3580 results about "Retrieval result" patented technology

The invention discloses a knowledge question-answering system based on a large language model. The method comprises the steps that a power operation and maintenance knowledge base module obtains data from a data source for preprocessing, constructs a power operation and maintenance field knowledge graph, converts text content into semantic vectors and integrates the semantic vectors to form a power operation and maintenance knowledge base; the problem understanding module is used for identifying and analyzing problems and converting the user problems into semantic vectors; the retrieval and reordering module is used for performing semantic retrieval in the electric power operation and maintenance knowledge base, recalling knowledge information related to question semantics, and ordering retrieval results based on semantic relevance, knowledge confidence and information importance; and the answer generation module is used for integrating the knowledge information and the user question into input of a large language model, generating prompt information, and generating an answer in a natural language form according to the prompt information by adopting the large language model based on data fine adjustment in the field of power operation and maintenance. According to the scheme, the deep learning technology is used for understanding semantics of user questions, and accurate retrieval is carried out in the knowledge base.

The invention belongs to the technical field of multi-modal data processing, and particularly relates to a multi-modal document retrieval enhancement generation method based on a large model, which comprises the following steps: receiving query content input by a user for a multi-modal document; processing the query content by adopting an embedded model, generating a query vector representing user query semantic information, and storing the query vector in a vector database; analyzing the multi-modal document to obtain long text information, segmenting the long text information into data blocks by adopting a recursive partitioning strategy, and numbering and marking the data blocks; carrying out vectorization processing on the data blocks by adopting an embedded model to generate document vectors, storing the document vectors into a vector database, and constructing a hierarchical index structure; retrieving in a vector database based on the query vector, and returning a retrieval result; and processing a retrieval result by utilizing a large language model to generate response content conforming to the query intention of the user. According to the method, the multi-modal document can be effectively analyzed and processed, and the accuracy and comprehensiveness of analysis are improved.

The invention discloses a traffic engineering large model intelligent question answering system and method based on a knowledge graph, and the method comprises the steps: extracting a structured degree feature, a semantic ambiguity feature and a context association feature through receiving and analyzing a natural language query statement inputted by a user, generating a retrieval intention vector, and carrying out the retrieval of the retrieval intention vector; and dynamically selecting a retrieval path according to the intention classification model. And according to the retrieval path, constructing a structured query statement or a semantic vector, and respectively retrieving in the knowledge graph and the vector database to obtain a first retrieval result and a second retrieval result. Further performing bidirectional verification through entity consistency, semantic similarity and relation connectivity indexes, screening a candidate result set, and constructing a reasoning chain; if the inference chain is broken, a large model inference gap complementation mechanism is adopted to generate relay nodes, a complete inference chain is formed, and inference type answer output is generated based on the complete chain. According to the method, the retrieval accuracy and reasoning continuity of the question-answering system are improved.

The invention belongs to the technical field of knowledge retrieval, and discloses a multi-modal large model-based dynamic vector knowledge base construction and retrieval method, which comprises the following steps of: obtaining a multi-source heterogeneous modal data set, and carrying out preprocessing and modal standardization processing on the multi-source heterogeneous modal data set to obtain a standardized multi-modal data set; performing feature extraction and semantic vector representation generation by using the pre-trained multi-modal large model, and constructing a multi-modal knowledge vector set; semantic association analysis and hierarchical clustering are carried out on the multi-modal knowledge vector set, and a structured vector knowledge base is constructed; performing semantic similarity calculation and relation modeling on the vector knowledge base to form a vector relation network; intention analysis and vector representation are performed based on mixed modal query information input by a user, and efficient similarity retrieval is realized in combination with a vector relation network; dynamic optimization is carried out through user feedback, personalized retrieval result adjustment is achieved, and the problem of limitation of a traditional retrieval system during multi-modal data processing is effectively solved.

A Generative Artificial Intelligence (Gen. AI) based chatbot apparatus implements classical AI models and Generative AI models to answer user queries with results retrieved from relational databases and unstructured knowledge bases. When a user query is received, it is determined if the intent of the user query can be determined with a confidence greater than a configured confidence limit. If yes, a structured query mapped to the intent is employed to answer the user query. If the intent determination confidence is less than the configured confidence limit then Gen. AI-based techniques using a plurality of LLMs are used to respond to the query. A Gen. AI switch is also implemented to switch between the plurality of LLMs to answer user queries with greater accuracy.

