2757 results about "Relvar" patented technology

The invention discloses a method for constructing a knowledge graph based on a large language model and a vector library, and relates to the technical field of knowledge graphs, and the method comprises the following steps: carrying out word segmentation, part-of-speech tagging and semantic analysis processing on a collected natural language text by using a pre-trained RoBERTa model to obtain a semantic representation vector of the natural language text; through a Seq2Seq framework and an attention mechanism, obtaining association between the semantic representation vector and the corresponding named entity, and obtaining the named entity through the obtained association; constructing a knowledge graph; fusing the constructed knowledge graph with a relational database of a preset field to generate a fused knowledge graph; named entities and corresponding attributes are obtained through dependency syntax analysis, the entity relation of the named entities is obtained, and a structured triple fusing the named entities, the attributes and the entity relation of the knowledge graph is constructed. In order to solve the problem of high entity linking difficulty in the prior art, the knowledge graph is converted into low-dimensional dense vector representation, and an inverted index database and the like are constructed, so that the difficulty is reduced.

Certain aspects of the present disclosure provide techniques for preprocessing data prior to generating vector representations. The data is preprocessed to generate one or more natural language texts describing entities and relationships included in the data. Vector representations of the natural language format texts are stored in a vector database. The embeddings may be retrieved to augment a prompt for a generative model. The embeddings may be selected by performing a search of the vector database using an embedding for the prompt to determine relevant or similar embeddings. The augmented prompt can be input into a large language model. Although the model may be unaware of the data from which the embeddings of the embedding database were generated, the augmented prompt may enable the model to use the data to improve breadth and depth of responses. The preprocessing of the data improves response results of the model.

The present application relates to the technical field of drug screening, and in particular to a method and system for enhancing the ability of a large language model to answer questions of a specific field. The method comprises the following steps: using a large language model to extract a relation triple from a data set, wherein the data set comprises structured data and unstructured data; on the basis of the relation triple, constructing a knowledge graph of G protein coupled receptors (GPCRs), and storing the knowledge graph in a form of a graph database, wherein the knowledge graph comprises a GPCR entity and a relation thereof; using a pre-trained large language model to search the graph database for related information of a specific field to obtain prompt information, wherein the specific field is the field of GPCRs; and on the basis of the prompt information and the pre-trained large language model, obtaining an answer result, wherein the answer result is returned to a user by the pre-trained large language model. In the present application, by constructing a knowledge graph, the large language model is endowed with accurate and real-time field knowledge of GPCRs, and the ability of the large language model to answer questions of the field of GPCRs is enhanced.

The embodiment of the invention discloses a retrieval generation method and device based on a large language model and a knowledge graph. The problems that retrieval semantic relevance is weak, and recalled data lacks global knowledge can be solved. The method comprises the following steps: performing vector analysis on a plurality of preset domain documents, and constructing a vector database; constructing a knowledge graph according to the entity description information and the relationship description information extracted from the plurality of field documents, and storing the knowledge graph into a graph database; performing feature extraction on a target question input by a user to obtain a multi-dimensional keyword corresponding to the target question; retrieving the multi-dimensional keywords through a preset search engine, a vector database and a graph database to obtain a target retrieval result; and determining target input information according to the target retrieval result, and inputting the target input information into the target language model to obtain target output response information.

The invention discloses a database query method and device, electronic equipment and a nonvolatile storage medium. The method comprises the steps that a knowledge graph corresponding to a to-be-queried database is determined, and the knowledge graph is used for representing a logic structure and an incidence relation of data in the to-be-queried database; a similarity score between the user question text and each graph node in the knowledge graph is determined, a target node is determined in each graph node of the knowledge graph according to the similarity score, and the similarity score is used for representing a correlation degree between the graph node and the user question text; and generating database mode information corresponding to the to-be-queried database according to the target node, and generating a structured query language statement corresponding to the user question text by adopting a large language model according to the database mode information. According to the method and the device, the technical problem of poor accuracy of generating the query statement of the database by utilizing the large language model due to the fact that the field information in the database is numerous and the relationship is complex is solved.

