462 results about "Psychological status" patented technology

The invention discloses a multi-modal interview automatic quality analysis and evaluation method and system based on a large model, and the method comprises the steps: collecting and storing multi-modal data, such as texts, audios, videos and behavior interaction, and carrying out the preprocessing of the multi-modal data to form a standardized data set; utilizing a preset interview structure and a large model to dynamically guide the process, adjusting the topic rhythm according to real-time feedback, and recording stage conversion information to form logic trajectory data for process coherence management; automatically coding text data through a large language model, extracting features such as keywords and performing topic clustering, performing cross validation and semantic fusion in combination with data analysis results of each modal, and generating deep analysis results such as psychological states; and generating a comprehensive assessment report containing qualitative description, quantitative score and psychological abnormality or cognitive disorder risk prompts based on a deep analysis result, thereby providing a basis for psychological health assessment and cognitive competence evaluation. According to the method, automatic analysis of multi-modal data is realized, and evaluation scientificity and efficiency are improved.

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 system platform for psychological assessment and emotion feedback. The system platform comprises a multi-source data acquisition module, an emotion data analysis module, a psychological state assessment module, an intervention scheme generation module, an emotion change feedback module, a scheme optimization module and a data security protection module. The emotion recognition accuracy is improved by synchronously acquiring physiological-behavior-environment data and combining with a dynamic feature fusion method, a personalized intervention strategy is generated based on a reinforcement learning algorithm, real-time feedback is realized, and a data security protection system is constructed by adopting federal learning and homomorphic encryption technologies, so that the data security is ensured.

The invention provides a psychological crisis multi-stage joint control method and system based on a psychological large model, and aims to realize real-time monitoring, accurate evaluation and intelligent intervention of psychological states through a multi-modal data fusion and deep learning technology. The system collects multi-source information such as texts, voices, videos, physiological signals and behavior data, performs cross-modal analysis by using models such as Transform, LSTM and CNN, constructs personalized psychological portraits, and analyzes and predicts the psychological state change trend in combination with a time sequence. According to the method, a psychological crisis dynamic grading model is adopted, the psychological state of a user is divided into a normal grade, a mild grade, a moderate grade and a severe grade, multi-grade intelligent intervention is provided based on different risk grades, and the multi-grade intelligent intervention comprises AI self-service adjustment, psychological counseling matching, social support enhancement, emergency medical intervention and the like. The psychological intervention strategy is optimized in combination with reinforcement learning, the intervention mode is dynamically adjusted according to user feedback, and individuation and adaptability are improved.

The invention relates to the field of pilot psychological competency evaluation, and discloses a civil aviation pilot psychological competency evaluation method and system fused with biofeedback, and the method comprises the following steps: S1, collecting the physiological data and behavior data of a pilot in a baseline test situation, and building individualized psychological load baseline data; s2, in the virtual situation, collecting multi-modal data, including physiological data and behavior data, of the pilot in real time; S3, analyzing the data collected in real time through a multi-modal data fusion model, and generating a psychological state score of the pilot; s4, providing real-time feedback and situation difficulty adjustment based on the psychological state score; and S5, generating a personalized psychological competency assessment report. The psychological state analysis of multi-modal data fusion is matched with the real-time physiological data acquisition, so that the dynamic monitoring and accurate evaluation effects on the psychological load of the pilot are realized, and the defect that the psychological state of the pilot is difficult to dynamically capture in real time in the prior art is overcome.

The invention belongs to the field of student psychological health management, and particularly relates to a student psychological evaluation system based on AI reinforcement learning optimization. According to the system, the psychological state of the student is dynamically evaluated through multi-modal data acquisition, deep data processing and reinforcement learning optimization, and the problems of singleness and hysteresis in a traditional psychological evaluation method are solved. The system comprises a data acquisition layer, a data processing layer, a reinforcement learning optimization layer, a gamification evaluation module, a feedback and intervention layer and a user interface layer. The data acquisition layer acquires behavior, physiological and social data in multiple channels; the data processing layer performs cleaning, feature extraction and fusion on the data; the reinforcement learning optimization layer adjusts the psychological evaluation strategy in real time; the gamification evaluation module enhances the student participation degree through a task and reward mechanism; and the feedback and intervention layer provides real-time feedback and personalized intervention suggestions. The psychological evaluation accuracy, participation and intervention effect can be improved, and the method is suitable for psychological health management scenes of schools and related educational institutions.

