1446 results about "Health data" patented technology

A spatiotemporal modeling system for Personal Health Database (PHDB) platforms integrates diverse health data types into a comprehensive 4D model of an individual's health status. By combining genomic, imaging, clinical, and real-time health data, the system creates a dynamic, time-based representation of the user's anatomy and physiology. This model enables real-time analysis, pattern recognition, and predictive forecasting of health outcomes. The system preprocesses and aligns data from various sources, constructs a detailed spatial framework, and continuously updates the model with new inputs. Through interactive visualizations, it provides users and healthcare providers with intuitive, personalized insights for improved health management and decision-making.

The invention relates to a digital chronic disease intelligent management platform based on an AI model and multi-dimensional data fusion, clinical diagnosis and treatment data, wearable equipment monitoring data, medication record data and environment monitoring data are acquired through a data acquisition module, and after standardized preprocessing is performed through a data fusion processing module, deep analysis is performed through an AI analysis module, and the data fusion processing module performs data fusion processing; in combination with medical knowledge of the knowledge base module, the intelligent decision-making module generates a personalized management scheme, and the personalized management scheme is implemented through the intervention execution module and the intelligent interaction module. Multi-dimensional health data are processed through an AI large model, a complex mode and an association relationship are automatically learned, and accurate disease prediction and risk assessment are realized; the pertinence of the scheme and the compliance of a patient are greatly improved; and real-time interaction and personalized guidance are provided, the participation degree and the self-management ability of the patient are effectively enhanced, and a benign health management cycle is formed.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

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 health management systems, and particularly discloses a personalized AI intelligent health management system based on multi-source data fusion. Comprising a multi-modal health data dynamic acquisition and fusion module, a personalized dynamic health portrait construction module, a hierarchical risk early warning and root cause inference module, a self-adaptive personalized intervention strategy generation and closed-loop optimization module and a privacy protection and federated learning module which are connected in sequence. According to the system, standardized acquisition, quality verification and feature level fusion of different types of health data are realized through a multi-modal health data dynamic acquisition and fusion module, the problems of multi-source data dispersion and poor fusion effect in the prior art are solved, and a high-quality data basis is provided for subsequent health analysis; through an embedded dual adaptive calibration mechanism and a dynamic updating unit, portrait calibration is carried out in combination with a user individual historical baseline and a group generality mode, and the change of a user health state can be adapted in real time.

The invention discloses an intelligent monitoring decision-making method based on a knowledge graph and federal learning, and the method comprises the following steps: S1, collecting and preprocessing multi-source health data of a user, and generating a health data set; s2, constructing a local medical knowledge graph and performing knowledge embedding modeling to generate a knowledge representation vector; s3, constructing a health risk assessment model, and performing modeling in combination with knowledge representation and health data; s4, initializing a federated learning architecture, setting a client and an aggregation end, and distributing a model structure and parameters; s5, locally training the model by each federated client, and uploading parameters to an aggregation end to complete parameter aggregation; s6, combining the updated model with the real-time health data and a knowledge graph reasoning result to generate a personalized monitoring decision; and S7, collecting user feedback and newly added data, updating the knowledge graph and the model, and entering a new round of optimization. The method is used for realizing personalized health risk assessment and intelligent monitoring fusing the knowledge graph and federal learning while ensuring privacy.

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 relates to the technical field of data processing, and discloses an old people health state assessment data processing system based on multi-modal data, and the system comprises a medical data integration module which obtains electronic medical record data and a medical examination report through an FHIR interface, and extracts a structured health index; the cross-modal causal fusion processing module is used for fusing the monitoring data and the medical text through an image, text and image interlayer architecture; the health state evolution modeling module is used for mapping the health feature vectors into physiological function, cognitive level and athletic ability three-dimensional state indexes; an evaluation report backtracking module; and a decision output module. Through an image, text and image interlayer architecture, deep semantic fusion of multi-modal features is realized under the constraint of medical pathology rules, feature weight adaptive distribution is dynamically guided based on a medical causal atlas, a high-dimensional fusion vector retaining key pathology information is generated, the semantic integration ability of health data is remarkably improved, and the health data fusion efficiency is improved. And the reliability of discrimination and decision making is improved.

