工矿自动化2025，Vol.51Issue(9)：72-80,9.DOI:10.13272/j.issn.1671-251x.2025070028
基于动态图卷积Transformer的瓦斯浓度预测模型
Gas concentration prediction model based on dynamic graph convolutional Transformer
摘要
Abstract
Accurate prediction of gas concentration is crucial for preventing gas disasters.The prediction accuracy is influenced by both the temporal variation patterns of gas concentration and the spatiotemporal distribution characteristics of gas diffusion.Existing model-driven prediction methods struggle to handle long-term and large-scale gas concentration prediction tasks,while data-driven prediction methods do not consider the impact of dynamic spatial features,resulting in poor generalization performance.To capture the spatiotemporal dependency of gas concentration changes and improve the prediction accuracy,a Temporal-Dynamic Graph Convolutional Transformer with Multi-Scale Mechanism(TDMformer)was proposed to construct a gas concentration prediction model.Based on the ITransformer framework,a temporal-variable attention mechanism was designed to model the temporal and variable features simultaneously.A dynamic graph convolutional network was integrated to describe the topology of underground gas sensor networks and capture the spatial dependency of gas concentration data.A multi-scale gated Tanh unit was introduced to enhance the multi-scale feature extraction capability.The experimental results showed that,compared with Graph-WaveNet,GRU,Transformer,AGCRN,DSformer,STAEformer,and FourierGNN,the root mean square error of the TDMformer model decreased by 24.87％,26.37％,21.69％,19.57％,11.90％,10.84％,and 9.20％,respectively.The mean absolute error decreased by 17.09％,25.58％,26.89％,14.56％,11.10％,5.75％,and 4.53％,respectively.The coefficient of determination increased by 5.94％,6.51％,4.79％,4.12％,2.21％,2.08％,and 1.76％,respectively,verifying that this model had higher prediction accuracy and better data fitting performance.关键词
瓦斯浓度预测/Transformer/ITransformer/动态图卷积网络/时序-变量注意力机制Key words
gas concentration prediction/Transformer/ITransformer/dynamic graph convolutional network/temporal-variable attention mechanism分类
矿业与冶金引用本文复制引用
董立红,赵楠楠,王丹,秦昳..基于动态图卷积Transformer的瓦斯浓度预测模型[J].工矿自动化,2025,51(9):72-80,9.基金项目
国家自然科学基金项目(62476216) （62476216）
陕西省重点研发计划项目(2024GX-YBXM-146) （2024GX-YBXM-146）
陕西省教育厅青年创新团队科研计划项目(23JP091). （23JP091）
