Real Time Data Fusion and Intelligent Decision-Making Algorithm for Low-Voltage Distribution Network Based on Cloud Edge Collaboration

As the distribution network scale expands and intelligence levels improve, network edge devices are generating an ever-increasing amount of data and computing, causing significant stress on data storage, cloud computing, and transmission bandwidth. Traditional cloud computing models struggle with real-time data analysis, processing, and response due to transmission delays and resource constraints, leading to non-real-time information flow handling that undermines the stability of energy flow and control reliability. Addressing these challenges, this article introduces a real-time data fusion and intelligent decision-making algorithm tailored for low-voltage distribution networks, leveraging cloud-edge collaboration. By harnessing the strengths of both cloud and edge computing, this algorithm ensures real-time data capture, processing, analysis, and decision-making. Experimental results demonstrate that our proposed algorithm not only resolves the real-time limitations of traditional cloud computing models but also enhances the intelligence and operational efficiency of distribution networks, thus bolstering the stable operation and sustainable growth of power systems.