51 results about "Intervention approach" patented technology

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 technical field of medical care, in particular to an AI-based personalized mental health assessment and intervention method, which comprises the following steps: S1, data acquisition: acquiring multi-dimensional data of a user; s2, data preprocessing: preprocessing the collected multi-dimensional data; s3, psychological health portrait construction: generating a personalized psychological health portrait of the user; s4, social relation psychological influence analysis: identifying an emotion change mode, a negative emotion propagation path and a social isolation risk of the user in social interaction, and dynamically analyzing emotion propagation characteristics in a social network structure and potential influence factors of a user psychological state; s5, making a personalized psychological health intervention scheme: generating the personalized psychological health intervention scheme; the method disclosed by the invention not only reveals the potential influence of social interaction on mental health, but also provides a data-driven scientific basis for further personalized mental health intervention.

The invention provides a neurosurgery tumor patient personalized nursing intervention method based on big data. The neurosurgery tumor patient personalized nursing intervention method comprises the steps that physiological parameters, tumor characteristic data, behavior data and environment data of a patient are collected through a multi-mode sensor and an electronic medical record system; preprocessing the data to generate a standardized data set; extracting dynamic time sequence features and spatial correlation features based on a multi-modal fusion feature extraction algorithm; calculating an intensity parameter, a frequency parameter and a priority parameter of nursing intervention through a personalized intervention parameter generation algorithm according to the features; generating a personalized nursing intervention scheme in combination with the nursing rule base; a dynamic adjustment scheme is fed back through real-time data, and intervention parameters are corrected through a feedback compensation mechanism; and outputting the corrected intervention scheme to the nursing terminal equipment for execution. Accurate personalized nursing intervention can be achieved, the nursing scheme is dynamically adjusted, and the nursing effect and rehabilitation quality of neurosurgical tumor patients are improved.

The invention discloses an elderly falling early warning and intervention method based on emotion recognition and behavior pattern analysis. The elderly falling early warning and intervention method comprises the following steps: S1, collecting a facial expression data set, a voice signal data set and a behavior data set of an elderly; s2, analyzing the collected facial expression data set by using a convolutional neural network model; s3, analyzing the voice signal data set by using a long short-term memory network model; s4, an ElderSense model is set; and S5, when the analysis result shows that the risk score is higher than a preset first threshold value, sending an early warning signal to the old people through the intelligent equipment, and when the analysis result shows that the falling risk is higher than a preset second threshold value, sending an early warning signal to the old people and caregivers thereof through the intelligent equipment. The falling risk of the old people can be comprehensively evaluated from multiple angles, and the accuracy of falling risk judgment in a complex situation is improved.

The invention discloses an intelligent evaluation system and intervention method for mental health of teenagers, and the method comprises the following steps: collecting multi-mode psychological signal data of the teenagers, and carrying out the preprocessing; establishing a cognitive energy field, and generating a cognitive energy time sequence curve; performing spectrum resonance analysis, and combining the spectrum resonance analysis with a cognitive energy time sequence curve; constructing a psychological topological graph, and calculating a topological complexity index; when the topology complexity index is continuously abnormal or the cognitive energy disturbance amplitude exceeds a set threshold value, a psychological risk early warning result is generated; after the psychological unstable state is detected, a cognitive energy feedback parameter set is generated, and cognitive energy balance intervention is executed; and after the intervention is finished, calculating an energy balance improvement rate, and when the energy balance improvement rate exceeds an energy balance threshold, judging that the intervention is finished. According to the method, multi-mode psychological signals and cognitive energy field modeling are fused, intelligent evaluation and dynamic intervention of the psychological state of the teenagers are achieved, and the method has the advantages of being high in accuracy, high in interpretability and closed in intervention loop.

