183 results about "Affective computing" patented technology

The invention relates to the technical field of artificial intelligence and emotion calculation, in particular to an audio and video dual-mode emotion recognition method and system based on adapter fusion. The method comprises the following steps: acquiring a video frame sequence and an audio signal, and preprocessing the video frame sequence and the audio signal; constructing an emotion recognition model; based on a bimodal feature extraction module, a space adapter and a global adapter are embedded in sequence, and corresponding modal enhanced space features and global features are obtained in sequence; generating intermediate representations of the corresponding modes based on the global features, and performing feature fusion according to the intermediate representations to obtain fusion features of the corresponding modes; the fusion features are spliced, time sequence features are extracted, and final features are obtained; inputting the final features into a classifier to obtain a predicted emotion category, training an emotion recognition model by adopting a loss function, and determining an optimal emotion recognition model; and inputting a to-be-recognized video frame sequence and an audio signal into the emotion recognition model, and outputting a recognition result.

The invention provides a virtual human design and application platform, method and device based on artificial intelligence, and relates to the technical field of virtual digital humans. The method comprises the steps of performing local anonymization on multi-modal input data on user equipment, encoding generated anonymized multi-modal features to obtain a multi-modal feature vector, and inputting the multi-modal feature vector into an emotion calculation model to obtain a user emotion intensity quantized value; inputting the multi-modal feature vector into a context sensing model, and generating a user intention vector after context correction in combination with a knowledge graph; generating an updated personality parameter matrix according to the user emotion intensity quantized value and the user intention vector; and outputting voice waveform data, facial muscle motion parameters and skeleton joint coordinate data based on the personality parameter matrix, and driving the virtual digital human three-dimensional model to perform real-time rendering. According to the scheme, the naturalness, emotional resonance and long-term user retention rate of virtual digital human interaction can be improved, and user privacy data security is protected.

The invention discloses an emotion detection system based on facial recognition, and relates to the technical field of computer vision and emotion calculation. A video stream time sequence analysis module is used for extracting a facial micro-expression image sequence of continuous frames, a time sequence feature vector containing a micro-expression intensity gradient, an illumination robustness coefficient and a facial action unit cooperation feature is generated, and a multi-mode dynamic sensing module is combined to carry out real-time analysis on an emotion classification probability, voice emotion parameters and physiological signals. And the fusion decision module performs dynamic weighted fusion on the multi-modal data based on the scene adaptive weight, and finally generates a comprehensive emotion score. Through multi-modal time sequence modeling and a dynamic weight optimization mechanism, the accuracy and environmental adaptability of emotion recognition are remarkably improved, and real-time perception and accurate decision making of customer emotion are realized in a target scene.

The invention relates to the technical field of artificial intelligence customer service, provides an intelligent customer life cycle management AiCRM method and system, and is used for solving the problems of low customer service accuracy and poor response timeliness in the prior art. The method comprises the steps that in the interaction process of a target client and an enterprise, client voice, expression and text data are acquired, multi-modal sentiment analysis is carried out, and a client sentiment feature set is generated; dynamically updating a customer emotion evolution graph based on the customer emotion feature set, and generating an accurate customer emotion score by adopting an emotion calculation model in combination with historical data and real-time interaction information; and when the score is lower than a preset baseline, the system automatically generates a personalized service strategy adjustment scheme matched with the current emotional state and the historical change trend of the customer. According to the application, the customer service accuracy and the response timeliness are improved.

The invention discloses a multi-modal interactive fusion virtual reality emotion computing system, which comprises a multi-modal interactive fusion virtual reality emotion computing device, a multi-modal interactive fusion virtual reality emotion computing device and a multi-modal interactive fusion virtual reality emotion computing device, the virtual reality emotion computing device based on multi-modal interaction fusion further comprises a distributed multi-modal sensing device, a user adaptation interaction module, an interaction feedback module, an emotion extraction module, a multi-modal fusion module and a self-adaptation emotion computing model. The multi-modal fusion module is used for carrying out full-dimensional monitoring and intelligent decision making on a complex scene, the user adaptive interaction module can realize accurate identification and response to user requirements, and the multi-modal fusion module is used for making up for information limitation of a single modal, so that effective interaction and accurate identification can be realized on the whole.

