As the core link of the air traffic control system, the evaluation and management of controllers’ workload are of great significance for the rapid development of China’s civil aviation industry and aviation safety. To address the problems of strong subjectivity and lag in the current controller evaluation methods, we propose to construct a flight conflict network and control network based on inter-aircraft flight conflict, controller control relationship and control transfer relationship by using the interdependent network theory. Based on the urgency of inter-aircraft conflict and the control difficulty of controllers, we set the side rights and construct an interdependent network model. Based on the constructed network model, the controller workload is evaluated by selecting the interdependent network index characteristics. Finally, the experimental analysis is carried out by program simulation and control data of Takasaki Airport. The results demonstrate that the method is able to evaluate the controller’s workload. At the same time, it can quickly and accurately identify the key control nodes and provide assistance for controllers to allocate their efforts reasonably. Our proposed controller workload evaluation method enables quantitative analysis and predicts the future workload of controllers based on the potential conflict relationships between air posture and aircraft. The method has strong timeliness and objectivity. At the same time, the constructed interdependent network model can realize the identification of key aircraft in the control area and reflect the impact of key aircraft on the whole network, which can help controllers to perform better control work.
Sector partition is an important task of air traffic control, and a reasonable sector partition can improve the utilization rate of airspace and protect the flight safety of aircrafts. Since the sector partition during flat hours is not well suited to the complex air situation, this paper proposes a sector optimization method based on Voronoi diagram and improved K-means. Firstly, a conflict network is constructed based on the air situation, and a comprehensive sector control workload measurement method is proposed by combining aircraft velocity obstacle relationship and complex network theory. Based on the workload value, a cluster center is determined as the generating element of Voronoi diagram by using the improved K-means method, and then the sector is optimized by using the division method of Voronoi diagram. In this paper, the data of Xiamen airspace control sectors are collected as a simulation scenario for calculation and analysis. The simulation results show that the average variance of the optimized sector control workload is reduced by 66.04% during the peak hours and 13.88% during the flat hours compared with the original sector. The method achieves the purpose of balancing the sector workload, verifies the effectiveness of the sector optimization method, and provides a reference basis for the existing sector partition work.
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.
Unmanned Aerial Vehicle (UAV) swarm surveillance has many advantages: flexible deployment, no casualties, high swarm survival rate, and high cost-effectiveness. It has become a force that we cannot ignore on the battlefield. As the key technology to ensure the survival rate of UAV swarms and improve detection efficiency, mission planning technology is the basis for realizing the autonomous detection of UAV swarms in the future. This paper introduces the method of UAV distributed mission planning. The mainstream UAV planning methods are discussed. We focus on the improved artificial potential field (IAPF) approach. The modeling method of discrete rasterization of task space is adopted in complex scenes of multiple target types. Compared with the simulation results of hybrid artificial potential field and ant colony optimization (HAPF-ACO), the superiority of the proposed method in search performance is verified.
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