386 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 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 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 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 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 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 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 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.

The invention discloses a psychological state assessment method and device. The method comprises the steps that firstly, based on a sensor group and a camera module, electrocardio information, face information, gait information and plantar pressure information of a target are obtained; then constructing an input feature vector based on the electrocardio information, the face information, the gait information and the plantar pressure information; and finally, inputting the input feature vector into a preset model to obtain an evaluation result. According to the embodiment of the invention, the multi-dimensional feature data can be processed, so that the psychological state is evaluated by using the multi-dimensional feature data to obtain the evaluation score, and the evaluation accuracy can be improved.

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 application relates to the technical field of data processing, and particularly discloses a student psychological state recognition method and system based on multidimensional data analysis, which realizes the precision and efficiency of student mental health management. First, data cleaning and standardization processing are integrated to eliminate invalid information and unify data dimensions, thereby significantly improving analysis reliability. Through a collaborative analysis mechanism, different dimensional data features can be dynamically associated, and the adaptive adjustment function of the feature extraction process can automatically optimize algorithm weights according to data quality, thereby ensuring the extraction accuracy of key psychological indicators. Through data-driven closed-loop management, mental health monitoring is changed from passive coping to active prevention, the efficiency of school psychological crisis intervention is effectively improved, scientific support is provided for educational decision-making, and the precise allocation of mental health resources is promoted.

The invention discloses a lung cancer patient psychological health assessment intervention system based on a virtual platform, and belongs to the field of psychological health. A lung cancer patient psychological health assessment intervention system based on a virtual platform comprises a virtual environment creating module, a psychological state monitoring module, a psychological health assessment module and a psychological health intervention module. The problem that an existing mental health assessment and intervention mode is limited is solved, an immersive environment is created through the virtual reality technology, a real lung cancer patient treatment scene is restored, a basis is provided for accurate assessment, psychological state assessment is more accurate by comprehensively obtaining multi-dimensional data such as physiology, voice and actions of a patient in real time, and the accuracy of assessment is improved. The emotion type and the psychological problem severity are quickly and accurately recognized through the constructed psychological health assessment model, a scientific basis is provided for subsequent intervention, a personalized scheme is formulated according to an assessment result, and the psychological stress of a patient can be effectively relieved by combining virtual role interaction.

The invention relates to the technical field of psychological health service and digital twinning, and discloses a psychological health digital twinning construction and dynamic intervention system, which comprises a multi-modal data acquisition module, a psychological digital twinning modeling module, a psychological state prediction module, a dynamic intervention decision module and a feedback optimization module, each module realizes real-time data interaction through a distributed data bus; wherein the multi-modal data acquisition module is responsible for acquiring individual physiological, psychological, behavior and environment multi-dimensional data, and the psychological digital twin modeling module constructs a dynamic virtual mapping body of an individual psychological state based on the data. According to the method, real-time monitoring of the psychological state is realized through multi-modal data acquisition and twinborn dynamic mapping, and the hysteresis of traditional evaluation is overcome; on the basis of a two-stage matching algorithm, interference strategies are screened in combination with user static characteristics and dynamic states, and limitation of a homogenization scheme is avoided.

The invention provides an intelligent psychological counseling system based on a multi-mode large model and a cognitive behavior therapy, and the system comprises the steps: recognizing voice from a user side through the multi-mode large model, and obtaining text information and emotional state information; analyzing the text information by using a large language model, carrying out conversation with the user side, and extracting psychological state information of the user side from the conversation; a cognitive behavior treatment strategy matched with the user side is determined according to the emotional state information, so that a voice response to the user side is generated according to the matched cognitive behavior treatment strategy by using a multi-mode large model; by combining a multi-modal large model and a large language model, accurate and efficient natural language understanding of a user side is realized, highly anthropomorphic voice response is provided, dependence on professional psychological consultants is reduced, limitation of psychological consultation services on time and regions can be broken through, large-scale consultation service requirements can be met in an all-weather and low-cost manner, and the psychological consultation service experience is improved. The man-machine interaction experience in the consultation process is improved, and high-quality and high-practicability psychological consultation and treatment services are provided.

