1831 results about "Graph database" patented technology

The implementations herein disclose advanced systems and methods for integrating, analyzing, and reasoning over heterogeneous data at scale. In some implementations, the system comprises a synergistic data processing infrastructure featuring: a graph database core for unified data representation; specialized loaders for concurrent ingestion and processing of structured, unstructured, and time series data; a natural language reasoning engine leveraging large language models; and a multi-modal user interface.

The invention relates to an intelligent question-answering system optimization method and device based on a knowledge graph, and the method comprises the steps: obtaining original knowledge data of a target knowledge domain, and constructing a knowledge graph structure model; extracting term information of entity nodes in the knowledge graph structure model, and constructing an entity term set; receiving a natural language question input by a user, executing a semantic understanding operation based on the standardized expression set to obtain a structured question semantic representation, and matching the question semantic representation with the case training set to obtain context semantic features; constructing a cue word template, and executing a query instruction generation operation to obtain a target query statement of the graph database; submitting the target query statement to a graph database to execute data retrieval operation, and obtaining query result data corresponding to the question semantic representation; and performing personalized rendering processing on the query result data based on the user portrait information to generate final question and answer return content. The method has the effect of improving the query accuracy.

The invention discloses an industrial innovation knowledge graph dynamic construction method based on a large language model, and belongs to the technical field of data processing. According to the method, multi-source heterogeneous data are integrated, cleaning, format conversion and standardization processing are performed, entities, relationships and attributes are automatically extracted by adopting a large model driven zero sample extraction technology, a high-precision triple library is constructed by combining entity embedding alignment and context anaphora resolution, and a knowledge graph is generated based on a graph database. According to the method, incremental data are captured in real time through a dynamic sensing layer, graph dynamic updating is achieved through an atomization updating mechanism, and data timeliness is guaranteed in combination with timestamps and multi-source verification. According to the method, the knowledge graph construction efficiency and coverage rate are improved, the defects that a traditional method is high in manual dependency degree, long-tail knowledge is missing, updating lags and the like are effectively relieved, and accurate support is provided for technical innovation decision making.

The invention discloses a multi-source heterogeneous data knowledge graph construction method for railway disaster prevention monitoring, and relates to the technical field of knowledge graph construction, and the method comprises the steps: gathering multi-source heterogeneous data related to railway disaster prevention monitoring, and constructing a domain ontology model used for guiding knowledge extraction and fusion; extracting entities, attributes and relationships among the entities from different modal data after standardization preprocessing by using a targeted extraction algorithm; obtaining fused structured knowledge based on a multi-strategy knowledge fusion process of domain ontology constraint and confidence evaluation; the fused structured knowledge is stored in a graph database, and construction of the knowledge graph in the railway disaster prevention monitoring field is completed; through combination of domain ontology construction, a mixed knowledge extraction engine and a multi-strategy knowledge fusion technology, deep semantic fusion of multi-source heterogeneous data in the railway field is realized. The invention aims to construct a knowledge graph capable of comprehensively and accurately reflecting complex characteristics in the railway disaster prevention field.

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 data sharing system and method for realizing multi-agent interaction, and belongs to the technical field of agents. According to the method, production command instructions are labeled, a large language model is trained, and task types are identified; disassembling the production command instruction into a subtask sequence; constructing a knowledge relationship graph through the graph database; establishing a unified data interface, and constructing a data sharing center; dividing agent types according to the power transmission and distribution service scene, and determining a function boundary of each agent; a multi-agent simulation environment is used for testing the cooperation efficiency of the subtasks, and the execution sequence of the subtask sequence is optimized; sub-tasks are dynamically allocated according to the intelligent agent load and the skill matching degree; the intelligent agent accesses the knowledge relation graph through a unified data interface, and real-time pushing of data change is achieved through a message queue; the decision of the large language model is optimized through cue word engineering, specific operation is executed by utilizing a tool library component, and the intelligent agent executes operation according to a subtask sequence.

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 discloses an archive knowledge base construction and retrieval method and system based on multi-modal data fusion. The method comprises the steps that heterogeneous archive data are cleaned, image features are extracted through CNN, text features are extracted through Transform, audio is converted into text and then subjected to similarity, a unified feature vector is generated, and metadata is constructed according to archive code association; creating a graph database instance, defining nodes and relationship types, importing entities and relationships, and storing feature vectors and metadata; the features are mapped to a high-dimensional shared semantic space, positive and negative sample pairs are constructed to update embedded layer parameters, self-attention is used in modalities, a shared attention mechanism is used between modalities, weights are adjusted according to archive features, and unified knowledge representation is generated; segmenting the steering quantity of the multi-modal data, storing the steering quantity into a database, and adopting hierarchical indexing and optimizing as required; related document fragments are retrieved through RAG technology vectors, answers are generated with the help of a large language model, and session feedback is provided. The file retrieval efficiency and accuracy are improved.

