With the continuous increase of China’s air traffic volume, the number of aircraft and support vehicles in the airfield area is increasing. In view of the increasingly complex surface traffic situation, the risk of conflict in the airfield area is increasing. In this paper, first, taking the large moving targets in the airfield as the network nodes and the potential conflict relationship between the targets as the connecting edge, the network model of the moving targets in the airfield is constructed. On this basis, the community structure detection algorithm is used to detect the active target community in the flight area, and the K ‐means clustering algorithm is used to divide the risk level of the network in time according to the state characteristics of the cluster, so as to identify the high‐risk moments in the airfield area. Finally, for the three indicators of node selection degree, point strength and weighted clustering coefficient at high‐risk time, the combination weighting method is used to identify the potential conflict of nodes and accurately identify high‐risk nodes in space. Finally, taking Xi’an Xianyang Airport as an example, the experimental results show that the conflict classification and risk identification method proposed in this paper can accurately identify the conflict risk in the flight area in time and space.
Zhou et al. (Thu,) studied this question.