CN103714050B - Resource modeling framework for manufacturing multi-layer cloud with multi-granularity characteristic - Google Patents

Resource modeling framework for manufacturing multi-layer cloud with multi-granularity characteristic Download PDF

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CN103714050B
CN103714050B CN201310689629.7A CN201310689629A CN103714050B CN 103714050 B CN103714050 B CN 103714050B CN 201310689629 A CN201310689629 A CN 201310689629A CN 103714050 B CN103714050 B CN 103714050B
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李小平
刘宁
朱夏
张跃
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Southeast University
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Abstract

本发明公开了一种具有多粒度特性的多层云制造资源建模框架,为云制造环境中异构制造资源提供统一描述规范,实现资源全局共享并提高资源利用率。此资源建模框架共分为三层,从下至上依次是资源模型层、功能模型层、语义元模型层。资源模型层与功能模型层通过多对多映射关系进行关联,使得资源与其对应的功能特性松散耦合在一起,提高建模框架的灵活性。功能模型层与语义元模型层通过转换关系进行关联,使得资源描述具有准确的语义。

The invention discloses a multi-layer cloud manufacturing resource modeling framework with multi-granularity characteristics, which provides a unified description specification for heterogeneous manufacturing resources in a cloud manufacturing environment, realizes global resource sharing and improves resource utilization. This resource modeling framework is divided into three layers, which are resource model layer, functional model layer and semantic metamodel layer from bottom to top. The resource model layer and the function model layer are associated through a many-to-many mapping relationship, which makes the resources and their corresponding functional characteristics loosely coupled together, and improves the flexibility of the modeling framework. The functional model layer and the semantic meta-model layer are associated through the conversion relationship, so that the resource description has accurate semantics.

Description

具有多粒度特性的多层云制造资源建模框架A multi-layer cloud manufacturing resource modeling framework with multi-granularity characteristics

技术领域technical field

本发明涉及一种在云制造环境中为资源语义描述而实现的具有多粒度特性的多层云制造资源建模框架,属于制造资源描述技术领域。The invention relates to a multi-layer cloud manufacturing resource modeling framework with multi-granularity characteristics implemented for resource semantic description in a cloud manufacturing environment, and belongs to the technical field of manufacturing resource description.

背景技术Background technique

资源描述是资源发现与资源选择的基础,资源描述的充分性与准确性决定了资源利用率的高低,因此资源描述是云制造关键问题之一。资源描述规范依赖于有效的资源建模框架,当前主要的资源建模框架设计技术包括基于标准的资源建模框架设计、基于本体的资源建模框架设计。Resource description is the basis of resource discovery and resource selection. The adequacy and accuracy of resource description determines the level of resource utilization. Therefore, resource description is one of the key issues in cloud manufacturing. The specification of resource description depends on an effective resource modeling framework. The current main resource modeling framework design technologies include standard-based resource modeling framework design and ontology-based resource modeling framework design.

基于标准的资源建模框架设计方法主要采用web 服务相关的一系列标准,例如:应用WSDL对web服务进行描述,通过UDDI对服务进行注册,基于关键字方法对服务进行查找。但这种资源描述方式缺乏机器可理解的资源语义,资源发现不全面、不准确、导致资源利用率较低。The standard-based resource modeling framework design method mainly uses a series of standards related to web services, such as: using WSDL to describe web services, registering services through UDDI, and searching services based on keyword methods. However, this resource description method lacks machine-understandable resource semantics, and resource discovery is incomplete and inaccurate, resulting in low resource utilization.

基于本体的资源建模框架设计方法从多个方面改善了基于标准的资源建模框架设计方法的不足。例如:OWL-S提供了资源描述的语义信息,为自动化资源发现与组合提供了基础。WSMO使用状态机对资源行为进行建模,实现资源内部能力描述与资源组合之间的能力编排。Ontology-based resource modeling framework design method improves the deficiency of standard-based resource modeling framework design method from many aspects. For example: OWL-S provides semantic information of resource description, and provides a basis for automatic resource discovery and combination. WSMO uses a state machine to model resource behavior, and realizes capability orchestration between resource internal capability description and resource combination.