The invention discloses an employment and entrepreneurship support system based on artificial intelligence, relates to the technical field of occupational planning, and aims to solve the problem that a traditional support mode is insufficient in individuation and accuracy. The system comprises a multi-modal data acquisition module used for acquiring facial expressions, whole body dynamics and voice data of a user in at least 30 minutes of video dialogue in real time; a dialogue text is processed through a large language model based on a Transform architecture, continuous time sequence modeling is carried out on multi-modal dynamic behaviors by applying a liquid time constant network, and deep fusion reasoning is carried out in combination with multiple groups of multi-head Transform attention mechanisms. The core analyzes the real thought, psychological state, behavior pattern and core ability of the user through consistency verification, generates a structured dynamic user insight abstract, and customizes personalized vocational development or entrepreneurship planning according to the structured dynamic user insight abstract. According to the method, the potential of the user can be deeply informed, high-precision personalized planning is provided, the decision-making quality and success rate are improved, and the method has dynamic adaptation and continuous learning capabilities.

The invention discloses an intelligent psychological state assessment method and system based on multi-modal alignment, and relates to the technical field of psychological assessment, and the method comprises the following steps: S1, constructing a training sample set of a psychological state assessment model; s2, preprocessing video data, voice data and text data in the training data sample set; s3, performing alignment and fusion by using multi-modal cross attention, taking the text data as a main modal, and taking the video data and the voice data as assistance of the text data to obtain multi-modal fusion features for psychological state analysis; s4, training by using the aligned and fused multi-modal fusion features to obtain a psychological state analysis model; and S5, obtaining a multi-dimensional psychological analysis result of the testee through the psychological state analysis model obtained through training. According to the method, the multi-modal deep features of the teenagers are extracted by pre-training the large model, and the feature expression ability of the data is improved by using the modal alignment technology.

The invention discloses a psychological state pre-screening system and method based on multi-modal data. The psychological state pre-screening system comprises a data acquisition module, a data preprocessing module, a psychological state analysis module, a psychological state evaluation and screening module and a feedback and early warning module. The system collects multi-modal data such as facial expressions, voices, texts, physiological signals and the like, and performs psychological state analysis by using a deep learning algorithm. The psychological state score is calculated through a multi-modal data fusion algorithm, and feedback or early warning is provided, so that the user or related mechanisms can perform further intervention. According to the method, the accuracy and the real-time performance of psychological state screening can be improved, and the problem of misjudgment of a single data source is avoided. The system can be integrated to a smart phone, a smart bracelet, a computer and other equipment, realizes low-cost and non-perceptual mental health monitoring, is suitable for personal health management, enterprise employee mental monitoring, psychological counseling auxiliary diagnosis and school psychological screening, and is beneficial to early discovery and intervention of mental health problems.

The invention discloses a Multi-agent-based psychological counseling platform and method. The counseling platform comprises an evaluation agent used for dynamically extracting psychological features based on dialogue behaviors of a visitor agent and updating results to a traits library module; the planning agent is used for constructing a personalized psychological counseling scheme based on the traits library module and dynamically adjusting a counseling process and a strategy according to counseling feedback; the consultant agent is used for carrying out dialogue interaction with the visitor agent according to the personalized consultation scheme and implementing emotion pacification and psychological intervention; the visitor agent is used for expressing psychological troubles, feeding back psychological state changes and promoting the consultation process; the historical dialogue long and short-term memory module is used for storing multiple rounds of psychological counseling interaction contents and comprises historical dialogue short-term memory and historical dialogue long-term memory; and the dynamic probability memory retrieval module is used for generating probability distribution based on the current consultation context and dynamically calling memory nodes from the historical dialogue long and short term memory module or the traits library module.

The invention provides a data processing method and device for an integrated virtual employee care Saas platform. The data processing method comprises the steps of obtaining associated data of employees through the integrated virtual employee care Saas platform; the associated data comprises historical behavior data, working state data and emotion fluctuation data; adjusting and optimizing a preset psychological state recognition algorithm based on the associated data to obtain an adjusted and optimized psychological state recognition algorithm; evaluating the associated data based on the tuning psychological state recognition algorithm, generating a psychological state evaluation result of the employee, and automatically pushing care scheme information when the psychological state evaluation result of the employee triggers an early warning threshold value; and encrypting and storing the associated data by adopting a dynamic encryption algorithm. According to the invention, the accuracy of psychological state assessment is improved, and the data security is guaranteed.

