Application of Mirror Materials in Wood Furniture Manufacturing Process Based on Big Data Analysis

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Abstract

With the development of the national economy, the interior decoration work has received extensive attention, and the traditional decoration methods have been unable to meet its actual work needs. The application of mirror materials in interior decoration can not only enrich the technical methods of interior decoration, but also improve the decoration quality and comfort, so as to meet people's growing decoration needs, achieving the purpose of promoting the sound development of interior decoration. Mirror material is one of the decorative materials widely used in modern indoor environment. It not only has decoration, but also can expand space. In recent years, the advent of the era of big data has promoted the reform of the design industry. Using big data thinking to assist the innovative design of furniture products will also become an important trend in the development of the furniture industry. According to the characteristics of customer demand in the era of big data, this paper designs and develops a set of customer demand analysis system based on big data by using cloud computing and big data mining technology. By collecting the behavior data of users when using products, this paper combines the mirror material with the manufacturing process of wooden furniture. The mining algorithm is applied to find out the improvement goal and direction of wood furniture manufacturing process based on mirror material, so as to enhance the competitiveness of furniture enterprises. The method proposed in this paper can obtain the furniture design elements according to the functional zoning and theme information and input the furniture design elements and zoning elements into the design element matching evaluation model to obtain the design matching element information.

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APA

Yang, Y. (2022). Application of Mirror Materials in Wood Furniture Manufacturing Process Based on Big Data Analysis. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/8283152

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