307 results about "Human computer dialogue" patented technology

The invention relates to the field of natural language understanding in an artificial intelligence technology man-machine dialogue system, in particular to a psychological accompanying dialogue method based on multi-agent collaboration and a storage medium. The psychological accompanying dialogue with individuation, high naturalness and accurate emotion recognition is provided, the user experience is effectively improved, and the psychological support requirement of the user is met. According to the main scheme, the method comprises the steps of performing multi-mode emotion evaluation on user input to obtain user emotion state description; retrieving related dialogue states and external knowledge from a shared memory module according to user questions and user emotional state description; according to the retrieved dialogue state and external knowledge, the strategy planning module generates a reply strategy to obtain a strategy group; the dialogue content generation module calls a corresponding vertical domain agent according to the generated reply strategy to generate specific reply content; and storing the user questions and model replies in the current round into the short-term memory of the shared memory module, and updating the long-term memory. The method is used for AI dialogues.

The invention relates to the technical field of artificial intelligence, and discloses a man-machine dialogue history and semantic retrieval-based prompt automatic construction method, which comprises the following steps of S1, performing text preprocessing on historical dialogue data and user input, including dynamically generating query keywords, verifying and analyzing a file uploaded by a user, and extracting core contents; s2, utilizing a pre-training language model to map the historical dialogue and the user request into semantic vectors, matching in combination with the keyword weight, and constructing a historical vector set and a user request vector; and S3, extracting a context fragment most relevant to the user request from the historical dialogue vector based on a convex optimization method. The context information highly related to the user request is accurately extracted through the sparse optimization technology, and the background information, the user request and the generation instruction are organically integrated in combination with the dynamic Prompt template design, so that the semantic correlation and context coherence of the generated reply are improved, and the interference of redundancy and irrelevant information is avoided.

The invention discloses a cockpit scene recommendation method and device, a storage medium, a program product and a vehicle, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the preprocessing of man-machine conversation text data of a voice burying point log of the vehicle, obtaining the natural language description of a user, carrying out the similarity screening in a preset scene knowledge base, and obtaining a voice burying point log of the vehicle; obtaining corresponding scene knowledge; splicing the user natural language description with the scene knowledge to obtain a scene recommendation prompt word; and performing scene action reasoning on the scene recommendation prompt word through the large model agent, and outputting a scene action execution list corresponding to the scene knowledge. According to the method, man-machine conversation text data is converted into user natural language description, corresponding scene knowledge is screened, scene action reasoning is carried out based on the powerful text understanding and generating capacity of a large model agent, and the intelligent scene recommendation service of the cockpit can be automatically realized according to the voice description of a vehicle owner; and the accuracy of cabin scene recommendation is improved.

The embodiment of the invention discloses a household appliance fault diagnosis method, terminal equipment and a server. The terminal equipment execution method comprises the following steps: acquiring fault description data input by a user; sending a first query instruction to a server, wherein the first query instruction is used for instructing the server to query target knowledge from a knowledge base according to the fault description data; the knowledge base is generated according to a fault tree structured file, the fault tree structured file comprises at least one fault tree associated with the target household appliance, the fault tree comprises a plurality of nodes, and the nodes are configured with a fault representation image, a fault log, a repair mode and a fault phenomenon corresponding to one fault type; generating prompt word information according to the fault description data and the target knowledge and sending the prompt word information to a server; displaying corresponding response content on the man-machine conversation page according to response information sent by the server; the response information comprises the fault type and the recommended repair mode of the target household appliance. Therefore, the fault diagnosis accuracy and the fault repair efficiency of the household appliance can be improved.

