293 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 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 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 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.

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 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 discloses a student psychological risk perception method based on multiple modes, and relates to the technical field of emotion calculation and intelligent education. The method comprises the following steps: firstly, extracting a facial expression feature vector and a voice intonation feature vector respectively by using a convolutional neural network and Fourier transform through a collected video stream and an audio stream; then adaptive denoising processing is carried out on environmental interference, timestamp alignment and dynamic time warping are carried out on the denoised multi-modal data, time sequence synchronization is ensured, and corrected multi-modal sequence data are formed; then, dynamic emotion track features are extracted from the sequence data, a preliminary emotion state label is generated by comparing the dynamic emotion track features with a baseline threshold value, and the threshold value is adaptively updated in combination with historical data so as to improve the judgment accuracy; and finally, aggregating the emotional state labels of a plurality of students to generate a visual group emotional thermodynamic diagram so as to realize macroscopic perception of group psychological risks. The accuracy, robustness and visualization degree of student psychological state analysis are effectively improved, and an efficient technical means is provided for campus psychological early warning.

The invention discloses a psychological state dynamic evaluation and early warning system based on multi-modal behavior data, which belongs to the field of medical care informatics and comprises a multi-modal behavior data hierarchical coding module, a time sequence causal atlas construction and reasoning module, a double-stage self-adaptive early warning decision module and a context awareness intervention strategy generation module. A cross-modal association mode is extracted through a double-layer coding mechanism, a time sequence graph containing a causal relationship is constructed, causal reasoning is performed, a double-stage mechanism of short-term mutation detection and long-term trend prediction is adopted to generate graded early warning, and an optimal intervention strategy is selected based on a deep Q network according to a user situation. According to the method, the accuracy, timeliness and intervention effectiveness of psychological health assessment are improved, and dynamic monitoring and early warning of the psychological state are realized.

The invention relates to the technical field of psychological assessment, in particular to a psychological health assessment method based on VR (virtual reality), which comprises the following steps: based on virtual scene event nodes, analyzing event types, interaction states and priorities, screening adaptive nodes, adjusting an acquisition window, judging amplitude variation of brain waves, identifying segmentation moments, and combining extreme values and response intervals. And matching a high-risk reference, and classifying psychological risk indexes. According to the method, through dynamic mapping of user interaction behaviors and virtual situation events, whole-course closed-loop management of data streams is achieved, an acquisition window is adjusted in a linkage mode according to multi-source parameters, signal segmentation is defined in real time according to actual brain wave motion and a scene cognitive state, brain wave extreme values and behavior response time sequences are synchronously extracted, and coupling analysis is conducted on multi-dimensional features; dynamic grouping of psychological risks is jointly set based on multiple index intervals, timeliness guarantee is obtained for data in a high-frequency interaction scene, and an evaluation result can reflect continuous evolution of psychological states of a user in a complex environment.

The invention relates to the technical field of personalized intelligent dialogue and digital accompanying for old people, and discloses an AI interaction-based personalized knowledge graph dialogue generation method for old people, which comprises the following steps of: reading a role conversion event record from a family structure sub-graph of a personalized knowledge graph for old people, and generating a role fall feeling feature template; aI dialogue initiation records of the old in recent late night periods are obtained, the number of days of continuous late night dialogues, the average dialogue duration and the active listing frequency are counted, and an aloneness level label is generated; generating a personalized spiral blocking intervention scheme based on the spiral strength coefficient and the spiral type label; and executing the collaborative psychological support dialogue script to generate a dialogue, and outputting a continuous personalized psychological support dialogue sequence. According to the method, the technical challenge that in the AI interaction-based elderly dialogue service, when the psychological state is worsened spirally due to the fact that the sense of fall and the sense of loneliness of roles are enhanced mutually, it is difficult to generate effective intervention dialogues by means of the personalized knowledge graph is solved.

