149 results about "Emotion perception" patented technology

An end-side-cloud three-in-one active chat robot system for high-emotional merchants comprises an end side, a local edge server and a cloud end, and the end side is used for collecting multi-modal data of a user and performing local lightweight real-time processing and response execution; the local edge server is used for receiving and fusing the multi-modal features and the context information from the end side, and carrying out sentiment calculation, dialogue management and active trigger decision making with medium complexity; the cloud end is used for operating a super-large-scale model and providing global knowledge management, long-term user portrait storage and model training optimization; and the end side, the local edge server and the cloud end carry out cooperative communication through an encrypted channel to form a distributed intelligent processing architecture. According to the method, global optimization is realized by integrating end-side lightweight sensing, edge multi-modal fusion and cloud long-term memory. A composite finite state machine (FSM) active questioning mechanism is combined with sentiment calculation, and is different from traditional rule type triggering. Off-line and on-line fusion scheduling and multi-agent role playing are combined to be applied to a high-emotional-quotient interaction scene, and the simulation is improved. Multi-modal emotion perception circulation is introduced, and the problem of single text emotion misjudgment is solved.

The invention discloses a real-time emotion perception and voice interaction system for an intelligent cabin. The system comprises a multi-modal data acquisition module used for synchronously acquiring a facial image, a voice signal and text input of a driver; the visual feature enhancement unit is used for carrying out restoration and emotion distribution extraction on the low-quality image; the audio noise reduction and feature extraction unit is used for extracting voice emotion features; the text emotion coding unit is used for fusing relative position coding and context semantic information; the cross-modal fusion module is used for outputting an emotion classification result by integrating visual, audio and text features; the personalized emotion database is used for storing historical emotion data of the user and performing emotion trend prediction and early warning judgment; the large language model feedback module is used for generating structured cue words according to the emotion recognition result and the driving situation and generating natural language feedback; the voice synthesis and output module is used for adjusting voice parameters and performing feedback output through a vehicle-mounted multi-channel; according to the invention, the emotion expression of human-vehicle interaction is enhanced.

The invention discloses a classroom real-time learning emotion knowledge and intelligent intervention system, and relates to the technical field of artificial intelligence auxiliary education. Comprising a time sequence perception modeling module, a conflict identification analysis module, an intervention priority control module, a decision fusion output module, a stability prediction and early warning module and a closed-loop vibration suppression regulation and control module, the time sequence perception modeling module establishes a unified time baseline and a phase reference field, and performs joint modeling on multi-source classroom learning condition data under the constraint of the unified time baseline and the phase reference field; time sequence semantic fusion features are extracted, and a contradictory candidate trajectory set is generated. According to the method, multi-modal time sequence semantic features are fused, a learning situation conflict source is identified, conflict intensity is quantified, an intervention priority and a punishment mechanism are constructed to guarantee rhythm stability, an evidence chain and an attention mechanism are adopted to generate a consistent decision, oscillation detection and time reversal regulation are combined, closed-loop control of the intervention process is achieved, strategy oscillation is effectively avoided, and the method is suitable for popularization and application. And the intervention continuity and stability are improved.

The invention discloses a vending machine multi-mode emotion perception and personalized dialogue generation method and system, and the method comprises the steps: collecting a face image and a voice signal of a user through a camera and a microphone, and outputting a dual-mode emotion tag through the analysis of a face expression and a voice emotion; fusing the label and the dialogue text to generate a structured Prompt containing emotion, intention and context; based on an Agent framework multi-modal large model, combining context history and a personalized strategy to generate a reply text matched with the emotional scene; according to the text and the comprehensive emotion label, TTS parameters are dynamically adjusted, and anthropomorphic voice is output; in multiple rounds of dialogues, styles are kept consistent through an emotion smoothing formula, and emotion-verbal skill-conversion data are precipitated in combination with a user feedback optimization strategy. The interaction limitation of a traditional vending machine is broken through, multi-mode emotion perception, personalized personification interaction and continuous evolution are achieved, the vending machine is promoted to be upgraded to an emotional shopping guide platform, user experience and sales transformation are improved, and a technical model is provided for intelligent retail.

