253 results about "Health data" patented technology

Provided is a system for standardizing health data for use by machine-learned models in responding to user queries. A computing system obtains health data for a user from one or more health data sources. The computing system generates standardized health data, wherein the standardized health data is generated by converting the health data into a standardized format usable by a machine-learned model. The computing system receives a user health query from the user. The computing system generates a model input for the machine-learned model comprising the user health query and the standardized health data. The computing system provides the model input to the machine-learned model. The computing system receives a model output from the machine-learned model. The computing system transmits the model output for display to the user.

Provide a system. 【Solution means】 Means for analyzing contract data and collecting individual authentication information, credit information, and living environment, Means for evaluating the credit risk of each individual using the collected data, Means for setting a virtual guarantee entity based on the evaluation, Means for providing services by setting a virtual guarantee entity, Means for receiving data necessary for generating submission materials and automatically generating the materials, Means for monitoring the user's living environment and health status and sending notifications, Means for analyzing health data and sending real-time notifications in case of abnormalities, A system including the above.

This invention provides a method, system, and apparatus for dynamic fusion of multi-scale health time-series features, relating to the field of dynamic feature fusion technology. The method includes: collecting multi-source health data from subjects, resampling it onto a time grid to obtain aligned data; extracting single-point feature vectors at each time point in the time grid to form a feature sequence; weighting the single-point feature vectors within a time window according to time decay weights, using a time window as the decreasing unit; calculating the moving average feature across multiple time scales; inputting the moving average feature into an attention scoring network to calculate the relevance score of each time scale within the current time window, and obtaining attention weights through normalization; and weighted fusion of the moving average feature to obtain a dynamic feature vector. This solution can adaptively adjust the focus on short-term or long-term information according to different users, different disease stages, and different scenarios, improving the model's generalization ability.

The present disclosure provides a scientific instrument support apparatus comprising a first logic, a second logic, and a third logic. The first logic subscribes to events from a device driver, receives signature data events including a generated signature event, generates signature data with a specified signature identifier in response to the generated signature event, and transmits the signature data to the second logic. The second logic receives the signature data from the first logic, processes the signature data, writes the processed signature data to an instrument health database, and transmits the signature data to a cloud-based database. The third logic presents a graphical user interface based on the processed signature data. The apparatus enables efficient collection, processing, and presentation of scientific instrument data for improved monitoring and analysis.

1. The name of the design product: medical and preventive core wrist ECG and blood pressure recording smart watch (G1). 2. The use of the design product: for smart watch, detecting human ECG, blood pressure, pulse rate, heart rate and blood oxygen saturation monitoring, sleep monitoring, stress and emotion monitoring, and recording and analyzing health data. 3. The design points of the design product: the combination of shape and pattern. 4. The picture or photo that best indicates the design points: perspective view 1.

The present application relates to the field of data processing, in particular to a real-time abnormality monitoring method and system for industrial production line production, the method comprising: collecting equipment state data to form a multi-dimensional time series and dividing it into period data blocks aligned with the production rhythm; determining a reference period length and a period jitter standard deviation based on offline health data; establishing an analysis matrix for the current period data block with rhythm-adaptive adjustment and singular value decomposition; calculating the spectral smoothness of each independent component, identifying components below the critical smoothness threshold as a trend component set; calculating the parameter drift degree and rhythm instability weight, fusing the two to obtain a non-steady-state weighted drift strength, and performing abnormality determination. The present application solves the problems of phase shift caused by rhythm fluctuation and difficulty in extracting weak fault features in traditional methods through rhythm-adaptive alignment, spectral smoothness screening and multi-index fusion evaluation, significantly improving the accuracy and robustness of monitoring.

We provide the system. [Solution] A means for receiving voice input from a user and converting that voice input into text data, A means for analyzing the aforementioned text data and identifying the user's intent, A means for obtaining necessary information and generating an appropriate response based on the intent of the identified user, A means of converting the generated response into audio information and outputting it to the user, A means of analyzing information based on voice input, acquiring past health data, and providing health guidance, A means of notifying patients of the time to take their medication, A system that includes this.

Provide a system. 【Solution means】means for collecting and analyzing the status data of the care recipient; means for collecting and analyzing the status data of the care recipient; means for generating a care plan based on the status data; means for providing the care plan to the caregiver; means for providing the caregiver with information resources for guiding care techniques; means for evaluating the stress level of the caregiver and providing relaxation methods; means for providing a community function that enables information sharing with other caregivers; means for real-time monitoring of the health data of the care recipient using a smart device; means for notifying the generated care plan and displaying information based on the care history; A system including the above.

