172 results about "Intervention protocols" patented technology

The invention discloses a psychological accompanying method based on multi-modal emotion recognition, and belongs to the technical field of psychological health services, and the method comprises the steps: S1, synchronously collecting physiological signals, voice features, facial expressions and interactive behavior data of a user through a multi-modal sensor; s2, performing fusion analysis on the multi-modal data by using a deep learning model, and identifying a current emotional state and an emotional intensity level of the user; and S3, dynamically generating an adaptive psychological accompanying intervention scheme based on a preset emotion-intervention strategy mapping rule in combination with historical emotion data and personalized preferences of the user. According to the method, the physiological signals, the voice features, the facial expressions and the interactive behavior data are synchronously collected through the multi-modal sensor, the deep learning model is used for fusion analysis, and compared with single-modal recognition, the emotion state and the intensity level of the user can be judged more comprehensively and accurately, the emotion misjudgment risk is reduced, and a reliable basis is provided for subsequent intervention.

The invention discloses a cerebral apoplexy multi-mode early screening intelligent evaluation system based on a large model, and relates to the technical field of medical health information, the cerebral apoplexy multi-mode early screening intelligent evaluation system comprises an intelligent management platform, and the intelligent management platform is in communication connection with the following modules: a multi-source heterogeneous data fusion engine, the data integration module is used for integrating multi-modal data including clinical data and terminal health data and constructing a health portrait of a patient; and the cerebral apoplexy knowledge graph construction platform is used for constructing a cerebral apoplexy domain knowledge graph in combination with evidence-based medical knowledge. By combining the digital twinning technology and the intelligent risk assessment engine, the influence of different intervention schemes on the cerebral apoplexy risk can be simulated, personalized intervention suggestions are generated, a patient is helped to reduce the cerebral apoplexy risk and change from passive prediction to active intervention, the patient is helped to take effective measures earlier, the health condition is improved, and the patient experience is improved. The occurrence of cerebral apoplexy is prevented, so that the disability rate and the death rate caused by cerebral apoplexy are reduced.

The invention discloses a health management service system and method based on AI optimization. The method comprises the following steps: S1, collecting multi-source health data of a user and constructing a structured health data set; s2, constructing a health state graph containing node attributes, edge connection weights and time indexes; s3, inputting the health state atlas into a linear graph neural network to generate a health state embedded vector; s4, collecting context information of a user, and fusing to generate personalized state perception representation and a health target embedding vector; s5, inputting the improved general value layering model to generate a structured intervention action candidate set; s6, screening and outputting a personalized health intervention scheme based on the matching score; s7, constructing a state evolution sequence, and inputting the state evolution sequence into a semi-supervised anomaly detection algorithm for monitoring and recognition; and S8, when the risk threshold is exceeded, triggering early warning and dynamically adjusting the intervention scheme. According to the method, collaborative optimization of personalized modeling and intelligent intervention strategies is realized, and the method is suitable for health management service scenes.

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 a constipation prediction system based on an artificial intelligence large model, and relates to the technical field of medical health information. In order to solve the problems that complex risk factors of constipation are difficult to comprehensively capture, so that the feature coverage range of a prediction model is limited, group classification and risk level dynamic evaluation are not performed on users, so that an intervention scheme is high in universality, but accurate management requirements of different groups are difficult to meet. Multi-source data such as electronic medical records and physiological parameters are integrated through the multi-modal data processing module, refined classification of user groups is achieved, feature causal relationships are analyzed in combination with a knowledge graph technology, constipation prediction accuracy is improved, the intelligent prediction module dynamically adapts to a model according to group risk levels, and constipation prediction accuracy is improved. The probability distribution is output, a visual causal path report is generated, and the dynamic decision-making module adjusts an intervention scheme through doctor-patient cooperation based on a knowledge graph matching strategy set and optimizes a strategy in real time by using an effect feedback mechanism, so that personalized health management is realized.

