The practical engineering of satellite tracking telemetry and command (TT&C) is often disturbed by unpredictable external factors, including the temporary rise in a significant quantity of satellite TT&C tasks, temporary failures and failures of some TT&C resources, and so on. To improve the adaptability and robustness of satellite TT&C systems when faced with uncertain dynamic disturbances, a hierarchical disturbance propagation mechanism and an improved contract network dynamic scheduling method for satellite TT&C resources were designed to address the dynamic scheduling problem of satellite TT&C resources. Firstly, the characteristics of the dynamic scheduling problem of satellite TT&C resources are analyzed, and a mathematical model is established with the weighted optimization objectives of maximizing the revenue from task completion and minimizing the degree of plan disturbance. Then, a bottom-up distributed dynamic collaborative scheduling framework for satellite TT&C resources is proposed, which includes a task layer, a resource layer, a central internal collaboration layer, and a central external collaboration layer. Dynamic disturbances are propagated layer by layer from the task layer to the central external collaboration layer in a bottom-up manner, using efficient heuristic strategies in the task layer and the resource layer, respectively. We use improved contract network algorithms in the center internal collaboration layer and the center external collaboration layer, the original scheduling plan is quickly adjusted to minimize the impact of disturbances while effectively completing dynamic task requirements. Finally, a large number of simulation experiments were carried out and compared with various comparative algorithms. The results show that the proposed algorithm can effectively improve the solution effect of satellite TT&C resource dynamic scheduling problems, and has good application prospects.

Efficient airport airside ground movement (AAGM) is key to successful operations of urban air mobility. Recent studies have introduced the use of multi-objective multigraphs (MOMGs) as the conceptual prototype to formulate AAGM. Swift calculation of the shortest path costs is crucial for the algorithmic heuristic search on MOMGs, however, previous work chiefly focused on single-objective simple graphs (SOSGs), treated cost enquires as search problems, and failed to keep a low level of computational time and storage complexity. This paper concentrates on the conceptual prototype MOMG, and investigates its node feature extraction, which lays the foundation for efficient prediction of shortest path costs. Two extraction methods are implemented and compared: a statistics-based method that summarises 22 node physical patterns from graph theory principles, and a learning-based method that employs node embedding technique to encode graph structures into a discriminative vector space. The former method can effectively evaluate the node physical patterns and reveals their individual importance for distance prediction, while the latter provides novel practices on processing multigraphs for node embedding algorithms that can merely handle SOSGs. Three regression models are applied to predict the shortest path costs to demonstrate the performance of each. Our experiments on randomly generated benchmark MOMGs show that (ⅰ) the statistics-based method underperforms on characterising small distance values due to severe overestimation; (ⅱ) A subset of essential physical patterns can achieve comparable or slightly better prediction accuracy than that based on a complete set of patterns; and (ⅲ) the learning-based method consistently outperforms the statistics-based method, while maintaining a competitive level of computational complexity.