481 results about "Health condition" patented technology

The invention discloses a precise numerical control machine tool operation state intelligent control method and system, and particularly relates to the technical field of numerical control machine tools. Multi-dimensional operation data, including working temperature, load, current, pressure, cutting force and tool wear data, of a machine tool are collected in real time, a Z-score algorithm is adopted for data preprocessing, abnormal values are extracted, a prediction model is constructed and trained based on a machine learning algorithm, abnormal data are analyzed in real time, and the abnormal values of the machine tool are analyzed in real time. And accurately identifying the operation state of the machine tool and evaluating the health condition. According to the health state evaluation result, an intelligent control strategy is automatically generated, and the feeding speed, the spindle rotating speed and the cutting depth of the machine tool are dynamically adjusted, so that the operation state of the machine tool is optimized, potential faults are found and dealt with in time, the machining quality is improved, the faults are reduced, the production efficiency and the equipment stability are improved, and the maintenance cost is reduced.

The invention relates to the technical field of data processing, in particular to a pneumology department intelligent early warning system based on data fusion. The method comprises the following steps: monitoring and collecting multi-source physiological data of a patient to obtain original physiological data; preprocessing the original physiological data to obtain preprocessed physiological data; performing intelligent fusion processing on the preprocessed physiological data to obtain comprehensive physiological feature data; performing anomaly detection analysis on the comprehensive physiological feature data to obtain an analysis result of the breathing state; performing risk grade division based on the analysis result of the breathing state; and making medical suggestions based on the risk levels. The technical problems that in the implementation process of intelligent early warning of the pneumology department, a current respiration monitoring system is inaccurate in multi-source physiological data fusion and low in patient health condition judgment accuracy are solved.

Camera and / or other sensor analysis of skin, used to diagnose and monitor health conditions. A computer, receiving skin data from a user, where the skin data is images and / or other data of the skin. The computer processes the skin data identify skin conditions, their status, and to compare the skin data with information indicative of health conditions that are not skin diseases. This can be done by finding similar skin data to other data received from other people who have specified health conditions and / or by looking for changes from previous images. The skin data can be captured by a smart phone camera (alone or with digital peripheral attachments) or a dedicated capture device such as a periscope, one way mirror, or cubicle. An augmented reality marking system can be used to mark areas on the user's skin to compare against other previous skin images and / or data.

The invention provides a lightweight non-contact multi-parameter physiological detection method and system, a storage medium and electronic equipment, and relates to the field of non-contact multifunctional physiological detection. According to the method, double-spectrum information of thermal infrared and visible light is fully utilized, respiratory wave signals are extracted from a thermal infrared spectrum by adopting affine transformation and double-light fusion technologies, and iPPG signals are extracted from a double-light video based on a time-frequency domain feature analysis method. Meanwhile, a lightweight signal noise reduction network is designed, automatic noise reduction processing is carried out on the respiration signals and the iPPG signals in a multi-task learning mode, and the incidence relation between different physiological parameter estimation tasks is excavated, so that accurate estimation of the three physiological parameters including the respiration rate, the heart rate and the heart rate variability can be achieved under the scene that computing resources are limited, and the accuracy of estimation of the three physiological parameters including the respiration rate, the heart rate and the heart rate variability is improved. The physical health condition of an individual is analyzed in real time, and objective and accurate physiological perception information is provided for mental health screening.

The invention relates to the technical field of state monitoring, in particular to an intelligent cervical vertebra state data monitoring system which comprises a data monitoring prediction module, a cervical vertebra risk analysis module, a cervical vertebra intervention making module and an intervention effect evaluation module. According to the invention, through real-time data analysis and continuous monitoring of the posture and pressure change of the cervical vertebra, high-sensitivity tracking of the health condition of the cervical vertebra is realized, the system adopts dynamic probability estimation, the health state can be updated in real time, the accuracy of abnormal recognition is improved, and the accuracy of abnormal recognition is improved by integrating data analysis of the posture and the pressure. The system can identify potential health risks in an initial stage, early warning of risk factors enables medical intervention to be carried out more timely and effectively, the demand and cost of long-term treatment are reduced, a targeted intervention plan is adjusted according to real-time data, it is ensured that each intervention measure accurately matches the current demand of a patient, and the patient experience is improved. The intervention process is optimized, and the cervical vertebra rehabilitation speed and effect are improved.

