基于深度学习的通信电源效能优化方法

Optimization Method for Communication Power Supply Efficiency Based on Deep Learning

分析通信系统的电源管理需求和挑战,提出智能电源管理、数据驱动的能效评估、可再生能源集成、实时热管理以及能源储存和备份等电源效能优化建议,通过深度学习模型实现电源的实时调整。使用深度学习技术,实现动态电源调整,降低功耗并提高性能。可再生能源的集成减少了对传统能源的依赖,实时热管理降低了过热风险。基于深度学习的通信电源效能优化方法潜力巨大,提高了能效、性能和可持续性。在实施过程中,还需要充分考虑系统需求,改进硬件和软件系统。

The article analyzes the power management needs and challenges of communication systems,and proposes power efficiency optimization recommendations such as intelligent power management,data-driven energy efficiency assessment,renewable energy integration,real-time thermal management,and energy storage and backup,and real-time power tuning through deep learning models.Using deep learning techniques,dynamic power tuning is realized to reduce power consumption and improve performance.Renewable energy integration reduces dependence on traditional energy sources,and real-time thermal management reduces the risk of overheating.Deep learning-based optimization methods for communication power efficiency have great potential to improve energy efficiency,performance and sustainability.The implementation process also requires full consideration of system requirements and improvement of hardware and software systems.