针对将组件完全恢复到最初运行状态的维修方式导致无法提供准确维修方案，对于实际系统，具有更多的维修细节，需要更复杂的维修方式描述组件维修过程的问题。以水下采油树关键部件为研究对象，提出基于维纳过程的多级维修方式下的视情维修方法。根据历史运行数据建立组件连续状态退化模型。根据历史维修数据结合K近邻分类方法，提出维修方式分级方法。通过多目标优化算法得到系统在使用周期内的维修方案。结果表明，考虑多级维修方式能够为多组件系统提供更准确的维修方案，有效平衡系统可靠性、可用性与维修成本之间的矛盾。

The maintenance measure of fully restoring components to their initial operational state results in the inability to provide accurate maintenance plans. In real-world systems, there are more maintenance details that require a more complex description of the component repair process. A condition-based maintenance method was proposed for key components of subsea Christmas trees, utilizing a multi-level maintenance measures based on Wiener processes. A continuous degradation model of components was established based on historical operating data. A maintenance classification method was proposed by combining historical maintenance data with the K-nearest neighbors classification method. The system's maintenance plan over its operational cycle was obtained through multi-objective optimization algorithms. The results demonstrate that considering a multi-level maintenance measures can provide more accurate maintenance plans for multi-component systems, effectively balancing the trade-off between system reliability, availability, and maintenance costs.