The invention relates to a self-adaptive interaction method based on multi-modal emotion calculation and a robot. The system realizes dynamic management of emotional values of different interaction objects by constructing a time sequence and a real time axis, and improves the memory weight in combination with key events. The system comprises a random event generation unit which simulates an environment event and gives an emotional influence; the user emotion judging unit is used for calculating emotion energy based on real-time input and historical interaction; the user impression dynamic adjusting unit is used for realizing impression weight attenuation and updating; the system emotional energy calculation unit fuses the virtual environment and the real time information to generate a system emotional state; the emotion response strategy unit is used for dynamically generating personalized emotion output according to the system emotion, the user emotion and the user impression; the aggressiveness detection unit prevents aggressiveness content from being output, and interaction safety is guaranteed. The system improves personalization, dynamic adaptation and safety of emotional response, and is suitable for scenes of intelligent accompanying, emotional counseling, customer service and the like.

The invention discloses a cross-subject electroencephalogram emotion recognition method and device based on domain self-adaption and adversarial fusion, and belongs to the field of electroencephalogram signal processing and emotion calculation. The invention provides a cross-subject electroencephalogram emotion recognition method based on domain self-adaption and adversarial fusion, and aims to solve the problems that in the prior art, electroencephalogram signal preprocessing is insufficient in emotional feature retention capacity and cross-subject model generalization is poor, and the method specifically comprises the steps that an electroencephalogram signal data set is acquired, and electroencephalogram signals are preprocessed; after fractional order Fourier transform is carried out on the preprocessed electroencephalogram signals, differential entropy features are extracted; constructing a pre-training model; constructing a classification model; the classification model adopts an encoder in a pre-trained model after pre-training, and then a classifier is added; and adopting the classification model to realize target domain electroencephalogram signal emotion classification. Experiments show that the average accuracy of cross-subject emotion recognition on an SEED data set reaches 88.29%, and the method is suitable for scenes such as brain-computer interfaces and mental health monitoring.

The invention discloses a multi-modal emotion calculation enabling adaptive interactive language learning system and a matching method thereof, and belongs to the technical field of artificial intelligence, and the system comprises a multi-modal emotion real-time perception and quantification module MHAE-Net, a personalized adaptive dialogue and intervention strategy module ADIS-RL, and a virtual dialogue partner interactive interface module EC-NLG. The multi-modal emotion real-time perception and quantification module MHAE-Net is composed of a voice signal acquisition and processing unit, a voice emotion analysis unit, a text emotion analysis unit, a facial expression emotion analysis unit, a physiological signal emotion analysis unit and a multi-modal emotion fusion and decision-making unit. The interactive strategy is dynamically adjusted, the oral anxiety of the language is effectively relieved, the oral confidence is improved, the system adopts the multi-mode emotion calculation technology, the accuracy and robustness of emotion recognition are improved, the targeted interactive strategy is designed for different emotion states, and personalized emotion support and language practice are provided for learners.

The invention discloses a facial emotion analysis method based on spatial-temporal feature network construction. The method comprises the following steps: S1, video frame segmentation and feature extraction; s2, dividing the video into a plurality of time periods according to the set time window length and time step length; and for each time period, constructing a facial feature network representing association between facial features based on the feature sequences of all frames in the time period. The networks obtained through calculation in all the time periods are synthesized, and a dynamic network sequence is obtained; s3, performing dynamic network feature extraction and dynamic key point analysis based on the facial feature dynamic network; and S4, performing emotion calculation or psychological state evaluation based on the dynamic network features or the dynamic key points. The method solves the problems that an existing method is difficult to comprehensively capture complex dynamic characteristics of facial expressions and lacks dynamic correlation modeling and dynamic key point recognition capabilities.

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

The invention discloses a 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.

