227 results about "Human computer dialogue" patented technology

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 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.

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.

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 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 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.

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 relates to the technical field of artificial intelligence, and discloses a smart park man-machine conversation intention recognition system based on natural language processing drive, comprising: step 1, a natural language processing module for receiving and preprocessing natural language input of a park user; 2, the intention recognition module communicates with the natural language processing module, modular design is adopted, preprocessing, intention recognition, RAG retrieval generation, chain reasoning and self-checking, interaction control and other parts are included, and parallel development and maintenance are facilitated; a multi-stage verification and rollback mechanism enables the result to be more reliable and the update to be more controllable; by means of weak / semi-supervised learning, intelligent word segmentation and multi-space embedding, rich semantics can be extracted even if labels are few; the RAG module gives consideration to high correlation and duplicate removal, and can dynamically access the newest laws and regulations and data; event triggering type updating is supported, regular increment updating is also achieved, and continuous self-adaption is achieved through closed-loop feedback; and the timely and stable delivery of the message is ensured by the channel reliability control and the push priority strategy.

1. Name of the product in this design: Dialog box body of a home decoration graphical user interface for electronic devices. 2. Intended use of this design: for use in electronic devices. 3. The key design features of this product are the graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. For electronic devices, which are conventional devices, the rear view, left view, right view, top view, and bottom view are omitted. 6. Purpose of the graphical user interface: The home decoration dialog graphical user interface is used for human-computer interaction, and the dialog box body is used for human-computer dialogue. 7. Human-computer interaction methods of graphical user interface: Human-computer interaction can be carried out through clicking, input, etc., to realize human-computer questioning and answering. 8. Description of the changing states of the graphical user interface: The main view displays the home decoration dialog graphical user interface. Questions can be entered through the input box at the bottom of the dialog box, and the questions and answers are displayed in the chat box in the middle of the dialog box. During the machine's response after a question is entered, the input box displays the current response progress, as shown in the changing state diagram 1. You can click on the status bar to view the changing state diagram 2, which displays the progress list in a pop-up window. 9. Other situations requiring explanation: This design is a partial design, wherein the pink-coated part is the part for which protection is not sought, and the other parts are the parts for which protection is sought.

The application is suitable for the technical field of man-machine conversation, and provides a multi-round conversation generation method and device based on emotion judgment and terminal equipment, the method comprising: obtaining conversation information; identifying first emotion information in the conversation information through an emotion classifier, wherein the emotion classifier identifies and classifies emotions contained in the conversation information based on a classification model, and the first emotion information represents the emotion of the conversation information; determining second emotion information based on the first emotion information by using an emotion transfer mathematical model, wherein the second emotion information is one kind of emotion information in multiple first emotion information, and the emotion transfer mathematical model is used to determine the second emotion information based on conversation time and conversation round; and outputting the current round of conversation containing the second emotion information according to the second emotion information through a conversation generation model. The application can improve the recognition accuracy of the emotion classifier and improve the experience of users using the man-machine conversation system.

The invention relates to an intelligent spoken language learning system based on man-machine conversation interaction data. The intelligent spoken language learning system comprises a scene selection system, an intelligent conversation system and a learning evaluation system. According to the intelligent spoken language learning system based on the man-machine conversation interaction data, a man-machine conversation interaction mode is adopted, user voice is converted into texts, the texts are submitted to the A I intelligent large model system for natural language processing, the texts returned by the system are converted into voice after processing is completed, each dialog can be fed back in time, and the learning efficiency is improved. The user is notified or guided to perform chat-type dialogues, grammar, pronunciation and the like of the user are corrected in real time in the dialogues, corresponding suggestions are given, and the learning improvement effect of the user is guaranteed. And a learning improvement solution is provided for students through a continuous practice scene.

Embodiments of the present disclosure provide a method and apparatus for identifying user emotions in human-computer dialogue. The method includes: obtaining first voice data, wherein the first voice data is the user's voice data in the current round; extracting a first audio feature representing the emotion and a first text feature representing the emotion from the first voice data; obtaining at least one of a second text feature and a second audio feature, wherein the second text feature is a weighted sum of the user's text features in each of the previous N rounds, and the second audio feature is a weighted sum of the user's audio features in each of the previous N rounds, where N is a positive integer; concatenating the first audio feature, the first text feature, and at least one of the second text feature and the second audio feature into a concatenated feature; and identifying the user's first emotion in the current round based on the concatenated feature using a trained multimodal model.

The invention discloses a man-machine dialogue psychological stress identification and intervention method and system, a medium and a product, and relates to the field of natural language data processing, and the method comprises the steps: carrying out man-machine dialogue based on a man-machine dialogue strategy through employing a preset man-machine dialogue model; acquiring a multi-modal signal of the detected object in a man-machine conversation process in real time, and performing feature extraction and mapping on the multi-modal signal to obtain a multi-modal fusion answer space; performing psychological stress identification on the multi-modal fusion answer space to obtain a real-time psychological stress level of the tested object; a man-machine conversation strategy of the man-machine conversation model is adaptively adjusted according to the real-time psychological stress level; and executing corresponding operation based on the adjusted man-machine dialogue strategy by utilizing the man-machine dialogue model. The accuracy and robustness of psychological pressure recognition are improved, the man-machine conversation strategy can be automatically adjusted according to the recognition result, and the emotional stability and psychological assistance value of man-machine conversation are improved.

