3181 results about "Health data" patented technology

The invention relates to the technical field of privacy data protection, and discloses a privacy data protection method and system based on homomorphic encryption and federated learning, and the method comprises the steps: processing original health data collected by Internet of Things equipment of a plurality of pension institutions, and obtaining a preprocessing data set and a privacy risk assessment result; encrypting to obtain a double encrypted data set; constructing a decentralized federated learning framework, and calculating global training parameters; performing noise injection processing and model iteration training to obtain a privacy protection model; performing trusted execution environment deployment on the privacy protection model, and performing identity verification through role-based multi-level access control and zero-knowledge proof to obtain a security access strategy; edge node distributed deployment is conducted on the privacy protection model, cross-mechanism health data analysis is conducted through a secure multi-party computing protocol, a collaborative analysis result is obtained, various privacy protection technologies are adopted, and efficient utilization of data is achieved while data privacy is protected.

The invention provides a dynamic health adaptive monitoring method and system using artificial intelligence, and relates to the technical field of health monitoring, and the method comprises the steps: carrying out the noise reduction through a hierarchical diffusion probability model through multi-source heterogeneous health data, extracting preliminary features through combining with an improved attention auto-encoder, learning discriminative features through a contrast learning network, and carrying out the recognition of the discriminative features; inputting the discriminative features into an adaptive wavelet neural network for multi-scale feature decomposition, inputting the discriminative features into a dynamic graph neural network to obtain a health feature tensor, obtaining time sequence features by using a hierarchical memory enhancement network, and generating a health knowledge graph in combination with a dynamic routing capsule network and a causal reasoning network; and inputting the time sequence characteristics and the health knowledge graph into a deep Bayesian network for health risk assessment, and obtaining an adaptive monitoring scheme through a model prediction control network by using a hierarchical reinforcement learning network and combining a multi-agent collaborative optimization system and a deep deterministic strategy gradient network.

A computing system may include non-transitory computer-readable media storing instructions that when executed by the one or more computer processors, causes the computing system to perform operations comprising: causing presentation of a graphical user interface, the graphical user interface being associated with patient health data, wherein the graphical user interface is configured to: display one or more user interface elements, receive information identifying a logging action and at least one classification associated with the logging action, generate, by the processor, one or more models based on the received information, based on the generated one or more models, automatically associate the one or more user interface elements with one or more logging actions and update the graphical user interface with at least a portion of the one or more user interface elements.

The invention discloses a multi-source data fusion method and system based on cloud computing, and particularly relates to the technical field of multi-source data fusion, and the method comprises the steps: obtaining the real-time information of multi-source data, generating a health score and a label through a health evaluation model, and marking a data state and an abnormality type; performing format conversion and semantic enhancement processing on the health data to generate structured data; based on a knowledge graph reasoning technology, extracting a logical relationship to generate a fusion rule, and calculating a rule weight and confidence in combination with a health label, a data source confidence and a semantic feature; a fusion result of conflict data is judged through a semantic consistency index, if the fusion result does not conform to expectation, historical data and rules are loaded according to a verification model, a verification score is calculated, an optimization process is triggered after the verification score is compared with a threshold value, health assessment model parameters, a semantic embedding model and fusion rule logic are optimized, and fusion accuracy and reliability are improved.

An example operation includes one or more of accessing, by an application providing assistance to a vehicle, sensor data associated with an environment inside and outside of a vehicle and profile data associated with a vehicle occupant, determining, by the application, an initial condition of the vehicle occupant based on the sensor data and the profile data, responsive to the initial condition being above a health condition threshold, accessing, by the application, health data associated with the vehicle occupant from a mobile device, determining, by the application, an updated condition of the vehicle occupant based on the health data, creating, by the application, an alert to notify the occupant based on the updated condition and one or more current driving conditions of the vehicle identified by the sensor data, and performing, by the vehicle, one or more vehicle actions based on the alert and the one or more current driving conditions.