The invention discloses a knowledge graph construction method based on a fine-tuning large language model, and the method comprises the following steps: S1), data preprocessing: S2), extracting knowledge points and relationships in a data source, and obtaining preprocessed knowledge points and relationships; s3) disambiguating knowledge points and relationships; and S4) constructing a knowledge graph, forming a dynamic extensible knowledge graph, regularly using the AI model to re-analyze the text data in the later use process, and updating the knowledge graph. According to the method, the knowledge points are automatically extracted by utilizing the knowledge point extraction fine tuning model to determine the entity and the relation edge, the knowledge points are further identified through multiple disambiguation means, and the complex relation between the knowledge points is constructed, so that the efficiency and the accuracy of data processing are effectively improved; and finally, storing the data subjected to ambiguity elimination processing into a graph database model to construct a knowledge graph, thereby realizing automatic construction and updating of the knowledge graph.

The invention provides a case quality intelligent evaluation method and system based on a large language model, and the method comprises the steps: building an evaluation standard library covering various cases based on a law normative file and judgment practice; designing a complete evaluation reasoning chain based on the evaluation standard library; constructing a multi-agent system based on the evaluation reasoning chain; based on the multi-agent system, realizing distributed task allocation for unplanned online tasks by applying an imprecise alternating direction multiplier method algorithm; based on the distributed task allocation result, combining planning and reinforcement learning technologies to solve a relational multi-agent case association problem; based on the multi-agent case association processing result, applying a forced zero method sparse graph technology to realize effective learning based on a case graph; and constructing a case quality assessment knowledge graph based on the effective learning result of the case graph. By adopting the technical scheme, the accuracy, comprehensiveness and efficiency of case quality evaluation are improved.

The invention discloses a data anomaly diagnosis method and system based on a knowledge graph and a large model. According to the method, multi-source data including structured database records, semi-structured technical documents and unstructured expert experience descriptions are obtained and preprocessed, a knowledge graph containing entity, relation and attribute levels is established, domain adaptation training is conducted on a large language model through the knowledge graph, and the large language model is obtained. And constructing a hierarchical reasoning framework to realize data exception identification, diagnosis and solution generation. The method solves the technical problems of non-uniform knowledge representation, limited diagnosis capability, single reasoning logic and the like of a traditional data anomaly diagnosis method, and has the following advantages: uniform representation and storage of multi-source heterogeneous data are realized; the domain understanding ability of the large language model is improved through knowledge injection; the accuracy and efficiency of abnormality diagnosis are improved by adopting a hierarchical reasoning framework; and experience accumulation and continuous optimization are supported, and high practicability and expansibility are achieved.

The invention discloses a data knowledge-based method based on semantic fusion, and relates to the technical field of computer information processing.The method comprises the steps that a requirement set serves as input, knowledge requirement analysis, concept modeling, relation modeling and constraint declaration are completed, and a semantic model OB is constructed; taking the original data set Sraw as input, completing standardization processing and structure segmentation under the support of a semantic model OB, and forming an entity corpus and a semantic unit set; based on the semantic unit set, structured and unstructured triple extraction, semantic verification and graph loading are executed, and an initial knowledge graph is constructed; performing entity alignment, relationship merging, rule reasoning and versioning release on the initial knowledge graph to generate a graph; introducing a quality evaluation mechanism, and outputting an optimized atlas and an evaluation document; the KG opt deployment is online, and query packaging, visualization, service arrangement and incremental maintenance are completed. The method aims at solving the problems that multi-source heterogeneous data are not uniform in structure and inconsistent in semantics.

