面向飞行员认知负荷评估的数字化仿真方法研究

doi:10.16182/j.issn1004731x.joss.25-0311

摘要/Abstract

摘要：

操作者的认知负荷是影响其任务绩效的重要因素。飞行员作为飞机的主要操作者，在执行复杂任务时将面临大量信息的判断和处理，极易导致认知过载和人为失误。评估任务过程中认知负荷水平将有助于改善设计、减少人误、提高系统安全性。针对多任务场景，建立一种仿真模型用于任务过程中飞行员认知负荷动态预计。基于多资源理论，引出多任务的认知负荷量化方法；考虑认知能力、任务时限、任务优先级和时间随机性，基于时间随机的增广加权Petri网，建立任务过程动态模型；基于实验测量数据计算认知负荷预计值并验证认知负荷评估模型的有效性。结果表明：敏感生理参数与认知负荷预计值显著相关。

关键词: 认知负荷, 多资源理论, 多任务, Petri网, 任务过程动态模型

Abstract:

The operator's cognitive load constitutes a critical determinant of task performance. Pilots, as the primary operators of aircraft, must face an overwhelming volume of information during complex missions, which significantly heightens the risk of cognitive overload and operational errors. Evaluating cognitive load during tasks helps reduce human errors and improve system safety by optimizing design schemes. A simulation model was established to dynamically predict the pilots' cognitive load during tasks for multi-task scenarios. Based on the multiple resource theory, a method for quantifying cognitive load in multi-task conditions was established. By considering cognitive capacity, task time constraints, task priority, and temporal randomness, a dynamic model of task process was constructed based on the random-time extended weighted Petri net. Based on the experimental data, cognitive load values were predicted, and the effectiveness of the cognitive load evaluation model was verified. The results indicate that the sensitive physiological parameter is significantly correlated with predicted values of cognitive load.

Key words: cognitive load, multiple resource theory, multi-task scenario, Petri net, dynamic model of task process

中图分类号:

TP391.9

范增,何明君,邢翔宇 . 面向飞行员认知负荷评估的数字化仿真方法研究[J]. 系统仿真学报, 2025, 37(8): 1921-1932.

Fan Zeng,He Mingjun,Xing Xiangyu . Research on Digital Simulation Method for Cognitive Load Evaluation of pilots[J]. Journal of System Simulation, 2025, 37(8): 1921-1932.

图/表 13

图1

表1

图2

图3

图4

图5

图6

表2

表3

实验测量数据

测量数据

指标

绩效测量

TRACK任务：目标圆心与中心距离的均方根偏差(RMSD)

其余任务：反应时间(RT)和正确率

主观测量

NASA-TLX量表主观认知负荷

资源需求水平

生理测量

眼动数据(注视点、注视时间)

脑电数据( $θ$ [4~7 Hz]、 $α$ [8~12 Hz]频带功率)

表3

表4

图7

图8

表5

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			任务2
			知觉				认知		反应
			VF	VA	AS	AV	CS	CV	RS	RV
任务1	知觉	VF	0.8	0.6	0.6	0.4	0.7	0.5	0.4	0.2
		VA		0.8	0.4	0.6	0.5	0.7	0.2	0.4
		AS			0.8	0.4	0.7	0.5	0.4	0.2
		AV				0.8	0.5	0.7	0.2	0.4
	认知	CS					0.8	0.6	0.6	0.4
	认知	CV						0.8	0.4	0.6
	反应	RS							0.8	0.6
	反应	RV								1.0

任务编号	子任务
1*	TRACK
2*	SYSMON
3*	COMM
4*	RESMAN
1	TRACK	SYSMON
2	TRACK	COMM
3	TRACK	RESMAN
4	TRACK	SYSMON	COMM
5	TRACK	SYSMON	RESMAN
6	TRACK	COMM	RESMAN

子任务绩效	任务	显著性
SYSMON	1&4	p = 0.031*
COMM	2&4	p = 0.645
RESMAN	3&6	p = 0.005**
COMM	2&6	p = 0.866
RESMAN	1&5	p = 0.097
SYSMON	3&5	p = 0.258

分析阶段	被试	CCPZ	PO1
全任务过程	被试1	0.04	0.13*
	被试5	0.29**	0.29**
	被试6	0.21**	0.16**
	被试7	0.03	0.16**
	所有被试均值	0.14**	0.28**
高负荷阶段	被试1	0.19	0.44*
	被试3	0.64**	0.51**
	被试4	0.13	0.40*
	被试7	0.22	0.43*
	所有被试均值	0.12	0.40*