The invention discloses a robot interaction system and method based on machine learning and emotion calculation, and the method comprises the steps: a robot collects the voice, facial expression, body language and physiological signal data of a user through a multi-mode perception device, and carries out the noise elimination, signal enhancement and feature extraction of the data; utilizing a deep neural network and an emotion classification algorithm to identify the emotion state of the user, identifying mixed emotion and performing label classification; a personalized emotion model is established through machine learning in combination with historical emotion data of the user, an emotion change rule is reflected, and long-term learning and adaptation are carried out; based on the sentiment analysis result, a response conforming to the user sentiment state is generated; through user feedback and interaction, the system continuously optimizes emotion understanding and reaction ability and updates emotion archives; emotion computing tasks are shared through edge computing or cloud computing resources, and real-time response to complex emotion computing tasks is ensured.

A facial expression recognition method based on grid attention and pyramid segmentation attention belongs to the technical field of deep learning image processing and emotion calculation, and comprises the following steps: introducing a grid attention mechanism and a pyramid segmentation attention mechanism on a ResNet101 large model, capturing local expression detail features through a grid attention module, establishing multi-scale global feature association by using a pyramid segmentation attention mechanism; hierarchical segmentation is carried out on the backbone network, and information from multiple hierarchies is effectively fused; and dynamically balancing the contribution degree of each level of features through learnable parameters, and integrating the extracted features to judge the expression category. Experiments show that the model achieves the recognition accuracy superior to that of a traditional model on a public data set in natural scenes such as complex illumination and posture change. According to the method, a solution with high robustness is provided for facial expression recognition in a complex environment, and the method has important application value in the fields of intelligent human-computer interaction, mental health assessment and the like.

The invention relates to the technical field of artificial intelligence and emotion calculation, and discloses an emotion state evaluation method based on multi-modal data, and the method comprises the steps: obtaining and decoupling an implicit signal, an explicit signal and situation data of an evaluated object; and respectively generating implicit features, explicit features and context vectors through double-flow decoupling coding and context knowledge graph coding. And the core engine determines the internal emotional state and the extrinsic emotional representation in parallel, and dynamically regulates and controls a bridging conversion process from the internal state to the predicted extrinsic representation by utilizing the situation vector. Finally, the emotion regulation intensity is calculated by comparing actual and predicted extrinsic representations, and the emotion regulation intensity, the internal state and the extrinsic representations are jointly used as a multi-dimensional evaluation result to be output. According to the method, the dynamic relationship and the adjustment strategy between the internal feeling and the external expression can be disclosed, a deeper and more three-dimensional analysis view angle is provided for the fields of mental health, human-computer interaction and the like, and the method has remarkable application value.

The invention relates to the technical field of computer vision and emotion calculation, in particular to an emotion real-time recognition and long-term monitoring method based on facial expressions. According to the method, an emotion recognition result is obtained by recognizing a high-definition facial image, and the emotion recognition result, environment information and physiological state data are fused to obtain time-space aligned multi-modal data; performing emotional causal analysis based on the multi-modal data, and judging emotional causes by combining a rule engine and a machine learning model: outputting a real-time emotional state recognition result and a periodic emotional report according to the emotional causes, and performing differentiated feedback according to the emotional causes. According to the method, through multi-source data fusion and a causal inference mechanism, the accuracy and interpretability of emotion recognition are effectively improved, and the technical span from passive recognition to personalized active intervention is realized.

The invention discloses a skin electric signal multi-task feature fusion-based mixed emotion recognition method, which comprises the following steps of: acquiring a skin electric signal in a mixed emotion state, performing preprocessing such as filtering and baseline correction, extracting time domain, frequency domain and nonlinear manual features, and extracting deep spatial-temporal features in combination with a CNN-LSTM model; a gating network is designed to realize self-adaptive fusion of manual features and deep spatial-temporal features, so that a multi-task collaborative learning framework with emotion classification as a main task and titer / wakeup degree prediction as an auxiliary task is constructed, and accurate distinguishing of positive, negative and mixed emotions is realized through joint optimization of model parameters. According to the method, classification of mixed emotions is realized, and an extensible technical reference can be provided for the fields of emotion calculation, intelligent health monitoring and the like.