The invention relates to the technical field of psychosomatic health management, in particular to a psychological immune system evaluation system and intervention method for cancer patients. And the data acquisition module is used for acquiring and fusing the immune biochemical indexes of the patient, the continuous physiological time sequence signals generated by the wearable device, the digital psychological assessment data and the clinical treatment stage marks, and outputting a structured multi-dimensional time sequence data set through time alignment and feature extraction operation. Through multi-modal data fusion and integrated machine learning analysis, the psychosomatic health risk of the patient can be predicted based on the continuous physiological and psychological data change trend of the patient, and corresponding intervention measures can be triggered before the patient has obvious clinical symptoms by outputting the quantized risk coefficient and the multi-stage early warning signal, so that the accuracy of the patient is improved. Therefore, an intervention mode is promoted to be converted from traditional intervention after evaluation to predictive and preventive intervention.

The invention discloses an emotion rehabilitation intervention method based on face recognition. The method comprises the steps that S1, a user face image sequence is collected through a front camera; s2, performing image preprocessing to generate a standardized image tensor set; s3, facial dynamic features are extracted through an improved BiFormer network, emotional states are recognized, and a multi-dimensional emotional state sequence and a facial expression coding sequence are generated; s4, jointly analyzing the emotion change trend and the expression behavior mode of the user through a multi-mode RETNet network, and outputting a psychological state grade and a future emotion fluctuation score sequence of the user; s5, matching an emotion rehabilitation intervention strategy through a preset rule engine; s6, executing the emotion rehabilitation intervention strategy, collecting user data in real time, and generating a user response scoring sequence; and S7, performing incremental updating on the multi-modal RETNet network and a preset rule engine based on the user response score sequence. According to the invention, the accuracy of emotional state recognition and the intelligent level of intervention strategy pushing are improved.

The invention provides a college faculty worker and student psychological health monitoring and intervention method and system, and relates to a college ideological and political care system, and the method comprises the steps: employing a Monte Carlo simulation method to generate a large number of negative emotion propagation paths, carrying out the statistics of the coverage range and influence of negative emotion nodes, determining the influence range and key propagation nodes of negative emotion, and carrying out the calculation of the negative emotion propagation paths. Quantitative indexes reflecting the propagation penetration degree of the negative emotion among different communities are obtained, wherein the quantitative indexes comprise the negative emotion propagation speed, the influence range and the duration time; in a college ideological and political education system, positive emotion information is pushed to a target community and key nodes through a social network platform, the negative emotion propagation dynamic state is monitored, according to the real-time change of emotion distribution in an emotion propagation network, an emotion intervention strategy is dynamically adjusted, diffusion of negative emotions is controlled, and community emotion management measures are enhanced; and community emotion interaction mode analysis is perfected.

The invention discloses a depressive emotion intervention method and system. The method comprises the following steps: displaying a system interface with blue sky and white cloud as a background to a user, and confirming login information; performing stimulation presentation according to the presentation strategy; training the user according to the intervention rule; training data of the user is collected and analyzed; and adjusting the presentation condition of the stimulation according to the collected data so as to improve the depressive emotion of the user. According to the method, the psychological defense of the user is reduced by simulating the interface design of the natural landscape, the randomly presented positive, neutral and negative pictures and the user name are used as stimulation, and positive feedback and personalized difficulty adjustment are combined, so that the user is effectively guided to pay attention to the positive emotion, and the implicit self-esteem of an individual is improved, thereby improving the depressive emotion.

According to the multi-model-based user emotion monitoring intervention method and device and the computer program product provided by the invention, the communication style of the target object is simulated by training multiple types of models, a virtual image is synchronously generated to match mouth shapes, expressions and actions, and interaction with more emotion support can be provided for the user. Meanwhile, an emotion analysis model is introduced to carry out emotion recognition, early warning and risk level analysis, and when emotion abnormity is recognized, a pacific dialogue is carried out based on a second model obtained by learning a psychological intervention strategy library and a psychological knowledge graph. And sudden emotional abnormal conditions can be detected and responded in real time, and professional pacifying dialogues can be intervened.

