknown")
|
||
action_type = cycle_data.get("action_type", "unknown")
|
||
total_time = cycle_data.get("total_time", 0.0)
|
||
step_times = cycle_data.get("step_times", {})
|
||
version = cycle_data.get("version", "unknown")
|
||
|
||
# 更新聊天ID名称映射
|
||
if chat_id not in self.name_mapping:
|
||
# 尝试获取实际的聊天名称
|
||
display_name = self._get_chat_display_name_from_id(chat_id)
|
||
self.name_mapping[chat_id] = (display_name, cycle_time.timestamp())
|
||
|
||
# 对每个时间段进行统计
|
||
for idx, (_, period_start) in enumerate(collect_period):
|
||
if cycle_time >= period_start:
|
||
for period_key, _ in collect_period[idx:]:
|
||
stat = stats[period_key]
|
||
|
||
# 基础统计
|
||
stat[FOCUS_TOTAL_CYCLES] += 1
|
||
stat[FOCUS_ACTION_RATIOS][action_type] += 1
|
||
stat[FOCUS_CYCLE_CNT_BY_CHAT][chat_id] += 1
|
||
stat[FOCUS_CYCLE_CNT_BY_ACTION][action_type] += 1
|
||
stat["focus_action_ratios_by_chat"][chat_id][action_type] += 1
|
||
stat[FOCUS_TOTAL_TIME_BY_CHAT][chat_id] += total_time
|
||
stat[FOCUS_TOTAL_TIME_BY_ACTION][action_type] += total_time
|
||
|
||
# 版本统计
|
||
stat[FOCUS_CYCLE_CNT_BY_VERSION][version] += 1
|
||
stat[FOCUS_ACTION_RATIOS_BY_VERSION][version][action_type] += 1
|
||
|
||
# 阶段时间统计
|
||
for stage, time_val in step_times.items():
|
||
stat[FOCUS_AVG_TIMES_BY_STAGE][stage].append(time_val)
|
||
stat[FOCUS_AVG_TIMES_BY_CHAT_ACTION][chat_id][stage].append(time_val)
|
||
stat[FOCUS_AVG_TIMES_BY_ACTION][action_type][stage].append(time_val)
|
||
stat[FOCUS_AVG_TIMES_BY_VERSION][version][stage].append(time_val)
|
||
|
||
# 专门收集执行动作阶段的时间，按聊天流和action类型分组
|
||
if stage == "执行动作":
|
||
stat["focus_exec_times_by_chat_action"][chat_id][action_type].append(time_val)
|
||
# 按版本和action类型收集执行时间
|
||
stat["focus_exec_times_by_version_action"][version][action_type].append(time_val)
|
||
break
|
||
except Exception as e:
|
||
logger.warning(f"Failed to process cycle data: {e}")
|
||
continue
|
||
|
||
def _calculate_focus_averages(self, stats: Dict[str, Any]):
|
||
"""
|
||
计算Focus统计的平均值
|
||
"""
|
||
for _period_key, stat in stats.items():
|
||
# 计算全局阶段平均时间
|
||
for stage, times in stat[FOCUS_AVG_TIMES_BY_STAGE].items():
|
||
if times:
|
||
stat[FOCUS_AVG_TIMES_BY_STAGE][stage] = sum(times) / len(times)
|
||
else:
|
||
stat[FOCUS_AVG_TIMES_BY_STAGE][stage] = 0.0
|
||
|
||
# 计算按chat_id和action_type的阶段平均时间
|
||
for chat_id, stage_times in stat[FOCUS_AVG_TIMES_BY_CHAT_ACTION].items():
|
||
for stage, times in stage_times.items():
|
||
if times:
|
||
stat[FOCUS_AVG_TIMES_BY_CHAT_ACTION][chat_id][stage] = sum(times) / len(times)
|
||
else:
|
||
stat[FOCUS_AVG_TIMES_BY_CHAT_ACTION][chat_id][stage] = 0.0
|
||
|
||
# 计算按action_type的阶段平均时间
|
||
for action_type, stage_times in stat[FOCUS_AVG_TIMES_BY_ACTION].items():
|
||
for stage, times in stage_times.items():
|
||
if times:
|
||
stat[FOCUS_AVG_TIMES_BY_ACTION][action_type][stage] = sum(times) / len(times)
|
||
else:
|
||
stat[FOCUS_AVG_TIMES_BY_ACTION][action_type][stage] = 0.0
|
||
|
||
# 计算按聊天流和action类型的执行时间平均值
|
||
for chat_id, action_times in stat["focus_exec_times_by_chat_action"].items():
|
||
for action_type, times in action_times.items():
|
||
if times:
|
||
stat["focus_exec_times_by_chat_action"][chat_id][action_type] = sum(times) / len(times)
|
||
else:
|
||
stat["focus_exec_times_by_chat_action"][chat_id][action_type] = 0.0
|
||
|
||
# 计算按版本的阶段平均时间
|
||
for version, stage_times in stat[FOCUS_AVG_TIMES_BY_VERSION].items():
|
||
for stage, times in stage_times.items():
|
||
if times:
|
||
stat[FOCUS_AVG_TIMES_BY_VERSION][version][stage] = sum(times) / len(times)
|
||
else:
|
||
stat[FOCUS_AVG_TIMES_BY_VERSION][version][stage] = 0.0
|
||
|
||
# 计算按版本和action类型的执行时间平均值
|
||
for version, action_times in stat["focus_exec_times_by_version_action"].items():
|
||
for action_type, times in action_times.items():
|
||
if times:
|
||
stat["focus_exec_times_by_version_action"][version][action_type] = sum(times) / len(times)
|
||
else:
|
||
stat["focus_exec_times_by_version_action"][version][action_type] = 0.0
|
||
|
||
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
|
||
|
||
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
|
||
|
||
self.name_mapping = last_stat["name_mapping"] # 上次完整统计数据的名称映射
|
||
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"))
|
||
|
||
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)
|
||
focus_stat = self._collect_focus_statistics_for_period(stat_start_timestamp)
|
||
|
||
# 统计数据合并
|
||
# 合并四类统计数据
|
||
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])
|
||
stat[period_key].update(focus_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"])
|
||
local_storage["last_full_statistics"] = {
|
||
"name_mapping": self.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
|
||
"""
|
||
|
||
output = [
|
||
f"总在线时间: {_format_online_time(stats[ONLINE_TIME])}",
|
||
f"总消息数: {stats[TOTAL_MSG_CNT]}",
|
||
f"总请求数: {stats[TOTAL_REQ_CNT]}",
|
||
f"总花费: {stats[TOTAL_COST]:.4f}¥",
|
||
"",
|
||
]
|
||
|
||
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} {:>9.4f}¥"
|
||
|
||
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",
|
||
]
|
||
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]
|
||
output.append(data_fmt.format(name, count, in_tokens, out_tokens, tokens, cost))
|
||
|
||
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"]
|
||
output.extend(
|
||
f"{self.name_mapping[chat_id][0][:32]:<32} {count:>10}"
|
||
for chat_id, count in sorted(stats[MSG_CNT_BY_CHAT].items())
|
||
)
|
||
output.append("")
|
||
return "\n".join(output)
|
||
|
||
def _format_focus_stat(self, stats: Dict[str, Any]) -> str:
|
||
"""
|
||
格式化Focus统计数据
|
||
"""
|
||
if stats[FOCUS_TOTAL_CYCLES] <= 0:
|
||
return ""
|
||
|
||
