| 引用本文: | 滕飞,刘鉴竹,祝锦烨,等.高铁大风预警模式挖掘.[J].国防科技大学学报,2020,42(2):55-63.[点击复制] |
| TENG Fei,LIU Jianzhu,ZHU Jinye,et al.Pattern mining of gale warning for high-speed railway[J].Journal of National University of Defense Technology,2020,42(2):55-63[点击复制] |
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| 高铁大风预警模式挖掘 |
| 滕飞1,刘鉴竹1,祝锦烨1,勾红叶2 |
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(1. 西南交通大学 信息科学与技术学院, 四川 成都 611756;2. 西南交通大学 土木工程学院, 四川 成都 610031)
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| 摘要: |
| 高铁大风预警的传统方法基于风速预测,当瞬时值高于限速阈值时触发报警,存在大量的误报警,不必要的限速控制影响了高铁行车效率。创新地提出了基于序列模式的预警方法,旨在挖掘报警事件前序数据中的频繁模式,找出报警事件的变化规律,通过滤除与非预警序列共有的频繁模式,得到预警序列独有的序列特征,构建了预警模式库。经兰新高铁沿线的监测数据验证,该方法在提高预测准确率的基础上降低了漏报率,同时有效地减少了模式匹配所需的时间,为提前预警预留充分的时间窗口,更加符合实际应用的需求。 |
| 关键词: 模式挖掘 大风预警 时间序列 频繁序列 Spark |
| DOI：10.11887/j.cn.202002007 |
| 投稿日期：2019-09-20 |
| 基金项目:四川省科技计划资助项目(2019YJ0214,2018JY0549,2018JY0294) |
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| Pattern mining of gale warning for high-speed railway |
| TENG Fei1, LIU Jianzhu1, ZHU Jinye1, GOU Hongye2 |
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(1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China;2. School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China)
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| Abstract: |
| The traditional method of alarming high-speed rail traffic in gale is based on an instantaneous threshold. Although it covers all alarm events, there are a lot of unnecessary alarms, which affect the efficiency of high-speed rail traffic. An early warning method based on sequence pattern was proposed. It aimed at mining frequent patterns in the preorder data and finding out the changing rules of alarm events. The unique sequence characteristics of early warning sequences were obtained by filtering out the public frequent patterns of non-early warning sequences, and a database of early warning patterns was constructed. Through the verification of monitoring data along Lanzhou-Urumchi high-speed railway, the method can improve the accuracy of prediction, and reduce the rate of missing reports concurrently. It reduces the time required for pattern matching effectively, and reserves sufficient time windows for early warning, which can accord more with the practical application requirements. |
| Keywords: pattern mining gale warning time series frequent sequence Spark |
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