JOURNAL ARTICLE
Industrial internet process management and platform construction in intelligent manufacturing based on blockchain.
Published In: Advanced Control for Applications, 2024, v. 6, n. 4. P. 1 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Zhang, Yan; Xiang, Luoyong; Chen, Wen 3 of 3
Abstract
The current lack of effective means for information technology to penetrate into the intelligent manufacturing process of products has led to its disconnection from the core links of industrial systems and low efficiency. Therefore, a nonstandard mechanical product lifecycle model and an industrial internet platform data access mechanism have been proposed using blockchain technology, and an industrial internet platform has been constructed based on the two, which has been experimentally verified. The experimental results showed that the calculation methods in the model could overall reduce the average storage of abnormal data in blockchain products by 53.6%. In addition, the maximum time cost of scalar multiplication did not exceed 20 ms, which was lower than that of bi‐linear pairing. In the comparison of different schemes, the highest encryption computation cost of the research scheme did not exceed 100 ms, and the highest decryption computation cost did not exceed 200 ms, which was generally lower than the comparison scheme. At the same time, the max, mini, and average time consumption of encrypting order data during decryption operations were 5.74, 1.10, and 2.77 s, respectively. When the simulation request reached 2000, there were only a few exceptions on the platform server, indicating that it could support at least 2000 simultaneous requests from applications. Overall, the industrial internet process management platform constructed through research has high practicality and can meet the actual needs of intelligent manufacturing enterprises. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Advanced Control for Applications. 2024/12, Vol. 6, Issue 4, p1
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:25780727
- DOI:10.1002/adc2.180
- Accession Number:181778192
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