AI-enabled operation and maintenance automation: intelligent prediction and optimization mechanism based on federated learning

With the continuous improvement of information technology and industrial intelligence, operation and maintenance automation has become a key link to ensure system stability and operational efficiency. Traditional O&M methods have obvious limitations in dealing with heterogeneous data from multiple sources and real-time response requirements, and more efficient and intelligent solutions are urgently needed. The introduction of AI technology brings new capabilities such as predictive maintenance, fault warning and resource optimization to the O&M system, but its reliance on a large amount of centralized data training faces challenges such as data silos, security risks and privacy compliance. Federated learning, as an emerging distributed machine learning framework, realizes collaborative modeling and knowledge sharing among multiple nodes under the premise of guaranteeing localized data storage and privacy protection, providing a new technical path for intelligent O&M. This paper focuses on the core proposition of “AI-enabled operation and maintenance automation”, proposes an intelligent prediction and optimization mechanism based on federated learning, constructs an operation and maintenance model architecture that takes into account both performance and privacy, and focuses on the model construction, optimization method, security mechanism, and its application effect in real scenarios. By introducing the federated learning mechanism, this paper effectively reduces the risk of data leakage and communication burden while improving the prediction accuracy and system response speed. The experimental results verify the superior performance of the proposed method in typical scenarios such as industrial equipment management and data center resource scheduling, providing theoretical support and practical guidance for the construction of a safe, intelligent, and efficient next-generation operation and maintenance system.