6 results about "Concept network" patented technology

The present invention relates to the field of computer vision technology, and discloses a long-term visual positioning method based on an association guidance network. The model training stage includes perceptual network training and concept network training; after the concept network training is completed, the concept network is used as a pre-training model to train the perceptual network, and the concept network is optimized at the same time; the association guidance mechanism performs information interaction; in the image retrieval stage, the trained perceptual network is used to perform image retrieval. The invention uses domain adaptive learning and an association guidance mechanism to use the features in the concept network to guide the feature learning in the perceptual network, so that the features finally obtained by the model training are robust under environmental changes. In addition, the present invention uses a concept database that does not require additional data to perform self-inspirational learning on the concept network, so as to better guide the perceptual network with domain features, thereby improving the final image retrieval performance.

The invention relates to the technical field of financial intelligent customer service, and discloses a financial institution customer service full-process digital system based on semantic understanding. According to the system, an original request of a client is accessed through an interaction port, and a deep semantic kernel and interaction environment information are obtained through multi-level analysis. And the semantic mapping module associates the deep semantic kernel to the financial institution ontology concept network to generate a structured business intention framework. And the system synchronously calls a three-dimensional historical file of the customer, and in a context fusion layer, space-time alignment and logic stitching are carried out on the intention framework, the environment information and the historical file to generate an enhanced customer intention portrait with a multi-dimensional association tag. According to the method, deep semantic understanding and panoramic intention construction of complex and implicit appeals of customers are realized, so that core support is provided for accurate and personalized intelligent service response of the whole process.

The application discloses a semantic understanding template generation method and device, a storage medium and an electronic device, relates to the technical field of smart homes, and the semantic understanding template generation method comprises the following steps: generating a concept network according to historical voice interaction data of a smart device, wherein the concept network is used for storing entity relationships between entity words and entity word labels; identifying voice interaction data received by the smart device to obtain target entity words contained in the voice interaction data; obtaining a target entity word label corresponding to the target entity words from the concept network, and generating a target semantic understanding template by using the target entity word label, wherein semantic information corresponding to the target semantic understanding template is an interactive intention of a target object. By adopting the technical solution, the technical problem of how to improve the understanding accuracy of the interactive intention of the user is solved.

The application provides a user portrait generation method and device and electronic equipment. The method comprises: obtaining user data of a target user, wherein the user data comprises structured user data and unstructured user data; performing data recognition processing on the user data based on a concept network and a preset topic model to determine label information of the target user and probability information corresponding to the label information, wherein the probability information is used to represent the credibility of the corresponding label information; and generating a user portrait of the target user according to the label information and the probability information corresponding to the label information. In this process, the concept network knowledge graph is introduced, the concept network is introduced into the data feature recognition process, and the user portrait construction accuracy is improved. The unstructured user data is introduced into the construction of the user portrait, the user data is enriched, and the user portrait construction accuracy is improved.

The retrieval knowledge graph library generation method based on the combination of scene graph and concept net comprises the following steps: 1) model pre-training: pre-training input data on a neural network to detect the object types and their positions appearing in a picture; 2) scene graph training: performing unbiased training on the pre-training result of the model, and finally outputting a file related to scene graph information by applying a neural network model to predict the relationship between different types in the picture; 3) automatic expansion of the knowledge graph; 4) testing the trained scene graph model; 5) extracting and processing the file information related to the scene graph and the concept net, and then importing the file information into a retrieval database to finally form the retrieval database; merging the nodes and relationships with high similarity in the scene graph, and fusing the corresponding knowledge graph libraries of the scene graph and the concept net, and finally merging the scene graph and the concept net into a knowledge graph library containing all information; and 6) database regular update.

The invention relates to the technical field of digital education resource automatic generation, and discloses a digital teaching video courseware automatic generation method. The method comprises the steps that teaching material logic is analyzed to construct a concept network, semantic vectors are marked for key nodes, and according to the semantic vectors, multimedia materials are accurately matched to form standardized content primitives; and constructing a dynamic content topology based on a network structure and primitive semantics, planning a content generation path and calculating a node expected state. In the process of driving the primitives to be assembled in a serialized mode along the path, state deviation is monitored in real time, a track is dynamically optimized and generated, and finally, courseware is output after assembly products are subjected to coherence stitching and rendering. According to the method, the logic fluency of content evolution is ensured through dynamic path planning and real-time optimization, and meanwhile, the matching precision of materials and teaching concepts is improved by utilizing semantic vector matching, so that the self-adaption and intelligence of the courseware generation process are realized, and the quality of the generated courseware and the teaching effect are improved.