A Model of Proactive Safety Based on Knowledge Graph

TL;DR

This paper introduces a hybrid proactive safety model that combines data-driven and knowledge-driven methods, utilizing safety data and risk knowledge graphs to enhance accident prevention in industrial settings.

Contribution

It presents a novel integrated model that effectively utilizes safety data and knowledge graphs for proactive safety management, bridging the gap between data collection and actionable safety knowledge.

Findings

Effective identification of safety issues in CSTR scenario

Demonstrated practicality and efficacy of the model

Potential for industrial safety applications

Abstract

In contemporary safety management, despite the abundance of safety data gathered from routine operation tasks and safety management activities, actions cannot prevent all accidents effectively due to a lack of effective utilization of these data as safety knowledge. To bridge this gap, this paper proposes a hybrid proactive safety model integrating data-driven and knowledge-driven approaches. The model comprises three main steps: proactive safety actions to generate safety data, data-driven approaches to mine safety data, and knowledge-driven approaches to depicting risk knowledge graphs. Application of this model to a continuous stirred tank reactor (CSTR) scenario demonstrates its efficacy in identifying and addressing safety issues proactively. The results demonstrate the effectiveness and practicality of the proposed proactive safety model, suggesting its endorsement within both academic and industrial applications.