419 results about "Knowledge retrieval" patented technology

The invention discloses a retrieval generation method and system based on fusion of a large model and a knowledge graph, and relates to knowledge graph, knowledge query and large model technologies, and the method comprises the following steps: obtaining original document data, and preprocessing the original document data; extracting a triple structure from the preprocessed document data by using a large model to construct a vectorized knowledge base and a knowledge graph; performing GraphRAG retrieval and multi-hop path reasoning on the received user query through a pre-constructed vectorization knowledge base and a knowledge graph; according to the structured knowledge of GraphRAG retrieval and multi-hop path reasoning and the context of the document, a result meeting the query requirement is generated through the large model. According to the embodiment of the invention, the knowledge graph is automatically constructed and updated through the powerful semantic understanding capability of the large model, and the GraphRAG technology is combined, so that accurate retrieval and multi-step reasoning of complex multi-hop and high-relevance problems are realized, and the depth and accuracy of knowledge retrieval are realized.

The invention belongs to the technical field of artificial intelligence and information retrieval, and provides a multi-modal retrieval method and system based on a lightweight knowledge graph and an index table, and the method comprises the steps: obtaining multi-modal source data, and extracting a structured semantic tag set; constructing a lightweight knowledge graph and a metadata index table; analyzing a natural language query input by a user to obtain a semantic query vector and a keyword set, executing semantic retrieval in the knowledge graph to obtain a text candidate result, and executing keyword matching in the metadata index table to obtain a non-text candidate result; for each non-text meta record, fusing the cross-modal similarity between the non-text meta record and the text candidate result, performing index matching on an original score and a graph semantic evidence score, calculating a comprehensive correlation score, and performing reordering; and generating a natural language answer containing a non-text record link according to a reordering result. The method is suitable for efficient cross-modal knowledge retrieval in a high-security and low-resource scene.

The invention provides a vertical field data construction method based on a large model, which belongs to the technical field of data processing and artificial intelligence, and comprises the following steps: converting a vertical field source document into an intermediate format text, and segmenting the intermediate format text into a plurality of text blocks; inputting the text blocks into a pre-trained generative language model, guiding the pre-trained generative language model according to pre-designed cue words to generate a plurality of candidate questions according to the content of the text blocks, and performing preliminary screening and fine screening on each candidate question to obtain a question set; pre-defining a mode of a knowledge graph according to field characteristics of the vertical field, processing all text blocks based on an information extraction model, and constructing a field knowledge graph; and performing local context retrieval on each final question in the question set based on the text block of the question source, performing global knowledge retrieval based on the domain knowledge graph, and generating a final answer and a final thinking chain. The method is suitable for different vertical fields, the data quality can be effectively improved, and the problem generation accuracy is guaranteed.

The invention discloses a document review method based on multi-agent collaboration and retrieval enhancement generation. The method comprises the following steps: firstly, analyzing a document review rule input by a user into a rule semantic intermediate representation through a natural language processing technology, and calling a retrieval enhancement generation (RAG) module to expand related knowledge to form a structured rule library; secondly, multi-modal analysis and chapter segmentation are carried out on a to-be-examined document, and elements such as texts, tables, pictures and formulas are expressed in a unified mode; tasks such as rule analysis, document segmentation, knowledge retrieval, matching comparison and report generation are completed through a multi-agent cooperation mechanism; finally, when ambiguity exists in rule and document matching, an RAG module is introduced to retrieve supplementary evidences from an external knowledge base, explanatory comparison is conducted in combination with the generative model, and the accuracy and authority of judgment are improved.

The invention relates to the technical field of data analysis, and discloses a feedback generation method and device based on a business knowledge graph, equipment and a medium, and the method comprises the steps: collecting multi-source business data, cleaning and segmenting the multi-source business data to form standardized text fragments, and carrying out the entity recognition and relation extraction of the text fragments to construct the business knowledge graph; receiving a natural language query instruction, extracting query intention features and key entity references, mapping the key entity references into the business knowledge graph, and performing association traversal under the guidance of the query intention features to obtain associated knowledge sub-graphs; and serializing the associated knowledge sub-graph into a knowledge background context, and jointly inputting the knowledge background context and a natural language query instruction into a pre-training language generation model to generate natural language feedback information. The method can be applied to business scenes such as financial science and technology, medical health and the like, semantic structuring is carried out on text knowledge, reasoning is carried out in knowledge association in combination with query semantics, accurate question answering is achieved, and knowledge retrieval efficiency is improved.

