57 results about "Affective response" patented technology

The invention discloses an AI digital human interaction system based on emotion recognition, relates to the technical field of emotion calculation, and improves the accuracy of emotion recognition through multi-modal emotion data acquisition and processing of an emotion primary recognition module and a secondary recognition module. The system effectively distinguishes complex emotions, such as fatigue and depression, of the trainee in the virtual education task by calculating the first emotion coefficient and the second emotion coefficient; secondary emotion recognition is carried out, so that misjudgment is reduced, and the emotion state recognition accuracy is ensured; through an emotion feedback module, the system dynamically adjusts the expression, intonation and interaction frequency of the AI digital human according to a second emotion coefficient, so that the emotion feedback is more natural, and the emotion recognition sense of the student is enhanced; besides, the culture adaptation analysis module collects cross-culture emotion data based on the culture background of the trainee, calculates and evaluates a culture adaptation coefficient, and automatically adjusts an emotion recognition and feedback strategy, thereby improving the adaptability of the system in a multi-culture environment and the accuracy of emotion expression.

The invention discloses a text-driven advertisement video generation method based on emotion feedback, relates to the technical field of digital advertisement and man-machine interaction, and aims to dynamically adjust advertisement content and improve individuation, interactivity and conversion rate by collecting emotion data such as facial expression, voice features and eye movement tracks of a user in real time. A text generation video model and a multi-mode emotion recognition technology are combined, and feature fusion and feedback processing are utilized, so that the advertisement video better conforms to the emotion of the user; multi-round feedback and self-adaptive optimization ensure that advertisements transmit core information and flexibly respond to user emotional states at the same time, attraction and participation degree are greatly improved, an innovative feature fusion algorithm further improves individuation and real-time response capability of generated videos, a brand new solution is provided for the field of digital advertisements, and the method is suitable for popularization and application. And the development of individuation and emotional interaction is promoted.

The invention discloses a cognitive training emotion interaction method and system based on a large model. The method comprises the following steps: acquiring a cognitive evaluation result of a patient, and pushing a corresponding cognitive training scheme to the patient; before cognitive training, acquiring the current emotional psychological state of the user to judge whether the cognitive training requirement is met or not, and if not, performing emotion regulation until the cognitive training requirement is met; during cognitive training, acquiring multi-modal data of the patient and inputting the multi-modal data into a preset large model to perform emotion calculation, so as to identify the current emotional psychological state of the patient again; and based on the current emotional psychological state of the patient, carrying out interaction feedback of a corresponding mode on the patient so as to carry out personalized emotional interaction with the patient. According to the method, the emotional state of the user is recognized in advance before cognitive training, emotion feedback of different modes can be performed according to the current emotional psychological state of the patient in the cognitive training, so that the training compliance of the patient is improved, and the cognitive training effect is improved.

An AI-driven emotion recognition system for learning adaptation in educational institutions, comprehensive: • an emotion recognition unit that tracks students' facial expressions in real time and categorizes the resulting expressions into a specific emotional class from a given set using a Convolutional Neural Network (CNN) trained on a large emotion dataset; • a real-time processing unit that extracts facial features and dynamic emotional characteristics of students through adaptive filter algorithms; • an LMS integration unit, adapted to combine discovered emotional knowledge with online and offline learning environments for customizable learning adjustments; • a personalization engine programmed to intelligently adapt training content, response approaches, and training materials according to the identified emotional states, so that each learner receives individually tailored and more intensive learning experiences; • a feedback loop that sends educators real-time alerts and recommended videos to address emotional feedback or lesson gaps, allowing teachers to tackle negative signals in the moment (e.g., frustrated face, etc.); • AI data storage and adaptive learning model unit that stores anonymized data for emotion recognition, updates the AI ​​model and improves it through learning over time, and ensures that the use of the information is in compliance with data protection regulations; • A user interface module specifically designed for educators, providing a visual overview of students' emotional patterns and real-time analysis of their emotions and engagement as a basis for important decisions that lead to improvements in teaching processes.

