96 results about "Question asking" patented technology

The invention relates to the technical field of artificial intelligence, and particularly provides an intelligent question asking method and device, based on a large model and semantic modeling, the method comprises the following steps: S1, a user interaction module receives a natural language question input by a user; s2, the semantic modeling module collects and integrates related data sources; s3, an analysis rule is built in the rule analysis module; s4, the large model processing module understands and answers the natural language question input by the user; s5, the SQL conversion module generates a universal logic SQL for the natural language problem input by the user through rules and natural language analysis; s6, the data processing and indexing module establishes a data indexing system; s7, establishing a dynamic feedback and learning mechanism; and S8, establishing a security and privacy protection mechanism. Compared with the prior art, the accuracy and timeliness of intelligent question answering of the user questions can be improved, and accurate answering of specific big data scene tasks is achieved.

The invention provides a method and device for scoring and questioning according to interview question answers and electronic equipment, and belongs to the technical field of artificial intelligence, in the method, multiple similarity measurement algorithms are integrated to achieve multi-angle quantitative evaluation of applicant answers, a large model scoring link based on deep learning is creatively added, and the evaluation efficiency is improved. Therefore, the language understanding depth which cannot be reached by pure mathematical calculation is compensated. According to the double-track scoring architecture, on one hand, the objectivity and the consistency of scoring are guaranteed by means of an accurate mathematical model; on the other hand, subjective understanding and interpretation of scoring are enhanced by means of a large model with high semantic analysis ability, the accuracy and comprehensiveness of evaluation are improved, the process is automatically achieved, efficiency is high, and the method can rapidly, accurately and comprehensively evaluate the applicant answers. In addition, the scoring reason and the question-asking problem given by the large model provide valuable information for mining the knowledge mastering condition of the applicant.

The invention belongs to the technical field of intelligent question-answering systems, and particularly relates to an intelligent question-answering inference system based on a knowledge graph, which is characterized in that firstly, a knowledge graph construction module fuses multi-source data to generate a structured graph, and after a user inputs a natural language question, a question-answering analysis module completes intention classification and entity disambiguation and converts the question into structured query; an inference engine module fuses symbol rules and graph neural network inference through a hybrid inference sub-module, and a dynamic weight adjustment sub-module optimizes weights according to errors and attenuation factors to generate an inference result; the knowledge updating module incrementally updates the atlas in real time and detects conflicts, the interactive interface module visually presents a result, and the evaluation optimization module iteratively optimizes parameters in combination with offline evaluation and online feedback. The whole process is from user question asking to result output, accurate reasoning and continuous performance improvement are achieved, and multi-field question and answer requirements are met.

The invention provides a knowledge enhancement type sports video understanding method based on large-model dual-mode reasoning, and belongs to the field of video understanding. Firstly, a sports video needing questioning and a question text are obtained, and the sports video, the question text and cue words are input into a reactive reasoning agent; the reactive reasoning agent classifies the questions according to the question texts and the cue words, and if the questions belong to simple questions, the reactive reasoning agent answers the questions according to the input sports videos; if the questions belong to complex questions, the questions are answered through a deep reasoning agent, and the deep reasoning agent is composed of a dynamic motion divider, a key segment selector and a fine-grained matcher based on the sports knowledge graph. According to the method, a dual-mode reasoning system is innovatively introduced, the dynamics and domain specificity of the sports video and the diversity and complexity of questions asked by the user are fully considered, and the performance of a (multi-mode) large language model in a sports video understanding task is remarkably improved.

