194 results about "Psychological evaluation" patented technology

The invention discloses an intelligent psychological intervention system based on multi-modal fusion, which is characterized in that a three-dimensional evaluation system is constructed by integrating speech sentiment analysis, keyboard dynamics monitoring and physiological signal acquisition, and time sequence alignment and feature weighted fusion of multi-source data are realized by adopting a cross-modal Transform model. The core of the system comprises an adaptive intervention engine which defines a multi-dimensional state space based on a hierarchical reinforcement learning architecture, optimizes an intervention strategy through a PPO algorithm, and realizes dynamic emotion interaction in AR and VR scenes in combination with a digital twin training module; according to the clinical decision support system, physiological behavior characteristics and psychological assessment trends are integrated by using a multi-time scale risk prediction model, and a personalized early warning threshold system is constructed, so that the psychological state recognition accuracy is improved, the intervention intensity self-adaptive adjustment response time is shortened, and the high-risk signal early warning timeliness reaches the minute level; and the problems of evaluation hysteresis and strategy stiffness of traditional psychological intervention are obviously improved.

The invention relates to the technical field of multi-modal data, provides a pilot competency dynamic evaluation method, system and device based on multi-modal data and a storage medium, and solves the problems of low accuracy of pilot competency evaluation and poor pertinence of training guidance. The method comprises the steps of collecting flight control data, physiological signal data, psychological assessment data, subjective scale data and international civil aviation organization core competency information, performing alignment fusion on multi-source data by adopting a time synchronization algorithm, and identifying an attention fixation mode based on a hidden Markov model. And constructing a workload index by fusing a subjective scale and a physiological entropy value, inputting the multi-modal features into a pre-trained competency assessment model, outputting three levels of psychological assessment indexes including a basic ability layer, a dynamic presentation layer and a risk early warning layer, and finally generating a personalized training report. According to the invention, the accuracy of pilot competency evaluation and the pertinence of training guidance are improved.

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

The invention provides a psychological analysis method and system based on multi-modal features, and relates to the technical field of psychological assessment. The method comprises the following steps: synchronously obtaining voice data, video data, text data and physiological signal data of a user; respectively denoising the data to obtain each piece of denoised modal data; performing feature extraction on each modal data to obtain each modal feature; mapping each modal feature vector to a unified semantic space through an attention weighting mechanism to generate a fusion feature matrix; a trained psychological analysis model is adopted to perform multi-task learning on the fusion feature matrix, and emotion classification, pressure indexes and psychological health risk scores are synchronously output; and updating model parameters of the psychoanalysis model through an online incremental learning mechanism based on the real-time feedback data of the user and the newly added sample. Through accurate data denoising, efficient feature extraction and flexible model optimization, comprehensive and accurate analysis of the psychological state of the user is realized.

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

The invention discloses a multi-role collaborative management architecture based on RBAC + HIS synchronization, and the architecture comprises a role authority management module which builds a static role authority baseline based on an RBAC model, and associates the real-time organization architecture data of an HIS system through a dynamic rule engine, and generates a multi-level role authority tree; the data synchronization middleware adopts an API gateway to realize one-way data push between the HIS system and the psychological assessment platform, comprises a field-level encryption unit and an incremental data verification unit, and supports isolated transmission of patient medical records, medical advices and assessment tasks according to inpatient areas; the multi-terminal cooperation engine realizes authority mapping of the doctor workstation, the mobile nursing terminal and the patient self-service terminal through distributed session management, a conflict detection mechanism is built in, and a secondary authentication process is triggered when cross-inpatient-area operation is detected. According to the invention, dynamic authority management is realized, data secure transmission and multi-terminal cooperation are guaranteed, auditing tracing is enhanced, and business continuity when the system is abnormal is ensured.

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

The invention relates to the technical field of talent training, and discloses an artificial intelligence-based large-health customized talent training system, which comprises a multi-modal data acquisition module, which is used for acquiring user biological index data, behavior log data, psychological assessment data and industry demand data; and the data processing and knowledge fusion module is used for performing time sequence alignment and missing value filling on the acquired data, and constructing a skill association knowledge graph based on industry demand data. According to the invention, through the personalized ability modeling and reinforcement learning recommendation module, the personalized training path of each user is dynamically generated, it is ensured that each user can obtain training matched with health requirements, ability levels and industry requirements, and compared with a universal training scheme in the prior art, the training efficiency is greatly improved. An accurate and customized training scheme can be provided according to real-time biological indexes, psychological evaluation and behavior data of the user, and the training effect is remarkably improved.