The invention relates to the field of human-computer interaction, and discloses a human-computer interaction robot facial expression implementation control method, which comprises the following steps: dynamically capturing the face, sound and posture of a user in a human-computer interaction scene, and generating an initial emotion perception sequence; performing hierarchical cross mapping on the initial emotion perception sequence, and performing combinatorial analysis on facial expressions, voices and intonations and body movement features to form an emotion matching vector; based on the emotion matching vector, utilizing a priority regulation model to predict the emotion trend of the user at the next moment, and identifying an expression enhancement point and an emotion attenuation area; mapping the generated facial micro-expression adjusting instruction to a robot facial driving unit, and performing real-time correction and conflict resolution on an expression action sequence; and reversely fusing a user fixation point, facial muscle micro-motion and voice emotion which are acquired in real time into an emotion matching vector, and dynamically updating a perception weight and expression regulation and control parameters. The method has the advantage of improving the emotion matching degree of the robot expressions.

The invention relates to the technical field of music recommendation, in particular to an intelligent audio recommendation control method and system. The method comprises the following steps: detecting a real-time face image of a user in an initial music playing process, carrying out multi-period emotion evolution modeling, and constructing a user emotion state evolution curve; identifying the initially played music, and performing emotion resonance matching based on the emotional state evolution curve of the user to obtain emotion resonance response data; constructing a global music feature map of the user music library; performing deep rhythm demand analysis on the emotion resonance response data according to the global music feature map, and extracting an emotion demand adaptive music set; adaptive music recommendation decision making is carried out based on the emotion demand adaptive music set, and recommendation control execution is carried out. The music emotion demand of the user is predicted through accurate user emotion perception, adaptive music is efficiently and accurately recommended, the satisfaction degree of the user is improved, and good auditory experience is provided.

The invention discloses a pet accompanying robot state control system and method capable of dynamically updating characters, and the method comprises the steps: sensing the current state of a user, and recognizing the emotion state of the user through a multi-mode emotion calculation method based on the current state of the user; each feedback state of the pet robot is generated; based on the selected specific feedback state, the appropriate pet robot feedback expression, action and sound effect are intelligently matched; and synchronously controlling the pet robot to realize integral state output of man-machine interaction. According to the method, the specific feedback state is output by combining the multi-mode emotion perception with the personality of the pet robot, so that the personalized interaction experience of the pet robot is improved. Meanwhile, through a dynamic updating mechanism of the character of the pet robot, the pet robot can evolve continuously, and the emotional accompanying experience of thousands of people in long-term use of the user is improved.

The invention discloses an emotional diary intelligent glasses system driven by a multi-modal agent. The emotional diary intelligent glasses system comprises a multi-modal perception layer, a unified vector space processing layer and an agent collaborative decision-making layer. The multi-modal sensing layer constructs a full-dimensional sensing network by integrating a camera, a microphone array and an eye movement tracker, and collects image, sound and eye movement information; the unified vector space processing layer realizes cross-modal information unified representation by using a multi-modal large model; the agent collaborative decision-making layer comprises an emotion perception agent, a scene understanding agent, a diary generation agent and a personalized adaptive agent. According to the method, deep understanding of the emotional state and the environment scene of the user is realized through a multi-modal fusion perception and agent cooperation mechanism, the personalized emotional diary is generated in combination with chain thinking and reflection, and the content insight and the user experience are effectively improved.

The invention discloses a personalized interaction method and system based on multi-modal emotion perception, and relates to the technical field of intelligent interaction, and the method comprises the steps: collecting user historical interaction event data, and extracting interaction content features and user behavior features; obtaining interaction emotion data of the user in real time through a multi-mode sensor; fusing the interactive emotion multi-modal data to generate a dynamic emotion quantized value; generating an emotion correction factor; dynamically generating a personalized interaction strategy; and executing the interaction strategy, and collecting user response data to update a historical interaction event library. The method has the advantages that the key problem that a traditional interaction system is difficult to perceive the real-time emotional state of the user is effectively solved, a closed-loop optimization system of perception-decision-execution-feedback is constructed, and the user interaction satisfaction degree and the situation adaptive capacity of the whole system are continuously improved.

