面向化工企业事故的根原因关联分析

化工事故发生的根原因多是由人的不安全行为、机械或物的不安全状态等引发，其本质是企业管理上的缺陷。挖掘根原因间、根原因与事故间的关联关系是预防事故、提升企业安全管理水平的关键。由于事故调研根原因分析与安全管理指标体系存在稀疏关联现象，难以挖掘管理缺陷与事故演化间的关联关系。为此，本文通过协同过滤算法填补事故调研中缺失的评分数据；基于加权支持度计数的关联规则算法挖掘事故根原因间、根原因与事故属性间的强关联规则。实验结果表明，基于加权支持度的关联分析算法相比于现有的算法，能推荐更多危险程度高的企业潜在安全隐患及安全隐患与事故间的演化关联，从而能科学指导企业安全生产，实现面向生产过程的风险预警和事故预防。

Root Cause Correlation Analysis of Chemical Enterprise Accident

The root causes of chemical accidents are mainly caused by the unsafe behavior of people, the unsafe state of machinery or materials, etc. Their essence is the defect of enterprise management. Mining the relationships between root causes, root causes and accidents is the key to prevent accidents and improve the level of enterprise safety management. Since the existing root cause analysis of accident investigation and the safety management index system are sparsely correlated, it is difficult to mine the correlations between management defects and accident evolution. So, collaborative filtering algorithm is used to fill in the missing score data in the accident investigation. The association rule algorithm based on the weighted support degree is used to mine the strong association rules between the root causes of the accidents, the root cause and the accident attribute. The experimental results show that compared with the existing algorithms, the association analysis algorithm based on weighted support can recommend more high-risk enterprises potential safety hazards and the evolutionary correlations between safety hazards and accidents, so as to scientifically guide the safe production of enterprises, realize the risk warning and accident prevention for the production process.