基于多源异构数据融合的高校实验室事故致因演化机理与关键路径识别

Identification of Evolution Mechanism and Critical Paths of Accident Causes in University Laboratories Based on Multi-Source Heterogeneous Data Fusion

摘要: 针对传统高校实验室事故分析数据源单一、信息碎片化、静态化等问题，本文以2015~2025年公开权威渠道发布的85起有效实验室安全事故为研究样本，整合官方通报、新闻报道、学术文献及安全检查资料构建多源异构数据库。采用自然语言处理技术完成文本信息结构化提取，引入动态贝叶斯网络与复杂系统理论，建立人–物–环境–管理四维致因分析框架，揭示实验室事故从隐患萌生、风险累积到耦合触发的非线性演化机理，并识别化工、生物、材料等不同学科的关键致险路径。研究表明，违规操作占比62.35%，为事故直接主因，管理缺陷为深层根因，化工类实验室事故占比最高，达45.88%。本文可为高校实验室风险预警、分类管控与应急处置提供真实数据支撑与科学决策依据。

Abstract: To address the limitations of single data source, fragmented information and static analysis in traditional university laboratory accident research, this paper takes 85 valid laboratory safety accidents published by authoritative channels from 2015 to 2025 as research samples, and constructs a multi-source heterogeneous database by integrating official bulletins, news reports, academic documents and safety inspection materials. Natural language processing technology is used to extract structured text information. With the introduction of dynamic Bayesian network and complex system theory, a four-dimensional causal analysis framework of human-material-environment-management is established to reveal the nonlinear evolution mechanism of laboratory accidents from hidden danger initiation, risk accumulation to coupling trigger, and identify the critical risk paths of different disciplines such as chemistry, biology and materials. The results show that illegal operation accounts for 62.35%, which is the direct main cause of accidents; management defects are the deep root cause; and chemical laboratories account for the highest proportion of accidents, reaching 45.88%. This paper can provide real data support and scientific decision-making basis for risk early warning, classified management and emergency disposal of university laboratories.