Research on knowledge graph representation method of mine pressure hazard events in stope whole life cycle
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摘要: 知识图谱是认知智能研究不可或缺的组成部分。针对采场矿压危害事件传统分析方法对煤矿开采过程中应力时空演化过程及隐患演化与风险防控关系等表达存在不足的问题，本文基于采场全生命周期提出一种采场矿压危害事件知识图谱表示方法：首先，从地质灾害的角度，分析采场应力及分布的动态特性，对矿压地质灾害中的各节点进行动态关联，提出顾及时空过程的采场矿压地质灾害知识图谱表示方法。其次，从事故的角度，分析采场矿压事故隐患演化与风险防控过程，将隐患耦合演化与防治措施融入知识图谱表示模型中。最后，运用Neo4j构建矿压危害事件知识图谱。结果表明：依据该表示方法构建的知识图谱，不仅刻画了灾害事件中不同对象的时空演化过程，还描述了隐患耦合演化过程以及对应的风险防控措施，为采场矿压危害的防治提供了智能研究途径。Abstract: Knowledge graph is an indispensable part of cognitive intelligence research.Aiming at the problem that the traditional analysis method of stope mine pressure hazard events has insufficient expression of the stress time and space evolution process and the relationship between hazard evolution and risk prevention and control in the coal mining process, this paper proposes a knowledge graph representation method that based on the entire life cycle of stope for mining pressure hazard events.Firstly, from the perspective of geological disasters, the dynamic characteristics of stress and distribution in stope were analyzed.The dynamic correlation of various nodes in mine pressure geological disasters was established, and a knowledge graph representation method for mine pressure geological disasters considering spatiotemporal processes was proposed.Secondly, from the perspective of accidents, the evolution of hazard and the process of risk prevention and control in mine pressure accidents were analyzed.The evolution of hazard and preventive measures were integrated into the knowledge graph representation model.Finally, Neo4j was used to build the knowledge graph of mine pressure hazard events.The results indicate that the knowledge graph constructed based on this representation method not only depicts the spatiotemporal evolution processes of different objects in disaster events but also describes the coupling evolution process of hazards and corresponding risk prevention and control measures.This provides an intelligent research pathway for the prevention and control of mining pressure hazards in intelligent mining.
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Key words:
- intelligent mining /
- stope pressure /
- knowledge graph /
- hazard coupling /
- risk prevention and control
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图 2 采场矿压危害事件知识图谱表示模型
Figure 2. Knowledge graph representation model of stope mine pressure hazard events
图 4 灾害角度节点及关系表示示例
Figure 4. Example of disaster angle node and relation representation
图 5 隐患演化与风险防控体系知识图谱表示
Figure 5. Knowledge graph representation of hazard evolution and risk prevention and control system
图 6 事故角度节点及关系表示示例
Figure 6. Example of accident angle node and relation representation
表 1 采场矿压危害知识图谱概念节点(部分)
Table 1. Concept nodes of stope mine pressure hazard knowledge graph (Part)
类型 名称 说明 概念节点(角度) 采场矿压显现地质灾害 采场矿压灾害角度顶层概念 孕灾环境 孕灾环境概念 致灾因子 致灾因子概念 承灾体(受体) 承灾体概念 开采过程中矿山压力的变化 矿压地质灾害中致灾因子的主要内容 井工开采活动 孕灾环境中主要因素之一 煤层赋存环境 孕灾环境中主要因素之一 人 承灾体中人的概念 财产 承灾体中财产的概念 资源与环境 承灾体中资源与环境的概念 …… 灾害角度其他概念节点 概念节点(事故角度) 采场矿压显现安全事故 采场矿压事故角度顶层概念 隐患 蛰伏、濒危、活动隐患的总称 安全栅 预防、控制、保护措施的总称 人的不安全行为 隐患的分类概念 管理的缺失 隐患的分类概念 机械的不安全状态 隐患的分类概念 环境的不安全状态 隐患的分类概念 预防措施 预防蛰伏与濒危隐患发展的措施概念 控制措施 控制活动隐患发展的措施概念 保护措施 保护受体的措施概念 …… 事故角度其他概念节点 
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表 2 矿压危害知识图谱中对象、状态、属性节点(部分)
Table 2. Object, state and attribute nodes in knowledge graph of mine pressure hazard (Part)
类型 名称 说明 对象节点 某矿开采过程中矿山压力的变化 事件中矿压显现实例 作业工人 事件中人物实例 液压支架 事件中设备实例 采煤机 事件中设备实例 刮板输送机 事件中设备实例 …… 其他对象节点 状态节点 发生期 矿压显现发生期状态 发育期 矿压显现发育期状态 稳定期 矿压显现稳定期状态 设备正常 设备正常状态 设备受损 设备受损状态 设备中断 设备中断状态 …… 其他状态节点 属性节点 设备名称 矿压地质灾害中设备的名称 设备位置 矿压地质灾害中设备的位置 中断时间 矿压地质灾害中设备中断的时间 采高 井工开采活动中的开采高度 控顶距 井工开采活动中的控顶距离 工作面推进速度 井工开采活动中的工作面推进速度 煤层倾角 煤岩层赋存环境中的煤岩倾角 开采深度 煤岩层赋存环境中的开采深度 围岩性质 煤岩层赋存环境中的围岩性质 …… 其他属性节点
