大数据视域下实验室安全风险感知与应对  

Laboratory safety risk perception and response in big data perspective

作　　者：李曾[1] 王皓[2] 李牧天 王碧瑾 LI Zeng;WANG Hao;LI Mutian;WANG Bijin(College of Chemistry and Life Science,Beijing University of Technology,Beijing 100124,China;Center for Network and Information Technology,Beijing University of Technology,Beijing 100124,China;Center for Student Data Management and Service,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学化学与生命科学学院,北京100124 [2]北京工业大学网络与信息技术中心,北京100124 [3]北京工业大学学生数据管理与服务中心,北京100124

出　　处：《实验技术与管理》2025年第1期251-256,共6页Experimental Technology and Management

摘　　要：随着信息化、智能化手段的飞速发展,有效利用大数据技术提升实验室安全管理水平,是高校安全管理人员面临的挑战。该文基于高校实验室安全管理现状,将优化安全管理路径作为核心目标,提出一种大数据视域下的实验室安全风险感知模型。模型将实验室大数据分为物理空间、知识空间和管理空间数据,这些数据会经过要素、表征和态势三个阶段表征安全事件,且空间与空间、空间与阶段、阶段与阶段间存在着相互联系。准确理解并有效运用这些关系,对提升实验室安全管理水平有重要意义。[Objective]The primary objective of this study is to explore and propose strategies for enhancing safety risk perception and response in university laboratory environments through the application of big data technologies.The research addresses the increasing complexity and diversity of safety risks in modern laboratories,where growing resource density and research activity intensity outpace the allocation of human resources and safety measures.By leveraging big data,this study seeks to improve the efficiency and effectiveness of laboratory safety management systems.This approach aims to better protect the lives and well-being of faculty,staff,and students and ensure the smooth progress of scientific research and teaching activities.[Methods]This study employs a comprehensive approach that integrates theoretical analysis with practical applications.First,it reviews the current state of laboratory safety management in universities,highlighting the challenges posed by the growing abundance of laboratory resources and the intensification of research activities.The analysis also highlights emerging issues related to big data,such as difficulties in data cleaning and correlation analysis,data redundancy,and the diminishing relevance of traditional safety management practices.To address these challenges,this study proposes a framework based on physical knowledge management(PKM)space for laboratory safety risk perception and response.Within the physical space,data on experimental conditions,personnel,and equipment is collected and monitored.This data is then transformed into training data for specific professional models in the knowledge space,where advanced risk models are developed to assess and predict potential risks across various scenarios.Finally,in the management space,administrators utilize key data to fine-tune risk models based on their expertise or manual adjustments.They also integrate the risk model’s warning mechanisms with emergency response plans.This study further employs various data processing and a

关 键 词：大数据 实验室安全 风险感知 

分 类 号：X923[环境科学与工程—安全科学]