An Improved Algorithm and Implementation of Data Mining for Intelligent Manufacturing Association Rules Based on Pattern Recognition

Abstract

With the deepening of the research of artificial intelligence theory, the research of pattern recognition has made further development, and the applicable fields have also been continuously expanded. Pattern recognition can be used in text and speech recognition, remote sensing nuclear medicine diagnosis. This article optimizes and analyzes intelligent manufacturing based on pattern recognition, and uses improved association rule data mining algorithms to improve it from many aspects, Association rules are not limited by the number of dependent variables and can discover associations between data in large databases. Data analysis is carried out based on factors in different production stages of intelligent manufacturing to demonstrate the feasibility of the improved algorithm of association rule data mining. The experimental simulation results show that the application of the improved pattern recognition model in this research has made the estimated value of the remanufactured product remanufactured to a maximum of 4.05, and the production effect has also been greatly improved, the floating range is between 6.13 and 7.90, and the overall evaluation can reach 9.06. The experimental results show that the effect of the improved pattern recognition model is greatly improved.