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 belongs to the technical field of medical treatment, and discloses a wearable intelligent method and device for monitoring the mental health of community old people, and the method comprises the steps: collecting the physiological indexes of the community old people in real time through an intelligent chest card, and achieving the dynamic collection of multi-dimensional data; data are transmitted to the cloud platform through Bluetooth or Wi-Fi, the equipment supports a data caching mechanism, and key data are temporarily stored when connection is abnormal; a dynamic evaluation model is constructed based on AI learning, and a multi-factor decision tree model and a natural language processing NLP algorithm are combined; a group psychological health trend and an environment risk thermodynamic diagram are presented in real time through a chest card APP, main factors influencing psychological health of the old are analyzed, and community managers are assisted in making an intervention plan. By making and applying the community old people mental health monitoring and early warning model, a unified mental health assessment system is provided for old people in different states, the applicability, universality and compliance of mental assessment are improved, home rehabilitation is achieved, and the overall medical cost is reduced.

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 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 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 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.

The invention discloses a patient psychological assessment method and device based on information interaction and a storage medium, and relates to the technical field of medical information processing.The method comprises the steps that a scene interaction module is constructed, and a multi-modal data set of a patient is collected under multiple scene samples; the method comprises the following steps: performing space-time alignment on a multi-modal data set, inputting a multi-modal fusion psychological assessment model, learning correlation among different modals, outputting a plurality of psychological assessment indexes corresponding to a plurality of scene samples, performing fusion analysis, obtaining fusion psychological assessment indexes, and sending the fusion psychological assessment indexes to an interaction upper computer for medical record storage. The technical problems that in the prior art, a traditional psychological assessment method depends on manual observation, the assessment dimension is single, emotion changes cannot be comprehensively captured, and the assessment accuracy, real-time performance and comprehensiveness are poor are solved, and the purposes of automatically assessing the psychological state of the patient by fusing multi-dimensional data, improving the accuracy and accuracy of assessment and the like are achieved. And the accuracy, the real-time performance and the comprehensiveness of psychological assessment are improved.

The invention relates to the technical field of psychological state assessment, and particularly discloses a psychological state assessment method and system based on environmental perception, and a readable storage medium, and the method comprises the following steps: collecting an environment scene and an assessed object behavior image through an RGB camera, and extracting an object category, a spatial layout and a dynamic event through a semantic segmentation model; acquiring an environment voiceprint through a microphone, and recognizing a voice category by using a voice recognition model; synchronously acquiring facial micro-expression feature points, voice rhythm features and head, neck and shoulder vibration frequencies of the evaluated object; and generating a semantic vector from the object / scene label through a semantic embedding model. According to the psychological state assessment method based on environmental perception, through three core innovations of quantitative modeling of environmental semantics, causal reasoning and a dynamic attention mechanism and personalized adaptive calibration, the neglect limitation of traditional psychological assessment on environmental factors is broken through, and the accuracy, robustness and practicability of psychological state assessment are remarkably improved.

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 a high-precision multi-modal psychological normal form assessment method and system. The method comprises the following steps: constructing and training a multi-modal psychological normal form assessment model; preprocessing the to-be-detected data; performing long-range dependency relationship modeling on the coding result to obtain an enhanced single-mode coding result; performing feature fusion on the enhanced single-modal coding result to obtain a fused modal feature, and optimizing the fused modal feature; carrying out classification evaluation of a psychological normal form of multi-modal input on the optimized modal features to obtain a multi-modal evaluation result; and performing classification evaluation of the psychological normal form of single-mode input on the enhanced single-mode coding result to obtain a single-mode evaluation result. According to the method, high robustness is shown in multi-modal sentiment analysis, the calculation cost is controllable, and a reliable solution is provided for actual deployment of the multi-modal psychological assessment technology.

The invention discloses an intelligent psychological sand table assessment and healing system based on VR, and relates to the technical field of psychological assessment and healing. A VR-based intelligent psychological sand table assessment and healing system is characterized in that a user side comprises a VR head-mounted display device, a spatial positioning and gesture interaction module and a virtual sand table scene generation engine, and a system terminal comprises a user behavior data acquisition and analysis module, an intelligent analysis and assessment engine and an immersive healing guide module. According to the invention, the regional limitation is broken through the virtual reality technology, the efficient and personalized psychological healing service is provided, the deficiency of professional resources in low-resource regions is made up, the psychological health intervention effect is improved, and the improvement of the overall social psychological health level is facilitated. The system adopts a modular design, is easy to expand and maintain, supports simultaneous online of multiple users, ensures data security and privacy protection, is suitable for multiple scenes such as schools, communities and medical institutions, effectively reduces psychological assessment and healing thresholds, and promotes popularization of psychological health.

The invention discloses a campus safety risk monitoring method and system, relates to the technical field of campus safety, and solves the problem that existing campus safety monitoring cannot be combined with other data to find potential risks. Timely early warning cannot be carried out, and a normative processing flow is prompted; according to the scheme, the method comprises the steps that monitoring data and psychological evaluation data are acquired; determining important attention personnel according to the psychological evaluation data; the risk level of the personnel behavior is identified according to the monitoring data, an early warning signal is generated based on a graded early warning rule, and the graded early warning rule comprises: early warning signals are generated for medium-risk behaviors or all high-risk behaviors of the key concerned personnel; the early warning signal is pushed to the corresponding responsibility management personnel and the nearby patrol personnel; according to the accident type fed back by the nearby patrol personnel, an associated guidance plan is pushed, and the associated guidance plan is used for guiding standard handling of the accident; generating a keyword according to the accident type, and carrying out public opinion monitoring on a website; the problems are solved.