The invention discloses a digital human student driving method based on a large language model agent, and the method specifically comprises the steps: initializing digital human student configuration information, and loading a knowledge system and background setting; during teaching, voice of a teacher is converted into characters in real time, and accurate transmission of information is ensured; teaching content is accurately analyzed by means of an intention recognition module, and teaching intentions and stages are recognized, so that digital human students can respond in time; during question asking, the digital person student feeds back according to knowledge reserve and state, such as hand raising or doubt; through short-time memory updating and dynamic knowledge base adjustment, continuous learning adapts to the teaching progress, and diversified learning behaviors and emotional responses are shown. According to the digital human student driving method, the teaching process is divided into a plurality of stages, and input and output of stage intelligent agents are subjected to formatted constraint, so that digital human students can simulate real classroom interaction and show different learning behaviors, emotional responses and classroom performance, and the method is used for teaching simulation practice of normal teachers.

The invention discloses an intelligent accompanying robot system based on multi-modal interaction and a control method, which adopt a dual-chip collaborative framework to realize multi-modal emotion interaction. According to the equipment, a quantized and optimized lightweight face recognition and voice detection model is locally operated in an off-line manner through an ESP32-S3 chip, and facial expressions and voice emotions of a user are analyzed in real time; meanwhile, an STM32F103RCT6 chip is adopted to be responsible for peripheral control, and the peripheral control comprises OLED expression display, body movement driven by a servo motor and a music feedback module, so that visual and dynamic emotion feedback is achieved. A task polling mechanism is utilized, face and voice recognition tasks are efficiently and alternately processed under limited computing resources, and low power consumption and high responsiveness of the system are ensured. The interactive naturalness and the user immersion of the intelligent toy are improved, and the intelligent toy has a good personalized emotion regulation effect and is suitable for the fields of child accompanying, psychological counseling, special crowd assistance and the like.

The invention discloses a psychological counseling assisting system and method based on user feature analysis, and the method comprises a data collection module which is used for responding to an online psychological counseling starting signal of a target user, acquiring a facial image of a target user acquired by a camera device, acquiring voice data of the target user acquired by a sound receiving device, and acquiring physiological signal data of the target user acquired by intelligent monitoring equipment worn by the target user; the feature analysis module is used for extracting facial features, sound features and physiological features and analyzing emotional state information of the target user in real time according to the facial features, the sound features and the physiological features; the feedback module is used for generating emotion feedback information according to the emotion state information of the target user and sending the emotion feedback information to the display equipment; and the display device is used for displaying the emotion feedback information so that the psychological consultant can continue to provide psychological consultation service for the target user after reference.

The invention discloses a multi-mode emotion resonance system with an emotion feedback closed loop, and belongs to the field of artificial intelligence and human-computer interaction. The system comprises eight modules including a multi-modal emotion acquisition module, a modal alignment and fusion module, a high-dimensional emotion modeling module, an individualized context construction module, a resonance generation and adjustment module, a behavior prediction triggering module, an interactive feedback deposition module and a strategy closed-loop evolution module. Micro expressions and acoustic and physiological features are sensed through multi-source signals, a subjectively-adjusted emotion vector field and a dynamic context map are constructed, a resonance reaction path is generated through heterogeneous map mapping and multi-layer reasoning, closed-loop linkage of sensing, reasoning, resonance, response and regeneration is finally achieved, and continuous emotional sharing and strategy self-adaptive optimization among individuals are supported. The method has the beneficial effects that closed-loop adjustment of cross-modal emotion recognition and individualized emotional response is realized, and the emotion understanding depth and response adaptability in human-computer interaction are remarkably improved.

The invention relates to an intelligent sight interaction system for emotional communication. The system comprises a sight capture module, a head posture recognition module, an emotion analysis module, a digital human generation module, an interaction control module and a multi-mode output module. According to the system, sight line tracking is carried out through collected eye images, head posture correction is carried out based on face key points and a 3D model, sight line / expression / posture cooperative processing is carried out through a multi-mode sentiment analysis model, and the technical means of dynamically generating digital human sight line and expression parameters is combined; the problems of tracking errors and single emotion feedback caused by head movement in traditional sight line interaction are solved, high-precision sight line capture and emotional digital human real-time response are achieved, man-machine interaction reaches the naturalness degree close to real sight contact, and meanwhile the adaptability to the emotional state of a user is improved.

