基于认知过程的飞行员脑力负荷动态预测

刘承平; 肖旭; 赵竞全

doi:10.13700/j.bh.1001-5965.2022.0053

基于认知过程的飞行员脑力负荷动态预测

doi: 10.13700/j.bh.1001-5965.2022.0053

刘承平^{1, 2},
肖旭²,
赵竞全^1, ,

1.
北京航空航天大学航空科学与工程学院，北京 100191
2.
中国电子科技集团公司电子科学研究院，北京 100041

基金项目: 国家自然科学基金委员会与中国民用航空局联合基金(U1733118)；国家自然科学基金(71301005)

详细信息

通讯作者:
E-mail：zjq206@buaa.edu.cn

中图分类号: V7；R857.1
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- 被引次数: 0
出版历程
- 收稿日期: 2022-01-27
- 录用日期: 2022-03-04
- 网络出版日期: 2022-03-11
- 整期出版日期: 2023-11-30

Pilots’ mental workload dynamic prediction based on cognitive process

LIU Chengping^{1, 2},
XIAO Xu²,
ZHAO Jingquan^{1
, ,}

1.
School of Aeronautic Science and Engineering，Beihang University，Beijing 100191，China
2.
China Academy of Electronics and Information Technology of China Electronics Technology Group Corporation，Beijing 100041，China

Funds: Joint Fund of the National Natural Science Foundation of China and the Civil Aviation Administration of China (U1733118); National Natural Science Foundation of China (71301005)

More Information

Corresponding author: E-mail：zjq206@buaa.edu.cn

摘要

摘要:
现代军用飞机座舱驾驶系统信息高度密集、任务复杂多变。为探讨信息加工类型与多任务协同对飞行员脑力负荷的影响，依据ACT-R认知模块与脑力负荷决定性因素的关联性，将飞行员的脑力负荷划分为感知负荷与认知负荷，并基于四维多资源干扰理论，考虑多任务协同作用时的资源干扰对脑力负荷的影响，提出了基于认知过程的飞行员脑力负荷动态预测模型。为校验所提模型，选取16名被试完成4种模拟飞行任务的脑力负荷评价实验，结果显示：不同飞行任务在飞行绩效、NASA-TLX主观评价、平均扫视时间与扫视频率下的主效应显著（P<0.05），总脑力负荷预测值与NASA-TLX主观评价、扫视频率和心率呈显著正相关，平均脑力负荷预测值与飞行绩效、瞳孔直径和平均扫视时间呈显著正相关。所提模型对飞行员脑力负荷的动态预测与评价具有应用价值。
- 人机工效 /
- 脑力负荷 /
- 注意力分配 /
- 认知过程 /
- 预测模型
Abstract:
Modern military flight systems are highly information-intensive and their tasks are complex and changeable. In order to explore the influence of information processing types and multi-task coordination on pilots’ mental workload, a quantitative prediction model based on the cognitive process was proposed. The ACT-R cognitive module and the mental workload determinants were used to separate the pilot’s mental workload into perceptual workload and cognitive burden. The multi-task resource interference for mental workload was calculated based on multiple resources. 16 subjects were selected to complete the multi-factor mental workload experiment. The results showed that the main effects of flight performance, NASA-TLX, average saccade time and scanning rate were significant (P<0.05). Subjective evaluation, RRCV and HR were significantly positively correlated with the total mental workload. And the average mental workload was significantly positively correlated with flight performance, pupil diameter and average saccade time. In order to anticipate and assess the mental workload of pilots, the prediction model offered a certain application value.
- ergonomics /
- mental workload /
- attention allocation /
- cognitive process /
- predicton model

HTML全文

图 1 基于认知过程的脑力负荷分析模型框架

Figure 1. Framework of mental workload analysis model based on cognitive process

下载: 全尺寸图片幻灯片

图 2 实验模拟器与T型布局的下视显示器

Figure 2. Experimental simulator and head down display with T-layout

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图 3 实验显示界面

Figure 3. Experimental interfaces

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图 4 起飞和降落的脑力负荷动态变化

Figure 4. Dynamic mental workload during take-off and landing

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表 1 脑力负荷理论预测值

Table 1. Mental workload theory prediction

飞行任务	Z_T/(bit·s)	$ \overline Z $/bit
起飞	442.3	2.891
巡航	867.5	3.614
巡航探测	1216.1	5.067
降落	1078.6	3.145

下载: 导出CSV

表 2 实验描述性统计结果

Table 2. Descriptive statistical results and ANONA results of experiment

飞行任务	飞行绩效/m	主观评价分数	心率/（次数·min⁻¹）	心率变异系数/%	瞳孔直径/mm	平均扫视时间/s	扫视频率/Hz
起飞	162±155	66.11±10.03	87.13±10.07	6.6±1.22	3.659±1.001	0.079±0.013	0.154±0.123
巡航	1361±762	69.96±8.69	87.50±9.28	4.74±1.89	3.723±1.003	0.093±0.013	0.244±0.111
巡航探测	2553±1554	78.39±7.10	89.06±8.92	4.75±2.36	3.957±1.110	0.111±0.022	0.297±0.105
降落	1102±1179	75.54±9.54	88.31±8.78	5.86±1.91	3.659±0.902	0.085±0.014	0.232±0.123
注：数据为均值±标准差形式。

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