The invention relates to a method convenient for data consanguinity collection and analysis, which comprises the following steps of: obtaining original consanguinity data comprising a task execution log, application metadata and a cross-system dependency relationship, and carrying out standardization processing on the original consanguinity data to generate structured consanguinity information comprising an asset unique identifier, an upstream and downstream association relationship and a data operation type; structured consanguinity information is synchronously written into a graph database and a distributed data warehouse, the graph database stores real-time association topology, the distributed data warehouse stores full-amount historical versions, and a transaction consistency algorithm is adopted to ensure the atomicity of double-write operation, so that the data storage efficiency is improved. Single-asset-level consanguinity tracking is performed based on real-time topology of a graph database, global consanguinity analysis is performed based on batch computing power of a distributed data warehouse, a direct dependence path, a deep association network and a closed-loop link detection result are generated, and a closed-loop management mechanism from data acquisition, analysis to optimization is formed. And the problem that an analysis result is disjointed from an acquisition end in a traditional scheme is solved.

The invention discloses a three-dimensional environment reconstruction optimization method based on multi-sensor fusion data, and relates to the field of three-dimensional environment reconstruction optimization, and the three-dimensional environment reconstruction optimization method based on the multi-sensor fusion data comprises the following steps: S1, collecting multi-source sensor data, and constructing a data set under a unified coordinate system; s2, generating dense visual point cloud, and extracting laser point cloud features to construct a model; s3, establishing a local three-dimensional model, and generating a local environment image; s4, shadow parameters are extracted through shadow geometric analysis, and time sequence optimization is carried out; s5, consistency verification and correction are carried out, and three-dimensional reconstruction data are output; and S6, comparing the reconstruction data with the navigation map database, and carrying out map optimization updating. According to the method, time synchronization and space calibration are carried out on data acquired by the depth camera and the laser radar, complete and accurate three-dimensional information modeling of the target environment is realized, and the geometric precision of environment reconstruction and the image detail reduction capability are improved.

The invention discloses an intelligent monitoring management method and system based on archive digitization, and relates to the technical field of data management, and the method comprises the steps: collecting and preprocessing multi-source archive data, employing a multi-mode BERT model to carry out the feature fusion of different data sources, and generating a unified semantic representation; semantic labeling is performed on archive data through a multi-label classification model, a semantic graph of archive content is constructed by using a graph database, an association relationship between archives is represented, a semantic index tree is constructed based on the semantic graph, and rapid positioning and calling of the archive content are optimized; and recording the change of each file version, positioning the change position based on a semantic index tree, identifying the semantic change of the file through a semantic difference comparison algorithm, recording hash, carrying out granularity division on the file content through the semantic boundary of each level of node in the index tree, and generating a user access strategy. According to the invention, dynamic perception and risk early warning of user behaviors are realized, and the intellectualization and safety of the archive management system are effectively improved.

The invention provides a prospecting prediction method based on a multi-agent technology. The method specifically comprises the steps that S11, keyword extraction is conducted on mineral problems input by a user; s12, establishing a vector database of the mineral knowledge text and a graph database of the mineral knowledge graph, and optimizing the extracted keywords to generate cue words; s13, constructing a large prospecting model to think the cue words, expanding the cue words, and obtaining a complete statement for professional expression of the user question again; s14, performing multi-task decomposition on the complete statement to obtain a plurality of sub-tasks, and scheduling the plurality of sub-tasks to corresponding agents based on a graph database of the mineral knowledge graph to perform step-by-step prediction; and S15, integrating prediction results of the intelligent agents and feeding back the integrated prediction results to a user. According to the method, prospecting prediction is promoted to be converted from manual driving to intelligent driving, and emerging technical support is provided for prospecting breakthrough.

The invention discloses a medical history recognition method and system based on a large model and an expert strategy, and the method comprises the steps: collecting data in a medical network, and carrying out the cleaning and standardization processing of the data; extracting medical entities and relationships thereof from the text, and optimizing the rule set; the extracted entities and relations are fused into a knowledge graph, a multi-relation graph is constructed, and attributes are defined; storing the knowledge graph in a graph database, and receiving new data through a real-time data stream processing system; basic information and symptom description of a patient are collected, and key information is extracted by using a large language model; an inquiry path is dynamically adjusted, and specific symptoms are clearly and fuzzily answered or deeply discussed by questioning; and generating a structured electronic medical record by using the large language model. Through an intelligent pre-inquiry process, the system can collect detailed medical history information and generate a standardized electronic medical record before a patient officially sees a doctor, so that the information verification and supplement time when a doctor receives the doctor is greatly shortened.