The invention discloses a multi-modal emotion calculation method and system, and mainly relates to the technical field of artificial intelligence and human-computer interaction. Comprising the following steps: synchronously collecting voice, visual and tactile multi-modal data of a user, and carrying out feature extraction on each modal data; performing cross-modal fusion on the extracted multi-modal features, including time sequence alignment and semantic association modeling, and generating a global emotion feature vector; performing dynamic emotion reasoning based on the global emotion feature vector, and outputting an emotion category and emotion intensity; generating a tactile feedback signal according to the emotion category and the emotion intensity, and driving an actuator to output; and updating the emotion memory graph based on user interaction data to complete personalized model self-adaption. The method has the beneficial effects that high-precision and low-delay emotion recognition and natural tactile feedback in a cross-culture scene are realized, and the core bottleneck of dynamic drift adaptation failure and emotion-behavior feedback decoupling in the prior art is solved.

The invention relates to the technical field of handwriting analysis, and discloses a handwriting analysis method and system based on emotion calculation multi-modal recognition, and the method comprises the steps: obtaining handwriting data of a writer, and obtaining handwriting features through employing a multi-modal collection technology, specifically, dividing a writing process into a plurality of writing time periods according to time, and each time period does not exceed 5 minutes; collecting pen point pressure data by using a pressure sensor, collecting writing speed data by using a speed sensor, and collecting font structure data by using an image sensor; for each section of handwriting, executing a feature extraction strategy, fusing dynamic and static features, and generating a multi-modal feature set; inputting the feature set into a pre-trained emotion calculation model; multi-modal fusion breaks through the limitation of a traditional single mode, handwriting features are comprehensively described through a 200 + index, dynamic and static dimensions are combined, and the comprehensiveness of analysis is improved; the 300 + submodel and the million-level sample library ensure the analysis precision, the deep learning architecture captures feature association, and the generalization ability of the model is enhanced through cross validation and online learning.

The invention discloses a student psychological risk perception method based on multiple modes, and relates to the technical field of emotion calculation and intelligent education. The method comprises the following steps: firstly, extracting a facial expression feature vector and a voice intonation feature vector respectively by using a convolutional neural network and Fourier transform through a collected video stream and an audio stream; then adaptive denoising processing is carried out on environmental interference, timestamp alignment and dynamic time warping are carried out on the denoised multi-modal data, time sequence synchronization is ensured, and corrected multi-modal sequence data are formed; then, dynamic emotion track features are extracted from the sequence data, a preliminary emotion state label is generated by comparing the dynamic emotion track features with a baseline threshold value, and the threshold value is adaptively updated in combination with historical data so as to improve the judgment accuracy; and finally, aggregating the emotional state labels of a plurality of students to generate a visual group emotional thermodynamic diagram so as to realize macroscopic perception of group psychological risks. The accuracy, robustness and visualization degree of student psychological state analysis are effectively improved, and an efficient technical means is provided for campus psychological early warning.

The invention provides an emotion and cognition driven children education interaction system and method, and relates to the technical field of man-machine interaction. Emotion is quantified through an emotion calculation module by using a titer awakening degree two-dimensional continuous model, and an emotion migration slope is predicted in combination with a time sequence convolutional network; accurate description and prospective prediction of the emotion dynamics of children are realized, and the accuracy and initiative of emotion guidance are remarkably improved; secondly, the constructed knowledge graph endows the nodes with three-dimensional dynamic attributes of mastery degree, interest intensity and associated confusion degree, and real-time updating is carried out according to interaction data, so that the cognitive development trajectory of children is dynamically described, and a solid foundation is provided for personalized teaching; and finally, proposals are actively pushed by setting collaborative windows of interest, cognitive correction and the like, and a structured knowledge injection interface is provided, so that parents are converted from passive supervisors to common constructors of education contents, and deep fusion of family intelligence and artificial intelligence is realized.

The invention relates to the field of intelligent speech synthesis and emotion calculation, and discloses a digital human tour guide speech generation method, system and device and a storage medium, the digital human tour guide speech generation method comprises the following steps: S1, constructing a user mental model comprising an initial knowledge state of a user for a knowledge graph; s2, on the basis of the model, predicting and evaluating candidate narrative paths, planning an optimal path and determining an expected mental state; s3, multi-modal explanation content is generated and broadcasted according to the optimal path; s4, collecting real-time feedback of the user to obtain a real mental state; and S5, comparing the real state with the expected state, calculating a prediction deviation, and dynamically calibrating the user mental model for subsequent planning according to the prediction deviation. According to the method, prospective path planning is carried out by constructing the mental model, closed-loop calibration and robustness evaluation are combined, and personalized explanation which is accurate, stable and free of lag adjustment is achieved.

