Edge Deployment and Real-time Warning: Architecture Design and Optimization of Action Recognition Systems in Power Scenarios

In contemporary society, economic growth and scientific advancements serve as relentless catalysts for ongoing innovation across multiple industries. Notably, the technology sector’s extensive adoption of avant-garde technologies hinges on steadfast and dependable power supply. As the power grid expands and its intelligence level advances, stricter demands arise for real-time safety alerts concerning power grid equipment operating environments. To tackle this, the present article introduces an action recognition system integrating edge deployment and real-time alerts. Leveraging the prowess of deep learning (DL) technology, this system monitors and issues real-time warnings for power grid equipment operating environments using DL models. By embracing edge deployment, it stations computing resources and data processing capabilities directly at power grid equipment sites, significantly enhancing the immediacy and precision of early warnings. Experimental findings reveal that, when compared to conventional centralized monitoring systems, this system boasts superior real-time performance and reduced latency, thereby offering a more robust safeguard for the secure operation of power grid equipment.