The invention provides a man-machine dialogue method based on a large language model, and the method comprises the steps: starting a dialogue from a starting node of a predefined dialogue process, and transmitting initial problem information configured by the starting node to a user; initial reply information input by the user for the initial question information is received, and a first intention recognition node is determined based on the initial reply information; displaying a node question corresponding to the current node to the user, and receiving intention reply information input by the user for the node question; determining subsequent nodes according to the intention reply information and a candidate option set predefined by the current node through a large language model; and displaying reply information corresponding to the reply node to the user until the determined node is the reply node. The combination of the graphical process and the large language model enables the dialogue path to advance according to the preset logic, reduces the risk of deviating from the core target, and enhances the naturalness and flexibility of the dialogue by using the semantic understanding capability of the large language model, so that the reply of the user can be better understood.

The invention discloses a man-machine interaction method and electronic equipment, and relates to the field of AI. The method is applied to the electronic equipment and comprises the steps that a user instruction is received, multiple target APIs are obtained according to a scene intention in a scene where the user instruction represents the scene intention, and the multiple target APIs correspond to the scene intention. And outputting a result of calling the plurality of target APIs, wherein the result of calling the plurality of APIs can be used for realizing a user demand corresponding to the scene intention. Thus, the electronic equipment can directly output the API calling result meeting the user requirement under the scene intention based on the scene intention of the user, the API is the direct exposure of the application function, the more abundant, comprehensive and convenient response result is achieved by directly outputting the result of calling the multiple APIs, the user requirement can be met, and the man-machine conversation effect is improved.

The invention discloses a spoken English man-machine conversation interaction training method and system, and relates to the technical field of spoken English training, spoken English audio signals of a user are monitored in real time through a microphone, and the frequency spectrum information of the audio signals is refined into each frame of spectrogram and cepstrum coefficient feature vectors by using Fourier transform and feature extraction technologies; therefore, the voice signal characteristics of the user can be completely captured; three core dimensions of semantic similarity, grammatical structure similarity and fluency are incorporated into an evaluation model, a comprehensive oral English training evaluation system is formed, a text sequence is generated by analyzing a time sequence dependency relationship through a convolutional neural network, and the oral English training evaluation model of a deep learning technology is combined, so that the oral English training evaluation accuracy is improved. According to the method, the expressions of the user in the aspects of semantics, grammar and fluency can be accurately measured, the oral training level of the user can be comprehensively judged by the system by comparing the comprehensive evaluation coefficient with the preset qualified threshold value, and a personalized learning guide is provided according to a result.

The invention provides an RAG-based man-machine conversation method and device, a storage medium and a program product, and the method comprises the steps: obtaining conversation context information associated with an input question, and completing the input question according to the conversation context information to determine a rewriting optimization question; performing RAG retrieval according to the rewriting optimization questions to determine at least one corresponding related background knowledge, and constructing large model cue words according to the related background knowledge and the rewriting optimization questions; and inputting the big model cue word into the man-machine conversation big language model, and determining a reply answer for the input question according to the output content of the man-machine conversation big language model. Therefore, through continuous tracking and analysis of the dialogue context information and rewriting of user questions, the retrieval accuracy, the generation intelligence and the dialogue consistency of the system are effectively improved, and then the application performance and the user experience in a multi-round human-computer interaction scene are improved.

The invention provides an intelligent tour guide man-machine conversation method and system based on knowledge graph retrieval, and relates to the technical field of artificial intelligence and natural language processing. The method comprises the following steps: collecting relevant data of scenic spots in a preset range, carrying out structured processing on all the relevant data of the scenic spots, and constructing a knowledge graph in the tour guide field; establishing indexes for entities and relationships in the knowledge graph, and introducing an adaptive query optimization mechanism; selecting a pre-trained language model and performing LoRA fine tuning on the pre-trained language model to obtain a fine tuning language model; a user initiates a dialogue request; the dialogue request accesses the fine tuning language model to obtain a preliminary answer result, and accesses the knowledge graph to obtain knowledge graph information; and matching and fusing the preliminary answer result and the knowledge graph information, and outputting a corrected answer result. According to the invention, knowledge can be effectively fused between the tour guide explanation field and the general field, and the accuracy and comprehensiveness of answers are improved.