现有的资源建模框架无法直接应用于对云制造资源的描述中,因为云制造资源具有数量多、种类多、异构、动态、多粒度等特性。从资源信息的充分性而言,基于标准的资源建模框架缺乏对云制造资源多粒度功能特性以及云制造资源所特有的QoS信息的描述。从资源语义的准确性而言,基于本体的资源建模框架仅提供了对资源功能特性输入、输出信息的语义标注,缺乏对资源上下文信息以及资源间互操作信息的语义描述。因此,本发明基于以上不足,设计一种具有多粒度特性的多层云制造资源建模框架。Existing resource modeling frameworks cannot be directly applied to the description of cloud manufacturing resources, because cloud manufacturing resources have characteristics such as large number, variety, heterogeneity, dynamics, and multi-granularity. From the adequacy of resource information, the standard-based resource modeling framework lacks the description of the multi-granularity functional characteristics of cloud manufacturing resources and the specific QoS information of cloud manufacturing resources. In terms of the accuracy of resource semantics, the ontology-based resource modeling framework only provides semantic annotations of input and output information of resource functional characteristics, and lacks semantic descriptions of resource context information and resource interoperability information. Therefore, based on the above deficiencies, the present invention designs a multi-layer cloud manufacturing resource modeling framework with multi-granularity characteristics.

发明内容Contents of the invention

发明目的:本发明提供一种具有多粒度特性的多层云制造资源建模框架,为云制造资源描述提供必要的信息与准确的语义。从不同抽象层次展现云制造资源的多粒度特性,多层资源建模框架可以适应云制造环境中所需资源的动态特性和不同功能的抽象层次表达,从而可以实现资源共享。Purpose of the invention: The present invention provides a multi-layer cloud manufacturing resource modeling framework with multi-granularity characteristics, which provides necessary information and accurate semantics for cloud manufacturing resource description. The multi-granularity characteristics of cloud manufacturing resources are displayed from different abstraction levels, and the multi-layer resource modeling framework can adapt to the dynamic characteristics of the required resources in the cloud manufacturing environment and the abstract level expression of different functions, so that resource sharing can be realized.

技术方案:一种具有多粒度特性的多层云制造资源建模框架,可以在云制造环境中实现分布的各资源提供资源抽象和共享。此模型框架共分为三层,分别是底层的资源模型层,中层的功能模型层和高层的基于语义的元模型层。最底层描述各种物理资源,它是云制造的基础,由地理位置分布的企业中所拥有的异构资源组成;中间层描述资源所具有的各种功能,为云制造平台提供统一的资源功能视图;最高层定义资源相关的概念以及资源之间的关系,为资源应用提供充分的语义信息,促进协同企业之间的语义互操作。Technical solution: a multi-layer cloud manufacturing resource modeling framework with multi-granularity characteristics, which can provide resource abstraction and sharing for distributed resources in the cloud manufacturing environment. This model framework is divided into three layers, which are the resource model layer at the bottom layer, the functional model layer at the middle layer and the meta-model layer based on semantics at the high layer. The lowest layer describes various physical resources, which is the basis of cloud manufacturing, and is composed of heterogeneous resources owned by geographically distributed enterprises; the middle layer describes various functions of resources, and provides unified resource functions for the cloud manufacturing platform View; the highest layer defines resource-related concepts and the relationship between resources, provides sufficient semantic information for resource applications, and promotes semantic interoperability between collaborative enterprises.

具有多粒度特性的多层云制造资源建模框架包括以下几个关系:The multi-layer cloud manufacturing resource modeling framework with multi-granularity features includes the following relationships:

多对多映射关系,关联资源模型层与功能模型层,通过将云制造资源与其对应的功能特征解耦,提高资源建模的灵活性,资源可以动态的加入或者退出云制造系统,符合云制造系统的动态特性,一个资源具有多个功能特性,并且一个功能特性可以由不同的资源提供,多对多映射关系反映了资源的多功能、多任务、多目的的特性;The many-to-many mapping relationship, associating the resource model layer with the functional model layer, improves the flexibility of resource modeling by decoupling cloud manufacturing resources from their corresponding functional features. Resources can dynamically join or exit the cloud manufacturing system, which is in line with cloud manufacturing The dynamic characteristics of the system, a resource has multiple functional characteristics, and a functional characteristic can be provided by different resources, and the many-to-many mapping relationship reflects the multi-functional, multi-task, and multi-purpose characteristics of resources;

语义转换关系,关联功能模型层与基于语义的元模型层,通过元模型层定义的概念与关系为异构的资源信息进行语义标注,确保异构资源在不同的上下文环境中能够被正确的解释,保证资源的语义一致性。Semantic conversion relationship, associating the functional model layer with the semantic-based meta-model layer, and semantically annotating heterogeneous resource information through the concepts and relationships defined in the meta-model layer to ensure that heterogeneous resources can be interpreted correctly in different contexts , to ensure the semantic consistency of resources.

