Response characteristics of lifting operation driven by visual information

doi:10.16265/j.cnki.issn1003-3033.2022.04.029

Abstract

Abstract:

In order to improve safety of lifting operation, optimal visual field cone and maximum visual field cone were analyzed considering visual angle and visual distance, and driving level of visual information on lifting operation was measured from the angle of geometric optics. Then, with lifting drivers as subjects, a virtual test scene was constructed according to operation tasks, visual cognition parameters were collected by using eye movement tracking technology, and eye movement measurement experiment of visual information driving level was designed. Finally, Work Breakdown Structure (WBS) and kinetic element analysis method were used to develop act therbligs coding system, and driving effects of visual information on lifting operation rate and error were studied. The results show that the higher driving level is, the faster lifting operation will be, and there is a strong exponential relationship between them. The driving level with correct operation behavior is generally higher than that with wrong behavior, namely, it is not easy to have wrong operation when driving level of visual information is high.

Key words: lifting operation, visual information, behavioral response, eye tracking, behavior code

JIN Lianghai, WANG Licheng, CHEN Yun, WU Zhipeng. Response characteristics of lifting operation driven by visual information[J]. China Safety Science Journal, 2022, 32(4): 198-204.

Figures/Tables 12

Fig.1

Fig.2

Fig.3

Tab.1

Fig.4

Tab.2

Tab.3

Fig.5

Tab.4

Fig.6

Tab.5

Fig.7

References 17

[1]	晋良海, 张荣坤, 郑霞忠, 等. 隧洞钻孔作业工效及低位作业姿势研究[J]. 中国安全科学学报, 2016, 26(4):66-71.
	JIN Lianghai, ZHANG Rongkun, ZHENG Xiazhong, et al. Reseaech on simulation of drilling work posture and optimization of lowing work posture for tunnel[J]. China Safety Science Journal, 2016, 26(4):66-71.
[2]	BAEKHEE L, YOON C, KIHYO J, et al. Ergonomic design of a main control room of radioactive waste facility using digital human simulation[C]. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2012:1912-1916.
[3]	王全伟, 徐格宁, 文豪. 起重机司机行为建模与操作可靠度分析[J]. 人类工效学, 2016, 22(3):58-65.
	WANG Quanwei, XU Gening, WEN Hao. Behavior modeling and operation reliability analysis of crane driver[J]. Chinese Journal of Ergonomic, 2016, 22(3):58-65.
[4]	晋良海, 尹洁, 张荣坤, 等. 塔机驾驶员的持续性视觉注意负荷研究[J]. 中国安全生产科学技术, 2018, 14(3):157-162.
	JIN Lianghai, YIN Jie, ZHANG Rongkun, et al. Study on continuous visual attention load of tower crane driver[J]. Journal of Safety Science and Technology, 2018, 14(3):157-162.
[5]	晋良海, 李佳炘, 郑霞忠, 等. 空间冲突下起重作业安全轨迹规划模型[J]. 中国安全科学学报, 2020, 30(3):47-52. doi: 10.16265/j.cnki.issn1003-3033.2020.03.008
	JIN Lianghai, LI Jiaxin, ZHENG Xiazhong, et al. Trajectory planning model for lifting operation considering spatial conflict[J]. China Safety Science Journal, 2020, 30(3):47-52. doi: 10.16265/j.cnki.issn1003-3033.2020.03.008
[6]	郑霞忠, 郭雅薇, 石法起, 等. 塔机作业模糊认知可靠性与失误分析方法研究[J]. 中国安全科学学报, 2016, 26(6):98-103.
	ZHENG Xiazhong, GUO Yawei, SHI Faqi, et al. Research on fuzzy CREAM of crane operation[J]. China Safety Science Journal, 2016, 26(6):98-103.