The invention belongs to the technical field of music recommendation, and provides a personalized music recommendation method, device, equipment and medium based on emotion perception, and the method comprises the steps: collecting user motion monitoring parameters and physiological state data, and carrying out the personalized motion rhythm calculation to obtain a user motion rhythm model; according to the physiological state data and a user exercise rhythm model, heart rate frequency band-beat synchronous matching and intelligent index hierarchical management are carried out, and a music index candidate pool is obtained; performing multi-scale wavelet transform decomposition on the physiological state data stream, and performing user personalized emotion perception to generate a real-time emotion sub-state; performing exercise emotion trend prediction on the user exercise rhythm model according to the real-time emotion sub-state to generate a user exercise emotion situation map; and performing multi-target music recommendation optimization processing from the music index candidate pool according to the user motion emotion situation map to obtain a recommendation result. The method has the beneficial effect that the personalized music recommendation accuracy is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology, medical health and the like, and discloses an action generation method, device, equipment and medium based on multi-modal emotion perception.The action generation method comprises the steps that multi-modal data of a target object is obtained, weights are distributed based on the emotion recognition degree, and fused emotion features are generated through fusion; determining an emotion type, an emotion intensity value and a change rate, generating a personalized adjustment coefficient and a scene adjustment coefficient in combination with personalized features and scene features of the target object, processing the initial action parameter to obtain a final action parameter, executing an emotion action, obtaining feedback data, and updating an action parameter mapping relation based on the feedback data. The emotion perception comprehensiveness is improved by fusing the multi-modal data, the action parameters are dynamically adjusted by combining the personalized features and the scene features, the action parameter mapping relation is updated based on the feedback data, and the accuracy, personalization and real-time performance of emotion response are improved.

The invention discloses an adjusting and optimizing algorithm model based on elder care emotion accompanying. The model comprises a multi-mode emotion perception module for receiving voice, images and physiological signals and extracting features such as timbre, facial expression and pulse to generate emotion feature vectors; the emotion recognition module is used for predicting the real-time emotion state of the old people through fusion of CNN and LSTM and multi-modal features; the strategy adjusting and optimizing module adopts a Bayesian optimization or genetic algorithm to automatically adjust and optimize the voice intonation, the interaction rhythm and the dialogue strategy of the accompanying system; the feedback learning module iteratively updates the model based on the old people feedback data, and optimizes the interaction effect; and the personalized accompanying generation module generates accompanying strategies such as voice consolation and music recommendation according to the tuning result. The multi-mode emotion perception module is composed of a voice monitoring sub-module, an expression monitoring sub-module and a physiological signal monitoring sub-module. The strategy tuning module comprises a self-adaptive parameter search sub-module and a multi-target optimization sub-module; and the feedback learning module comprises a user feedback collection and reinforcement learning sub-module, so that the interaction experience and the emotion adaptability of the accompanying system are improved.

The invention discloses a multi-chess game robot control method and system based on behavior and emotion adaptive game difficulty adjustment. The method comprises the following steps: initializing a multi-chess game scene; interaction object behavior and emotion perception; calculating the instantaneous state time sequence of the interaction object; performing two-way mapping calculation on the difficulty coefficient to obtain a final difficulty coefficient; and according to the output final difficulty coefficient state, a game strategy of a preset AlphaZero reinforcement learning model and kinematics parameters of the mechanical arm are adjusted, so that dual anthropomorphism of the game level and the action performance of the robot is achieved. Executing control and safety detection; according to the method, behaviors and emotions of the interaction object are sensed in real time, self-adaptive adjustment of the game difficulty is achieved in the two dimensions of the game strategy and the physical action, and the interaction experience and the intelligent level of man-machine game are effectively improved.

The invention discloses a multi-round dialogue logic optimization method in an intelligent question answering system based on a knowledge graph, and belongs to the technical field of artificial intelligence and natural language processing, and the method comprises the following steps: S1, constructing and dynamically updating a triple knowledge graph; s2, based on a graph neural network and a graph attention mechanism, performing time sequence and emotion perception modeling on cross-round semantic association in the triple knowledge graph, and outputting a dialogue context vector; s3, inputting a dialogue context vector and a current user input feature into the generative model, performing semantic mapping through an independent intention, entity and behavior embedding encoder, and generating a robot behavior in combination with logic constraint and emotion-driven weight adjustment from a triple knowledge graph; s4, optimizing the generative model based on a task-associated adversarial training mechanism; and S5, carrying out multi-modal emotion fusion and consistency verification. According to the method, dynamic, coherent and emotional intelligent multi-round dialogue logic optimization can be realized.

