109 results about "Psychological intervention" patented technology

The invention relates to the technical field of medical rehabilitation, in particular to an artificial intelligence-based orthopedic full-cycle rehabilitation management system, which comprises a rehabilitation demand evaluation module, a three-dimensional pathological modeling module, a multi-modal training generation module, a rehabilitation effect prediction module, a dynamic parameter adjustment module and a full-cycle management module. Multi-dimensional data such as bone mineral density, joint motion range, gait mechanics and psychological state of a patient are collected, an artificial intelligence algorithm is combined for analysis, a personalized rehabilitation scheme is generated, and rehabilitation parameters are predicted and adjusted in real time. The system can dynamically correct the training intensity, the nutritional supplement dosage, the psychological intervention mode and the rehabilitation schedule, and the safety and reliability of data are guaranteed through the block chain technology; according to the system, biomechanics, nutriology, psychology and environmental factors are comprehensively fused, the system has strong adaptive ability and full-period management ability, real-time optimization can be performed according to the rehabilitation process of a patient, and the rehabilitation effect of the orthopedics department and the satisfaction degree of the patient are remarkably improved.

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

Aiming at the problems of insufficient language understanding depth, weak personalized dialogue generation ability, lack of continuous learning and long-term user state modeling and the like in the current emotion recognition and psychological intervention technology, the invention provides a user emotion recognition and psychological intervention method combined with a large language model (LLM). According to the method, the potential emotional state is identified by analyzing free text information input by a user by utilizing the powerful capabilities of a large language model in the aspects of natural language understanding, emotional modeling and text generation; constructing a multi-round dialogue context, and reasoning a psychological change trend of the user; in combination with a psychological knowledge base, personalized and mild psychological intervention dialogue content with a dredging effect is generated. The system supports recognition and classification of various emotional states such as depression, anxiety and alonity, is suitable for various interaction scenes (such as APPs, webpages and social robots), and can greatly improve the precision of emotion recognition and the timeliness and effectiveness of psychological intervention. The emotion recognition and psychological intervention method based on the large language model provides solid technical support for constructing an intelligent, continuous and personalized psychological health management system, and has wide application prospects and profound social significance.

The invention relates to the technical field of psychological health management, and comprises an AI-based college psychological health comprehensive service system and method, and the system comprises a psychological behavior scoring module, an interactive behavior analysis module, a psychological health assessment module, a social intervention matching module, and a dynamic adaptation optimization module. According to the method, based on behavior data of learning, work and rest, social contact and the like, the time matching degree of behavior categories is calculated, the comprehensiveness of individual psychological health scoring is improved, social interaction data is combined, the social behavior resonance degree is analyzed, the psychological integrating degree between individuals is measured, semantic emotion polarity change is combined, and psychological state fluctuation is quantified; based on psychological health state change analysis, individuals with high social mode similarity are screened, pertinence of intervention matching is enhanced, fluctuation trends of individual social states are synchronously identified, psychological intervention strategies are adjusted according to changes of psychological health scores, personalized adaptability is improved, and fine adjustment of psychological health management of college students can be achieved.

The invention relates to the field of healing dialogues, and particularly discloses a psychotherapy-healing-oriented large-model dialogue agent which realizes deep analysis of core emotions, cognitive states and potential demands of a user by integrating real-time input, historical dialogue abstracts and emotional memory and adopting a structured coding and feature fusion technology. Based on the emotion signal and the semantic analysis result, matching a predefined emotional strategy label, and ensuring that a response strategy accords with psychological intervention logic. The psychological theory is converted into an executable dialogue framework through key element recognition and instruction embedding, and the integrating degree and safety are improved in cooperation with multi-candidate generation and an emotional quality evaluation mechanism based on semantic embedding. According to the scheme, by continuously optimizing emotion representation and strategy matching, the defects of a traditional model in the aspects of emotion tracking, professional fusion and safety control are effectively overcome, and the depth and reliability of psychological support are remarkably improved.

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

The invention relates to the technical field of artificial intelligence and psychological health, and discloses a multi-mode psychological counselor skill recommendation method and equipment. The method comprises the following steps: acquiring video, audio and text data in a psychological consultation session, and converting the video, audio and text data into structured data; obtaining the current emotional state of the consultant through multi-modal analysis according to the video, audio and text data after the structure conversion; judging whether psychological intervention skill recommendation is performed or not; if yes, obtaining recommended psychological intervention skills through a trained condition triggering model according to the dialogue between the psychological consultant and the consultant and the current emotional state; and according to the psychological intervention skills and the dialogue between the psychological consultant and the consultant, a dialogue text is generated through a large language model to help the psychological consultant to reply. According to the embodiment of the invention, the recommendation effect of the psychological intervention skills can be improved.