进一步地,资源模型层,接收分布式企业注册的异构资源信息,根据企业原有的资源描述方式设计资源信息对应的XML模式,将异构的资源信息转换为对应的XML文档,使得异构的资源信息同构化。Furthermore, the resource model layer receives the heterogeneous resource information registered by distributed enterprises, designs the XML schema corresponding to the resource information according to the original resource description method of the enterprise, and converts the heterogeneous resource information into corresponding XML documents, so that heterogeneous Resource information isomorphism.

进一步地,功能模型层,从资源对应的XML文档中抽取资源功能信息,将同类资源功能信息合并,根据功能信息合并的结果,设计功能信息对应的XML模式,建立功能信息XML文档视图。Further, the function model layer extracts resource function information from XML documents corresponding to resources, merges function information of similar resources, designs XML schemas corresponding to function information according to the result of function information merger, and establishes function information XML document views.

进一步地,元模型层,对通用的本体构件进行扩展,提供概念、关系、约束、公理为资源相关语义进行标注;从四个方面定义云制造资源语义信息、资源非功能特征语义信息、具有多粒度特性的资源功能特征语义信息、云制造上下文环境语义信息和支持云制造服务等级协议机制的语义信息。Furthermore, the meta-model layer extends the common ontology components, provides concepts, relationships, constraints, and axioms to mark resource-related semantics; defines cloud manufacturing resource semantic information, resource non-functional feature semantic information, and multiple Semantic information of resource function characteristics of granular characteristics, semantic information of cloud manufacturing context environment, and semantic information of supporting cloud manufacturing service level agreement mechanism.

进一步地,多对多关系:对于资源模型层中的每一个注册资源,顺序查找功能信息视图,将资源与其对应的功能特性进行链接,若不存在与注册资源对应的功能特性,则在功能信息视图中创建新的功能特征项,将资源与新特征项进行链接,将资源与功能特征链接关系记录到资源功能映射表中;对于功能视图中的每一个功能特性,在资源注册表中查找提供此功能特性的所有资源,将其与对应资源链接在一起,并将关联信息记录到功能特性映射表中;若资源注册表中不存在提供此功能特性的资源,则删除该功能特征项。Further, many-to-many relationship: For each registered resource in the resource model layer, search the functional information view sequentially, and link the resource with its corresponding functional characteristics. If there is no functional characteristic corresponding to the registered resource, then in the functional information Create a new functional characteristic item in the view, link the resource with the new characteristic item, and record the link relationship between the resource and the functional characteristic in the resource function mapping table; for each functional characteristic in the functional view, find the provided Link all resources of this functional characteristic with corresponding resources, and record the associated information in the functional characteristic mapping table; if there is no resource providing this functional characteristic in the resource registry, delete the functional characteristic item.

进一步地,语义转换关系:根据语义元模型中定义的概念,对资源与功能特性中涉及的等价词汇进行标注;根据语义元模型中定义的关系,对资源之间与功能特性之间的关系进行标注;根据语义元模型中定义的约束,对资源与功能特性所需满足的约束条件进行描述;根据语义元模型中定义的公理,对资源之间与功能特性之间的逻辑关系进行标注,提供机器可理解的推理信息。Further, semantic conversion relationship: according to the concepts defined in the semantic meta-model, mark the equivalent vocabulary involved in resources and functional characteristics; according to the relationship defined in the semantic meta-model, the relationship between resources and functional characteristics Marking; according to the constraints defined in the semantic metamodel, describe the constraints that resources and functional characteristics need to meet; according to the axioms defined in the semantic metamodel, mark the logical relationship between resources and functional characteristics, Provide machine-understandable reasoning information.

