The application discloses a
movable type printing plate number spraying
robot collaborative control method based on
deep learning, relates to the technical field of
machine vision, and comprises the following steps: S1, outputting a hole space topology graph; S2, outputting a space-time
characteristic matrix; S3, through improving a TDN model, based on a graph Laplacian spectrum transverse
diffusion constraint and a dynamic anti-disturbance hedging principle, combinedly extracting fluid transverse
diffusion and longitudinal accumulation evolution characteristics and dimensionally outputting a space-time deformation prediction
tensor; S4, generating a targeted defect gradient
tensor; S5, generating an inhibition
instruction data packet; S6, outputting a anti-muddle character spraying ready
signal; and S7, executing a spraying operation. The application overcomes the limitations of fluid response
lag,
physical space structure
distortion and neglecting
dynamic balance constraints of a traditional method, and provides an efficient solution for
movable type printing plate number spraying
robot collaborative control.