Production Scheduling Optimization of Clothing Intelligent Manufacturing System Based on Association Rule Algorithm and Big Data Platform

At this stage, the requirements for refined production management of enterprises are getting higher and higher, and the amount of data has also increased significantly. The traditional management model has been unable to meet the requirements of the new era. In order to further improve the efficiency of enterprise management, enterprises have begun to introduce and apply new technologies such as networking and sensors are used to build a clothing intelligent manufacturing system. Aiming at the complexity and demand uncertainty of multiple batches and types of fabrics in clothing enterprises, a method for mining association rules of clothing fabrics based on a priori algorithm is proposed. First, multi-dimensionally merge the fabric attribute information of multiple batches and types of enterprises. Then, traverse the frequent itemsets generated by the fabric collection and calculate the support respectively. At the same time, applying artificial intelligence technology to clothing manufacturing can achieve the goal of saving human and financial resources and improving labor efficiency by combining artificial intelligence technology with machine sewing. It is believed that in the next few years, artificial intelligence will become the main interactive interface between enterprises and customers, with artificial intelligence dynamic adaptive intelligent manufacturing as the platform side, and modular intelligent manufacturing units as the manufacturing side, to achieve dynamic interaction with the customer demand side, Improve customer satisfaction and achieve real smart clothing manufacturing. It is revealed that the new business model of the clothing industry brought by cloud customization services brings new changes to enterprises and customers, making customers change from passive consumption to active consumption, allowing enterprises to take customer needs as the starting point, actively select consumer groups, and respond to the possible occurrences in practice. To put forward suggestions to promote Chinese clothing enterprises to innovate service concepts, pay attention to responsibility and experience, enhance brand building, and seek greater enterprise benefits.