The invention discloses a power plant operation and maintenance knowledge intelligent query method based on a large language model and an RAG technology. The method comprises the following steps: constructing a power plant operation and maintenance knowledge vector library covering structured, semi-structured and unstructured data; receiving a natural language question of a user, inputting an improved instruction to align a preprocessor, and generating a question semantic vector and an intention tag; relevant knowledge fragments are retrieved and sorted through a semantic matching retriever in combination with the intention labels; constructing a large language model cue word structure based on the retrieval result and the original question, generating candidate answers and recording a reference path; and finally, performing term specification and consistency verification according to the expert rule base, and outputting a structured and traceable final answer. According to the invention, the improved RAG technology is fused to realize intelligent query of the operation and maintenance knowledge of the power plant.

The invention discloses a Text2SQL (Structured Query Language) implementation method and system, and relates to the field of data processing, and the method comprises the following steps: firstly, receiving a natural language query, and analyzing a query intention, field classification and a key entity through a planner; the searcher obtains domain knowledge, entity information, a database table structure and a historical query mode in a multi-path parallel mode based on the planning result; the generator constructs an SQL framework according to the retrieval result and generates an initial statement; the verifier carries out grammar, table field, authority and logic multi-dimensional verification on the SQL, and if the verification fails, iteration adjustment is carried out to generate logic; when the SQL is executed, the result is formatted and a natural language explanation containing query logic, a data source and a calculation method is generated if the SQL is executed successfully, and a diagnosis and error correction mechanism is started for correction and then rechecking is performed if the SQL is executed unsuccessfully. According to the method, through deep fusion of domain knowledge, whole-process verification error correction and interpretability enhancement, the accuracy, robustness and user interaction experience of SQL conversion in a professional scene are improved.

The invention discloses a cross-modal information fusion and retrieval method and system driven by semantic comprehension, and the method comprises the steps: obtaining text, image and audio original data, and extracting an initial feature set of each modal through a deep neural network; dynamically distributing each modal weight coefficient based on an attention mechanism, and performing weighted fusion on the initial feature set to obtain cross-modal fusion feature representation; through a cross-modal semantic association analysis model, high-dimensional semantic association features are extracted from the fusion feature representation, and semantic enhancement feature vectors are generated; constructing a cross-modal semantic graph network based on the vector, complementing missing modal features, and generating an optimized multi-modal feature set; and inputting the optimized feature set and the query sample into a contrast learning model, calculating a semantic similarity score, and generating a cross-modal retrieval result sorting list according to the score.

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 relates to the technical field of knowledge question answering, in particular to an intelligent question answering method and system based on multi-module collaborative optimization, and the method comprises the steps: inputting a question to be answered into a knowledge question answering model, and outputting a knowledge question answering result by the knowledge question answering model; a knowledge range judgment module in the model judges whether the problem can be solved by relying on own knowledge, and if not, a dynamic retrieval module is entered; the dynamic retrieval module performs similarity retrieval on the content of the memory knowledge base according to the question to be answered, and if the retrieval result does not meet the requirement, the multi-level question rewriting module is entered; the multi-level question rewriting module rewrites questions to be answered and inputs the rewritten questions into the knowledge screening module; the knowledge screening module outputs the screened documents according to the rewritten questions, the auto-reflection optimization module generates preliminary answers according to the documents and the questions and judges whether the preliminary answers are reasonable or not, auto-reflection optimization is carried out if the preliminary answers are not reasonable, and a new solution is provided for development of the intelligent question and answer technology.

The invention provides an intelligent retrieval method and system for unstructured asset content based on a large model, and relates to the technical field of data retrieval, and the method comprises the steps: carrying out semantic expansion on retrieval keywords through a knowledge graph enhancement module, obtaining asset feature vectors through a multi-modal feature extraction model, and constructing a hierarchical asset feature index database; the comprehensive similarity is calculated through a weighted fusion algorithm, and retrieval results are optimized and reordered through semantic comprehension and correlation analysis capacity of the pre-trained large model. According to the method, the retrieval precision and efficiency of the unstructured asset content are improved, and intelligent association analysis and semantic understanding of cross-modal assets are realized.

