66 results about "Glucose fluctuations" patented technology

The invention relates to the technical field of child health monitoring and artificial intelligence, and provides a T1DM child blood glucose monitoring and early warning system. An existing blood glucose monitoring system is generally only based on single blood glucose data acquisition, and blood glucose fluctuation of children in different situations is difficult to accurately capture in real time. Through a multi-modal data fusion technology, multi-modal physiological features are integrated, and deep feature extraction and fusion analysis of data are realized. The system further combines a Q-learning algorithm to generate a personalized blood glucose early warning strategy, and utilizes a hidden Markov model (HMM) to identify the behavior situation of the child and optimize the early warning threshold setting. Meanwhile, data privacy is protected in a data distributed processing process through a federated learning framework and a differential privacy protection technology. The real-time performance and accuracy of blood glucose monitoring are improved, and intelligent and personalized health management and early warning services are provided for children and guardians of the children on the premise of protecting the privacy of the children.

The invention relates to the technical field of patient information processing, in particular to an intelligent health management system for diabetic patients, which comprises the steps of acquiring continuous blood glucose monitoring data of patients, extracting blood glucose values, timestamps and patient identity information, establishing a blood glucose data set, performing data cleaning on the blood glucose data set, and removing missing values and repeated values. According to the method, after the data are cleaned and analyzed, the generated blood glucose fluctuation index can reveal the blood glucose fluctuation condition in a short time, and therefore the possible health risk is predicted. The fluctuation indexes are standardized and compared with a set threshold value, potential abnormal blood glucose events are recognized, and the response speed and preventability of disease management are improved. Meanwhile, the health risk of the patient can be quantitatively evaluated through calculation of the abnormal accumulated score, early warning is automatically triggered when the abnormal accumulated score exceeds a safety threshold value, and intervention is carried out in time. And the management efficiency and the treatment pertinence of diabetes mellitus are improved.

The invention relates to the technical field of blood glucose prediction, in particular to a blood glucose fluctuation prediction method based on historical data. The method comprises the following steps: acquiring corresponding historical blood glucose monitoring data, daily behavior data, physiological status data and medical intervention data of a user, performing time axis alignment and abnormal value cleaning treatment, and extracting basic features and associated influence features corresponding to blood glucose fluctuation; performing space-time dimension fusion on the basic features and the associated influence features, and generating a blood glucose fluctuation prediction curve of the user in a future preset time period; performing comparative analysis on the blood glucose fluctuation prediction curve and actual blood glucose monitoring data of the user, calculating a prediction deviation degree and a trend goodness of fit, and generating a corresponding deviation analysis result; feature weight distribution of the dynamic prediction model is optimized based on the deviation analysis result, and a personalized blood glucose fluctuation prediction report is generated and comprises a risk early warning threshold value and an intervention suggestion triggering condition. According to the method, prediction efficiency and personalized management of blood glucose fluctuation can be realized.

The invention relates to the technical field of medical information processing, in particular to a type 2 diabetes precise typing and dynamic treatment system which comprises a clinical feature extraction module, a metabolism dynamic modeling module, a precise typing module, a treatment parameter generation module and a dynamic treatment output module. Wherein the clinical feature extraction module is used for collecting a multi-dimensional clinical data set of a patient for continuous 14 days; the metabolism dynamic modeling module is used for extracting blood glucose fluctuation characteristics and pancreas islet function compensation characteristics; the precise typing module is used for dividing metabolism-driven subtypes to which the patients belong through a clustering algorithm; the treatment parameter generation module is used for mapping a subtype-treatment rule base and outputting a matched personalized treatment parameter combination; and the dynamic treatment output module outputs a dynamic treatment scheme. According to the invention, through a dynamic treatment mechanism based on typing, linkage control of individual difference identification and real-time adjustment of the medication scheme of the type 2 diabetes patients is realized, and the accuracy and adaptability of treatment are improved.

