As a crucial component in the evolution of modern warfare toward digitization and intelligentization, unmanned aerial vehicle (UAV) equipment requires a more precise and efficient operation and maintenance (O&M) system. Based on the Department of Defense Architecture Framework (DoDAF) 2.0, the integration of Multi-Agent Systems (MAS) and military simulation technology provides a comprehensive, rational, and feasible theoretical foundation for the construction and validation of an intelligent O&M system for UAV equipment. Firstly, starting from the O&M tasks of UAV equipment in intelligent warfare, this study analyzes the capability requirements for intelligent UAV O&M by following the generation path of scenarios, activities, and capabilities. Three core capabilities are proposed: situational awareness, decision support, and mission execution. Secondly, various O&M tasks are decomposed into behaviors of multiple types of agents, and based on this, an intelligent O&M system for UAV equipment is designed using a “cloud-edge-terminal” distributed architecture. Finally, simulations are conducted to model and validate UAV equipment maintenance tasks. Experimental results demonstrate that the MAS-based UAV O&M system significantly enhances support efficiency, accuracy, and response speed, offering a novel solution for O&M in future UAV operations.

The intelligent detection of surface damage is crucial for ensuring safe and stable equipment operation. This study addressed the low efficiency, strong subjectivity, and inadequate representation capabilities of traditional single-feature-based damage identification methods by proposing an intelligent equipment surface damage classification method employing multi-feature fusion. The innovations of this method comprise the construction of a seven-dimensional model integrating brightness, texture, frequency domain, edge, and morphological features to comprehensively characterize the visual characteristics of six typical types of damage (fine crazing, inclusions, patches, pitted surfaces, rolled-in oxide scale, and scratches) and the application of a Bayesian probabilistic classification model based on adaptive kernel density estimation to identify damage type and determine the associated confidence. Experimental results obtained using a self-assembled dataset comprising 1619 training samples and 180 test samples indicated that the proposed method achieved a comprehensive accuracy of 97.77%, significantly outperforming traditional deep neural networks and Ant Forest algorithms. Thus, the proposed method provides an effective solution for the intelligent and efficient detection of equipment surface damage.