| 高速铁路灾害监测系统大数据分析 |
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| 引用本文： | 赵方霞,王瑞,包云,王娇娇.高速铁路灾害监测系统大数据分析[J].铁路计算机应用,2018,27(10):49-53. |
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| 作者姓名： | 赵方霞 王瑞 包云 王娇娇 |
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| 作者单位： | 中国铁道科学研究院集团有限公司 电子计算技术研究所, 北京 100081 |
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| 基金项目： | 中国铁路总公司科技研究开发计划重大课题（2016X004-B ；Z2016-T005 ；2017T001-A） |
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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。
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| 关 键 词： | 铁路安全 灾害监测 大数据 预警 |
| 收稿时间： | 2018-02-04 |
Big data analysis for high-speed railway disaster monitoring system |
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| Affiliation: | Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited,Beijing 100081, China |
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| 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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