面向產(chǎn)品設(shè)計的資源主動服務(wù)與優(yōu)化配置管理研究
發(fā)布時間：2018-07-21 11:18
【摘要】：隨著企業(yè)間的競爭日趨激烈,實現(xiàn)快速、有效、個人化的產(chǎn)品供應(yīng)是企業(yè)發(fā)展的必然要求。在產(chǎn)品設(shè)計過程中,為縮短產(chǎn)品研發(fā)周期和提高資源服務(wù)效率,實現(xiàn)設(shè)計資源的主動服務(wù),解決設(shè)計資源服務(wù)需求的主動獲取問題,并設(shè)法從海量的網(wǎng)絡(luò)設(shè)計資源中找到適合設(shè)計人員真正需求的資源進行調(diào)度,快速高效地將合適的設(shè)計資源主動推送給所需的設(shè)計人員。為避免超大規(guī)模且復(fù)雜多變的數(shù)據(jù)源給網(wǎng)絡(luò)設(shè)計資源管理系統(tǒng)的維護人員帶來的巨大挑戰(zhàn),提出網(wǎng)絡(luò)設(shè)計資源的優(yōu)化配置與自動化管理方法。針對上述問題,本文做出如下研究:(1)設(shè)計資源服務(wù)相關(guān)的概念與理論基礎(chǔ)。對相關(guān)的基礎(chǔ)理論進行定義及分類分析,為研究如何進行高效的設(shè)計資源服務(wù)提供了前提條件。深入地了解相關(guān)領(lǐng)域中的資源流向特點和傳遞過程,分別對設(shè)計資源、設(shè)計資源服務(wù)需求和云設(shè)計資源三個領(lǐng)域進行定義及分類分析。(2)基于情境感知的設(shè)計資源服務(wù)需求的主動獲取方法。為實現(xiàn)設(shè)計資源服務(wù)需求的主動獲取,在分析情境感知推理層特點的基礎(chǔ)上,構(gòu)建主動獲取設(shè)計資源服務(wù)需求的情境感知服務(wù)體系。采用貝葉斯方法使設(shè)計資源類別偏好情境化,并根據(jù)不同的情境特點,為選擇合適的且能融合到推薦中的方法,提出融合資源類別偏好的協(xié)同過濾獲取算法,通過多個設(shè)計資源服務(wù)需求的期望值計算及其大小比較,實現(xiàn)了基于情境感知的設(shè)計資源服務(wù)需求的主動獲取。(3)面向設(shè)計人員需求與偏好的云設(shè)計資源節(jié)點的主動反饋方法。提出面向產(chǎn)品設(shè)計的云設(shè)計資源建模方法,在云設(shè)計資源響應(yīng)能力模型和設(shè)計人員需求與偏好模型的基礎(chǔ)上,構(gòu)建基于負反饋的云設(shè)計資源調(diào)度機制,通過對該調(diào)度機制的求解,實現(xiàn)了云設(shè)計資源響應(yīng)能力與設(shè)計人員需求的相似度匹配,并將匹配度較高的云設(shè)計資源節(jié)點反饋給設(shè)計人員,提高了資源利用率。(4)云設(shè)計資源的自適應(yīng)優(yōu)化配置管理方法。建立一種基于神經(jīng)網(wǎng)絡(luò)和多目標(biāo)遺傳算法的云設(shè)計資源自適應(yīng)配置模型,利用神經(jīng)網(wǎng)絡(luò)預(yù)測算法對資源負載進行預(yù)測,并根據(jù)預(yù)測值提出虛擬機遷移請求。為提供最優(yōu)的虛擬機遷移策略,將基于混合分組編碼的多目標(biāo)優(yōu)化遺傳算法引入虛擬機資源管理,節(jié)省了虛擬機遷移時間并減少了物理節(jié)點數(shù)量,實現(xiàn)了云設(shè)計資源的自適應(yīng)優(yōu)化配置管理。仿真及結(jié)果分析表明,該研究方法能在保證能耗與服務(wù)等級協(xié)議超標(biāo)率較低的前提下,提高云設(shè)計資源服務(wù)效率和質(zhì)量。
[Abstract]:With the increasingly fierce competition among enterprises, the realization of rapid, effective, personalized product supply is an inevitable requirement for the development of enterprises. In the process of product design, in order to shorten the period of product research and development and improve the efficiency of resource service, to realize the active service of design resource, and to solve the problem of active acquisition of design resource service demand. From the massive network design resources, we try to find the resources that are suitable for the designers' real needs for scheduling, and quickly and efficiently push the appropriate design resources to the designers who need them. In order to avoid the huge challenge to the maintainers of the network design resource management system caused by the large and complex data sources, the optimal configuration and automatic management method of the network design resources are proposed. To solve the above problems, this paper makes the following research: (1) the concept and theoretical basis of designing resource services. The related basic theories are defined and classified, which provides a prerequisite for the research on how to design resource services efficiently. Deeply understand the characteristics and transfer process of resource flow in related fields. Design resource service requirement and cloud design resource are defined and classified. (2) Context-aware design resource service requirement acquisition method. In order to realize the active acquisition of design resource service requirements, a situation-aware service system is constructed on the basis of analyzing the characteristics of context-aware reasoning layer. The Bayesian method is used to situate the design resource category preference. In order to select the appropriate method which can be fused into the recommendation, a collaborative filtering algorithm is proposed according to the different situation characteristics. By calculating the expected value of multiple design resource service requirements and comparing their sizes, we realize the active acquisition of design resource service requirements based on situational awareness. (3) an active feedback method for cloud design resource nodes based on designers' needs and preferences. A method of cloud design resource modeling for product design is proposed. Based on the response ability model of cloud design resources and the demand and preference model of designers, a negative feedback based scheduling mechanism for cloud design resources is constructed. By solving the scheduling mechanism, the similarity between the response ability of cloud design resources and the designer's requirements is realized, and the node of cloud design resources with high matching degree is fed back to the designer. (4) an adaptive optimal configuration management method for cloud design resources. An adaptive resource allocation model for cloud design based on neural network and multi-objective genetic algorithm is established. The neural network prediction algorithm is used to predict the resource load, and a virtual machine migration request is proposed based on the prediction value. In order to provide the optimal migration strategy of virtual machine, the multi-objective optimization genetic algorithm based on hybrid block coding is introduced into virtual machine resource management, which saves the migration time of virtual machine and reduces the number of physical nodes. The adaptive optimal configuration management of cloud design resources is realized. Simulation and result analysis show that the proposed method can improve the efficiency and quality of cloud design resources on the premise of low energy consumption and low rate of service level agreement.
