基于隐性知识的复杂系统创新设计系统关键技术研究

项目名称： 基于隐性知识的复杂系统创新设计系统关键技术研究

项目编号： No.51475097

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 机械、仪表工业

项目作者： 李少波

作者单位： 贵州大学

项目金额： 82万元

中文摘要： 对于存在于专利技术中却很难进行明确表述与逻辑说明的隐性知识，已成为企业竞争优势的源泉。有效的挖掘与利用这类知识对于复杂系统的创新设计具有重要意义。项目着眼于复杂系统创新设计的基础问题，研究隐性知识的获取方法，建立隐性知识的重构与运用模式。从认知角度，构造综合了发散思维与逻辑思维的基于进化的创新性概念设计过程模型。基于博弈理论，构建实现创新性概念设计过程模型成长的可以充分利用隐性知识的博弈进化算法。最后，以创新性概念设计模型为依据，以博弈进化算法为复杂系统不断优化的工具，建立基于隐性知识与博弈进化的复杂系统创新设计系统，并以混合动力系统、混合动力挖掘机等复杂系统为对象，应用本系统进行产品创新方案设计验证相关成果。相关研究的进展和突破，将进一步丰富复杂系统创新设计系统的理论体系，为我国复杂系统创新设计提供一种有效的手段。

中文关键词： 隐性知识；智能设计；优化设计；知识工程；进化博弈

英文摘要： Tacit knowledge existed in patent technology is very difficult to description clear and logically, which has remarkable effect on enterprise competitive edge. It is significant in the innovative design of complex systems to mining and make good use of tacit knowledge effectively. Firstly,the fundamental problem of innovative design of complex system is focused on, and the algorithm to obtain the tacit knowledge, the technology to refactor the tacit knowledge as well as to use it will be investigated. From the cognitive perspective, the innovative conceptual design process model will be established by synthesizing the divergent thinking and logical thinking.Secondly, based on game theory, a kind of evolutionary game algorithm will be proposed, which can make good use of tacit knowledge and has the evolutionary mechanism for the innovative conceptual design process model. Finally, the creative design system for complex system based on based on tacit knowledge and evolutionary game theory will be established by using innovative conceptual design process model and evolutionary game algorithm, and then the innovation design of hybrid systems and hybrid excavator will be taken as applied instance to verify that system and the related work. The research progress and breakthrough will further enrich the theoretical system of complex system innovation design system and provides an effective means for innovative design of complex system in our country.

英文关键词： Tacit Knowledge;Intelligent design;Optimization design;Knowledge engineering;Evolutionary game