68 results about "Knowledge base question answering" patented technology

The invention discloses a large language model knowledge base question answering system based on a multi-path fusion recall retrieval algorithm, and relates to the technical field of artificial intelligence application, and the system comprises the steps of S1, data collection, S2, data preprocessing, S3, knowledge base construction, S4, analysis and clustering, S5, knowledge recall, S6, weight adjustment, and S7, weight fusion. According to the method, a dynamic weight distribution module is arranged, so that various paths of recall weights such as keyword matching, semantic similarity and a knowledge graph can be flexibly adjusted according to different semantic scenes such as a technical scene and a product query scene, a recall result can be effectively optimized, knowledge related to problems can be accurately screened, and the method is high in practicability and high in practicability. And irrelevant information interference is reduced, the answer quality and the system efficiency are improved, and the user satisfaction is improved.

The invention provides a knowledge base question-answering system optimization method and device based on hybrid fine tuning and multi-dimensional evaluation and a readable storage medium thereof, and provides the following scheme: constructing a hybrid progressive fine tuning framework, fusing low rank adaptation (LoRA) and direct preference optimization (DPO), and realizing domain knowledge migration through hierarchical dynamic parameter configuration; establishing a logic-semantic-knowledge three-dimensional quantitative evaluation system, and forming closed-loop optimization by using dynamic weight fusion and a visual decision system; and designing a multi-domain prompt template library with layered parameter freezing, sparse constraint and attention driving, and realizing model lightweight and cross-domain logic constraint. According to the method, the adaptive bottleneck of a general model and domain characteristics is broken through, the small sample training efficiency and the generated content compliance are improved, the computing resource consumption is reduced, an efficient and reliable knowledge service base is provided for professional scenes such as laws, medical treatment and finance, and the technical advantages of specialization, light weight and interpretability are achieved.

The invention discloses a privatized AI question and answer method, system and device and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a file archive data set, and constructing a local digital knowledge base; selecting an AI semantic large model architecture; performing annotation enhancement on a local digital knowledge base by using an AI semantic large model architecture, and constructing a knowledge base AI question and answer model; performing permission marking on the local digital knowledge base based on a user access control rule, and establishing a knowledge base retrieval mechanism; and integrating and fusing the knowledge base retrieval mechanism and the knowledge base AI question-answering model to generate a retrieval enhanced AI question-answering model, performing semantic question-answering retrieval on the request question information, and outputting a user request question-answering result. The technical problem that in the prior art, data security, intelligent question answering and information retrieval efficiency are insufficient is solved, and the technical effect of improving intelligence and data access control of the question answering system is achieved.

The invention discloses a knowledge base question answering method and system based on intention recognition and a medium, and the method comprises the steps: receiving a user question, inputting the user question into a large language model subjected to intention recognition training, and carrying out the preliminary classification; determining a corresponding search strategy according to a classification result obtained by the preliminary classification; if the search strategy is knowledge base search questions and answers, selecting a knowledge base matched with the search strategy for knowledge retrieval, inputting the retrieved reference knowledge and cue word templates into a large language model, and answering the user questions by the large language model; and if the search strategy is large-model direct question answering, selecting a cue word template, inputting the cue word template into the large-language model, and answering the user question by the large-language model. According to the method, user questions can be preliminarily classified through user intention recognition, different search strategies are adopted for different categories, and knowledge base question answering efficiency and accuracy are effectively improved.

The invention relates to the technical field of natural language processing, and discloses a knowledge base question and answer platform construction method based on a large language model, which comprises a knowledge acquisition module, a data preprocessing module, a text processing module, a vectorization module, a question understanding module, a mixed retrieval module, a prompt generation module, an answer generation module and an answer quality analysis module. A secondary inquiry processing module and a feedback learning module; according to the method, a semantic segmentation algorithm is combined with semantic retrieval and keyword retrieval, so that the flexibility is high; normalized prompts are constructed, input is performed according to correlation sorting, and the accuracy of answers is improved; multi-dimensional confidence evaluation is introduced, strict multi-layer security and compliance filtering is set, and the reliability of the system is ensured; the relevance of multiple rounds of dialogues is judged and complemented, so that interaction is more natural and efficient; knowledge is collected and updated in real time, a knowledge base and a retrieval strategy are continuously optimized, and a closed loop of data-application-feedback-tracing-optimization is formed.

