民机驾驶舱人机交互脑力负荷预测模型  

A Model for Prediction of Pilot’s Mental Workload of Human Machine Interface in Civil Flight Deck

作　　者：卫宗敏 WEI Zong-min(Development of Aviation Safety Management,Civil Aviation Management Institute of China,Beijing 100102,China)

机构地区：[1]中国民航管理干部学院航空安全管理系,北京100102

出　　处：《科学技术与工程》2021年第13期5270-5274,共5页Science Technology and Engineering

基　　金：国家自然科学基金(61972021)。

摘　　要：针对民机驾驶舱人机交互中飞行员脑力负荷问题,设计不同难度的人机交互飞行实验任务,分析飞行作业过程中飞行员脑力负荷在评估指标上的变化。在此基础上,提出一种新的改进的多元线形回归方法,探索人机交互中飞行人员脑力负荷变化预测模型的构建方法。结果显示:飞行作业过程中脑力负荷在反应时间、正确率、NASA-TLX、SDNN 4个评估指标上变化显著;在RRI Count、Max RRI、Minimum RRI、Mean RRI、Max/Min 5个评估指标上变化不显著。改进的多元线形回归模型可对不同飞行难度下个体脑力负荷水平进行预测和等级划分,平均预测准确率为87.5%。提出的预测模型与实测数据吻合性较好,能够较准确地反映民机驾驶舱人机交互中飞行员脑力负荷变化特性,可为未来民机驾驶舱人机交互中工效学评价与优化设计提供依据。Aiming at the pilot’s mental task,human machine interface flight task was implemented at different levels,mental workload changes in different evaluation indicators during the flight was analyzed,a new improved multiple linear regression model was proposed,and a construction method of pilots’mental workload change prediction model in human-machine interaction was discovered.The results suggest that the mental workload of pilots during flight operation changed significantly in the evaluation indicators of response time,accuracy rate,NASA-TLX and the SDNN,while the evaluation indicators of RRI Count,Max RRI,Minimum RRI,Mean RRI,and Max/Min do not change significantly.By the improved multiple linear regression model,the individual mental workload with different levels of flight difficulty can be predicted and ranked,with average prediction accuracy of 87.5%.Accordingly,the proposed prediction model fit well with the measured data,and can accurately reflect the characteristics of flight deck human-machine interaction mental workload change,which can provide a basis for ergonomic evaluation and optimization design of future aircraft flight deck human-machine interaction.

关 键 词：民机驾驶舱 脑力负荷 人机交互 预测 数学模型 多元线形回归 

分 类 号：R857.14[医药卫生—航空、航天与航海医学] V7[医药卫生—临床医学]