Big data analysis for high-speed railway disaster monitoring system
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摘要: 基于高速铁路灾害监测系统的大数据分析研究，通过分布式文件系统存储、MapReduce/Spark计算框架、数据挖掘等技术，对高速铁路灾害监测系统的灾害规律分析、灾害预测、运用规则优化、监测点布设优化、设备选型、设备状态分析等进行研究。以大风规律和设备运行状态为例进行分析，结果表明，50%左右的大风集中在15～16 m/s之间，通过优化大风报警阈值，可有效降低大风报警次数；电源故障是灾害系统设备的主要故障，需对其进行重点监测和维护。该研究可解决目前灾害监测系统运用和维护中遇到的问题，为灾害监测数据综合分析与应用研究提供技术支持。Abstract: Based on the big data analysis for high-speed railway disaster monitoring system, by means of distributed file system storage, MapReduce/Spark computing framework and data mining technology, this paper studied on the disaster law analysis, disaster prediction, application rule optimization, monitoring point layout optimization,equipment selection and equipment state analysis of high-speed railway disaster monitoring system. Taking the rule of strong wind and equipment running as an example to analyze, the analysis results show that about 50% of the gale is concentrated between 15 m/s and 16 m/s. By optimizing the warning threshold of strong wind, the number of gale warning can be reduced effectively. Power failure is the main failure of disaster system equipment, so it needs to be monitored and maintained. This study can solve the problems encountered in the application and maintenance of disaster monitoring system, and provide technical support for comprehensive analysis and application research of disaster monitoring data.
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Keywords:
- railway safety /
- disaster monitoring /
- big data /
- early warning
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[1] Imai, T., Fujii, T., Tanemoto, K., et al. New Train Regulation Method Based on Wind Direction and Velocity of Natural Wind Against Strong Winds[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2002, 90: 1601-1610. [2] Sanquer, S., Barré, C., Virel, M., et al. Effect of Cross-Winds on High Speed Trains- Development of a New Experimental Methodology[J].Journal of Wind Engineering and Industrial Aerodynamics, 2004, 92(7): 535-545. [3] Kobayashi, N., Shimamura, M. Study of a Strong Wind Warning System[J]. JR EAST Technical Review, 2003(2): 61-65. [4] Delaunay, D., Célon, L.M., Sacré, C., et al. Designing a Wind Alarm System for the TGV-Mediterranée[C]//Proc. 11th Int.Conf. on Wind Engineering, Lubbock (Texas), 2003. [5] 贾永兴，梅元贵. 日本高速铁路强风预警系统的发展[J]. 铁道机车车辆，2008，28（4）：16-19. [6] 中国铁道科学研究院. 国外高速铁路自然灾害及异物侵限监测系统调研报告[R]. 北京：中国铁道科学研究院，2012. [7] 王 彤. 高速铁路防灾安全监控系统研究与开发[J]. 中国铁路，2009（8）： 25-28. [8] 中国铁路总公司. 高速铁路自然灾害及异物侵限监测系统总体技术方案（暂行）[Z]. 北京：中国铁路总公司， 2013. [9] Kostas,C.,Psannis,K.E., Kim,B.G., et al. Secure integration of IoT and Cloud Computing[J]. Future Generation Computer Systems, 2018, 78(3): 964-975. [10] Raguseo,E. Big data technologies: An empirical investigation on their adoption, benefits and risks for companies[J].International Journal of Information Management, 2018, 38(1):187-195. [11] 史天运，刘 军，李 平，等. 铁路大数据平台总体方案及关键技术研究[J]. 铁路计算机应用，2016，25（9）：1-6. [12] 马小宁，李 平，史天运. 铁路大数据应用体系架构研究[J].铁路计算机应用，2016，25（9）： 7-13.
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