The invention discloses a psychological disease pre-diagnosis information processing method and system, and the method comprises the steps: collecting a multi-modal behavior signal and a physiological parameter signal of a user, and carrying out the differential privacy protection processing based on edge calculation and the noise separation processing based on wavelet transform, obtaining a standardized behavior feature sequence and a time sequence physiological feature vector; extracting behavior node features and psychological state markers from the standardized behavior feature sequence, and constructing a three-dimensional incidence matrix in combination with the time sequence physiological feature vector; and calculating a potential risk probability based on a graph neural network model, generating a pre-diagnosis grading result, fusing objective behavior environment data through a progressive protocol, outputting a personalized intervention strategy, and finally generating a comprehensive pre-diagnosis report. According to the method, accurate modeling of psychological-physiological-behavior dynamic association is realized through dynamic knowledge graph construction and cross-modal fusion analysis, meanwhile, data privacy protection is considered, and the accuracy and practicability of psychological health monitoring are effectively improved.

The invention belongs to artificial intelligence, and relates to a psychological assessment method based on a large language model Agent, which comprises the following steps of: extracting user-level cue words from each round of dialogue with a user by utilizing a user psychological feature extraction module, connecting the user-level cue words with system-level cue words obtained when a large language model is loaded, performing entity recognition on the cue words spliced together through a large language model; matching the extracted entity nodes with symptom nodes in the psychological state knowledge graph by utilizing a user psychological state retrieval module, and retrieving all corresponding psychological state node sets; the Agent questioning module is used for initiating questioning according to the answer of the user; according to the psychological assessment report generation module, the large language model searches a self-scale with the highest similarity with the user state according to all features extracted by dialogue with the user, and sends a report with the highest similarity with the condition that the user completes the self-scale to the user; the use experience of the user and the effectiveness of the evaluation result are improved.

The invention discloses a rehabilitation skill action standardization method combining multimode physiological signals and human body posture analysis, and belongs to the technical field of rehabilitation, and the method comprises the following steps: 1, constructing a preset standard action library; step 2, user data multi-modal acquisition and processing; step 3, comparing the user data with the standard library and scoring; and 4, performing closed-loop feedback correction. According to the rehabilitation skill action standardization method and system combining the multimode physiological signal and the human body posture analysis, through posture and physiological state monitoring and data processing, the action standardization of a practicer is ensured, and the rehabilitation effect is improved; the state of the practicer is analyzed through the physiological coordination and the mind concentration degree, it is ensured that the practicer can have both shapes and minds, and the rehabilitation effect is improved; according to the physiological and psychological states of the practicer, personalized feedback and correction suggestions are provided, and the pertinence of rehabilitation training is improved.

The invention discloses an intelligent evaluation system fusing behavior data and a scale, and the system comprises a data collection module which is used for collecting multi-dimensional behavior data; the data processing and feature extraction module is used for cleaning and preprocessing the data and extracting and calculating features; the analysis and evaluation module performs analysis and evaluation based on the behavior characteristics and scale scores; and the result output and explanation module is used for displaying the psychological state evaluation result generated previously to the user and explaining the evaluation result displayed by the user. According to the method, by collecting the multi-dimensional behavior data of the user in the answering process and combining the traditional psychological scale score, the limitation that the traditional psychological scale evaluation method excessively depends on self-reporting and lacks behavior data analysis and integration is overcome, more comprehensive, accurate and objective evaluation of the psychological state of the user is achieved, and powerful support is provided for the field of psychological health.

The invention relates to the technical field of psychological health education, and discloses a psychological health education-based dredging auxiliary platform, which comprises a psychological evaluation module, a data analysis module, an intervention execution module, a user archive library, a real-time monitoring module and the like. And the data analysis module is connected with the user archive library, generates a psychological state trend and intervention demand curve according to historical psychological data and behavior record data, and interacts data with other modules. And the intervention execution module receives related data, generates a real-time grooming instruction, and stores and records the instruction. The real-time monitoring module and the health rating module collect data to generate a corresponding matrix. The cognitive model optimization module optimizes parameters through a semantic association analysis network and the like. The state prediction model adopts a sliding time pane analysis method, and the health rating module adopts a fuzzy analytic hierarchy process. The platform realizes dynamic evaluation and accurate intervention, and improves the psychological health dredging effect.

