融合本体和深度学习的高速铁路应急预案数字化方法研究

Method of Digitalization of High-speed Railway Emergency Plan Integrating Ontology and Deep Learning

应急预案是对高速铁路突发事件进行科学高效处置的核心,通常以纸质文本、电子文档等方式存储,存在着数字化程度不足、查询效率不高、全文检索困难、智能关联性差等不足。提出一种融合本体和深度学习的高速铁路应急预案数字化方法,即通过深度学习算法对突发事件消息文本中的类型、等级、时间、地点等关键信息进行提取,采用本体方法对突发事件应急预案进行预防预警、分级响应、应急处置、后期处置4阶段数字化构建,通过基于目标树的语义查询智能生成应急处置流程。案例分析表明本文提出的方法可以提高应急处置效率和应急预案数字化管理水平,减少突发事件对高速铁路安全运营的影响。

Emergency plan is the core of scientific and efficient response to high-speed railway emergencies.The usual way of storing emergency plans in the form of paper and electronic documents features some deficiencies such as insufficient digitalization,low query efficiency,difficulty in full-text retrieval,and poor intelligent relevance.To address these deficiencies,this paper proposed a digital method that integrates ontology and deep learning for high-speed railway emergency plan,which mainly uses deep learning algorithm to extract the emergency message’s key fields such as emergency type,level,time and location.Then this paper built the digital emergency response plan in four stages such as prevention and early warning,graded response,emergency response,post-event handling through ontology.Finally,the emergency response process was intelligently generated by semantic query based on target tree.The case analysis shows that the method proposed in this paper can improve the emergency response efficiency and digital management of emergency plans,and reduce the impact of emergency events on the safe operation of high-speed railway.