The invention relates to the technical field of data processing, in particular to a method and system for processing data based on a large language model.The method comprises the following steps that based on inter-sentence punctuation positioning and part-of-speech tagging, sentence blocks are divided to generate functional partitions, themes and relational words are extracted to judge semantic chain starting points, a trigger index sequence is constructed, and logic jump points and breakpoint positions are recognized; and mapping the label structure to the language model output analysis deviation, and generating a label mapping combination list. According to the method, semantic turning nodes can be captured by analyzing the semantic direction change trend, the relation between the semantic turning nodes and verb and noun combinations is judged, the position of a trigger point of an actual information transfer effect is extracted, and the break point area of a semantic path is recognized through the positioning of key word starting and stopping blocks and the logical judgment of noun group cross combination; the path integrity has a clear fracture identifier, a traceable semantic mapping structure path is established in a language model, and the accuracy, coherence and hierarchy clearness of semantic reconstruction are enhanced.

The invention relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention discloses an agent long-term memory modeling method based on a memory network, and relates to the field of agents, and the method comprises the steps: storing vector data to a vector database, and storing structured metadata to a relational database; receiving an input request of a user, and executing vector similarity retrieval in the vector database to obtain semantic similar fragments; executing structured data query in the relational database to obtain structured metadata; the memory abstract is retrieved; forming a candidate data set; taking a filtered result as memory information, inputting the memory information into a large language model through a cue word project, and generating reply content; the input request of the user and the generated reply content are combined to form a new interaction record; processing the new interaction record and historical interaction records stored in a vector database and a relational database through a large language model to generate a memory abstract; aiming at the insufficient long-term memory emotion interaction coherence of the intelligent agent, the emotion interaction coherence is improved.

The invention relates to a digital finance and taxation auditing method based on artificial intelligence, and the method comprises the following steps: S1, obtaining electric finance and taxation multi-source data, and carrying out the preprocessing of the data, and obtaining the preprocessed electric finance and taxation multi-source data; s2, constructing a finance and taxation knowledge graph according to the preprocessed power finance and taxation multi-source data; s3, constructing a multi-modal risk detection model, and obtaining a risk event list and a whole-course data traceability path according to the finance and taxation knowledge graph and the real-time electric finance and taxation multi-source data; s4, according to the risk event and traceability, based on a rule model, generating a structured decision instruction set including all key information and traceability links; and S5, converting the structured decision instruction set and the finance and tax knowledge graph into a standard relation graph for input, and dynamically displaying fund flow, risk events, abnormal aggregation and traceability paths among enterprises through D3. Js rendering. According to the invention, the efficiency and reliability of electric power digital finance and taxation auditing are significantly improved.

The invention belongs to the technical field of data query in the electric power field, and particularly relates to an electric power field SQL intelligent agent construction method based on a KMDI chain. According to the method, a plurality of field knowledge bases are constructed based on professional knowledge such as a data structure, data coding and a data relation in the electric power field, and a chain type process composed of three links of knowledge matching and decision making, key knowledge distillation and key knowledge injection is designed and an SQL intelligent agent is constructed; under the support of a large language model, the data coding conversion of the electric power technical terms in the natural language questions is realized, and the data query SQL statements matched with the requirements are accurately generated, so that the business personnel in the electric power field can directly realize the access and operation of the data through the natural language.

The invention discloses a combinatorial optimization automatic modeling-oriented training data generation method and system, and the method comprises the steps: receiving a problem description text, extracting key information through semantic analysis and knowledge reasoning, and constructing a structured optimization problem representation; reading the structured representation, extracting a target function component from the binary label matrix, extracting a constraint function component from the constraint relation adjacent matrix, extracting a variable definition component from the variable dependency matrix, and integrating the components to form a model component set; a value interval parameter is generated for the variable, a coefficient parameter is generated for the constraint matrix, a weight parameter is generated for the target function, and a parameterized model instance is obtained; and applying corresponding templates to scene elements, variables and constraint descriptions to generate a problem description text. Through the systematic information extraction and parameter generation method, automatic generation of high-quality training data is realized, and the diversity and practicability of the data are improved.

