基于BLS的铁路安全事件文本分类研究

doi:10.16265/j.cnki.issn1003-3033.2022.06.2262

中国安全科学学报 ›› 2022, Vol. 32 ›› Issue (6): 103-108.doi: 10.16265/j.cnki.issn1003-3033.2022.06.2262

基于BLS的铁路安全事件文本分类研究

尚麟宇¹(), 尹明², 肖畅³, 程君¹

1 中国铁道科学研究院集团有限公司通信信号研究所,北京 100081
2 国能朔黄铁路发展有限责任公司, 河北沧州 062350
3 中国铁道学会,北京 100844

收稿日期:2022-01-12 修回日期:2022-04-13 出版日期:2022-06-28 发布日期:2022-12-28
作者简介:
尚麟宇 (1988—),男,辽宁沈阳人,硕士,助理研究员,主要从事铁路通信信号技术方面的工作。E-mail: shanglinyu@rails.cn。
尹明,高级工程师
肖畅,工程师
程君,副研究员
基金资助:
国能集团科技创新项目(GJNY-20-231); 中国铁道科学研究院集团有限公司科研项目(2020YJ044)

Research on text classification of railway safety incidents based on BLS

SHANG Linyu¹(), YIN Ming², XIAO Chang³, CHENG Jun¹

1 Signal & Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China
2 CHN Energy Shuohuang Railway Development Co., Ltd.,Cangzhou Hebei 062350, China
3 China Railway Society, Beijing 100844, China

Received:2022-01-12 Revised:2022-04-13 Online:2022-06-28 Published:2022-12-28

摘要/Abstract

摘要：

为预防铁路安全事件的发生,利用文本挖掘相关技术和宽度学习系统(BLS),探讨铁路安全事件分类,包括设备问题、施工问题、作业问题、外部环境问题4大类。通过清洗及结构化 314条文本数据,运用Jieba分词+自定义词表+通用停用词表完成中文分词;基于卡方检验建立223个特征词,基于词频-逆文档频率(TF-IDF)计算特征词权重;基于BLS完成事件成因分类,设计3种基于BLS的分类方法。结果表明:该系统通过挖掘铁路安全事件报告的文本信息,能够形成有效的分类模型;利用BLS自身节省算力的特性,并通过添加特征增强节点的方式,可提高分类准确性,从而提高行业管理水平。

关键词: 宽度学习系统(BLS), 铁路安全事件, 文本分类, 词频-逆文档频率(TF-IDF), 文本挖掘

Abstract:

In order to prevent railway safety incidents, text mining related technologies and BLS were utilized to study effective incident classification mechanism, including four categories of equipment, construction, operation and external environmental problems. 314 pieces of text data were cleaned and structured, and Chinese word segmentation was built based on Jieba word segmentation + custom thesaurus+ custom stop word list. Then, 223 feature words were constructed based on Chi square test, and their weights were calculated based on TF-IDF. Finally, accident causes were classified according to BLS, and three classification methods were designed. The results show that the system can form an effective classification model through mining text information of railway safety event reports. And it can save computing power by utilizing features of BLS, and improve classification accuracy by adding feature enhancement nodes, so as improve industry management level.

Key words: broad learning system (BLS), railway safety incident, text classification, term frequency-inverse document frequency (TF-IDF), text mining

尚麟宇, 尹明, 肖畅, 程君. 基于BLS的铁路安全事件文本分类研究[J]. 中国安全科学学报, 2022, 32(6): 103-108.

SHANG Linyu, YIN Ming, XIAO Chang, CHENG Jun. Research on text classification of railway safety incidents based on BLS[J]. China Safety Science Journal, 2022, 32(6): 103-108.

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分类编码	分类名称	细分类
F₁	设备问题	接触网断线、电缆故障、隧道问题、桥梁问题、断轨问题、钢轨损伤、信号设备故障、车载设备故障、供电设备故障
F₂	施工问题	路基下沉、路基上拱、损伤电缆、违章施工、施工不到位、施工侵限、碰触接触网
F₃	作业问题	司机违章开车、司机操纵不当、臆测行车、人员违规操作、调车问题、错误编组
F₄	外部环境问题	人为摆障、异物上道、进入闲杂人员、机动车肇事、治安问题、沿线火情、雨雪灾害

F₁		F₂		F₃		F₄
词条	p值	词条	p值	词条	p值	词条	p值
故障	6.22×10^-9	施工	3.82×10^-25	调车	6.29×10^-20	摆放	2.51×10^-6
分路不良	1.58×10^-6	挖断	6.37×10^-6	盲目	6.93×10^-14	地方	1.50×10^-5
占用	4.31×10^-5	拆除	3.52×10^-5	作业	1.86×10^-10	人为	7.47×10^-5
限速	5.67×10^-5	改造	0.000 10	挤坏	4.56×10^-8	闲杂人员	8.83×10^-5
热备	7.79×10^-5	红光带	0.000 14	关闭	1.27×10^-7	入网	0.000 21
内部	0.000 11	大修	0.000 22	越过	1.27×10^-7	防尘	0.000 21
牵引	0.000 41	大机	0.000 22	信号机	2.25×10^-6	防护网	0.000 10
交路	0.000 45	挖掘机	0.000 22	甩车	2.75×10^-6	碾轧	0.000 10
病害	0.000 77	电缆	0.000 52	值班员	8.06×10^-6	撞坏	0.001 25
更换	0.000 96	施工单位	0.000 71	抱闸	8.46×10^-6	落石	0.001 25

分类	训练数量	测试数量	合计
F₁	58	42	100
F₂	25	15	40
F₃	41	11	52
F₄	96	26	122
总计	220	94	314

模型	准确率/%	耗时/ms
BLS	87.23	3.9
BLS_Enhance	89.36	6.0
BLS_Feature&Enhance	91.49	5.0

基于BLS的铁路安全事件文本分类研究

Research on text classification of railway safety incidents based on BLS

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