Preliminary study on a standard digitalization platform for the nuclear industry

The nuclear industry relies on a comprehensive set of standards to ensure safety, quality, and regulatory compliance. The advancement of digital technologies, particularly artificial intelligence, knowledge graphs, and semantic modeling, offers significant opportunities to enhance the management, integration, and application of these standards. This study identifies major challenges to effective standardization, including inconsistent terminology and isolated implementation across domains. To address these issues, a unified digital standardization platform is proposed. A comparative analysis of prominent international platforms, such as ASTM Compass, ISO/IEC SMART Standards, and China’s i-Knowledge platform, is conducted to identify best practices in terms of functionality, data governance, and application scenarios. The te chnical architecture supporting artificial intelligence-based indexing, semantic search capabilities, knowledge graph development, and collaborative filtering recommendations is described in detail, accompanied by system logic diagrams. The concept of risk - informed quality management is reformulated into a sequential framework that includes risk information integration, intelligent decision support, human–machine collaboration, and the continuous evolution of standards. This framework is illustrated through a comprehensive data flow diagram. The results highlight that the digitalization of standards, particularly the embedding of machine-readable semantics, is essential for developing an intelligent and data-driven quality assurance framework within the nuclear industry.