128 results about "Emotional perception" patented technology

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to an intelligent customer service method and system based on a large model, and the method comprises the steps: generating a unique session ID based on a user identity, and associating a historical context; multi-modal input of texts, voices, images and the like is carried out, and data standardization is carried out through a unified interface; realizing mixed intention recognition by combining a rule engine and a large model; constructing a dynamic customer portrait through multiple rounds of dialogue records, historical work orders and business data; according to the question emergency degree and customer value grading response, analyzing customer emotion in real time, adjusting a reply style, and outputting an answer; the manual operation process of the traditional customer service is converted into an AI automatic closed loop; the method has the beneficial effects that customer intention deep understanding, multi-mode interaction, emotion perception and service process automation are realized through a large language model (LLM) technology, and the service efficiency and the user satisfaction are remarkably improved.

The invention relates to an emotion accompanying robot system based on multi-dimensional perception and an interaction method, and belongs to the technical field of intelligent robots. The system comprises a sensing module which captures micro-expression, voice, body temperature, touch and other multi-source data through a biological radar array, multispectral imaging and a touch sensing fabric; the decision-making module quantifies the emotion intensity by using an emotion state calculation engine, constructs a user personalized emotion file in combination with the dynamic knowledge graph, and generates a dynamic emotion graph; and the execution module realizes anthropomorphic emotion expression through bionic face driving, joint compliance control and thermal feedback. The interaction method comprises the steps of data capture, emotion file construction, dynamic graph generation, interactive execution and feedback adjustment, definition of an emotion intensity quantification formula, a graph edge weight model and the like. According to the method, physiological signals, environment situations and bionic expression are fused, multi-modal precise emotion perception and safe interaction are achieved, man-machine naturalness is improved, the method is suitable for elderly accompanying, psychological counseling and other scenes, and the emotion accompanying effect is enhanced.

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 schizophrenia early warning and evaluation system based on human brain multi-region signals, and relates to the technical field of early warning and evaluation, and the system comprises a neural connection analysis unit which is used for recognizing neural connection characteristics of a brain network based on biomarker characteristics, comparing the neural connection characteristics with a database, and determining the neural connection characteristics of the brain network; clustering individuals with similar neural connection features according to a comparison result to generate a risk group; the emotion perception analysis unit is used for acquiring facial emotion states of the risk group in a preset time period and predicting evolution trends of neural connection features in different facial emotion states; and the early warning evaluation unit is used for inputting the evolution trend of the neural connection characteristics into a pre-constructed evaluation model, outputting a risk level evaluation result and formulating an early warning measure based on the risk level evaluation result. According to the method, neural connection features and emotion perception analysis are combined, so that early-stage neural connection abnormity and emotion turning intervals of schizophrenia can be accurately identified and positioned.

The invention provides an analgesia feedback and control method and system based on multi-modal data, electronic equipment and a storage medium, and relates to the technical field of analgesia feedback and control. The method related to the technology comprises the following steps: acquiring pain associated data based on a multi-mode sensor and an emotion perception assembly; generating a comprehensive pain index based on the pain sense associated data, and matching an analgesia scheme based on the comprehensive pain index; or directly outputting an analgesia scheme based on the pain associated data; and performing analgesia control based on the analgesia scheme. According to the analgesia feedback and control technology, data, such as electroencephalogram, voice emotion and environment, associated with pain sense are integrated, the limitation of a single signal is broken through, and analgesia feedback and control are more accurate and intelligent.

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 belongs to the technical field of robots, and provides an accompanying robot control method and system based on emotion perception classification. The method comprises the following steps: acquiring multi-dimensional emotion related data of a target object, and analyzing based on the emotion related data to obtain an emotion anomaly judgment probability; when the emotion abnormity judgment probability is higher than a probability threshold value, performing voice interaction with the target object, and processing voice interaction data and emotion related data by using a multi-modal emotion classification model to perform emotion perception classification on the target object to obtain an emotion type and an emotion degree; and when the emotion type is an abnormal emotion and the emotion degree exceeds the regulation and control capability of the accompanying robot, determining external personnel, including emotion regulation and control personnel and / or safety personnel, based on the emotion type and the emotion degree, and scheduling the external personnel to perform emotion intervention on the target object. The method can break through the emotion processing limitation of the accompanying robot, and remarkably improves the accompanying service quality and the user emotion care experience.

The invention discloses a human-computer interaction qi and collateral dredging robot adjusting method which comprises the following steps: acquiring basic information of a user, and predicting initial massage parameter setting of the user; identifying a user operation intention by adopting a natural language processing technology, and generating a massage parameter adjustment scheme; mFCC features are calculated through fast Fourier transform, and the voice emotion state of the user is recognized; constructing a user posture emotional state recognition model, recognizing the posture emotional state of the user, and judging the emotional state of the user in combination with the voice emotional state of the user; according to the recognition result of the emotion type of the user, generating a massage parameter adjustment prompt of the qi and collateral dredging robot; the instant response of the user after the massage parameters are adjusted is obtained in real time, and the massage parameter adjusting effect is judged and optimized. Through combination of emotion perception and personalized adjustment, more intelligent, convenient and comfortable massage experience is provided, the man-machine interaction efficiency is improved, and more efficient, more intelligent and more convenient massage service is realized.

