201 results about "Health intervention" patented technology

The invention relates to the technical field of artificial intelligence and health management, in particular to a health management method and system based on multi-dimensional data analysis, and the method comprises the steps: collecting the physiological, behavior, environment and psychological data of a user, and carrying out the modeling through a recurrent neural network, and generating health state data; extracting a key causal relationship of the health risk based on a causal analysis model of a Bayesian network; comprehensively analyzing health changes through the short-term time sequence model and the long-term trend prediction model, and generating health risk data; a personalized health intervention scheme is designed by using a conditional generative adversarial network, and safe sharing and dynamic management of the scheme are realized through a block chain technology. According to the method, the health risk can be accurately identified, scientific intervention measures can be designed, the health management process is dynamically optimized, powerful technical support is provided for an intelligent old-age nursing scene, and the health management efficiency and effect are improved.

The invention relates to the technical field of health state analysis, in particular to an intelligent health state analysis system and method based on artificial intelligence, and the method comprises the following steps: S1, collecting multi-modal health data; s2, data preprocessing and anomaly detection: preprocessing the collected multi-modal data; s3, multi-modal data fusion and feature selection: carrying out feature fusion on data of different modals through a deep learning model, and carrying out feature extraction on time sequence data by adopting a convolutional neural network (CNN); s4, health state prediction and trend analysis: performing prediction analysis on the health state of the user based on a long and short term memory network (LSTM), and generating a health state trend chart of the user in combination with historical health data and real-time data; and S5, personalized health intervention suggestions are generated: personalized health management suggestions are automatically generated, a more comprehensive health assessment result is provided, the user can better know the own health condition, and personalized management is carried out.

The invention discloses a health status assessment method and system based on a multi-modal biosensor, and the method comprises the steps: synchronously collecting a plurality of physiological signals of a plurality of target species objects, and generating a multi-modal physiological data flow with consistent time and space; inputting the multi-modal physiological data stream into a deep learning model, and outputting a high-dimensional feature vector fused with time-space characteristics; inputting the high-dimensional feature vector into a cross-species adaptation model based on transfer learning, and outputting a cross-species universality state vector representing the current health abnormal state and confidence of the target object; inputting the cross-species universality state vector into a lightweight risk assessment model, and outputting a quantitative assessment result reflecting the instant health risk level of the target object; and according to a quantitative evaluation result, automatically generating and triggering a personalized health intervention instruction for the target object. By utilizing the embodiment of the invention, the physiological difference of different species can be dynamically adapted, and the accuracy and the universality of health state evaluation are improved.

The invention relates to the technical field of health management systems, and discloses a health management scheme recommendation system based on big data analysis. The system comprises a health data acquisition module, a health characteristic quantification module, a health state identification module, a health scheme prediction module and a health parameter coupling module. The health data acquisition module synchronously acquires three types of time sequence data of physiological indexes, health behaviors and environmental exposure of a user and performs timestamp alignment; a health feature quantification module extracts features from the aligned data and generates corresponding feature matrixes and vectors; the health state recognition module classifies the feature data according to a preset rule and generates a health state label set; the health scheme prediction module is combined with a health intervention measure knowledge base to generate an initial scheme set through an association rule mining algorithm; and the health parameter coupling module corrects the intervention intensity parameter of the initial scheme based on the mapping relationship between the human physiological response and the behavior intervention parameter. According to the system, multi-dimensional health data can be integrated, and an accurate personalized health management scheme is generated.

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 relates to the technical field of medical treatment, in particular to an intracranial aneurysm rupture risk assessment and management system based on an internet hospital, which comprises a patient end module, a patient end module, a management end module and a management end module, and the patient end module uploads medical history data and performs automatic filing and labeling processing on basic health data through a system standardization questionnaire and a multi-modal data acquisition mechanism; according to the method, the consistency and comparability of the health data are realized through vectorization coding and standardization processing; a blind deconvolution technology is adopted to improve the definition of a cerebrovascular image, and risk assessment is optimized in combination with the image and clinical features; privacy is protected through a self-adaptive watermarking technology, and data security and compliance are ensured in combination with double encryption and de-identification processing; the system timely identifies risks and pushes personalized health intervention through dynamic monitoring and intelligent early warning, and the accuracy and initiative of chronic disease management are improved. In addition, the block chain technology ensures data traceability and tampering prevention, and the doctor-patient trust is enhanced.

