Knowledge Graph Reasoning and Security Assurance Decision-Making Based on Online Retrieval Augment Generation

The Sino-Russian pipeline network, a critical infrastructure for energy supply, faces multifaceted risks ranging from geopolitical instability and international sanctions to sophisticated cyber threats. This paper proposes a novel framework leveraging Knowledge Graph (KG) reasoning and online Retrieval Augmented Generation (RAG) to enhance security assurance and risk prevention decision-making for this vital energy corridor. Our approach integrates a dynamically updated KG with large language models (LLMs) to facilitate real-time risk assessment, scenario planning, and proactive mitigation strategies. We detail the methodology for KG construction, LLM integration, and RAG implementation, and demonstrate its efficacy through simulated experiments. Our results indicate that this framework significantly improves the accuracy and timeliness of risk identification and response, offering a robust solution for safeguarding critical energy infrastructure.