The design of a real-time monitoring and intelligent optimization data analysis framework for power plant production systems by 5G networks

Abstract

The current power plant production systems face issues such as insufficient monitoring accuracy, data transmission delays, and low energy utilization efficiency. In response, this study proposes a real-time monitoring and intelligent data analysis system based on Fifth-Generation Mobile Communication Network (5G) technology. Building upon an analysis of the limitations inherent in traditional systems, the experiment capitalizes on the extensive connectivity capabilities of 5G to design an intelligent monitoring architecture tailored for power plant production environments. To enhance system performance, the study introduces an innovative resource scheduling and data analysis model that combines an improved Hybrid Advantage Actor-Critic (A3C) algorithm with a Dueling Deep Q-Network (DQN) algorithm. This model integrates the global optimization capabilities of the A3C algorithm with the efficient learning mechanism of the Dueling DQN algorithm to optimize communication resource scheduling and energy storage management within a 5G Cloud Radio Access Network (C-RAN) environment. Simulation experiments demonstrate that this approach significantly improves system energy efficiency, optimizes resource utilization, and reduces energy waste. The results show that data transmission delays decreased by 25%, energy utilization increased by 18.25%, and renewable energy consumption rose by 12.55%. This study offers a new technical approach for the intelligent upgrade and green, efficient operation of power plant production systems, providing both theoretical and practical support for the optimization of power systems in the 5G era.