88 results about "Emotion assessment" patented technology

The invention provides an emotion evaluation method and device, equipment and a medium. The method comprises the following steps: receiving interaction feedback information of a user for an emotion evaluation problem of a current round; generating an interactive input text of the current round based on the interactive feedback information, historical information interacting with the user and prompt information of emotion evaluation; inputting the interaction input text of the current round into the large language model to obtain an emotion evaluation problem of the next round output by the large language model, and returning and receiving interaction feedback information of the user for the emotion evaluation problem of the next round until interaction is finished; and performing emotion evaluation based on the interaction feedback information of each round. According to the emotion evaluation method provided by the invention, the emotion evaluation problem is automatically generated through the large language model, man-machine interaction is carried out, and the dependence on professional evaluation personnel is reduced. And through an interactive feedback mechanism, the questioning content can be adjusted in real time according to the emotion of the user, and personalized requirements are met.

The invention relates to the technical field of psychological analysis, in particular to a student psychological interview assisting method and system based on an AI large model, and the method comprises the following steps: obtaining a student answer text, carrying out emotion vocabulary marking by using the AI large model, recognizing a grammatical role and an emotion direction of emotion vocabularies in the text, and carrying out the quantitative analysis of the intensity of each emotion word. And evaluating the emotion direction and intensity of each sentence of the student to obtain emotion state information. According to the method, the AI large model is introduced to perform emotion analysis on the answer text of the student, so that emotion evaluation is more comprehensive and detailed, the rate and amplitude of emotion fluctuation of the student are monitored in real time, the emotion fluctuation curve is drawn, the duration time is recorded, dynamic data support is provided for psychological health evaluation, the accuracy and real-time performance of psychological evaluation are improved, and the psychological evaluation efficiency is improved. The psychological health risk of the student is accurately identified by combining emotion fluctuation and psychological pressure analysis, so that the evaluation result is more personalized, and targeted and prospective support is provided for psychological experts.

The invention discloses a time interference electrical stimulation system for suppressing, controlling and adjusting, which comprises an image acquisition module for acquiring functional magnetic resonance images to acquire functional connection data; the stimulation module is used for respectively generating time interference electrical stimulation waveforms in the plurality of stimulation areas so as to stimulate the corresponding stimulation areas and adjusting stimulation parameters; the evaluation module is electrically connected with the image acquisition module and the stimulation module, acquires cognitive emotion evaluation data, and obtains an emotion and cognitive neural circuit regulation result by combining the emotion evaluation data and the function connection data as an inhibition control capability evaluation index; and the control module is electrically connected with the stimulation module and the evaluation module, and controls the stimulation module to adjust the stimulation parameters based on the emotion and cognitive neural circuit adjustment result. According to the system, effective time interference electrical stimulation is carried out on a plurality of inhibition control intervention brain regions, stimulation parameters are improved by implementing stimulation effect evaluation, and a favorable adjustment means is provided for inhibition control of multi-region and complex network modulation.

The invention discloses a self-adaptive telephone traffic distribution method and system based on customer service emotion. Comprising the following steps: acquiring a first emotional state data set of a plurality of first customer service staff in an idle state in a first time period and a first customer evaluation value after each first customer service staff answers a first incoming call in a second time period, and acquiring a call answering skill value of each first customer service staff; for each first customer service, analyzing the first emotional state data set and the first customer evaluation value of the first customer service by using a target emotional evaluation model to obtain a first emotional value of the first customer service in a first time period, and determining the call receiving priority of each first customer service for answering the second incoming call according to the call receiving skill value and the first emotional value; and in response to an answering request for the second incoming call, determining a target customer service answering the second incoming call according to the call answering priority of each first customer service. According to the invention, the technical problem that the telephone traffic distribution result cannot adapt to the actual working state of the customer service because the analysis of the emotional state of the customer service is neglected in the related telephone traffic distribution technology is solved.

