College of Communication and Information Engineering, Xi’an University of Science and Technology
Shaanxi Changwu Tingnan Coal Industry Co. , Ltd.

煤矿井下供水系统是煤矿安全生产的生命线,供水管网水流量的预测是供水系统优化调度的基础,预测的重要性对供水调度有重要影响。文章提出了一种融合多模态数据特征的煤矿井下供水管网流量预测方法,该方法通过图深度学习的方法实现了对井下管网空间拓扑结构、历史时间依赖、井下实际生产工况、周期相关等多种数据模态特征的融合,具体的,使用添加空间注意机制的图卷积神经网络获取井下管网监测点的空间拓扑关系,然后利用循环神经网络中的门控循环单元获取监测点的时间依赖,并融合煤矿生产规律与不同周期的流量数据形成最终预测结果,通过陕西亭南煤矿实际数据进行实验,结果表明,提出的预测方法相较于SVM、LSTM、STGCN等方法能更准确地预测井下流量未来的趋势,预测偏差分别降低了9.3%、6.84%和3.65%。

The underground water supply system of coal mine is the lifeline of safe production in coal mines, and the prediction of water flow in the water supply pipe network is the basis for the optimal scheduling of the water supply system, and the precision of prediction has an important impact on water supply dispatching. We proposed a flow prediction method for coal mine underground water supply pipe network by integrating multi - modal data features, which was different from previous methods, and fused various data modal features such as spatial topology, historical time dependence, actual underground production conditions, and cycle correlation of underground pipe network through the method of graph deep learning. Specifically, we used a graph convolutional neural network with added spatial attention mechanism to obtain the spatial topology relationship of monitoring points in the underground pipeline network, and used the gated recurrent unit in the recurrent neural network to obtain the time dependence of monitoring points, then combined the coal mine production law with the flow data of different cycles to form the final prediction results. According to the test data of a mine in Shaanxi, the proposed prediction method was able to predict the future trend of underground flow more accurately than SVM, LSTM, STGCN methods, reducing the prediction deviation by 9. 3%, 6. 84% and 3. 65%, respectively.

陕西省重点研发计划(2022GY-152,2023-YBGY-066)

赵安新, 刘 鼎, 郭仕林, 等. 融合多种模态特征的井下供水管网流量预测 [J]. 煤炭工程, 2024, 56(2):24-30.