The invention belongs to the technical field of intelligent AI, and discloses an AI-based leadership scene simulation partner training intelligent system, which simulates a real leadership scene through the AI technology, helps a user to carry out leadership training in a virtual environment, and comprises a drilling scene setting module, a scene simulation module and an evaluation feedback module. Through a highly-simulated virtual scene and dynamic interaction, the effectiveness of leadership training is remarkably improved, meanwhile, the behavior and response of a real subordinate are simulated through an AI training partner, the dialogue strategy is dynamically adjusted in combination with emotion calculation and trust value management, immersive training experience is provided for a user, and the training efficiency is improved. The method comprises the following steps of: obtaining a multi-dimensional scoring system, immediately reminding and providing verbal skill guidance to help a user to adjust a communication strategy through an AI coach when the user speaks and deviates from a target, and finally quantitatively evaluating the performance of the user through an AI scorer based on the multi-dimensional scoring system, and generating a visual report to meet the requirement of large-scale training of an enterprise.

The invention relates to the field of work satisfaction evaluation, and discloses an express industry work satisfaction evaluation method based on a productivity model, and the method comprises the following steps: collecting the work data of a courier, including the workload, the work efficiency and the customer evaluation; collecting emotional data of the courier, wherein the emotional data comprises emotional fluctuations and emotional labels obtained through voice analysis and text analysis; constructing a productivity evaluation model based on the collected work data, and evaluating the work productivity of the courier; constructing an emotion calculation model based on the collected emotion data, and evaluating the emotional state of the courier; calculating the work satisfaction degree of the courier based on the results of the productivity evaluation and the emotional state evaluation; and based on a work satisfaction evaluation result, constructing an optimization algorithm to dynamically adjust the work task and the work time. Through a dynamic evaluation method based on a productivity model, in combination with a multi-dimensional working state score and a real-time feedback mechanism, the accuracy of work satisfaction evaluation is effectively improved.

The invention discloses a multi-user emotion recognition and digital human feedback method based on behavior data, and belongs to the field of artificial intelligence emotion calculation. Sensing user approaching, identifying and confirming the identity of a registered user, distributing an identifier, distributing an identifier for a visitor, and establishing an independent session channel in a multi-user scene; capturing time sequence dialogue behavior data containing voice rhythm features, dialogue interaction modes and linguistic features in real time; inputting the data into a sequence information processing model based on a state space theory and provided with a data dependence selection mechanism and an emotion analysis model spliced by static context features, and outputting multi-dimensional emotion and cognitive state tags; generating an emotion support strategy through a decision-making model in combination with the personal file of the user; and generating a multi-mode control instruction, and driving the digital human to synchronously present special effects such as expressions and the like. According to the method, privacy concerns are eliminated through non-intrusive collection, the method is adaptive to multi-user scenes, deep cognitive states can be recognized, and interaction effectiveness is improved for a long time.

The invention relates to the technical field of digital human voice interaction, and discloses a digital human live voice interaction system fused with emotion calculation. The system constructs an emotional response time window by acquiring a user voice input data stream, the starting point of the emotional response time window is the end moment of the user voice input data stream, and the end point is obtained by subtracting the necessary duration of voice response synthesis from the preset maximum response cut-off moment. The system obtains an emotional state vector of the user in real time and judges whether the emotional state vector reaches an emotional intensity threshold value or not; and if so, predicting the total generation duration of the digital human voice response. And when the residual duration of the emotional response time window is equal to the total generation duration, taking the window starting point as a voice response starting moment, and controlling the digital human to start voice response generation. According to the system, the voice response matched with the emotional state of the user is generated through refined time window management and emotional state perception, invalid response and interaction delay are reduced, and the naturalness of digital human live broadcast voice interaction and the user experience are improved.