The invention discloses a healing landscape intervention method and device based on user feedback, and the method comprises the steps: carrying out the intervention of a to-be-intervened user according to a preset intervention scheme, and obtaining the feedback data of the to-be-intervened user after the intervention; wherein the preset intervention scheme comprises a preset healing landscape video; collecting user multi-dimensional data of the to-be-intervened user, and preprocessing the feedback data and the user multi-dimensional data to obtain a plurality of personal emotion indexes corresponding to the to-be-intervened user; and according to the plurality of personal emotion indexes, constructing a healing isomerism map, and according to the healing isomerism map, adjusting the preset intervention scheme. According to the invention, the intervention effect of psychological intervention based on the healing landscape can be improved.

The invention relates to the technical field of atlas construction and recognition, in particular to a teenager psychological question recognition and intervention method fusing a teaching atlas and grading, which comprises the following steps: S1, data acquisition is carried out, a family system atlas is constructed, and the family system atlas represents family members and family rules through nodes, representing the relationship strength between the members and the rule execution consistency probability through edges; s2, jointly inputting the home system atlas and user question description input in real time into a situation-risk joint inference model, and outputting an enhanced question identifier fusing a situation type, a risk level and a most fragile relationship path; and S3, taking the most fragile relationship path as a core retrieval key, retrieving an intervention strategy from the strategy knowledge base, and generating a targeted micro-action scheme. According to the method, the family system atlas is constructed, and the most fragile relation path mechanism is introduced, so that the structural modeling and context awareness inference of the family structure and relation dynamics are realized.

The application discloses a kind of emotion evaluation and intervention method and system based on multi-modal data.Through terminal synchronous acquisition voice, face, screen tremor and so on multi-modal data;Reaction mode vector is generated based on context information and data verification fusion;The composite emotional state of user is determined using emotional emergence model.At the same time, personalized emotional causal diagram is constructed and updated, and the enhanced state vector is generated by extracting key causal path features.The vector is input into the intervention agent to output intervention actions;After execution, the emotional state is evaluated again and the reward is calculated, and the system model is updated online.The application realizes accurate emotion evaluation and closed-loop adaptive intervention that strikes the root.

The invention provides a bidirectional emotion interaction and intervention method based on electroencephalogram. The two-way emotion interaction and intervention method based on the electroencephalogram comprises an electroencephalogram signal two-way emotion interaction system and an intervention system, according to the electroencephalogram bidirectional emotion interaction system, a closed-loop structure of'emotion recognition-bidirectional interaction-personalized intervention 'is constructed by adding a bidirectional emotion transmission module and a personalized intervention module on the basis of a traditional unidirectional emotion recognition structure. According to the electroencephalogram-based bidirectional emotion interaction and intervention method provided by the invention, by realizing real bidirectional emotion interaction, the limitation of unidirectional monitoring can be overcome, the emotion synchronization problem in a remote interaction scene can be effectively solved, emotion connection can be enhanced, and the practical value of application can be improved by constructing a recognition-feedback-regulation closed-loop system; and the interactivity and the care degree among the users can be enhanced.

The invention discloses a man-machine dialogue psychological stress identification and intervention method and system, a medium and a product, and relates to the field of natural language data processing, and the method comprises the steps: carrying out man-machine dialogue based on a man-machine dialogue strategy through employing a preset man-machine dialogue model; acquiring a multi-modal signal of the detected object in a man-machine conversation process in real time, and performing feature extraction and mapping on the multi-modal signal to obtain a multi-modal fusion answer space; performing psychological stress identification on the multi-modal fusion answer space to obtain a real-time psychological stress level of the tested object; a man-machine conversation strategy of the man-machine conversation model is adaptively adjusted according to the real-time psychological stress level; and executing corresponding operation based on the adjusted man-machine dialogue strategy by utilizing the man-machine dialogue model. The accuracy and robustness of psychological pressure recognition are improved, the man-machine conversation strategy can be automatically adjusted according to the recognition result, and the emotional stability and psychological assistance value of man-machine conversation are improved.