The invention relates to the technical field of intelligent work order scheduling platform design, and discloses an intelligent work order scheduling system, method and device and a medium, and the method comprises the steps: the system achieves the efficient and intelligent scheduling of a device fault work order through integrating a plurality of function modules. Firstly, the work order access module receives equipment fault information of different channels, and it is ensured that the information is comprehensive and timely. Then, the portrait construction module constructs a multi-dimensional ability portrait by using the historical service data of the maintenance personnel, and provides a data basis for intelligent matching; and then, the intelligent distribution module accurately matches and associates the most suitable maintenance personnel with the work order according to the portrait data and the work order parameters. And the order grabbing interaction module generates a to-be-grabbed work order list and an order grabbing interface, so that the scheduling flexibility is enhanced, and the scheduling efficiency is improved. The execution tracking module monitors the work order processing progress in real time, and transparent and controllable scheduling is ensured. And finally, the knowledge base question and answer module provides professional knowledge support for the maintenance personnel and helps the maintenance personnel to quickly solve the work order problem.

The embodiment of the invention provides a retrieval enhancement generation method and system based on multivariate fusion, and the method comprises the steps: firstly structuring a knowledge base document, generating a knowledge graph, and importing an ES to establish an index; inputting question sentences, carrying out word segmentation and other processing, querying instance nodes from the map, and outputting meeting conditions according to answer sentence patterns; if not, fragmenting and blocking the document according to a title level, obtaining candidate results through vector, ES and atlas retrieval, normalizing scores, merging and optimizing the scores of the candidate retrieval results according to a retrieval source, calculating comprehensive scores, and outputting the comprehensive scores in a descending order; and finally, carrying out semantic integrity aggregation on the combined candidate results, and inputting into a large language model to obtain a final answer. According to the method, the relevance of the retrieved content is greatly improved, the semantic integrity of the retrieved content is improved, the model magic view problem is reduced, and the question and answer accuracy and the answer quality of a knowledge base are improved.

The invention discloses an energy field intelligent knowledge base question answering system based on multi-model cooperation, and aims to solve the problems of data type identification, image structured extraction and cross-modal indexing in energy field multi-modal document question answering. The system comprises 13 core modules, dynamic cooperation of a multi-mode large model and a large language model is achieved through a collaborative scheduling module, data types involved in user problems can be recognized, and corresponding modules can be called; structured information of images such as charts and flow charts can be extracted from documents and coded; and cross-modal retrieval is realized through a unified semantic vector. When answers are integrated, an attached source is quoted, and traceability is ensured. The system improves the accuracy and efficiency of complex document question answering in the energy field, and is suitable for professional document question answering scenes containing multiple types of images.

Aspects of the subject disclosure may include systems and methods, for example, including receiving a user input in natural language, retrieving a first answer including a list of a first number of documents relevant to the user input by searching indexed documents in a knowledge base, applying the first answer to a large language model reader, resulting in a second answer, re-ranking the first answer by using the second answer, resulting in a third answer including a re-ranked list of the first number of documents, and generating a final response including the second answer and one or more documents among the third answer.

The invention provides an intelligent product recommendation method, system and device based on a Dify platform and a medium, and belongs to the technical field of artificial intelligence information recommendation, and the method comprises the steps: uploading structured product data and a question and answer text based on Dify, generating a product knowledge base according to the structured product data, and generating a question and answer knowledge base according to the question and answer text; receiving a question input by a user, and calling a Dify semantic analysis service to extract a structured query object; according to the extracted structured query object, performing retrieval matching on the question and answer knowledge base to obtain an optimal answer, and calling the AI large model to generate the optimal answer when retrieval matching fails; extracting a keyword from the optimal answer, and screening an optimal product from a product knowledge base by using the keyword; and returning the screened optimal product and the matched answer text to the user. Through combination of the product knowledge base, the question and answer knowledge base, the Dify semantic analysis service and the AI model, the product recommendation efficiency and accuracy are improved.

