29 results about "Glucose fluctuations" patented technology

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 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 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 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.

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 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.

This invention discloses a dietary inference system and method combining blood glucose monitoring and mobile phone behavior. The system includes: a data acquisition module; a data processing and feature extraction module; a multimodal fusion and joint inference module; a user interaction and calibration module; and a result output and visualization module, which presents the inference results to the user or uploads them to a health management platform. The method includes the following steps: synchronously acquiring continuous blood glucose monitoring time-series data from the user and multimodal behavioral perception data from the mobile terminal; constructing a joint inference model by aligning the timestamps, extracting features, and fusing the two types of data. This invention integrates the physiological response of continuous blood glucose monitoring with direct behavioral observation from smartphones, and utilizes both the "behavioral precursors" and "physiological consequences" of events, making the detection of eating events more reliable and effectively eliminating blood glucose fluctuations caused by simple exercise or stress.

The invention relates to the technical field of blood glucose monitoring, and discloses a dynamic feedback correction blood glucose real-time monitoring method and system and a storage medium, and the method comprises the steps: obtaining intertissue fluid data and a venous blood reference value, and determining a sensor drift deviation based on a real-time difference; correcting interstitial fluid data based on the drift deviation and calculating a blood glucose fluctuation rate; if the fluctuation rate exceeds a preset threshold value, adjusting the feedback interval to a high-frequency mode; executing calibration correction in a high-frequency mode, fusing the venous blood reference value to correct interstitial fluid data, and generating a corrected blood glucose monitoring result; evaluating the resource consumption level based on the correction result, if there is an optimization demand, adjusting the feedback interval to a target interval value in a stationary mode, and determining the target interval value as a final feedback rhythm; monitoring parameters are updated based on the final feedback rhythm, so that the system adapts to individual differences and dynamic changes. The blood glucose monitoring accuracy, the system operation efficiency and the individualized adaptive capacity are improved.

The invention discloses an insulin infusion control method and system based on an insulin pump, and belongs to the technical field of insulin pump control. The method comprises the steps that user basic data and user blood glucose data and motion data within a preset time period are obtained, and the obtained data are preprocessed; inputting the preprocessed multi-dimensional data into the trained blood glucose prediction model, and predicting the blood glucose change trend of the user in a period of time in the future; adjusting the basic rate of an insulin pump based on the blood glucose change trend; the system comprises a data acquisition module, a data processing module, a data analysis module and an adjustment module. By adopting the method and the system, the intelligent control model is constructed by fusing multi-dimensional data to predict the blood glucose change trend of the user, so that the insulin infusion basic rate is dynamically and accurately adjusted, the blood glucose fluctuation risk is reduced, and the treatment safety and comfort of a patient are improved.

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

This invention discloses a wearable patch-type insulin precision drug delivery control system, belonging to the field of medical devices and wearable precision drug delivery technology. It includes a skin-fitting sensing module, a real-time blood glucose acquisition module, a human metabolic parameter detection module, a drug delivery calculation and control module, a microneedle patch drug delivery execution module, a closed-loop feedback correction module, and a wearable battery life and protection module. This invention achieves fully automated control of the entire process—blood glucose monitoring, parameter correction, precise drug delivery, and dynamic optimization—through an integrated technical solution based on pre-fitting condition verification, synchronous monitoring of blood glucose and metabolic parameters, progressive drug delivery calculation, painless microneedle drug delivery, and closed-loop feedback self-correction. By setting a pre-fitting sensing verification mechanism, it achieves synchronous and linked acquisition of dynamic blood glucose data and human metabolic parameters, accurately capturing differences in blood glucose fluctuations under different exercise and metabolic states.

