基于数据挖掘的施工危险识别注意投入与分配研究  

Attention input and allocation of construction hazard identification based on data mining

作　　者：张帅 韩豫[1,2] 裴中玉[1,2] 张泾杰 吴晗 张梦月 ZHANG Shuai;HAN Yu;PEI Zhongyu;ZHANG Jingjie;WU Han;ZHANG Mengyue(College of Civil Engineering and Mechanics,Jiangsu University,Zhenjiang Jiangsu 212013,China;Urban Environment and Construction Safety Behavior Systems Research Centre,Jiangsu University,Zhenjiang Jiangsu 212013,China;College of Management,Jiangsu University,Zhenjiang Jiangsu 212013,China)

机构地区：[1]江苏大学土木工程与力学学院,江苏镇江212013 [2]江苏大学城市环境与工程安全行为系统研究中心,江苏镇江212013 [3]江苏大学管理学院,江苏镇江212013

出　　处：《中国安全生产科学技术》2023年第1期115-121,共7页Journal of Safety Science and Technology

基　　金：国家自然科学基金项目(72071097);教育部人文社会科学研究规划基金项目(20YJAZH034);江苏省第十六批“六大人才高峰”高层次人才项目(SZCY-014)。

摘　　要：为分析施工情境中危险识别注意资源动态投入分配规律,基于动态时间规整算法,挖掘危险识别注视轨迹序列,以表征注意资源投入分配变化,并采用k-means聚类、注视熵、Needleman-Wunsch全局序列对齐算法和统计等方法,深入挖掘注意资源在危险目标中投入和分配等时空变化规律。研究结果表明:当事人危险识别各阶段注意资源呈现从显著目标到高危目标的投入变化趋势,危险识别注意资源分配随情境复杂因素呈现零散、均匀的空间特征,分配无序程度提高。In order to analyze the dynamic input allocation law of hazard identification attention resources in the construction situation,the gaze track sequence of hazard identification was mined based on the dynamic time warping algorithm to characterize the change in attention resource input allocation.The k-means clustering,gaze entropy,Needleman-Wunsch global sequence alignment algorithm and statistics methods were applied to deeply mine the spatiotemporal change in the input and allocation of attention resources on dangerous targets.The results showed that the attention resources in each stage of the client’s hazard identification presented a changing trend of input from significant targets to high-risk targets.The allocation of hazard identification attention resources presented the scattered and uniform spatial characteristics with the complex factors of situation,and the degree of disorder in allocation increased.

关 键 词：施工安全 危险识别 数据挖掘 注意资源 注视轨迹 

分 类 号：X947[环境科学与工程—安全科学]