The invention discloses a dialect recognition and emotion feedback multi-mode interaction system based on large model driving, and relates to the technical field of intelligent man-machine interaction, the system comprises a dialect emotion interaction engine, an article situation analysis module and a situation self-adaptive response module, and the problems of dialect emotion transmission distortion and situation understanding splitting in the prior art can be solved. The dialect emotion interaction engine generates a target dialect acoustic parameter through a pronunciation decoupling architecture, and combines a thermodynamic diagram to dynamically compensate and optimize emotion expression; the article situation analysis module improves the emotion recognition robustness in a complex environment based on an article-emotion knowledge graph and cross-modal arbitration; the context adaptive response module generates adaptive dialect responses according to the emotions and the item semantics. According to the system, technologies such as end-to-end generation and adversarial learning are adopted, zero-sample dialect adaptation and progressive capacity upgrading are achieved, the emotion requirements of old dialect users can be accurately matched, and the interaction experience is improved.

The invention relates to the technical field of child psychotherapy assistance, and discloses a heuristic story generation method and device based on sand table toy placement, and the method comprises the steps: recognizing the type, spatial relation and placement time sequence of toys in a sand table through a multi-modal perception fusion technology, and combining symbolic meaning analysis, role relation network construction and emotion recognition to generate a heuristic story. A heuristic story generation algorithm is utilized to automatically generate a personalized story conforming to the psychological state of the child, and the story content can be dynamically adjusted according to real-time emotion feedback of the child, so that the problems that traditional sand table treatment depends on subjective experience of a therapist, is low in efficiency and lacks standardization are solved, and the accuracy and effectiveness of sand table game treatment are improved.

The invention provides a drawing projection marriage matching test system based on a visual converter and a method thereof. According to the system, drawing behaviors, emotion feedback and interaction data of a user are collected, multi-modal feature extraction is carried out through a visual converter, and drawing styles, psychological features and emotion preferences of the user are deeply analyzed through a self-supervised clustering algorithm. A unique emotion feedback loop is embedded into a system, and micro expressions, gestures and emotion changes shown by a user in the drawing process are captured in real time, so that more accurate personalized marriage matching recommendation is realized. The system generates a comprehensive love and marriage matching report, provides customized love and marriage suggestions in combination with potential characters, behavior modes and emotional requirements of users, and is matched with emotional management and mental health support tools. The innovative system improves the scientificity, interactivity and deep emotion association of love and marriage matching, has high application value, and can be widely applied to the fields of love and marriage platforms, emotion consultation and intelligent mental health.

The invention relates to the technical field of automatic driving, and discloses a driving decision sharing method and system of an automatic driving vehicle and a medium, and the driving decision sharing method of the automatic driving vehicle comprises the steps: collecting facial expressions, sound features and physiological signal data of passengers through multiple sensors, and constructing a multi-channel emotion perception system; extracting cultural background features of passengers, and constructing an emotion recognition model sensitive to cultural dimensions; historical driving data and emotion feedback of the user are analyzed, and unique driving habits and preference features of the user are extracted; combining the emotion evaluation values of the culture layer and the individual layer to construct a culture individual double-layer emotion model; based on the current emotional state and the individual preference, generating a personalized comfort score, and selecting an optimal driving decision by adopting a multi-objective optimization mode; according to the invention, the user acceptability and commercialization potential of the automatic driving system are improved, and the technical problem that the automatic driving system in the prior art lacks the ability to perceive the emotional state and driving preference of passengers is solved.

An electronic device includes a context scanner, an emotion feedback module, and a processing unit. The context scanner scans an area during a predetermined time period to detect position and proximity information of the electronic device of a user. The position information indicates the geographic location of the electronic device within the area. The proximity information indicates that the electronic device is in the proximity of a second electronic device of a second user. The emotion feedback module detects the user's emotional response during the time period by sensing physiological and physical signals of the user. The processing unit executes an algorithm that determines the relationship between the position information, the proximity information and the emotional response. The processing unit generates a report based on the relationship. The report indicates how the user's presence in the geographic location and the user's proximity to the second user influence the user's well-being.

A user emotion feedback method, apparatus, and computer-readable storage medium for expressing emotions in social circle interactions through virtual figures. The method displays a first user interface containing a social circle message posted by a second account on a first client logged in with a first account. Based on a trigger operation on a user feedback control for the social circle message, feedback information of the first account's user emotion is displayed through a first virtual figure corresponding to the first account. The user feedback control is configured to provide emotion feedback on social circle messages, enabling expressive and personalized emotional responses through virtual figure representations.