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 discloses a knowledge question-answering method and system based on a large language model and a semantic abstract. The system comprises a tree structure abstract generation subsystem which comprises a knowledge base configuration module, a file management module, a document analysis module and a knowledge block management module and is used for automatically generating tree structure hierarchical knowledge blocks for large manual documents or multi-chapter manual documents; the dual-channel retrieval engine subsystem comprises a question rewriting module, a semantic abstract retrieval module, a detail knowledge block retrieval module, a father-son retrieval module and a prompt word construction module, and all the modules work cooperatively to ensure that semantic abstract knowledge blocks and detail knowledge blocks related to the question of the user are retrieved. According to the method, by introducing the semantic abstract based on the tree structure and the two-channel retrieval, perception of global knowledge and accurate extraction of local fine-grained knowledge can be achieved at the same time, and then the ability of a knowledge base question-answering system in processing the general and inductive problems is improved.

The invention provides a pension knowledge base question and answer method and system based on deep learning, and relates to the field of natural language processing and graph neural networks. The method comprises the following steps: collecting pension policy text data from a plurality of channels; carrying out feature division on the pension policy text data, carrying out structured classified storage according to a feature division result, and constructing a pension knowledge base; performing semantic probability screening on the currently input oral user question, and converting the oral user question into a standardized user question; performing semantic matching on the standardized user question and the old-age care knowledge base to obtain a preliminary matching result; constructing a knowledge graph by taking the pension policy text data as nodes and the incidence relation between the pension policy text data as edges; and inputting the preliminary matching result into a graph neural network GNN, learning the knowledge graph through the graph neural network GNN, performing multi-hop reasoning on the preliminary matching result, and outputting a final question and answer result.

The invention provides a graph knowledge base question and answer optimization method and device, equipment and a storage medium, and relates to the technical field of computer systems.The optimization method comprises the steps that when query information is detected, intention recognition is conducted on the query information, a recognition result is obtained, and a corresponding preset graph knowledge base is determined according to the recognition result; when the preset graph knowledge base is a document class graph knowledge base, determining target entity data according to the query information, and inputting the target entity data into the document class graph knowledge base for query to obtain related entity data and a corresponding related entity relationship; and based on a preset large language model, obtaining a final question and answer result according to the related entity data and the corresponding related entity relationship. Through intention recognition and selection of the preset graph knowledge base, the core intention of user query can be accurately positioned, so that the corresponding graph knowledge base is quickly determined, invalid search in irrelevant knowledge bases is avoided, and the query efficiency is remarkably improved.

The invention discloses an intelligent evaluation system and method for knowledge base question and answer application, and relates to the technical field of artificial intelligence, natural language processing and multi-agent collaborative systems. The system comprises a data preprocessing module, a data synthesis module, a data screening module, a data scoring module and a self-adaptive weight adjustment module. The data preprocessing module carries out data preprocessing on the enterprise original document and generates a data knowledge base; the data synthesis module adopts a large language model to perform repeated question and answer on each paragraph of the data knowledge base to generate question and answer pairs; the data screening module screens out high-quality samples through a screening agent collaborative screening MACA framework mechanism; the data scoring module performs question and answer scoring through a scoring agent collaborative scoring MACA framework mechanism; and the adaptive weight adjustment module dynamically updates the weight of each screening scoring dimension by adopting an exponential weighted moving average algorithm.

The invention relates to the cross technical field of artificial intelligence and knowledge engineering, in particular to a local knowledge base question-answering system based on a new energy automobile and a construction method thereof, and the method comprises the following steps: integrating a knowledge graph, a ChatGLM2-6B language model and a LangChain framework to construct a local knowledge base question-answering system; obtaining a ternary collaborative architecture for structured management, retrieval and semantic question and answer of heterogeneous data in the new energy field; and based on the ternary collaborative architecture, correspondingly designing a plurality of functional modules for providing an intelligent question-answering solution for the field of new energy vehicles, and forming the local knowledge base question-answering system for the new energy vehicles. According to the'knowledge graph-pre-trained large model-LangChain 'ternary collaborative architecture provided by the invention, structured management, efficient retrieval and high-precision semantic question and answer of heterogeneous data in the field can be realized, and structural management, efficient retrieval and high-precision semantic question and answer of multi-source heterogeneous data in the new energy automobile field can be realized; and local deployment, large model generation and interpretable retrieval can be combined.