The invention relates to the technical field of artificial intelligence and biological characteristic analysis, in particular to an AI-based personalized postpartum rehabilitation medical beauty evaluation system and method. The method comprises the following steps: generating a standardized action sequence according to a rehabilitation stage and a historical record of a user and guiding execution; collecting dynamic biological characteristic data in real time; aligning data and distributing dynamic weights by using a multi-modal fusion model guided by time sequence semantics, constructing a three-dimensional response graph by combining a tension response function, a collaborative exponential function and a stable fluctuation function, and generating individual embedded vectors through a graph neural network; identifying a potential high-risk time period through map potential energy offset analysis and disturbance sensitivity factors; integrating a user state, a historical response and an intervention knowledge graph, recommending a dynamically adaptive intervention scheme by using a multi-channel attention network, and continuously optimizing through a feedback closed loop; precise evaluation and personalized intervention recommendation of the postpartum rehabilitation process are realized through multi-modal fusion and dynamic modeling, and the rehabilitation efficiency and safety are improved.

The invention provides an integrated doctor-patient cooperation and health management platform and a data processing method thereof, and relates to the technical field of health management, and the integrated doctor-patient cooperation and health management platform comprises a multi-source data access layer, a credibility evaluation and correction engine, a semantic fusion and knowledge graph layer, a patient digital twin module, a cooperation interaction module, a privacy protection and joint training module and a contract and audit layer. Dynamic modeling of health data is realized through a space-time diagram neural network, a risk prediction and intervention scheme with a confidence interval is generated in combination with Bayesian reasoning and Monte Carlo simulation, and then digital twins of a patient are constructed and personalized simulation is performed; and meanwhile, the safety and performance of cross-mechanism joint modeling are guaranteed by adopting hybrid synchronous-asynchronous federal learning and a dynamic privacy budget mechanism, and the traceability and compliance of the whole process are realized through a block chain contract. The accuracy and transparency of medical data processing can be remarkably improved, close cooperation between doctors and patients is promoted, and comprehensive health management of the patients is achieved.

The invention relates to the technical field of intelligent medical treatment, in particular to a household intelligent health management system and a method thereof.The system collects household environment parameters and user physiological indexes through a data collection subsystem, constructs a medical knowledge graph of a three-layer structure through a knowledge graph driving subsystem, and dynamically updates the medical knowledge graph; the multi-target health intervention subsystem generates a candidate intervention scheme set based on a knowledge graph, the digital twinborn verification subsystem predicts and selects a scheme with a health improvement index higher than a target by establishing a user, environment and interactive twinborn model, and the adaptive interaction subsystem pushes the verified scheme to a user in a personalized manner and collects feedback information. Meanwhile, the digital twinborn verification subsystem transmits the feedback information to the knowledge graph driving subsystem to trigger dynamic updating of the knowledge graph, the system comprehensively collects health influence factors, comprehensive data support is provided for health risk assessment, and personalized health management is achieved.

The invention provides a psychological tutoring system based on a digital human technology, and relates to the technical field of psychological health. The system captures the emotional state of a user in real time in a multi-modal interaction mode, generates a real-time emotional state group of the user by combining multi-dimensional data fusion of facial micro-expressions, voice spectrums and dialogue texts, and maps the real-time emotional state group to a three-dimensional emotional space for accurate emotion recognition. Based on an emotion-behavior mapping rule, the system dynamically adjusts the digital person and provides an intervention scheme. And when the emotion fluctuation of the user is detected to exceed a preset threshold value, the system automatically triggers a digital human hiding mechanism, gradually weakens visual, auditory and action stimulation, and provides psychological pacification and guidance. According to the method, the accuracy of psychological state recognition and the pertinence of intervention are remarkably improved, real-time and continuous psychological health support is provided for the user, and the method is suitable for psychological health assistance and emotional state analysis.

The invention relates to the technical field of old people health management, and discloses a personalized health auxiliary management system based on artificial intelligence, which comprises a multi-mode sensing module, an intelligent preprocessing module, a dynamic knowledge graph module, a personalized inference engine module and a self-adaptive communication and interaction module. The multi-mode sensing module is used for acquiring physiological, environmental and behavioral data; the intelligent preprocessing module desensitizes and cleans data through federal learning, and extracts core features; the dynamic knowledge graph module constructs and updates an exclusive graph containing basic and dynamic nodes and associated edges; the personalized inference engine module generates a layered intervention scheme; and the adaptive interaction module adapts to the cognitive ability and synchronizes the scheme. According to the invention, health information integration and dynamic intervention are realized, the management accuracy and compliance are improved, and the method is suitable for home non-clinical scenes.