The present invention relates to the fields of child health monitoring and artificial intelligence technology, and provides a child scoliosis monitoring and early warning system. Existing methods rely on static inspections, and it is difficult to capture abnormal dynamic changes of the spine in real time. The present invention acquires multimodal data through posture sensors, acceleration sensors, and image acquisition devices, combines convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to perform deep feature extraction, and uses hidden Markov models (HMMs) to identify children's behavioral contexts and dynamically adjust the early warning threshold. The system uses a Q-learning algorithm to generate personalized health intervention strategies, and uses differential privacy and homomorphic encryption technologies to ensure data security. Finally, the system generates a three-level health warning signal to immediately remind children to adjust their postures and notify guardians. The present invention realizes real-time monitoring and personalized intervention of spinal health, provides an efficient and safe health management solution, and promotes the early detection and correction of scoliosis.

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 discloses an ear-nose-throat department symptom monitoring method and system based on multi-modal data fusion, and relates to the technical field of medical monitoring. Comprising the steps of monitoring physiological data in real time, calling historical medical record data, collecting environment data, and preprocessing the monitored, called and collected data; carrying out feature fusion on the preprocessed data, extracting potential associated features, and carrying out multilevel and sequential feature learning; on the basis of the extracted features, an improved long-short-term memory network is used for conducting symptom prediction, in combination with historical trends of the blood sugar and the blood pressure of the patient, correlation between the symptoms and ear-nose-throat symptoms is evaluated in real time, and personalized symptom early warning is output; and sending a health prompt to the patient through the intelligent equipment or the mobile terminal application in real time. According to the invention, conventional symptom early warning can be carried out, and personalized health intervention measures can be provided according to individual health conditions.

The invention relates to the technical field of intelligent wearable health management, in particular to a health management method and device based on multi-modal data, equipment and a storage medium. Comprising the steps of obtaining multi-feature-dimension original data to construct an original data set; performing data preprocessing and feature extraction on the original data set to generate a feature vector set; performing trend capture on the feature vector set according to a time sequence to obtain a time sequence feature vector of each modal; calculating a cross-modal association weight according to each modal time sequence feature vector, outputting a joint feature vector based on the cross-modal association weight, and generating a health index value of each evaluation target dimension according to the joint feature vector; and forming a state vector by the health index value and the historical behavior data of the user, calculating action probability distribution according to the state vector, and sampling and outputting a health intervention suggestion based on the probability distribution. The problem of health assessment deviation caused by insufficient multi-modal data fusion and insufficient time sequence association mining in the prior art can be solved.

The invention discloses a health examination data analysis method and system based on artificial intelligence, and the method comprises the steps: receiving multi-modal health examination original data of a user, and generating a multi-modal feature matrix fused with time-space correlation; inputting the multi-modal feature matrix into a lightweight dual-channel network to obtain a decoupled dual-modal feature group; constructing a hidden Markov chain based on the bimodal feature group, and generating a health state transition trajectory diagram with a timestamp; inputting the health state transition trajectory diagram into a discriminator of the generative adversarial network, and outputting a confidence score and a pathology trigger threshold of a high-risk node; and activating a rule engine according to a pathological trigger threshold, and generating a hierarchical health intervention instruction set in combination with individual living habit data. By using the embodiment of the invention, the physical examination data can be efficiently analyzed, and the accuracy of disease early warning and health intervention is optimized.

The invention relates to the technical field of service robots, and discloses an elderly care service robot based on an emotion adjustment system, and the robot comprises a multi-mode emotion recognition module which is used for carrying out the comprehensive perception of the emotional state of the elderly through combining various emotion signals of voice, facial expression and limb movement; the personalized emotion adjusting module is used for generating personalized emotion responses meeting the requirements of the old people; the emotion memory module is used for recording emotion changes, important life events and personal preferences of the elderly; the scene self-adaption module is used for sensing the living environment of the old people in real time and adjusting the emotional expression mode of the robot according to the environment information; the health intervention module is used for generating a health management scheme through long-term monitoring of the emotional state and health of the old people; and a safety monitoring module. According to the invention, the emotion state of the elderly is analyzed through the multi-modal fusion network, the emotion misjudgment probability is reduced, and the man-machine interaction experience and the emotion support effect of the elderly are remarkably improved.