The invention provides a diabetic health level-to-level management system based on association rule analysis, and relates to the technical field of data processing, and the system is used for collecting basic data of a diabetic at different follow-up visit time points, identifying evolution characteristics of the diabetic in a time dimension, calculating an evolution characterization quantity, and determining the health level-to-level management of the diabetic according to the evolution characterization quantity. Dividing the follow-up data sequence into a plurality of evolution data segments, determining the evolution data segments at different follow-up time points as rule antecedent data and rule consequent data, and judging whether candidate rule units are consistent in a time evolution direction or not to obtain a time sequence association rule set; and calculating the stable bearing degree of the same time sequence association rule in each follow-up visit process to obtain a rule bearing capacity, calculating a comprehensive characterization value of the patient, and establishing a health grade interval according to the distribution characteristics of the comprehensive characterization value. According to the method, by constructing the time sequence association rule, it is avoided that short-term blood glucose fluctuation is wrongly associated with complications.

The invention relates to a noninvasive blood glucose dynamic monitoring method and system for wearable equipment. According to the method, physiological and behavior signals of a user are continuously collected and fused into a unified feature vector, and the vector is synchronously analyzed by utilizing a cascaded neural network model subjected to personalized training, so that a real-time blood glucose value is predicted; more importantly, the instantaneous influence of a specific behavior on blood glucose is quantified, the anti-fact blood glucose state is calculated if the behavior does not exist, then a process contribution value representing the health effort degree of a user is calculated according to the attribution information, and finally a monitoring report fusing a numerical result and behavior attribution is generated. According to the scheme, interpretable attribution of blood glucose fluctuation is achieved, a monitoring focus is extended from a single blood glucose result to a behavior process causing the result, and an accurate data basis is provided for management and excitation of a fair, anti-speculation and health encouraging process.

This invention relates to the field of fracture risk prediction technology, and particularly to a method and system for constructing a fracture risk prediction model for diabetic osteoporosis. The method involves collecting bone metabolism data and hip and spine bone density computed tomography images from diabetic patients to construct a multi-dimensional three-dimensional model of bone tissue microstructure. Based on this model, blood glucose fluctuation-bone strength mapping is performed to calculate the trabecular fragility increment and identify the pre-gradient feedback characteristics of brittle mechanical signals to obtain brittle mechanical pre-gradient feedback data. Phase shift analysis of bone stress signals is then performed on the above data to extract phase shift feature data and construct a fracture risk prediction model for diabetic osteoporosis. Finally, dynamic assessment and remote monitoring of the patient's fracture risk are conducted. This method integrates three-dimensional bone microstructure modeling, blood glucose fluctuation impact analysis, and artificial intelligence prediction, improving the early warning accuracy of diabetic osteoporotic fractures.

This invention provides a health classification management system for diabetic patients based on association rule analysis, belonging to the field of data processing technology. The system is used to: collect basic data of diabetic patients at different follow-up time points; identify the evolutionary characteristics of diabetic patients in the time dimension; calculate the evolutionary representation quantity; divide the follow-up data sequence into multiple evolutionary data segments; determine the evolutionary data segments at different follow-up time points as rule antecedent data and rule consequent data; determine whether candidate rule units are consistent in the temporal evolution direction; obtain a set of temporal association rules; calculate the stability carrying capacity of the same temporal association rule in each follow-up process; obtain the rule carrying capacity; calculate the patient's comprehensive representation value; and establish a health level interval based on the distribution characteristics of the comprehensive representation value. This invention avoids incorrectly associating short-term blood glucose fluctuations with the occurrence of complications by constructing temporal association rules.

The application relates to a vascular fistula state prediction method, system, device and medium. The method comprises the following steps: acquiring continuous dynamic blood glucose monitoring data of a target patient in a preset monitoring period; performing standardization preprocessing on the continuous dynamic blood glucose monitoring data to generate blood glucose concentration time series data; extracting a multi-dimensional dynamic injury dose feature vector of the target patient based on the blood glucose concentration time series data; inputting the dynamic injury dose feature vector and the static clinical feature variable of the target patient into a trained fistula failure risk prediction model to output the risk probability of fistula failure of the target patient in a preset future time window. By using the method, dynamic and quantitative evaluation of the blood glucose fluctuation, an invisible risk source, can be realized, and the blood glucose fluctuation is successfully embedded into a clinical prediction framework, the limitations of traditional dependence on static average indexes are effectively overcome, and the fistula risk early warning can be jumped from reflecting a long-term average state to capturing a real-time and cumulative biological damage process.