This application provides a health data processing method, system, device, medium, and product. The method includes: classifying the sensitivity of a user's plaintext health data to obtain plaintext health data at each sensitivity level; encrypting the plaintext health data at each sensitivity level using the corresponding encryption algorithm to obtain encrypted health data at each sensitivity level; performing a health assessment based on the encrypted health data at each sensitivity level to obtain encrypted health assessment results at each sensitivity level; and decrypting the encrypted health assessment results at each sensitivity level to obtain plaintext health assessment results. This application can ensure the accuracy of health assessments while protecting data privacy.

The invention relates to a motor vehicle comprising at least one passenger seat, the vehicle comprising a footrest (FR) configured to activate the blood circulation of a passenger, characterized in that the vehicle comprises a controller (C) configured to communicate with the footrest (FR) and with a health data collection means (MC) configured to measure at least one health data point among the passenger's blood pressure, heart rate, perspiration rate, and / or body temperature, said footrest (FR) comprising a vibrator and / or a sliding device capable of activating automatically when insufficient blood circulation of the passenger is detected, the footrest (FR) being positioned opposite the passenger seat. The invention further relates to a method for activating a vibrator and / or a sliding device of a footrest (FR) as previously described. Abstract figure: Fig. 1

A service provider can store a health database associated with a user profile. The service provider can transmit, to a first user device, the health database. The service provider can receive, from the first user device, a first health sync object, The first health sync object can be based on first health information collected at the first user device. The first health sync object can include a first sync identity that includes a first hardware identifier indicative of the first user device and a first database identifier indicative of the health database. The service provider can identify a second user device to receive the first health sync object. The service provider can determine that the first sync identity is different from a second sync identity associated with the second user device. The service provider can transmit, to the second user device the first health sync object.

Disclosed in the present application are a device health prediction method and a computer-readable storage medium. The method comprises: determining first health data of a target device, wherein the first health data comprises battery health data of the target device within the current time period; and on the basis of the first health data, determining battery health prediction data of the target device within a future preset time period. The solution can efficiently and accurately predict the battery health condition of a target device within a future preset time period. Therefore, early warning can be further performed prior to a potential fault of a device, such that health management can be effectively implemented on the device, the occurrence of the device fault can be prevented, and economic losses caused by the device fault can be reduced, thereby reducing operation and maintenance costs and improving the operating efficiency.

The present disclosure provides a health information generation method, a storage medium and an electronic device, the method comprising: acquiring multi-modal health data of a user, the multi-modal health data comprising at least: basic information data and physiological information data, the basic information data being used to represent personal basic characteristics of the user, and the physiological information data being used to represent physiological state of the user; determining health suggestion information of the user according to the multi-modal health data; and outputting the health suggestion information. In the health information generation method provided by the present disclosure, the device can generate health suggestion information for the current user based on the real-time acquired multi-modal health data. The fusion analysis of the health data of the user in different modes is realized, and the limitation of only processing a single index in the related technical solutions is broken through, and more real-time, scientific and comprehensive health suggestion information can be provided for the user.

The application relates to the technical field of intelligent health management, in particular to a health risk early warning method based on artificial intelligence, which comprises the following steps: collecting user health data through a multi-parameter health detection device, pre-processing the data to form a standardized multi-dimensional time series data set, inputting the data set into a pre-trained health risk early warning AI model for analysis, outputting a health risk level and a key risk indicator, when the risk level exceeds a threshold value, generating and sending early warning information to the user, and based on the key risk indicator and user historical data, generating personalized health management suggestions from a health management knowledge base and sending the suggestions to the user. The health risk early warning method based on artificial intelligence comprehensively analyzes multi-dimensional health data and deeply mines the data by using an AI model, realizes accurate and forward-looking early warning of health risks, provides personalized intervention guidance, and effectively improves the intelligent level and initiative level of personal health management.

This invention provides a method and system for early warning of line structure health. By collecting multi-source heterogeneous monitoring data of the line structure, and based on the reliability differences of different types of monitoring data, an adaptive weight fusion algorithm is designed to fuse the multi-source heterogeneous monitoring data to obtain comprehensive feature values. Historical health data, historical minor hazard data, and historical serious hazard data are selected as training samples to train an improved attention ELM model, completing model training. The comprehensive feature values ​​collected and fused in real time are used to construct feature vectors according to time windows, which are then input into the trained improved attention ELM model to output the probability distribution of the line health status level. The probability of the line health status level and the comprehensive feature values ​​are compared with the corresponding probability thresholds and warning thresholds to perform graded warnings, achieving accurate identification and graded warning of early hazards in the line structure, improving the reliability and real-time performance of the warnings.

