Blood glucose monitoring and early warning method, device and equipment and storage medium

Abstract

The embodiment of the invention provides a blood glucose monitoring and early warning method, device and equipment and a storage medium. The method comprises the following steps: acquiring historical blood glucose data and body index data of a user; determining a mean value and a standard deviation of the historical blood glucose data, and determining a blood glucose threshold value of the user based on the mean value and the standard deviation; key risk factors in the body index data and the weight corresponding to each key risk factor are determined, and the blood glucose risk score of the user is determined according to the key risk factors and the corresponding weights; and determining a blood glucose threshold value of the user and a target early warning level corresponding to the blood glucose risk score from a plurality of preset early warning levels, and executing a corresponding target early warning strategy in response to the condition that the target early warning level is greater than the preset early warning threshold value. The embodiment of the invention aims to improve the accuracy and flexibility of blood glucose monitoring and early warning.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a blood sugar monitoring and early warning method, a blood sugar monitoring and early warning device, a computer device, and a computer-readable storage medium. Background Art

[0002] Blood glucose analysis refers to the process of in-depth research and interpretation of an individual's blood glucose data to obtain information about the user's blood glucose level, volatility and trends. Blood glucose analysis can provide personalized, real-time interpretation and guidance of blood glucose data to help users effectively manage diseases, improve blood glucose control levels, and reduce potential risks and complications.

[0003] In the related art, the current patient blood sugar analysis is mainly carried out by real-time monitoring of the patient through a blood sugar monitor, judging the user's blood sugar level based on the changes in the monitored blood sugar content, and warning the user when the user's blood sugar level exceeds the set threshold. However, the related art usually uses static thresholds or simple personalized parameters for setting, which cannot be adaptively adjusted as the user's blood sugar control status changes. In addition, the related art is also unable to reflect the blood sugar change pattern caused by individual differences, resulting in poor flexibility and accuracy of blood sugar monitoring warnings. Summary of the invention

[0004] The present application provides a blood glucose monitoring and early warning method, a blood glucose monitoring and early warning device, a computer device and a computer-readable storage medium, aiming to improve the accuracy and flexibility of blood glucose monitoring and early warning.

[0005] To achieve the above objectives, the present application provides a blood sugar monitoring and early warning method, comprising:

[0006] Obtain the user's historical blood sugar data and body index data;

[0007] Determine a mean value and a standard deviation of the historical blood glucose data, and determine a blood glucose threshold value of the user based on the mean value and the standard deviation;

[0008] Analyzing the physical indicator data by using a correlation analysis model to identify key risk factors in the physical indicator data and weights corresponding to each of the key risk factors, and analyzing the key risk factors and corresponding weights by using a risk scoring model to obtain a blood sugar risk score for the user; and,

[0009] A target warning level corresponding to the blood glucose threshold of the user and the blood glucose risk score is determined from a plurality of preset warning levels, and in response to the target warning level being greater than the preset warning threshold, a corresponding target warning strategy is executed.

[0010] To achieve the above objectives, the present application also provides a blood sugar monitoring and early warning device, comprising:

[0011] The acquisition module is used to obtain the user's historical blood sugar data and body index data;

[0012] a determination module, configured to determine a mean value and a standard deviation of the historical blood glucose data, and determine a blood glucose threshold value of the user based on the mean value and the standard deviation;

[0013] The determination module is further used to analyze the physical indicator data through a correlation analysis model, identify key risk factors in the physical indicator data and the weight corresponding to each key risk factor, and analyze the key risk factors and the corresponding weights using a risk scoring model to obtain a blood sugar risk score for the user;

[0014] An early warning module is used to determine the target early warning level corresponding to the user's blood sugar threshold and the blood sugar risk score from a plurality of preset early warning levels, and in response to the target early warning level being greater than the preset early warning threshold, execute the corresponding target early warning strategy.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a computer device, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the steps of any one of the blood glucose monitoring and early warning methods provided in the embodiments of the present application when executing the computer program.

[0016] In addition, to achieve the above-mentioned purpose, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor enables the processor to implement the steps of any one of the blood glucose monitoring and early warning methods provided in the embodiments of the present application.

