Qian Yu, Tai Jiwen, Tang Shengxiang, Zhou Zijie
School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China.
Commercial Aircraft Corporation of China Ltd Sichuan Branch, Chengdu 610200, China.
Heliyon. 2024 Oct 1;10(19):e38855. doi: 10.1016/j.heliyon.2024.e38855. eCollection 2024 Oct 15.
The traditional evaluation method of student pilots ' manual manipulation level relies too much on teachers' subjective judgment. A quantitative evaluation method based on flight data is proposed to compensate for the shortcoming of the traditional method. According to the theory of the core competence of a pilot, the evaluation index system of manual manipulation level of student pilots in a typical scene is constructed. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) categorizes pilot manipulation levels into four grades. The improved support vector machine (SVM) model with particle swarm optimization (PSO) is trained using index data and rank labels as inputs. The data is split into training and verification sets with the cross-validation method. The training data for Guanghan Airport are taken as an example to verify the model's reliability. The model's prediction accuracy is compared with that of K-nearest neighbor and random forest models. The results show that the PSO-SVM model has higher prediction accuracy. The research can provide a theoretical reference for quantitatively evaluating manual manipulation levels and improving flight training quality.
传统的学生飞行员手动操纵水平评估方法过于依赖教师的主观判断。为弥补传统方法的不足，提出一种基于飞行数据的定量评估方法。依据飞行员核心能力理论，构建典型场景下学生飞行员手动操纵水平评估指标体系。逼近理想解排序法（TOPSIS）将飞行员操纵水平分为四个等级。以指标数据和等级标签作为输入，训练带有粒子群优化（PSO）的改进支持向量机（SVM）模型。采用交叉验证法将数据划分为训练集和验证集。以广汉机场的训练数据为例验证模型的可靠性。将该模型的预测准确率与K近邻和随机森林模型的预测准确率进行比较。结果表明，PSO-SVM模型具有更高的预测准确率。该研究可为定量评估手动操纵水平和提高飞行训练质量提供理论参考。