The invention provides a method for generating an SQL (Structured Query Language) by a natural language based on bidirectional mapping and semantic parsing, which relates to the technical field of database query and comprises the following steps of: extracting natural language query elements and packaging the natural language query elements into structured data, and establishing a mapping relationship from a query field to a service attribute and a physical data table by adopting a bidirectional Hash index technology; and automatically identifying multi-table association keys, performing semantic extension and compliance verification, generating an abstract syntax tree, performing processing according to user permission, and finally converting the abstract syntax tree into an SQL statement conforming to a target database syntax specification. According to the method, the accuracy and the efficiency of converting the natural language into the SQL are improved, and the flexibility and the safety of the system are enhanced.

The invention discloses a fault root cause positioning method and system driven by a dynamic knowledge graph, and relates to the technical field of fault root cause localization, and the method comprises the steps: collecting and obtaining a multi-source fault associated data set, carrying out the entity association extraction of the multi-source fault associated data set, and obtaining a fault entity set and an entity relationship set; performing graph node cascading and incremental learning updating, and constructing a fault updating knowledge graph; monitoring and acquiring target fault data, performing mode matching reasoning, and generating a fault mode candidate root cause set; and performing similarity matching on the fault mode candidate root cause set in combination with a historical fault case library, and determining a target fault root cause positioning result. The technical problem of low fault diagnosis efficiency caused by inaccurate fault root cause positioning and knowledge graph updating lagging in the prior art is solved, and the technical effects of realizing accurate positioning of the fault root cause and dynamic improvement of the knowledge graph and improving the fault diagnosis efficiency and accuracy are achieved.

The invention relates to a knowledge-enhanced large model data analysis agent visualization method, which comprises the following steps of: performing syntactic analysis by using a PCFG algorithm, identifying syntactic components and relationships of sentences, and splitting the sentences into sub-sentences with complete logic; extracting keywords and entity words of the sentences by combining a word segmentation technology with a business word segmentation library; recommending and summarizing candidate segmented words through a recall strategy, carrying out rough arrangement, fine arrangement and fine arrangement on the candidate segmented words, and correcting and complementing a segmented word result; splitting and analyzing the natural language problem based on the fine-tuned large model, and generating SQL statements meeting business requirements; analyzing the data set generated by the SQL, extracting field information and business meanings, and identifying an association relationship between fields; and automatically recommending chart styles according to data features and user preferences, assembling an analysis report and distributing the analysis report according to a specified frequency. According to the method, knowledge enhancement and large model technologies are combined, so that the efficiency and effect of data analysis and visualization are remarkably improved.

The invention discloses a large model knowledge base construction method based on multi-granularity retrieval. The method comprises the steps of obtaining a target text and analyzing a document structure, splitting and reorganizing text data according to three levels of titles, fragments and sentences, and constructing three levels of knowledge of the titles, the fragments and the sentences; using a large language model to construct a generative index as question-level knowledge according to the sentence-level knowledge; using a vector model to generate a first vector representing index keys of fragment-level knowledge and / or sentence-level knowledge, and a second vector representing index keys of title-level knowledge and / or question-level knowledge; and respectively storing the first vector and the second vector into a vector database, and storing the index values and the index types corresponding to the first vector and the second vector into a relational database. According to the method, multi-granularity document knowledge base construction is achieved, the expression modes of knowledge under different granularities are expanded, the accuracy of user query and knowledge matching is improved, and the problem that information is lost or incomplete in an existing method is solved.

The invention relates to a government affair file information extraction and question answering method and device and a medium, and the method comprises the steps: carrying out the entity extraction of a government affair file through employing a BERT-CRF joint model, and obtaining a structured entity set; performing relation extraction on the structured entity set to generate a semantic relation set between the entities; constructing a knowledge graph according to the structured entity set and the semantic relationship set, storing entity nodes into a graph database, and storing an embedded vector of an entity text into a vector database; when a query request of a user is received, relation query of the graph database and semantic retrieval of the vector database are carried out, sub-graph structures and semantic matching vectors related to query are extracted, and a mixed retrieval result is obtained; and inputting the mixed retrieval result into a large language model, and generating a question and answer response text conforming to a preset format by applying a dynamic prompt template. According to the method, the document processing efficiency and accuracy are effectively improved, and a solid technical support is provided for intelligent management of government affair documents.

