摘要
针对矿压时序数据强非线性与长程依赖特性导致的预测难题,本文提出一种融合扩展长短期记忆网络(xLSTM)与长序列预测模型(Informer)的xLSTM-Infomer预测方法。较于传统单一模型,该模型利用xLSTM精细捕捉局部动态特征的能力,并结合Informer全局长程依赖高效建模的特性,实现了对矿压演化规律的长期预测。为验证模型的预测能力,本文以新疆硫磺沟煤矿的倾斜厚煤层工作面为背景,对工作面矿压数据进行预测,实验结果表明,与LSTM、Informer等基准模型相比,本文所建立的模型预测性能有较高提升,不同部位预测结果的决定系数R^(2)均在93%以上,最高的R^(2)达到了98.21%,且较于对比模型在MAE与RMSE的指标上也均处于最低水平,同时模型在复杂工况下也能表现出较高的预测精度,这为实现智能矿压监测与灾害预警提供了可靠的技术支撑。
Aiming at the prediction problem caused by the strong nonlinearity and long distance dependence of mine pressure time series data,this paper proposed an xLSTM-Infomer prediction method that combined extended long short term memory network(xLSTM)and long sequence prediction model(Informer).Compared with the traditional single model,this model used the ability of xLSTM to capture local dynamic features finely,and combined the characteristics of Informer's global long distance dependence and efficient modeling to realize the long-term prediction of the evolution law of mine pressure.In order to verify the prediction ability of the model,this paper took the inclined thick coal seam working face of Liuhuanggou Coal Mine in Xinjiang as the background to predict the mine pressure data of the working face.The experimental results showed that compared with the benchmark models such as LSTM and Informer,the prediction performance of the model established in this paper was higher.The determination coefficient(R^(2))of the prediction results of different parts was above 93%,and the highest R^(2) reached 98.21%.Compared with the comparison model,the indexes of MAE and RMSE were also at the lowest level,and the model could also show higher prediction accuracy under complex working conditions.The study provided a reliable technical support for the realization of intelligent mine pressure monitoring and disaster forewarning.
作者
王永胜
崔志瀛
赵亮
董文哲
赵文广
Wang Yongsheng;Cui Zhiying;Zhao Liang;Dong Wenzhe;Zhao Wenguang(School of Energy and Mining Engineering,Shandong University of Science and Technology,Qingdao 266590,China;Liuhuanggou Mine,Yankuang Xinjiang Mining Co.,Ltd.,Changji 831100,China)
出处
《煤炭与化工》
2026年第1期25-31,共7页
Coal and Chemical Industry
