Abstract: Provided is a product quality detection method in a complex industrial process fusing prior knowledge. Related data of product production in the complex industrial process is acquired to serve as samples; The acquired samples are preprocessed and a training data set is constructed by the preprocessed samples; a product quality detection model in the complex industrial process fusing prior knowledge is constructed; the product quality detection model is trained by the training data set; and the trained quality detection model is used for product quality detection to obtain product quality. The present invention designs that feature extraction on multiple scales is supported by a multi-scale feature extraction network with convolutional kernels with different sizes, and designs that the extracted features of each process are fused by a multi-process feature fusion method of the prior knowledge, thereby obtaining more accurate feature representation and increasing the production benefit.

Abstract: Provided is a product quality detection method in a complex industrial process fusing prior knowledge. Related data of product production in the complex industrial process is acquired to serve as samples; The acquired samples are preprocessed and a training data set is constructed by the preprocessed samples; a product quality detection model in the complex industrial process fusing prior knowledge is constructed; the product quality detection model is trained by the training data set; and the trained quality detection model is used for product quality detection to obtain product quality. The present invention designs that feature extraction on multiple scales is supported by a multi-scale feature extraction network with convolutional kernels with different sizes, and designs that the extracted features of each process are fused by a multi-process feature fusion method of the prior knowledge, thereby obtaining more accurate feature representation and increasing the production benefit.