The invention discloses a psychological health education auxiliary system based on big data, and relates to the technical field of psychological health education, the psychological health education auxiliary system uses a camera to collect eye movement data, combines CNN to extract static features and LSTM to capture time sequence changes, improves anxiety detection objectivity, combines embedded online learning, short video watching or classroom scenes, and improves anxiety detection accuracy. Daily data collection is carried out, the anxiety trend is continuously tracked by adopting a dynamic analysis method, and the change of the psychological state is monitored and evaluated in real time; the psychological intervention content can be dynamically adjusted based on anxiety scores and personalized feedback strategies, for example, relaxation training, psychological adjustment videos or emotion evaluation questionnaires are pushed, so that psychological health education is more personalized, and invalid or generalized psychological education information is reduced; in addition, a reinforcement learning algorithm is introduced, recommendation content is continuously optimized through interaction data of a tested object, the intervention effect is improved, and psychological education resources can adapt to dynamic changes of individual psychological states.

The invention relates to the technical field of large language models, in particular to a multi-modal digital human interaction method and system based on a large language model, and the method comprises the steps: obtaining video data, audio signals and text data of a single conversation when a user interacts with a digital human; extracting an envelope line of the audio signal, and determining an emotion evaluation value and a semantic contrast ratio of a single dialogue; identifying emotions contained in each frame of image in the video data to form an emotion sequence of a single dialogue; obtaining a semantic deviation value of a single dialogue; obtaining a semantic guide value of a single dialogue; and generating a corresponding emotional dialogue in combination with the emotional dialogue generation model. According to the method and the device, the deviation between the understanding of the intention expressed by the user and the perception of the emotion can be reduced, and the emotion state of the user can be more accurately judged, so that the generated emotion reply content is matched with the emotion state of the user, and the user interaction experience is improved.

The invention discloses a wearable emotion monitoring and evaluating system based on brain-heart-enteric nervous system electric signals, and belongs to the field of bio-electricity signal processing and recognition. According to the system, electroencephalogram (EEG) and electrocardio (ECG) are integrated, and an EGG signal closely related to nervous system activity is creatively integrated, so that the system can provide comprehensive emotion evaluation. The signals respectively represent different emotional response mechanisms, the emotion change can be more accurately captured through the fusion of the multi-modal signals, and the accuracy and reliability of emotion detection are improved. The system can collect and analyze physiological signals of a user in real time, provide instant emotion feedback, help the user to recognize emotion fluctuation in an early stage and take corresponding measures, and avoid emotion out-of-control or long-term emotion lowering.

The invention relates to a method and device for emotion evaluation, a medium and a program product. The method comprises the steps of obtaining multi-modal emotion evaluation data of a to-be-evaluated object in a process of performing a plurality of predetermined emotion evaluation tasks on the to-be-evaluated object; performing feature extraction processing on each item of data included in the emotion evaluation data to obtain corresponding feature information; fusing the feature information corresponding to each data to obtain corresponding fused feature information; and outputting a corresponding emotion evaluation result based on the fused feature information by using a trained emotion prediction model. According to the method, the trained emotion prediction model is used, the corresponding emotion evaluation result is output based on the feature information of the multi-modal emotion evaluation data, model pre-training and automatic iteration updating are carried out by adopting the meta-learning algorithm, individual differences are rapidly adapted, and the performance, efficiency and robustness of model prediction are improved.

The invention relates to the technical field of text language analysis, in particular to a method for evaluating emotion in client evaluation text content, which comprises the following steps of: S1, establishing a basic emotion database through the client evaluation text content, performing similarity screening, invalid deletion and coding on data, and removing texts without evaluation value to obtain a basic emotion database; carrying out natural language preprocessing on the residual evaluation texts; s2, performing sentiment classification on the evaluation of the customer based on a deep learning model, tracking the change of the evaluation of the customer through time sequence analysis, judging the change of the customer on products and services at different time points by using clustering analysis, and constructing a personalized portrait of the customer, in the method, the change of the evaluation of the customer is analyzed and tracked through A I, and a theme label is distributed for an evaluation text; and meanwhile, when the BERT algorithm model is trained, the training effect of the model is enhanced through MLM mask, NSP prediction and customer personalized portrait, and the purpose of truly extracting semantics and emotions in the text is achieved.

