75 results about "Psychological evaluation" patented technology

The invention discloses a psychological health analysis method and apparatus, an electronic device and a storage medium, through the application, dynamic psychological assessment is generated through multi-modal data fusion analysis, and personalized scenarized dynamic interaction content is generated based on an assessment result and an interaction history, so that the psychological health analysis efficiency is improved. Meanwhile, continuous learning and correlation analysis are carried out on the psychological state of the user by utilizing a multi-dimensional memory architecture, so that the system can understand the change of the user state and adjust an interaction strategy like a human consultant, and therefore, the technical problems of insufficient emotional distraction and credibility of the user due to the adoption of a standardized reply mode in the prior art can be solved, and the user experience is improved. The technical effects of enhancing the emotion affinity of the system, establishing a continuous and credible interaction relationship and improving the durability of the psychological intervention effect are achieved.

The invention relates to the technical field of cognitive behavior therapy, in particular to a scheme intelligent pushing and task self-adaptive management method based on cognitive behavior therapy, which comprises the following steps: S1, acquiring physiological signals, behavior logs and environment interaction data of a user through a multi-modal data acquisition module; s2, constructing a dynamic psychological assessment model based on a cognitive behavior theory, and fusing multi-source data by adopting a Bayesian network to generate a user cognitive state map; s3, generating a personalized intervention scheme through a reinforcement learning algorithm according to the cognitive state map and a preset CBT intervention rule base; according to the method, through multi-modal data acquisition, dynamic psychological assessment, reinforcement learning scheme generation, task decomposition optimization, difficulty adaptive adjustment and digital twinborn simulation, full-process closed-loop management from data acquisition to intervention optimization is constructed, and efficient, safe and personalized cognitive behavior treatment scheme intelligent pushing and task adaptive management are realized.

The invention relates to the technical field of block chain data sharing, and discloses a student psychological assessment data sharing method and system based on a block chain. The method comprises the following steps: when a user logs in through a browser client, a system triggers a zero-knowledge proof generation process; and the client signs the timestamp and the random number by using a local false identity private key to form a zero-knowledge proof request packet, and submits the zero-knowledge proof request packet to the alliance chain node through a secure channel. And the node calls an identity verification contract to verify the signature, generates a session token containing a role type and a validity period after the signature passes, writes the token in an alliance chain account book in a hash manner and returns the token to the client. The client encrypts and stores the token, and a subsequent data request is carried through an HTTP header; and when it is detected that the token is expired or abnormal, the client automatically re-triggers the zero-knowledge proof process. The method depends on the block chain and zero knowledge proof technology, optimizes identity verification and session management, guarantees data sharing safety, compliance and stability, and is suitable for student psychological assessment data sharing among multiple subjects.

The invention discloses an intelligent management system and method for vocational skill training, and relates to the technical field of vocational skill training management systems. The system comprises a student information management subsystem which integrates biological feature recognition, a dynamic password and SSO single sign-on to realize cross-platform secure access; a personal ability graph is constructed based on behavior logs and psychological assessment data, and attribute tags of unmarked nodes are automatically expanded by using a graph neural network. According to the system, attribute tags of unmarked nodes are automatically expanded through a graph neural network, a dynamic personal ability graph is constructed in combination with behavior logs and psychological assessment data, and accurate user portraits and personalized recommendation are supported; modeling time series data by using an LSTM neural network, and quantifying an attention concentration score; and student grades are divided based on a K-means clustering algorithm, and differentiated resource packages are pushed, so that individualized teaching is realized.

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 relates to the technical field of intelligent monitoring of mental health, and discloses a social function prediction system for teenager patients with depression. The system comprises a multi-source data acquisition module, a psychological feature fusion module and a prediction engine module. The multi-source data acquisition module is used for acquiring social interaction records of patients, constructing behavior dynamic characteristics, performing mode recognition and generating behavior mode data. And the psychological feature fusion module performs difference comparison on the behavior data and a healthy group benchmark, screens key behavior indexes, and calculates correlation degree values of the key behavior indexes and psychological assessment parameters in real time. The prediction engine module evaluates the social function degradation type based on the correlation degree, locates vulnerable nodes in the development process, and identifies specific dysfunction points. According to the system, continuous dynamic monitoring of social function states is realized, early warning of degradation risks and accurate positioning of weak links are realized through behavior-psychological correlation analysis, and technical support is provided for targeted intervention.

