283 results about "Health evaluation" patented technology

The invention belongs to the technical field of bridge monitoring, and relates to a system and a method for fusing multi-source data of a bridge structure. Comprising a heterogeneous topological graph construction and manifold embedding technology module, a multi-scale space-time cognitive convolutional neural network module, a continuous manifold space-time alignment and Bayesian fusion module and a structure health index calculation and state evaluation module. The heterogeneous topological graph construction and manifold embedding technology module is used for obtaining a heterogeneous topological graph, a node embedding vector and a manifold model parameter; the multi-scale space-time cognitive convolutional neural network module is used for performing deep feature extraction on the heterogeneous topological graph to obtain multi-scale fusion features; the continuous manifold space-time alignment and Bayesian fusion module is used for obtaining space-time alignment parameters and fusion state vectors; the structure health index calculation and state evaluation module is used for carrying out structure health monitoring and state evaluation on the bridge to obtain a final health evaluation result; therefore, the intelligence, automation and reliability levels of the structure monitoring system are improved.

The invention relates to the technical field of data processing, and discloses a comprehensive evaluation method and system for the health degree of a distribution network cable. The method comprises the following steps: collecting cable joint multi-source monitoring signals, normalizing the monitoring signals to obtain a degradation degree feature vector, correcting multi-physics field coupling model parameters, obtaining a recessive degradation index through finite element calculation to obtain an enhanced feature vector, and performing time-frequency domain decomposition to extract multi-scale feature parameters to obtain a comprehensive feature matrix; a double attention mechanism calculates a feature weight and a time sequence correlation degree to obtain a deterioration trend prediction value, and fuzzy integral is fused with a multi-classifier output probability to obtain a health degree evaluation grade and an early warning result. According to the invention, the early defect identification accuracy and the degradation trend prediction precision are improved.

The invention discloses a glioma T cell prediction and prognosis evaluation method based on pathological images, particularly relates to the field of patient prognosis health evaluation, and aims to solve the problems that existing pathological evaluation is difficult to combine with tumor structure heterogeneity and immune infiltration distribution, and the future progress risk of a patient cannot be predicted based on follow-up visit pathological data. A spatial heterogeneity map of a tumor core area and an invasion edge is constructed in a digital pathological section, density gradients of T cells in different areas are calculated to generate a distribution heterogeneity coefficient, interaction processing is performed on the two types of characteristics in combination with historical follow-up visit records, and a time sequence neural network constructed based on a gating structure is combined to obtain a high-efficiency characteristic of the tumor. And outputting disease progress probabilities of a plurality of follow-up visit time points in the future to form a prognosis trajectory prediction curve, calculating a quantitative recurrence risk score according to curve slope change and an immune fluctuation mode, and finally generating an individualized management scheme, thereby realizing quantitative prediction evaluation and risk management of the prognosis trend of the glioma patient.

The invention discloses a power equipment health evaluation method and system based on time sequence analysis and probability modeling, equipment and a medium, and relates to the technical field of power equipment state monitoring and fault prediction, and the method comprises the steps: obtaining and preprocessing multi-source operation data of power equipment, outputting a predicted value and a confidence interval of a future parameter through a time sequence prediction model, calculating a residual sequence of an actual observation value and a predicted value, fitting distribution through a probability distribution model, establishing a statistical characteristic model of a normal operation state of the equipment, performing anomaly judgment, calculating a health degree index of the equipment based on a deviation degree and a dynamic weight of a monitoring parameter and weighted accumulation, and dividing equipment state grades according to the index. Quantitative evaluation of the health state of the equipment is realized. According to the method, accurate quantification and early abnormity identification of the health state of the power equipment are realized, a reliable basis is provided for predictive maintenance, and the intelligent level and the safety guarantee capability of power grid operation and maintenance are remarkably improved.

