A three machine digital twin and collaborative modeling method for fully mechanized working face
-
摘要: 针对现有煤矿设备数字孪生建模方法主要侧重对单一设备进行建模，缺少三机耦合协同关系分析的问题，提出了综采工作面三机数字孪生及协同建模方法。采用智能体建模方法构建包含感知单元、控制单元和执行单元的采煤机、液压支架、刮板输送机智能体模型，依据三维建模流程构建对应的可视化模型，以智能体模型驱动三维模型运动，二者结合构成三机数字孪生模型；采用离散事件建模方法构建涵盖三机数字孪生模型交互过程的协同工艺模型，按照时序梳理三机开采工艺，形成三机协同工艺时序表。数字孪生模型用于描述综采三机的状态与行为，进行个体层面的仿真计算；协同工艺模型用于表征数字孪生模型之间的时序动作转换，实现对三机协同过程整体的推演。采煤机数字孪生模型的摇臂升降仿真实验结果表明，与真实设备测量数据对比，模型误差小，摇臂倾角平均误差为2.3°；液压支架数字孪生模型的连续升柱动作仿真实验结果表明，模型与真实设备的一致性好，与真实设备测量数据对比，角度平均误差为0.14°，行程平均误差为6.3 mm；结合煤矿实际生产日志对构建的三机协同模型进行虚实仿真实验，结果表明，所构建的综采工作面三机数字孪生模型与真实设备实现了相互映射，仿真结果与真实记录接近，三机协同模型可以较为准确地反映协同开采过程。综采工作面三机数字孪生及协同建模方法为综采设备及其协同关系的数字孪生建模提供了新思路。Abstract: The existing coal mine equipment digital twin modeling method mainly focuses on single equipment modeling. It lacks three machine coupling collaborative relationship analysis. In order to solve the above problems, the paper puts forward three machine digital twin and collaborative modeling method for fully mechanized working face. By adopting an intelligent modeling method, the method constructs agent-based models of a coal mining machine, a hydraulic support and a scraper conveyor which comprise a sensing unit, a control unit and an execution unit. The method constructs corresponding visual models according to a three-dimensional modeling process. The method drives the three-dimensional models to move by the intelligent models. The combination of the two forms a digital twin model of three machines. A discrete event modeling method is used to construct a collaborative process model covering the interaction process of the three machine digital twin model. The three machine mining process is sorted out according to the time sequence to form a three machine collaborative process time sequence table. The digital twin model is used to describe the state and behavior of the three machines in fully mechanized mining and to simulate the calculation at the individual level. The collaborative process model is used to represent the sequential action transformation between digital twin models and realize the deduction of the whole three machine collaborative process. The simulation of rocker lifting and lowering for the digital twin model of the shearer is carried out. The simulation results show that compared with the measured data of real equipment, the model error is small, an average error of rocker arm dip angle is 2.3°. The simulation of continuous column lifting action for the digital twin model of hydraulic support is carried out. The simulation results show good consistency between the model and real equipment. Compared with the measured data of the real equipment, the average angle error is 0.14° and the average stroke error is 6.3 mm. Combined with the actual production log of the coal mine, the virtual and real simulation experiment of the three machine collaborative model is carried out. The results show that the three machine digital twin model of the fully mechanized working face and real equipment realize mutual mapping. The simulation results are close to the real records. The three machine collaborative model can accurately reflect the collaborative mining process. The method of three machine digital twin and collaborative modeling for fully mechanized working face provides a new idea for the digital twin modeling of fully mechanized coal mining equipment and its collaborative relationship.
