Laboratory quality management is the key to ensuring the accuracy and reliability of testing data, but the traditional manual management mode is inefficient and difficult to meet the increasing certification requirements. This article proposes an intelligent laboratory quality management system that integrates artificial intelligence technologies such as knowledge graphs, data mining, and machine learning, achieving full process intelligent management and risk warning of laboratory resources, processes, and results. The innovation points of the system include: ontology based quality knowledge graph construction, multi-objective reinforcement learning for resource allocation and process optimization, and quality trend prediction for spatiotemporal sequences. The application in X laboratory has shown that the system can significantly improve the standardization, effectiveness, and efficiency of quality management, providing the possibility for the establishment of an intelligent certification system.

laboratory certification  /  intelligent quality management  /  knowledge graph  /  reinforcement learning  /  spatiotemporal convolutional network