The invention relates to the technical field of power battery health state evaluation and anomaly recognition, in particular to a remote health state analysis and anomaly recognition method for an electric bicycle battery, which comprises the following steps of: under an access working condition, applying small-signal active excitation and synchronously acquiring to form an opportunity diagnosis excitation data window; time-frequency coupling modeling including equivalent impedance modeling, harmonic wave and intermodulation fingerprint modeling, Koopman operator modeling and entropy coefficient difference is carried out, and a spectral measurement geometric distance is constructed through optimal transmission to generate a multi-domain response packet, a physical invariant vector and an uncertainty matrix; according to the geometric distance and consistency score gating and hierarchical standardization, obtaining a health state estimation record and an abnormal event set by adopting a shape-limited health mapping model; and in combination with mapping Jacobian and dual potential, generating an opportunity diagnosis incentive plan of a next period by adopting D-optimal test design, and updating a group health manifold, thereby realizing cross-environment, cross-equipment and traceable remote health assessment and early anomaly recognition.

The invention relates to the field of intelligent evaluation, and particularly discloses a robot joint wear evaluation method and system based on a neural network, and the method comprises the steps: synchronously obtaining an original vibration time sequence and a working condition data time sequence of a robot joint, and constructing a feature extraction network with working condition data as a dynamic condition; and pure degradation feature codes only related to the internal wear state are decoupled and extracted. And then, based on the pure degeneration feature coding sequence, a degeneration trend time sequence modeling network is driven to accurately capture a long-term monotonous evolution law of the degeneration trend time sequence modeling network, and a time sequence hidden state representing the real abrasion degree of the joint is generated. And finally, decoding prediction is carried out on the hidden state of the time sequence, accurate evaluation of the remaining service life of the robot joint is achieved, it can be ensured that the real degradation state of the joint is accurately tracked all the time under the working condition of wide changes, and therefore accurate and intelligent health evaluation of the robot joint is achieved.

The invention discloses a boiler tube group diagnosis and maintenance method, and relates to the technical field of boiler monitoring, and the method comprises the following steps: building a thermodynamic simulation model, and carrying out reference simulation to obtain a reference system response parameter and a reference thermodynamic parameter; based on the simulation model, multiple times of simulation are carried out by adjusting the operation condition and the fault state, and a tube panel group health evaluation model is constructed by utilizing machine learning; introducing a thermal medium into the target tube group system, collecting actual measurement data, and screening out a problem tube panel group through the health evaluation model; and performing three-dimensional laser scanning and thermal imaging image acquisition on the defective tube panel group, fusing temperature information and space coordinates through coordinate mapping to construct a three-dimensional temperature field data set, positioning an abnormal tube section needing to be maintained according to the three-dimensional temperature field data set, and performing local maintenance. According to the method, step-by-step positioning and targeted maintenance from system-level abnormity to pipe section-level defects can be realized, and the diagnosis accuracy and maintenance efficiency of the boiler pipe group are remarkably improved.

The invention belongs to the technical field of electric energy metering box performance testing, and particularly relates to an electric energy metering box performance testing system based on data analysis, which comprises a multi-dimensional data acquisition module, a self-adaptive preprocessing module, a multi-modal feature fusion module, a dynamic health assessment module, an intelligent decision output module and an operation supervision center. The multi-dimensional data acquisition module outputs an original signal, the original signal is corrected by the self-adaptive preprocessing module and then flows into the multi-modal feature fusion module to generate a high-order feature, the dynamic health assessment module generates a health index based on a feature vector, and a data acquisition strategy is reversely regulated according to an assessment result. A complete test and analysis link is formed from original data collection to final maintenance decision generation, quantitative characterization and trend pre-judgment of the performance of the electric energy metering box are achieved, the real-time performance is guaranteed, meanwhile, the long-term state pre-estimation precision is improved, and the intelligent level and the automation level are high.