The invention relates to a method for determining an individualized nerve regulation target spot, and the method comprises the steps: obtaining nerve image data, which are repeatedly measured for multiple times, of a subject and corresponding clinical psychological assessment data in a preset time period; calculating a brain activity index of each voxel in a preset brain region; establishing a plurality of candidate mathematical models to describe the relationship between the brain activity indexes and the clinical scores; selecting an optimal model through statistical test of fitting residual errors; generating a parameter weight map representing'symptom-activity 'association strength based on the optimal model; and finally, determining an individualized nerve regulation target according to the numerical distribution of the map. The invention also relates to a corresponding device, a processor and a computer readable storage medium thereof, and solves the problem of inaccurate target positioning caused by neglecting symptom heterogeneity and individual brain function difference in the prior art through a core methodology of ''longitudinal tracking-multi-model fitting-residual optimal selection''. The curative effect of nerve regulation and control treatment is obviously improved.

The invention discloses an auxiliary evaluation system for future scene guidance. The auxiliary evaluation system comprises an evaluation management module, an evaluation control module and an evaluation process module, the evaluation management module is used for defining an evaluation stage, an evaluation standard and a risk probability calculation method; the method comprises the following execution steps: defining an evaluated session scene, evaluation content and an evaluation stage; determining an evaluation standard and a scoring dimension, and determining a method for calculating a risk probability according to the evaluation standard; setting an evaluation plan according to the risk probability; the evaluation control module is used for executing the evaluation plan and initiating an evaluation event to the evaluation process module; the evaluation process module is used for responding to the evaluation event, providing evaluation content for the user side according to the evaluation stage, calculating the risk probability of the evaluation stage according to the feedback result of the user side, and generating an evaluation result and an evaluation plan. According to the technical scheme, assessment obstacles caused by high frequency and multi-mode characteristics in the future scene guidance psychological assessment process can be effectively solved, and the assessment process is dynamic and procedural.

This invention discloses a psychological assessment system based on heart rate variability analysis, belonging to the field of medical data analysis and psychological assessment technology. It solves the problems of existing heart rate variability analysis being limited to physiological indicators and traditional psychological assessments lacking objective physiological support. The system is built on a B / S architecture, integrating two core modules: ECG data reception and recording, and graphical analysis reports. It collects raw ECG data via Bluetooth 4.0, automatically calculates core heart rate variability feature values ​​such as SDNN, LF, and HF, achieves hierarchical and quantitative assessment of heart rate variability, and generates integrated graphical reports. It also features dual-layer access control, encrypted data transmission, and backup and recovery functions. This invention achieves deep integration of physiological data and psychological assessment, improving the scientific rigor and accuracy of psychological assessments. The system is highly stable and adaptable, suitable for clinical psychological screening, employee psychological monitoring, and other scenarios. The corresponding product, "Heart Rate Variability Analysis Software," has obtained a Class II medical device registration certificate.

The invention discloses an AI-assisted insomnia typing and personalized intervention system, which belongs to the field of medical artificial intelligence, and comprises a multi-modal data acquisition module used for synchronously acquiring psychological assessment data, physiological index data, clinical diagnosis data and dynamic behavior data of an insomnia patient, the psychological assessment data comprises a DCPR semi-structured interview text, a PSSS scale score, a PSQI scale score, an AIS scale score, an ESS scale score, an HAMA scale score and an HAMD scale score; according to the method, the DCPR semi-structured interview text, the EEG / PSG physiological signal and the wearable device behavior data are subjected to time-space synchronization integration, the semantic features of the psychological text are analyzed through BiLSTM + attention mechanism, and precise classification of healthy anxiety type, persistent psychosomatic type and other psychosomatic syndrome subtypes is realized in combination with hierarchical clustering and random forest algorithms, so that the accuracy of psychosomatic syndrome subtypes is improved, and the accuracy of psychosomatic syndrome subtypes is improved. The limitation of traditional ICSD-3 / DSM-5 single-dimension diagnosis is broken through, and the clinical significance of subtype distinguishing is improved.

