150 results about "Health intervention" patented technology

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 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 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 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 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 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.

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 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.

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 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 discloses an artificial intelligence health management system based on multi-source data fusion, and relates to the field of intelligent health management, and the system comprises a multi-modal data collection and processing module which is used for obtaining related data; the target dynamic adjustment module is used for decomposing the health target of the user and adjusting exercise and diet schemes; the health file construction and storage module is used for generating a dynamic health trend graph and realizing data encryption storage and cross-device synchronization; the panoramic health analysis module is used for associating the multi-dimensional health data to generate risk tags; the personalized health intervention module is used for calling a food red-black list to generate customized courses and recipes; and the intelligent reminding and execution module is used for triggering reminding and executing a priority strategy. Risk assessment is realized through multi-modal data fusion and block chain encryption, and safe, reliable and full-scene coverage intelligent health management service is provided for a user depending on a personalized rule base and an offline early warning mechanism.

The invention relates to a dynamic health record automatic updating and management method based on a cloud platform. The method comprises the following steps: acquiring multi-source heterogeneous data and performing event packaging to generate a standard health event stream; performing cross-modal time sequence alignment processing on the standard health event stream to form a time sequence event sequence; an event-state double-layer time sequence knowledge graph is constructed, and real-time updating of health state vectors is achieved by means of the dynamic reasoning ability of a graph neural network in combination with a time sequence event sequence; and dynamic updating of the archive is completed through vectorization archiving. By the adoption of the method, the defects of traditional health archives in the aspects of data fusion depth, updating timeliness and state characterization precision can be effectively overcome, an accurate and real-time data basis is provided for chronic disease management and other long-term health intervention scenes, and the initiative and individuation level of health management is remarkably improved.

The embodiment of the invention provides a health state monitoring method and device and electronic equipment, and relates to the technical field of computers. The method comprises the following steps: acquiring multi-source data of a cockpit domain; the multi-source data comprises physiological data of the user, behavior data of the user and environment data of a cockpit domain; obtaining a health risk score of the user based on the multi-source data and the health state evaluation model; the health state evaluation model is used for evaluating the health state of the user to obtain a health risk score; and determining a corresponding health intervention strategy based on the health risk score of the user. The method can be used for carrying out real-time monitoring and dynamic intervention on the health state of the user in the intelligent cabin driving process, and is used for solving the problems of scene limitation, insufficient data processing, risk response lagging and single intervention strategy in the existing cabin health monitoring technology.

The invention discloses a student physical health dynamic monitoring system based on deep learning. Student eye image data passing a quality threshold is used as qualified student eye image data; cutting according to the face key point positioning result to obtain student eye image data of the interested areas of the eyes and the sub-orbital area; generating CLAHE enhanced student eye image data; extracting an original image stream student eye feature map and a CLAHE enhanced stream student eye feature map; obtaining a fused student eye feature map; outputting a student eye fatigue area pixel-level mask map and a student eye fatigue area pixel-level confidence coefficient heat map; and generating a student eye fatigue sub-item index result, and outputting a student individualized eye fatigue index. According to the invention, large-scale, non-contact and fine-grained physical health monitoring for student groups is realized, and the refinement and traceability of health intervention are improved.

The invention relates to the technical field of teenager physical health management, and discloses a teenager physical health management system based on physical test results, and the system comprises a data collection terminal, a health intervention device and a cloud management platform which are in communication connection. The data collection terminal collects teenager physical test motion indexes and physiological signals in real time, and the health intervention device receives an instruction of the cloud management platform to adjust the training intensity. The data monitoring unit records an index abnormal starting point and a recovery termination point to determine a health risk interval, and controls the equipment to operate at a safety threshold and generate a physical characteristic curve for physical prediction mapping; the evaluation processing unit corrects the intervention path to generate an execution strategy; and a conventional regulation and control unit and an emergency regulation and control unit of the health management unit control equipment to execute corresponding operation under normal and abnormal working conditions respectively. According to the system, real-time monitoring, accurate evaluation, scientific intervention and risk processing of teenager physical health are realized, teenager physical test safety is effectively guaranteed, and the physical health condition is improved.