The invention provides a management method and system of a vehicle networking control unit. The method comprises the steps of obtaining various operation data of a vehicle-mounted T-BOX; the types of the operation data comprise hardware state data, software operation data and vehicle operation state data; extracting a plurality of operation feature representations from the plurality of operation data; inputting the plurality of operation feature representations into a pre-trained fault prediction set model to obtain a fault prediction result of the vehicle-mounted T-BOX; wherein the fault prediction set model comprises a fault prediction overall model and at least one fault prediction sub-model for a specific fault type of the vehicle-mounted T-BOX; inputting the various operation data into a pre-constructed health state evaluation model, and determining a health state evaluation result of the vehicle-mounted T-BOX; and integrating the fault prediction result and the health state evaluation result of the vehicle-mounted T-BOX, and executing a fault processing action. In this way, the accuracy of vehicle-mounted T-BOX fault prediction, the comprehensiveness of health assessment and the initiative of fault processing can be improved, and the fault occurrence rate is effectively reduced.

The invention relates to the technical field of health evaluation, in particular to a group image health evaluation method and system based on intestinal flora, and the method comprises the steps: collecting intestinal flora samples of a target group through a standardized process, and obtaining microbial sequence information through a high-throughput sequencing technology; microflora diversity characteristics and core flora characteristics are extracted through strain identification and relative abundance analysis, individual flora characteristics are compared with a healthy population reference database, health state scores are calculated, health grades are divided, population health grade distribution is counted, and the population health grade distribution is calculated. And generating a group health evaluation report containing a visual chart and text analysis. According to the method, a complete technical system from sample collection to health strategy making is established, systematic evaluation of the group health state from the perspective of intestinal flora is achieved, and the limitation of a traditional method in group health early evaluation is overcome. The method is suitable for health monitoring of different scales of groups, and provides effective technical support for public health management and health service.

The invention discloses a remote intelligent control system for a drip irrigation head water pump based on the Internet of Things, relates to the technical field of agricultural intelligent irrigation, and is used for solving the problems of low precision and poor management efficiency of a traditional drip irrigation technology. The system comprises a drip irrigation area parameter acquisition module which acquires soil temperature and humidity, equipment operation parameters and meteorological data in a layered manner through a multi-source sensor network; the data processing module performs differential filling, space-time alignment and integration on the data; the intelligent analysis module realizes drip irrigation demand judgment, quantity value calculation, opportunity optimization and equipment health assessment; and the control execution module converts the instruction into equipment operation and supports remote intervention, so that precise management and control of drip irrigation are realized, and the precision and management efficiency of drip irrigation are improved.

The invention discloses a laser scanning type overhead conductor health detection system and method, and relates to the technical field of power equipment detection, and the method comprises the steps that a laser scanning module scans the contour of an overhead conductor according to a laser scanning line, detects the environmental interference, and corrects the scanning track of the laser scanning line in real time based on the environmental interference information; acquiring wire scanning data; the data processing module extracts wire contour data based on the wire scanning data, fits a wire diameter according to the wire contour data, and matches a standard wire diameter library to obtain wire diameter data; and the data analysis module calculates the wear rate according to the wire diameter data of the wire, evaluates the health state of the wire in combination with the multi-element wire information, and obtains a health evaluation result. According to the invention, the track deviation of the laser under environmental disturbance can be compensated in a complex scene, the lead diameter measurement precision is improved, the health state of the lead is evaluated by fusing the lead diameter data with the multi-element lead information, and the reliability of the lead health state evaluation result is improved.