Techniques for constraining the results of a generative language model to valid information using knowledge-grounded documentation. A generative language model may generate invalid results, including compound entities and incorrect entity relations. The techniques include, for a given user inquiry, determining a set of documented information, from a particular knowledge base, that corresponds to the user inquiry. The techniques further include determining a subgraph from a knowledge graph representing the knowledge base, as well as determining a trie data structure representation of the set of documented information. The user inquiry and subgraph are provided as input to a trained generative language model for generating a response to the user inquiry. The techniques include using the trie data structure to validate that the generated response corresponds to real information from the set of documented information.

The invention relates to the technical field of artificial intelligence, and discloses a rapid labeling method based on an artificial intelligence large model, which comprises the following steps of: 1, extracting an entity set and a relationship set of a specific field through a document set of a target field, identifying nouns, terms and keywords related to the field in a document by using an entity identification technology to form an entity set, and marking the entity set and the relationship set; the method comprises the following steps of: establishing an entity set, analyzing logic and semantic relevance among entities by utilizing a relation extraction technology, generating a relation set, combining the entity set with the relation set to construct a knowledge triad, and the knowledge triad comprises an association relation among the entities. By introducing the domain knowledge graph, the entity and relation information extracted from the target domain document is structured into the knowledge triad, and the knowledge triad is matched with the to-be-annotated data to optimize the annotated semantic information, so that the domain adaptability and semantic consistency of the annotation result are realized, and higher accuracy and consistency effects of the annotation data in the professional field are obtained.

The invention discloses a multi-modal large model optimization method and system based on defect detection and analysis. The method comprises the following steps: S1, collecting a multi-modal original sample with power business scene characteristics; s2, performing standardization preprocessing on the original data to obtain a fine-tuned standard data set; and S3, establishing relational mapping among the labels of the training data set to form a semantic relation graph of the labels, and extracting a topological structure relation of the semantic relation graph to generate the power text knowledge base. S4, various task function modules of the computer vision large model are trained, so that the computer vision large model has various tasks for processing the electric power scene. And S5, fusing the trained Clip semantic large model and the trained computer vision large model into a multi-modal large model by using a Transform structure. According to the method, the multi-modal large model subjected to fine tuning through the power text knowledge base not only maintains the specific analysis and reasoning performance under various power scenes, but also can ensure the generalization ability of the large model to various algorithm tasks.

The invention provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

The invention discloses a multi-table combined natural language query SQL (structured query language) generation method, which comprises the following steps of: 1, constructing a database meta-knowledge graph, and establishing a triple storage comprising a table structure, a primary and foreign key relationship and business description for each data table; 2, receiving a natural language query request, and calculating the topic relevancy between query semantics and each database table through a pre-trained topic matching model; 3, dynamically constructing a view, and logically associating the database tables of which the theme relevancy exceeds a threshold value to form a temporary view; 4, generating a context enhancement prompt, and combining the temporary view structure, the field semantic description of the view and the view content sample to form a structured prompt; 5, inputting the natural language query and the structured prompt into the large language model to generate candidate SQL statements; and step 6, executing verification and iterative optimization on the candidate SQL statements, verifying logic correctness through SQL execution plan analysis and result sampling, and triggering and prompting a reconstruction mechanism when detection is abnormal.