The invention discloses an ear-nose-throat department symptom monitoring method and system based on multi-modal data fusion, and relates to the technical field of medical monitoring. Comprising the steps of monitoring physiological data in real time, calling historical medical record data, collecting environment data, and preprocessing the monitored, called and collected data; carrying out feature fusion on the preprocessed data, extracting potential associated features, and carrying out multilevel and sequential feature learning; on the basis of the extracted features, an improved long-short-term memory network is used for conducting symptom prediction, in combination with historical trends of the blood sugar and the blood pressure of the patient, correlation between the symptoms and ear-nose-throat symptoms is evaluated in real time, and personalized symptom early warning is output; and sending a health prompt to the patient through the intelligent equipment or the mobile terminal application in real time. According to the invention, conventional symptom early warning can be carried out, and personalized health intervention measures can be provided according to individual health conditions.

The invention provides a service area health degree evaluation system and method based on deep learning, and the system comprises an index system building module which is used for building a service area health degree evaluation index system; the data acquisition module is used for collecting data in the service area; the data preprocessing module is used for preprocessing the collected original data in the service area; the health degree label obtaining module is used for providing labels for training of the model; the deep learning model module is used for modeling by using the preprocessed data and training a model based on a label; and the health degree evaluation module is used for comprehensively evaluating the overall health condition of the service area by adopting the trained deep learning model in combination with the data of the current service area. According to the invention, comprehensiveness and scientificity of an evaluation result are ensured; meanwhile, by means of the health degree label obtaining module, the health degree of the service area is scored in combination with quantitative indexes and expert experience, and the accuracy and practicability of health degree labels are ensured.

The invention provides an electric power tower state real-time monitoring and common fault identification system based on a digital twinborn technology. According to the system, a high-precision digital twinborn model module is constructed, and a sensor network module is integrated so as to acquire key data of a tower in real time. The data processing center module utilizes an advanced clock synchronization technology to ensure data precision, cooperates with the common fault identification system module and the health state evaluation module, and adopts finite element analysis and Paris laws to accurately evaluate the health condition of the tower. The system further comprises an intelligent maintenance decision support module which provides a scientific maintenance strategy. In addition, the data visualization platform module and the user interaction interface module enhance the user experience, so that the system is not limited to monitoring and evaluation, and optimization of maintenance decision and individuation of user interaction are also realized. The overall system design pays attention to real-time performance, accuracy and intelligence, and aims at improving the operation and maintenance efficiency and safety of the electric power tower.

The invention provides a hip joint impact syndrome intelligent diagnosis method, system and device based on multi-modal data fusion and a computer readable storage medium. The intelligent diagnosis method for the hip joint impact syndrome based on multi-modal data fusion comprises the following steps: acquiring a hip joint X-ray image and health condition text information of a patient; wherein the health condition text information comprises general information, medical history information, symptom information and physical examination information of the patient; inputting the hip joint X-ray image and the health condition text information into a preset hip joint impact syndrome diagnosis model, and outputting a hip joint impact syndrome diagnosis result; according to the hip joint impact syndrome diagnosis model, text features and image features are extracted and fused for diagnosis. According to the embodiment of the invention, the hip joint impingement syndrome can be diagnosed more accurately.

