105 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 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 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 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 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 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 provides an electromechanical product innovative design problem analysis method and system based on a pre-training model. The method and system are used for solving the problem that the accuracy and efficiency of product problem analysis are low. The method comprises the following steps: acquiring an innovative design problem description text and a technique analysis tool type for a target mechanical and electrical product; searching a structured prompt template corresponding to the technical analysis tool type from a prompt template library, wherein a corresponding relationship between the technical analysis tool type and the structured prompt template is stored in the prompt template library; filling the innovative design problem description text into a structured prompt template to obtain a technique analysis prompt engineering text; reasoning the technical analysis prompt engineering text through the pre-training model to obtain a technical analysis result of the target mechanical and electrical product, the technical analysis prompt engineering text being used for guiding a thinking chain prompt reasoning process of the pre-training model.

The invention provides a multi-modal large model combined question analysis and explanation generation method and system, and relates to the technical field of multi-modal learning, and the method comprises the steps: carrying out the standardization processing of a multi-modal input question, and obtaining the standardized question data; separating structured question information to perform deep feature extraction, driving three-level knowledge structure linkage matching of the question solving thought library, and generating a plurality of initial question solving paths; performing knowledge blind area coverage screening, and positioning a target problem solving path; recursive knowledge point reinforcement is carried out, and a non-blind area credible problem solving chain is generated; step-by-step analysis logic derivation is carried out, and a target analysis logic framework is output; and performing cross-modal directional rendering according to real-time explanation modal selection, and delivering and outputting a multi-modal analysis explanation stream. The technical problems that in the prior art, deep understanding of personalized requirements and knowledge mastering levels of users is lacked, customized answers cannot be provided for the users, and the learning effect of the users and tool practicability are affected are solved.

The invention provides a large language model interaction implementation method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: determining annotation data of a plurality of to-be-called tools from a tool knowledge base on the basis of user questions; generating an execution topological graph based on the annotation data of each to-be-called tool; the execution topological graph reflects a calling relation and a dependency relation between the to-be-called tools; based on the user problem and the execution topological graph, the large language model is called to generate the workflow data under the constraint of the execution topological graph, so that on one hand, the problem analysis efficiency can be improved, and on the other hand, the execution topological graph is directly used as the constraint, and Token consumption caused by inputting a large number of dependency relationships into the large language model is avoided. And finally, carrying out availability verification on the workflow data on the basis of the execution topological graph, and carrying out tool calling on the basis of the workflow data after the availability verification is passed to obtain a final calling result so as to ensure the accuracy and availability of the workflow data.

The invention discloses a dialogue generation method and device, a customer service system, a storage medium and a program product, and the method comprises the steps: receiving request data inputted by a user, and carrying out the data preprocessing of the request data; performing intention analysis and emotion detection on the preprocessed request data to obtain user intention information and a user emotion state; performing data search on the user intention information to obtain problem analysis data; generating an initial reply text according to the user intention information and the question analysis data; and adjusting the initial reply text based on the emotional state to obtain a dialogue text. By adopting the embodiment of the invention, the intention and the perception emotional state of the user can be accurately analyzed, and multi-modal interaction is realized by fusing multi-element information, so that real-time response and efficient solution of questions of the user are realized.

The embodiment of the invention provides a multi-modal problem solving method and a multi-modal problem solving system.The multi-modal problem solving method comprises the steps that an input image is obtained, and the input image comprises a problem text and a problem graph; analyzing the question text to obtain a question analysis result and a target graphic element; performing vector graphic marking on the target graphic element in the topic graphic to obtain a vector graphic marking result; and based on a vector graphic marking result, reasoning a question analysis result to obtain an answer text. Attention on image details is improved in a vector graphic marking mode, image perception precision is improved, a vector graphic marking result can serve as an intermediate representation in a processing process, interpretability of a problem solving process and understanding of a verification method on the image details are enhanced, and a problem analysis result is reasoned based on the vector graphic marking result, so that the accuracy of image perception is improved. Direct perception of possible errors can be avoided, and the problem solving accuracy is improved.

The invention discloses an intelligent question analysis and calibration method based on a multi-modal large model, and relates to the technical field of image processing, and the method mainly comprises the steps: firstly, carrying out the OCR processing of an input multi-modal question picture, and generating structured OCR data containing coordinates, types and subject attributes through a general OCR engine in combination with a subject exclusive recognition plug-in; performing question region identification and segmentation by taking the data as a drive, and establishing a correlation graph among a whole question region, an answering region and an auxiliary region; a reference answer matched with the question type is generated based on the OCR data; further generating deep answer analysis taking the OCR information as a tracing basis in combination with the OCR data and the reference answer; and finally, based on the analysis result, the answer and the OCR data, generating a quantitative scoring standard by taking the analysis integrity, the answer correctness and the data use accuracy as standards. According to the invention, full-automatic processing from multi-modal topic picture input to final quantitative scoring standard generation is realized.

