11 results about "Relation discovery" patented technology

The application discloses a kind of individual and group-oriented behavior internet modeling and prediction recommendation method, the method includes the following steps: step 1: establish the behavior preference model of behavior internet based on hypergraph;Step 2: extract the relationship between behaviors based on the model of step 1;Step 3: discover the behavior mode of user based on the behavior relationship of step 2;Step 4: predict the behavior trend of user based on the behavior relationship of step 2 and the behavior mode of step 3;Step 5: personalized service recommendation is carried out for user based on step 2, step 3, step 4.The model of behavior internet is proposed in the application, and the personalized behavior internet is obtained by cause-effect inference and relationship discovery, and the recommendation is carried out by using the deep learning method based on external knowledge, which improves the recommendation efficiency and effect for individual and group, overcomes the shortcomings that the traditional recommendation method based on deep learning is difficult to fully utilize personalized knowledge, and realizes the personalized and efficient recommendation of service.

The invention relates to the technical field of knowledge graphs, and provides a semantic graph and structure graph fused entity relationship automatic generation method, which comprises the following steps: constructing a semantic graph according to text content, and constructing a structure graph according to a text structure; calculating the semantic association degree between each keyword vector and each text paragraph structure vector; performing semantic association degree sorting on keywords related to each text paragraph structure to construct an association degree matrix; calculating the semantic correlation between every two keywords, and constructing a plurality of undirected relation pairs according to the semantic correlation so as to automatically screen and generate an entity set in the semantic correlation knowledge graph; based on the text evidence and each undirected relation pair, performing relation calculation on two keywords in each undirected relation pair to construct a semantic association knowledge graph; a large model hint project for relationship discovery is improved based on a high quality relationship sample library. According to the method, entity relationship automatic labeling can be carried out on massive and dynamically changing text contents.

The application discloses a Web table abnormal data discovery method based on a text semantic mapping relationship. The application aims at discovering abnormal data with fuzzy or even wrong semantic information in a Web table. The method mainly comprises three parts: a semantic representation module, a column type inference module and an error discovery module. First, the semantic representation module represents the meaning of cell text. For a cell in a table, the string text in the cell is represented as a semantic vector according to context information. Then, the column type inference module infers the type of the column where the cell is located, and obtains the mode information of the column. Finally, based on the mapping relationship between the column type and the semantic vector of the cell text of the main column cell and the target cell, abnormal data in the table is discovered and labeled.

This invention relates to the field of causal relationship discovery technology for electromechanical equipment monitoring data, specifically to a method for causal relationship discovery based on hierarchical knowledge embedding in electromechanical equipment monitoring data. The method includes: collecting electromechanical equipment monitoring data; establishing a hierarchical knowledge constraint matrix based on the knowledge information of the electromechanical equipment; establishing a causal discovery model; weighting and adjusting the causal strength calculated by the causal discovery model based on the hierarchical knowledge constraint matrix, or masking and modulating the attention matrix of the causal discovery model based on the hierarchical knowledge constraint matrix; processing the electromechanical equipment monitoring data based on the knowledge-embedded causal discovery model to obtain a causal network adjacency matrix; performing conflict edge detection based on the hierarchical knowledge constraint matrix, updating the causal network adjacency matrix, and using the updated causal network adjacency matrix as the causal relationship analysis result. This invention can improve the performance of causal relationship identification in monitoring data.

The application discloses a user active relationship discovery method and system for information recommendation, and the method comprises the following steps: constructing a user relationship multi-attribute heterogeneous graph; calculating the relationship score between a target user and other users based on the relationship between the vertices in the user relationship multi-attribute heterogeneous graph; obtaining the active relationship group of the target user according to a preset score threshold and the relationship score between the target user and other users; determining the N users most similar to the target user from the active relationship group of the target user based on a TOP-N algorithm, and taking the N users most similar to the target user as the active relationship users for information recommendation of the target user. The active relationship group of the target user is calculated according to the association relationship between users, so that the discovery of the active relationship of the user is realized, and the accuracy of information recommendation is further improved.

The invention provides a training method of an archive data reconstruction model, an archive data anomaly detection method and device, equipment and a medium. The method comprises the steps of obtaining an archive record to be detected; vectorizing the archive record to be detected to obtain a corresponding input vector; inputting the input vector into an archive data reconstruction model obtained based on normal archive record unsupervised training to obtain a corresponding reconstruction vector; calculating a reconstruction error between the input vector and the reconstruction vector; and when the reconstruction error is greater than a preset error threshold value, determining that the to-be-detected archive record is abnormal data. Through the pre-trained archive data reconstruction model, the internal association and non-linear relationship of each field in the archive record can be identified, novel anomalies of unknown types and undefined rules are found, the limitation of traditional hard coding rules is broken through, and accurate detection of deep and hidden defects in archive data is realized.

