97 results about "Health intervention" patented technology

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 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 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 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 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 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 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.

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.

The invention relates to the technical field of drinking water containers, and provides a method and device for displaying the effective water intake of a water cup and the water cup. The cup cover comprises a top cover and a bottom cover, the top cover can open and close the opening end of the bottom cover, the bottom cover is detachably connected with the opening end of the cup body, the water cup effective drinking amount display method comprises the steps that the water cup states including the effective drinking state and the ineffective water consumption state are obtained based on the cover opening behavior, in the effective drinking state, the top cover is opened relative to the bottom cover, and in the ineffective water consumption state, the top cover is opened relative to the bottom cover; the bottom cover is connected relative to the cup body, and the bottom cover is detached relative to the cup body in an invalid water consumption state; the effective water drinking amount of the water cup in the effective water drinking state or the invalid water consumption amount of the water cup in the invalid water consumption state is obtained; and the effective water intake and the ineffective water consumption of the water cup are displayed. The water drinking amount of the user can be accurately displayed, complex behaviors of the user can be recognized, health intervention can be carried out, and therefore the credibility degree of effective water drinking amount display of the water cup is improved.

The invention discloses a multi-dimensional physiological signal monitoring system for mental stress of nursing personnel. The system comprises a signal acquisition module, a context acquisition module, a processing analysis module and an output module. The signal acquisition module is used for acquiring multi-dimensional physiological data such as an electrocardiosignal, a motion signal and an environment signal of a nurse in real time; the context acquisition module acquires a clinical operation log from a hospital information system; the processing analysis module is used for distinguishing psychological pressure from physiological interference through a multi-modal signal fusion filtering technology, intelligently associating an effective pressure event with clinical operation and determining the context of a pressure source; and the output module is used for generating a personalized pressure analysis report and a management instrument panel containing the clinical work context. The problems that in the prior art, pressure monitoring specificity is poor, and a pressure source is unknown are solved, accurate monitoring and intelligent source tracing of the psychological pressure of nursing personnel in a real clinical environment are achieved, and a scientific basis is provided for individual pressure management and organization health intervention.

The invention provides a psychological health analysis and intervention method based on multi-Agent collaborative assessment, which comprises the following steps of: shunting information and dialogue contents input by a user through an initial shunting Agent module, and then performing parallel and independent multi-dimensional psychological assessment by using a plurality of different assessment Agent modules. Meanwhile, each assessment Agent module corresponds to a psychological dimension and is provided with a special vertical domain knowledge base, then, structured output results of the assessment Agent modules are received and integrated through the central coordination and treatment agent, a collaborative and unified psychological health assessment report is generated, and finally, based on the assessment report, a psychological health assessment report is established. The treatment intervention Agent module is used for generating a personalized mental health intervention scheme matched with the user, and a precise intervention scheme is provided for the user.

The invention provides a female age prediction system and method fusing facial images and biochemical indexes, and belongs to the field of artificial intelligence and health management, and the system comprises a data collection and preprocessing module, a dual-channel feature extraction module, a multi-modal feature topology fusion module, an age prediction and health evaluation module, and a personalized health intervention module. According to the method, a biochemical feature topological space and a facial feature topological space are innovatively constructed, feature fusion is realized through a cross-modal attention interaction network and a topological consistency keeping mechanism, the system predicts the biological age and generates a three-dimensional health risk score based on the fused features, and dynamic calibration is carried out in combination with female physiological cycle data, so that the health risk of the female is evaluated. According to the method, the technical contradictions that the traditional detection method is high in cost due to high precision and obvious in error due to low cost are solved, and accurate and reliable female physiological age evaluation is provided.

Provided herein are systems and methods for communicating with user devices over networked environments. A server may provide, to a user device over a communication network, an instruction to present a graphical user interface (GUI) on a user device to prompt a user to input values for event parameters for events on the user device according to a data protocol. The server may receive, from the user device, a plurality of values for a plurality of event parameters The server may determine, based on inputting via the GUI, a metric. The server may apply the plurality of values and the metric into a machine learning model to generate a likelihood to satisfy a target associated with the data protocol. The server may, responsive to the likelihood not satisfying a threshold, transmit a subsequent instruction.

The invention relates to the technical field of plateau medicine and health information, and provides a plateau health risk assessment and personalized management method. The method comprises the following steps: firstly, collecting plateau environment exposure data and multiple physiological index data of a user; calculating the plateau health risk level of the user by using a pre-trained multi-factor fusion evaluation model and combining the basic features of the user; and finally, calling a corresponding management strategy library according to the risk level, and generating and outputting a personalized management scheme containing plateau adaptation suggestions, health intervention measures and risk early warning. Through data driving and an intelligent algorithm, dynamic and accurate assessment and personalized management guidance of plateau health risks are realized, and the defects of one-sided assessment and lack of personalized guidance in the prior art are effectively overcome.