The invention relates to the technical field of artificial intelligence, and discloses a blood glucose management system based on artificial intelligence. According to the system, blood glucose monitoring is reminded through the blood glucose monitoring terminal, a blood glucose curve is drawn every week, abnormal blood glucose conditions are judged for reminding and alarming, the diet terminal is used for guiding low-sugar diet recommendation, high-sugar food intake is avoided, the mastering degree of a patient and family members for food classification is improved, and the exercise terminal determines the exercise time and exercise amount of the patient according to the blood glucose value. Parameters are input into an intelligent system, the system converts and selects a motion mode, time and intensity, a drug treatment terminal calculates the insulin injection amount of a patient every day and every meal according to the blood glucose level and food intake, the insulin dosage is adjusted according to the calculated numerical values, blood glucose fluctuation is reduced, and the treatment effect is improved. The education and self-management terminal guides a patient to improve the cognitive degree of diabetes and provides personalized propaganda and education guidance for the patient, and the complication reminding terminal controls the blood sugar of the patient within a normal range, guides a good lifestyle of the patient and guides behavior specifications.

The invention discloses a real-time adjusting method for insulin infusion volume, which adopts a closed-loop control method, and dynamically adjusts the infusion volume of an insulin pump based on a comparison result of a predicted blood glucose parameter and a preset blood glucose range through collected blood glucose data and predicted future blood glucose data, thereby realizing more accurate blood glucose control. The invention further discloses a closed-loop infusion system designed based on the real-time adjustment method. The closed-loop infusion system comprises a controller, a blood glucose detection system, an insulin pump and a continuous glucose monitoring system. Compared with open-loop control, the closed-loop control can monitor the blood glucose level of the patient in real time and automatically adjust the insulin infusion amount according to real-time data, and the high-automation system can more accurately maintain the blood glucose within the target range and reduce blood glucose fluctuation. Moreover, the closed-loop control system reduces the requirements of patients or medical staff for manually calculating and adjusting the insulin dosage, so that personal errors are reduced, automatic adjustment can better adapt to individual differences and dynamic changes of the patients, the burden of the patients on blood glucose monitoring and insulin dosage adjustment is reduced, and the safety and effectiveness of treatment are improved.

The invention discloses a diabetic health data management method and system, and relates to the technical field of data management. A diabetic health data management system comprises a health data monitoring module and a health data management module. According to the method, blood glucose fluctuation is considered, multi-dimensional signals such as a molecular marker, a sleep cycle and bioelectricity feedback are introduced, and a patient molecular physiological interaction index is established through mapping of a unified time axis and correction of a causal inference model; misjudgment caused by accidental fluctuation is effectively reduced by capturing cross-channel collaborative abnormity; according to the method, the patient individual metabolism adaptation domain and the similar patient metabolism adaptation domain are constructed, a double-gating judgment mode is adopted, excessive early warning of health data management caused by a single threshold standard is avoided, and personalized applicability under patient individual difference is guaranteed.

The invention, which relates to the technical field of the emergency internal medicine department, discloses an intelligent assessment and early warning system for the illness state of the emergency internal medicine department, and the system carries out quantitative assessment based on pathological characteristics of multiple dimensions through a comprehensive assessment and early warning module and a blood sugar fluctuation and fundus disease analysis module. The integration of the data can form a comprehensive fundus lesion risk factor Rrisk, and the fundus lesion risk of a diabetic patient is subjected to all-around quantitative evaluation. According to the system, fundus lesion risks of patients are divided through set risk levels, and intervention suggestions of different levels are provided in combination with a comprehensive fundus lesion risk factor Rrisk. When the patient is at a high risk level, the system can trigger related early warning in time to remind medical staff to take targeted treatment or further examination, and serious consequences such as disease development to blindness and the like are avoided. The multi-dimensional comprehensive evaluation not only improves the diagnosis accuracy of the fundus lesion of the patient, but also provides powerful decision support for clinical treatment.