The invention provides a creativity psychological assessment method based on artificial intelligence and a reinforcement learning model, and relates to the technical field of creativity psychological assessment. According to the method, the attention entropy value is calculated through the eye movement data, the multi-dimensional feature vector is constructed in combination with standardization processing of the prefrontal lobe Gamma wave band electroencephalogram signals, quantitative characterization of the attention distribution and cranial nerve activity cooperation state is achieved, the one-sidedness of traditional single signal evaluation is broken through, and meanwhile, the method has the advantages of being simple in structure and convenient to operate. A reinforcement learning task decision engine dynamically matches an evaluation task according to a real-time cognitive state, so that a task type is highly matched with individual cognitive features, more real creativity performance is captured, a reinforcement learning model driven by a multi-dimensional reward mechanism is continuously optimized, evaluation not only reflects a surface behavior result, but also improves the performance of the system. And the internal law of the cognitive process can be mined, and a quantitative basis is provided for creativity psychological mechanism research.

The application discloses a kind of self-help evaluation and individualized healing push of love loss mood, it is related to artificial intelligence technical field, which comprises the following steps: collecting multidimensional psychological evaluation source data, constructing user communication image, user behavior image and user psychological image based on multidimensional psychological evaluation source data, and inputting the comprehensive evaluation model constructed in advance, obtaining the user comprehensive risk score, and dividing the user state level and the user current healing stage based on the user comprehensive risk score;Based on the user state level and the user current healing stage, match the basic healing scheme from the preset healing scheme template library based on the healing scheme generation algorithm, fine-tune the basic healing scheme in combination with the recent behavior preference of user, to obtain the individualized healing plan;The individualized healing plan is pushed to user end, which helps to solve the problem that the prior art cannot realize accurate self-help evaluation of love loss mood and push individualized healing scheme.

The invention provides a psychological health analysis and intervention method based on multi-Agent collaborative assessment, which comprises the following steps of: shunting information and dialogue contents input by a user through an initial shunting Agent module, and then performing parallel and independent multi-dimensional psychological assessment by using a plurality of different assessment Agent modules. Meanwhile, each assessment Agent module corresponds to a psychological dimension and is provided with a special vertical domain knowledge base, then, structured output results of the assessment Agent modules are received and integrated through the central coordination and treatment agent, a collaborative and unified psychological health assessment report is generated, and finally, based on the assessment report, a psychological health assessment report is established. The treatment intervention Agent module is used for generating a personalized mental health intervention scheme matched with the user, and a precise intervention scheme is provided for the user.

The invention relates to the technical field of psychological assessment, and discloses a multi-modal psychological feature modeling and assessment method based on artificial intelligence, and the method comprises the steps: carrying out the preprocessing of a house tree person drawing image, and extracting image features from the preprocessed house tree person drawing image; a score vector model is generated, image features are converted into structured vectors, a Sigmoid function is optimized and trained, scores of different psychological dimensions are calculated through the Sigmoid function, accurate quantitative evaluation can be provided for the psychological state, emotional tendency and the like of an individual, meanwhile, an SHAP method is used for interpretable analysis, the prediction result of the model can be analyzed, and the prediction accuracy of the model is improved. And the contribution of each feature to the result is quantified, so that the transparency of the model is enhanced, a tool for understanding the model decision process is provided for a user, the credibility of the model is improved, and the psychological feature evaluation accuracy is improved.

This invention relates to the field of psychological assessment technology, specifically a VR-based mental health assessment method, comprising the following steps: analyzing event types, interaction states, and priorities based on virtual scene event nodes; selecting suitable nodes; adjusting the acquisition window; judging changes in EEG amplitude; identifying segmented time points; combining extreme values ​​and response intervals; matching high-risk benchmarks; and classifying psychological risk indicators. In this invention, user interaction behavior and virtual situation events are dynamically mapped to achieve closed-loop management of the entire data flow. The acquisition window is adjusted in conjunction with multi-source parameters, signal segments are defined in real-time based on actual EEG fluctuations and scene cognitive states, EEG electrode values ​​and behavioral response timing are extracted synchronously, multi-dimensional features are coupled and analyzed, and the dynamic grouping of psychological risks is based on the joint setting of multiple indicator intervals. Data is guaranteed to be timely in high-frequency interaction scenarios, and the assessment results can reflect the continuous evolution of the user's psychological state in complex environments.

