524 results about "Heart rate" patented technology

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand / supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data / distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and / or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a “high” range, provide medically relevant detections including medication non-compliance. Novel methods of power management and patient monitoring are disclosed.

The invention discloses an integrated pediatric vital sign intelligent monitoring and early warning method and system, and relates to the technical field of biomedical signal processing. The method is used for solving the problems of large monitoring interference and early warning lag in a dynamic environment. The method comprises the following steps: firstly, synchronously collecting a multi-modal signal to generate a motion intensity envelope signal and an R wave peak value interval sequence, carrying out orthogonal projection denoising on a photoelectric volume pulse wave signal by utilizing motion envelope based on a recursive least square algorithm, and outputting a reconstructed blood oxygen waveform; then, calculating a cross correlation coefficient of a heart rate trend and a motion envelope, quantifying a physiological coupling degree to generate an independent heart rate anomaly index, and accurately identifying a non-motion related risk; finally, the real-time blood oxygen value is analyzed, the index is used for dynamically increasing the low oxygen threshold value so as to tighten the alarm boundary, physiological parameter linkage grading early warning is achieved, and the recognition sensitivity of the compensation period risk is improved while interference resistance is guaranteed.

The invention discloses an emotional interactive intelligent nursing system and method based on multi-modal perception, and belongs to the technical field of intelligent medical treatment and intelligent old-age care crossing. The matrix type pressure sensor array is integrated in the nursing mattress, multi-point pressure distribution of the body of the old people on the bed surface is obtained in real time, and high-resolution body pressure mapping is achieved. Compared with an existing single-point type or linear pressure monitoring structure, the system can accurately recognize the spatial position of a local pressed area, and early recognition and quantitative evaluation of a bedsore risk area are achieved. By fusing the pressure matrix, the heart rate, the blood oxygen, the environment temperature and humidity, the urine wetting and other multi-mode sensing data, the monitoring dimension is remarkably expanded, the health condition can be comprehensively evaluated from multiple physiological indexes, compared with a single physiological parameter monitoring mode, the more comprehensive information obtaining capacity is achieved, and the monitoring precision and the nursing efficiency are effectively improved.

The invention relates to the technical field of emotional robots, and discloses an embracing emotional robot which comprises an inner shell, low-cost and high-simulation breathing simulation is achieved through cooperative control of an air pump and an air valve, and the embracing emotional robot is designed by combining simulation body temperature brought by a heating piece and synchronous action of a rotatable arm. The emotion robot can naturally rotate forwards when being embraced and is matched with simulated respiration and simulated body temperature, so that the immersion and emotion interaction experience of a user are remarkably improved; the operation cycle of the air pump strictly follows biological breathing characteristics, the inspiration stage accounts for 2 / 3 of the breathing cycle, the speed of the inspiration stage changes fast and slow, the expiration stage accounts for 1 / 3 of the expiration stage, smooth pressure relief is achieved through the air valve, and the highly-simulated breathing mode can effectively guide a user to breathe synchronously, so that parasympathetic nerves are activated, the anxiety level and heart rate fluctuation are reduced, and the user experience is improved. And the whole structure adopts a modular design, complex functions such as breathing simulation, embracing and body temperature simulation can be realized, the maintenance is convenient, and the device is particularly suitable for long-term accompanying scenes.

The invention discloses an electrocardiosignal-based sleep apnea detection method and system and a storage medium. The method comprises the following steps: acquiring a target electrocardiosignal to be analyzed, performing R peak identification on the target electrocardiosignal, calculating an RR interval between two adjacent R peaks, and acquiring a voltage amplitude of the electrocardiosignal at the R peak position as an R peak amplitude; on the basis of the RR interval, the RR interval is converted into an instantaneous heart rate sequence, resampling operation is carried out on the heart rate sequence, interpolation processing is carried out on non-uniform sampling points obtained after resampling so as to obtain a continuous and equally-spaced heart rate curve, and signals of a breathing-related frequency band are extracted through filtering to serve as electrocardio derived breathing signals; and splicing the RR interval, the R peak amplitude and the electrocardio-derived respiration signal to form a multi-channel feature, and inputting the multi-channel feature into a trained sleep respiration detection model to obtain sleep apnea state judgment information. Therefore, the apnea state can be detected more comprehensively, and the accuracy of sleep apnea detection is improved.