The invention provides a man-machine conversation method and device, a storage medium and a program product. The method comprises the steps that whether an input problem relates to at least one target professional knowledge point or not is detected; when the input question relates to at least one target professional knowledge point, constructing a large model reading understanding cue word according to each target professional knowledge point and the input question; and inputting the large model reading understanding cue word into the large language model to determine a corresponding reply answer. Therefore, through an intelligent target knowledge point detection and cue word construction mechanism, the dialogue system can automatically adjust the answer strategy according to questions in different fields, and more accurate and professional answers can be provided for users.

According to the man-machine conversation model training method and device based on the generative pre-training model provided by the invention, the model is trained by using the data sets of different dimensions, so that the applicability of the model to the preset field is improved. Moreover, a reinforcement learning data set and a score output by the reward model are used to train the model to obtain a dialogue model, and the reinforcement learning data set comprises a question belonging to a preset field, a first answer corresponding to the question, a second answer corresponding to the question, first security data and second security data. The first security data indicates that the first answer meets the use rule, and the second security data indicates that the second answer does not meet the use rule, so that the fusion training is beneficial to improving the stability, accuracy and applicability of the model. The dialogue device obtained based on the method has relatively high understanding and reasoning capabilities for data in a preset field, and also has relatively high anti-interference capability.

The present invention relates to the field of big data technology, and discloses an information recommendation method based on user portrait, comprising the following steps: receiving a human-computer dialogue chat record in a first scenario in real time through a log collection system Flume; performing desensitization processing on the chat record to obtain first data; performing stop word removal processing on the first data to obtain second data; extracting keywords of the second data through a preset word graph; obtaining a first tag set according to the keywords; removing duplicates from the first tag set data to obtain a second tag set; generating a user interest portrait based on the second tag set, and storing the user interest portrait in a database; receiving a recommendation instruction, and obtaining a user interest portrait according to the recommendation instruction; obtaining information to be recommended corresponding to the recommendation instruction according to the user interest portrait. The present invention also provides an information recommendation device, equipment and storage medium based on user portrait, and the information recommendation method based on user portrait provided by the present invention improves the accuracy of user portrait.

A human-machine conversation method and apparatus, a device, and a storage medium. The method comprises: acquiring a user statement of a current round; determining, from one or more candidate tables, one or more target tables that match the user statement; parsing the user statement of the current round to obtain a first query statement; according to the first query statement, querying from the one or more target tables to obtain target data; and generating a system reply of the current round according to the target data.

The invention relates to the technical field of semantic recognition of man-machine conversation, and provides an AI agent interaction method and system based on semantic analysis, and the method comprises the steps: collecting voice data of a user, converting the voice data into text data, and obtaining the collection time of each character in the text data; determining the information complexity of the text data of the user and the repeated interaction rate of the voice data, performing syntactic analysis on the text data, and screening the deviation voice data of the user in combination with the entity number difference, the repeated interaction rate and the information complexity; determining a voice habit characteristic value of the user and a real intention probability of deviation voice data; and according to the real intention probability of the deviation voice data, re-screening voice data from the deviation voice data, and according to the re-screened voice data and the deviation voice data, realizing AI agent interaction based on semantic analysis. According to the method, the accuracy of AI agent semantic analysis and user real intention recognition can be improved.

The invention provides a man-machine conversation-based software cost intelligent evaluation interaction method and system, and relates to the technical field of software cost evaluation, and the method comprises the steps: obtaining project description information, carrying out intention recognition and entity extraction, and carrying out semantic matching in combination with a knowledge graph to obtain candidate cost references; according to the method, the estimation reference value is generated based on multi-dimensional similarity calculation and weight self-adaptive adjustment, user feedback correction is supported, accurate evaluation and dynamic adjustment of the software cost are achieved, and the accuracy, transparency and efficiency of software cost evaluation are improved.

