Global dynamic collaborative management and control of diversified business in coal mines driven by digital twins
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摘要: 煤矿作为典型的多业务协同的复杂生产系统,在动态化销量需求及不确定性生产环境的作用下,存在安全、生产、经营等业务协同性和联动性较差等问题。数字孪生技术可为实现煤矿全局业务系统数据融合、协同管控和智能联动提供技术保障。从专业业务内部的融合、专业业务之间的融合2个方面分析了煤矿多元业务的融合及协同管控,其中专业业务内部的融合包括安全监测类业务融合、生产协同类业务融合、经营类业务融合3个部分。构建了基于数字孪生的业务动态协同管控架构,该架构包括物理对象感知层、虚拟空间仿真层、协同管控决策层。提出了“决策算法前摄性静态规划+孪生模型预测性协同管控+实时数据动态性协同管控”三重驱动的煤矿全局业务动态协同管控模式:在孪生世界构建煤矿“安全−生产−经营”多元业务虚拟模型,进行安全保障、生产联动、经营管理等前摄性静态规划,制订最优的业务协同管控初始工作计划;采用数值仿真模拟方式,在信息世界中实现虚拟模型的预测性运行,当管控措施效果得到验证后,及时将决策指令下发到物理世界中,从而将扰动由事件发生后的被动处理变成事件发生前的主动管控,提升管控决策的有效性;当煤矿企业业务运行过程中出现孪生世界未预测到的扰动时,根据实时监测数据,借助决策算法判断扰动事件等级,触发对应应急预案,实现企业多元业务的动态性协同管控。Abstract: As a typical complex production system with multi business collaboration, coal mines face problems such as poor collaboration and linkage in safety, production, and operation under the dynamic sales demand and uncertain production environment. Digital twin technology can provide technical support for achieving data fusion, collaborative control, and intelligent linkage in the global business system of coal mines. This article analyzes the integration and collaborative control of diversified businesses in coal mines from two aspects: internal integration of professional businesses and integration between professional businesses. The internal integration of professional businesses includes three parts: safety monitoring business integration, production collaboration business integration, and operational business integration. We have constructed a business dynamic collaborative management and control architecture based on digital twins. It includes a physical object perception layer, a virtual space simulation layer, and a collaborative management and control decision-making layer. A triple-driven dynamic collaborative control model for the coal mine global business has been proposed. It includes "proactive static planning of decision algorithms, predictive collaborative control of twin models, and real-time data dynamics collaborative control". In the twin world, the multi-business virtual model for coal mine "safety - production - operation" is constructed. The proactive static planning for safety assurance, production linkage, and business management is carried out to formulate the optimal initial work plan for business collaborative control. The numerical simulation method is used to achieve the predictive operation of virtual models in the information world. After the effectiveness of control measures is verified, decision instructions are promptly issued to the physical world. Therefore, the disturbances from passive processing after the event are transformed into active control before the event, improving the effectiveness of control decisions. When there are unforeseen disturbances in the twin world during the operation of coal mining enterprises' business, based on real-time monitoring data, decision algorithms are used to determine the level of disturbance events. The corresponding emergency plans are triggered. The dynamic collaborative management and control of the diversified business of the enterprise are achieved.
