1702 lines
71 KiB
Python
1702 lines
71 KiB
Python
from collections import defaultdict
|
||
from datetime import datetime, timedelta
|
||
from typing import Any, Dict, Tuple, List
|
||
import asyncio
|
||
import concurrent.futures
|
||
|
||
|
||
from src.common.logger import get_logger
|
||
from src.manager.async_task_manager import AsyncTask
|
||
|
||
from ...common.database.database import db # This db is the Peewee database instance
|
||
from ...common.database.database_model import OnlineTime, LLMUsage, Messages # Import the Peewee model
|
||
from src.manager.local_store_manager import local_storage
|
||
|
||
logger = get_logger("maibot_statistic")
|
||
|
||
# HFC统计相关的键
|
||
HFC_TOTAL_CYCLES = "hfc_total_cycles"
|
||
HFC_CYCLES_BY_CHAT = "hfc_cycles_by_chat"
|
||
HFC_CYCLES_BY_ACTION = "hfc_cycles_by_action"
|
||
HFC_CYCLES_BY_VERSION = "hfc_cycles_by_version"
|
||
HFC_AVG_TIME_BY_CHAT = "hfc_avg_time_by_chat"
|
||
HFC_AVG_TIME_BY_ACTION = "hfc_avg_time_by_action"
|
||
HFC_AVG_TIME_BY_VERSION = "hfc_avg_time_by_version"
|
||
HFC_ACTIONS_BY_CHAT = "hfc_actions_by_chat" # 群聊×动作交叉统计
|
||
|
||
# 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"
|
||
ONLINE_TIME = "online_time"
|
||
TOTAL_MSG_CNT = "total_messages"
|
||
MSG_CNT_BY_CHAT = "messages_by_chat"
|
||
|
||
|
||
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):
|
||
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)
|
||
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)))
|
||
.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_oneline_time = timedelta(seconds=online_seconds)
|
||
|
||
days = total_oneline_time.days
|
||
hours = total_oneline_time.seconds // 3600
|
||
minutes = (total_oneline_time.seconds // 60) % 60
|
||
seconds = total_oneline_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_oneline_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"
|
||
|
||
|
||
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"])
|
||
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_7_days", timedelta(days=7), "最近7天"),
|
||
("last_24_hours", timedelta(days=1), "最近24小时"),
|
||
("last_3_hours", timedelta(hours=3), "最近3小时"),
|
||
("last_hour", timedelta(hours=1), "最近1小时"),
|
||
]
|
||
"""
|
||
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_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)
|
||
)
|
||
|
||
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)),
|
||
asyncio.create_task(loop.run_in_executor(executor, self._generate_html_report, stats, now)),
|
||
]
|
||
|
||
# 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),
|
||
}
|
||
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):
|
||
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_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
|
||
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):
|
||
# 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):
|
||
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_hfc_data_for_period(self, collect_period: List[Tuple[str, datetime]]) -> Dict[str, Any]:
|
||
"""
|
||
收集指定时间段的HFC统计数据
|
||
|
||