The invention provides an intelligent reply method for a user service, a medium and electronic equipment, and can input current input information and historical context information of a user into a deep learning model to obtain an identified user intention, and then input the user intention and a question decomposition instruction into the deep learning model to obtain decomposed sub-intentions, thereby improving the user service reply efficiency. Acquiring skill indication information according to the sub-intention, inputting the sub-intention, the skill indication information and the user associated information into a deep learning model to acquire reply content, inputting the reply content and a content retouching instruction into the deep learning model, acquiring the reply content after combined retouching, and feeding back the reply content to the user, so that the user intention can be accurately understood; and accurate reply contents and services related to the business are provided according to the user association information in combination with the user demand, so that the problem that the intelligent customer service cannot accurately reply the intention of the user in combination with the business information in the prior art is solved, man-machine conversation can be more natural, smoother and more concise, and the conversation efficiency and service experience of the user are effectively improved.

The invention relates to the technical field of official document exchange, and discloses an intelligent voice interaction official document exchange system based on a large model and a man-machine conversation control method, and the system comprises a confidential document management subsystem which is used for the transmitting and receiving business management of a document; the voice interaction module is used for receiving and analyzing a user voice instruction; the control module is used for scheduling each sub-module in the system to complete an operation instruction; the data processing module is used for analyzing and processing contents, states and circulation of documents and letters based on a large model; and the feedback module is used for feeding back the execution result to the user in a voice or text form. According to the technical scheme, a three-member management platform is adopted to perform labor division management and real-time safety auditing on the system permission, the technical effect of improving the system permission distribution reasonability and the overall operation safety is achieved, and compared with the technical scheme of concentrated permission and lack of effective auditing and safety management in the prior art, the technical scheme has the advantages that the system permission distribution reasonability and the overall operation safety are improved. And the defects of permission abuse and security vulnerability hidden dangers are overcome.

The invention discloses a man-machine dialogue method and system based on retrieval enhancement, and relates to the technical field of artificial intelligence. The man-machine dialogue method comprises the steps that firstly, innovative example retrieval is introduced, personalized example pairs with rich information are selected, and then four cognitive relationships are recognized through a preset common sense conversion model; the understanding of the situation and implicit psychological state of the help seeker is deepened. Finally, the multi-source knowledge is fused through the support decoder, it is ensured that response generation has the same psychology and cognitive consciousness, and the dialogue experience feeling of the user is improved.

The invention relates to a man-machine conversation method and device, computer equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: for a to-be-processed problem sent by a user terminal, obtaining a vector similarity corresponding to the to-be-processed problem; the vector similarity is determined based on the vector of the to-be-processed question and the vector corresponding to each question and answer text in the question and answer library; under the condition that the vector similarities are smaller than a preset threshold value, determining a target classification result corresponding to the to-be-processed problem based on the large model and the classification prompt word; the target classification result is one of question and answer texts in a question and answer library and performance functions in a function library; under the condition that the target classification result is the performance function in the function library, performing parameter extraction on the to-be-processed problem based on the large model and the parameter cue word to obtain a function parameter corresponding to the performance function; and determining an output result of the performance function through the function parameter, and returning the output result to the user terminal. By adopting the method, the reply accuracy can be improved.

The invention discloses a task type multi-round dialogue system enhancement method based on a general intention. The method comprises the following steps: loading a general intention recognition model of a task type multi-round dialogue; obtaining current user input and a corresponding man-machine conversation history; inputting the current user input and the corresponding man-machine conversation history into a general intention recognition model, and outputting a corresponding general intention list by the model; extracting the intention and task elements of the dialogue from the general intention enhanced task dialogue large model input by the current user and the corresponding man-machine dialogue history, and generating a corresponding reply; and repeating the steps until the dialogue task is completed, and ending the dialogue. According to the method, the capability of a large language model thinking chain is utilized, a general intention is introduced into a task type multi-round dialogue system in a large language model era to serve as an auxiliary means, the intention recognition capability in the task type multi-round dialogue system is effectively improved, and the success rate of the whole dialogue of the system is effectively increased.

