303 results about "Psychological health" patented technology

The invention discloses a multi-modal interview automatic quality analysis and evaluation method and system based on a large model, and the method comprises the steps: collecting and storing multi-modal data, such as texts, audios, videos and behavior interaction, and carrying out the preprocessing of the multi-modal data to form a standardized data set; utilizing a preset interview structure and a large model to dynamically guide the process, adjusting the topic rhythm according to real-time feedback, and recording stage conversion information to form logic trajectory data for process coherence management; automatically coding text data through a large language model, extracting features such as keywords and performing topic clustering, performing cross validation and semantic fusion in combination with data analysis results of each modal, and generating deep analysis results such as psychological states; and generating a comprehensive assessment report containing qualitative description, quantitative score and psychological abnormality or cognitive disorder risk prompts based on a deep analysis result, thereby providing a basis for psychological health assessment and cognitive competence evaluation. According to the method, automatic analysis of multi-modal data is realized, and evaluation scientificity and efficiency are improved.

The invention relates to the technical field of psychological health monitoring, in particular to a self-injury behavior early warning and intervention method and system based on multi-modal physiological data and AI.The method comprises the steps that physiological signals, behavior data, text data and environment parameters of a user are collected in real time based on a contact sensor and non-contact sensing equipment, and the physiological signals, the behavior data, the text data and the environment parameters of the user are obtained; generating a multi-source original data set; performing time synchronization and spatial correlation analysis on the multi-source original data set to form a fusion feature vector; inputting the fusion feature vector into a preset multi-modal deep learning model, obtaining a self-injury behavior risk score value, and dividing risk levels based on the self-injury behavior risk score value; and triggering a hierarchical intervention strategy of AI psychological counseling, emergency contact notification and medical resource linkage according to the risk level. The method has the effect of improving the precision and quality of teenager mental health assessment and self-injury behavior early warning results.

The invention relates to the field of voice signal processing, and discloses a multi-level labeling method and device in a dialogue scene. Through the endpoint detection and automatic speech recognition method of the dialogue audio data, noise and mute in real speech can be removed, and effective speech segments can be obtained. And carrying out clustering analysis based on the effective voice segments, obtaining target voice segments and text data of the corresponding speakers, and carrying out emotion feature extraction through multi-modal emotion analysis. And finally, realizing automatic annotation of the inquiry strategy or intention understanding of the corresponding speaker by utilizing a large language model. According to the method, the recorded conversation voice can be subjected to multi-level labeling, an efficient and accurate information management and analysis tool is provided for a conversation scene, and the arrangement efficiency of conversation records can be greatly improved. The annotation information can be used for training and improving an artificial intelligence system, so that the artificial intelligence system plays a greater role in future psychological consultation service, and the overall quality and efficiency of psychological health service are improved.

The invention relates to an immersive VR psychological detection system and method based on multi-modal AI. The system comprises a data acquisition and processing module which is used for acquiring a multi-modal data set of a user in a virtual reality scene based on unified clock synchronization, performing time-space alignment and noise reduction standardization processing on the multi-modal data set, and extracting key biological characteristics. And the correlation model construction module performs space-time correlation mapping through a spatial transformation network, constructs a three-dimensional space attention model, and generates a real-time fluctuation curve after inputting the key biological characteristics into the trained model. And the state report generation module identifies a real-time fluctuation curve by using a time sequence analysis model, performs backtracking analysis in combination with the psychological state conversion node and a multi-modal cross validation result, and finally generates a three-dimensional interactive report. By adopting the method, multi-modal data fusion can be realized, the dynamic change of the psychological state of the user can be effectively captured, the psychological state of the user can be comprehensively and deeply analyzed, and a scientific basis is provided for psychological health assessment and intervention.