[0017] The blood glucose monitoring early warning method, blood glucose monitoring early warning device, computer equipment and computer readable storage medium disclosed in the embodiment of the present application. Among them, the method includes obtaining the user's historical blood glucose data and physical index data, determining the mean and standard deviation of the historical blood glucose data, and determining the user's blood glucose threshold based on the mean and standard deviation, and analyzing the physical index data through a correlation analysis model, identifying the key risk factors in the physical index data and the weights corresponding to each key risk factor, and using the risk scoring model to analyze the key risk factors and the corresponding weights to obtain the user's blood glucose risk score. Thus, the target early warning level corresponding to the user's blood glucose threshold and blood glucose risk score can be determined from multiple preset early warning levels, and in response to the target early warning level being greater than the preset early warning threshold, the corresponding target early warning strategy is executed. The present application can determine the user's blood glucose threshold and identify the key risk factors and generate a blood glucose risk score by analyzing the user's historical blood glucose data and physical index data, thereby achieving accurate blood glucose management and dynamic early warning, and improving the accuracy and flexibility of blood glucose monitoring and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is a scenario diagram of a blood sugar monitoring and early warning method provided in an embodiment of the present application;

[0020] Figure 2 It is a flow chart of a blood sugar monitoring and early warning method provided in an embodiment of the present application;

[0021] Figure 3 is a schematic block diagram of a blood sugar monitoring and early warning device provided in an embodiment of the present application;

[0022] Figure 4 It is a schematic block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0024] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor do they have to be executed in the order described. For example, some operations / steps can also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions. In addition, although the functional modules are divided in the device schematic, in some cases, the module division can be different from that in the device schematic.

[0025] The term "and / or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0026] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0027] like Figure 1 As shown, the blood sugar monitoring and early warning method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. The application environment includes a terminal device 110 and a server 120, wherein the terminal device 110 can communicate with the server 120 through a network. Specifically, the server 120 can obtain the user's historical blood sugar data and body index data; determine the mean and standard deviation of the historical blood sugar data, and determine the user's blood sugar threshold based on the mean and the standard deviation; analyze the body index data through a correlation analysis model, identify the key risk factors in the body index data and the weights corresponding to each of the key risk factors, and use the risk scoring model to analyze the key risk factors and the corresponding weights to obtain the user's blood sugar risk score; and determine the user's blood sugar threshold and the target warning level corresponding to the blood sugar risk score from multiple preset warning levels, and in response to the target warning level being greater than the preset warning threshold, execute the corresponding target warning strategy, thereby sending the target warning strategy to the terminal device 110. Among them, the server 120 can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms. The terminal device 110 can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, and this application does not limit this.

[0028] Please continue reading Figure 2, Figure 2 Schematic diagram of a blood sugar monitoring and early warning method provided in an embodiment of the present application. Figure 2 As shown, the blood sugar monitoring and early warning method can be implemented through steps S11 to S14.

[0029] Step S11: Obtain the user's historical blood sugar data and body index data.

[0030] Among them, historical blood sugar data is used to characterize the user's blood sugar monitoring records over a period of time. For example, it can be blood sugar data for a preset time period (such as within a week or a day). The blood sugar data can be fasting blood sugar data or postprandial blood sugar data, etc. This application does not limit this.

[0031] Furthermore, physical indicator data may include but are not limited to weight, height, blood pressure, and amount of exercise, etc., which is not limited in this application.

[0032] Step S12: Determine the mean and standard deviation of the historical blood glucose data, and determine the user's blood glucose threshold based on the mean and standard deviation.

[0033] Optionally, the user's blood glucose threshold includes an upper threshold and a lower threshold, and the user's blood glucose threshold is determined based on the mean and the standard deviation, including: determining the product of the standard deviation and an adjustable parameter, and determining the sum of the product and the mean as the upper threshold; and, subtracting the mean from the product to obtain the lower threshold.

[0034] Among them, the mean is the average value of the user's blood sugar level, which can be the user's average blood sugar level and is the benchmark for the user's overall blood sugar status; the standard deviation represents the fluctuation range of the user's blood sugar data, which can be used to measure the stability of the user's blood sugar. The smaller the standard deviation, the more stable the blood sugar; the larger the standard deviation, the more obvious the fluctuation and the possible risk. The upper threshold is used to represent the highest value of the user's blood sugar, which can identify the user's blood sugar level is too high; the lower threshold is used for the lowest value of the user's blood sugar, which can identify the user's blood sugar level is too low (such as fasting blood sugar is too low.)

