6 results about "Shared learning" patented technology

This invention discloses a heterogeneous government data collection and analysis system based on data governance and privacy protection, belonging to the field of intelligent analysis technology. The system includes: a data collection and cleaning module, which intelligently collects and cleans heterogeneous government data using adaptive algorithms; a privacy protection and dynamic desensitization module, which achieves comprehensive data privacy protection through dynamic desensitization technology and secure multi-party computation technology; an intelligent analysis and collaborative computing module, which, by introducing federated learning technology, enables multi-party collaborative model training without sharing original data, and shares the learned model parameters to achieve cross-departmental and cross-regional joint data analysis; and a data sharing and management module, which establishes a cross-departmental data sharing and verification mechanism through blockchain technology to ensure the transparency and traceability of data exchange. This invention can achieve efficient and intelligent government data collection, privacy protection, and analysis with low resource consumption.

Provide a system. 【Solution means】 Means for collecting learning information of individual learners, Means for analyzing the collected learning information to identify the learners' strong and weak areas, Means for generating an individualized learning plan suitable for the learner based on the identified strong and weak areas, Means for presenting the generated individualized learning plan to the learner, Means for providing an immediate response to inquiries from the learner, Means for notifying the learner's learning progress, Means for providing an individually optimized learning experience in the local community where the learner resides, Means for sharing the learner's educational progress with parents or educational institutions, A system including the above.

This invention discloses a method and apparatus for compressing and inheriting ancestor model knowledge into descendant models through optimal transmission. The method includes: constructing an ancestor model containing multiple Transformer network layers and obtaining a unified feature representation for each layer of the ancestor model; constructing a difference metric between each layer of the ancestor model and candidate learning genes; modeling the cross-layer knowledge mapping process as an entropy-regularized optimal transmission problem; solving the entropy-regularized optimal transmission problem to obtain a cross-layer knowledge transmission matrix; performing cross-layer weighted fusion of ancestor model parameters based on the cross-layer knowledge transmission matrix to construct shared learning gene parameters; constructing descendant models of different scales based on the learning gene parameters; and applying the constructed descendant models to the training and inference of downstream tasks. This invention can complete structured knowledge inheritance and model reconstruction without accessing the original training data of the ancestor model, and has good deployability and generalization ability.

The application discloses a loading and unloading equipment health state monitoring method based on multi-similar equipment shared learning, real-time collection of operation data of multiple similar loading and unloading equipment, server initialization of a basic model for each loading and unloading equipment, self-adaptive learning of each loading and unloading equipment according to collected data and performance evaluation of the basic model, server fusion of the basic models of the multiple loading and unloading equipment into a global model and generation of an individualized model according to local data of each loading and unloading equipment, health state prediction by using the individualized model and provision of early warning information, maintenance plans and suggestions according to the health state prediction result. The application fully utilizes the similarity between the loading and unloading equipment in structure and function, realizes deep mining and effective utilization of a large amount of monitoring data in a shared learning mode, and greatly improves the accuracy and real-time performance of monitoring.

The invention provides a spatial multi-omics integration method and device, equipment and a storage medium. Relates to the technical field of spatial multi-omics. The method comprises the steps of obtaining a double-omics data set, and constructing an adjacent graph containing a space graph and a feature graph based on the double-omics data set; by means of an in-omics integration module, graph convolution is performed on the space graph and the feature graph, space and feature potential representations are spliced and input into a multi-layer perceptron, and in-omics fusion representation is obtained; through an inter-omics integration module, performing weighted summation on the intra-omics fusion representation by using a multi-head attention mechanism to obtain a multi-omics potential representation; a space omics integration model is trained by means of a double learning strategy, multi-omics potential representation and original normalized space mapping are guaranteed through private learning in omics, specific feature coding is enhanced by introducing self-supervised contrast learning, alignment of different omics representations is restrained through shared learning among omics, and space multi-omics integration is achieved. According to the method, more complex histological structure characteristics can be captured, and the analysis capability of a complex space structure is remarkably improved.

A wireless signal spectral analysis method using a multi-task learning-based spectral analysis learning model, the wireless signal spectral analysis method may be provided. The analysis method according to an embodiment of the present disclosure may include: receiving a target signal of a target band, obtaining a training dataset through pre-processing of the target signal, performing wireless signal spectral analysis learning using the training dataset, and analyzing the target signal using a trained spectral analysis learning model, wherein the performing of wireless signal spectral analysis learning comprises: configuring task specific layers for respectively performing individual learning for a plurality of tasks to be analyzed and a shared layer for performing shared learning; learning, in the shared layer, correlation data that meets a predefined criterion in the training dataset; and individually learning, in each of the plurality of task specific layers, using an individual dataset required for each task in the training dataset and a result of learning the correlation data.