66 results about "Emotion assessment" patented technology

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 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 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 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.

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

This application relates to the technical field of large language models, and specifically to a multimodal digital human interaction method and system based on a large language model. The method includes: obtaining video data, audio signals, and text data of a single conversation when a user interacts with a digital human; extracting the envelope of the audio signal to determine the emotion evaluation value and semantic contrast of the single conversation; identifying the emotions contained in each frame of the video data to form an emotion sequence for the single conversation; obtaining the semantic deviation of the single conversation; obtaining the semantic guidance value of the single conversation; and combining with an emotional conversation generation model to generate a corresponding emotional conversation. This application can reduce the deviation in the understanding of the user's expressed intentions and the perception of emotions, more accurately judge the user's emotional state, so that the generated emotional response content matches the user's emotional state, and improve the user's interactive experience.

The task-based thermal imaging and near-infrared mania assessment method, system, and device of this application captures the user's facial features, pupil features, and behavioral characteristics during an irritability psychology experimental paradigm assessment; constructs a multimodal fusion neural network; and inputs the facial features, pupil features, and behavioral features into the multimodal fusion neural network to obtain the user's mania assessment results. This application integrates thermal imaging and eye tracking technology to achieve real-time and accurate detection of emotional states such as manic mood disorder. This multimodal fusion technology breaks through the limitations of traditional methods and provides a new solution for the screening and assessment of emotional disorders. It has broad application prospects, especially in emotional management and mental health monitoring involving high-risk populations.

The invention discloses a physiological data acquisition method for an air traffic controller, and relates to the technical field of data acquisition, and the method comprises the steps: obtaining the multi-modal physiological data of the air traffic controller at the current moment; inputting the multi-modal physiological data into the trained fatigue-emotion evaluation model to obtain a predicted fatigue score and predicted emotion state probability distribution at the current moment; based on the predicted fatigue score, the predicted emotional state probability distribution and the multi-modal physiological data, processing tasks of different priorities are generated; processing tasks are executed according to priorities through a hierarchical scheduling algorithm, and physiological data collection is completed. Through multi-modal data fusion and model evaluation, the physiological state of the controller is accurately obtained, tasks are processed according to priorities, timely response of key data is guaranteed, reliable physiological information support is provided for air traffic control, and the flight safety guarantee level is improved.

The application discloses a kind of based on action unit AU and micro-expression question and answer mood evaluation method and device, obtain the question and answer video of user to be detected, the first image about non-basic question and answer in question and answer video is input into face detection model and obtains first face image, first face image is input into the multi-task identification model based on AU and micro-expression, output multiple first AU scores and multiple first micro-expression scores of first face image. By the corresponding relationship between multiple AU, multiple micro-expression and multiple emotional parameters, multiple first AU scores and multiple first micro-expression scores are weighted to obtain multiple target emotional values corresponding to multiple emotional parameters. Compare multiple target emotional values with multiple basic emotional values corresponding to multiple emotional parameters to obtain comparison result. The question and answer mood of user to be detected is evaluated by comparison result. In this way, the analysis standard of question and answer mood is set, and the fairness and credibility of evaluating question and answer mood are improved.

An emotion inference device according to one aspect of the present invention includes: a subjectivity information acquisition unit for acquiring subjectivity information related to subjectivity of a target person; a psychological characteristic database for containing information on a plurality of psychological characteristics related to a sense of unity; a psychological characteristic calculation unit for calculating, on the basis of the subjectivity information and the information in the psychological characteristic database, a plurality of psychological characteristic scores respectively corresponding to the plurality of psychological characteristics; a behavior information acquisition unit for acquiring behavior information related to behavior of the target person; a behavior information processing unit for calculating, on the basis of the behavior information, target person behavior data by analyzing the behavior of the target person; an event information acquisition unit for acquiring event information related to the content of an event; an audience information acquisition unit for acquiring audience information related to an audience around the target person; an audience information processing unit for calculating color phase information on the entire audience at an event site on the basis of the audience information; and a sense-of-unity calculation unit for calculating sense-of-unity inference data in which the psychological characteristics related to the sense of unity are quantified by using the plurality of psychological characteristic scores, the target person behavior data, and the color phase information.

The invention provides a quick emotion assessment method and device under a variable gravity condition. The method comprises the following steps: acquiring electroencephalogram data and video data under a microgravity environment; performing data preprocessing on the electroencephalogram data and the video data to obtain electroencephalogram features and video features; substituting the electroencephalogram features and the video features into a pre-trained basic emotion recognition model to obtain an emotion recognition result; wherein the basic emotion recognition model comprises a single-mode basic emotion recognition model and a multi-mode basic emotion recognition model; the single-mode basic emotion recognition model and the multi-mode basic emotion recognition model are obtained by training a classifier based on features extracted from the video data and the electroencephalogram data and corresponding emotion categories. According to the invention, the accuracy and real-time performance of emotion recognition in a variable gravity environment are improved.

The invention discloses a head physiotherapy instrument control method and system based on a multi-element stimulation mode, and relates to the technical field of head physiotherapy, and the method comprises the steps: calculating a comprehensive emotional state value through a fusion algorithm according to the geometric features, deep expression features, voice emotion scores and skin electric emotion scores of a face region, and obtaining a comprehensive emotional state value; judging an emotion category according to the emotion state value, and selecting a physiotherapy instrument mode according to the emotion category; when the physiotherapy instrument is used, a skin conductance value is collected in real time, heart rate variability data is collected through the photoelectric volume pulse wave sensor, and the physiotherapy instrument mode is dynamically adjusted through the skin conductance value and the heart rate variability data. According to the method, the facial image, the voice signal and the skin conductance data are comprehensively collected, deep learning and machine learning algorithms are combined, the emotional state of the user is accurately evaluated, and the problems of low emotional evaluation precision, fixed treatment mode and the like in the prior art are solved.

An object of a system according to an embodiment is to realize efficient delivery through selection of a deliverer and optimization of a delivery route.SOLUTION: A system according to an embodiment includes a deliverer selector, a delivery route generator, a health monitor, and an emotion estimator. The deliverer selection section analyzes the past delivery history of the deliverer to select the most efficient deliverer. The delivery route generation section generates an optimal delivery route for the deliverer selected by the deliverer selection section. The health monitoring unit monitors the health condition of the courier. The emotion estimator analyzes the emotional state of the delivery person.SELECTED DRAWING: Figure 1

The invention discloses an emotion evaluation and sound-light therapy healing method based on multi-modal signal fusion. The method comprises the following steps: S1, collecting an electroencephalogram signal and a photoelectric volume pulse wave signal of a user; s2, extracting a first feature value representing the relaxation degree of the user, and extracting a second feature value representing the heart rate variability of the user; s3, calculating a real-time emotional state index of the user; s4, matching a corresponding acoustic-optical therapy healing scheme from a preset therapy healing strategy library; and S5, controlling an acoustic unit and an optical unit to execute the acoustic-optical therapy healing scheme. The invention belongs to the technical field of emotion evaluation and healing, and particularly provides a method for realizing more accurate evaluation of the emotional state of a user by fusing electroencephalogram and pulse wave signals, dynamically matching and executing an optimal sound-light healing scheme based on an evaluation result, realizing personalized and adaptive closed-loop healing, and improving the healing efficiency. The emotion evaluation and acoustic-optical therapy healing method and system based on multi-modal signal fusion can effectively improve the emotion healing effect.