:param collect_period: 统计时间段
|
||
"""
|
||
if not collect_period:
|
||
return {}
|
||
|
||
# 为每个时间段初始化空的统计数据
|
||
stats = {
|
||
period_key: {
|
||
HFC_TOTAL_CYCLES: 0,
|
||
HFC_CYCLES_BY_CHAT: defaultdict(int),
|
||
HFC_CYCLES_BY_ACTION: defaultdict(int),
|
||
HFC_CYCLES_BY_VERSION: defaultdict(int),
|
||
HFC_AVG_TIME_BY_CHAT: defaultdict(lambda: {"decision": 0, "action": 0, "total": 0}),
|
||
HFC_AVG_TIME_BY_ACTION: defaultdict(lambda: {"decision": 0, "action": 0, "total": 0}),
|
||
HFC_AVG_TIME_BY_VERSION: defaultdict(lambda: {"decision": 0, "action": 0, "total": 0}),
|
||
HFC_ACTIONS_BY_CHAT: defaultdict(lambda: defaultdict(int)), # 群聊×动作交叉统计
|
||
|
||
}
|
||
for period_key, _ in collect_period
|
||
}
|
||
|
||
try:
|
||
import json
|
||
from pathlib import Path
|
||
|
||
hfc_stats_file = Path("data/hfc/time.json")
|
||
if not hfc_stats_file.exists():
|
||
logger.info("HFC统计文件不存在，跳过HFC统计")
|
||
return stats
|
||
|
||
# 读取HFC统计数据
|
||
with open(hfc_stats_file, 'r', encoding='utf-8') as f:
|
||
hfc_data = json.load(f)
|
||
|
||
# 处理每个chat_id和版本的统计数据
|
||
for stats_key, chat_stats in hfc_data.items():
|
||
chat_id = chat_stats.get("chat_id", "unknown")
|
||
version = chat_stats.get("version", "unknown")
|
||
last_updated_str = chat_stats.get("last_updated")
|
||
|
||
if not last_updated_str:
|
||
continue
|
||
|
||
# 解析最后更新时间
|
||
try:
|
||
last_updated = datetime.fromisoformat(last_updated_str.replace('Z', '+00:00'))
|
||
if last_updated.tzinfo:
|
||
last_updated = last_updated.replace(tzinfo=None)
|
||
except:
|
||
continue
|
||
|
||
# 对于"全部时间"，所有数据都包含
|
||
# 对于其他时间段，只包含在时间范围内更新的数据
|
||
applicable_periods = []
|
||
for period_key, period_start in collect_period:
|
||
if period_key == "all_time" or last_updated >= period_start:
|
||
applicable_periods.append(period_key)
|
||
|
||
if not applicable_periods:
|
||
continue
|
||
|
||
# 处理整体统计
|
||
overall = chat_stats.get("overall", {})
|
||
total_records = overall.get("total_records", 0)
|
||
avg_step_times = overall.get("avg_step_times", {})
|
||
|
||
# 计算决策时间和动作时间
|
||
action_time = avg_step_times.get("执行动作", 0)
|
||
total_time = overall.get("avg_total_time", 0)
|
||
decision_time = max(0, total_time - action_time)
|
||
|
||
for period_key in applicable_periods:
|
||
stats[period_key][HFC_TOTAL_CYCLES] += total_records
|
||
stats[period_key][HFC_CYCLES_BY_CHAT][chat_id] += total_records
|
||
stats[period_key][HFC_CYCLES_BY_VERSION][version] += total_records
|
||
|
||
# 处理按动作类型的统计
|
||
by_action = chat_stats.get("by_action", {})
|
||
for action_type, action_data in by_action.items():
|
||
count = action_data.get("count", 0)
|
||
action_step_times = action_data.get("avg_step_times", {})
|
||
action_total_time = action_data.get("avg_total_time", 0)
|
||
|
||
# 计算该动作类型的决策时间和动作时间
|
||
action_exec_time = action_step_times.get("执行动作", 0)
|
||
action_decision_time = max(0, action_total_time - action_exec_time)
|
||
|
||
for period_key in applicable_periods:
|
||
stats[period_key][HFC_CYCLES_BY_ACTION][action_type] += count
|
||
|
||
# 群聊×动作交叉统计
|
||
