面向复杂飞行任务的脑力负荷多维综合评估模型

针对多显示界面多飞行任务状态下的脑力负荷评价问题，设计了飞行仪表监控、飞行数字计算及飞行雷达探测3种不同类型的飞行任务，综合采用多种不同测评方法在飞行试验平台上开展脑力负荷实验测量和综合评价模型研究。实验结果表明:随着飞行任务类型的增多，NASA任务负荷指数（NASA-TLX）的主观评价分值显著增高；飞行正确探测率逐渐下降，反应时间显著延长；事件相关电位（ERP）测量技术中的P3a的峰值（在Fz电极处）逐步降低，心电（ECG）测量技术中的SDNN指标的数值逐步降低，眼电（EOG）测量技术中的眨眼次数没有显著变化。在此基础上，基于贝叶斯判别分析方法，建立了面向复杂飞行任务的脑力负荷多维综合评估模型，并将该综合评估模型与基于单一指标、双指标、三指标、四指标的模型进行了比较，结果显示:所提多维综合评估模型对于多显示界面多飞行任务中的显示界面脑力任务设计的等级分类预测准确率最高，其平均分类预测准确率为82.22%。所提多维综合评估模型为大型复杂系统中显示界面脑力任务设计提供了有效的量化方法和科学依据，有助于歼击机和运输类飞机设计人员优化显示界面脑力任务设计，并为相关型号飞机显示系统的适航审定工作提供新的验证工具。 

A multi-dimensional comprehensive evaluation model of mental workload for complex flight missions

To solve the problems of mental workload assessment in multiple flight tasks of the aircraft cockpit multi-display interfaces, we design three different types of flight missions of the multi-display interfaces, i.e. flight monitor, flight calculation, and radar detection, to systematically develop the experimental measurement and the theoretical modeling of the mental workload via the conjunctive use of many kinds of measuring technique. Our experimental results reveal that, with increasing flight mission modes, the changes are:the subjective assessment scores of NASA-Task Load Index (NASA-TLX) increase significantly, the accuracy rate of the flight operation decreases gradually, and the response time becomes obviously longer; the value of the P3a component index in the Event-Related Potential(ERP) measurement technique at Fz electrode reduces gradually, the value of SDNN index in the Electrocardiogram (ECG) measurement also decreases gradually, and no obvious change in the number of blinks in the Electrooculogram (EOG) measurement is further confirmed. Based on the Bayesian discriminant analysis method, a multi-dimensional comprehensive evaluation model of mental workload for complex flight tasks was established, and the comprehensive evaluation model was compared with models based on a single indicator, dual indicators, three indicators, and four indicators. The results showed that the five-index model founded by the Bayesian-Fisher discrimination and classification method shows a much higher accuracy rate for the level discrimination and prediction results of mental workload in comparison with other index models. Its average discrimination accuracy rate is 82.22%. Obviously, This model provides an effective quantitative method and scientific basis for the display interface mental task design in large and complex systems, and helps fighter and transportation aircraft designers to optimize the display interface mental task design, but also provide a unique compliance verification tool for the airworthiness certification of flight deck display interface.