The invention discloses a semantic analysis-based teenager psychological problem AI dynamic assessment method, and relates to the technical field of artificial intelligence psychological assessment. The method comprises the following steps: acquiring semantic data such as dialogue text content, emotion keyword information and semantic coherence information of a patient in different scenes, dividing a stable and change evaluation stage according to the change trend of the number of emotion keywords along with dialogue rounds, and screening dialogue scenes of key semantic types; in a key scene, a generative driving evaluation index is obtained by combining semantic data similarity conditions, round information and semantic coherence information, and a dynamic evaluation node possibility is obtained by combining a dialogue text in a change evaluation stage, a round corresponding to the semantic coherence information and repeated expression data; screening evaluation nodes, then constructing an evaluation chain tree structure, and determining psychological state dynamic evaluation schemes of different patients. The method can dynamically capture the psychological state change of the patient.

The invention discloses a psychological assessment method and system based on dialogue content, and relates to the technical field of semantic processing, and the method comprises the steps: obtaining question and answer dialogue data of a user and a system in a multi-round interaction process; encoding each round of question sentences and answer sentences by using a semantic analysis model, and extracting semantic embedding vectors and topic feature vectors; calculating a semantic matching degree between adjacent question and answer pairs, and marking the semantic matching degree as a semantic deviation sample when the matching degree continuously decreases; constructing a topic embedding sequence of multiple rounds of dialogues and counting a fuzzy word proportion to form a topic and a statistical feature vector; inputting the semantic deviation samples, the themes and the statistical features into a machine learning classification model to obtain a psychological situation classifier; and outputting an evaluation result during dialogue operation. The problem that psychological reasons for topic avoidance of users in multiple rounds of dialogues are difficult to quantify in real time is solved.

The application discloses a virtual reality scene driven psychological intervention adaptive training method and system, relates to the virtual reality intervention training field, and has the technical scheme as follows: a virtual training scene with configurable parameters is generated according to psychological evaluation data; a second training target vector with logical complementarity with the first training target vector is acquired; second behavior data of a second user is received, and the second behavior data is mapped into virtual role behavior in a virtual training scene corresponding to the first user; first behavior data of the first user in the virtual training scene is collected, and the first behavior data is transmitted to the second user, so that the first behavior data is mapped into virtual role behavior in a virtual training scene corresponding to the second user. The application can not only quickly construct a virtual role, but also enhance the authenticity and reaction efficiency of the virtual role, reduce the required computing resources of the virtual reality scene driving, and improve the skill transfer rate.

The invention relates to the technical field of artificial intelligence psychological assessment, and discloses a drawing projection psychological state assessment system based on artificial intelligence and a knowledge graph. According to the system, a user drawing image and behavior data are collected, topological symbol segmentation is carried out on drawing content by using a convolutional neural network, structural features of a house contour, a tree form and a figure action are identified, and time sequence behavior features in the drawing process are extracted. The features are mapped through a knowledge graph constructed based on a psychological theory and are converted into normative psychological semantic indexes. And integrating and analyzing the indexes by adopting a dynamic weight reasoning model, and finally generating a multi-dimensional quantitative evaluation report containing personality characteristics, personality structures and psychological states. According to the scheme, standardization and objectification of drawing projection analysis are realized, and the accuracy of feature analysis and the theoretical interpretability of the result are improved.

The present application belongs to the technical field of computer vision, in particular to a psychological condition evaluation method based on channel attention and variable-length sequence processing, comprising: obtaining and processing psychological evaluation video data of a subject to construct an original feature tensor; constructing an adaptive multi-feature spatiotemporal attention network to obtain aligned features; constructing an adaptive aligned channel attention module to obtain enhanced features; inputting the enhanced features into a regression prediction head network, and performing end-to-end training on the network based on a joint loss function to evaluate the subject's psychology; and the present application solves the technical problems of large differences in actual psychological evaluation task duration, significant sequence length changes, and low processing efficiency and large information loss of variable-length sequences in the prior art.