[0035] It should be understood that due to the different blood sugar levels and fluctuation characteristics of each user, fixed standard values ​​(such as fasting blood sugar 3.9-6.1 mmol / L) may not be suitable for all users. Therefore, calculating personalized thresholds through mean and standard deviation is more in line with the user's actual health status.

[0036] Specifically, the product of the standard deviation and the adjustable parameter can be determined, and the sum of the product and the mean can be determined as the upper threshold; and the mean and the product can be subtracted to obtain the lower threshold. In this way, the upper and lower thresholds can dynamically reflect the user's blood sugar level and fluctuation range, avoid the limitations of fixed thresholds, achieve personalized blood sugar management, and lay a scientific foundation for subsequent risk analysis and early warning strategies.

[0037] Step S13: Analyze the physical indicator data through the correlation analysis model, identify the key risk factors in the physical indicator data and the weight corresponding to each key risk factor, and use the risk scoring model to analyze the key risk factors and the corresponding weights to obtain the user's blood sugar risk score.

[0038] Among them, key risk factors include parameters that have a significant impact on blood sugar (such as glycated hemoglobin, body mass index, etc.).

[0039] For example, the correlation analysis model can be used to determine the relationship between different variables in the physical indicator data, so as to identify key risk factors. The correlation analysis model may include a random forest model, a Bayesian network model, and a graph model, etc., which are not limited in this application.

[0040] For example, the correlation analysis model can be used to analyze the relationship between the various parameters in the body index data and their potential impact on blood sugar risk, so as to identify the key risk factors affecting the user's blood sugar level and their corresponding weights. For example, if weight has a greater impact on blood sugar risk, the weight may be 0.4; if eating habits have a smaller impact, the weight may be 0.1. Among them, each key risk factor and its corresponding weight form a mapping relationship.

[0041] For example, the identified key risk factors and their weights can be input into the risk scoring model for further calculation. The risk scoring model can calculate the user's blood glucose risk score based on the input parameter values ​​and their weights. Among them, the blood glucose risk score can be a quantitative result (such as a score range from 0 to 100) used to assess the user's blood glucose risk level. A low score indicates a low blood glucose risk; a high score indicates a high blood glucose risk, and the user needs to pay attention to related health issues.

[0042] It should be noted that the present application does not limit the risk scoring model. For example, the risk scoring model includes a Bayesian risk model, a support vector machine model, and a neural network model, etc., and the present application does not limit this.

[0043] In the above implementation mode, the physical indicator data is analyzed through a correlation analysis model to identify the key risk factors in the physical indicator data and the weights corresponding to each key risk factor, and the key risk factors and the corresponding weights are analyzed using a risk scoring model to obtain the user's blood sugar risk score, thereby providing a data basis for subsequent warnings.

[0044] Step S14: Determine a target warning level corresponding to the user's blood glucose threshold and blood glucose risk score from a plurality of preset warning levels, and execute a corresponding target warning strategy in response to the target warning level being greater than the preset warning threshold.

[0045] For example, the blood sugar risk score is calculated by step S13 and is used to measure the user's current blood sugar risk status. The preset warning level is a plurality of warning levels defined within the system, which are used to classify and trigger responses according to the risk score. For example, the preset warning level may include a normal level: 0 to 30, indicating that the blood sugar risk is low and no special attention is required; a moderate warning level: 31 to 60, prompting the user to pay attention to living habits and make appropriate adjustments; a high warning level: 61 to 90, suggesting that the user takes immediate action and may require medical intervention; a severe warning level: 91 to 100, prompting the user that there is a high health risk and recommending emergency medical treatment.

[0046] It should be noted that the warning threshold is used to represent a minimum trigger condition set by the system for each warning strategy, which is used to determine whether specific warning measures need to be executed. For example, if the warning threshold is a moderate warning level, no alarm needs to be triggered when it is below this level.

[0047] For example, the target warning level can be compared with the preset warning threshold. If the target warning level is greater than the preset warning threshold, the corresponding warning strategy is triggered. If the target warning level is less than or equal to the preset warning threshold, the warning strategy is not executed, and only data is recorded or general suggestions are provided.

[0048] The above implementation methods can achieve accurate blood sugar management and dynamic early warning based on blood sugar risk score, and improve the accuracy and flexibility of blood sugar monitoring and early warning.

