Knowledge Graph Enhanced Large Language Model for Missile Design

Missile technology, central to precision-guided weapons, demands systematic improvements in overall design methods for enhancing intelligence, precision, and cost-efficiency. Traditional empirical design heavily relies on subjective expert knowledge, leading to inconsistencies and high iterative costs. To address these issues, this study proposes an innovative framework integrating knowledge graph (KG) and large language model (LLM) technologies into missile overall design. We construct a multi-level knowledge association fusion model based on missile modular design philosophy and ontological modeling, achieving structured knowledge representation. Leveraging LLM-based open information extraction with chain-of-thought prompting, we systematically organize and fuse missile design experiences and instances. Furthermore, employing retrieval-augmented generation (RAG), we implement an interpretable missile design knowledge Q&A recommendation system. The developed KG encompasses 18,150 entities and 40,017 relations, demonstrating strong connectivity and comprehensive coverage. The resulting recommendation system effectively supports missile design layouts and instance-specific inquiries, significantly advancing missile overall design technology toward agile, cost-effective, and precise development.