6129 results about "Interaction method" patented technology

The invention discloses an intelligent multi-modal virtual digital human interaction system based on an AI language large model. The system comprises a high-authenticity face generation module; the high-authenticity face generation module uses an AdaAN network, based on adaptive feature fusion and voice driving and time sequence modeling of voice features, feature information related to voice is extracted, the extracted voice features are processed through a deep neural network, it is ensured that the voice and facial expressions are highly aligned in time and space, and the face recognition accuracy is improved. Collecting a bio-electricity signal, mapping the signal to facial muscle movement, generating a final facial expression, and interacting with a user; the system further comprises an intelligent interaction module, a training optimization and efficient generation module, an efficient integration module, a multi-modal data acquisition module, an AI large model core processing module, a digital human image generation and driving module, an interaction scene adaptation module and a feedback optimization module. The invention further discloses a multi-mode digital human interaction method which has wide application value.

The invention relates to the technical field of building data interaction, in particular to a building engineering interaction method and system based on a BIM model and a medium. The method comprises the following steps: collecting construction site environment parameters and structure response data by using a distributed sensor network; preprocessing the data by an edge computing node; generating standardized environment data and key structure indexes; comparing environment threshold values to identify abnormal events; the method comprises the following steps of: displaying spatial positioning and risk levels through a visual interface, receiving a regulation and control instruction input by a user, dynamically adjusting construction parameters of a BIM model, generating an optimized construction progress scheme, comparing the optimized scheme with an original model to identify a construction conflict area, and generating a conflict resolution report. Through the advanced monitoring technology and construction management technology, the response speed and decision-making efficiency of the building engineering project are improved, and the safety guarantee and resource utilization efficiency in the construction process are improved.

The invention relates to a multi-modal intelligent robot system and an interaction method, and belongs to the technical field of robot digital data processing, and the method comprises the following steps: S1, multi-modal input collection; s2, carrying out multi-modal input preprocessing; s3, performing multi-modal information fusion, inputting the information into a pre-trained multi-modal feature fusion model, and performing feature fusion on each modal feature through a quantitative feature data fusion mechanism to obtain a user interaction intention vector and a user emotional state vector; s4, interaction response generation: matching a preset interaction response strategy library based on the user interaction intention vector to obtain basic response content, and performing emotion adaptation adjustment on the basic response content in combination with the user emotion state vector to generate multi-modal response content including text information, voice information and action information; s5, interactive feedback is executed; the method has the beneficial effects that the robot is fused with multi-modal information through the basic response content and the user emotional state vector, and interaction is dynamically optimized.

The embodiment of the invention provides a government affair digital human dynamic interaction method and system based on a multi-mode large model. The method is applied to the technical field of government affair intelligent services, and comprises the following steps: acquiring a policy announcement text, and performing cleaning and structuring processing to obtain a structured policy data set; and extracting old and new policy data, performing difference comparison, marking key change fields, and generating policy change data. Abstracting and element extraction are carried out on the change data to form structured semantic fragments, and the structured semantic fragments are incrementally embedded into the policy knowledge graph. And generating a question and answer pair sample based on the updated knowledge graph, and carrying out self-supervised fine tuning training on the multi-modal large model. A user inputs multi-modal data, and the model generates a government affair response and feeds back the government affair response. According to the scheme, the multi-modal large model can continuously keep the latest policy knowledge; the model is enabled to generate accurate government affair response with consistent context while understanding multi-modal input such as text, voice and image, and timeliness, accuracy and interactive experience of policy interpretation are improved.

The invention relates to the technical field of wireless communication networks, in particular to a vehicle ad hoc network based on intelligent networked vehicle WiFi and a V2V interaction method, a vehicle motion state and network load data are acquired through a vehicle-mounted sensor, AP / STA modes are dynamically switched by combining a deep Q network (DQN) model and an epsilon-greedy strategy, and an adaptive topological structure is constructed. And expanding OLSR protocol message embedding path delay fields, calculating and optimizing multi-hop routing selection in combination with pheromone gradient, and realizing message priority classification and differential privacy protection through a federated learning model. And dynamically adjusting an AP / STA proportion threshold based on a particle swarm algorithm, and triggering node role reconfiguration to balance network load. According to the method, the existing WiFi hardware is reused, the deployment cost is reduced, cross-layer routing optimization and a distributed decision mechanism are combined, and the network real-time performance, the expansibility and the group cooperation capability in a dynamic scene are improved.