The invention belongs to the field of student psychological health management, and particularly relates to a student psychological evaluation system based on AI reinforcement learning optimization. According to the system, the psychological state of the student is dynamically evaluated through multi-modal data acquisition, deep data processing and reinforcement learning optimization, and the problems of singleness and hysteresis in a traditional psychological evaluation method are solved. The system comprises a data acquisition layer, a data processing layer, a reinforcement learning optimization layer, a gamification evaluation module, a feedback and intervention layer and a user interface layer. The data acquisition layer acquires behavior, physiological and social data in multiple channels; the data processing layer performs cleaning, feature extraction and fusion on the data; the reinforcement learning optimization layer adjusts the psychological evaluation strategy in real time; the gamification evaluation module enhances the student participation degree through a task and reward mechanism; and the feedback and intervention layer provides real-time feedback and personalized intervention suggestions. The psychological evaluation accuracy, participation and intervention effect can be improved, and the method is suitable for psychological health management scenes of schools and related educational institutions.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as medical health fund, fusion science and technology and mental health recuperation, and discloses a guidance method, device, equipment and medium based on multi-modal perception.The guidance method comprises the steps that physiological index data and motion trail data are collected, and a multi-modal body data set is generated; inputting a pre-trained mind and body association model to generate a mind and body state mapping relation; combining the three-dimensional scene model library and the dynamic attention parameters to generate an immersive interaction scene, and analyzing a scene interaction instruction based on a domain knowledge graph; and fusing the user portrait and the real-time state vector, generating a comprehensive decision parameter, and screening personalized guidance content from the guidance content library according to the feature similarity between the comprehensive decision parameter and the scene interaction instruction and outputting the personalized guidance content. According to the method, through multi-modal data acquisition and body and mind state modeling, accurate perception of the physiological state and behavior characteristics of the user is realized, and the accuracy of personalized guidance is improved in combination with immersive scene interaction and knowledge graph analysis.

The invention relates to the technical field of natural language processing, and provides a psychological medical map retrieval method based on an enhanced large model, which comprises the following steps of: aiming at a user input problem, extracting a subject entity by adopting a large language model; obtaining a user intention label by adopting a light-weight large model; based on a user intention label, controlling a relationship screening and path expansion process of the knowledge graph, and constructing a semantic path set; and judging whether the semantic path set has enough semantic support to answer the user question, and if so, outputting an answer according to the question input by the user and the semantic path set. On the basis of an existing reasoning framework, a fine-tuning lightweight large language model is introduced to perform multi-level semantic intention recognition on user questions, and a relation path selection process in a knowledge graph is controlled accordingly, so that a mechanism of scoring and sequencing paths hop by hop depending on the large language model in an original framework is replaced; therefore, the user can obtain scientific and complete psychological health knowledge, and the reliability of psychological health service is improved.

The invention relates to the technical field of model evaluation, in particular to a psychological health dialogue model evaluation method and device and a medium, and the method comprises the steps: obtaining a psychological consultation data set, extracting the psychological archive of a visitor from the psychological consultation data set, and building a role information base; selecting a visitor psychological file, setting role playing cues, and establishing a virtual visitor and a virtual psychological counselor; inputting the virtual visitor and the virtual psychological counselor into the psychological health dialogue model, setting dialogue constraints, and generating a psychological counseling dialogue; scoring the psychological counseling dialogue by adopting a plurality of different psychological counseling scales to obtain a psychological counseling dialogue score; and calculating the passing rate of all the psychological counseling dialogues by adopting the psychological counseling dialogue scores. According to the method, a psychological counseling dialogue is generated by establishing a virtual visitor and a virtual psychological counseling teacher, and evaluation of a psychological health dialogue model is completed based on the psychological counseling dialogue.

The invention discloses a psychological health monitoring and self-adaptive nursing decision-making system and method based on electroencephalogram signals, and relates to the field, and the method comprises the following steps: S1, collecting electroencephalogram signals and eye movement characteristics, and generating a multi-modal original signal data set; s2, electroencephalogram alpha wave asymmetry and eye movement characteristics are extracted, and delta-alpha spectrum overlapping pathological interference is eliminated; s3, inputting an evaluation model and a rule base, matching a DLB pathological marker and a symptom association rule, and marking a mental health risk level; and S4, matching an intervention strategy, and dynamically updating the evaluation model and the symptom association rule. The multi-modal fusion strategy effectively covers an association path of a DLB patient from molecular pathology to clinical symptoms, further provides a high-reliability data basis for subsequent risk level marking, realizes extraction of pure alpha wave energy and correction of prefrontal lobe alpha wave asymmetry aiming at frequency spectrum overlapping interference of delta waves and alpha waves in electroencephalogram signals, and improves the accuracy of risk level marking. Emotion abnormal degree misjudgment caused by false increase of the FAA value is avoided.

The invention discloses a building decoration intelligent design method and system based on a user portrait, and relates to the technical field of smart home and artificial intelligence, and the system comprises the following components: user data collection and portrait construction, a biological rhythm analysis model, an intelligent design algorithm, an interactive design platform, and implementation and feedback. Based on biology, psychology and environmental science theories, a biological rhythm analysis model is established, the potential influence of different building decoration elements on the biological rhythm of a user can be predicted and evaluated, through application of an intelligent design algorithm, the system can automatically match and optimize a building decoration design scheme, and the design efficiency is improved. The design scheme is ensured to meet the personalized requirements of the user, and meanwhile, the biological rhythm harmony of the user is promoted to the maximum extent, which is of great significance for improving the physical and psychological health level of the user and improving the quality of the living environment.