output = ["Focus系统统计:", f"总循环数: {stats[FOCUS_TOTAL_CYCLES]}", ""]
|
||
|
||
# 全局阶段平均时间
|
||
if stats[FOCUS_AVG_TIMES_BY_STAGE]:
|
||
output.append("全局阶段平均时间:")
|
||
output.extend(f" {stage}: {avg_time:.3f}秒" for stage, avg_time in stats[FOCUS_AVG_TIMES_BY_STAGE].items())
|
||
output.append("")
|
||
|
||
# Action类型比例
|
||
if stats[FOCUS_ACTION_RATIOS]:
|
||
total_actions = sum(stats[FOCUS_ACTION_RATIOS].values())
|
||
output.append("Action类型分布:")
|
||
for action_type, count in sorted(stats[FOCUS_ACTION_RATIOS].items()):
|
||
ratio = (count / total_actions) * 100 if total_actions > 0 else 0
|
||
output.append(f" {action_type}: {count} ({ratio:.1f}%)")
|
||
output.append("")
|
||
|
||
# 按Chat统计（仅显示前10个）
|
||
if stats[FOCUS_CYCLE_CNT_BY_CHAT]:
|
||
output.append("按聊天流统计 (前10):")
|
||
sorted_chats = sorted(stats[FOCUS_CYCLE_CNT_BY_CHAT].items(), key=lambda x: x[1], reverse=True)[:10]
|
||
for chat_id, count in sorted_chats:
|
||
chat_name = self.name_mapping.get(chat_id, (chat_id, 0))[0]
|
||
output.append(f" {chat_name[:30]}: {count} 循环")
|
||
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
|
||
|
||
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
|
||
"""
|
||
|
||
tab_list = [
|
||
f'<button class="tab-link" onclick="showTab(event, \'{period[0]}\')">{period[2]}</button>'
|
||
for period in self.stat_period
|
||
]
|
||
# 添加Focus统计、版本对比和图表选项卡
|
||
tab_list.append('<button class="tab-link" onclick="showTab(event, \'focus\')">Focus统计</button>')
|
||
tab_list.append('<button class="tab-link" onclick="showTab(event, \'versions\')">版本对比</button>')
|
||
tab_list.append('<button class="tab-link" onclick="showTab(event, \'charts\')">数据图表</button>')
|
||
|
||
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
|
||
model_rows = "\n".join(
|
||
[
|
||
f"<tr>"
|
||
f"<td>{model_name}</td>"
|
||
f"<td>{count}</td>"
|
||
f"<td>{stat_data[IN_TOK_BY_MODEL][model_name]}</td>"
|
||
f"<td>{stat_data[OUT_TOK_BY_MODEL][model_name]}</td>"
|
||
f"<td>{stat_data[TOTAL_TOK_BY_MODEL][model_name]}</td>"
|
||
f"<td>{stat_data[COST_BY_MODEL][model_name]:.4f} ¥</td>"
|
||
f"</tr>"
|
||
for model_name, count in sorted(stat_data[REQ_CNT_BY_MODEL].items())
|
||
]
|
||
)
|
||
# 按请求类型分类统计
|
||
type_rows = "\n".join(
|
||
[
|
||
f"<tr>"
|
||
f"<td>{req_type}</td>"
|
||
f"<td>{count}</td>"
|
||
f"<td>{stat_data[IN_TOK_BY_TYPE][req_type]}</td>"
|
||
f"<td>{stat_data[OUT_TOK_BY_TYPE][req_type]}</td>"
|
||
f"<td>{stat_data[TOTAL_TOK_BY_TYPE][req_type]}</td>"
|
||
f"<td>{stat_data[COST_BY_TYPE][req_type]:.4f} ¥</td>"
|
||
f"</tr>"
|
||
for req_type, count in sorted(stat_data[REQ_CNT_BY_TYPE].items())
|
||
]
|
||
)
|
||
# 按模块分类统计
|
||
module_rows = "\n".join(
|
||
[
|
||
f"<tr>"
|
||
f"<td>{module_name}</td>"
|
||
f"<td>{count}</td>"
|
||
f"<td>{stat_data[IN_TOK_BY_MODULE][module_name]}</td>"
|
||
f"<td>{stat_data[OUT_TOK_BY_MODULE][module_name]}</td>"
|
||
f"<td>{stat_data[TOTAL_TOK_BY_MODULE][module_name]}</td>"
|
||
f"<td>{stat_data[COST_BY_MODULE][module_name]:.4f} ¥</td>"
|
||
f"</tr>"
|
||
for module_name, count in sorted(stat_data[REQ_CNT_BY_MODULE].items())
|
||
]
|
||
)
|
||
|
||
# 聊天消息统计
|
||
chat_rows = "\n".join(
|
||
[
|
||
f"<tr><td>{self.name_mapping[chat_id][0]}</td><td>{count}</td></tr>"
|
||
for chat_id, count in sorted(stat_data[MSG_CNT_BY_CHAT].items())
|
||
]
|
||
)
|
||
|
||
# Focus统计数据
|
||
# focus_action_rows = ""
|
||
# focus_chat_rows = ""
|
||
# focus_stage_rows = ""
|
||
# focus_action_stage_rows = ""
|
||
|
||
if stat_data.get(FOCUS_TOTAL_CYCLES, 0) > 0:
|
||
# Action类型统计
|
||
total_actions = sum(stat_data[FOCUS_ACTION_RATIOS].values()) if stat_data[FOCUS_ACTION_RATIOS] else 0
|
||
_focus_action_rows = "\n".join(
|
||
[
|
||
f"<tr><td>{action_type}</td><td>{count}</td><td>{(count / total_actions * 100):.1f}%</td></tr>"
|
||
for action_type, count in sorted(stat_data[FOCUS_ACTION_RATIOS].items())
|
||
]
|
||
)
|
||
|
||
# 按聊天流统计
|
||
_focus_chat_rows = "\n".join(
|
||
[
|
||
f"<tr><td>{self.name_mapping.get(chat_id, (chat_id, 0))[0]}</td><td>{count}</td><td>{stat_data[FOCUS_TOTAL_TIME_BY_CHAT].get(chat_id, 0):.2f}秒</td></tr>"
|
||
for chat_id, count in sorted(
|
||
stat_data[FOCUS_CYCLE_CNT_BY_CHAT].items(), key=lambda x: x[1], reverse=True
|
||
)
|
||
]
|
||
)
|
||
|
||
# 全局阶段时间统计
|
||
_focus_stage_rows = "\n".join(
|
||
[
|
||
f"<tr><td>{stage}</td><td>{avg_time:.3f}秒</td></tr>"
|
||
for stage, avg_time in sorted(stat_data[FOCUS_AVG_TIMES_BY_STAGE].items())
|
||
]
|
||
)
|
||
|
||
# 按Action类型的阶段时间统计
|
||
focus_action_stage_items = []
|
||
for action_type, stage_times in stat_data[FOCUS_AVG_TIMES_BY_ACTION].items():
|
||
for stage, avg_time in stage_times.items():
|
||
focus_action_stage_items.append((action_type, stage, avg_time))
|
||
|
||
_focus_action_stage_rows = "\n".join(
|
||
[
|
||
f"<tr><td>{action_type}</td><td>{stage}</td><td>{avg_time:.3f}秒</td></tr>"
|
||
for action_type, stage, avg_time in sorted(focus_action_stage_items)
|
||
]
|
||
)
|
||
# 生成HTML
|
||
return f"""
|
||
<div id=\"{div_id}\" class=\"tab-content\">
|
||
<p class=\"info-item\">
|
||
<strong>统计时段: </strong>
|
||
{start_time.strftime("%Y-%m-%d %H:%M:%S")} ~ {now.strftime("%Y-%m-%d %H:%M:%S")}
|
||
</p>
|
||
<p class=\"info-item\"><strong>总在线时间: </strong>{_format_online_time(stat_data[ONLINE_TIME])}</p>
|
||
<p class=\"info-item\"><strong>总消息数: </strong>{stat_data[TOTAL_MSG_CNT]}</p>
|
||
<p class=\"info-item\"><strong>总请求数: </strong>{stat_data[TOTAL_REQ_CNT]}</p>
|
||
<p class=\"info-item\"><strong>总花费: </strong>{stat_data[TOTAL_COST]:.4f} ¥</p>
|
||
|
||
<h2>按模型分类统计</h2>
|
||
<table>
|
||
<thead><tr><th>模型名称</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th></tr></thead>
|
||
<tbody>
|
||