The invention relates to the technical field of data processing, and discloses a medical knowledge base question answering method and system, electronic equipment and a storage medium. Performing mixed retrieval on the medical problem based on a medical knowledge base to obtain a set number of text blocks as retrieval results, constructing cue words based on the medical problem and the retrieval results, and generating a preliminary answer containing a preliminary source identifier through a large language model; source verification and source tracing processing is carried out on the preliminary answer to generate a final answer, and the source verification and source tracing processing comprises the steps of analyzing the preliminary answer to obtain a plurality of independent claim points, carrying out sentence-level and block-level correlation judgment on text blocks pointed by the claim points and the preliminary source identifier based on a reordering model, and executing source judgment based on a threshold value; generating a corresponding source mark; according to the medical knowledge retrieval method and system, the accuracy and integrity of medical knowledge retrieval can be improved, and the fact accuracy of answer generation is ensured.

The invention relates to the technical field of intelligent legal question answering, and discloses a knowledge base question answering method and system based on a large language model agent. According to the method, a legal question and answer task is planned and decomposed through a large language model agent; extracting question keywords, and calling a combined query tool to retrieve related legal provisions, cases and other documents from a database; embedding the content related to the question in the document into a large language model agent, and assisting the large language model agent to generate an answer; according to the method, the workflow of the intelligent agent is reasonably planned by utilizing thinking chain prompt so as to excite the capability of a large model, and the problems of low retrieval efficiency and high cost of a vector database are solved by directly retrieving problem-related documents from a structured database; and the content most relevant to the question is embedded into the intelligent agent through the memory module, so that the problem of low legal question and answer result accuracy caused by insufficient knowledge in the large-model legal field is solved.

The present invention proposes an optimization method, device and readable storage medium for a knowledge base question-answering system based on hybrid fine-tuning and multi-dimensional evaluation, and proposes the following solutions: constructing a hybrid progressive fine-tuning framework that integrates Low-Rank Adaptation (LoRA) and Direct Preference Optimization (DPO), and achieving domain knowledge transfer through hierarchical dynamic parameter configuration; establishing a three-dimensional quantification evaluation system of "logic-semantics-knowledge", and forming a closed-loop optimization by using dynamic weight fusion and a visual decision-making system; designing a multi-domain prompt template library with hierarchical parameter freezing, sparse constraints and attention driving to achieve model lightweight and cross-domain logical constraints. The present invention breaks through the adaptation bottleneck between the general model and domain characteristics, improves the small-sample training efficiency and the compliance of the generated content, reduces the consumption of computing resources, provides an efficient and reliable knowledge service foundation for professional scenarios such as law, medicine, finance, etc., and has technical advantages of specialization, lightweight and interpretability.

The invention discloses a knowledge base question and answer method based on dynamic intention analysis and mixed retrieval, and relates to the technical field of artificial intelligence and natural language processing. Comprising the following steps: preprocessing user question data, including word segmentation, entity recognition and completion error correction; performing intention recognition based on the preprocessed user question data, outputting an intention label and extracting a plurality of scene parameters; performing weighted summation on each scene parameter and the corresponding weight coefficient to obtain a scene threshold value; comparing the scene threshold value with the initial threshold value, and if the scene threshold value is greater than the initial threshold value, triggering label slot complementation; obtaining a tag retrieval result and a vector retrieval result through a tag knowledge base based on the tag after slot completion; adjusting the weight ratio of the tag retrieval result to the vector retrieval result based on the scene threshold value, and obtaining a fusion retrieval result through weighted fusion; and generating a natural language answer based on the fused retrieval result and the contextual parameters. According to the scheme, the matching accuracy and retrieval efficiency of questions and answers and prompt scenes can be optimized.