The invention discloses an intelligent voice question-answering system and a knowledge reasoning method for operation and maintenance of power equipment. The system comprises a knowledge base construction module and a core question and answer engine module, the knowledge base construction module is used for constructing a power equipment knowledge graph and a fault case library, and the core question and answer engine module comprises a natural language understanding unit, a multistage semantic retrieval unit and a voice interaction ambiguity resolution unit. A natural language understanding unit identifies intent categories and power entities. The multi-stage semantic retrieval unit executes the following steps: matching in an ontology layer of the knowledge graph; performing extended query to obtain detailed information; and similar case matching is carried out in the fault case library. The voice interaction ambiguity resolution unit is used for executing disambiguation processing; analyzing the confidence and clarifying the dialogue; and triggering knowledge retrieval and reasoning and generating a sorting answer. The operation and maintenance efficiency is improved, the dependence on experts is reduced, the operation and maintenance quality and safety are improved, knowledge precipitation and sharing are promoted, the field work experience is improved, and scientific decision making is assisted.

The invention relates to the technical field of electric digital data processing, in particular to a flood control plan generation method based on a knowledge graph and a large language model, which comprises the following steps: acquiring a flood control plan query problem; extracting water conservancy entities, relationships between the entities and keywords, and obtaining semantic vector representation of a flood prevention plan query problem; retrieving in a pre-constructed knowledge base to obtain a knowledge retrieval result; performing duplicate removal on the knowledge retrieval result to obtain a target knowledge set; and utilizing the large language model to generate a flood control plan text corresponding to the flood control plan query problem. According to the scheme, deep fusion of the structured knowledge and the semantic vector is realized, and the structured information of the knowledge graph is combined with the semantic vector, so that an information island is broken, efficient organization and accurate matching of a semantic space are realized, the breadth and depth of knowledge retrieval are improved, generation and reasoning of an intelligent plan are effectively supported, and the accuracy of knowledge retrieval is improved. And the generated plan is more comprehensive and accurate.

The invention discloses an operation and maintenance knowledge base analysis system and method based on deep learning, and is applied to unified operation and maintenance management of a hospital information system and medical equipment. The system collects operation and maintenance data such as operation logs, monitoring indexes, alarm events, work orders, maintenance records and the like, constructs a minute-level time window through equipment identification and technology triggering time, and merges multi-source heterogeneous data into operation and maintenance sub-events and operation and maintenance events. Log, index and text feature extraction is performed on each operation and maintenance sub-event, multi-modal feature fusion is performed to generate an operation and maintenance event feature vector, the operation and maintenance event feature vector is stored in a vector index database and is associated with an operation and maintenance knowledge graph, and similar historical event retrieval and root cause and processing scheme recommendation of a new operation and maintenance event are realized. In combination with the adoption and processing results of recommended schemes by operation and maintenance personnel, the system performs hierarchical management on the operation and maintenance knowledge and performs incremental updating on the feature modeling network, so that the operation and maintenance knowledge retrieval accuracy and the fault positioning efficiency are improved.

The invention relates to the technical field of document processing, and discloses a document knowledge retrieval method and system, electronic equipment and a storage medium. Based on a language model, synchronously generating abstract contents and a semantic association query set for each text fragment, and aggregating the abstract contents of all text fragments corresponding to the same document to form a fragment abstract set; constructing a multi-path retrieval pool based on the text fragment, the abstract content and the semantic association query set, and respectively constructing a plurality of independent retrieval data sources; the method comprises the following steps: constructing a dual-granularity abstract index system, receiving a query request of a user, executing query routing processing based on dual-granularity abstract index and a hierarchical retrieval process based on a metadata structure, returning retrieval result data, performing fusion calculation and duplicate removal processing on the retrieval result data recalled by multiple paths, and obtaining a retrieval result. And meanwhile, the document abstract short sentence group is injected into a search suggestion function of a search engine. According to the method, the retrieval accuracy can be improved, and the user experience is improved.

The invention provides a medical information retrieval enhancement method and device based on a large model and related equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: in response to a medical information query request, executing double-path joint retrieval of keywords and semantics in a pre-constructed medical knowledge base to obtain a candidate data set containing at least one medical information query result, the medical knowledge base is a database which analyzes the multi-modal medical data based on a multi-modal medical data deep analysis large model and is constructed according to the analyzed medical text data; and adopting a plurality of sorting tools to resort the medical information query results in the candidate data set, and performing fusion processing on a plurality of resorting results obtained by resorting to obtain a medical information query result after retrieval enhancement. According to the method and the device, the precision improvement and response efficiency optimization of medical knowledge retrieval can be realized, a high-credibility knowledge enhancement service is provided for a clinical decision support system, and the method and the device have important application value.

