Prediction Research on the Aggregation Degree of Regional Science and Technology Innovation Talents

Abstract

In order to provide data support for regional science and technology innovation talents strategy formulation and achieve regional social economic sustainable development, the improved unbiased GM(1,1) model and the improved gray target model are used by the paper to predict the regional science and technology talents aggregation. Firstly, based on the relevant research results, the factors affecting the aggregation degree of regional science and technology innovation talents is analyzed by the paper, and the index system of the aggregation degree of regional science and technology innovation talents has been constructed. Secondly, the unbiased GM(1,1) model has been selected as the basic model and the improved unbiased GM(1,1) has been constructed by smoothed processing of the raw sequence, the improvement of the background value and the dynamic processing of the model. A reasonable prediction of the future development trend of the factors technology innovation talents has been made, and the improved gray target model has been used to synthesize the forecast data of each index to determine the predicted value of the final aggregation degree of regional science and technology innovation talents. Finally, the relevant data on the aggregation of science and technology innovation talents in Shaanxi Province has been collected by the paper, and the future development of the aggregation degree of science and technology innovation talents in Shaanxi Province has been predicted. The empirical results show the validity and accuracy of the model.