云制造环境下板材余料资源的服务匹配方法

在云制造环境下,针对中小型企业加工后的板材余料造成极大资源浪费的问题,提出了一种基于语义相似度算法同时融入QoS(quality of service,服务质量)信息的分层次资源描述模型.首先,建立板材余料和板材加工设备资源的本体模型;然后,基于语义相似度算法对板材余料和板材加工设备的各个属性参数进行匹配,得到初选服务...     

导向辊生产设备制造资源集合匹配方法

目的 为解决云制造环境下导向辊生产工序外协时设备制造资源搜索效率低的问题,本文提出一种导向辊生产设备云制造资源集合与生产工序云制造需求匹配的方法,用于降低导向辊制造资源的寻优范围。方法 首先建立导向辊生产设备制造资源的可拓物元模型,通过可拓聚类算法实现导向辊生产设备制造资源的聚类,接着利用集合中制造资源的可用加工方法属性与制造需求的加工方法属性进行配对,通过配对结果完成导向辊生产设备制造资源集合与生产工序制造需求的匹配。结果 使用该匹配方法后,成功将10个导向辊制造资源样本聚类为7种制造资源集合KN1—KN7,并将其与导向辊的7种生产工序制造需求实现匹配。结论 实验结果表明,该方法能够实现导向辊生产设备云制造资源集合与生产工序需求的匹配,提高了后续导向辊生产工序外协资源的寻优效率。     

Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved Artificial Bee Colony optimisation algorithm

Cloud Manufacturing (CMfg) ambitions to create dedicated manufacturing clouds (i.e. virtual enterprises) for complex manufacturing demands through the association of various service providers’ resources and capabilities. In order to insure a dedicated manufacturing cloud to match the level of customer’s requirements, the cloud service selection and composition appear to be a decisive process. This study takes common aspects of cloud services into consideration such as quality of service (QoS) parameters but extend the scope to the physical location of the manufacturing resources. Unlike the classic service composition, manufacturing brings additional constraints. Consequently, we propose a method based on QoS evaluation along with the geo-perspective correlation from one cloud service to another for transportation impact analysis. We also insure the veracity of the manufacturing time evaluation by resource availability overtime. Since the composition is an exhaustive process in terms of computational time consumption, the proposed method is optimised through an adapted Artificial Bee Colony (ABC) algorithm based on initialisation enhancement. Finally, the efficiency and precision of our method are discussed furthermore in the experiments chapter.     

Cloud manufacturing resources fuzzy classification based on genetic simulated annealing algorithm

To solve the problem of fuzzy classification of manufacturing resources in a cloud manufacturing environment, a hybrid algorithm based on genetic algorithm (GA), simulated annealing (SA) and fuzzy C-means clustering algorithm (FCM) is proposed. In this hybrid algorithm, classification is based on the processing feature and attributes of the manufacturing resource; the inner and outer layers of the nested loops are solving it, GA obtains the best classification number in the outer layer; the fitness function is constructed by fuzzy clustering algorithm (FCM), carrying out the selection, crossover and mutation operation and SA cooling operation. The final classification results are obtained in the inner layer. Using the hybrid algorithm to solve 45 kinds of manufacturing resources, the optimal classification number is 9 and the corresponding classification results are obtained, proving that the algorithm is effective.     

Cloud manufacturing service composition and optimal selection with sustainability considerations: a multi-objective integer bi-level multi-follower programming approach

The process of service composition and optimal selection (SCOS) is an important issue in cloud manufacturing (CMfg). However, the current studies on CMfg and SCOS have generally focused on optimising the allocation of resources against quality of service (QoS), in terms of e.g. cost, quality, and time. They have seldom taken the perspective of sustainability into discussion, although sustainability is indispensable in the CMfg environment. Addressing this gap, we aim to (1) propose a comprehensive method to assess the sustainability of cloud manufacturing (SoM) in terms of the economic, environmental, and social aspects; (2) establish a multi-objective integer bi-level multi-follower programming (MOIBMFP) model to simultaneously maximise SoM and QoS from the perspectives of both platform operator and multiple service demanders; and (3) design a hybrid particle swarm optimisation algorithm to solve the proposed MOIBMFP model. The experimental results show that the proposed algorithm is more feasible and effective than the typical multi-objective particle swarm optimisation algorithm when solving the proposed model. In other words, the proposed model and algorithm suggest better alternatives to meet the needs of the platform operator and service demanders in the CMfg environment.     

