中国安全科学学报 ›› 2021, Vol. 31 ›› Issue (2): 173-178.doi: 10.16265/j.cnki.issn 1003-3033.2021.02.024

• 职业卫生 • 上一篇    下一篇

基于脑电数据的管制架次对管制员疲劳影响研究

王莉莉 教授, 朱敏   

  1. 中国民航大学 空中交通管理学院, 天津 300300
  • 收稿日期:2020-11-30 修回日期:2020-01-11 出版日期:2021-02-28 发布日期:2021-08-18
  • 作者简介:王莉莉 (1973—),女,陕西兴平人,博士,教授,主要从事空中交通人为因素、空域规划方面的研究。E-mail:llwang@cauc.edu.cn。
  • 基金资助:
    国家自然科学基金委员会与中国民用航空局联合资助项目(U1633124);中央高校基本业务科研费项目(3122016A002)。

Research on influence of controlled sorties on controllers' fatigue based on EEG data

WANG Lili, ZHU Min   

  1. School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China
  • Received:2020-11-30 Revised:2020-01-11 Online:2021-02-28 Published:2021-08-18

摘要: 为量化研究管制区域内飞机架次的时空变化对管制员疲劳的影响,设计相应测试方案进行试验研究。有针对性地选取20名在职管制员分别在大、小夜班的班前和班后进行试验,采集完成不同流量等级下雷达模拟机任务过程中的管制员的脑电(EEG)信号数据,从数据中提取疲劳指标值,运用SPSS20.0软件统计分析指标值,根据管制员的岗龄将其分成2组进行比较,线性拟合班前班后的管制员疲劳指标值。结果表明:岗龄≥10年的管制员疲劳指标值及其波动性明显低于岗龄<10年的管制员;班前管制员疲劳指标值随管制架次的增加中后期呈下降趋势;班后管制员的疲劳指标值随管制架次的增加而增长,随时间呈3次曲线变化。

关键词: 管制员, 脑电(EEG)信号, 疲劳指标, 岗龄, 管制架次

Abstract: In order to quantify impact of changes in flight number at different work stages in control area on fatigue of controllers, a test was designed and conducted. Firstly, 20 in-service controllers were selected to conduct experiments before and after large and small night shifts. Their EEG signals were collected during tasks of radar simulator at different flow levels before fatigue indicators value were extracted and analyzed by using SPSS20.0. Then, controllers were divided into two groups for comparison, and fatigue index value before and after shift was linearly fitted. The results show that fatigue index value and volatility of these controllers with job age≥10 years are significantly lower than those with that <10 years. That value of pre-shift controllers shows a downward trend in middle and later periods as number of control flights increases while that of post-shift controllers increases logarithmically along with increase of control frequency, and changes over time with a cubic curve.

Key words: controller, electroencephalogram (EEG) signal, fatigue index, job age, number of control flights

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