The invention discloses an anorectal postoperative rehabilitation intelligent supervision method and system based on multiple modes, and belongs to the technical field of intelligent supervision. A temperature, humidity, air quality and other multi-mode sensor group is arranged in each anorectal postoperative rehabilitation management inpatient area, the body temperature, heart rate and wound surface images of a patient are synchronously collected, and the wound surface area and seepage color are extracted through image processing; the method comprises the following steps: by taking a time node as a unit, adding a time label to an environment and patient basic data set, constructing a multi-modal comprehensive data set in a unified format, calculating an environment comprehensive index and a physiological comprehensive index, fusing the environment comprehensive index and the physiological comprehensive index to calculate a postoperative rehabilitation risk value, setting a threshold interval to generate low, medium and high three-level risk labels, and the patient risk label set is pushed to the doctor end in real time, a dynamic supervision system is constructed, the anomaly recognition rate and the clinical response speed are improved, the complication occurrence rate is reduced, the rehabilitation period is shortened, and reliable data support is provided for nursing quality improvement and evidence-based medical research.

The invention discloses a sleep quality evaluation method and system and a storage medium, and the method comprises the steps: inputting electrocardiosignal segmentation data into a trained sleep staging and breath detection multi-task model, and outputting a preliminary sleep staging result and an apnea detection result; based on the acceleration segmented data, extracting posture features of each corresponding time period, inputting the posture features into a decision tree posture classifier to obtain sleeping posture categories of each time period, and correcting a preliminary sleep staging result and an apnea detection result based on a preset posture and heart rate combined correction rule; and finally, calculating the corrected sleep staging result and the apnea result based on a preset sleep scoring rule to obtain a sleep quality evaluation result. Therefore, accurate quantitative evaluation of sleep staging and sleep quality is realized, and a reliable basis is provided for accurate intervention and monitoring of subsequent sleep health.

Provided are a measurement method and system and a related apparatus. The method is applied to a first electronic device and comprises: acquiring user information; acquiring first physiological data of a user in a resting state, the first physiological data comprising a first blood pressure value and a first heart rate; outputting a first prompt, the first prompt being used for prompting the user to start exercising; measuring second physiological data after the user finishes exercising, the second physiological data comprising a second blood pressure value and a second heart rate; acquiring an exercise load of the user; determining, on the basis of the user information, the first physiological data, the second physiological data, and the exercise load, a first evaluation result, the first evaluation result comprising cardiac reserve function evaluation; and outputting the first evaluation result. In this way, not only can the blood pressure status of a user be evaluated, but also the cardiac reserve function of the user can be judged, such that the user knows his / her own health status in time.

A method and system for deriving one or more hemodynamic parameters based on blood-velocity and arterial diameter measures, or parameters proportional thereto, each sampled recurrently or continuously over a time period to obtain for each a data series spanning a time window (i.e. a waveform). This is used, preferably in combination with at least one further physiological parameter, for example heart rate, to derive one or more hemodynamic parameters. A transfer function or machine learning model is used to process the inputs to obtain the estimated hemodynamic parameters.

The invention discloses an undisturbed sleep monitoring method and a related device, and the method comprises the steps: carrying out the feature extraction of a first piezoelectric signal, obtaining a body movement feature, and enabling the first piezoelectric signal to be obtained through the superposition of a plurality of preprocessed original piezoelectric signals; frequency domain analysis is carried out on the second piezoelectric signals, frequency components corresponding to wave crests are recognized in a sleep heart rate related frequency band, a frequency pair corresponding to each moment is extracted, a frequency set is obtained, the frequency pair comprises a plurality of preorder frequency components in the first frequency sequence and the frequency of each frequency component, and the frequency set is obtained; the first frequency sequence is obtained by arranging frequency components in a descending order according to amplitude intensity; determining heart rate characteristics at each moment according to the frequency set; performing feature extraction on the acquired piezoresistive signals to obtain piezoresistive features; and determining the sleep state according to the body movement characteristics, the heart rate characteristics at each moment and the piezoresistance characteristics. The reliability and accuracy of sleep state recognition can be improved.

