飞机地面除冰保障过程动态预测

Dynamic prediction for aircraft ground deicing operation process

针对冰雪气象下除冰保障过程的精细化管理及预测精度低下的问题,提出了一种基于时空关联动态贝叶斯网络的飞机地面除冰保障过程动态预测方法。在系统性分析除冰保障过程的基础上,设计了面向离港除冰队列的时空关联节点判别方法,基于K最近邻算法简化关联节点并构建了变结构动态贝叶斯网络模型。基于核注意力机制的除冰保障节点先验概率密度估计方法,结合条件概率更新结果构建了面向不同状态的飞机地面除冰保障过程动态预测方法。实验结果表明:所提方法在考虑除冰保障演化不确定性的基础上实现了各节点的动态预测,平均绝对误差为2.34 min,整体精度相比静态贝叶斯网络方法最大提升8.66%,为地面除冰运行战术组织及决策控制提供了有效依据。

Aiming at the problem of fine management and low prediction accuracy of deicing operation process under ice and snow weather,a prediction method for aircraft ground deicing operation process based on the temporal and spatial correlation dynamic Bayesian network is proposed.A spatial-temporal correlation node identification method for departure deicing queue is developed based on a systematic analysis of the deicing operation process.The correlation node is then simplified using the K-nearest neighbor algorithm,and a dynamic Bayesian network model with variable structure is created.A priori probability density estimation method for deicing operation nodes based on kernel attention mechanism is studied.Combined with the conditional probability updating results,a dynamic prediction method for aircraft ground deicing support process for different states is constructed.A dynamic prediction approach for the aircraft ground deicing support process is built using the conditional probability updating findings in combination.The average absolute error is 2.34 min,and the whole accuracy is increased by 8.66%compared with static Bayesian network method,which can provide an effective decision-making basis for the tactical organization and control of ground deicing operations.