Real-time Data Processing and Intelligent Monitoring System of Ferrite Magnetic Sensors under Edge Computing Architecture

In the field of modern industrial monitoring, the traditional centralized computing architecture is difficult to meet the real-time and reliability requirements of ferrite magnetic sensors in complex industrial environments. This study proposes a real-time data processing and intelligent monitoring system for ferrite magnetic sensors based on edge computing architecture. The system adopts a four-level modular design. The edge perception layer deploys lightweight embedded nodes (ARM Cortex-M7 + ferrite sensor), the edge computing layer builds a distributed processing unit based on the KubeEdge edge containerization framework, the cloud collaboration layer dynamically updates the edge model parameters through federated learning, and the visualization platform integrates the time series database (TDengine) and the low-code dashboard (Node-RED). The maximum delay of the edge computing architecture is 395 milliseconds, the minimum delay is 217 milliseconds, and the average data processing throughput is about 10549 items/second. The system improves the monitoring performance of ferrite magnetic sensors.