To provide a technique for facilitating management of advance payments received in a business system by facilitating the management of advance payments received from business partners and suppressing automatic issuance of invoices.SOLUTION: A server of a business system for storing business data regarding a user's business, executes, as functions thereof: a step (S121) of receiving text information including a question requesting advice regarding the user's business by input from the user; a step (S122) of analyzing the received text information and extracting the question regarding the user's business; a step (S123) of executing analysis by referring to the business data regarding the user's business and generating answer information that answers the question regarding the user's business; and a step (S124) of outputting the generated answer information.SELECTED DRAWING: Figure 5

The invention relates to a method and system for relieving large model illusion, and belongs to the technical field of computers. The method comprises the following steps: identifying and analyzing the intention of a question input by a user through a first-level agent, and judging whether an answer is obtained by calling a local knowledge base, a search engine or the capability of a large model; if a search engine is called, pulling the question through a first-level agent, and inputting the content of each webpage, website information, the question input by the user and a prompt cue word into a large model to obtain an answer, wherein the previous set number of webpages are related to the content inquired by the search engine; if a local knowledge base is called, analyzing the question through a secondary agent and generating a query instruction corresponding to the question, performing interactive query with professional databases of industries to which at least two questions with different query modes belong through the query instruction to obtain a query result, and inputting the question input by a user, a prompt prompt word and the query result into a large model to obtain an answer; if the capacity of the large model is called, an answer is obtained through the large model according to the question.

This invention discloses a knowledge graph-based intelligent question-and-answer method for audit guidance, relating to the field of digital auditing. Through knowledge graph framework design and ETL data warehouse technology, this invention extracts, cleans, and transforms audit business data relevant to business environment optimization audits, achieving the fusion of various business data to provide data support for intelligent question-and-answer. By constructing an intelligent question-and-answer system, and designing a multi-task-oriented approach based on real-world audit scenarios, it utilizes word segmentation, matching, and query modules to achieve knowledge graph-based intelligent question-and-answer for business environment optimization audit guidance. This reduces the workload of the audit process, freeing auditors from tedious and repetitive tasks, allowing them to focus more on risk identification, problem analysis, and root cause analysis, effectively improving audit efficiency and quality.

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 invention provides an intelligent customer service processing method and system, and is suitable for the technical field of data processing, and the method comprises the steps: carrying out the classification processing of a plurality of pieces of current user question information and a plurality of pieces of historical customer service dialogue information, and generating a plurality of pieces of user question category information; based on a plurality of preset user question analysis vectors, performing analysis processing on the plurality of pieces of user question category information to obtain a plurality of pieces of user question analysis information; and generating multiple pieces of current customer service processing information according to the multiple pieces of user question analysis information and the multiple pieces of user group attribute information. The present question intention and demand of the user are accurately grasped, more targeted and accurate services are provided for different user groups in combination with the past dialogue condition and group characteristics of the user, the semantic comprehension ability is effectively improved, the customer service better meets the actual demand of the user, and the user experience satisfaction degree is improved.

The invention relates to the technical field of data processing, and particularly discloses a transformer substation operation and maintenance intelligent question answering method and platform based on a large model, and the method comprises the steps: obtaining historical question answering data and historical fault data, collecting real-time operation data, and obtaining a real-time operation and maintenance problem; analyzing the historical question and answer data, constructing a semantic recognition model and a matching model, and optimizing the matching model; analyzing historical fault data, and constructing a fault detection model of each equipment label; and constructing a large model, and determining a real-time question label and a real-time question answer. The method can achieve the precise classification of questions, guarantees the accuracy of knowledge specification answers, provides a targeted fault processing strategy, forms a classification-matching-decision full-process intelligent response, avoids the problem of universal model answer generalization, solves a pain point that fault processing lacks precise strategy support, improves the operation and maintenance question and answer efficiency and decision reliability, and improves the operation and maintenance efficiency. And the professional and complexity requirements of the operation and maintenance scene of the transformer substation are met.

To provide a technique for facilitating management of advance payments received in a business system by facilitating the management of advance payments received from business partners and suppressing automatic issuance of invoices.SOLUTION: A server of a business system for storing business data regarding a user's business, executes, as functions thereof: a step (S121) of receiving text information including a question requesting advice regarding the user's business by input from the user; a step (S122) of analyzing the received text information and extracting the question regarding the user's business; a step (S123) of executing analysis by referring to the business data regarding the user's business and generating answer information that answers the question regarding the user's business; and a step (S124) of outputting the generated answer information.SELECTED DRAWING: Figure 5

The invention provides a metadata-driven intelligent log analysis system and method, and the system comprises a dialogue interaction module which is used for extracting a technical problem proposed by a user; the keyword extraction and query module is used for carrying out keyword extraction, service attribute labeling and query on the technical problem based on the service metadata pre-identifier; the query statement dynamic construction module dynamically constructs a query statement and performs log data query; the time conversion module performs context conversion on the keyword query result and the log data query result based on a time sequence, and outputs a final log query result; and the hierarchical structured RAG module performs context structured processing on the query result and the technical problem, and outputs a final solution through large model analysis. According to the method, natural language question input is supported, the use threshold is remarkably reduced, accurate retrieval of operation and maintenance data and business context enhancement are achieved through collaborative association of keywords and metadata, and the accuracy of question analysis is improved.

