基于呼吸参数的模拟管制工作负荷预测模型研究

doi:10.16097/j.cnki.1009-6744.2025.04.029

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (4): 317-325.DOI: 10.16097/j.cnki.1009-6744.2025.04.029

基于呼吸参数的模拟管制工作负荷预测模型研究

张荣^*，张茜，史文萱，靖晴

中国民航大学，安全科学与工程学院，天津300300

收稿日期:2025-02-20 修回日期:2025-05-13 接受日期:2025-05-21 出版日期:2025-08-25 发布日期:2025-08-25
作者简介:张荣(1988—)，男，河北张家口人，副教授，博士。
基金资助:
天津市自然科学基金(23JCQNJC00030)；中央高校基本科业务费自然科学重点项目(3122025102)。

Prediction of Simulation Control Workload Based on Respiratory Parameters

ZHANG Rong^*, ZHANG Xi, SHI Wenxuan, JING Qing

College of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China

Received:2025-02-20 Revised:2025-05-13 Accepted:2025-05-21 Online:2025-08-25 Published:2025-08-25
Supported by:
Natural Science Foundation of Tianjin, China(23JCQNJC00030)；Key Program of the National Natural Science for the Central Universities of Ministry of Education of China (3122025102)。

摘要/Abstract

摘要： 为探究人体呼吸参数与管制工作负荷的关系，本文选取27名被试开展模拟管制实验，对不同类型工作负荷下被试的呼吸参数进行采集分析。首先，根据Spearman秩相关系数计算结果，分别获取与脑力和体力管制工作负荷显著相关的呼吸参数。然后，基于有序Logistic模型方法，以显著相关的呼吸参数为自变量，5类不同严重程度的脑力和体力管制工作负荷为因变量，构建脑力负荷和体力负荷严重程度预测模型并进行似然比和拟合优度检验。进一步，绘制ROC(Receiver Operating Characteristic)曲线，检验预测模型的性能；最后，使用交叉表评价方法预测模型的准确率。结果表明：呼吸参数中，呼吸周期与脑力负荷显著相关，呼吸周期、呼吸幅值和吸呼比与体力负荷显著相关。在0.05的显著性水平下，构建的脑力负荷和体力负荷严重程度预测模型拟合效果良好，整体AUC(Area Under Curve)分别为0.679和0.753，模型均具有一定的检测性能。交叉表评价结果表明，模型对脑力和体力负荷中的高负荷状态预测效果最好，准确率分别高达88.9％和83.3％。本文研究结果能够为基于呼吸参数的管制工作负荷监测提供一定参考价值。

关键词: 航空运输, 预测模型, Spearman秩相关系数, 有序Logistic模型, 管制工作负荷, 呼吸参数

Abstract: To investigate the relationship between the human respiratory index and controlled workload, a simulated air traffic control experiment was conducted by the selected 27 subjects to collect and analyze their respiratory parameters under different types of workloads. First, according to the calculation results of Spearman's rank correlation coefficient, the respiratory parameters which are significantly correlated with mental and physical control workloads were obtained. Then, based on the ordered logistic model method with significantly correlated respiratory parameters as independent variables and five graded levels of mental and physical regulatory workload as dependent variables, the prediction models for the severity of mental and physical workload and the likelihood ratio test were constructed and goodness-of-fit tests were carried out. Furthermore, the ROC (Receiver Operating Characteristic) was plotted to evaluate model performance, and finally Confusion Matrix was used to verify the prediction accuracy of model. The results showed that among the respiratory parameters, the respiratory cycle was significantly correlated with mental workload, and the respiratory cycle, respiratory amplitude and inspiration-exhalation ratio were significantly correlated with physical workload. At a significance level of 0.05, the constructed prediction models for mental workload and physical workload severity demonstrated the satisfactory goodness of fit and exhibited certain detection capabilities, with the overall AUC (Area Under Curve) values reaching 0.679 and 0.753 respectively. The evaluation results of Confusion Matrix showed that the prediction model demonstrated optimal performance in identifying high mental and physical workload conditions, achieving highly prediction accuracies of 88.9% and 83.3% respectively. The research results of this article can provide a certain reference value for monitoring control workload based on respiratory parameters.

Key words: air transportation, prediction model, Spearman's rank correlation coefficient, ordered Logistic model, control workload, respiratory parameter

中图分类号:

张荣, 张茜, 史文萱, 靖晴. 基于呼吸参数的模拟管制工作负荷预测模型研究[J]. 交通运输系统工程与信息, 2025, 25(4): 317-325.

ZHANG Rong, ZHANG Xi, SHI Wenxuan, JING Qing. Prediction of Simulation Control Workload Based on Respiratory Parameters[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(4): 317-325.

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参考文献

[1] 温瑞英,王洪勇.基于岭回归-BP神经网络的管制工作负荷预测方法[J].交通运输系统工程与信息,2015,15 (1): 123- 129. [WEN R Y, WANG H Y. A forecasting method of controller's workload based on ridge regression-BP neural network[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(1): 123-129.]