The invention discloses a transcutaneous electrical stimulation device based on a large language model, and relates to the technical field of wearable devices. The transcutaneous electrical stimulation equipment based on the large language model comprises a data acquisition and preprocessing module, a feature extraction and fusion module, a model training and prediction module and a real-time information processing and response module, real-time data processing and intervention capabilities are integrated, human body data can be analyzed in real time, electrical stimulation parameters can be automatically adjusted, an instant treatment or adjustment scheme is provided for the problems of arrhythmia, chronic pain and the like, and the timeliness and effectiveness of treatment are greatly improved. And secondly, by fusing the physiological signal features and the symptom text features, the equipment can more comprehensively understand the health condition of the user, so that a more accurate treatment scheme is provided. Besides, the equipment also has the functions of anomaly detection and trend analysis, can predict potential health risks, and triggers an alarm in time when a serious anomaly is detected, thereby effectively guaranteeing the life safety of the user.

The invention provides a power module scheduling method, apparatus and device, and a storage medium. The method comprises the steps of obtaining one or more health parameters of each power module in a plurality of power modules of a charging pile; based on the one or more health parameters of each power module, determining a total health degree corresponding to each power module; determining a scheduling sequence of the plurality of power modules based on the total health degree of each power module; and scheduling the power modules to charge the target equipment according to the scheduling sequence of the plurality of power modules. According to the scheme, the total health degree of each power module determined based on the one or more health parameters of each power module can accurately reflect the health condition of the power module, so that the power modules are scheduled according to the scheduling sequence of the plurality of power modules determined based on the total health degree to charge the target equipment; the use of each power module can be balanced, and the service life of the charging pile is prolonged.

Camera and / or other sensor analysis of skin, used to diagnose and monitor health conditions. A computer, receiving skin data from a user, where the skin data is images and / or other data of the skin. The computer processes the skin data identify skin conditions, their status, and to compare the skin data with information indicative of health conditions that are not skin diseases. This can be done by finding similar skin data to other data received from other people who have specified health conditions and / or by looking for changes from previous images. The skin data can be captured by a smart phone camera (alone or with digital peripheral attachments) or a dedicated capture device such as a periscope, one way mirror, or cubicle. An augmented reality marking system can be used to mark areas on the user's skin to compare against other previous skin images and / or data.

The invention is suitable for the technical field of escalator monitoring, and provides an escalator health assessment method and device based on deep learning and terminal equipment. The method comprises the steps that escalator operation data are collected through a sensor, and the escalator operation data comprise escalator vibration signals, escalator temperature, escalator operation noise and escalator operation current; local features used for representing the health state of the escalator are extracted from the escalator operation data through a convolutional neural network, the local features of various escalator operation data are fused through an attention mechanism, and global health state representation of the escalator is obtained; and an escalator domain knowledge graph is constructed, when the global health state characterization shows that the escalator state is abnormal, the reason of the escalator state abnormality is reasoned based on the relationship between entities in the escalator domain knowledge graph, and the escalator health condition is evaluated according to the reason of the escalator state abnormality. The escalator operation data can be comprehensively analyzed, the health state of the escalator is effectively evaluated, and the safety, reliability and operation efficiency of the escalator are improved.

The invention discloses a human body physiological signal acquisition and analysis system in an Internet of Things environment. An operation method of the system comprises the following steps: step 1, acquiring a multi-modal physiological signal; 2, intelligent prediction and early warning of the body health state; 3, the adaptability of the system is enhanced through an environment sensing function; 4, mental health analysis is conducted on the user, and the data collection module is used for comprehensively collecting physiological signals and mental health indexes of the user; the intelligent analysis module is used for establishing a prediction model by using artificial intelligence and a machine learning algorithm, analyzing and predicting multi-modal data, and early warning possible health risks in advance; the user service module is used for providing personalized health suggestions and intervention measures according to the physical and mental health conditions of the user and helping the user to improve the health state; the system has the characteristics of comprehensive human body data acquisition and accurate physiological and psychological data analysis.

The invention discloses a blood glucose management method, a user interface and a related device, and the method can determine a first meal time based on a historical blood glucose value of a user, and determine a second meal time based on one or more of the meal time input by the user, the limb movement of the user and the heart rate. Third meal time is determined based on the first meal time and the second meal time, and the blood glucose health condition of the user is evaluated according to the third meal time. Therefore, according to the method, the dining time of the user can be predicted in combination with the dining time, determined in various modes, of the user in the historical diet event, and the electronic equipment can evaluate the blood glucose condition of the user more accurately.