{model_rows}
|
||
</tbody>
|
||
</table>
|
||
|
||
<h2>按模块分类统计</h2>
|
||
<table>
|
||
<thead>
|
||
<tr><th>模块名称</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th></tr>
|
||
</thead>
|
||
<tbody>
|
||
{module_rows}
|
||
</tbody>
|
||
</table>
|
||
|
||
<h2>按请求类型分类统计</h2>
|
||
<table>
|
||
<thead>
|
||
<tr><th>请求类型</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th></tr>
|
||
</thead>
|
||
<tbody>
|
||
{type_rows}
|
||
</tbody>
|
||
</table>
|
||
|
||
<h2>聊天消息统计</h2>
|
||
<table>
|
||
<thead>
|
||
<tr><th>联系人/群组名称</th><th>消息数量</th></tr>
|
||
</thead>
|
||
<tbody>
|
||
{chat_rows}
|
||
</tbody>
|
||
</table>
|
||
|
||
|
||
</div>
|
||
"""
|
||
|
||
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
|
||
)
|
||
|
||
# 添加Focus统计内容
|
||
focus_tab = self._generate_focus_tab(stat)
|
||
tab_content_list.append(focus_tab)
|
||
|
||
# 添加版本对比内容
|
||
versions_tab = self._generate_versions_tab(stat)
|
||
tab_content_list.append(versions_tab)
|
||
|
||
# 添加图表内容
|
||
chart_data = self._generate_chart_data(stat)
|
||
tab_content_list.append(self._generate_chart_tab(chart_data))
|
||
|
||
joined_tab_list = "\n".join(tab_list)
|
||
joined_tab_content = "\n".join(tab_content_list)
|
||
|
||
html_template = (
|
||
"""
|
||
<!DOCTYPE html>
|
||
<html lang="zh-CN">
|
||
<head>
|
||
<meta charset="UTF-8">
|
||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||
<title>MaiBot运行统计报告</title>
|
||
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
|
||
<style>
|
||
body {
|
||
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
|
||
margin: 0;
|
||
padding: 20px;
|
||
background-color: #f4f7f6;
|
||
color: #333;
|
||
line-height: 1.6;
|
||
}
|
||
.container {
|
||
max-width: 900px;
|
||
margin: 20px auto;
|
||
background-color: #fff;
|
||
padding: 25px;
|
||
border-radius: 8px;
|
||
box-shadow: 0 2px 10px rgba(0,0,0,0.1);
|
||
}
|
||
h1, h2 {
|
||
color: #2c3e50;
|
||
border-bottom: 2px solid #3498db;
|
||
padding-bottom: 10px;
|
||
margin-top: 0;
|
||
}
|
||
h1 {
|
||
text-align: center;
|
||
font-size: 2em;
|
||
}
|
||
h2 {
|
||
font-size: 1.5em;
|
||
margin-top: 30px;
|
||
}
|
||
p {
|
||
margin-bottom: 10px;
|
||
}
|
||
.info-item {
|
||
background-color: #ecf0f1;
|
||
padding: 8px 12px;
|
||
border-radius: 4px;
|
||
margin-bottom: 8px;
|
||
font-size: 0.95em;
|
||
}
|
||
.info-item strong {
|
||
color: #2980b9;
|
||
}
|
||
table {
|
||
width: 100%;
|
||
border-collapse: collapse;
|
||
margin-top: 15px;
|
||
font-size: 0.9em;
|
||
}
|
||
th, td {
|
||
border: 1px solid #ddd;
|
||
padding: 10px;
|
||
text-align: left;
|
||
}
|
||
th {
|
||
background-color: #3498db;
|
||
color: white;
|
||
font-weight: bold;
|
||
}
|
||
tr:nth-child(even) {
|
||
background-color: #f9f9f9;
|
||
}
|
||
.footer {
|
||
text-align: center;
|
||
margin-top: 30px;
|
||
font-size: 0.8em;
|
||
color: #7f8c8d;
|
||
}
|
||
.tabs {
|
||
overflow: hidden;
|
||
background: #ecf0f1;
|
||
display: flex;
|
||
}
|
||
.tabs button {
|
||
background: inherit; border: none; outline: none;
|
||
padding: 14px 16px; cursor: pointer;
|
||
transition: 0.3s; font-size: 16px;
|
||
}
|
||
.tabs button:hover {
|
||
background-color: #d4dbdc;
|
||
}
|
||
.tabs button.active {
|
||
background-color: #b3bbbd;
|
||
}
|
||
.tab-content {
|
||
display: none;
|
||
padding: 20px;
|
||
background-color: #fff;
|
||
border: 1px solid #ccc;
|
||
}
|
||
.tab-content.active {
|
||
display: block;
|
||
}
|
||
</style>
|
||
</head>
|
||
<body>
|
||
"""
|
||
+ f"""
|
||
<div class="container">
|
||
<h1>MaiBot运行统计报告</h1>
|
||
<p class="info-item"><strong>统计截止时间:</strong> {now.strftime("%Y-%m-%d %H:%M:%S")}</p>
|
||
|
||
<div class="tabs">
|
||
{joined_tab_list}
|
||
</div>
|
||
|
||
{joined_tab_content}
|
||
</div>
|
||
"""
|
||
+ """
|
||
<script>
|
||
let i, tab_content, tab_links;
|
||
tab_content = document.getElementsByClassName("tab-content");
|
||
tab_links = document.getElementsByClassName("tab-link");
|
||
|
||
tab_content[0].classList.add("active");
|
||
tab_links[0].classList.add("active");
|
||
|
||
function showTab(evt, tabName) {{
|
||
for (i = 0; i < tab_content.length; i++) tab_content[i].classList.remove("active");
|
||
for (i = 0; i < tab_links.length; i++) tab_links[i].classList.remove("active");
|
||
document.getElementById(tabName).classList.add("active");
|
||
evt.currentTarget.classList.add("active");
|
||
}}
|
||
</script>
|
||
</body>
|
||
</html>
|
||
"""
|
||
)
|
||
|
||
with open(self.record_file_path, "w", encoding="utf-8") as f:
|
||
f.write(html_template)
|
||
|
||
def _generate_focus_tab(self, stat: dict[str, Any]) -> str:
|
||
# sourcery skip: for-append-to-extend, list-comprehension, use-any
|
||
"""生成Focus统计独立分页的HTML内容"""
|
||
|
||
# 为每个时间段准备Focus数据
|
||
focus_sections = []
|
||
|
||
for period_name, period_delta, period_desc in self.stat_period:
|
||
stat_data = stat.get(period_name, {})
|
||
|
||
if stat_data.get(FOCUS_TOTAL_CYCLES, 0) <= 0:
|
||
continue
|
||
|
||
# 生成Focus统计数据行
|
||
focus_action_rows = ""
|
||
focus_chat_rows = ""
|
||
focus_stage_rows = ""
|
||
focus_action_stage_rows = ""
|
||
|
||
# Action类型统计
|
||
total_actions = sum(stat_data[FOCUS_ACTION_RATIOS].values()) if stat_data[FOCUS_ACTION_RATIOS] else 0
|
||
if total_actions > 0:
|
||
focus_action_rows = "\n".join(
|
||
[
|
||
f"<tr><td>{action_type}</td><td>{count}</td><td>{(count / total_actions * 100):.1f}%</td></tr>"
|
||
for action_type, count in sorted(stat_data[FOCUS_ACTION_RATIOS].items())
|
||
]
|
||
)
|
||
|
||
# 按聊天流统计（横向表格，显示各阶段时间差异和不同action的平均时间）
|
||
focus_chat_rows = ""
|
||
if stat_data[FOCUS_AVG_TIMES_BY_CHAT_ACTION]:
|
||
# 获取前三个阶段（不包括执行动作）
|
||
basic_stages = ["观察", "规划器"]
|
||
existing_basic_stages = []
|
||