This specification provides a method and system for processing pet data, including: obtaining target-related information of a target pet, wherein the target-related information includes basic information, historical claims information, and hardware perception information of the target pet; the hardware perception information is collected from the target pet by hardware perception devices based on the target pet's environment; determining the target disease of the target pet based on the target-related information; and determining and pushing a target health intervention plan corresponding to the target disease to the owner of the target pet based on a pre-constructed target knowledge graph, wherein the target knowledge graph is used to represent the correspondence between diseases and health intervention plans. This aims to improve the pet's health status, ensuring the pet is as healthy as possible and reducing the pet insurance payout rate.

This invention provides a health management method, device, equipment, and storage medium based on assisted diagnosis. The method includes: standardizing patient medical record data, examination report data, and health monitoring data to construct a digital patient file; performing inference calculations through multiple intelligent analysis models, and adaptively weighting and fusing them according to the reliability coefficients and uncertainty measures of each model to generate a comprehensive probability ranking of multiple candidate results; generating assisted diagnostic results and recommended examination items based on this ranking; and calculating risk assessment indicators by combining the temporal characteristics of historical health data to generate personalized dynamic monitoring plans and health intervention suggestions. This invention improves output stability through multi-model fusion, avoiding the limitations of a single model; provides rich references for clinical diagnosis through multi-candidate ranking; and achieves dynamic risk assessment through temporal analysis, forming a complete closed loop from assisted diagnosis to health management, thus improving reliability and practicality.

The present application relates to the technical field of medical health management, in particular to a chronic disease patient health management system based on big data, comprising: constructing a full-dimension personal health record containing static and dynamic information. The data analysis module performs abnormal pattern mining on continuous sign data, and generates a trend graph by associating periodic test data with historical records. The data correlation module constructs a risk correlation matrix based on environmental exposure data, which quantifies the contribution of environmental behavior factors to health indicators. The risk modeling module integrates the above information. The prediction and intervention generation module uses a chronic disease progression prediction model to calculate and synchronously output individualized disease risk warning signals and the corresponding phased health intervention programs. The present application realizes the quantitative attribution of health risk factors and dynamic, phased precision health management.

This invention discloses a user health intervention feature management system and method based on multi-source data, belonging to the field of medical information technology. The method includes: verifying user identity and connecting to a medical management platform to obtain medical data (including medical time, diagnosis, and medication); constructing a supplementary extended database containing non-medical and non-platform medical databases; integrating relevant data to form a user-coordinated dataset; parsing the dataset to obtain a user medical feature library; opening a verification feedback interface based on the feature library; and managing and adjusting the user medical database based on feedback data. This invention solves the technical problems of fragmented, incomplete, and difficult-to-achieve personalized and precise services from existing user medical data, achieving the technical effect of effectively managing user medical data and providing continuous high-quality data support for medical services.

The application provides a dynamic modeling method and system for multi-field skill AI automatic semantic labeling, and relates to information retrieval, medical health and cross-field skill fusion scene. Through nonlinear dynamic modeling and biomedical stability control, the semantic label automatic generation and real-time matching of talent skill and health intervention effectiveness are realized. The application innovatively integrates the sound wave frequency of fetal brain promoting music method, maternal action instruction and blood type nutrition scheme into the "skill-time" frequency domain space, combines the Gevrey smoothing operator and fractional derivative evolution equation, accurately separates the temporary fetal movement fluctuation and low-frequency core characteristics, and breaks through the characterization limitation of traditional models on nonlinear biological coupling effect. By introducing the "intervention effect-time" frequency domain component and local dependency constraint, the short-term effectiveness and long-term value of music intervention on fetal neurodevelopment are quantified, and the model distortion problem caused by extreme data or cross-field parameter resonance in traditional methods is solved.

The present application relates to the technical field of data processing and strategy optimization, and particularly relates to a diet structure health intervention method and system based on physical data of obese children. The present application firstly acquires physical data and nutrition data and groups children; further acquires intervention synchronism of each physical data according to deviation characteristics of each physical data compared with corresponding preset physical standard and similarity of changes in time domain of deviation characteristics of all physical data; further adjusts the diet structure of the target child according to the difference in intervention synchronism of each physical data of the target child and the children in the same group at the current period, in combination with the difference between the nutrition data and the preset recommended diet structure. By analyzing the individualized differences in the physical angle and the diet intake angle of the children, the diet structure of the children is continuously adjusted, the diet intervention adaptability is improved, and the intervention effect is ensured.

The invention discloses a health monitoring and early warning method and system based on multi-source data fusion, and relates to the technical field of medical information, and the method comprises the steps: carrying out the health semantic mapping and medical association marking of multi-source monitoring health data, and forming an associated health data set; identifying health data missing items with data missing in the associated health data set, and outputting user health completion data; performing association relation extraction on the user health completion data, and constructing a health state association map; identifying a state change result of the health data node in the health state association map in the time dimension and an association influence transmission path in the data association relationship, and generating health early warning data; and converting the health early warning data into early warning prompt information, and outputting a health intervention suggestion. According to the method, association consistency construction and data integrity improvement of the multi-source health data are realized through cooperation of medical association marking processing and cross-source inference supplementation.

