In order to detect the key nodes in the aviation network, a node detection algorithm is proposed. In view of the fallibility of previous methods, this algorithm integrates the improved closeness sorting algorithm and the importance evaluation matrix. Firstly, the model of the aviation network is constructed. Based on the closeness sorting algorithm, a weight function is proposed, which is set to reflect the position information of the node. Then, considering the edge weights, the importance matrix is constructed, and the importance of each node is obtained. Experimental results show that the algorithm is capable of simulating the actual aviation network, using the advantages of the two methods to the most extent, at the same time, considering the route traffic.

To address the controller workload with the forecast, the capacity of the air traffic management system is effectively enhanced. It should be based on a specific analysis of the controller workload. In the current controller workload studies, there is no clear means to analyze the process of controller workload development propagation. In this paper, we propose a new method for analyzing the factors influencing the controller workload. This method takes into account the influence of various situations in the actual work of controllers and objectively quantifies the complexity of work conditions. A complex network is constructed by treating various factors as nodes and the complexity relationships between these nodes as edges. The complexity network was then tested using the contagion model. The sum of the number of times of infecting other nodes and being infected in the detection result was defined as the infection capacity of the nodes, and the point with the strongest infection capacity was controlled and analyzed. The results show that the point with the strongest infection capacity is the key factor for the development of controller workload generation. In addition, the analysis of the key factors using a backpropagation neural network shows that the prediction of the controller workload can be made by the key factors. It will provide a new effective method to control controller workload and improve air traffic control capability.

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At present, the flight training, army operation increase constantly, the army ATC faces rigorous challenges. The safety evaluation on ATC can enhance the control effect and guarantee fly safety. In this paper, an evaluation index system is established based on ‘human factor, machine factor, environment factor and management factor’. On the basis of AHP and cloud model, the safety evaluation model is built. To validate the model, an experiment is operated, the result is accord with the real situation, which proves the scientifically and feasibility of this model.