2198 results about "Glucose blood" patented technology

The invention discloses an insulin infusion decision-making system and method combining reinforcement learning and metabolism simulation, and belongs to the field of clinical decision-making assistance. Comprising an acquisition processing module, a metabolism simulation module, a dynamic updating module, a path simulation module, a strategy control module, a monitoring constraint module, a decision analysis module, a correction processing module, a training optimization module and an injection control module. The method can accurately reflect the metabolic characteristics of the patient, makes the insulin decision more targeted, realizes automatic adjustment of the model, keeps real-time perception and response to the state of the patient, avoids the risk caused by model aging or error accumulation, enhances the long-term control ability, improves the control efficiency while guaranteeing the blood glucose stability, and reduces the cost. The fault-tolerant capability of the system is improved, the interpretability and safety guarantee of clinical decisions are improved, and the credibility of doctors is enhanced.

The invention relates to the technical field of health early warning, in particular to a human health early warning method and system based on physical examination data analysis, which comprises the following steps: collecting physical examination data of a human body, analyzing index changes of blood pressure, blood sugar, cholesterol and body mass index of the human body, sorting the physical examination data of the human body through time sequence sorting, and generating a time sequence sorting result. According to the method, time sequence sorting and spatial dimension evaluation are performed on the physical examination data, so that the health risk can be accurately identified and the early warning level can be dynamically evaluated, the data analysis precision is improved, the risk prediction dimension is increased, early warning is more timely and accurate, and the accuracy of early warning is improved by performing layout evaluation on the physical examination data points in a finite dimension space. Potential health abnormalities can be identified on multiple levels, and multi-dimensional analysis provides a new way for identifying complex health problems, so that health management is more active and predictable, health monitoring and risk management are optimized, and the health condition of a patient is improved.

The invention relates to the technical field of intelligent data management, in particular to a multi-source data fusion type intelligent data management system based on edge computing. The system comprises a multi-modal data acquisition unit which acquires multi-source heterogeneous data in real time; the edge preprocessing and enhancement unit integrates a long short-term memory network and a cross attention mechanism, and the lightweight anomaly detection unit deploys a structured pruning and quantized double-branch hybrid model at edge nodes, and performs real-time classification and anomaly scoring on multi-modal fusion feature tensors; and the grading early warning unit is used for receiving the fused comprehensive abnormal score, mapping the comprehensive abnormal score into a discrete alarm grade, and generating a standardized message body containing the grade, a timestamp and details. According to the design, through local tolerance self-adaption and LSH Hash accelerated matching, the coupling complexity of the electrocardio-blood pressure-blood glucose multiple signals is quantified, and the problem that the complex coupling relation among multiple physiological parameters is difficult to capture through a single signal is solved.

The invention discloses a dynamic blood glucose monitoring and intelligent pump speed adjusting system and method, and relates to the technical field of biomedical engineering.The dynamic blood glucose monitoring and intelligent pump speed adjusting method comprises the steps that precise monitoring is conducted through a multi-mode sensing module, an enzyme electrode sensor adopts an anti-interference coating, a Raman spectrum dynamic correction algorithm is combined, and motion artifacts and temperature drift are restrained; the metabolic feature fusion calibration module is integrated with a time convolution network, time sequence modeling is carried out on diet, exercise and historical blood glucose data, an individualized metabolic parameter database is constructed, and self-adaptive signal drift compensation is achieved; the self-adaptive pump control module innovatively designs a double-layer fuzzy PID controller, a base layer generates an infusion rate according to a real-time blood glucose state and insulin activity, an optimization layer introduces an LSTM metabolism prediction model, and the slope of an infusion curve is dynamically adjusted in combination with future behavior pre-judgment; in the edge-cloud collaborative architecture, a lightweight TCN model is locally deployed to guarantee real-time control, and a cloud end optimizes a global digital twinborn model through federal learning and generates a multi-dimensional metabolic risk early warning report.

The invention relates to the technical field of disease prediction, in particular to an intelligent risk prediction method based on a neural network, and the method comprises the steps: collecting blood glucose and body mass index data, carrying out the clustering analysis, and constructing a multi-risk group feature; a risk transfer track before attack is recognized by combining a historical index change path and time sequence analysis; and calculating an individual risk coefficient by using deep learning, and comparing the individual risk coefficient with a risk threshold to generate early warning information, thereby realizing dynamic accurate evaluation. According to the method, by collecting the blood glucose level and body weight index data and conducting standardization processing, risk group division based on health index clustering can be achieved, a group recognition system with health feature differentiation is constructed, and the structured understanding of the individual health trend is enhanced. And on the basis of the clustered group, extracting a fluctuation path of individual historical indexes by means of a time sequence mode, identifying key transfer characteristics before disease attack, and enhancing the traceability of the disease formation process.