The invention belongs to the technical field of health management, and particularly relates to an artificial intelligence health management method and system based on big data, and the method comprises the following specific steps: S1, data collection: collecting physiological data, environment and behavior data and medical historical data; s2, data preprocessing: integrating multi-source data according to the credibility of data sources by adopting a weighted fusion algorithm, and generating a unified user health data matrix; s3, AI intelligent analysis: constructing a dynamic evolutionary Transform time sequence prediction model combined with a medical knowledge graph, and realizing health risk prediction and user portrait generation; and S4, performing health intervention: forming a closed-loop intervention mechanism of scheme generation-scheme tracking-scheme adjustment. By integrating physiological, environment, behavior and medical data, the problem of analysis one-sidedness caused by single data can be solved, for example, when the blood glucose of a diabetic user is increased, the system can accurately position reasons in combination with diet, environment and medication data.

The invention discloses an accurate health index prediction method and system based on multi-source data fusion, and relates to the technical field of health management. The method comprises the following steps: acquiring multi-dimensional data of a user; according to the preprocessed multi-dimensional data, biological harmony characteristics are constructed, wherein the biological harmony characteristics comprise a physiological health comprehensive index, a psychological health fluctuation index, an activity intensity adaptation index and an environment stress index; calculating a comprehensive health dynamic index according to the biological harmony characteristics; performing threshold judgment on the comprehensive health dynamic index, and providing corresponding health suggestions; constructing an LSTM model based on the historical comprehensive health dynamic index to perform trend prediction; and the health state and the health trend prediction result are monitored in real time through a visual billboard. Comprehensive health assessment is performed by integrating multi-dimensional data, personalized health intervention is provided, and a health threshold is dynamically adjusted according to personal information of a user, so that accurate and efficient health management and monitoring are realized.

The invention discloses a refrigerator food material management and health intervention system and method. The system comprises an identity authentication module, a multi-mode acquisition module, a control module, a health management engine and an interaction module. The identity authentication module outputs a user identity signal; the multi-modal acquisition module comprises a visual acquisition unit, a quality sensing unit and a voice input unit; the control module is in communication connection with other modules to dynamically construct a food material database; the health management engine generates an intervention instruction; the interaction module responds to the intervention instruction and outputs health suggestions; triple biological characteristic collaborative verification is achieved through the identity authentication module, closed-loop linkage of a dynamic food material database and a health management engine is formed in combination with a spatio-temporal data binding mechanism of the multi-modal acquisition module and the control module, and full-life-cycle management of food materials from storage to taking out is achieved. The automation degree of inventory updating and health intervention is improved, and the efficiency of household food material management and the health risk prevention and control capability are remarkably improved.

The invention provides a management method and system for health detection and intervention in early pregnancy and a medium, and the method comprises the steps: firstly, obtaining multi-source original data of women in early pregnancy, and executing standardized preprocessing, thereby forming a health information sequence, and then extracting a health feature vector; secondly, primary threshold verification, intermediate threshold and trend continuity verification and advanced threshold and multi-dimensional cross verification are carried out in sequence, and accurate classification of risk levels is realized; then, based on the risk level and the specific abnormal features, generating a personalized intervention instruction set and executing pushing; and finally, according to health data changes before and after intervention, dynamically updating thresholds at all levels through an adaptive algorithm, and realizing continuous self-optimization. According to the pregnancy health monitoring system and method, full-process closed-loop management is constructed, so that the accuracy and timeliness of pregnancy health monitoring are effectively improved, the early warning reliability is enhanced, and dynamic personalized health intervention and system self-evolution capability are realized.