有益效果:本发明提供了云制造资源多粒度功能特性的描述方式,提供了云制造资源应用所需的QoS信息,对云制造上下文环境进行建模,提供了支持服务等级协议机制的描述信息。本发明体现了云制造资源的特性,模型框架保证了合作的异构企业的信息交互性,提供了制造资源和制造功能对应的动态关系,保证了云制造的灵活性,有效的对分布制造资源进行管理。Beneficial effects: the present invention provides a description method of multi-granularity functional characteristics of cloud manufacturing resources, provides QoS information required for the application of cloud manufacturing resources, models the cloud manufacturing context environment, and provides description information supporting service level agreement mechanisms. The invention embodies the characteristics of cloud manufacturing resources, the model framework ensures the information interaction of cooperative heterogeneous enterprises, provides the dynamic relationship between manufacturing resources and manufacturing functions, ensures the flexibility of cloud manufacturing, and effectively distributes manufacturing resources to manage.

附图说明Description of drawings

图1为语义元模型概念框架图;Figure 1 is a conceptual framework diagram of the semantic metamodel;

图2为多粒度功能特性描述图;Fig. 2 is a description diagram of multi-granularity functional characteristics;

图3为多主轴车床功能特性描述示例图;Figure 3 is an example diagram for describing the functional characteristics of a multi-spindle lathe;

图4为基于历史经验的云制造上下文描述示例图;Figure 4 is an example diagram of cloud manufacturing context description based on historical experience;

图5为具有多粒度特性的多层云制造资源建模框架图。Figure 5 is a framework diagram of multi-layer cloud manufacturing resource modeling with multi-granularity characteristics.

具体实施方式detailed description

下面结合具体实施例,进一步阐明本发明,应理解这些实施例仅用于说明本发明而不用于限制本发明的范围,在阅读了本发明之后,本领域技术人员对本发明的各种等价形式的修改均落于本申请所附权利要求所限定的范围。Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

具有多粒度特性的多层云制造资源建模框架(如图5所示),共分为三层,分别是底层的资源模型层,中层的功能模型层和高层的基于语义的元模型层(如图1所示)。最底层描述各种物理资源,它是云制造的基础,由地理位置分布的企业中所拥有的异构资源组成;中间层描述资源所具有的各种功能,为云制造平台提供统一的资源功能视图;最高层定义资源相关的概念以及资源之间的关系,为资源应用提供充分的语义信息,促进协同企业之间的语义互操作。The multi-layer cloud manufacturing resource modeling framework with multi-granularity characteristics (as shown in Figure 5) is divided into three layers, namely, the bottom resource model layer, the middle layer functional model layer and the high layer semantic-based meta-model layer ( As shown in Figure 1). The lowest layer describes various physical resources, which is the basis of cloud manufacturing, and is composed of heterogeneous resources owned by geographically distributed enterprises; the middle layer describes various functions of resources, and provides unified resource functions for the cloud manufacturing platform View; the highest layer defines resource-related concepts and the relationship between resources, provides sufficient semantic information for resource applications, and promotes semantic interoperability between collaborative enterprises.

具有多粒度特性的多层云制造资源建模框架包括以下几个关系:The multi-layer cloud manufacturing resource modeling framework with multi-granularity features includes the following relationships:

多对多映射关系,关联资源模型层与功能模型层,通过将云制造资源与其对应的功能特征解耦,提高资源建模的灵活性,资源可以动态的加入或者退出云制造系统,符合云制造系统的动态特性,一个资源具有多个功能特性,并且一个功能特性可以由不同的资源提供,多对多映射关系反映了资源的多功能、多任务、多目的的特性;The many-to-many mapping relationship, associating the resource model layer with the functional model layer, improves the flexibility of resource modeling by decoupling cloud manufacturing resources from their corresponding functional features. Resources can dynamically join or exit the cloud manufacturing system, which is in line with cloud manufacturing The dynamic characteristics of the system, a resource has multiple functional characteristics, and a functional characteristic can be provided by different resources, and the many-to-many mapping relationship reflects the multi-functional, multi-task, and multi-purpose characteristics of resources;

语义转换关系,关联功能模型层与基于语义的元模型层,通过元模型层定义的概念与关系为异构的资源信息进行语义标注,确保异构资源在不同的上下文环境中能够被正确的解释,保证资源的语义一致性。Semantic conversion relationship, associating the functional model layer with the semantic-based meta-model layer, and semantically annotating heterogeneous resource information through the concepts and relationships defined in the meta-model layer to ensure that heterogeneous resources can be interpreted correctly in different contexts , to ensure the semantic consistency of resources.