The invention discloses an infusion state and vital sign intelligent monitoring method based on multi-modal fusion, and relates to the technical field of sign monitoring, and the method comprises the following steps: respectively collecting infusion dripping speed, pipeline liquid state, infusion pressure and breathing and heart rate signals of a patient; filtering the original signal of each sensor; key features are extracted from the processed signals, wherein the key features comprise parameters including the average dripping speed, the liquid existence proportion, the pressure change rate, the respiratory rate and the heart rate; carrying out normalization and confidence weighted fusion on the extracted infusion and vital sign characteristics, calculating an infusion state index and a vital sign index, and further obtaining a combined abnormal index; performing three-level anomaly judgment according to the fused index; monitoring data and alarm information are uploaded to a hospital information system in real time. By introducing a judgment algorithm based on multi-modal data fusion and confidence coefficient weighting, monitoring and graded alarming of the infusion state and the vital signs of the patient can be achieved.

The invention relates to the technical field of Internet of Things and artificial intelligence, in particular to a real-time dynamic sensing method based on artificial intelligence, which comprises the following steps: S1, a multi-source data acquisition step: synchronously acquiring multi-dimensional real-time dynamic data of a user through a plurality of heterogeneous sensors deployed in a monitoring space, the sensor at least comprises a falling monitoring millimeter wave radar used for monitoring human body existence, a sign monitoring millimeter wave radar used for monitoring heart rate and breath, a sleep monitoring millimeter wave radar or an AI intelligent mattress used for monitoring on-bed / off-bed states and body positions, and an AI intelligent camera used for monitoring behaviors. According to the real-time dynamic sensing method based on artificial intelligence, false alarms possibly generated by a single sensor are effectively eliminated through multi-modal data cross validation. For example, a millimeter wave radar and an AI camera perform dual confirmation on a fall event, and the accuracy is far higher than that of a single technology.

The embodiment of the invention provides a near-infrared heart rate detection method and device based on background perception, relates to the technical field of physiological signal detection, and can effectively perceive background noise so as to reduce the influence of the background noise on heart rate detection precision. The method provided by the embodiment of the invention comprises the following steps: acquiring an image sequence by utilizing near-infrared imaging equipment, and extracting face key points; determining regions of interest, calculating an average value of pixel values in each region of interest, obtaining pulse signals, and calculating a signal-to-noise ratio of each pulse signal; extracting background regions, calculating an average value of pixel values in each background region, and obtaining a background noise signal; performing Fourier transform on the pulse signal to obtain a frequency power signal; performing Fourier transform on the background noise signal to obtain a spectrum power signal; pulse frequency power with background noise influence removed is obtained, and a heart rate predicted value is determined; and determining the weighted sum of the heart rate predicted values as a heart rate detection result.

The invention discloses a multi-person collaborative human factor risk monitoring method and device based on multi-modal perception, and relates to the technical field of human factor engineering, intelligent perception and group decision behavior modeling. The cognitive state, emotion change and physiological load of participants are sensed in real time by fusing EEG, motion state, positioning track, heart rate, blood pressure and other multi-source information, and the collaborative decision behavior and cognitive consistency of a group in a high-pressure task are evaluated through interactive modeling. The method is suitable for multi-personnel, multi-role and task-intensive cooperation scenes such as emergency management, military drilling, industrial safety, complex traffic guidance and the like.

The invention discloses an electrocardio data processing and coronary heart disease risk prediction method, device and system and a medium. The method comprises the steps of obtaining exercise electrocardio data output through at least one electrocardiogram lead; analyzing the exercise electrocardiogram data to obtain a heart rate sequence corresponding to each electrocardiogram lead; extracting a QRS wave group corresponding to each heart beat from the exercise electrocardio data; dividing a plurality of heart rate intervals according to the obtained heart rate sequence; according to the heart rate interval to which the heart rate corresponding to each QRS wave group belongs, classifying the QRS wave group into a group corresponding to the corresponding heart rate interval; determining an average QRS wave group corresponding to each heart rate interval according to all the QRS wave groups in each group; extracting the high-frequency energy of the average QRS wave group corresponding to each heart rate interval; and determining an energy sequence according to the high-frequency energy corresponding to each heart rate interval. According to the scheme, high-frequency components and dynamic load information which are more sensitive to ischemia are utilized, so that the detection rate and the identification capability of coronary heart diseases, particularly early and mild lesions, can be remarkably improved.

