fix(statistic): 增强统计数据处理的健壮性
此前的统计处理逻辑在某些统计项（如模型调用、消息数）在特定周期内未产生数据时，会因为直接访问字典键而引发 `KeyError`，导致统计任务失败。 本次提交通过以下方式解决了该问题： - 在访问统计字典时，全面使用 `dict.get()` 并提供默认值，避免因键不存在而崩溃。 - 使用 `setdefault` 来确保在计算平均耗时等指标前，相关的数据结构已被初始化。 - 简化并重构了历史总览数据的合并逻辑，使其在处理不完整数据时更加稳健。
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minecraft1024a
@@ -417,7 +417,12 @@ class StatisticOutputTask(AsyncTask):
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avg_key = f"AVG_TIME_COST_BY_{items.upper()}"
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std_key = f"STD_TIME_COST_BY_{items.upper()}"
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for item_name in period_stats[category_key]:
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# Ensure the stat dicts exist before trying to access them, making the process more robust.
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period_stats.setdefault(time_cost_key, defaultdict(list))
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period_stats.setdefault(avg_key, defaultdict(float))
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period_stats.setdefault(std_key, defaultdict(float))
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for item_name in period_stats.get(category_key, {}):
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time_costs = period_stats[time_cost_key].get(item_name, [])
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if time_costs:
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avg_time = sum(time_costs) / len(time_costs)
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@@ -614,37 +619,31 @@ class StatisticOutputTask(AsyncTask):
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# 统计数据合并
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# 合并三类统计数据
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for period_key, _ in stat_start_timestamp:
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stat[period_key].update(model_req_stat[period_key])
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stat[period_key].update(online_time_stat[period_key])
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stat[period_key].update(message_count_stat[period_key])
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stat[period_key].update(model_req_stat.get(period_key, {}))
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stat[period_key].update(online_time_stat.get(period_key, {}))
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stat[period_key].update(message_count_stat.get(period_key, {}))
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if last_all_time_stat:
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# 若存在上次完整统计数据，则将其与当前统计数据合并
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for key, val in last_all_time_stat.items():
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# 确保当前统计数据中存在该key
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# If a key from old stats is not in the current period's stats, it means no new data was generated.
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# In this case, we carry over the old data.
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if key not in stat["all_time"]:
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stat["all_time"][key] = val
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continue
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# If the key exists in both, we merge.
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if isinstance(val, dict):
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# 是字典类型，则进行合并
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# It's a dictionary-like object (e.g., COST_BY_MODEL, TIME_COST_BY_TYPE)
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current_dict = stat["all_time"][key]
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for sub_key, sub_val in val.items():
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# 普通的数值或字典合并
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if sub_key in stat["all_time"][key]:
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# 检查是否为嵌套的字典类型（如版本统计）
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if isinstance(sub_val, dict) and isinstance(stat["all_time"][key][sub_key], dict):
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# 合并嵌套字典
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for nested_key, nested_val in sub_val.items():
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if nested_key in stat["all_time"][key][sub_key]:
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stat["all_time"][key][sub_key][nested_key] += nested_val
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else:
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stat["all_time"][key][sub_key][nested_key] = nested_val
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else:
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# 普通数值累加
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stat["all_time"][key][sub_key] += sub_val
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if sub_key in current_dict:
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# For lists (like TIME_COST), this extends. For numbers, this adds.
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current_dict[sub_key] += sub_val
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else:
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stat["all_time"][key][sub_key] = sub_val
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current_dict[sub_key] = sub_val
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else:
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# 直接合并
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# It's a simple value (e.g., TOTAL_COST)
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stat["all_time"][key] += val
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# 更新上次完整统计数据的时间戳
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@@ -686,10 +685,10 @@ class StatisticOutputTask(AsyncTask):
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"""
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output = [
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f"总在线时间: {_format_online_time(stats[ONLINE_TIME])}",
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f"总消息数: {stats[TOTAL_MSG_CNT]}",
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f"总请求数: {stats[TOTAL_REQ_CNT]}",
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f"总花费: {stats[TOTAL_COST]:.4f}¥",
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f"总在线时间: {_format_online_time(stats.get(ONLINE_TIME, 0))}",
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f"总消息数: {stats.get(TOTAL_MSG_CNT, 0)}",
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f"总请求数: {stats.get(TOTAL_REQ_CNT, 0)}",
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f"总花费: {stats.get(TOTAL_COST, 0.0):.4f}¥",
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"",
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]
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@@ -700,21 +699,21 @@ class StatisticOutputTask(AsyncTask):
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"""
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格式化按模型分类的统计数据
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"""
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if stats[TOTAL_REQ_CNT] <= 0:
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if stats.get(TOTAL_REQ_CNT, 0) <= 0:
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return ""
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data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.4f}¥ {:>10} {:>10}"
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output = [
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" 模型名称 调用次数 输入Token 输出Token Token总量 累计花费 平均耗时(秒) 标准差(秒)",
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]
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for model_name, count in sorted(stats[REQ_CNT_BY_MODEL].items()):
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for model_name, count in sorted(stats.get(REQ_CNT_BY_MODEL, {}).items()):
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name = f"{model_name[:29]}..." if len(model_name) > 32 else model_name
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in_tokens = stats[IN_TOK_BY_MODEL][model_name]
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out_tokens = stats[OUT_TOK_BY_MODEL][model_name]
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tokens = stats[TOTAL_TOK_BY_MODEL][model_name]
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cost = stats[COST_BY_MODEL][model_name]
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avg_time_cost = stats[AVG_TIME_COST_BY_MODEL][model_name]
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std_time_cost = stats[STD_TIME_COST_BY_MODEL][model_name]
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in_tokens = stats.get(IN_TOK_BY_MODEL, {}).get(model_name, 0)
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out_tokens = stats.get(OUT_TOK_BY_MODEL, {}).get(model_name, 0)
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tokens = stats.get(TOTAL_TOK_BY_MODEL, {}).get(model_name, 0)
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cost = stats.get(COST_BY_MODEL, {}).get(model_name, 0.0)
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avg_time_cost = stats.get(AVG_TIME_COST_BY_MODEL, {}).get(model_name, 0.0)
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std_time_cost = stats.get(STD_TIME_COST_BY_MODEL, {}).get(model_name, 0.0)
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output.append(
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data_fmt.format(name, count, in_tokens, out_tokens, tokens, cost, avg_time_cost, std_time_cost)
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)
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@@ -726,12 +725,12 @@ class StatisticOutputTask(AsyncTask):
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"""
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格式化聊天统计数据
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"""
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if stats[TOTAL_MSG_CNT] <= 0:
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if stats.get(TOTAL_MSG_CNT, 0) <= 0:
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return ""
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output = ["聊天消息统计:", " 联系人/群组名称 消息数量"]
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output.extend(
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f"{self.name_mapping.get(chat_id, (chat_id, 0))[0][:32]:<32} {count:>10}"
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for chat_id, count in sorted(stats[MSG_CNT_BY_CHAT].items())
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for chat_id, count in sorted(stats.get(MSG_CNT_BY_CHAT, {}).items())
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)
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output.append("")
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return "\n".join(output)
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