China Huadian Co., Ltd.
Buliangou Coal Mine, Inner Mongolia Mengtai Buliangou Coal Industry Co., Ltd.
School of Mechanics and Civil Engineering, China University of Mining and Technology

目前煤矿巷道支护方案设计仍以人工设计、工程类比、FLAC模型模拟为主，存在主观性强、普适性低、未充分利用煤矿支护大数据等问题，而基于专家系统的设计方法规则设定程序繁琐，工程量大，智能化程度较低。将案例推理（CBR）和深度学习技术引入巷道支护方案设计领域，基于煤矿支护规程、支护规范及煤矿巷道地质报告等文本大数据，提出了一种煤矿巷道支护方案智能设计方法。获取346份不同煤矿的巷道支护资料，抽取结构化数据并划分为输入、输出参数，通过常属性变量滤波和高相关性滤波方法对输入、输出参数进行优化。建立CBR模型，并将抽取的结构化数据导入CBR模型，形成支护方案比选案例库，计算新的巷道支护方案与历史方案的相似度，输出相似度最高的3条历史方案进行对比，实现相似案例比选。分别采用BP神经网络和基于长短期记忆（LSTM）网络建立煤矿巷道支护方案自动生成模型，通过对比预测指标，确定采用基于LSTM模型与CBR模型结合，建立煤矿巷道支护方案智能设计系统。将该系统用于不连沟煤矿掘进F6226工作面辅运巷支护方案设计，通过试验验证了系统生成方案下巷道两帮变形量和顶板最大位移均小于人工设计方案，巷道顶板及两帮完整性较好，围岩承载能力增强，支护效果明显。

Currently, the design of coal mine roadway support schemes is still mainly based on manual design, engineering analogy, and FLAC model simulation, which has problems such as strong subjectivity, low universality, and insufficient utilization of coal mine support big data. The design method based on expert systems has cumbersome rule setting procedures, large engineering quantities, and low intelligence. Case based reasoning (CBR) and deep learning techniques are introduced into the field of roadway support scheme design. Based on text big data such as coal mine support regulations, support standards, and coal mine roadway geological reports, an intelligent design method for coal mine roadway support scheme is proposed. The method obtains 346 sets of roadway support data from different coal mines, extracts structured data and divides it into input and output parameters, and optimizes the input and output parameters through constant attribute variable filtering and high correlation filtering methods. The method establishes a CBR model and imports the extracted structured data into the CBR model to form a case library of support scheme comparison and selection. The method calculates the similarity between the new roadway support scheme and the historical scheme, and outputs the three historical schemes with the highest similarity for comparison, achieving similar case comparison. BP neural network and long short term memory (LSTM) network are respectively used to establish automatic generation models for coal mine roadway support schemes. By comparing the prediction indicators, it is determined to use the combination of LSTM model and CBR model to establish an intelligent design system for coal mine roadway support scheme. The system is used for the design of auxiliary transportation roadway support scheme in the F6226 working face of Buliangou Coal Mine excavation. Through experiments, it is verified that the deformation of the two sides of the roadway and the maximum displacement of the roof under the system generated scheme are smaller than those under the manual design scheme. The integrity of the roadway roof and two sides is good, the bearing capacity of the surrounding rock is enhanced, and the support effect is significant.

国家自然科学青年基金资助项目（71901206）。

陈万辉，郭瑞，韩伟，等. 煤矿巷道支护方案智能设计研究[J]. 工矿自动化，2024，50（8）：76-83, 90.

CHEN Wanhui, GUO Rui, HAN Wei, et al. Research on intelligent design of coal mine roadway support scheme[J]. Journal of Mine Automation，2024，50（8）：76-83, 90.