Railway Emergency Response Decision Support System Based on Semantic Understanding and Generation Models
This study aims to propose a novel railway emergency response decision support system based on semantic understanding and generation models,which is capable of efficiently generating emergency response strategies,validating and updating response processes,and intelligently distributing response tasks.First,the system integrates semantic understanding and generation models with a professional railway emergency response knowledge base for dynamic strategy generation,providing decision support;then the strategy validation and update module leverages the multi-faceted quantifiable features of Petri nets to verify and update generated strategies,ensuring their timeliness and effectiveness.Finally,the emergency response task distribution module employs a BERT-based deep learning model for named entity recognition and relation extraction,constructing a detailed"task-ownership-department"knowledge graph.Through the Neo4j graph database and Cypher query language,it achieves intelligent task distribution.This comprehensive decision support system offers an effective solution for the intelligent scheduling in the railway emergency response domain and new directions for the application of large language models in this field.
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