面向数字孪生水网的空天地水工一体化激光雷达感知:进展与展望OA

To address the bottlenecks of information fragmentation,inadequate physical mechanism coupling,and low cross-medium sensing accuracy in traditional monitoring for digital twin water networks,this study constructs an air-space-ground-water-engineering integrated LiDAR collaborative sensing framework spanning macro-basin,linear river channel,and micro-hydraulic structure scales.By systematically analyzing the transmission characteristics and attenuation mechanisms of laser pulses in complex media(atmosphere-vegetation-water-structure),this paper focuses on core technologies including multi-source heterogeneous data processing and cross-medium refraction correction,and proposes a physics-data dual-driven full-element inversion method.Integrating satellite laser altimetry,airborne green LiDAR,terrestrial scanning,and underwater acoustic-optic detection,the system achieves high-fidelity characterization of topographic evolution,water resource dynamics,structural defects,and flow capacity.In lake monitoring,engineering supervision,and flood disaster response,it significantly enhances the physical fidelity of hydrological simulations and the timeliness of emergency decision-making.Addressing challenges of high-turbidity signal attenuation and data assimilation,future research will focus on physics-informed neural networks(PINNs),edge-cloud collaborative architectures,and multi-spectral payloads to realize full-element dynamic mapping,thereby supporting the intelligent scheduling of national water networks.