Abstract:

Against the backdrop of the accelerated development of industrial intelligence, industrial data has become a key production factor. However, its low quality, heterogeneity, fragmentation, and semantic disconnection have restricted cross-system applications. This paper proposes a technical architecture for industrial data fusion and interoperability, systematically studying three key technologies: data standardization, data quality processing, and data annotation. It constructs a standardized promotion path, introduces a large model-driven dynamic self-closed-loop quality processing system, and establishes a scenario-oriented annotation evaluation model to achieve controllable and quantifiable assessment of the annotation process. The related research provides methods and technical support for the construction of a high-quality industrial data application environment.

Key words: industrial data, data integration, interoperability, data standardization