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表 1 影响因素等级划分
Table 1. Classification of different factors
等级 影响
程度Ideg影响
范围Isco影响
时长Iper可恢复
程度IrecⅠ级 影响很小 单一子系统 很短 容易恢复 Ⅱ级 影响较小 业务单元 较短 不容易恢复 Ⅲ级 影响很大 业务群 较长 难以恢复 Ⅳ级 影响严重 煤矿整体 很长 无法恢复 -
[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. doi: 10.13199/j.cnki.cst.2019.08.001WANG 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. doi: 10.13199/j.cnki.cst.2019.08.001 [2] 李首滨. 智能化开采研究进展与发展趋势[J]. 煤炭科学技术,2019,47(10):102-110. doi: 10.13199/j.cnki.cst.2019.10.012LI Shoubin. Progress and development trend of intelligent mining technology[J]. Coal Science and Technology,2019,47(10):102-110. doi: 10.13199/j.cnki.cst.2019.10.012 [3] 罗香玉,李嘉楠,郎丁. 智慧矿山基本内涵、核心问题与关键技术[J]. 工矿自动化,2019,45(9):61-64. doi: 10.13272/j.issn.1671-251x.2019020046LUO Xiangyu,LI Jianan,LANG Ding. Basic connotation,core problems and key technologies of wisdom mine[J]. Industry and Mine Automation,2019,45(9):61-64. doi: 10.13272/j.issn.1671-251x.2019020046 [4] 丁恩杰,廖玉波,张雷,等. 煤矿信息化建设回顾与展望[J]. 工矿自动化,2020,46(7):5-11. doi: 10.13272/j.issn.1671-251x.17624DING Enjie,LIAO Yubo,ZHANG Lei,et al. Review and prospect of coal mine informatization construction[J]. Industry and Mine Automation,2020,46(7):5-11. doi: 10.13272/j.issn.1671-251x.17624 [5] 王国法,任怀伟,赵国瑞,等. 煤矿智能化十大“痛点”解析及对策[J]. 工矿自动化,2021,47(6):1-11. doi: 10.13272/j.issn.1671-251x.17808WANG Guofa,REN Huaiwei,ZHAO Guorui,et al. Analysis and countermeasures of ten 'pain points' of intelligent coal mine[J]. Industry and Mine Automation,2021,47(6):1-11. doi: 10.13272/j.issn.1671-251x.17808 [6] 李梅,杨帅伟,孙振明,等. 智慧矿山框架与发展前景研究[J]. 煤炭科学技术,2017,45(1):121-128,134. doi: 10.13199/j.cnki.cst.2017.01.021LI Mei,YANG Shuaiwei,SUN Zhenming,et al. Study on framework and development prospects of intelligent mine[J]. Coal Science and Technology,2017,45(1):121-128,134. doi: 10.13199/j.cnki.cst.2017.01.021 [7] 王国法,任怀伟,赵国瑞,等. 智能化煤矿数据模型及复杂巨系统耦合技术体系[J]. 煤炭学报,2022,47(1):61-74. doi: 10.13225/j.cnki.jccs.YG21.1860WANG Guofa,REN Huaiwei,ZHAO Guorui,et al. Digital model and giant system coupling technology system of smart coal mine[J]. Journal of China Coal Society,2022,47(1):61-74. doi: 10.13225/j.cnki.jccs.YG21.1860 [8] 胡青松,钱建生,李世银,等. 再论智能煤矿建设路线——基于人工智能3.0视角[J]. 煤炭科学技术,2022,50(1):256-264. doi: 10.3969/j.issn.0253-2336.2022.1.mtkxjs202201026HU Qingsong,QIAN Jiansheng,LI Shiyin,et al. Re-discussion on construction route of intelligent coal mine:based on perspective of artificial intelligence 3.0[J]. Coal Science and Technology,2022,50(1):256-264. doi: 10.3969/j.issn.0253-2336.2022.1.mtkxjs202201026 [9] 王海军,武先利. “互联网+”时代煤矿大数据应用分析[J]. 煤炭科学技术,2016,44(2):139-143. doi: 10.13199/j.cnki.cst.2016.02.026WANG Haijun,WU Xianli. Analysis on application of coal mine big data in age of 'Internet +'[J]. Coal Science and Technology,2016,44(2):139-143. doi: 10.13199/j.cnki.cst.2016.02.026 [10] 马小平,胡延军,缪燕子. 物联网、大数据及云计算技术在煤矿安全生产中的应用研究[J]. 