The invention discloses a manic emotion assessment method, system and device based on task state thermal imaging and near infrared. The method comprises the following steps: acquiring expression features, pupil features and behavioral features of a user in an irritable psychological experiment normal form assessment process; constructing a multi-modal fusion neural network; and inputting the expression features, the pupil features and the behavioral features into a multi-modal fusion neural network to obtain a manic emotion evaluation result of the user. Thermal imaging and eye movement tracking technologies are integrated to achieve real-time and accurate detection of emotional states such as manic emotional disorder, the limitation of a traditional method is broken through through the multi-modal fusion technology, a new solution is provided for screening and evaluation of emotional disorder, and particularly, the method has a good application prospect in emotional management and mental health monitoring of high-risk people. Wide application prospects are realized.

The invention belongs to the field of financial science and technology and data analysis, and particularly relates to a financial transaction emotion analysis and price trend prediction method based on data fusion, which is mainly used for solving the problems of complexity and real-time demand of stock price prediction in a financial market. According to the method, a fundamental analysis module, an emotion evaluation module and a price prediction module are integrated, multiple data sources are integrated, an advanced machine learning algorithm is adopted, and the real dynamic state of the market is more comprehensively and accurately captured. The fundamental analysis module is used for systematically analyzing financial data, announcement information and other related indexes of the stock and providing deep understanding of a target stock; the emotion evaluation module analyzes text data in social media and financial forums and combines various emotion analysis methods to generate dynamic indexes reflecting market emotions; the price prediction module carries out modeling based on data of different time scales such as minute, hour and day lines, and the prediction precision is improved through a layer-by-layer feedback mode.

The invention discloses a psychiatric patient abnormity monitoring and early warning system, which relates to the technical field of medical health information, and comprises the steps of collecting patient information in real time, preprocessing the collected data, extracting features, fusing to form a multi-modal feature set, carrying out preliminary emotional state evaluation on the multi-modal features, and carrying out early warning. Setting an initial early warning threshold according to the basic information of the patient and the preliminary evaluation result, dynamically adjusting the early warning threshold according to the historical data and the emotion evaluation result, when the emotional state of the patient exceeds the dynamically adjusted early warning threshold, analyzing the behavior pattern and the physiological signal of the patient, extracting text data from the social media interaction record, and sending the text data to the user. And identifying the emotion triggering content by using a natural language processing technology. And specific events causing emotion changes are identified in combination with the information, and personalized intervention suggestions are generated. According to the method and the system, medical staff can respond to emergency situations quickly, customized treatment schemes can be provided for patients, and finally the purpose of improving mental health conditions is achieved.

The invention provides a data processing method and device, electronic equipment and a computer readable storage medium, and belongs to the technical field of data processing. The method comprises the following steps: acquiring multi-modal emotion data of a target object; obtaining a first emotion entropy value according to the multi-modal emotion data of the target object; acquiring brain wave data of the target object; obtaining a second emotion entropy value according to the brain wave data of the target object; obtaining a target emotion entropy value according to the first emotion entropy value and the second emotion entropy value; and performing grade division on the target emotion entropy according to an emotion evaluation criterion to obtain an emotion state grade of the target object, and setting an emotion atmosphere scene according to the emotion state grade. Through emotion label identification of the multi-modal emotion data and the brain wave data, the emotion score of the target object is obtained more accurately, and the ability of adjusting the environment atmosphere is improved.

