Multimodal information fusion of manufacturing digital industrial scene based on federated learning optimization algorithm

The volume of data generated by the manufacturing industry has exploded at an exponential rate in structured, semi-structured and unstructured forms, increasing the risk of errors in the multi-modal data fusion process. To address this challenge, this study proposes an innovative multimodal information fusion strategy aimed at improving data processing capabilities in manufacturing digital industrial scenarios by integrating federated learning optimization algorithms. The multi-modal data in the digital industrial scene of manufacturing industry is collected and redundant components are eliminated. The feature information is extracted from the dimensionally reduced data and input into a multi-modal data fusion framework optimized by federated learning to generate a tensor integrating three key features. The fuzzy set theory is used to guide the local decision process of each module in the framework, and the fuzzy membership degree corresponding to the multi-modal information is calculated to realize the multi-modal information fusion effectively. The experimental results show that the information fusion degree of this method is not less than 0.35, and the fused information has high integrity and consistency. In most cases, the fusion error can be significantly reduced.