173 results about "Question analysis" patented technology

Provided in the present application is an intelligent question answering method based on a collaboration between a large language model and a knowledge graph, relating to the technical fields of artificial intelligence and natural language processing, the method comprising: decomposing a complex question into a plurality of simple questions, and analyzing the degree of association between the simple questions and a basic function so as to form a multi-hop reasoning path; automatically extracting structured information from the simple questions on the basis of a multi-task learning framework of a large model, so as to construct a knowledge graph; and constructing a cumulative reasoning learning framework on the basis of a logic reasoning large model, and performing iterative verification on a process result formed by the knowledge graph on the basis of the multi-hop reasoning path, so as to correct the reasoning path until a correct answer is inferred.

The invention discloses a civil administration service question and answer method based on a large model and knowledge graph retrieval enhancement, and the method comprises the steps: S10, inputting a user question, and carrying out the question analysis and entity recognition; s20, the problem complexity is judged, if the problem is a single-hop problem, knowledge graph single-hop retrieval is carried out, and if the problem is a multi-hop problem, knowledge graph multi-hop retrieval is carried out; s30, performing semantic matching sorting to generate sub-answers; and S40, based on the sub-answers and the user question, performing synthesis to generate a final answer. According to the method, the structured knowledge of the knowledge graph and the natural language processing capability of the large language model are fused; a question decomposition module is used to enhance the interpretability of multi-hop information retrieval and answers; and using contextual learning (ICL) and thinking chain (CoT) prompts to generate an individually processed explicit inference chain to improve authenticity; the defects of traditional civil administration service questions and answers in the aspects of knowledge accuracy, reasoning ability and interpretability are overcome.

The invention relates to the technical field of multi-round dialogue processing, in particular to a multi-round dialogue processing method based on RAG technology dynamic cue word retrieval. Comprising the following steps of question input by a user, initial retrieval and cue word generation, question splicing and answer generation, historical dialogue management and context coding, multi-round question analysis and retrieval enhancement, complex question decomposition and parallel retrieval, multi-source information fusion and generation model optimization, iterative feedback and model tuning, and exception handling and fault-tolerant mechanism. According to the method, related information is retrieved from an external knowledge base, the model can utilize wider and more specific knowledge to generate answers and better track dialogue history and context, so that answers consistent and coherent with the dialogue history are generated, the model can generate answers on a short input sequence by retrieving and splicing related cue words, and meanwhile, the model can be used for analyzing the dialogue history. Cue words are dynamically generated according to user input and retrieval results, so that the model can better adapt to different dialogue scenes and user requirements.

The invention provides a multi-course fusion knowledge graph construction and real question analysis method and system. The method comprises the following steps: carrying out knowledge point alignment operation, carrying out integration operation on cross-course knowledge points, carrying out quantitative analysis on the knowledge points by utilizing a cosine similarity calculation method, and identifying and aligning synonymous knowledge points in different courses; performing knowledge point disambiguation operation, and processing the situation that the knowledge points have the same name and different meanings to obtain integrated knowledge points and an integrated knowledge graph; performing knowledge point standardization operation on the integrated knowledge points, and performing standardization processing on the integrated knowledge graph to obtain standardized knowledge points and a standardized knowledge graph; converting the normalized knowledge points, extracting to obtain key features, and processing according to the key features to obtain semantic feature sequence vectors; matching to obtain a matching result and applicable knowledge points; and verifying and evaluating the matching result, and mounting the question to the corresponding knowledge point. The technical problems that the knowledge point extraction result is one-sided and the result accuracy is not high are solved.

An analysis system connects to a set of data sources and perform natural language questions based on the data sources. The analysis system connects with the data sources and retrieves metadata describing data assets stored in each data source. The analysis system generates an execution plan for the natural language question. The analysis system finds data assets that match the received question based on the metadata. The analysis system ranks the data assets and presents the ranked data assets to users for allowing users to modify the execution plan. The analysis system may use execution plans of previously stored questions for executing new questions. The analysis system supports selective preprocessing of data to increase the data quality.

