基于北斗监测数据的高陡边坡变形Transformer-CNN预测模型

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Resources Development Company Limited， Chongqing 40ooo0，P.R.China; 3.Collge of Design，Sichuan Fine Arts Institute， Chongqing 401331，P.R. China）

Abstract： High and steep slopes are common during theconstruction oflarge-scale projects，and their deformation often leads togeological hazards，posing significant threats tolifeand property.Efficiently collecting displacement data and developinganaccuratepredictive model are therefore essential. This study proposes a Transformer-CNN hybrid model that integrates convolutional layers and residual structures into the Transformer architecture.The optimized model is applied to displacement data obtained from the Beidou satelite system in a large water conservancy project in Chongqing.Experimental results indicate that the Transformer-CNN model achieves lower MAE，MSE，and RMSE values compared to single-model approaches，demonstrating superior prediction accuracy.These findings suggest that the proposed model offers a practical solution for predicting and analyzing slope deformation in similar engineering projects.

Keywords： Transformer-CNN; Beidou dataset; time series; displacement prediction; slope deformation

在大型水利工程建设过程中，高陡边坡由于受力特点极其复杂，在外部施工等环境因素的影响下，易发生坍塌、滑坡等严重地质灾害，给现场施工带来不利影响，直接威胁人员的生命和财产安全。（剩余14363字）