为降低阶段地表变形目标设定中对施工人员经验依赖程度，提高盾构施工的安全性和效率，提出一种科学、有效的地表变形阶段目标规划方法。首先，对不同地质条件下各阶段地表变形量与盾构掘进效率和周边环境的关系进行分析； 然后，采用机器学习方法建立盾构掘进效率评估模型和最终地表变形预测模型； 最后，基于预测结果综合考虑推进效率、环境扰动和施工成本,得到工程综合效益评价，并应用粒子群优化算法计算得到各阶段地表变形控制值，以实现最优综合效益。该模型已被内置在“智驭号”盾构自主掘进控制系统中，并应用于南京地铁5号线隧道工程。工程应用结果表明，该模型通过对地表变形阶段控制目标的优化，有效提升了自主驾驶盾构施工的安全性和高效性。

To address the issue of relying heavily on the experience of construction personnel when setting surface deformation targets for each stage of shield tunneling, the relevance of surface deformation at each stage to the efficiency of shield tunneling and the impact on the surrounding environment under different geological conditions are analyzed. Based on this analysis, a stage target planning method for surface deformation is proposed. The proposed method utilizes machine learning to establish an evaluation model for the efficiency of shield tunneling and a prediction model for the final surface deformation. The predicted results are then comprehensively evaluated, considering tunneling efficiency, environmental impact, and construction cost, to obtain an engineering comprehensive benefit evaluation. Optimization algorithms are used to calculate surface deformation control values for each stage, aiming to achieve optimal comprehensive benefits. The model was integrated into the ZhiYu selfdriving shield system and applied to the Nanjing metro line 5 tunneling project. The results of the engineering application show that the model has effectively improved the safety and efficiency of selfdriving shield tunneling by optimizing the control targets for each stage of surface deformation.