山西省经济转型发展中科技创新人才供需矛盾分析

发布时间：2018-04-13 19:07

本文选题：科技创新人才 + 灰色神经网络预测　；参考：《太原理工大学》2014年硕士论文

【摘要】：山西资源型经济的顺利转型必须依靠先进的科技水平,科技创新人才作为知识生产、传播与扩散的主体,是技术进步的主导力量,在经济转型发展过程中,区域内的科技创新人才队伍建设的水平直接关系到区域经济增长方式的优化升级。山西省作为经济改革试验区,是国家规定的经济转型重点区域,全方位、深层次的经济转型需要高质量、全能型的科技创新人才队伍作为支撑。因此,对科技创新人才在数量、质量、产业布局等方面的相关研究,能够提高区域经济发展的技术、创新能力,为实现最优化的经济发展模式提供人才资源保障。本文立足于山西省资源型经济转型发展方向,通过分析转型过程中所需科技创新人才类型与山西省目前人才结构现状,在人力资本的杰出贡献性、人才结构与产业结构互动关系性、人才结构调整方向性的理论作为指导的前提下,借助于山西省科技创新人才方面的相关统计数据,建立了以BP神经网络为基并且引入灰色预测理论的灰色神经网络预测模型,对山西省2014—2020年科技创新人才从总量、学历层次质量、产业布局三方面进行了预测,利用科技创新人才的动态供给来源,分析了供需之间的矛盾缺口,指出：(1)科技创新人才在总量上仍将供给不足,供需矛盾显著；(2)科技创新人才存在供需结构矛盾,制造业、高技术产业对科技创新人才有较大需求量,增长速度都很快；(3)科技创新人才对学历层次的需求表现为对研究生高学历科技创新人才需求的增长速度明显高于本科及以下学历人才。基于此,分别以政府、高校、企业作为科技创新的主体提出了人才培养的政策建议。本文的选题立足于山西省经济转型发展的关键时期对科技创新型人才提出的挑战,使研究对象有了切实的环境依据,也使得本文在数据统计分析时便于确定合理的时间界限,同时也为山西省在经济转型期的人才培养指明了方向。选择的灰色神经网络预测模型在预测精度上高于单一的灰色预测和BP神经网络预测,使预测结果有较强的现实指导意义。即使预测是借助于一定的理论指导和数学模型,但其本身受时间因素的影响,会导致预测结果精度的降低,为了顺利的实现经济转型,还需在实践过程中对预测结果进行不断的调整,完善科技创新人才的培养策略。
[Abstract]:The smooth transformation of Shanxi's resource-based economy must depend on the advanced scientific and technological level. As the main body of knowledge production, dissemination and diffusion, the talents of scientific and technological innovation are the leading force of technological progress, and in the process of economic transformation and development,The level of scientific and technological innovation talents in the region is directly related to the optimization and upgrading of regional economic growth mode.Shanxi Province, as the experimental area of economic reform, is the key area of economic transformation stipulated by the state. The all-round and deep-level economic transformation needs the support of high-quality, all-purpose scientific and technological innovation talents.Therefore, the research on the quantity, quality and industrial layout of scientific and technological innovation talents can improve the technology and innovation ability of regional economic development, and provide the guarantee of human resources for realizing the optimal economic development model.Based on the development direction of Shanxi resource-based economy transformation, this paper analyzes the types of scientific and technological innovation talents needed in the process of transformation and the present situation of talent structure in Shanxi Province, and makes outstanding contributions to human capital.Under the guidance of the theory of talent structure adjustment direction, with the help of the relevant statistical data of scientific and technological innovation talents in Shanxi Province,Based on BP neural network and introducing grey prediction theory, the prediction model of scientific and technological innovation talents in Shanxi Province from 2014 to 2020 is established, which is based on BP neural network, and forecasts the total amount, educational level quality and industrial layout of Shanxi Province from 2014 to 2020.Using the dynamic supply sources of scientific and technological innovation talents, this paper analyzes the contradiction gap between supply and demand, points out that the total quantity of scientific and technological innovation talents will still be insufficient, and the contradiction between supply and demand is obvious. 2) there is a structural contradiction between supply and demand of scientific and technological innovation talents, and the manufacturing industry.The high-tech industry has a great demand for scientific and technological innovation talents.The demand of scientific and technological innovation talents for the educational level is obviously higher than that for graduate students with high academic qualifications and below.Based on this, the government, universities and enterprises as the main body of scientific and technological innovation put forward the policy recommendations of talent training.The topic of this paper is based on the challenge to the talents of science and technology innovation put forward in the key period of the economic transformation and development of Shanxi Province, which makes the research object have a practical environmental basis, and makes this paper easy to determine the reasonable time limit when the data is statistically analyzed.At the same time also for Shanxi Province in the economic transformation of talent training pointed out the direction.The prediction accuracy of the selected grey neural network prediction model is higher than that of the single grey prediction model and the BP neural network prediction model, which makes the prediction results more practical and instructive.Even if the prediction is based on certain theoretical guidance and mathematical model, but it is affected by time factors, it will lead to the reduction of the accuracy of the prediction results, in order to achieve a smooth economic transformation,In the process of practice, the forecast results should be adjusted constantly, and the training strategy of scientific and technological innovation talents should be perfected.
【学位授予单位】：太原理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：C964.2

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