The application discloses a recruitment interview evaluation method and system based on artificial intelligence and a storage medium, relates to the technical field of recruitment interview evaluation, and solves the technical problem that the prior art only completes morphological analysis and pragmatic reasoning through voice recognition and corpus comparison, does not deeply mine dynamic characteristics of voice signals, and does not perform correlation analysis on personality descriptions in resumes and real-time emotional performances in interviews, so that the evaluation dimension is single, and it is difficult to comprehensively and objectively reflect the language expression ability, psychological state and personality matching degree of a job seeker; the application obtains voice data and emotional data of a job seeker; the voice data is preprocessed to obtain a formant sequence corresponding to the voice data; a voice evaluation coefficient is calculated based on the formant sequence; an emotional similarity is calculated based on the emotional data; an interview evaluation coefficient is calculated based on the voice evaluation coefficient and the emotional similarity; and whether the job seeker is qualified is judged based on the interview evaluation coefficient, so that the above technical problem is solved.

The invention provides a psychological state detection method and an emotion relieving method. The psychological state detection method comprises the following steps: S1, a preparation step; s2, a relieving step; s3, a first detection step: the AI language interaction system obtains the known and determined objective information according to the file of the existing detected person, and repeatedly inquires the detected person; the objective information comprises at least one of age, occupation, present medical history, past medical history and family genetic disease history of the detected person; and S4, a second detection step: after the detected person is determined to be in an abnormal state, the AI language interaction system sequentially provides a minnisuda polynomial personality questionnaire, a symptom self-evaluation scale, an anxiety self-evaluation scale and a depression self-evaluation scale for the detected person to fill in. According to the method, the detected person is prevented from having relatively large subjective rejection consciousness, the psychological privacy of the detected person is protected, the psychological worry of the detected person is eliminated, a manual inquiry and detection mode is effectively replaced, and the efficiency and the detection accuracy are improved.

The invention relates to the technical field of child psychotherapy assistance, and discloses a heuristic story generation method and device based on sand table toy placement, and the method comprises the steps: recognizing the type, spatial relation and placement time sequence of toys in a sand table through a multi-modal perception fusion technology, and combining symbolic meaning analysis, role relation network construction and emotion recognition to generate a heuristic story. A heuristic story generation algorithm is utilized to automatically generate a personalized story conforming to the psychological state of the child, and the story content can be dynamically adjusted according to real-time emotion feedback of the child, so that the problems that traditional sand table treatment depends on subjective experience of a therapist, is low in efficiency and lacks standardization are solved, and the accuracy and effectiveness of sand table game treatment are improved.

The invention is suitable for the technical field of emotion detection, and provides an anxiety risk detection system based on multi-scale time sequence feature fusion, and the system comprises a short-term feature obtaining module which is used for obtaining short-term branch features of multi-modal data of a user; the short-term branch features are used for describing psychological state changes of the user in a recent short time period; the long-term feature acquisition module is used for acquiring long-term branch features of the multi-modal data; the long-term branch features are used for describing a stable psychological trend of the user in a long time period; the attention fusion module is used for fusing the short-term branch features and the long-term branch features based on an attention mechanism to obtain a fusion result; and the detection module is used for processing the fusion result to obtain an anxiety risk detection result of the user. The accuracy and interpretability of anxiety risk detection can be improved.

The embodiment of the invention belongs to the technical field of psychological state detection, and discloses a psychological state assessment interaction system and device, and the corresponding system comprises an AI interaction module which is used for interacting with a user and obtaining feedback information of the user in the interaction process, and the feedback information comprises voice reply data, text reply data and physiological data of a question; the deep learning model is used for extracting feature data of the feedback information; the support vector machine model is used for evaluating the psychological state of the user according to the feedback information; input data of a kernel function of the support vector machine model is feature data; and the VR video generation module is used for generating a corresponding VR video according to the psychological state and pushing the VR video to the user, and the VR video has an interaction function with the user so as to further determine a lower state of the psychological state of the user and autonomously modify the VR video according to the lower state. The emotion, cognition and behavior characteristics can be extracted from the dialogue of the user, and the psychological state is quantified.

The invention discloses a psychological state text classification method based on large model generative data enhancement, and relates to the technical field of natural language processing. According to the method, patient psychological semantic clusters are automatically found by clustering a limited number of original samples, and then a natural language semantic template of each patient psychological semantic cluster is extracted; then, under the dual control of emotional polarity and psychological themes by utilizing a large language model, according to the natural language semantic templates, psychological state texts with consistent themes and diversified expressions are generated as enhanced samples; according to the method, high-quality sample generation is carried out by utilizing a natural language semantic template obtained based on clustering through a large language model so as to expand a model training sample, and the authenticity and diversity of the generated enhanced sample can be ensured through the large model generation type data enhancement method; therefore, the classification accuracy and generalization ability of the psychological state text classification model obtained through training are improved.