The invention discloses a dynamic interaction method based on cooperation of a multi-modal dynamic fusion large model and an intelligent agent. The method comprises the following steps: performing feature extraction on user voice information to obtain a voice coding vector, a text semantic vector and an emotion feature vector; performing dynamic weight feature fusion on the voice coding vector, the text semantic vector and the emotion feature vector through a multi-modal dynamic fusion large model to obtain a fusion feature vector; inputting the fusion feature vector into an intention-scene coupling network, and identifying to obtain a user intention label; and identifying according to the user behavior log to obtain a user portrait tag, inputting the user intention tag and the user portrait tag into an autonomous decision-making agent, generating a target decision-making action through a lightweight policy network, and then interacting with the user according to the target decision-making action. The intelligent interaction efficiency and accuracy of the customer service system are improved, the interaction experience of the user is also improved, and the method can be widely applied to the technical field of artificial intelligence.

The invention provides an interaction method and system based on deep learning, and relates to the technical field of computer vision, and the method comprises the steps: collecting a continuous image sequence, extracting feature points, calculating the position variation and gradient variation of the feature points, constructing a two-dimensional feature vector, and carrying out the clustering through a Gaussian mixture model, thereby obtaining a stable feature class. And weighting the feature points based on a feature weight distribution function, calculating feature similarity and constructing a matching relationship. And calculating a dynamic parallax threshold in combination with the vehicle movement speed, screening reliable matching feature pairs, and solving pose transformation parameters. A feature change sequence is extracted through a feature detection network, a motion feature mapping matrix is constructed, and a compensation depth map is generated in combination with depth information. And scene reconstruction is carried out based on the compensation depth map and the pose transformation parameters, the scene confidence score is calculated, the pose parameters are calibrated, a high-precision pose parameter sequence is obtained, and the robustness of feature matching and the accuracy of pose estimation can be improved.

Provided in the embodiments of the present application are a video generation method and interaction method based on a digital human, and a device, a storage medium and a program product. In the embodiments of the present application, text-to-speech processing is performed on the basis of voice features of a user and an emotion label, speech-to-expression processing is performed on the basis of a mapping relationship between the voice features of the user and expression coefficients, and a digital human model is rendered on the basis of speech signals and the expression coefficients, so as to obtain video data of the digital human model. Thus, voice features of a user are accurately simulated, so as to ensure that a speech output of a digital human sounds natural and is also highly personalized, thereby realizing personalized driving of the digital human, and improving the realism of the digital human in terms of voice and dynamic images. Thus, the user experience is improved, and the interactivity of the digital human and the authenticity and immersion are enhanced.

The invention relates to the technical field of artificial intelligence and robots. According to the multi-modal perception and interaction method and device in the body environment, the method comprises the steps that environment entropy estimation processing is carried out through a dynamic weighting multi-modal feature fusion algorithm, and an environment entropy value representing the disorder degree of the environment is generated; performing cross-modal alignment processing to generate a fusion environment understanding map; performing dynamic decision processing through the task adaptive reinforcement learning model to generate an interaction instruction; driving an execution mechanism to execute the interaction action to obtain an execution result of the interaction action; carrying out dynamic adjustment processing on the weight of the environment entropy value to generate an updated environment entropy weight; and performing local knowledge node incremental updating processing on the meta-knowledge base to generate an optimized meta-knowledge base so as to solve the problems of insufficient consistency of cross-modal data and poor environmental understanding robustness caused by large distribution deviation between virtual features generated by a generation model in a noise or data missing scene and a real environment in related technologies.