-
图 1 数字孪生模型、智能体模型与三维模型的关系
Figure 1. Relation among digital twin model, agent model and 3D model
图 5 采煤机左侧摇臂倾角数字孪生数据与真实数据对比曲线
Figure 5. Comparison curves between digital twin data and real data of dip angle of shearer left rocker arm
图 7 真实液压支架与三维模型对比
Figure 7. Comparison of real equipment and 3D model of hydraulic support
图 8 液压支架姿态各变量连续变化曲线
Figure 8. Continuous change curves of various variables of hydraulic support attitude
图 9 真实刮板输送机与三维模型对比
Figure 9. Comparison of real equipment and 3D model of scraper conveyor
表 1 采煤机关键感知数据项
Table 1. Key perception data items of shearer
数据类型及传感器 关键感知数据项 结构尺寸 滚筒：直径、截深
摇臂：长度、旋转锚点
机身：长度、宽度、厚度倾角传感器 左右摇臂升降角度 行程传感器 左右滚筒采高卧底 测速传感器 采煤机行进速度 编码器/红外发射器 采煤机位置 
下载: 导出CSV
表 2 液压支架运动仿真结果
Table 2. Hydraulic support motion simulation reaults
项目 后连杆
角度/（°）前连杆
角度/（°）立柱杆
角度/（°）平衡杆
角度/（°）掩护梁
角度/（°）顶梁
角度/（°）立柱
长度/ mm平衡杆
长度/ mm支护
高度/mm初始值 100.00 100.00 80.00 20.00 20.00 0 3000.00 1000.00 3800.00 最终值 107.17 122.89 77.62 32.50 40.85 0 3163.70 1137.40 3800.00 测量值 107.02 123.03 77.51 32.21 40.75 0.05 3158.59 1130.21 3793.39 误差 0.15 0.14 0.11 0.29 0.10 0.05 5.11 7.19 6.61
下载: 导出CSV
表 3 部分三机协同工艺时序表数据
Table 3. Partial three machine collaborative process schedule data
事件名称 动作执行
对象指令持续
时间/s动作
指令采煤机开始
位置/架采煤机结束
位置/架割底煤 采煤机 5 牵启 170 170 3 左降 170 170 4 右牵 170 168 4 加速 168 150 机尾顺序移架 175号支架 3 降柱 165 164 4 移架 164 163 4 升柱 163 162
下载: 导出CSV
-
[1] 张帆,葛世荣,李闯. 智慧矿山数字孪生技术研究综述[J]. 煤炭科学技术,2020,48(7):168-176. doi: 10.13199/j.cnki.cst.2020.07.017ZHANG Fan,GE Shirong,LI Chuang. Research summary on digital twin technology for smart mines[J]. Coal Science and Technology,2020,48(7):168-176. doi: 10.13199/j.cnki.cst.2020.07.017 [2] 王国法,王虹,任怀伟,等. 智慧煤矿2025情景目标和发展路径[J]. 煤炭学报,2018,43(2):295-305. doi: 10.13225/j.cnki.jccs.2018.0152WANG Guofa,WANG Hong,REN Huaiwei,et al. 2025 scenarios and development path of intelligent coal mine[J]. Journal of China Coal Society,2018,43(2):295-305. doi: 10.13225/j.cnki.jccs.2018.0152 [3] 王国法,杜毅博. 智慧煤矿与智能化开采技术的发展方向[J]. 煤炭科学技术,2019,47(1):1-10.WANG Guofa,DU Yibo. Development direction of intelligent coal mine and intelligent mining technology[J]. Coal Science and Technology,2019,47(1):1-10. [4] 王国法,刘峰,庞义辉,等. 煤矿智能化——煤炭工业高质量发展的核心技术支撑[J]. 煤炭学报,2019,44(2):349-357.WANG Guofa,LIU Feng,PANG Yihui,et al. Coal mine intellectualization:the core technology of high quality development[J]. Journal of China Coal Society,2019,44(2):349-357. [5] 陶飞,刘蔚然,刘检华,等. 数字孪生及其应用探索[J]. 计算机集成制造系统,2018,24(1):1-18.TAO Fei,LIU Weiran,LIU Jianhua,et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems,2018,24(1):1-18. [6] 陶飞,刘蔚然,张萌,等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统,2019,25(1):1-18.TAO Fei,LIU Weiran,ZHANG Meng,et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems,2019,25(1):1-18. [7] 葛世荣,王世博,管增伦,等. 数字孪生——应对智能化综采工作面技术挑战[J]. 工矿自动化,2022,48(7):1-12.GE Shirong,WANG Shibo,GUAN Zenglun,et al. Digital twin:meeting the technical challenges of intelligent fully mechanized working face[J]. Journal of Mine Automation,2022,48(7):1-12. [8] 王宏伟,武亚丹,陈龙. 液压支架数字孪生体联合建模方法[J]. 工矿自动化,2022,48(10):13-19.WANG Hongwei,WU Yadan,CHEN Long. Hydraulic support digital twin joint modeling method[J]. Journal of Mine Automation,2022,48(10):13-19. [9] 孙继平. 煤矿智能化与矿用5G和网络硬切片技术[J]. 工矿自动化,2021,47(8):1-6.SUN Jiping. Coal mine intelligence,mine 5G and network hard slicing technology[J]. Industry and Mine Automation,2021,47(8):1-6. [10] 谢嘉成. VR环境下综采工作面“三机”监测与动态规划方法研究[D]. 