According to the COMTRADE data and large model agent driven label generation analysis method, a large language model agent serves as a core scheduling layer, an atomization model and vector knowledge base construction are fused, and the process from multi-source heterogeneous data analysis to semantic label generation is achieved; the method specifically comprises the following steps: firstly, establishing a vector knowledge base fusing electric energy quality domain knowledge and COMTRADE file standard features, and realizing multi-manufacturer format adaptive analysis and professional semantic unification; secondly, constructing an atomization COMTRADE data processing model, and disassembling tasks of analysis, calibration, feature extraction, anomaly detection and label generation into reusable atomic units; then, dynamic scheduling and collaborative execution of atomic units are realized by utilizing planning and reasoning capabilities of an intelligent agent of a large model, and a multi-level label is generated based on semantic understanding and knowledge retrieval; the method has high compatibility, high intelligence and high expansibility, and can quickly adapt to new equipment, new fault types and complex power grid scenes.

The invention relates to the cross technical field of artificial intelligence and data retrieval, and particularly provides an RAG question and answer optimization method and device.Firstly, original knowledge documents of various formats and natural language query of a user are received on an input layer, then a knowledge base construction module and a knowledge retrieval module are arranged on a processing layer, and a knowledge retrieval module is arranged on the processing layer; the middleware layer is provided with an MCP Server module and a vector database, the MCP Server module is a standard interface agent, and the vector database is a distributed storage engine for efficient approximate nearest neighbor search and mixed retrieval; and finally, deploying an LLM generation module at an output layer for generating a final answer based on the retrieved enhanced context. Compared with the prior art, the method has the advantages that irrelevant or low-signal-to-noise-ratio knowledge contacted by a large language model (LLM) can be effectively reduced, and the accuracy, the reliability and the practicability of knowledge base questions and answers are comprehensively improved.

The invention relates to the technical field of artificial intelligence, and discloses a Transformer training method for paint surface AI defect identification, which comprises the following steps: synchronously acquiring RGB images and three-dimensional point cloud data through a six-axis mechanical arm to form a multi-modal data set; generating synthetic defect data by using fluid dynamics and a ray tracing model to expand a sample set; realizing feature alignment by adopting cross-modal contrast learning; aggregating defect structure capsules through a dynamic routing attention network; performing knowledge retrieval and fusion in combination with the prototype memory matrix; and finally, the sorting mechanical arm is driven to execute sorting or rechecking operation based on the multi-task output and the uncertainty score. According to the method, the problems of insufficient structural representation of complex defects, weak generalization ability of rare defects and low reliability of sorting decision are solved, and high-precision and high-reliability automatic defect detection and sorting are realized.

The invention discloses a medical diagnosis-oriented multi-modal credible dialogue type retrieval enhancement generation system, and relates to the field of artificial intelligence, in the system, a multi-modal image analysis module generates a structured image feature data packet based on a multi-modal large model according to an original medical image; the knowledge database construction module is used for constructing a multi-center collaborative medical knowledge database; the image knowledge retrieval module is used for obtaining image feature associated knowledge; the clinical knowledge retrieval module is used for obtaining a candidate knowledge set; a multi-dimensional weighting reordering module obtains a knowledge list; the medical inquiry generation module is used for generating structured preliminary diagnosis based on the user questions, the image abstract and the knowledge list; the credibility verification module is used for verifying the structured preliminary diagnosis to obtain a final diagnosis report; according to the method, the medical knowledge retrieval precision can be improved, the image information fusion capability is enhanced, and high-credibility and traceable intelligent diagnosis and treatment auxiliary service oriented to doctor-patient scenes is realized.

The invention discloses a medical informatization data intelligent analysis system based on a large model, and belongs to the technical field of medical informatization and artificial intelligence, and the system comprises a data collection and standardization module, a medical knowledge graph construction module, a knowledge enhancement inference analysis module, a data quality evaluation module and a structured output module. The system collects multi-source heterogeneous medical data from an electronic medical record system, a laboratory information system, a medical image information system and a hospital information system, performs standardization processing, automatically constructs a medical knowledge graph, and performs intelligent analysis and reasoning on the medical data by using a knowledge retrieval enhanced medical field large language model. Meanwhile, the data quality is evaluated in four dimensions of integrity, accuracy, timeliness and relevance, and finally a structured analysis report is generated. The multi-source medical data can be effectively integrated, the accuracy and interpretability of medical data analysis are improved, and intelligent support is provided for clinical decision making.

