Research on big data processing and analysis technology in automation systems

The rapid development of industrial automation systems has generated massive industrial data volumes. To address the complexity of big data processing and analysis in automation systems, this research proposes a multi-dimensional data analysis framework based on distributed computing architecture. The framework implements an enhanced MapReduce algorithm for heterogeneous data processing and performs in-depth analysis of industrial process data through sequential stages of data preprocessing, feature extraction, and pattern recognition. Experimental validation demonstrates that this framework significantly improves data processing efficiency, achieving 95.8% accuracy with an 18.3% enhancement over conventional methodologies. The framework exhibits notable advantages in application scenarios including equipment failure prediction and production quality control, thereby providing technical foundation for intelligent upgrades of automation systems.