云制造模式下建材装备企业制造任务执行关键技术研究
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摘要
随着云计算、物联网等先进技术的迅猛发展和制造业所面临的挑战,云制造模式和技术应运而生,其核心在于实现制造资源的协同和共享,为制造业的升级转型提供了新的推动力。然而,由于云制造模式的开放性及多用户等特征,导致多用户任务执行过程涉及到制造任务的异构性、任务资源服务的竞争性、状态监控和任务执行后的评价等问题。对此,本文从四个方面来研究云制造模式下制造任务的优化执行,并以建材装备企业为应用对象进行实践研究。主要研究工作可以概括为以下几个方面：
(1)针对制造任务执行过程中的任务异构性问题,提出了云制造任务的信息模型和语义建模框架,首先应用本体建模技术建立了初始云制造任务本体(OCMT_Ontology),以建材装备企业制造任务文本库为基础结合文本处理技术,通过本体自学习模型建立并完善通用云制造任务本体(GCMT_Ontology);在此基础基础之上,通过匹配GCMT_Ontology,构建云制造任务子本体(CMTS_ontology)来实现云制造任务的语义描述。
(2)提出了云制造任务执行服务链构建框架,在分析单用户资源服务优选的基础上,重点研究多用户任务资源服务优选模型,针对多用户对资源服务的竞争,提出了应用演化博弈论的方法构建多用户资源服务的博弈模型,按照任务执行是否延期分四种类型求解博弈模型的演化稳定策略,并分析其动态复制方程的演化相图。最后,依据制造子任务的时序逻辑关系来构建可执行的协同制造资源服务链。
(3)提出云制造模式下任务执行过程状态监控框架,构建了基于制造任务子本体的多源数据一致映射模型,按照制造任务的结构和执行服务链实现任务执行进度的数据融合,根据制造资源实时监控的数据,应用隐性马尔科夫模型实现制造资源状态的实时监控。
(4)提出了云制造任务执行过程评估模型,并建立了不同执行阶段的评估指标及模糊化处理方法。提出直觉模糊OWA-TOPSIS的方法对任务执行过程进行评估,构架了六类不同的TOPSIS数据累积方法和最佳和最差理想点的识别方法,并通过案例研究验证了提出方法的实用性。
(5)从实际应用出发,针对建材装备制造企业产品的制造过程,开发了云制造模式下任务执行过程管理系统,并在协同制造的多个企业间实现任务执行计划管理、过程跟踪和评价。
With the rapid development of advanced technologies such as cloud computing, Internet of things and the challenges in manufacturing industries, cloud manufacturing model and technology is proposed. And its aims are to realize the collaboration and sharing of manufacturing resources, and provide new powers to transformation and upgradation of manufacturing industries. However, the openness of cloud manufacturing model and multi-user characteristic, cause that multi-user tasks execution process involves heterogeneity of manufacturing tasks, competition of resource services, condition monitoring and evaluation of task execution and so on. In view of this, this paper researches the optimization execution of manufacturing task in cloud manufacturing mode from four aspects, and choose the building material equipment enterprises as the application case study in practice. The main research work can be summarized as the following aspects:
(1) To the heterogeneity problem in the process of manufacturing task execution, put forward the cloud manufacturing task information model and semantic modeling framework. And apply ontology modeling technology to establish the original cloud manufacturing task ontology (OCMT_Ontology), then establish and perfect the common cloud manufacturing task ontology (GCMT_Ontology) by self-learning model of ontology on the basis of the text corpus of manufacture tasks in building material equipment enterprise and text pre-processing technology, Furthermore, build the cloud manufacturing task sub-ontology (CMTS_ontology) by matching with GCMT_Ontology and implement the semantic description of cloud manufacturing tasks.
(2) Propose the construction framework of cloud manufacturing task execution service chain. On the basis of the analysis of single user resource service optimal-selection problem, multi-user task resource service optimal-selection model is focused. And construct the game model by applying the evolutionary game theory to solve the competetion problem of multi-user resource services.Four types of scenarios are developed to obtain the evolution stable strategy of game model and the diagrams of replicator dynamics are analyzed simultaneously. Finally, build the executable collaborative manufacturing resource service chain based on the sequential and logic relationship of manufacturing subtasks.
(3) Construct the state monitoring framework of task execution process in cloud manufacturing. And build the multi-source data mapping model of manufacturing task ontology.On basis of this, achieve data fusion of task execution progress according to the structure of manufacturing tasks and resource service chain, and realize the real-time condition monitoring of manufacturing resources with the hidden markov model.
(4) Advance the evaluation model of cloud manufacturing task execution process and build the evaluation attributions and fuzzification methods of different attributions. And then propose intuitionistic fuzzy OWA-TOPSIS method to evaluate the performance of task execution process, in which six different types of data aggregation methods and corresponding positive-ideal and negative-ideal solutions identification methods are advanced. And then, the practicability of the proposed method is verified by a case study.
