2 results about "Cognitive module" patented technology

This invention relates to the field of UAV communication technology, and in particular to an autonomous identification method for multimodal interference in UAVs based on a hierarchical cognitive architecture. By introducing homomorphic encryption and secure multi-party computation, it achieves encrypted domain processing and fusion of multimodal data throughout its entire lifecycle, avoiding the risk of leakage of original information during transmission and computation, and constructing a data security barrier in high-adversarial environments. Furthermore, at the cognitive layer, it integrates an attention mechanism and a causal reasoning engine, elevating multimodal features to a deep semantic understanding level of intent and evolutionary patterns, generating a spatiotemporal interference cognitive situation map, breaking through the limitation of traditional methods that can only identify interference types. Finally, the decision layer adopts hierarchical reinforcement learning to generate the optimal anti-interference strategy in real time based on the dynamic situation, and optimizes the perception and cognition modules through feedback of evaluation results, forming a closed-loop self-evolutionary mechanism.

The application provides a training method, an inference method and a system of a perception-cognition-control multi-modal large model based on a Riemannian manifold. The method comprises the following steps: obtaining Euclidean features corresponding to multi-modal training samples, and inputting the Euclidean features into a perception-cognition-control multi-modal large model to be trained; obtaining feature point representations corresponding to the multi-modal training samples through a multi-modal mapping alignment module in the large model, and determining a multi-modal alignment loss based on the similarity between the feature point representations; taking the feature point representations corresponding to first modal training samples and second modal training positive samples as inputs of a cognition module, and taking the output of the cognition module as an input of a control module; determining a spatial consistency loss based on the input and output of the cognition module and the output of the control module; and iteratively updating based on multiple loss terms to obtain a logically integrated multi-modal large model. The application can overcome the geometric mismatch defect of the Euclidean space.