The long-term safe operation of high-speed maglev lines depends on continuous monitoring and evaluation of track structures and surrounding environmental conditions. However, traditional manual inspection suffers from low efficiency and limited coverage, making it difficult to meet the requirements of high-frequency and high-precision detection. To address these challenges, this paper investigates the operation and maintenance needs of high-speed maglev lines and designs UAV-based inspection schemes for fine-grained track detection and wide-area environmental monitoring and further proposes a functional architecture for an intelligent edge-cloud collaborative inspection system. In addition, this study systematically analyzes the key technical challenges in implementing the proposed system, including small-object perception, motion-blur suppression, out-of-distribution target identification, and robustness under adverse environmental conditions. Future work will focus on constructing a dedicated dataset and developing key detection algorithms to support engineering implementation and real-world deployment of the system. This study provides feasible insights and technical references for advancing intelligent inspection systems for high-speed maglev lines.