The invention discloses a psychology and artificial intelligence-based dispute party psychological recognition and intervention method, system and device and a medium, and the method comprises the steps: constructing a prompt word bank for guiding dispute parties to express willingness; preprocessing speech and behavior data of dispute parties, inputting the speech and behavior data into a pre-trained large language model, and identifying willingness and emotional states of the parties to obtain willingness labels and emotional labels; establishing a psychological willingness intervention strategy library; analyzing the current mediation case, and selecting a willingness intervention strategy matched with the current mediation situation and the willingness of the party from a psychological willingness intervention strategy library in combination with the willingness label and the emotion label; generating intervention content based on the willingness intervention strategy; a pre-trained large language model is used for monitoring willingness and emotion changes of dispute parties in real time, feedback opinions of the parties and mediators for current intervention content are collected, and willingness intervention strategies and intervention content are adjusted. According to the invention, accurate identification of the willingness of the party and intelligent generation of the intervention content are realized.

The invention discloses an augmented reality chest surgery department nursing evaluation intervention method, and relates to the technical field of chest surgery department nursing, and the technical scheme comprises the following steps: collecting multi-mode nursing data, AR equipment environment perception data and a nursing intervention instruction in a chest surgery department perioperative period; performing judgment and demand mapping on the nursing intervention instruction to obtain a target nursing intervention scene and an AR dynamic interaction rule; based on the clinical priority of the nursing intervention instruction, the rehabilitation contribution degree of the patient and the AR interaction real-time performance, grading AR dynamic interaction rules to obtain a functional nursing task set; sensing data features according to an AR interaction protocol specification and an AR device environment corresponding to the target nursing intervention scene; the method has the advantages that emergency tasks can be executed automatically and preferentially, conventional tasks can be delayed, the highest equipment resource authority is allocated to the emergency tasks, intervention response time is shortened, potential safety hazards caused by intervention response lag are reduced, and nursing timeliness and safety are improved.

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

The present invention discloses a user emotion analysis method for psychological counseling, including: S1: constructing a mental health emotion dictionary using the SO-PMI algorithm; S2: after text preprocessing, extracting the current text sequence of the user, and using the mental health emotion dictionary to extract the historical emotion word sequences of both parties in the conversation; S3: inputting the three sequences into a BiLSTM model to obtain corresponding feature vectors, and using the Ebbinghaus forgetting curve to assign weights to the historical emotion word sequences; S4: using an interactive attention mechanism to obtain inertial features and interactive features; S5: fusing the inertial features, interactive features and user text features and inputting them into a Softmax layer to calculate the emotion tendency probability. The present invention enhances the emotion features by using the Ebbinghaus forgetting curve to assign weights to the historical emotion word sequences, and mines the emotional interaction between the user himself and between the user and the counselor, improves the accuracy of emotion analysis, and provides technical support for identifying the user's emotion state, developing psychological assessment tools and intervention methods.

The present application relates to the technical field of student psychological state evaluation and teaching intervention, and specifically discloses a student psychological state dynamic evaluation and teaching intervention method and system, which comprises the following steps: collecting various signals of students in the teaching process; calculating an emotional probability distribution; combining course difficulty, student portraits and teaching events to construct enhanced input features; using a multi-scale mixed time sequence network architecture to capture the second-level state fluctuation and minute-level trend evolution of the enhanced input features, and fusing to obtain unified time sequence representation; predicting the mean and variance of the future K time step prediction distribution; and if it is judged to trigger teaching intervention, selecting the optimal teaching intervention strategy according to the matching degree of the strategy and the current emotional state of the student, etc. The present application can capture the second-level state fluctuation and minute-level trend evolution, accurately fit the state evolution law in the education scene, accurately determine the student state, trigger teaching intervention, and improve the classroom teaching effect.