[0049] The blood glucose monitoring and early warning method disclosed in the embodiment of the present application includes obtaining the user's historical blood glucose data and physical index data, determining the mean and standard deviation of the historical blood glucose data, and determining the user's blood glucose threshold based on the mean and standard deviation, and analyzing the physical index data through a correlation analysis model, identifying the key risk factors in the physical index data and the weights corresponding to each key risk factor, and using the risk scoring model to analyze the key risk factors and the corresponding weights to obtain the user's blood glucose risk score. Thus, the target early warning level corresponding to the user's blood glucose threshold and blood glucose risk score can be determined from multiple preset early warning levels, and in response to the target early warning level being greater than the preset early warning threshold, the corresponding target early warning strategy is executed. The present application can determine the user's blood glucose threshold and identify key risk factors and generate a blood glucose risk score by analyzing the user's historical blood glucose data and physical index data, thereby achieving accurate blood glucose management and dynamic early warning, and improving the accuracy and flexibility of blood glucose monitoring and early warning.

[0050] Optionally, the product of the adjustable parameters is determined based on the user's historical blood glucose data. In response to a change value of the historical blood glucose data within a preset time period being lower than a preset threshold, the product of the adjustable parameters is determined to be within the range of 1.5 to 2; in response to a change value of the historical blood glucose data within a preset time period being greater than or equal to a preset threshold, the product of the adjustable parameters is determined to be within the range of 2.5 to 3.

[0051] Among them, the product of adjustable parameters refers to the product value of multiple adjustable parameters (such as weight, diet, amount of exercise, etc.), which serves as a comprehensive quantitative indicator for adjusting the sensitivity of the blood glucose management system. Historical blood glucose data is the change in the user's blood glucose level within a preset time period, which is used to evaluate the effectiveness or stability of current blood glucose management. The preset time period is used to represent a fixed time window (such as 7 days, 30 days) for observing the changing trend of the user's blood glucose data. The preset threshold is used to represent the reference value used for comparison, indicating the sensitivity of blood glucose changes.

[0052] For example, when the change value of the user's historical blood sugar data within a preset time period is lower than the preset threshold, it means that the user's blood sugar level fluctuates less and the blood sugar management status is relatively stable. In this case, the product of the adjustable parameters can be adjusted to a range of 1.5 to 2, indicating that the system is more relaxed about blood sugar fluctuations with lower changes. In this way, overreaction to small fluctuations can be reduced, unnecessary intervention can be avoided, and priority attention can be given to other high-risk users or high-volatility data points.

[0053] For example, when the change value of the user's historical blood sugar data within a preset time period is greater than or equal to a preset threshold, it indicates that the user's blood sugar fluctuates greatly, and there may be potential risks or management problems. In this case, the product of the adjustable parameters can be adjusted to a range of 2.5 to 3, indicating that the system is more sensitive to high-fluctuation blood sugar states. As a result, high-fluctuation states can be closely monitored and timely intervention can be made. Alternatively, an early warning strategy can be triggered, such as suggesting that the user adjust their lifestyle or undergo a medical examination.

[0054] The above implementation method can more intelligently and accurately adapt to the blood sugar management needs of different users by dynamically adjusting the product of adjustable parameters.

[0055] Optionally, the key risk factors and the corresponding weights are analyzed using a risk scoring model to obtain the user's blood glucose risk score, including: analyzing the key risk factors through the risk scoring model, identifying the factor value corresponding to each key risk factor, and determining the product of the factor value and the corresponding weight; adding the product corresponding to each key risk factor to obtain the user's blood glucose risk score.

[0056] Among them, the risk scoring model is a mathematical or statistical model that combines multiple risk factors and their weights to calculate a comprehensive risk score. Key risk factors are important indicators that affect blood sugar risk, such as body mass index, blood sugar fluctuations, eating habits, exercise frequency, family medical history, etc. The weight reflects the degree of influence of each risk factor on the overall blood sugar risk, which is determined by expert experience, data analysis or machine learning methods. The blood sugar risk score is a quantitative indicator used to assess the user's blood sugar risk level, and is usually used to guide subsequent intervention strategies or early warning measures.

[0057] Specifically, the key risk factors can be analyzed through the risk scoring model, the factor value corresponding to each key risk factor can be identified, and the product of the factor value and the corresponding weight can be determined. Then, the product corresponding to each key risk factor is added to obtain the user's blood sugar risk score. This method is not only easy to operate, but also highly adaptable, and can effectively support users' blood sugar risk management and health intervention.