A battery state-of-energy health assessment method and apparatus, and an electronic device and a readable storage medium. The method comprises: acquiring state-of-energy health data, wherein the state-of-energy health data is the ratio of dischargeable energy of a battery under fixed C-rate operating conditions at different states of aging to dischargeable energy of the battery under the same C-rate operating conditions at an initial stage of the battery (S101); acquiring charging energy of the battery of a target vehicle at different temperatures, and acquiring the ratio of the charging energy to theoretical charging energy (S102); and using a preset algorithm to assess the state-of-energy health data and the ratio of the charging energy to the theoretical charging energy, in order to obtain an assessment result (S103). In the method, acquiring battery state-of-energy health data and the ratio of charging energy to theoretical charging energy achieves comprehensive assessment of battery state-of-energy health from two perspectives, so as to more rationally estimate the residual driving range of electric vehicles, thereby achieving more accurate estimation results.

The invention provides a physiological data monitoring report generation method and system applied to diabetes management. The physiological data monitoring report generation method comprises the following steps: acquiring physiological monitoring data of a diabetic patient in a preset monitoring period; state fragment division and mode recognition are conducted on the physiological monitoring data, a time sequence state fragment set of the diabetic is obtained, metabolic load and steady-state migration quantitative characterization is conducted on the basis of the time sequence state fragment set, a metabolic evolution graph of the diabetic is generated, and the metabolic evolution graph of the diabetic is obtained by combining a preset medical knowledge base and historical health data of the diabetic. Semantic analysis is carried out on the metabolic evolution map, and a health condition key insight set of the diabetic patient is output; and according to the personalized context and expression preference of the diabetic patient, converting the health condition key insight set into a health guidance report conforming to the personalized context and expression preference. The accuracy, comprehensiveness and user acceptability of the physiological data monitoring report in diabetes management can be effectively 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 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 artificial intelligence and robots, and discloses a robot health state authentication method and system based on multi-dimensional fusion, and the method comprises the steps: collecting multi-source data, and carrying out the standardization preprocessing; 17 health dimensions are estimated based on an algorithm model; fusing the health dimensions to generate a comprehensive health index; authenticating the health state, and generating a health authentication report; reporting and coding, and storing to a block chain; the system comprises a multi-source data acquisition module, a dimension calculation engine, a correlation analysis module, a dynamic authentication generator, a block chain evidence storage module and an application interface layer. According to the method, a 17-dimensional health index system is constructed, hardware multiplexing is realized by applying an algorithm model based on current, speed, vision, network data and the like, an expensive physical sensor is effectively replaced, and more comprehensive health data can be obtained by matching with a chemical risk inversion model based on vision and network data to generate an environmental chemical index.

The invention discloses a power transformation equipment working condition detection system and method based on self-adaption, and relates to the field of power equipment state monitoring. The method comprises the following steps: collecting multi-source monitoring data of power transformation equipment and carrying out standardization processing to generate standard time sequence data; dividing a time window based on working conditions, extracting characteristic parameters, and constructing a characteristic track; establishing and updating an adaptive reference library containing standard reference points and health tolerance boundaries under different working conditions based on historical health data; comparing the real-time characteristic track with a standard reference point and a health tolerance boundary of a corresponding working condition in a self-adaptive reference library, calculating a deviation degree and generating an operation health index; and determining a dynamic early warning threshold value according to the historical operation health index data, and judging the state of the equipment by comparing the real-time operation health index with the dynamic early warning threshold value and a preset rigid alarm threshold value. And early discovery of weak degradation of the internal coordination relation of the power transformation equipment is realized by constructing a comparison mechanism of a self-adaptive health benchmark and a feature trajectory.

The invention discloses an internal medicine drug curative effect evaluation method based on big data analysis of an electronic health record, and the method comprises the steps: extracting basic health data, diagnosis and treatment time sequence data and drug intervention data from the electronic health record, and carrying out the time-space alignment to generate a dynamic feature set; subgroups are obtained based on disease typing standard hierarchical clustering, and historical data and real world data are fused through transfer learning to construct a subgroup curative effect reference matrix; collecting data after medication in real time, and generating an evaluation vector containing short-term physiological response, middle-term symptom improvement and long-term prognosis risk through deep learning; dynamically matching the evaluation vector with the reference matrix, and introducing an individual weight coefficient to correct deviation; taking the deviation correction value as input, constructing a self-adaptive evaluation model through reinforcement learning, and performing iterative optimization; and generating an individualized report containing the curative effect level, the medication suggestion and the risk early warning, and quantifying the curative effect level through a fuzzy comprehensive evaluation method. According to the method, individual differences are accurately captured, full-cycle dynamic evaluation is realized, and the curative effect evaluation accuracy and the clinical decision-making efficiency are improved.