面向云制造服务的制造资源优化配置研究

为了降低云端制造服务成本,解决云制造环境下无需求偏好的制造资源优化配置的难题,充分考虑制造资源需求企业和云平台运营方的利益以及双方在制造资源配置服务过程中涉及到的服务质量（quality of sevice,QoS）因素和柔性因素,构建了云环境下代表制造资源需求企业和云平台运营方利益的多目标优化资源配置模型,并基于改进NSGA-Ⅱ算法对模型算例进行了求解,计算结果表明了该模型和算法的可行性、有效性和稳定性。     

考虑资源需求差异的印包设备网络协同制造资源匹配方法

目的 包装印刷装备行业存在制造资源分散、产业协同不足和效率低等问题,针对网络协同制造中的制造资源匹配问题提出一种有效方法。方法 从不同子任务资源需求差异视角出发,构建基于TQCS制造资源评价指标体系及制造任务约束体系,通过层次分析法计算不同子任务的权重,以资源与任务的匹配度最大为目标函数,提出基于莱维飞行–遗传算法的网络协同制造资源匹配方法。结果 改进的资源匹配方法相较于传统方法,能够得到成本更低、时间更短的方案,并且改进的遗传算法的寻优能力更高。结论 相较于传统方法,改进的制造资源匹配方法的目标函数更合理、权重取值更客观、寻优能力更好,能够得到更为合理的制造资源匹配方案。     

Dynamic Intelligent Supply-Demand Adaptation Model Towards Intelligent Cloud Manufacturing

As a new mode and means of smart manufacturing, smart cloud manufacturing (SCM) faces great challenges in massive supply and demand, dynamic resource collaboration and intelligent adaptation. To address the problem, this paper proposes an SCM-oriented dynamic supply-demand (S-D) intelligent adaptation model for massive manufacturing services. In this model, a collaborative network model is established based on the properties of both the supply-demand and their relationships; in addition, an algorithm based on deep graph clustering (DGC) and aligned sampling (AS) is used to divide and conquer the large adaptation domain to solve the problem of the slow computational speed caused by the high complexity of spatiotemporal search in the collaborative network model. At the same time, an intelligent supply-demand adaptation method driven by the quality of service (QoS) is established, in which the experiences of adaptation are shared among adaptation subdomains through deep reinforcement learning (DRL) powered by a transfer mechanism to improve the poor adaptation results caused by dynamic uncertainty. The results show that the model and the solution proposed in this paper can perform collaborative and intelligent supply-demand adaptation for the massive and dynamic resources in SCM through autonomous learning and can effectively perform global supply-demand matching and optimal resource allocation.     

基于改进NSGA-Ⅱ的云制造服务组合优化问题研究

为了实现企业在云制造环境下的节能,提出一种新的云制造服务组合优化方法,该方法既能降低能耗,又能在考虑不确定性的情况下提高服务质量。然后利用双目标模型和改进的NSGA-Ⅱ解决制造服务组合优化问题。实例研究结果表明:该模型从云制造服务平台(CMSP)的角度有效地控制了能耗,改进后的NSGA-Ⅱ在解决该问题上具有与MOPSO和标准的NSGA-Ⅱ相比的准确性和收敛性优势。     

Natural Language Semantic Construction Based on Cloud Database

Natural language semantic construction improves natural language comprehension ability and analytical skills of the machine. It is the basis for realizing the information exchange in the intelligent cloud-computing environment. This paper proposes a natural language semantic construction method based on cloud database, mainly including two parts: natural language cloud database construction and natural language semantic construction. Natural Language cloud database is established on the CloudStack cloud-computing environment, which is composed by corpus, thesaurus, word vector library and ontology knowledge base. In this section, we concentrate on the pretreatment of corpus and the presentation of background knowledge ontology, and then put forward a TF-IDF and word vector distance based algorithm for duplicated webpages (TWDW). It raises the recognition efficiency of repeated web pages. The part of natural language semantic construction mainly introduces the dynamic process of semantic construction and proposes a mapping algorithm based on semantic similarity (MBSS), which is a bridge between Predicate-Argument (PA) structure and background knowledge ontology. Experiments show that compared with the relevant algorithms, the precision and recall of both algorithms we propose have been significantly improved. The work in this paper improves the understanding of natural language semantics, and provides effective data support for the natural language interaction function of the cloud service.     