The invention discloses a building electromechanical BIM model information rapid retrieval method and system, and the method comprises the steps: generating composite retrieval parameters fusing semantic keywords and three-dimensional coordinate constraints according to a multi-mode retrieval instruction inputted by a user; on the basis of the composite retrieval parameters, constructing a dynamic search space by utilizing a hierarchical graph convolutional network, and generating a candidate model index structure of multi-dimensional feature coding; inputting the candidate model index into a multi-objective optimization engine, performing real-time optimization on a search path by adopting a dynamic pruning algorithm driven by reinforcement learning, and outputting a candidate model set of which the confidence coefficient is higher than a preset confidence threshold after pruning; and on the basis of the candidate model set, associated equipment nodes are expanded through a knowledge graph embedding and complementing technology, and an enhanced retrieval result set containing the hidden associated equipment is generated. By utilizing the embodiment of the invention, efficient, multi-dimensional and multi-modal information accurate positioning and quick retrieval can be realized in a large-scale complex BIM model.

The invention discloses a privacy enhanced intelligent search method and system based on multi-round iteration. The method comprises the following steps: performing hierarchical semantic analysis on a query input by a user; splitting the complex query into sub-queries based on a task dependency graph algorithm; according to the sub-query, retrieving an evidence fragment from the multi-source data, constructing a semantic element coverage matrix to detect a knowledge gap, and if an uncovered element exists, generating a supplementary sub-query for iterative completion until a preset termination condition is met; integrating cross-modal data through a federated learning technology, and generating a structured knowledge graph fragment in combination with semantic vector alignment and an evidence fusion algorithm; performing dynamic desensitization processing on the retrieval result; and a closed-loop iterative updating mechanism is formed based on a user explicit and implicit feedback optimization retrieval strategy. The problems of traditional intelligent search in the aspects of semantic understanding depth, complex problem reasoning, search result accuracy and integrity, user privacy security and the like are effectively solved.

The invention relates to the technical field of document retrieval and information processing in the data processing technology, in particular to a document retrieval method based on multistage indexing and feature clustering, which comprises the following steps: performing high-dimensional space mapping on multi-modal features such as texts and images through a quantum embedding layer to generate cross-modal joint feature representation; a first-level index of a multi-level index architecture is dynamically initialized based on a meta-clustering algorithm, and semantic blocks of a second-level index are divided in combination with a multi-head self-attention mechanism. And an optimal transmission matrix is generated by using a Sinkhorn algorithm to align cross-node feature distribution. The multi-target mixed retrieval strategy is fused with vector retrieval, keyword retrieval and graph retrieval results, and weight distribution is dynamically adjusted. Through collaborative optimization of quantum calculation, federated learning and causal reasoning, a closed-loop technical architecture from feature analysis to dynamic index construction is formed, the problems of insufficient cross-modal fusion, static clustering deviation and semantic association deficiency are solved, and the precision, efficiency and dynamic adaptability of heterogeneous document retrieval are improved.

The invention provides a cross-modal image-text retrieval processing method and a cross-modal image-text retrieval processing system, which are applied to the field of information retrieval, and the method comprises the following steps: obtaining a query text input by a user in an image-text retrieval process; encoding the query text through a text encoder to generate a query text feature vector; through a cross-modal image-text retrieval model, similarity matching is carried out based on query text feature vectors and multi-modal embedding representations stored in an external knowledge base, related results corresponding to the multi-modal embedding representations larger than a matching threshold value are returned, and the multi-modal embedding representations are used for representing joint features of images and texts; when the related result comprises the image and the text at the same time, the related result and the query text are input into a preset multi-modal large model, image question answering with text assistance is carried out, and a retrieval result output by the multi-modal large model is obtained; according to the method, the semantic association between the image and the text can be better captured, so that the accuracy of image-text retrieval is improved.

