Exploring Strategies for Upgrading Management Systems in Food and Agricultural Laboratory Information Management Systems Based on Association Rule Mining Algorithms

With the increasing emphasis on food safety, ensuring the accuracy, efficiency and traceability of qualified laboratory testing processes for food and agricultural products has become a key challenge. However, despite certification, many laboratories still have problems with quality management and technical workflow. This article aims to explore the strategy of intelligent and information upgrading of the laboratory management system to solve these problems. By using association rule mining algorithms, we conduct in-depth data analysis of laboratory records and quality control data, discover hidden patterns and correlations, and provide information for system optimization. The proposed method is achieved through the secondary development of the existing laboratory information management system (LIMS), enabling the laboratory to build an intelligent management platform with customized functions and enhanced decision-making capabilities. The results show that workflow efficiency, anomaly detection and data-driven quality control have been significantly improved. This study provides a valuable reference for the development of intelligent and data-driven laboratory systems, and helps to build a more comprehensive and efficient food safety regulatory framework for agricultural products.