Collaboration of large equipment complete service under cloud manufacturing mode

This paper focuses on the need of the large equipment manufacturing industry to adapt collaborative operation to transform the industry to cloud manufacturing services and to solve the new problem of federal resources coordination in complete service operation. We systematically study federal resources cooperation under cloud manufacturing mode to complete a large complex project. The primary research contents are divided into four points. First, a system structure of cloud manufacturing service mode is presented. Second, a synergy logic framework from the global system perspective is designed based on generalised partial global planning. Third, a multi-level system coordination mechanism is established by integrating various methods, including the bidding game mechanism for enterprise external resources, the planning control mechanisms for enterprise internal resource and the global collaborative optimisation mechanism for enterprise global federal resources. Finally, a cloud manufacturing service platform for a typical enterprise is developed by combining theory with practice. The results can realise collaborative management in resource selection and configuration, service process planning control and service information feedback in cloud manufacturing service, as well as achieve overall synergy effect for the system.     

面向中小包装企业的云制造服务平台研发与应用

目的提高中小包装企业的综合竞争能力,满足客户的个性化需求。方法研发了一种采用多家企业、多个订单、多方协作、多维度服务交易的中小包装企业的云制造服务平台,基于创新智能服务的思想设计出了云制造服务平台的工作流程和核心功能。结果针对云制造服务平台的交易协同逻辑、供需智能匹配方法等关键问题进行了研究,实现了基于关键字的语义智能搜索、交易协同、订单跟踪以及全过程的协同化智能管理。结论以包装产品的研发、设计、生产、销售、物流过程应用为例,仿真分析订单完成效率提升20.5%,提高了整体包装效率、质量与顾客满意度,证明了云制造服务在包装行业的可行性和必要性。     

高压电器设备包装材料优选研究

目的 实现高压电器设备包装材料的优选，提高包装材料选择的一致性和经济型，降低制造成本。方法 建立一种多目标材料选择优选模型，包括包装承载力、包装可靠性、包装成本、资源消耗、包装绿色性等5个优化目标，使用组合隶属度函数构建评价指标集，运用灰关联法与基于可能度排序算法的模糊层次分析法相结合的方法，实现材料优选。结果 候选材料的关联系数分别为0.745、0.606、0.669、0.749。结论 关联程度最大的包装材料为最终优选包装材料。     

面向用户个性化需求的云服务商推荐

针对当前不具备专业知识的用户难以从海量云服务中选择满足其偏好的云服务商的问题，构建了满足用户需求偏好的云服务商推荐模型。该模型包括以下3部分：首先，从用户角度，通过模糊评价的方法确定并衡量用户对云服务的需求偏好；其次，从云服务商角度，通过模糊评价法和熵权法确定并衡量其满足用户需求的能力；最后，利用相似距离公式，将用户与候选服务商的相似性程度进行排序，向用户推荐最匹配的云服务商。算例结果表明，与传统的推荐方法相比，该模型能够更好地针对用户对云服务各项指标的偏好进行推荐，提高了用户选择云服务商的准确性。     

Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet. The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric. In a hyperconverged cloud ecosystem environment, building high-reliability cloud applications is a challenging job. The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings. The emergence of cloud computing is significantly reshaping the digital ecosystem, and the numerous services offered by cloud service providers are playing a vital role in this transformation. Hyperconverged software-based unified utilities combine storage virtualization, compute virtualization, and network virtualization. The availability of the latter has also raised the demand for QoS. Due to the diversity of services, the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical, common, and impactful parameters. It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs. This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters: service quality, downtime of servers, and outage of cloud services.     