The invention provides a student course learning evaluation method and device and electronic equipment, and relates to the technical field of education informatization. The method comprises the following steps: acquiring reply data of questions asked by a student in a learning process of a target course; performing feature extraction on the answer content to obtain answer content features and semantic category labels; standard answers corresponding to the questions are extracted according to the knowledge graph, and candidate answer features are generated; selecting a corresponding target answer feature according to the semantic category label; calculating the semantic similarity of the answer content features and the target answer features, and determining whether the answer content is correct or not according to the semantic similarity and a similarity threshold value; counting the number of correct answer contents, and calculating the answer accuracy; and according to the answer correct rate and the answer state, determining a knowledge mastering score of the student for the target course, and according to the knowledge mastering score and a score threshold, determining a mastering degree of the student for the target course. According to the invention, the accuracy of student course mastering degree evaluation can be improved.

The invention provides a rapid data acquisition method based on fusion of intelligent number asking and an industrial connection technology, and the method comprises the steps: obtaining industrial protocols of a plurality of devices and configuration parameters corresponding to the devices, and generating an acquisition model of each device; generating a structural fingerprint for each acquisition model; performing matching degree analysis on the plurality of acquisition models through multi-modal similarity calculation on the basis of the structure fingerprint, screening the plurality of acquisition models according to a matching degree analysis result to perform uniqueness management, and performing association mapping on the equipment and the corresponding acquisition model to obtain a de-duplicated acquisition model; edge gateway configuration accurate issuing of the collection model after duplicate removal is realized through a two-way transmission channel, and consistency is guaranteed by combining real-time structure fingerprint return and matching degree analysis. Through the AI-driven process arrangement and semantic understanding technology, the modeling efficiency, the information consistency and the issuing capability of the data acquisition system are remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a man-machine interaction-based question answer feedback method, device, equipment and medium, the method comprises the following steps: obtaining a question help request of a target user, obtaining a question frequency, and if the question frequency is less than a preset frequency threshold, sending a question answer to the target user; if yes, intention recognition is conducted on the question help-seeking request to obtain question key information, and if yes, context reloading is conducted on an obtained historical question record according to the question help-seeking request to obtain the question key information, statement splitting is conducted on the question key information to obtain a key task; and searching related information of the key task from the obtained external professional knowledge base, generating a user preference behavior according to the historical question record, generating a question answer of the question help request according to the related information, and sending the question answer to a preset target user side. According to the invention, the efficiency of answering various questions for the user is improved.

The invention discloses a content-based news duplicate removal method and device and a nonvolatile storage medium. The method comprises the steps of obtaining a to-be-processed news set, deleting to-be-processed news with text structure similarity greater than or equal to preset similarity, and obtaining residual to-be-processed news; determining a content element and a category system of each piece of residual to-be-processed news, and classifying the plurality of pieces of residual to-be-processed news with the same content element and category system into a to-be-deduplicated set; for each piece of target residual to-be-processed news in the to-be-deduplicated set, triggering a text analysis model by adopting a first cue word to output a plurality of questions related to the target residual to-be-processed news to obtain a question list, and triggering the text analysis model by adopting a second cue word to output summary information of the target residual to-be-processed news; and according to the first semantic similarity of the question list and the second semantic similarity of the summary information, determining to-be-deleted news in the to-be-deduplicated set.

The invention discloses a voice interaction product test method and device based on artificial intelligence and a product, and the method comprises the steps: generating a corresponding question according to a selected question class through a first artificial intelligence large model; the questioning question is converted into questioning voice; the voice interaction product needing to be tested responds to the questioning voice and outputs corresponding answering voice; the answer voice is converted into a character answer; and evaluating the matching accuracy of the character answers and the corresponding questions by using the second artificial intelligence large model, and outputting the accuracy as a test result. The invention discloses a voice interaction product test method and device based on artificial intelligence and a product, and the method comprises the steps: generating a questioning question through a first artificial intelligence large model, evaluating the matching accuracy of a character answer and the corresponding questioning question through a second artificial intelligence large model, and outputting a test result. The system has the characteristics of high automation degree of voice interaction product testing, full testing and accurate testing effect.

