针对飞机状态数据向地面传输过程中出现传输异常情况而导致无法对飞机状态进行实时监控的问题，提出一种基于随机森林（random forest, RF）、时域卷积网络（temporal convolutional network, TCN）、麻雀搜索算法（sparrow search algorithm, SSA）、Informer模型的飞行状态预测方法。利用随机森林算法对真实飞行数据进行特征重要性分析，将筛选后的参数作为预测的输入变量；引入TCN弥补Informer模型在处理复杂飞行数据序列时信息丢失或信息模糊的问题，TCN通过其卷积结构能够有效捕捉局部时间特征和短期依赖，将TCN与Informer的特征矩阵进行融合，能够同时捕捉飞行状态序列中的局部与全局信息，从而提升预测的准确性和可靠性；并使用麻雀搜索算法对模型超参数进行优化，以获得更好的预测性能。以飞行俯仰角、滚转角、偏航率为预测对象进行实验验证，通过与其他模型进行对比分析。实验结果表明：该模型在单步预测与多步预测任务中均具有良好的预测性能，可为飞机状态预测提供参考。

Aiming at the problem that aircraft status data cannot be monitored in real time due to transmission abnormalities during transmission to the ground, a flight status prediction method based on random forest (RF), temporal convolutional network (TCN), sparrow search algorithm (SSA), and Informer model was proposed. search algorithm (SSA), and Informer model for flight state prediction. Random forest algorithm was used to analyze the feature importance of real flight data, and the filtered parameters were used as input variables for prediction. TCN was introduced to make up for the problem of information loss or blurring of information in the Informer model when dealing with complex flight data sequences, and TCN was able to capture the local time features and short-term dependence effectively through its convolutional structure, and fusion of TCN and Informer's feature matrix was carried out to capture the flight state prediction simultaneously. The fusion of TCN and Informer's feature matrix can capture both local and global information in the flight state sequence, thus improving the accuracy and reliability of prediction, and the sparrow search algorithm was used to optimize the hyperparameters of the model in order to obtain better prediction performance. Experimental validation was carried out using the flight pitch angle, roll angle, and yaw rate as the prediction objects, and through comparison and analysis with other models. The experimental results show that the proposed model has good prediction performance in both single-step prediction and multi-step prediction tasks, and it can provide a reference for the prediction of aircraft state.