This application relates to an energy knowledge base question answering method and an energy knowledge base question answering device. The method includes: receiving an original question containing a role identifier, and determining a job vocabulary corresponding to the role identifier; splicing the lexical units in the original question that match the job vocabulary to obtain a job-limited text, mapping the job-limited text to a predetermined slot structure to obtain an intent lexical unit group; determining the latest approval node in the energy knowledge base that matches the role identifier and the intent lexical unit group; filtering in the energy knowledge base with the latest approval node to obtain a filtered business segment, matching the intent lexical unit group with the filtered business segment weighted by an approval weight, and obtaining a candidate business segment, where the approval weight is determined according to the level of the approval node corresponding to the filtered business segment; screening out a target business segment from the candidate business segments based on the intent lexical unit group; and generating a reply text based on the target business segment and the intent lexical unit group. Using this method can improve the accuracy of the reply.

The application discloses a knowledge base question answering method and system under full-process large language model small sample learning, the method comprises the following steps: question analysis, identifying candidate triples and question types of original user questions through a large language model; query template generation, generating a knowledge base query statement template with placeholders for user questions; knowledge base linking, obtaining matched expressions for the placeholders in the query statement template in combination with a large language model and an entity information index that has been constructed; answer generation, generating a knowledge base query language and returning a final result. Through the small sample learning capability of the large model, the retrieval process is redesigned for the knowledge base question answering task, a model does not need to be trained separately, and the question answering process is simplified; through the powerful knowledge reserve of the large model, the question answering effect and generalization capability are improved.

The application discloses a data retrieval method, a server, a terminal and a storage medium, which are used for providing a knowledge base question and answer retrieval function for VDI users through a server without occupying the storage space of the server, and providing a safer, more reliable and more intelligent question and answer retrieval scheme. The method comprises the following steps: acquiring first text information from a first remote terminal in one or more remote terminals, wherein the first text information corresponds to a first embedding vector; if the similarity of the first embedding vector and a second embedding vector in a vector database is greater than or equal to a threshold value, determining a retrieval result of the first text information according to the second text information corresponding to the second embedding vector and the first embedding vector, wherein the second text information is obtained by text segmentation according to source documents of the one or more remote terminals.

The application discloses a knowledge base question answering method and device, computer equipment and a storage medium. The method comprises the following steps: in response to a user query, extracting a structured query element corresponding to a preset query dimension; the preset query dimension at least comprises a domain keyword; based on the structured query element, searching in a preset knowledge base to obtain a candidate knowledge record set; the preset knowledge base comprises a plurality of knowledge records, and each knowledge record at least comprises an identifier field and a domain keyword field, and each identifier is associated with a semantic vector generated based on the content of the corresponding knowledge record; encoding the user query into a query vector, and performing similarity matching between the query vector and the semantic vector associated with the identifier of each knowledge record in the candidate knowledge record set; and filtering a target knowledge record set from the candidate knowledge record set according to the matching result, and generating an answer corresponding to the user query based on the target knowledge record set. Thus, the accuracy of the answer is improved.

The present invention relates to the field of intelligent railway technology and provides a method for constructing a railway multimodal knowledge base question-answering system with a hybrid RAG architecture. The method comprises: 1. server local model deployment and fine-tuning; 2. data processing and database construction; establishing a railway specification knowledge base database management system based on MongoDB content storage and PostgreSQL vector search library; optimizing and merging search results for multiple sets of data based on hybrid search and RRF algorithms, and finally performing re-ranking and search filtering; 3. constructing a multimodal knowledge base; using multimodal embedding to embed images and text into a vector database, while simultaneously storing the corresponding original images and text in a document store; obtaining images from the document store during hybrid similarity search, and passing the original images and text blocks to a large model to generate answers; and 4. multi-platform expansion. The present invention can preferably construct a railway multimodal knowledge base question-answering system.

The application discloses an intelligent evaluation system and method for a knowledge base question answering application, and relates to the technical fields of artificial intelligence, natural language processing and multi-agent collaborative system. The system comprises a data preprocessing module, a data synthesis module, a data screening module, a data scoring module and an adaptive weight adjustment module. The data preprocessing module performs data preprocessing on enterprise original documents and generates a data knowledge base. The data synthesis module repeatedly asks and answers each paragraph of the data knowledge base by using a large language model to generate a question and answer pair. The data screening module screens out high-quality samples through screening agent collaboration and a MACA framework mechanism. The data scoring module scores the question and answer pair through scoring agent collaboration and a MACA framework mechanism. The adaptive weight adjustment module dynamically updates the weight of each screening and scoring dimension by using an exponentially weighted moving average algorithm.