[0058] Optionally, the preset warning levels include at least normal level, mild level, moderate level, high level, severe level and crisis level, and each preset warning level corresponds to a blood glucose threshold range and a blood glucose risk score range, respectively. The target warning level corresponding to the user's blood glucose threshold and blood glucose risk score is determined from multiple preset warning levels, including: determining the target blood glucose threshold range where the user's blood glucose threshold is located and the target blood glucose risk score range where the blood glucose risk score is located; determining the target warning level based on the target blood glucose threshold range and the target blood glucose risk score range.

[0059] For example, the warning level is used to characterize the risk classification determined based on the user's blood sugar threshold and blood sugar risk score, representing the urgency of blood sugar management and intervention. The preset levels include the following six: normal level (3.9-6.1mmol / L), blood sugar values ​​and risk scores are within the normal range, no special intervention is required; mild level (6.2-7.0mmol / L), blood sugar values ​​and / or risk scores are slightly beyond the normal range, and attention should be paid to adjusting lifestyle habits; moderate level (7.1-8.0mmol / L), blood sugar abnormalities have a certain development trend, and regular monitoring and consultation with a doctor are recommended; high level (8.1-9.0mmol / L), blood sugar risk is significantly increased, and active health management measures need to be taken; severe level (9.1-10.0mmol / L), blood sugar conditions are more dangerous, and immediate medical intervention may be required; crisis level, blood sugar is seriously out of control, and emergency medical treatment is required.

[0060] For example, the user's blood glucose value (such as fasting blood glucose or postprandial blood glucose) can be obtained and matched with multiple preset blood glucose threshold ranges to determine the target blood glucose threshold range. For example, if the user's fasting blood glucose value is 6.5mmol / L, and compared with the preset blood glucose threshold range, it is found that 6.5mmol / L falls within the mild abnormal range (6.2-7.0mmol / L), then the target blood glucose threshold range can be determined to be mildly abnormal.

[0061] The above implementation method can accurately locate the user's target warning level through dual analysis of blood sugar threshold and blood sugar risk score. This method not only scientifically quantifies risk assessment, but also provides users with personalized health intervention strategies.

[0062] Optionally, executing the corresponding target warning strategy includes at least: displaying abnormal warning information and intervention strategies through a display interface, wherein the intervention strategies are obtained by analyzing physical indicator data through a deep learning model.

[0063] For example, intervention strategies are personalized suggestions based on the user's current health status, designed to help users alleviate abnormal blood sugar or reduce risks. Depending on the warning level, intervention strategies may include the following types: mild risk, adjust diet structure, increase exercise appropriately; moderate risk, closely monitor blood sugar, check regularly, and consult a doctor whether medication needs to be adjusted; severe risk and above. Contact a medical institution and recommend immediate medical intervention measures.

[0064] For example, abnormal warning information and intervention strategies can be clearly and intuitively presented through the display interface on smart devices (such as mobile phones, bracelets, tablets, etc.).

[0065] For example, deep learning models (such as CNN, RNN or Transformer models) can be used to extract important features of blood sugar and related physical indicators, such as blood sugar fluctuation patterns, medication effects, etc. Then specific indicators can be identified to find abnormal changes or potential risks. Furthermore, the model can combine the analysis results with the medical knowledge base to generate personalized intervention recommendations.

[0066] In the embodiment of the present application, abnormal information can be displayed to the user in a timely manner through the display interface, and personalized intervention suggestions can be generated in combination with the deep learning model, which can effectively help the user manage blood sugar and prevent further aggravation of risks, so as to realize the convenience and scientificity of intelligent health management.

[0067] See also Figure 3 , Figure 3 1 is a schematic block diagram of a blood sugar monitoring and early warning device provided in an embodiment of the present application. The blood sugar monitoring and early warning device can be configured in a server to execute the aforementioned blood sugar monitoring and early warning method.

[0068] like Figure 3 As shown, the blood sugar monitoring and early warning device 200 includes: an acquisition module 201, a determination module 202 and an early warning module 203.

[0069] An acquisition module 201 is used to acquire the user's historical blood sugar data and body index data;

[0070] A determination module 202, configured to determine a mean and a standard deviation of the historical blood glucose data, and determine a blood glucose threshold of the user based on the mean and the standard deviation;

[0071] The determination module 202 is further configured to analyze the physical indicator data through a correlation analysis model, identify key risk factors in the physical indicator data and weights corresponding to each of the key risk factors, and analyze the key risk factors and corresponding weights using a risk scoring model to obtain a blood sugar risk score for the user;

[0072] The warning module 203 is used to determine the target warning level corresponding to the blood glucose threshold of the user and the blood glucose risk score from multiple preset warning levels, and execute the corresponding target warning strategy in response to the target warning level being greater than the preset warning threshold.