The invention relates to the technical field of wind power operation and maintenance, and discloses a wind power blade crack diagnosis method and system based on knowledge graph large model enhancement, and the method comprises the steps: S1, edge end image collection and crack semantic recognition: an edge end carries out the crack detection and semantic feature extraction of a collected wind power blade image sequence through an ECL-Net lightweight network, generating structured semantic information and transmitting the structured semantic information to a cloud; s2, cloud knowledge graph retrieval and semantic reasoning: the cloud performs semantic retrieval and causal reasoning on the structured semantic information through a KFR-Net knowledge graph reasoning network based on a pre-constructed wind power blade operation and maintenance knowledge graph, and outputs a knowledge retrieval result; and S3, multi-modal information is fused through an MMR-Cogntive multi-modal memory retrieval-cognitive generation network, and a structured maintenance report including crack detection summarization, cause analysis, maintenance measures and risk assessment is generated. The method has the advantage that intelligent closed-loop management of wind power blade cracks from detection to maintenance suggestions is realized.

The invention discloses an electric power equipment safety long text knowledge retrieval method based on sparse and dense joint enhancement, which comprises the following steps of: encoding an electric power equipment safety long text and retrieval query by utilizing a pre-training language model for performing supervision and fine tuning on an electric power equipment safety field label data set, and generating a semantic word vector; a weighted sparse vector fusing semantic information and a dense semantic vector enhanced through nonlinear transformation are generated through fine-grained semantic enhancement branches; respectively calculating sparse similarity based on a common item weight product and dense similarity adopting a double-distance weighted fusion strategy combining a cosine distance and an Euclidean distance; and carrying out weighted summation on the two similarity scores through the learnable weight to obtain a final similarity, and realizing accurate sorting retrieval of the long text knowledge. The method gets rid of dependence on global features of sentence vectors, can accurately capture local key information in a long text, and has high retrieval precision and semantic robustness.

The invention discloses a knowledge retrieval enhancement method and system for a keyword knowledge graph. The method comprises the following steps: constructing a keyword knowledge graph, forming a bidirectional collaborative index with a vector knowledge base, extracting global core keywords in a document through a large model, mining relationships of same words, similar words and ambiguous words among the keywords, applying a type consistency constraint, and binding the keywords and document information as an association relationship. And accurate and efficient retrieval is realized by combining keyword expansion, retrieval range constraint and result fusion. The method has the advantages that recall integrity is improved, semantic consistency is enhanced, accurate association is achieved, dynamic scenes are adapted, and finally efficient and accurate retrieval is achieved.

The invention relates to the technical field of knowledge graph question answering, and discloses a knowledge graph fused question answering system which comprises a knowledge graph construction module, a natural language understanding module, a knowledge graph query module and an answer output module which are connected in sequence. The knowledge graph construction module performs knowledge extraction on the unstructured text to obtain triple data to construct a knowledge graph; the natural language understanding module converts a question input by a user in a natural language form into a structured query statement; the knowledge spectrogram query module performs retrieval in a knowledge graph according to the structured query statement and returns knowledge sub-graphs related to the query statement; and the answer output module inputs the knowledge sub-graph into an end-to-end generative model based on a copy network mechanism, generates a natural and smooth text answer and presents the text answer to the user. Through cooperative work of all the modules, efficient knowledge extraction, accurate intention understanding, efficient knowledge retrieval and high-quality answer generation are achieved, and the question and answer requirements of users can be met more efficiently and accurately.

The invention discloses a molecular knowledge iteration discovery method and device and electronic equipment, and belongs to the technical field of molecular knowledge iteration.The molecular knowledge iteration discovery method comprises the steps that semantic analysis and knowledge retrieval are conducted on a molecular prediction task based on a large language model, and molecular physicochemical property characteristics are obtained through reasoning; identifying substructure features with the capability of discriminating the prediction result in the sample molecules; carrying out coding fusion processing on the molecular physicochemical property characteristics and the substructure characteristics to obtain molecular representation; determining a prediction model based on the molecular representation and reasoning to obtain a feature contribution degree, and performing interpretability analysis on the feature contribution degree; the scheme provided by the invention is suitable for various molecular prediction and generation tasks, and dynamic feature integration and substructure mining can be realized only by simply configuring a specific task; the expandability ensures efficient operation under different molecular data sets and application scenes, and the method has wide scientific research and industrial application potential.

