96 results about "Emotional perception" patented technology

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 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 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 a robot dynamic control method and system combined with multi-modal emotional interaction, and relates to the technical field of robot control and artificial intelligence. The method comprises the steps of collecting multi-modal data and performing entity extraction to obtain a first interaction entity set and a second interaction entity set; acquiring a real-time emotion category through a current emotion recognition component; performing trend analysis based on the historical entity data to obtain a predicted emotion category; respectively inputting the real-time emotion category and the predicted emotion category into a pre-constructed control parameter mapping model, querying to obtain a first model mapping parameter group and a second model mapping parameter group, and calculating a corresponding dynamic control parameter set; and finally, fusing the two parameter sets, generating a smooth control sequence by using a Bezier curve, and driving the robot to carry out anthropomorphic and adaptive dynamic response. According to the method, the problems of emotion perception lagging and abrupt control in the prior art are solved, accurate emotion recognition, trend prediction and smooth behavior control are realized, and the naturalness and intelligence of man-machine interaction are improved.

The invention provides a man-machine emotion interaction assembly, method and device, a robot, a medium and a program product, and relates to the technical field of artificial intelligence. The man-machine emotional interaction assembly comprises a pressure tracking module which is used for capturing a micro gesture contacted by a user and generating a corresponding sensing signal; the sound receiver is used for collecting user voice; the processing module is connected with the pressure tracking module and the sound receiver and comprises an emotion pressure sensing model and a sensing session model, the emotion pressure sensing model is used for performing emotion sensing based on the sensing signal to obtain a sensing result, and the sensing session model is used for performing semantic and emotion analysis on the user voice; generating response information based on the analysis result and the sensing result; and the tactile stimulation module is connected with the processing module, receives the stimulation instruction generated by the processing module, and executes tactile stimulation based on the stimulation instruction. Through the technical scheme disclosed by the invention, the man-machine emotion interaction component effectively relieves the pressure and nervous emotion of the user by reappearing a secure physical prompt.

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 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 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 current document is directed to methods and systems that provide an emotional compass that reflects the emotional states of human beings, dynamics of the emotional states over time periods, emotional synchronization between humans and between humans and automated interactive systems, and the degree to which the emotional state of one human is influenced or affected by the emotional state of another human or by an automated interactive system. The emotional compass can be used for continuous or intermittent emotional orientation by both automated systems and human analysts. The emotional awareness provided by the emotional compass facilitates continuous or intermittent strategy selection for achieving various different types of emotion-related results.

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 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.

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 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.

The invention discloses a human emotion world generation method based on MBTI character injection, and the method comprises the steps: carrying out the preprocessing of collected multi-source signals one by one, carrying out the weighted fusion through an attention mechanism, mapping a fusion feature to a two-dimensional space comprising the pleasure degree and the activation degree through a multi-layer perceptron, and obtaining a current emotion state coordinate; according to an emotional state coordinate sequence comprising a plurality of historical emotional state coordinates, a historical emotional trajectory is obtained through a Linear Attention network, and a personalized emotional evolution trajectory is obtained in combination with the current emotional state coordinates; according to the MBTI character code of the user, an embedded vector is obtained and serves as the hidden state of the Linear Attention network, and the personalized emotional evolution trajectory is modulated; and adopting a diffusion model to generate a plurality of emotion evolution trajectories according to the modulated personalized emotion evolution trajectory and the embedded vector. The method has the effects of improving the emotion perception robustness, realizing efficient long-range modeling, generating highly personalized prediction and supporting uncertainty decisions.

