电气工程自动化中多传感器数据融合技术的研究

Key Points

• The aim is to explore the theoretical framework and key technologies of multi-sensor data fusion in electrical engineering automation.
• Systematic review of multi-sensor data fusion theories and applications in electrical engineering.
• Analysis of mainstream fusion algorithms including Kalman filtering, Dempster-Shafer theory, and deep learning methods.
• Identification of challenges in heterogeneous data synchronization and real-time processing.
• Outlined main technologies and implementation pathways for typical applications like power system monitoring and fault diagnosis.
• Comparative analysis of the performance of different fusion algorithms under defined conditions.
• Proposed future research directions addressing real-time constraints, security, and data synchronization.