In this paper, we introduce a first-of-its-kind forecasting-driven, incentive-inherent service provisioning framework for distributed air-ground integrated networks that explicitly accounts for human-machine coexistence. In our framework, vehicular-UAV agent pairs (APs) are proactively dispatched to overloaded hotspots to augment the computing capacity of edge servers (ESs), which in turn gives rise to a set of challenges that we jointly address: highly uncertain spatio-temporal workloads, spatio-temporal coupling between road traffic and UAV capacity, forecast-driven contracting risks, and heterogeneous quality-of-service (QoS) requirements of human users (HUs) and machine users (MUs). To address these challenges, we propose FUSION, a two-stage optimization framework, consisting of an offline stage and an online stage. In the offline stage, a liquid neural network-powered module performs multi-step spatio-temporal demand forecasting at distributed ESs, whose outputs are exploited by an enhanced ant colony optimization-based routing scheme and an auction-based incentive-compatible contracting mechanism, to jointly determine ES-AP contracts and pre-planned service routes. In the online stage, we formulate the congestion-aware task scheduling as a potential game among HUs, MUs, and heterogeneous ES/UAVs, and devise a potential-guided best-response dynamics algorithm that provably converges to a pure-strategy Nash equilibrium. Experiments on both synthetic and real-world datasets show that FUSION consistently achieves higher social welfare and improved resource utilization, while maintaining latency and energy costs comparable to state-of-the-art baselines and preserving individual rationality, budget balance, and near-truthfulness.

翻译：本文首次提出了一种面向分布式空地一体化网络的预测驱动、激励内生的服务供给框架，该框架明确考虑了人机共存场景。在我们的框架中，车载无人机智能体对（APs）被主动调度至过载热点区域，以增强边缘服务器（ESs）的计算能力，这进而引发了一系列我们联合应对的挑战：高度不确定的时空工作负载、道路交通与无人机容量间的时空耦合、预测驱动的合约风险，以及人类用户（HUs）与机器用户（MUs）异构的服务质量（QoS）需求。为应对这些挑战，我们提出了FUSION——一个包含离线与在线阶段的两阶段优化框架。在离线阶段，一个由液态神经网络驱动的模块对分布式边缘服务器进行多步时空需求预测，其输出被一个增强的基于蚁群优化的路由方案和一个基于拍卖的激励相容合约机制所利用，以联合确定边缘服务器-智能体对合约及预规划的服务路线。在线阶段，我们将拥塞感知任务调度建模为人类用户、机器用户与异构边缘服务器/无人机之间的势博弈，并设计了一种势引导的最佳响应动态算法，该算法可证明收敛至纯策略纳什均衡。在合成数据集和真实数据集上的实验表明，FUSION在保持与最先进基线相当的延迟和能耗成本的同时，持续实现了更高的社会福利和资源利用率，并保持了个人理性、预算平衡和近似真实性。