11 results about "Emotional Changes" patented technology

The invention discloses a multi-dimensional personality evaluation method for dynamic emotion changes, and relates to the technical field of personality evaluation, the system is composed of a plurality of functional modules, and the method comprises the following steps: step 1, recording physiological data in real time through a wearable device, capturing micro-reactions by using a computer vision technology, and determining whether the micro-reactions exist or not; the micro-reactions comprise facial micro-expressions, limb actions and voice and intonation changes; based on a preset dynamic simulation social scene, observing an emotion regulation strategy of the subject in pressure; step 2, using an LSTM model to carry out time sequence modeling on the multi-modal acquisition data, capturing dynamic evolution of emotions, and combining an attention mechanism to dynamically adjust the weight of each modal; based on six basic emotions and nine mental disorder tendencies, establishing a classification model, and capturing association between emotions and personalities; reinforcement learning is introduced, and model parameters are dynamically optimized according to feedback of a subject; and predicting the emotion trend of the subject in a period of time in the future through an autoregressive integral moving average model.

The invention discloses an AI psychological counseling method and system for generating an emotional map and a differentiation report, and relates to the technical field of artificial intelligence and psychology crossing. The invention discloses an AI psychological counseling method and system for generating an emotional map and a differentiated report. Through interaction of voice and characters, a visual emotional map is generated, personalized action suggestions are generated, and a visitor report or a psychological counselor report is generated. According to the method, a visual emotional map is generated through voice and character analysis, changes in the emotional map can be seen more visually, meanwhile, a proper psychological AI model is adapted, personalized action suggestions and corresponding related reports are generated, fine emotional fluctuations can be effectively captured through the accurate voice and character capturing capacity, and therefore the emotional effect is improved. A multi-dimensional emotion analysis model is arranged in the system, emotion fluctuation is monitored in real time, a detailed emotion change trend chart is generated, and the scientific basis is provided for psychological counseling.

This invention relates to the field of EEG emotion recognition technology, specifically to an emotion recognition method and system based on two-level KL divergence. The method includes: acquiring EEG data and calculating the first KL divergence between the left and right hemispheres in each region before and after external sensory stimulation, at each set frequency band; averaging the first KL divergence of each segment in each hemisphere as the feature before and after external sensory stimulation; calculating the second KL divergence based on this feature; and concatenating the feature before and after external sensory stimulation with the second KL divergence as input to a classification model. This invention deeply mines the emotionally sensitive features in EEG signals through two-level progressive distribution difference calculation and combines it with a classification network to achieve model training and accurate classification, thereby achieving precise recognition of subtle emotional changes.

This invention discloses an emotional intervention system integrating multi-sensory feedback, relating to the field of emotional intervention technology, and includes the following steps: S1, scene library construction; S2, scene-based preference collection; S3, personalized intervention scene generation; S4, real-time dynamic adjustment. This invention realizes the transformation of intervention content from standardized output to personalized customization. Based on a preference adaptation mechanism of children's autonomous feedback, it can accurately capture each child's unique interests, making the intervention content more aligned with their cognitive habits and emotional needs. This helps to stimulate children's active participation in the intervention process, reducing resistance. Furthermore, it achieves dynamic adaptation between the intervention content and the child's real-time state, enabling timely responses to fluctuations in children's attention and emotional changes during the intervention process. This avoids guidance failure due to rigid content, ensuring the intervention content remains attractive to children, thereby helping to maintain their focused attention.

The invention relates to the technical field of natural language processing, and discloses a common-situation reply generation model training method and device based on knowledge distillation. The method comprises the steps that a first sample sequence is input into a teacher model for reply generation, and a first reply sequence is obtained; inputting the emotional common sense sequence into an emotional encoder, and inputting the cognitive common sense sequence into a cognitive encoder to obtain an emotional global representation vector and a cognitive global representation vector; inputting the fusion representation vector into a decoder of the student model for reply generation to obtain a second reply sequence; performing emotion classification prediction to obtain emotion classification probability distribution; and carrying out joint training on the student model and the teacher model to obtain a common-situation reply generation model. According to the scheme, emotional changes and potential intentions of the user can be accurately captured, and personalized and diversified replies are generated in combination with a knowledge distillation technology and external common knowledge; dialogue historical information can be comprehensively and deeply analyzed, and it is ensured that generated replies are closely related to contexts, and logic coherence is achieved.

