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Research on the Countermeasures of Digital Intelligence Transformation and Upgrading of Innovative Manufacturing Enterprises

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Business Intelligence and Information Technology (BIIT 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 107))

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Abstract

In China, the rapid development of the digital economy has brought transformation opportunities to innovative manufacturing enterprises. New-generation information technologies such as big data, 5G, and artificial intelligence provide powerful tools for transforming digital intelligence and upgrading innovative manufacturing enterprises. In this paper, the concept and characteristics of innovative manufacturing enterprises are introduced, and the economic transformation process of innovative manufacturing enterprises is divided into five main stages. Secondly, from the data-driven perspective, starting from the development needs of innovative manufacturing enterprises, a digital intelligence platform and data mining framework are built for them. Finally, the transformation countermeasures of innovative manufacturing enterprises are given. This paper helps the innovative manufacturing enterprises scientifically complete the transformation and upgrading from digitization to digital intelligence. It provides a new perspective and ideas for the deep integration of the digital economy and real economies’ deep integration.

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Acknowledgment

This work was supported by the grants of the Heilongjiang Social Science (Evolution path and development countermeasures of big data industry ecosystem in Heilongjiang Province: 20JYB037).

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Li, Y., Zhai, L., Yang, C., Liu, K. (2022). Research on the Countermeasures of Digital Intelligence Transformation and Upgrading of Innovative Manufacturing Enterprises. In: Hassanien, A.E., Xu, Y., Zhao, Z., Mohammed, S., Fan, Z. (eds) Business Intelligence and Information Technology. BIIT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-030-92632-8_11

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