The invention discloses a multi-
modal code
stream transmission bandwidth dynamic allocation optimization method, which comprises the steps of firstly performing
feature extraction on to-be-transmitted multi-
modal data including images and voiceprints, calculating the number of features, further considering the influence of feature similarity among the multi-
modal data on transmission marginal benefits, and optimizing the
transmission bandwidth dynamic allocation of the multi-modal code
stream. The suitability of an analysis result and a real environment is improved; a
time sequence neural network is utilized to calculate a code
stream demand predicted value, a
bandwidth allocation optimization model with maximization of bandwidth transmission efficiency as an optimization target is constructed according to image feature quantities and voiceprint feature quantities, the optimization model is further modeled as a
decision process, a deep actor-commentator
algorithm is utilized to solve the
decision process, and the
decision process is optimized. A playback experience
pool in a decision process is constructed by utilizing prediction deviation of a
time sequence neural network, so that the
algorithm learns an optimal strategy of each parameter under different prediction precisions, the adaptability of the
bandwidth allocation method to different monitoring scenes is enhanced, and the problem of sparse training samples is solved.