The present invention discloses modified-release bilayer tablets consisting of two layers, layer 1 consisting 500 mg sustained release metformin and layer 2 consisting 250 mg normal release metformin and 5mg / 10mg delayed-release dapagliflozin. This formulation is meticulously designed to regulate blood glucose levels effectively over an extended period. Immediate-release metformin swiftly addresses acute glucose spikes, while sustained-release metformin ensures prolonged glucose control, minimizing fluctuations throughout the day. The delayed-release dapagliflozin component allows for controlled and timed release, managing persistent hyperglycemia synergistically with metformin. By optimizing efficacy and minimizing glucose fluctuations, this innovative formulation offers a promising solution for stable glycemic control in individuals with diabetes. Present invention aims to improve patient outcomes and enhance overall quality of life by maintaining stable glucose levels and reducing the risk of complications associated with uncontrolled diabetes.

The invention belongs to the technical field of intelligent medical treatment, and provides a pregnancy blood glucose fluctuation prediction and intervention recommendation system based on multi-source time sequence data and a blood glucose dynamic prediction method, and the system comprises a data acquisition layer, a data processing layer, an algorithm calculation layer and an application interaction layer. The data acquisition layer comprises a CGM access module, a glucometer access module, an intelligent wearable device access module and an EHR / EMR access module; the data processing layer comprises a data cleaning module, a time sequence database and a feature engineering module; the algorithm calculation layer comprises a blood glucose prediction module, a user blood glucose portrait generation module and an intervention recommendation module; the application interaction layer is an operation and feedback entrance for doctors and users and comprises a doctor end, a user end, an early warning module and an interaction module. According to the method, the multi-source time series data related to the blood glucose fluctuation are integrated, the dynamic correlation model is established for predicting the blood glucose fluctuation, the monitoring blank is filled, the monitoring frequency is optimized, and the user compliance is improved.

The invention relates to the technical field of non-invasive monitoring, in particular to a diabetes blood glucose fluctuation trend prediction method based on foot temperature and humidity change, which comprises the following steps: continuously collecting temperature, humidity and motion data of multiple areas of a foot through a foot wearable device, extracting a feature set related to blood glucose fluctuation from the preprocessed data, and calculating the blood glucose fluctuation trend according to the feature set; and fusing the feature set and context event information, inputting the fused feature set and context event information into a multi-task deep learning prediction model, visually displaying a prediction result in the forms of a trend arrow, a probability interval and a risk level, and triggering early warning in advance when a hyperglycemia or hypoglycemia event is predicted to be about to occur. The method is completely noninvasive and high in compliance, prospective trend prediction can be carried out based on existing diabetic foot monitoring wearable equipment, individual physiological characteristics of a user can be continuously learned through a built-in personalized self-adaptive mechanism, dynamic calibration is carried out on the model through incremental learning, and the prediction accuracy is improved. And the prediction precision is continuously improved along with the use time.

The invention relates to the technical field of medical instruments, and discloses a dynamic blood glucose monitoring system after kidney transplantation based on artificial intelligence, the system comprises a data acquisition and transmission module, an AI decision support module, a risk early warning and report generation module, a feedback and optimization module and a system integration module; an AI drug disturbance analysis module is arranged, when postoperation blood glucose is monitored in real time, an immunosuppressor-blood glucose dynamic response model is established, the causal relationship between the blood concentration of a specific drug and glucose metabolism is analyzed, and a drug correlation event label is labeled in real time; by means of the method, whether blood glucose fluctuation is triggered by immunosuppressor metabolism abnormality or not can be accurately recognized, it is avoided that drug-induced hyperglycemia is misjudged as an dietary event, the accuracy of treatment intervention decision making is guaranteed, and the rejection risk is reduced.