The invention relates to the technical field of intelligent operation and maintenance of industrial equipment, and discloses an industrial equipment predictive maintenance method based on an adaptive neural network. According to the method, equipment operation data and sensor ontology data are acquired through a multi-source sensor network, an equipment health index and sensor data confidence coefficient are calculated based on principal component analysis, and a comprehensive health feature vector is constructed in combination with change rate and correlation analysis; inputting the feature vector and the state identifier into a hybrid neural network model, extracting a time sequence feature through an attention mechanism, and outputting an equipment health state, a residual life prediction value and a prediction confidence coefficient in parallel; based on a multi-dimensional evaluation result, a hierarchical maintenance strategy is generated and executed through a decision of data confidence management; according to the invention, through dual health assessment of the equipment and the sensor, the attention mechanism enhanced prediction model and data-driven closed-loop optimization, the accuracy and reliability of fault prediction of the industrial equipment and the self-adaptive capability of the system are improved.

The invention discloses a computer-aided old-age language erosion assessment method and assessment system, and relates to the technical field of old-age health assessment, and the method comprises the following steps: S1, collecting the voice data, text input data and interactive behavior data of an old-age user; s2, preprocessing the data, and extracting voice acoustic features, language structure features and cognitive behavior features; s3, inputting the extracted features into a pre-trained language erosion evaluation model, and outputting a language ability score and an erosion type classification result; and S4, generating a visual evaluation report, wherein the visual evaluation report comprises the language ability degradation degree, key obstacle points and intervention suggestions. According to the method, the score and classification result is automatically output through the multi-modal data acquisition and pre-trained deep learning model, so that the evaluation time is greatly shortened, the subjective deviation is eliminated, the result objectivity is ensured, the problems of low manual evaluation efficiency and high subjectivity are solved, and the effect of automatic evaluation is realized.

The application relates to the physiological parameter monitoring technical field of intelligent wearable devices, in particular to an intelligent ring health monitoring method based on multi-sensor cooperation and related equipment. The method comprises the following steps: acquiring motion, optical and temperature sensing data, combining multi-dimensional rules and a user preset log to determine a scene, performing differentiated data acquisition, and generating a scene-matched health evaluation result after signal noise reduction, feature extraction and fusion analysis. The application can balance monitoring accuracy, low energy consumption and personalization, and improve the effectiveness of health monitoring.

The invention provides an infrared physiotherapy cabin system and method based on animal and environment cooperative regulation and control, and relates to the field of animal physiotherapy and intelligent monitoring control. Comprising an animal state and cabin environment monitoring module, an infrared physiotherapy and cabin environment regulation and control module, an animal health assessment and curative effect assessment module, a personalized treatment and intelligent decision module and a man-machine interaction and safety supervision module. A credibility result is formed through multi-modal data synchronous alignment and data quality evaluation, health state characterization is constructed under credibility gating, a risk level is output, and infrared physiotherapy control parameters and cabin environment regulation and control parameters are generated through cooperative control under the constraint of a stage control boundary and a treatment target and are issued and executed. Continuously adjustable parameters are subjected to online fine adjustment in the physiotherapy process, and rollback and interlocking protection is executed when the risk is increased or the credibility is reduced; after the period is finished, the curative effect is evaluated, data recording and tracing archiving are completed, and collaborative closed-loop infrared physiotherapy regulation and control of the animal state and the in-cabin environment are achieved.

The invention relates to the technical field of vehicle wire harness fault monitoring, in particular to a wire harness performance monitoring system based on multi-source data analysis, and the system comprises a multi-source heterogeneous data sensing module which is used for collecting the electrical parameter analog quantity of a wire harness, environment vibration frequency data and vehicle operation state parameters, and outputting an original multi-source data stream; and the data preprocessing and feature extraction module is used for receiving the original multi-source data stream and carrying out timestamp synchronization and denoising preprocessing on the original multi-source data stream. According to the invention, by establishing a multi-source heterogeneous data fusion mechanism, unified normalization and structured processing of sensor data and vehicle operation data of different types, different dimensions and different time scales are realized, and a hierarchical fusion calculation method of a physical failure model and a deep learning model is combined to construct a health index SOH of a wire harness system. Effective mapping from original monitoring data to health state quantitative representation is realized to a certain extent, and the coordination and rationality of a wiring harness health assessment result are improved.