(5)Develop the task execution management system for practical application following the product manufacturing process of the building material equipment enterprises in could manufacturing. And implement the collaborative manufacturing task execution plan management, process tracking and evaluation among the collaborative enterprises.
(1)针对制造任务执行过程中的任务异构性问题,提出了云制造任务的信息模型和语义建模框架,首先应用本体建模技术建立了初始云制造任务本体(OCMT_Ontology),以建材装备企业制造任务文本库为基础结合文本处理技术,通过本体自学习模型建立并完善通用云制造任务本体(GCMT_Ontology);在此基础基础之上,通过匹配GCMT_Ontology,构建云制造任务子本体(CMTS_ontology)来实现云制造任务的语义描述。
(2)提出了云制造任务执行服务链构建框架,在分析单用户资源服务优选的基础上,重点研究多用户任务资源服务优选模型,针对多用户对资源服务的竞争,提出了应用演化博弈论的方法构建多用户资源服务的博弈模型,按照任务执行是否延期分四种类型求解博弈模型的演化稳定策略,并分析其动态复制方程的演化相图。最后,依据制造子任务的时序逻辑关系来构建可执行的协同制造资源服务链。
(3)提出云制造模式下任务执行过程状态监控框架,构建了基于制造任务子本体的多源数据一致映射模型,按照制造任务的结构和执行服务链实现任务执行进度的数据融合,根据制造资源实时监控的数据,应用隐性马尔科夫模型实现制造资源状态的实时监控。
(4)提出了云制造任务执行过程评估模型,并建立了不同执行阶段的评估指标及模糊化处理方法。提出直觉模糊OWA-TOPSIS的方法对任务执行过程进行评估,构架了六类不同的TOPSIS数据累积方法和最佳和最差理想点的识别方法,并通过案例研究验证了提出方法的实用性。
(5)从实际应用出发,针对建材装备制造企业产品的制造过程,开发了云制造模式下任务执行过程管理系统,并在协同制造的多个企业间实现任务执行计划管理、过程跟踪和评价。
With the rapid development of advanced technologies such as cloud computing, Internet of things and the challenges in manufacturing industries, cloud manufacturing model and technology is proposed. And its aims are to realize the collaboration and sharing of manufacturing resources, and provide new powers to transformation and upgradation of manufacturing industries. However, the openness of cloud manufacturing model and multi-user characteristic, cause that multi-user tasks execution process involves heterogeneity of manufacturing tasks, competition of resource services, condition monitoring and evaluation of task execution and so on. In view of this, this paper researches the optimization execution of manufacturing task in cloud manufacturing mode from four aspects, and choose the building material equipment enterprises as the application case study in practice. The main research work can be summarized as the following aspects:
(1) To the heterogeneity problem in the process of manufacturing task execution, put forward the cloud manufacturing task information model and semantic modeling framework. And apply ontology modeling technology to establish the original cloud manufacturing task ontology (OCMT_Ontology), then establish and perfect the common cloud manufacturing task ontology (GCMT_Ontology) by self-learning model of ontology on the basis of the text corpus of manufacture tasks in building material equipment enterprise and text pre-processing technology, Furthermore, build the cloud manufacturing task sub-ontology (CMTS_ontology) by matching with GCMT_Ontology and implement the semantic description of cloud manufacturing tasks.
(2) Propose the construction framework of cloud manufacturing task execution service chain. On the basis of the analysis of single user resource service optimal-selection problem, multi-user task resource service optimal-selection model is focused. And construct the game model by applying the evolutionary game theory to solve the competetion problem of multi-user resource services.Four types of scenarios are developed to obtain the evolution stable strategy of game model and the diagrams of replicator dynamics are analyzed simultaneously. Finally, build the executable collaborative manufacturing resource service chain based on the sequential and logic relationship of manufacturing subtasks.
(3) Construct the state monitoring framework of task execution process in cloud manufacturing. And build the multi-source data mapping model of manufacturing task ontology.On basis of this, achieve data fusion of task execution progress according to the structure of manufacturing tasks and resource service chain, and realize the real-time condition monitoring of manufacturing resources with the hidden markov model.
(4) Advance the evaluation model of cloud manufacturing task execution process and build the evaluation attributions and fuzzification methods of different attributions. And then propose intuitionistic fuzzy OWA-TOPSIS method to evaluate the performance of task execution process, in which six different types of data aggregation methods and corresponding positive-ideal and negative-ideal solutions identification methods are advanced. And then, the practicability of the proposed method is verified by a case study.
(5)Develop the task execution management system for practical application following the product manufacturing process of the building material equipment enterprises in could manufacturing. And implement the collaborative manufacturing task execution plan management, process tracking and evaluation among the collaborative enterprises.
引文
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