The invention relates to the technical field of medical health information processing, in particular to a patient diet digital visual management method and system based on multi-source data. The method comprises the following steps: acquiring blood glucose data of a patient, and generating a blood glucose fluctuation curve; meanwhile, diet images or log data of the patient are obtained, structured nutritional ingredient analysis is conducted in combination with the nutrition knowledge graph, and diet nutrition data are extracted; the motion behavior information of the patient is obtained in combination with wearable equipment and the like, and motion feature data are extracted; and respectively associating the blood glucose fluctuation curve with the diet nutrition data and the motion characteristic data, and constructing blood glucose diet data and blood glucose motion data. The blood glucose diet data and the blood glucose movement data are integrated through a multi-factor conjoint analysis model, the relative influence of various behaviors on blood glucose fluctuation is recognized, and diet multi-source influence data are obtained. According to the method, the influence data are presented in a graphic mode, behavior traceability and visual feedback are achieved, and the patient is assisted in scientific diet and exercise intervention management.

The invention provides a personalized diet recommendation method for pregnancy diabetes management, and the method comprises the steps: processing diet option data inputted by a patient through an optimized blood glucose response model, and generating an alternative diet recommendation list if the carbohydrate content in the diet option data causes a predicted blood glucose fluctuation trend to exceed a safety range, determining a personalized diet scheme; analyzing the matching degree of the historical blood glucose fluctuation sequence and the personalized dietary scheme according to an iterative blood glucose reaction model, and if the matching degree is judged to be lower than a preset threshold value, re-classifying interaction modes among dynamic factors to obtain refined blood glucose influence weight analysis; and integrating multi-dimensional data from the obtained refined blood sugar influence weight analysis to generate final real-time personalized diet recommendation, and if it is judged that the final implementation of the personalized diet recommendation can stabilize the abnormal blood sugar fluctuation during pregnancy, outputting a complete diabetes management guidance sequence.

The invention discloses a rice cake capable of regulating postprandial blood sugar and inhibiting starch aging, and belongs to the field of food processing and preservation. The rice cake is prepared from the following raw materials in parts by mass: 100 to 300 parts of polished round-grained rice flour, 100 to 200 parts of glutinous rice flour, 5 to 30 parts of pre-gelatinized corn starch, 5 to 30 parts of wheat type B starch granules and 100 to 300 parts of water. The pre-gelatinized corn starch-wheat type B starch composite preservative prepared by mixing the pre-gelatinized starch and the wheat type B starch in proportion can effectively inhibit hardness increase caused by starch aging and retrogradation in the storage process of the rice cake and avoid taste deterioration. Meanwhile, by adding the composite starch, the digestibility rate of the rice cake is remarkably reduced, and the metabolic regulation characteristic similar to that of low glycemic index food is endowed to the product. The characteristic of a cleaning label formed by natural components of the rice highly conforms to the development trend of healthy food, and the rice is especially suitable for functional rice product development of people needing to control postprandial blood sugar fluctuation.

The present invention relates to blood glucose monitoring, and specifically to a blood glucose monitoring system based on a multi-wavelength light source and multi-modal data, a multi-wavelength tunable silicon photonic Raman spectroscopy module, which adaptively selects an optimal wavelength combination according to skin type and detection depth, performs multi-wavelength synchronous excitation on a target skin area, and converts the collected Raman spectral signals into a Raman spectral sequence, which is then transmitted to a blood glucose monitoring model for analysis; a multi-modal physiological parameter acquisition unit, which collects a user's multi-modal physiological parameters through multiple types of sensors, forms physiological parameter time series data, and transmits the data to a blood glucose monitoring model for analysis; and a blood glucose monitoring model, which synchronously analyzes the Raman spectral sequence and the physiological parameter time series data, and dynamically correlates blood glucose fluctuations with cardiovascular status. The technical solution provided by the present invention can effectively overcome the defect of the prior art that it is difficult to accurately and efficiently monitor blood glucose in combination with the user's skin type and physiological parameters.