The invention relates to the technical field of internet data services, and particularly discloses a multi-graph hidden danger prediction method based on retrieval enhancement and full-graph sub-graphs. The method constructs a complete technical closed loop from data preparation, model training to intelligent application by combining external knowledge retrieval, multi-granularity visual perception and hierarchical reasoning, and specifically comprises three core stages: firstly, automatically constructing a high-quality field specific training data set by using a pre-training large model; secondly, specialized cultivation of a lightweight target multi-modal large model is carried out through supervised fine tuning and reinforcement learning alignment; and finally, introducing'full graph-sub graph 'multi-vision input and regulation knowledge retrieval enhancement in a prediction stage, performing hierarchical reasoning, and outputting a structured hidden danger report.

The invention discloses an intelligent diagnosis method and system based on a large model and knowledge retrieval enhancement, and the method comprises the steps: obtaining multi-source heterogeneous data through a distributed collection network, and building a standardized data flow; extracting multi-dimensional dynamic features to construct a state model, and identifying an abnormal mode; the key innovation lies in that the deep semantic understanding ability of a large language model is combined with a structured knowledge graph, and the exceptions are primarily screened and rechecked through a dual verification mechanism; and finally, closed-loop optimization from diagnosis to treatment is realized. The multi-source data fusion greatly expands the fault perception dimension, the dual verification mechanism of knowledge retrieval and semantic understanding effectively filters false alarms, and the false alarm rate is significantly reduced; a closed-loop feedback mechanism enables the system to have continuous learning ability, and the diagnosis precision is continuously improved along with operation time; the accuracy of fault identification and the timeliness of system response are greatly improved, and technical support is provided for conversion of operation and maintenance of electromechanical equipment from passive response to active intervention.

The invention discloses a field event element extraction method and system based on prompt learning and knowledge completion, and the method comprises the steps: converting an extraction task into a constrained generation problem through structured prompt, achieving the stable extraction under a small number of labeled samples through the existing knowledge prior of a large language model, reducing the dependence on large-scale labeled data, and improving the extraction efficiency. And the cross-domain adaptive capacity is improved. A vectorization knowledge retrieval mechanism is introduced, related evidences are obtained from an external knowledge source, elements which are not clearly expressed in a text are complemented, the integrity of information is enhanced, and the accuracy and credibility of a result are improved through knowledge verification. And through a self-adaptive fusion mechanism of prompt and knowledge, the model can flexibly balance text context and external knowledge, and the robustness of complex context and fuzzy expression is improved. And finally, standardized and structured element data are output, a convenient interface is provided for downstream event atlas construction and analysis tasks, and the automation degree of domain knowledge processing and the system integration efficiency are improved.

The invention provides an automobile software development question and answer method and device, a storage medium and equipment, in the method, an automobile software demand document is used as a data basis, question and answer processing is carried out in a mode of combining retrieval enhancement generation and a large language model, and based on an automobile software development problem, the question and answer efficiency is improved. Searching a retrieval result related to the automobile software development problem in the knowledge base by adopting a keyword retrieval method and a vector retrieval method, so as to more comprehensively mine potential related information in the knowledge base; and then, reordering the retrieval results to obtain context information, and inputting the context information and the automobile software development problem into a large language model to generate an automobile software development question and answer result. According to the method, rich and accurate context information is injected into the large language model, so that model response better meets actual requirements, time and energy of a user for manually searching massive documents are saved, difficulty of the user for learning automobile software demand documents is reduced, and knowledge retrieval efficiency is improved.

The invention relates to the technical field of artificial intelligence and intelligent traffic, in particular to a traffic accident liability intelligent affirmation method and system based on a multi-agent cooperation mechanism and a large language model. The whole process of credible information verification, responsibility affirmation reasoning and standard document generation is simulated. Wherein the legal expert agent adopts a competing mechanism, and the fairness and the accuracy of affirmation are improved through preliminary affirmation, double-party defense and final judgment. The reasoning ability of a large language model and accurate knowledge retrieval of a retrieval enhancement generation technology are fused, the real-time performance and accuracy of legal clause quotation are ensured, and a road traffic accident identification document conforming to specifications is automatically generated. The method effectively solves the problems that traditional manual identification is low in efficiency and high in subjectivity, and an existing intelligent method lacks interpretability and legal accuracy.