The invention discloses a multi-mode interaction method for an accompanying robot, and relates to the technical field of robot interaction.The multi-mode interaction method comprises the steps that voice, visual and tactile signals are converted into quantum states through multi-mode quantum state coding, emotion weights are dynamically fused, and unified quantum representation is constructed; the dynamic quantum decision engine analyzes environmental noise and user emotion intensity in real time based on a quantum measurement theory, generates an adaptive strategy through dynamic modal weight distribution, and solves a multi-modal instruction conflict; holographic reinforcement learning optimization is combined with quantum acceleration calculation and classical reinforcement learning, the reward function weight is dynamically adjusted, cross-modal knowledge migration is achieved through the quantum tunneling effect, and the system strategy is continuously optimized. The problems that a traditional multi-mode interaction system is rigid in mode switching, high in emotion recognition error rate, lack of self-adaptive optimization of a feedback strategy and the like are solved, and the interaction efficiency and the user experience are improved.

The invention discloses a database interaction method, device and equipment based on multi-agent cooperation and a medium, and relates to the technical field of database interaction. The method comprises the steps of obtaining a query request, performing semantic partitioning and vectorization, and obtaining a retrieval vector set; and selecting a search source and searching to obtain an information block. And obtaining candidate knowledge through knowledge base retrieval. And integrating the information blocks and the candidate knowledge to obtain a candidate information set. And constructing a dynamic demand understanding model according to the query request and the candidate information set, and obtaining a task state matrix and a tool calling task. And calling a corresponding auxiliary tool and operating, updating the task state matrix according to an operation result and returning the task state matrix to the task planning agent to update the tool calling task until the tool calling task is no longer output, and obtaining a task result. And when the database interaction module is called, generating a chart according to an output result of the database interaction module, and outputting the chart and a task result together. Otherwise, directly outputting a task result.

The present application relates to a question answering interaction method and apparatus based on a large language model data vectorization query, and an electronic device and a computer-readable medium. The method comprises: performing semantic recognition on input data of a user, and determining a question category corresponding to the input data; determining a target vector database on the basis of the question category; converting the input data into an input vector; using the input vector to perform a vector query in the target vector database, and performing splicing to generate a query result; generating a prompt question by means of the input data and the query result; inputting the prompt question into a large language model, so as to acquire a logical analysis result; and when the logical analysis result is a positive result, performing question answering interaction on the basis of the logical analysis result. The present application can provide an intelligent question answering method for a user, thereby improving the answering quality and accuracy of a man-machine intelligent dialogue and question query.

The invention provides an interaction method, system and device and a medium, and relates to the technical field of man-machine interaction, and the method comprises the steps: obtaining an interaction process of a main control agent and a target user in a current round of interaction process according to the intention information and state information of the target user in the current round of interaction process; according to the interaction process, controlling an execution agent to perform response operation on current input information to obtain a response operation result of the current input information; the current input information is information input by the target user in a current round of interaction process; and outputting response information corresponding to the current input information according to the response operation result. According to the method, the interaction process and the dialogue content are flexibly adjusted according to the real-time intention and the real-time state of the user, and the fluency, naturalness and adaptability of interaction, the efficiency and real-time performance of interaction processing and the user experience feeling are remarkably improved.

The invention provides an AR intelligent shopping guide interaction method and system applied to store management, and the method comprises the steps: firstly obtaining an interaction data set of a user in a store, covering a user position movement track, a commodity attention action sequence and voice consultation content, carrying out the user intention analysis of the interaction data set, and carrying out the user intention analysis of the interaction data set; the method comprises the steps of obtaining commodity demand characteristics and interaction preference characteristics, generating AR shopping guide interaction content, including commodity three-dimensional display information and an interaction guide instruction, adaptive to a user based on the commodity demand characteristics and the interaction preference characteristics, aligning and fusing the AR shopping guide interaction content with a store physical scene space, generating an AR shopping guide interaction scene capable of being displayed in real time, and displaying the AR shopping guide interaction scene in real time. And finally, responding to user interaction operation in the AR shopping guide interaction scene, generating feedback information and updating the interaction data set to trigger interactive process iterative optimization, thereby providing personalized and immersive shopping guide experience, and improving store operation efficiency and customer satisfaction.