The invention relates to the technical field of data processing and analysis, in particular to an intelligent large health system and a data processing method thereof, and the method comprises the steps: collecting the data of a multi-source heterogeneous data source in real time, and comprehensively capturing the multi-dimensional health data of a user; a multi-modal health knowledge graph is dynamically constructed and optimized through an efficient data preprocessing and feature alignment technology, and the integration value and the utilization efficiency of data are improved; a graph neural network and a time sequence analysis model are utilized to realize accurate evaluation of the health state of the user and timely prediction of future risks, and predictability and pertinence of health management are effectively improved; based on a reinforcement learning algorithm, a highly personalized intervention sequence can be generated according to the individual condition of a user, the accuracy of health intervention is enhanced, and the positive change of the health behavior of the user is promoted; an intervention instruction is executed through intelligent equipment, user feedback is monitored in real time, an intervention strategy is dynamically adjusted, and the flexibility and adaptability of intervention measures are ensured.

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 belongs to the field of intelligent voice, and particularly relates to an intelligent health robot combining intelligent dialogue and health intervention, which comprises a voice recognition module and a rehabilitation guidance module, and is characterized in that the voice recognition module comprises a voice acquisition unit, a voice compensation unit and a multi-mode recognition unit; the method comprises the following steps: acquiring user voice by a voice acquisition unit, compensating formant features and pause discrimination by a discrimination filter, and obtaining a to-be-recognized voice feature space; correcting voice missing words and tones through a voice compensation unit to obtain a corrected to-be-recognized voice feature space; the multi-modal recognition unit outputs the voice text and the demand intention space according to the voice text and the demand intention space, and obtains an intention emotion state space by combining a preset evaluation interval; and finally, the rehabilitation guidance module calls an enhanced reasoning strategy library to generate a rehabilitation strategy intervention scheme according to the demand intention space and the emotional state space, and performs real-time voice output to realize intelligent dialogue and health intervention.

The invention provides a whole-house health intervention lighting system and a lighting method thereof, and the system comprises a distributed multi-mode sensing layer which is used for non-inductively collecting the physiological, behavior and environment information of a user through a sensor module integrated in a lighting lamp; the edge calculation and model analysis layer is used for carrying out preprocessing and feature extraction on the data acquired by the distributed multi-mode perception layer and providing a model self-interpretation function; the decentralized federated artificial intelligence decision-making layer is used for constructing a personalized health model based on a federated learning framework, performing health state prediction and generating an intervention instruction; the cooperative multi-target intervention planning layer is used for converting the intervention instruction into a whole-house cooperative illumination control strategy; and the system health and fault-tolerant reconstruction layer is used for monitoring the state of the system and realizing function reconstruction when a fault occurs, and the organic cooperation among the layers realizes the capabilities of non-inductive monitoring, privacy protection, continuous learning and active intervention of the lighting system.

The invention discloses an individual health risk dynamic assessment system based on multi-modal biological feature fusion, and relates to the technical field of medical health information processing. The method comprises the following steps: constructing a high / low-dimension data combination rule; dynamically adjusting the feature fusion weight according to the clinical feature importance weight and the medical efficiency value calculated by the detection time interval; a cost difference value formula is introduced to quantify the economical efficiency of combined evaluation and independent evaluation; calculating a health risk comprehensive coefficient of the monitoring scene by combining the health data credibility score and the index type / amplitude / duration weighted risk value; the risk level is rechecked through expert assessment or a secondary model, so that the assessment accuracy is ensured; updating the individual credibility score according to the evaluation result; and for the result exceeding the confidence interval, classifying and optimizing a feature fusion algorithm, a model parameter or a data combination strategy. The system realizes accurate, economic and real-time individual health risk dynamic assessment, and is suitable for scenes of chronic disease management, health intervention and the like.

A computer implemented preventative health care and health intervention application is described. The application includes a scoring system to score an overall health of the member, including breakdowns scoring the member's initial physical health, mental health, and an initial score for the lifestyle of the member. One or more health care providers are assigned to and / or selected by the user. The health care providers assess which health intervention programs are best suited to meet the goals of the member and to improve the score for the member's health. The score is re-evaluated when the member meets a minimum threshold of participation and attendance in the one or more selected health and health intervention programs. The score evaluation includes review of attendance, participation information, data collected from sensors and wearable devices, as well as based on predictive analytics integrated into the preventative health care and health intervention program.

The invention relates to the technical field of intelligent closestools, in particular to an intelligent closestool with human health monitoring analysis and early warning functions, which comprises an identity recognition module, a multi-source sensing module, an internet of things module, an intelligent analysis module and a user terminal module, the intelligent analysis module performs deep integration and analysis on the multi-modal time series data in the cloud database based on a large language model, generates a personalized health portrait, identifies a potential pathological phenomenon, triggers disease early warning, and constructs a health assessment model through a model fine tuning method in combination with a medical knowledge base; and outputting a personalized health analysis report and a health intervention suggestion. The multi-source sensing module is integrated, multi-dimensional health data can be collected in real time, the problems that in the prior art, the data collection dimension is single and means are limited are solved, the complex health state of the human body is comprehensively reflected, the situation that an existing analysis method is single is broken through, and the potential value of data is fully mined; and a complex scene with individual difference and coexistence of multiple disease species can be better handled.