The embodiment of the invention provides a reply method and device based on a large language model, electronic equipment and a storage medium, relates to the technical field of intelligent questions and answers, and is suitable for the fields of financial science and technology, digital medical treatment and the like. The method comprises the steps of obtaining candidate question and answer texts of a target knowledge base based on a preset knowledge category, and obtaining question extraction indication parameters; performing question extraction on the candidate question and answer text according to the question extraction indication parameter to obtain candidate question information; obtaining target problem information; performing problem analysis on the target problem information according to a preset problem analysis indication parameter to obtain problem analysis information; performing question splitting on the target question information according to the question analysis information to obtain reference sub-question information; and performing answer extraction on the candidate question and answer text according to the reference sub-question information and the candidate question information to obtain target answer information. According to the embodiment of the invention, the reply accuracy can be improved.

An analysis system connects to a set of data sources and perform natural language questions based on the data sources. The analysis system connects with the data sources and retrieves metadata describing data assets stored in each data source. The analysis system generates an execution plan for the natural language question. The analysis system finds data assets that match the received question based on the metadata. The analysis system ranks the data assets and presents the ranked data assets to users for allowing users to modify the execution plan. The analysis system may use execution plans of previously stored questions for executing new questions. The analysis system supports selective preprocessing of data to increase the data quality.

The invention provides a mathematical question and answer process teaching system, which comprises a user interaction module, a question analysis and standardization module, a cognitive diagnosis engine, a generative teaching module, a mathematical knowledge graph database and a user portrait database, teaching content and guiding questions are presented to the user in a multi-mode form; the question analysis and standardization module is used for performing semantic understanding and structured analysis on mathematical questions and generating standardized question description and at least one standard question solving path; according to the method, the structured reasoning of the knowledge graph is combined with the natural language generation ability of the generative AI, so that a closed loop from'problem solving 'to'diagnosis' to'heuristic teaching 'is realized, thinking blockage points of students can be accurately positioned and personalized tutoring can be provided like human teachers, and the learning efficiency and the autonomous thinking ability are remarkably improved.

The invention provides an automatic data analysis method based on multi-agent cognitive collaboration, which comprises the following steps: S1, multiple agents perform modeling processing, and each agent has unique functions and tasks in a multi-agent system; s2, the user side is used for collecting complex problems proposed by a user; s3, the controller coordinates multiple agents to work, wherein the multiple agents comprise a problem analysis module, an SQL module, an OA module and a data analysis module; s4, analyzing a complex problem into a plurality of sub-problems by a problem analysis module; s5, the SQL module searches a table structure according to the sub-problem requirements, compiles an SQL and operates the SQL; s6, the QA module is responsible for checking whether the sql is correct or not; according to the method, the search technology and the large model generation technology are combined, knowledge outside the model can be obtained through the search technology, a tool can be directly operated to solve user problems, and more complex problems can be solved in multiple rounds of interaction.

The embodiment of the invention provides a knowledge question-answering system and a data processing method. The knowledge question-answering system comprises a multi-modal knowledge graph, a question analysis module, a knowledge query module and an answer generation module, the question analysis module can perform semantic analysis on a target question currently asked by a user based on a large language model, extract target entity information and target user intention in the target question, and generate a knowledge query result; the knowledge query module can construct target query logic according to the target entity information and the target user intention and query the multi-modal knowledge graph according to the target query logic to obtain a target prompt information list, and the answer generation module determines a target answer according to all attribute information in the target prompt information list to generate prompt words and sends the prompt words to the user. And based on the large language model, generating a target answer according to the target answer generation cue word and returning the target answer to the user. According to the knowledge question-answering system, the accuracy and timeliness of the system can be improved, and the generalization ability of the system can also be improved.

The invention relates to the technical field of natural language processing, in particular to a method and system for achieving intelligent number asking based on a large model, and the method comprises the following steps: integrating data from various data sources including a database, a data warehouse, WEB service and Excel files; the data is cleaned, converted, standardized and normalized through the data management center, so that the data quality is ensured, and basic data support is provided for subsequent intelligent question analysis; the beneficial effects are that the method for realizing intelligent number asking based on the large model provided by the invention responds to complex and changeable data requirements in real time, helps an enterprise to quickly make a data-based decision, and reduces the time cost of waiting for the assistance of an IT department or a data analysis team; through effective integration and intelligent search of mass data resources, various data in an enterprise can be fully mined and utilized, and idleness and waste of the data resources are avoided.

