301 results about "Emotion perception" patented technology

The invention discloses an intelligent real-time emotion evaluation method for social media and online text data based on a multi-modal knowledge graph, and the method comprises the steps: firstly obtaining and preprocessing multi-modal original data; secondly, designing single-mode feature extraction of dialogue texts, voices and facial expressions based on BERT-BiLSTM, AST and ResNet-50 pre-training models; furthermore, sharing among multiple modes and private emotion semantic information are considered, and complementation of cross-mode emotion information is promoted by enhancing repeated emotion information and utilizing uniqueness of private information. Meanwhile, a multi-mode knowledge graph method is provided from bottom to top to carry out emotion real-time evaluation. Analyzing user behaviors by using DeepFM and generating user features, and capturing external knowledge layer features in combination with K-BERT; finally, on the basis of a TransformerEncoder structure, cross-modal emotion information interaction and feature fusion are achieved, real-time recognition and classification of multi-modal emotion perception are completed through a Softmax layer, and therefore the purpose of intelligent real-time emotion evaluation is achieved.

The invention belongs to the field of artificial intelligence, and provides a dialogue interaction system based on multi-mode emotion perception and knowledge graph dynamic enhancement. A user edge terminal obtains multi-modal data, a lightweight Transform fusion network is adopted, the multi-modal data is converted into a fusion feature vector through cross-modal attention fusion and dynamic weight adjustment, and the fusion feature vector and historical conversations in a preset round are compressed in real time; the cloud service platform inputs the compressed data into DKGE, mining and fusing feature vectors and entity knowledge and emotional relations implied in historical dialogues in real time in the dialogue interaction process to update a dynamic knowledge graph, performing knowledge enhancement processing based on the dynamic knowledge graph, and constructing an initial reply prototype of the current round of interaction of the user; inputting the fusion feature vector and the updated dynamic knowledge graph into a dialogue strategy model, and determining a response strategy and a knowledge calling direction of the current round of dialogue; and the edge terminal generates real-time interaction reply information according to the initial reply prototype, the response strategy and the knowledge calling direction.

The invention provides an AI-assisted large-model intelligent question and answer accuracy improvement method, and belongs to the technical field of intelligent question and answer. Comprising the steps of data collection, data cleaning, data labeling, dynamic knowledge base construction, knowledge base updating trigger mechanism establishment, pre-training language model improvement, post-processing language understanding enhancement, combination of rule-based and machine learning reasoning, reasoning result verification and optimization, dialogue strategy optimization, emotion perception and response, multi-language search and language recognition search. And multi-dimensional evaluation index setting and regular evaluation and feedback improvement are realized. The intelligent question and answer accuracy improving method is high in understanding ability, timely in data updating, outstanding in reasoning ability and high in language recognition and dialogue ability.

The invention relates to the field of multi-modal interaction analysis, in particular to a multi-modal interaction method and system of a digital human agent. The method comprises the following steps: acquiring a real-time face image and a voice signal input stream of an interactive user based on an intelligent agent; performing real-time micro-expression recognition and deep emotion analysis based on the real-time facial image to obtain real-time emotion features of the user; performing time sequence evolution analysis on the real-time emotion characteristics of the user, performing holographic user emotion deep mining, and constructing a user emotion holographic characteristic spectrum; carrying out adaptive acoustic gain processing on the voice signal input stream, and carrying out voice-emotion association analysis based on the user emotion holographic characteristic spectrum to generate a voice-emotion linkage mapping spectrum; and carrying out eyeball fixation point migration tracking based on the user emotion holographic feature map and the real-time face image, and generating a user interaction depth intention signal. Through the real-time deep semantic understanding and emotion perception ability, the intelligent agent interaction intelligence and response accuracy are improved.