The invention provides an evaluation-intervention integrated system and equipment oriented to personalized rehabilitation scheme optimization. The evaluation-intervention integrated system comprises a personalized scheme recommendation module, a curative effect evaluation module and an intervention scheme optimization and adjustment module. The personalized scheme recommendation module generates a personalized rehabilitation treatment scheme by collecting multi-dimensional data and combining with a machine learning algorithm. And the curative effect evaluation module monitors and comprehensively evaluates the rehabilitation effect of the patient in real time, and generates a detailed curative effect report. And the intervention scheme optimization and adjustment module dynamically adjusts the treatment scheme and optimizes the treatment content, intensity and frequency according to the curative effect evaluation result and patient feedback. Through intelligent analysis and a feedback mechanism, an accurate and customized rehabilitation treatment scheme is provided for each patient, dynamic adjustment is performed according to a real-time evaluation result, and the rehabilitation treatment accuracy and the treatment effect are remarkably improved. The system also supports real-time data sharing and cooperation among patients, doctors and rehabilitation experts, and can perform cross-region and cross-professional personalized treatment management.

The invention relates to a comprehensive management system and method for elderly loneliness based on a mobile phone APP, and the system employs a three-level architecture of a host client-a control client-a data server, and achieves the intelligent and precise intervention. The host client integrates a multi-mode data acquisition function, an aging-suitable interaction interface function, a video / game intervention function and an emergency response function, and supports the elderly to autonomously complete mental health monitoring intervention. The control client is responsible for data cleaning, quality control and standardized management and provides reliable input for AI analysis. And the data server generates a personalized intervention scheme by relying on an AI model in combination with manual auditing, and dynamically optimizes a strategy. According to the system, wearable monitoring, CBT (Cathode Based Therapy) and an intelligent algorithm are combined, the goals of reducing the loneliness score of the old people and improving the social interaction frequency are expected to be achieved in future trial points, and the application prospects of the system in the aspects of accurate evaluation, risk early warning and intervention are embodied. By reducing medical resource consumption and enhancing intergenerational linkage, a replicable scheme is provided for healthy aging.

The invention discloses a psychological health full-process intelligent evaluation and intervention system, and mainly relates to the technical field of psychological health evaluation and intervention. Comprising a gamification general measurement module used for collecting multi-modal initial data of a user through multiple gamification tasks; the multi-modal fine screening module is connected with the gamification general survey module and is used for collecting multi-dimensional fine data of the user when the general survey risk score reaches a preset threshold value; the self-adaptive intervention module is connected with the multi-modal fine screening module and used for matching a corresponding intervention scheme according to the risk level and continuously collecting physiological data of the user in the intervention process to dynamically adjust the intervention scheme; and the data interaction and control module is connected with the gamification general measurement module, the multi-mode fine screening module and the self-adaptive intervention module and is used for realizing data transmission and cooperative control among the modules. The method has the beneficial effects that the evaluation precision is remarkably improved, and meanwhile, the intervention response speed is increased.

The invention relates to the technical field of reinforcement learning, and discloses a chronic disease intervention scheme generation method and system based on reinforcement learning and multi-modal data, and the method comprises the steps: obtaining the multi-modal data of a patient, the multi-modal data at least comprising electronic medical record data, voice data, image data, text data and physiological time sequence data; performing feature extraction on the multi-modal data to obtain a multi-modal feature vector; on the basis of the multi-modal feature vectors, health state vectors are constructed, and the health state vectors at least comprise a physiological risk score, a treatment compliance score and a lifestyle health degree score; designing a reward function according to the dynamic change of the health state vector; optimizing the strategy network in a predefined intervention action space according to the reward function by utilizing a reinforcement learning algorithm so as to output an optimal intervention action; and converting the optimal intervention action into personalized natural language interaction content through a generative AI model. According to the invention, the efficiency, precision and patient compliance of chronic disease management can be significantly improved.

The invention discloses a postoperative monitoring system and method for a metabolic weight loss surgery patient, and the system comprises a multi-source data collection module which is used for obtaining physiological parameters, behavior data and environment data of the patient in real time, the physiological parameters comprise body weight, blood sugar and body composition indexes, and the behavior data comprise exercise amount and diet records; the environment data comprises temperature, humidity and geographic position; the central processing platform is connected with the multi-source data acquisition module and comprises a data fusion unit for performing space-time alignment and dimension reduction processing on the multi-source heterogeneous data by adopting a feature level fusion algorithm; the dynamic risk assessment unit is used for constructing a patient personalized metabolism model based on the fused data, and dynamically generating nutrition loss and complication recurrence risk indexes through a machine learning algorithm; and the intelligent early warning module triggers a graded early warning signal according to the risk index, generates a personalized intervention scheme and pushes the personalized intervention scheme to the doctor-patient terminal.

