2554 results about "Emotionality" patented technology

This invention relates to a gait emotion recognition method, system, storage medium, and computer device based on spatiotemporal graph convolution. The method includes the following steps: S1, data augmentation by reversing the temporal direction of gait; S2, obtaining deep emotion features and prior emotion features respectively through a spatiotemporal graph convolutional network and prior feature statistical methods; S3, performing nonlinear mapping on the prior emotion features using a feature mapping layer; S4, inputting the fused features of the deep emotion features and prior emotion features into an emotion classifier to obtain the emotion category. The feature mapping layer of this invention achieves more effective feature fusion by performing nonlinear mapping on prior features; it also introduces causal temporal convolution to replace general temporal convolution, effectively extracting fine-grained temporal features by enhancing temporal correlation and cross-period feature fusion. Furthermore, a walking direction recognition auxiliary task is designed to accelerate the training and convergence speed of the model, enhancing the ability to extract temporal-dependent features and the performance of emotion recognition.

The invention discloses a digital human emotion recognition and feedback system based on a large model, and relates to the technical field of large models.During operation of the system, multi-modal data of a driver are collected in real time through an embedded sensor network in various driving environments, and the emotion, driving behaviors and driving environment parameters of the driver are obtained; the method comprises the steps of constructing fatigue, anger and other multi-dimensional emotion features of a driver to form an emotion feature vector, analyzing an emotion change trend through threshold comparison, obtaining an evaluation state, dynamically generating a feedback strategy for optimizing a driving state based on an emotion state and a driving scene of the driver, and embedding a generative large model into a module. Diversified feedback responses including audio reminding, driving mode adjustment and entertainment content recommendation are automatically generated, feedback strength and form are adaptively adjusted through multi-dimensional parameters, a feedback generation effect is dynamically adjusted, and an emotion state analysis result and vehicle behavior monitoring data are integrated, so that a user experience is improved. And evaluating the potential dangerous state of the driver in real time.

The invention discloses an intelligent psychological intervention system based on multi-modal fusion, which is characterized in that a three-dimensional evaluation system is constructed by integrating speech sentiment analysis, keyboard dynamics monitoring and physiological signal acquisition, and time sequence alignment and feature weighted fusion of multi-source data are realized by adopting a cross-modal Transform model. The core of the system comprises an adaptive intervention engine which defines a multi-dimensional state space based on a hierarchical reinforcement learning architecture, optimizes an intervention strategy through a PPO algorithm, and realizes dynamic emotion interaction in AR and VR scenes in combination with a digital twin training module; according to the clinical decision support system, physiological behavior characteristics and psychological assessment trends are integrated by using a multi-time scale risk prediction model, and a personalized early warning threshold system is constructed, so that the psychological state recognition accuracy is improved, the intervention intensity self-adaptive adjustment response time is shortened, and the high-risk signal early warning timeliness reaches the minute level; and the problems of evaluation hysteresis and strategy stiffness of traditional psychological intervention are obviously improved.

The invention provides a digital human interaction control method and device fusing emotion semantics and logical reasoning and a storage medium. The method comprises the following steps: analyzing multi-modal input data of a user, constructing emotion-semantics joint representation, and generating a logic decision path; and through a cognitive fusion module, emotion-semantic representation and a logic decision path are fused, and an interaction response adapting to emotion and logic consistency is generated. The system optimizes an emotion semantic model and a logical reasoning rule on line according to user feedback and interaction history, and real-time interaction of emotion dynamic and logical rules is achieved. The system can dynamically adjust the logic decision path based on the multi-mode emotional state of the user, and improves the naturalness and situation adaptability of interaction. A dynamic time warping algorithm and a factorization machine are introduced to process a multi-modal feature fusion problem, and the accuracy and robustness of emotional state recognition are improved. The online optimization mechanism enables the model and the rule to be evolved continuously, and reasoning errors are corrected automatically through user feedback, so that error circulation is avoided.

