智能制造创新生态系统中服务资源的动态自组织研究

发布时间：2018-06-04 04:01

本文选题：智能制造 + 创新生态系统　；参考：《合肥工业大学》2017年博士论文

【摘要】：生态化、有机式的智能制造创新生态系统是科技进步、国际竞争及生态发展的必然结果,它不仅具有开放式创新、绿色创新、可持续创新、网络创新等多种创新模式的优势,同时由于新兴信息技术与创新过程及创新过程管理的深度融合,创新资源及其组织呈现出创新资源服务化、创新能力社会化及创新过程协同化等新的特点。创新资源的组织在范围上跨越了企业边界,从形式上体现为智能创新服务单元的联合,使得创新联盟的形态发生根本变化,创新决策跨越了整个价值链,社会化网络协同成为主要的创新活动方式。这些变化促使创新系统从以往关注创新要素构成及资源配置的静态结构性问题,逐渐演变为跨价值链的多层次创新服务资源之间相互作用机制的动态自组织问题,对自组织理论在智能制造创新生态系统中的研究和具体应用提出了新的挑战。本文针对智能制造创新生态系统中服务资源所呈现出的新特点,对创新资源的动态自组织问题展开了相关研究。首先基于创新客体不断智能互联化的发展趋势,研究了智能互联产品结构下创新服务群体的成员选择问题;然后根据创新生态系统中的小群体服务特性,给出了服务群体自主协同的任务分配方法;最后结合社会化创新服务群体所呈现出的新的网络结构特征,提出了基于结构熵的创新服务群体网络自适应演化方法。主要研究内容和创新点总结如下:(1)针对智能制造中产品形态的变化,围绕网络部件、物理部件等多种形态的产品结构,定义了智能互联产品中空间、能量、信息、物质及逻辑等五种部件依赖关系。指出社会化服务资源网络协作的产品创新是创新生态系统的主要业务模式,并基于创新服务群体与产品结构之间的协同对应关系,分析了创新生态系统中服务资源的协同机制,构建了服务资源交互协同过程管理的参考框架。为改善产品协同创新的有效性和准确性提供了理论支持,对智能制造创新生态系统中多样性服务资源协同创新的交互过程管理提供了参考依据。(2)提出了智能互联产品结构下创新服务群体的成员选择方法。基于产品结构与创新群体的结构同构性,分析了智能互联产品结构对研发成员选择的影响机理,指出产品部件之间的依赖交互模式界定了研发人员之间协同交互的能力范围,并运用多领域矩阵方法提取出服务群体满足产品结构要求的理想协同矩阵,且将其作为成员选择标准之一。从科技信息交互的维度度量了不同研发人员之间的实际协同交互能力,并据此计算得出其实际协同矩阵。综合比较理想协同矩阵和实际协同矩阵计算得出创新群体的协同赤字,用以刻画研发成员对产品结构协同要求的满足水平。最后结合研发团队的协同成本构建产品研发成员的选择模型。实验验证该方法可提升对创新服务资源的组织管理及协调控制水平,可以为最大限度的利用社会化服务资源,改善企业调动社会知识及其整合能力提供技术支持。(3)构建了创新服务群体自主协同的研发任务分配机制。智能制造创新生态系统中的服务群体是由多个社会化创新服务单元所构成的成员有限的小群体,首先从创新主体所具有的任务信息、任务资源及对任务偏好等不同角度分析了小服务群体研发任务的协调分配过程,并基于创新主体对任务目标的感知状态及所拥有的实现该任务目标的资源条件,定义了创新任务可行性的四种状态。利用创新任务不同可行性状态之间的路径转换,来刻画成员动态变化对群体创新任务可行性的不同影响方式,最终结合群体偏好给出了服务群体创新任务的协商分配机制,形成了一个基于小群体协商的创新服务群体自主协同的动态任务分配方法,是本研究在群体决策理论方面的创新。(4)从协同进化的角度,对智能制造创新生态系统中服务群体的协同演化过程进行了深入研究。针对创新服务群体所具有的不同网络结构特征,给出对社会化创新服务单元的多粒度划分及其相互关系的异质性分析。依据创新服务单元之间的不同相互关系对创新网络稳定性的影响,将不同异质性关系划分为正向关系和负向关系两大类。给出了正向关系的多维邻近性表征及度量方法,并运用生态位理论对竞争关系进行了重新定义和建模,用以描述负向关系强度。最后基于熵理论,实现了对创新服务单元多粒度性及其不同相互关系对网络结构稳定性影响的有效度量,构建了基于结构熵的创新服务群体网络结构自适应演化方法,为智能制造创新生态系统中服务群体的协同演化提供了新的研究视角。本文针对新兴信息技术环境下智能制造创新资源及其组织所呈现出的新特点,充分利用已有研究成果,系统分析了智能制造创新生态系统的形成、运行及演化等自组织过程,并就不同过程围绕基于智能互联产品结构的创新服务资源选择、创新服务群体自主协同的研发任务分配以及创新服务群体的网络协同演化等方面展开了研究,初步形成了较为完整的智能制造创新生态系统自组织的理论体系,为智能制造创新生态系统的研究开拓了更为广阔的空间。
[Abstract]:Ecological, organic and intelligent manufacturing innovation ecosystem is the inevitable result of scientific and technological progress, international competition and ecological development. It not only has the advantages of open innovation, green innovation, sustainable innovation, network innovation and other innovative models, but also because of the advanced integration of new information technology and innovation process and innovation process management. The new resources and their organizations show new characteristics such as service of innovation resources, socialization of innovation ability and synergy of innovation process. The organization of innovative resources across the boundary of the enterprise, form the form of the union of intelligent innovation service unit, making the form of innovation alliance change radically, and the innovation decision spanned the whole price. Value chain, social network collaboration has become the main innovative way of innovation. These changes have prompted the innovation system to gradually evolve into a dynamic self-organization problem of the interaction mechanism of multi-layer innovation service resources across the value chain, and the self-organization theory in intelligence. The research and application in the manufacturing innovation ecosystem put forward a new challenge. In this paper, the new characteristics of the service resources in the intelligent manufacturing innovation ecosystem are presented, and the dynamic self-organization of the innovative resources is studied. First, based on the development trend of the continuous intelligent interconnection of the innovative object, the intelligent interconnection is studied. According to the characteristics of small group service in the innovative ecosystem, the task allocation method of the independent collaboration of service groups is given. Finally, a new service group network based on the structure entropy is proposed in the light of the new network structure features presented by the socialized innovation service group. The main research contents and innovation points are summarized as follows: (1) according to the changes in the product form in intelligent manufacturing, five components, such as space, energy, information, material and logic in intelligent interconnected products, are defined, and the social service resource network is pointed out by the variety of product structures such as network components and physical components. The product innovation of collaterals collaboration is the main business model of the innovation ecosystem, and based on the cooperative correspondence between the innovation service group and the product structure, the cooperative mechanism of service resources in the innovative ecosystem is analyzed, and the reference frame of the service resource interaction and coordination process management is constructed. The effectiveness and the effectiveness of the collaborative innovation of the product are improved. The accuracy provides theoretical support and provides a reference for the interactive process management of