The invention belongs to the technical field of electric power data security management and intelligent question answering, and particularly relates to an electric power data security policy large model question answering system and method based on relation pooling. The system comprises a power data security policy knowledge base, a language model, a question processing module, an answer generation module and a user output module. Data of the electric power data security policy knowledge base comes from an electric power data security policy file; the language model converts a natural language problem into a structured query for knowledge base retrieval; the question processing module retrieves policy terms related to the natural language question to serve as retrieval results; the answer generation module generates a final answer according to the natural language question and a retrieval result of the knowledge base; by introducing the intelligent question-answering system combining the large language model and the knowledge base, the problems of low efficiency, inaccurate interpretation, incapability of dynamically updating and the like in the existing power data security policy query and management are solved.

The invention provides a multi-round dialogue interaction method and system based on context reconstruction and multi-library retrieval, and the method comprises the steps: firstly obtaining a multi-round dialogue data set of a target user, which comprises a plurality of dialogue round sequences composed of user input statements and system response statements; performing context reconstruction processing on the multi-round dialogue data set to generate context reconstruction characteristics of each dialogue round sequence, covering current round semantic focus offset and historical round semantic dependence intensity, and retrieving a matched multi-database retrieval result from a preset heterogeneous database based on a dynamic context window, a candidate response statement set and confidence are included. Performing multi-strategy matching on context reconstruction features and retrieval results, generating a response generation strategy to adjust candidate response semantic priorities and confidence thresholds, and finally feeding back the response generation strategy to a dialogue service system to trigger response optimization operation, and updating heterogeneous database retrieval weights and context window dynamic adjustment parameters.

The invention discloses an agricultural pest question-answering method based on knowledge graph adaptive hybrid retrieval enhancement. The method comprises the following steps: 1) data acquisition and arrangement; 2) construction of a knowledge graph and a vector library; 3) constructing a question and answer pre-classification system; 4) building and training a question type automatic classification model: automatically identifying and classifying the questions input by the user by using a small classification model to form a self-adaptive retrieval classifier; 5) self-adaptive knowledge retrieval based on question types: according to the queried question types, self-adaptively and dynamically selecting different retrieval methods of the knowledge graph and the vector library; 6) selecting a corresponding thinking chain reasoning method according to retrieval results of the knowledge graph and the vector library, and constructing a Prompt cue word with high pertinence; according to the method, the knowledge graph and vector library retrieval are fused, so that the answering precision and accuracy are improved, and by integrating a thinking chain (CoT) reasoning framework, the decision-making process of disease and insect pest problem analysis and answering has a clear logic chain.

The invention discloses an intelligent automobile question and answer method based on a large model and retrieval enhancement generation, and the method comprises the steps: 1, receiving a user query, carrying out the preliminary retrieval of a user query vector through a mixed index retrieval module, and generating a candidate result; 2, judging whether the relevancy of the retrieval result meets a preset threshold value or not, and triggering query conversion enhancement and re-retrieval when the relevancy of the retrieval result does not meet the preset threshold value; thirdly, retrieval results are rearranged according to the correlation degree; and step 4, the system generates a query related prompt word, combines the original user query as input, and generates and returns a question and answer result. The invention further discloses a system for implementing the method, and the system has wide application value.

The invention relates to the technical field of knowledge graph retrieval, in particular to a genuine medicinal material intelligent retrieval method and system based on a graph. The method comprises the following steps: performing semantic granularity analysis on a retrieval request input by a user, constructing a multi-level semantic edge and generating a hierarchical semantic graph structure; semantic enhancement is carried out on the map relation through semantic annotation, and a multi-condition intention is extracted in combination with dimensions such as regions, drug properties and channel tropism; further, the system executes multi-hop path combination, a structured semantic path conforming to the composite intention is mined, edge nodes in the path are inferred and complemented, and a genuine medicinal material retrieval result with a closed structure and complete semantics is generated. Compared with a traditional keyword matching and static field retrieval mode, the method has higher semantic perception ability and reasoning intelligence, and the accuracy and adaptability of the system in processing fuzzy, composite and path-incomplete retrieval scenes are remarkably improved.