The invention provides an intelligent data quality monitoring method and system. The method comprises the following steps: performing data processing on multi-source heterogeneous data by utilizing a unified data model; inputting historical data as a training set into the pre-training model for training optimization to obtain an optimized pre-training model, and performing threshold determination on the standardized distribution data stream by using the optimized pre-training model in combination with an adaptive threshold module; inputting the processed abnormal event list into a context awareness and dynamic priority management module to carry out de-duplication optimization transmission; carrying out root analysis on the abnormal event list after the de-weighting optimization by utilizing a root analysis tool; generating a repair strategy for the root analysis result by using the assistance of a repair suggestion engine; and inputting the repair strategy and the visual report into a cross-platform monitoring interface, and generating a repair suggestion and an early warning notification. According to the method, the data consanguinity map is constructed based on the graph database, minute-level traceability of abnormal events is achieved, and the troubleshooting time is shortened by 50%.

The invention relates to an enterprise-level simulation knowledge graph construction method based on multi-modal data integration, and belongs to the technical field of knowledge graphs. The method comprises the following steps: integrating structured data, semi-structured data and unstructured data through a multi-modal data warehouse; performing knowledge extraction on the semi-structured data and the non-structured data to obtain entities and relationships, and performing knowledge fusion; storing the fused entities and relationships by using a graph database, and constructing a simulation knowledge graph; a vector database is embedded in combination with a simulation knowledge graph, semantic extension search is realized through multi-modal joint search, similar cases are searched through a simulation result graph, and a simulation scheme comparison matrix is automatically generated. The multi-modal data is effectively integrated, the comprehensiveness and accuracy of knowledge graph construction are improved, more powerful, efficient and intelligent support is provided for simulation analysis of enterprises, and the enterprises can be assisted in rapidly making scientific decisions in complex and changeable business scenes.

The invention discloses a method for constructing a cross-modal knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the collection, structural analysis, modal recognition and classification, cleaning and standardization processing, so as to form structured multi-modal data; extracting entities and relationships of the identified modals from the structured multi-modal data, and summarizing the entities and relationships to form a multi-modal knowledge element set; mapping different modal entities in the multi-modal knowledge element set to a unified semantic space, and generating a unified entity relationship set through semantic matching, alignment and fusion; and normalizing the data into knowledge triples, and storing and organizing the knowledge triples through a graph database to form a cross-modal knowledge graph. According to the method, the problems of difficulty in multi-modal heterogeneous information alignment and difficulty in entity relationship extraction can be relieved, semantic association is enhanced, and knowledge graph integrity and multi-scene adaptability are improved.

The invention provides a charger fault diagnosis method based on a knowledge graph, which realizes efficient and self-adaptive fault diagnosis and maintenance through multi-technology fusion, constructs a structured knowledge base, integrates charger parts, fault modes and detection methods, stores entities and causal relationships and detection association by adopting a graph database, extracts information through a BERT model, and realizes fault diagnosis and maintenance based on the knowledge graph. Marking causal strength and dynamically adjusting the causal strength, converting spoken description of a user into structured data, matching a fault path in a knowledge graph, dynamically adjusting a weight, analyzing time sequence data, matching a time sequence mode in the knowledge graph in combination with dynamic time warping, generating a candidate path, optimizing a diagnosis path weight, and designing a multi-target reward function; a visual report is generated, the fault probability in the future seven days is predicted, maintenance suggestions are generated in combination with risk levels, knowledge maps and models are continuously optimized through user feedback, diagnosis accuracy and maintenance efficiency are improved, and manual intervention requirements are reduced.

The invention provides an intelligent agent tool calling knowledge optimization method based on empirical path graph evolution, which comprises the following steps: when an intelligent agent successfully completes a task for the first time, recording an intelligent agent tool calling sequence, input and output parameters and an execution result, generating a structured calling log, the calling log is converted into a standardized calling path knowledge unit; performing structured representation and semantic representation on the calling path knowledge unit, storing the structured representation in a graph database, and storing the semantic representation in a vector database; task intentions, tool entities and calling paths are used as heterogeneous nodes, an experience path knowledge graph is constructed, the experience path knowledge graph is used for recording the multi-dimensional relation among tasks, paths and tools, execution performance attributes and feedback attributes are added to path nodes in the graph, and agent tool calling knowledge optimization is completed. And the purpose of improving the tool calling efficiency and robustness of the intelligent agent in the multi-task environment is achieved.

