Study On Demand Forecasting Model Of Regional Science And Technology Talents

In the knowledge economy era, science and technology has growing integrationof economic and social development, and its development level decides the regionalindustrial structure and core competitiveness. Science and technology talents as themost fundamental scientific and technological facilitators, has became the mostimportant resources of regions. The study on regional science and technology talentsdemand forecasts is basic research for regional development, which is not only relatedto the development rule of the talents, but also to regional sustainable development,and it is an important source of information for regional planning.The current regional science and technology talents demand forecasting hasencountered new difficulties, such as changes in standards, the shortage of historicalinformation, the combined effects of multiple factors, and so on. This paper aims toaddressing the above problems encountered in the study, focusing on regional scienceand technology talents'status quo, trying to innovate the quantitative predictingmethods, establishing the advanced demand forecast model, providing scientificreference for the regional government and talents employing units to make decisions.In this paper, we use combined research methods: theoretical analysis andempirical research, quantitative analysis and qualitative analysis. Key activitiesinclude: defining the regional science and technology talents standards, formulatingthe Index System corresponding economic, social and technological factors from thepoint of operational view; establishing the demand forecast model based on BP neuralnetwork using Matlab for computing platforms. We select Henan and Inner Mongoliafor empirical analysis, analyzing the regional science and technology talents from thepersonnel structure, the pattern of distribution, and the comparative advantage. At theend of this paper, we apply the model to forecasting the science and technologytalents demand in these regions, inspecting and verifying the accuracy of this model.