The invention relates to the technical field of medical data processing, in particular to a multi-modal body feeling information processing method and system for physiological state assessment, and the method comprises the steps: collecting multi-modal parameters; constructing a consistency feature; carrying out individual normal behavior zone modeling; compressing and generating a consistent track; extracting deviation path features; carrying out similar matching on physiological trajectories; state type and stage judgment; carrying out self-adaptive correction on the behavior belt; and outputting a health intervention prompt. According to the method, the multi-source physiological signals are introduced at the same time, the time sequence cooperation relation of different types of parameters is utilized, and the cross-modal consistency characteristic matrix is constructed, so that the evaluation process does not depend on isolated change of a single signal any more, and comprehensive judgment is carried out based on linkage evolution of multi-dimensional information; the problems that due to the fact that a single physiological signal and a static evaluation benchmark are relied on, physiological state evaluation is prone to being influenced by individual differences and long-term state changes, and misjudgment and drifting are generated are effectively solved.

The invention relates to the technical field of health assessment, in particular to a traditional Chinese medicine health assessment method and system based on intelligent analysis, and the method comprises the steps: carrying out the multispectral image collection of the face and tongue picture of a user, carrying out the color gradation quantification processing and texture feature extraction of the obtained image data, and obtaining a face color gamut feature matrix and a tongue picture texture feature vector; and based on the tongue picture-complexion associated features and a syndrome description text input by the user, performing intelligent classification pre-judgment on traditional Chinese medicine syndrome types to obtain an initial syndrome label set, and based on user physique data, correcting the initial syndrome label set to obtain a syndrome feature fusion vector. Through the health fluctuation index and the health trend early warning signal, the change of the health condition of the user can be dynamically evaluated, and a personalized health intervention scheme is generated on the basis, so that the fluctuation of the health condition can be reflected in real time, intervention measures can be taken in time, and potential health risks can be prevented.

The invention relates to the technical field of digital health management, in particular to a health behavior intervention and incentive method based on social network analysis, which comprises the following steps: acquiring health behavior data, social network data and interaction data of a user through a social platform and a health management application, preprocessing the acquired data, ensuring the accuracy and consistency of the data, and improving the user experience. Modeling by adopting a social network analysis technology, extracting social nodes and structural features, identifying a user behavior mode through clustering analysis, and evaluating health risks; on the basis of the analysis results, a personalized intervention strategy is generated, and through multi-dimensional incentive mechanisms such as material reward, social reward and psychological incentive, the user is pushed to continuously keep good health behaviors; according to the invention, accurate and long-term health management can be realized, and the problems of low efficiency and singleness in a traditional health intervention scheme are solved.

The invention provides an employee health risk monitoring method and system based on big data, and relates to the technical field of health management and data analysis, and the method comprises the steps: S1, collecting the multi-modal health data of an employee, including but not limited to physiological data, behavior data, working environment data, social interaction data and genome data, and S2, fusing and analyzing the collected multi-modal health data by adopting an integrated neural network model and an adaptive learning algorithm. According to the big data-based staff health risk monitoring method and system, through the combination of the integrated neural network and the deep causal inference model, the multi-modal health data of the staff can be deeply analyzed, and the complex causal relationship between the health data can be effectively identified. Through the dynamic analysis of the historical health data, the current health state and the working environment of the employees, the model can achieve the precise health risk prediction, and provides personalized health intervention suggestions according to the individual differences of the employees.

The invention discloses an old people lower limb motion function analysis method and system based on the Internet of Things technology, and belongs to the technical field of data analysis, and the method comprises the steps: deploying a data collection device to collect original data, preprocessing the original data to obtain a first data set, dividing the motion into gait cycles through data calculation, step length, step frequency, support time, swing time and joint angle of each gait cycle are calculated and integrated into a second data set, time sequence dynamic features are extracted to obtain a dynamic feature vector, a gait quality score and a gait degradation trend are obtained through model calculation, gait anomaly detection is performed to obtain a gait anomaly recognition result and a gait risk score, and a gait anomaly recognition result is obtained. And constructing a health intervention suggestion generation model. According to the old people lower limb motion function analysis method and system based on the Internet of Things technology, multi-dimensional data from different sources are fused, and a gait analysis model is adjusted in real time by using an adaptive algorithm, so that accurate early warning and personalized analysis are realized.