The invention discloses a multimedia memoir generation method and device based on multi-dimensional emotion perception, relates to the technical field of artificial intelligence, can be applied to financial science and technology and medical health business scenarios, and comprises the steps that in the AI interaction process, multi-dimensional emotion related data expressed by user recall is collected in real time; comprehensively analyzing the multi-dimensional emotion related data, and determining a real-time emotion type and emotion intensity of the user; according to the real-time emotion type and the emotion intensity, corresponding visual feedback materials and auditory feedback materials are dynamically and adaptively generated, and an immersive interaction scene is constructed based on the visual feedback materials and the auditory feedback materials; in the immersive interaction scene, intercepting a plurality of audio and video material slices when the emotion of the user reaches a peak value; and integrating the plurality of audio and video material slices and the core content expressed by the user memory to obtain the multimedia memoir of the user. The emotion information of the user in the memory expression process can be effectively captured and fused.

The invention relates to a multifunctional intelligent communication method based on multi-modal emotion perception and dynamic encryption, and belongs to the crossing field of communication technology, artificial intelligence and information security. The method comprises the following steps: S1, extracting and fusing texts, voices and images input by a user through a multi-modal emotion recognition model in combination with historical emotion data and behavior modes in a knowledge base to analyze emotion features, and introducing an attention mechanism to perform multi-modal feature weighting to realize accurate classification of emotion types and intensities; based on a classification result, dynamically triggering a response strategy through an AI agent decision engine; and S2, through a content sensitivity analysis module, using a deep learning model to identify the sensitivity level of the chat content in real time, coordinating a plurality of encryption agents based on an MCP, dynamically adjusting the intensity of an encryption algorithm, generating a dynamic key by using a logistic chaotic encryption algorithm, constructing a multi-level key management system, and ensuring the security and efficiency of end-to-end communication.

The invention relates to the technical field of vehicle intelligent control, in particular to an atmosphere lamp adaptive control system based on driver emotion perception, which comprises a multi-mode emotion perception module, an emotion dynamic modeling module, a light regulation and control strategy generation module, an intelligent scene switching module and a safety auxiliary intervention module. The system collects data through the biological signal sensor array and the driving environment monitoring unit, constructs an emotion dynamic model by using deep learning, and generates a light regulation and control strategy in combination with a multi-target optimization algorithm, thereby achieving the dynamic adaptation of the atmosphere lamp effect and the driver emotion state. The system can improve the driving safety and comfort, supports the module extension and maintenance, and is suitable for various driving scenes.

The invention discloses an emotion perception quantification method considering scene semantic information based on a graph structure, which comprises the following steps of: firstly, performing label conversion preprocessing on an emotion perception data set of a streetscape image, and providing a data source for training an emotion perception model; and carrying out transfer learning by adopting a scene graph generation model based on an attention mechanism to obtain entity representation and entity relationship representation for emotion perception. Through the representations, node features and edge features are generated, and multi-dimensional image features are fused to construct richer graph structure features. Then, screening the features by applying a confidence screening rule, and carrying out feature fusion and training by using a graph convolutional network based on the screened features; the method has the advantages that the emotion-related features in the streetscape image are structured into highly explained entity and relation features, and finally quantitative model training of emotion perception is realized to reveal the relation between the city appearance and the emotion perception, deepen the understanding of the physical appearance of the city street and further assist in decision making of city planning.

The invention discloses an intelligent music recommendation method based on emotional perception and acoustic features, and relates to the technical field of intelligent recommendation systems and emotional computation.The method comprises the steps that physiological signals, music acoustic features and historical behavior data of a user are collected; preprocessing the multi-source features and mapping the multi-source features to the same dimension to construct a fusion matrix; building a double-branch deep learning model, fusing features through an attention mechanism and training parameters; an evolutionary algorithm is adopted to optimize hyper-parameter screening optimal combination; generating a recommendation list matched with the real-time emotion and preference; and continuously collecting user interaction data, and regularly and incrementally training the dynamic update model. According to the method, emotion and behavior dual-drive recommendation is achieved by fusing physiological signals and acoustic features, emotion perception is accurate, recommended content fits the real-time mood, model optimization is efficient, recommendation precision and diversity are remarkably improved, and the music consumption experience of a user is greatly improved.