The invention discloses a psychotherapy and healing robot based on high human emotion fitting degree simulation analysis, and the robot comprises a multi-mode perception layer which is used for collecting the interaction data of physiology, movement and environment; the multi-modal sensing layer comprises a heterogeneous data acquisition module, a spatial-temporal feature extraction network and an attention fusion mechanism module; the dynamic decision-making layer is used for generating an intervention strategy based on the interaction data; the dynamic decision-making layer comprises a reinforcement learning strategy engine and a hierarchical intervention selection tree; the generative interaction layer is used for generating a co-estrus response conforming to ethical specifications based on the intervention strategy; the generative interaction layer comprises an ethical constraint system and an emotional response generator; the brain science verification layer is used for monitoring neural feedback in real time through EEG and adjusting an intervention strategy; and the brain science verification layer comprises a neural feedback regulation module and a multi-mode feedback design module. Therefore, a precise and personalized psychological intervention decision closed loop is provided, and the defects of an existing AI psychological product in the aspects of emotion recognition, intervention strategies and effect quantification are overcome.

The invention relates to a clinical psychological treatment effect evaluation method and system based on machine learning, and the method comprises the steps: obtaining biological signals, behavior patterns and psychological state data of a patient during treatment, constructing a three-dimensional tensor structure with aligned timestamps, and carrying out the standardization of the three-dimensional tensor structure to generate a multi-dimensional data matrix; dimensionality reduction, reconstruction and verification are carried out on the matrix through a deep auto-encoder network, and unified feature vector representation is output; utilizing an improved support vector regression algorithm to establish a nonlinear mapping model of the feature vector and the curative effect score; according to the method, the curative effect score is predicted in real time, when the score is abnormal or fluctuation exceeds a threshold value, a personalized treatment scheme optimization mechanism based on the knowledge graph is triggered, deep fusion of multi-source heterogeneous data is achieved, evaluation precision and treatment adaptability are improved through a dynamic optimization mechanism, and intelligent decision support is provided for clinical psychological intervention.

The invention relates to the field of psychotherapy, and discloses a pain psychotherapy simulation system based on a virtual reality technology, which is used for solving the problem that when a female patient experiences a physiological cycle, a patient biofeedback signal collected by the system can be interfered, and comprises the following steps: combining a head-mounted display, a handle and other control equipment; the method comprises the steps that a patient enters an immersive virtual world through equipment, physiological data of the patient are obtained, the physiological data comprise heart rate data, skin conductivity and brain wave data, an initial disease pain index is calculated according to the physiological data of the patient, a regulatory factor is calculated according to physiological period information, and the initial disease pain index is calculated according to the regulatory factor; the actual disease pain index is calculated according to the initial disease pain index and the regulatory factor, and psychological intervention is performed according to the actual disease pain index, so that the accuracy of evaluating the state of the treated part of the patient by the system is effectively improved, and the use experience of the patient is improved.

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

The invention provides an old people postoperative rehabilitation nursing system based on artificial intelligence, and relates to the field of intelligent nursing, and the old people postoperative rehabilitation nursing system comprises the steps: firstly, carrying out the encryption processing and data normalization of a collected sentiment log data set of a target old people object, and obtaining the time sequence distribution of an encrypted sentiment log; and then performing content summarization and semantic embedding on the time sequence distribution of the encrypted sentiment logs by adopting a data analysis mode based on artificial intelligence to obtain time sequence distribution of sentiment log summary semantic features, and obtaining an emotion recognition result based on context causal association coding features of the time sequence distribution of the sentiment log summary semantic features. And finally, judging whether a psychological intervention prompt signal is generated based on an emotion recognition result. Therefore, by continuously monitoring and analyzing the emotion fluctuation and the law of the senior citizens, the emotion state can be more accurately captured, and then the timeliness, the effectiveness and the intelligent degree of psychological intervention are improved.

The invention provides a remote rehabilitation system for a stroke patient, relates to the technical field of stroke rehabilitation, and aims to remotely diagnose the patient, periodically generate a personalized rehabilitation scheme for the patient, and timely adjust and update a training scheme according to the exercise effect of the patient. The system comprises a data acquisition terminal, a data transmission terminal, a control terminal, a doctor monitoring terminal and a patient terminal, a personalized rehabilitation training scheme generation algorithm is applied, multi-dimensional data is fused to form a comprehensive feature vector, and rehabilitation potential and requirements are evaluated. Personalized schemes covering exercise, cognition and psychological intervention are customized for the patient, dynamic adjustment is conducted according to feedback, and precise rehabilitation is achieved. The system has the functions of real-time monitoring, intelligent reminding and abnormity early warning, the rehabilitation effect is optimized, the medical burden is relieved, the life quality of patients is improved, and the system is suitable for medical institutions at all levels.