The invention relates to the technical field of big data and the field of financial science and technology, and discloses a database load optimization method based on dynamic fragmentation, which comprises the following steps: establishing an association relationship between fragments and storage nodes by configuring basic logic fragments and dynamic logic fragments; monitoring a data access record, identifying hotspot data and marking the hotspot data as a to-be-migrated data block; when the fragment load exceeds a threshold value, migrating the high-load data block to the dynamic logic fragment; and when the access frequency is reduced, moving the low-load data block back to the basic logic fragment. According to the method, through configuration and management of the dynamic logic fragments, in combination with real-time monitored access records and load state analysis, the high-load data blocks are recognized and migrated to the dynamic logic fragments for storage, and the overload problem of the basic logic fragments is relieved. Meanwhile, the low-load data blocks are moved back to the basic logic fragments, dynamic adjustment and balance of storage resources are further achieved, the load balancing capacity of the system is improved, and the requirements for high concurrency and real-time load changes in a financial scene are met.

The invention provides a relational database backup recovery method and system based on big data, and relates to the technical field of database backup recovery, and the method comprises the following steps: obtaining total backup data and incremental backup data of a relational database, and obtaining merged backup data; based on the features and the access mode of the merged backup data, constructing a multi-dimensional index structure at the same time, and integrating a data dependency graph, a classification result and cold and hot layering information into a multi-dimensional index; decompressing the target backup data to obtain recovered backup data, and performing intelligent verification on the recovered backup data by using a data integrity constraint rule; if the intelligent verification is passed, the recovered backup data is imported into the relational database, and data recovery at a specified time point is completed; and if the intelligent verification is not passed, intelligent data restoration and complementation are carried out, and then verification, verification and importing are carried out again until the intelligent verification is passed.

According to the multi-modal automatic knowledge graph construction method based on the large language model, a multi-modal data stream is preprocessed, features are extracted, and the multi-modal data stream is mapped to a unified semantic space through a cross-modal alignment network after being processed through the large language model, a visual converter and a time sequence neural network. In the space, entities and categories are recognized based on a large language model, a triple is generated by combining a multi-modal feature judgment entity relationship, mapping fusion is performed through an ontology alignment algorithm driven by a graph neural network and a predefined domain ontology, finally knowledge is stored in a graph database, and dynamic updating is performed by means of incremental learning and online reasoning. Standardized APIs and visualization components are provided. According to the method, the construction efficiency and the automation degree of the knowledge graph are remarkably improved, the cross-modal information fusion and knowledge maintenance capability is enhanced, and the application requirements of intelligent retrieval, recommendation, decision support and the like are met.

The invention discloses an information extraction method and device based on a large language model, equipment and a storage medium, and relates to the technical field of information extraction, and the method comprises the following steps: converting an original text into a to-be-extracted character string; extracting entities and relationships from the to-be-extracted character string according to the pre-labeled sample; retrieving from a professional knowledge database according to the original text, the entity and the relationship to obtain a related professional knowledge text; according to the original text, the entity, the relationship and the related professional knowledge text, constructing cue words; and inputting the original text, the entity, the relationship and the cue word into a large language model, so that the large language model outputs extraction information of the original text. According to the method, entities and relationships can be accurately extracted from character strings by using a small amount of annotation data through pre-annotation samples, the extracted entities and relationships are retrieved in a professional knowledge database according to an original text, and cue words are generated in combination with retrieval contents, so that a large language model is helped to understand information needing to be extracted, and the extraction efficiency is improved. And then proper information is extracted.

The invention relates to the technical field of data processing and governance, in particular to a monitoring method and system for a data governance process, and the method comprises the steps: constructing a quantum distribution model through extracting multi-modal features, so as to represent a complex relation between feature variables; analyzing the abnormal propagation path based on a causal chain model and a recursive reasoning algorithm, and generating basic abnormal feature data; performing parameter optimization on the governance rule by using a memetic evolutionary algorithm to generate optimized rule data; model parameters of the monitoring system are updated through multi-round iteration, and dynamic optimization of the system state is achieved. According to the method, the depth and precision of anomaly detection are remarkably improved, meanwhile, efficient iteration of a monitoring system is achieved, the treatment requirement under the complex multi-modal data environment is met, and the key problems that in the prior art, the analysis depth is insufficient, the real-time performance is poor, and the traceability is limited are solved.