The invention provides tableted candies capable of blocking sugar absorption as well as a preparation method and application of the tableted candies, and relates to the technical field of functional foods. The tablet candy capable of blocking sugar absorption sequentially comprises an enteric-coated layer, a slow-release layer and a quick-release layer from inside to outside, the enteric-coated layer accounts for 25-35% of the total mass of the tablet candy, the slow-release layer accounts for 40-50% of the total mass of the tablet candy, and the quick-release layer accounts for 20-30% of the total mass of the tablet candy. By means of the three-layer structure, the whole-course coverage from preprandial prevention, in-meal blocking to postprandial adjustment is achieved, the action time can last for 2-3 h, and the action time is highly matched with a typical postprandial blood sugar fluctuation curve. Meanwhile, by adopting a tablet candy dosage form, the tablet candy is convenient to take and good in taste, and can be taken without water. The product is stored for 12 months under a normal-temperature sealing condition, the content of each active component is kept at 95% or above, the integrity of the microcapsule is good, and the quality requirements of commercial products are fully met.

The invention relates to a dynamic nutrition adjustment method and system based on metabolic adaptability, and the method comprises the steps: collecting historical metabolic rate data and real-time biosensor data, carrying out the preprocessing, and recognizing and outputting a structured metabolic data set containing a platform period marker; calculating a metabolic attenuation slope, a nutrient responsivity and an energy gap elastic coefficient, and generating a metabolic adaptability coefficient through weighted fusion; dynamically adjusting the macroscopic nutrient proportion and the total calorie value, and outputting a nutrition adjustment instruction; carrying out strengthening processing on the nutrition adjustment instruction; according to the nutrition enhancing instruction and a pre-configured user diet preference database, optimizing and calculating a specific food material combination, verifying a blood glucose fluctuation level, and outputting an executable meal replacement formula; and calculating the metabolic response rate and dynamically correcting the calculation weight of the metabolic adaptability coefficient based on the executed metabolic rate feedback data. According to the invention, the accuracy, timeliness and individual fitness of nutrition intervention are improved.

The invention relates to the technical field of food, in particular to a low-glycemic five-black-wolfberry-sesame ball and a production process thereof.The low-glycemic five-black-wolfberry-sesame ball is characterized in that the low-glycemic five-black-wolfberry-sesame ball is prepared from, by weight, 27%-45% of black sesame, 20%-30% of wolfberry, 9%-15% of mulberries, 8%-12% of peeled walnut kernels, 8%-12% of pumpkin seeds, 4%-8% of red date particles, functional adhesives and flavor auxiliary materials; the maltitol liquid and the liquid polydextrose are adopted to construct a composite sugar substitute bonding system, the food blood glucose generation index (GI value) of the product is successfully reduced to 32, compared with a traditional sesame ball with honey or granulated sugar as an adhesive, the sesame ball keeps high protein and high dietary fiber, greatly slows down fluctuation of postprandial blood glucose, and has the advantages of being good in stability and good in stability. A healthy food which not only can nourish bodies but also has a blood glucose management function can be provided for diabetic patients, hyperglycemia crowds and blood-glucose-controlling and fat-reducing crowds, and the health burden caused by too high sugar content of a traditional tonic is solved.

To improve user health, we provide a method and system for optimizing a user's glucose state via a mobile application. [Solution] The health management method manages glucose status by using a continuous glucose monitoring (CGM) device to receive the user's blood glucose level 502, determine the time in range (TIR) ​​value 504, determine the TIR state 506, receive the glucose fluctuation (GV) value 508, determine the GV state 510, determine the starting state based on the TIR state and GV state 512, determine that the starting state corresponds to a non-ideal state 514, and generate an optimized path 516 to reach an ideal state based on one or more account vectors, such as addressing self-management behaviors including diet, activity, and medication use.