The invention discloses a mental health monitoring and nursing robot with body-multi-dimensional intelligent cooperation. The robot comprises a mobile robot platform equipped with a differential driving chassis, a laser SLAM navigation module and the like; a multi-mode sensing network integrating an RGB-D depth camera, a thermal imager and the like processes data in real time through an edge computing unit; an intelligent decision-making engine based on a micro-service architecture covers a patrol recognition module, a vital sign monitoring module, a psychological support Transform dialogue model, a multi-modal emotion recognition module and the like; and the man-machine interaction interface is connected through an end-to-end encrypted 5G network. A medicine compliance management module, an RFID medicine box and a privacy protection scheme of medicine taking action recognition and federal learning and differential privacy are further arranged. The method has the advantages that nursing efficiency is improved by more than 40%, adverse event occurrence rate is reduced by 35%, psychological intervention coverage rate is improved, medicine taking compliance is improved, sublingual Tibetan medicine detection rate is improved to more than 95%, data privacy is safe and controllable, and intelligent upgrading of mental health nursing is promoted.

The invention relates to the technical field of emotion analysis, and provides a psychological catharsis interaction system based on artificial intelligence, which comprises a data acquisition module, an emotion recognition module, a virtual catharsis scene module and an emotion analysis and guidance module, physiological data and behavior data of students are collected through VR equipment and a sensor, an emotion recognition model is constructed in combination with feature extraction and a multi-modal feature fusion technology, and emotion category recognition is achieved; dynamically generating an adaptive virtual catharsis scene based on an identification result; the emotion analysis and guidance module optimizes hyper-parameters of a TCN-GRU model through an improved grey wolf optimization algorithm, constructs an enhanced TCN-GRU model, extracts long-term and short-term features from virtual catharsis interaction data, analyzes emotion evolution and provides personalized psychological guidance; the system can generate an accurate emotion evolution map and psychological state scores, and provides a scientific and accurate psychological intervention scheme for students.

The invention relates to the technical field of psychological intervention adjustment, in particular to a dynamic structured self-adaptive training method which is applied to a training framework comprising an acquisition terminal, a processing terminal and an execution terminal. The acquisition terminal is used for acquiring a physiological signal, a behavior signal and an environment context of an individual, the processing terminal fuses the information and calculates an anxiety index and an uncertainty probability value, and when the uncertainty probability value is lower than a preset threshold value, an initial task graph is constructed according to a target task and the environment context. The initial task graph is subjected to structure and parameter updating through a preset rule generator in combination with a reward function and an anxiety index, an individualized task graph is generated, cross-age migration is further carried out based on physiological and behavior signals, an execution graph bound with an execution terminal is obtained, and the execution terminal carries out intervention according to the execution graph. The problem that an existing training method is difficult to give consideration to behavior complexity and psychological bearing capacity is solved, and training individuation and stability are improved.

The invention discloses a neural feedback intervention system and method based on meditation and meditation, and belongs to the technical field of neural intervention. The problems that existing meditation intervention equipment is mostly based on directional voice guidance or respiratory regulation, personalized and dynamic psychological intervention cannot be performed by fully combining with a neural feedback technology, and an expected standard is difficult to realize are solved, the electroencephalogram signal of an intervention object is monitored in real time, and the meditation intervention accuracy is improved. Mapping the features into a specified interval by using a neural feedback index normalization model to obtain a normalized neural feedback index; converting the audio signal into an audio signal through a time domain signal processing technology; personalized psychological intervention content is provided for an intervention object in combination with a system based on a meditation technology; in the intervention process, the physiological status of the intervention object is monitored in real time, the current intervention scheme can be properly adjusted in time, the effect of meditation training and the experience of the intervention object are effectively improved, and high practicability and market prospects are achieved.