The invention relates to the technical field of mental health assessment, in particular to a medical scene-oriented non-contact mental health assessment and intervention method, which comprises the following steps of: performing data enhancement on expression data and physiological data, and screening out enhanced expression data and enhanced physiological data; performing emotion recognition according to the enhanced expression data by using an emotion classification model to obtain an emotion recognition result of the detected object; and establishing a contact type psychological health assessment model by using a neural network, and carrying out transfer learning on the contact type psychological health assessment model to obtain a non-contact type psychological health assessment model. According to the method, the defects of the collected physiological data and expression data are repaired by utilizing the repair network, the quality of analysis data is improved, meanwhile, a contact type psychological assessment process based on the physiological data is converted into a non-contact type psychological assessment process by utilizing a transfer learning mode, external equipment does not need to be added to make contact with the body of a tumor patient, and the workload of the tumor patient is reduced. And unease and panic of tumor patients are avoided.

The invention discloses an emotion intervention method and device, a vehicle and a storage medium. The method comprises the steps that video data and audio data of a driver and passengers in the vehicle driving process are acquired; performing emotion state detection on the driver according to the video data and the audio data to obtain an emotion score of the driver; when it is determined that the driver and the passenger are in a negative emotional state according to the emotional score, an intervention strategy is determined according to the emotional score, and the intervention strategy is used for smoothing the negative emotion of the driver and the passenger; and performing emotion intervention on the driver and passengers according to the intervention strategy. According to the method, when it is determined that the driver is in the negative emotional state based on the detected emotional score of the driver in the vehicle driving process, emotional intervention is conducted on the driver according to the emotional score so as to flatten the negative emotions of the driver, the situation that the driver is distracted to drive, and consequently the driving risk of vehicle driving is increased can be avoided, and the driving safety is improved. And the driving safety of vehicle driving can be improved.

This invention discloses a method for medication risk warning and dynamic intervention using a smart pillbox for elderly patients with chronic diseases, relating to the fields of intelligent medical devices and chronic disease management technology. The method first constructs four types of chronic disease knowledge graphs: medication behavior, physiological indicators, drug interactions, and environmental conditions. Then, it processes the graphs using a GNN module with a GRU-like gating mechanism, weightedly fusing multi-time-step features using a time-step attention mechanism, and integrating similar patient features based on Jaccard similarity to generate comprehensive risk features. During the training phase, the risk level and type are output after feature optimization using a collaborative attention mechanism. In the inference phase, a similarity propagation mechanism is used to address the cold-start problem for newly enrolled patients, matching a three-level intervention rule base to generate personalized strategies, and iteratively optimizing the model based on intervention feedback. This invention achieves proactive and precise warning and dynamic intervention, improving risk assessment performance and patient medication adherence in different scenarios.

The application provides a psychological and social adaptation intervention method and system for a teenager with a malignant tumor, and the method comprises the following steps: obtaining a social anxiety feature based on the physiological stability, eye movement data, text emotion vector and voice emotion vector of the patient; subtracting the positive contribution of physiological stability and cognitive participation from the social anxiety feature, correcting through a clinical cycle bias factor, and obtaining a psychological adaptation risk index; predicting the change gradient of the psychological adaptation risk index in a future preset time period, mapping the psychological adaptation risk index and the change gradient to augmented reality environment parameters when the change gradient is lower than a preset threshold, and realizing dynamic matching of the psychological state; calculating the emotional correlation value of the psychological adaptation risk index of multiple patient nodes, and inserting a common task node when the emotional correlation value is bottom synchronous, dynamically adjusting the augmented reality environment parameters and the task difficulty, and realizing the establishment of a social safety zone and the enhancement of peer support. The application intervenes in the psychological and social adaptation of the patient based on the real-time physical physiological state of the patient, and ensures the intervention opportunity and intensity.

The application discloses a kind of AI mental resilience real-time intervention method and system for college student group, the method includes the following steps: acquisition multimodal data;UniStressBERT model is constructed, text data is input into model, and text semantic feature and text emotion score are obtained;Individual baseline standardization is carried out to multimodal data, and standardized data are calculated;Risk monitoring is carried out based on text emotion score and standardized data by SRT algorithm;Scene data is encoded, and scene coding data are obtained;According to text semantic feature, standardized data, scene coding, mental resilience hidden state is calculated;Based on mental resilience hidden state, the PRI score of mental resilience dimension is calculated;Risk level is determined according to PRI score, and intervention strategy is executed according to risk level.The application greatly improves the recognition accuracy of campus stress event through model extraction and PRI dynamic evaluation, and enhances the intervention level.