The invention discloses an air jet spinning production line intelligent control method and system based on artificial intelligence, and relates to the technical field of production line intelligent control. According to the air jet spinning production line intelligent control method based on artificial intelligence, operation process time sequence data, equipment vibration signal data and energy consumption correction time sequence data of a target production line are obtained, and an initial setting set is generated based on a tension-rotating speed coupling model and the operation process time sequence data; carrying out variational mode decomposition and feature fusion evaluation on the equipment vibration signal to obtain an equipment health evaluation value, and constructing a health evaluation prediction function; energy consumption correction time sequence data is processed through working condition deviation calibration and trend extrapolation, a predicted energy consumption evaluation value is generated, and an energy consumption evaluation prediction function is constructed. A health evaluation prediction function is taken as a dynamic safety constraint, energy consumption minimization is taken as a target, an initial setting set is optimized, and optimized spindle speed and air pressure setting values are generated; therefore, the control precision of the production process is improved.

The invention relates to the technical field of anti-interference transmission, in particular to an anti-interference transmission optimization method and system for building structure health monitoring signals. According to the method, a building structure dynamic response signal is collected through a distributed wireless sensor and digital processing is completed; deploying a lightweight imitation learning model at a local control end, performing interference mode identification on a transmission channel of monitoring data, and extracting interference features; generating a self-adaptive anti-interference transmission strategy according to the interference characteristics, and stably transmitting the monitoring data to a central server to realize multi-node data fusion and health state evaluation; and finally, performing parameter updating on the simulation learning model according to a health evaluation result returned by the server, thereby realizing high-reliability anti-interference transmission and self-adaptive optimization of the building monitoring signal. According to the invention, the effective transmission rate and the data perfectness rate of building structure health monitoring signals in a complex interference environment are improved, and the signal transmission reliability and the intelligent level are effectively improved.

The invention provides a mattress type sleep monitoring data real-time processing method based on edge calculation, relates to the technical field of medical health data processing, and aims to perform macroscopic state layer calculation, pathological event layer calculation and stability layer calculation at the same time in a layering manner based on a constructed dynamic sensing matrix, generate a structured sleep health report, and realize real-time sleep monitoring through one-time sleep monitoring. The comprehensive information about the sleep macrostructure, the respiratory pathology event burden and the sleep stability can be synchronously output; through a sleep health assessment report, total sleep duration and sleep efficiency calculated by the macroscopic state layer, apnea and type and severity calculated by the pathological event layer, and a sleep micro-awakening state and sleep fragmentation degree calculated by the stability layer can be clearly known, and a multi-dimensional and integrated comprehensive assessment result with high medical value is realized. The information amount of the sleep health portrait is enriched, and a comprehensive and reliable data support is provided for accurately positioning the root of the sleep problem and formulating a personalized intervention or treatment scheme.

The invention provides an aero-engine health management system based on intelligent oil monitoring, and relates to the technical field of aero-engine system oil monitoring. Comprising a sensor module, a data acquisition and processing module, a problem diagnosis module, a health assessment module, a maintenance decision module and a man-machine interaction module. Generating a comprehensive feature vector by receiving data acquired by the sensor module; based on a machine learning algorithm, performing problem diagnosis on the comprehensive feature vector output by the data acquisition and processing module, and identifying potential problems of an engine system; the diagnosis model outputs a diagnosis conclusion according to the fault label; according to the diagnosis result and the lubricating oil performance parameters, performing health state evaluation on the transmission system, and predicting the remaining service life; according to the health state evaluation result, a maintenance strategy is formulated and provided for the user through the man-machine interaction module; the health state, fault alarm and residual life prediction are displayed in real time; and a maintenance strategy is actively pushed, and the user is allowed to carry out alarm confirmation and report export.