The invention relates to the technical field of intelligent assessment of psychological states, and discloses an intelligent dynamic assessment method for psychological states of teenagers based on multi-modal data. The method comprises the steps that multi-modal psychological data of a target assessment object is acquired, the multi-modal psychological data comprises voice emotion information and expression behavior relations, and dynamic features are extracted from the multi-modal psychological data; performing feature fusion processing on the psychological data by adopting a trained machine learning model to generate a multi-modal feature set containing a mapping relation between modal type identifiers and time sequence codes; based on the multi-modal feature set, performing time sequence correlation analysis on the dynamic features through a state analysis network to obtain a fusion result containing emotion dimension features and cognitive mode features; and inputting a fusion result into an evaluation model for dynamic state deduction processing, and outputting psychological state evaluation elements. According to the invention, comprehensive and dynamic assessment of the psychological state can be realized.

The application discloses a student intelligent psychological state evaluation system, and relates to the technical field of data communication. First, the quality of student published data is checked and automatically corrected to ensure the reliability of subsequent processing input. Then, data sequence clusters are divided through clustering analysis, and resource scheduling strategies are dynamically adjusted based on the similarity confidence of each cluster to realize the optimization of analysis frequency and sampling rate of different clusters. Meanwhile, the resource scheduling strategy in the cluster is dynamically adjusted, and the optimal task allocation plan is intelligently calculated and issued according to the scheduling exposure index and resource proportion index of each sequence cluster to realize the fine matching of computing power and manpower. Finally, the real-time monitoring of key performance indicators improves the real-time performance and accuracy of student psychological state evaluation, and significantly optimizes the resource utilization efficiency and stability of the system in a high-concurrency scenario.

The invention relates to the field of physiological parameter detection and medical diagnosis measurement, in particular to a multi-mode psychological state quantitative evaluation method and device and a medium thereof. The method comprises the following steps: acquiring multi-source physiological and behavior signals and situation metadata, performing clock synchronization and equipment identifier mapping processing, performing quality feature extraction, artifact detection and slice completion processing, and generating a quality passing fragment set; de-noising normalization, time windowing and feature stack construction, modal gating and attention weighting processing are executed, and unified time sequence embedding is generated; through domain alignment parameter estimation, double-domain drift correction and multi-task inference, a psychological state quantitative index and uncertainty thereof are output; and finally, generating a structured evaluation report and updating parameters through consistency comparison, online self-distillation updating and individual baseline refreshing. According to the method, the anti-interference performance, the individual suitability and the result credibility of psychological state assessment in a natural scene are effectively improved.

The invention provides a psychological tutoring system based on a digital human technology, and relates to the technical field of psychological health. The system captures the emotional state of a user in real time in a multi-modal interaction mode, generates a real-time emotional state group of the user by combining multi-dimensional data fusion of facial micro-expressions, voice spectrums and dialogue texts, and maps the real-time emotional state group to a three-dimensional emotional space for accurate emotion recognition. Based on an emotion-behavior mapping rule, the system dynamically adjusts the digital person and provides an intervention scheme. And when the emotion fluctuation of the user is detected to exceed a preset threshold value, the system automatically triggers a digital human hiding mechanism, gradually weakens visual, auditory and action stimulation, and provides psychological pacification and guidance. According to the method, the accuracy of psychological state recognition and the pertinence of intervention are remarkably improved, real-time and continuous psychological health support is provided for the user, and the method is suitable for psychological health assistance and emotional state analysis.

The invention discloses a depression identification auxiliary decision-making system based on multi-modal fusion. The depression identification auxiliary decision-making system comprises a data acquisition module; a data processing module; a feature extraction module; a depression identification model module; the model training module is used for training the corresponding depression recognition model module; the model integration module is used for fusing the plurality of groups of trained depression recognition model modules together and establishing a multi-mode depression recognition model for depression recognition; according to the method, features can be extracted from multiple information sources, data of different modes have complementarity in the aspect of capturing human physiological and psychological states, and the comprehensive data processing enables the system to more comprehensively capture the physiological and psychological states of a depression patient; depression recognition is carried out by extracting the multi-modal physiological data and constructing the multi-modal data fusion model, the defects of a single-modal data recognition method are overcome, and the depression recognition accuracy is improved.

