Abstract: Abstract: Background　Perioperative real-world data (RWD), despite its comprehensive capture of patient information and clinical treatment processes, is seldom utilized in clinical research. Objective　This study aims to construct a perioperative data resource model system (WIRE) to integrate multi-source RWD during the perioperative period and to advance the application of intelligent technology in real-world studies.Methods　The research team, grounded in the HL7 Reference Information Model (RIM) and incorporating characteristics of medical Internet of Things data along with clinical data models, designed the WIRE system to achieve integration at the data model layer of perioperative medical information. Concurrently, the team explored general methods for real-world studies (RWS) of perioperative intelligent early warning technology based on WIRE and developed early warning models for intraoperative hypoxemia and hypotension, culminating in the creation of a risk early warning system integrated with these models.Results　The perioperative specialty data resource libraries based on WIRE were successfully established at the Sixth Medical Center of the General Hospital of the People's Liberation Army and Guangdong Provincial People's Hospital, respectively aggregating data from 6, 483 and 27, 939 surgical cases, providing ample data resources for the early warning models. The intraoperative hypotension early warning model and the intraoperative hypoxemia prediction models for 3-minute and 5-minute early warnings have a higher accuracy, recall, and F1 scores compared to the performance of anesthesiologists(P＜0.05). Moreover, the developed risk early warning system enabled real-time alerting for the potential occurrence of intraoperative complications in surgical patients.Conclusion　WIRE can effectively integrate RWD from the perioperative period of surgical patients, facilitating the advancement of clinical research and providing practical references for the industrial application of clinical data elements.