A Research and Application of Key Technologies for Multi-source Heterogeneous Data in Intelligent Manufacturing

Abstract

Currently, most enterprises have adopted information software and digital equipment and gradually established digital factories. They conduct enterprise data collection and decision-support activities, generating large volumes of multi-source heterogeneous data across all stages of the product life cycle. However, current data utilization methods remain simplistic, and the goal of leveraging multi-source heterogeneous data to drive manufacturing value has yet to be fully realized. To address this issue, this study first defines the concept and characteristics of multi-source heterogeneous data in intelligent manufacturing, based on an analysis of its relationship with industrial big data. Then, integrating principles from data science, a technological framework for multi-source heterogeneous data is proposed. The key technologies involved in each stage of data processing are investigated, and typical applications of such data in intelligent manufacturing are discussed. Finally, this paper analyzes the challenges and future development directions of multi-source heterogeneous data processing in intelligent manufacturing. The goal is to provide theoretical and technical support for integrating intelligent manufacturing with data science.