The present application relates to the technical field of intelligent analysis of financial data, and particularly relates to a method for automatically discovering reconciliation constraints for high-sparse heterogeneous financial data tables. The present application identifies a locally consistent record set by record clustering and field sub-cluster generation based on a field non-empty mode, then discovers reconciliation relationships within a locally effective sample range, and then introduces an inverse frequency weight and enhances sparse fields, so that fields that only appear in specific business scenarios but play a key role in reconciliation structure identification are not easily covered by high-frequency fields. The common non-empty constraints and splitting of sufficient field candidate sub-clusters are performed to gradually shrink the candidate field set, avoid irrelevant fields from being mixed into the same linear solution space, and improve the concentration and interpretability of numerical reconciliation relationship discovery.

The invention provides an open type relation extraction method and device based on a large language model, and the method comprises the steps: constructing a corresponding example set and an instruction prompt for each training example in a training data set, and enabling the example set to comprise examples which belong to the same relation with the corresponding training example; inputting each training instance, the corresponding instance set and the instruction prompt into a first large language model for training; a corresponding example set and an instruction prompt are constructed for each training example, and the example set does not comprise examples belonging to the same relationship with the corresponding training example; inputting each training instance, the corresponding instance set and the instruction prompt into the second large language model, and adopting the trained relation predictor as a teacher to guide the second large language model to train; performing new relationship prediction on the to-be-tested instance by adopting the trained relationship discoverer and relationship tester; therefore, the accuracy of relation prediction is effectively improved.

The invention discloses a key information infrastructure association evaluation system and method based on a large language model, and the method comprises the steps: firstly extracting information from multi-source heterogeneous data through an entity extraction and relationship discovery engine based on large language model driving, and constructing and updating a unified risk knowledge graph; then, parallelly starting intrusion trace analysis and service logic security analysis, and writing an analysis result back to the knowledge graph for enriching; thirdly, calculating and generating an attack path from an attack entrance to a core target and a corresponding script based on the knowledge graph containing the threat analysis result; and finally, performing multi-mode empirical verification on the generated attack script, and feeding back a verification result to the knowledge graph to form a closed loop. According to the scheme provided by the invention, risk discovery, association analysis, attack simulation and closed-loop verification of the key information infrastructure can be automatically carried out, isolated security data points are converted into linked attack paths, and the evaluation depth, breadth and efficiency are remarkably improved.

The invention discloses a dynamic visualization system and method for a certificate asset relation topology. The system comprises a data input layer, a relation discovery module, a topology calculation engine, a visual rendering module, an interaction control module and a real-time monitoring module. The data input layer collects certificates, IT assets and configuration data; the relationship discovery module adopts a depth-first search algorithm to identify relationships of deployment, dependency, protection and the like of certificates and IT assets, and stores the relationships after triple verification of domain name matching, configuration analysis and network detection; the topology calculation engine realizes layout optimization based on an improved force-oriented layout algorithm; the visual rendering module adopts WebGLGPU acceleration and LOD technology to distinguish asset types and relationships through visual coding; the interaction control module provides interaction such as dragging and zooming. According to the method, dynamic visualization of the certificate and IT asset relationship is realized, the management efficiency is improved, the certificate change risk is reduced, the large-scale data visualization performance is improved, and the method is suitable for certificate asset relationship management of a complex IT environment of an enterprise.

The application relates to the field of marine information technology and data processing, and discloses a marine survey historical data correlation analysis and retrieval system based on a knowledge graph, which comprises a self-adaptive relation kernel compiling engine, a user query intention, a field semantic graph, a dynamically selected and compiled relation kernel encapsulating a field algorithm, a generated relation discovery execution plan, a dynamic hypergraph construction module executing the plan, a calculation of generated relation instances representing high-order correlations as hyperedges by calling the relation kernel to act on original data, a dynamically constructed dynamic hypergraph with space-time data field nodes as vertices and the relation instances as hyperedges, and a pattern matching on the dynamic hypergraph to retrieve complex correlation patterns and provide traceable analysis results. The application realizes the calculation-driven dynamic construction of data relations, can efficiently and flexibly discover and analyze high-order correlations in marine space-time data.