The unmanned aerial vehicle （UAV） multi-modal ultra-wide spectrum cognitive instrument constructs an intelligent remote sensing system by deeply integrating visible light， infrared， synthetic aperture radar （SAR）， and wireless spectrum sensors. It aims to overcome fundamental bottlenecks in traditional UAV remote sensing： Limited endurance severely constraining detection range， insufficient payload capacity restricting multi-modal perception， weak onboard computing capability causing real-time processing delays， and finite communication capacity hindering high-fidelity situational assessment. To address endurance challenges， the design employs a hybrid energy configuration combining piston engines and lithium batteries with a vertical take-off and landing （VTOL） flying-wing layout， significantly enhancing operational longevity. For payload limitations， it develops a compound-eye multi-camera array for wide-field high-resolution imaging and integrates a W-band miniaturized SAR radar with submillimeter-level vibration compensation technology， enabling air-time-frequency multi-dimensional collaborative perception. To resolve real-time processing constraints， a spatiotemporal registration framework and lightweight deep learning model establish a multi-level fusion mechanism （data-feature-semantic layers）， elevating detection accuracy for low-observable targets beyond 90%. Targeting communication bottlenecks， innovative generative coding combined with knowledge-graph-driven situational reconstruction achieves high-fidelity 3D situational generation under 400-fold compression， quantified via a no-reference quality assessment model for semantic fidelity.Validated in defense reconnaissance for real-time tracking of concealed targets in complex electromagnetic environments and in emergency response for flood monitoring and 3D reconstruction， the instrument demonstrates practical value in complex scenarios. Future research should deepen cross-modal semantic understanding optimization and dynamic cooperative control of UAV swarms to advance intelligent remote sensing toward real-time， autonomous cognitive evolution.