The invention provides a method and system for monitoring the running state of a photovoltaic power station in real time, and relates to the technical field of photovoltaic power station monitoring, and the method comprises the steps: integrating a multi-mode sensor array in a photovoltaic module junction box, and collecting the sensor data of the photovoltaic power station in real time; performing localization preprocessing on the sensor data through an edge computing node, and transmitting different priority data to a cloud based on a dynamic hybrid communication protocol; sensor data are fused at the cloud, a four-dimensional digital twinborn model is constructed, and time-space continuous meteorological prediction, component aging and dust retention evolution data are fused; recognizing the fault mode of the photovoltaic power station, and dynamically optimizing the string topological structure of the photovoltaic power station in combination with the real-time operation state of the photovoltaic power station, the equipment health data and the fault mode recognition result; by integrating the distributed sensor, the edge calculation module and the intelligent alarm mechanism, the problems of delay, insufficient intelligent analysis and response lag in the prior art can be effectively solved.

The invention relates to the technical field of wind power generation, and discloses a wind turbine generator gearbox fault diagnosis method and device and computer equipment, and the method comprises the steps: collecting multi-source data of a wind turbine generator gearbox; respectively extracting a time domain feature, a frequency domain feature, a temperature feature and a working condition feature of the multi-source data; performing weighted feature fusion on the time domain feature, the frequency domain feature, the temperature feature and the working condition feature to obtain a real-time fusion feature; obtaining historical health data of the wind turbine generator gearbox, establishing a health degree reference of the wind turbine generator gearbox based on the historical health data, and constructing a health degree model based on the real-time fusion features and the health degree reference; and performing fault diagnosis on the wind turbine generator gearbox by adopting the health degree model. According to the method, multi-source data of the gearbox of the wind turbine generator is combined, and gearbox health state evaluation and fault type identification are realized through dynamic feature fusion and deep learning modeling.

The invention relates to the technical field of smart communities, and discloses a smart community resident health management method based on machine learning, and the method comprises the steps: continuously collecting the health feedback data of residents, such as the physical condition change after outdoor activities, carrying out the correlation analysis of the health feedback data with the previous health data and environment data, dynamically updating and optimizing a correlation model, and obtaining the health feedback data of the residents; the prediction precision of the model and the effectiveness of suggestions are improved, the relationship between the use condition of the community public space and the resident health indexes is analyzed, key public facilities and areas influencing resident health are identified, data support and decision basis are provided for community planning and facility reconstruction, and the method is suitable for popularization and application. Community resident health and environment condition analysis reports are generated regularly, and resident health index change trends are visually displayed for reference of community managers and residents. According to the invention, intelligent analysis and application of environment and health data are realized, and technical support is provided for building a healthy community.

The invention relates to the technical field of intelligent power grid dispatching, in particular to a power grid dispatching method and system, and the method comprises the steps: collecting environment data, equipment health data and economic and market data, and generating fusion data through edge computing equipment; a multi-model integrated deep learning model is adopted to predict power demand and supply, a reinforcement learning algorithm is combined to simulate a power grid dispatching scene, and an optimal dispatching strategy is generated; and dynamically adjusting the output power of the power station and the power grid node load distribution, and updating the scheduling strategy in real time. According to the method, the prediction precision is improved through multi-model integration, a scene simulation strategy is optimized by utilizing reinforcement learning, and coordinated optimization of global and local states is realized in combination with multi-level feedback analysis, so that the stability and economy of power grid operation are remarkably improved, meanwhile, carbon emission is reduced, and the method is suitable for complex scenes of modern intelligent power grid dispatching.

The invention provides an integrated sharing method and platform for health data of old people, and relates to the technical field of data processing.The method comprises the steps that multi-source heterogeneous health data of the old people are accessed, a rule template in a historical grading preprocessing rule base is matched based on data source capacity and data features, and data cleaning and standardization are completed; further identifying a sensitive field based on a medical data standard and an industry privacy specification, generating a sensitive field vector, carrying out sensitive level scoring, and calling a data desensitization strategy library to execute desensitization operations such as de-identification and masking; an access control function is constructed based on user attributes, access context and field sensitivity levels, authorization levels are determined, differential encryption is performed, and authorization views and data tokens are generated for secure access. Through the method, the problems of heterogeneous sources, complex processing, insufficient privacy protection and lack of flexibility of a sharing mechanism of old-age health data in the prior art can be solved.

