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高空多飞机的联合通信系统的安全性,关系到空中安全。由于双机、多机的高空联合通信过程都以地面服务器为基础,通信系统采用多区域、多服务器联合工作的模式,通信设备以节点的形式,被人为分成若干个固定数据块储存区,对不同的无人机来说,所属的不同区域的故障特征表达方式不同。传统的故障数据检测方法进行无人机通信故障检测时,很难对地空区域进行准确的故障区域划分,也没有考虑不同区域不同故障特征的表达,故障反推对应过程容易出现混乱,导致故障检测的准确率降低。提出一种基于神经网络算法的云计算环境下高空双机通信中的单方故障挖掘方法。对数据储存节点进行分类,利用神经网络建立故障数据子数据块节点挖掘模型,从而完成了故障数据的挖掘。实验结果表明,利用改进算法进行云计算环境下高空双机通信中的单方故障挖掘,能够有效提高挖掘的准确率和挖掘效率。
The safety of the joint communications system at high altitudes and aircraft is related to air safety. Because the two-aircraft, multi-aircraft high-altitude joint communications process are based on the ground server, communication system using multi-regional, multi-server joint mode of operation, communication equipment in the form of nodes, is artificially divided into a number of fixed data block storage area, For different UAVs, the fault features of different regions belong to different ways. When using the traditional fault data detection method to detect the UAV communication fault, it is very difficult to divide the ground fault region accurately and not to consider the expression of different fault features in different regions. The corresponding fault backstepping process is prone to confusion, resulting in failure The accuracy of detection is reduced. This paper proposes a single-party fault mining method in high-altitude two-plane communication based on neural network algorithm in cloud computing environment. The data storage nodes are classified, and the neural network is used to establish the fault data sub-data block node mining model, thus completing the fault data mining. The experimental results show that the improved algorithm can improve the accuracy of mining and the efficiency of mining by using unilateral fault mining in high-altitude dual-plane communication under cloud computing environment.