The invention discloses a hybrid PDF (Portable Document Format) document analysis and knowledge fragment construction method and device, and belongs to the technical field of digital document intelligent processing. The method comprises the following steps: firstly, carrying out layout element identification and semantic classification on a PDF page through a deep learning model, and calculating the confidence of the PDF page; when the confidence coefficient is lower than or equal to a threshold value, a correction module based on vertical and horizontal projection analysis is automatically switched to carry out boundary detection and result error correction; the fused layout result is converted into structured JSON data, and the structured JSON data is further mapped into a Markdown format with a reserved title level; and finally, knowledge fragment segmentation is carried out based on a Markdown hierarchical relationship, so that each fragment carries a complete title context path. According to the method, the problems of semantic deficiency, insufficient stability and context splitting in the prior art are effectively solved, and the robustness and accuracy of complex format PDF and low-quality scanning copy analysis and the interpretability of downstream knowledge retrieval are remarkably improved.

The invention discloses a welding structure performance intelligent evaluation method and system based on a multi-modal large model, and belongs to the technical field of welding. The system comprises the steps that a data collection and access layer collects multi-modal data and writes the multi-modal data into a unified tracing identifier; the preprocessing and calibration alignment layer carries out space-time calibration alignment and outputs an aligned multi-modal sample; the multi-modal large model layer obtains fusion representation through quality sensing gating fusion, and uncertainty quantification and distribution external detection are carried out; the mechanism constraint and state inference layer performs constraint calibration in combination with knowledge retrieval, and outputs a crack state, a health index, a residual life interval and a key evidence index; the interpretable and decision support layer outputs confidence and risk levels, triggers a recheck strategy and generates an audit report; and the closed-loop learning and digital ledger layer returns a maintenance verification result to update a model version. According to the method, space-time alignment, quality perception fusion, mechanism constraint reasoning and closed-loop updating can be realized, and accurate state estimation and explainable evidence can be output.

The application discloses a standard document writing method and system based on knowledge retrieval enhancement, comprising the following steps: acquiring field multi-source heterogeneous knowledge by knowledge retrieval and constructing a field knowledge graph and a content increment model; performing semantic extraction, structure division, content segmentation and text granularity perception; determining first matching information and second matching information; matching standard paragraph structures and standard sentence structures to generate a standard template; performing compliance review to obtain a compliant text and recommended text; performing abnormality screening and abnormality correction to obtain a compliance value; inputting the compliant text, the recommended text and the compliance value into the content increment model to perform normalized conversion to obtain standard materials; and inputting the standard materials of each sentence into the standard template to obtain a standard document. The method can improve the efficiency and accuracy of standard document writing, has good interpretability, and can be directly applied to a standard document writing system based on knowledge retrieval enhancement.

The invention provides a question and answer method and device based on retrieval enhancement generation, electronic equipment and a computer readable storage medium, and relates to the technical field of retrieval enhancement generation. The question and answer method comprises the steps of obtaining a user question, a knowledge memory library and a strategy network of a current round, and evaluating a similarity threshold value of the current round based on the user question; according to the knowledge memory library of the current round, a candidate document block set of the current round is obtained through matching, and candidate document blocks refer to document blocks with the cosine similarity between the candidate document blocks and the user question being larger than or equal to the similarity threshold value; determining the context of the user question according to the strategy network of the current round and the candidate document block set; and splicing the user question and the context thereof to obtain an intention prompt, inputting the intention prompt into a large language model, and outputting an answer to the user question. The problems of low accuracy and high delay in related technologies are at least solved. The method is suitable for knowledge retrieval and question and answer scenes.

The invention discloses an automatic design and optimization system for a hydraulic speed control loop, which takes a large language model intelligent body as a core, realizes automatic design and optimization of the hydraulic speed control loop through semantic comprehension, knowledge retrieval and parameter optimization, and specifically comprises an LLM intelligent body, a data processing module and a data processing module, the analysis module is used for analyzing natural language input containing design requirements and performance targets of a user, generating a loop structure in combination with a local knowledge base and calling an optimization algorithm; the local knowledge base comprises a basic hydraulic loop, a key empirical relationship and a typical structure template which are artificially extracted; and the simulation and optimization module is used for executing a swarm intelligence optimization algorithm to optimize and adjust parameters of the loop structure. The invention constructs a hydraulic speed control loop automatic design and optimization system with semantic understanding, automatic generation and adaptive optimization capabilities, and aims to realize full-process intelligent design from user demand input to loop structure generation and performance optimization.