Feature Vector Extraction Algorithm Based on Big Data in Engineering Quality

Abstract

With the advent of the information age, the network has played a role in promoting the development of various industries. As a construction enterprise, it is necessary to integrate new technologies to achieve scientific management and construction. Engineering quality control management is the lifeblood of determining the merits of a project, which is the life of construction engineering and the key to winning users, developing enterprises and occupying the market. Based on the current problems encountered in the construction quality control of China’s construction industry, a comprehensive evaluation system based on network big data in the paper is proposed, and the data of method in the engineering quality risk eigenvector model are extracted, processed and analyzed. In the paper, the engineering quality risk feature vector model is designed. The genetic algorithm is used to solve the function as a nonlinear optimization problem. The vector feature extraction algorithm is optimized. The data projection vector of the feature vector data processing is used to define the quality influencing factor evaluation value. The quality of the project is analyzed. After testing and analyzing the model, it proves that the data based on big data extraction is more objective and reasonable from engineering quality risk analysis, risk generation mechanism and optimization risk indicators, which provides reference for China’s construction engineering enterprises.