摘要
目前针对高速铁路应急救援处置流程监管,主要依靠应急管理人员指挥及自身经验完成,对应急救援进度预警研究还存在不足。根据高速铁路行车事故应急救援过程,利用随机网络GERTS模型并确定节点参数,将预测结果作为合理的救援计划时间,对比关键工序实际救援时间与计划时间,判断进度延误趋势,以进度偏差为指标进行预警,完成高速铁路交通事故应急救援进度预警模型的构建,并以某次高速铁路事故应急救援过程为例,说明模型的具体预警过程。通过高速铁路交通事故应急救援进度预警模型,可对应急救援进度进行预警,帮助应急管理人员更加深入了解救援实际情况,及时采取有效措施,提高救援效率,保障救援及时完成。
At present,the supervision of the emergency rescue and disposal process of high speed railways mainly depends on the command of emergency managers and their own experience,and the research on early warning of emergency rescue progress is still insufficient.On the basis of the emergency rescue process of high speed railway traffic accidents,the stochastic network GERTS model was used to determine the node parameters,and the predicted results were taken as the reasonable rescue planning time.This paper also compared the actual rescue time and planning time of the key process,judged the trend of progress delay,and took the schedule deviation as the index for early warning.The paper constructed the early warning model of emergency rescue progress in high speed railway traffic accidents and took the emergency rescue process in a high speed railway accident as an example to illustrate the specific early warning process of the model.Through the early warning model of emergency rescue progress in high speed railway traffic accidents,this paper can warn the progress of emergency rescue and help emergency managers to further understand the actual situation of rescue and take effective measures in time to improve the efficiency of rescue and ensure that the rescue is completed in a timely manner.
作者
赵涛
左静
尚梦星
ZHAO Tao;ZUO Jing;SHANG Mengxing(School of Electrical and Automation,Lanzhou Jiaotong University,Lanzhou 730070,Gansu,China;General Sign Electric Station,Survey and Design Branch,China Railway NO.3 Engineering Group Co.,Ltd.,Taiyuan 030000,Shanxi,China)
出处
《铁道运输与经济》
北大核心
2023年第4期127-132,共6页
Railway Transport and Economy
基金
甘肃省自然科学基金项目(20JR5RA398)
甘肃省云计算重点实验室开放课题(019-419104)。
关键词
高速铁路
突发事故
应急救援时间预测
进度偏差
进度预警
随机网络
High Speed Railway
Sudden Accident
Emergency Rescue Time Forecast
Schedule Deviation
Progress Warning
Stochastic Network