The invention discloses a student psychological assessment early warning method and system based on group behavior clustering analysis, and the method comprises the steps: obtaining multi-source campus behavior time series data, extracting group behavior feature vectors, and dynamically dividing a plurality of behavior normal form groups; calculating a multi-dimensional behavior baseline of each behavior normal form group; calculating a group relative deviation degree between the current behavior feature vector of the target student and the multi-dimensional behavior baseline of the behavior normal form group to which the target student belongs; analyzing the continuous increasing trend of the relative deviation degree of the group based on a sliding time window to judge adaptive behavior deviation; and performing attribution analysis on the target student with adaptive behavior deviation, generating a semantic risk early warning signal, and outputting targeted assistance suggestions according to the association intervention strategy library. According to the invention, through dynamic group division and relative deviation analysis, accurate and explainable early warning and effective intervention guidance of student behavior risks are realized.

The application provides a pain analysis and treatment scheme generation method and system based on machine learning, which obtains pain information of a target patient, extracts feature information in the pain information, and enters the feature information into a feature information table; identifies a pain type of the target patient based on the feature information table; generates inquiry content based on the pain type; receives reply content of the target patient to the inquiry content; generates a pain treatment scheme of the target patient based on the reply content; obtains the feature information table by extracting pain information and mental and psychological evaluation results of the target patient, determines the pain type of the target patient according to the feature information table, and inquires the target patient according to the pain type and the mental and psychological state to supplement relevant information, and then generates a treatment scheme for the pain of the target patient, improves the accuracy of pain type identification based on the feature information table, and thus a more suitable treatment scheme can be recommended for the pain type of the target patient.

The invention discloses a disease auxiliary diagnosis system and method based on multi-modal structured causal reasoning, and belongs to the technical field of intelligent auxiliary diagnosis. The system comprises a behavior feature layer which obtains behavior features representing behavior changes of a target object by synchronously collecting audio data and facial image video data of the target object; the scale entry layer is used for mapping the behavior characteristics into entry vectors of a psychological assessment clinical scale by utilizing a regression model; the symptom latent variable layer is used for aggregating entry vectors into symptom latent variables reflecting different clinical symptom dimensions according to the corresponding relation between scale entries and the clinical symptom dimensions; a disease category layer: obtaining a disease category influence probability used for analyzing the influence of symptoms on the disease risk by constructing a structured causal relationship graph; and the result interpretation layer is used for outputting a readable causal interpretation path and a feature contribution degree based on the causal structure diagram and the disease type prediction result. According to the invention, objective, structured and interpretable auxiliary decision-making of mental disease diagnosis can be realized.

The application provides a kind of juvenile emotion adjustment method, system and electronic equipment based on picture projection and electroencephalogram feedback, belongs to juvenile mental health intervention, digital psychological adjustment and auxiliary psychological evaluation technical field.The method comprises: obtaining self-defined and / or standardized pictures to form a set to be played;In turn show and synchronously collect electroencephalogram data;Subjective results are obtained by projecting and analyzing pictures, and objective indexes are obtained by analyzing electroencephalogram;The subjective and objective results are matched and fused to generate a psychological state auxiliary evaluation;According to the evaluation result, the picture is screened and adapted, and a personalized healing package for home use is generated.The application is compliant and has no risk of unqualified, has low threshold and is easy to popularize;With photographing and selecting pictures as the core, the compliance of teenagers is high;Combined with electroencephalogram objective quantification, the intervention is accurate and effective;No professional equipment and site are needed, and the cost is low, which supports home continued use and has scientific research and industrialization value.

The invention relates to an interview response psychological twinborn modeling method based on multi-modal data fusion, and relates to the technical field of twinborn modeling, and the method comprises the steps: determining a plurality of key test scenes, and obtaining multi-modal performance test data of a plurality of test users under the plurality of key test scenes; determining a plurality of user types based on the multi-modal performance test data of the plurality of test users in the plurality of key test scenes; obtaining multi-modal interview response data of a plurality of test users; constructing a psychological expression knowledge graph based on the multi-modal interview response data of the plurality of test users; obtaining multi-modal performance test data of an interviewer in a plurality of key test scenes, and determining a user type to which the interviewer belongs; obtaining multi-modal interview response data of the interviewer; based on the multi-modal interview response data of the interviewer, the user type to which the interviewer belongs and the psychological expression knowledge map, response psychological twinborn modeling of the interviewer is carried out, and the method has the advantage of improving the accuracy of interview response psychological assessment.