stats[period_key][HFC_ACTIONS_BY_CHAT][chat_id][action_type] += count
|
||
|
||
# 累加时间统计（用于后续计算加权平均）
|
||
# 这里我们需要重新设计数据结构来存储累计值
|
||
if chat_id not in stats[period_key][HFC_AVG_TIME_BY_CHAT]:
|
||
stats[period_key][HFC_AVG_TIME_BY_CHAT][chat_id] = {"decision": 0, "action": 0, "total": 0, "count": 0}
|
||
if action_type not in stats[period_key][HFC_AVG_TIME_BY_ACTION]:
|
||
stats[period_key][HFC_AVG_TIME_BY_ACTION][action_type] = {"decision": 0, "action": 0, "total": 0, "count": 0}
|
||
if version not in stats[period_key][HFC_AVG_TIME_BY_VERSION]:
|
||
stats[period_key][HFC_AVG_TIME_BY_VERSION][version] = {"decision": 0, "action": 0, "total": 0, "count": 0}
|
||
|
||
# 累加加权值（时间*数量）
|
||
stats[period_key][HFC_AVG_TIME_BY_CHAT][chat_id]["decision"] += decision_time * total_records
|
||
stats[period_key][HFC_AVG_TIME_BY_CHAT][chat_id]["action"] += action_time * total_records
|
||
stats[period_key][HFC_AVG_TIME_BY_CHAT][chat_id]["total"] += total_time * total_records
|
||
stats[period_key][HFC_AVG_TIME_BY_CHAT][chat_id]["count"] += total_records
|
||
|
||
stats[period_key][HFC_AVG_TIME_BY_ACTION][action_type]["decision"] += action_decision_time * count
|
||
stats[period_key][HFC_AVG_TIME_BY_ACTION][action_type]["action"] += action_exec_time * count
|
||
stats[period_key][HFC_AVG_TIME_BY_ACTION][action_type]["total"] += action_total_time * count
|
||
stats[period_key][HFC_AVG_TIME_BY_ACTION][action_type]["count"] += count
|
||
|
||
stats[period_key][HFC_AVG_TIME_BY_VERSION][version]["decision"] += decision_time * total_records
|
||
stats[period_key][HFC_AVG_TIME_BY_VERSION][version]["action"] += action_time * total_records
|
||
stats[period_key][HFC_AVG_TIME_BY_VERSION][version]["total"] += total_time * total_records
|
||
stats[period_key][HFC_AVG_TIME_BY_VERSION][version]["count"] += total_records
|
||
|
||
except Exception as e:
|
||
logger.error(f"收集HFC统计数据失败: {e}")
|
||
|
||
# 计算加权平均时间
|
||
for period_key in stats:
|
||
for stat_type in [HFC_AVG_TIME_BY_CHAT, HFC_AVG_TIME_BY_ACTION, HFC_AVG_TIME_BY_VERSION]:
|
||
for key, time_data in stats[period_key][stat_type].items():
|
||
if time_data.get("count", 0) > 0:
|
||
count = time_data["count"]
|
||
stats[period_key][stat_type][key] = {
|
||
"decision": time_data["decision"] / count,
|
||
"action": time_data["action"] / count,
|
||
"total": time_data["total"] / count
|
||
}
|
||
else:
|
||
stats[period_key][stat_type][key] = {"decision": 0, "action": 0, "total": 0}
|
||
|
||
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
|
||
|
||
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 = local_storage["last_full_statistics"] # 上次完整统计数据
|
||
|
||
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)
|
||
|
||
# HFC统计数据收集
|
||
hfc_stat = self._collect_hfc_data_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(hfc_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():
|
||
# 跳过已删除的SUCCESS_RATE相关key
|
||
if key in ["hfc_success_rate_by_chat", "hfc_success_rate_by_action", "hfc_success_rate_by_version"]:
|
||
continue
|
||
|
||
# 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():
|
