feat(report): 丰富统计报告，增加多种高级可视化图表
新增了五种高级数据可视化图表，以提供更深入的模型性能和使用情况分析： - **Token使用对比图**: 直观展示各模型的输入与输出Token数量。 - **供应商请求占比图**: 显示不同服务供应商的请求分布。 - **平均响应时间图**: 对比各模型的平均响应速度。 - **模型效率雷达图**: 从多个维度（请求量、TPS、速度、成本、容量）综合评估模型性能。 - **响应时间分布散点图**: 展示每次请求的具体响应时间，分析性能稳定性。 此外，对报告的UI/UX进行了全面优化，包括采用卡片式布局、实现可交互的图例以筛选数据、以及改进图表样式和提示信息，提升了报告的可读性和易用性。
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minecraft1024a
@@ -302,6 +302,13 @@ class StatisticOutputTask(AsyncTask):
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PIE_CHART_COST_BY_MODULE: {},
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BAR_CHART_COST_BY_MODEL: {},
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BAR_CHART_REQ_BY_MODEL: {},
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BAR_CHART_TOKEN_COMPARISON: {},
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SCATTER_CHART_RESPONSE_TIME: {},
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RADAR_CHART_MODEL_EFFICIENCY: {},
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HEATMAP_CHAT_ACTIVITY: {},
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DOUGHNUT_CHART_PROVIDER_REQUESTS: {},
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LINE_CHART_COST_TREND: {},
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BAR_CHART_AVG_RESPONSE_TIME: {},
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}
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for period_key, _ in collect_period
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}
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@@ -475,6 +482,91 @@ class StatisticOutputTask(AsyncTask):
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"labels": [item[0] for item in sorted_models],
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"data": [round(item[1], 4) for item in sorted_models],
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}
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# 1. Token输入输出对比条形图
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model_names = list(period_stats[REQ_CNT_BY_MODEL].keys())
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if model_names:
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period_stats[BAR_CHART_TOKEN_COMPARISON] = {
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"labels": model_names,
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"input_tokens": [period_stats[IN_TOK_BY_MODEL].get(m, 0) for m in model_names],
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"output_tokens": [period_stats[OUT_TOK_BY_MODEL].get(m, 0) for m in model_names],
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}
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# 2. 响应时间分布散点图数据（限制数据点以提高加载速度）
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scatter_data = []
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max_points_per_model = 50 # 每个模型最多50个点
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for model_name, time_costs in period_stats[TIME_COST_BY_MODEL].items():
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# 如果数据点太多，进行采样
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if len(time_costs) > max_points_per_model:
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step = len(time_costs) // max_points_per_model
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sampled_costs = time_costs[::step][:max_points_per_model]
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else:
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sampled_costs = time_costs
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for idx, time_cost in enumerate(sampled_costs):
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scatter_data.append({
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"model": model_name,
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"x": idx,
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"y": round(time_cost, 3),
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"tokens": period_stats[TOTAL_TOK_BY_MODEL].get(model_name, 0) // len(time_costs) if time_costs else 0
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})
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period_stats[SCATTER_CHART_RESPONSE_TIME] = scatter_data
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# 3. 模型效率雷达图
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if model_names:
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# 取前5个最常用的模型
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top_models = sorted(period_stats[REQ_CNT_BY_MODEL].items(), key=lambda x: x[1], reverse=True)[:5]
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radar_data = []
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for model_name, _ in top_models:
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# 归一化各项指标到0-100
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req_count = period_stats[REQ_CNT_BY_MODEL].get(model_name, 0)
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tps = period_stats[TPS_BY_MODEL].get(model_name, 0)
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avg_time = period_stats[AVG_TIME_COST_BY_MODEL].get(model_name, 0)
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cost_per_ktok = period_stats[COST_PER_KTOK_BY_MODEL].get(model_name, 0)
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avg_tokens = period_stats[AVG_TOK_BY_MODEL].get(model_name, 0)
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# 简单的归一化（反向归一化时间和成本，值越小越好）
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max_req = max([period_stats[REQ_CNT_BY_MODEL].get(m[0], 1) for m in top_models])
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max_tps = max([period_stats[TPS_BY_MODEL].get(m[0], 1) for m in top_models])
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max_time = max([period_stats[AVG_TIME_COST_BY_MODEL].get(m[0], 0.1) for m in top_models])
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max_cost = max([period_stats[COST_PER_KTOK_BY_MODEL].get(m[0], 0.001) for m in top_models])
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max_tokens = max([period_stats[AVG_TOK_BY_MODEL].get(m[0], 1) for m in top_models])
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radar_data.append({
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"model": model_name,
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"metrics": [
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round((req_count / max_req) * 100, 2) if max_req > 0 else 0, # 请求量
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round((tps / max_tps) * 100, 2) if max_tps > 0 else 0, # TPS
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round((1 - avg_time / max_time) * 100, 2) if max_time > 0 else 100, # 速度(反向)
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round((1 - cost_per_ktok / max_cost) * 100, 2) if max_cost > 0 else 100, # 成本效益(反向)
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round((avg_tokens / max_tokens) * 100, 2) if max_tokens > 0 else 0, # Token容量
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]
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})
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period_stats[RADAR_CHART_MODEL_EFFICIENCY] = {
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"labels": ["请求量", "TPS", "响应速度", "成本效益", "Token容量"],
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"datasets": radar_data
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}
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# 4. 供应商请求占比环形图
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provider_requests = period_stats[REQ_CNT_BY_PROVIDER]
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if provider_requests:
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sorted_provider_reqs = sorted(provider_requests.items(), key=lambda item: item[1], reverse=True)
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period_stats[DOUGHNUT_CHART_PROVIDER_REQUESTS] = {
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"labels": [item[0] for item in sorted_provider_reqs],
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"data": [item[1] for item in sorted_provider_reqs],
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}
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# 5. 平均响应时间条形图
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if model_names:
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sorted_by_time = sorted(
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[(m, period_stats[AVG_TIME_COST_BY_MODEL].get(m, 0)) for m in model_names],
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key=lambda x: x[1],
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reverse=True
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)
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period_stats[BAR_CHART_AVG_RESPONSE_TIME] = {
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"labels": [item[0] for item in sorted_by_time],
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"data": [round(item[1], 3) for item in sorted_by_time],
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}
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return stats
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@staticmethod
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