基于机器学习组合模型的远程塔台管制员情景意识水平  

Remote Tower Controllers'Situational Awareness Level Based on Machine Learning Combinatorial Modeling

作　　者：张兆宁[1] 郝邈 ZHANG Zhao-ning;HAO Miao(School of Air Traffic Management,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学空中交通管理学院,天津300300

出　　处：《科学技术与工程》2024年第27期11928-11936,共9页Science Technology and Engineering

基　　金：国家重点研发计划(2020YFB1600103)。

摘　　要：有效识别远程塔台管制员情景意识水平(situation awareness,SA)的主要影响因素,能够更好地为远程塔台管制设计和使用提供参考依据。首先对管制员分别在传统塔台和远程塔台环境下进行数据采集试验,分析两种环境下主、客观指标的差异性。其次,采用假设检验的方法来验证眼动指标作为评价远程塔台管制员SA的可行性。接着基于敏感眼动指标采用K-means聚类和支持向量机(support vector machine,SVM)组合模型分析方法对远程塔台管制员SA水平进行分类识别。结果表明:采用Poly核函数进行模型训练,对SA识别的准确率达到了99.72%。研究结果证实了K-means聚类与支持向量机模型组合模型可作为分析远程塔台管制员SA的有效方法。Effectively identifying the main influencing factors of remote tower controllers'situation awareness(SA)level can better provide a reference basis for remote tower control design and use.First,data collection tests were conducted on controllers in traditional and remote tower environments to analyze the differences of subjective and objective indicators in the two environments.Secondly,hypothesis testing was used to verify the feasibility of eye movement metrics as an evaluation of SA for remote tower controllers.Then,a combination of K-means clustering and support vector machine(SVM)modeling analysis was used to classify and identify the SA level of remote tower controllers based on the sensitive eye movement indicators.The results showed that the accuracy of SA recognition reached 99.72%by using Poly kernel function to train the model.The results confirm that the combined model of K-means clustering and support vector machine model can be used as an effective method to analyze the SA of remote tower controllers.

关 键 词：管制员 情景意识 远程塔台 聚类 支持向量机模型 

分 类 号：V355[航空宇航科学与技术—人机与环境工程] X914[环境科学与工程—安全科学]