Research on High-Efficiency Optimization and Real-Time Response of Embedded Computer Systems in Intelligent Electronic Devices

The optimization of real-time performance in embedded computer systems of intelligent electronic devices presents significant challenges due to dynamic workloads, energy constraints, strict latency requirements, and operational cost considerations. Conventional static or heuristic-based methods often fail to simultaneously satisfy these competing objectives, leading to suboptimal system performance, increased energy consumption, and reduced responsiveness. To address these limitations, this study proposes a Deep Reinforcement Learning (DRL) framework that leverages agent-based strategies, including Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), to dynamically manage computational resources and adjust operational parameters in real time. The DRL agent continuously observes system states, executes actions, and learns from rewards that quantify throughput, energy efficiency, latency, and overall stability. Extensive simulation results demonstrate that the proposed DRL approach significantly outperforms conventional methods such as Energy-Aware Task Scheduling (EATS), Dynamic Voltage and Frequency Scaling (DVFS), and standard RL, achieving notable improvements in system efficiency, task processing speed, energy consumption, and reliability. By adaptively balancing performance and energy trade-offs, the DRL framework enables intelligent devices to maintain high throughput while minimizing latency and power usage, offering a robust solution for modern embedded applications operating under dynamic and constrained conditions.