The invention relates to the field of data processing, in particular to an emotion recognition method based on a large model and a related device. The method comprises the following steps: firstly, acquiring to-be-processed multi-modal data; the multi-modal data at least comprises text information, audio information and image information input by a user; the image information at least comprises a facial expression image and / or a limb action image. And inputting the multi-modal data into an emotion recognition model for emotion state analysis to obtain a predicted emotion state corresponding to the user. And finally, performing confidence evaluation based on the predicted emotional state, and generating a user emotion recognition result based on the predicted emotional state and a corresponding evaluation result. According to the method, richer emotion details are captured from the multi-modal data through the emotion recognition model, cross-modal emotion intelligent recognition is realized, the accuracy of emotion recognition is further improved by combining confidence evaluation, the emotion recognition efficiency is improved, and the application range of emotion recognition is expanded.

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 discloses a large-model complaint intention recognition method based on sentiment analysis, and the method comprises the steps: obtaining call voice data of a customer, and converting the call voice data into text data; preprocessing the text data, and removing noise and marking components; performing emotion feature analysis on the text data, extracting an emotion feature value, and generating an emotion vector; the emotion vectors and the text data are input into a large model together, and the large model combines the emotion feature values and context semantics to generate intention feature vectors; constructing an emotion-intention state vector, inputting the emotion-intention state vector into an asynchronous dominant actor commentator algorithm model, and generating a corresponding complaint intention probability value; judging a complaint intention probability, and generating risk early warning; and preferentially distributing high-risk customers and responding to customer demands. Through combination of voice data preprocessing, text semantic feature extraction, emotion intensity quantitative analysis and a multi-modal fusion algorithm and efficient risk assessment based on an asynchronous dominant actor reviewer model, the early warning and response capabilities of customer complaint risks are significantly improved.

The invention discloses a robot anthropomorphic interaction method based on multi-modal emotion recognition and customized portrait generation. The method comprises the following steps: S1, dynamically fusing multi-modal emotions; the method comprises the following steps: S1, synchronously acquiring voice, visual and text signals through a multi-source heterogeneous sensor, capturing a user voice stream by a high-fidelity microphone array, and extracting acoustic characteristics such as intonation and speed, S2, performing cross-modal reasoning; s3, synchronously generating contents; step S4: style migration; step S5, anthropomorphic voice and expression generation; according to the method, man-machine interaction emotion is analyzed and generated by utilizing a large language model and multi-modal information fusion, the singleness of interaction emotion and the deficiency of emotional sharing ability are avoided, a strong emotion interaction characteristic is achieved, the image of the robot is obtained through a generative technology and can be migrated to any image, the limitation that a specific image is independently made is broken through, and the interaction effect of the robot is improved. The advantage that one robot can be suitable for different scenes is achieved.

The invention provides a multi-modal sentiment analysis method and device based on multi-agent collaboration, and the method comprises the steps: carrying out the clock synchronization collection and preprocessing of multi-modal data, and obtaining a multi-modal data package with a time sequence mark; and performing feature extraction on the multi-modal data packet by using a plurality of intelligent agents, and mapping the extracted feature vector and a preset emotion ontology to obtain a multi-modal emotion vector. And detecting the difference degree between the vectors in the multi-modal emotion vectors, and starting a conflict detection algorithm based on feature credibility to perform evaluation when a significant difference is detected to obtain an evaluation result. And performing feature fusion operation on the multi-modal emotion vector according to the evaluation result and a preset weight coefficient to obtain an emotion recognition result, and performing timestamp association storage on the emotion recognition result and the corresponding multi-modal data. By introducing a difference detection and evaluation mechanism, the abnormal feature vector is effectively prevented from influencing the fusion result, and the reliability of modal sentiment analysis is improved.