The invention discloses a motor dynamic efficiency optimization regulation and control system, which relates to the technical field of dynamic efficiency optimization regulation and control and comprises a data acquisition module, an efficiency characteristic module, a health evaluation module, a state evaluation module, an optimization execution module and a parameter updating module. A comprehensive index comprehensively reflecting the running state of the motor is constructed, conversion from single efficiency optimization to system-level comprehensive optimization is achieved, a parameter updating module forms complete closed-loop control, the system can dynamically adjust key parameters according to the actual running effect, the self-learning and self-optimization functions are achieved, and the system reliability is improved. The system can dynamically adjust key parameters according to actual operation effects, has self-learning and self-optimization functions, realizes optimal balance of safety and economy through a multi-mode operation strategy, ensures equipment safety, and can dynamically optimize construction efficiency on the premise of safety.

According to an embodiment, disclosed is a system comprising a processor configured to receive an image of a breast of a patient and patient data comprising genetic data; extract features from the image and the patient data, using one or more machine learning models, wherein the features comprise a presence of a calcification and a calcification pattern to generate a breast calcification vector; augment the breast calcification vector with the genetic data; determine, using the machine learning models, a first risk for a breast cancer; a second risk to one or more organs of the patient, wherein the organs comprises one or more of heart, kidney, lungs, pancreas, and brain; predict, a third risk based on one or more of a healing response, a tumor flow and growth, inflammation and degeneration, a disease relapse, an adverse event, and a clinical response; and determine, an overall risk to the patient.

The invention discloses a pediatric spleen and stomach dynamic monitoring method and system based on an AI chip, and belongs to the field of medical health information technology and artificial intelligence chip application. The pediatric spleen and stomach dynamic monitoring method comprises the steps that a multi-mode original data set is collected, signal preprocessing is conducted, and a feature data set is obtained; key indexes of spleen and stomach syndromes are identified from the feature data set based on the viscera differentiation theory, and a dialectical result is obtained; inputting the feature data set into a dialectical verification model to generate a spleen and stomach health assessment value; when the spleen and stomach health assessment value is smaller than an assessment threshold value, a first-level health early warning signal is generated; and when the duration of the first-stage health early warning signal is greater than a duration threshold, generating a second-stage combined early warning signal according to pre-acquired user behavior log data. A multi-modal data fusion technology, a dynamic noise reduction algorithm and a grading early warning mechanism are adopted, and doctors can be assisted in achieving precise evaluation of the spleen and stomach states of children and personalized traditional Chinese medicine intervention.

The invention discloses a coast nitrogen migration dynamic evaluation system based on multi-source data, relates to the technical field of water environment health evaluation, and establishes a causal link based on a multi-source nitrogen data pool, identifies a key driving factor node and a non-dominant nitrogen input path, and generates a migration graph. According to the coast nitrogen migration dynamic evaluation system based on the multi-source data, a multi-source nitrogen data pool is constructed, a nitrogen concentration fluctuation mode is extracted, a future migration trend map is output in combination with historical interference data, risk levels are judged according to migration trends, regulation and control measures are matched, and ecological restoration suggestions are output. A migration path, a key driving factor, system generation time, space and event three-label migration map are recognized through brain-like reasoning, periodic modeling and trend prediction of nitrogen flux are achieved, dynamic early warning and differentiated ecological intervention are supported, and compared with the prior art, the method has the advantages that the efficiency is high, and the cost is low. The system has the advantages of accurate path identification, distinguishable non-dominant input, strong prediction capability and perfect visual interaction.

The invention discloses a river water quality inversion and health evaluation method and system based on satellite remote sensing, terminal equipment and a medium, and relates to the technical field of environment remote sensing monitoring. The method comprises the following steps: acquiring a time-space matched multispectral remote sensing image and ground actually measured water quality parameter data set of a research area; preprocessing the image and extracting a water body area image; constructing a data sample set based on the water body area image and the water quality parameter data set; establishing a plurality of inversion models for each water quality parameter, and selecting an optimal inversion model through performance verification; and calculating a comprehensive health index and a health level of the river in the water body area through objective weight determination. According to the method, the problems of low monitoring efficiency, insufficient inversion precision and high evaluation subjectivity in the prior art are solved, efficient and accurate inversion and objective health evaluation of large-range river water quality are realized, and decision support is provided for environment management.