The invention discloses a human resource management service system based on Internet of Things, and relates to the technical field of Internet of Things and human resource management, and the system comprises a data collection module, a data analysis module, a data processing module and a local database, and is used for collecting customer service center environment, employee physiological behaviors and business multi-source data; according to the method, regional load and health risks are evaluated in two dimensions, a visual risk distribution diagram is generated, future overload risk levels are pre-judged, differentiated environment intervention and cross-regional task scheduling are matched, health intervention strategies are matched, the intervention effect is quantitatively verified through correlation analysis, a data splitting barrier is broken, accurate and preventive intelligent management and control of human resources of a customer service center are achieved, and the risk assessment efficiency is improved. Employee fatigue is effectively relieved, and resource allocation efficiency and customer service experience are remarkably improved.

The invention discloses a ring type blood glucose noninvasive detection device based on multi-modal sensing fusion, and relates to the technical field of medical detection equipment. A baseline construction module; an interference decoupling module; a cross-mode experience verification module; a result feedback module; by integrating optical, electrochemical and mechanical sensors, the accuracy and anti-interference capability of blood glucose noninvasive detection are improved by using a multi-modal data fusion technology, and the traditional single-modal limitation is broken through; meanwhile, a self-supervised learning algorithm is adopted to construct a personalized digital twin baseline, dynamic adjustment is performed in combination with data such as diet and exercise of the user, and customized health intervention suggestions are generated, so that the design not only improves the clinical applicability of noninvasive detection, but also improves the continuity of blood glucose monitoring through a closed-loop management mode, reduces the risk of complications, and improves the accuracy of blood glucose monitoring. And an innovative scheme is provided for chronic disease management.

The invention provides an individualized nursing scheme intelligent generation method and system based on big data, and relates to the technical field of individualized nursing scheme intelligent generation. Multi-source health data of a target nursing object is collected, feature coding is carried out on the multi-source health data, and a structured health file is generated; performing similar mapping on the health state of the target nursing object according to the diagnosis mapping table and the structured health file to obtain a health state set, and determining an individualized state index according to the health state set; state difference detection is carried out according to the real-time monitoring data and the structured health record, a state change trend vector is generated, fusion analysis is carried out according to the state change trend vector and the individualized state index, and a dynamic health intervention index is generated; and generating an individualized nursing scheme of the target nursing object according to the dynamic health intervention index. According to the invention, the dynamic adaptation of the nursing scheme and the real-time health state of the patient can be realized, and the accuracy and timeliness of the nursing scheme are improved.

This invention provides a method, device, electronic device, and storage medium for mental health intervention. The method includes: acquiring user assessment behavior characteristic data, including consistency scores for answers, reasonableness scores for completion time, and regularity scores for response patterns; determining a credibility index based on the assessment behavior characteristic data, determining a warning level based on the credibility index, and determining a preset first intervention strategy based on the warning level; after the preset first intervention strategy is applied to the user, acquiring user characteristic data and forming a user characteristic vector based on the user characteristic data; assigning a corresponding preset second intervention strategy to the user through a trained classification model and the user's clustering categories for psychological intervention, acquiring intervention effect labels, and stopping the push of the preset second intervention strategy to the user if the levels of several pre-effect labels are lower than the preset label levels. This method can improve the effectiveness of mental health intervention.

The application discloses a human health monitoring method and system based on proportion philosophy and individual self-adaption. A multi-level nested proportion model is constructed by non-invasively collecting multi-dimensional signals of a human body. A golden section 0.618 is taken as a harmonious anchor point to calculate an individual exclusive health benchmark zero point and a stable interval. Through real-time proportion comparison and fine ladder deviation analysis, the structure harmony and function state are judged. The health trend early warning is realized by combining multiple retests. The application breaks away from the limitation of unified medical indicators, realizes personalized, global and original health evaluation, can early identify imbalance and sub-health state, and is suitable for scenes such as daily monitoring, physical evaluation and health intervention effect judgment.