Multi-Modal Big Data Modeling and Analysis Techniques for Industrial Internet of Things

The rapid advancement of the Industrial Internet of Things (IIoT) has resulted in the generation of vast amounts of multimodal data within the industrial sector. This data encompasses various levels, domains, and modalities, including sensor data, equipment logs, and environmental information, exhibiting significant fragmentation and heterogeneity. Current technologies face substantial challenges in managing and facilitating the flow of data across different levels, domains, and modalities, hindering the ability to fully leverage the potential value of this data. Furthermore, the long-tail distribution and small sample characteristics of industrial data render traditional data analysis methods insufficient for addressing the complexities inherent in IIoT. Consequently, there is an urgent need for novel big data modeling and analysis techniques capable of efficiently managing, analyzing, and applying multimodal data to enhance data utilization in IIoT. We develope a multimodal big data circulation and management system for the IIoT, enhancing data collection and aggregation capabilities, thereby improving circulation management efficiency. Furthermore, we propose data generation and integration techniques to support the operational and scheduling technologies for multimodal big data in the IIoT, and finally validate our study in real industrial scenarioThe novelty of this paper lies in its comprehensive approach to addressing the challenges posed by the vast, fragmented, and heterogeneous multimodal data generated within the Industrial Internet of Things (IIoT). Unlike traditional data management and analysis techniques that often struggle to handle the complexities of such data, the proposed system offers innovative solutions in data generation and integration techniques and tailored big data analysis for industrial scenarios.