122 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 provides an agent evaluation method and device based on multiple rounds of dialogues, electronic equipment and a medium. The method comprises the following steps: constructing an evaluation sample set only containing an input sample-output sample pair; simulating a user to ask a to-be-evaluated intelligent agent according to an input sample in the evaluation sample set on the basis of the confrontation intelligent agent; determining a feedback result of the current round of dialogue based on the judgment agent according to the questioning question, the corresponding questioning result and an output sample corresponding to the input sample; and determining an evaluation result of the to-be-evaluated intelligent agent according to the current dialogue round and the corresponding feedback result. According to the method, an adversarial agent is introduced to simulate a user to ask a to-be-evaluated agent, and meanwhile, a judgment agent is introduced to judge the reasonability and accuracy of question answering of the to-be-evaluated agent in each round of dialogue, so that the problems that time and labor are consumed when an evaluation data set is constructed by an existing agent evaluation method; according to the method, the problems that the intellectual property is poor and the middle interaction process cannot be effectively simulated are solved, comprehensive evaluation of the intellectual body in the aspects of understanding ability, coherence, reply quality and the like is achieved, and powerful support is provided for optimization of an intelligent dialogue system.

The invention relates to the field of artificial intelligence, and discloses an automatic interview method and device based on artificial intelligence, computer equipment and a storage medium, and the method comprises the steps: obtaining keyword information for a delivery post in a resume file of a target object, and employing a large language model based on the keyword information and post requirements of the delivery post, generating a question for the target object; generating a corresponding questioning video based on the questioning question, obtaining the answer content of the target object for the questioning video, and determining an evaluation result corresponding to the answer content based on a preset evaluation standard by adopting a large language model; and generating a corresponding evaluation video based on the evaluation result to enable the target object to obtain the evaluation result, thereby realizing automatic generation of the question question and answer content evaluation result in the interview process, and interacting with the target object in a mode of generating the question video and the evaluation video, so that the interview process does not need an interviewer, and the interview efficiency is improved. Therefore, the automation degree of the interview process is improved.

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 relates to an intelligent medical question-answering system based on a large model, and belongs to the technical field of intelligent medical question-answering. The system comprises a basic module which is used as middleware between a user and the LLM and serves as a routing gateway, and through the basic module, the system can be connected to any supported LLM without modifying any code; the function module is used for analyzing the questions asked by the user, constructing proper prompt words to ask the LLM, formatting the response of the LLM and returning the formatted response to a system user interface; the memory module is used for realizing association between the case data of the user and the historical dialogues of the user by means of a vector database; when the big model is helped to answer, specific answers can be made to specific users, and meanwhile, historical dialogue information is owned; and the safety module is used for carrying out safety prompt.

The invention relates to the technical field of intelligent robots, and provides an intelligent question answering method and device, electronic equipment and a storage medium, the intelligent question answering method comprises the following steps: obtaining voice information corresponding to a question asked by a user, and carrying out text conversion on the voice information to obtain a question text of the user; obtaining a user portrait model of the user, wherein the user portrait model is generated according to at least one of a mood feature, an emotion feature, a dialogue habit feature and an age feature of the user; and according to the question text and the user portrait model, querying a plurality of corresponding candidate answers, further determining a target answer from the plurality of candidate answers, performing secondary processing on the target answer to enable the processed target answer to accord with the cognitive level of the age group of the user, and outputting the target answer. The accuracy and correlation of question answering are effectively improved, and the user experience and loyalty are improved.

The invention discloses a question answering method and device based on a large model, a storage medium and electronic equipment. And determining a domain knowledge base, dividing each document contained in the domain knowledge base, and determining each text block according to a division result. And for each text block, predicting a question asked by the text block as a prediction question corresponding to the text block, and determining index information according to each text block and the prediction question corresponding to each text block. And when a target question of the user is received, matching the target question with the index information to obtain related information. Because the index information is determined according to the prediction problem corresponding to each text block and each text block, that is, the index information comprises multiple types, the related information in the method is determined by integrating the similarity between the target problem and the multiple types of index information, the correlation with the target problem is stronger, and the accuracy of the target problem is improved. Domain knowledge with higher correlation with the target question can be provided for the large model, and the large model is guided to output a more accurate target answer.

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.

One or more embodiments of the invention provide a business question-answering system and method, electronic equipment and a medium. The system comprises a question receiving module, a public knowledge acquisition module and an answer generation module. The question receiving module is used for receiving a business question asked by a user; the public knowledge obtaining module is used for obtaining first knowledge matched with the business problem from a public knowledge base based on the business problem, and the first knowledge is obtained by summarizing multiple pieces of public knowledge with association in the public knowledge base; and the answer generation module is used for generating answer information for the business question according to the business question and the public knowledge.

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 an automatic data labeling method and device and a storage medium, relates to the technical field of data processing, and is used for automatically labeling data and improving labeling efficiency and labeling quality at the same time. The method comprises the following steps: manually pre-labeling data of each task to obtain pre-labeled data; automatically deriving a plurality of different cue word schemes for each task; according to the cue word schemes, all tasks are imported into a multi-mode large model for independent question asking, and first output results corresponding to the cue word schemes are obtained; performing result extraction by adopting an unsupervised voting algorithm to generate a voting statistical result of each task; if the number of the positive correlation marks reaches or exceeds an election threshold value, judging that the task is a positive correlation sample; inputting the pre-annotated data and the positive correlation sample into a single-mode small model for training iteration; and automatically labeling the target data to be labeled in batches by using the single-mode small model, and outputting a labeling result.

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 discloses a method for checking and asking necessary questions based on a lora fine tuning model, which comprises the following steps of: firstly, extracting a question data set marked as unnecessary questions from a database, and then respectively carrying out semantic analysis on questions asked by law enforcement officers and each unnecessary question in the question data set by adopting the lora fine tuning model; according to the method and the device, the question asking accuracy and the work efficiency of the law enforcement officers can be effectively improved, unnecessary repeated labor is reduced, and the efficiency and the quality of law enforcement and case handling are improved.

The invention discloses a user question answer output method for a hot rolling production line intelligent operation and maintenance online question-answering system. The method comprises the following steps: obtaining operation and maintenance documents in the iron and steel industry, and screening to generate expert question-answering pairs; carrying out vectorization processing on the expert question-answer pairs and then storing the expert question-answer pairs in a vector database; performing vectorization processing on professional documents of the iron and steel industry and fault maintenance and disposal reports of a production line over the years, and loading the vectorized documents and the fault maintenance and disposal reports into a vector database; when a user asks a question, vectorizing the question of the user, comparing the question of the user with an expert question-answer pair pre-stored in a vector database, and directly outputting an answer of the expert question-answer pair as an answer of the question of the user when the similarity is high; if the similarity is low, inputting the user question and the similar expert question and answer pairs and paragraph texts in the vector database into a large language model, and summarizing and outputting a congruent answer of the user question by the large language model according to the user question and the similar expert question and answer pairs and paragraph texts. According to the method, the accuracy of outputting answers for the user questions is greatly 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 relates to the technical field of tourism application, and discloses a scene-driven large model travel question and answer service system, which comprises a priori knowledge system, a posterior knowledge system, a von Neumann thinking architecture and a knowledge connection mechanism, and can automatically call corresponding knowledge and generate personalized answers according to the specific situation of questions asked by a user. Wherein priori knowledge is stored in a knowledge base or memory, system performance is improved, tokens are effectively saved, and reaction time is shortened. By combining an advanced knowledge system and an artificial intelligence technology, the intelligent service level in the text travel field can be effectively improved, and personalized and accurate information support is provided for users.

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.