Construction of multi-source sensor data fusion and construction state recognition model for intelligent construction

With the continuous development of intelligent construction technology, construction sites have put forward higher requirements for real-time and accurate information sensing and state identification. The wide application of multi-source sensors provides rich heterogeneous data for the construction site, and how to efficiently fuse the data from different sources and construct an accurate construction state recognition model is still an important challenge in current research. In this paper, a construction state recognition model construction method for multi-source sensor data fusion is proposed around the intelligent construction scenario. The common sensor types and their data characteristics are sorted out on the construction site, and the key technical path of multi-source data fusion is analyzed; the state identification model architecture that integrates multiple data layers is designed, and deep learning and feature engineering are combined to achieve high-precision identification; the feasibility and accuracy of the model are verified through actual cases, and the results show that the model has significant advantages in improving the efficiency of construction state identification and the level of intelligent management. The results show that the model has significant advantages in improving the efficiency of construction status identification and intelligent management level. The results show that the model has significant advantages in improving the efficiency of construction state identification and the level of intelligent management. The research results can provide theoretical support and technical reference for the optimization of intelligent construction site construction and construction management.