| Citation: | LI B，XING Z W，WANG L W. Dynamic prediction for aircraft ground deicing operation process[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（1）：224-233 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0189
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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.
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