The invention discloses an agricultural machinery driving fatigue grade division method based on multi-source physiological information, solves the problems of insufficient robustness and detection precision of the agricultural machinery driving fatigue grade division method, and belongs to the technical field of human-computer interaction, traffic safety and artificial intelligence cross application. The method comprises the steps that electrocardiosignals and face image data of a driver are collected, feature extraction is carried out, and a heart rate feature vector and a face feature vector of a current sliding window are obtained; fusing the standardized heart rate features and facial features to obtain fused feature representation; taking the fusion feature representation as an observation vector, inputting the observation vector into a time sequence model, and estimating potential fatigue strength; according to the standardized heart rate features and facial features, obtaining respective corresponding edge fatigue probabilities, and fusing the two edge fatigue probabilities into a joint fatigue confidence coefficient; and calibrating the potential fatigue strength by using the joint fatigue confidence, and carrying out ordered grading by using the calibrated fatigue strength to obtain a predicted fatigue grade.

The invention relates to the technical field of intelligent driving, and provides an intelligent driving method and system based on driver biofeedback. The method comprises the following steps: firstly, acquiring multi-mode biological signals such as heart rate, electrodermal response and eye movement track of a driver and a vehicle movement signal in real time through an in-vehicle sensor; carrying out cross-modal feature fusion and analysis on the signals by utilizing a driver state analysis model, carrying out parallel calculation on tensity, motion sickness and attention level indexes of the driver, and generating a quantized comfort level score; furthermore, in a trajectory planning stage, a comfort level score corresponding to each candidate trajectory is predicted through a personalized comfort level prediction model, the comfort level score is used as an optimization target parallel to safety and efficiency for comprehensive decision making, and finally an optimal trajectory is selected for execution. According to the invention, the automatic driving system can sense and respond to the subjective state of the driver in real time, the tension and motion sickness of passengers can be effectively relieved, and the riding comfort and personalized experience are improved.

The invention discloses an animal non-constraint physiological monitoring system based on pressure signals, which comprises a pressure signal acquisition module, a signal processing module, a storage module, a communication module and a power supply module which are respectively connected to a processor module, the signal processing module specifically comprises a preprocessing unit, a basic feature extraction unit, a pose feature extraction unit, a physiological parameter detection unit, a sleep monitoring unit, an instantaneous health event detection unit, an emotion evaluation unit, a safety monitoring unit and a summarizing unit so as to monitor the heart rate, breath, sleep, postures, emotions and safety of a target animal. According to the invention, unbound physiological monitoring is carried out on the animal, the method is more friendly to the animal, the monitoring mode is more suitable for the animal, and the monitoring result is more accurate.

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand / supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data / distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. A power management system provides at least 2 strategies of evaluating power level including at least obtaining a voltage measure and measuring and calculating a cumulative capacity measure and comparing these two measure to their respective thresholds.

The invention provides an intelligent drug delivery method and system for pre-pump injection of m-hydroxylamine. The method comprises the following steps: determining an initial pumping rate, a target blood pressure range and a heart rate alarm threshold value of m-hydroxylamine according to initial information of a target patient; pre-pump injection of m-hydroxylamine is carried out according to the initial pump injection rate at a first preset time before lumbar anesthesia puncture operation; after the lumbar anesthesia puncture operation is completed, the current systolic pressure and the current heart rate of the target patient are collected based on the preset frequency; according to the current systolic pressure, the current heart rate, the target blood pressure range and the heart rate alarm threshold value, the initial pumping rate of m-hydroxylamine is adjusted; and carrying out pump injection of m-hydroxylamine based on the adjusted pump injection rate. By means of the method, pre-pumping injection is started before lumbar anesthesia puncture operation, occurrence of hypotension is preempted from the time dimension, the initial pumping injection rate is set based on information of a patient, the pumping injection rate is intelligently adjusted according to real-time feedback of the initial pumping injection rate, and therefore the occurrence rate of hypotension after cesarean section lumbar anesthesia is reduced.