The invention discloses a construction system of a non-contact emotion perception interpretable model based on a millimeter wave radar. The construction system comprises a data acquisition and preprocessing module, a machine learning pre-analysis model module, a model evaluation module, a model interpretable analysis module, a perception and prediction module and a data storage module which are connected in sequence. The invention further discloses a construction method of the non-contact emotion perception interpretable model based on the millimeter-wave radar, chest micro-motion signals of the human body are collected through the millimeter-wave radar, physiological features related to the emotion are extracted, an emotion perception feature importance analysis method is provided based on an SHAP analysis method, and the non-contact emotion perception interpretable model based on the millimeter-wave radar is constructed. And constructing an interpretable machine learning model for driver emotion perception. The method has the characteristics that the model performance and accuracy are improved, the limitation of a single model is avoided, and the interpretability of emotion perception model logic and input feature matrix dimension reduction is realized.

This invention relates to an audiovisual bimodal emotion recognition system and method for assisting robot decision-making, belonging to the field of artificial intelligence and pattern recognition technology. The invention first extracts and aligns features from visual and audio signals; then, it uses domain adversarial learning to remove speaker identity interference and learn cross-domain invariant emotion features; simultaneously, it introduces mask contrast learning to enhance robustness to speech loss; next, it utilizes a video-guided attention gating mechanism to achieve dynamic adaptive feature fusion; finally, it inputs the emotion recognition results into a decision-making module and performs end-to-end joint training with the recognition task using reward signals based on interaction effects. This invention effectively solves the problems of poor generalization ability, insufficient robustness to modal loss, static fusion strategies, and perception-decision disconnect in existing technologies, providing assistive robots with more accurate, robust, and context-adaptive emotion perception capabilities, thereby driving more natural and effective human-computer interaction.

The application discloses a method and system for emotion perception and empathetic interaction adjustment of embodied agents. Non-contact acquisition of user physiological signals: millimeter wave radar measures respiratory rate and heart rate, a camera measures heart rate and / or heart rate variability through remote photoplethysmography rPPG, and wearable device signals can be optionally fused; real-time evaluation of signal quality of each mode and adaptive fusion, estimation of emotional / stress state containing physiological arousal based on individualized baseline; adjustment of robot behavior strategy based on the estimation, output of embodied behavior parameters such as speech speed, action amplitude, close distance and dialogue content; the behavior strategy adopts reinforcement learning, and physiological arousal is included in the reward function, so that the robot learns embodied behaviors for reducing user discomfort and improving comfort, and an empathetic interaction closed loop of perception-estimation-decision-execution-re-perception is formed. The application does not need long-term wearing, can more truly reflect the internal state, is robust, safe and privacy-friendly, and is suitable for companion and education robots.

This invention discloses an emotion-aware voice interaction method and system for plush toys, relating to the field of voice processing technology. The method and system include: S1, collecting basic voice interaction data from within the plush toy and preprocessing it; S2, performing word-frame binding, acoustic change localization, and semantic parsing on the basic voice interaction data to generate candidate boundary positions, device control voice segments, and emotional expression voice segments; S3, generating instructions, generating data packets, and performing flow verification feedback based on the results of flow assignment reliability analysis; S4, performing local control execution, cloud-based emotion understanding, feedback execution, feedback execution switching, and data archiving. This solves the problem of existing interaction methods lacking intra-sentence boundary and segment attribution determination for device control intent and emotional expression intent within the same continuous voice, which easily leads to incorrect flow assignment between local control execution and emotional dialogue response.

The invention discloses an outbound system interaction method based on voice recognition, and relates to the technical field of outbound system interaction, and the method comprises the steps: obtaining callback data and combining an anger grade coefficient, calculating emotion preferential money of an anger customer, informing the customer in the form of coupons, and issuing the coupons; according to the method, the regional emotion coefficient and the emotion data are combined, the emotion perception index is scientifically calculated by utilizing a formula, the customer emotion can be objectively distinguished into satisfaction or anger, and the anger emotion is finely recognized; the angry level coefficient is calculated through the voice feature data, and the severity of the angry emotion of the client is quantified; the emotion preferential fund is dynamically calculated based on the recovery data and the anger level coefficient, and is automatically issued through voice and information in the form of coupons, so that real-time response and compensation to the emotion of the customer are realized, the anger emotion of the customer is effectively recovered, the customer satisfaction is remarkably improved, and the customer experience is improved. And therefore, the practicability and competitiveness of the outbound system in marketing and customer service scenes can be enhanced.