The invention belongs to the technical field of genes, and discloses intelligent wearable equipment for real-time monitoring and intervention of chronic fatigue syndromes. The equipment comprises a user input module, an intelligent screening module, a control center, a dynamic risk assessment module, a health monitoring module, a cloud server, an intelligent scheme generation module, a display module, a sound production module, an intelligent supervision module and an intelligent optimization module. According to the method, through a multi-modal data fusion technology, the accuracy of CFS early recognition is remarkably improved, and clinical verification shows that the screening accuracy reaches 92.3%; due to the innovative dual-mode design (treatment mode / conventional monitoring mode), the system can flexibly adjust the monitoring intensity according to user requirements, and the energy consumption is optimized while the effect is ensured; a cognitive behavior therapy is digitalized, psychological intervention is implemented in an intelligent interaction mode, and user compliance is improved by 56%.

The invention belongs to the technical field of artificial intelligence, and relates to an AI psychological tutoring system with emotion intelligence and privacy calculation double engines, comprising: a data acquisition and fusion module for generating a fusion input stream with time-space alignment; the emotional state analysis module is used for generating a dynamic emotional state matrix; the privacy strategy generation module is used for triggering a privacy calculation engine to construct a differential privacy domain according to the dynamic emotional state matrix and generating hierarchical data grids; the privacy recoding module is used for executing privacy recoding on the fused input stream according to the hierarchical data grids to generate an anti-reverse engineering ciphertext packet; the self-adaptive strategy generation module is used for generating a self-adaptive tutoring strategy tree; and the closed-loop intervention execution module is used for driving the terminal equipment to execute feedback of the self-adaptive tutoring strategy tree and generating a closed-loop psychological intervention flow. According to the invention, the problem that a real-time capture and response mechanism for user feedback is lacked due to the fact that most interaction modes are one-way output is solved.

The invention discloses a psychological intervention suggestion training method of a large language model. The method specifically comprises the following steps: S1, data preparation; s2, data processing; s3, constructing a training sample; s4, establishing a vocabulary set; s5, data training; the method has the beneficial effects that data in the field of psychological intervention is acquired from multiple media, and the acquired data text information is processed, so that the situation that a model autonomously removes redundant data is avoided, and the training time and training energy are saved; and by configuring a scientific and reasonable training sample set, the accuracy of the model relative to the psychological intervention field can be effectively improved, so that predicted answers output by the large language model can contain more contents in the psychological intervention field, and the blank of training of the psychological intervention field on the large language model is filled.

The invention discloses a psychological assessment and psychological measurement platform, which relates to the technical field of artificial intelligence and comprises a multi-dimensional data acquisition module, a precise measurement and assessment analysis module, a personalized report generation module, a customized psychological intervention suggestion module and a visual presentation module. By integrating various psychological assessment tools and an advanced data analysis technology, comprehensive and accurate psychological health assessment and psychological feature analysis are provided for individuals, measurement and assessment are performed by adopting an advanced multi-dimensional data acquisition mode and combining artificial intelligence and a big data analysis technology, and data processing and analysis functions provided by the platform are more accurate and accurate. A detailed personalized report can be generated in real time, an intelligent evaluation system of the platform can provide customized psychological intervention suggestions according to basic information, behavior data and physiological data input by a user, and the user is helped to understand psychological characteristics and behavior modes of the user more visually in a data visualization mode.

The application belongs to the technical field of virtual reality, and particularly relates to a sound-driven VR serious game interaction control method for semi-blind people. The method comprises the following steps: constructing a 3D sound scene space and a motion guide field, and converting a motion path into a sound change rule; collecting and fusing user pose and physiological feedback signals in real time; dynamically adjusting sound source parameters and tactile feedback according to pose deviation to realize non-visual orientation guidance; performing boundary verification and area determination through ray projection to ensure interaction safety; synchronously processing collision by using a bounding box model and quaternion interpolation; and establishing a mapping model of emotional parameters and sound characteristics to synthesize exciting sound effects. Through multi-modal collaborative interaction, the application significantly improves the perception accuracy and safety of semi-blind users in a virtual space, realizes the cooperation of psychological intervention and motion rehabilitation, and has excellent real-time response capability.

The invention is applied to the technical field of dialogue interaction systems, and particularly discloses an induced dialogue interaction system based on a psychological intervention artificial intelligence model, which comprises a voice recognition module, a deep language module, a voice output module, a closed feedback module and an abnormal condition processing module, the voice recognition module is connected with the deep language module through wireless signals, and the deep language module is connected with the voice output module through wireless signals. According to the induced dialogue interaction system based on the psychological intervention artificial intelligence model, psychological theories such as a social emotion selection theory, a communication adaptation theory and the like are introduced in the system, and an old people age layering optimization deep language model is combined, so that the recognition and fitness reply ability of the model to common emotion expression of old people is enhanced; meanwhile, by means of intervention boundary setting, psychological intervention scenes of the old people are accurately adapted, and intervention safety and effectiveness are guaranteed.