The invention discloses an interactive rich media textbook and courseware design method based on artificial intelligence, and relates to the field of textbook system research and development, and the method comprises the steps that a resource center provides required resources for a classroom creating and sharing module and a teaching and research ecological platform; the classroom creating and sharing module introduces a virtual simulation scene, a 3D model, a model hotspot, an interactive scene, VR experience and a classroom record based on resources provided by the resource center, designs teaching courseware in combination with teacher experience, and forms a man-machine collaborative personalized teaching mode; the teaching and research ecological platform carries out emotion calculation and social analysis on responses of students by utilizing data mining and artificial intelligence analysis technologies based on a personalized teaching mode, analyzes the response degrees of the students to teaching courseware, and generates a visual knowledge point graph according to resources provided by the resource center; the visual knowledge point graph is used for covering the whole teaching link of pre-class preview, in-class interaction and after-class review, personalized teaching is achieved, and the whole teaching process is throughout.

The invention provides a customer service response system and method based on artificial intelligence. The customer service response system comprises a client, an intelligent access network, a core AI engine system, a multi-modal understanding system, a federated learning coordinator, a knowledge management system, an emotion calculation engine system, a dynamic decision system and a digital ethical arbitration system. According to the method, multi-dimensional data such as texts, voices and vision are fused through a multi-modal understanding system, more accurate user intention recognition is achieved in combination with a cross-modal entanglement network and intention conflict detection, and the misjudgment problem caused by traditional single-modal analysis is reduced; the emotion calculation engine is combined with physiological signals, voice emotion analysis and micro-expression recognition to dynamically sense the emotion state of the user, so that customer service interaction is more similar.

The invention discloses an EEG (electroencephalogram) emotion recognition method, device and system based on CNN-Transform in the technical field of emotion calculation and brain-computer interfaces. The method comprises the following steps: acquiring an original EEG signal; segmenting the original EEG signal, and extracting features of a plurality of frequency bands in each segment; differential entropy features and power spectrum density features are extracted from the multi-band features in a set time window; based on the mixed features of the differential entropy and the power spectrum density, comprehensive space-time frequency features are extracted through a CNN-Transform model; and inputting the space-time frequency characteristics into a multilayer perceptron to obtain an emotion recognition result. According to the technical scheme, the CNN-Transform network is utilized to effectively capture the space-time frequency interaction characteristics in the electroencephalogram signals, and the problems of insufficient characteristic coupling modeling, insufficient neuroscience prior utilization and the like of a traditional method are solved, so that the efficiency and the accuracy of electroencephalogram emotion recognition are remarkably improved.

The invention provides an intelligent triage method and system for outpatient and emergency treatment, and relates to the technical field of medical information. The method comprises the steps that physiological data, symptom description text data, facial image data and voice audio data of a patient are collected through a multi-mode sensor; analyzing the image and audio data by using an emotion calculation engine to generate emotion state data; processing the physiological and text data through an analysis model to generate illness state emergency degree data; fusing emotion and illness state data to form a preliminary triage result; in combination with the real-time medical resource state data, a triage decision-making scheme including department allocation, processing priority and personalized pacifying strategies is generated through a dynamic decision-making device; finally, guiding and pacifying are executed through the execution terminal and the interaction robot, automation, intelligence and humanization of the triage process are achieved, the triage efficiency and accuracy are remarkably improved, medical resource configuration is optimized, and care for psychological needs of patients is enhanced.

The invention relates to the technical field of artificial intelligence and social computing, and discloses a user-based digital duplicate interaction system and privacy protection method.The user-based digital duplicate interaction system comprises a dynamic personality modeling engine used for extracting behavior features, emotion features and personality features of a user through a multi-source heterogeneous data fusion framework and generating a five-dimensional personality feature matrix; the emotion migration training module adopts a double-loop adversarial training framework, comprises a main loop network and an adversarial network, and is used for generating an emotion response mode; and the multi-modal interaction control module can realize a three-layer decision architecture, including semantic understanding, emotion calculation and decision generation, and is used for processing interaction between the user and the digital clones. Through adoption of a differential privacy technology, privacy protection is carried out on interaction data between a user and a digital avatar. The differential privacy ensures that the influence of single user data on a statistical result is limited within a certain range by adding a proper amount of noise in the data, so that the privacy of the user is protected.