Research on Multimodal Mechanism Data Fusion Technology in Transmission Lines

Aiming at the data diversity and heterogeneity problems in transmission line monitoring, this paper studies the intelligent monitoring technology based on multimodal mechanism data fusion. By fusing multimodal data such as temperature, vibration, and current, a mechanism modeling method suitable for transmission lines is proposed. This paper designs a fusion algorithm with feature extraction, data cleaning, fusion algorithm, and multi-level model training as the core, which optimizes the complementarity and collaborative analysis capabilities of each modal data. To verify the effectiveness of this method, this paper conducts detailed simulation experiments through Matlab and Python tools. The data analysis results show that the algorithm significantly reduces the false alarm rate in the monitoring process while improving the accuracy of fault warning, further verifying the practicality of multimodal data fusion in intelligent monitoring of transmission lines. This study provides new ideas for building a more efficient and intelligent power grid monitoring system.