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一种基于文本挖掘的方法,以全面了解中国铁路运营设备故障报告

Xiaorui Yang¹, Honghui Li², Yi Xu¹

¹School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.

Scientific reports

|July 30, 2025

概括

通过使用先进的文本挖掘分析历史铁路运营设备故障报告,这项研究构建了一个知识图表,以揭示故障因素关系,增强铁路安全和预测能力.

科学领域:

铁路工程铁路工程是指铁路工程.
数据科学数据科学数据科学
人工智能的人工智能

背景情况:

铁路运营设备故障对安全和效率构成风险.
分析历史故障报告对于提高铁路安全至关重要,但面临着文本挖掘的局限性.

研究的目的:

为了解决分析铁路运行设备故障 (ROEF) 报告的局限性.
为ROEF报告分析开发一种先进的文本挖掘方法.
构建知识图,以可视化与ROEF相关的实体和关系.

主要方法:

利用来自中国铁路局的历史ROEF报告.
开发了一个命名实体识别 (NER) 模型,将BERT,BiLSTM和CRF与注意层相结合.
使用Neo4j构建了一个知识图,用于存储和可视化提取的数据.

主要成果:

从非结构化的ROEF报告中成功提取了关键实体和关系.
构建了一个知识图,说明ROEF因子的拓关系.
展示了分析和可视化历史失败模式的潜力.

结论:

拟议的文本挖掘和知识图表方法有效地分析了ROEF报告.
这种方法增强了对故障因子相互依存性的理解,有助于故障预测.
该研究填补了铁路运营设备故障数据的挖掘和分析的关键缺口,有助于提高铁路安全.

关键词:

贝尔特 (BERT) 公司这就是BiLSTM.在CRF的基础上.知识图表知识图表铁路运营设备的故障文本挖掘 (Text Mining) 是一种文字挖掘方式.

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