Abstract
The driving force of high-quality development of regional economy is inseparable from the support of technology. With the support of big data, we need to solve this problem in order to solve the difficulty of large-scale experimental testing and accurately reflect the feasibility growth of data sample changes. This paper proposes a discrete dynamic modeling technology based on big data background to analyze the development and change of regional economy. The reliability AMSAA model is usually used for dynamic discrete modeling. It can be combined with the change data provided by big data to form a dynamic modeling method for reliability growth evaluation. Then, the Bayesian regression method is used to predict the change parameters of the model, and the spatial econometric method is used to analyze the regional economic change. The results show that compared with the traditional methods, the discrete dynamic modeling method is more accurate and can effectively solve the problem of reliable growth under the condition of big data. After introducing the spatial effect measurement model, it can also reflect the main factors of the growth and change of regional economic real output value. In addition to the development of high and new technology, terrain factors, investment, and government support have also had different effects. Therefore, according to the above results, it is proved that the discrete dynamic modeling technology can accurately obtain the experimental data and provide reliable technical support for dynamic data processing.
Highlights
With the continuous development of regional economy, the statistical work related to the current situation of regional economic development and influencing factors has become a key task [1]
In the big data survey, we found that there is a linear relationship between technical efficiency and regional economic development. e AMSAA model is a widely used reliability growth model, and its interval estimation coefficient of MTBF is urgently needed in engineering. is paper gives some results, but it is far from showing the overall needs of engineering practice. e AMSAA model and Bayesian method are used to study the experiment. e purpose of introducing Bayesian method is to be applicable to any capacity data sample, and the reliability dynamic modeling can objectively evaluate the parameter distribution for analysis
Compared with the traditional discrete linear mathematical model, the dynamic modeling technology can deal with the error caused by data changes and improve the numerical accuracy in the process of analyzing regional economy
Summary
With the continuous development of regional economy, the statistical work related to the current situation of regional economic development and influencing factors has become a key task [1]. In the face of the above situation, the research on the impact of technical efficiency on regional economic change is proposed, and the discrete dynamic system modeling technology supported by big data is adopted [11]. E first part introduces the current situation of technical efficiency in the influencing factors of high-quality development of regional economy under the background of big data and the application of prediction modeling methods adopted by various countries in the exploration of influencing factors of regional economic development. E second part first uses the dynamic modeling method of complex system reliability growth to explore the impact of technical efficiency on regional economic development and uses spatial econometrics based on big data to analyze the factors of regional economic change. Is paper is mainly divided into three parts. e first part introduces the current situation of technical efficiency in the influencing factors of high-quality development of regional economy under the background of big data and the application of prediction modeling methods adopted by various countries in the exploration of influencing factors of regional economic development. e second part first uses the dynamic modeling method of complex system reliability growth to explore the impact of technical efficiency on regional economic development and uses spatial econometrics based on big data to analyze the factors of regional economic change. e third part analyzes the research results of complex system discrete dynamic modeling technology in the impact of technical efficiency on regional economic development, analyzes the research results of regional economic change factors under spatial econometric research, and analyzes the causes of regional economic development and change
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