The invention relates to a full-life-cycle medical health management method and system, and relates to the technical field of wisdom medical treatment, and the method comprises the steps: constructing a full-process closed-loop system from data collection, risk assessment to intervention optimization, effectively solving the problems of insufficient precision, low efficiency and high cost of traditional health management, and improving the health management efficiency. By means of an analysis mechanism of medical specialized training and Sudoku intelligent matching, personalized health intervention based on personal and family collaborative data is achieved, the service matching degree and safety are improved, dynamic evaluation and closed-loop optimization can be conducted on an intervention scheme by continuously tracking execution data and quantifying health index changes, and the intervention efficiency is improved. While the compliance of the user and the health result are improved, the manual dependence and repetition cost are reduced, and a scientific, efficient and extensible solution is provided for full-life-cycle health management.

This invention relates to a method and apparatus for predicting and analyzing health intervention factors based on the Eight Principles of Traditional Chinese Medicine (TCM) diagnostic methods. The method includes: constructing a deep learning model, denoted as the first prediction model, for predicting TCM interventions based on the four diagnostic methods of TCM; constructing a first dataset through volunteer data collection and expert group classification; training the first prediction model based on the first dataset; after model training, feeding the user-input four diagnostic vector X into the first prediction model for prediction, and performing health intervention factor analysis based on the model's output prediction vector Y and an intervention factor library to obtain a corresponding health intervention report, which is then fed back to the current user. This invention can improve the personalized prediction level of health intervention factors and enhance the accuracy and completeness of health intervention factor analysis.

The invention discloses a cancer rehabilitation period patient and caregiver physiological health collaborative monitoring and intervention system, which comprises a multi-source physiological data acquisition module, a physiological interaction modeling module, a collaborative health intervention module and a closed-loop feedback regulation and control module, the multi-source physiological data acquisition module synchronously acquires heart rate variability, electrodermal response and body movement acceleration data of a patient and a caregiver through a wearable device and outputs a physiological stress index, a physiological load index, a physiological synchronization index and a rhythm matching index, and the physiological interaction modeling module constructs a double-person physiological state interaction model based on cross-correlation analysis. Key intervention nodes are recognized, the collaborative health intervention module determines the optimal intensity ratio and time sequence cooperation parameters of intervention of the two parties through a collaborative optimization algorithm, the closed-loop feedback regulation and control module executes intervention and monitors the effect, and when deviation exceeds a threshold value, a feedback signal is transmitted back to trigger scheme adjustment; a complete technical link of two-person physiological data collaborative acquisition, interactive modeling, collaborative intervention and closed-loop feedback is realized.

The invention discloses a multi-modal data fusion intelligent analysis system for motion evaluation, and belongs to the technical field of intelligent health monitoring and motion analysis, the system comprises a Vue + Django front-end and rear-end architecture, the Vue + Django front-end and rear-end architecture comprises a Vue front-end interaction module and a Django rear-end processing module, the Vue front-end interaction module uses AXIOS to access a RESTful API through an HTTP protocol, and interaction with the Django rear-end processing module is realized; the Vue front-end interaction module comprises a report generation unit, the system is adopted, a set of complete technical scheme from data collection to health intervention is established, and the technical problems that a traditional evaluation system is single in dimension, data is split, intervention guidance is weak and the like are effectively solved.

This application discloses a central robot and its home-based health and elderly care implementation method. The method includes: acquiring a user's health events based on multimodal perception data from multiple non-visual sensors, detection data from health monitoring devices, and a digital twin space; activating visual sensors when preset conditions are met, and performing fusion processing with the digital twin space and the user's health profile to obtain the user's scene context; generating health intervention content based on the scene context and a pre-stored health knowledge graph, and mobilizing corresponding execution modules for multimodal output; determining the user's interaction information based on the perception data from the sensing modules and the digital twin space, and adjusting the corresponding execution modules for multimodal output. Implementing the technical solution of this application can avoid errors in perception from a single device or module, reduce the risk of user privacy leakage, provide more professional closed-loop health services, and eliminate the need for complex user operations.

The invention relates to the technical field of health management, and discloses a personalized health management scheme data mining method, which comprises the following steps of: acquiring multi-source heterogeneous health record data of a target user, performing time-space alignment processing on the multi-source heterogeneous health record data, and generating a standard health data sequence; constructing a dynamic health entropy value to divide a health level interval; inputting the health level interval into a preset health intervention knowledge base, determining an intervention measure primitive matched with the health feature vector set, performing association mapping on the intervention measure primitive and the dynamic health entropy, and constructing an intervention mapping matrix; taking minimization of the dynamic health entropy as an optimization target, performing combined screening and priority ranking on intervention measure elements in the intervention mapping matrix, and generating a personalized health candidate set; performing expected effect deduction on the individual health candidate set to obtain an optimized health management scheme; according to the invention, the efficiency of personalized health management scheme data mining can be improved.