The invention discloses a structural health monitoring method for a high-temperature and ultrahigh-pressure reactor based on an unmanned aerial vehicle platform, the unmanned aerial vehicle platform carries a non-contact thermal imaging module and a contact ultrasonic detection module, and the structural health monitoring method comprises the following steps: receiving and processing data from the non-contact thermal imaging module, identifying a suspected abnormal area on the surface of the reactor; generating a control instruction based on the identified suspected abnormal area, and guiding the unmanned aerial vehicle platform to move the contact type ultrasonic detection module to a corresponding position for contact type detection; and the data processing module is used for receiving and processing data from the contact type ultrasonic detection module so as to verify and evaluate the suspected abnormal area. According to the technical scheme provided by the invention, full-coverage structure health assessment can be carried out on the high-temperature and ultrahigh-pressure reactor through the remote unmanned aerial vehicle platform, and potential internal defects and surface abnormalities can be accurately identified.

The invention discloses an emotion recognition method, system and device for child psychological health and a storage medium, and relates to the technical field of psychological health assessment. Obtaining dialogue data between the child and the human-computer interaction interface, and performing natural language and acoustic feature processing on the dialogue data to obtain a current comprehensive emotion value; if the current comprehensive emotion value is smaller than the negative emotion threshold value, marking a time point corresponding to the voice information as an emotion starting moment; acquiring emotional dialogue text information according to the emotional starting moment; identifying the emotional dialogue text information, and calculating a first attribution score and a second attribution score associated with each candidate emotional reason; calculating the first attribution score and the second attribution score to obtain a final attribution score; and determining a core emotion reason according to the final attribution score, and generating an intervention measure based on the current comprehensive emotion value and the core emotion reason so as to intervene the child according to the intervention measure. By implementing the provided technical scheme, the accuracy of children mental health intervention is improved.

The invention discloses a multi-modal disease prediction algorithm based on a dynamic health record, and the algorithm comprises the following steps: multi-modal health data collection: synchronously obtaining multi-source data comprising a time sequence physiological signal, image data, a clinical diagnosis result and a user behavior log through a non-contact sensing device and a clinical data interface; dynamic health archive construction: establishing an individual health feature network based on a graph database, and realizing association storage of real-time data and historical baselines through a time sequence alignment mechanism to form a dynamically updated health portrait; multi-modal feature fusion: carrying out cross-modal feature extraction on the acquired data; the method has the advantages that by integrating multi-modal data such as RGB / near-infrared images, rPPG physiological signals, near-infrared spectrum biochemical indexes, clinical data and behavior logs, cross-dimensional health assessment of physiology-biochemistry-behavior is achieved; the defect that traditional single-mode data cannot describe the health state completely is overcome, and the accuracy of disease risk prediction is remarkably improved.

The invention relates to the technical field of intelligent health monitoring, and provides a body fat scale data multi-dimensional health score calculation and generation method and system. The method comprises the steps of obtaining body data and basic information of a user; processing the body data and the basic information according to a preset multi-dimensional health scoring model; enabling each piece of body data to correspond to a scoring interval of the scoring dimension to which the body data belongs, obtaining a corresponding basic score, calculating a weighted score of each scoring dimension according to the weight coefficient of each scoring dimension, and summing the plurality of weighted scores to obtain an initial health score; and correcting the initial health score according to the basic information, and generating a final multi-dimensional health score. Aiming at the defects of singleness, no differentiation and non-dynamic health scoring of an existing intelligent body fat scale, a set of complete intelligent health assessment system is formed through multi-dimensional model construction, basic information correction and data driving optimization.