The invention relates to the technical field of robot health physical examination, and discloses a robot noninvasive health physical examination and monitoring platform. A health data acquisition module of the system integrates physiological parameters acquired by a multi-modal sensor to generate a standardized health data set; the physical sign load analysis module calculates parameter fluctuation difference to generate a physical sign load distribution state value; the physical examination parameter optimization module screens optimal physical examination parameters to generate an operation parameter set; the environment adaptability adjusting module generates an environment response physical examination scheme in combination with the environment and body position data; a health threshold value dynamic setting module adjusts an abnormal judgment threshold value to generate a grading early warning threshold value; the health trend prediction module generates a health risk prediction value through fuzzy reasoning; and the physical examination feedback control module adjusts operation parameters and scanning frequency to generate a self-adaptive regulation and control strategy. According to the platform, the accuracy and the intelligent level of health physical examination are improved, and the non-invasive health monitoring requirement of the user can be better met.

The invention relates to a valve health degree monitoring and maintaining method and system based on an electric actuator, and belongs to the technical field of mechanical control and automation. The method comprises the steps that valve maintenance data of the electric actuator are obtained, a position-pressure bimodal grid is constructed based on the valve maintenance data, pressure testing is conducted on a valve, torque-opening curves in the opening and closing processes are analyzed, and valve health degree data are obtained; an electric execution monitoring model is constructed, a valve torque curve is obtained, maintenance information labels are added to grid nodes according to the valve maintenance information through a labeling binding method, and a valve health maintenance scheme is generated; for the pressure test, identifying switching time delay, hysteresis and leakage data in a valve torque profile and an operation log through a curve feature extraction method to obtain a valve scheme optimization strategy; and executing the maintenance scheme optimized by the valve scheme optimization strategy, and automatically outputting the updated valve health maintenance scheme based on a recursive execution feedback method.

The invention relates to the technical field of intelligent robot health monitoring, and discloses a health monitoring management system based on an intelligent robot. The system comprises a data cleaning unit, an attribute extraction unit, a period division unit, a coordinate conversion unit and a mode analysis unit. The data cleaning unit collects multi-source health data of the robot, and generates an integrated health data set through format standardization, data mode unification, structural difference analysis and redundancy elimination according to a time sequence. The attribute extraction unit separates a monitoring timestamp, a health index code and an individual type label; a period division unit divides the integrated data set into time slices according to timestamps to form a period health data set; the coordinate conversion unit maps the health state to a standard health coordinate system by using the periodic data set and the health index code to generate a health distribution map; and the mode analysis unit classifies health change events according to the atlas, the periodic data set and the individual type labels, calculates a target periodic health activity degree and outputs a health change trend index.

The invention relates to the technical field of report optimization, in particular to a user medical history-based physical examination report conclusion priority adjustment method, which comprises the following steps of: acquiring multi-source data, and preprocessing the multi-source data to obtain a structured multi-source data set; based on the structured multi-source data set, mining an association relationship between health data and diseases through association analysis, constructing a health trend prediction model in combination with a deep Q network, and obtaining a multi-dimensional diagnosis analysis result; determining a diagnosis conclusion priority in combination with a multi-dimensional diagnosis analysis result and a priority calculation formula; calculating a comprehensive risk score through the data fusion score, and dividing risk levels according to the comprehensive risk score to match corresponding stage intervention strategies; and inputting a multi-dimensional diagnosis analysis result, intervention strategy execution feedback data and user health preference into a comprehensive diagnosis decision model, and obtaining a differentiated health association strategy in combination with a diagnosis conclusion priority. According to the scheme, the severity of the disease is accurately identified by dynamically adjusting the priority.

