The application relates to the technical field of unmanned aerial vehicle scheduling and intelligent optimization, and discloses a multi-unmanned aerial vehicle cooperative task
allocation method and device for complex task scenarios, which has the technical scheme as follows: global multi-
source data acquisition and space-time heterogeneous
hypergraph representation, complex task causal emergence decoupling and counterfactual deduction optimization, hierarchical federated heterogeneous matching degree accurate representation, distributed external generalization causal element
reinforcement learning dynamic allocation decision, distributed conflict resolution and digital twin
closed loop iterative optimization; by adopting frontier technologies such as space-time heterogeneous
hypergraph representation, causal emergence reasoning, hierarchical
federated learning, causal element
reinforcement learning and digital twin
closed loop optimization, a causal driving full-link
algorithm architecture is constructed to break through the inherent limitations of traditional correlation-based data-driven algorithms, so that efficient cooperative operation of a large-scale heterogeneous unmanned aerial vehicle cluster in a
strong coupling task and an extreme dynamic environment can be realized.