The invention discloses an elderly psychological state assessment early warning method and system, and relates to the technical field of elderly psychological health monitoring, and the method comprises the steps: collecting electroencephalogram signals of an elderly assessment object in a daily fixed time period, calculating the power spectral density ratio of the electroencephalogram signals, and constructing a personal reference feature set of the elderly assessment object; obtaining a current electroencephalogram signal of the aged evaluation object, calculating a power spectral density ratio of the current electroencephalogram signal, comparing the power spectral density ratio with the personal reference feature set, and determining a short-term fluctuation amplitude and an accumulated deviation amount; and judging the abnormal degree of the psychological state according to the short-term fluctuation amplitude and the accumulated deviation, and generating a graded early warning signal containing a risk grade and a timestamp. According to the invention, the detection accuracy and early warning timeliness of the abnormal psychological state of the elderly are improved, and a further solution is provided for psychological health management of the elderly.

The invention discloses an elderly emotion evaluation system based on multi-modal physiological and behavior signals. The elderly emotion evaluation system is composed of a multi-modal signal acquisition module, an emotion induction task design module and a multi-modal feature processing module. By collecting multi-mode signals such as electroencephalogram, electrocardio, voice and facial expressions, the system can synchronously record physiological and behavior changes of a user in a specific situation. The emotion induction task adopts scientifically designed audio and video contents, and six typical emotions such as happiness, tension, boring, anger, calm and sadness are guided. Then, the system extracts emotion related features from the multi-modal signals by using a deep learning method, models a cooperative relationship among the multi-modal signals by using an attention mechanism fusion technology, and generates unified emotion feature representation; by comprehensively analyzing and identifying the emotional state of the elderly user, the system provides important reference for early monitoring of emotional health of the elderly, psychological state evaluation and personalized intervention, and has social and clinical values.

The invention relates to the technical field of psychological health, and discloses a psychological health multi-modal evaluation method, system and device and computer equipment, and the method comprises the steps: obtaining multi-modal data, and carrying out the feature extraction of the multi-modal data, and obtaining multi-modal features; performing emotion-oriented cross-modal attention mechanism analysis on the multi-modal features to respectively obtain a preset-dimension emotion scale vector, an emotion semantic feature, a multi-dimensional emotion feature and an uncertainty quantitative feature; fusing the preset dimension emotion scale vector, the emotion semantic feature, the multi-dimensional emotion feature and the uncertainty quantitative feature to obtain a multi-modal fusion feature; and obtaining a mental health assessment result based on the multi-modal fusion features and a preset multi-task learning framework. Through a cross-modal attention mechanism, deep semantic fusion of five modals of vision, audio, physiology, text and behavior is realized, and intelligent mapping from original multi-modal data to accurate psychological state judgment is realized through a deep learning technology.

The invention relates to the field of psychological health, and provides an employee psychological health management system and method based on cloud computing, and the system comprises a psychological archive management module which is used for collecting, classifying, storing and updating the psychological health data of employees, and calculating the comprehensive psychological health indexes of the employees; the intelligent psychological assessment module is used for acquiring psychological data of the employees and assessing psychological states of the employees by applying an advanced regression model to obtain psychological assessment results; the remote psychological counseling module is used for evaluating the psychological counseling effect of the employee to obtain a first evaluation value of the psychological counseling effect; the personalized psychological assistance plan generation module is used for generating a psychological assistance plan for the employee; and the virtual reality psychological treatment module is used for providing psychological treatment service for the employees through the virtual reality technology and evaluating the treatment effect to obtain a second evaluation value of the virtual reality psychological treatment effect. According to the invention, the psychological health state of the employee can be comprehensively and accurately improved.

The invention relates to the field of psychological disease early warning, and discloses a psychological disease early warning method and system based on artificial intelligence, and the method comprises the steps: carrying out the feature extraction and data fusion of collected and preprocessed physiological and behavior data, carrying out the emotion analysis of the emotional state of a user through the fused data, and carrying out the early warning of psychological diseases. The method comprises the following steps: preliminarily judging whether individualized emotion is abnormal or not, comparing physiological features and behavior features of abnormal emotion according to an analysis result of emotion abnormality, performing time sequence analysis on the physiological features and behavior features of known psychological disease symptoms, detecting whether a user has psychological diseases or not, and further evaluating and analyzing the severity of the diseases. According to the method and the system, the disease is subjected to graded early warning, so that the user is helped to realize own emotion change, the psychological disease is discovered and intervened as soon as possible, the analysis and detection of the psychological state are more accurate and reliable, the detection efficiency of the psychological disease is improved, and the disease risk is reduced.