The invention provides a data reordering retrieval method and system based on RAG, and the method comprises the steps: carrying out the dynamic semantic partitioning processing of an original document, generating corresponding document blocks, storing the document blocks in a vector database, constructing a hierarchical index, analyzing a received query request, extracting keywords in the query request, and carrying out the retrieval of the query request. Boolean keyword matching is carried out through an inverted index in the hierarchical index, semantic retrieval of keywords is carried out in a vector database, an initial candidate set is generated after multi-source retrieval results are fused, a query request and the initial candidate set are input into a generative reordering model, and a query result is obtained; according to the method, the initial candidate set is selected, the corresponding score is generated through the cross-modal attention mechanism in the generative reordering model, the initial candidate set is reordered according to the score, the final retrieval result is obtained, and the reordered document blocks are displayed, so that the overall retrieval speed is increased, the limitation of a single retrieval mode is avoided, and the accuracy of the retrieval result is improved.

The invention discloses a traditional Chinese medicine intelligent inquiry method and system based on a knowledge graph and medical case enhancement RAG, and the method comprises the following steps: collecting traditional Chinese medicine related data from a plurality of sources, and carrying out the data cleaning, formatting and standardization processing, and constructing a knowledge graph containing traditional Chinese medicine symptoms, disease causes, prescriptions, and the mutual relation of the symptoms, the disease causes, the prescriptions; expressing a knowledge structure in the graph in a triple form; searching a local sub-graph related to query based on query keyword extraction and generalization by utilizing a Leiden community detection algorithm; a mixed retrieval strategy is adopted, global search and local search are combined, recalled contents are sorted and scored, global and local retrieval results are fused, and a large language model is uniformly output. The system aims at improving the intelligent level and individuation ability of traditional Chinese medicine diagnosis, firstly, a knowledge graph covering entities such as traditional Chinese medicine theories, symptoms, pathogenesis and prescriptions and relationships of the entities is constructed, mass traditional Chinese medicine data are systematically integrated, deep understanding and semantic association of traditional Chinese medicine complex diagnosis and treatment logic are ensured, and the traditional Chinese medicine diagnosis and treatment efficiency is improved. Through deep combination of the structured knowledge base and the generated model, the diagnosis and treatment accuracy is improved, and the model fine tuning and updating cost is reduced.

The invention relates to the technical field of fault analysis, and particularly provides a monitoring fault analysis method fused with a multi-modal knowledge base, which comprises the following steps: collecting original data of a monitoring fault log, and preprocessing and storing the original data; performing data cleaning and feature extraction on the obtained original data of the monitoring fault log; constructing a searchable knowledge base based on the cleaned data; when the system triggers an alarm, mixed retrieval is executed through a dynamic routing mechanism; aggregating the plurality of retrieval results to generate an executable repair scheme; iteratively optimizing the decision process through manual feedback; and continuously optimizing the knowledge base and the diagnosis model to form a closed loop iteration mechanism. According to the scheme, the accuracy and response efficiency of fault diagnosis are improved.

The invention provides a retrieval enhancement generation method and system based on a large language model. The method comprises the steps that input query information of a user is obtained; performing rewriting and key information extraction on the input query information based on a large language model to obtain a first query instruction and a second query instruction; according to the input query information, identifying a user intention based on a large language model and performing intention classification to obtain an intention classification result; according to the first query instruction and the second query instruction, performing multi-path mixed retrieval through a preset multi-source knowledge base to obtain an initial retrieval result; according to the intention classification result, performing result rearrangement and screening on the initial retrieval result to obtain an enhanced cue word; and inputting the input query information and the enhanced cue word into the large language model to obtain a retrieval enhanced generation result. According to the method, the query demand and intention of the user can be accurately recognized, related information is retrieved in an open domain in a high-precision manner, and the instantaneity, the accuracy, the specialty and the reply quality of information retrieval are improved.

The invention discloses a domain-adaptive retrieval enhancement generation method and system, and the method comprises the steps: carrying out the document analysis and semantic partitioning of a document through a semantic understanding model and a document rule, and storing document fragments; on the basis of the large language model and a preset question library, generating a question and disassembling the question; performing correlation evaluation and sorting on the initial retrieval results in combination with a retrieval technology; candidate content most matched with the cue words is screened out through a large language model, and question-retrieval fragments are scored and marked; performing fusion on the initial retrieval result and the scoring and labeling result to construct positive and negative samples, and constructing a supervised data set; and based on a preset loss function, utilizing the supervised data set to carry out supervised training on the knowledge semantic search model, replacing the RAG mixed search module, and carrying out continuous iteration. By means of the technical scheme, new domain knowledge and operation feedback data can be flexibly applied, and domain self-adaptive retrieval enhancement generation is achieved.

