动态、准确的管制系统运行态势预测是航空运输系统各相关单位开展协同决策的关键基础。本文基于航空器间的冲突情况、管制员对航空器的管控情况以及管制移交情况构建管制-飞行状态相依网络，探究、分析其演化规律，采用相关性分析和主成分分析证明了所选五项指标的合理性。通过计算平均节点度、平均点强等拓扑指标的最大Lyapunov指数证明各时间序列均具有混沌特性，并选择长短期记忆人工神经网络方法对各时间序列的演化规律进行预测。仿真结果表明该预测方法能对管制系统的演化过程进行有效的预测，五项指标的预测精度有96%的概率在20%以内。

Dynamic and accurate operation situation prediction of control system is the key basis for collaborative decision-making among relevant units of air transport system. In this paper, a control-aircraft state interdependent network model was established based on the conflict relation of aircrafts, the command relation of controllers to aircrafts and the transfer relation of controllers. The evolution law of the interdependent network is explored and analyzed. Correlation analysis and principal component analysis are used to prove the rationality of the five indicators selected It is proved that each time series has chaotic characteristics by calculating the maximum Lyapunov exponent of topological indexes such as average node degree and average point weight. And the long short-term memory method is selected to predict the evolution law of each time series. The simulation results show that the proposed method can effectively predict the evolution process of the control system. The prediction accuracy of the five indicators has a probability of 96% within 20%.