This invention relates to the field of new energy cable management technology, specifically to an intelligent management method and system for new energy cables based on the Internet of Things (IoT). The system includes: an array of sensors deployed on the new energy cable itself and at monitoring points along the cable line to continuously collect real-time operational status data such as cable sheath temperature, conductor current load, joint vibration amplitude, and ambient air humidity; the data is transmitted to an edge computing node for preprocessing and standardization to form a cable health data package; this data package is then input into an improved fault probability prediction algorithm based on a historical fault database and an environmental stress evolution model to calculate the instantaneous probability values ​​of different levels of faults in each monitoring section at a specific time in the future; adaptive inspections are triggered based on these probability values, generating intelligent work orders that are sent to a mobile inspection terminal; and the algorithm parameters are calibrated using the returned on-site verification results. This method can accurately predict fault risks, rationally allocate inspection resources, continuously optimize algorithm accuracy, and ensure the safe and stable operation of new energy cables.

This application provides a health data processing method and terminal based on an ear-worn wearable device, belonging to the field of health data technology. The method collects ear acupoint health data and the carbon monoxide concentration in the user's exhaled breath. Based on the health data, it determines psychological stress data. When a psychological stress parameter exceeding a psychological stress threshold is found, target control information corresponding to the psychological stress data is determined. When the number of stimulators controlled by the target control information exceeds a certain threshold, a reminder message is sent to the user, and ear acupoint stimulation is performed based on the target control information until the detected psychological stress data returns to normal. This solution acquires and analyzes various health data from the user's ear, provides visual analysis results to the user, and simultaneously uses sensors to generate pressure or electrical stimulation of ear acupoints based on the analysis results, cycling until the data returns to normal, thus providing beneficial regulation through health warnings and feedback stimulation.

This invention discloses an adaptive monitoring and early warning system for ship main engines, comprising a multi-source heterogeneous data acquisition and cleaning module, a dynamic operating condition self-identification module, an adaptive condition monitoring model library, an operating condition matching and model scheduling unit, a real-time monitoring and anomaly triggering unit, a multi-dimensional contribution tracing and decision support module, and a model adaptive update engine. The system performs anomaly detection by calling PCA-based sub-monitoring models to calculate and statistically analyze data through data cleaning, online operating condition identification and matching scheduling. When an anomaly is triggered, the system locates the suspected root cause by calculating the contribution of variables and generates maintenance suggestions based on a fault case library. If no anomaly is triggered, the system uses health data to continuously update model parameters and control limits through a sliding window mechanism. This invention achieves dynamic adaptive tracking of main engine operating conditions, self-learning evolution of monitoring models, and a closed loop from anomaly detection to intelligent decision support, improving the accuracy and reliability of ship main engine condition monitoring.

The application relates to the field of biological medicine, and discloses a cardiovascular disease risk cycle evaluation method for a patient, which comprises the following steps: acquiring a multi-source longitudinal health data sequence of the patient; performing standardization and missing value processing to generate a structured feature matrix with unified time granularity; inputting the structured feature matrix into a pre-trained time-aware deep state space model to output a state trajectory vector sequence representing the dynamic characteristics of the cardiovascular system; calculating a risk evolution curvature index, combining with a turning point threshold to identify a risk acceleration period, a platform period or a reversal period; and generating a risk grade label, an intervention suggestion and a visual atlas according to the above. The system comprises six units of data acquisition, preprocessing, modeling, curvature calculation, cycle identification and evaluation output. The application realizes high-sensitivity identification of a key turning point by continuously tracking the risk dynamics of an individual, and improves the accuracy and timeliness of cardiovascular disease prevention.

Some embodiments provide for a method for identifying an intervention for a subject in furtherance of preventing development of cardiovascular disease in the subject, the method comprising: obtaining cardiometabolic health data for the subject; determining, using at least some of the cardiometabolic health data, a first trained machine learning (ML) model and for each of multiple time intervals, one or more measures of risk that the subject develops cardiovascular disease to obtain multiple measures of risk; determining, using the multiple measures of risk that the subject develops cardiovascular disease and at least one second trained ML model, benefit of administering to the subject one or more therapeutic interventions designed to reduce risk of cardiovascular disease by targering one or more modifiable causes of the cardiovascular disease; and identifying, using the determined benefit of administering the therapeutic intervention(s), at least one therapeutic intervention to recommend being administered to the subject.

The invention relates to a patient decision support system and operating method based on self-care algorithms that evaluate patient activity for patients diagnosed with liver cirrhosis, which is intended to monitor and manage patients' health conditions, provides regular data tracking and personalised recommendations, tracks patients' daily and weekly activities, medication use, diet, and exercise routines, collects and evaluates the data, and presents user-specific recommendations, and which uses artificial intelligence and care algorithms to determine, on the basis of the patient's health data, which treatment and care pathways are the most appropriate.