The invention discloses an AI virtual coach training system based on standard action matching and deviation feedback, which relates to the technical field of AI virtual coach training systems and comprises a user modeling module, an action acquisition module, a template matching module, a deviation calculation module, a feedback generation module, an interactive presentation module and a learning optimization module. The user modeling module is used for modeling a registered user by adopting a body parameter acquisition and health data analysis method to obtain a user personalized feature vector; the action acquisition module is used for capturing actions executed by a user in real time by adopting a multi-source sensor fusion method to obtain a time sequence containing key point coordinates; and the template matching module is used for comparing the time sequence of the key point coordinates with corresponding actions in a preset standard action template library by adopting an improved dynamic time warping (DTW) algorithm to obtain an optimal matching path and a corresponding minimum matching cost.

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

The invention relates to the technical field of health event prediction, in particular to an AI-based abnormal health event prediction system, which comprises a data acquisition module, a data preprocessing module, a causal analysis and health trend deduction module, a dynamic risk assessment and hierarchical management module, a feedback and optimization module and a user interaction and suggestion module, the data acquisition module collects health data of a user in real time; the data preprocessing module is used for preprocessing the collected health data; the causal analysis and health trend deduction module deduces the health trend of the user; the dynamic risk assessment and level-to-level management module dynamically assesses the health risk of the user; and the feedback and optimization module optimizes the health trend prediction model. According to the method and the system, the user is helped to take proper actions to prevent or deal with potential health problems, refined and personalized health management is realized, and the overall health level of the user is improved.

Embodiments described herein provide for a healthcare ecosystem of computing devices participating as nodes of a distributed ledger (e.g., blockchain). The distributed ledger includes NFTs representing corresponding patients, where the initial NET behaves logically as a token scaffold that represents a “digital twin” of the particular patient. The initial patient token represents personal profile data of patient personal data for the patient. The nodes may generate new data and / or new sub-tokens (or other blockchain entry type) representing new data, which the nodes associate with the token scaffold. The sub-tokens contain data for representing physical or digital specimens, health data, and data derived from the health data (e.g., cell lines, organoids, other model systems). As the nodes generate and associate new sub-tokens to the patient's token scaffold over time, the token scaffold graphs and facilitates integration of patient-specific data and evolves into a robust electronic health record (EHR) and digital twin.

A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.

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 the technical field of pension information management, and discloses a cloud-based pension information management system, which comprises a health data acquisition module, a privacy protection processing module, a risk prediction module, a resource scheduling module and a comprehensive service module. The health data acquisition module synchronously acquires data in a multi-mode manner and encrypts and uploads the data by means of wearable equipment and an Internet of Things sensor; the privacy protection processing module guarantees data security by using federated learning and differential privacy encryption; the risk prediction module accurately predicts health risks based on a graph neural network; the resource scheduling module optimizes resource configuration through deep reinforcement learning; and the comprehensive service module generates a personalized service chain according to the knowledge graph. The system solves many problems of traditional pension information management, realizes multi-dimensional data real-time acquisition and privacy protection, accurately predicts health risks, efficiently schedules resources, provides personalized services, improves the pension service quality and the life quality of old people, and promotes the intelligent development of the pension industry.

The invention discloses a user data intelligent protection method and system based on differential privacy, and belongs to the technical field of data protection, and the method specifically comprises the steps: collecting and preprocessing user health data; setting a dynamic privacy budget according to the data sensitivity, the analysis purpose and the privacy demand, and adding random noise to generate disturbance data by adopting a multi-strategy fused data disturbance strategy; statistical analysis is carried out, and an improved differential privacy algorithm is used to assess the privacy leakage risk, the protection degree and the data availability; based on an evaluation result, a privacy budget and a disturbance strategy are iteratively adjusted through a feedback loop mechanism; the health data analysis method provided by the invention not only can protect user privacy, but also can ensure data availability.

The invention relates to the technical field of health management and artificial intelligence, in particular to an old people health analysis system and method based on combination of medicine and nursing. The method comprises the steps of collecting and processing multi-modal health data, and generating a health data vector set; mapping the vector set to a standardized health knowledge ontology, constructing a health map initial structure and generating a map node vector; generating a health portrait and an evolution prediction result based on time sequence modeling; identifying risk features according to the health portrait and the prediction result, and generating a medical care service strategy set; and issuing the strategy set to an execution unit and collecting feedback, and finally updating a graph structure and optimizing model parameters based on feedback data. According to the invention, multi-modal data-driven old people health dynamic evolution modeling and personalized service strategy closed-loop optimization are realized, and the intelligent level and response efficiency of medical and nursing services are improved.