The invention discloses a video semantic question-answering system for a monitoring scene, and the system comprises a video collection module which is composed of a plurality of cameras and forms a monitoring network; the video preprocessing module is used for optimizing original video data through three-stage processing of key frame extraction, noise reduction and time alignment; the multi-modal feature extraction module comprises a video feature extraction channel and a text feature extraction channel; the multi-modal interactive reasoning module adopts a cross-modal neural network model; the answer generation module is used for outputting corresponding structured answer branches and natural language branches based on joint representation prediction answers; and an interaction and visualization interface module. The invention provides a video semantic question-answering system oriented to a monitoring scene, which supports a user to carry out semantic query on monitoring video contents in a natural language form, and the system automatically analyzes problems, analyzes video data and generates structured or natural language answers. And the combined understanding and response capability of open semantics, dynamic behaviors and attribute states is realized.

The invention relates to an adversarial collaborative retrieval enhancement method, provides an adversarial collaborative retrieval enhancement framework (AC-RAG), and improves the performance of a large language model (LLMs) in a natural language processing (NLP) task in a specific field. In the pre-detection stage, a detector preliminarily judges whether a task needs to be retrieved or not; in the question analysis stage, the detector decomposes query into sub-tasks and provides retrieval clues for the answers; in the retrieval and integration stage, the answers retrieve and integrate information according to the subtasks; in the post-detection stage, the detector evaluates the information integrity, and retrieval is repeated if necessary. According to the framework, a neutral regulator is introduced, cooperation among agents is optimized, the problems of retrieval illusion and semantic difference are remarkably reduced, and retrieval accuracy and system efficiency are improved. In addition, in the model optimization stage, a detector and an answering device are finely adjusted through reasoning path data, so that the model is evolved continuously. Experimental results show that the AC-RAG disclosed by the invention exceeds the existing method in a plurality of vertical fields, and shows excellent performance of the AC-RAG in complex query.

The invention discloses an intelligent question answering system, which relates to the technical field of intelligent question answering and comprises a question input module, an answer presentation module and a feedback collection module which are arranged on a front-end user interface, the question analysis module, the answer generation module and the knowledge base are arranged on the back-end server; the question input module is used for realizing question input; the problem analysis module can accurately analyze the meaning of the problem and identify the key point and implicit demand of the problem; the answer generation module retrieves a knowledge base to generate answers based on an analysis result of the question analysis module; the answer presentation module is used for presenting answers; the feedback collection module is used for collecting evaluation and feedback of the user on the answer and generating feedback information; and the knowledge base is used for storing domain knowledge and feedback information. According to the method, the user experience is improved, the question answering efficiency is improved, meanwhile, development of an intelligent question answering system can be promoted, and the method can adapt to diversified application scenes.

The invention provides an intelligent questioning and answering system based on a heterogeneous graph RAG technology, and relates to the field of intelligent questioning and answering. A heterogeneous graph model is constructed, alternative knowledge nodes are extracted from the heterogeneous graph model, and questions input by a user are obtained; semantic coding is carried out on the alternative knowledge nodes and the questions input by the user by adopting a data processing technology based on deep learning, so that a semantic matching degree is automatically obtained according to principal component significant fusion representation between the semantic features of the alternative knowledge nodes and the semantic features of the input questions; and automatically determining whether to return the alternative knowledge node or not based on comparison between the semantic matching degree and a preset threshold value. The intelligent question answering system based on deep learning and heterogeneous graphs has higher flexibility and stronger generalization ability, and can capture complex structures and meanings in natural languages, thereby improving the accuracy of question analysis.