The invention provides a multi-mode-based man-machine interaction method and system, and relates to the technical field of man-machine interaction. Voice input of a user is received, environment information is collected, facial expressions of the user are captured, and voice features, environment features and facial features are obtained based on the input; utilizing a pre-trained emotion recognition model to generate emotion features of the user, and utilizing a pre-trained user behavior model to generate behavior features according to the current operation information of the user; after the features are obtained, the first weights and the second weights of the features are dynamically adjusted, feature weighted fusion is carried out, and comprehensive features are obtained; the multiple candidate responses are generated based on the comprehensive characteristics, the final response is screened out through a preset scoring mechanism and output, comprehensive understanding of the intention and the emotional state of the user is achieved through comprehensive utilization of multiple data sources, and meanwhile the weight of each characteristic can be dynamically adjusted so that more intelligent and humanized interaction experience can be generated.

The invention discloses a smart home voice interaction control method and system, and relates to the technical field of smart home. The problems that in the prior art, the intention of a user cannot be accurately understood through non-voice information such as emotion and expression of the user, a control strategy is solidified and lacks individuation, and user requirements and emotion cannot be met in real time are solved. By fusing voice and non-voice information, accurate recognition of user intentions is enhanced, recognition accuracy of voice instructions is improved, emotional states and requirements of users are understood more comprehensively, particularly in a noisy environment or a complex situation, recognition and response effects of the system are obviously improved, and user experience is improved. By utilizing the emotion analysis and context understanding technology of the user, the interaction mode is dynamically adjusted according to the emotion state of the user, the emotion demands of different users can be flexibly adapted, more humanized services are provided, the control strategy is continuously optimized according to the feedback of the user, the user satisfaction is improved, and the user experience is improved. Therefore, the intelligent degree and the user experience of the smart home system are improved.

The invention relates to the technical field of emotion recognition, and discloses an electroencephalogram and synchronous physiological signal emotion recognition method based on cross-modal comparative learning and multi-scale characterization, in a pre-training stage, features are extracted from physiological signals of different modalities through encoders corresponding to the modalities, and modal characterization related to emotion stimulation is learned; the modal representations are mapped to a potential space through a mapping layer to calculate the similarity, then the contrast loss of each modal is calculated, and contrast learning in the modal is carried out; performing cross-modal comparative learning on the representation based on each modal extracted by an encoder, and aligning the representation of different modals to a shared potential space; according to the invention, the encoder is pre-trained at different time scales by using short-term and long-term emotion changes in each mode; in the fine tuning stage, emotion recognition training is carried out by using a pre-trained encoder, an initialized modal fusion module and a classifier, and the model weight is optimized based on the emotion label of the physiological signal.

The invention discloses a scene-based emotional interactive accompanying doll system and method, and relates to the technical field of artificial intelligence, and the system comprises a data collection module, a feature analysis module, a portrait construction module, an interactive decision module, an execution control module and a database module. The data acquisition module is used for acquiring interaction data and scene data; the feature analysis module generates an emotion feature vector and a scene feature vector by using a multi-modal feature network, and determines a user emotion state and a scene type; the portrait construction module is used for constructing a user portrait; the interaction decision module comprises an emotion evolution unit, a scene interaction unit and a physiological regulation unit and is used for generating an execution instruction sequence; the execution control module is used for controlling the doll to complete emotion interaction behaviors. Through the scene perception and sentiment analysis technology, accurate recognition and personalized interaction response of the doll to the user sentiment state are achieved, and intelligent sentiment accompanying service is provided.

Provided are a conversational artificial intelligence (AI) system and method based on real-time multimodal emotion recognition. The system includes a model server configured to provide a machine learning-based conversational model, a terminal configured to perform a conversation with the machine learning-based conversational model through the model server, display a virtual human responding to a user during a conversation with the user, and capture a facial image of the user during the conversation, and a multimodal empathetic conversation-generation system configured to access the model server and receive a response to a question of the user from the terminal, and assess an emotion of the user from the facial image of the user and control, based on the assessed emotion, an expression of the virtual human displayed on the terminal.