The invention relates to a personalized healthcare management system and method. The system integrates advanced technologies, immersive technology, and a data exchange platform to provide dynamic, personalized health insights and proactive management in a secure, interactive environment. It employs machine learning techniques to examine health-related data, forecast health risks, and propose preventive actions. The system also provides customised content based on user's progress, comprehension, and evolving health conditions. It combines health data, monitoring devices, and continuous health monitoring, and uses environmental data to forecast and alert users of potential health risks. The system also employs secure data technology for data privacy and security, and a machine learning framework for developing and deploying predictive models.

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.

The invention discloses an old people chronic disease risk prediction method and system based on a time sequence knowledge graph, and belongs to the technical field of medical health information, and the method comprises the following steps: S1, obtaining a health data set of an old people group; s2, on the basis of an ESOA-XGBoost algorithm, performing feature selection and sorting on each piece of health data in the health data set so as to determine correlation among variables, and generating a health data feature set; s3, constructing and training a time sequence knowledge graph prediction model including an STKGR-PR model and a T i PNN model, and predicting a relationship among the target entity, other entities and time sequence information through the time sequence knowledge graph prediction model; s4, predicting the old chronic disease risk of the old people group according to the trained time sequence knowledge graph prediction model; through the method, an elderly chronic disease risk prediction result with a good prediction effect and high accuracy can be obtained.

The invention belongs to the technical field of intelligent lighting, and discloses an intelligent adjustment method and system for the spectrum of a quantum dot light-emitting lamp. The objective of the invention is to solve the problem that an existing lighting system cannot meet personalized healthy lighting requirements. The method comprises the following steps: firstly, acquiring user biological rhythm, environment spectrum, use scene, user preference and physiological health data through multi-dimensional data acquisition, and constructing a feature data set; then human factor optical feature extraction and cross-situation demand analysis are carried out, and a spectral response model is established; carrying out quantum dot material photoelectric characteristic mapping and spectrum synthesis modeling based on the model, and determining a luminescence parameter space; constructing a spectrum optimization model, performing multi-objective optimization by comprehensively considering visual comfort, energy efficiency and health influence, and generating a dynamic spectrum regulation and control strategy; closed-loop verification and adaptive optimization are carried out based on real-time physiological feedback, and an intelligent spectrum adjustment scheme is formed. According to the invention, the advantage of spectrum adjustability of the quantum dot material is fully exerted, and real personalized intelligent illumination is realized.

Systems and methods are disclosed for preserving patient privacy while transmitting health data from one geographic region to another geographic region for data analysis. One method includes receiving patient-specific health data including patient privacy information at a first region; removing the patient privacy information from the patient-specific health data to generate anonymous health data; storing the patient privacy information at the first region; and transmitting the anonymous health data to a second region for analysis.

The invention discloses an intelligent maintenance system and method for an electric actuating mechanism based on fault prediction, and particularly relates to the technical field of equipment maintenance. The method comprises the following steps: acquiring historical operation data in a preset operation period, generating a historical health data set, extracting characteristic parameters to obtain a historical characteristic parameter set, and constructing a trend change characteristic matrix according to the change trend of the historical characteristic parameters; constructing a multi-dimensional state space by using the trend change feature matrix, calculating the state aggregation degree of the fault event in the space, and determining an early warning index set; meanwhile, collecting real-time operation data to extract real-time feature parameters, forming real-time feature state vectors, and performing correlation calculation on the real-time feature state vectors and early warning indexes in a space to obtain real-time risk correlation factors; and finally, generating a risk probability prediction model according to the real-time risk association factor, the historical characteristic parameter set and the trend change characteristic matrix, predicting a fault probability and outputting a maintenance scheduling suggestion, thereby realizing intelligent maintenance of the electric actuating mechanism.