The invention provides an interaction method based on artificial intelligence equipment, belongs to the technical field of artificial intelligence, and remarkably optimizes the efficiency of an AI equipment interaction system through distributed resource dynamic scheduling, multi-framework heterogeneous collaboration and full-link intelligent monitoring. Based on an elastic capacity expansion and contraction and priority scheduling strategy of Kubernetes, GPU resource islands are eliminated, and the computing power distribution efficiency is improved; a Flink checkpoint mechanism and Spark RDD blood relationship tracking are integrated, training task breakpoint continuous calculation and millisecond-level fault recovery are achieved, and repeated data processing is avoided; gPU node performance indexes are collected in real time through Prometheus Exporter, and in combination with Grafana visual early warning, the anomaly detection response speed is shortened to the second level; seamless access of frames such as TensorFlow / PyTorch is supported, heterogeneous device protocol conversion is achieved through a unified API gateway, and the cross-platform migration cost is reduced.

The invention provides a model-based interaction method and system, wearable equipment and a storage medium, and belongs to the technical field of intelligent interaction.The method comprises the steps that in response to a received interaction instruction, voice data, a gesture image, eye movement data and an environment image are obtained based on the interaction instruction; extracting user intention features based on the voice data, the gesture image and the eye movement data, and determining scene type features based on the environment image; determining an interaction theme based on the interaction instruction, obtaining user historical interaction information associated with the interaction theme from a context memory database, and generating a context feature vector based on the user historical interaction information; and inputting the user intention feature, the scene type feature and the context feature vector into an intention recognition model based on quantum enhancement to obtain a user intention, and generating interaction response data based on the user intention. According to the invention, the accuracy of user intention recognition can be improved, and the intelligence of interaction is improved.

The invention relates to the technical field of electric digital data processing, in particular to an artificial intelligence algorithm-driven digital human language understanding and corresponding reaction interaction method. The method comprises the following steps: collecting historical language data, text data, action instruction data and physical environment information of a user to obtain structured training data; vectorizing the text data to obtain a text semantic vector; environment feature vectors are extracted from the physical environment information; splicing the text semantic vector and the environment feature vector to obtain an environment enhanced text vector; and constructing a pre-training language model based on the environment enhanced text vector, and performing secondary pre-training to generate an optimized language model parameter. According to the invention, by fusing the user language and the physical environment information and combining with the multi-module intelligent component, context perception, efficient understanding and multi-modal natural interaction of the digital human in a complex scene are realized, and the intelligence, adaptability and user experience of the system are remarkably improved.

The invention relates to the technical field of vehicle-mounted communication, and discloses a CAN network-based vehicle-mounted equipment data transmission and interaction method, which comprises the following steps of: S101, acquiring a message sent by vehicle-mounted equipment, and analyzing to extract vehicle operation data; s102, receiving the vehicle operation data by the CAN gateway controller, extracting signal content, performing classification optimization, and performing compression packaging; s103, sending the packaged vehicle operation data to an external terminal device; s104, the external terminal device receives and analyzes the packaged vehicle operation data; and S105, the remote terminal sends an encrypted control instruction to the vehicle-mounted gateway, the gateway verifies the encrypted control instruction, converts the encrypted control instruction into a CAN message and executes the CAN message, the ECU generates a feedback signal and returns the feedback signal, and the gateway feeds the feedback signal back to the remote terminal through wireless communication to complete closed-loop interaction. According to the invention, the real-time transmission of safety key data is ensured, and the stability of wireless communication is improved.

The invention discloses a personalized memory auxiliary interaction method based on a mental cognition mechanism, and particularly relates to the field of agent interaction. Comprising the following steps: determining a desire type and a personal role abstract of an input text, and taking the desire type and the personal role abstract as semantic memory; taking the current input text as scene memory, summarizing all the input texts in the dialogue date at the end of the current dialogue date to obtain a daily event abstract, and taking the daily event abstract as abstract memory; performing similarity retrieval in a memory bank according to the current input text to obtain related historical semantic memory, historical scene memory and historical abstract memory, and taking the retrieval result as working memory; and generating a response text according to the working memory, and performing interaction. The role and personality characteristics of the user can be deeply understood and memorized, personalized response can be generated, and historical dialogues can be effectively associated.