The invention discloses an intelligent question and answer processing method and device, equipment and a storage medium. When the method provided by the embodiment of the invention is executed, the intelligent question and answer model can firstly obtain the questioning question to be processed and identify the real-time performance of the question. Wherein if the answer to the question changes with time, the question is a real-time question, or if the answer to the question needs to be replied based on the update information, the question is also a real-time question. And if the problem is a real-time problem, generating a preliminary reply to the problem. And then evaluating the confidence coefficient of the preliminary reply based on question and answer data in a knowledge base associated with the intelligent question and answer model. If the confidence coefficient is greater than or equal to a confidence threshold value, directly outputting the preliminary reply; on the contrary, if the confidence coefficient is smaller than a confidence threshold value, outputting a rejection answer or an uncertainty, and obtaining a question reply of the questioning question obtained based on the RAG query. The response accuracy and the resource utilization efficiency of the intelligent question and answer are improved.

The invention relates to the technical field of computers, in particular to the technical field of artificial intelligence, and provides an answer generation method and device, electronic equipment and a storage medium, which are used for improving the accuracy of answers generated by a dialogue system and improving the question answering efficiency due to the fact that the number of questions asked by an object for the same question is reduced. The method comprises the steps of obtaining a to-be-queried question input based on an object, and generating at least one initial candidate question; for each initial candidate question, executing the following operations: generating at least one initial answer corresponding to the initial candidate question, and fusing the initial candidate question with the at least one initial answer to generate a fused candidate question; on the basis of the fused candidate questions, querying again to generate candidate answers corresponding to the fused candidate questions; the target answer corresponding to the to-be-queried question is generated based on the candidate answers corresponding to the at least one fusion candidate question, and the target answer generated based on the re-query is more accurate.

The invention provides an assessment interview method and device for enterprise employees and a storage medium, and the method comprises the steps: determining an initial problem in a preset assessment interview problem library based on the post type and historical performance data of the enterprise employees; questioning by using the initial question so as to receive answer information of the enterprise employees; determining the semantic integrity of the answer information; if the semantic integrity is smaller than a preset integrity threshold value, generating a question-chasing question of the initial question according to a semantic element type missing in the answer information; asking questions by utilizing the question-asking questions to receive supplementary answer information of the enterprise employees; and according to the answer information, supplementing the answer information, and determining an assessment interview evaluation result of the enterprise employees. According to the method and the device, the questioning content can be adjusted according to the post types and the historical performance data of the enterprise employees, and the questioning can be selectively performed according to the answer information of the enterprise employees, so that the questioning of the assessment interviews is more accurate and deep, and the accuracy of the result obtained by the assessment interviews is improved.

The invention relates to the technical field of voice interaction, in particular to an intelligent voice interaction method and system, and the method comprises the steps: obtaining a voice signal of a current round of question voice, and converting the voice signal into a voice text; performing weighted summation on the word vectors of the words according to the semantic importance of the words in the voice text to obtain question semantics; obtaining the prediction probability of each standard problem in the current round according to the prediction model; calculating the similarity between the question semantics and each standard question, and obtaining the questioning question of the current round according to the similarity and the prediction probability; and inputting the questioning question and the standard question and response text of each round before the current round into a text to generate a large model, obtaining the response text of the current round, and converting the response text into broadcast voice. Through the technical scheme provided by the invention, the problem proposed by the user can be accurately recognized, so that the accurate response text is obtained, and the user experience during voice interaction is improved.

The invention relates to an intelligent deep questioning method and system, and belongs to the technical field of natural language processing and intelligent evaluation. The method comprises the following steps: acquiring a candidate response, converting the candidate response into a standardized text, analyzing the text in combination with multi-dimensional monitoring data, positioning competency missing elements, generating a missing identification set, and marking a sorting question-chasing point; constructing a dynamic self-adaptive score matrix in which competency is linked with a questioning point, fusing monitoring data to calibrate scores and confidence, and generating a questioning technology intervention scheme; a virtual interviewer role library is preset, roles are dynamically matched, and the intervention logic and the missing identification set are combined to generate specialized questioning content; and when the core element capability element characterization is insufficient or the confidence coefficient does not reach the standard, generating a target element cognition questioning problem, and after element extraction and fusion, outputting a multi-dimensional competency score and a confidence coefficient report. According to the invention, the upgrading of the questioning from mechanical questioning to intelligent technical intervention is realized, the questioning targeting and the evaluation accuracy are improved, and reliable support is provided for accurate talent selection.