The invention relates to a context redundancy optimization method (CRORALLM) oriented to large language model retrieval enhancement. According to the method, the input context of the large language model is optimized by adopting a strategy of clustering first and then abstracting, so that the redundant influence is reduced, and the content comprehensiveness is ensured while the retrieval accuracy is improved. CRORALLM firstly classifies a candidate document set in order in a clustering mode, so that content themes can be recognized more effectively, and the subsequent abstract generation quality is improved; then, a fine-tuning abstract model is used for analyzing the correlation of the clustering document set according to the problem, and a weighted abstract is generated to serve as a new context: texts with high relevancy with the problem are reserved, texts with low relevancy with the problem are subjected to high-degree abstracting, and texts irrelevant to the problem are filtered. And finally, the contexts are used for large language model retrieval enhancement, so that the problems of information overload and redundant noise are solved, and the model answering accuracy is improved. According to the method, four base models at home and abroad such as Qwen are selected in an experiment, a common data set is adopted, different noise ratios are set for the experiment, and the result shows that the model accuracy of the CRRALLM is obviously improved under the different noise ratios, so that the method can effectively optimize the context in large language retrieval enhancement and improve the accuracy of large language model answering.

The invention discloses an intelligent test question generation method based on a large language model, and the method comprises the steps: obtaining a training courseware, and configuring question types needed by an examination and the number of test questions corresponding to each question type; generating a corresponding test question prompt word project according to a question type required by an examination, extracting courseware text information according to the obtained training courseware, configuring the courseware text information and the number of test questions corresponding to the question type as parameters to the corresponding test question prompt word project, generating a test question prompt word project to be debugged, and debugging the test question prompt word project to be debugged. Debugging the test question cue word project to be debugged; inputting the debugged test question prompt word engineering into the large language model in sequence, and generating test questions, answers and question analysis of the number of test questions corresponding to the question type required by the test; and storing the test questions, answers and question analysis of the test question quantity corresponding to the question type required by the test. The corresponding test paper can be quickly generated according to user requirements, the professional requirement for teaching and research personnel is reduced, the workload is further reduced, and the test question generation efficiency is improved.

The invention discloses a wrong question analysis and personalized recommendation method and device based on a large model and a server, and relates to the technical field of learning assistance and artificial intelligence combination, and the method comprises the steps: obtaining input question-answering information, inputting the processed question-answering information into a preset artificial intelligence model, analyzing the question semantics and the question answering process of the question-answering information, and obtaining a question-answering result; error reasons are positioned, and weak knowledge points are analyzed; based on the error reason of the question answering information, extracting error question core features; on the basis of the extracted wrong question core features, questions which reach a preset association degree with wrong question reasons are retrieved from a preset question bank, and / or three questions related to the wrong question reasons are dynamically generated, the questions which reach the preset association degree with the wrong question reasons are output, and the questions are preferentially matched with the wrong question reasons. The invention provides a method and an intelligent system which are combined with a large model technology, can deeply analyze error causes, supports multi-modal data and can dynamically generate questions, and can realize precise and personalized learning assistance.

The embodiment of the invention provides a multi-mode problem solving method, a multi-mode problem solving system, computing equipment and a medium, and the multi-mode problem solving method comprises the steps that an input image is acquired, and the input image comprises a problem text and an initial problem graph; based on the initial topic graph, analyzing the topic text to obtain a topic analysis result and a target graph element; reasoning the question analysis result based on the target graphic element to obtain an answer text; based on the answer text, performing vector graphic marking on the target graphic element to obtain a vector graphic marking result; and based on a vector graph marking result, rendering the target graph element on the initial topic graph to obtain a rendered topic graph. Students can visually see the answering process and the final result, and the learning effect and the question solving experience are improved.

The invention discloses an in-patient doctor standardized training examination system based on a knowledge graph and the knowledge graph, and aims at simulating the flow and the examination range of the in-patient doctor standardized training examination to meet the relevant examination requirements, and the system comprises two subsystems of an intelligent medical question and answer system and an examination management system, the front end of the intelligent medical question-answering system uses a WeChat applet framework to realize intelligent medical question-answering and examination functions, and the rear end module comprises a knowledge graph module, an intelligent question-answering system module, an inquiry module responding to doctors, a medical science popularization question module responding to users and the like. The examination management system has the functions of user answering, answer submitting and the like; the test result analysis comprises answer result display and question analysis. According to the invention, a knowledge basis is provided for the standardized training examination, and the actual requirements of the standardized training examination of the resident physician can be well met.