The invention discloses a personalized medical record analysis recommendation method and system based on a patient portrait. The method comprises the following steps: S1, generating a preprocessed health data set; s2, according to the preprocessed health data set, detecting a health event of the patient through a predefined health event triggering rule and an event classification model; s3, based on the priority of the health event, setting a trigger condition to start an updating process of the patient portrait; s4, the preprocessed health data set is combined with health event information, and a dynamic patient portrait is generated through a multi-modal data fusion technology and a time sequence modeling technology; s5, calculating a similarity index and selecting an optimally matched similar medical record; s6, according to the intelligent medical record analysis result and the medical knowledge graph, a diagnosis and treatment suggestion for the current patient is generated in combination with the rule base. The intelligent level and the precision level of the personalized medical record analysis recommendation system are remarkably improved.

The invention discloses a chronic disease patient information management system and method based on intelligent AI, and relates to the technical field of information management. The information management system comprises a patient information management module, a data acquisition module, a data management module, a data analysis and evaluation module, a risk early warning module, a health management module and a remote medical service module. According to the chronic disease patient information management system and method based on the intelligent AI, learning and analysis are carried out based on comprehensive health data of the patient, so that accurate assessment of the health risk of the patient is realized, the current health data of the patient is integrated, and multi-dimensional information of medical history and living habits of the patient is combined, so that the assessment result is more comprehensive and accurate, and meanwhile, the assessment efficiency is improved. The health risk possibly faced by the patient in the future is predicted, early warning is given out in advance, sufficient time is provided for medical institutions to formulate intervention measures, potential health problems can be found and handled in time, and disease deterioration is prevented.

The invention discloses an insurance intelligent decision engine system based on a multi-modal user portrait, and relates to the technical field of insurance industry, the system comprises a semantic representation space construction unit used for performing feature decoupling on collected multi-modal data by using variational modal decomposition, extracting modal features, and constructing a semantic representation space based on each modal feature; a user portrait generation unit; a user classification result acquisition unit; and the insurance decision-making unit is used for analyzing the multi-source health data in the user portrait by using a cross-modal alignment technology, constructing a layered health risk assessment model, and generating an insurance recommendation strategy and a risk avoidance scheme in combination with a user classification result. According to the method, resource waste and efficiency loss caused by scattered storage and repeated development of data are avoided through multi-modal data fusion and construction of a unified semantic representation space; and in combination with a hierarchical label system, the user portrait can be automatically generated, and the intelligent level of insurance business is enhanced.

The invention belongs to the field of machine tool health state monitoring, and discloses a machine tool feed shaft health monitoring method based on an optimization condition auto-encoder. The sensor collects vibration signals and power signals of the machine tool feed shaft in a healthy state and a partial fault state; preprocessing and denoising the vibration signal and the power signal through wavelet packet decomposition; after denoising, calculating time domain and frequency domain eigenvalues to form a machine tool eigenvector, generating a time-frequency matrix, converting the time-frequency matrix into an RGB image, and constructing a machine tool multi-source data set; a machine tool multi-source data set is divided, a training set, a verification set and a test set are obtained, and the training set only contains health data; the machine tool feature vector and the training set are input into a machine tool feed shaft health state monitoring model based on an optimization condition variational auto-encoder for training, and the trained model is used for machine tool feed shaft health state monitoring. According to the method, the health state of the feed shaft is monitored only by using the health data, so that the influence of imbalance of the fault data and the health data on the data-driven monitoring method is reduced.

The invention discloses an interactive personal health management system based on artificial intelligence and an operation method. Different from a part of existing modes, limited health data and brief basic information which are acquired by a single acquisition device and serve as a unique health information source of a user are not limited, but only serve as health basic information, and a concept and an implementation scheme of'health supplement information 'are provided. On the basis of obtaining the health basic information, through alternate or simultaneous use of multiple types of collection devices and multiple collection ways and modes, more health supplement information of the user is obtained, and a comprehensive AI health information source of the user is jointly constructed. The AI health information source is analyzed and calculated through an artificial intelligence health algorithm, once the AI health information source changes, the AI health assessment result is adjusted accordingly, an infinite loop mechanism is formed, the user health assessment result is promoted to be infinitely close to precision, and health information is interacted with the user in real time through the AI health digital human in a natural language.