Scientific Development Research

Aiming at the problems of inconsistent knowledge expression and difficult experience reuse in HAZOP analysis of chemical processes, this paper proposes to construct an ontology knowledge graph to realize the structured and intelligent application of domain knowledge. By defining core concepts, relationships, and rules, and integrating multi-source information such as process parameters, deviations, causes, consequences, and safety measures, a standardized HAZOP ontology model is established. Semantic description language and graph database technology are used to realize knowledge storage and reasoning, improving the systematicness and traceability of the analysis process. Case verification shows that this method can effectively support the accumulation, sharing, and intelligent auxiliary decision-making of HAZOP analysis knowledge, and enhance risk identification capabilities.

HAZOP analysis; Ontology modeling; Knowledge graph; Chemical process; Risk identification