3results about How to "Extensive knowledge" patented technology

The invention relates to the technical field of smart tourism, in particular to a personalized scenic spot content generation method, system and device based on a multi-modal knowledge graph and a medium, and the method comprises the steps: collecting multi-modal data of a scenic spot, and constructing a structured multi-modal scenic spot knowledge graph based on the multi-modal data; obtaining personalized data provided by the tourist, and constructing a personalized portrait of the tourist based on the personalized data; in response to an operation of acquiring an image in a scenic spot, identifying the scenery in the image, and extracting a corresponding initial knowledge subset from the multi-modal scenic spot knowledge graph according to an identification result; performing personalized screening and expansion on the knowledge nodes in the initial knowledge subset by calculating the matching degree between the knowledge nodes in the initial knowledge subset and the tourist interest in the personalized portrait of the tourist, and dynamically binding multi-modal resources to generate a personalized knowledge graph; and presenting the personalized knowledge graph. According to the invention, the multi-mode scenic spot knowledge experience of which the structure is matched with the interest and the knowledge background can be provided for tourists.

The invention relates to a computer-implemented method for updating a view of a spatial scene, comprising the following steps: S1) receiving image points of the view of the spatial scene, S2) acquiring a plurality of acquisition points of the scene by means of a camera, S3) assigning the acquisition points to respective corresponding image points of the view as a function of position information, S4) determining a corresponding deviation, and S5) updating the view of the spatial scene by means of the deviation. S5) excluding from the update those acquisition points with deviations below a tolerance threshold, S6) summarizing a plurality of image points of the view into a sub-view, S7) determining a spatial environment for sub-view image points of a subset with deviations above the tolerance threshold, S8) determining whether at least one acquisition point is located within a volume spanned by the determined spatial environment and an image sensor of the camera, and if the at least one acquisition point is located within the volume spanned by the determined spatial environment and the image sensor of the camera, if the at least one acquisition point is located within the volume spanned by the determined spatial environment and the image sensor of the camera. S9) excluding the sub-view image points from the update if the deviation of at least one acquisition point located within the volume is above a motion threshold, S10) updating only the image points of the view not excluded from the update based only on the acquisition points not excluded from the update, S11) providing an updated view.

This application relates to a method, apparatus, computer device, storage medium, and computer program product for training a knowledge distillation model. The method includes: adding a first weight matrix and a second weight matrix to the linear layer of the student model to be trained, based on the original weight matrix of the linear layer; obtaining a first predicted label for sample data using a teacher model, and obtaining a second predicted label for the sample data using the student model to be trained; the sample data carrying sample labels; determining the model loss value of the student model to be trained using the first predicted label, the second predicted label, and the sample labels; freezing the original weight matrix, and adjusting the first and second weight matrices using the model loss value to train the model, thereby obtaining the trained student model. This method can improve the training efficiency of the student model and reduce processing resource consumption.