The invention discloses an intelligent fish tank for children enlightenment education, and the fish tank comprises a fish tank main body which is internally provided with a voice interaction module, an audio playing module, an image recognition module and a wireless transmission module; wherein the voice interaction module is used for carrying out dialogue interaction with children, the audio playing module is used for playing audio, the image recognition module is used for recognizing expressions and objects, and the wireless transmission module is used for carrying out data transmission with external terminal equipment. Intelligent upgrading and child-friendly design of the fish tank equipment are achieved, and the technical defect that traditional equipment lacks interaction ability is overcome. The system can recognize the emotional state of a user and make intelligent feedback, establishes a natural AI intelligent man-machine conversation mechanism, effectively improves the interaction experience of the child user, enables the child to carry out natural science popularization and accompanying conversation in the fish culture and observation process, is a child enlightenment education tool with interaction and accompanying functions, and improves the interestingness of the child. And the requirements of families on children education and accompanying are met.

The embodiment of the invention provides an online man-machine conversation optimization method, system and device, electronic equipment, a storage medium and a program product. In the scheme provided by the embodiment, when a first dialogue generation model executes a streaming response based on user input data, a currently generated first dialogue text fragment is subjected to repeated detection, and when it is detected that repeated content exists in the first dialogue text fragment, the first dialogue generation model is controlled to interrupt execution of the streaming response; in addition, reply generation constraint information is determined according to the repeated content in the first reply text segment, and then reply generation cue words are generated based on the reply generation constraint information and the non-repeated content in the first reply text segment. And after the dialogue generation cue word is input into the first dialogue generation model, the first dialogue generation model is triggered to determine a dialogue generation starting point according to the non-repeated content in the first dialogue text fragment, and streaming response is continuously executed from the dialogue generation starting point.

The present application relates to the field of human-computer dialogue technology, and specifically relates to an electronic device, a semantic parsing method, a medium, and a human-computer dialogue system thereof. The semantic parsing method includes: obtaining corpus data to be parsed; calculating the degree of intention correlation between the characters included in the corpus data to be parsed and the intention represented by the corpus data to be parsed, and the degree of slot correlation between the characters and the slot represented by the corpus data to be parsed; predicting the slot of the corpus data to be parsed based on the semantic information of the character, the semantic information of the context of the character, as well as the degree of intention correlation and slot correlation of the character. By identifying multiple intentions close to the user's true intention from the user's speech, and then using the identified multiple intentions to predict slot information, the accuracy of slot filling is improved, and accordingly, the speed or efficiency of slot filling is also improved, thereby improving the accuracy of semantic parsing in human-computer dialogue.

The invention relates to a control method and device of clothes processing equipment and the clothes processing equipment. The control method of the clothes processing equipment comprises the steps that clothes information of clothes to be processed is acquired based on man-machine conversation; obtaining a target processing program matched with the clothes information and a target processing parameter corresponding to the target processing program; and processing the to-be-processed clothes according to the target processing program and the target processing parameters. According to the embodiment of the invention, the method can guide the user to provide the expression related to the to-be-processed clothes through the man-machine conversation, converts the oral and fragmented expression of the user into the formatted clothes information, and recommends the target processing program and the target processing parameters suitable for the to-be-processed clothes for the user according to the clothes information. Therefore, the problem that the user does not know which processing program and parameter are selected to process the to-be-processed clothes in daily life is solved, and better clothes processing service is provided for the user.

The invention belongs to the technical field of intelligent service robots, and discloses a wheelchair type robot capable of automatically climbing stairs and fusing bionic motion control and a deep learning algorithm, which is particularly suitable for the vertical movement requirement of the disabled and the going-out requirement of the blind in a barrier-free environment deficiency scene. Comprising a chassis, a seat, a front base, a rear base, a driving assembly, an environment information collecting assembly, a calculation control unit, a power supply assembly, a man-machine conversation assembly with a display screen and an operation handle.

The present invention discloses a method for implementing a process-driven dialogue system and a dialogue robot based on a workflow engine. Before designing a process-driven dialogue system, business requirement analysis should be carried out first, and at the same time, corresponding corpus information such as dialogue intents, corpus, slot values, etc. should be prepared. Secondly, break down the complex business into processes, and then create a BPMN file to draw a dialogue flow chart. After that, build a process engine, deploy the corresponding dialogue flow chart, upload the corresponding corpus to the database, and write the corresponding corpus into the natural language understanding configuration file of the traditional dialogue system. Train the traditional dialogue system while deploying. After training is completed, conduct tests. Finally, human-machine dialogue can be carried out, and then the dialogue system can be iteratively optimized. The present invention uses a process engine and a traditional dialogue system, and can quickly build a dialogue robot with low code, and ordinary users can also quickly build a dialogue system.

The invention relates to the technical field of artificial intelligence, in particular to a papermaking employee virtual practical training method and system combined with a large model. According to the papermaking employee virtual practical training method and system combined with the large model, immersive interaction with a high-fidelity virtual papermaking production line is achieved through data collection, virtual simulation and large model interaction and reasoning. According to the method, a large-scale language model is introduced, so that the method has strong natural language understanding, deep reasoning and multi-modal information fusion capabilities, complex and changeable virtual environment states can be analyzed in real time, and intelligent diagnosis and guidance are performed in combination with natural language questions of employees; and even non-preset or multi-factor coupled fault scenes which cannot be processed by a traditional rule system can be processed, man-machine dialogue interaction similar to experienced experts is realized, and the intelligent level of the system on solving complex problems is remarkably improved.