[7]	刘名强, 李英攀, 王芳, 等. 基于RVM的装配式建筑吊装作业安全预警模型[J]. 中国安全科学学报, 2018, 28(4):109-114. doi: 10.16265/j.cnki.issn1003-3033.2018.04.019
	LIU Mingqiang, LI Yingpan, WANG Fang, et al. An RVM based safety early warning model for hoisting operation in fabricated building project[J]. China Safety Science Journal, 2018, 28(4):109-112. doi: 10.16265/j.cnki.issn1003-3033.2018.04.019
[8]	段在鹏, 黄月铃, 黄萍. 新入职起重机司机操作失误研究[J]. 安全与环境学报, 2019, 19(5):1649-1655.
	DUAN Zaipeng, HUANG Yueling, HUANG Ping. On the operational errors of the novice on-post crane drivers[J]. Journal of Safety and Environment, 2019, 19(5):1649-1655.
[9]	GABRIEL R, AVIAD S, BARAK F. AHP-based analysis of the risk potential of safety incidents : case study of cranes in the construction industry[J]. Safety Science, 2016, 91:298-309. doi: 10.1016/j.ssci.2016.08.027
[10]	MARC O E, MARTIN S B. Humans integrate visual and haptic information in a statistically optimal fashion[J]. Nature, 2002, 415(6870):429-433. doi: 10.1038/415429a
[11]	KOH S, MAEDA N, IKEDA C, et al. The effect of ocular surface regularity on contrast sensitivity and straylight in dry eye[J]. Investigative Ophthalmology & Visual Science, 2017, 58(5):DOI: 10.1167/iovs.17-21894. doi: 10.1167/iovs.17-21894
[12]	李艳兵. 维修可达性分析及评价技术研究[D]. 天津: 中国民航大学, 2014.
	LI Yanbing. Research on evaluation technique and maintenance accessibility analysis[D]. Tianjin: Civil Aviation University of China, 2014.
[13]	孙凯鹏, 李新军. 复杂背景下单个运动物体的实时视觉追踪算法[J]. 北京航空航天大学学报, 2008, 34(3):344-347.
	SUN Kaipeng, LI Xinjun. Real time visual tracking algorithm for single moving object in clustered environment[J]. Journal of Beijing University of Aeronautics and Astronautics, 2008, 34(3):344-347.
[14]	姜婷婷, 吴茜, 徐亚苹, 等. 眼动追踪技术在国外信息行为研究中的应用[J]. 情报学报, 2020, 39(2):217-230.
	JIANG Tingting, WU Xi, XU Yaping, et al. The application of eye tracking technology in information behavior research[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(2):217-230.
[15]	GILBRETH F B. Units, methods, and devices of measurement under scientific management[J]. The Journal of Political Economy, 1913, 21(7):618-629. doi: 10.1086/252279
[16]	徐海卿, 周鋐, 任永连. 考虑驾驶员反应时间和注意力的噪声品质主观评价[J]. 汽车工程, 2013, 35(8):740-743.
	XU Haiqing, ZHOU Hong, REN Yonglian. Subjective evaluation on noise sound quality with consideration of driver's response time and attention[J]. Automotive Engineering, 2013, 35(8):740-743.
[17]	郭应时, 王畅, 付锐, 等. 城市道路环境中驾驶人应激响应时间特性[J]. 中国公路学报, 2013, 26(6):135-142.
	GUO Yingshi, WANG Chang, FU Rui, et al. Driver's reaction time under city road conditions[J]. China Journal of Highway and Transport, 2013, 26(6):135-142.

试验组别	1	2	3
主试情况	2名熟练试验员	2名熟练试验员	2名熟练试验员
被试视力	视力正常	视力正常	视力正常
作业任务	定点停放货物	绕杆避让	击落木块

行为类别	行为内容	行为编码
有效行为	伸左臂	E-SL1
	伸右臂	F-E-SR2
	翻左腕	G-E-TL3
	翻右腕	H-E-TR4
辅助行为	平视	I-A-H5
	俯视	J-A-O6
	躯干弯曲	K-A-B8

按键	动作	分类
w	躯干弯曲	辅助行为
d	俯视	辅助行为
e	平视	辅助行为
4	翻左手腕	有效行为
3	翻右手腕	有效行为
2	伸左手臂	有效行为
1	伸右手臂	有效行为

项目		视觉信息驱动水平	操作速率
视觉信息驱动水平	Person相关性	1	0.749^**
视觉信息驱动水平	显著性(双侧)	—	0.000
操作速率	Person相关性	0.749^**	1
操作速率	显著性(双侧)	0.000	—

视觉信息驱动水平	极小值	极大值	均值
正确操作组	0.317	0.815	0.579
失误操作组	0.278	0.703	0.477