Emergency Disposal Decision Generation Method for Flight Test Based on Knowledge Graph

The purpose of this study is to propose a new emergency response decision support method for real-time monitoring in flight test. Based on semantic understanding and generation model, the system can effectively generate emergency handling strategies, verify and update handling processes, and intelligently distribute handling tasks. Firstly, by integrating semantic understanding and generation model with flight test monitoring and emergency response professional knowledge base, the dynamic generation module of disposal strategy can independently generate emergency response strategy and provide decision support. Then, the processing strategy verification and update module realizes the verification and update of the generation strategy by using the multi-dimensional quantifiable characteristics of Petri Net model to ensure the real-time and effectiveness of the strategy. Finally, the intelligent distribution module of emergency handling task uses the deep learning model based on BERT to identify named entities and extract relationships, and constructs a detailed knowledge graph of < handling task-belonging-topic>, and realizes the intelligent distribution of tasks through Neo4j graph database and Cypher query language. Through this comprehensive decision support method, this study provides an effective solution for safety-assisted decision making in the field of flight monitoring and emergency response, and opens up a new direction for the application of large language models in this field.