工矿自动化,2014,40(4):5-9. doi: 10.13272/j.issn.1671-251x.2014.04.002MA Xiaoping,HU Yanjun,MIAO Yanzi. Application research of technologies of Internet of things,big data and cloud computing in coal mine safety production[J]. Industry and Mine Automation,2014,40(4):5-9. doi: 10.13272/j.issn.1671-251x.2014.04.002 [11] 王星,武讲,张阳,等. 智能露天煤矿信息综合管控平台设计及关键技术研究[J]. 煤炭工程,2022,54(3):33-39.WANG Xing,WU Jiang,ZHANG Yang,et al. Design and key technology research of information integrated management and control platform for intelligent open-pit coal mine[J]. Coal Engineering,2022,54(3):33-39. [12] 徐正国,徐华龙. 基于多源异构数据的宁煤公司煤矿协同调度平台[J]. 煤矿安全,2021,52(12):172-176. doi: 10.13347/j.cnki.mkaq.2021.12.029XU Zhengguo,XU Hualong. Coal mine collaborative scheduling platform of Ningxia Coal Industry Company based on multi-source heterogeneous data[J]. Safety in Coal Mines,2021,52(12):172-176. doi: 10.13347/j.cnki.mkaq.2021.12.029 [13] 韩安,陈晓晶,贺耀宜,等. 智能矿山综合管控平台建设构思[J]. 工矿自动化,2021,47(8):7-14. doi: 10.13272/j.issn.1671-251x.17814HAN An,CHEN Xiaojing,HE Yaoyi,et al. Construction conception of intelligent mine integrated management and control platform[J]. Industry and Mine Automation,2021,47(8):7-14. doi: 10.13272/j.issn.1671-251x.17814 [14] 马小平,代伟. 大数据技术在煤炭工业中的研究现状与应用展望[J]. 工矿自动化,2018,44(1):50-54. doi: 10.13272/j.issn.1671-251x.2018.01.2017100022MA Xiaoping,DAI Wei. Research status and application prospect of big data technology in coal industry[J]. Industry and Mine Automation,2018,44(1):50-54. doi: 10.13272/j.issn.1671-251x.2018.01.2017100022 [15] 陈晓晶. 煤矿智能化综合管控平台研究与建设实践[J]. 智能矿山,2022,3(6):43-54.CHEN Xiaojing. Research and construction practice of intelligent integrated management and control platform for coal mines[J]. Journal of Intelligent Mine,2022,3(6):43-54. [16] 张倍宁,李伟东,汪义龙,等. 基于三级管控架构的智能化综合管控平台[J]. 煤矿安全,2022,53(6):137-142.ZHANG Beining,LI Weidong,WANG Yilong,et al. Development of a three-level intelligent integrated management and control platform[J]. Safety in Coal Mines,2022,53(6):137-142. [17] 毛善君,夏良,陈华州. 基于安全生产的智能煤矿管控系统[J]. 煤矿安全,2018,49(12):102-107. doi: 10.13347/j.cnki.mkaq.2018.12.026MAO Shanjun,XIA Liang,CHEN Huazhou. Design of intelligent coal mine management and control system based on safety production[J]. Safety in Coal Mines,2018,49(12):102-107. doi: 10.13347/j.cnki.mkaq.2018.12.026 [18] 徐华龙. 智能矿山一体化管控平台关键技术研究[J]. 煤矿安全,2022,53(12):144-149,154.XU Hualong. Research on key technologies of intelligent mine integrated management and control platform[J]. Safety in Coal Mines,2022,53(12):144-149,154. [19] 邢震,韩安,陈晓晶,等. 基于工业互联网的智能矿山灾害数字孪生研究[J]. 工矿自动化,2023,49(2):23-30,55.XING Zhen,HAN An,CHEN Xiaojing,et al. Research on intelligent mine disaster digital twin based on industrial Internet[J]. Journal of Mine Automation,2023,49(2):23-30,55. [20] 李明建. 煤矿多灾种融合预警技术与装备[J]. 智能矿山,2022,3(7):156-157.LI Mingjian. Multi disaster integrated warning technology and equipment for coal mines[J]. Journal of Intelligent Mine,2022,3(7):156-157. [21] 周达坚. 数字孪生框架下的工业园区“产−运−存”联动优化决策方法[D]. 广州: 广东工业大学, 2019.ZHOU Dajian. Digital-twin-based optimal decision-making method for a synchronized production-transportation-storage system in industrial park[D]. Guangzhou: Guangdong University of Technology, 2019. -