The invention provides a hand rehabilitation training method, device and equipment based on emotion feedback and a medium, and relates to the technical field of intelligent medical treatment. The specific implementation mode comprises the steps that in the process of hand rehabilitation training of a target patient, the emotional state of the patient is monitored, emotional monitoring data of the patient is obtained, and emotional features are extracted from the emotional monitoring data; inputting the sentiment features into a sentiment classification model to obtain a current sentiment state category of the patient; the sentiment classification model is a classification model pre-constructed based on a support vector machine; and dynamically adjusting the current hand rehabilitation training scheme of the patient according to the current emotional state category of the patient. The problems that in the prior art, emotion monitoring and feedback are lacked, the training mode is single, and the patient participation degree is low are solved, an emotion recognition and feedback mechanism is fused, a personalized hand rehabilitation training scheme is intelligently recommended and dynamically adjusted according to the emotion state and the rehabilitation progress of the patient, and the rehabilitation effect and the patient positive degree are improved.

This invention discloses a method, device, electronic device, storage medium, and process for implementing an emotional intelligent agent and its memory system based on a state machine, belonging to the field of artificial intelligence and human-computer interaction technology. The method includes: constructing a multi-dimensional emotional state machine, defining state vectors containing basic and advanced emotions, and combining a natural decay mechanism and an empathy adjustment algorithm to achieve dynamic emotion updates; establishing a hierarchical memory system with emotion binding, binding emotional states as metadata to memory entries, and achieving memory compression, archiving, and enhanced retrieval through a three-level architecture of short-term, medium-term, and long-term combined with importance scoring; adopting a hierarchical streaming response mechanism, splitting the response process into two layers: rapid emotional feedback and substantive content generation, and reducing interaction latency using streaming output and speculative execution; and using emotional states as the core driving source to synchronously control text tone, speech synthesis intonation, and virtual character animation. This invention, by integrating an emotional state machine and a hierarchical memory architecture, enhances the agent's personality continuity and long-term companionship ability, achieving low-latency, multimodal synchronous natural human-computer interaction.

The invention discloses a pet digital twinning generation and interaction method and system based on multi-modal data and user definition, and a virtual pet with high-fidelity restoration and personalized creation functions is constructed through multi-modal data processing or user parameter input. The technology covers the following two core scenes. In the digital twinning mode, a highly restored virtual body (such as the appearance, behavior and emotional response of a pet 'Rifu') is generated based on pet pre-birth data (such as images, videos and voices), and the emotional accompanying requirement of an owner is met; the user-defined mode supports a user to create a special pet (such as a cat image and a small ball playing habit of'seven-seven ') through a preset template or natural language description, and the requirements of people with limited pet keeping conditions are met. The system realizes immersive pet accompanying experience of full-scene users through a dual-mode architecture, dynamic emotion response and virtual and real scene synthesis, and breaks through the application limitation of the traditional technology.

The present invention relates to the field of generative artificial intelligence (AI), virtual reality, IoT sensor-based system More particularly, the present disclosure relates to a biofeedback VR system for enhancing emotional intelligence and skill development of users. The system (108) includes a plurality of biofeedback sensors (114), processor (202) embedded with a virtual reality simulation engine (214) and an artificial intelligence (AI) engine (212) and a computing device (104) with an application connected to the processor (202). The simulation is dynamically adjusted based on the emotional responses and physiological reactions of primary user (110). The present disclosure relates to a biofeedback VR method for enhancing emotional intelligence and skill development of users and also relates to a system with dynamic adjustment ensuring that the emotional intensity of simulations is in sync with the user's current emotional state, fostering a conducive learning environment for Emotional Intelligence (EI) growth.

The invention relates to the technical field of data processing, and discloses a modeling method, device and equipment for robot emotion perception and a medium, which can be applied to the financial and medical fields, and the method comprises the following steps: obtaining a dynamic own emotion state vector by combining a mapping relationship between a fusion external emotion feature vector and an emotion state vector of weighted fusion; constructing time sequence emotion trajectory data based on the dynamic self emotion state vector, and predicting the time sequence emotion trajectory data to obtain an emotion state prediction result; and evaluating and updating an emotional state prediction result, and outputting an emotional perception model. In the invention, aiming at the problems of single perception dimension and poor model generalization ability in an existing robot emotion perception process, multi-modal external perception data can be fused, an own emotion state vector can be constructed and dynamically updated, and model parameters are optimized in combination with time sequence prediction and scene feedback, so that the robot emotion perception method has the advantages that the perception dimension is simple, and the model generalization ability is poor. The common emotional and emotion feedback effect in the man-machine interaction process is enhanced, and the generalization ability is good.