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[1] SHANG F H, DING Q Y, DU R S, et al. Construction and application of the user behavior knowledge graph in software platforms[J]. Journal of Web Engineering, 2021: 387-411. [2] LI D F, MADDEN A. Cascade embedding model for knowledge graph inference and retrieval[J]. Information Processing & Management, 2019, 56(6): 102093. [3] 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. https://www.cnki.com.cn/Article/CJFDTOTAL-MKZD201803009.htmSHI Qinfu, LIU Xiulei, LIU Xuhong, et al. Research on coal mine safety ontology[J]. Industry and Mine Automation, 2018, 44(3): 42-49. https://www.cnki.com.cn/Article/CJFDTOTAL-MKZD201803009.htm [4] JI S X, PAN S R, CAMBRIA E, et al. A survey on knowledge graphs: representation, acquisition, and applications[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(2): 494-514. doi: 10.1109/TNNLS.2021.3070843 [5] 侯运炳, 张弘, 毛善君, 等. 基于高精度三维动态地质模型的采煤机自适应智能截割技术研究[J]. 矿业科学学报, 2023, 8(1): 26-38. doi: 10.19606/j.cnki.jmst.2024.01.004HOU Yunbing, ZHANG Hong, MAO Shanjun, et al. Adaptive intelligent cutting technology of the shearer based on the high-precision three-dimensional dynamic geological model[J]. Journal of Mining Science and Technology, 2023, 8(1): 26-38. doi: 10.19606/j.cnki.jmst.2024.01.004 [6] ZHANG G Z, CAO X G, ZHANG M Y. A knowledge graph system for the maintenance of coal mine equipment[J]. Mathematical Problems in Engineering, 2021, 2021: 2866751. [7] 赵丽丽. 基于知识图谱的煤矿建设安全管理知识问答研究[D]. 徐州: 中国矿业大学, 2022.ZHAO Lili. Research on question and answer research of coal mine construction safety management based on knowledge graph[D]. Xuzhou: China University of Mining and Technology, 2022. [8] 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. https://www.cnki.com.cn/Article/CJFDTOTAL-MESS202011007.htmLIU 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. https://www.cnki.com.cn/Article/CJFDTOTAL-MESS202011007.htm [9] 刘思含, 刘旭红, 刘秀磊, 等. 基于词向量和条件随机场的煤矿安全事故本体概念抽取[J]. 煤炭技术, 2018, 37(9): 178-181. https://www.cnki.com.cn/Article/CJFDTOTAL-MTJS201809066.htmLIU Sihan, LIU Xuhong, LIU Xiulei, et al. Concept extraction of coal mine safety accident ontology based on word embeddings and CRFs[J]. Coal Technology, 2018, 37(9): 178-181. https://www.cnki.com.cn/Article/CJFDTOTAL-MTJS201809066.htm [10] STUDER R, BENJAMINS V R, FENSEL D. Knowledge engineering: principles and methods[J]. Data & Knowledge Engineering, 1998, 25(1/2): 161-197. [11] 刘杰, 王恩元, 赵恩来, 等. 深部工作面采动应力场分布变化规律实测研究[J]. 采矿与安全工程学报, 2014, 31(1): 60-65. https://www.cnki.com.cn/Article/CJFDTOTAL-KSYL201401010.htmLIU Jie, WANG Enyuan, ZHAO Enlai, et al. Distribution and variation of mining-induced stress field in deep workface[J]. Journal of Mining & Safety Engineering, 2014, 31(1): 60-65. https://www.cnki.com.cn/Article/CJFDTOTAL-KSYL201401010.htm [12] 赵同彬, 张洪海, 陈云娟, 等. 支承压力分布演化规律及对煤岩体破坏的影响[J]. 辽宁工程技术大学学报: 自然科学版, 2010, 29(3): 420-423. https://www.cnki.com.cn/Article/CJFDTOTAL-FXKY201003022.htmZHAO Tongbin, ZHANG Honghai, CHEN Yunjuan, et al. Evolution of abutment pressure distribution and impact on coal-rock damage[J]. Journal of Liaoning Technical University: Natural Science, 2010, 29(3): 420-423. https://www.cnki.com.cn/Article/CJFDTOTAL-FXKY201003022.htm [13] 侯运炳, 何尚森, 周殿奇, 等. 特厚煤层大采高综放工作面覆岩结构及支架工作阻力研究[J]. 矿业科学学报, 2017, 2(1): 42-48. http://kykxxb.cumtb.edu.cn/article/id/46HOU Yunbing, HE Shangsen, ZHOU Dianqi, et al. Analysis of overburden structure and support working resistance of working face in fully-mechanized top coal caving with large mining height in ultra thick coal seam[J]. Journal of Mining Science and Technology, 2017, 2(1): 42-48. http://kykxxb.cumtb.edu.cn/article/id/46 [14] 郑建伟, 鞠文君, 赵曦, 等. 