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fix：修复统计

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This commit is contained in:
SengokuCola
2025-06-25 00:59:18 +08:00
parent babf43d211
commit 6653bf3bd9
2 changed files with 265 additions and 188 deletions
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449
src/chat/utils/statistic.py
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@@ -58,12 +58,6 @@ FOCUS_CYCLE_CNT_BY_VERSION = "focus_cycle_count_by_version"
FOCUS_ACTION_RATIOS_BY_VERSION = "focus_action_ratios_by_version"
FOCUS_AVG_TIMES_BY_VERSION = "focus_avg_times_by_version"
# 新增: 后处理器统计数据的键
FOCUS_POST_PROCESSOR_TIMES = "focus_post_processor_times"
FOCUS_POST_PROCESSOR_COUNT = "focus_post_processor_count"
FOCUS_POST_PROCESSOR_SUCCESS_RATE = "focus_post_processor_success_rate"
FOCUS_PROCESSOR_TIMES = "focus_processor_times" # 前处理器统计
class OnlineTimeRecordTask(AsyncTask):
"""在线时间记录任务"""
@@ -501,10 +495,6 @@ class StatisticOutputTask(AsyncTask):
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)),
# 新增：前处理器和后处理器统计字段
FOCUS_PROCESSOR_TIMES: defaultdict(list), # 前处理器时间
FOCUS_POST_PROCESSOR_TIMES: defaultdict(list), # 后处理器时间
FOCUS_POST_PROCESSOR_COUNT: defaultdict(int), # 后处理器执行次数
}
for period_key, _ in collect_period
}
@@ -567,10 +557,6 @@ class StatisticOutputTask(AsyncTask):
step_times = cycle_data.get("step_times", {})
version = cycle_data.get("version", "unknown")
# 新增：获取前处理器和后处理器时间
processor_time_costs = cycle_data.get("processor_time_costs", {})
post_processor_time_costs = cycle_data.get("post_processor_time_costs", {})
# 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
@@ -608,15 +594,6 @@ class StatisticOutputTask(AsyncTask):
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)
# 新增：前处理器时间统计
for processor_name, time_val in processor_time_costs.items():
stat[FOCUS_PROCESSOR_TIMES][processor_name].append(time_val)
# 新增：后处理器时间统计
for processor_name, time_val in post_processor_time_costs.items():
stat[FOCUS_POST_PROCESSOR_TIMES][processor_name].append(time_val)
stat[FOCUS_POST_PROCESSOR_COUNT][processor_name] += 1
break
except Exception as e:
logger.warning(f"Failed to process cycle data: {e}")
@@ -674,20 +651,6 @@ class StatisticOutputTask(AsyncTask):
else:
stat["focus_exec_times_by_version_action"][version][action_type] = 0.0
# 新增：计算前处理器平均时间
for processor_name, times in stat[FOCUS_PROCESSOR_TIMES].items():
if times:
stat[FOCUS_PROCESSOR_TIMES][processor_name] = sum(times) / len(times)
else:
stat[FOCUS_PROCESSOR_TIMES][processor_name] = 0.0
# 新增：计算后处理器平均时间
for processor_name, times in stat[FOCUS_POST_PROCESSOR_TIMES].items():
if times:
stat[FOCUS_POST_PROCESSOR_TIMES][processor_name] = sum(times) / len(times)
else:
stat[FOCUS_POST_PROCESSOR_TIMES][processor_name] = 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
@@ -930,7 +893,7 @@ class StatisticOutputTask(AsyncTask):
# format总在线时间
# 按模型分类统计
"\n".join(
model_rows = "\n".join(
[
f"<tr>"
f"<td>{model_name}</td>"
@@ -944,7 +907,7 @@ class StatisticOutputTask(AsyncTask):
]
)
# 按请求类型分类统计
"\n".join(
type_rows = "\n".join(
[
f"<tr>"
f"<td>{req_type}</td>"
@@ -958,7 +921,7 @@ class StatisticOutputTask(AsyncTask):
]
)
# 按模块分类统计
"\n".join(
module_rows = "\n".join(
[
f"<tr>"
f"<td>{module_name}</td>"
@@ -973,7 +936,7 @@ class StatisticOutputTask(AsyncTask):
)
# 聊天消息统计
"\n".join(
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())
@@ -996,6 +959,261 @@ class StatisticOutputTask(AsyncTask):
]
)
# 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"]))
)
