Research on theory and application of pan-scene data for behavioral safety

doi:10.16265/j.cnki.issn1003-3033.2017.02.001

Abstract

Abstract: In order to further excavate the safety rules in the statistical data,the theoretical connotations of pan-scene data were analyzed on the basis of scene research.The pan-scene data on unsafe behavior were described by 7 dimensions namely time, position, behavioral individual, unsafe action, behavioral nature, behavior trace and risk level.The pan-scene data can be collected from real scenes and abstract scenes.Then,the application of the pan-scene data was implemented through the single dimensional statistical analysis and multi-dimensional association rules mining.871 pan-scene data on unsafe behaviors in 225 gas explosion accidents were collected.Then,a statistical analysis and association rules mining were carried out for the data.The results show that the theory of the pan-scene data can be used for mining and releasing data value effectively and analyzing the intrinsic characteristics of unsafe behavior from multi dimensions deeply.

Key words: pan-scene data, unsafe behavior, data mining, association rule, gas explosion accident

CLC Number:

X936

TONG Ruipeng, CHEN Ce, LIU Silu, LU Heng, MA Jianhua. Research on theory and application of pan-scene data for behavioral safety[J]. China Safety Science Journal, 2017, 27(2): 1-6.

References

[1] GELLER E S. Behavior-based safety and occupational risk management[J]. Behavior Modification, 2005, 29(3): 539-561.
[2] COOPER M D. Exploratory analyses of the effects of managerial support and feedback consequences on behavioral safety maintenance[J]. Journal of Organizational Behavior Management, 2006, 26(3):1-41.
[3] HEINRICH H W, PETERSEN D, ROOS N. Industrial accident prevention[M]. New York:McGraw-Hill,1980:115.
[4] SALMINEN S T. Epidemiological analysis of serious occupational accidents in southern Finland[J]. Scandinavian Journal of Public Health, 1994,22(3):225-227.
[5] SCOBLE R, ISRAEL S. Age of context: mobile, sensors, data and the future of privacy[M]. Charlestone: Patric Brewster Press, 2014:34-40.
[6] 郭聖煜, 骆汉宾, 滕哲,等.地铁施工工人不安全行为关联规则研究[J].中国安全生产科学技术,2015,11(10):185-190.
GUO Shengyu, LUO Hanbin, TENG Zhe, et al. Research on association rules of unsafe behavior for metro construction workers[J].Journal of Safety Science and Technology,2015,11(10):185-190.
[7] 徐晟, 骆汉宾.基于图示语言的工人行为安全培训系统[J]. 土木工程与管理学报,2014,31(3):51-55.
XU Sheng, LUO Hanbin. A behavior safety training system based on visual language[J]. Journal of Civil Engineering and Management,2014,31(3):51-55.
[8] GUO Shengyu, LUO Hanbin, YONG Li.A big data-based workers behavior observation in China metro construction[J].Procedia Engineering,2015,123:190-197.
[9] 佟瑞鹏, 陈策, 李媛媛,等.面向行为安全的多维场景数据模型与应用[J].中国安全科学学报,2016,26(11):1-6.
TONG Ruipeng, CHEN Ce, LI Yuanyuan, et al. Research on multidimensional scene data model and its applicationfor behavioral safety[J].China Safety Science Journal,2016,26(11):1-6.
[10] YIN Wentao, FU Gui, GAO Ping,et al. Analysis and statistics of workers' unsafe acts in coal mine gas explosion[C]. Proceedings of the 2^nd International Symposium of Mine Safety Science and Engineering, 2013:731-735.
[11] GAO Ping, FU Gui, PENG Xiaofeng, et al. Grey relational analysis of unsafe acts in coal mine roof accidents[J].Energy Science and Technology Part A, 2014,32(5):4 163-4 170.
[12] AGRAWAL R, SRIKANT R. Fast algorithms for mining association rules[C].20^th International Conference on Very Large Databases, 1994:487-499.
[13] 殷文韬.煤矿瓦斯爆炸事故的不安全动作原因研究[D].北京:中国矿业大学(北京),2014.
YIN Wentao. Study on unsafe act of coal mine gas explosion accidents in China[D].Beijing: China University of Mining & Technology (Beijing),2014.
[14] 李润求, 施式亮, 罗文柯.煤矿瓦斯爆炸事故特征与耦合规律研究[J].中国安全科学学报,2010,20(2):69-74.
LI Runqiu, SHI Shiliang, LUO Wenke. Research on cross-coupling characteristics and laws of gas explosion accidents in coal mines[J].China Safety Science Journal,2010,20(2):69-74.