This invention discloses an environmental parameter sensing and adaptive adjustment system and method based on a photoconductive film. It integrates six-dimensional environmental sensors—light intensity, ultraviolet radiation, temperature, humidity, air pressure, and air quality—at the edge of the photoconductive film, and utilizes the film's directional light guidance and stray light filtering mechanisms to reduce environmental interference. The system includes an environmental sensing module, an environmental parameter processing module, a health data interface module, a correlation analysis module, an adaptive adjustment module, and an environmental early warning module. The correlation analysis module has preset environmental-health correlation patterns such as low temperature-increased blood pressure, high humidity-decreased blood oxygen, high ultraviolet radiation-skin health, poor air quality-respiratory diseases, and sudden changes in air pressure-headaches and joint pain, set based on clinical research data. The adaptive adjustment module pairs with smart home devices through a unified identity authentication center, sending control commands via Wi-Fi / Bluetooth / infrared to automatically adjust air conditioners, humidifiers, dehumidifiers, air purifiers, and lighting according to environmental parameters. The environmental early warning module supports single-indicator early warning, correlation early warning, trend early warning, and seasonal disease early warning, with customizable thresholds. The multi-environmental data fusion module uses weighted averaging and the 3σ principle for anomaly removal, integrating data from multiple devices such as mobile phones, watches, vehicles, and smart home devices. The sensors are automatically calibrated daily using meteorological bureau data, with the calibration process simultaneously linked to health data. This invention solves the problems of scattered environmental sensing devices, disconnect from health data, and inability to proactively adjust, serving as the infrastructure for intelligent health management.

The application discloses a kind of wireless communication methods of home health online monitoring, it is related to home health monitoring communication technical field, including the following steps: install home health monitoring equipment, collect the home health data of user;Online monitoring receiver is placed in housing, and the test point of each room is analyzed;According to test point, the transmission wave of the wireless communication signal of each room is drawn;According to transmission wave, the best placement point of online monitoring receiver in housing is analyzed;Online monitoring receiver is installed at the best placement point, and after installation is completed, home health data is monitored;The application is used to solve the problem that existing home health monitoring communication technology is still too dependent on the signal and network speed of equipment and the efficiency of receiving home health monitoring data is uncontrollable, so that users cannot receive home health monitoring data in time.

This invention belongs to the technical field of closed-loop management methods for health data in hospital convenience services, and particularly relates to a method and system for closed-loop management of health data in smart hospital convenience services throughout the entire process. It constructs a full-domain identity binding system based on blockchain technology, configuring identifiers and dynamic attribute tags for health data and assigning digital identity certificates to terminals / systems / roles; relying on a distributed graph database and tracking framework, it deploys a lightweight data collection agent and constructs a federated dynamic lineage graph based on medical parsing rules; it establishes a multi-dimensional operation traceability mechanism, expanding the collection of behavioral fingerprints and operation context data beyond regular logs, and adopts a dual-mode storage approach of blockchain evidence storage and local encrypted backup; it constructs an AI intelligent audit engine, transforming regulations into an audit rule base through a large language model, and combining unsupervised learning modeling to identify abnormal operations in medical scenarios.

Systems and methods for efficient information retrieval for clinical data are provided. A document including one or more patient health records can be obtained and tokenized to generate a sequence of tokens, each corresponding to a word or sub-word within the document. A plurality of segments each containing a token sequence comprising a sub-set of the tokens are then generated. Next, a transformer model is used to process each of the plurality of segments to generate a corresponding word-level encoding for each segment. The word-level encodings for each of the segments are combined and fused to obtain document-level contextual data. The combined and fused word-level encodings can then be analyzed, such as to identify named entities and relationships between them.

This invention relates to the field of data security processing technology, specifically to a method and system for protecting and sharing the privacy of health data of the elderly. The method includes the following steps: collecting heart rate and blood pressure sequences and constructing a timestamp dimension matrix; calculating perturbation mask coefficients and converting the timestamp dimension matrix into a desensitized physiological vector; receiving institutional query requests and extracting feature hash codes; using the feature hash codes to perform homomorphic encryption operations on the desensitized physiological vector to obtain a closed-state comparison index; and outputting the shared analysis results when the closed-state comparison index meets a security threshold. In this invention, by generating desensitized physiological vectors through differential perturbation mapping of high-frequency dynamic temporal physiological features, the strong binding storage risk between vital signs and identity identifiers in the global static encryption mechanism is broken. Simultaneously, homomorphic encryption operations are used to perform fine-grained logical comparisons in the encrypted state, avoiding the security risk of the querying party directly reading the complete plaintext record under the fixed password verification mode.