While UAV-aided reconnaissance data collection grows rapidly, its path optimization is severely hindered by environmental dynamics, node mobility, and overlapping security/interference risks. This paper addresses the problem of data collection from mobile reconnaissance nodes in interference environments, incorporating safety flight constraints and dynamic constraints. We propose an integrated path optimization framework combining initial path guidance with dynamic replanning capabilities. The system first predicts reconnaissance node and interference source positions while analyzing channel conditions to generate an optimal initial path within the designated communication flight corridor. Subsequently, the framework addresses non-convex path planning challenges using slack variables and equivalent constraints, with the successive convex approximation algorithm providing efficient iterative solutions. Numerical results show that in dynamically changing scenarios, the proposed method achieves high reliability and planning efficiency for real-time, data-collection-oriented path optimization.

With the escalating demand for electromagnetic spectrum resources in the 5G/6G era, efficient management of the electromagnetic space has become a critical challenge. This paper proposes the concept of an Electromagnetic Digital Twin (EDT) and an innovative framework for constructing Electromagnetic Twin Space (ETS) to achieve high-fidelity dynamic mapping and real-time optimization of the electromagnetic space through digital twin technology. We elaborate on the EDT concept, introducing a three-layer architecture comprising Physical Device Twin (PDT), Propagation Environment Twin (PET), and Electromagnetic Situation Twin (EST), thereby systematically integrating digital twin technology into the electromagnetic domain. Furthermore, we designed the ETS construction framework, clarifying the four key links between ETS construction and operation and their associated technologies. Through a case study, we demonstrate the effectiveness of a GAN-based EST, which achieves significantly better prediction accuracy than traditional methods. The findings show that incorporating building information and transmitter parameters substantially enhances the accuracy of EST, as evidenced by the RMSE metrics of the constructed electromagnetic situation. Moreover, the trained GAN model can generate electromagnetic situations under various building scenarios and transmitter locations, providing a valuable experimental platform for transmitter deployment.