8results about How to "Show well" patented technology

This invention discloses a concept abstraction method and apparatus based on pooling networks. It automatically abstracts concepts by splitting and merging entity sets corresponding to triple relationships in the input graph to obtain a pooling graph, and then obtains an output graph based on the pooling graph. The representation of the pooling graph is initialized using the representation of the input graph learned by a graph neural network model. The representation of the pooling graph is updated using the graph neural network model and, together with the representation of the input graph, initializes the representation of the output graph. The representation of the output graph is then updated again using the graph neural network model. The representation of the output graph is used as input to a knowledge representation learning model to train the model, ensuring that the entity representations contain conceptual information. Comparative learning losses are calculated for the input graph-pooling graph, pooling graph-output graph, and input graph-output graph relationships. Finally, reliable candidate triples are determined based on the knowledge scores of the candidate triples, achieving more accurate automatic information completion.

The application discloses a kind of aspect-level sentiment analysis method based on multilevel knowledge enhancement, belong to sentiment analysis field. Including the following steps: S1: text word vector is obtained using GloVe word embedding tool, the syntax dependency tree of sentence is constructed using Stanza, and the dependency graph is constructed accordingly;S2: the context representation of sentence is extracted by inputting word vector into BiLSTM, and the dependency graph is updated using sentiment dictionary and sensitive relationship set, to realize the sentiment and syntax enhancement of sentence;S3: the enhanced dependency graph is input into GCN to model node features, to obtain specific aspect representation;S4: aspect word is enhanced using concept atlas to obtain aspect word representation, which is fused with specific aspect representation to obtain aspect representation;S5: aspect representation and context representation are coordinated and optimized using interactive attention, to obtain the final representation of sentence, so as to determine aspect sentiment tendency.The application can effectively improve the accuracy of specific aspect sentiment classification, and help merchants accurately locate problems in products or services.

This utility model discloses a bedside table with a concealed compartment component, relating to the field of furniture technology. It includes a bedside table body with several drawer boxes inside. Each drawer box has a sliding rail on both sides, and sliders fixed to the left and right sides of the drawer box, sliding within the sliding rails. A concealed compartment is located at the rear of the drawer box, containing several upper and lower storage compartments. This utility model's sliding pull-out mechanism and multi-compartment layout achieve secure storage of personal items and privacy protection. The concealed compartments fit perfectly with the bedside table, ensuring concealment and effectively utilizing cabinet space, increasing storage capacity without increasing size. The sliding rail and slider structure facilitates access to each storage compartment while enhancing the overall uniformity and aesthetics. It breaks through the traditional bedside table design, meeting daily storage needs while providing private storage space, balancing functionality and aesthetics.

The application discloses a kind of sparse point cloud and GCN fusion's citrus defect detection method and system, the point cloud of citrus data set is input into GCN_PointNet++ model in the application, the GCN_PointNet++ model combines GCN with PointNet++ network and carries out feature transmission by PaConv module;GCN_PointNet++ model is distilled by BIFPN, and the total distillation loss function is obtained by combining BIFPN distillation label to guide student network training, and the best point cloud segmentation result is obtained, that is, citrus defect segmentation point cloud model;Based on citrus defect segmentation point cloud model, three-dimensional reconstruction is carried out on citrus, three-dimensional point cloud mesh file is established, and the area of defects is calculated according to the surface formed by the mesh.The application realizes the accurate segmentation of citrus defect point cloud and the accurate quantification of citrus defect area.

The invention relates to the field of molecular oil refining, in particular to a structure-oriented lumped data storage and retrieval method and device. The method comprises the following steps: according to chemical structures of oil product molecules, combining codes corresponding to basic structure units of the oil product molecules to obtain corresponding structure codes representing the oil product molecules; classifying the structure codes corresponding to the oil product molecules according to preset properties to obtain the structure codes of the oil product molecules under various properties; constructing a database for storing the oil product molecules according to the structure codes of the oil product molecules under various properties; according to a retrieval condition provided by a user, the structure code corresponding to the retrieval condition is inquired from the database, and the retrieval condition corresponds to a preset property. According to the scheme, data processing, storage and retrieval in the molecular oil refining process are optimized, and the oil refining efficiency and the product quality are improved.

A performance mechanism for biomimetic limb movement includes a torso, forelimbs, and hindlimbs. The forelimbs include a fore thigh, fore calf, and forefoot. One end of a first follower link is hinged to the fore thigh, and the other end is hinged to a first drive rocker. The first drive rocker is hinged to a first hinge seat fixed to the torso. The hindlimbs include a hind thigh, hind calf, and hindfoot. One end of a fourth follower link is hinged to the hind thigh, and the other end is hinged to a second drive rocker. The second drive rocker is hinged to a third hinge seat fixed to the torso. Drive assemblies connected to the first and second drive rockers are located at the front and rear of the torso, respectively. This performance mechanism features limbs that can be automatically and independently controlled. The limbs are compact, agile, and easy to adjust. They can perform biomimetic limb movements in a coordinated manner, achieving multiple biomimetic coordinated postures as needed, such as leisurely walking or vigorous running.

This invention discloses a method for characterizing multi-stage carbonate reservoirs based on time-varying Gaussian window transient extraction transform, used for characterizing multi-stage carbonate reservoirs. The method includes: inputting the seismic signal to be analyzed, where is time; constructing a short-time Fourier transform containing the time-varying window width parameter to be optimized, where represents frequency; determining the optimal window width parameter at each time step based on the local Rényi entropy minimization criterion; substituting and calculating its time partial derivative; constructing a transient extraction operator based on the time partial derivative results, including a local maximum criterion, an energy derivative zero-point criterion, and a group delay migration criterion; and proposing a time-varying Gaussian window transient extraction transform under the action of this operator for characterizing multi-stage carbonate reservoirs.

The present application belongs to the field of computer vision and agricultural information technology, and specifically relates to a tea disease detection method based on an attention mechanism. The detection method comprises: using an improved YOLOv5s model as a basic model; introducing an SE attention mechanism into the YOLOv5s model backbone network, and replacing the original C3 module with an seC3 module; adding a coordinate attention mechanism at the end of the backbone network; collecting tea disease image data, and preprocessing the collected tea disease image data; using a labeling tool to label the disease area of the preprocessed tea disease image data to generate a labeled data set; training the constructed YOLOv5-SE model using the labeled data set to obtain the model weight after training; and applying the trained YOLOv5-SE model to unlabeled tea disease images for disease detection and identification. This method can better capture and represent the key features in the tea disease image, thereby improving the accuracy and reliability of disease detection.