The application relates to the technical field of drug efficacy prediction, in particular to a multi-modal data prediction model for diabetes gene therapy response, which comprises an unmedicated data collection module, a medicated data collection module and a data prediction module.The unmedicated data collection module collects unmedicated data samples of a user; the medicated data collection module collects data samples of the user after taking medicine; the data samples comprise explicit data and implicit data; the explicit data is body information and medicine information related to blood glucose change; and the implicit data is blood glucose values corresponding to the explicit data; the data prediction module inputs fused features into a convolutional network model to generate drug efficacy prediction values.The application generates a prediction residual corresponding to the explicit data after taking medicine through a blood glucose value prediction module, identifies blood glucose fluctuation characteristics caused by non-drug factors by using the prediction residual, separates the net influence of medicine on blood glucose, and thus improves the accuracy of drug efficacy prediction.

The present disclosure relates to a use of Lacticaseibacillus rhamnosus FMBL L23004 CNN in the preparation of a medicament for preventing or treating diabetes mellitus. The Lacticaseibacillus rhamnosus FMBL L23004 CNN was deposited with the China Center for Type Culture Collection (CCTCC) on 26 Jun. 2023 under the accession number CCTCC NO: M 20231099. The Lacticaseibacillus rhamnosus FMBL L23004 CNN strain has the effect of inhibiting the activity of α-glucosidase, α-amylase, and dipeptidyl peptidase IV. Administration of the strain can significantly lower oral glucose tolerance and insulin tolerance in Type 2 Diabetes Mellitus (T2DM) mice, reduce abnormal postprandial blood glucose fluctuations, decrease fasting blood glucose levels, lower fasting insulin levels, reduce blood lipid levels, and alleviate insulin resistance. The strain significantly improves the composition of the gut microbiome, increases the abundance and diversity of beneficial gut bacteria, and reduces the relative abundance of pathogenic bacteria.

A transdermal glucose monitoring system is provided. The system includes: a continuous glucose monitoring (CGM) device, configured to monitor glucose concentration in subcutaneous interstitial fluid of a patient; a patient user interface, configured to acquire information of the CGM device, receive glucose concentration data, and display a warm-up progress indicator of the CGM device; and a cloud server, configured to complete data sharing and authorize access to data. The provided patient user interface focuses on the warm-up time of the CGM device and provides a positive and intuitive display thereof, along with visual management of glucose monitoring data analysis, thereby enhancing the convenience of glucose monitoring. Furthermore, the system supports a second monitoring device, enabling family and friends to promptly understand the patient's glucose monitoring status and infer the causes of glucose fluctuations.

This invention discloses an intelligent assessment and early warning system for emergency internal medicine conditions, belonging to the field of emergency internal medicine technology. This system, through a comprehensive assessment and early warning module and a blood glucose fluctuation and fundus disease analysis module, performs quantitative assessment based on multiple dimensions of pathological characteristics. The fusion of these data forms a comprehensive fundus disease risk factor, Rrisk, providing a comprehensive quantitative assessment of the fundus disease risk in diabetic patients. The system classifies patients' fundus disease risks according to set risk levels and, combined with the comprehensive fundus disease risk factor Rrisk, provides intervention suggestions at different levels. When a patient is at a high-risk level, the system can promptly trigger relevant early warnings, reminding medical staff to take targeted treatment or further examinations to prevent the condition from progressing to serious consequences such as blindness. This multi-dimensional comprehensive assessment not only improves the diagnostic accuracy of fundus diseases but also provides strong decision support for clinical treatment.

This invention relates to the field of urine glucose monitoring technology, and more particularly to a method and system for monitoring urine glucose data from adult diapers. The method first interacts with a nursing management system to obtain daily care milestones for the patient, and connects to a wearable terminal to generate a visual monitoring interface. Secondly, at preset milestones, multiple sensors integrated into the inner layer of the diaper collect data, combining health parameters to identify abnormalities and predict the level of blood glucose abnormalities. Subsequently, based on historical data from multiple milestones, a blood glucose fluctuation trend curve is generated on the interface. Finally, a personalized intervention plan is formulated based on the abnormality level and the trend curve, implementing targeted nursing care. This invention achieves non-invasive continuous monitoring through multi-source signal fusion to combat interference; it possesses the ability to predict blood glucose trends, transforming passive alarms into proactive prevention; and through a visual interface and intelligent decision-making closed loop, it significantly improves the accuracy, efficiency, and comfort of nursing care for diabetic patients.