资源模型层,接收分布式企业注册的异构资源信息,根据企业原有的资源描述方式设计资源信息对应的XML模式,将异构的资源信息转换为对应的XML文档,使得异构的资源信息同构化。The resource model layer receives the heterogeneous resource information registered by the distributed enterprise, designs the XML schema corresponding to the resource information according to the original resource description method of the enterprise, and converts the heterogeneous resource information into the corresponding XML document, so that the heterogeneous resource information isomorphism.

功能模型层,从资源对应的XML文档中抽取资源功能信息,将同类资源功能信息合并,根据功能信息合并的结果,设计功能信息对应的XML模式,建立功能信息XML文档视图。The function model layer extracts the resource function information from the XML document corresponding to the resource, merges the function information of similar resources, designs the XML schema corresponding to the function information according to the result of the function information merger, and establishes the function information XML document view.

元模型层,对通用的本体构件进行扩展,提供概念、关系、约束、公理为资源相关语义进行标注;从四个方面定义云制造资源语义信息、资源非功能特征语义信息、具有多粒度特性的资源功能特征语义信息(如图2所示)、云制造上下文环境语义信息和支持云制造服务等级协议机制的语义信息。The meta-model layer extends the common ontology components and provides concepts, relationships, constraints, and axioms to mark resource-related semantics; defines cloud manufacturing resource semantic information, resource non-functional feature semantic information, and multi-granularity features from four aspects. Resource functional feature semantic information (as shown in Figure 2), cloud manufacturing context semantic information and semantic information supporting cloud manufacturing service level agreement mechanism.

多对多关系:对于资源模型层中的每一个注册资源,顺序查找功能信息视图,将资源与其对应的功能特性进行链接,若不存在与注册资源对应的功能特性,则在功能信息视图中创建新的功能特征项,将资源与新特征项进行链接,将资源与功能特征链接关系记录到资源功能映射表中;对于功能视图中的每一个功能特性,在资源注册表中查找提供此功能特性的所有资源,将其与对应资源链接在一起,并将关联信息记录到功能特性映射表中;若资源注册表中不存在提供此功能特性的资源,则删除该功能特征项。Many-to-many relationship: For each registered resource in the resource model layer, search the functional information view sequentially, and link the resource with its corresponding functional characteristics. If there is no functional characteristic corresponding to the registered resource, create it in the functional information view New functional feature items, link resources with new feature items, and record the link relationship between resources and functional features in the resource function mapping table; for each feature in the function view, find and provide this feature in the resource registry Link all the resources with the corresponding resources, and record the associated information in the feature mapping table; if there is no resource providing this feature in the resource registry, delete the feature item.

语义转换关系:根据语义元模型中定义的概念,对资源与功能特性中涉及的等价词汇进行标注;根据语义元模型中定义的关系,对资源之间与功能特性之间的关系进行标注;根据语义元模型中定义的约束,对资源与功能特性所需满足的约束条件进行描述;根据语义元模型中定义的公理,对资源之间与功能特性之间的逻辑关系进行标注,提供机器可理解的推理信息。Semantic conversion relationship: according to the concepts defined in the semantic metamodel, mark the equivalent vocabulary involved in resources and functional characteristics; according to the relationship defined in the semantic metamodel, mark the relationship between resources and functional characteristics; According to the constraints defined in the semantic meta-model, describe the constraints that resources and functional characteristics need to meet; according to the axioms defined in the semantic meta-model, label the logical relationship between resources and functional characteristics, and provide machine-readable comprehension of reasoning information.