for stage in basic_stages:
|
||
# 检查是否有任何聊天流在这个阶段有数据
|
||
stage_exists = False
|
||
for _chat_id, stage_times in stat_data[FOCUS_AVG_TIMES_BY_CHAT_ACTION].items():
|
||
if stage in stage_times:
|
||
stage_exists = True
|
||
break
|
||
if stage_exists:
|
||
existing_basic_stages.append(stage)
|
||
|
||
# 获取所有action类型（按出现频率排序）
|
||
all_action_types = sorted(
|
||
stat_data[FOCUS_ACTION_RATIOS].keys(), key=lambda x: stat_data[FOCUS_ACTION_RATIOS][x], reverse=True
|
||
)
|
||
|
||
# 为每个聊天流生成一行
|
||
chat_rows = []
|
||
for chat_id in sorted(
|
||
stat_data[FOCUS_CYCLE_CNT_BY_CHAT].keys(),
|
||
key=lambda x: stat_data[FOCUS_CYCLE_CNT_BY_CHAT][x],
|
||
reverse=True,
|
||
):
|
||
chat_name = self.name_mapping.get(chat_id, (chat_id, 0))[0]
|
||
cycle_count = stat_data[FOCUS_CYCLE_CNT_BY_CHAT][chat_id]
|
||
|
||
# 获取该聊天流的各阶段平均时间
|
||
stage_times = stat_data[FOCUS_AVG_TIMES_BY_CHAT_ACTION].get(chat_id, {})
|
||
|
||
row_cells = [f"<td><strong>{chat_name}</strong><br><small>({cycle_count}次循环)</small></td>"]
|
||
|
||
# 添加基础阶段时间
|
||
for stage in existing_basic_stages:
|
||
time_val = stage_times.get(stage, 0.0)
|
||
row_cells.append(f"<td>{time_val:.3f}秒</td>")
|
||
|
||
# 添加每个action类型的平均执行时间
|
||
for action_type in all_action_types:
|
||
# 使用真实的按聊天流+action类型分组的执行时间数据
|
||
exec_times_by_chat_action = stat_data.get("focus_exec_times_by_chat_action", {})
|
||
chat_action_times = exec_times_by_chat_action.get(chat_id, {})
|
||
avg_exec_time = chat_action_times.get(action_type, 0.0)
|
||
|
||
if avg_exec_time > 0:
|
||
row_cells.append(f"<td>{avg_exec_time:.3f}秒</td>")
|
||
else:
|
||
row_cells.append("<td>-</td>")
|
||
|
||
chat_rows.append(f"<tr>{''.join(row_cells)}</tr>")
|
||
|
||
# 生成表头
|
||
stage_headers = "".join([f"<th>{stage}</th>" for stage in existing_basic_stages])
|
||
action_headers = "".join(
|
||
[f"<th>{action_type}<br><small>(执行)</small></th>" for action_type in all_action_types]
|
||
)
|
||
focus_chat_table_header = f"<tr><th>聊天流</th>{stage_headers}{action_headers}</tr>"
|
||
focus_chat_rows = focus_chat_table_header + "\n" + "\n".join(chat_rows)
|
||
|
||
# 全局阶段时间统计
|
||
focus_stage_rows = "\n".join(
|
||
[
|
||
f"<tr><td>{stage}</td><td>{avg_time:.3f}秒</td></tr>"
|
||
for stage, avg_time in sorted(stat_data[FOCUS_AVG_TIMES_BY_STAGE].items())
|
||
]
|
||
)
|
||
|
||
# 聊天流Action选择比例对比表（横向表格）
|
||
focus_chat_action_ratios_rows = ""
|
||
if stat_data.get("focus_action_ratios_by_chat"):
|
||
if all_action_types_for_ratio := sorted(
|
||
stat_data[FOCUS_ACTION_RATIOS].keys(),
|
||
key=lambda x: stat_data[FOCUS_ACTION_RATIOS][x],
|
||
reverse=True,
|
||
):
|
||
# 为每个聊天流生成数据行（按循环数排序）
|
||
chat_ratio_rows = []
|
||
for chat_id in sorted(
|
||
stat_data[FOCUS_CYCLE_CNT_BY_CHAT].keys(),
|
||
key=lambda x: stat_data[FOCUS_CYCLE_CNT_BY_CHAT][x],
|
||
reverse=True,
|
||
):
|
||
chat_name = self.name_mapping.get(chat_id, (chat_id, 0))[0]
|
||
total_cycles = stat_data[FOCUS_CYCLE_CNT_BY_CHAT][chat_id]
|
||
chat_action_counts = stat_data["focus_action_ratios_by_chat"].get(chat_id, {})
|
||
|
||
row_cells = [f"<td><strong>{chat_name}</strong><br><small>({total_cycles}次循环)</small></td>"]
|
||
|
||
# 添加每个action类型的数量和百分比
|
||
for action_type in all_action_types_for_ratio:
|
||
count = chat_action_counts.get(action_type, 0)
|
||
ratio = (count / total_cycles * 100) if total_cycles > 0 else 0
|
||
if count > 0:
|
||
row_cells.append(f"<td>{count}<br><small>({ratio:.1f}%)</small></td>")
|
||
else:
|
||
row_cells.append("<td>-<br><small>(0%)</small></td>")
|
||
|
||
chat_ratio_rows.append(f"<tr>{''.join(row_cells)}</tr>")
|
||
|
||
# 生成表头
|
||
action_headers = "".join([f"<th>{action_type}</th>" for action_type in all_action_types_for_ratio])
|
||
chat_action_ratio_table_header = f"<tr><th>聊天流</th>{action_headers}</tr>"
|
||
focus_chat_action_ratios_rows = chat_action_ratio_table_header + "\n" + "\n".join(chat_ratio_rows)
|
||
|
||
# 按Action类型的阶段时间统计（横向表格）
|
||
focus_action_stage_rows = ""
|
||
if stat_data[FOCUS_AVG_TIMES_BY_ACTION]:
|
||
# 获取所有阶段（按固定顺序）
|
||
stage_order = ["观察", "规划器", "执行动作"]
|
||
all_stages = []
|
||
for stage in stage_order:
|
||
if any(stage in stage_times for stage_times in stat_data[FOCUS_AVG_TIMES_BY_ACTION].values()):
|
||
all_stages.append(stage)
|
||
|
||
# 为每个Action类型生成一行
|
||
action_rows = []
|
||
for action_type in sorted(stat_data[FOCUS_AVG_TIMES_BY_ACTION].keys()):
|
||
stage_times = stat_data[FOCUS_AVG_TIMES_BY_ACTION][action_type]
|
||
row_cells = [f"<td><strong>{action_type}</strong></td>"]
|
||
|
||
for stage in all_stages:
|
||
time_val = stage_times.get(stage, 0.0)
|
||
row_cells.append(f"<td>{time_val:.3f}秒</td>")
|
||
|
||
action_rows.append(f"<tr>{''.join(row_cells)}</tr>")
|
||
|
||
# 生成表头
|
||
stage_headers = "".join([f"<th>{stage}</th>" for stage in all_stages])
|
||
focus_action_stage_table_header = f"<tr><th>Action类型</th>{stage_headers}</tr>"
|
||
focus_action_stage_rows = focus_action_stage_table_header + "\n" + "\n".join(action_rows)
|
||
|
||
# 计算时间范围
|
||
if period_name == "all_time":
|
||
from src.manager.local_store_manager import local_storage
|
||
|
||
start_time = datetime.fromtimestamp(local_storage["deploy_time"]) # type: ignore
|
||
else:
|
||
start_time = datetime.now() - period_delta
|
||
|
||
time_range = f"{start_time.strftime('%Y-%m-%d %H:%M:%S')} ~ {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
|
||
# 生成该时间段的Focus统计HTML
|
||
section_html = f"""
|
||
<div class="focus-period-section">
|
||
<h2>{period_desc}Focus统计</h2>
|
||
<p class="info-item"><strong>统计时段:</strong> {time_range}</p>
|
||
<p class="info-item"><strong>总循环数:</strong> {stat_data.get(FOCUS_TOTAL_CYCLES, 0)}</p>
|
||
|
||
<div class="focus-stats-grid">
|
||
