Innovative Economic Development Path Using Double-Cycle New Pattern Data Discovery for Industrial Internet of Things

Abstract

The use of smart sensors and actuators to improve industrial and manufacturing processes is known as the industrial internet of things (IIoT). Innovative economic growth is one of the key elements required for the success of IIoT in contemporary industry. Innovation in IIoT is essential for fostering high-quality economic growth and achieving a competitive edge. This study aims to conduct in-depth research on the development path of an innovative economy based on data mining under the new pattern of double circulation in order to enhance industrial innovation capability, realize the modernization of the industrial chain, and accelerate the development of industrial innovation in IIoT. The first step is to use the five urban agglomerations for path analysis of innovative economic development. The five metropolitan agglomerations’ pertinent facts are provided, and their industrial structure’s composition and proportion as well as the input and output of innovation are all examined. We then constructed a model for analyzing the link between technological innovation and economic growth. The investigation of the innovative economic development path based on data mining is achieved using the Microsoft time series technique and the expectation-maximization algorithm to examine the data of innovative economic development. The experiment demonstrates that the method proposed in this study has strong data mining stability and ideal data clustering advantages. In IIoT, it can be used to effectively increase data mining’s efficiency and innovation capacity for the growth of a knowledge-based economy and creating a new pattern in which innovation and market growth are mutually reinforcing.