The invention discloses a barrier-free communication system based on emotion recognition feedback, and the system comprises an emotion recognition module which is used for receiving and analyzing a voice signal in real time, and recognizing an emotion state in voice through employing an emotion recognition algorithm; the emotion feedback module is used for generating and displaying emotion feedback information, and the emotion recognition algorithm executes the following operations: S1, performing preprocessing operation on a voice signal; s2, capturing spectrum features of the voice signals, and performing feature extraction on the text data; s3, fusing the extracted features by adopting a weighted average algorithm; s4, training the fused features and constructing an emotion classification model; s5, optimizing model parameters of the sentiment classification model; and S6, performing feature extraction on the new language signal, performing sentiment classification, identifying the sentiment category of the voice signal, and outputting the sentiment category. According to the method, more comprehensive and accurate emotion feature representation can be formed, accurate emotion state information is obtained, and the robustness and accuracy of emotion recognition are improved.

The invention provides a recommendation system-oriented agent system construction method, device and equipment. The method comprises the following steps of: performing stratified sampling to obtain a representative user sample set; basic attributes, social features, unique grades and initial social relations of the agents are determined, and user portraits are defined; establishing a fact memory and an emotion memory of the agent, and determining an initial relation weight; dynamically calculating and updating the relation strength between the agent and other users through the relation weight; regularly reducing the relation weight according to the attenuation coefficient and the time period; configuring an action selection strategy based on social interaction, relation weight updating and emotional reaction; expanding the action list, and setting the influence of action consequences; and completing initialization and preparing to start interaction. According to the method, the social relation of the user samples is reserved, the agent is generated on the basis, user behaviors can be accurately reflected, the complex social relation between the samples can be simulated, and interaction based on the social relation is carried out in the simulation environment.

The invention discloses an emotion recognition method and system, electronic equipment, a storage medium and a program product in the technical field of artificial intelligence, and the method comprises the steps: extracting text feature data and target type features from emotion reaction data of a target object perceived by a user, and generating personalized features according to personality test questionnaire data of the user. Aligning various time sequence features of the target type features in a time dimension, and fusing various time sequence features under various time steps based on an attention mechanism to obtain fused emotional reaction features; and carrying out dimension reduction processing on the fused emotion reaction features, the text feature data and the personalized features to obtain multi-modal feature data for emotion recognition. The emotion recognition method and device can solve the problem that the emotion recognition precision cannot meet user requirements due to insufficient consideration of personalized factors in related technologies, and in the emotion recognition process, the personalized factors are fully considered, so that the emotion recognition precision is effectively improved.

The application relates to the technical field of automatic driving and discloses a driving decision sharing method, system and medium of an automatic driving vehicle, wherein the driving decision sharing method of the automatic driving vehicle comprises the following steps: collecting facial expressions, sound features and physiological signal data of passengers through multiple sensors to construct a multi-channel emotion perception system; extracting cultural background features of the passengers to construct an emotion recognition model sensitive to cultural dimensions; analyzing user historical driving data and emotional feedback to extract unique driving habits and preference features of the users; combining emotional evaluation values of the cultural layer and the individual layer to construct a cultural-individual double-layer emotion model; generating a personalized comfort degree score based on a current emotional state and individual preferences, and selecting the best driving decision in a multi-objective optimization mode; the application improves user acceptance and commercialization potential of an automatic driving system, and solves the technical problem that the automatic driving system lacks emotion state and driving preference perception ability in the prior art.

The invention provides an intelligent control method and device for a bionic animal toy. The intelligent control method comprises the following steps: analyzing behavior semantics of a target user based on a three-dimensional space trajectory of a hand joint point of the target user to generate a behavior intention vector; synchronously determining an emotion feature tensor when the target user interacts with the bionic animal toy, and further constructing a time-space association map of emotion and behavior of the target user in the interaction process based on the emotion feature tensor and the behavior intention vector; determining humanization state characteristics of the bionic animal toy according to the historical interaction data, and generating a motion primitive sequence based on the space-time correlation map and the state characteristics; and converting the motion primitive sequence into a steering engine control instruction stream to control the bionic animal toy to execute an interactive action with emotion feedback. By adopting the scheme of the invention, the behavior semantics and emotion characteristics of the user can be extracted by constructing a multi-modal interaction clue analysis mechanism so as to realize personalized emotion feedback of the bionic animal toy.