# 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:
"""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 = []
@@ -1024,8 +1242,8 @@ class StatisticOutputTask(AsyncTask):
# 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]:
# 获取所有阶段（包括后处理器）
basic_stages = ["观察", "并行调整动作、处理", "规划器", "后期处理器", "动作执行"]
# 获取前三个阶段（不包括执行动作）
basic_stages = ["观察", "并行调整动作、处理", "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
@@ -1092,99 +1310,6 @@ class StatisticOutputTask(AsyncTask):
]
)
# 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"):
# 获取所有action类型（按全局频率排序）
all_action_types_for_ratio = sorted(
stat_data[FOCUS_ACTION_RATIOS].keys(), key=lambda x: stat_data[FOCUS_ACTION_RATIOS][x], reverse=True
)
if all_action_types_for_ratio:
# 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)
# 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"):
@@ -1226,8 +1351,8 @@ class StatisticOutputTask(AsyncTask):
# 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，确保与实际Timer名称一致）
stage_order = ["观察", "并行调整动作、处理", "规划器", "后期处理器", "动作执行"]
# 获取所有阶段（按固定顺序）
stage_order = ["观察", "并行调整动作、处理", "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()):
@@ -1250,36 +1375,6 @@ class StatisticOutputTask(AsyncTask):
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)
# 新增：前处理器统计表格
focus_processor_rows = ""
if stat_data.get(FOCUS_PROCESSOR_TIMES):
processor_rows = []
for processor_name in sorted(stat_data[FOCUS_PROCESSOR_TIMES].keys()):
avg_time = stat_data[FOCUS_PROCESSOR_TIMES][processor_name]
processor_rows.append(f"<tr><td>{processor_name}</td><td>{avg_time:.3f}秒</td></tr>")
focus_processor_rows = "\n".join(processor_rows)
# 新增：前处理器统计表格
focus_processor_rows = ""
if stat_data.get(FOCUS_PROCESSOR_TIMES):
processor_rows = []
for processor_name in sorted(stat_data[FOCUS_PROCESSOR_TIMES].keys()):
avg_time = stat_data[FOCUS_PROCESSOR_TIMES][processor_name]
processor_rows.append(f"<tr><td>{processor_name}</td><td>{avg_time:.3f}秒</td></tr>")
focus_processor_rows = "\n".join(processor_rows)
# 新增：后处理器统计表格
focus_post_processor_rows = ""
if stat_data.get(FOCUS_POST_PROCESSOR_TIMES):
post_processor_rows = []
for processor_name in sorted(stat_data[FOCUS_POST_PROCESSOR_TIMES].keys()):
avg_time = stat_data[FOCUS_POST_PROCESSOR_TIMES][processor_name]
count = stat_data[FOCUS_POST_PROCESSOR_COUNT].get(processor_name, 0)
post_processor_rows.append(
f"<tr><td>{processor_name}</td><td>{avg_time:.3f}秒</td><td>{count}</td></tr>"
)
focus_post_processor_rows = "\n".join(post_processor_rows)
# 计算时间范围
if period_name == "all_time":
from src.manager.local_store_manager import local_storage
@@ -1342,24 +1437,6 @@ class StatisticOutputTask(AsyncTask):
<tbody>{focus_action_stage_rows}</tbody>
</table>
</div>
<div class="focus-stats-grid">
<div class="focus-stat-item">
<h3>前处理器平均时间</h3>
<table>
<thead><tr><th>处理器名称</th><th>平均耗时</th></tr></thead>
<tbody>{focus_processor_rows}</tbody>
</table>
</div>
<div class="focus-stat-item">
<h3>后处理器统计</h3>
<table>
<thead><tr><th>处理器名称</th><th>平均耗时</th><th>执行次数</th></tr></thead>
<tbody>{focus_post_processor_rows}</tbody>
</table>
</div>
</div>
</div>
"""

2
template/bot_config_template.toml
View File

@@ -196,7 +196,7 @@ enable_kaomoji_protection = false # 是否启用颜文字保护
[log]
date_style = "Y-m-d H:i:s" # 日期格式
log_level_style = "lite" # 日志级别样式,可选FULL，compact，lite
color_text = "title" # 日志文本颜色，可选none，title，full
color_text = "full" # 日志文本颜色，可选none，title，full
log_level = "INFO" # 全局日志级别（向下兼容，优先级低于下面的分别设置）
console_log_level = "INFO" # 控制台日志级别，可选: DEBUG, INFO, WARNING, ERROR, CRITICAL
file_log_level = "DEBUG" # 文件日志级别，可选: DEBUG, INFO, WARNING, ERROR, CRITICAL