Miao, Jianjun and Lan, Shundong and Lv, Zhihan (2021) Application of Visual Sensing Image Processing Technology under Digital Twins to the Intelligent Logistics System. Advances in Civil Engineering, 2021. pp. 1-13. ISSN 1687-8086
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Abstract
Since the founding of the People’s Republic of China, the advantages of logistics are neglected, and the scale operation and the welfare in the industry are difficult to achieve due to the influence of the economic system and social environment. Therefore, a new intelligent logistics distribution management system based on machine vision and visual sensor image processing technology is constructed to respond to the shortcomings of the traditional system, including slow efficiency, huge cost, complex data, and low degree of informatization. Through the analysis and research on the visual sensor image processing technology and the order processing, receipt management, distribution management, scheduling management, and return management that affect logistics distribution, a simulation experiment is used to verify that the visual sensor image processing technology is rigorous, intelligent, and efficient and has high precision. The intelligent logistics distribution management system can effectively solve the problems existing in the traditional logistics distribution management. The experimental results show that the visual sensor image processing technology can collect and analyze the target image and effectively track and monitor it in the logistics distribution process. The average distribution precision of the intelligent logistics distribution management system reaches more than 99.5%, which is greatly improved compared with 90% of the traditional logistics distribution. And it can greatly improve the distribution efficiency, which increases by about 26.5%. The study realizes the information management of the logistics system and automatically completes all the work according to the designed program, so that the real-time dynamic distribution can be transmitted to the urban logistics distribution at any time.
Item Type: | Article |
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Subjects: | STM Repository > Engineering |
Depositing User: | Managing Editor |
Date Deposited: | 06 Mar 2023 07:20 |
Last Modified: | 06 May 2024 06:24 |
URI: | http://classical.goforpromo.com/id/eprint/327 |