为探究大面积航班延误传播过程和演变规律，避免以往主要集中在对现实数据观察和延误传播网络分布等表象的研究。借鉴经典的SEIR（susceptible-explored-infected-recovered）模型思想，在考虑到节点关闭对大面积航班延误影响的基础上，将机场节点的状态新增为五类，构建基于SEIDR（susceptible-explored-infected-death-recovered）的大面积航班延误传播模型并将其应用于空中交通网络。采用相轨线分析和相关参数分析结合的方法，得出大面积航班延误的传播阈值和蔓延规律，并进一步分析各机场节点之间的传播参数对大面积航班延误传播规律的影响。最后以2022年某次大面积航班延误为例进行分析验证。结果表明：所建立的新模型更能够准确地描述大面积航班延误的演变过程和传播规律。

To explore the process and evolution of large-scale flight delay propagation, and avoid previous research mainly focusing on observing real data and the distribution of delay propagation networks. Drawing inspiration from the classic susceptible-explored-infected-recovered(SEIR) model and taking into account the impact of node closure on large-scale flight delays. Based on this, the state of airport nodes was added to five categories, and a large-scale flight delay propagation model based on susceptible-explored-infected-death-recovered(SEIDR) was constructed and applied to air traffic networks. Using a combination of phase trajectory analysis and related parameter analysis, the propagation threshold and propagation law of large-scale flight delays were obtained, and the influence of propagation parameters between airport nodes on the propagation law of large-scale flight delays was further analyzed. Finally, a large-scale flight delay in 2022 was taken as an example for analysis and verification. The results show that the established model can more accurately describe the evolution process and propagation law of large-scale flight delays.