The invention discloses a GIS equipment expansion joint hidden danger intelligent detection system based on deep learning, and relates to the field of intelligent monitoring, and the system comprises an acquisition preprocessing module, a multi-modal data fusion module, a defect detection and classification module, an intelligent hidden danger evaluation module and an optimization feedback module. According to the invention, data of a plurality of sensors are combined, and the health condition of the equipment is comprehensively evaluated through a data fusion technology. The multi-source data fusion can effectively improve the accuracy of a fault detection model, avoids misjudgment caused by limitation of a single data source, and provides more comprehensive and accurate equipment health state evaluation. Through real-time monitoring and early warning, long-term health assessment can be carried out on the equipment, and serious faults of the equipment in the using process are avoided.

The invention relates to a multi-modal data feature layer fused sow weight estimation method, which relates to a detection method, and comprises the following steps: acquiring point clouds of sows at different angles through a sow point cloud acquisition device, and taking sow time sequence data such as variety, fetal number and reproduction stage and reconstructed sow real-time three-dimensional point clouds as input; a dual-branch network fused in a feature layer is established, meanwhile, deep features are automatically extracted from one-dimensional data and three-dimensional point clouds, time sequence data features and real-time data features are fused, and current body weight estimation of the sow and body weight curve estimation of the whole breeding stage are completed. Through the method, the accuracy of weight estimation is improved, the final output of the method is the weight real-time value of the current reproduction stage and the weight prediction value of the whole reproduction stage, and the real-time weight value is beneficial for obtaining the physical health condition of the sow at the current stage and adjusting the feeding strategy; therefore, the sows can reach the optimal health state and maintain the optimal production performance, and the minimum feed cost is maintained.

The invention relates to the technical field of health assessment, in particular to a traditional Chinese medicine health assessment method and system based on intelligent analysis, and the method comprises the steps: carrying out the multispectral image collection of the face and tongue picture of a user, carrying out the color gradation quantification processing and texture feature extraction of the obtained image data, and obtaining a face color gamut feature matrix and a tongue picture texture feature vector; and based on the tongue picture-complexion associated features and a syndrome description text input by the user, performing intelligent classification pre-judgment on traditional Chinese medicine syndrome types to obtain an initial syndrome label set, and based on user physique data, correcting the initial syndrome label set to obtain a syndrome feature fusion vector. Through the health fluctuation index and the health trend early warning signal, the change of the health condition of the user can be dynamically evaluated, and a personalized health intervention scheme is generated on the basis, so that the fluctuation of the health condition can be reflected in real time, intervention measures can be taken in time, and potential health risks can be prevented.

The disclosure relates to methods, compositions, and combinations for detoxification in a patient comprising performing therapeutic plasma exchange (TPE) on the patient. The disclosure also relates to methods, compositions, and combinations for removing toxins in a patient as a result of chronic exposure to the toxins, comprising performing one or more TPE sessions on the patient and administering an oral composition to the patient. The methods, compositions, and combinations of the disclosure may also be used to replenish biomarkers indicative of a healthy state of the body in order to improve immune system function or patient longevity, and / or reduce oxidative stress or inflammation. The methods, compositions, and combinations of the disclosure may also be used to reduce the level of atherosclerosis, improve cognitive function, prevent early cancer development, promote immune health, and / or treat health conditions or diseases such as autoimmune diseases, cancers, Alzheimer's disease, Parkinson's disease, chronic fatigue syndromes, or fibromyalgia. Additionally, the methods, compositions, and combinations of the disclosure may comprise an intravenous supplementation, wherein each TPE session is followed by administration to the patient of an IV composition. The disclosure further relates to oral compositions and IV compositions for use in the methods, compositions, and combinations of the disclosure.