The invention provides a multi-source heterogeneous data integrated coronary heart disease accurate prediction method, which comprises the following steps: acquiring various physiological signals of a patient in real time, the physiological signals comprising ST segment change characteristics and basic cycle function parameters in electrocardiosignals, and obtaining a multi-source signal data set; according to the joint data set, analyzing instantaneous fluctuation characteristics of a vascular resistance index in a postprandial hyperlipemia window period, extracting a vascular resistance fluctuation amplitude from the instantaneous fluctuation characteristics, and if it is detected that the fluctuation amplitude exceeds a preset threshold range, marking the window as a high-risk time window; if the score value exceeds a preset threshold value, triggering a coronary heart disease early warning signal according to the comprehensive risk score value in combination with a feature mode of a coronary heart disease hidden period in historical data; and storing the current high-risk time window characteristics and the blood sugar and blood fat curve change trend through a triggered coronary heart disease early warning signal to obtain structured risk archive data.

The invention provides a diabetic neuropathy dynamic early warning method based on a multi-modal biological signal, and the method comprises the steps: integrating a multi-modal sensor through an intelligent wearable device, and constructing a personalized prediction model in combination with a deep learning algorithm; through an artificial intelligence algorithm, diabetic neuropathy (DN) development risk prediction and unperceptual hypoglycemia real-time early warning are realized. According to the time-frequency domain characteristics and clinical data of the multi-mode signal, the time window of the DN from the progress to the serious stage can be predicted; recognizing an unperceptual hypoglycemia risk by combining ECG and HR features with a blood glucose trend prediction model, and triggering grading intervention measures; based on the individual physiological response mode of the patient, the algorithm threshold is dynamically optimized, and the accuracy of prediction and early warning is improved. According to the method, HR, BP, ECG and other multi-dimensional signals are integrated, and the prediction precision is improved; continuous monitoring and instant feedback are supported; the self-adaptive algorithm adapts to individual differences of patients, and the limitation of a traditional general model is overcome.

The invention discloses a measurement method, system and related device, the method is applied to first electronic equipment, the first electronic equipment comprises a first electrode group and a second electrode group, and the distance between the first electrode group and the second electrode group is greater than a first distance; under the condition that a first electrode group and a second electrode group are implanted into subcutaneous tissue, the method comprises the following steps: determining a first physiological parameter through the first electrode group; electrocardiosignals are determined through the first electrode set and the second electrode set. Therefore, the electrocardiosignal of the user can be obtained anytime and anywhere through the first electronic equipment, and one or more other physiological parameters (such as blood glucose, blood ketone, blood lactic acid and uric acid) of the user can be measured at the same time, so that the health monitoring efficiency is improved.

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

The invention discloses a diabetic complication monitoring auxiliary system and method in the technical field of complication monitoring, and the system comprises a data collection module which is used for obtaining local multi-modal data of a user in real time; the federal learning module is used for integrating the local multi-modal data of the user with other anonymous medical databases through a distributed architecture and constructing a multi-family cross-institution complication prediction model; the causal reasoning module is used for analyzing the causal relationship among the blood glucose fluctuation, the glycosylated hemoglobin and the target complication based on an anti-factual causal reasoning algorithm, and outputting a causal effect value to correct the prediction deviation; the prediction module is used for generating occurrence probabilities of future specific complications based on the causal effect values and risk scores output by the complication prediction model, and classifying the occurrence probabilities according to risk levels; and the early warning module is used for generating personalized intervention suggestions according to the risk levels. According to the scheme, the data leakage risk in the data acquisition process is reduced through the federal learning distributed architecture, and the prediction reliability is improved.

The invention discloses a method and system for reducing data errors of a dynamic glucometer, and the method comprises the steps: collecting a current signal of fluid between subcutaneous tissues in real time through an implantable sensor, and synchronously recording the working environment parameters of the sensor; carrying out denoising and normalization processing on the current signal, and carrying out temperature compensation and drift correction on the signal according to a working environment parameter; extracting time sequence features and frequency domain features from the signals processed in the step S2; training a machine learning model based on the time sequence feature data and the frequency domain feature data, wherein the machine learning model is used for predicting the blood glucose concentration and identifying abnormal data points; correcting a model prediction result through residual analysis and weighted adjustment, and performing secondary correction on abnormal data points in combination with event data input by a patient; and outputting the optimized blood glucose data to a terminal display device in a time sequence curve form. Through the process, the influence of abnormal state data on blood glucose monitoring is reduced, and the data stability is improved.