The invention provides an intelligent network operation and maintenance method and system based on performance analysis and a large language model, and belongs to the field of network operation and maintenance. The method comprises the following steps: firstly, embedding a large language model into a network application performance analysis system; performance analysis platform authentication information and an API path are configured, a semantic analysis template, an inference rule base, a graph model and an alarm threshold value are initialized, and a report generation template and a scheduling strategy are configured; receiving an operation and maintenance problem, a query request or a diagnosis demand input by a user in a natural language form; based on a semantic analysis template configured in the large language model, intention recognition and keyword extraction are carried out, a special language DSL in the field of structured query is generated, network application performance analysis is carried out, performance feedback data is obtained, index analysis is carried out, an analysis result is fed back to the large language model, a natural language is generated according to the feedback result, and the natural language is stored in a database. And displaying according to the report generation template. The problem analysis efficiency, the automation degree and the network operation and maintenance efficiency and effect are improved.

The invention provides a knowledge reasoning and evidence generating method based on problem decomposition, and belongs to the technical field of problem decomposition. The knowledge reasoning and evidence generation method based on question decomposition comprises question analysis, knowledge base and data, reasoning engine, evidence generation and answer production, learning and feedback, and comprises natural language processing, question classification and question understanding, the question analysis uses an NLP technology to analyze and understand questions proposed by users, and the NLP technology is used to analyze and understand the questions proposed by the users; the method comprises the following steps: determining the field or type of a question, decomposing the complex question into a series of simpler sub-questions by using a comparator module to complete relation analysis to generate a final answer, and outputting the answering process of each sub-question as evidence to display the reasoning logic of the model to answer the question, so as to realize the high consistency of the evidence and a decision; the inconsistency of the questions and the answers is ensured, and the accuracy of the answers is improved.

The application discloses an intelligent question and answer system, and relates to the technical field of intelligent question and answer, which comprises a question input module, an answer presentation module and a feedback collection module arranged in a front-end user interface, a question analysis module, an answer generation module and a knowledge base arranged in a back-end server, the question input module is used for realizing question input, the question analysis module can accurately analyze the meaning of a question, identify the key points and implied requirements of the question, the answer generation module retrieves the knowledge base to generate an answer based on the analysis result of the question analysis module, the answer presentation module is used for presenting the answer, the feedback collection module is used for collecting the evaluation and feedback of the user on the answer and generating feedback information, and the knowledge base is used for storing field knowledge and feedback information, so that the user experience is improved, the question and answer efficiency is improved, the development of the intelligent question and answer system is promoted, and the application scenarios can be adapted to diversification.

The embodiment of the invention provides an intelligent question answering method and system based on a chemical safety knowledge graph, and belongs to the technical field of chemical safety. The method comprises the following steps: collecting question information of a user, and processing the question information into text information; performing question parsing on the text information to obtain a query language; based on the query language, performing target object query in a pre-constructed chemical safety knowledge graph; and based on the queried target object, generating and pushing answer information to the user side. According to the scheme, quick questioning and answering of professional knowledge in the technical field of chemical safety are achieved, and the problem that it is difficult for users to obtain professional knowledge suggestions in the technical field of chemical safety at present is solved.

The invention discloses a multi-modal large model-based multimedia file generation method, and relates to the technical field of education content generation. Task alignment and evidence readiness, question analysis and question solving trajectory diagram construction, compiling into a unified instruction of a split white board and a parastyle, controlled generation, co-optimization in generation, posterior judgment, traceable release and learning write-back are sequentially completed; according to the scheme, a problem solving track graph is used as a unique true source, a semantic structure which can do a problem is mapped into a shot, blackboard writing and oral playing, and a suitable age target and a cognitive load are synchronously constrained; in the generation stage, the fact and safety scores are calculated according to the shot, local regeneration is triggered, and rework of the whole section is reduced; source credentials are written before publishing, and learning conditions are written back in combination with watching and evaluation, so that a correct, right-age, traceable and self-evolutionary closed loop is formed. And unified numbering, continuous input processing and output are realized, fixed-point backspacing and sampling inspection are supported, uncertainty and cost are reduced, and convenience is brought to campus distribution and home check verification.