The invention discloses an intelligent evaluation system fusing behavior data and a scale, and the system comprises a data collection module which is used for collecting multi-dimensional behavior data; the data processing and feature extraction module is used for cleaning and preprocessing the data and extracting and calculating features; the analysis and evaluation module performs analysis and evaluation based on the behavior characteristics and scale scores; and the result output and explanation module is used for displaying the psychological state evaluation result generated previously to the user and explaining the evaluation result displayed by the user. According to the method, by collecting the multi-dimensional behavior data of the user in the answering process and combining the traditional psychological scale score, the limitation that the traditional psychological scale evaluation method excessively depends on self-reporting and lacks behavior data analysis and integration is overcome, more comprehensive, accurate and objective evaluation of the psychological state of the user is achieved, and powerful support is provided for the field of psychological health.

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 present invention relates to the field of health information processing technology, and specifically discloses an artificial intelligence-based student psychological data analysis and early warning system. The system sets a data set complexity determination module, a data slice determination module, a GPU compatible matching module, and a psychological data analysis and early warning module. The psychological health monitoring platform collects multi-source student psychological data sets, evaluates their comprehensive complexity, and determines the number of psychological data slices and their respective complexities based on this. The platform monitors the execution data of each GPU in real time, evaluates its initial execution quality indicators and compatible complexity. Subsequently, through data processing, the comprehensive complexity of the psychological data slices is matched with the compatible complexity of each GPU, and the allocation of psychological data slices to GPUs is completed. Finally, the platform uses this allocation relationship to analyze the students' psychological data, and ultimately achieves analysis and early warning of the students' psychological data.

The invention provides a health scheme recommendation method, device and equipment based on a large model and a knowledge graph. The method comprises the following steps: acquiring multi-modal data of a cardiovascular disease patient; the multi-modal data is preprocessed, and fusion of the multi-modal data is achieved through time alignment and feature alignment; features of the preprocessed multi-modal data are extracted, a multi-dimensional feature matrix is constructed, and the multi-dimensional feature matrix is identified and labeled; inputting the multi-dimensional feature matrix into a pre-constructed fusion behavior recognition model, enhancing the model performance through time sequence modeling and multi-modal fusion, realizing the recognition of psychological disorders, and obtaining a recognition result; and integrating the patient data, the recognition result and a pre-constructed psychological disorder knowledge graph, and adopting collaborative filtering, content recommendation and reinforcement learning methods to generate a personalized psychological health management scheme. Precise recognition and personalized management of the psychological disorder of the patient with the cardiovascular disease are achieved, and the psychological health level and life quality of the patient can be improved.

The invention discloses a psychological support strategy generation engine based on multi-modal data fusion, and relates to the technical field of psychological health, the engine comprises a multi-modal data acquisition module, a multi-modal data fusion processing module, a dynamic recovery trajectory modeling module, a recovery process prediction and strategy generation module and a strategy evaluation and feedback adjustment module; multi-modal data such as physiology, behaviors and voice are deeply combined with core theories such as cognitive behavior therapy and positive psychology through an innovative mechanism, data processing is performed according to theoretical constraint cleaning, and data compliance is ensured; fusing the matching degree of the mid-quantized data and the theoretical dimension, and associating objective data with theoretical indexes; dynamic modeling strengthens sensitive features such as a critical period of wound recovery, so that evaluation reflects physiological and behavior changes and embodies intrinsic features such as psychological toughness, a real psychological state can be accurately captured, a recovery risk can be recognized in advance, a scientific basis is provided for accurate intervention, and pertinence and foresight of psychological support are effectively improved.

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

The invention relates to the cross technical field of biomedical engineering and psychotherapy, in particular to an automatic control system for classified exposure therapy of phobia based on naked eye 3D. The system comprises an electroencephalogram signal feature extraction module, a heart rate variability feature extraction module, a skin electric response feature extraction module, an eye movement feature extraction module, a physiological load state feature extraction module, an exposure therapy process feature extraction module, a first feature fusion module, a second feature fusion module and a stimulation parameter adaptive control module. The method combines multi-mode biological signal real-time analysis and naked eye 3D stimulation parameter self-adaptive adjustment, and is suitable for clinical psychological treatment mechanisms and psychological health intervention scenes.