This invention provides a multi-model-based user emotion monitoring and intervention method, device, and computer program product. By training multiple models to simulate the communication style of the target object, it simultaneously generates a virtual avatar matching lip movements, facial expressions, and gestures, providing users with more emotionally supportive interactions. Simultaneously, it introduces an emotion analysis model for emotion recognition, early warning, and risk level analysis. When abnormal emotions are identified, it uses a second model learned from a psychological intervention strategy library and a psychological knowledge graph to conduct reassuring dialogue. It can detect and respond to sudden emotional abnormalities in real time and intervene with professional reassuring dialogue.

This application belongs to the field of emotion monitoring technology, and provides a method and system for emotion monitoring and intervention based on multimodal neurochemical sensing. It acquires electroacoustic and optical fluorescence spectral signals from a user's target tissue region through a wearable device, and identifies neurotransmitter system activity information through signal fusion and decoding. This information is then input into an individualized dynamic emotion monitoring model to obtain the user's current emotional state type and intensity. Combining these two indicators, an emotion instability index is calculated in real time. When the index exceeds an individualized critical threshold, a personalized intervention plan matching the instability characteristics is generated and executed. The critical threshold can also be dynamically adjusted based on the intervention effect and real-time physiological state. This invention achieves non-invasive neurotransmitter monitoring and emotion monitoring, assessment, and intervention, improving monitoring accuracy and the timeliness and personalization of intervention, and is applicable to fields such as medicine and education.

This invention discloses a method and system for intelligent assessment and tiered intervention of career development risks for college students based on multimodal behavioral trajectories, belonging to the fields of artificial intelligence and educational informatization technology. The method includes: collecting students' explicit behavioral data, implicit ability-related data, psychological and motivational-related data, and market environment data; and performing cleaning, standardization, temporal alignment, and windowed fusion on these data; extracting joint features from structured and unstructured data to obtain integrated career development features; identifying risk-driving factors based on causal inference, establishing a career development risk assessment model, and outputting a comprehensive risk level; generating tiered intervention strategies based on the comprehensive risk level, driving factors, historical intervention records, and behavioral response characteristics; and updating the risk assessment model and intervention strategies based on intervention feedback. This invention enables dynamic assessment, tiered early warning, and closed-loop intervention of career development risks for college students.

The invention discloses an emotional state evaluation and intervention method and system based on user listening behaviors, and solves the technical problem that in the prior art, a recommendation system takes user dominant preference as a core and neglects dynamic evaluation of user recessive psychological emotional states. The method comprises the following steps: acquiring listening behavior data of a user, preprocessing the data, and performing multi-source feature extraction; constructing a user dynamic portrait library according to the multi-source features; constructing and training an emotional state evaluation model; evaluating a user emotional state category C through an emotional state evaluation model; calculating a user emotional state risk index R; and judging whether intervention conditions are met or not according to the numerical value of the emotional state risk index R, and if so, performing guide intervention. According to the method and the device, the emotion state of the user is evaluated, and when the risk index triggers the threshold value, the system automatically starts the reverse recommendation and intervention strategy, so that the user is helped to dissociate from unhealthy emotions, and the user experience is improved. The method can be widely applied to the computer technical field.

An object of a system according to an embodiment is to analyze data related to behavioral psychology of a child and propose an appropriate approach method.SOLUTION: A system according to an embodiment includes an inspection execution unit, a data analysis unit, a proposal generation unit, and a cooperation unit. The examination conducting unit conducts a behavioral psychological test of the child. The data analysis unit analyzes the inspection data collected by the inspection execution unit. The proposal generation unit proposes an analysis result and an approach method to a guardian or an education site based on the inspection result analyzed by the data analysis unit. The cooperation unit cooperates with a medical institution or an education-related institution as necessary.SELECTED DRAWING: Figure 1