Objective: To develop a smart blood donation service assistant for popularizing donation-related knowledge to blood donors via intelligent Q&A support, thereby enabling precise service delivery. Methods: Based on the operational scenarios of the Zhejiang Provincial Blood Center, the system utilized the open-source Dify platform for agent orchestration, and integrated with the DeepSeek model as the language processing engine to support online real-time interaction. External tools, including the Amap API and MySQL database queries, were encapsulated via the Model Context Protocol (MCP). A professional blood knowledge base for Retrieval-Augmented Generation (RAG) was constructed using the BGE-M3 embedding model. An innovative dual-large language model collaborative verification mechanism was introduced to design the overall framework. The system was deployed privately using Docker containerization, and offline closed-loop optimization was achieved through customized Python scripts. Results: An interactive interface for blood donors was developed by integrating the chatflow Web component from Dify. The intelligent assistant Agent can recommend optimal blood donation sites and navigation routes by invoking the Amap API based on the donor's location. The Blood Donation Knowledge Agent enables timely responses to inquiries, along with reasonable suggestions and reminders. This agent specializes in the field of voluntary blood donation, empowering the assistant to answer doubts and questions for blood donors in the form of intelligent question-and-answer interaction. It also guides users through preliminary self-assessments, helping potential donors identify eligibility issues beforehand, thereby effectively increasing the on-site success rate of blood donation and reducing resource waste. Conclusion: The smart blood donation assistant validates the feasibility of the "Dify+MCP+RAG" technical architecture within the blood transfusion informatization field. The assistant not only improves the service experience for blood donors, but also, ensures the sustainable evolution of the system through its modular design and closed-loop optimization mechanism, thus providing valuable insights for the intelligent transformation of traditional blood donation service systems.

【Objective】 To investigate the deferral causes of voluntary blood donors in primary blood screening in Hangzhou, so as to take steps to reduce the deferral rate. 【Methods】 The causes of donor deferral in 8 blood donation sites in Hangzhou from January 2019 to December 2020 was statistically analyzed. 【Results】 A total of 103 325 donors(49 335 in 2019, 53 990 in 2020)were registered in 8 blood donation sites in Hangzhou From 2019 to 2020, among which 87 435 (44 462 in 2019, 42 973 in 2020)were successfully donated, and 15 890 (4 873 in 2019, 11 017 in 2020) were deferred, with a deferral rate of 15.38%(9.88% in 2019, 20.41% in 2020). The main reasons leading to donation deferral were Alt, medical history and lipemic blood. Significant differences were noticed in deferral items as medical history, HBsAg, TP, ALT and lipemic blood by gender and donation history, and not in Hb by gender. 【Conclusion】 Promoted publicity, specified primary blood screening, especially the pre-donationTP detection can effectively reduce the proportion of high-risk blood donors, cut down blood deferral rate, thus avoiding the waste of blood resources and balancing blood supply and demand.