The invention relates to a precise health monitoring method based on multi-source data fusion. The method comprises the following steps of 1, collecting multi-source data; step 2, data preprocessing; step 3, multi-source data fusion; 4, health analysis and decision making; step 5, dynamic weight adjustment; and step 6, feedback optimization. The hierarchical fusion strategy is adopted to process heterogeneous data, a machine learning algorithm is combined to establish an individualized health model, physiological state changes are tracked in real time, a targeted intervention scheme is generated, and in terms of technical implementation, the system supports collaborative decision making of various analysis models, including rule-based behavior reasoning, time sequence feature analysis and environment threshold judgment, and the system has the advantages of being simple in structure and convenient to use. The contribution degrees of information of different sources are balanced through an adaptive weight distribution mechanism, time-frequency analysis and dimension reduction technologies are fused in the feature extraction process, the dynamic mode of the health state is effectively captured, cross validation optimization is adopted in model training, and the reliability of an evaluation result is ensured.

The invention discloses a chronic disease management system for hospitals, which relates to the technical field of chronic disease management systems and comprises a patient file module, a risk assessment module, an intervention scheme module, a follow-up management module, a monitoring and early warning module and a decision support module. The patient archive module is used for collecting and sorting personal information and medical history information of patients by adopting an automatic data integration method to obtain a complete patient health archive; the risk assessment module is used for analyzing the health information in the patient health record by adopting an intelligent assessment algorithm to obtain a chronic disease risk assessment result; the intervention scheme module is used for formulating a personalized health intervention scheme according to the risk assessment result, and the personalized health intervention scheme comprises diet, exercise and medication suggestions; and the follow-up management module is used for executing the follow-up plan in the intervention scheme through multiple communication modes, tracking the rehabilitation condition of the patient, recording and feeding back, and generating a follow-up report.

The invention relates to the technical field of medical data processing, and discloses an operation period treatment risk intelligent prompting method, which comprises the following steps of: acquiring physiological index time sequence data and a treatment scheme execution parameter matrix of a perioperative period patient, generating a composite state tensor through multi-source feature correlation calculation, constructing a risk-intervention correlation nodal topological map, and calculating the risk-intervention correlation nodal topological map. Inputting a risk transfer model to simulate a risk transfer path and outputting an association influence probability value set, establishing a bidirectional association equation through space-time association mapping operation to generate a risk-intervention coupling prediction curve, calculating a risk prompt factor, and performing operation on the risk prompt factor and an association influence probability value to generate a standardized thermodynamic diagram; and extracting the operation quantitative index set, dynamically comparing the operation quantitative index set with a preset safety threshold value, and generating an intelligent prompt scheme containing a risk early warning level and an intervention scheme adjustment strategy when the index breaks through the threshold value. According to the method, the accuracy and effectiveness of risk prompting during the operation period are improved.

The invention provides a trauma memory integration evaluation and treatment system. Comprising the following steps: acquiring patient description information, generating a trauma event scene based on the description information, collecting multi-modal data of a patient in the scene, identifying a trauma recall activation state by utilizing the multi-modal data, combining the activation state with individual psychological characteristics and database contents to generate an intervention scheme, and circularly updating the scene and the scheme until a termination condition is met. The importance of the multi-modal features is determined through a machine learning model, a trauma recall activation score is generated through weighted fusion, and quantitative and objective evaluation of the activation state is achieved; and a personalized intervention scheme is dynamically retrieved or generated in combination with psychological characteristics of the patient, so that the pertinence and effectiveness of treatment are improved. The technical problems that existing trauma recall activation evaluation depends on subjective judgment, and an intervention strategy lacks individuation and dynamic optimization can be solved.