The invention provides an operational research course knowledge graph construction method based on multi-source data fusion. The method comprises the following steps: firstly, discussing logical association of courses, majors and students, collecting data such as textbooks, exercises and teaching programs by taking operational research knowledge points as a core and utilizing technologies such as OCR (Optical Character Recognition) and crawlers, and carrying out preprocessing and manual labeling; then, an improved deep learning model is adopted for entity recognition and relation extraction, BERT + BiLSTM + CRF is adopted for entity recognition, and a dynamic context pooling enhancement model is fused to improve the capture ability of a complex knowledge boundary; bERT + BiLSTM is adopted for relation extraction, a multi-head attention mechanism is combined, and hidden logical relation mining is enhanced. And finally, constructing a multi-level knowledge network which takes knowledge points as nodes and logic relations as edges, and embedding the multi-level knowledge network into a Neo4j graph database for visualization. The map can optimize a teaching path, provides personalized learning recommendation, and is widely applied to the fields of wisdom education, knowledge retrieval and the like.

The invention discloses a hyper-converged server multi-resource integration system and a scheduling method, belongs to the technical field of computer resource management, and aims at solving the problems that a traditional hyper-converged server is dispersed in resource scheduling, low in storage efficiency, difficult to identify abnormities and the like. Multi-source data is obtained by means of distributed acquisition nodes, a distributed time sequence database architecture is adopted for storage, a standard data set containing a multi-dimensional index is constructed, and efficient storage and rapid retrieval of the data are achieved. Positioning target data to construct a graph model, building a layered and partitioned distributed graph database, and capturing change events in real time to realize dynamic updating. And mining a resource causal relationship by using a graph neural network, and screening an effective causal chain. Meanwhile, according to a graph model, monitoring weights of nodes and connecting edges are calculated, differential monitoring is implemented, potential abnormal points are accurately identified, and a resource scheduling strategy is generated in combination with a causal relationship. Real-time feedback adjustment and database updating are performed during execution, multi-resource deep integration and intelligent scheduling are achieved, and the resource utilization rate and stability of the system are remarkably improved.

The invention relates to a synchronous line loss intelligent diagnosis and analysis system based on an electric power knowledge graph. The synchronous line loss intelligent diagnosis and analysis system comprises a data acquisition layer, a data access layer, a data storage layer, a data analysis layer and a visualization layer, the data acquisition layer is used for acquiring power grid node data; the data access layer is used for carrying out dynamic synchronization on power grid node data based on Apache Kafka; the data storage layer comprises a time sequence database and a graph database; the data analysis layer comprises a detection module and an inference engine; and the visualization layer is used for dynamically displaying a propagation path and node characteristics of an abnormal region based on a topological structure of the knowledge graph, and querying an abnormal reason of the node and the influence on the whole system by clicking the node of the device. According to the invention, intelligent detection and diagnosis of the line loss abnormal area are realized, and the problem root cause is rapidly identified.

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.

The invention relates to an industrial equipment maintenance intelligent question-answering system based on multi-agent collaboration. Wherein the input unit is used for receiving text, voice, image or equipment scanning and other multi-mode user input information and analyzing the information into structured problem information; the scheduling unit performs semantic understanding and problem classification on the structured problem information based on the fine-tuned cross-language pre-training language model and an incremental training mechanism; the processing unit calls a corresponding domain agent according to the classification result, and generates an intelligent question and answer processing result including predictive maintenance suggestions, structured reply content and semantic annotation information; and the fusion unit fuses the local knowledge base, the graph database and the networking retrieval information, performs multi-hop semantic reasoning on the intelligent question and answer processing result, and generates multi-modal reply information including text description, image screenshots, prediction curves and recommendation links. The system can support multi-language and multi-mode intelligent question answering and predictive maintenance in a complex industrial maintenance scene.

Systems and methods are disclosed for condition identification. One or more processors may receive a member data object with indicators and dimensions, access a member-specific graph network with nodes representing attributes and weighted edges indicating associations, modify the nodes and edges based on the member data object, generate a multi-modal graph database by combining the modified member-specific graph network and a disease graph network, apply the multi-modal graph database to an attention-based graph neural network (GNN) that identifies associations between nodes by dynamically allocating attention weights to edges, generate an embedding data object with node identifiers and vectors representing features and relationships, select a target node associated with condition data, apply the embedding data object to a classification layer that outputs predicted conditions for the target node, and generate the probability of predicted conditions appearing in the target node.

