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科技创新人才关键要素之系统研究
中文摘要

面对世界科技发展的新形势和日趋激烈的国际竞争,我国已把走创新型国家发展道路作为我国面向2020年的战略选择。其中,科技创新作为在科学技术领域的创造与革新的活动,是解决发展中面临的重大问题的根本,是建设创新型国家的一个重要方面。因此,如何推进科技自主创新,以科技创新带动建设创新型国家进程,成为当前我国面临的重大问题。科技创新人才的缺乏是科技创新活动中最显著的问题,而以往针对科技创新人才的研究人多停留在定性分析层面,缺少系统研究成果,难以挖掘科技创新人才所具有的关键要素。论文将管理科学、系统工程、脑认知和机器智能的相关理论应用于科技创新人才关键要素的系统研究中,从定性到定量综合集成,概念加工与数据加工协同运用,通过理论建模分析科技创新人才相关指标,并运用实证研究挖掘不同层次科技创新人才的关键要素,对我国科技创新人才的培养有着重要的理论意义和社会价值。 论文以科技创新人才为研究对象,从多学科层面系统分析科技创新人才所具备的关键要素,提出了影响科技创新人才的指标体系框架。在科技创新人才指标体系的基础上,按照定性分析、数据方案设计、定量关联分析、多学科系统建模、仿真与实证分析、构建决策支持系统原型这一完整的研究体系,对科技创新人才关键要素进行了系统研究。论文的主要研究工作和创新点如下: (1)从良好的知识修养、优秀的人格素质、健康的体魄和健全的心理四个层面系统分析了科技创新人才七商关键要素,定义了七商概念并详细分析了七商指标要素,进一步构建了科技创新人才关键要素指标体系,为后续系统研究科技创新人才提供理论基础。 (2)基于科技创新人才关键要素指标体系,定性分析了七商及七商指标的关联关系,构建了科技创新人才匹配模型,用高层次科技创新人才的七商指标数据作为衡量标准,利用统计方法计算匹配阈值,以判别样本是否符合科技创新人才标准。进而通过构建基于多层交互式权重和熵值理论的模糊综合评价模型对样本个体的科技创新层级等级进行系统评价,确定样本个体具体的科技创新层次。 (3)通过定量研究七商指标间的关联关系,构建科技创新人才无向加权小世界复杂网络的拓扑模型,综合分析基于局部、全局和综合属性的网络节点重要性评价指标,计算科技创新人才复杂网络节点重要度,挖掘影响科技创新人才的关键要素。进而,利用“加权平均最短路径”和“自然连通度”网络抗毁性测度指标分析不同层次的科技创新人才复杂网络在蓄意和随机攻击策略下的抗毁性变化情况,以研究不同层次科技创新人才的关键要素培养。 (4)研究人脑的生理基础与科技创新人才关键要素的关联,研究不同脑功能结构对个体七商相关指标的影响。基于人类的认知过程从概念加工、数据加工、双向协同加工三种认知模式研究科技创新关键要素在加工过程中起到的作用,并采用概念加工人才分类方法——贝叶斯分类器挖掘不同层次人才的差别。定量地研究不同素质在科技创新人才成长中的作用,利用时间序列模型进行典型样本发展曲线推导,研究杰出科技创新人才以及一般人员的数据双向加工中的七商各个要素的作用。利用收集到的案例数据通过基于谱系聚类的先验决策模型,分析科技创新人才成长所需的关键指标。 (5)通过研究机器智能和人类智慧之间的异同,分析机器智能在科技创新活动中的辅助功能,提出了人类智慧和机器智能的协同发展模式。采用特征选择方法,定量分析科技创新人才发展的关键要素,进而利用SVM研究不同层次科技创新人才的主要差别。然后,利用Apriori算法定量分析七商之间的促进、抑制或协同关系,利用神经网络进行不同类型的人才分类对未知样本的正确分类提供了一种新的模型,为提出科技创新人才的培养方案提供依据。 (6)科技创新人才关键要素决策系统支持系统管理员从发布测评任务到具体的单位或企事业单位人才完成七商测评的流程,主要用户包括后台管理员、人才管理员、部门、人才、企事业单位和政府机关。基于各类用户的需求不同赋予各类用户不同的权限。然后,通过匹配模型、模糊综合评价模型、复杂网络模型、 K-means聚类模型、贝叶斯分类器模型、层次聚类模型、谱系聚类模型、支持向量机模型等实现决策支持,给出个人用户七商情况的测评并给出提高七商等级的建议,实现政府机关、企事业单位对各部门人才的管理、测评和建议。 论文通过以上研究内容,对科技创新人才关键要素进行系统研究,从多学科角度建立了研究模型,并利用所收集的样本数据进行实证研究。通过对相关理论分析和实证研究结论的详细解析,挖掘不同层次科技创新人才的关键要素,进而利用所构建的科技创新人才管理和支持系统为不同层次科技创新人才的培养提供理论和决策支持。 关键词 科技创新人才;关联分析:综合评价;复杂网络;脑认知;机器智能;决策支持系统原型

