Construction and application of mine electromechanical equipment accident knowledge graph
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摘要: 针对难以从煤矿机电设备事故表象和部分监控数据判断设备事故根本原因,以及缺少能够利用历史数据、经验知识的有效手段来提高设备事故处理效率等问题,构建了煤矿机电设备事故知识图谱。首先设计四组元本体模型的数据关系,确定本体及本体之间的关系类型;然后根据设计的数据关系,采用机器学习和规则模板相结合的方法从数据库、文本中抽取实体、关系和属性;最后基于Python语言,通过py2neo库用Cypher语句对实体、关系和属性进行创建并存入Neo4j图数据库,实现知识图谱的构建和更新。煤矿机电设备事故知识图谱在煤矿机电设备事故诊断、风险管理和智能问答等方面的应用可使用户高效利用煤矿机电设备事故相关知识,帮助设备维护人员快速查找事故链条、定位事故原因并提出维修方案,达到降低事故率、减少事故处理时间的目的。Abstract: It is difficult to judge the root cause of equipment accident from the appearance of coal mine electromechanical equipment accident and part of monitoring data, and there is a lack of effective methods to improve the efficiency of equipment accident treatment by using historical data and experience knowledge. In order to solve the above problems, the mine electromechanical equipment accident knowledge graph is constructed. Firstly, the data relationships of the four-group ontology model are designed, and the ontology and the relationship types between the ontologies are determined. Secondly, according to the designed data relationships, a combination method of machine learning and rule templates is used to extract entities, relationships and attributes from databases and texts. Finally, based on the Python language, through the py2neo library, the entities, relationships and attributes are created and stored in the Neo4j graph database with Cypher statements, so as to realize the construction and update of the knowledge graph. The application of mine electromechanical equipment accident knowledge graph in mine electromechanical equipment accident diagnosis, risk management and intelligent question and answer can enable users to effectively use related knowledge of mine electromechanical equipment accident, help equipment maintenance personnel to quickly find the accident chain, locate the cause of the accident and put forward maintenance schemes, so as to achieve the purpose of reducing the accident rate and the accident handling time.
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表 1 Neo4j图数据库元素描述
Table 1. Element description of Neo4j graph database
Neo4j
图数据库元素作用 表达对象 标签 描述本体概念 设备、事故、原因等本体概念 节点 描述实体 采煤机、异响、漏电等具体对象 关系 描述实体间关系 包含、涉及、导致等关系 属性 描述实体和关系的属性 设备厂家、型号等实体属性 -
[1] 王国法, 刘峰, 孟祥军, 等. 煤矿智能化(初级阶段)研究与实践[J]. 煤炭科学技术,2019,47(8):1-36.WANG Guofa, LIU Feng, MENG Xiangjun, et al. Research and practice on intelligent coal mine construction(primary stage)[J]. Coal Science and Technology,2019,47(8):1-36. [2] 王国法, 任怀伟, 庞义辉, 等. 煤矿智能化(初级阶段)技术体系研究与工程进展[J]. 煤炭科学技术,2020,48(7):1-27.WANG Guofa, REN Huaiwei, PANG Yihui, et al. Research and engineering progress of intelligent coal mine technical system in early stages[J]. Coal Science and Technology,2020,48(7):1-27. [3] 李涛, 王次臣, 李华康. 知识图谱的发展与构建[J]. 南京理工大学学报,2017,41(1):22-34.LI Tao, WANG Cichen, LI Huakang. Development and construction of knowledge graph[J]. Journal of Nanjing University of Science and Technology,2017,41(1):22-34. [4] 刘峤, 李杨, 段宏, 等. 