The invention discloses an internet-based self-service psychological assessment and guidance system and method, and particularly relates to the field of internet, the system comprises a multi-source data acquisition module, a data preprocessing module, a layered assessment analysis module, a personalized guidance scheme generation module, a real-time feedback adjustment module and an effect tracking evaluation module; the real-time feedback adjustment module is used for collecting user feedback data and dynamically optimizing the scheme in the grooming process; the real-time feedback adjustment module is used for collecting user feedback data and dynamically optimizing the scheme in the grooming process; the effect tracking evaluation module is used for recalculating the comprehensive psychological state index after 7 days of continuous dredging, and evaluating the improvement effect; through multi-source data fusion and standardized layered evaluation, an accurate psychological state evaluation result is obtained, the problem of large result deviation caused by single evaluation dimension and dependence on a subjective questionnaire is solved, and the beneficial effects of comprehensively describing the psychological state of the user and improving the evaluation accuracy are achieved.

The invention provides a psychological questionnaire data analysis method and system based on a hidden variable model, and belongs to the technical field of data analysis. The method comprises the steps that a first questionnaire data set is acquired, the first questionnaire data set comprises a plurality of effective filling results about a preset psychological questionnaire, and the preset psychological questionnaire comprises a plurality of psychological evaluation question items and a plurality of grade score options corresponding to each psychological evaluation question item; performing standardization processing on the first questionnaire data set to obtain a second questionnaire data set; taking the plurality of psychological evaluation items as a plurality of observation variables and constructing an undirected graph, and separating the undirected graph according to the second questionnaire data set and a first preset transformation independent noise condition to form all single factor sets; and according to the second questionnaire data set and a second preset transformation independent noise condition, determining a causal direction among all hidden variables corresponding to all the single factor sets to form a causal structure diagram. The accuracy of identifying the causal structure between the psychological hidden variables can be improved.

This invention discloses an intelligent assessment method, system, and readable storage medium based on observable indicators, belonging to the field of information data processing technology. The method includes: acquiring the definition of a target psychological construct from a standardized psychological scale or psychological theoretical system; converting it into one or more observable indicator units according to observability rules and consistency rules with psychological theories; constructing an enhanced knowledge base for observable indicator retrieval; structurally binding the observable indicator units with evidence from psychological literature; acquiring multimodal behavioral data and extracting features; aligning the data with the observable indicator units to generate a set of behavioral evidence; and performing inference assessment based on a collaborative mechanism of rules, knowledge base, and machine learning models. This invention overcomes the limitations of existing technologies that rely on subjective self-assessment or black-box model prediction by constructing a computational representation layer from psychological constructs to observable indicators, achieving objective psychological assessment that requires no subject self-assessment, has a theoretical basis, and is traceable.

This application relates to a method, apparatus, device, and medium for dynamic optimization of health plans based on a large-scale model. The method includes: acquiring user-set health goals, user historical behavior data, and self-psychological assessment scores; the user historical behavior data includes historical health plan executions, historical plan execution status, and user interaction logs; constructing a current user personality vector based on the user's historical behavior data and self-psychological assessment score; constructing a set of failed personality vectors based on historical health plan executions and historical plan execution status; based on the health goals, conducting an intervention path game driven by personality adversarial interaction based on the current user personality vector and the set of failed personality vectors to obtain a health plan intervention path; the health plan intervention path includes behavioral suggestions, corresponding time period constraints, and behavioral contextual considerations. This method integrates personality modeling and adversarial game theory to optimize the personalization and resilience of health plan paths.

According to the analysis method and device of the EAP listening feeling analyzer and the analyzer, the EAP listening feeling analyzer can directly analyze and evaluate mental health according to the voice information of a person needing psychological evaluation, the person needing psychological evaluation does not need to subjectively answer questions, and the analysis efficiency is improved. According to the psychological evaluation method, subjectivity of a person needing psychological evaluation during psychological evaluation can be reduced, the obtained psychological evaluation result is more objective and accurate, the quantization problem corresponding to each sub-mode is judged through character information and voice emotion, and the accuracy of the judgment result can be improved. The data storage and management module is used for storing all data generated by the data acquisition module, the voice-to-character module, the voice and intonation analysis module and the psychological report generation module, and providing storage and encryption services for the data acquisition module, the voice-to-character module, the keyword recognition module, the voice and intonation analysis module and the psychological report generation module; and confidentiality and subsequent checking can be facilitated.