The application belongs to the technical field of
tactile communication and intelligent human-computer interaction, and discloses a multi-point tactile intelligent encoding and decoding device,
system and method. An acquisition end obtains original tactile signals, and extracts features through a neural network self-
encoder. A weighted fusion module based on an attention mechanism dynamically groups and quantizes residual errors of the encoded features in combination with
channel state information (CSI), so as to realize low-code-rate compression transmission. A
perception end inversely quantizes received data and reconstructs signals, and optimizes output tactile signals in combination with a multi-scale
perception calibration mechanism, so that the output tactile signals are closer to original inputs in the human
perception dimension. A multi-point tactile feedback device obtains vibration feedback corresponding to real-time multi-channel vibration tactile signals based on the reconstructed signals. The application realizes efficient compression, low-
delay transmission and high-quality reconstruction of tactile signals by fusing a
deep learning architecture and a psychological
tactile perception model, and is suitable for
virtual reality,
remote operation, medical robots and other high-precision tactile interaction scenes.