The invention belongs to the technical field of
nuclear graphite decommissioning treatment, and particularly relates to a decommissioning
nuclear graphite potential prediction method and
system based on multi-
modal vision, and the method comprises the steps: collecting the multi-
modal image data of the surface morphology, temperature distribution,
radiation intensity and internal structure of decommissioning
nuclear graphite, carrying out the preprocessing, and carrying out the prediction of the potential of the decommissioning nuclear
graphite. A
deep learning model is utilized to extract key features, a
feature fusion technology is adopted to generate a multi-dimensional feature map, a key region is focused in combination with an attention mechanism, the attention of the model on the features is enhanced, then a
feature extraction network is constructed through a U-Net
image segmentation algorithm for training, and the optimized model can display the state and prediction result of nuclear
graphite in real time. And an appropriate
waste treatment scheme is selected in an assisted manner. According to the method, the limitation of a traditional single detection mode is overcome, the potential of the decommissioning nuclear
graphite is accurately predicted through multi-dimensional data fusion and intelligent analysis, and the safety of the
nuclear reactor decommissioning process is improved.