The invention provides a class learning condition analysis system and method based on an AI large model, and the system comprises a multi-person single-question analysis subsystem which is used for carrying out the error cause analysis and the number of persons through employing different analysis strategies according to different question types, and carrying out the error cause analysis through employing different preset strategies according to the question setting modes of different subjects. Obtaining a first analysis report; the multi-person multi-question knowledge point analysis subsystem is used for aggregating question information for different knowledge points and obtaining a second analysis report based on a multi-modal LLM model; and the multi-person and multi-question ability analysis subsystem is used for aggregating question information for different abilities and obtaining a third analysis report based on a multi-modal LLM model. The first analysis report, the second analysis report and the third analysis report output under the multi-person single question, the multi-person multi-question knowledge points and the multi-person single question ability are integrated, and a final learning condition analysis report is given.

The application provides a large model-based intelligent question analysis method, device and equipment and a storage medium. The application performs step-by-step semantic analysis on a question through a thinking chain reasoning framework, simulates a complete thinking process of a human being from understanding a problem to disassembling a structure, breaks through the limitation of only making sequence translation in the prior art, and significantly improves the accuracy of complex SQL generation. A multi-layer progressive retrieval mechanism is adopted, thinking chain information is used to drive focusing from a business scene to field association layer by layer, the retrieval result is ensured to be highly matched with the query requirement, a dynamically generated personalized SQL sub-template is used, and the adaptation capability for an unseen question is greatly enhanced. A multi-dimensional feature vector integrating semantic similarity, structural complexity and business matching degree is constructed, adaptive selection of a decoding strategy is realized, an optimal generation path can be used in different complexity scenes, and efficiency and quality are taken into account.

The invention relates to the technical field of Internet, and discloses a strategy content co-creation method and system based on artificial intelligence, and the method comprises the steps: displaying a content analysis page used for AI strategy note co-creation and aiming at a target analysis object; in response to a questioning trigger operation on the analysis questioning component, displaying questioning analysis content corresponding to the target analysis object on the content analysis page; displaying a note editing page used for AI strategy note co-creation; and in response to an editing trigger operation on the note title editing component, displaying the policy note title content in the note title area, and / or in response to an insertion note trigger operation for the question analysis content, displaying the question analysis content in the note body area to obtain a target co-created policy note. Therefore, the generation accuracy, the generation reliability, the generation efficiency and the generation convenience of the target co-creation strategy note can be improved, so that the co-creation experience of the user on the strategy note is improved.

An object of the system according to the embodiment is to allow a viewer to ask a question in real time during network broadcasting and obtain an answer on the spot.SOLUTION: A system according to an embodiment includes a dialogue reception unit, a data analysis unit, and an answer generation unit. The dialogue reception unit receives a question from a viewer. The data analysis unit analyzes data on the basis of the question received by the dialogue reception unit. The answer generation unit generates an answer based on the data analyzed by the data analysis unit.SELECTED DRAWING: Figure 1

The system according to this embodiment aims to uncover users' latent needs and contribute to improving the quality of services. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, an analysis unit, and a proposal unit. The collection unit collects user attribute information. The generation unit generates questions based on the information collected by the collection unit. The analysis unit analyzes the answers obtained from the user based on the questions generated by the generation unit. The proposal unit makes specific improvement suggestions based on the analysis results obtained by the analysis unit.

The invention discloses a financial question analysis method and device based on a large language model, equipment and a medium, and relates to the technical field of natural language processing, and the method comprises the steps: retrieving a target sub-graph corresponding to a to-be-analyzed financial question from a knowledge graph, and carrying out the topology analysis of the target sub-graph, so as to obtain a structural representation vector; determining a graph structure type by using the structure representation vector, and determining a cue word template based on the graph structure type; determining priorities corresponding to the nodes on the basis of semantic relevancy and time relevancy between the nodes in the target sub-graph and the financial problem to be analyzed and PageRank scores of the nodes, sorting and recombining the nodes according to the priorities to obtain a target logic flow, and filling the cue word template by using the target logic flow to obtain a cue word template; obtaining a target prompt word; and inputting the target cue word into the large language model so as to generate an analysis result. A structural representation vector is obtained through topology analysis, and the problem of confusion of context organization of a generated result is solved.

The invention relates to the technical field of data processing, in particular to a traceable intelligent question-answering system and method based on an electric power operation instruction, and the system comprises a data collection module, a data processing module, a question analysis module, an intelligent answering module, and a question-answering pushing module. Through cooperative operation of all the modules, the problems caused by unprocessed unstructured data and lack of traceable questions and answers of power intelligent guidance in the prior art are effectively solved, comprehensive collection and efficient processing of multi-modal data are achieved, the power problem can be accurately analyzed, targeted guidance answers can be generated, and the method has the advantages of being high in practicability and easy to popularize. And meanwhile, the traceability of questions and answers is guaranteed, the electric power work question and answer guidance efficiency is remarkably improved, the safety of electric power work is enhanced, and comprehensive, intelligent and reliable guidance support is provided for electric power work.