||
# 检查是否是HFC的嵌套字典时间数据
|
||
if key in [HFC_AVG_TIME_BY_CHAT, HFC_AVG_TIME_BY_ACTION, HFC_AVG_TIME_BY_VERSION] and isinstance(sub_val, dict):
|
||
# 对于HFC时间数据，需要特殊处理
|
||
if sub_key not in stat["all_time"][key]:
|
||
stat["all_time"][key][sub_key] = {"decision": 0, "action": 0, "total": 0}
|
||
|
||
# 合并嵌套的时间数据
|
||
for time_type, time_val in sub_val.items():
|
||
if time_type in stat["all_time"][key][sub_key]:
|
||
stat["all_time"][key][sub_key][time_type] += time_val
|
||
elif key == HFC_ACTIONS_BY_CHAT and isinstance(sub_val, dict):
|
||
# 对于群聊×动作交叉统计的二层嵌套字典，需要特殊处理
|
||
if sub_key not in stat["all_time"][key]:
|
||
stat["all_time"][key][sub_key] = {}
|
||
|
||
# 合并二层嵌套的动作数据
|
||
for action_type, action_count in sub_val.items():
|
||
if action_type in stat["all_time"][key][sub_key]:
|
||
stat["all_time"][key][sub_key][action_type] += action_count
|
||
else:
|
||
stat["all_time"][key][sub_key][action_type] = action_count
|
||
else:
|
||
# 普通的数值或字典合并
|
||
if sub_key in stat["all_time"][key]:
|
||
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
|
||
local_storage["last_full_statistics"] = {
|
||
"name_mapping": self.name_mapping,
|
||
"stat_data": stat["all_time"],
|
||
"timestamp": now.timestamp(),
|
||
}
|
||
|
||
return stat
|
||
|
||
# -- 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 _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
|
||
]
|
||
# 添加图表选项卡
|
||
tab_list.append('<button class="tab-link" onclick="showTab(event, \'charts\')">数据图表</button>')
|
||
# 添加HFC统计选项卡
|
||
tab_list.append('<button class="tab-link" onclick="showTab(event, \'hfc_stats\')">HFC统计</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())
|
||
]
|
||
)
|
||
# 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"]))
|
||
)
|
||
|
||
# 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HFC统计内容
|
||
tab_content_list.append(self._generate_hfc_stats_tab(stat))
|
||
|
||
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_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):
|
||
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
|
||
|
||
# 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):
|
||
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_hfc_stats_tab(self, stat: dict[str, Any]) -> str:
|
||
"""生成HFC统计选项卡HTML内容"""
|
||
|
||
def _get_chat_display_name(chat_id):
|
||
"""获取聊天显示名称"""
|
||
if chat_id in self.name_mapping:
|
||
return self.name_mapping[chat_id][0]
|
||
else:
|
||
return chat_id
|
||
|
||
def _generate_overview_section(data, title):
|
||
"""生成总览部分"""
|
||
total_cycles = data.get(HFC_TOTAL_CYCLES, 0)
|
||
if total_cycles == 0:
|
||
return f"<h3>{title}</h3><p>暂无HFC数据</p>"
|
||
|
||
def _generate_chat_action_table(actions_by_chat):
|
||
"""生成群聊×动作选择率表格"""
|
||
if not actions_by_chat:
|
||
return "<h4>按群聊的动作选择率</h4><p>暂无数据</p>"
|
||
|
||
# 获取所有动作类型
|
||
all_actions = set()
|
||
for chat_actions in actions_by_chat.values():
|
||
all_actions.update(chat_actions.keys())
|
||
|
||
if not all_actions:
|
||
return "<h4>按群聊的动作选择率</h4><p>暂无数据</p>"
|
||
|
||
all_actions = sorted(all_actions)