The invention discloses a diabetic patient data management method and system based on big data. The method comprises the steps that basic information and stable blood glucose data of a patient are collected, similar cases are matched through a cloud database to obtain influence factors, and a personalized data collection scheme is generated; customizing a user terminal and establishing a data management container, adaptively acquiring blood glucose and influence factor data, calculating a change degree through a sliding window algorithm and performing early warning; and dynamically updating influence factor weights by adopting a time-varying weight model, and generating an intervention strategy according to the weights. The system comprises a cloud server, a comprehensive management terminal, a user terminal and other modules, and realizes data acquisition-analysis-intervention closed-loop management. According to the scheme, through big data analysis and the dynamic weight model, personalized accurate management of the diabetic patient is realized, the acquisition strategy and the intervention scheme can be adjusted according to individual differences, and the risk early warning accuracy and the management efficiency are improved.

The invention discloses a personalized medical health service recommendation system based on big data analysis, and belongs to the technical field of medical health services. The synchronization module is used for generating a standardized data stream according to dynamic blood glucose monitoring data and an original heart rate variability signal; the analysis module is used for generating a diet time sequence data stream according to the intake event timestamp; the blood glucose feature extraction module is used for extracting data from the standardized data stream; the insulin sensitivity analysis module is used for generating an insulin sensitivity factor matrix; the nutrition analysis module is used for generating a carbohydrate equivalent time distribution vector and a dietary fiber intake intensity value; the feature fusion module is used for generating a fusion feature vector according to the blood glucose drift trend coefficient, the insulin sensitive factor matrix and the carbohydrate equivalent time distribution vector; the risk assessment module is used for generating a hypoglycemia risk probability value according to the fusion feature vector; and the recommendation generation module is used for generating a personalized service recommendation set according to the hypoglycemia risk probability value.

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 discloses a system and a method for intelligently monitoring blood sugar of a patient before and after diet based on food components, and belongs to the technical field of blood sugar monitoring, and the system comprises a food component quantification module which is used for converting a food image or description into accurate macro nutrient and micronutrient data; the multi-modal data fusion module is used for preprocessing and fusing the multi-modal data of the patient; the patient blood sugar prediction module is used for constructing a patient blood sugar prediction model to analyze the multi-modal fusion data of the patient and predicting the blood sugar of the patient; and the difference analysis optimization module is used for comparing actual blood glucose with predicted blood glucose after meal and analyzing difference reasons. The problems that blood glucose of a patient before and after eating cannot be intelligently monitored, and accurate quantification and prediction of the relation between diet and blood glucose cannot be achieved are solved. The system can intelligently monitor the blood glucose of the patient before and after eating, can help the diabetic to realize accurate quantification and prediction of the relationship between diet and blood glucose, and can effectively control the blood glucose and reduce the risk of complications.

The invention relates to a diabetes exercise rehabilitation and diet collaborative management method, device and equipment and a medium. The method comprises the following steps: acquiring individual feature data, physiological data and daily behavior data of a diabetic patient, and constructing a personalized portrait of a user; generating an exercise plan and a diet plan according to a set period based on the personalized portrait of the user; in response to the obtained real-time user data, dynamically correcting the exercise plan and the diet plan to obtain an exercise and diet collaborative plan; the real-time user data comprises blood glucose data, diet intake data and exercise data; the exercise and diet collaborative plan comprises exercise items arranged based on time points and diet arrangement; generating a corresponding push instruction according to the time point; the pushing instruction is used for indicating to push corresponding exercise items or diet arrangements to the terminal of the diabetic patient. By adopting the method, the problem of inadaptation caused by a rigid fixed plan can be avoided by an exercise-diet collaborative intervention mechanism, and the management response capability of external variable change is improved.