The invention relates to a risk assessment method and system for acute attack of asthma in children. The method comprises the following steps: acquiring multi-modal data of a target child and preprocessing; extracting specified features to form basic feature vectors, adjusting dynamic feature weights in combination with individual differences of children and real-time scenes, generating weighted feature vectors, fusing the weighted feature vectors into recent medication compliance to obtain fused feature vectors, and obtaining the acute attack probability of asthma in a specified time period in the future. Pushing a conventional nursing scheme or an emergency intervention scheme according to the comparison between the probability and a first threshold value and a second threshold value, and if the threshold value is not reached, inputting the fusion feature vector into an XGBoost model to output a preliminary risk level and calibrating the preliminary risk level; and generating a dynamic intervention scheme. Through accurate integration of multi-modal data, adaptation of individuals and scenes by dynamic weights and combination of hierarchical model prediction and individual historical calibration, the accuracy and personalization of risk assessment are remarkably improved, deep adaptation of an assessment result and an intervention scheme is realized, and the problems of missed judgment, misjudgment rate and insufficient or excessive intervention of acute attack of children asthma are effectively reduced.

The invention relates to the technical field of health monitoring, in particular to a health state alarm response system based on multi-dimensional monitoring, which comprises a dynamic threshold module, a physiological monitoring module, an anomaly positioning module, an inducement output module, an anomaly intervention module and an early warning response module, according to the method, multi-dimensional original data of a user is collected, meanwhile, a timestamp alignment and scene label binding technology is adopted, an exclusive physiological index normal fluctuation interval library is constructed for different large classes of scenes, then the problem of misjudgment of a static threshold value is fundamentally solved, the cause of'abnormity 'is deeply and accurately positioned, and the accuracy of the abnormal condition is improved. The abnormity-scene-behavior-cause full-link association not only enables the user to clearly know the cause of the abnormity, but also provides data support for the medical personnel to formulate a targeted intervention scheme, and reasonably formulates the targeted intervention scheme based on the intervention list, thereby avoiding the ineffectiveness of general suggestions, enabling resources to be inclined to high-risk users, and improving the user experience. The complication occurrence rate is effectively reduced.

The invention relates to the technical field of personalized intelligent dialogue and digital accompanying for old people, and discloses an AI interaction-based personalized knowledge graph dialogue generation method for old people, which comprises the following steps of: reading a role conversion event record from a family structure sub-graph of a personalized knowledge graph for old people, and generating a role fall feeling feature template; aI dialogue initiation records of the old in recent late night periods are obtained, the number of days of continuous late night dialogues, the average dialogue duration and the active listing frequency are counted, and an aloneness level label is generated; generating a personalized spiral blocking intervention scheme based on the spiral strength coefficient and the spiral type label; and executing the collaborative psychological support dialogue script to generate a dialogue, and outputting a continuous personalized psychological support dialogue sequence. According to the method, the technical challenge that in the AI interaction-based elderly dialogue service, when the psychological state is worsened spirally due to the fact that the sense of fall and the sense of loneliness of roles are enhanced mutually, it is difficult to generate effective intervention dialogues by means of the personalized knowledge graph is solved.

The invention discloses a dementia intervention scheme recommendation method and system based on a knowledge graph, and relates to the technical field of intelligent medical treatment and artificial intelligence. And constructing a dementia intervention knowledge graph containing patients, symptoms, intervention measures and environmental elements. And performing intelligent semantic reasoning on the short-term state, the long-term behavior mode and the nursing environment of the patient by fusing multi-level reasoning and situational reasoning, generating a candidate intervention scheme matched with the current state of the patient, and outputting a personalized recommendation result. In the intervention implementation process, the knowledge graph association relation is dynamically updated based on patient behavior feedback and symptom changes, and continuous optimization and self-adaptive adjustment of an intervention scheme are achieved. According to the method, the individuation level, long-term adaptability and safety of dementia intervention recommendation are improved, and the method is suitable for clinical and long-term nursing scenes.

The invention discloses a medical critical risk prediction method based on physical sign data monitored by a smart watch, and relates to the technical field of critical monitoring, and the method comprises the steps: constructing a multi-dimensional physical sign fusion analysis model, collecting dynamic physiological parameters in real time through the smart watch, and updating a personalized health baseline; based on the personalized health baseline, detecting a pathological fluctuation mode in the dynamic physiological parameters through a time sequence convolutional network; inputting the identified abnormal waveform features into a risk prediction model optimized by transfer learning, and calculating a coupling risk index of the multi-organ system; establishing a three-level response early warning mechanism based on the coupling risk index of the multi-organ system; and generating an intervention scheme based on the coupling risk index of the multi-organ system. Compared with the prior art, the intelligent wearable device has the advantages that the multi-organ risk conduction model is combined, the severe risk is accurately predicted, a risk identification-early warning-disposal dynamic management system is formed, and the clinical practical value of the intelligent wearable device in a pre-hospital emergency scene is effectively improved.

