2results about How to "Achieve authentication" patented technology

This invention provides a system for real-time dynamic adjustment of grouping across devices, including an identity authentication module, a distributed synchronization module, a rule engine module, a trigger engine module, and an algorithm engine module. These modules are coupled and interact through standardized data interfaces. By establishing a three-element association relationship between users, devices, and groups, unified management and authentication of user identities are achieved in a multi-device environment. A dynamic adjustment strategy is employed to achieve static allocation and dynamic adjustment of user permissions. Multi-dimensional grouping rules are formulated to filter users meeting certain conditions into the same group. A multi-dimensional trigger strategy is used to dynamically adjust groups. Intelligent calculation and optimization of dynamic grouping are performed based on the K-means clustering algorithm combined with a greedy strategy. Data synchronization between devices is achieved based on a distributed architecture, enabling real-time dynamic adjustment and efficient collaboration of grouping across devices.

This invention primarily addresses the performance issues caused by the limited computing power of wireless terminal devices in industrial control systems, as well as the high latency problems of traditional authentication methods. It proposes a physical layer authentication scheme based on ShuffleNet and a self-attention mechanism. First, a method based on an improved variational autoencoder and generative adversarial networks is proposed to augment CSI data, and a denoising autoencoder based on particle swarm optimization is used to reduce the dimensionality of the data. Then, a lightweight SE module is introduced as the implementation of the self-attention mechanism, further enhancing the model's focus on key features and improving its discriminative power and feature learning ability in complex scenarios. Finally, the ShuffleNet-SE model is used to identify CSI data features, classifying legitimate and illegitimate devices in the industrial control system.