A Resource Storage Model and Supply-Demand Matching Method Based on Cloud Manufacturing

This paper analyzed on real-time database system, and applied the cloud manufacturing technology and distributed system technology to the database system model. Through the analysis on the real time database, we can got the distributed real-time data system types and data storage model, establish two kinds of memory T-mode and S-mode, database storage model and make the production of block. Finally establish the work flow of distributed real-time database system, get relevant application program. A large number of real-time data collection and administration can be realized, which can improve the real-time information data processing efficiency and system model performance. Through the establishment of model and analysis of its working process in the real-time distributed system real time database application path, it can provide guidance for the establishment of distributed real-time database system model.

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Research Progress of Cloud Manufacturing in China: A Literature Survey

Volume 2: Systems; Micro and Nano Technologies; Sustainable Manufacturing ◽

10.1115/msec2013-1168 ◽

2013 ◽

Cited By ~ 3

Author(s):

Qun Lin ◽

Kai Xia ◽

Lihui Wang ◽

Liang Gao

Keyword(s):

Combinatorial Optimization ◽

Cloud Manufacturing ◽

Literature Survey ◽

Research Progress ◽

Future Research ◽

Trust Evaluation ◽

Matching Method ◽

Broad Perspective ◽

Academic Researchers ◽

Intelligent Searching

Cloud manufacturing has been of considerable interest to Chinese academic researchers over the last decade. This paper presents a broad perspective of the research on cloud manufacturing in China. The topics studied mainly include design of cloud manufacturing architecture, resource and capability virtualization, combinatorial optimization of virtual resource and capability, design and collaboration of cloud manufacturing services, intelligent searching and matching method and trust evaluation. The present literature survey also includes two successful cases applying cloud manufacturing in China to verify the feasibility of the cloud manufacturing architecture and services. Potentially interesting directions for future research in this area are also identified.

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A classification matching method for manufacturing resource in cloud manufacturing environment

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s179396231750057x ◽

2017 ◽

Vol 08 (02) ◽

pp. 1750057 ◽

Cited By ~ 2

Author(s):

Wei-Jiao Feng ◽

Chao Yin ◽

Xiao-Bin Li ◽

Liang Li

Keyword(s):

Description Logic ◽

Cloud Manufacturing ◽

Matching Method ◽

Information Matching ◽

Implementation Stages ◽

Service Oriented ◽

Geographically Distributed ◽

Manufacturing Resource ◽

Dynamic Description ◽

Demand Classification

Cloud manufacturing (CMfg), combining the idea and technologies of cloud computing and Internet of Things, is an emerging service-oriented manufacturing model. The supply–demand matching of manufacturing resources is one of the key technologies for implemention. However, resources in CMfg system are geographically distributed, functional of similar and dynamically changeable, and these features make it difficult to obtain higher accuracy for existing matching methods. In order to select the most satisfied resources in CMfg, a semantics-based supply–demand classification matching method (SDCM) is proposed. Firstly, the implementing framework of SDCM is constructed. Then, combined with the theories of ontology and dynamic description logic, a semantics-based SDCM algorithm is designed, which includes four implementation stages, respectively, basic information matching, IOPE parameters (Input, Outputs, Preconditions, Effects) matching, QoS (Quality of Service) matching and comprehensive matching. Finally, a case verifies the feasibility and effectiveness of the proposed method.

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Research on Task-Service Network Node Matching Method Based on Multi-Objective Optimization Model in Dynamic Hyper-Network Environment

Micromachines ◽

10.3390/mi12111427 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1427

Author(s):

Chenglei Zhang ◽

Jiajia Liu ◽

Hu Han ◽

Xiaojie Wang ◽

Bo Yuan ◽

...

Keyword(s):

3D Printing ◽

Optimization Model ◽

Cloud Manufacturing ◽

Network Node ◽

Network Environment ◽

Multi Objective Optimization ◽

Matching Method ◽

Service Network ◽

Multi Objective ◽

Mathematical Algorithm

In order to reduce the cost of manufacturing and service for the Cloud 3D printing (C3DP) manufacturing grid, to solve the problem of resources optimization deployment for no-need preference under circumstance of cloud manufacturing, consider the interests of enterprises which need Cloud 3D printing resources and cloud platform operators, together with QoS and flexibility of both sides in the process of Cloud 3D printing resources configuration service, a task-service network node matching method based on Multi-Objective optimization model in dynamic hyper-network environment is built for resource allocation. This model represents interests of the above-mentioned two parties. In addition, the model examples are solved by modifying Mathematical algorithm of Node Matching and Evolutionary Solutions. Results prove that the model and the algorithm are feasible, effective and stable.

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