The invention discloses a knowledge tracking method and system based on a hypergraph neural network and emotion perception, and belongs to the field of knowledge tracking. The method comprises the steps of firstly obtaining a practice data set composed of practice data of a student account, then converting a relationship between exercises and knowledge points in the practice data into matrix representation, constructing an exercises-knowledge point incidence matrix, forming an exercises-knowledge point hypergraph by the practice data of the student account based on the exercises-knowledge point incidence matrix, and finally obtaining an exercises-knowledge point hypergraph. And constructing a knowledge tracking model comprising the hypergraph neural network, an emotion perception module and a prediction module, training the knowledge tracking model, inputting the exercise data to be subjected to knowledge tracking into the trained knowledge tracking model, and outputting a predicted value of a next exercise answering result of the student. According to the method, the knowledge mastering conditions of the students in different emotional states are obtained through combination of the hypergraph structure and the emotional factors, the prediction performance of knowledge tracking is improved, and the method has important significance on precise and personalized development of an intelligent learning guiding system.

The invention relates to an artificial intelligence-based interactive digital human generation method, which comprises the following steps of defining basic features and generation requirements of a digital human through a condition vector, generating a potential space representation by using an encoder, generating a preliminary image by using a decoder, and adjusting the image in combination with a style migration technology to adapt to a target environment, and improving the resolution and details by using a super-resolution network, and finally generating a static digital human image. And collecting multi-modal data, and integrating features through a fusion function to generate a unified representation. Based on conditional vectors, random noise, and multi-modal unified features, expressions and speech representations conforming to emotions or states are generated. And a basis is provided for expression and voice response of the digital human by identifying the emotion of the user. Real-time response is generated through a reaction function, and more accurate and intelligent interaction is achieved by combining reinforcement learning optimization behaviors and response strategies. A high-quality personalized digital human image can be generated, and intelligent response is realized through multi-modal interaction and emotion perception.

The invention relates to the technical field of financial risk assessment, and discloses a financial risk assessment system based on emotion perception and a dynamic knowledge graph, and the technical scheme is characterized in that the system comprises a multi-source data collection module which is in butt joint with a plurality of data sources and collects structured data and unstructured data; carrying out standardization processing on the data to obtain standard data; the emotion perception engine is used for quantifying customer emotion features; the dynamic knowledge graph construction module is used for attenuating the weight of historical data according to the time dimension and updating the knowledge graph in real time when an entity extraction model is used for extracting an entity and a corresponding relation and incremental updating is carried out on the knowledge graph; the multi-modal risk assessment model is used for fusing structured data, emotion features and knowledge graph embedding and outputting a risk score; and the dynamic feedback and optimization module iteratively updates the risk assessment logic according to the risk score and the market feedback, and performs incremental training on the multi-modal risk assessment model through misjudgment data.

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 an international remote translation system based on AI and an implementation method thereof, and belongs to the technical field of artificial intelligence. Comprising a voice input module, an emotion perception module, a predictive simultaneous interpretation module, a language adaptation module based on few-sample learning, a dynamic translation quality evaluation and feedback module and a multi-language cooperative processing module, and can automatically recognize an input language and dynamically adjust a translation model in a multi-language environment. The emotion information in the voice and the text is captured in real time, the translation path is optimized in combination with the context weight, the emotion transmission accuracy and the translation context consistency are remarkably improved, the term requirements of different fields can be rapidly met, the translation quality is continuously optimized through dynamic feedback, the translation accuracy and smoothness are ensured, and the translation efficiency is improved. And the method has wide applicability in multi-language, cross-cultural communication and professional field application, and has relatively high innovativeness and practical value.

The invention discloses an intelligent simulation system and method based on multi-modal interaction and a dynamic teaching strategy, and the system comprises a multi-modal interaction emotion perception module, a dynamic teaching strategy generation module, a situational multi-post training module, an automatic guidance and evaluation module, and an integration and optimization module. Specifically, the multi-modal interaction emotion perception module comprises a visual information acquisition module and a voice information acquisition module; and the visual information acquisition module is used for capturing facial expression, eye movement and gesture information of the student through a camera. The invention belongs to the technical field of simulation training and intelligent teaching, and achieves the technical effects that the limitation of the prior art is broken through by organically combining the technologies of multi-modal emotion perception, dynamic teaching strategy generation, situational multi-post training, multi-dimensional intelligent evaluation and the like, and compared with the prior art, the system has the advantages that the system is simple in structure and convenient to use. The method has obvious technical advantages in the aspects of real-time emotion perception, self-adaptive teaching strategies, cross-role interaction, multi-dimensional evaluation and the like.