采场全生命周期及其应力的时空演化特征分析[J]. 煤炭学报, 2019, 44(4): 995-1002. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB201904003.htmZHENG Jianwei, JU Wenjun, ZHAO Xi, et al. Dynamic evolution characteristic on stope pressure in whole life cycle of stope[J]. Journal of China Coal Society, 2019, 44(4): 995-1002. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB201904003.htm [15] 李建伟, 刘长友, 卜庆为. 浅埋厚煤层开采覆岩采动裂缝时空演化规律[J]. 采矿与安全工程学报, 2020, 37(2): 238-246. https://www.cnki.com.cn/Article/CJFDTOTAL-KSYL202002003.htmLI Jianwei, LIU Changyou, BU Qingwei. Spatio-temporal evolution of overburden fissures in shallow thick coal seam mining[J]. Journal of Mining & Safety Engineering, 2020, 37(2): 238-246. https://www.cnki.com.cn/Article/CJFDTOTAL-KSYL202002003.htm [16] 王益鹏, 张雪英, 党玉龙, 等. 顾及时空过程的台风灾害事件知识图谱表示方法[J]. 地球信息科学学报, 2023, 25(6): 1228-1239. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXX202306012.htmWANG Yipeng, ZHANG Xueying, DANG Yulong, et al. Knowledge graph representation of typhoon disaster events based on spatiotemporal processes[J]. Journal of Geo-Information Science, 2023, 25(6): 1228-1239. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXX202306012.htm [17] 刘文超, 赵毅鑫. 红庆河矿典型工作面冲击地压灾变机理及防治[J]. 矿业科学学报, 2023, 8(6): 803-816, 827. doi: 10.19606/j.cnki.jmst.2023.06.007LIU Wenchao, ZHAO Yixin. Mechanism and prevention of typical coal burst disaster at the working face of Hongqinghe coal mine[J]. Journal of Mining Science and Technology, 2023, 8(6): 803-816, 827. doi: 10.19606/j.cnki.jmst.2023.06.007 [18] CHEN Q H, LONG D B, YANG C, et al. Knowledge graph improved dynamic risk analysis method for behavior-based safety management on a construction site[J]. Journal of Management in Engineering, 2023, 39(4): 04023023. doi: 10.1061/JMENEA.MEENG-5306 [19] TUDORACHE T, HITZLER P, JANOWICZ K. Ontology engineering: current state, challenges, and future directions[J]. Semantic Web, 2020, 11(1): 125-138. doi: 10.3233/SW-190382 [20] MIZOGUCHI R. Tutorial on ontological engineering Part 2: ontology development, tools and languages[J]. New Generation Computing, 2004, 22(1): 61-96. doi: 10.1007/BF03037281 [21] LI D F, MADDEN A. Cascade embedding model for knowledge graph inference and retrieval[J]. Information Processing & Management, 2019, 56(6): 102093. [22] DZIEKANIAK G V. An ontology development about ontologies component[J]. Perspectivas Em Ciencia Da Informacao, 2010, 15(1): 173-184. doi: 10.1590/S1413-99362010000100010 [23] NOY N F, MCGUINNESS D L. Ontology development 101: a guide to creating your first ontology[J]. Stanford Knowledge Systems Laboratory, 2001. [24] 李爱华, 徐以则, 迟钰雪. 本体构建及应用综述[J]. 情报理论与实践, 2023, 46(11): 189-195. https://www.cnki.com.cn/Article/CJFDTOTAL-QBLL202311024.htmLI Aihua, XV Yize, CHI Yuxue. Review of ontology construction and applications[J]. Information Studies: Theory & Application, 2023, 46(11): 189-195. https://www.cnki.com.cn/Article/CJFDTOTAL-QBLL202311024.htm [25] 国家质量监督检验检疫总局, 中国国家标准化管理委员会. 自然灾害承灾体分类与代码: GB/T 32572-2016[S]. 北京: 中国标准出版社, 2016.General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration of the People's Republic of China. Classification and coding for natural disaster exposure: GB/T 32572-2016[S]. Beijing: Standards Press of China, 2016. [26] CHEN Y L, SUN Z M, HOU Y B, et al. Hazard identification & risk control in aluminum production[J]. Process Safety and Environmental Protection, 2022, 165: 336-346. doi: 10.1016/j.psep.2022.07.017 -
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