The invention discloses a neurology patient body index monitoring system, and particularly relates to the technical field of body monitoring. The environment interaction and adaptation module is used for monitoring and adjusting the surrounding environment of a patient to optimize the health state of the nervous system; the emotion and psychological state monitoring module is used for evaluating the emotion and psychological state of the patient in real time by utilizing an advanced sensing technology and data analysis; and the cognitive function and learning support module is used for monitoring and promoting the cognitive function of the patient to improve neuroplasticity and cognitive ability through interaction and training. By integrating the environment interaction and adaptation module, the emotion and psychological state monitoring module, the cognitive function and learning support module and the comprehensive health ecosystem module, intelligent adjustment of the surrounding environment of a patient, real-time monitoring of the emotion and psychological state, improvement of the cognitive function and comprehensive integration of health resources are achieved; the system optimizes the environmental comfort through intelligent illumination, noise monitoring and air quality adjustment.

The invention relates to the technical field of health assessment, in particular to a psychological health assessment system and method for old people, and the system comprises a psychological monitoring module, a psychological analysis module, an adjustment strategy module and an intervention execution module. According to the method, the behaviors of the old people are monitored, data are recorded, abnormal indexes of the psychological state are accurately analyzed, the behaviors are matched with the psychological state through the psychological mode atlas, the accuracy of psychological health assessment and the response speed to changes are greatly improved, and the psychological health can be effectively monitored and timely adjusted by adjusting assessment parameters in real time; after the optimized psychological state data is deeply analyzed, the tuning effect can be more accurately evaluated, personalized psychological intervention measures are provided for the elderly, the personalized degree of the intervention measures is enhanced, the individual requirements are better met, and by analyzing feedback in the intervention period and recognizing key change points, the optimization effect is improved. And the accuracy and the effect of mental health intervention are obviously improved.

The invention discloses an intelligent evaluation system and intervention method for mental health of teenagers, and the method comprises the following steps: collecting multi-mode psychological signal data of the teenagers, and carrying out the preprocessing; establishing a cognitive energy field, and generating a cognitive energy time sequence curve; performing spectrum resonance analysis, and combining the spectrum resonance analysis with a cognitive energy time sequence curve; constructing a psychological topological graph, and calculating a topological complexity index; when the topology complexity index is continuously abnormal or the cognitive energy disturbance amplitude exceeds a set threshold value, a psychological risk early warning result is generated; after the psychological unstable state is detected, a cognitive energy feedback parameter set is generated, and cognitive energy balance intervention is executed; and after the intervention is finished, calculating an energy balance improvement rate, and when the energy balance improvement rate exceeds an energy balance threshold, judging that the intervention is finished. According to the method, multi-mode psychological signals and cognitive energy field modeling are fused, intelligent evaluation and dynamic intervention of the psychological state of the teenagers are achieved, and the method has the advantages of being high in accuracy, high in interpretability and closed in intervention loop.

The invention discloses a campus psychological assessment multi-mode emotion recognition and privacy protection method and system, and the method comprises the steps: collecting physiological signal data, voice signal data and facial expression data in a non-contact manner, carrying out the preprocessing of the collected data, extracting the feature vector of the signal data, and carrying out the recognition of the feature vector of the signal data; the method comprises the following steps of: inputting a modal-invariant basic model to carry out multi-modal fusion, then inputting the modal-invariant basic model into a lightweight multi-modal emotion recognition model to carry out emotion recognition, generating a visual report of a user emotion state and emotion intensity according to an emotion recognition result, calculating a DASS-21 index according to the visual report, generating a standard evaluation scale, and providing emotion guidance. According to the method, the physiological signals, the voice signals and the facial expression data are fused, the emotion features are captured from multiple angles, the emotion recognition accuracy is improved, the psychological state can be more comprehensively described through the multi-modal fusion mode, and the root of the psychological problem can be more accurately positioned in an auxiliary mode.