The present application relates to the technical field of sleep promotion, in particular to a sleep auxiliary device for relieving arthralgia, comprising a binding part worn on the joint position of a patient, the surface of the binding part is provided with a plurality of multi-modal stimulation mechanisms for mechanically stimulating the joint acupoints; the auxiliary device further comprises a power source, a gyroscope, a surface electromyography sensor and a heart rate sensor; the power source provides stimulation power for the plurality of multi-modal stimulation mechanisms; the multi-modal stimulation mechanism comprises a shell with an open bottom, a pressing device arranged in the shell and a bionic massage device; the pressing device is used for pressing the joint acupoints; the bionic massage device is in transmission connection with the pressing device and is used for driving the pressing surface of the pressing device to swing. The device can automatically switch between the two modes of soft pressing and active pressing and rubbing according to the sleep and wake state information of the patient, relieve arthralgia, significantly reduce the interference to the sleep process and effectively improve the sleep continuity and quality of the patient.

1. The name of the design product: physical examination all-in-one machine. 2. The use of the design product: for measuring and managing blood pressure, heart rate, body temperature, blood oxygen, exhaled gas alcohol concentration, etc., and printing measurement results. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The invention relates to the technical field of biomedical signal processing and medical electronics, in particular to a blood pressure measuring method for non-invasive exercise blood pressure monitoring. The method aims to solve the technical problem of low blood pressure measurement accuracy caused by broadband noise interference and failure of a fixed parameter model in a motion state in the prior art. According to the technical scheme, the method comprises the steps that a cardiac cycle reference signal and a Korotkoff sound signal are synchronously collected, a sampling window is calculated in a self-adaptive mode by utilizing a cycle triggering feature point gating mechanism and combining a heart rate, and time domain precise interception of the signals is achieved; carrying out root mean square calculation and Savitzky-Golay filtering on the intercepted signal, and keeping waveform characteristics while denoising; constructing a multi-dimensional feature regression model containing a peak value, skewness, kurtosis and dynamic template similarity so as to accurately judge systolic pressure and diastolic pressure; an automatic recharging mechanism is triggered by monitoring signal quality, and strong interference is physically avoided. The method has the advantages of being high in anti-noise capacity, high in feature extraction robustness and accurate and reliable in measurement result.

The invention discloses an intelligent monitoring wrist strap for plateau adaptive sports and a system thereof. The intelligent monitoring wrist strap comprises a monitoring main body, a left wrist strap and a right wrist strap, one side of the monitoring main body is provided with a power supply lamp, a gait monitoring lamp and an osteoporosis early warning lamp, the other side of the monitoring main body is provided with a charging port, and the monitoring main body is internally provided with a micro-processing chip, an air pressure sensor, a temperature sensor, an ultraviolet sensor and an alarm microphone; an osteoporosis early warning module is arranged at the lower part of the monitoring main body, a display screen is arranged at the upper part of the monitoring main body, and an air pressure sensor, a temperature sensor, a blood oxygen detection module, a heart rate detection module, a gait monitoring module, an osteoporosis early warning module and a wireless transmission module are integrated on the micro-processing chip. According to the plateau environment characteristics and the skeletal system chronic disease risk, the motion state and health data of the wearer are dynamically monitored, and individualized motion guidance and early warning information is generated, so that the risk of plateau crowds in the motion process is reduced, and the individualized health management level is improved.

The invention discloses a wearable real-time nursing monitoring device for physiological signs. The wearable real-time nursing monitoring device comprises an electrocardio garment body, a sensor integration module, a micro-processing module, an electrode slice and a heating module, the electrocardio garment body is made of a flexible fabric material, has a shape fitting the curve of a human body and is used for being worn on the trunk of the human body; the sensor integration module is embedded in a fabric of the electrocardiograph garment body, and the sensor integration module comprises an electrode sensor, a heart rate sensor, a respiration sensor, a temperature sensor, a motion sensor, an optical sensor and an electrocardiogram analysis module; the intelligent electrocardiograph garment has the advantages that the electrocardiograph sensor, the heart rate sensor, the breathing sensor, the temperature sensor, the motion sensor and the optical sensor are embedded into the electrocardiograph garment in a fabric integration mode, multiple key vital signs can be collected synchronously for a long time in a non-sensitive mode in the natural living state of a user, and the flexible electrodes are matched with the special electrocardiograph analysis module, so that the user experience is improved. And the continuous and dynamic electrocardiogram monitoring capability is realized in a wearable clothing form.