一种QoS感知的语义Web服务组合群决策算法

针对QoS信息不确定和存在多个决策者的语义Web服务组合问题,基于多属性群决策理论给出了一个自治的语义Web服务组合群决策算法(AGSC).该算法能够对以实数型、区间型和语言型数据描述的复杂的QoS信息进行综合评估,从而为多客户提供正确、高效的决策支持,为其优选出最佳的组合服务执行计划.利用真实Web服务的质量对该算法进行了实验验证,结果表明该算法具有优秀的决策灵敏性和稳定性,并能有效地反映决策群中个体角色的变化.     

Correlation-aware manufacturing service composition model using an extended flower pollination algorithm

Due to the emergence of cloud computing technology, many services with the same functionalities and different non-functionalities occur in cloud manufacturing system. Thus, manufacturing service composition optimisation is becoming increasingly important to meet customer demands, where this issue involves multi-objective optimisation. In this study, we propose a new manufacturing service composition model based on quality of service as well as considerations of crowdsourcing and service correlation. To address the problem of multi-objective optimisation, we employ an extended flower pollination algorithm (FPA) to obtain the optimal service composition solution, where it not only utilises the adaptive parameters but also integrates with genetic algorithm (GA). A case study was conducted to illustrate the practicality and effectiveness of the proposed method compared with GA, differential evolution algorithm, and basic FPA.     

基于改进HMM和Pearson相似度分析的滚动轴承自适应寿命预测方法

基于数据驱动思想,提出了一种相同工况下的滚动轴承寿命预测方法。针对轴承全寿命监测数据,根据K-means聚类算法划分轴承运行状态空间,考虑到隐马尔科夫模型主链为状态链的不足,对状态转移矩阵重新定义,将主链改进为寿命链,建立了基于改进HMM的全寿命状态驻留时间模型;将观测轴承数据、实时与建模数据进行Pearson相似度分析,构造寿命比例调节系数,实现寿命模型参数的动态修正和观测轴承寿命的自适应预测。采用美国辛辛那提大学实验中心轴承试验数据开展了应用研究,通过一组轴承全寿命数据实现了对其它轴承不同阶段及全寿命的预测,与传统的隐马尔科夫模型、灰色模型预测等方法预测结果相比,所提算法兼具较好的预测准确性和模型的泛化性。     

一种零件综合质量评定方法研究

零件的质量评定是柔性智能制造中十分重要的环节。现有的自动化识别装置一般采用非人工接触的光学检测系统,但由于工况环境复杂,诸多干扰因素均会影响零件质量检测与评定的准确性。另外,工业现场的连续作业对工控机硬件的运行速度、光学检测系统的环境适应性以及质量评定算法的预测准确性都提出了更高的要求。基于此,提出一种基于机器视觉与机器学习的零件综合质量评定方法。首先,借助机器视觉技术完成被测零件图像的实时采集与处理,并利用灰度匹配算法与几何匹配算法对零件的图像与CAD（computer aided design,计算机辅助设计）机械加工图进行比较,求解得到灰度匹配分数与几何匹配分数这2个几何特征参数。然后,针对零件表面的缺陷（如划伤、磨损、边缘缺料及锈蚀等）,在图像预处理（灰度化、图像增强、高斯降噪和二值化）的基础上,求解得到图像灰度的均值和标准差这2个表面缺陷特征参数。最后,借助主成分分析（principal component analysis, PCA）对零件的四维特征数据集进行降维处理,并利用K最近邻（K-nearest neighbor, KNN）算法对降维后的数据集进行训练和预测,完成零件综合质量评定;在此基础上,比较KNN算法与其他机器学习算法的准确率、召回率和特异度等指标,以验证其可行性。实验结果表明,所搭建的光学检测与处理系统在不同光源条件下的识别准确率达到96.15%以上;当相机的快门时间设定为100 μs时,该系统的图像处理速度达到45.2 帧/s。所提出的零件综合质量评定方法具有较高的准确率与处理速度,适用于复杂工况下零件的综合质量评定。