The present invention discloses a method and system for translating graph data query languages for knowledge base question answering, which realizes the translation between graph data query languages (SPARQL, Cypher, and nGQL). Taking the translation from SPARQL to Cypher as an example, the corresponding method includes: 1) preprocessing SPARQL and Cypher statements; 2) training a structure-to-sequence translation model from the source SPARQL query skeleton graph structure to the target Cypher query skeleton sequence on parallel corpora; 3) inputting the query skeleton graph structure obtained by preprocessing the input source SPARQL statement into the translation model to generate the target Cypher query skeleton sequence, and restoring the skeleton sequence to the target Cypher statement according to the mapping dictionary. The corresponding system includes a preprocessing module, a source query language encoding module, and a target query language generation module, which helps with the migration of the knowledge base question answering system. The present invention uses a graph neural network to encode the structural semantic information of the source query statement, realizes end-to-end translation between graph data query languages, helps save labor costs, and contributes to the construction and migration of the knowledge base question answering system.

The application relates to the technical field of intelligent query of low-altitude flight regulations, and discloses a low-altitude regulation knowledge base question-answering system based on adaptive RAG, which comprises the following modules: a specification document preprocessing module, which is used for data cleaning of low-altitude multi-source heterogeneous regulation documents, extraction of multi-dimensional information, and establishment of a multi-dimensional label system; an adaptive RAG knowledge base construction module, which is used for structured storage of a clause unit and a label system structure and construction of a double-mode knowledge base; an adaptive routing and differentiated retrieval module, which is used for complexity discrimination of user queries and adaptive selection of different retrieval paths; a context integration and generation module, which is used for splicing of retrieval results, source information and user queries after sorting of the retrieval results, and generation of an interpretable answer based on reference materials; and a feedback optimization module, which is used for closed-loop optimization through periodical incremental fine-tuning of the model. The application can deeply understand the space-time constraint characteristics of the low-altitude field, dynamically resolve multi-source regulation conflicts, and realize efficient and accurate intelligent question-answering.

The application relates to the technical field of information retrieval processing, and discloses an aviation knowledge base question answering method and system based on vector retrieval and a large model, which comprises the following steps: analyzing a business document into rule units to generate rule vectors; receiving a question to generate a question vector; judging whether a query condition satisfies necessary conditions; if the necessary conditions are not satisfied, outputting supplementary question information; if the necessary conditions are satisfied, screening the rule units, performing retrieval, and obtaining recalled rule units; judging whether there is a rule conflict among the recalled rule units; if there is a rule conflict, judging whether there is a substitution relationship or an exception relationship; if there is a substitution relationship or an exception relationship, determining an effective rule unit; if there is no substitution relationship or exception relationship, outputting verification information; if there is no rule conflict, determining the recalled rule units as the effective rule units; generating a question and answer result according to the effective rule units; and outputting a final question and answer result. The application improves the accuracy, pertinence and stability of an aviation knowledge base question and answer result.

The invention relates to the field of knowledge base questions and answers, in particular to a data mining system in a digital archive based on network and digital resources, which comprises a question understanding module, an information matching module, a dynamic adjustment module and an answer generation module. By supporting a multi-hop reasoning mode, information fault and insufficient reasoning depth in complex question query are avoided, the real-time similarity between an intention recognition result and an entity is calculated, the connection number in an archive knowledge base is combined, a focus entity is selected, the pertinence is improved, and the real-time similarity between a candidate answer entity and the focus entity is obtained based on the incidence relation between the candidate answer entity and the focus entity. Factors such as policy reference frequency and business association strength are considered, a to-be-selected path is formulated, the next hop entity type is predicted through a probability model, the accuracy of path selection is ensured, the reasoning progress is monitored in real time, the reasoning strategy is dynamically adjusted through a hop strategy adjusting unit, and the adaptive capacity and reasoning efficiency of the question and answer reasoning process are improved. Effective feedback is provided for user consultation, and user experience is improved.