Research on Real-Time Data Processing System Combining Edge Computing and Artificial Intelligence

With the exponential growth of Internet of Things (IoT) devices, vast amounts of diverse data require real-time processing. However, traditional cloud computing approaches often struggle to meet these demands due to issues related to latency and bandwidth constraints. As an emerging computing paradigm, edge computing significantly reduces data transmission latency and improves processing efficiency. In this paper, a real-time data processing system combining edge computing is proposed to realize efficient processing and intelligent analysis of distributed data. Firstly, this paper systematically analyzes the potential advantages and challenges of edge computing and artificial intelligence in real-time data processing, and designs a system architecture that integrates them. The architecture consists of four main modules: data acquisition, edge processing, intelligent analysis and feedback control, aiming to realize real-time data processing with low latency and high performance. Specifically, edge devices perform preliminary data screening and pre-processing by deploying lightweight artificial intelligence algorithms; Then, the optimized deep learning model performs complex data analysis and prediction on the edge nodes; Finally, the processing results are fed back to the data source in time to achieve closed-loop control. Compared with the traditional cloud computing model, the system combining edge computing and artificial intelligence shows significant advantages in terms of latency, bandwidth utilization and processing throughput.