Intelligent control system for digital instruments based on deep convolutional neural networks and edge computing

With the continuous advancement of digital and intelligent technologies, the application of digital instruments in industrial automation and intelligent control has become increasingly widespread. However, traditional centralized computing architectures often face challenges such as computational delays and bandwidth limitations when handling large-scale data processing and real-time control tasks, making it difficult to meet real-time requirements. To address this issue, this paper proposes an intelligent control method for digital instruments that combines deep convolutional neural networks (CNNs) and edge computing. By offloading data processing and model inference tasks to edge nodes, the method avoids the latency associated with transmitting data to the cloud, thereby enhancing the system's real-time response capability. The trained CNN model is deployed on edge devices, enabling rapid device state assessment and control feedback through local inference. Experimental results demonstrate that the proposed method outperforms traditional machine learning methods in terms of accuracy, response time, and computational efficiency, with significant advantages in high real-time and low-latency scenarios. This research provides a practical solution for the efficient operation of intelligent control systems for digital instruments and holds broad application prospects.