The invention discloses a pseudo-
random number generation method and device based on
deep learning and a medium, and relates to the field of
random number generation, the method comprises the following steps: collecting built-in hardware
noise of a target
system, and setting the built-in hardware
noise as a basic entropy source; acquiring
server environment data of the target
system in real time, and generating an auxiliary entropy source based on the environment data; performing normalized mixing on the basic entropy source and the auxiliary entropy source to obtain a mixed entropy
pool; extracting a mixed entropy seed from the mixed entropy
pool, inputting the mixed entropy seed into a trained
deep learning model, and generating a pseudo-random number sequence through the
deep learning model; and converting the pseudo-random number sequence into uniform distribution based on a preset distribution sequence, and carrying out randomness test on the pseudo-random number sequence through a test tool. The method can better adapt to environmental changes through the training model, and can dynamically adjust the characteristics of the generated pseudo-random number sequence according to the input data, thereby generating the pseudo-random number better meeting the current environment and application requirements.