针对由高低温等复杂工况导致的飞机起降系统失效问题，提出了一种基于数字孪生的起降系统可靠域分析方法，实现参数的全局优化和可靠域的动态确定。首先，建立了具有5个层级的数字孪生框架，阐述了各层级功能。其次，结合长鼻浣熊算法(COA)的高效率、强平衡性以及无需设置控制参数的优势，优化了支持向量机(SVM)，并建立了COA-SVM数字孪生模型，实现了系统效率、行程和过载三个性能指标功能函数的映射。然后，提出了基于群体交互的长鼻浣熊算法，用于求解由高低温、多失效模式张成高维参数空间产生的优化问题，进而确定起降系统在低温环境下的可靠域。最后，以考虑温度效应的起降系统落震为例进行验证，建立的数字孪生模型能够准确反映物理模型，并发现低温环境下起降系统的失效概率较大。基于数字孪生技术开展可靠域分析，为飞机起降系统的优化设计提供了新的策略和技术路径。

Aiming at the failure issues of aircraft landing gear systems caused by complex operating conditions such as high and low tem-peratures, this study proposes a reliability domain analysis method based on digital twin technology to achieve global parame-ter optimization and dynamic determination of reliability domains. First, a five-level digital twin framework is established, with the functionalities of each layer elaborated. Second, leveraging the high efficiency, robust balancing capability, and absence of control parameter requirements of the Coati Optimization Algorithm (COA), an optimized Support Vector Machine (SVM) is developed, and a COA-SVM digital twin model is constructed. This model establishes functional mappings for three perfor-mance indicators: system efficiency, stroke, and overload. Subsequently, a population interaction-enhanced Coati Optimization Algorithm is proposed to address the optimization challenges arising from high-dimensional parameter spaces formed by multi-failure modes under extreme temperatures, thereby determining the reliability domain of the landing system in low-temperature environments. Finally, a landing gear drop test incorporating temperature effects is conducted for validation. The established digital twin model accurately reflects the physical system and reveals that the failure probability of the landing gear systems increases under low-temperature conditions. The proposed digital twin-driven reliability analysis provides novel strategies and technical pathways for the optimal design of aircraft landing gear systems.