An Online Blast Furnace Condition Recognition Method Based on Spatiotemporal Texture Feature Coupling and Diffusion Networks

Real-time and accurate identification of the blast furnace (BF) condition is essential for maintaining stability and improving energy efficiency in steelmaking. However, the harsh environment inside the BF makes direct acquisition of the BF condition extremely difficult. To address this challenge, this study proposes an online BF condition recognition method based on spatiotemporal texture feature coupling and diffusion networks (STFC-DN). The method employs a multi-domain Swin-Transformer module (MDSTM) combined with wavelet decomposition and channel attention to extract the gas flow region. A temporal feature pyramid network module (T-FPNM) is then used to capture both the global and local spatiotemporal characteristics of this region. Heuristic clustering and an idempotent generative network (IGN) are introduced to obtain standardized BF condition features, enabling intelligent classification through multi-metric similarity analysis. Experimental results show that the proposed STFC-DN achieves an average accuracy exceeding 98% when identifying four BF conditions: normal, hanging, oblique stockline, and collapsing, with an inference speed of approximately 28 FPS. This approach demonstrates both high accuracy and real-time capability, showing strong potential for advancing the intelligent and sustainable development of the steel industry.