8 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 invention relates to the technical field of Internet of Things cooperative task information recommendation, and provides a cooperative task information recommendation association degree calculation method and device based on information source semantic feature measurement and a storage medium. In order to solve the problem of low supply and demand matching precision caused by fuzzy semantic association evaluation between an information source and a task demand in a decentralized Internet of Things environment, the method comprises the following steps: establishing a distributed local information directory on each Internet of Things platform, and constructing a heterogeneous information source semantic feature vector model; traversing all local information source semantic feature vectors, extracting an available vector set, and monitoring a change event of a local heterogeneous information source semantic feature vector model in real time to update the vector set; constructing a collaborative task information demand semantic model, and obtaining a task demand semantic vector; constructing a domain information concept tree through domain expert knowledge; and performing correlation calculation based on a domain information concept tree on the available information source semantic feature vector set and the task information demand semantic vector to obtain a correlation two-dimensional matrix.

The invention relates to a clinical path-based diagnosis and treatment knowledge graph construction method and system, and the method comprises the steps: determining an ontology concept of a diagnosis and treatment knowledge graph, building an ontology concept tree, extracting an ontology according to the ontology concept tree, and marking the relation of the extracted entities to obtain a training sample; pre-training encoders of a Bert model by using the diagnosis and treatment text, then training the Bert model with multiple layers of decoders by using the training sample, outputting a hierarchical label by each layer of decoder in the Bert model with the multiple layers of encoders, and obtaining an entity relationship based on the hierarchical labels; and inputting a medical text of a to-be-extracted entity relationship into the trained Bert model to obtain a triple, constructing a diagnosis and treatment knowledge graph of a diagnosis and treatment clinical path by adopting the triple, and performing knowledge completion and entity disambiguation on the triple.

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 present application relates to the technical field of situational awareness processing, and in particular to a situational awareness processing method, electronic equipment, medium and program product, the method comprising obtaining data to be processed, the data to be processed being the operation data corresponding to the device to be processed at different functional nodes; identifying the data features corresponding to each functional node from the data to be processed, and judging whether there is an abnormal functional node based on the preset threat features, the abnormal functional node being a functional node containing the preset threat features in the corresponding data features; identifying the abnormal node identifier corresponding to each abnormal functional node, and determining the node threat portrait corresponding to the abnormal functional node based on the abnormal node identifier and the preset threat features contained; constructing a security threat concept tree based on each abnormal functional node and the node threat portrait corresponding to each abnormal functional node, and feeding back the security threat concept tree according to the preset feedback frequency. The present application can improve the accuracy of determining security threats.

The invention discloses an ontology-based large language model self-evolution method and device, and the method comprises the steps: generating an ontology concept related to a professional field from a large language model through a question and answer interaction mode based on a first prompt template for ontology knowledge extraction, and enabling the professional field to comprise the medical, legal or financial field; the ontology concepts generated by the large language model step by step are organized into a concept tree corresponding to the domain ontology, each edge on the concept tree is marked as a knowledge path, the confusion degree and the certainty degree of the large language model on each knowledge path on the concept tree are calculated, and a reliable path and a to-be-reinforced path in the concept tree are marked based on the certainty degree; on the basis of the to-be-enhanced path, more natural and smoother new knowledge is generated through interaction with the large language model, fine adjustment is conducted on the large language model in combination with the new knowledge, in this way, deficient professional knowledge can be accurately supplemented, model self-evolution independent of external resources is achieved through the characteristics of the ontology, and the professional ability of the model is improved.

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.