The invention relates to the technical field of
color printing product quality detection, and discloses a green
color printing product defect intelligent diagnosis method based on
deep learning. Visible light, hyperspectral and
infrared thermal
imaging data of a green
color printing product are collected, multi-scale
feature extraction is carried out, defect feature vectors such as surface texture,
chromatic aberration distribution and heat conduction abnormity are obtained, and a cross-
modal attention mechanism is utilized to fuse the defect feature vectors into a unified defect representation vector. And determining a defect type and a repair scheme through a dynamic classification
algorithm in combination with the defect
knowledge base. The
algorithm comprises the steps of generating an initial candidate defect set, constructing a
topological graph, optimizing defect type judgment and the like. A multi-dimensional
anomaly detection model is adopted to process sudden defects, and a
causal inference engine is utilized to adaptively adjust a repair scheme. And constructing a defect rule
knowledge graph to verify the compliance of the repair scheme. The method is accurate in detection, intelligent in diagnosis and efficient in repair, and effectively improves the quality detection and repair level of the green color printing product.