Research on Risk Analysis of Emergencies in Heavy-Haul Railway Operations Based on Knowledge Graph

Heavy-haul railways are a critical component of China’s dedicated freight rail network, serving as the primary land transport channel for energy and resource intermodal transportation. Their safe operation and transportation is essential for ensuring the reliable delivery of energy and raw materials. Taking the Shuohuang Heavy-haul Railway as a case study, based on the hazards identified across its entire operational chain, an ontology model structured as "professional module–task–process–hazard–risk attribute–management object" is constructed in this paper. Based on this model, a knowledge graph for heavy-haul railway operational emergencies is established. The study analyzes the connectivity between different nodes (e.g., work processes and hazards) in the knowledge graph and their potential relationships with risk values. Using directed graph-based degree centrality analysis, a risk assessment method incorporating node centrality is proposed. Risk values are computed at both the hazard and process levels, followed by risk ranking and analysis. The risk ranking results demonstrate that considering node centrality yields rankings that better reflect the complex division of labor in heavy-haul railway transportation system, thereby providing more effective support for emergency risk management. The research results can provide decision-making support for the prevention and control of emergencies in heavy-haul railway operations, as well as safety management.