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 biology, in particular to a DPP-IV inhibitory peptide extracted from buffalo cheese protein hydrolysate and application of the DPP-IV inhibitory peptide. An active component with the molecular weight smaller than 3 kDa is obtained through alkaline protease enzymolysis, ultrafiltration separation and LC-MS / MS identification, and the DPP-IV enzyme inhibitory activity IC50 value of the active component is 12.6 mg / mL. Four new DPP-IV inhibitory peptide fragments are found, are amino acid sequences in a sequence table SEQ ID NO: 1-4, have good DPP-IV inhibitory activity, and have good application prospects in being developed into functional foods or medicines for regulating blood sugar. In conclusion, the enzymolysis product and the peptide fragment can be prepared into antidiabetic drugs or functional foods, and have the advantages of being natural, safe and efficient. The invention provides a new strategy for high-valued utilization of buffalo milk resources and treatment of type 2 diabetes.

The invention discloses a preparation method and application of a glucose sensor based on an acupuncture needle, the sensor is prepared based on a specific structural design and a molecular recognition mechanism by taking the acupuncture needle as a substrate, and high-specificity and high-sensitivity detection of glucose concentration is realized through drain current change. According to the present invention, the biosensor has the ultra-wide dynamic detection range (0-40 mmol.L <-1 >), covers the clinical range from hypoglycemia to severe hyperglycemia, has the low detection limit of 54 mol.L <-1 >, has excellent grid regulation and control ability, shows the significant on-off ratio and the rapid response characteristic during the detection process, and meets the requirements of the biosensor on sensitivity and real-time performance; and the device has quick response capability to grid voltage change, so that the device has good stability and repeatability under repeated operation, and a direction and a basis can be provided for the development of a blood glucose monitoring technology with continuous, real-time and accurate characteristics.

The invention discloses a medicinal and edible homologous compound based on probiotic fermentation synergy and application of the medicinal and edible homologous compound in blood sugar regulation and control, and belongs to the technical field of microorganisms and fermentation engineering. The preparation method comprises the following steps: respectively carrying out two-stage fermentation on aloe, mulberry leaves and liquorice by utilizing saccharomyces cerevisiae BLCC4-0032 and plant lactobacillus DFR-1, so as to obtain a fermentation mixture; the preparation method comprises the following steps: fermenting a fermentation mixture, adding balsam pear peptide into the fermentation mixture to obtain a medicinal and edible compound, forming a metabolic closed loop, and cooperatively regulating a'blood sugar-liver-intestinal flora axis', thereby providing a new solution for diabetes intervention, and opening up a new path for preventing non-alcoholic fatty liver and intestinal inflammation caused by glucose and lipid metabolism disorder.

The application provides a Huangdache oligosaccharide with GLP-1 agonist activity and application thereof, and belongs to the technical field of medicines. 3 The oligosaccharide component ELYP-3 has a molecular weight of 3.2*10 3 Da, and the molar ratio of monosaccharides is rhamnose:galacturonic acid:glucose:galactose:arabinose = 1:32.42:45.37:9.11:6.54. The Huangdache oligosaccharide fragment obtained by glycosidase degradation has significant hypoglycemic and weight loss activities, the preparation process is simple, specific and green, and provides scientific basis and theoretical support for the development of natural GLP-1 agonists. Meanwhile, the functional oligosaccharide provides a wide application prospect for the development of medicines with blood glucose regulation.

In an automated insulin delivery device, fuzzy logic may be applied to the responding to the possibility of a user taking additional action that may affect the blood glucose concentration. Fuzzy sets may be defined for empirically derived different likelihoods of the user taking such additional action based on correlated factors. A membership function may be provided for each fuzzy set. The membership function may provide a probability of membership in the set based on a parameter. Each fuzzy set may have a response that is reflective of the likelihood of additional user action associated with the fuzzy set. The responses by the AID device to each of these cases may reflect the probability of each such case occurring as evidenced by empirical data.

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, which relates to the field of diabetes nursing, discloses a multi-model fusion personalized diet recommendation system for diabetes mellitus, comprising a recipe recommendation decision module based on a system architecture, a blood sugar prediction module, and an intelligent question and answer interaction module. The nutritional requirements and the blood sugar control target of the patient are met at the same time; the blood glucose prediction module predicts the postprandial blood glucose level according to the current blood glucose level of the user and the recommended recipe; the intelligent question and answer interaction module provides convenient natural language interaction, collects user feedback and dynamically adjusts recommended content. Compared with the prior art, the method has the advantages that scientific diet recommendation is provided, the recommendation result is optimized through a user feedback mechanism, and the recommendation accuracy and practicability are ensured; a convenient health management tool is provided, and the compliance of the patient on diet management is enhanced; the health condition of the diabetic patient can be improved, the burden of a medical system is relieved, and the public health level is improved.