There is provided a computer implemented method of dynamic generation of audio content of a panel including questions asked by a moderator and responses by responders, comprising: accessing user interest(s) of a target user, accessing a social network graph that includes the target user, selecting questions correlated with the user interest(s) of the target user, selecting responses to questions by responders, wherein the responders are linked to the target user in the social network graph, wherein the responders are associated with user interests correlated with the user interest(s) of the target user, dynamically assembling audio content(s) from voice records and / or from audio recordings generating by converting text to voice, each audio content including a sub-set of the selected questions and a sub-set of the responses, and providing dynamically assembled audio content(s) for selection thereof for playing on a speaker.

The invention discloses a problem dynamic recommendation method, device and equipment based on an HITS algorithm. The method comprises the steps of obtaining a questioned question set and a to-be-recommended question set; initializing the hub degree of each asked question; initializing the authority of each question to be recommended; for each to-be-recommended question, iteratively updating the authority of the to-be-recommended question based on the initial value of the authority of the to-be-recommended question and the latest value of the hub degree of each questioned question; for each questioned question, iteratively updating the hub degree of the questioned question based on the initial value of the hub degree of the questioned question and the latest value of the authority degree of each question to be recommended; and after iteration is finished, generating a question recommendation result according to the authority of each question to be recommended. Questioned questions are abstracted as hub pages in the HITS algorithm, to-be-recommended questions are abstracted as authoritative pages in the HITS algorithm, and dynamic recommendation of the questions is realized through iterative updating of hub degrees and authoritative degrees.

The invention provides a role recommendation method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the field of natural language processing and the field of large models. According to the specific implementation scheme, the method comprises the following steps: recalling roles for replying a question to obtain a plurality of candidate roles; on the basis of the recall mode and the recall parameter corresponding to each candidate role, respectively determining the role score of each candidate role; and based on the role score of each candidate role, determining a target role for replying the question in the plurality of candidate roles.

The invention discloses an electric power standard knowledge question answering method and system and related equipment. The method comprises the steps of obtaining a question asked by a user; a semantic routing module determines to distribute the problem to a standard business scene model or a power industry large model according to the correlation between the problem and the power industry standard business scene; performing intention query and routing of various standard questions based on the questions, and distributing the questions to corresponding standard service scene models for processing; different standard service scene modules perform processing according to different types of problems, perform standard problem query of corresponding routing scenes according to the problems to obtain context reference, and send the context reference to the power industry large model; and the power industry large model judges and generates an answer according to the question. Different types of questions can be automatically answered according to user questions, standard and power grid business fusion is promoted, and the guiding effect of technical standards on power grid grassroots is enhanced.

The invention provides a knowledge question and answer method and device, electronic equipment and a storage medium, and the method comprises the steps: receiving a question input by a user, generating a question vector corresponding to the question according to the question, determining a target corpus vector matched with the question vector from a preset first database according to the question vector, and sending the target corpus vector to the user; determining a target corpus vector, determining an identifier of the target corpus vector as a target corpus identifier, determining a knowledge type corresponding to the target corpus identifier from a corpus data table in a preset second database according to the target corpus identifier, and according to the knowledge type corresponding to the target corpus identifier and a target corpus, determining a knowledge type corresponding to the target corpus identifier from a knowledge data table corresponding to the knowledge type in the second database, and obtaining query knowledge information, generating reply information corresponding to the question according to the query knowledge information, and pushing the reply information to the user. Therefore, interactive personalized question and answer service can be provided for the user, and the question and answer efficiency and accuracy are improved.