The invention relates to the technical field of pregnant woman delivery time prediction, in particular to a pregnant woman delivery time prediction system based on big data analysis, and the system carries out the weighted fusion of the ratio of the abdominal circumference of a fetus to the femur length, a fetus weight estimation value and a fetus weight increase rate to obtain a fetus metabolic load index; calculating to obtain an insulin resistance index change rate according to the fasting insulin level and the fasting blood glucose level; calculating the blood glucose fluctuation amplitude of the continuous blood glucose monitoring data of the maternal body, and calculating the area abnormal increment under the blood glucose curve; analyzing the blood glucose fluctuation amplitude, the insulin resistance index change rate and the abnormal increment of the area under the blood glucose curve to obtain a maternal metabolic stress index; and when a dynamic imbalance critical value obtained by carrying out load-stress imbalance analysis on the fetal metabolic load index and the maternal metabolic stress index exceeds a preset dynamic imbalance critical threshold value, generating predicted delivery time and outputting early warning information. And the accuracy of predicting the delivery time of the pregnant woman with the gestational diabetes mellitus is effectively improved.

This application provides a personalized dietary recommendation method for managing gestational diabetes, comprising: processing patient-input dietary option data by optimizing a blood glucose response model; determining if the carbohydrate content in the dietary option data causes the predicted blood glucose fluctuation trend to exceed the safe range, and generating an alternative dietary recommendation list to determine a personalized dietary plan; analyzing the matching degree between historical blood glucose fluctuation sequences and the personalized dietary plan based on an iterative blood glucose response model; determining if the matching degree is lower than a preset threshold, and reclassifying the interaction patterns between dynamic factors to obtain a refined blood glucose impact weight analysis; integrating multidimensional data from the obtained refined blood glucose impact weight analysis to generate a final real-time personalized dietary recommendation; and determining if the final implementation of the personalized dietary recommendation can stabilize abnormal blood glucose fluctuations during pregnancy, and outputting a complete diabetes management guidance sequence.

The present disclosure describes an analysis method and system for blood glucose fluctuation caused by insulin leakage adsorbed by an NC membrane, the method comprising collecting a leakage image, adsorbing leakage at a target position by a nitrocellulose membrane after a patient injects insulin, and taking a picture of the nitrocellulose membrane after adsorption of the leakage and a reference object to obtain a leakage image; analyzing the leakage image to determine a leakage area in the leakage image; determining a leakage area based on a resolution of the leakage image, the leakage area, and a reference object pattern; determining a leakage volume corresponding to the leakage area based on an adsorption capacity of the nitrocellulose membrane and the leakage area; and determining an impact of the leakage volume on blood glucose fluctuation of the patient based on an injection dose, the leakage volume, a first blood glucose level and a second blood glucose level of the patient, the first blood glucose level and the second blood glucose level being blood glucose indicators before and after the patient injects insulin, respectively. Thus, the leakage volume can be effectively, conveniently and non-invasively obtained to analyze the impact of the leakage volume on blood glucose fluctuation.

The invention provides a multifunctional intelligent blood glucose monitoring method and system which are applied to an integrated household blood glucose monitoring intelligent workstation. The method comprises the steps that in response to a monitoring starting instruction of a user, a central control unit calls a perioperative period nursing voice library to play standardized operation guidance; the automatic blood sampling and sterilizing device is controlled to execute a sterile sampling process, and automatic puncture and blood sample conveying are completed in a micro-negative pressure environment; acquiring a real-time blood glucose value, reading a clinical state tag of the user, and calling the irritable hyperglycemia risk assessment model to generate a grading assessment result; dynamically adjusting the disinfection strategy of the ultraviolet bacteriostasis bin based on the biological load risk of the detected numerical value; monitoring data and evaluation results are stored in a database, and a perioperative period blood glucose fluctuation file is constructed. According to the system, through software and hardware cooperation, sterile precise monitoring and complication risk early warning of the blood glucose of the patient in the perioperative period are achieved.