The invention provides an emotion recognition method and device, and relates to the technical field of emotion recognition. Performing multi-modal feature extraction according to the emotion perception data of the user, determining character features of the user, covering multi-dimensional state information of the user and tracking character changes of the user in real time; the attention weight of each feature item in each single-mode feature and the weight of each single-mode feature are calculated in sequence by taking the character features of the user as guidance, multi-mode fusion features of character perception strongly related to the character of the user are generated, and an emotion recognition result is obtained through an emotion recognition model. The technical problem that the emotion recognition accuracy is limited due to the fact that an emotion recognition method in the prior art only depends on single-modal features or simple multi-modal fusion and ignores individual character differences of users is solved, deep fusion of comprehensive representation of user states and character features of the users is achieved, and the emotion recognition accuracy is improved. And the emotion recognition accuracy and the individual suitability are improved.

The embodiment of the invention provides an emotion perception robot control method and device based on an LLVM core, and the method and device achieve the effective perception of emotion through multi-modal features and a neural network. A language processing mechanism is constructed, and a reliable interaction strategy is established in combination with emotion adjustment and dialogue understanding. Expression control is introduced, and coordination of emotional expression is ensured through action planning and speech synthesis. According to the method, the defects of a traditional technology in the aspects of emotion recognition, language processing, emotion expression and the like are effectively overcome, and technical guarantee is provided for an emotion perception robot.

The application provides an emotion perception-based Internet of Things information technology customer service method and system, and relates to the technical field of information technology services.The method comprises the following steps: obtaining real-time message data of a user and a customer service, and transmitting the data to a cloud server in real time through an Internet of Things platform; performing text analysis on the obtained real-time message data, and analyzing the current emotional state of the user through an emotion analysis method; analyzing the intention of the user according to the real-time message data, and generating reply content through a reply database according to the current emotional state of the user; continuously monitoring the interaction data between the user and the customer service, and dynamically adjusting the reply content.The application can analyze the emotional state of the user through emotion perception technology, thereby better understanding the demand and emotional state of the user, adjusting the reply content according to the emotional state of the user, and providing more personalized, warm and considerate services that are closer to the psychological demand of the user.

The invention relates to the technical field of intelligent human-computer interaction, in particular to an intelligent housekeeper-oriented multi-modal time sequence emotion perception system, which comprises a multi-modal data synchronous acquisition module, a multi-branch feature extraction network, a dynamic weight distribution module, an emotion evolution analysis module, a culture adaptation fusion layer and a hardware perception interface layer. The system integrates facial expression, voice, physiology and environment signals, and combines scene adaptive weighting, time sequence trend fitting, culture expression correction and equipment parameter adaptation to realize high-precision and robust real-time emotion recognition.

The invention discloses a pet emotion regulation generation system under the perspective of human-pet interaction psychology, and relates to the technical field of artificial intelligence, and the system comprises a multi-mode emotion perception module, an emotion state recognition module, a personalized regulation strategy generation module, a human-pet bidirectional interaction execution module and a dynamic optimization learning module. By fusing multi-modal data and a deep neural network model, high-precision recognition of the pet emotional state is realized, and a personalized interaction adjustment strategy is generated. The invention aims to solve the problem of insufficient accuracy and individuation of pet emotion regulation in the prior art, and can significantly improve the accuracy and generalization ability of emotion recognition, improve the pertinence and continuity of the regulation strategy, and enhance the overall coordination and adaptability of the system.

The invention discloses a mediation strategy dynamic adjustment system based on multi-modal emotion perception. The core of the system comprises six modules: obtaining text, voice and video data through a multi-modal data acquisition module; high-accuracy emotion perception is realized through an emotion recognition and fusion analysis module; key emotional events are captured through an emotional event detection and early warning module; strategies such as verbal skills and rhythms are intelligently recommended / adjusted by means of a mediation strategy dynamic adjustment module in combination with a knowledge base; a visual interface and a feedback channel are provided through the user interaction and feedback module; and compliance is guaranteed by a data security and privacy protection module. The system can realize real-time accurate sensing of multi-modal emotions, dynamically adapt to a mediation strategy, reduce experience dependence of mediators, and support online and offline multi-mediation modes. The trial application shows that the multi-modal emotion recognition accuracy rate reaches 92%, the strategy suggestion adoption rate reaches 87%, the mediation success rate is increased by 15%, mediation intelligence and standardization can be enhanced, and the method has wide popularization prospects and social benefits.

