Application of Data Integration in Dataspace in Multi-value Chain Collaboration of Electric Power Manufacturing Industry

The manufacturing industry is currently in a critical period of intelligent change, and the generation of massive amounts of data makes data management and data integration increasingly important. With the continuous upgrading of data management technology, how to handle diversified data and effectively collect multi-source heterogeneous data while ensuring data security has become the key to intelligent data management in current manufacturing enterprises. This paper analyzes the factors influencing the supply value chain in multi-value chain synergy, taking the external supply value chain of an electric power manufacturing company as an example. The gray correlation method is used to sort out the factors. Then the к-means method is used for data mining and cleaning. A dataset of key factors affecting the external value chain is established, and a data integration architecture for the dataspace of power manufacturing enterprises is constructed and empirically analyzed. The research results show that the data integration architecture can effectively tap into the management potential of power manufacturing enterprises in the external supply value chain and provide information solutions for the operation and management of power manufacturing enterprises.