[1]	LIU Yao, JIAO Shuangjian. Application of ST-GCN in unsafe action identification of construction workers [J]. China Safety Science Journal, 2022, 32(4): 30-35.
[2]	ZHAO Chufan, MI Chuanmin, ZHOU Zhipeng, FANG Jing. Key cause chains selection of electric personal accidents based on combinatorial association analysis model [J]. China Safety Science Journal, 2022, 32(4): 99-106.
[3]	SHI Juan, CHANG Dingyi, ZHENG Peng. An early warning model of unsafe behaviors of construction workers based on BP neural network [J]. China Safety Science Journal, 2022, 32(1): 27-33.
[4]	CHEN Shu, XI Junbo, WANG Jianping, CHEN Yun. Mining association rules of near-misses of hydropower projects construction [J]. China Safety Science Journal, 2021, 31(8): 75-82.
[5]	LI Miaohui, LI Jizu. Study on influence of group dynamics on miners' unsafe behavior intention [J]. China Safety Science Journal, 2021, 31(7): 48-55.
[6]	KUANG Yuqi, ZHAO Tingsheng, JIANG Ling,ZHANG Wei. Mining and analysis of association rules in tower crane accident cases [J]. China Safety Science Journal, 2021, 31(7): 137-142.
[7]	CAO Lulu, LIU Yan. Research on relationship between unsafe psychology and behavior of workers in confined space [J]. China Safety Science Journal, 2021, 31(6): 70-75.
[8]	AN Yu, LI Ziqi, WANG Hui, GUO Zimeng, ZHANG Jiangbonan. Risk assessment on unsafe behaviors of university laboratories based on FBN [J]. China Safety Science Journal, 2021, 31(5): 160-167.
[9]	LI Jing, WANG Zhen, QIN Yaru, WANG Zhen, LUO Jianghao. Study on influence of different noise intensity on operation error rate of mine workers [J]. China Safety Science Journal, 2021, 31(2): 179-184.
[10]	ZHANG Mingyuan, XU Qin. Analysis on propagation model and intervention effects of unsafe behaviors of construction workers [J]. China Safety Science Journal, 2021, 31(12): 1-9.
[11]	CAO Huiling, HU Yanjie. Association analysis on CAD design and manufacturing defect cases [J]. China Safety Science Journal, 2021, 31(11): 54-63.
[12]	LU Jintao. Structural similarity construction method of "scenario-response" model for emergency drills [J]. China Safety Science Journal, 2021, 31(10): 182-188.
[13]	CHEN Mei, QI Shenjun, NIU Dayu, ZHANG Yunbo, WU Guolai. Impact of subjective social support on psychological state and unsafe behavior of construction workers [J]. China Safety Science Journal, 2021, 31(1): 30-37.
[14]	WANG Rubing, WAN Xin, MAO Peng, LI Dezhi, WANG Xiang. Relevance study between unsafe behaviors of passengers and metro accidents based on fsQCA [J]. China Safety Science Journal, 2020, 30(7): 152-158.
[15]	WEN Tingxin, WANG Guitong, KONG Xiangbo, LIU Mengxiao, BO Jingkai. Identification of miners' unsafe behaviors based on transfer learning and residual network [J]. China Safety Science Journal, 2020, 30(3): 41-46.