A machine learning prediction system can analyze a dataset of users with self-reported symptoms and associated data from a wearable device to impact measure the impact of an acute health condition (such as the flu) at the population level. The machine learning prediction system can train a machine learning model to recognize individual acute health condition patterns based on differences in user activity with respect to the characteristics of determined baseline periods. For example, per-individual normalized change with respect to baseline aggregated at the population level can be used to determine individual acute health condition patterns and predict the onset of certain acute health conditions using a trained machine learning model. In response to predictions, the machine learning prediction system can take interventions to manage the impact of a predicted acute health condition on an individual.

The invention relates to the technical field of data processing, and discloses an operation and maintenance task processing intelligent distribution management system and method based on data analysis, and the system comprises a data collection module; a life cycle analysis module; a task plan making module; an operation and maintenance personnel task distribution module; and a path planning and dynamic adjusting module. Based on data analysis, in combination with the equipment operation state, the skill of the operation and maintenance personnel and the load condition, the execution sequence and route of the operation and maintenance tasks are optimized through intelligent allocation and route planning, task allocation and route can be dynamically adjusted according to the health condition of the equipment, the task priority and the real-time information of the operation and maintenance personnel, and the operation and maintenance efficiency is improved. The operation and maintenance task is efficiently and accurately completed, and the overall working efficiency and the resource utilization rate are improved.

The invention discloses application of a composition in preparation of an iron supplementing preparation. The composition comprises dogwood and / or a dogwood extract and mangosteen and / or a mangosteen extract. Through compatibility of the active components, the composition provided by the invention can promote absorption of an iron element by a body by promoting intestinal iron absorption and / or inhibiting hepcidin and other mechanisms, plays a role in supplementing iron, further can be used for treating indications such as functional iron deficiency and the like, provides a more effective preparation for improving iron deficiency of the body, and has wide application prospects. Meanwhile, the preparation prepared from the composition also has the effects of regulating physique, tonifying deficiency and qi, regulating the health condition of the body and the like.

To solve a problem of supporting individual health management by photographing a face, using artificial intelligence to analyze a health condition, a risk of disease and a mental state, and proposing appropriate advice and an improvement method.SOLUTION: A system includes means for: photographing a face; using artificial intelligence to analyze a health condition, a risk of disease and a mental state; and proposing appropriate advice and an improvement method.SELECTED DRAWING: Figure 1

The invention discloses a pair of intelligent eyesight monitoring and eye disease prediction glasses, and aims to solve the problems that most of existing eye health monitoring devices are used for regular inspection, cannot monitor and predict the eye health condition in real time and are difficult to take intervention measures in time, the glasses comprise glasses legs and a glasses main body, and the glasses main body is provided with an inner side facing a wearer; the glasses body further comprises a miniature camera, an optical sensor, a master control system and a battery system. The miniature camera is connected with the main control system through an application-specific integrated circuit, and the miniature camera is installed on the inner side of the glasses body facing the eyes of a wearer and used for shooting eye images of the wearer in real time and capturing the pupil size, eyeball movement and sight line direction of the wearer; the optical sensor is connected with the main control system and is used for detecting the ambient light intensity and color and adjusting the brightness of the lenses of the glasses main body; the eye health condition of the teenagers can be monitored and predicted in real time, and accurate health data support is provided for parents, teachers and ophthalmologists.

The invention belongs to the technical field of sleep quality analysis, and particularly relates to a sleep quality analysis system and method based on a medical bed sensor, so as to solve the technical problem that an existing method excessively depends on duration statistics and neglects sleep continuity and periodic rhythmicity. The analysis method comprises the following steps: S1, acquiring sign signal data acquired by a sensor on a medical bed in a sleep cycle, and dividing the sign signal data into a plurality of continuous time slices; s2, extracting physiological indexes of respiratory rate, heart rate and body movement amplitude in each time slice, and calculating variation depth of the corresponding time slice; s3, generating a variation depth baseline; calculating an unsteady state disturbance factor according to the occurrence frequency and the duration of the micro-awakening disturbance fragment; and S4, generating a sleep quality comprehensive score. According to the analysis method, the comprehensiveness and objectivity of the evaluation result are improved, and the sleep health condition is reflected more truly.

