Low-altitude safety is key to the sustainable development of the low-altitude economy. Drone swarms pose greater risks than individual unmanned aerial vehicles due to their scale and coordination. This paper proposes a situation awareness strategy for the defense of drone swarm. Multi-scale drone target detection is achieved through an anchor-free structure, drone swarm formation recognition is realized by Graph Neural Networks, and the situation of drone swarm is calculated by constructing macroscopic quantitative descriptors. It breaks through the feature extraction and fusion algorithm for multi-scale drones, graph neural networks for intra-layer and inter-layer feature extraction, and macroscopic quantitative descriptors based on divergence and curl to construct scale-invariant and rotation-invariant features. It achieves the detection of whether it is a drone swarm, the identification of which drone swarm it is, and the calculation of the degree of the drone swarm, providing a basis for the classification and graded handling of drone swarm, and effectively promoting the modernization of the low-altitude safety governance capacity.