The invention relates to the technical field of artificial intelligence, in particular to an emotional support dialogue generation method integrating sensory reasoning and mental memory enhancement, which comprises the following steps: performing structured psychological state extraction on dialogue content input by a user, and generating personal clues and fact clues by utilizing an emotional mental theory framework; dynamically selecting an emotional dominant or rational dominant response mode through a personal fact classifier, and determining a generated emotional state; encoding the historical memory and the emotional state by adopting a Sensory-BERT encoder, calculating semantic similarity between the historical memory and the emotional state, and dynamically filtering redundant information through a forgetting elimination mechanism based on the semantic similarity to remove outdated memory so as to obtain updated memory; inputting the emotional state and the updated memory into a large language model to generate a targeted response; semantic coherence is guaranteed through a mask self-attention mechanism, and repeated suggestions are avoided in multiple rounds of dialogues. According to the method, high-quality, high-efficiency and high-individuation generation of the emotional support dialogue is realized.

To solve the problem that it is general to generate a response on the basis of single input and an ability for continuously tracking emotions and intentions of a user is limited, and thus, it is difficult to maintain a natural dialog in a conventional AI dialog system.SOLUTION: The present invention applies a technique named dead-reckoning for estimating a current position from a position and speed in the past in navigation and aviation to a dialog with AI, extracts important information from dialog data in the past of a user, and estimates current needs and emotions. Thus, the intentions of the user are understood, directivity of the dialog is defined, the emotions and degrees of interest are grasped as speed and force of the dialog, a psychological state of the user and context of the dialog are more accurately analyzed, and a response adaptable to the analyzed psychological state and context are generated. For that purpose, the emotions of the user are estimated, and directivity of the dialog is analyzed to generate a personalized response by a response generation unit to be constituted of a user emotion analysis unit, a dynamic dialog navigation analysis unit, a personalized response generation unit, and a learning and evolution unit.SELECTED DRAWING: Figure 3

The invention discloses an educational resource recommendation method and system based on artificial intelligence. The method comprises the following steps: acquiring audio data, interaction data and task data acquired by a user terminal; performing feature extraction on the audio data, the interaction data and the task data to obtain an emotion feature vector, a learning rhythm vector and a content feature vector; inputting the emotion feature vector and the learning rhythm vector into a pre-constructed emotion recognition model to obtain a psychological state vector; the cognitive load is calculated based on the learning rhythm vector and the content feature vector, and then the cognitive load is corrected through the psychological state vector; matching a state interval of the corrected cognitive load according to a preset threshold interval; and according to the state interval, adjusting a difficulty coefficient of the recommended course, rearranging a course content sequence and an auxiliary learning prompt, generating structured data, and outputting the structured data as an intelligent auxiliary learning recommendation result. According to the invention, online learning interactivity and teaching quality in rural and remote areas are effectively improved.

The invention relates to the technical field of VR, AI and psychological therapy crossing, and discloses a psychological universe intelligent psychological tutoring integrated platform which comprises a holographic sensing module used for acquiring multi-modal data of a user in real time; the intelligent psychological engine module is used for receiving and analyzing the multi-modal data and generating a multi-dimensional psychological state vector; and the dynamic meta universe scene and personalized intervention module is used for adjusting virtual scene parameters and behaviors of the intelligent non-player character in real time according to the multi-dimensional psychological state vector. According to the method, multi-modal data such as behaviors, eye movement and physiology are acquired and deployed at edge computing nodes, and attention weight calculation is adopted to carry out weighted fusion and comprehensive analysis on the multi-modal data, so that a unified multi-dimensional psychological state vector is generated; therefore, the technical defects that in the prior art, data are dispersed, and effective integrated analysis cannot be carried out are overcome, and objective, quantitative and comprehensive evaluation on the psychological state of the user is achieved.

