为提高智能网联汽车运维智能化程度及运行可靠性，创建高效精密的预测性维护格局。运用数字孪生建模方式，搭建关键部件的动态映射模型，把粒子滤波与长短期记忆神经网络等算法组合起来，完成状态识别与寿命预测。以实车平台为例，实施多场景验证，研究关键部件于退化演化进程中的健康评估及维护响应举措。此技术有良好的故障预警精度与环境耐受的鲁棒性，为智能运维提供有可行性的技术通道。

To enhance the intelligence level and operational reliability of intelligent connected vehicle operation and maintenance, and to establish an efficient and precise predictive maintenance pattern. By using the digital twin modeling method, a dynamic mapping model of key components is built, and algorithms such as particle filtering and long short-term memory neural networks are combined to complete state recognition and life prediction. Taking the real vehicle platform as an example, multi-scenario verification is implemented to study the health assessment and maintenance response measures of key components during the degradation and evolution process. This technology features excellent fault early warning accuracy and robustness in environmental tolerance, providing a feasible technical channel for intelligent operation and maintenance.