The invention discloses an intelligent customer service question answering method and system based on a large model and a database, and belongs to the technical field of customer service question answering, and the method specifically comprises the steps: extracting customer-related data from a customer service database, carrying out the preprocessing, obtaining a question asked by a customer, determining the relevance between the question asked by the customer and the customer-related data, and obtaining a question asked by the customer; the method comprises the following steps: setting a relevancy threshold value, screening to obtain first relevancy data, setting a preset time range, screening to obtain second relevancy data according to questions asked by customers in the preset time range, answering the questions asked by the customers by using a large model, generating preliminary answers, and generating personalized recommended answers based on historical dialogues and sentiment analysis. Replying the questions proposed by the client; by accessing the customer service database, the smart customer service can comprehensively understand the information of each customer, customized recommendation answers are provided according to customer portraits, the communication styles and demands of the customers are adapted, and the customer experience is improved.

The invention discloses a college interview simulation method and system and a computer program product, and relates to the technical field of simulation training, and the method comprises the steps: obtaining personal resume data inputted by a current user, and constructing a user feature database according to the personal resume data; generating an initial interview question based on the user feature database and a preset field interview knowledge base; collecting an initial answer of the current user for the initial interview question, and generating a question-chasing question according to the initial answer through a preset natural language model; and inputting the initial interview question, the initial answer, the question asking question and the corresponding question asking answer into a preset evaluation model to obtain an interview evaluation report. Through personalized question generation and evaluation feedback, the reality and effectiveness of user interview preparation can be efficiently improved.

The application discloses a kind of based on knowledge verification's trusted question and answer method, including the following steps: identifying and eliminating the reference and omission content in user's asking question, restating user's asking question, and extracting the keyword expressing user's intention.Use the keyword set after restating as query, retrieve relevant articles, calculate the relevance score of the content retrieved and the asking question, and filter out the highest relevance score multi-modal knowledge.Generating multi-modal statements about search results, multi-round, multi-path verification and reasoning are carried out on the multi-modal statements.The authenticity of the iterated multi-modal statements is verified, and the filtered and verified knowledge and evidence are integrated to generate the final trusted user reply.Compared with the prior art, the application can eliminate references and omissions by injecting associated historical context information during the question representation process, and obtain trusted multi-modal knowledge using retrieval enhancement and self-checking mechanisms, thereby more accurately answering the questions asked by the user.

An object of the system according to the embodiment is to quickly and appropriately answer questions asked by students in a recorded class video.SOLUTION: A system includes a question input unit, an analysis unit, an answer generation unit, and a provision unit. The question input part inputs a question from the student. The analysis unit analyzes the question input by the question input unit. The answer generation unit generates an answer based on the question analyzed by the analysis unit. The providing unit provides the answer generated by the answer generating unit to the student.SELECTED DRAWING: Figure 1

This invention discloses a disease retrieval method and apparatus based on a knowledge graph-enhanced large language model, relating to the fields of large language models and knowledge graph question answering technology. The method includes: constructing a question-answering dataset based on triples; storing and visualizing a knowledge graph of five specialized diseases using Neo4j; obtaining medical questions posed by users; progressively expanding the input questions to obtain sub-questions based on thought chain reasoning; performing relational path retrieval and knowledge subgraph retrieval based on a pre-trained model; fine-tuning the large language model to generate factual knowledge text based on the sampled answer subgraph; constructing a knowledge-enhanced prompt template by combining the input questions and factual knowledge, and feeding it into the large language model to obtain the final medical answer. This solution utilizes a knowledge graph-enhanced large language model, guiding it to reason and retrieve within efficient knowledge paths, enabling accurate and professional answers to users' specialized medical questions.