To provide a system capable of easily performing appropriate measures tailored to a physical condition and a health status of an individual user.SOLUTION: A system includes means for inputting user's physical symptoms, means for preparing a prompt sentence to instruct to specify nutrients required to improve the symptoms on the basis of the inputted symptoms, means for specifying the nutrients required to improve the user's physical symptoms by using the prepared prompt sentence and a generative AI model, means for recommending foods or supplements containing specified nutrients, and means for generating a link to purchase the recommended foods or supplements to display them on a user's terminal screen.SELECTED DRAWING: Figure 1

The invention provides a fan tower drum deformation real-time monitoring and early warning system, method and equipment based on Internet of Things safety, and relates to the technical field of wind power generation. The system comprises a sensor module used for collecting deformation data of a fan tower, and the deformation data comprises strain data, inclination data, micro-strain data, vibration data, wind load spectrum and temperature and humidity parameters of distributed optical fibers; the data analysis and early warning module is used for inputting the standard deformation data into the deformation prediction model and outputting a deformation prediction value; inputting the standard deformation data into a health assessment unit to obtain a health score; and sending red early warning information under the condition that the variable quantity of the deformation predicted value within the target duration is greater than a first threshold value and / or the health score is less than a second threshold value. According to the method, composite early warning is carried out through the abnormal modes of the associated multiple parameters, and the health condition of the fan tower can be judged more accurately.

The embodiment of the invention discloses an ovarian health assessment method and related equipment, the method is applied to first electronic equipment, the first electronic equipment can display a first interface, and the first interface comprises a first control; receiving a first operation acting on the first control; acquiring first data, wherein the first data comprises heart rate variability data of the user in a preset time period; according to the physical sign data of the user, determining an ovary health assessment result; the user sign data comprises first data; the ovarian health assessment result is used for indicating the ovarian health condition of the user in a preset time period; after the preset time period, a second interface is displayed, and the display content of the second interface comprises the ovarian health assessment result. By adopting the method, the ovarian function abnormity of the user can be found in time.

The invention provides a real-time monitoring system for blades of a wind generating set, which belongs to the field of blade monitoring and comprises a monitoring module for acquiring a plurality of parameters of the blades of the wind generating set and determining the real-time light transmittance of the blades of the wind generating set monitored by a corresponding camera; the filtering module is used for acquiring light transmittance data in different time periods and detecting and filtering abnormal values; the analysis and prediction module is used for analyzing and predicting the running state of the leaf based on a machine learning algorithm of a cloud platform according to the filtered light transmittance data; the obtaining module is used for obtaining the health condition of the blade according to the running state and obtaining a repairing strategy for the blade of the wind generating set according to the health condition; and the display module is used for converting the repairing strategy of the blade of the wind generating set into a chart by using a data visualization tool and performing multi-angle display. The problem that the blade is damaged due to errors in judgment of the abnormal phenomenon of the blade caused by the fact that the running state of the fan blade cannot be obtained in real time is solved.

The invention relates to the field of digital twin driving, and discloses an electric actuator energy consumption optimization method and system based on digital twin driving, and the method comprises the steps: collecting the current, torque, rotating speed, temperature, vibration and environment wind speed data of an electric actuator in real time, carrying out the online recognition of the thermal-friction coupling parameters of the actuator through a multi-scale sliding window method, and carrying out the online recognition of the thermal-friction coupling parameters of the actuator; the operation state of the actuator is dynamically updated in combination with the load damping change and the long-term health condition; the load evolution trajectory of the actuator is extracted, transient fluctuation and long-term degradation trend analysis are combined, and the energy consumption deviation and the performance bottleneck existing in the operation process of the actuator are recognized; according to the method, related actuator components and mutual influences thereof are comprehensively analyzed, parameters, feature weights and updating frequencies of the digital twin model are dynamically adjusted, and a real-time energy consumption optimization control strategy is generated. The method has the advantage that the real-time response capability and the long-term self-adaptive precision are improved.