<div class="focus-stat-item">
|
||
<h3>全局阶段平均时间</h3>
|
||
<table>
|
||
<thead><tr><th>阶段</th><th>平均时间</th></tr></thead>
|
||
<tbody>{focus_stage_rows}</tbody>
|
||
</table>
|
||
</div>
|
||
|
||
<div class="focus-stat-item">
|
||
<h3>Action类型分布</h3>
|
||
<table>
|
||
<thead><tr><th>Action类型</th><th>次数</th><th>占比</th></tr></thead>
|
||
<tbody>{focus_action_rows}</tbody>
|
||
</table>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="focus-stat-item">
|
||
<h3>按聊天流各阶段时间统计</h3>
|
||
<table class="chat-stage-table">
|
||
<thead></thead>
|
||
<tbody>{focus_chat_rows}</tbody>
|
||
</table>
|
||
</div>
|
||
|
||
<div class="focus-stat-item">
|
||
<h3>聊天流Action选择比例对比</h3>
|
||
<table class="chat-action-ratio-table">
|
||
<thead></thead>
|
||
<tbody>{focus_chat_action_ratios_rows}</tbody>
|
||
</table>
|
||
</div>
|
||
|
||
<div class="focus-stat-item">
|
||
<h3>Action类型阶段时间详情</h3>
|
||
<table class="action-stage-table">
|
||
<thead></thead>
|
||
<tbody>{focus_action_stage_rows}</tbody>
|
||
</table>
|
||
</div>
|
||
</div>
|
||
"""
|
||
|
||
focus_sections.append(section_html)
|
||
|
||
# 如果没有任何Focus数据
|
||
if not focus_sections:
|
||
focus_sections.append("""
|
||
<div class="focus-period-section">
|
||
<h2>暂无Focus统计数据</h2>
|
||
<p class="info-item">在指定时间段内未找到任何Focus循环数据。</p>
|
||
<p class="info-item">请确保 <code>log/hfc_loop/</code> 目录下存在相应的JSON文件。</p>
|
||
</div>
|
||
""")
|
||
|
||
return f"""
|
||
<div id="focus" class="tab-content">
|
||
<h1>Focus系统详细统计</h1>
|
||
<p class="info-item">
|
||
<strong>数据来源:</strong> log/hfc_loop/ 目录下的JSON文件<br>
|
||
<strong>统计内容:</strong> 各时间段的Focus循环性能分析
|
||
</p>
|
||
|
||
{"".join(focus_sections)}
|
||
|
||
<style>
|
||
.focus-period-section {{
|
||
margin-bottom: 40px;
|
||
padding: 20px;
|
||
border: 1px solid #ddd;
|
||
border-radius: 8px;
|
||
background-color: #fafafa;
|
||
}}
|
||
|
||
.focus-stats-grid {{
|
||
display: grid;
|
||
grid-template-columns: 1fr 1fr;
|
||
gap: 20px;
|
||
margin: 20px 0;
|
||
}}
|
||
|
||
.focus-stat-item {{
|
||
background-color: white;
|
||
padding: 15px;
|
||
border-radius: 6px;
|
||
border: 1px solid #eee;
|
||
}}
|
||
|
||
.focus-stat-item h3 {{
|
||
margin-top: 0;
|
||
color: #2c3e50;
|
||
border-bottom: 1px solid #3498db;
|
||
padding-bottom: 5px;
|
||
}}
|
||
|
||
@media (max-width: 768px) {{
|
||
.focus-stats-grid {{
|
||
grid-template-columns: 1fr;
|
||
}}
|
||
}}
|
||
|
||
/* 为横向表格添加特殊样式 */
|
||
.focus-stat-item table.action-stage-table,
|
||
.focus-stat-item table.chat-stage-table,
|
||
.focus-stat-item table.chat-action-ratio-table {{
|
||
width: 100%;
|
||
overflow-x: auto;
|
||
display: block;
|
||
white-space: nowrap;
|
||
}}
|
||
|
||
.focus-stat-item table.action-stage-table tbody,
|
||
.focus-stat-item table.chat-stage-table tbody,
|
||
.focus-stat-item table.chat-action-ratio-table tbody {{
|
||
display: table;
|
||
width: 100%;
|
||
}}
|
||
|
||
.focus-stat-item table.action-stage-table tr:first-child,
|
||
.focus-stat-item table.chat-stage-table tr:first-child,
|
||
.focus-stat-item table.chat-action-ratio-table tr:first-child {{
|
||
background-color: #3498db;
|
||
color: white;
|
||
font-weight: bold;
|
||
}}
|
||
|
||
.focus-stat-item table.action-stage-table tr:first-child td,
|
||
.focus-stat-item table.chat-stage-table tr:first-child td,
|
||
.focus-stat-item table.chat-action-ratio-table tr:first-child td {{
|
||
background-color: #3498db !important;
|
||
color: white !important;
|
||
font-weight: bold;
|
||
}}
|
||
|
||
/* 为聊天流表格添加额外样式 */
|
||
.focus-stat-item table.chat-stage-table td:first-child,
|
||
.focus-stat-item table.chat-action-ratio-table td:first-child {{
|
||
min-width: 150px;
|
||
max-width: 200px;
|
||
word-wrap: break-word;
|
||
white-space: normal;
|
||
}}
|
||
|
||
.focus-stat-item table.chat-stage-table small,
|
||
.focus-stat-item table.chat-action-ratio-table small {{
|
||
color: #7f8c8d;
|
||
font-size: 0.8em;
|
||
}}
|
||
|
||
/* 聊天流Action比例表格的特殊样式 */
|
||
.focus-stat-item table.chat-action-ratio-table {{
|
||
border-spacing: 0;
|
||
}}
|
||
|
||
.focus-stat-item table.chat-action-ratio-table td {{
|
||
text-align: center;
|
||
padding: 8px;
|
||
}}
|
||
|
||
.focus-stat-item table.chat-action-ratio-table td:first-child {{
|
||
text-align: left;
|
||
font-weight: bold;
|
||
}}
|
||
</style>
|
||
</div>
|
||
"""
|
||
|
||
def _generate_versions_tab(self, stat: dict[str, Any]) -> str:
|
||
"""生成版本对比独立分页的HTML内容"""
|
||
|
||
# 为每个时间段准备版本对比数据
|
||
version_sections = []
|
||
|
||
for period_name, period_delta, period_desc in self.stat_period:
|
||
stat_data = stat.get(period_name, {})
|
||
|
||
if not stat_data.get(FOCUS_CYCLE_CNT_BY_VERSION):
|
||
continue
|
||
|
||
# 获取所有版本（按循环数排序）
|
||
all_versions = sorted(
|
||
stat_data[FOCUS_CYCLE_CNT_BY_VERSION].keys(),
|
||
key=lambda x: stat_data[FOCUS_CYCLE_CNT_BY_VERSION][x],
|
||
reverse=True,
|
||
)
|
||
|
||
# 生成版本Action分布表
|
||
focus_version_action_rows = ""
|
||
if stat_data[FOCUS_ACTION_RATIOS_BY_VERSION]:
|
||
# 获取所有action类型
|
||
all_action_types_for_version = set()
|
||
for version_actions in stat_data[FOCUS_ACTION_RATIOS_BY_VERSION].values():
|
||
all_action_types_for_version.update(version_actions.keys())
|
||
all_action_types_for_version = sorted(all_action_types_for_version)
|
||
|
||
if all_action_types_for_version:
|
||
version_action_rows = []
|
||
for version in all_versions:
|
||
version_actions = stat_data[FOCUS_ACTION_RATIOS_BY_VERSION].get(version, {})
|
||
total_cycles = stat_data[FOCUS_CYCLE_CNT_BY_VERSION][version]
|
||
|
||
row_cells = [f"<td><strong>{version}</strong><br><small>({total_cycles}次循环)</small></td>"]
|
||
|
||
for action_type in all_action_types_for_version:
|
||
count = version_actions.get(action_type, 0)
