Optimization of Regional Economic Industrial Structure Based on Edge Computing and Fuzzy K -Means Clustering

Abstract

This paper proposes a fuzzy $K$ -means clustering-based optimization method for regional economic industrial structure. This paper discusses the multisubject of regional industrial planning, as well as its position and function in planning, based on research into regional industrial planning system design. This paper proposes a regional industrial planning model based on regional economic differences after summarizing the general methods of regional industrial planning and the disadvantages of traditional regional industrial planning. This paper uses edge computing technology to conduct a category analysis of various industrial cities and develops three models to study the evolution law of industrial structure: alternative evolution model, unbalanced evolution model, and diffusion evolution model. The optimization model of industrial structure is established by analyzing and sorting the various influencing factors of the evolution of urban industrial structure. This paper conducts a simulation experiment to verify the content arrangement of regional industrial planning and the design theory of regional industrial structure optimization system. The findings of this study have theoretical and practical implications for the study of regional industrial structure. For the optimization of regional industrial structure, it has a certain reference value.