The invention discloses a student psychological health prediction method and system based on action recognition, and the method comprises the steps: obtaining video data in multiple scenes of a school, processing and tracking students in the video data, and constructing a space-time database; associating time-space databases of different students, setting influence weights according to the interaction frequency and the physical distance, and establishing a social relation graph; acquiring characteristics of group behaviors in the space-time database, and performing interaction activeness analysis according to the social relation graph; establishing a dynamic social relation graph weight matrix according to interaction activeness analysis, and inputting various data of the interaction activeness into the matrix to carry out bidirectional prediction among the students; a thermodynamic diagram of the class is constructed according to the bidirectional prediction in the step S4, and intervention suggestions are fed back to the teacher in real time; by blocking a negative emotion propagation chain in time and optimizing psychological counseling resource allocation, the initiative and systematicness of campus psychological health management are remarkably enhanced.

The invention relates to the field of intelligent consultation of health information, and particularly discloses an intelligent health information consultation system and method based on interactive voice response, and the method comprises the steps: firstly obtaining a psychological consultation voice signal of a consultant and psychological health information data of a plurality of different emotion labels in a database, and enabling the psychological consultation voice signal and the psychological health information data to serve as input data; then, the deep learning technology is used for conducting feature extraction and analysis on the input data, and then feature information is comprehensively analyzed to obtain a classification result used for representing psychological emotion labels of consultants. In this way, the system can generate personalized psychological health voice responses based on the psychological emotion labels. Therefore, timely, convenient and confidential psychological support can be effectively provided for the consultant, resonance of the consultant is further caused, and the consultant is helped to relieve pressure and dispatch mind of the consultant.

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

The invention discloses a brain heuristic multi-expert multi-mode emotion recognition method and system, equipment and a medium, and belongs to the technical field of artificial intelligence and biomedical signal processing. The method comprises the following steps: by simulating a brain function partitioning mechanism, dividing an electroencephalogram signal into a plurality of brain regions according to neuroanatomy prior, and designing a special expert network for each region; a global-local double-current encoder is adopted to cooperatively extract spatial-temporal characteristics of each brain region signal, and meanwhile, a multi-scale large-kernel convolution module is utilized to extract peripheral physiological signal characteristics; and finally, dynamically fusing multi-expert features through an adaptive routing network to realize sentiment classification. Expert load balancing and bifurcation regularization joint loss are introduced into the model in training, and effective cooperation and feature diversity of experts are ensured. According to the method, excellent recognition precision is obtained in practice, it is verified through interpretability analysis that the decision-making process conforms to neuroscience cognition, and a high-precision and high-reliability solution is provided for application of brain-computer interfaces, mental health monitoring and the like.

The invention discloses a non-contact AI vision real-time multi-dimensional physiological and psychological health testing device and method, and relates to the technical field of data processing, and the device comprises a non-contact AI vision module which is used for collecting psychological test answers and real-time physiological and action expression information; and the physiological health assessment module is used for assessing the health condition based on the standard physiological state. And the psychological health assessment module is used for inputting a scoring model through psychological test questions and answers to generate a psychological health score. The credibility correction module is used for correcting the score in combination with the real-time physiological information, and the conflict tracking compensation module is used for optimizing the evaluation result based on the action expression information. And the mechanism adjustment module is used for dynamically optimizing a test mechanism according to the psychological and physiological health assessment result, so that the technical effects of improving test objectivity and comprehensiveness and improving test accuracy are achieved.

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

The invention is suitable for the field of artificial intelligence and psychological health services, and provides a multi-mode psychological counseling system based on artificial intelligence. According to the system, face image data, voice data and text data of a user are collected through a multi-modal data collection module; the psychological counseling strategy generation module determines a corresponding psychological counseling strategy according to the comprehensive emotional state of the user and personal information of the user, and the psychological counseling strategy generation module generates a psychological counseling strategy according to the psychological counseling strategy according to the psychological counseling strategy and the personal information of the user. And finally, the interaction feedback module communicates with the user according to the policy, and optimizes the policy according to feedback information in the communication. The emotional state of the user can be judged according to various information sources such as facial expressions, voice emotions and voice texts of the user. Therefore, the accuracy and comprehensiveness of emotion recognition are improved.

The invention relates to the technical field of psychological health intelligent evaluation and collaborative intervention, and discloses a family-school psychological health collaborative method and system.The method comprises the steps that a multi-dimensional data model is built based on the psychological theory, and family-school double-end data collection is achieved through standardized indexes; constructing a multi-dimensional psychological state diagram structure, and realizing home-school data deep fusion by adopting a time sequence dynamic coding method; a time sequence modeling and multi-level risk assessment method is adopted, and a dynamic mental health risk assessment result and an intelligent early warning signal are output; a cross-platform collaborative intervention management method is adopted, and a personalized intervention strategy and a collaborative task allocation scheme are output; and a multi-dimensional effect evaluation and reinforcement learning optimization method is adopted to realize continuous optimization of an intervention strategy. Through artificial intelligence technologies such as the dynamic graph neural network and deep reinforcement learning, intelligent fusion and cooperative intervention of family-school psychological health data are realized, and the early recognition accuracy and intervention effect of psychological health problems are improved.