The invention relates to the technical field of blood glucose monitoring, in particular to an intelligent blood glucose monitoring and early warning method and system based on multi-source data fusion. Comprising the following steps: S1, acquiring blood glucose monitoring information of a monitored object, and determining first combined blood glucose monitoring information and second combined blood glucose monitoring information according to the blood glucose monitoring information; the first combined blood glucose monitoring information is an absolute difference between the blood glucose instantaneous information and the blood glucose continuous information; the second combined blood glucose monitoring information is a mean value of the blood glucose instantaneous information and the blood glucose continuous information; acquiring environment information of the monitored object, and determining a first environment variable quantity and a second environment variable quantity according to the environment information. According to the method, the dynamic deviation parameter and the early warning threshold value are fused, the measurement deviation and the physiological delay are quantified in combination with the extreme value features, double-source interference suppression and individualized early warning optimization of blood glucose monitoring are achieved in the plateau environment, and the risk identification accuracy and adaptability are improved.

The present application relates to a blood glucose concentration analysis method, apparatus, device, storage medium, and computer program product. The method comprises: obtaining intensity sets corresponding to multiple photoplethysmographic wave sets of a target subject within a target time period; the intensity sets include average AC intensity and average DC intensity; performing curve fitting on the multiple intensity sets to obtain fitting coefficients and fitting bias; comparing the fitting bias with the bias range corresponding to the target subject, and if it is determined that the fitting bias is not within the bias range, adjusting the fitting bias to obtain an adjusted fitting bias; based on the adjusted fitting bias, performing curve fitting on the multiple intensity sets to obtain adjusted fitting coefficients, and using the adjusted fitting coefficients as the target subject's blood glucose concentration during the target time period. This method can improve the accuracy of blood glucose concentration analysis.

The invention provides a power management and power consumption control device and method of a continuous blood glucose detection circuit. Relates to the technical field of continuous blood glucose detection, solves the problems of power consumption control and switching in the prior art, and is characterized in that a power input end of a charge pump is connected with an anode of an external power supply; the control input end of the charge pump is connected with a mode control signal end; the power output end of the charge pump is connected with the first control end of the power switch device; the switching signal control end of the charge pump is connected with the second control end of the power switching device through the delayer; the output end of the power switch device is connected with the working voltage input end, and the working voltage output end is connected with the grounding capacitor; the power output end of the charge pump is connected with the input end of the power-on reset module, the power switch switching signal end of the power-on reset module is connected with the power switch switching signal end of the charge pump, and different working modes can be achieved, so that power consumption control is achieved.

The invention relates to the technical field of medical health, in particular to a chronic disease patient-oriented home nursing management platform, comprising: a terminal sensing module comprising a monitoring bracelet and a glucometer and used for obtaining real-time data of a patient; the monitoring bracelet is used for monitoring physiological indexes and motion data of a patient in real time; the glucometer is used for monitoring blood glucose data of a patient in real time; the edge calculation module comprises home gateway equipment and an edge intelligent analysis system; the cloud service module comprises a data processing center and an intelligent decision-making system; the intelligent decision-making system comprises a health risk assessment model, an exercise scheme optimization model and an expert knowledge base comprising an exercise guidance strategy and a life rhythm management strategy; and the optimization module is used for comprehensively coordinating the generated results, dynamically adjusting motion scheme parameters according to patient feedback and execution conditions, and pushing an optimized personalized scheme to a user terminal for execution.

The invention relates to an Akecman mucophilic protein strain LWHK0003, which has the effects of preventing non-alcoholic steatohepatitis, resisting obesity, preventing NASH induced by GAN diet, maintaining constant blood sugar and resisting inflammation.

The invention discloses a blood glucose prediction method based on multi-scale data processing, and belongs to the technical field of blood glucose prediction. The method comprises the following steps: firstly, setting three sliding windows, generating small-scale, medium-scale and large-scale sub-sequence sets on a normalized historical blood glucose sequence, and extracting corresponding feature vectors; constructing two types of attention sequences by calculating the difference between the small-scale feature vector and the medium-scale and large-scale feature vectors; a scale feature fusion unit is combined with the attention degree sequence to obtain small-medium and small-large scale weighted features; and finally, processing the weighted features by adopting a two-channel feature blood glucose prediction network, and outputting a predicted blood glucose value. According to the method, through a fusion strategy of multi-scale feature extraction and attention guidance, the accuracy of blood glucose prediction is improved.