|
||
|
||
# 生成表头
|
||
action_headers = ""
|
||
for action in all_actions:
|
||
action_display = action
|
||
if action == "no_reply":
|
||
action_display = "不回复"
|
||
action_headers += f"<th>{action_display}</th>"
|
||
|
||
# 生成表格行
|
||
table_rows = ""
|
||
for chat_id in sorted(actions_by_chat.keys()):
|
||
chat_actions = actions_by_chat[chat_id]
|
||
chat_total = sum(chat_actions.values())
|
||
|
||
if chat_total == 0:
|
||
continue
|
||
|
||
chat_display_name = _get_chat_display_name(chat_id)
|
||
table_rows += f"<tr><td>{chat_display_name}</td>"
|
||
|
||
# 为每个动作生成百分比
|
||
for action in all_actions:
|
||
count = chat_actions.get(action, 0)
|
||
percentage = (count / chat_total * 100) if chat_total > 0 else 0
|
||
table_rows += f"<td>{count} ({percentage:.1f}%)</td>"
|
||
|
||
table_rows += f"<td>{chat_total}</td></tr>"
|
||
|
||
return f"""
|
||
<h4>按群聊的动作选择率</h4>
|
||
<table>
|
||
<thead>
|
||
<tr><th>群聊名称</th>{action_headers}<th>总计</th></tr>
|
||
</thead>
|
||
<tbody>
|
||
{table_rows}
|
||
</tbody>
|
||
</table>
|
||
<p class="info-item"><strong>说明：</strong>显示每个群聊中不同动作类型的选择次数及占比。</p>
|
||
"""
|
||
|
||
cycles_by_chat = data.get(HFC_CYCLES_BY_CHAT, {})
|
||
cycles_by_action = data.get(HFC_CYCLES_BY_ACTION, {})
|
||
cycles_by_version = data.get(HFC_CYCLES_BY_VERSION, {})
|
||
avg_time_by_chat = data.get(HFC_AVG_TIME_BY_CHAT, {})
|
||
avg_time_by_action = data.get(HFC_AVG_TIME_BY_ACTION, {})
|
||
avg_time_by_version = data.get(HFC_AVG_TIME_BY_VERSION, {})
|
||
actions_by_chat = data.get(HFC_ACTIONS_BY_CHAT, {})
|
||
|
||
# 按群聊统计表格
|
||
chat_rows = ""
|
||
for chat_id in sorted(cycles_by_chat.keys()):
|
||
cycles = cycles_by_chat[chat_id]
|
||
time_data = avg_time_by_chat.get(chat_id, {"decision": 0, "action": 0, "total": 0})
|
||
decision_time = time_data.get("decision", 0)
|
||
action_time = time_data.get("action", 0)
|
||
total_time = time_data.get("total", 0)
|
||
chat_display_name = _get_chat_display_name(chat_id)
|
||
chat_rows += f"""
|
||
<tr>
|
||
<td>{chat_display_name}</td>
|
||
<td>{cycles}</td>
|
||
<td>{decision_time:.2f}s</td>
|
||
<td>{action_time:.2f}s</td>
|
||
<td>{total_time:.2f}s</td>
|
||
</tr>"""
|
||
|
||
# 按动作类型统计表格 - 添加说明
|
||
action_rows = ""
|
||
for action_type in sorted(cycles_by_action.keys()):
|
||
cycles = cycles_by_action[action_type]
|
||
time_data = avg_time_by_action.get(action_type, {"decision": 0, "action": 0, "total": 0})
|
||
decision_time = time_data.get("decision", 0)
|
||
action_time = time_data.get("action", 0)
|
||
total_time = time_data.get("total", 0)
|
||
# 为no_reply添加说明
|
||
action_display = action_type
|
||
if action_type == "no_reply":
|
||
action_display = f"{action_type} (不回复决策)"
|
||
action_rows += f"""
|
||
<tr>
|
||
<td>{action_display}</td>
|
||
<td>{cycles}</td>
|
||
<td>{decision_time:.2f}s</td>
|
||
<td>{action_time:.2f}s</td>
|
||
<td>{total_time:.2f}s</td>
|
||
</tr>"""
|
||
|
||
# 按版本统计表格
|
||
version_rows = ""
|
||
for version in sorted(cycles_by_version.keys()):
|
||
cycles = cycles_by_version[version]
|
||