The invention discloses a digital human contradictory dispute mediation method based on intelligent perception and emotion regulation. The method comprises the following steps: firstly, acquiring voice information and visual information of two contradictory parties; calculating a voice emotion score by using the voice information, calculating a visual emotion score by using the visual information, and carrying out weighted calculation to obtain a comprehensive emotion score; then generating an initial speech speed, an initial expression and an initial gesture of the digital human; matching a proper response strategy from the knowledge base and transmitting the response strategy to the digital person; and finally, mapping voice information in the voice emotion score to a voice synthesis parameter, generating emotional voice signals consistent with both parties of a contradictory dispute in language by combining a response strategy, and outputting the emotional voice signals by a digital person for contradictory dispute mediation. In the mediation process, the digital person monitors the comprehensive emotion score and the contradictory scene in real time for dynamic adjustment. The real emotional state of the user is accurately captured based on intelligent perception, emotion mediation is carried out based on emotion perception, and the reality sense of digital human interaction and the user trust degree are improved.

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 vehicle-mounted interaction system and method for adjusting the emotion of a driver, a vehicle and a medium, and relates to the technical field of intelligent driving. The vehicle-mounted interaction system comprises an emotion sensing module, a data processing module and a feedback control module which are connected with one another, the emotion sensing module is used for acquiring multi-modal information of a driver, and the multi-modal information comprises facial data, physiological data and voice data; the data processing module is used for fusing the multi-modal information and determining an emotional state level of the driver; and the feedback control module is used for controlling operation parameters of the multi-channel actuator according to the emotional state level, and the multi-channel actuator comprises an auditory channel, a visual channel, an olfactory channel and a tactile channel. By adopting the vehicle-mounted interaction system, the emotion of the driver can be actively sensed and cooperatively adjusted, and the driving safety and comfort are effectively 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 discloses a case mediation sentiment analysis and decision support method based on deep learning, and the method comprises the following steps: S1, collecting multi-source data in a mediation process, carrying out the preprocessing, and constructing a multi-modal input feature set; s2, constructing a dual-channel collaborative attention network emotion recognition model; s3, optimizing model structure hyper-parameters by using a wild goat optimization algorithm; s4, reasoning multi-modal features, and outputting a structured emotion vector; s5, constructing a multi-node mediation state map, and depicting an emotional evolution path; s6, generating a mediation behavior scheme based on the multi-node mediation state map and the strategy rule; and S7, collecting feedback data, and updating the model and the strategy library. The invention aims to improve the emotion perception accuracy and the mediation decision intelligent level in the case mediation process through a multi-modal emotion recognition and strategy generation mechanism.

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 relates to a color temperature-based blind person color pattern emotion perception enhancement method and system, and the method comprises the steps: collecting a target image, and extracting the contour information and color information in the image; performing HSV judgment to obtain a cooling and heating classification result; mapping the cooling and heating classification result to a preset temperature interval, and generating a temperature control signal; according to the contour information, driving a target pin in the tactile array to protrude to form a tactile contour area; a temperature control signal is applied to the heating unit corresponding to the touch contour area, so that the temperature of the protruding pin is adjusted to be the target temperature corresponding to the cold and warm classification result. The method has the beneficial effects that the image color temperature is mapped into five grades of temperature gradients, so that a blind user can perceive a complete color emotion pedigree through touch, accurate touch expression of color emotion information is realized, and a visual color temperature-temperature sensing corresponding relation is established.

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 provides an emotion perception and guest room management method based on voiceprint recognition, and the method comprises the steps: generating a service configuration adjustment scheme matched with the emotion tendency of a user according to a demand prediction result, carrying out the fine adjustment of service contents according to the real-time change captured by dynamic data, and obtaining adjusted service parameters; if there is a deviation between the optimized emotional periodic change rule and the actual user feedback, updating the voiceprint feature matrix and the emotional tendency distribution by re-collecting the long-term data record, and determining a new periodic fluctuation feature; and according to the new periodic fluctuation characteristics, the construction of an emotion portrait model and the personalized demand prediction process are executed circularly, the pertinence of service configuration adjustment is continuously improved, and a final optimized service quality improvement scheme is obtained.

The invention relates to the technical field of artificial intelligence session processing, and discloses a context intention recognition-based instruction chain generation method, which comprises the following steps of: extracting an event type from a user session request, determining an event risk level, establishing an intelligent session window for a low-risk or medium-risk event, and generating a context intention recognition instruction chain; dynamically capturing user input and context information to form basic session features, and inputting the basic session features into a dominant intention recognition network to obtain first intention information and confidence; generating and displaying a reply based on the first intention, and performing real-time scoring through an emotion perception model; when the emotion or intention confidence of the user is lower than a threshold value, constructing an enhanced session feature and inputting the enhanced session feature into the implicit intention recognition network to obtain second intention information; and finally, generating an ordered instruction chain in combination with the first intention and the second intention. Therefore, accurate intention recognition, intelligent reply and automatic business operation in multiple rounds of sessions are 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 a robot lacrimation control method and a microfluid lacrimation execution device for a robot, and relates to the technical field of bionic robot emotional interaction.The robot lacrimation control method comprises the steps that multi-modal sensing data of the robot is obtained, determining a first code used for judging an emotional state and a second code used for judging a physical stimulation response of the robot according to the multi-modal sensing data; and according to the first code and / or the second code, determining an emotion code of the robot, and according to the emotion code, determining a tear instruction for controlling the operation of a microfluid tear execution device of the robot, so that the robot tears. The emotional codes are generated through an emotional state and physical stimulation double-path mechanism, the simulated lacrimation effect matched with the human emotional state can be achieved, the purpose of rich physiological feedback is achieved, and the lacrimation performance is improved through a liquid storage device, a lacrimation channel, a lacrimation outlet and a miniature pressure pump of the lacrimation execution device. And the robot can realize accurate tear control on the basis of emotion perception of physiological feedback.

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.