The invention discloses a wake-up method based on expression recognition, a robot and an interaction device capable of performing wake-up control through facial expressions of a user. The wake-up method and the robot are suitable for scenes such as psychological care, accompanying robots, child / old-age auxiliary equipment and the like. The device has multi-modal fusion perception and facial emotion active recognition awakening capability, and can remarkably improve the awakening accuracy, the humanization degree and the technical depth. By integrating an expression recognition awakening mechanism, the naturalness and initiative of man-machine interaction are improved, and the limitation of awakening depending on a single mode such as voice and touch control in the prior art is improved. The robot supports voice wake-up, touch wake-up and button wake-up modes at the same time, and a multi-mode fusion wake-up mechanism is achieved. The naturalness and usability of the psychological support robot in the man-machine interaction process are improved, and the psychological support robot is particularly suitable for non-contact emotion accompanying and psychological intervention of children, old people and language barrier users.

1. The name of the design product: intelligent robot. 2. The use of the design product: intelligent robot for data collection, processing, result display of psychological test, and content display, user interaction and process implementation of psychological intervention training. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The invention discloses a psychological intervention adaptability training method and system driven by a virtual reality scene, and relates to the field of virtual reality intervention training, and the technical scheme is characterized in that a virtual training scene with configurable parameters is generated according to psychological assessment data; obtaining a second training target vector which is logically complementary with the first training target vector; receiving second behavior data of a second user, and mapping the second behavior data into a virtual role behavior in the virtual training scene corresponding to the first user; and collecting first behavior data of the first user in the virtual training scene, and transmitting the first behavior data to the second user so as to map the first behavior data into a virtual character behavior in the virtual training scene corresponding to the second user. The invention also discloses a method. According to the method, the virtual character can be quickly constructed, the authenticity and the reaction efficiency of the virtual character can be enhanced, the computing resources required for driving the virtual reality scene are reduced, and the skill migration rate can be improved.

The invention discloses an intelligent monitoring method and system for the mental health state of teenagers, and particularly relates to the technical field of mental health monitoring. The method comprises the following steps: collecting emotional response data and intervention feedback data generated by teenager individuals in a psychological intervention process, and constructing an initial psychological state feature sequence; performing quantitative evaluation on a matching effect between the intervention measure and the teenager psychological state based on the initial psychological state feature sequence to obtain intervention measure dynamic effectiveness evaluation index data; obtaining intervention measure utility attenuation trend prediction data and intervention measure instant utility response feature data by analyzing and identifying a long-term interaction change trend and a short-term action effect between the intervention measure and a psychological state; the psychological state matching adaptation degree data is generated through feature combination and optimization, the intervention measures are dynamically optimized, the real-time optimization intervention strategy is output, and the stability and the individualized adaptation degree of the psychological intervention process are improved.

The present application relates to a smart classroom system, comprising: a classroom comprising a public area for a plurality of testees [1] to simultaneously perform psychological assessment and a private area for a single testee to perform psychological intervention; a plurality of pairs of tables and chairs, wherein each table is provided with a first user terminal; the shared information presentation mechanism comprises a main body and an information presentation device arranged on the main body; the wearable virtual reality equipment is used by a testee during psychological intervention; the second user terminal is used by a psychological intervener during psychological intervention; the server generates psychological drama sand table placement information and a first evaluation result in a scene that multiple persons perform psychological evaluation at the same time; in a scene of performing psychological intervention on the testee, the wobble information is respectively sent to the paired second user terminal and the wearable virtual reality device by the server, and the server generates a second evaluation result in response to a second operation of the testee on the psychological drama sand table presented on the wearable virtual reality device.

The invention discloses a psychological stress state assessment method based on multi-knowledge domain information, and relates to the technical field of signal analysis. According to the psychological stress state assessment method based on the multi-knowledge-domain information, key factors are drafted through multiple knowledge domains, multi-modal data are generated in combination with physiological information, psychological information and the key factors, the data analysis confidence is improved, and meanwhile, the psychological stress state assessment method based on the multi-knowledge-domain information is established through a key factor association network and topology analysis mode. According to the psychological stress state evaluation method, the relationship between factors is disclosed, feature selection is optimized, model performance is improved, feature fusion and classification are carried out in cooperation with a deep learning model, the psychological stress state is accurately evaluated, the accuracy and reliability of psychological stress state evaluation are improved, and powerful support is provided for subsequent psychological intervention and treatment.