[2] BACKS R W, et al. Cardio-respiratory indices of mental workload during simulated air traffic control[C]. Proceedings of the IEA/HFES 2000 Congress, 2000, 44: 89-92.

[3] HOSKOVA-MAYEROVA S, KALVODA J, BAUCER M, et al. Development of a methodology for assessing workload within the air traffic control environment in the Czech republic[J]. Sustainability, 2022, 14: 7858.

[4] LISA R F, GLENN F W, CAROLYNE R S. Electrophysiological, behavioral, and subjective indexes of workload when performing multiple tasks: Manipulations of task difficulty and training[J]. Biological Psychology, 1999, 42(3): 129-145.

[5] HAN L, ZHANG Q, CHEN X, et al. Detecting work related stress with a wearable device[J]. Computers in Industry, 2017, 90: 42-49.

[6]杨文臣,谢碧珊,房锐,等.山区双车道公路机动车碰撞事故严重度致因比较分析与预测[J].交通运输系统工程与信息,2021,21(1): 190-195. [YANG W C, XIE B S, FANG R, et al. Comparative analysis and prediction of motor vehicle crash severity on mountainous two-lane highways[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(1): 190-195.]

[7]刘继新,曾逍宇,尹旻嘉,等.基于累积Logistic回归模型的管制员应激程度预测[J].重庆交通大学学报(自然科学版),2019, 38(3): 97-102. [LIU J X, ZENG X Y, YI W J, et al. Stress level prediction of controller based on cumulative logistic regression model[J]. Journal of Chongqing Jiaotong University (Natural Science), 2019, 38(3): 97-102.]

[8] 潘恒彦,王永岗,李德林,等.弯坡组合路段追尾事故风险评估与影响因素分析[J].哈尔滨工业大学学报, 2023, 55(11): 36-46. [PAN H Y, WANG Y G, LI D L, et al. Risk assessment and influence factors analysis of rear-end collision on curved slope combination section [J]. Journal of Harbin Institute of Technology, 2023, 55 (11): 36-46.]

[9] 庄焱,董春娇,李鹏辉,等.电动汽车与行人碰撞事故时空特征与严重程度建模[J]. 交通运输工程学报, 2024, 24(6): 299-310. [ZHUNG Y, DONG C J, LI P H, et al. Spatiotemporal characteristics and severity modelling of electric vehicle-pedestrian collision accidents[J]. Journal of Traffic and Transportation Engineering, 2024, 24(6): 299-310.]

[10] 张荣, 梁馨月.航空人员疲劳检测方法研究[J].计算机工程与应用,2024, 60(20): 84-95. [ZHANG R, LIANG X Y. Research on fatigue detection methods for aviation personnel[J]. Computer Engineering and Applications, 2024, 60(20): 84-95.]

[11] PETERO A, GIANLUCA B, GIANLUCA F D, et al. Reliability over time of EEG-based mental workload evaluation during air traffic management(ATM) tasks[C]. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015: 7242-7245.

[12] 王莉莉, 顾秋丽.管制员个体工作负荷多维量化研究[J]. 中国安全科学学报,2024, 34(6): 1-9. [WANG L L, GU Q L. Study on multidimensional quantification of individual workload of controllers[J]. China Safety Science Journal, 2024, 34(6): 1-9.]

[13] 王岩韬, 刘毓.基于复杂网络的航班运行风险传播分析[J]. 交通运输系统工程与信息, 2020, 20(1): 198 205. [WANG Y T, LIU Y. Flight operation risk propagation based on complex network[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(1): 198-205.]

[14] 刘强, 严修, 谢谦,等.基于事故综合强度的公交事故严重程度分析[J].交通运输系统工程与信息,2022, 22(6): 152-159. [LIU Q, YAN X, XIE Q, et al. Bus accident severity analysis based on comprehensive accident intensity[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 152-159.]

[15] CHEN S, EPPS J. Using task-induced pupil diameter and blink rate to infer cognitive load[J]. Human-Computer Interaction, 2014, 29(4): 390-413.

[16] 张文会, 徐海彬,周舸,等.信控交叉口行人过街冲突严重程度评价模型[J]. 交通运输系统工程与信息, 2023, 23(6): 90-99. [ZHANG W H, XU H B, ZHOU G, et al. Evaluation model for pedestrian vehicle conflict degree at signalized intersections[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(6): 90-99.]

[17] RAE C D, BAUR J A, BORGES K, et al. Brain energy metabolism: A road map for future research[J]. Journal of Neurochemistry, 2024, 168(5): 910-954.

[18] PETER B S, ALAN S, LUBOS S. The anabolic role of the Warburg, Cori-cycle and Crabtree effects in health and disease [J]. Clinical Nutrition, 2021, 40(5): 2988-2998.

基于呼吸参数的模拟管制工作负荷预测模型研究

Prediction of Simulation Control Workload Based on Respiratory Parameters

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