|
||
ratio = (count / total_cycles * 100) if total_cycles > 0 else 0
|
||
row_cells.append(f"<td>{count}<br><small>({ratio:.1f}%)</small></td>")
|
||
|
||
version_action_rows.append(f"<tr>{''.join(row_cells)}</tr>")
|
||
|
||
# 生成表头
|
||
action_headers = "".join(
|
||
[f"<th>{action_type}</th>" for action_type in all_action_types_for_version]
|
||
)
|
||
version_action_table_header = f"<tr><th>版本</th>{action_headers}</tr>"
|
||
focus_version_action_rows = version_action_table_header + "\n" + "\n".join(version_action_rows)
|
||
|
||
# 生成版本阶段时间表（按action类型分解执行时间）
|
||
focus_version_stage_rows = ""
|
||
if stat_data[FOCUS_AVG_TIMES_BY_VERSION]:
|
||
# 基础三个阶段
|
||
basic_stages = ["观察", "规划器"]
|
||
|
||
# 获取所有action类型用于执行时间列
|
||
all_action_types_for_exec = set()
|
||
if stat_data.get("focus_exec_times_by_version_action"):
|
||
for version_actions in stat_data["focus_exec_times_by_version_action"].values():
|
||
all_action_types_for_exec.update(version_actions.keys())
|
||
all_action_types_for_exec = sorted(all_action_types_for_exec)
|
||
|
||
# 检查哪些基础阶段存在数据
|
||
existing_basic_stages = []
|
||
for stage in basic_stages:
|
||
stage_exists = False
|
||
for version_stages in stat_data[FOCUS_AVG_TIMES_BY_VERSION].values():
|
||
if stage in version_stages:
|
||
stage_exists = True
|
||
break
|
||
if stage_exists:
|
||
existing_basic_stages.append(stage)
|
||
|
||
# 构建表格
|
||
if existing_basic_stages or all_action_types_for_exec:
|
||
version_stage_rows = []
|
||
|
||
# 为每个版本生成数据行
|
||
for version in all_versions:
|
||
version_stages = stat_data[FOCUS_AVG_TIMES_BY_VERSION].get(version, {})
|
||
total_cycles = stat_data[FOCUS_CYCLE_CNT_BY_VERSION][version]
|
||
|
||
row_cells = [f"<td><strong>{version}</strong><br><small>({total_cycles}次循环)</small></td>"]
|
||
|
||
# 添加基础阶段时间
|
||
for stage in existing_basic_stages:
|
||
time_val = version_stages.get(stage, 0.0)
|
||
row_cells.append(f"<td>{time_val:.3f}秒</td>")
|
||
|
||
# 添加不同action类型的执行时间
|
||
for action_type in all_action_types_for_exec:
|
||
# 获取该版本该action类型的平均执行时间
|
||
version_exec_times = stat_data.get("focus_exec_times_by_version_action", {})
|
||
if version in version_exec_times and action_type in version_exec_times[version]:
|
||
exec_time = version_exec_times[version][action_type]
|
||
row_cells.append(f"<td>{exec_time:.3f}秒</td>")
|
||
else:
|
||
row_cells.append("<td>-</td>")
|
||
|
||
version_stage_rows.append(f"<tr>{''.join(row_cells)}</tr>")
|
||
|
||
# 生成表头
|
||
basic_headers = "".join([f"<th>{stage}</th>" for stage in existing_basic_stages])
|
||
action_headers = "".join(
|
||
[
|
||
f"<th>执行时间<br><small>[{action_type}]</small></th>"
|
||
for action_type in all_action_types_for_exec
|
||
]
|
||
)
|
||
version_stage_table_header = f"<tr><th>版本</th>{basic_headers}{action_headers}</tr>"
|
||
focus_version_stage_rows = version_stage_table_header + "\n" + "\n".join(version_stage_rows)
|
||
|
||
# 计算时间范围
|
||
if period_name == "all_time":
|
||
from src.manager.local_store_manager import local_storage
|
||
|
||
start_time = datetime.fromtimestamp(local_storage["deploy_time"]) # type: ignore
|
||
else:
|
||
start_time = datetime.now() - period_delta
|
||
time_range = f"{start_time.strftime('%Y-%m-%d %H:%M:%S')} ~ {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
|
||
# 生成该时间段的版本对比HTML
|
||
section_html = f"""
|
||
<div class="version-period-section">
|
||
<h2>{period_desc}版本对比</h2>
|
||
<p class="info-item"><strong>统计时段:</strong> {time_range}</p>
|
||
<p class="info-item"><strong>包含版本:</strong> {len(all_versions)} 个版本</p>
|
||
|
||
<div class="version-stats-grid">
|
||
<div class="version-stat-item">
|
||
<h3>版本Action类型分布对比</h3>
|
||
<table class="version-comparison-table">
|
||
<thead></thead>
|
||
<tbody>{focus_version_action_rows}</tbody>
|
||
</table>
|
||
</div>
|
||
|
||
<div class="version-stat-item">
|
||
<h3>版本阶段时间对比</h3>
|
||
<table class="version-comparison-table">
|
||
<thead></thead>
|
||
<tbody>{focus_version_stage_rows}</tbody>
|
||
</table>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
"""
|
||
|
||
version_sections.append(section_html)
|
||
|
||
# 如果没有任何版本数据
|
||
if not version_sections:
|
||
version_sections.append("""
|
||
<div class="version-period-section">
|
||
<h2>暂无版本对比数据</h2>
|
||
<p class="info-item">在指定时间段内未找到任何版本信息。</p>
|
||
<p class="info-item">请确保 <code>log/hfc_loop/</code> 目录下的JSON文件包含版本信息。</p>
|
||
</div>
|
||
""")
|
||
|
||
return f"""
|
||
<div id="versions" class="tab-content">
|
||
<h1>Focus HFC版本对比分析</h1>
|
||
<p class="info-item">
|
||
<strong>对比内容:</strong> 不同版本的Action类型分布和各阶段性能表现<br>
|
||
<strong>数据来源:</strong> log/hfc_loop/ 目录下JSON文件中的version字段
|
||
</p>
|
||
|
||
{"".join(version_sections)}
|
||
|
||
<style>
|
||
.version-period-section {{
|
||
margin-bottom: 40px;
|
||
padding: 20px;
|
||
border: 1px solid #e74c3c;
|
||
border-radius: 8px;
|
||
background-color: #fdf2f2;
|
||
}}
|
||
|
||
.version-stats-grid {{
|
||
display: grid;
|
||
grid-template-columns: 1fr;
|
||
gap: 30px;
|
||
margin: 20px 0;
|
||
}}
|
||
|
||
.version-stat-item {{
|
||
background-color: white;
|
||
padding: 15px;
|
||
border-radius: 6px;
|
||
border: 1px solid #eee;
|
||
}}
|
||
|
||
.version-stat-item h3 {{
|
||
margin-top: 0;
|
||
color: #c0392b;
|
||
border-bottom: 1px solid #e74c3c;
|
||
padding-bottom: 5px;
|
||
}}
|
||
|
||
@media (max-width: 768px) {{
|
||
.version-stats-grid {{
|
||
grid-template-columns: 1fr;
|
||
gap: 20px;
|
||
}}
|
||
}}
|
||
|
||
/* 版本对比表格样式 */
|
||
.version-stat-item table.version-comparison-table {{
|
||
width: 100%;
|
||
overflow-x: auto;
|
||
display: block;
|
||
white-space: nowrap;
|
||
}}
|
||
|
||
.version-stat-item table.version-comparison-table tbody {{