The invention relates to the technical field of medical auxiliary evaluation, in particular to a perioperative pain evaluation system for a brain tumor surgery patient, which comprises a surgery pain data acquisition module for acquiring an HRV signal, an intracranial pressure waveform, a subjective VAS score and pain part data of the patient; the feature extraction module processes the data through abnormal value detection and sliding window filtering, extracts HRV and ICP features in combination with time domain analysis, and outputs standardized data after integrating the HRV and ICP features with subjective feedback features; the AI pain quantification module is used for generating a pain risk probability value based on an AI model constructed by an XGBoost algorithm, triggering three-level early warning and associating pain properties and causes of a patient; the intervention pushing module triggers a multi-terminal prompt and pushes an intervention scheme, and a patient performs graphic interaction and feedback through a mobile phone terminal; and the management and tracing module is used for associating single-time whole-process data, recording an intervention effect and automatically upgrading an unexpected scheme. Therefore, the problems that in the prior art, intracranial pressure and pain coupling is not quantified, intervention lags and the like are solved.

The invention provides a big data resource service-based patient hierarchical intervention method and system, which are applied to the technical field of medical information, and are used for acquiring full-cycle health data of a patient, generating and updating a patient disease course trajectory sequence, analyzing health index change and trend under short, medium and long time scales, and calculating health scores and steady-state coefficients, so as to realize hierarchical intervention of the patient. A layered intervention scheme is generated, detection and personalized intervention of the health state of the patient are achieved, the timeliness and pertinence of medical intervention can be improved, and the disease management effect is optimized.

The invention relates to the technical field of health management, in particular to an obese patient remote dynamic intervention system based on AI early warning, which comprises a multi-dimensional data acquisition module, an AI risk layering and early warning module, a personalized intervention scheme generation module, a remote collaborative management module and an effect evaluation and optimization module, the multi-dimensional data acquisition module is used for acquiring patient related data; the AI risk layering and early warning module constructs a multi-dimensional dynamic score card model to carry out risk scoring and layering, and obesity health risk early warning is generated; a personalized intervention scheme generation module constructs a multidisciplinary intervention strategy matrix, and dynamically generates an intervention scheme according to an early warning result and individual features of a patient; the remote collaborative management module realizes information interaction and collaborative intervention of multi-party resources; and the effect evaluation and optimization module tracks the intervention effect and optimizes the model and the intervention scheme. Therefore, the problems of data lag, intervention scheme solidification, untimely early warning and the like in obese patient management are solved.

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 discloses a multi-modal health knowledge generation and integration system, which is applied to the technical field of fusion and application of health data, and comprises a multi-modal data acquisition and preprocessing module used for acquiring multi-modal health data and preprocessing the multi-modal health data; the multi-level fusion framework design sub-module comprises data-level fusion based on a time alignment algorithm, feature-level fusion based on a Transform cross-modal attention mechanism and decision-making-level fusion based on an ensemble learning algorithm; and the intelligent knowledge generation sub-module comprises health knowledge graph construction based on a graph neural network, personalized health risk report and intervention scheme generation based on a conditional generative adversarial network, visual image feature contribution degree based on gradient weighted class activation mapping and text data key factor analysis based on an SHAP value. According to the method, the bottleneck of a data island is fundamentally broken through, and the generation quality and the application value of health knowledge are improved.

The invention discloses a question and answer evaluation method and system for autism intervention, and relates to the field of medical electronic systems.The method comprises the steps that historical data are extracted, noise reduction processing is carried out to obtain noise reduction data after noise reduction processing, and a database is built according to the noise reduction data, a word frequency-inverse document frequency algorithm and a BERT model; extracting question and answer information of a plurality of different autism stages in the database, and constructing a judgment model according to the question and answer information and the database; obtaining current question and answer data, and obtaining a judgment result of the child according to the current question and answer data and the judgment model; the teacher resume in the database is extracted, multiple recommendation teachers are determined according to the teacher resume and a judgment result, the two are combined to form a multi-level semantic feature matrix, local word frequency information is reserved, global context understanding is fused, and therefore the problem that feature extraction of a single model is one-sided is solved; accurate recognition capability of autism core symptoms (such as social obstacles and communication defects) is remarkably improved, and a more reliable behavior characteristic basis is provided for an intervention scheme.