太原: 太原理工大学, 2018.XIE Jiacheng. Method of on monitoring and dynamic planning for "three machines" in a fully mechanized coal mining face under VR environment[D]. Taiyuan: Taiyuan University of Technology, 2018. [11] 葛世荣,张帆,王世博,等. 数字孪生智采工作面技术架构研究[J]. 煤炭学报,2020,45(6):1925-1936.GE Shirong,ZHNAG Fan,WANG Shibo,et al. Digital twin for smart coal mining work face:technological frame and construction[J]. Journal of China Coal Society,2020,45(6):1925-1936. [12] 洪飞. 基于数字孪生和数据驱动的新型煤矿智能支护监控系统设计[J]. 煤矿现代化,2021,30(5):116-118,122. doi: 10.13606/j.cnki.37-1205/td.2021.05.051HONG Fei. Design of novel coal mine intelligent support monitoring system based on digital twin and data driven[J]. Coal Mine Modernization,2021,30(5):116-118,122. doi: 10.13606/j.cnki.37-1205/td.2021.05.051 [13] 吴云超,傅琛,张宁馨. 面向数字孪生战场的智能体建模框架构建[J]. 指挥信息系统与技术,2022,13(4):19-25,31.WU Yunchao,FU Chen,ZHANG Ningxin. Construction of agent modeling framework for digital twin battlefield[J]. Command Information System and Technology,2022,13(4):19-25,31. [14] 王龙,黄锋. 多智能体博弈、学习与控制[J]. 自动化学报,2023,49(3):1-34. doi: 10.16383/j.aas.c220680WANG Long,HUANG Feng. An interdisciplinary survey of multi-agent games,learning,and control[J]. Acta Automatica Sinica,2023,49(3):1-34. doi: 10.16383/j.aas.c220680 [15] 李梅,康济童,刘晖,等. 基于BIM与GIS的矿山巷道参数化三维建模技术研究[J]. 煤炭科学技术,2022,50(7):25-35.LI Mei,KANG Jitong,LIU Hui,et al. Research on parametric 3D modeling technology of mine roadway based on BIM and GIS[J]. Coal Science and Technology,2022,50(7):25-35. [16] 卢阳. 基于感知的并行离散事件仿真组件连接关系建模技术研究[D]. 长沙: 国防科学技术大学, 2012.LU Yang. Research on component connection modeling technology of parallel discrete event simulation based on perception [D]. Changsha: National University of Defense Technology, 2012. [17] 蔡安江,刘俊强,刘亚东,等. 基于隐式数字孪生的采煤机自主调高策略研究[J]. 矿业研究与开发,2022,42(11):188-194.CAI Anjiang,LIU Junqiang,LIU Yadong,et al. Research on the strategy of independent height adjustment of shearer based on implicit digital twin[J]. Mining Research and Development,2022,42(11):188-194. [18] 杨桂香,卢洪坤,梁敏富. 两柱式液压支架姿态角矢量解算模型[J]. 煤矿机械,2022,43(11):31-33.YANG Guixiang,LU Hongkun,LIANG Minfu. Two-column hydraulic support attitude angle vector solution model[J]. Coal Mine Machinery,2022,43(11):31-33. [19] 苏岐芳, 陈科. 两类求解非线性方程的高阶算法[J/OL]. 数学的实践与认识: 1-10[2023-02-06]. http://kns.cnki.net/kcms/detail/11.2018.o1.20221125.1406.032.html.SU Qifang, CHEN Ke. Two classes of higher-order algorithms for solving nonlinear equations [J/OL]. Mathematics in Practice and Theory: 1-10 [2023-02-06]. http://kns.cnki.net/kcms/detail/11.2018.o1.20221125.1406.032.html. [20] 李祖旭. 刮板输送机形态监测技术研究[D].徐州: 中国矿业大学, 2022.LI Zuxu. Research on shape monitoring technology of scraper conveyor[D].Xuzhou: China University of Mining and Technology, 2022. [21] 张文静. 基于PLC采煤机与刮板输送机联动控制技术研究[J]. 山东煤炭科技,2022,40(12):135-137.ZHANG Wenjing. Research on linkage control technology of shearer and scraper conveyor based on PLC[J]. Shandong Coal Science and Technology,2022,40(12):135-137. [22] 胡相捧, 刘新华. 两柱掩护式液压支架初撑过程的机构演化机理[J/OL]. 煤炭科学技术:1-12[2023-02-06]. https://doi.org/10.13199/j.cnki.cst.2022-1055.HU Xiangpeng, LIU Xinhua. Mechanism evolution mechanism of active support process of two-leg shield[J/OL]. Coal Science and Technology: 1-12 [2023-02-06]. https://doi.org/10.13199/j.cnki.cst.2022-1055. -
点击查看大图
图(11) / 表(3)
计量
- 文章访问数: 1361
- HTML全文浏览量: 35
- PDF下载量: 117
- 被引次数: 0
