Intelligent Power Optimization and Allocation Algorithm for Digital Media Content Generation Based on Deep Learning

This paper proposes an intelligent power optimization and allocation algorithm based on the combination of deep learning and reinforcement learning, aiming to reduce power consumption and improve task execution efficiency in the process of digital media content generation. By introducing multi-task scenarios and rich data sets, the proposed model can dynamically predict the power demand of tasks and perform real-time power allocation with high accuracy. Experimental results show that the hybrid deep learning and reinforcement learning model significantly outperforms the traditional static power allocation method and single deep learning model in terms of power consumption and task execution efficiency, reducing the average power consumption by about 15%, shortening the task execution time by about 7%, and achieving a power allocation accuracy of 96.5%. The algorithm is applicable to various digital media content generation tasks, including video rendering, image processing, 3D modeling, and audio synthesis, providing an effective solution for intelligent power management and energy-saving applications, and has broad application prospects.