为了进一步提高煤矿井下智能化采煤工作面系统自主运行和人机交互能力,达到真正的无人化开采境界,提出数字孪生智采工作面系统(Digital Twin Smart Mining Workface)的概念、架构及构建方法,融合应用 5G 通信技术、物联网技术和仿生智能技术,从而搭建一个智采工作面的数字孪生远程操作平台。

 首次定义数字孪生智采工作面是一个数据可视化、人机强交互、工艺自优化的高逼真采煤工作面三维镜像场景,它由物理工作面、数字工作面和数据信息 3 个部分组成。 

介绍了DTSMW 系统涉及的物理工作面、虚拟工作面、孪生数据、信息交互、模型驱动、边缘计算、沉浸式体验、云端服务、信息物理系统、智能终端等 10 项关键技术。 

新的 DTSMW 系统具有开采过程仿真、优化和监控功能,可以实现开采工艺数字孪生、开采过程数字孪生、设备性能数字孪生、生产管理数字孪生和生产安控数字孪生。 

研究了智采工作面的仿生智能特性,阐述了物理模块(躯干)、信息模块(大脑)、通信模块(神经)、控制模块(脑肌)、孪生模块(映像)的基本功能特征,特别描述了采煤机、液压支架和刮板输送机的仿生智能要素。

 针对DTSMW 系统数据的高度依赖性,首次将智采工作面复杂信息归纳为 3 条信息流,用于描述采煤过程的环境、控制和能量状态。 环境信息流和控制信息流来自煤岩体对采煤机、液压支架、煤流运输机组的输入信息及其调控信息,能量信息流来自开采装备对煤层、岩层变量调控所产生的能量交换状态信息。

 针对智采工作面的巨大信息流量,提出了管理 DTSMW 信息流的数据主线(Digital Thread)方法,将信息流的数据分为周期性数据、随机性数据和突发性数据进行建模处理,以确保数字孪生智采工作面的数据驱动及稳定运行。 通过对比分析,DTSMW 系统比现有远程集控中心的智能性提高了一个层次,可为中级智采工作面实现无人化运行提供新的监控系统架构。

This paper proposes the concept, architecture and construction methods of the digital twin smart mining workface system (DTSMW) in order to further improve the autonomous operation and human-machine interaction ca- pability of intelligent coal mining workface system,and achieve the true state of unmanned mining. The fusion applica- tion of 5G communication technology,internet of things technology and bionic intelligence technology is also intro- duced,which is much helpful for building a digital twin remote operating platform of smart mining workface. The digital twin smart mining workface is firstly defined as a three-dimensional mirror scenario of coal mining process with data vi- sualization,human-machine interaction and process optimization. The new DTSMW system consists of three parts as physical workface,digital workface and data transmission. The key technologies in the DTSMW system include physical workface,virtual workface, twin data, information interaction, model driver, edge computing, immersive experience, cloud service,information physics system, intelligent equipment, etc. The DTSMW system could achieve the digital twins of mining model,mining process,equipment performance,production management and safety monitoring,which will improve the mining process simulation,optimization and monitoring capabilities. In the paper,the bionic intelli- gence characteristics of the smart mining workface are described,and the basic intelligence characteristics of physical module (torso),information module ( brain),communication module ( nerve),control module ( brain muscle),twin module (image) are discussed. Especially,the bionic intelligence features of the shearer,hydraulic support and scrap- er conveyor are described in detail. The complex information data of smart mining workface are summarized into three flows to correlate the interaction state from environmental,control and energy in the coal mining process. The environ- mental data flow and control data flow come from the input information and the adaptive control information to the coal cutting,hydraulic support and coal flow transport unit. The energy information flow comes from the energy exchange state in mining equipment corresponding to coal seam and roof pressure variations. In view of the huge amount of data flow in the DTSMW system,the digital thread method is proposed for data management,dividing the data flow into pe- riodic data,random data and sudden data for accurate modeling and processing,to ensure the stable data-driven of the DTSMW system. Through a comparative analysis,it is recognized that the DTSMW system could improve the intelli- gence of the existing remote control center in underground coal mining workface,and provide a new monitoring system architecture for the future unmanned operation of higher level smart mining workface.