| Innovation ecosystem of intelligent manufacturing is an ecological and organic system, which is the synthetic result of technical development, international competition and eco-development. It has the integrated advantages of open innovation, green innovation, sustainable innovation, network innovation and so on. However, the deep integration of emerging information technologies and innovation process and innovation management are making the innovation more personalized, the resources more service-oriented, the creativity more socialized and more collaboration needed during the innovation process. The organization of innovation resources is beyond the enterprise boundaries. In most cases-, they are organized by the means of intelligent innovation service unit alliance, changing the form of innovation alliance. Socialized network cooperation is the main mode of innovation activities. As a result of these changes, the research focus is changing from the static structural problems of innovation system to the dynamic self-organization problems of socialized service resources of the innovation ecosystems. Therefore, it puts forward some new challenges to the study and application of self-organization theory in the innovation ecosystem.In this paper, the dynamic self-organization of innovation resources is studied based on the new characteristics of innovation service resources. Firstly, in view of the innovation objects being smart and connected, this paper studied the partner selection of innovation service groups under the smart and connected product structure. Then,according to the characteristics of small service group in the innovation ecosystem,a self-coordination task allocation method is proposed for the innovation service group.Finally,a new evolutionary method of service network based on structure entropy is proposed, where the new network structure characteristics of the social innovation service groups are considered.Contributions of this dissertation include:(1) A new product mode is given under the context of the innovation ecosystem development. Cyber components and physical components are the basic elements of smart and connected products. Spatial, energy, informational, material and logical dependencies are the interdependent modes between different components. Cyber components and the logical dependency not only amplify the capabilities and value of the physical components,but also enable them to exist outside the physical components,and finally form the product cloud. It provides a reference frame for the interactive process management of collaborative innovation in the innovative ecosystems.(2) Based on the isomorphism between the product and the innovation group, the paper analyzed the influence mechanism of the product structure on the R&D partner selection, and pointed out that the interaction mode between product components defines the capability areas of R&D partners. Multi-domain matrix is used to extract the ideal synergy matrix from the product structure, which is used as the partner selection criteria. Based on the technological information interaction among different R&D partners, the real synergy matrix of them is calculated. Synergy deficit is proposed to characterize the satisfaction level of R&D members to product structure coordination requirements. Finally, the selection model of product R&D members is constructed with their collaborative cost. The experiment proves that this method can improve the synergy level of the innovation service group, and it provides the technical support for maximizing the utilization of social service resources.(3) A conciliation mechanism for self-organizing dynamic small groups in the innovation ecosystem is proposed. The service group in the innovation ecosystem is a small group of multiple social innovation service units. This paper analyzed the self-coordination process of tasks allocation for such small group from the perspective of task information, task resources and task preferences. Based on the perceived state of the task of the innovation subject and the resources he has, four feasibility states of the innovation task are defined. The different state transitions of the innovation task are used to describe the influences of the members' dynamic changes on the feasibility of the group innovation task. Finally, a conciliation mechanism of the .innovation task of the service group is given where the group preferences are considered.(4) Based on the new network structure characteristics of innovation service groups,the heterogeneity analysis of multi-granularity partition and the mutual relations between social innovation service units is given. According to their different influences on the innovation network stability, the heterogeneity relations are divided into two types: positive and negative. Multiple proximities are used to characterize the strength of positive relations. The competition relation is redefined and modeled by the niche theory, which is used to describe the negative relationship intensity. Finally, based on the entropy theory, the effective measurement of influence of the granularity of innovation service units and their mutual relations on the network structure stability is realized. An adaptive evolutionary method of the innovation service network structure is proposed by using the structure entropy. It provides a new perspective of research on the co-evolution of service groups in the innovation ecosystem.According to the new characteristics of intelligent manufacturing innovation resources and their organization, this dissertation analyses the self-organization process of intelligent manufacturing innovation ecosystem from the views of its formation,operation and evolution. And for the different self-organization stages, we focus on the innovation service resource selection, innovation task allocation and innovation network evolution respectively, forms a systematic self-organization theory of intelligent manufacturing innovation ecosystem. Hope it can exploit an even wider space for future researches. |
PDF Full Text Request