The application discloses a voice emotion recognition method and system, the method collects voice sample data through a recording device, pre-processes voice sample data and to-be-recognized voice data, extracts voice features of pre-processed voice sample data and to-be-recognized voice data, and establishes an emotion classifier model based on the extracted voice features of the voice sample data; the voice feature data of the to-be-recognized voice data is input into the emotion classifier model, the emotion classifier model compares and analyzes the input feature data to determine the most possible emotion category, and outputs the analysis result after processing. The method provides a voice emotion recognition method with high accuracy, effectively analyzes and understands the emotional state contained in the voice, thereby providing accurate emotional feedback or analysis results, can run in a real-time or near real-time environment, quickly processes and analyzes the voice, and meets the demand of an application scene with high speed requirement.

A method, system, and medium for affective music recommendation and composition. A listener's current affective state and target affective state are identified, and an audio stream, such as a music playlist, is generated with the intent of effecting a controlled trajectory of the listener's affective state from the current state to the target state. The audio stream is generated by a machine learning system trained using data from the listener and / or other users indicating the effectiveness of specific audio segments, or audio segments having specific features, in effecting the desired affective trajectory. The audio stream is presented to the user as an auditory stimulus. The machine learning system may be updated based on the affective state changes induced in the listener after exposure to the auditory stimulus. Over time, the machine learning system gains a robust understanding of the relationship between music and human affect, and thus the machine learning system may also be used to compose, master, and / or adapt music configured to induce specific affective responses in listeners.

The present disclosure provides a signal system, method and application for urban street view visual emotion prediction, comprising a monitoring module, a processing module, a first construction module, a second construction module and an output module; the monitoring module collects the brain electrical signals and emotional evaluation reaction indicators when the subjects watch the street view images; the processing module obtains the relative power of the brain electrical signals in the single-image presentation segment and the multi-image sequence segment; the first construction module uses the relative power of the brain electrical signals in the multi-image sequence segment and the emotional evaluation reaction indicators to construct a brain electrical signal emotion prediction model, which is applied to the brain electrical signals in the single-image presentation segment to obtain the emotional response of any street view image; the second construction module is used for constructing a machine learning model to obtain a street view image emotion prediction model based on the brain electrical signal recognition, and is applied to the street view images in the output module to obtain a city street view visual emotion map; the present disclosure has the advantages of sensitive expression and accurate emotion capture.

The invention discloses an advertising effect analysis method and system based on artificial intelligence, and relates to the technical field of commercial advertising prediction, the advertising effect analysis method based on artificial intelligence can significantly improve the accuracy and effectiveness of advertisement putting, and through multi-modal biological feature collection, the advertising effect is improved. Data of brain waves and eye movement tracks when consumers watch advertisements are obtained in real time, the nerve excitement degree, the attention concentration degree and the emotional response of the consumers are deeply known, a nerve behavior decoding engine constructs a three-layer cognitive mapping model based on the data, the real-time transformation probability of the consumers is accurately predicted, and the conversion efficiency of the consumers is improved. The dynamic advertisement gene pool specifically adjusts visual, auditory and narrative elements of the advertisement according to the prediction result, personalized customization of the advertisement content is achieved, the accurate advertisement putting mode can better meet the requirements and interests of consumers, the matching degree of the advertisement and the consumers is improved, and therefore the conversion rate of the advertisement is effectively improved.

The present application relates to the technical field of artificial intelligence, and discloses a method and system for cognitive analysis of emotional companions in a virtual-real fusion scene, comprising: firstly, sensing the emotional state of a user in the virtual-real fusion scene, identifying the emotional demand category in the interaction task, and querying the corresponding emotional response level; then, analyzing the emotional barrier limit triggered by each level, positioning the emotional fluctuation node of the user in the accompanying process, analyzing the interaction frequency and feedback time limit of the emotional companion at the fluctuation node, quantitatively calculating the perceived response amount, identifying the emotional feedback content based on the perceived response amount, analyzing the matching degree of the emotional feedback content with the virtual-real fusion scene, and finally determining the emotional accompanying state of the emotional companion to the user. The present application can improve the accurate judgment and continuous optimization of the emotional accompanying state of the user.