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 relates to the field of computers, in particular to a micro-service fault positioning method and device based on causal inference and a knowledge graph. The method comprises the following steps: firstly, collecting an original calling link, a multi-dimensional time sequence index and log data of a micro-service system, analyzing the correlation of micro-service indexes by using a partial correlation algorithm, then inferring the causal relationship of the indexes by using a structural causal model, and mapping the causal relationship of the indexes to specific service calling by using a principal component analysis method. And constructing a service dependency relationship. And then defining a knowledge graph ontology, constructing a fault dependency graph according to an index causal relationship and a service dependency relationship, and fusing the fault dependency graph with a calling link graph to form a complete fault dependency knowledge graph with multi-level influence. And finally, storing the graph in a graph database, taking exception as a service node starting point, and outputting a fault root cause candidate set through exception detection, random walk and node score calculation. According to the invention, the efficiency and accuracy of fault positioning are improved, and the stable operation of the micro-service system is ensured.

The invention relates to the technical field of intelligent questioning and answering, in particular to an intelligent questioning and answering method, device and equipment based on combination of a large model and a knowledge graph. The method comprises the steps that user input is received, and whether the user input is a document question and answer is judged; if the user input is the document question and answer, calling an RAG module and an LLM module to judge whether the user input is related to the uploaded document; if the user inputs a non-document question and answer, or the user input is unrelated to the uploaded document, calling an LLM module to judge whether the user input is related to the legal field; if the user input is related to the legal field, calling a KBQA module and an LLM module to generate a retrieval result input by the user in a preset graph database of the legal field; and based on the retrieval result and the user input, calling the LLM module to generate a reply result input by the user. According to the method and the device, accurate reply aiming at user input can be realized.

The invention provides an asset library model security risk situation assessment system and method, and the system comprises a data collection and processing subsystem, a multilayer asset association graph construction module which constructs the function dependence, data flow dependence and physical connection relation of assets in a resource library based on a graph database; the risk propagation probability calculation module is used for simulating a conduction path of an attack from an edge node to a core resource by adopting a Monte Carlo method; and the time sensitivity analysis module is used for dynamically adjusting threat weights in different time periods through a sliding window mechanism. According to the method, real-time comprehensive data is provided for evaluation through a multi-source streaming data fusion engine and distributed edge computing nodes; accurately analyzing asset association and risk propagation by applying a multi-layer asset association map and a Monte Carlo method; by means of a reinforcement learning decision framework and incremental model updating, self-adaptive decision optimization is achieved, and novel attacks are coped with in time; based on a visual interface and a closed-loop feedback control loop, the visual effect and the evaluation accuracy are improved.

The invention relates to the technical field of electric power communication, in particular to an electric power communication resource topological optimization method and system based on a graph database. The specific implementation process comprises the following steps: constructing an electric power communication resource topological graph comprising communication exchange nodes, data transmission links and attribute information through a graph database; analyzing and quantifying the received business type into a routing constraint condition according to a business service level protocol; performing dynamic weighting calculation on each candidate communication path meeting the routing constraint condition, evaluating comprehensive performance indexes and outputting an optimal communication path; and converting the optimal communication path into a topology configuration instruction capable of guiding network equipment to carry out data deployment, and carrying out dynamic optimization on the power communication resource topology. According to the method, the service quality requirements of different services are quantified into dynamically adjusted routing constraints and weights, so that reasonable allocation of power communication resources and effective optimization of network topology are realized, and the working efficiency of a power communication resource network is improved.

The invention provides a government affair hotline service knowledge graph construction method and system based on a large language model, and relates to the technical field of natural language processing and government affair informationization, and the method comprises the steps: obtaining a government affair hotline service scene, and defining a mode layer of a multi-scene knowledge graph; unstructured government affair hotline question and answer data are collected, semantic fusion, logic extraction and ambiguity elimination are carried out on the data based on a chained thinking mechanism of a large language model, and a fused government affair hotline question and answer set is generated; driving a large language model to identify knowledge entities in the set by adopting a step-by-step prompt strategy, extracting semantic relationships among the knowledge entities by adopting a semantic association analysis method, constructing triple knowledge data based on the knowledge entities and the semantic relationships, storing the triple knowledge data into a graph database, and performing dynamic updating and multi-dimensional retrieval on the multi-scene knowledge graph to obtain a multi-scene knowledge graph set; therefore, a large language model and a knowledge graph technology can be fused, government affair data are efficiently integrated, and the intelligent level of government affair service is improved.