As airspace congestion becomes more and more serious, it not only affects the efficiency and quality of aviation activities, but also poses a greater safety hazard. For in-depth exploration of the airspace network, a new airspace association network that differs from the traditional aviation approach of organizing the airspace into a network of sectors based on aircraft traffic flow is proposed. By judging the relationship between the size and location of the airspace and other properties, the establishment of an association network provides new ideas for airspace conflict detection and other aerial tasks. The three key factors of airspace use conflict were extracted and quantified, then the Analytic Hierarchy Process (AHP) was used to assign weights to basic symmetric operators corresponding to three factors to calculate the final airspace correction degree, which reveals the magnitude of correlation degree between the airspace caused by the combined action of the above three factors. Finally, an airspace association network model was established according to the association degree, using basic symmetric operators to explore the general symmetry and network characteristics in real-world airspace association networks, and the feature indices, such as degree, strength, average correlation, clustering coefficient, and structure information entropy of the network, are proposed. The results show that the nodes with higher node degree and node association strength are often key nodes in the network, and the areas with higher average association degree and clustering coefficient are often more prone to airspace conflicts. At the same time, it was also verified that the safety distance between airspace has the greatest impact on the characteristics of the airspace association network.

Aiming at the problem of the wide range and great difficulty in the future of battlefield airspace control, based on the unique advantages of an airspace grid system in an airspace grid representation and time–space binary computing, this paper designs a pre-clustering method for mission airspace based on airspace location correlation under the condition of future large-scale air combat missions in order to realize the block control of battlefield airspace. This method reduces the whole 3D battlefield space projection to a 2D plane and regards the task airspace projection as “obstacles” in the task area; Multi-A * algorithm is used to generate the airspace clustering line surrounding the task airspace, and the airspace association clustering problem is transformed into a multiple “start point-end point” path planning problem with autonomous optimization. Through the experiment, it was found that clustering the airspace can effectively improve the management and control efficiency of large-scale battlefield airspace.

Multi-UAV (unmanned aerial vehicle) mission collaboration is one of the current research hotspots in automation, artificial intelligence, and other fields. The difficulty and high cost of real flight verification have led to the problem that related knowledge learning focuses on theoretical derivation and ignores technical practice. In this paper, a multi-UAV mission simulation system is designed to show the greatest advantage of its collaborative mission planning results simulated and tested in 3D scenarios. It simultaneously reflects and records changes in UAV position, velocity, status, and other state values. The simulation program can be directly applied to real flight with only a few settings. First, the general system framework is presented. Second, according to the simulation requirements, the key modules involved in the software in the loop are given. Finally, through a path planning test for two UAVs, the effect of the system is demonstrated. The results show that the simulation verification based on the system is consistent with the real flight results in terms of functional implementation. It is applicable to the teaching process of related majors and can also provide support for the realization of many complex tasks in the future.