Abstract: To reduce the complex risks of drilling induced by uncertain geological conditions in deep strata, a digital twin technology-based intelligent early warning system was developed. This system integrates physical models with data-driven models. To address practical needs such as real-time warnings and risk reduction, four supporting technologies were proposed for the digital twin early warning system: microservice-based data integration, intelligent perception of the digital twin, multimodal fusion warning, and intelligent diagnosis of the digital twin. The overall architecture of the drilling digital twin early warning system was established and its functions and model design were detailed. It includes a five-layer interaction system: physical device layer, virtual entity layer, digital twin data layer, digital twin algorithm model layer, and digital twin warning service layer. Three application scenarios were designed: pre-drilling risk avoidance, real-time warning during drilling, and post-drilling analysis of block risk situations to optimize block drilling design. This system achieves digital integration of multi-source heterogeneous data, fusion of traditional physical models with intelligent models, and functions such as overflow, wellbore loss, and stuck pipe warning and type identification. This reduces the risks associated with deep drilling operations and optimizes drilling efficiency. The study results indicate that the "model + data" digital twin early warning architecture has the potential for early identification of drilling process risks and rapid diagnosis of risk types. Digital twin technology provides new approaches and methods for intelligent drilling early warning and monitoring, which is significant for advancing its application in smart and autonomous drilling.