The invention provides a health state evaluation and product matching method and system based on a knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the steps: obtaining user health data, building a health knowledge graph, mapping the user data into a feature vector, and employing a multi-scale time convolution network to extract a time sequence change feature; and constructing a dynamic health feature association network, generating a health state evaluation result by using a graph convolutional neural network and a causal discovery algorithm, calculating a collaborative enhancement coefficient based on a product feature vector, and predicting an intervention effect to determine an optimal product combination. According to the method, personalized health assessment and precise product matching are realized, and the health management efficiency is improved.

The invention discloses a cerebral apoplexy high risk group positioning evaluation system based on multi-modal data, and relates to the technical field of medical information science, the cerebral apoplexy high risk group positioning evaluation system comprises a cerebral apoplexy prevention and control management platform, and the cerebral apoplexy prevention and control management platform is in communication connection with the following modules: a multi-modal data acquisition and integration module, the multi-modal data collection module is used for collecting multi-modal data related to cerebral apoplexy from multiple channels and carrying out preprocessing operation on the collected multi-modal data. By integrating clinical data, image data, omics data and terminal health data, multi-dimensional information related to the cerebral apoplexy can be comprehensively captured, particularly, cerebral vessel digital twin is utilized to simulate hemodynamic characteristics, a plurality of data sources are fused in combination with a graph neural network, high-risk groups can be recognized more accurately, and the accuracy of cerebral apoplexy recognition is improved. The accuracy and reliability of risk prediction are remarkably improved, the problem of missing detection caused by dependence on a single data source in a traditional method is solved, and more powerful support is provided for early intervention.

The invention discloses a trusted data sharing method based on a block chain consensus mechanism. The method comprises the following steps: S1, constructing an alliance chain network; s2, completing KZG polynomial commitment generation, ML-KEM algorithm encryption storage and pointer registration; s3, the data provider medical institution completes an evidence storage transaction of the FROST threshold signature; s4, the target medical institution completes the access request transaction; s5, the data provider medical institution sends a KZG unpacking proof and a data item, the target medical institution obtains a storage pointer and a commitment value, KZG verification check is executed, and after verification is passed, an ML-KEM algorithm is executed for depacking to obtain a session key; and S6, the target medical institution completes the audit transaction, and after the alliance chain network broadcast, the consensus node executes rule verification of the access control contract and the audit contract. The multi-node electronic medical record sharing and tamper-proof evidence storage method is high in performance and traceability, and is suitable for credible circulation of sensitive health data among multiple medical institutions.

The embodiment of the invention discloses a skeleton health dynamic tracking and intervention method and system based on movement and nutrition. The method comprises the following steps: acquiring gait information, bone metabolite information and bone state measurement parameters of a target user by adopting wearable equipment; processing the gait information, the bone metabolite information and the bone state measurement parameters based on an artificial intelligence (AI) model to obtain bone state prediction parameters; according to the bone state prediction parameter and the health data of the target user, performing bone health state evaluation of the target user to obtain a health state prediction parameter; the health state prediction parameters comprise a bone health state instant evaluation index and bone health dynamic trend data; generating a targeted exercise plan and a diet plan according to the health state prediction parameters; wherein the target motion planning is used for guiding the motion of the target user; the diet planning is used for guiding the diet of the target user.

The invention provides a cardiovascular trend prediction method and system based on time sequence medical health data, and relates to the technical field of medical health data analysis. The method comprises the following steps: collecting time sequence medical health data of a patient through a multi-source sensor, wherein the time sequence medical health data comprises dynamic physiological indexes such as heart rate, blood pressure and oxyhemoglobin saturation; performing time calibration and feature extraction on the acquired multi-source data; constructing a time sequence feature model based on the time sequence features, and fusing the time sequence feature model with the static information of the patient to generate a comprehensive feature vector; using a deep learning model to train historical data, and learning a dynamic change rule of cardiovascular health indexes; predicting the change trend of the cardiovascular health indexes in real time, and evaluating the risk level of cardiovascular diseases; the model parameters are optimized through online learning, the prediction precision is improved, the change trend of cardiovascular health indexes can be predicted in real time, and support is provided for early discovery and personalized medical treatment of cardiovascular diseases.