The invention relates to the field of deep learning, and discloses a heart failure patient home care intelligent monitoring method combined with deep learning, which comprises the following steps: continuously acquiring the heart rate, blood pressure, breathing and body movement states of a patient in a home environment; performing conjoint analysis of time and activity states on the data streams after priority sorting, and forming a dynamic health feature set in combination with historical health records of patients and environment variable information; comprehensively evaluating the dynamic health feature set by using a deep learning model, and identifying a potential abnormal mode of short-time change through the deep learning model; marking and grouping the physiological parameters and related behavior scenes corresponding to the candidate abnormal events, locally evaluating the current health state of the patient based on grouping information, and identifying a potential high-risk condition; and according to the local evaluation result and the overall health trend of the patient, generating real-time early warning and personalized nursing suggestions. The method has the advantage of improving the identification capability of short-time abnormity.

The invention provides an intelligent bracelet for old people, which comprises a bracelet main body, and a display screen is mounted on the bracelet main body; a health monitoring module is arranged in the bracelet main body and is used for monitoring heart rate, blood oxygen and body temperature of the elderly; a health monitoring module is arranged on one side of the bracelet main body, a key alarm module is arranged on one side of the health monitoring module, the key alarm module is arranged in the bracelet main body, the key alarm module comprises a key device, the key device is arranged on one side of the bracelet main body, the key alarm module further comprises a key monitoring unit and a key alarm unit, and a wireless connection module is arranged on one side of the key alarm module. The wireless connection module is arranged in the bracelet main body, and the wireless connection module is used for being connected with external equipment. Mounting blocks are arranged on the two sides of the bracelet body, and a wrist strap connecting piece is arranged between the two mounting blocks. Through cooperative arrangement of all the structures, flexible wrist wearing adjustment can be conveniently carried out according to wearing requirements of different old people, one-key alarm operation is facilitated, and meanwhile operation is easy and popularization is convenient.

The invention discloses a multi-modal sensing fusion-based pet health state dynamic monitoring method, which comprises the following steps of sequentially acquiring health state signals, and outputting original multi-modal data; performing time synchronization on the original multi-modal data, and outputting a de-noised multi-modal signal; extracting heart rate features, and outputting heart rate feature data; taking the blood oxygen signal and the motion signal as input, adopting an improved phase space reconstruction method to construct a phase space trajectory, and outputting blood oxygen characteristic data; dynamically correcting the body temperature signal, and outputting body temperature characteristic data; performing nonlinear dimension reduction on the multi-dimensional health features to obtain low-dimensional health feature vectors; uploading the health feature vector to a cloud server, and constructing a personalized health state model; and performing real-time analysis on the low-dimensional health feature vector based on a personalized health state model, generating a pet health monitoring report, and triggering risk early warning when an abnormal motion mode is identified. According to the invention, the pet health state is dynamically monitored.

The invention relates to the field of intelligent medical monitoring, in particular to a diabetic multi-source data real-time analysis method based on a large language model, and the method mainly comprises the steps: continuously collecting multi-source data of a patient from a CGM device, an intelligent watch and a mobile terminal, and enabling the multi-source data to at least comprise blood glucose data, heart rate and input data; constructing a deep learning model comprising four layers of attention mechanisms, inputting the fusion data into the deep learning model, predicting a blood glucose trend within a preset time by using a sliding time window to obtain a blood glucose prediction result, and pushing the blood glucose prediction result to the mobile terminal or the smart watch; and performing reinforcement learning on the large language model based on a deep Q network architecture, and when the blood glucose control time in the blood glucose data is lower than a preset threshold value, performing parameter updating on the large language model. According to the invention, personalized monitoring and intervention effects on the diabetic patient can be improved.

The invention relates to the technical field of image enhancement, in particular to an angiocardiography high-precision angiography enhancement method and system. The method comprises the steps that an ECG curve of electrocardiosignals before and after contrast agent injection of a patient and a blood vessel image are obtained; determining the compressible degree of each wave band according to the change of different wave bands and the change of the heart rate in the QT interval before and after the contrast agent is injected; determining the increment duration of each wave band in combination with the standard duration, the actual duration and the compressible degree; performing standardized adjustment on the ECG time phase of each frame of blood vessel image in combination with the increment time length and the actual time length of each wave band to obtain a standard time phase; determining a mask matched with each negative film according to the standard time phases of the negative films and the masks; and carrying out image subtraction on each negative film and the matched mask, and carrying out angiography enhancement processing. The blood vessel imaging precision after digital subtraction can be improved, the blood vessel artifact phenomenon is reduced, and the blood vessel imaging precision and reliability are enhanced.