According to the cold region city emotion perception evaluation and measurement method, a cold region city is selected as a research object, and physiological signals and subjective scores are comprehensively considered; street view images are scored by training the street view perception model, perception of the street view images is realized, physiological signals and subjective scores are combined to realize comprehensive evaluation of corresponding areas of the cold region city, and a comprehensive evaluation result is less subject to subjective influence and is stable; the method can analyze the influence of the winter street space of the cold region city on the residential emotion perception, accurately measure the quality of the cold region city street space influencing the residential emotion, has the advantages of high efficiency and easy popularization and application, can be widely applied to the field of urban planning living street space quality investigation and measurement, and has a wide application prospect. And a method and a basis are provided for cold region city planning street space optimization practice.

The invention provides a TTS speech synthesis method for emotion perception enhancement under multi-modal fusion driving, and the method comprises the following steps: S1, carrying out the collection and preprocessing of multi-modal data, the multi-modal data comprising text data, speech data and facial expression data; s2, extracting and analyzing emotion features; s3, emotion perception speech synthesis model training; s4, speech synthesis and post-processing; s5, performing model evaluation and optimization; by collecting and analyzing multi-mode data such as texts, voices and facial expressions, emotion features can be captured more comprehensively and accurately, complementary information among different modes is fully mined through application of a multi-mode fusion network and a collaborative attention mechanism, the emotion expression of the synthesized voices is closer to real emotions, and the user experience is improved. And the precision of emotion perception is greatly improved.

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 relates to the technical field of wisdom medical treatment, in particular to a cooperative intervention system and method based on multi-modal emotion perception and five-tone therapy, and realizes precision of emotion recognition and individuation of intervention strategies through cooperative intervention of multi-modal emotion perception and traditional Chinese medicine five-tone therapy. Physiological and behavior signals of a user in different states are comprehensively captured, emotion recognition is carried out in combination with a deep learning model, the accuracy and real-time performance of emotion recognition are remarkably improved, the system can intelligently match music tracks most suitable for the current emotion state based on the five-internal-organ-five-sound theory of traditional Chinese medicine and modern music psychology, and the user experience is improved. And an intervention strategy is continuously optimized through reinforcement learning, so that more targeted and effective emotion regulation is realized, and the method is particularly suitable for long-term management and intervention of negative emotions such as anxiety and depression.

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 an emotion recognition method and system based on a dialogue situation knowledge base and comparative learning. The precision and robustness of emotion recognition are improved. The system comprises an initial emotion retrieval module, an emotion perception enhancement module and an anchor point dynamic adjustment module, and the specific emotion recognition method comprises the following steps: generating a dialogue place, a character relationship, a theme and initial emotion by using a large language model, and constructing a strip-shaped situation knowledge base; adding a mask to each dialogue round of the target person, and splicing the initial emotion obtained by the retrieval module to the foremost of the data to obtain training data; the initial emotion and all mask sequences before target utterance prediction are jointly input into an emotion perception enhancement module for prediction; using an LSTM model to calculate and predict a mask emotion label of the target utterance; and transmitting the emotion label of the utterance of the target person into a pre-training model to obtain emotion label anchor points, carrying out comparative learning on the predicted emotion label and the emotion label, and adjusting the distance between the two anchor points.

The invention relates to the field of artificial intelligence and the field of financial science and technology, and discloses a response strategy generation and execution method, which comprises the following steps of: constructing a multi-task learning structure based on an enhanced spatio-temporal feature extraction model by collecting voice signals of a user and extracting emotional features; sentiment classification, sentiment intensity regression, phoneme recognition and gender recognition are used as joint tasks for training, and a multi-task learning model is obtained. After emotion features are input into the model, emotion category labels and emotion intensity are generated, a strategy table is inquired in combination with a current application scene, and a corresponding response strategy is determined and executed. Through the construction of the multi-task learning model and the joint task training, the emotion category and emotion intensity of the user are accurately identified, and the response strategy is dynamically adjusted in combination with the application scene, so that the emotion perception ability and the personalized service effect of the smart home system are improved, and the user experience and the interaction effect are remarkably improved.

