数据部分撰写框架
1. 数据来源
本研究的数据来源于校园食堂实地观察，数据采集时间为2024年12月2日至12月5日和2024年12月9日至12月12日（共计8天）（由于我们学校的课程安排为每两天一循环，故每种课程安排各观察4天），覆盖第三以及第四节课的午餐时间这两个高峰时段。数据包括每位学生进入食堂的时间戳、每个窗口的服务时间，以及服务窗口的数量。
2. 数据采集方法
数据通过当天时段的监控，人为清点每位学生进入食堂时系统记录时间戳。采集时间为每天的11:50-13:35。窗口数量通过直接观察得到。窗口平均服务时间是人为计时多次得到的平均值。
3. 数据预处理
数据清洗时，所有数据均符合标准，没有数据被剔除。在预处理阶段，我们将时间戳转换为相对于高峰时段的相对时间，并在高峰时段按2分钟间隔进行分组统计，在其他时段按5分钟间隔进行分组统计。
4. 数据特性分析
数据分析显示，学生到达时间分布呈现明显的高峰特性，特别是在上午11:50和下午12:10之间，平均每分钟有约40人到达，而其他时间段到达人数显著减少。窗口服务时间分布大致符合指数分布，均值为45秒。详细的统计结果见表1和图2。
5. 数据的潜在问题
本研究的数据主要集中于午餐时段，未能全面反映非高峰时段的食堂使用情况。此外，由于部分学生可能直接绕过打卡系统进入食堂，实际到达人数可能略高于记录值。
6. 数据展示
- 使用表格和图表清晰展示数据。
- 示例数据表：
| 时间段 | 到达人数（人） | 服务时间均值（秒） | 服务窗口数量 |
|---|---|---|---|
| 7:30-8:00 | 120 | 40 | 3 |
| 11:30-12:00 | 300 | 45 | 5 |
- 示例图表：绘制到达人数分布直方图或服务时间分布曲线。
The following is a part-by-part explanation of the uploaded chart:
The average wait time peaks at time , indicating that there are more customers in the system at this time and the wait time is longer.
After the peak, the waiting time declines rapidly and tends to approach zero at time . This indicates that after the peak, the customer arrival rate decreases, the system processing capacity improves, and the waiting time decreases significantly.
The change in average waiting time reflects the load of the system during peak and off-peak periods, providing data support for optimizing the allocation of service windows.
At , the number of people in the system reaches a peak , which coincides with the peak of the average wait time, indicating the highest load on the system.
Decreasing Trend: The number of people drops rapidly after the peak and stabilizes at time , indicating that the system is gradually returning to normal.
The trend illustrates the arrival distribution characteristics of customers and the system pressure during the peak period, which can provide an optimization reference for service resource allocation.
At Time , the probability of idleness is close to zero, indicating that the system has little to no idle time during the peak period.
The idle probability increases gradually with time, reaching at time . This indicates that the system is more likely to be idle during off-peak hours.
The change of idle probability demonstrates the dynamic change of system load over time, reflecting the efficiency of resource utilization during peak and off-peak periods.