In order to improve the energy consumption management efficiency of the embedded real-time system and reduce the impact of traditional dynamic voltage scaling technology on system stability, a dynamic energy consumption optimization scheme supported by whale algorithm based on historical cognition is proposed. Firstly, a nonlinear dynamic convergence factor control strategy is proposed, which can effectively accelerate the convergence speed of the algorithm. Secondly, using the historical optimum solutions as interference factors, a hybrid guided strategy is designed in the constriction and envelopment mechanism to balance the local development and global search capability of the algorithm. Finally, the frequency characteristics of the processor can be changed in real time according to the dynamic voltage scaling technology, the tasks 10, 30 and 50 are optimized by the algorithm, so as to verify the effectiveness of the improved algorithm.