【學(xué)位授予單位】：南昌航空大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F272;TB472
本文編號：2135352
[Abstract]:With the increasingly fierce competition among enterprises, the realization of rapid, effective, personalized product supply is an inevitable requirement for the development of enterprises. In the process of product design, in order to shorten the period of product research and development and improve the efficiency of resource service, to realize the active service of design resource, and to solve the problem of active acquisition of design resource service demand. From the massive network design resources, we try to find the resources that are suitable for the designers' real needs for scheduling, and quickly and efficiently push the appropriate design resources to the designers who need them. In order to avoid the huge challenge to the maintainers of the network design resource management system caused by the large and complex data sources, the optimal configuration and automatic management method of the network design resources are proposed. To solve the above problems, this paper makes the following research: (1) the concept and theoretical basis of designing resource services. The related basic theories are defined and classified, which provides a prerequisite for the research on how to design resource services efficiently. Deeply understand the characteristics and transfer process of resource flow in related fields. Design resource service requirement and cloud design resource are defined and classified. (2) Context-aware design resource service requirement acquisition method. In order to realize the active acquisition of design resource service requirements, a situation-aware service system is constructed on the basis of analyzing the characteristics of context-aware reasoning layer. The Bayesian method is used to situate the design resource category preference. In order to select the appropriate method which can be fused into the recommendation, a collaborative filtering algorithm is proposed according to the different situation characteristics. By calculating the expected value of multiple design resource service requirements and comparing their sizes, we realize the active acquisition of design resource service requirements based on situational awareness. (3) an active feedback method for cloud design resource nodes based on designers' needs and preferences. A method of cloud design resource modeling for product design is proposed. Based on the response ability model of cloud design resources and the demand and preference model of designers, a negative feedback based scheduling mechanism for cloud design resources is constructed. By solving the scheduling mechanism, the similarity between the response ability of cloud design resources and the designer's requirements is realized, and the node of cloud design resources with high matching degree is fed back to the designer. (4) an adaptive optimal configuration management method for cloud design resources. An adaptive resource allocation model for cloud design based on neural network and multi-objective genetic algorithm is established. The neural network prediction algorithm is used to predict the resource load, and a virtual machine migration request is proposed based on the prediction value. In order to provide the optimal migration strategy of virtual machine, the multi-objective optimization genetic algorithm based on hybrid block coding is introduced into virtual machine resource management, which saves the migration time of virtual machine and reduces the number of physical nodes. The adaptive optimal configuration management of cloud design resources is realized. Simulation and result analysis show that the proposed method can improve the efficiency and quality of cloud design resources on the premise of low energy consumption and low rate of service level agreement.
【學(xué)位授予單位】：南昌航空大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F272;TB472
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