The invention discloses a commodity pushing method and system based on a consumer address, and particularly relates to the technical field of positioning and message pushing, and the system comprises a data collection module which is used for monitoring and collecting consumer personal information and mobile phone historical record information data; according to the invention, based on the consumer outdoor information, the environmental noise information and the network connection information generated in the daily activities of the consumer, the demand degree of the consumer for commodity pushing is evaluated, the pushing frequency is preliminarily adjusted according to the evaluation result, and then the machine learning algorithm model is constructed to optimize the pushing process, so that the recommendation accuracy is improved, and the user experience is improved. After a system sends commodity push to consumers according to an algorithm, a post-push emotion evaluation model is constructed through emotion abnormal information, software retention information and commodity evaluation information of consumers who do not purchase commodities after information acquisition, and the emotion tendency of the consumers to user software is judged; and the commodity pushing frequency of the consumer is adjusted for the second time according to the emotion tendency of the user.

To provide a technology for forming an AI model that can derive an emotion index value without the need of a sensor device for detecting a biological signal.SOLUTION: A learning device causes an AI model (121 m, 122 m) to learn using a learning data set including learning input data that includes at least one of the pieces of information of input operation to a vehicle (V1) by a subject (U1) and information output from the vehicle (V1) in accordance with input operation to the vehicle (V1) and including an emotion index value in accordance with a biological signal of the subject as correct answer data.SELECTED DRAWING: Figure 7

The invention discloses an emotion management method and device, equipment and a storage medium, and belongs to the technical field of artificial intelligence. The invention provides a new medical mode, that is, emotion management is carried out on the user online. Specifically, under the guidance of basic theories of traditional Chinese medicine, emotion evaluation is carried out on the user based on multi-modal data collected by wearable equipment worn by the user, and the online emotion evaluation mode enables the user to carry out self-service evaluation on emotion problems anytime and anywhere, so that the user experience is improved, and the user experience is improved. And the prevention efficiency of the emotion problem is effectively improved. Besides, the mode can output an emotion evaluation report and early warning information based on the emotion evaluation result, so that the user can timely and detailedly know own emotion problems, and the man-machine interaction efficiency is high. Besides, the mode can also recommend personalized emotion intervention suggestions to the user based on the emotion evaluation result and the personal physique of the user, so that the user can conveniently carry out self-service intervention on emotion problems in time.

The invention provides a news recommendation mobile phone application implementation method and system based on deep learning, and relates to the technical field of intelligent information recommendation. The method comprises the following steps: acquiring multi-source data, including hot news ranking list data, user calendar application data, APP use data, news stay time data and facial expression data; and analyzing facial expressions by using an emotion evaluation model constructed by deep learning to determine user emotions, and judging that the user is not interested in current pushing when the news stays for a short time and the emotions are poor. And by combining calendar and APP use data, determining interest information through a news push model constructed by deep learning, and by applying multi-dimensional data fusion and the deep learning model, an interest change sign of a user can be captured in time, a recommendation strategy can be quickly adjusted, and accurate push is performed in combination with a hot news ranking list.

The invention discloses a depressive emotion early screening and intervention system based on cross-modal heterogeneous information. The depressive emotion early screening and intervention system comprises a tester information registration module, a sensor test template, a resting state data acquisition module, a task state data acquisition module, a depressive emotion evaluation module, a digital intervention module, a report display module and a terminal control module. According to the method, scale, physiological data and behavioral signals are combined, and multiple scientific evaluation paradigms are constructed to perform multi-dimensional, objective and effective evaluation on the depressive emotion; the system further provides a personalized digital intervention scheme through an evaluation result, and the depressive emotion is preliminarily relieved. According to the invention, early screening and intervention of the depression emotion state can be realized, the detection efficiency is improved, and the method can be applied to various scenes such as schools, hospitals and families.

The emotion assessment apparatus has a detecting unit detecting heartbeat information including the heart rate of a subject, an emotion assessment unit assessing whether the emotion of the subject is a negative emotion or a positive emotion based on the heartbeat information, a counting unit counting the number of heart rate variations within a predetermined time period, an emotional expression assessment unit assessing the mental state of the subject based on the number of heart rate variations, and using the assessment results from the emotion assessment unit to assess the mental state, and an output unit outputting the assessment results from the emotional expression assessment unit.

