Research on Multi-Layer Data Processing Technology of Deep Learning in Intelligent Systems

Abstract

Aiming at the difficulty of processing multi-source heterogeneous data in intelligent systems, a multi-layer data processing method based on deep learning is designed. A hierarchical feature learning framework is developed to realize the feature extraction and fusion of multi-modal data. Experimental data shows that this method achieves an accuracy of 96.3% when processing 3 million industrial data, with a processing delay of less than 15ms. The system adopts a distributed architecture deployment, supporting 500,000 QPS real-time processing capability. The research results have been applied in intelligent manufacturing and other fields, providing new ideas and technical support for improving industrial big data processing efficiency.