The invention relates to the technical field of drug effect prediction, in particular to a multi-modal data prediction model for diabetes gene therapy response, which comprises an untaken medicine data acquisition module for acquiring a data sample of untaken medicine of a user; the medicine taking data acquisition module is used for acquiring data samples after the user takes medicine, the data samples comprise dominant data and recessive data, the dominant data are body information and medicine taking information related to blood glucose changes, and the recessive data are blood glucose values corresponding to the dominant data; and the data prediction module inputs the fusion features into a convolutional network model to generate a drug effect prediction value. The prediction residual error corresponding to the dominant data after medicine taking is generated through the blood glucose value prediction module, the blood glucose fluctuation characteristic caused by non-medicine factors is recognized through the prediction residual error, the net influence effect of the medicine on blood glucose is separated, and therefore the medicine effect prediction accuracy is improved.

The present application provides a blood glucose monitoring method. The method comprises: determining a first early warning duration according to first blood glucose data, wherein the first blood glucose data comprises blood glucose data within a first time period, and the first early warning duration is used for performing hypoglycemia early warning. The first early warning duration is related to blood glucose fluctuation indicated by the first blood glucose data. If the blood glucose indicated by the first blood glucose data fluctuates little and changes slowly, the first early warning duration can be longer. If the first blood glucose data input into a model indicates that the blood glucose of a user fluctuates greatly and changes rapidly in historical time, the first early warning duration can be shorter. By means of implementing the described method, the electronic device can adaptively adjust the first early warning duration according to the characteristics of the blood glucose variability of a user, thereby improving the accuracy of hypoglycemia early warning.

The present invention discloses a quantitative assessment method for personalized glucose metabolism sensitivity and a personalized dietary carbohydrate intake recommendation system. Based on the repeated measurement of blood glucose fluctuations after a variety of standard meals by a continuous blood glucose monitor, the present invention creates a new quantitative index to characterize the differentiated glucose metabolism levels of different individuals. On this basis, the present invention also creates a personalized diet library, which can provide different individuals with customized maximum carbohydrate intake limits for each meal for the same food. Residents can use this tool to better understand their own glucose metabolism levels and the optimal carbohydrate intake limit for each food, thereby achieving precise management of glucose metabolism.

The invention relates to the technical field of medical health management, and particularly discloses a patient multi-parameter coupled venous insulin intelligent control method and a patient multi-parameter coupled venous insulin intelligent control system. The method realizes accurate regulation and control of the intravenous insulin infusion rate and ensures that the blood glucose level of a patient is maintained in a safe target interval, and specifically comprises the following steps: evaluating in-vivo energy conversion efficiency and night body sugar regulation efficiency, constructing a comprehensive feature vector by using the parameters, and inputting the comprehensive feature vector into a pre-trained random forest model; the blood glucose fluctuation range of a patient in the current and future preset time periods is predicted in combination with a blood glucose kinetic model, and an optimal insulin infusion rate adjustment strategy is calculated through an optimization algorithm based on a prediction result and a set target blood glucose interval, so that the insulin infusion rate is dynamically adjusted, and it is ensured that the blood glucose level is stably maintained in the target interval.

The invention belongs to the technical field of non-contact blood glucose monitoring, and provides a non-contact blood glucose monitoring device and method.The device comprises the steps that infrared video frames of the face of a user are obtained; compressing the original infrared video frame to obtain a compressed video frame; identifying an original infrared video frame, extracting key facial feature points, and dynamically selecting an optimal monitoring area based on facial physiological feature stability; continuous face information is extracted from the selected optimal monitoring area, remodeling is carried out, parallel large kernel and small kernel convolution paths are utilized, multi-scale context representation is generated, global modeling is carried out to capture a time dependency relationship, features are integrated, and the current blood glucose concentration of the user is obtained through estimation; face detection is carried out based on the compressed video frame, corresponding user information and blood glucose concentration historical records are called according to a detection result, and blood glucose fluctuation is determined in combination with the current blood glucose concentration. The accuracy of non-contact blood glucose monitoring is improved, and the detection precision is guaranteed.