To provide an emotion estimation device capable of starting emotion estimation processing early, an emotion estimation method, an emotion estimation program, and an emotion estimation system.SOLUTION: An emotion estimation device performs emotion estimation according to an emotion index value about a biological state in a person whose emotion is estimated on the basis of an estimation condition adjusted by calibration, and has a controller. The controller extracts and stores singular point information to be a characteristic of detected time series data of emotion index value during calibration, extracts correspondence information corresponding to the singular point information in the detected time series data of emotion index value during emotion estimation, and performs emotion estimation in accordance with difference between the singular point information and the correspondence information.SELECTED DRAWING: Figure 1

The invention relates to the technical field of medical information, in particular to an intelligent assessment and intervention system and method for psychological states of patients after hepatobiliary and pancreatic surgery. According to the method, technologies such as a BP neural network, speech recognition, natural language processing and an improved LSTM neural network are fused, and a psychological state evaluation model is constructed; the system comprises a psychological test module, a music intervention module and an evaluation module, and realizes data intercommunication of evaluation results. The psychological test module selects a proper scale for emotion evaluation according to an evaluation result; the music intervention module recommends playing music according to the evaluation result; the evaluation module analyzes the voice communication text, judges psychological problems by using a support vector machine algorithm, and constructs a postoperative health state data model; according to the system, through innovative technical means and overall design, the comprehensiveness of evaluation, the individuation of intervention and the continuity of service are improved, the defects of a traditional method are effectively overcome, and more accurate and comprehensive postoperative psychological evaluation and intervention service is provided for patients.

This application discloses an emotion assessment method and apparatus based on deep temporal modeling. The method includes: real-time acquisition of speech data and EEG signals of a target object; conversion of the acquired speech data into an audio sequence and the EEG signals into an EEG sequence according to a preset time interval; inputting the audio sequence into a first sub-network of an emotion extractor to obtain speech emotion results; inputting the EEG sequence into a second sub-network of the emotion extractor to obtain brain activity emotion results; evaluating the emotional state of the target object based on the speech emotion results and brain activity emotion results at different times to obtain multiple emotion scores; fusing the multiple emotion scores of the target object within a target time period to obtain a comprehensive emotion score evaluation; and determining the emotional state type of the target object according to a preset emotion score scale.

The invention discloses an emotional pressure monitoring method and system based on an intelligent ring and multi-modal data. The method comprises the following steps: continuously acquiring sensing data of multiple modals of a target user in a preset time period through the intelligent ring; based on a preset feature extraction algorithm, extracting a health feature parameter corresponding to each piece of sensing data; forming multi-modal feature data by using all the health feature parameters, and respectively inputting the multi-modal feature data into prediction models corresponding to a plurality of evaluation dimensions to obtain a plurality of output emotional pressure vectors; and according to the plurality of output emotional pressure vectors and an emotional recognition event corresponding to the preset time period, determining an emotional pressure condition of the target user in the preset time period. It can be seen that accurate emotional pressure analysis based on multi-modal data and dimension evaluation can be achieved, the comprehensiveness and real-time performance of user emotional health monitoring are improved, and the risk of emotional evaluation deviation caused by single dimension or event neglect is reduced.

The present invention provides an employee status monitoring method and system based on deep learning of facial multimodal information, relating to the technical field of employee status monitoring. The system includes a facial acquisition module, a facial processing module, a deep learning module and an emotion assessment module; the facial acquisition module is used to collect basic facial information and work facial information of employees; the facial processing module is used to analyze the collected data; the deep learning module is used to construct a convolutional neural network; the emotion assessment module evaluates the work facial information of employees based on the convolutional neural network to obtain the real-time status of employees; the present invention obtains facial data of employees in basic status and work status, compares and analyzes the facial data of employees in different states, and obtains the real-time status of employees, so as to solve the problem that the existing employee monitoring system does not obtain and analyze the facial status of employees, resulting in the inability to judge the fatigue status of employees.