The invention discloses an emotion-enhanced digital human driving and presenting system and method, and belongs to the technical field of computers.The system comprises an audio decoupling module, a potential diffusion model module, a human motion prior module, a training module and a rendered image generation module; predicting and reconstructing the facial expression and gesture action sequence to obtain a final action sequence; and generating a neural rendering image based on the final action sequence. According to the method, through voice input, emotion and style features in voice are captured, a cross attention feature fusion mechanism is introduced to enhance expression of emotion and personal style features, a voice-driven gesture generation task is expanded to a facial expression and gesture action synchronous generation task, the integrity of generated digital human actions is realized, and the user experience is improved. The emotional expressive force of facial expressions and gestures of the digital human is greatly improved.

The invention relates to the technical field of AI interaction, and discloses an AI multi-modal emotion interaction memory terminal, which realizes microsecond-level synchronization of voice, facial expression and text data through a multi-thread acquisition engine, dynamically allocates each modal weight by adopting a multi-head cross attention mechanism, and adaptively adjusts modal importance based on a conversation context hidden state; when the cross-modal confidence difference exceeds a threshold value, a gating LSTM conflict resolution module is activated, and the multi-source data collaboration problem is solved; the emotional memory modeling constructs an emotional state transition topology based on a graph convolutional network, protects user privacy in combination with a differential privacy mechanism, and realizes associated event storage of millisecond backtracking of short-term memory and long-term memory. The technology integrates multi-modal dynamic perception, privacy security calculation and adaptive learning ability, significantly improves the real-time performance and personification degree of emotion interaction, and can be applied to the fields of intelligent customer service, emotion accompanying, health monitoring and the like.

Aiming at the problems of insufficient language understanding depth, weak personalized dialogue generation ability, lack of continuous learning and long-term user state modeling and the like in the current emotion recognition and psychological intervention technology, the invention provides a user emotion recognition and psychological intervention method combined with a large language model (LLM). According to the method, the potential emotional state is identified by analyzing free text information input by a user by utilizing the powerful capabilities of a large language model in the aspects of natural language understanding, emotional modeling and text generation; constructing a multi-round dialogue context, and reasoning a psychological change trend of the user; in combination with a psychological knowledge base, personalized and mild psychological intervention dialogue content with a dredging effect is generated. The system supports recognition and classification of various emotional states such as depression, anxiety and alonity, is suitable for various interaction scenes (such as APPs, webpages and social robots), and can greatly improve the precision of emotion recognition and the timeliness and effectiveness of psychological intervention. The emotion recognition and psychological intervention method based on the large language model provides solid technical support for constructing an intelligent, continuous and personalized psychological health management system, and has wide application prospects and profound social significance.

An AI-driven system for personalized advertising in real time, where: ◯ an analytics unit to monitor and analyze user behavior in real time across multiple digital platforms such as websites, mobile applications, social media, and smart devices; the unit collects data on user engagement, browsing patterns, time spent, and content preferences to enable targeted, personalized advertising; ◯ a prediction module that predicts preferences and interests of a user, operatively connected to the user interaction data collection unit, wherein the prediction module uses machine learning models such as deep learning, recurrent neural networks (RNNs) and transformer-based architectures to predict interests of users based on historical interactions and inferred preferences; ◯ an emotion and sentiment analysis unit that assesses the user's mood in real time through computer vision, natural language processing (NLP) and voice analysis, whereby the analysis of facial expressions, voice pitch and linguistic mood is used to determine emotional states and receptivity to advertising content; ◯ an embodiment of a context awareness component in operational communication with the emotion analysis and mood unit, in some cases further augmented by various environmental and situational data such as the device type and its physical location, date and time, and the content processed in the device, processing methods, etc., in establishing adaptability and automatic ad placement to be as relevant as possible to the user and their status as prescribed; ◯ Use reinforcement learning algorithms and generative AI models to drive advertising with personalization engines. Creative elements, messaging, and presentations are dynamically adjusted in real time based on user responses to ensure advertising is personalized and always optimized for best performance; o a privacy-focused AI system with federated learning, differential privacy methods, and on-device AI processing to reduce targeted advertising while complying with international data protection laws such as the General Data Protection Regulation (GDPR) and California consumer privacy laws; o an operationally adapted ad delivery mechanism to engage with real-time bidding (RTB) networks, programmatic advertising exchanges and demand-side platforms (DSPs) and place advertisements through digital advertising networks, which guarantees the delivery of tailored advertising to the most relevant audience in real time; and o a contextual feedback loop in which the machine learning models used in the preference and interest prediction module and in advertising personalization The engine is continuously updated to reflect the latest user engagement data, improving personalization over time and optimizing advertising performance.