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 relates to the technical field of treatment and healing of depression patients, and discloses a depression patient AI treatment and healing interaction and note generation method based on natural language processing, which comprises the following steps: step 1, constructing a natural language processing model; step 2, psychological assessment and emotion analysis; 3, a personalized chemotherapy healing interaction strategy; step 4, a note generation function; and 5, evaluating and feeding back the treatment effect. According to the depression patient AI healing interaction and note generation method based on natural language processing, depression healing is converted into an online reinforcement learning problem, the rehabilitation effect of the patient is taken as an instant reward, an interaction strategy is taken as an action space, the quality of a strategy library is automatically improved through continuous iteration, and compared with static script type dialogue, the method has the advantages that the efficiency is greatly improved; the system can dynamically adjust parameters according to three factors of emotion level improvement, interaction duration and satisfaction, achieves the purpose of'understanding patients more than using ', enables a strategy library to be periodically and globally retrained after a large amount of crowd data is converged, remarkably shortens the single-time healing effectiveness time, and improves the overall rehabilitation efficiency.

The invention provides a psychological counseling auxiliary system. The system comprises a psychological analysis module, a psychological counseling resource library, a recommendation module and an interactive interface module, the psychoanalysis module is used for receiving voice data and image data of the user in multiple rounds of dialogues and analyzing keywords in the voice data and the image data and an emotional state of the user; the psychological counseling resource library is used for storing psychological counseling resources; the recommendation module is used for providing psychological consultation suggestions and resource recommendation for the user according to historical data, keywords and emotional states of the user; and the interactive interface module is used for displaying the recommended psychological counseling suggestions and resource recommendation. Based on the natural language processing technology and the emotion recognition technology, the method can improve the insight in the psychological state of the user in the consulting process, provides accurate psychological assessment in combination with voice, text and expression analysis, solves the limitation of the existing traditional consulting tool, improves the professional property of psychological consulting, and improves the consulting efficiency. And the working efficiency is improved.

The invention relates to the technical field of psychological evaluation, in particular to an AI model-based student negative emotion evaluation method, which comprises the following steps of: acquiring facial video data and mouse movement data of a student in real time; state data such as attention degree and hesitation degree of answering of the students are dynamically analyzed, and subsequent emotion test questions are dynamically adjusted according to an analysis result; after an emotion test question of a student is completed, the question weight is dynamically adjusted according to question difficulty, face video data and mouse movement data, the score of the test question is combined, and the emotion score of the student is quantified by adopting a weighted average method. And generating an emotion analysis report of the student according to the question difficulty, the face video data, the mouse movement data and the emotion score. By means of the mode, the problem that the accuracy of the emotion evaluation result of the student is low due to the fact that the face features of the tested student are analyzed in the testing process and data in multiple aspects such as the attention of the student is not considered can be solved.

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 relates to the technical field of medical information, in particular to an intelligent assessment and intervention system and method for psychological states of patients after hepatobiliary and pancreatic surgery. According to the method, technologies such as a BP neural network, speech recognition, natural language processing and an improved LSTM neural network are fused, and a psychological state evaluation model is constructed; the system comprises a psychological test module, a music intervention module and an evaluation module, and realizes data intercommunication of evaluation results. The psychological test module selects a proper scale for emotion evaluation according to an evaluation result; the music intervention module recommends playing music according to the evaluation result; the evaluation module analyzes the voice communication text, judges psychological problems by using a support vector machine algorithm, and constructs a postoperative health state data model; according to the system, through innovative technical means and overall design, the comprehensiveness of evaluation, the individuation of intervention and the continuity of service are improved, the defects of a traditional method are effectively overcome, and more accurate and comprehensive postoperative psychological evaluation and intervention service is provided for patients.

The present invention discloses a method and system for embodied perception evaluation of campus green spaces based on multimodal learning. The method specifically includes: inputting the output results of the visual feature encoding network and the auditory feature encoding network into an audiovisual modality feature fusion module for fusion processing to obtain an environmental joint representation; inputting the physiological response data set and the psychological evaluation data set into a physiological-psychological collaborative encoder for processing to obtain a physiological-psychological joint representation; inputting the environmental joint representation and the physiological-psychological joint representation into a global fusion module for deep fusion to obtain a final fusion feature; inputting the final fusion feature into a multi-task prediction channel for processing, and training and optimizing in combination with a loss function to obtain a comprehensive evaluation result of environmental comfort and an explanatory decision-making basis. The present invention realizes a comprehensive evaluation of the embodied perception of campus green spaces, improves the accuracy and scientific nature of the evaluation, and provides decision-making support for optimizing campus space design.