The invention relates to the technical field of brain-computer interfaces, in particular to an emotion perception interaction method and system for a brain-computer interface earphone, and the method comprises the following steps: obtaining an electroencephalogram signal and a voice signal of a user; preprocessing the electroencephalogram signal and the voice signal; electroencephalogram features are extracted from the preprocessed electroencephalogram signals, and voice features are extracted from the preprocessed voice signals; performing multi-modal feature fusion based on the electroencephalogram features and the voice features to obtain fusion features; identifying an emotional state of the user based on the fused feature; adjusting earphone output parameters according to the emotional state; obtaining user feedback; and updating the model based on user feedback; the deep fusion of the electroencephalogram and the voice signal is realized through the innovative DAMCLN algorithm, and the accuracy of emotion recognition is remarkably improved. Test results show that the emotion recognition accuracy of the method reaches 92.5% and is far higher than that of the prior art; intelligent parameter adjustment based on the emotional state of the user is realized, and the score of the user reaches 8.7.

The invention provides a complex emotion representing and sensing method based on an emotion vector, and the method employs the emotion vector to represent a complex emotion containing two or more emotions, the emotion vector is composed of a plurality of element emotion elements corresponding to human emotions, the value of each element represents the intensity of the element emotion, and the value of each element represents the intensity of the element emotion for the sensing of the emotion vector. According to the method, the approximation degree of the perceived emotion and the actual emotion is improved through a meta-emotion detection algorithm (ISL-MED) based on iterative self-learning. According to the invention, the confidence score of the element emotion examination is utilized to approximately represent the intensity of the element emotion, a plurality of element emotion detectors are designed to detect the complex emotion in the voice, and the detection results are combined into an emotion vector to represent the perceived emotion result. Evaluation of the emotion perception result is based on the distance or loss measurement between the labeled emotion vector and the perception emotion vector, and the actual voice emotion can be described more accurately.

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

The invention relates to the field of artificial intelligence, and discloses a hotspot short video automatic generation method and apparatus, a computer device and a storage medium, and the method comprises the steps of obtaining hotspot information, and determining a hotspot picture and a hotspot text corresponding to the hotspot information by using a large language model; determining a hotspot voice corresponding to the hotspot text, performing emotion perception on the hotspot voice, and determining an emotion contained in the hotspot voice and background music corresponding to the emotion; and generating a hotspot short video corresponding to the hotspot information based on the hotspot picture, the hotspot voice and the background music. According to the technical scheme provided by one or more embodiments disclosed by the invention, the hotspot picture and the hotspot text corresponding to the hotspot information can be determined by adopting the large language model, so that the short video generation speed is increased; the hotspot short video is generated based on the hotspot picture, the hotspot voice and the background music, and the generated short video content is coordinated and matched with the background music, so that the quality of the short video can be improved.

The invention discloses a scene layout optimization method and system based on multiple visual elements and emotion perception. Relates to the technical field of management optimization. The method comprises the steps of obtaining image data and a scene type of a target scene; feature information of visual elements of each category in the image data is extracted, and weight information of the visual elements of each category is determined according to the scene type and user feedback information; determining an emotion intensity value of the target scene according to the feature information and the weight information of all the types of visual elements, and determining a layout optimization suggestion of the target scene according to the emotion intensity value; and according to the layout optimization suggestion, generating image data of the target scene after layout optimization by using an image generation model. The technical problem that in the prior art, a scene layout optimization mode has the technical defects that a static weight distribution mechanism is rigid, comprehensive influence among a plurality of visual elements is not fully considered, and dynamic quantification capacity of emotional intensity is insufficient, so that the scene layout optimization effect is poor is solved.

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 relates to the technical field of artificial intelligence, in particular to an emotional dialogue method of a terminal digital person based on a large model, which comprises the following steps: after a user sends a query, a system retrieves the most matched memory fragment by combining the semantic and emotion information of the query, so that the dialogue content is consistent with both semantic logic and emotional information when being generated; and resonance with the emotion of the user can be generated. According to the method, the generated dialogue can be more humanized through an emotion perception retrieval and generation mechanism, the emotional state of the user can be effectively adapted, and the emotional interaction effect of a terminal digital person is improved.

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