time_data = avg_time_by_version.get(version, {"decision": 0, "action": 0, "total": 0})
|
||
decision_time = time_data.get("decision", 0)
|
||
action_time = time_data.get("action", 0)
|
||
total_time = time_data.get("total", 0)
|
||
version_rows += f"""
|
||
<tr>
|
||
<td>{version}</td>
|
||
<td>{cycles}</td>
|
||
<td>{decision_time:.2f}s</td>
|
||
<td>{action_time:.2f}s</td>
|
||
<td>{total_time:.2f}s</td>
|
||
</tr>"""
|
||
|
||
return f"""
|
||
<h3>{title} (总循环数: {total_cycles})</h3>
|
||
|
||
<h4>按群聊统计</h4>
|
||
<table>
|
||
<thead>
|
||
<tr><th>群聊名称</th><th>循环次数</th><th>决策时间</th><th>动作时间</th><th>总时间</th></tr>
|
||
</thead>
|
||
<tbody>
|
||
{chat_rows}
|
||
</tbody>
|
||
</table>
|
||
|
||
<h4>按动作类型统计</h4>
|
||
<table>
|
||
<thead>
|
||
<tr><th>动作类型</th><th>循环次数</th><th>决策时间</th><th>动作时间</th><th>总时间</th></tr>
|
||
</thead>
|
||
<tbody>
|
||
{action_rows}
|
||
</tbody>
|
||
</table>
|
||
<p class="info-item"><strong>时间说明：</strong>决策时间包括观察、处理、规划等步骤；动作时间是执行具体动作的时间。</p>
|
||
|
||
<h4>按版本统计</h4>
|
||
<table>
|
||
<thead>
|
||
<tr><th>版本</th><th>循环次数</th><th>决策时间</th><th>动作时间</th><th>总时间</th></tr>
|
||
</thead>
|
||
<tbody>
|
||
{version_rows}
|
||
</tbody>
|
||
</table>
|
||
|
||
{_generate_chat_action_table(actions_by_chat)}
|
||
"""
|
||
|
||
# 生成指定时间段的统计
|
||
sections = []
|
||
|
||
# 定义要显示的时间段及其描述（所有时间在最上方）
|
||
time_periods = [
|
||
("all_time", "全部时间"),
|
||
("last_24_hours", "最近24小时"),
|
||
("last_7_days", "最近7天")
|
||
]
|
||
|
||
for period_key, period_desc in time_periods:
|
||
period_data = stat.get(period_key, {})
|
||
if period_data.get(HFC_TOTAL_CYCLES, 0) > 0: # 只显示有数据的时间段
|
||
sections.append(_generate_overview_section(period_data, period_desc))
|
||
|
||
if not sections:
|
||
sections.append("<h3>暂无HFC数据</h3><p>系统中还没有HFC循环记录</p>")
|
||
|
||
sections_html = "<br/>".join(sections)
|
||
|
||
return f"""
|
||
<div id="hfc_stats" class="tab-content">
|
||
<h2>HFC (Heart Flow Chat) 统计</h2>
|
||
<p class="info-item"><strong>说明：</strong>此页面显示HFC模块的性能统计信息，包括各群聊、动作类型和版本的详细数据。</p>
|
||
|
||
{sections_html}
|
||
</div>
|
||
"""
|
||
|
||
def _generate_chart_tab(self, chart_data: dict) -> str:
|
||
"""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>
|
||
<canvas id="messageByChatChart" 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
|
||
}}
|
||
}};
|
||
|
||
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);
|
||
}}
|
||
|
||
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)
|
||
)
|
||
|
||
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)),
|
||
asyncio.create_task(loop.run_in_executor(executor, self._generate_html_report, stats, now)),
|
||
]
|
||
|
||
# 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_hfc_data_for_period(self, collect_period: List[Tuple[str, datetime]]) -> Dict[str, Any]:
|
||
return StatisticOutputTask._collect_hfc_data_for_period(self, collect_period)
|
||
|
||
@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 _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 _generate_hfc_stats_tab(self, stat: dict[str, Any]) -> str:
|
||
return StatisticOutputTask._generate_hfc_stats_tab(self, stat)
|