【摘要】 为增强铁路安全风险智能化管控能力，提出一种基于情景推演的铁路应急处置情景相似度计算方法。基于多维情景空间模型架构，对铁路突发事件情景进行知识表征；融合深度学习与自然语言处理技术，构建面向铁路事故报告文本的Sentence-BERT(SBERT)文本语义相似度匹配模型；通过Token Attention机制实现文本句子内部关键词的动态捕捉与识别，完成基于铁路事故报告的特征抽取、语义解析、分类归纳与信息检索，进而实现高精度的铁路事故情景匹配，可为构建铁路突发事件应急案例情景库奠定基础。更多还原

【Abstract】 To improve the intelligent management and control capability of railway safety risks, this paper proposed a scenario inference-based similarity calculation method for railway emergency disposal. It used the multi-dimensional scenario space model architecture to conduct knowledge representation of railway emergency scenarios, combined deep learning and natural language processing technologies to construct a Sentence-BERT(SBERT) textual semantic similarity matching model for railway accident report texts, adopted the Token Attention mechanism to dynamically capture and identify internal keywords in text sentences, completed feature extraction, semantic analysis, classification induction, and information retrieval based on railway accident reports, and further achieved high-precision railway accident scenario matching. This method lays a solid foundation for the construction of an emergency case scenario library for railway emergencies.更多还原