Research on Construction Safety Risk Warning Model Based on Digital Twins——Deep learning methods for high-rise building projects in the Chengdu Chongqing region

As the economic core area of western China, the Chengdu Chongqing region has a high concentration of high-rise building projects and an urgent need for construction safety risk prevention and control. This study integrates digital twin technology and deep learning algorithms to propose a dynamic risk warning model aimed at addressing the challenges of data silos, static analysis, and insufficient quantification of multi factor coupling effects in traditional risk assessment. By building a "physical virtual" real-time mapping digital twin platform that integrates BIM, IoT, and GIS technologies, multi-source data fusion in construction scenarios can be achieved; Design a GCN-LSTM hybrid neural network and combine attention mechanism to explore the risk coupling relationship between "human machine environment management"; And quantify the strength of risk interaction based on the N-K model. Empirical research has shown that the accuracy of model warning reaches 92.7%, the false alarm rate is reduced to 6.8%, and the response time is shortened by 40%, providing theoretical methods and technical tools for intelligent construction safety management.