The objective is to provide a program and information processing device that efficiently support learning specifically for qualification exams where past exam questions are highly valued. [Solution] The system includes a past question data acquisition unit that acquires past question data containing textual information about past questions asked in qualification exams, a necessary knowledge data generation unit that generates necessary knowledge data which is textual information about the knowledge needed to answer the questions in the acquired past question data, and an important knowledge data generation unit that generates important knowledge data which is textual information about common knowledge from a plurality of necessary knowledge data generated from each of a plurality of past question data, wherein at least one of the necessary knowledge data generation unit or the important knowledge data generation unit is configured to generate information based on a pre-trained model that has been trained in advance by inputting at least knowledge data which is textual information containing the knowledge necessary to pass the qualification exam.

The invention provides an LLM-based table data question and answer method, which comprises the following steps of: performing preliminary screening on a to-be-queried disordered table to obtain M rows of data and N columns of data related to a current question, and forming an initial sub-table; on the basis of LLM, whether the data contained in the initial sub-table can answer the current questioning question or not is evaluated; if the data contained in the initial sub-table cannot answer the current question, sequentially selecting a column of data from the (N + 1) th column of data in the candidate column set until a stop condition is met, and forming a target sub-table; on the basis of LLM, description information is added to the target sub-table, and a linguistic table is formed; and based on the LLM, reasoning the linguistic table to obtain the answer content of the current question. Therefore, the context length can be shortened, the table can be simplified as much as possible, the limitation of long context input is reduced, the information density is improved, and the efficiency and accuracy of table understanding and question answering are improved.

The invention provides a method and a device for improving recall capability of an RAG system. The method comprises the following steps: S1, carrying out structured analysis on a document; s2, performing semantic hierarchical segmentation on the document content by adopting a multi-scale semantic partitioning strategy to generate a multi-granularity index unit; s3, performing semantic enhancement and optimization on the query based on a query rewriting strategy of a large language model; and S4, combining the optimized query with the multi-scale semantic index, and executing a retrieval and generation task. According to the multi-scale semantic chunk strategy, the similarity is calculated by applying a vector model, knowledge is divided into a plurality of blocks with different semantic information, and the vector representation capability is enhanced. An LLM-based question rewriting strategy can effectively identify intention words and keywords in user query, and particularly for a multi-question question, the multi-question question can be split into a plurality of independent sub-questions for multi-path recall. According to the method, the accuracy and efficiency of intelligent query in the vertical field can be remarkably improved, and the query experience of a user is improved.

A processing device (10) comprises: a question extraction unit (11) that, using an LLM (20), extracts, from a plurality of dialogues between an expert and a user, questions asked by the expert in order to elicit information from the user; a proposal extraction unit (12) that, using the LLM (20), extracts, from the plurality of dialogues, proposals made by the expert in order to solve the problem of the user; a structuring unit (13) that, using the LLM (20), converts a dialogue between the expert and the user into a flow having information indicating a question, the user's answer to the question, and the proposal accepted by the user; and a chart construction unit (14) that statistically constructs, on the basis of a plurality of the flows, one flowchart indicating the tacit knowledge and judgment of the expert.

The application provides a large model-based intelligent question and answer method and system, which mainly proceeds from two aspects: 1, through accurate question classification and matching of different answering strategies, the answering efficiency and quality of the system are effectively improved. 2, in the "guess you want to ask" question recommendation link, the relevance and logicality of the dialogue context are fully considered, and an intention attenuation coefficient is introduced to calculate the weight of the recommended question, so as to improve the accuracy of the recommendation result. For standard answering type questions, the system searches and matches effective, concise and accurate answers in the database; for non-standard answering type questions, the system generates detailed and comprehensive answer content through the RAG method. In the recommendation process of the "guess you want to ask" question, the relevance and logicality of the current question and the historical dialogue of the user are comprehensively considered, and the current intention of the user is described through the intention attenuation coefficient, and accordingly the weight of the recommended question is adjusted, and then the rationality and accuracy of the recommendation result are improved.