This application provides a voiceprint recognition-based emotion perception and room management method, comprising: generating a service configuration adjustment plan that matches the user's emotional tendencies based on demand prediction results; fine-tuning the service content based on real-time changes captured by dynamic data to obtain the adjusted service parameters; if the optimized emotional cycle change pattern deviates from the actual user feedback, then updating the voiceprint feature matrix and emotional tendency distribution by collecting long-term data records again to determine the new periodic fluctuation characteristics; and based on the new periodic fluctuation characteristics, cyclically executing the construction of the emotional profile model and the personalized demand prediction process to continuously improve the targeting of service configuration adjustments and obtain the final optimized service quality improvement plan.

The invention provides an intelligent cabin system and a control method thereof, and belongs to the technical field of intelligent cabins, and the intelligent cabin system comprises an emotion perception module which is used for obtaining the emotion data of a user in a vehicle; the environment sensing module is used for acquiring environment data outside the vehicle; the central controller is used for receiving the in-vehicle user emotion data of the emotion sensing module and the out-vehicle environment data of the environment sensing module, generating vehicle body display content according to the in-vehicle user emotion data and / or the out-vehicle environment data, and controlling the vehicle body display module to display the vehicle body display content; and the vehicle body display module is used for displaying the vehicle body display content on the surface of the vehicle body. Linkage of in-vehicle emotion and out-of-vehicle display can be achieved, the interaction ability between the vehicle and the surrounding environment is enhanced, and the driving experience of a user is improved.

This application discloses a multi-agent emotion intervention system and method based on emotion focus, relating to the field of artificial intelligence application technology. The system includes an active emotion perception module, a user profile database, and a multi-agent intervention module. The multi-agent intervention module comprises multiple agent units. The active emotion perception module generates structured emotion quadruples based on multimodal data published by the user and stores these structured emotion quadruples in the user profile database. The multi-agent intervention module loads the target user's structured emotion quadruples from the user profile database and, according to a preset emotion-focused therapy process logic, controls multiple cooperating agent units to engage in dialogue with the target user. Each agent unit is responsible for a specific stage of the dialogue. This application can improve the reliability of emotion intervention.

The application belongs to the field of artificial intelligence, and provides a dialogue interaction system based on multi-modal emotion perception and dynamic enhancement of knowledge graph. A user edge terminal acquires multi-modal data, adopts a lightweight Transform fusion network, converts the fusion feature vector through cross-modal attention fusion and dynamic weight adjustment, and compresses the fusion feature vector and the historical dialogue within the preset round in real time; a cloud service platform inputs the compressed data into DKGE, mines the entity knowledge and emotional relationship implied in the fusion feature vector and the historical dialogue in real time during the dialogue interaction process, updates the dynamic knowledge graph, performs knowledge enhancement processing based on the dynamic knowledge graph, and constructs an initial reply prototype for the current round of user interaction; the fusion feature vector and the updated dynamic knowledge graph are input into a dialogue strategy model to determine the response strategy and knowledge calling direction of the current round of dialogue; and the edge terminal generates real-time interaction reply information according to the initial reply prototype, the response strategy and the knowledge calling direction.

The invention discloses an interactive intelligent teaching system based on multiple modes, and relates to the technical field of intelligent teaching management systems. The system comprises a multi-modal perception layer, a semantic and feature fusion engine, a cognition-emotion two-dimensional analysis module, a strategy decision center and a multi-dimensional interaction execution terminal. Multi-source data of vision, hearing, physiology, behaviors and the like are synchronously collected in real time, and an improved MLP regression network is utilized to construct a cognition-emotion two-dimensional state model of a learner; when the state of the student is monitored to be abnormal, the strategy decision center dynamically generates an optimal teaching path in combination with a knowledge graph and a historical successful case, and drives a virtual digital teacher to execute immersive intervention; according to the method, the problems of emotion perception deficiency, feedback lagging and insufficient individuation in traditional online teaching are solved, and intelligent closed loop and adaptive optimization of the teaching process are realized.