The present disclosure relates to an apparatus and a method for managing depression in a perinatal period. The apparatus comprises: a memory that stores at least one instruction for managing depression in a perinatal period; and a processor that performs an operation according to the instruction, wherein the processor analyzes, through a cognitive therapy model, questionnaire information and speech information collected from a terminal of a mother, so as to identify negative thought patterns and emotions of the mother, and evaluates the mental health level of the mother according to the identified negative thought patterns and emotions, the cognitive therapy model learns a trigger keyword related to depression of the mother, and, when a word showing similarity greater than or equal to a preset level with the learned trigger keyword is detected from the questionnaire information or the speech information, the mental health level of the mother can be evaluated according to the analysis result of the detected keyword.

The invention discloses a simulated flight emotional stability objective quantitative evaluation method and system, and the method comprises the steps: carrying out the physiological representation mapping of a data unit in a monitoring period of each stage of simulated flight based on a pre-trained simulated flight emotional evaluation model, and obtaining the global physiological features and stage-specific physiological features of an individual, performing dimension reduction based on the physiological relevancy to obtain a time sequence structure of a preset time window; collaboratively embedding a preset global physiological mapping feature and a stage-specific physiological mapping feature, and generating a self-correlation feature representation integrated with the core physiological feature through a self-correlation coding layer; extracting cross-layer correlation features between the self-correlation feature representation and the two types of time sequence structure features; and identifying an emotion quantitative evaluation result based on the cross-layer related features. According to the method, emotion-related physiological changes of each stage of simulated flight can be accurately captured, and the objectivity and accuracy of evaluation are improved.

The invention discloses a personalized emotion evaluation questionnaire generation method and system based on real-time physiological data and neural network modeling, relates to the technical field of emotion evaluation, and solves the problems of lack of personalization, data isolation and poor dynamic adaptability of an emotion evaluation questionnaire in the prior art. The method comprises the steps of obtaining multi-modal physiological data and performing dynamic feature extraction, predicting emotional state classification and emotional degree indexes of a user by utilizing an emotional state evaluation model, and automatically generating a personalized emotional evaluation questionnaire by utilizing a questionnaire generation model according to a model prediction result. And a feedback mechanism is constructed to continuously optimize the emotional state evaluation model and the questionnaire generation model, so that the problems of insufficient personalization, data isolation and poor dynamic adaptability in the prior art can be effectively overcome, automatic generation of the personalized emotional evaluation questionnaire is realized, the emotional evaluation efficiency is improved, and the user experience is improved. And the questionnaire content can be differentiated according to individual differences.

To provide a system that estimates the emotions of a student and provides support accordingly.SOLUTION: A system according to an embodiment comprises an acquisition unit, an estimation unit, a determination unit, and a support unit. The acquisition unit acquires emotion estimation information for estimating the emotions of an English class student. The estimation unit estimates the emotions of the student on the basis of the emotion estimation information acquired by the acquisition unit. The determination unit determines support according to the emotions of the student as estimated by the estimation unit. The support unit provides the student with the support determined by the determination unit.SELECTED DRAWING: Figure 1

An object of a system according to an embodiment is to effectively stimulate power of idea of a user.SOLUTION: A system according to an embodiment includes AR goggles, an eye tracking sensor, a voice recognition function, and an emotion estimation function. AR goggles are worn by a user. The eye tracking sensor is built into the AR goggles and tracks the user's eye gaze. The voice recognition function is built into the AR goggles and recognizes the voice of the user. The emotion estimation function is built into the AR goggles, and estimates the user's emotion.SELECTED DRAWING: Figure 1

To provide a more realistic game experience by determining the behavior of a game character on the basis of a player's emotion.SOLUTION: A system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and an execution unit. The acquisition unit acquires emotion estimation information for estimating a game player's emotion. The estimation unit estimates a player's emotion on the basis of the emotion estimation information acquired by the acquisition unit. The determination unit determines the behavior of a game character on the basis of the player's emotion estimated by the estimation unit. The execution unit executes the behavior of the character determined by the determination unit.SELECTED DRAWING: Figure 1