Research on Quality Monitoring Platform and Resource Scheduling Algorithm Based On Machine Vision

The quality monitoring platform and resource scheduling algorithm based on machine vision are currently the research areas of concern in the manufacturing and logistics industries. It is targeted to the industry and the actual production environment with the combination of manufacturing enterprises....

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Vydáno v:Procedia computer science Ročník 259; s. 1064 - 1071
Hlavní autor: Han, Wei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 2025
Témata:
Machine Vision
Minimize Cost
Quality Monitoring Platform
Resource Scheduling Algorithm
Quality Monitoring Platform
Minimize Cost
Machine Vision
Resource Scheduling Algorithm
ISSN:1877-0509, 1877-0509
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Shrnutí:The quality monitoring platform and resource scheduling algorithm based on machine vision are currently the research areas of concern in the manufacturing and logistics industries. It is targeted to the industry and the actual production environment with the combination of manufacturing enterprises. It is proposed to apply automated image processing and machine learning algorithms to the quality monitoring platform, which not only improves the inspection accuracy, but also reduces the defect detection cost of the product. To this end, the research in this paper stems from relevant literature and empirical research, focusing on the working principle and key technologies of a quality monitoring platform and resource scheduling algorithm based on machine vision. The research methods mainly include: data acquisition, image processing, feature extraction, classification algorithm research and resource scheduling strategy. The research results show that the quality monitoring platform based on machine vision has high inspection accuracy and stability, and can effectively extract product features and detect defects. At the same time, the resource scheduling algorithm can optimize resource scheduling and human power allocation, improve production efficiency and production consistency.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2025.04.060