The invention provides an operation system and an operation control method of an intelligent agent product. According to the system, through a knowledge base templated import and dynamic maintenance mechanism, batch rapid import of multi-format product documents can be supported, and manual operation is greatly reduced. According to the system, a fine-grained multi-role authority management system is constructed, authority boundaries of a super administrator, an operation administrator, customer service personnel and equipment developers are clearly divided, and the safety and manageability of the system are enhanced. The system can comprehensively collect and analyze operation data including question and answer logs, control logs, model calling times, cost and the like; a multi-dimensional operation result is generated through hotspot problem analysis, control frequency statistics and the like, meanwhile, the cost-benefit ratio is calculated, and a data basis is provided for identifying user requirements and optimizing an intelligent agent response strategy. According to the system, through standardization and configuration design of an equipment adaptation process, a novel equipment access template is provided, a data demand and a test process are defined, a new product access period is shortened, and the market response speed is improved.

The invention discloses an RAG and knowledge graph question-answering method and system for security classification and grading of cross-border data, and belongs to the crossing field of big data security management, artificial intelligence and compliance technologies. The method comprises the following steps: step S101, receiving and preprocessing user input; step S102, performing question analysis and semantic intention recognition; step S103, intelligent scheduling control decision making; step S104, reasoning and executing the knowledge graph; step S105, performing RAG generation reasoning execution; step S106, performing intelligent fusion and decision traceability; step S107, carrying out multi-dimensional visual display; step S108, audit record and report generation; and S109, updating the dynamic knowledge. The method has the advantages of intelligent question and answer, multi-source fusion, sustainable evolution, traceability of the whole process and the like, and can be widely applied to compliance management and intelligent decision-making scenes related to data cross-border flow, such as enterprises, governments, finance, medical treatment and the like.

The invention relates to the field of SQL query statement generation, in particular to an SQL query statement generation method and device, computer equipment and a storage medium. The method comprises the steps of obtaining a query problem statement, and performing intention recognition on the query problem statement to obtain an intention recognition result; when the intention recognition result indicates that the query question statement is an answerable statement, performing dimension analysis on the query question statement to obtain dimension data of the query question statement; according to the dimension data and the index data, performing statement splitting processing on the query question statement to obtain at least two question sub-statements; based on a preset SQL data table library, the dimension data and the index data, performing question analysis on all the question sub-statements, and generating an SQL analysis statement for each question sub-statement; and generating an SQL query statement corresponding to the query problem statement according to all the SQL analysis statements. According to the method, the natural language problem can be quickly and accurately converted into the SQL query statement, and the generation efficiency of the SQL query statement is improved.

The invention provides a data annotation method which can be applied to the field of big data or artificial intelligence. The method comprises the steps of obtaining business handling records in a historical time period, performing problem analysis on the business handling records, and obtaining a problem set; based on at least one question in the question set, a question statement is generated, the question statement is input into a pre-trained language large model, a prompt word set output by the language large model is obtained, and the prompt word set comprises the question and at least one corresponding emotion prompt word; inputting the cue word set as input into the large language model to obtain at least one text statement output by the large language model; and based on each text statement and each corresponding emotion prompt word, generating a labeled data set for training an emotion recognition model. The invention further provides a data annotation device and equipment, a storage medium and a program product.

The invention relates to the technical field of water affairs, and provides a water affair expert question and answer auxiliary method and system based on a water affair large model, and the method comprises the steps: carrying out the classification processing of water affair problems, obtaining a water affair problem text and a water affair problem image, carrying out the problem screening processing of the water affair problem text and the water affair problem image, and obtaining a water affair problem image; obtaining a target question text and a target question image; performing feature extraction on problems in the water affair problem set to obtain problem features; calculating feature association strength between the problem features, setting a problem priority corresponding to the target problem text, mining problem information corresponding to the target problem text, and generating a problem identifier corresponding to the target problem text; and searching question analysis corresponding to the water affair question set from a preset water affair large model, performing calibration processing on the question analysis to obtain calibration analysis, and generating a question and answer auxiliary report corresponding to the water affair expert. The main purpose of the invention is to improve the processing efficiency of water affair expert question and answer assistance.