The invention discloses an intelligent agent-based interactive simulation system and an interactive simulation method, and belongs to the field of artificial intelligence. The system comprises a control module serving as a core control center, a world module providing a simulation environment for multi-agent interactive simulation, a memory module serving as an agent memory library and a tool module integrating various auxiliary tools. When the system is used for performing a topic discussion simulation task, the modules cooperate to perform interactive simulation, so that each agent generates comments. The invention aims to improve the expandability of the social simulation system, so that the social simulation system can process large-scale individual interaction; the accuracy of a simulation result is improved, and the emotion and psychological states of individuals are considered; and a complex organization structure in a human society is effectively simulated, so that the authenticity of a simulation effect is enhanced.

The invention belongs to the technical field of psychological health condition evaluation, and particularly relates to a psychological evaluation system and method based on multi-modal data fusion. The psychological assessment system based on multi-modal data fusion comprises a multi-modal data acquisition module, a data preprocessing module, a multi-modal feature fusion module, a psychological state analysis module, an AI interaction suggestion module and a system management security module. The psychological state is comprehensively evaluated in combination with multi-dimensional information of eye movement, text and voice; the intelligent dialogue analysis function is achieved through the AI interaction and suggestion module, evaluation is upgraded from static result output to dynamic interaction guidance, the psychological state of a user can be more accurately understood, and personalized support is provided.

The invention discloses an emotional dialogue generation method and system fused with psychological information, and the emotional expression capability of generating response can be improved through the obtained global emotional latent variable fused with a speaker and a listener. By determining a multi-hop knowledge reasoning path set, obtaining context semantic representation of each knowledge reasoning path, and fusing the context semantic representation with dialogue history global context representation to obtain knowledge-enhanced context representation, the knowledge reasoning ability and semantic relevance in a dialogue context are improved; by constructing a knowledge representation matrix, taking a global emotion latent variable as a bias item, and dynamically adjusting the knowledge representation matrix and a value vector generated by knowledge-enhanced context representation through semantic features, multi-scale attention fusion representation is obtained, and the context adaptive capacity and emotion representation naturalness of generated response are effectively improved. According to the method, the psychological state of the user can be identified more accurately, and the dialogue response with humanization, estrus-sharing expression and interaction adaptability is generated.

The invention discloses a classroom psychological state recognition system based on multi-modal data, particularly relates to the field of image recognition, and is used for solving the problem that an existing classroom monitoring mode cannot effectively capture group emotions and lacks targeted intervention. The method comprises the following steps: detecting and positioning a multi-view video sequence through a target tracking module, extracting a student face image and establishing an identity frame sequence relationship; the emotion recognition module performs expression recognition on the image to generate an emotion intensity time sequence; the emotion analysis module performs time-frequency analysis on the sequence, extracts rhythm frequency and phase parameters and generates a region-level emotion curve according to seat layout aggregation; the drift calculation module calculates the phase difference and the frequency difference between the regions, and constructs a rhythm drift matrix; the propagation visualization module maps the matrix into a space vector field, and generates an emotion propagation path visualization model; and the intervention prompt module completes source positioning and intervention strategy generation based on the model, and realizes classroom psychological state identification and regulation.

The invention provides a psychological state and personality analysis system and method based on a non-perception technology. Comprising a data acquisition module, a feature extraction module, a psychological state analysis module, a psychological portrait module and a behavior prediction module. The device integrates high-precision computer vision, deep learning, millimeter wave radar, an rPPG sensor, infrared thermal imaging and voice recording equipment into a whole, and high-precision real-time evaluation on the psychological state of an individual is achieved; a multi-modal fusion technology is adopted, so that the psychological state and personality characteristics can be analyzed with high precision without active cooperation of a user; edge calculation and deep learning are combined, rapid data processing is achieved, and the real-time performance of psychological state analysis is improved; the invention also relates to a method of the system. According to the system, personality characteristics, emotional states, defense mechanisms, psychological demands and psychological-driven feature fusion algorithms are combined, the scientificity of psychological state and personality analysis is ensured, and the reliability and applicability of evaluation are improved.