|
||
display: table;
|
||
width: 100%;
|
||
}}
|
||
|
||
.version-stat-item table.version-comparison-table tr:first-child {{
|
||
background-color: #e74c3c;
|
||
color: white;
|
||
font-weight: bold;
|
||
}}
|
||
|
||
.version-stat-item table.version-comparison-table tr:first-child td {{
|
||
background-color: #e74c3c !important;
|
||
color: white !important;
|
||
font-weight: bold;
|
||
}}
|
||
|
||
.version-stat-item table.version-comparison-table td:first-child {{
|
||
min-width: 120px;
|
||
font-weight: bold;
|
||
}}
|
||
|
||
.version-stat-item table.version-comparison-table small {{
|
||
color: #7f8c8d;
|
||
font-size: 0.8em;
|
||
}}
|
||
|
||
/* 版本差异高亮 */
|
||
.version-stat-item table.version-comparison-table tr:nth-child(even) {{
|
||
background-color: #f9f9f9;
|
||
}}
|
||
|
||
.version-stat-item table.version-comparison-table tr:hover {{
|
||
background-color: #f5f5f5;
|
||
}}
|
||
</style>
|
||
</div>
|
||
"""
|
||
|
||
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_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
|
||
|
||
# 查询Focus循环记录
|
||
focus_cycles_by_action = {}
|
||
focus_time_by_stage = {}
|
||
|
||
log_dir = "log/hfc_loop"
|
||
if os.path.exists(log_dir):
|
||
json_files = glob.glob(os.path.join(log_dir, "*.json"))
|
||
for json_file in json_files:
|
||
try:
|
||
# 解析文件时间
|
||
filename = os.path.basename(json_file)
|
||
name_parts = filename.replace(".json", "").split("_")
|
||
if len(name_parts) >= 4:
|
||
date_str = name_parts[-2]
|
||
time_str = name_parts[-1]
|
||
file_time_str = f"{date_str}_{time_str}"
|
||
file_time = datetime.strptime(file_time_str, "%Y%m%d_%H%M%S")
|
||
|
||
if file_time >= start_time:
|
||
with open(json_file, "r", encoding="utf-8") as f:
|
||
cycles_data = json.load(f)
|
||
|
||
for cycle in cycles_data:
|
||
try:
|
||
timestamp_str = cycle.get("timestamp", "")
|
||
if timestamp_str:
|
||
cycle_time = datetime.fromisoformat(timestamp_str.replace("Z", "+00:00"))
|
||
else:
|
||
cycle_time = file_time
|
||
|
||
if cycle_time >= start_time:
|
||
# 计算时间间隔索引
|
||
time_diff = (cycle_time - start_time).total_seconds()
|
||
interval_index = int(time_diff // interval_seconds)
|
||
|
||
if 0 <= interval_index < len(time_points):
|
||
action_type = cycle.get("action_type", "unknown")
|
||
step_times = cycle.get("step_times", {})
|
||
|
||
# 累计action类型数据
|
||
if action_type not in focus_cycles_by_action:
|
||
focus_cycles_by_action[action_type] = [0] * len(time_points)
|
||
focus_cycles_by_action[action_type][interval_index] += 1
|
||
|
||
# 累计阶段时间数据
|
||
for stage, time_val in step_times.items():
|
||
if stage not in focus_time_by_stage:
|
||
focus_time_by_stage[stage] = [0] * len(time_points)
|
||
focus_time_by_stage[stage][interval_index] += time_val
|
||
except Exception:
|
||
continue
|
||
except Exception:
|
||
continue
|
||
|
||
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,
|
||
"focus_cycles_by_action": focus_cycles_by_action,
|
||
"focus_time_by_stage": focus_time_by_stage,
|
||
}
|
||
|
||
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 = [
|
||
"#3498db",
|
||
"#e74c3c",
|
||
"#2ecc71",
|
||
"#f39c12",
|
||
"#9b59b6",
|
||
"#1abc9c",
|
||
"#34495e",
|
||
"#e67e22",
|
||
"#95a5a6",
|
||
"#f1c40f",
|
||
]
|
||
|
||
# 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"""
|
||
<div id="charts" class="tab-content">
|
||
<h2>数据图表</h2>
|
||
|
||
<!-- 时间范围选择按钮 -->
|
||
<div style="margin: 20px 0; text-align: center;">
|
||
<label style="margin-right: 10px; font-weight: bold;">时间范围:</label>
|
||
<button class="time-range-btn" onclick="switchTimeRange('6h')">6小时</button>
|
||
<button class="time-range-btn" onclick="switchTimeRange('12h')">12小时</button>
|
||
<button class="time-range-btn active" onclick="switchTimeRange('24h')">24小时</button>
|
||
<button class="time-range-btn" onclick="switchTimeRange('48h')">48小时</button>
|
||
</div>
|
||
|
||
<div style="margin-top: 20px;">
|
||
<div style="margin-bottom: 40px;">
|
||
<canvas id="totalCostChart" width="800" height="400"></canvas>
|
||
</div>
|
||
<div style="margin-bottom: 40px;">
|
||
<canvas id="costByModuleChart" width="800" height="400"></canvas>
|
||
</div>
|
||
<div style="margin-bottom: 40px;">
|
||
<canvas id="costByModelChart" width="800" height="400"></canvas>
|
||
</div>
|
||
<div style="margin-bottom: 40px;">
|
||
<canvas id="messageByChatChart" width="800" height="400"></canvas>
|
||
</div>
|
||
<div style="margin-bottom: 40px;">
|
||
<canvas id="focusCyclesByActionChart" width="800" height="400"></canvas>
|
||
</div>
|
||
<div>
|
||
<canvas id="focusTimeByStageChart" width="800" height="400"></canvas>
|
||
</div>
|
||
</div>
|
||
|
||
<style>
|
||
.time-range-btn {{
|
||
background-color: #ecf0f1;
|
||
border: 1px solid #bdc3c7;
|
||
color: #2c3e50;
|
||
padding: 8px 16px;
|
||
margin: 0 5px;
|
||
border-radius: 4px;
|
||
cursor: pointer;
|
||
font-size: 14px;
|
||
transition: all 0.3s ease;
|
||
}}
|
||
|
||
.time-range-btn:hover {{
|
||
background-color: #d5dbdb;
|
||
}}
|
||
|
||
.time-range-btn.active {{
|
||
background-color: #3498db;
|
||
color: white;
|
||
border-color: #2980b9;
|
||
}}
|
||
</style>
|
||
|
||
<script>
|
||
const allChartData = {chart_data};
|
||
let currentCharts = {{}};
|
||
|
||
// 图表配置模板
|
||
const chartConfigs = {{
|
||
totalCost: {{
|
||
id: 'totalCostChart',
|
||
title: '总花费',
|
||
yAxisLabel: '花费 (¥)',
|
||
dataKey: 'total_cost_data',
|
||
fill: true
|
||
}},
|
||
costByModule: {{
|
||
id: 'costByModuleChart',
|
||
title: '各模块花费',
|
||
yAxisLabel: '花费 (¥)',
|
||
dataKey: 'cost_by_module',
|
||
fill: false
|
||
}},
|
||
costByModel: {{
|
||
id: 'costByModelChart',
|
||
title: '各模型花费',
|
||