The application discloses an emotional risk identification method and platform combined with public emotional behavior spectrum, and the method comprises the following steps: extracting behavior features, text features and sound features in historical user interaction sequences, and splicing to obtain multi-source splicing features; based on the multi-source splicing features, outputting public emotional behavior spectrum through a public emotional behavior spectrum identification model and outputting personal emotional behavior spectrum through a personal emotional behavior spectrum identification model; splicing the public emotional behavior spectrum and the personal emotional behavior spectrum for training an emotional risk identification model; obtaining current real-time user complaint data, inputting the emotional risk identification model, and outputting an emotional risk level. By adopting the technical scheme, individual and group emotional behavior spectrum modeling can not only depict the fine-grained emotional evolution path of individuals, but also capture the group emotional aggregation and diffusion process, so that accurate risk grading early warning of personal emotional behavior can be realized in the background of public emotional change trend.

A system is provided.SOLUTION: A system comprising: means for collecting biometric data by a wearable device; means for determining an emotional change detected by the wearable device; means for recording and summarizing audio associated with the emotional change; means for automatically generating a diary based on the summarized audio data; and means for notifying a user of the diary.SELECTED DRAWING: Figure 1

This invention provides a human-computer interaction method, device, electronic device, and medium, belonging to the field of artificial intelligence technology. It acquires current user voice data from AI dialogue, and inputs the extracted current user emotional features and user personality features extracted from the converted text data into preset emotion recognition models and preset personality recognition models, respectively, to obtain current user emotion type probability data and user personality type probability data. These data are then fused to obtain an emotion-personality fusion type and a fusion type probability. The emotion-personality fusion type with the highest probability value is used as the result of judging the user's current emotion, and the most suitable emotion guidance scheme is obtained based on this result. By incorporating individual user personality differences into the emotion analysis process, the judgment of the user's current emotional state is more accurate, and corresponding emotional guidance can be provided promptly and accurately according to the user's individual emotional changes, thus improving the user experience.

This application provides a method and related equipment for implementing a psychological and emotional companionship service based on an intelligent agent. It relates to the technical field of data processing, including receiving user input information to generate session data, extracting emotional features to construct an emotional feature vector, and constructing an emotional change sequence based on consecutive session rounds to determine emotional trend information; matching the emotional feature vector and emotional trend information with an emotional companionship strategy, generating emotional companionship feedback content and outputting it to the user terminal; performing emotion recognition on subsequent user session data, and adjusting the emotional companionship interaction strategy when the emotion recognition result does not meet preset improvement conditions. By calculating the emotional feature deviation vector and re-matching an alternative strategy when the emotion does not meet preset improvement conditions, dynamic recognition of user emotional change trends and adaptive adjustment of the emotional companionship strategy are achieved, overcoming the problem of accurately recognizing user emotional change trends during emotional companionship interaction.

The invention discloses an emotional state dynamic monitoring and feedback method, which relates to the technical field of emotional monitoring and comprises the following steps: S1, collecting voice data, S2, monitoring equipment data, S3, determining an emotional score value, S4, calculating a baseline score value and S5, adding label information. According to the method, the accuracy of emotion recognition is improved by combining a time sequence modeling and weighted scoring mechanism of RNN and LSTM, the model not only can extract deep emotion features from sequence data, but also can reflect the actual contribution of different emotion dimensions to the overall state through weight adjustment, so that the score better fits the characteristics of the user, and the user experience is improved. Meanwhile, dynamic calibration and context perception of emotion evaluation are realized by establishing an emotion baseline in a non-interaction state, and a baseline score and a fluctuation value form an'emotion normal state 'reference system of the user, so that the system can distinguish emotion change caused by external interaction from emotion fluctuation of the user, and the user experience is improved. Therefore, the real emotion influence of the interaction behavior can be more accurately judged.

The invention discloses an interaction and emotion response method of education agent hardware equipment, and relates to the technical field of education equipment interaction, and the method comprises the steps: S1, data collection: capturing an interaction feedback behavior of a user on emotion response in real time through an interaction feedback collection module of the education agent hardware equipment, meanwhile, acquiring emotional state change data of the user in real time through an emotional data acquisition module of the hardware equipment; according to the method, a human-computer interaction closed loop of response output-feedback acquisition-iterative optimization is constructed, an evaluation model is constructed in combination with multi-dimensional interaction feedback data and emotional state change data, real-time judgment on an emotional response effect is realized, layered iterative interaction mode optimization is triggered, and the evaluation efficiency is improved. The problems that in the prior art, emotion response lacks dynamic evaluation and automatic iteration and is insufficient in accuracy and adaptability are solved, continuous optimization of the emotion response based on user interaction behaviors is achieved, the emotion change and learning requirements of the user are effectively met, and the fitting degree of man-machine interaction and the learning experience of the user are improved.