Composition modeling for manufacturing resource cloud service

The lack of semantic information of description model and quality of service (QoS) constraints in manufacturing resource cloud service (MRCS) has caused problems such as uneven distribution, small sharing scope and low standardization of manufacturing resources. The paper studies composition modeling of MRCS for this problem. Firstly, a modeling method for MRCS based on semantic extension is proposed. The formalization method is used to classify manufacturing resources, and the 6-layer semantic information of function, interface, execution, state, QoS and physical-virtual resource mapping is attached to the MRCS, and a data model based on eXtensible Markup Language and a semantic model based on ontology are constructed. Then a composition method for MRCS with multi-constraint of QoS is proposed. A composition framework and process for cloud services with multi-constraint of QoS is planned. A planning method based on hierarchical task network (HTN) is used to decompose manufacturing tasks into atomic tasks. The manufacturing task is decomposed into atomic tasks and used as service requirements by a planning method based on HTN, and multiple independent and related cloud services are composed into a new cloud service according to application needs through interface semantic matching. The attribute weight of the scheme for MRCS is comprehensively calculated based on quality function deployment. The composite cloud service corresponding to each atomic task is used as an independent component service, and the interface semantic matching operation is repeated according to the workflow of the atomic task to form a complete composite cloud service with more powerful functions to realize the manufacturing task.