The embodiment of the invention relates to a man-machine conversation method and device, electronic equipment and a storage medium. The method comprises the steps that a consultation statement input by a user is acquired; determining whether the current man-machine conversation state is in a consultation task internal state or not; if in the consultation task internal state, based on the consultation statement, determining consultation key parameters; generating reply statements of the consultation statements based on the consultation key parameters; if the consultation statement is not in the consultation task internal state, determining a consultation intention category of the consultation statement based on a pre-trained intention classification model; and according to a preset reply statement generation mode corresponding to the consulting intention category, utilizing a preset language model to generate a reply statement of the consulting statement. According to the embodiment of the invention, the accuracy and universality of man-machine conversation are improved.

The embodiment of the invention provides a large model tool calling data enhancement method, system and device and a medium. The method is applied to the technical field of man-machine conversation systems and artificial intelligence data enhancement, and comprises the steps of obtaining historical service data and corresponding track data to form historical parameters; carrying out association binding among the historical parameters, and constructing dialogue chain data; preprocessing the historical parameters and the corresponding dialogue chain data, judging whether the dialogue chain data is complete or not, and if yes, entering the next step; comparing the historical parameters with the reference data, and dividing the data into completely matched data, partially matched data and unmatched data; performing similarity retrieval on the dialogue chain data corresponding to the partial matching data to obtain historical correction data; and correcting the partial matching data according to the historical correction data to obtain correction data, and outputting the correction data, the complete matching data and respective corresponding track data and session chain data. According to the efficient and controllable data enhancement method disclosed by the invention, the large model tool calling capability can be improved.

A human-computer interaction method and an electronic device, relating to the field of AI. The method is applied to an electronic device, and comprises: receiving a user instruction, and in a scenario in which the user instruction represents a certain scenario intent, acquiring multiple target APIs according to the scenario intent, the multiple target APIs corresponding to the scenario intent. Then, a result of calling the multiple target APIs is outputted, and the result of calling the multiple APIs can be used to implement a user requirement corresponding to the scenario intent. Thus, an electronic device, on the basis of a scenario intent of a user, can directly output a result of calling an API which meets user requirements under the scenario intent, and the API is directly exposed by an application function. A result of calling multiple APIs is directly outputted, so that a richer, more comprehensive, and more convenient response result is achieved. In addition, user requirements can be met, and a human-computer conversation effect is improved.

The application relates to the technical fields of artificial intelligence and man-machine natural language dialogue, and provides a general memory management method and system based on a language model, which extracts memory information needing to be memorized from dialogue content with the language model, generates temporary memory, integrates the temporary memory to obtain persistent memory about a dialogue user, and is integrated into a dialogue process of the dialogue user and the language model to form memory of the dialogue user, so that the dialogue of the dialogue system is controlled, memory type classification management is performed according to the importance of the memory information in the persistent memory, so that the persistent memory contains short-term memory and long-term memory, the importance of the memory information in the persistent memory is updated and managed, and the short-term memory and the long-term memory are forgotten and upgraded, so that flexible and efficient storage and calling of user memory independent of the model type are realized, and the humanized, continuous and personalized dialogue experience in the man-machine dialogue process is improved.

The invention discloses a simulated inquiry interaction analysis method, device and equipment based on man-machine conversation, and relates to the technical field of artificial intelligence and medical treatment, and the method comprises the steps: obtaining inquiry conversation data generated by interaction between a user object and a virtual user; performing multi-dimensional interaction analysis on the inquiry dialogue data based on a preset medical knowledge graph and preset diagnosis information to generate a corresponding evaluation index and a corresponding index score; and generating and outputting an evaluation report containing the index score and the user object-oriented inquiry dialogue improvement suggestion. According to the method, the objectivity and the accuracy of an evaluation result are ensured, prejudice and inconsistency caused by subjective experience difference in manual evaluation are overcome, the evaluation result is accurate, and a user can clearly know the expression level of the user in each dimension and can also obtain a targeted inquiry optimization direction.

