Coal origin identification based on visible-infrared spectroscopy and attention networks

Coal origin identification is a crucial process in the coal industry, which is important in ensuring coal quality and optimizing supply chain management. However, due to the diversity of coal mine resources and the increasing market demands for quality, coal origin identification has become more complex. This study proposes a coal origin identification method based on spectroscopy and advanced machine learning techniques with deep attention networks. Through an improved model architecture and optimization strategy, the method achieves efficient classification and precise recognition of coal samples. This method uses the attention network as the core to fully explore the potential spectral features in coal samples. Experimental results show that compared with traditional methods, this method has achieved significant improvements in multiple key indicators, verifying its superior performance and application potential. This study not only provides an efficient and reliable solution for coal origin identification, but also provides important support for the intelligent and precise development of the coal industry.