Research and Application of Multimodal Knowledge Graph in Construction Risk Management of Transmission and Transformation Engineering

This paper proposes a construction risk management method based on multimodal knowledge graph, aiming to improve the risk identification and prediction capabilities in the construction process. The system combines advanced natural language processing (NLP) algorithms, which can automatically extract and integrate construction-related information from multi-source heterogeneous data, and construct a comprehensive knowledge graph covering multimodal data such as text, images, and videos. In terms of algorithm design, an improved NLP model is used to conduct in-depth semantic analysis and association mining of text data, while integrating image recognition and video analysis technologies to achieve all-round data integration and analysis of complex construction scenes. In the system simulation stage, this paper conducts comprehensive verification through a large amount of actual construction data, focusing on evaluating the risk identification and prediction performance of the system in different scenarios. The simulation results show that the accuracy of this method in identifying construction risks has reached more than 95%, and the error rate of predicting construction risks is controlled within 5%, which significantly improves the accuracy and efficiency of construction risk management, fully verifies the practicality and reliability of the system, and provides strong technical support for construction management.