The invention relates to a psychological health assessment method and system based on a large language model and multi-modal data. The system comprises a data acquisition module used for acquiring synchronous audio, video and text data of a subject; the data preprocessing module is used for preprocessing the audio data and the video data; the feature extraction module extracts audio features, video features and voices and converts the audio features, the video features and the voices into pseudo-texts, and the multi-modal fusion module inputs the multi-modal pseudo-texts and original texts into a sharing embedding layer for unified vector representation; the large language model processing module is used for carrying out deep context semantic modeling on the fused multi-modal vector by adopting a Transform architecture large language model and outputting a deep semantic embedding matrix containing emotion and psychological states; and the classification model module is used for training a configurable convolutional neural network classifier based on the semantic embedding matrix, supporting dichotomy or multi-classification tasks and realizing psychological assessment detection. According to the scheme, the objectivity and accuracy of detection are remarkably improved.

The invention relates to the technical field of psychological assessment, in particular to a psychological state assessment method, system and terminal based on an interactive terminal, and the method comprises the steps: separating voice data and text data of a user into an emotion change sequence; based on the emotion tendency of the emotion change sequence, setting an emotion change quantity, and according to a matching relationship between the emotion change quantity and a dialogue situation, calculating a user response duration; obtaining the participation degree of the user at different moments by obtaining the response frequency corresponding to the response duration of the user; combining the degree of participation, the emotional variation and the emotional change probability into an emotional feature, and deducing an emotional classification and a transition label of the current emotional feature; on the basis of the emotion proportion of each emotion classification, dialogue strategy matching is carried out according to the participation amount of the emotion classification at adjacent moments, and a dialogue strategy is set; and the accuracy and effect of emotion recognition are improved.

The invention relates to the technical field of psychology and artificial intelligence, in particular to a personalized psychological portrait generation evaluation method, which comprises the steps of constructing a multi-source heterogeneous data acquisition system, updating a time sequence behavior pattern, performing multi-dimensional association analysis, dynamically adjusting an adaptive optimization algorithm and the like. According to the method, real-time capturing and modeling of the psychological state of the user can be achieved by fusing multi-source data, the accuracy and adaptability of the psychological portrait are improved by combining the graph neural network and the genetic algorithm, and the defects in the aspects of dynamic psychological state capturing, individual difference modeling and multi-modal data processing in the prior art are overcome; and technical support is provided for psychological health intervention, behavior prediction and psychological assessment in a complex environment.

The invention discloses a psychological state recognition method and system based on physiological signals, and relates to the technical field of intelligent preoperative evaluation and planning of thoracic surgery, and the method comprises the steps: S1, collecting multi-modal physiological signals, and forming an original signal set; s2, performing parallel preprocessing on the original signal set to generate a denoised time sequence signal; s3, dynamically generating a modal weight coefficient based on the quality parameters, and performing weighted fusion on the multi-modal time sequence signals by using the weight coefficient; and S4, inputting the fused feature vector into a personalized recognition model, and outputting psychological state probability distribution through an adaptive feature mapping layer in the model. And S5, according to the user feedback signal or the distribution offset of the continuous monitoring data, triggering online parameter updating of the personalized recognition model, and generating a recognition model after incremental optimization. According to the psychological state recognition method and system based on the physiological signals, the problem that psychological state recognition is inaccurate due to large individual difference, strong environmental interference and weak dynamic adaptability can be solved.

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence assisted psychological assessment system. The artificial intelligence assisted psychological assessment system comprises a data collection module, a data analysis module, a scale generation module, a result presentation module, a user interaction module, an online AI consultation module and a modal selection and decision optimization module, according to the artificial intelligence assisted psychological assessment method, personalized and accurate psychological health assessment and intervention are realized through the steps of multi-modal data collection, psychological state modeling, data analysis, scale generation, result presentation, modal optimization, online consultation and the like, and the assessment efficiency and accuracy are improved; according to the method, accurate and personalized psychological assessment and intervention are provided through multi-modal data collection and analysis, information entropy optimization, variational reasoning data analysis and personalized scale generation; compared with the prior art, the accuracy and efficiency of evaluation and the user experience are improved.