The invention relates to blood glucose monitoring, in particular to a blood glucose monitoring system based on a multi-wavelength light source and multi-modal data, which is characterized in that a multi-wavelength adjustable silicon photon Raman spectrum module adaptively selects an optimal wavelength combination according to a skin type and a detection depth, and performs multi-wavelength synchronous excitation on a target skin area; the collected Raman spectrum signal is converted into a Raman spectrum sequence, and the Raman spectrum sequence is transmitted to the blood glucose monitoring model for analysis; the multi-modal physiological parameter acquisition unit is used for acquiring multi-modal physiological parameters of a user through a multi-type sensor, forming physiological parameter time sequence data and transmitting the physiological parameter time sequence data to the blood glucose monitoring model for analysis; the blood glucose monitoring model synchronously analyzes a Raman spectrum sequence and physiological parameter time sequence data, and dynamically associates blood glucose fluctuation with a cardiovascular state; according to the technical scheme provided by the invention, the defect that accurate and efficient blood glucose monitoring is difficult to combine with the skin type and physiological parameters of the user in the prior art can be effectively overcome.

The invention relates to the technical field of blood glucose monitoring, and discloses a blood glucose information management system for dynamic blood glucose monitoring, which comprises a data acquisition module, a data processing module, an intelligent adjustment module, a database module and a user interaction module, wherein the data acquisition module is used for acquiring blood glucose data, motion data and microenvironment data of a user; the data processing module is used for calculating the blood glucose fluctuation data of the user and judging the motion state of the user; the intelligent adjusting module is used for dynamically adjusting the blood glucose monitoring frequency and correcting the blood glucose data of the user; the database module is used for recording blood glucose data of the user and basic data of the user; and the user interaction module carries out blood glucose abnormity early warning on the user based on the blood glucose data of the user and the blood glucose early warning threshold value. The dynamic adaptability of blood glucose monitoring is effectively improved, and the blood glucose monitoring requirements of different patients in different states can be met.

The embodiment of the invention discloses a blood glucose health dynamic regulation and control method and system based on an AI algorithm. The method comprises the following steps: acquiring first blood glucose data of a current time window of a monitoring target; according to the first blood glucose data and second blood glucose data of at least one historical time window, obtaining at least one first feature map of blood glucose fluctuation of the monitoring target; generating a second feature map according to the first feature map and the related information; obtaining a second prediction probability group according to the second feature map and the first prediction probability group of the at least one historical time window; the second prediction probability group comprises probabilities corresponding to different risk levels and confidence coefficients corresponding to the probabilities; determining the risk level of the monitoring target in the next time window according to the second prediction probability group; the risk level comprises a first type value and a second type value; the first type of values are related to hypoglycemia; the second type of values are related to hyperglycemia; and storing the second prediction probability group into the first queue.

The invention provides a processing module and system for glucose concentration before and after sleep, and the processing module is used for detecting the glucose concentration of a to-be-detected object and giving guidance information. The processing module is configured to determine the fluctuation type of a glucose concentration curve between the pre-sleep state and the post-sleep state on the basis of glucose data including the time-varying glucose concentration between the pre-sleep state and the post-sleep state of the to-be-detected object, and the fluctuation type reflects the variation trend of the glucose concentration between the pre-sleep state and the post-sleep state; a fluctuation type is obtained based on the glucose concentration, the maximum glucose fluctuation amplitude and the sleep glucose fluctuation amplitude obtained based on the glucose data at the time before sleep, and guidance information or suggestions related to the sleep behavior are generated according to the fluctuation type. Therefore, the time for seeking suggestions of doctors can be reduced.