yAxisLabel: '花费 (¥)',
|
||
dataKey: 'cost_by_model',
|
||
fill: false
|
||
}},
|
||
messageByChat: {{
|
||
id: 'messageByChatChart',
|
||
title: '各聊天流消息数',
|
||
yAxisLabel: '消息数',
|
||
dataKey: 'message_by_chat',
|
||
fill: false
|
||
}},
|
||
focusCyclesByAction: {{
|
||
id: 'focusCyclesByActionChart',
|
||
title: 'Focus循环按Action类型',
|
||
yAxisLabel: '循环数',
|
||
dataKey: 'focus_cycles_by_action',
|
||
fill: false
|
||
}},
|
||
focusTimeByStage: {{
|
||
id: 'focusTimeByStageChart',
|
||
title: 'Focus各阶段累计时间',
|
||
yAxisLabel: '时间 (秒)',
|
||
dataKey: 'focus_time_by_stage',
|
||
fill: false
|
||
}}
|
||
}};
|
||
|
||
function switchTimeRange(timeRange) {{
|
||
// 更新按钮状态
|
||
document.querySelectorAll('.time-range-btn').forEach(btn => {{
|
||
btn.classList.remove('active');
|
||
}});
|
||
event.target.classList.add('active');
|
||
|
||
// 更新图表数据
|
||
const data = allChartData[timeRange];
|
||
updateAllCharts(data, timeRange);
|
||
}}
|
||
|
||
function updateAllCharts(data, timeRange) {{
|
||
// 销毁现有图表
|
||
Object.values(currentCharts).forEach(chart => {{
|
||
if (chart) chart.destroy();
|
||
}});
|
||
|
||
currentCharts = {{}};
|
||
|
||
// 重新创建图表
|
||
createChart('totalCost', data, timeRange);
|
||
createChart('costByModule', data, timeRange);
|
||
createChart('costByModel', data, timeRange);
|
||
createChart('messageByChat', data, timeRange);
|
||
createChart('focusCyclesByAction', data, timeRange);
|
||
createChart('focusTimeByStage', data, timeRange);
|
||
}}
|
||
|
||
function createChart(chartType, data, timeRange) {{
|
||
const config = chartConfigs[chartType];
|
||
const colors = ['#3498db', '#e74c3c', '#2ecc71', '#f39c12', '#9b59b6', '#1abc9c', '#34495e', '#e67e22', '#95a5a6', '#f1c40f'];
|
||
|
||
let datasets = [];
|
||
|
||
if (chartType === 'totalCost') {{
|
||
datasets = [{{
|
||
label: config.title,
|
||
data: data[config.dataKey],
|
||
borderColor: colors[0],
|
||
backgroundColor: 'rgba(52, 152, 219, 0.1)',
|
||
tension: 0.4,
|
||
fill: config.fill
|
||
}}];
|
||
}} else {{
|
||
let i = 0;
|
||
Object.entries(data[config.dataKey]).forEach(([name, chartData]) => {{
|
||
datasets.push({{
|
||
label: name,
|
||
data: chartData,
|
||
borderColor: colors[i % colors.length],
|
||
backgroundColor: colors[i % colors.length] + '20',
|
||
tension: 0.4,
|
||
fill: config.fill
|
||
}});
|
||
i++;
|
||
}});
|
||
}}
|
||
|
||
currentCharts[chartType] = new Chart(document.getElementById(config.id), {{
|
||
type: 'line',
|
||
data: {{
|
||
labels: data.time_labels,
|
||
datasets: datasets
|
||
}},
|
||
options: {{
|
||
responsive: true,
|
||
plugins: {{
|
||
title: {{
|
||
display: true,
|
||
text: timeRange + '内' + config.title + '趋势',
|
||
font: {{ size: 16 }}
|
||
}},
|
||
legend: {{
|
||
display: chartType !== 'totalCost',
|
||
position: 'top'
|
||
}}
|
||
}},
|
||
scales: {{
|
||
x: {{
|
||
title: {{
|
||
display: true,
|
||
text: '时间'
|
||
}},
|
||
ticks: {{
|
||
maxTicksLimit: 12
|
||
}}
|
||
}},
|
||
y: {{
|
||
title: {{
|
||
display: true,
|
||
text: config.yAxisLabel
|
||
}},
|
||
beginAtZero: true
|
||
}}
|
||
}},
|
||
interaction: {{
|
||
intersect: false,
|
||
mode: 'index'
|
||
}}
|
||
}}
|
||
}});
|
||
}}
|
||
|
||
// 初始化图表（默认24小时）
|
||
document.addEventListener('DOMContentLoaded', function() {{
|
||
updateAllCharts(allChartData['24h'], '24h');
|
||
}});
|
||
</script>
|
||
</div>
|
||
"""
|
||
|
||
|
||
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)
|
||
|
||
def _statistic_console_output(self, stats: Dict[str, Any], now: datetime):
|
||
return StatisticOutputTask._statistic_console_output(self, stats, now)
|
||
|
||
def _generate_html_report(self, stats: dict[str, Any], now: datetime):
|
||
return StatisticOutputTask._generate_html_report(self, stats, now)
|
||
|
||
# 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)
|
||
|
||
def _collect_focus_statistics_for_period(self, collect_period: List[Tuple[str, datetime]]) -> Dict[str, Any]:
|
||
return StatisticOutputTask._collect_focus_statistics_for_period(self, collect_period)
|
||
|
||
def _process_focus_file_data(
|
||
self,
|
||
cycles_data: List[Dict],
|
||
stats: Dict[str, Any],
|
||
collect_period: List[Tuple[str, datetime]],
|
||
file_time: datetime,
|
||
):
|
||
return StatisticOutputTask._process_focus_file_data(self, cycles_data, stats, collect_period, file_time)
|
||
|
||
def _calculate_focus_averages(self, stats: Dict[str, Any]):
|
||
return StatisticOutputTask._calculate_focus_averages(self, stats)
|
||
|
||
@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)
|
||
|
||
def _format_focus_stat(self, stats: Dict[str, Any]) -> str:
|
||
return StatisticOutputTask._format_focus_stat(self, stats)
|
||
|
||
def _generate_chart_data(self, stat: dict[str, Any]) -> dict:
|
||
return StatisticOutputTask._generate_chart_data(self, stat)
|
||
|
||
def _collect_interval_data(self, now: datetime, hours: int, interval_minutes: int) -> dict:
|
||
return StatisticOutputTask._collect_interval_data(self, now, hours, interval_minutes)
|
||
|
||
def _generate_chart_tab(self, chart_data: dict) -> str:
|
||
return StatisticOutputTask._generate_chart_tab(self, chart_data)
|
||
|
||
def _get_chat_display_name_from_id(self, chat_id: str) -> str:
|
||
return StatisticOutputTask._get_chat_display_name_from_id(self, chat_id)
|
||
|
||
def _generate_focus_tab(self, stat: dict[str, Any]) -> str:
|
||
return StatisticOutputTask._generate_focus_tab(self, stat)
|
||
|
||
def _generate_versions_tab(self, stat: dict[str, Any]) -> str:
|
||
return StatisticOutputTask._generate_versions_tab(self, stat)
|
||
|
||
def _convert_defaultdict_to_dict(self, data):
|
||
return StatisticOutputTask._convert_defaultdict_to_dict(self, data)
|