The invention provides a personalized exercise prescription generation method based on an analytic hierarchy process, which comprises the following steps of: acquiring body index information, psychological state information, exercise preference information and exercise environment information of a user, determining a physiological health index, a psychological health index, environment suitability and user preference of the user according to the collected user information, and recommending a plurality of candidate exercises; constructing an analytic hierarchy process evaluation model based on four factors of a physiological health index, a psychological health index, environmental suitability and user preference of the user so as to comprehensively score candidate motions; an exercise prescription is generated for the user according to the comprehensive score of the candidate exercises, and parameters in the exercise prescription comprise recommended exercises, exercise intensity, exercise frequency and duration; and acquiring exercise execution data and tracking feedback data of the user, and adjusting parameters in the current exercise prescription.

The invention discloses an interpretable intelligent psychological painting analysis prototype system, which belongs to the technical field of artificial intelligence and psychology crossing, and comprises the following steps: simultaneously inputting a painting video sequence into an overall feature extractor and a local feature extractor, sending the output of the overall feature extractor to a classifier, ending if the classifier obtains forward painting, and ending if the classifier obtains reverse painting; if the classifier obtains the negative drawing, the negative drawing is input into the local feature extractor, the local feature extractor obtains the object category, the image description, the drawing sequence and the drawing time, then knowledge reasoning is conducted on four results of the local feature extractor, and finally a psychological diagnosis description result is obtained. According to the invention, intelligent psychological analysis of room-tree-person testing is realized, the public psychological health problem is effectively tested, and the vacancy of the crossing field of artificial intelligence and psychology is made up.

The invention relates to the technical field of psychological health monitoring, and particularly discloses a student psychological crisis intelligent early warning and monitoring system based on behavior feature recognition, and the system recognizes a student whose heart rate is abnormal, then extracts the learning time and work and rest features of the student, and calculates a learning time distribution abnormal index and a work and rest abnormal index. A comprehensive feature vector is constructed as the input of a machine learning model, the development trend of future psychological crisis is predicted, a psychological state evaluation model based on fuzzy logic further converts a complex non-linear relationship into a visual result, the psychological state level of students is output, and for the students in serious psychological crisis, the psychological state evaluation model can be used for evaluating the psychological state level of the students. The system starts an emergency response mechanism and arranges professional psychological consultation and support; for students who may develop into serious psychological crisis, personalized psychological counseling plans are made, and monitoring and dynamic adjustment strategies are provided.

The invention discloses a psychological health cloud service platform system and method based on multi-modal data analysis. The system comprises a multi-modal data acquisition module; a heterogeneous data fusion analysis module; a dynamic intervention decision module; a block chain evidence tracking module; an edge computing terminal; the output end of the multi-modal data acquisition module is electrically connected with the input end of an edge computing terminal, the output end of the edge computing terminal is electrically connected with the input end of a heterogeneous data fusion analysis module, and the output end of the heterogeneous data fusion analysis module is electrically connected with the input end of a dynamic intervention decision module. According to the method, multi-modal data are integrated to construct a dynamic psychological portrait, a double-flow network is adopted to model spatio-temporal characteristics, an intervention scheme is matched based on risk levels, block chain evidence storage closed-loop management is performed, and an edge computing terminal performs cooperative computing through 5G encryption, so that real-time security is guaranteed, and the method is suitable for basic education scenes.

The invention relates to an intelligent dialogue method, system and device based on multiple agents and a medium. The method comprises the following steps: carrying out preliminary early warning analysis of mental health risks on dialogue information of a user; if the preliminary early warning analysis result is high risk, generating a subsequent dialogue strategy for the dialogue information through an early warning dialogue agent, and performing advanced early warning analysis for dialogue content acquired based on the dialogue strategy; if the preliminary early warning analysis result is not high risk and it is identified that the user has a recommendation demand, outputting a recommendation result through a recommendation agent; and if the preliminary early warning analysis result is not high risk and the user does not have a recommendation demand, predicting a target psychological scene and a target emotion based on the dialogue information, determining an emotional skill combination based on the target psychological scene and the target emotion through an emotional dialogue agent, and performing a subsequent dialogue based on the emotional skill combination. The method can improve the problem that psychological support provided for the user is too limited.