The invention provides a scenic area guide system based on a smart phone APP, and the system comprises a user registration and login module which is used for supporting a tourist to carry out registration and login through a plurality of modes, such as a mobile phone number, an email box, and a social media account; the personalized route planning module is used for collecting the body information of the tourist and planning an optimal touring route for the tourist by comprehensively considering the real-time condition of the scenic spot; the real-time positioning and navigation module is used for acquiring the position information of the tourist in real time, displaying the current position of the tourist on a map and providing voice navigation service for the tourist; the man-machine conversation module is used for providing an intelligent customer service function; the emergency rescue and health monitoring module is used for monitoring health data of tourists in real time by being connected with the intelligent wearable equipment; and the environmental protection action participation module is used for pushing scenic area environmental protection knowledge, ecological protection ideas and related policies and regulations, and improving the environmental protection awareness of tourists. According to the invention, personalized, convenient and safe scenic spot touring experience with environmental education significance can be provided for tourists.

The embodiment of the invention provides a man-machine interaction method and device. The method comprises the following steps: performing emotion semantic analysis on obtained man-machine conversation data of a current conversation user, and determining a current emotion state of the current conversation user; and generating and displaying a corresponding visual element based on the current emotional state of the current dialogue user. According to the technical scheme provided by the embodiment of the invention, different emotional states of the user can be mapped to different visual elements, and visual feedback expression of the emotions of the user is realized, so that personalized and vivid feedback can be performed on the user in a dialogue process between the user and the AI.

The invention relates to an interactive content generation method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining a dialogue between a user and a virtual object, determining a plot node in the dialogue, and generating an initial plot tree; generating a plurality of candidate plots at the plot node, and generating a plot branch leading to each plot result according to a target plot selected by the user in the plurality of candidate plots and at least one plot result input by the user; the plot branches and the initial plot tree are combined, a target plot tree is obtained, and the target plot tree serves as a script. According to the method, the plot tree is generated in a man-machine conversation mode, and the generation efficiency can be improved; according to the method, key plot nodes are extracted, various plot trends are predicted, and a plurality of plot branches are automatically generated according to the plot trends and the outcome selected by the user, so that dynamic plot branch generation can be realized, and rich diversity is provided for content creation.

The invention discloses a large-model-driven digital human real-time interaction method, which relates to the technical field of digital humans and comprises the following steps: S1, inputting a question text through a man-machine conversation front end and sending the question text to a large-model rear end; s2, the large model rear end performs large model reasoning according to the question text and the knowledge base, generates an answer abstract and a complete answer, and sends the answer abstract and the complete answer to the digital human rear end and the man-machine conversation front end for display; s3, the classifier generates a plurality of classification labels according to the answer abstract; the digital human rear end retrieves and matches a preset video library according to the classification label to obtain a target preset video, and sends the target preset video to the man-machine conversation front end; s4, the digital human processing engine generates a digital human abstract video according to the answer abstract, and pushes the video to the player; and the player sequentially plays the target preset video and the digital human abstract video. And finally, smoother digital human real-time question and answer interaction experience is realized, and distortion experience is reduced.

A human-computer dialog method is provided, and includes: A terminal device (200) receives a speech signal; the terminal device (200) obtains a first instruction based on the speech signal; the terminal device (200) sends the speech signal or a text of the speech signal to a server (100), where the server (100) is configured to obtain a second instruction based on the speech signal, and the text of the speech signal is obtained by the terminal device (200) based on the speech signal; the terminal device (200) receives the second instruction sent by the server (100); and when a first obtained instruction is not capable of being directly executed, the terminal device (200) selects a target instruction from the first instruction and the second instruction for execution, where the target instruction is the first instruction or the second instruction. The terminal device (200) may select the target instruction from the two instructions, so that accuracy of a speech response can be improved.