1.多主轴车床功能特性描述示例1. Example of functional characteristics description of multi-spindle lathe

如图3所示,具有精车功能特性的多主轴车床描述示例。基于多粒度特性多层资源建模框架,多主轴车床的功能特性根据包含关系可以被扩展为三个不同层次上的多粒度功能特性,如:打孔、车、精车。这三个功能层次的描述粒度由粗至细,分别对应三个不同的粒度层次,联盟层、企业层、资源层。通过多粒度的功能特性的扩展,多主轴车床可以在三个不同的粒度层次上被发现并使用,提高了资源利用率。An example of a multi-spindle lathe with finishing features is depicted in Figure 3. Based on the multi-level resource modeling framework of multi-granularity characteristics, the functional characteristics of the multi-spindle lathe can be extended to three different levels of multi-granularity functional characteristics according to the inclusion relationship, such as: drilling, turning, and finishing turning. The description granularity of these three functional levels is from coarse to fine, corresponding to three different levels of granularity, alliance layer, enterprise layer, and resource layer. Through the expansion of multi-granularity functional characteristics, multi-spindle lathes can be found and used at three different granularity levels, which improves resource utilization.

2.基于历史经验的云制造上下文描述示例2. Example of cloud manufacturing context description based on historical experience

如图4所示,基于历史经验的云制造上下文描述示例。一个具有精车功能特性的多主轴车床,其资源提供者为NEV.Company@ChengDu,根据资源应用的历史经验,资源消费者ISS.Company@Anchorage曾发出过资源请求,具有机械加工类型功能,可执行车操作,可人力资源辅助,资源位置在上海,完工时间为3小时,基于以上需求,根据历史经验可发现此多主轴车床被选择提供服务,其服务的实际记录为无人工辅助,并且2小时完工,根据此次服务的情况,资源消费者可以给资源进行评级,表征资源对于此类需求的支持程度,若云平台接受到同类资源需求时,可将此资源的历史评级反馈给需求者,需求者根据评级直接决定是否选择此资源进行服务,增加了资源利用率与资源发现效率。As shown in Figure 4, an example of cloud manufacturing context description based on historical experience. A multi-spindle lathe with the function of fine turning, its resource provider is NEV.Company@ChengDu, according to the historical experience of resource application, the resource consumer ISS.Company@Anchorage has sent a resource request, with the function of machining type, can be The lathe operation can be assisted by human resources. The resource location is in Shanghai, and the completion time is 3 hours. Based on the above requirements and historical experience, it can be found that this multi-spindle lathe is selected to provide services. The actual record of its services is no human assistance, and 2 According to the situation of this service, resource consumers can rate resources to represent the degree of support of resources for such needs. If the cloud platform receives a demand for similar resources, it can feedback the historical rating of this resource to the demander , the demander directly decides whether to choose this resource for service according to the rating, which increases resource utilization and resource discovery efficiency.

Claims (2)