The invention is applicable to the technical field of data processing, and discloses a knowledge base sensitive question test method, which comprises the following steps of: firstly, constructing a semantic vector model of a knowledge base for efficiently retrieving knowledge points with high relevancy with a to-be-tested question to form a candidate answer set; performing intention judgment on user questions by using an intention recognition model, and distinguishing factual verification or emotion expression; selecting knowledge points with the highest confidence degree from the candidate answer set as answers according to the fact verification type questions; responding to emotion expression questions based on an emotion response library; on the basis, the answers are further optimized in combination with the context of the questions; judging whether the optimized answer contains sensitive content or not, and performing necessary revision on the answer according to a sensitivity judgment result; and outputting the revised answer as a test result. The method effectively improves the question answering accuracy and sensitivity processing capacity of the knowledge base, and is suitable for various scenes needing intelligent question answering and knowledge base application.

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 an event analysis method, system and device based on task generation and multiple modes, which are used for quickly and accurately mastering the development trend of an event. The method comprises the steps that structured event information is generated through multi-modal data, and an event relation is obtained; constructing an event graph; extracting a multi-modal feature, and obtaining a multi-modal emotion relationship vector by using a cross-modal attention mechanism; based on a preset graph neural network, combining the event graph to obtain an emotion feature vector; calculating a comparative learning loss value, and obtaining a target emotion feature based on the comparative learning loss value; generating a target event atlas by using a cross-modal attention mechanism; extracting relation characteristics, and combining the attention weight and a preset time sequence model to obtain an event prediction map; extracting a causal chain and reasoning, and obtaining a trigger condition and an emotion factor after reasoning; and combining the event prediction map and the target event map with the node feature sequence, the trigger condition and the emotion factor to obtain an event analysis result.

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 relates to the technical field of computer vision and human-computer interaction, and discloses an AI digital human intelligent interaction method, system and equipment based on multiple modalities, and the method comprises the steps: pre-awakening a digital human when a human face is detected, and further thoroughly awakening the digital human based on recognized preset voice information or preset gesture information; voice and video information of a user in the interaction process is obtained, a keyword extraction result, a gesture recognition result and an emotional state tag are generated, a pre-constructed knowledge base is utilized to retrieve related information, a big language generation model module is combined to generate an answer text, and the answer text is input into a preset voice synthesis model to generate emotional voice output. And based on the current emotional state label of the user, driving the digital human animation to be output in an emotional manner. According to the method and the system, the digital human for understanding the emotion of the user, generating personalized answers, providing voices with rich emotions and displaying natural expressions and actions can be created, better interaction with the user can be realized, and more humanized and effective services can be provided.

The invention discloses a soothing lamp intelligent control method and system based on voice interaction, and relates to the technical field of data encryption, and the method comprises the steps: receiving a voice command inputted by a user through a voice interaction device, awakening a control system, and starting a voice emotion recognition unit; the voice emotion recognition unit is used for performing emotion analysis on the voice command, extracting emotion characteristics and judging real-time emotion of the user; on the basis of continuous voice input of a user, voice signals are collected regularly, the emotional state is updated dynamically, and light setting is optimized in combination with environmental data; by recording historical voice interaction data and environment data of a user, an emotion mode and light preference of the user are learned, and light setting is continuously optimized. Light setting is continuously optimized through a self-learning mechanism, it is ensured that the system can conduct self-adaptive adjustment along with changes of user requirements, and light control is more intelligent and personalized.