知识图谱构建技术综述[J]. 计算机研究与发展,2016,53(3):582-600. doi: 10.7544/issn1000-1239.2016.20148228LIU Qiao, LI Yang, DUAN Hong, et al. Knowledge graph construction techniques[J]. Journal of Computer Research and Development,2016,53(3):582-600. doi: 10.7544/issn1000-1239.2016.20148228 [5] 曹现刚, 张梦园, 雷卓, 等. 煤矿装备维护知识图谱构建及应用[J]. 工矿自动化,2021,47(3):41-45.CAO Xiangang, ZHANG Mengyuan, LEI Zhuo, et al. Construction and application of knowledge graph for coal mine equipment maintenance[J]. Industry and Mine Automation,2021,47(3):41-45. [6] 刘鹏, 叶帅, 舒雅, 等. 煤矿安全知识图谱构建及智能查询方法研究[J]. 中文信息学报,2020,34(11):49-59. doi: 10.3969/j.issn.1003-0077.2020.11.007LIU Peng, YE Shuai, SHU Ya, et al. Coalmine safety: knowledge graph construction and its QA approach[J]. Journal of Chinese Information Processing,2020,34(11):49-59. doi: 10.3969/j.issn.1003-0077.2020.11.007 [7] 叶帅. 基于Neo4j的煤矿领域知识图谱构建及查询方法研究[D]. 徐州: 中国矿业大学, 2019.YE Shuai. Research on the construction and query method of knowledge graph in coalmine based on Neo4j[D]. Xuzhou: China University of Mining and Technology, 2019. [8] 魏卉子. 煤矿安全融合知识图谱构建研究[D]. 徐州: 中国矿业大学, 2020.WEI Huizi. Study on the construction of coal mine safety integration knowledge map[D]. Xuzhou: China University of Mining and Technology, 2020. [9] 鹿晓龙. 煤矿安全知识图谱构建技术研究[D]. 徐州: 中国矿业大学, 2021.LU Xiaolong. Study on construction of coal mine safety knowledge graph[D]. Xuzhou: China University of Mining and Technology, 2021. [10] 史秦甫, 刘秀磊, 刘旭红, 等. 煤矿安全本体研究[J]. 工矿自动化,2018,44(3):42-49.SHI Qinfu, LIU Xiulei, LIU Xuhong, et al. Research on coal mine safety ontology[J]. Industry and Mine Automation,2018,44(3):42-49. [11] 彭彬, 杨晨, 蓝锦煌, 等. 基于知识图谱的精细化工辅助研发平台[J]. 情报工程,2017,3(1):43-55.PENG Bin, YANG Chen, LAN Jinhuang, et al. Knowledge graph aided research and development platform for fine chemical industry[J]. Technology Intelligence Engineering,2017,3(1):43-55. [12] KALAYCI T E, BRICELJ B, LAH M, et al. A knowledge graph-based data integration framework applied to battery data management[J]. Sustainability,2021,13(3):1583-1599. doi: 10.3390/su13031583 [13] 杨雪蓉, 洪宇, 马彬, 等. 基于核心词和实体推理的事件关系识别方法[J]. 中文信息学报,2014,28(2):100-108. doi: 10.3969/j.issn.1003-0077.2014.02.015YANG Xuerong, HONG Yu, MA Bin, et al. Event relation recognition by event term and entity inference[J]. Journal of Chinese Information Processing,2014,28(2):100-108. doi: 10.3969/j.issn.1003-0077.2014.02.015 [14] LUO Ling, YANG Zhihao, YANG Pei. An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition[J]. Bioinformatics,2018,34(8):1381-1388. doi: 10.1093/bioinformatics/btx761 [15] 林越, 王坚, 凌卫青. 基于图数据库的本体查询与推理[J]. 机电产品开发与创新,2019,32(1):16-18. doi: 10.3969/j.issn.1002-6673.2019.01.005LIN Yue, WANG Jian, LING Weiqing. Ontology query and reasoning based on graph database[J]. Development & Innovation of Machinery & Electrical Products,2019,32(1):16-18. doi: 10.3969/j.issn.1002-6673.2019.01.005 -