The invention discloses a personalized design intelligent interaction method based on topological optimization, and the method comprises the steps: S1, constructing a user behavior monitoring system based on multi-modal data collection, and generating a high-dimensional data set containing time sequence features; s2, based on an adaptive deep neural network model, performing dynamic prediction and hierarchical modeling on the personalized demand of the user; s3, generating an optimal solution of the design parameters in real time through a dynamic topology reconstruction algorithm; s4, iteratively updating a design scheme generation rule through a reinforcement learning mechanism; s5, constructing a multi-objective design space exploration and optimization framework supported by the neural network, and rapidly screening and optimizing design parameters; s6, dynamic updating and local optimization of the design scheme are achieved through multi-dimensional data mapping and parameterization regulation and control means; and S7, establishing a data-driven continuous learning and evolution mechanism, continuously optimizing the user demand prediction model and designing an optimization algorithm. The method has the advantages of being high in dynamic adaptability, high in intelligent level and high in personalized meeting precision.

The invention relates to the technical field of interaction based on visual earphones, and discloses an interaction method and device based on a visual earphone, computer equipment and a medium, and the method comprises the steps: judging whether a user wearing the visual earphone sends an interaction instruction or not; analyzing content in the interaction instruction to determine a user demand; in response to the user demand, controlling a camera to scan current content to obtain a scanned picture; performing data processing on the scanned picture based on the user demand to obtain a processing result; and feeding back the processing result to the user. The method has the beneficial effects that the user demand is determined through the interaction instruction, and intelligent and personalized services can be provided according to the user demand. Through the camera integrated with the earphone, a user can obtain environment information in real time without depending on external equipment, and obtain instant feedback through the earphone, so that the practicability and interaction experience of the intelligent wearable equipment in a complex environment are greatly improved.

The embodiment of the invention relates to an interface interaction method and device, equipment and a storage medium. The method provided by the invention comprises the following steps: in response to receiving a creation request associated with a shooting position of a media resource, presenting a creation interface; obtaining a group of description contents of the shooting position via the creation interface, the group of description contents including guide information associated with the shooting position; and creating a shooting position based on the set of description contents. In this way, the efficiency of creating the shooting position by the user can be improved.

The invention provides an interaction method and device based on image recognition, and relates to the technical field of man-machine interaction, and the technical scheme is characterized in that the method comprises the steps: analyzing the moving speed and change frequency of target and non-target elements in a training scene, and calculating an environment dynamic factor; adjusting a high threshold value and a low threshold value of target identification confidence; comparing the target recognition confidence output by multi-modal image recognition with the adjusted high threshold and low threshold, and determining a visual feedback type; setting a visual feedback intensity parameter according to the determined visual feedback type and the target type; and displaying the set visual feedback in the visual field of the technical object in an overlapping manner. The interaction method and device based on image recognition provided by the invention have the advantages that the cognitive load of the technical object is reduced, and the rapid target recognition capability of the technical object in a complex and uncertain battlefield environment is improved.

The invention discloses a humanoid robot object interaction method guided by a diffusion model based on key frames. Comprising the following steps: S1, generating interaction between a human body and an object based on a diffusion model of a key frame; s2, guiding the humanoid robot to interact with the object based on the contact instruction; and S3, interaction between the humanoid robot and the object based on the action generation guidance. According to the method, in an action generation stage, key frames of a robot and an object within a period of time are predicted according to a current state, a motion sequence is recovered by using interpolation, and then a universal robot and object interaction strategy is finely adjusted based on a generated and simulated closed-loop structure and a fixed diffusion model weight. The interaction strategy is rewarded due to accurate simulation of aligned movement and contact information and successful object control, the gap between the output of the diffusion model and the training data set of the simulation environment is made up, and the reward does not need to be designed for a specific task.