The invention provides a personalized English education system and method based on multi-modal sentiment analysis. The system comprises a multi-modal interaction module for receiving and processing multi-modal data of students, an emotion recognition module for performing emotion analysis on the input multi-modal data, and a personalized module for evaluating real-time learning states of the students based on historical learning data of the students, and the core brain module is used for dynamically adjusting interactive feedback according to the emotional state and the real-time learning state. The emotion recognition module comprises emotion information fusion, the emotion information fusion adopts a weighting strategy, and the final output emotion state is adjusted through emotion consistency constraint and a conflict correction mechanism. According to the invention, through an emotion consistency loss function, a conflict correction mechanism and a knowledge graph-based super-outline control mechanism, the emotion and cognitive states of the students are accurately identified.

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 provides a dynamic self-adaptive multi-modal sentiment analysis fusion method and system, and relates to the technical field of multi-modal sentiment analysis. The method comprises the following steps: synchronously acquiring voice, text, facial expression and limb movement data of a target user to form a multi-modal data set; the method comprises the following steps: firstly, extracting emotional characteristics of each mode, and constructing a cross-mode correlation model to capture a collaborative and complementary relationship among different modes; and calculating a real-time confidence score and a complementarity index of each modal based on the weight matrix of the cross-modal correlation model. Then, according to the scores and the indexes, a weighted average or maximum entropy algorithm is dynamically selected to fuse multi-modal emotion features, and a comprehensive emotion feature vector is generated; and finally, inputting the vector into a pre-training deep learning model, and outputting an emotional state category of the user. According to the method, the user emotion can be accurately and comprehensively captured, efficient emotion recognition and classification are realized, and the robustness and flexibility of an emotion analysis system in a complex scene are improved.

The embodiment of the invention provides a vehicle-mounted emotion interaction method and device based on multi-dimensional recognition, and the method and device achieve the precise judgment of the emotion of a driver through innovatively constructing an emotion fusion recognition model and integrating the facial expression, voice emotion, driving behavior and physiological state features. And designing a scene-based self-adaptive interaction strategy, and establishing an interaction triggering threshold value for intelligent matching in combination with external environment data and a danger level. An interaction effect evaluation mechanism is introduced, an interaction strategy model is continuously optimized through an online learning module, and dynamic adjustment of personalized interaction content is achieved. According to the method, the defects of the traditional technology in the aspects of emotion recognition, interaction strategies, effect evaluation and the like are effectively overcome, and the intelligent level and the user experience of vehicle-mounted emotion interaction are remarkably improved.

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses an information processing method based on sentiment analysis, and the method comprises the steps: collecting data and information which comprise text data, user behavior data and event information; performing natural language processing and sentiment analysis on the text data, and identifying sentiment features of the text data; analyzing the user behavior data to determine a behavior pattern; analyzing the event information to determine an event change trend; generating an emotional state index based on the emotional characteristics, the behavior pattern and the event change trend; monitoring the online platform to obtain user emotion feedback; comprehensively analyzing the emotion state index and the user emotion feedback to generate an emotion analysis result; and generating target output content based on the emotion analysis result. Through combination of emotion analysis, behavior data and event change trends, the emotion state of the user can be captured in real time, decision suggestions are dynamically adjusted, and market or environment changes are effectively coped with.

The invention discloses a touch and talk pen intelligent children education method based on emotion recognition. The method comprises the steps that S1, facial expression data, voice and intonation data and touch behavior data of children are collected; s2, multi-modal high-quality emotion data matched with the actual emotion state is generated; s3, generating deep emotional features related to facial expressions, voices and tones and touch behaviors; s4, generating an emotional state label corresponding to the current emotional state of the child; s5, the generation model generates voice feedback content, image animation content and interactive game content which are adaptive to the current emotional state; s6, generating a personalized recommendation content list conforming to the emotional state of the child; and S7, displaying the education content in the personalized recommendation content list through a display screen, a voice output unit and a touch interaction unit of the touch and talk pen. According to the method, the fuzzy classification problem of the emotional state is solved in a touch and talk pen emotion recognition scene, and the adaptability of the model to the complex emotional state is remarkably improved.