An Effective Resource Matching Scheme Based on a Novel Unified Descriptive Model for Modern Manufacturing Industry Systems

In order to effectively solve the problem of heterogeneous design/manufacturing/service resources and isolation in the whole lifecycle and realize unified description of design/manufacturing/service resources and resource sharing across subjects and stages, this paper proposes a hierarchical and mod...

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Bibliographic Details
Published inElectronics (Basel) Vol. 11; no. 8; p. 1187
Main Authors Lin, Jie, Wu, Chunxue, Yared, Rami
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2022
Subjects
Algorithms
Classification
Cloud computing
Cognition & reasoning
Data replication
Efficiency
Entropy (Information theory)
Image retrieval
Knowledge representation
Manufacturing
Matching
Ontology
Programming languages
Resource sharing
Semantics
Set theory
System effectiveness
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ISSN2079-9292
2079-9292
DOI10.3390/electronics11081187

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Summary:In order to effectively solve the problem of heterogeneous design/manufacturing/service resources and isolation in the whole lifecycle and realize unified description of design/manufacturing/service resources and resource sharing across subjects and stages, this paper proposes a hierarchical and modularized ontology-based resource-unified descriptive model, according to the characteristics of design/manufacturing/service resources. We analyze all kinds of properties of the resources, design a specific descriptive model of ontology, function, and service, ensure the consistency and independence of resource descriptions, and use the OWL (Web Ontology Language) ontology descriptive language and Protégé tools to verify. Then, based on the unified descriptive model, a resource matching method based on multi-level tags is proposed, which matches the task request with the resources in the resource library, selects the resources that meet the task request, and guarantees the resource sharing across subjects and stages. The resource matching work first performs task description and decomposition, and uses information entropy and rough set theory to sort the importance of subtasks, then uses the semantic similarity algorithm to complete multi-level tags’ matching. Finally, two examples are used to prove the feasibility and effectiveness of the experiment.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11081187