英文摘要

In the face of the new situation of world science and technology development and the increasingly fierce international competition, China has taken the road of innovation and development as a strategic choice for itself in 2020. As an activity in the field of science and technology, science and technology innovation is the fundamental to solve the major problems during developing, and an important aspect of building an innovative country. Therefore, how to promote independent innovation of science and technology, and drive the process of an innovative country become the current major problems China facing. Scientific and technological innovation is the main body of scientific and technological innovation activities, and the lack of quantity and the low quality of innovation is the most significant problem that affects the role scientific and technological innovation talents playing in scientific and technological innovation activities. Unfortunately in the past, most of the researches on scientific and technological talents have been at the level of qualitative analysis, lacking systematic research results and made it difficult to find the key qualities of technological innovation talents. This paper applies the theories of management science, system engineering, brain cognition and machine intelligence to the research of the key qualities of scientific and technological innovation talents, using integrated integration from qualitative to quantitative analysis and synergistically using the concept processing and data processing. It is of great theoretical and social value to cultivate talents of scientific and technological innovation in China by analyzing the relevant indexes of scientific and technological innovation talents based on theories and digging the key qualities of scientific and technological innovation talents through empirical research at different levels. Scientific and technological innovation talents are researched in this paper, and the key qualities of scientific and technological innovation talents are analyzed systematically by many subjects, and the index system framework affecting scientific and technological innovation talents is put forward. This is a complete research system through qualitative analysis and quantitative data design, association analysis, multidisciplinary system modeling, and simulation and empirical analysis method is established, and the decision support system prototype system is used to assist decision-making, and the key quality of scientific and technological innovation talents is systematically studied. The main research work and innovations of this thesis are as follows: (1)From the four aspects of good knowledge cultivation, excellent personality quality, healthy physique and sound psychology, this paper analyzes the key qualities of scientific and technological innovation talents, defines the concept of seven quotients and analyzes the elements of seven business indicators in detail And constructs the key quality index system of scientific and technological innovation talents, and provides the theoretical basis for the follow-up system to study the talents of scientific and technological innovation. (2)Based on the key quality index system of scientific and technological innovation talents, the paper analyzes the relationship between the seven quotients and the seven quotients indicators qualitatively, constructs the science and technology innovation talent matching model, uses the high level scientific and technological innovation talents as the measure standard, uses the statistics method to calculate the matching threshold to determine whether the sample meets the standards of scientific and technological innovation. And then constructs the fuzzy comprehensive evaluation model based on the multi-layer interactive weight and entropy theory to systematically evaluate the level of scientific and technological innovation of the sample individual and determine the specific level of scientific and technological innovation of the sample individual. (3)Through the quantitative study of the relationship between the seven quotients indicators, building a scientific and technological innovation talent to the weighted world of small network topology model, a comprehensive analysis based on local, global and comprehensive attributes of the network node importance evaluation indicators, scientific and technological innovation talent The importance of complex network nodes, and the key qualities that affect the talents of scientific and technological innovation. Furthermore, the use of “weighted average shortest path” and “natural connectivity” network survivability measure index analysis of different levels of scientific and technological innovation talent complex network in the deliberate and random attack under the anti-destructive changes in the situation to study different levels of scientific and technological innovation the key quality of personnel training. (4)Studying the relationship between the physiological basis of human brain and the key quality of scientific and technological innovation talents, and studying the influence of different brain functions and structures on the seven quotients of individual. Based on the concept of human cognitive process, the effect of the key quality of scientific and technological innovation playing in the process of processing is studied from the respects of three kinds of cognitive model of processing, data processing and two-way cooperation processes. The effect of different qualities in science and technology innovation talents is quantitatively studied, the development of the typical sample curve are derived by using time series model, taking various elements of seven outstanding technological innovation talents as well as the general staff in the role of two-way data processing. By using the collected case data, the key indicators for the growth of scientific and technological innovation talents arc analyzed through a priori decision model based on pedigree clustering. (5)By studying the similarities and differences between machine intelligence and human intelligence, this paper analyzes the auxiliary function of machine intelligence in scientific and technological innovation activities, and puts forward the cooperative development model of human intelligence and machine intelligence. By using the method of feature selection, the key quality of scientific and technological innovation talents development is quantitatively analyzed, and then the main differences of scientific and technological innovation talents at different levels arc studied by using SVM. Then, the Apriori algorithm is used to quantitatively analyze the promotion, inhibition, or coordination between the seven firms, using neural networks to classify different types of talents provides a new model for the correct classification of unknown samples, and provides the basis for the training program of scientific and technological innovation talents. The administrator of Scientific and technological innovation personnel key quality decision-making system from the release of evaluation tasks to individuals, enterprises, government agencies and other three categories of users to complete the seven business evaluation process. First of all, based on the background administrator, talent managers, department managers, individuals, enterprises, government agencies and other users of the different needs of different users to give different permissions. And then through the matching model, fuzzy comprehensive evaluation model, complex network model, K-means clustering model, Bayesian classifier model, hierarchical clustering model, pedigree clustering model, support vector machine model to achieve decision support, Users of the seven quotients situation evaluation results, and proposed to improve the seven quotients-level targeted recommendations, so that individual users, enterprises and institutions, government agencies can manage and develop the key qualities of talent better. Through the above research contents, this paper systematically studies the key qualities of scientific and technological innovation talents, establishes the research model from the multi - disciplinary point of view, and makes use of the collected sample data to carry on the empirical research. Through the detailed analysis of relevant theoretical analysis and empirical research, we can tap the key qualities of scientific and technological innovation talents at different levels, and then use the constructed science and technology innovation talent management and support system to provide theoretical and decision support for the cultivation of scientific and technological talents at different levels. Keywords scientific and technological innovation talents; correlation analysis; comprehensive evaluation; complex network; brain cognition; machine intelligence; decision support system prototype

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