随着航空货运量的增加，货运计划频繁因货物需求扰动的影响而中断，因此重新安排航班计划是航空货运恢复的核心问题。在需求临时增加的扰动下，以航空公司的利润最大化为目标，提出了一个基于时空网络方法建立的航空货运恢复模型，重新安排飞机路线、货物路线及航班，并通过增加惩罚因子的方式尽可能地保留初始航班计划。为验证模型有效性，利用CPLEX求解器对模型进行求解，将所提出的基于时空网络的航空货运恢复模型与文献中的模型进行对比，结果表明：所提出的模型在计算效率与寻找最优值方面均有较大优势，且随着算例规模的增大，优势愈加明显。分析了模型的求解结果对时间窗宽度与飞机承载力的敏感性，结果发现：时间窗越窄，求解速度越慢，而随着时间窗宽度的增大，求解速度加快并趋于稳定；随着飞机承载力逐渐增大，模型的求解速度更快并趋于稳定。

With the increase of air cargo volume, cargo plans are frequently interrupted due to disruptions in cargo demand, so rescheduling flight schedules is the core issue for air cargo recovery. An air cargo recovery model based on spatio-temporal network method was proposed with the goal of maximizing the profits of airlines under the disturbance of temporary increase in demand. Aircraft routes, cargo routes and flights were reorganized in the model and the initial flight plan was preserved as much as possible by adding penalty factors. In order to verify the effectiveness of the model, the model was solved using CPLEX solver. The proposed spatio-temporal network-based air cargo recovery model was compared with the model in reference. The results show that the proposed model has significant advantages in computational efficiency and finding optimal values, and the advantages become more apparent with the increase of the case size. The sensitivity of the model's solution results to the time window width and aircraft carrying capacity was analyzed. The results show that the narrower the time window, the slower the solution speed, while as the time window width increases, the solution speed accelerates and tends to stabilize. As the carrying capacity of the aircraft gradually increases, the solving speed of the model becomes faster and tends to be stable.