基于料面视频图像分析的高炉异常状态智能感知与识别OA北大核心CSTPCD

The intelligent perception and precise recognition of blast furnace(BF)anomalies are important for BF regulation,optimization and stable operation.However,the opaque nature of the internal workings of the BF makes it difficult for traditional detection methods to directly perceive and accurately recognize various BF anomalies.The novel industrial endoscope can capture a large number of BF burden surface video images,providing a new way for direct observation of the furnace's operational status.Based on this,an intelligent perception and precise recogni-tion method for BF anomalies is proposed via burden surface video image analysis.Firstly,a method for extracting high-temperature gas flow regions based on multi-scale texture fuzzy C-means(MST-FCM)clustering is proposed to accurately obtain gas flow images and extract multi-features of gas flow images.Secondly,a high-dimensional fea-ture dimensionality reduction method based on feature encoding is proposed,which is combined with the adaptive K-means++algorithm to achieve coarse-grained perception of gas flow anomalies.On this basis,a fine-grained per-ception method for gas flow anomalies is proposed by refining Jacobi-Fourier moments(JFM)to extract the deep feature change trend of gas flow images.Finally,based on the perception results of gas flow anomalies,and com-bined with BF video images,a multi-level residual channel attention module(MRCAM)is put forward and the BF anomalies recognition model ResVGGNet is established.This model achieves precise and online recognition of gas flow anomalies,collapsing and hanging burden surface in the BF.Experimental results demonstrate that the pro-posed method can accurately recognize different BF anomalies with a fast recognition speed,providing crucial assur-ance for the smooth operation of the BF.