The invention relates to a power field Text2SQL (Structured Query Language) data analysis method based on LLM (Logical Language Model) and RAG (Random Access Gateway), and solves the defects that a retrieval result contains noise and redundancy and a large model still has a large number of illusion phenomena due to the absence of a vectorization model in a specific field and the singleness of knowledge base types and dimensions in the RAG technology compared with the prior art. The method comprises the following steps: constructing a data set of a Text2SQL data analysis method in the power field; constructing a multi-dimensional text knowledge base of the Text2SQL data analysis method in the power field; fine adjustment of an LLM base of a Text2SQL data analysis method in the electric power field; constructing an RAG retrieval module of the Text2SQL data analysis method in the power field; and constructing an Agent data analysis module of the Text2SQL data analysis method in the power field, and performing data analysis processing. According to the method, related information required by Text2SQL data analysis in the electric power field can be obtained by a large language model through retrieval on the basis of an RAG technology, meanwhile, the privacy and timely updating of the data are ensured by field data stored in a knowledge base form, and the model training cost is also greatly reduced by the LLM-based Text2SQL data analysis method taking a prompt project as a main part; and by setting the mixed retrieval parameters, the retrieval result has relatively high accuracy, and the effect of Text2SQL data analysis is ensured.

The invention provides a multi-modal heterogeneous model retrieval enhancement method and system, and the method comprises the steps: building a knowledge and application example double-corpus based on user multi-modal query, and designing a joint retrieval mechanism to obtain a result set; mapping and scheduling to obtain feature representation through special processing channels for texts, images and audios and a Spiking neural network with a segmented trapezoidal topological structure; constructing a three-stage cascade architecture of a basic model, an advanced model and human experts, and obtaining a decision path and answer candidate set in combination with a recursive and discarding decision mechanism; a Hamiltonian graph network is used for representing a multi-modal relation, and a gradient-free descent method is used for rapidly training and optimizing model parameters; an enhanced retrieval result is obtained through cross-modal semantic alignment and dynamic retrieval window adjustment; and high-quality response is obtained through context-aware sorting and retrieval enhanced reasoning. According to the method, the multi-modal information retrieval processing efficiency and the heterogeneous model reasoning response quality are improved.

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 the technical field of electric power engineering, and discloses an electric power engineering multi-modal RAG system based on a knowledge graph and multi-Agent collaboration, and the system comprises a multi-modal dynamic knowledge base construction module which is configured to carry out the structural processing, multi-dimensional knowledge organization and dynamic optimization of electric power engineering multi-modal data; the self-adaptive retrieval strategy engine module is configured to construct a weight decision network based on deep reinforcement learning and execute multi-channel parallel retrieval and result fusion; the iterative self-reflection reasoning module is configured to generate a reasoning path in combination with the retrieval result and verify evidence validity from multiple dimensions; the MCP tool intelligent calling module is configured to integrate multiple types of standardized MCP tools; and the multi-Agent collaborative framework is configured to provide multiple types of Agents which are specific in function and have a cross-module interaction capability. According to the method, the question and answer accuracy, the reasoning depth and the result interpretability in the complex multi-modal scene of the electric power engineering can be remarkably improved.

The invention discloses a government affair processing method based on a big language model of a knowledge graph and electronic equipment. The method comprises the steps of performing named entity and relation extraction on government affair related texts by utilizing the big language model; performing named entity classification and entity disambiguation on the named entities and the relationships, and constructing a knowledge graph; performing community detection on the constructed knowledge graph by using a Leiden graph calculation method to generate a hierarchical community structure, and generating a community abstract from bottom to top by using a large language model; and extracting entities from government affair questions input by a user by using the large language model, performing mixed retrieval on the entities by using the constructed knowledge graph and the retrieval model to obtain retrieval results related to the entities, and enhancing the retrieval results by using the large language model to generate result answers. According to the method, information extraction is carried out by utilizing the large language model, so that the information of the constructed knowledge graph is more complete, and the knowledge graph is combined with a retrieval enhancement generation method, so that the interpretability and comprehensiveness of content generated by the large language model in the field of government affairs are improved.