4 results about "Concept tree" patented technology

This application discloses a text label generation, model training, and text classification method and related equipment, which addresses the problem that category labels obtained in related technologies cannot accurately describe the category to which the sample corpus belongs, thus affecting the accuracy of the subsequently trained text classification model and the execution accuracy of text classification tasks based on the text classification model. The text label generation method includes: obtaining the meta-concept path corresponding to the sample text from a pre-constructed concept tree based on keywords in the sample text corresponding to the target classification task; the concept tree is used to represent the hierarchical relationship between multiple meta-concepts, and the meta-concept path is used to represent the hierarchical relationship between multiple target meta-concepts related to the sample text in the concept tree; searching for label words in a pre-set meta-concept table based on the concept vectors corresponding to the multiple target meta-concepts and the hierarchical relationship between the multiple target meta-concepts to determine the label words corresponding to the sample text and use them as the category labels corresponding to the sample text.

The present invention discloses an analysis and identification method, system and storage medium for electrocardiograms. The method includes: constructing a concept tree based on definitions in electrocardiogram and arrhythmia knowledge, and establishing an arrhythmia electrocardiogram data set; performing data enhancement processing on the arrhythmia electrocardiogram data set based on the concept tree to construct prior data; constructing an ECG prior model based on the prior data; optimizing the ECG prior model to obtain a target identification model; and analyzing and identifying an electrocardiogram to be analyzed based on the target identification model, and determining an identification result of the electrocardiogram to be analyzed. The present invention has high accuracy and high efficiency, and can be widely used in the field of computer technologies.

This invention provides a method for predicting cattle weight and making optimal slaughter decisions using AI, comprising: collecting and processing 3D point cloud data of cattle to obtain a 3D model, and extracting body shape feature vectors; inputting the feature vectors into a deep neural network for mapping to obtain real-time weight data; processing the weight data and applying a neural Pareto algorithm for parameter calibration to obtain weight prediction data; collecting market price data and predicting prices; evaluating economic benefits based on weight prediction, price prediction, and cost data through comparative concept tree search; and finally mapping the evaluation results to a feature space and solving for the optimal slaughter decision scheme through similarity search and dynamic programming algorithms. This invention achieves contactless and accurate estimation of cattle weight, combined with market price prediction and cost-benefit analysis, providing a scientific basis for breeding decisions and significantly improving the economic benefits of breeding.

The application discloses an analysis and identification method and system for electrocardiograms and a storage medium. The method comprises the following steps: constructing a concept tree according to the definition of electrocardiograms and arrhythmia knowledge, and establishing an arrhythmia electrocardiogram data set; performing data enhancement processing on the arrhythmia electrocardiogram data set according to the concept tree, and constructing prior data; constructing an ECG prior model according to the prior data; optimizing the ECG prior model to obtain a target identification model; and analyzing and identifying a to-be-analyzed electrocardiogram according to the target identification model, and determining an identification result of the to-be-analyzed electrocardiogram. The application has high accuracy and high efficiency, and can be widely applied to the technical field of computers.