The invention discloses a health data automatic processing and precise health management method, system and device based on artificial intelligence and a medium. The method solves the problems that existing health data collection is fragmented, risk identification is not accurate, and management lacks individuation and continuous optimization, and comprises the steps that multi-source heterogeneous health data is acquired, and data acquisition is deepened in combination with edge calculation and an NLP technology; performing standardized preprocessing and fusion on the data; a multi-modal fusion deep learning model is constructed, the health risk and the prediction trend are accurately identified, and interpretability is provided; a personalized health management decision model based on reinforcement learning is constructed, and customized suggestions are generated; and the causal graph model is utilized to carry out iterative optimization on the feedback data, so that the data processing efficiency and the risk identification accuracy can be improved, thereby realizing highly personalized and adaptive precise health management, and effectively reducing the disease risk and the medical cost.

The invention relates to the technical field of electrical digital data processing, and discloses a health data processing and alarming method for a family monitoring group, which comprises the following steps of: establishing a dynamic baseline for individualized long-term stable habits and a short-term baseline for reflecting a recent mode for a multi-mode data stream of a monitored person; two processing paths are executed in parallel, one processing path generates a system steady-state entropy value based on double-baseline difference aggregation so as to identify a long-term system evolution trend, and the other processing path performs multi-dimensional resonance judgment based on a difference value between an original data point and a short-term baseline so as to perceive a transient system impact event; according to the invention, by constructing a double-pedigree collaborative monitoring framework of slow-varying risks and acute events, mutually independent trend analysis and transaction detection in a traditional data processing mode are integrated into an integral processing structure with internal data collaboration and function complementation, and the problem of inherent information omission of a single processing mode is solved.

The invention relates to the technical field of pipe network detection, in particular to a target detection method, system and device based on image recognition and a medium, and the method comprises the following steps: obtaining a data packet in a mechanical mining process; preprocessing the data packet to obtain a target point cloud; performing target positioning of the underground pipe network according to the preprocessed data to obtain a target area; a baseline database of the underground pipe network in the target area is obtained, target detection is conducted on the change trend of the baseline database along with the time sequence, and health data are obtained; according to the acquired health data and the data packet, acquiring detection data of the target area; through time synchronization packaging and semantic fusion of heterogeneous sensor data, a target point cloud containing space-time correlation characteristics is constructed, and progressive monitoring of the cable health state is realized in combination with time sequence analysis of a dynamic baseline database. The problems of leak detection and false detection of cable detection in a complex environment are effectively solved.

The invention relates to an AI personalized nutrition evaluation and guidance method based on individual continuous diet data. The AI personalized nutrition evaluation and guidance method comprises the following steps: (1) calculating individual single meal food and nutrient intake; individual health indexes are collected, the individual daily energy demand amount is calculated, and the daily food and nutrient demand amount is determined; (2) carrying out instant personalized nutrition evaluation on food and nutrients ingested by an individual in a single meal according to a domestic public recommendation standard; (3) on the basis of individual nutrition and health data continuously recorded in the longitudinal direction of time, establishing a personalized nutrition intake-health model taking an individual as a unit, and carrying out personalized nutrition evaluation and health risk prompting; and (4) simulating the idea of nutrition guidance of professionals to create an AI nutrition guidance algorithm, and generating AI guidance. By adopting the AI personalized nutrition evaluation and guidance method based on the individual continuous diet data, a sustainable AI-assisted diet self-management solution suitable for daily life scenes is effectively provided.

The invention relates to a personalized exercise rehabilitation risk early warning method and device based on IMU dynamic instability assessment, and the method comprises the steps: collecting multi-dimensional exercise data of a subject, carrying out the posture fusion, and obtaining the limb posture, joint angle change rate and angular momentum of the subject; performing time sequence mode decomposition to extract a single gait cycle and a specific jump landing attitude; calculating a biomechanical feature vector of each motion primitive; inputting the single gait cycle, the specific jump landing attitude and the biomechanical feature vector into a hybrid injury induced motion model, and outputting a multi-dimensional injury risk index vector; and comparing the multi-dimensional injury risk index vector with historical health data and a moving target dynamic adjustment threshold value of the subject, and if the threshold value is exceeded, providing real-time auditory, tactile or visual feedback to the subject. According to the method, the influence of movement speed and rhythm change is overcome, dynamic instability in movement can be more sensitively reflected, and personalized risk assessment in a real sense is realized.