1.一种具有多粒度特性的多层云制造资源建模框架,其特征在于:包括底层的资源模型层,中层的功能模型层,以及高层的基于语义的元模型层;资源模型层描述各种物理资源,由地理位置分布中的企业所拥有的异构资源组成;功能模型层描述资源所具有的各种功能,为云制造平台提供统一的资源功能视图;元模型层定义资源相关的概念以及资源之间的关系;1. A multi-layer cloud manufacturing resource modeling framework with multi-granularity features, characterized in that: it includes a bottom-level resource model layer, a middle-level functional model layer, and a high-level semantic-based meta-model layer; the resource model layer describes each A physical resource, which is composed of heterogeneous resources owned by enterprises in geographical distribution; the functional model layer describes various functions of resources, and provides a unified resource function view for the cloud manufacturing platform; the meta-model layer defines resource-related concepts and the relationship between resources; 具有多粒度特性的多层云制造资源建模框架包括多对多映射关系和语义转换关系:The multi-layer cloud manufacturing resource modeling framework with multi-granularity features includes many-to-many mapping and semantic transformation: 多对多映射关系,关联资源模型层与功能模型层,通过将云制造资源与其对应的功能特征解耦,提高资源建模的灵活性,资源动态的加入或者退出云制造系统,符合云制造系统的动态特性,一个资源具有多个功能特性,并且一个功能特性由不同的资源提供,多对多映射关系反映了资源的多功能、多任务、多目的的特性;The many-to-many mapping relationship, associating the resource model layer with the functional model layer, improves the flexibility of resource modeling by decoupling cloud manufacturing resources from their corresponding functional features, and dynamically joins or exits the cloud manufacturing system, which is in line with the cloud manufacturing system The dynamic characteristics of a resource has multiple functional characteristics, and a functional characteristic is provided by different resources, and the many-to-many mapping relationship reflects the multi-functional, multi-task, and multi-purpose characteristics of resources; 语义转换关系,关联功能模型层与基于语义的元模型层,通过元模型层定义的概念与关系为异构的资源信息进行语义标注;Semantic conversion relationship, associating the functional model layer with the semantic-based meta-model layer, and semantically annotating heterogeneous resource information through the concepts and relationships defined in the meta-model layer; 资源模型层接收分布式企业注册的异构资源信息,根据企业原有的资源描述方式设计资源信息对应的XML模式,将异构的资源信息转换为对应的XML文档,使得异构的资源信息同构化;The resource model layer receives the heterogeneous resource information registered by the distributed enterprise, designs the XML schema corresponding to the resource information according to the original resource description method of the enterprise, and converts the heterogeneous resource information into the corresponding XML document, so that the heterogeneous resource information is the same structured; 功能模型层从资源对应的XML文档中抽取资源功能信息,将同类资源功能信息合并,根据功能信息合并的结果,设计功能信息对应的XML模式,建立功能信息XML文档视图;The function model layer extracts the resource function information from the XML document corresponding to the resource, merges the function information of similar resources, designs the XML schema corresponding to the function information according to the result of the function information merger, and establishes the function information XML document view; 元模型层对通用的本体构件进行扩展,提供概念、关系、约束、公理为资源相关语义进行标注;从概念、关系、约束、公理四个方面定义云制造资源的语义信息、资源非功能特征语义信息、具有多粒度特性的资源功能特征语义信息、云制造上下文环境语义信息和支持云制造服务等级协议机制的语义信息;The metamodel layer extends the general ontology components, provides concepts, relationships, constraints, and axioms to mark resource-related semantics; defines the semantic information of cloud manufacturing resources and the semantics of non-functional features of resources from the four aspects of concepts, relationships, constraints, and axioms Information, resource functional feature semantic information with multi-granularity characteristics, cloud manufacturing context semantic information and semantic information supporting cloud manufacturing service level agreement mechanism; 多对多映射关系对于资源模型层中的每一个注册资源,顺序查找资源功能视图,将资源与其对应的功能特性进行链接,若不存在与注册资源对应的功能特性,则在资源功能视图中创建新的功能特征项,将资源与新特征项进行链接,将资源与功能特征链接关系记录到资源功能映射表中;对于资源功能视图中的每一个功能特性,在资源功能映射表中查找提供此功能特性的所有资源,将其与对应资源链接在一起,并将关联信息记录到资源功能映射表中;若资源功能映射表中不存在提供此功能特性的资源,则删除该功能特征项。Many-to-many mapping relationship For each registered resource in the resource model layer, the resource function view is searched sequentially, and the resource is linked to the corresponding functional characteristics. If there is no functional characteristic corresponding to the registered resource, it is created in the resource function view The new functional feature item links the resource with the new feature item, and records the link relationship between the resource and the functional feature in the resource function mapping table; for each functional feature in the resource function view, look up the resource function mapping table to provide this Link all the resources of the functional characteristics with the corresponding resources, and record the associated information in the resource function mapping table; if there is no resource providing this functional characteristic in the resource function mapping table, delete the functional characteristic item. 2.如权利要求1所述的具有多粒度特性的多层云制造资源建模框架,其特征在于,语义转换关系根据语义元模型中定义的概念,对资源与功能特性中涉及的等价词汇进行标注;根据语义元模型中定义的关系,对资源之间与功能特性之间的关系进行标注;根据语义元模型中定义的约束,对资源与功能特性所需满足的约束条件进行描述;根据语义元模型中定义的公理,对资源之间与功能特性之间的逻辑关系进行标注,提供机器可理解的推理信息。2. The multi-layer cloud manufacturing resource modeling framework with multi-granularity characteristics as claimed in claim 1, wherein the semantic transformation relationship is based on the concept defined in the semantic meta-model, and the equivalent vocabulary involved in the resource and functional characteristics Labeling; according to the relationship defined in the semantic metamodel, label the relationship between resources and functional characteristics; according to the constraints defined in the semantic metamodel, describe the constraints that resources and functional characteristics need to meet; according to The axioms defined in the semantic meta-model mark the logical relationship between resources and functional characteristics, and provide machine-understandable reasoning information.
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