The invention relates to the technical field of artificial intelligence, and discloses an interaction method and device based on voice recognition, equipment and a storage medium, and the method comprises the steps: obtaining collected user voice; a recognition model is adopted to convert the user voice into voice characters, and the identity of a speaker and the emotion of the user are obtained through recognition; obtaining the above-mentioned reply text, converting the voice text, the speaker identity and the user emotion into a prompt word text by adopting a language model, and generating a target reply text according to the above-mentioned reply text and the prompt word text; and converting the target dialogue text into target dialogue voice by adopting a text-to-voice model, and controlling a loudspeaker to play the target dialogue voice. The method can be applied to business management program systems of financial science and technology, medical treatment and the like, the problem of double limitation of intelligence and emotional interaction faced by existing human-computer interaction is solved, and the industry service quality and the user stickiness are improved.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to an agent system based on a large language model and an interaction method, and the system comprises a data collection module which is used for receiving text, voice or other forms of data input by a user in real time; the natural language processing module is used for performing semantic understanding and grammatical analysis on the received data to generate an executable user intention; the knowledge reasoning module is used for reasoning an optimal response result or action scheme according to the requirements of the user and historical interaction records; the method has the beneficial effects that the interaction experience of the user is greatly improved through natural language processing, sentiment analysis and personalized response technologies. The system can perform accurate response according to the emotion and demand of the user, and can provide efficient and intimate services in applications such as intelligent customer service, intelligent assistant, personalized recommendation and the like.

The invention provides an intelligent interaction method, device and system based on a large model, a medium and equipment, and is applied to the technical field of artificial intelligence. The method comprises the steps of obtaining a query intention from user query content, performing knowledge retrieval to generate an item candidate list, generating a model cue word by using the list, and screening out a target business item. After the user confirms, relevant content is retrieved in the database, a new model prompt word is generated, and the large model is input again to generate query reply information and return to the user. According to the method, a large-scale language model and knowledge retrieval are utilized, so that the service efficiency and quality of the intelligent service assistant are improved, and the user satisfaction is improved.

The invention belongs to the field of data processing, and provides a road engineering low-carbon data credible interaction method and system, and the method comprises the steps: constructing a full life cycle stage division model, generating corresponding low-carbon data according to the business activity of each stage, and adding a stage identifier, a timestamp and a spatial position label to the low-carbon data; based on a preset engineering logic, a data causal relationship and a carbon emission factor model, constructing a data context tree comprising a plurality of low-carbon data nodes, and organizing the low-carbon data nodes in different space-time ranges and life cycle stages into a life cycle data context tree with a staged conduction relationship; before data interaction is carried out, multi-dimensional verification operation is carried out on target data based on the data context tree; and calculating a credible score of the target data based on a structural path verification result, a space-time consistency verification result and a carbon factor mapping verification result, and controlling the interactive access permission of the low-carbon data according to a score result.

The invention relates to the technical field of music feedback, in particular to a music feedback training interface interaction method and system based on electroencephalogram data, and the method comprises the following steps: collecting electroencephalogram data of a user based on an electroencephalogram collection device, and removing environmental noise and non-target signals through signal amplification and filtering processing to obtain a user electroencephalogram data set. According to the method, the emotion recognition accuracy and time sensitivity are improved by analyzing the brain wave frequency band intensity, judging the emotion state of the user and recording the emotion change time point, and the emotion recognition accuracy and time sensitivity are improved by matching music tracks of the emotion state, combining real-time monitoring of electroencephalogram reaction and adjusting the rhythm, volume and spatial sound effect setting of music. Dynamic adjustment of music output parameters is achieved, the emotion intervention requirement is optimized, emotion improvement and user satisfaction are evaluated according to brain wave changes of the user, and the improvement effect of music intervention on the psychological state of the user can be measured.

The invention provides a customer service interaction method, an interaction device, equipment, a storage medium and a program product, and relates to the technical field of computers. The customer service interaction method based on the large language model comprises the following steps: performing intention scene recognition on a received user question; identifying that the user question belongs to a specified intention scene, and determining a corresponding domain knowledge base matched with the user question; performing enhanced retrieval on the knowledge base in the corresponding field based on the user question to obtain a business knowledge fragment for enhancing the user question; and inputting the business knowledge fragments into a large language model, so that the large language model performs filtering type question reply processing based on the business knowledge fragments, and outputs reply information of the user question based on a processing result. According to the technical scheme, the real intention of the user in continuous interaction can be deeply analyzed, the content which the user wants to consult can be more accurately grasped, and the consulting requirement of the user is better met.