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Research on enabling technology architecture for collaborative optimization of manufacturing resources

Published:03 May 2024Publication History

ABSTRACT

With the vigorous development of industrial manufacturing and the increasing competition in the global market, the manufacturing industry has gradually shown multi-dimensional characteristics such as diversification of manufacturing resources, customization of customer needs, complexity of production systems, and dynamic management decision-making. The global manufacturing industry has entered a high-level and all-round competition stage. However, regarding the collaborative optimization allocation of manufacturing resources under the Industrial Internet, with the continuous enrichment of smart manufacturing terminals and manufacturing scenarios, traditional research is mostly based on static data and passive stylized response methods, paying less attention to the data resource perception, multi-party dynamic collaboration and foresight. Therefore, a technical architecture for collaborative optimization of manufacturing resources for the industrial Internet is systematically proposed. By dividing logical levels and supporting technologies from the bottom up, the research on active sensing and optimization collaboration of manufacturing resources will be extended from the algorithm level to the system level, and the technical barriers between upper and lower levels will be eliminated. This provides a comprehensive and systematic enabling technical support for the decision-making optimization of top-level intelligent applications and services, ensuring that optimization strategies, multi-source intelligent equipment and other production elements and all levels can be interconnected and cooperate to truly unleash the effectiveness of collaborative algorithms.

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                IoTAAI '23: Proceedings of the 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence
                November 2023
                902 pages
                ISBN:9798400716485
                DOI:10.1145/3653081

                Copyright © 2023 ACM

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                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 3 May 2024

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