collaborative innovation of diversity service resources in the intelligent manufacturing innovation ecosystem. (2) the selection method of the members of the innovative service group under the intelligent interconnected product structure is proposed. Based on the structure isomorphism of the product structure and the innovative group, the intelligent interlinked production is analyzed. The effect mechanism of product structure on the selection of R & D members is presented. It is pointed out that the interactive mode between product components defines the scope of collaborative interaction between R & D personnel, and uses a multi domain matrix method to extract the ideal collaboration matrix of the service group to meet the requirements of the product structure, and takes it as one of the member selection criteria. The interactive dimensions measure the actual cooperative interaction between different R & D personnel, and then calculate the actual synergy matrix. A comprehensive comparison of the ideal synergy matrix and the actual coordination matrix is used to calculate the synergistic deficit of the innovative group, which is used to describe the satisfaction level of the research and development members on the collaborative requirements of the product structure. Finally, the research group is combined with the R & D group. The cooperative cost of the team builds the selection model of the product R & D members. The experiment proves that this method can improve the level of organizational management and coordination control of innovative service resources, and can provide technical support for maximizing the use of social service resources and improving enterprises to mobilize social knowledge and its integration ability. (3) the innovation service group has been constructed. The service group in the intelligent manufacturing innovation ecosystem is a small group composed of several socialized innovation service units. First, it analyzes the coordination of the research and development task of the small service group from the task information, task resources and task preference. The allocation process, based on the perception state of the task target and the resource conditions of the task target, defines the four states of the feasibility of the innovation task. It uses the path transformation between the different feasibility states of the innovation task to describe the different influence parties on the feasibility of the dynamic transformation of the members to the group innovation task. In the end, the negotiation and distribution mechanism of the service group innovation task is given in combination with group preference, and a dynamic task allocation method based on the independent collaboration of the innovative service group based on small group negotiation is formed. It is the innovation of this research in the group decision-making theory. (4) from the perspective of synergy, the service of the intelligent manufacturing innovation ecosystem is served. According to the different network structure features of the innovative service groups, the multi granularity division and the heterogeneity analysis of the social innovation service units are given. The influence of the different relationships among the innovative service units on the stability of the innovation network will be different. The qualitative relationship is divided into two categories: positive relation and negative relationship. The multidimensional proximity characterization and measurement method of positive relation are given. The competitive relationship is redefined and modeled by using niche theory to describe the strength of negative relationship. Finally, based on entropy theory, the multi granularity and different phase of innovation service unit are realized. The effective measure of the influence of interrelationship on the stability of network structure is to construct an adaptive evolution method for the network structure of innovative service groups based on structure entropy, which provides a new research perspective for the cooperative evolution of the service groups in the intelligent manufacturing innovation ecosystem. The new characteristics of the fabric are made full use of the existing research results, and the self-organizing process of the formation, operation and evolution of the intelligent manufacturing innovation ecosystem is systematically analyzed, and the selection of innovative service resources based on the intelligent interconnected product structure, the distribution of R & D task distribution and the innovation service of the independent collaboration of the service group are innovating in different processes. The research on the cooperative evolution of the group network has been carried out, and a more complete theoretical system of self organization of the intelligent manufacturing innovation ecosystem has been formed, which has opened up a wider space for the research of the intelligent manufacturing innovation ecosystem.
【学位授予单位】：合肥工业大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：F273.1

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