A health intervention method for healthy and healthy people based on compliance prediction relates to the technical field of health intervention, and mainly comprises the following steps: collecting training sample data to perform model pre-training on a deep belief network, collecting actual data of healthy and healthy people to be intervened, and sending the actual data to the pre-trained deep belief network to perform re-diagnosis compliance prediction; and outputting a re-visit compliance prediction classification result, adjusting and optimizing a health intervention strategy for the crowd according to the prediction classification result, and sending intervention knowledge content according to the intervention strategy and the patient condition. The invention further provides a health intervention system for healthy and healthy people based on compliance prediction. The system comprises a data synchronization module, a compliance prediction module, an intervention knowledge base, an intervention rule updating module and an intervention content sending module. According to the invention, a compliance prediction model for healthy and healthy people is constructed based on the deep belief network, and is used for guiding, adjusting and optimizing a health intervention strategy for people.

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 discloses a child health information tracking management method and system for a kindergarten institution, and relates to the technical field of kindergarten information management. According to the child health information tracking management method for the kindergarten institution, basic information and health data of each child are collected, the health condition of each time period is evaluated, a time period health index is generated, whether the time period health index of each child is within a normal range or not is judged in real time according to a preset health threshold value, and if the index is abnormal, the kindergarten institution is started. The method comprises the following steps of: immediately marking health abnormity, comprehensively analyzing health, intake, activity and interaction data of the whole day at the end of each day, analyzing daily health comprehensive indexes of each infant, performing health assessment, and taking corresponding health intervention measures according to a result. By continuously tracking and analyzing key health indexes such as the body temperature, the heart rate, the respiration rate and the blood oxygen saturation degree of the infant, the health index of each time period is generated in real time, and a young supporting institution is allowed to recognize potential health risks in real time in the initial stage of the problem.

The invention relates to the technical field of medical information, in particular to a multi-source data health intervention method, which comprises the following steps: firstly, acquiring texts, vital signs, image vectors and wearable signals, generating a unified vector through multi-modal coding, and extracting a topological life vector; constructing a spiking neuro-causal diagram based on a unified vector, and injecting a life vector as an external field into a Sheng differential equation to generate a dynamic pathological manifold; compressing the manifold into a tensor network state, constructing a quantum optimization model in combination with a causal diagram, minimizing energy expectation and topological risk to obtain an optimal intervention sequence, and reinjecting a target gradient to adjust the manifold in real time; after the sequence is subjected to clinical logic verification, a commitment value and a zero-knowledge proof are generated and written into a Byzantine account book, and meanwhile a control instruction is issued to an execution terminal. According to the method, millisecond-level decision making is achieved, the false alarm rate is reduced, and chain traceability is provided.

The invention relates to the technical field of health education, and particularly discloses a digital one-stop health education platform based on artificial intelligence and a construction method thereof, and the platform comprises a health data management module which is used for collecting health data of a user from a plurality of data sources, and automatically generating a health file of the user in combination with personal health information inputted by the user; the health portrait generation module is used for generating a personalized health portrait based on the health file, and performing personalized demand analysis according to the health portrait in combination with a target set by a user; according to the invention, based on the established health portrait, customized health education content is obtained according to personal requirements, and a health scheme is adjusted at any time according to health data changes, so that individuation and dynamics of health management are ensured; the user can ask questions at any time to obtain real-time health guidance and professional suggestions; by means of artificial intelligence and big data analysis, the health trend can be predicted, early warning can be given out when potential health problems are found, and effective health intervention measures are provided.

The invention relates to the technical field of health management, and discloses a method and system for managing medical health care records of a user, and the method comprises the following steps: S1, collecting multi-modal medical data of the user, including electronic health records, medical image data, vital sign data and genomics data; s2, preprocessing the collected medical data, including data cleaning, format standardization and noise removal; and S3, carrying out time synchronization on the data of different modalities by adopting an optimized time alignment method. According to the method, probability modeling and topological data analysis are adopted, the health prediction precision is improved, false alarms and missing alarms are reduced, federated learning and differential privacy are combined, cross-mechanism collaborative modeling is achieved, data safety is enhanced, health intervention is optimized through reinforcement learning, a management strategy is dynamically adjusted, and the individuation precision is improved; the scheme has better adaptability, safety and intelligence.