Abstract: The wind-solar-storage power stations in the Tengger Desert and Badain Jaran Desert regions of Northwest China face the dual harsh environments of dust storms and extreme cold, leading to accelerated equipment performance degradation, lagging response of traditional operation and maintenance (O M) modes, and high costs. This paper constructs a theoretical system for intelligent inspection and active protection against dust storms and extreme cold weather. By establishing a kinetic model of dust deposition wear and extreme cold electrochemical failure, the mechanism of equipment performance degradation is revealed. A multi-modal collaborative perception architecture and edge lightweight detection network are proposed to address the contradiction between wide-area coverage and computational constraints. Environmental prediction and protection strategies are coupled to establish a dynamic optimization and reinforcement learning adaptive active protection decision-making framework. Energy management and cross-device resource optimization considering equipment health are achieved at the wind-solar-storage coordination level. Simulations show that this system can improve equipment availability by 5% to 8% and reduce comprehensive O M costs by more than 20%, providing theoretical support for the intelligent upgrade of new energy bases in desert regions.