This invention relates to the field of artificial intelligence conversation processing technology, and discloses a method for generating instruction chains based on contextual intent recognition. The method includes: extracting event types and determining event risk levels from user conversation requests; for low-risk or medium-risk events, establishing an intelligent conversation window; dynamically capturing user input and contextual information to form basic conversation features; inputting these features into an explicit intent recognition network to obtain first intent information and confidence level; generating and displaying a response based on the first intent, while simultaneously scoring it in real time using an emotion perception model; when the user's emotion or intent confidence level is below a threshold, constructing enhanced conversation features and inputting them into a implicit intent recognition network to obtain second intent information; finally, combining the first and second intents to generate an ordered instruction chain. This achieves accurate intent recognition, intelligent response, and automated business operations in multi-turn conversations.

The application 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 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, modeling time sequence and emotion perception of cross-round semantic association in the triple knowledge graph, and outputting a dialogue context vector; S3, inputting the dialogue context vector and current user input features into a generation model, performing semantic mapping through an independent intent, entity and behavior embedding encoder, and combining logical constraints and emotion-driven weight adjustment from the triple knowledge graph to generate a robot behavior; S4, optimizing the generation model based on a task-related adversarial training mechanism; and S5, performing multi-modal emotion fusion and consistency verification.The application can realize dynamic, coherent and emotion-intelligent multi-round dialogue logic optimization.

This application relates to the field of vehicle technology, and in particular to a method, device, vehicle, and storage medium for calming children's emotions. The method includes: acquiring the child's current state data; inputting the current state data into a preset emotion recognition model to obtain the child's dominant emotion, wherein the preset emotion recognition model is trained from state data labeled with emotions; matching a set of calming actions from a preset strategy library based on the dominant emotion, and controlling the vehicle to execute the calming actions based on the set of calming actions. This solves the problems of delayed emotion perception in children's emotional processing and static, singular calming strategies, enabling proactive execution of corresponding calming actions based on the child's emotions, thus ensuring driving safety.

This application provides a method, apparatus, electronic device, and storage medium for outputting perceived voice emotion tags, applied in the field of smart home device control. The method includes: acquiring user voice input information and corresponding text input information; extracting voice emotion features from the voice input information and text emotion features from the text input information; performing multimodal fusion of the voice and text emotion features to identify the user's emotional state; generating corresponding emotion tags based on the user's emotional state; and controlling at least one smart home device to perform corresponding scenario-based operations based on the emotion tags. The perceived voice emotion tag output method provided in this application enables smart home systems to proactively provide personalized service responses based on the user's current true emotions, achieving an intelligent upgrade from passive command reception to proactive emotion perception, effectively improving the naturalness of human-computer interaction and the comfort of the user experience.

The invention discloses a smart home user behavior prediction method based on semantic analysis, and relates to the technical field of natural language processing. The method comprises the following specific steps: data acquisition: respectively acquiring user interaction data, equipment operation historical data and user interaction feedback data through a voice acquisition device, a user input interface, a smart home gateway and a feedback interface, and uniformly storing the data after formatting processing; the speech acoustic features and the text features are extracted on the emotional semantic recognition level, the pre-training model is used for outputting the user emotional state, the defect that in the prior art, emotional perception is lacked is overcome, in the semantic ambiguity resolution aspect, a learning mechanism with user interaction feedback data as an optimization signal is adopted, and the user experience is improved. When the same number of times of correction of the same fuzzy instruction by the user reaches a preset threshold value, the semantic mapping rule is automatically generated and written into the rule base, and the semantic analysis priority can be adjusted according to the family structure change.