Technology path and assumptions of intelligent surrounding rock control at longwall working face
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摘要: 煤矿采场围岩的智能控制是实现煤矿智能化的重要组成部分。本文重新定义了采场围岩系统的内涵，明确了围岩控制的范围，包括工作面推进与垂直方向上基本顶范围内支架围岩“小结构”以及影响采场围岩控制的远场岩层；根据作用方式将采场围岩系统因素分为“可控因素”与“不可控因素”，由此确定了围岩智能控制的重点和基本原理。建立了采场围岩系统“多参量智能感知-精准分析模式判别-自主决策-快速执行-控制效果动态评价”智能控制的技术构架，进一步明确了实现智能围岩控制的科学问题和关键技术难题，并提出了工作面开采系统智能化、装备围岩自适应控制、复杂条件围岩智能控制、统一坐标系下的采场围岩系统稳定性分析4点关键技术设想。结合实际案例分析了支架工况信息的可挖掘性，并用堆叠LSTM实现对采场矿压的智能预测。Abstract: The intelligent surrounding rock control at longwall working face is the necessary condition to realize the intelligent mining of working face. This paper redefined the connotation of the surrounding rock system at longwall working face and clarified the scope of surrounding rock control and clarified the scope of surrounding rock control, which includes small structure of support surrounding rock within its basic topic range in the vertical direction, and the far field rock layer that will have an effect on surrounding rock control at longwall working face. The factors of surrounding rock system at longwall working face are classified, there are "controllable factors" and "uncontrollable factors" according to the mode of action. The points and basic principles of intelligent surrounding rock control are determined. Multi-parameter intelligent detection, precise analysis of pattern discriminations, self-determination, quick implementation and dynamic evaluation of control effects which is technological framework for intelligent control is established. Furthermore, scientific issues and critical technical problems of intelligent surrounding rock control at longwall working face are elaborated. The four key technology assumptions based on this are "intelligent mining system at working face", "adaptive intelligent control technology of equipment-surrounding rock", "intelligent control of complex conditions surrounding rock", and "stability analysis of surrounding rock system at longwall working face under unified coordinate system". The extractability of the information on the working conditions of support is analyzed with the actual case, and the intelligent prediction of mine pressure at longwall working face is realized by the stacked LSTM algorithm.
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图 4 煤矿采场数据特点与利用
Figure 4. Data characteristics and utilization at longwall working face
图 5 采场围岩系统驱动数据
Figure 5. Driving data of the surrounding rock system of longwall working face
图 6 大数据处理与智能算法模型关系
Figure 6. Big data processing and intelligent algorithm model relationship
图 8 支架围岩自适应效果评价逻辑
Figure 8. Logic for evaluating the adaptive effect of self adaptive of support-surrounding rock
图 10 复杂条件下智能开采技术设想路线
Figure 10. Intelligent mining technology concept under complex conditions
图 11 “开采环境-生产系统”统一坐标系模型及围岩智能控制方法
Figure 11. Unified coordinate system model of "mining environment-production system" and intelligent rock control method
图 15 基于堆叠LSTM预测矿压的方法流程及输出结果
Figure 15. Cyclic pressure prediction process based on stacked LSTM networks
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