Knowledge-Enhanced Recommendation System Design for Mechatronics IoT Cloud Platform

In recent years, with the rapid development of the Internet and the continuous maturation of artificial intelligence technology, mechatronics has embraced the Internet of Things and is progressively advancing towards digitalization and intelligentization. The exponential growth of the network has facilitated seamless sharing and communication of information across the mechatronics engineering field and other industrial sectors, resulting in the accumulation of extensive multimodal data comprising text, images, and electromagnetic waves. However, conventional recommendation algorithms have predominantly focused on textual information, overlooking the valuable insights present within multimodal data. Additionally, mechatronics encounters challenges pertaining to information transmission, processing, and security. To tackle these challenges, we propose a knowledge-enhanced recommendation system based on a mechatronics-oriented Internet of Things cloud architecture. This system harnesses the rich multimodal information generated within the mechatronics domain, employs entity recognition and mapping techniques to integrate images and textual data into a comprehensive knowledge graph, and employs a multi-hop cross-modal attention mechanism to significantly enhance product recommendations and “virtual assembly line construction.” Furthermore, the system facilitates key technological research in the Internet of Things for the mechatronics industry, enables the development of large-scale distributed application software and products, ensures control information security, and facilitates product inspection services. It also fosters close collaboration with relevant research projects and technical services, providing technological support for the continuous improvement of quality and efficiency in the mechatronics industry in Xinjiang.