[0073] The determination module 202 is further configured to determine the product of the standard deviation and the adjustable parameter, and determine the sum of the product and the mean as the upper threshold; and to obtain the lower threshold by subtracting the mean from the product.

[0074] The determination module 202 is also used to analyze the key risk factors through the risk scoring model, identify the factor value corresponding to each of the key risk factors, and determine the product of the factor value and the corresponding weight; add the products corresponding to each of the key risk factors to obtain the blood sugar risk score of the user.

[0075] The determination module 202 is also used to determine the target blood glucose threshold range where the user's blood glucose threshold is located and the target blood glucose risk score range where the blood glucose risk score is located; and determine the target warning level based on the target blood glucose threshold range and the target blood glucose risk score range.

[0076] The determination module 202 is further used to determine that the detection result is an abnormal result in response to the blood glucose threshold being outside a preset range, and to execute a fault warning strategy, wherein the fault warning strategy at least includes executing data repair and executing a system recovery program.

[0077] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0078] The method and apparatus of the present application can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer terminal devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices.

[0079] Exemplarily, the above method and apparatus may be implemented in the form of a computer program. The computer program may be implemented in Figure 4 Runs on the computer device shown.

[0080] See also Figure 4 , Figure 4 1 is a schematic diagram of a computer device provided in an embodiment of the present application. The computer device may be a server.

[0081] like Figure 4 As shown, the computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory may include a volatile storage medium, a non-volatile storage medium and an internal memory.

[0082] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any blood sugar monitoring and early warning method.

[0083] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0084] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any blood glucose monitoring and early warning method.

[0085] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that the structure of the computer device is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0086] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0087] Among them, in some embodiments, the processor is used to obtain the user's historical blood sugar data and physical indicator data; determine the mean and standard deviation of the historical blood sugar data, and determine the user's blood sugar threshold based on the mean and the standard deviation; analyze the physical indicator data through a correlation analysis model, identify the key risk factors in the physical indicator data and the weights corresponding to each of the key risk factors, and use a risk scoring model to analyze the key risk factors and the corresponding weights to obtain the user's blood sugar risk score; and determine the user's blood sugar threshold and the target warning level corresponding to the blood sugar risk score from multiple preset warning levels, and execute the corresponding target warning strategy in response to the target warning level being greater than the preset warning threshold.

[0088] In some embodiments, the processor is further configured to determine the product of the standard deviation and an adjustable parameter, and determine the sum of the product and the mean as the upper threshold; and, subtract the mean from the product to obtain the lower threshold.

[0089] In some embodiments, the processor is also used to analyze the key risk factors through the risk scoring model, identify the factor value corresponding to each of the key risk factors, and determine the product of the factor value and the corresponding weight; add the products corresponding to each of the key risk factors to obtain the blood glucose risk score of the user.

[0090] In some embodiments, the processor is further used to determine a target blood glucose threshold range within which the user's blood glucose threshold lies and a target blood glucose risk score range within which the blood glucose risk score lies; and determine the target warning level based on the target blood glucose threshold range and the target blood glucose risk score range.

[0091] In some embodiments, the processor is further used to display abnormal warning information and intervention strategies through a display interface, wherein the intervention strategy is obtained by analyzing the physical indicator data through a deep learning model.

[0092] In some embodiments, the processor is further used to determine that the test result is an abnormal result in response to the blood glucose threshold being outside a preset range, and execute a fault warning strategy, wherein the fault warning strategy at least includes executing data repair and executing a system recovery program.

[0093] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions, and when the program instructions are executed, any blood glucose monitoring and early warning method provided in the embodiment of the present application is implemented.

[0094] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped on the computer device.

[0095] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like.

[0096] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A blood sugar monitoring and early warning method, characterized in that: The method comprises: Obtain the user's historical blood sugar data and body index data; Determine a mean value and a standard deviation of the historical blood glucose data, and determine a blood glucose threshold value of the user based on the mean value and the standard deviation; Analyzing the physical indicator data by using a correlation analysis model to identify key risk factors in the physical indicator data and weights corresponding to each of the key risk factors, and analyzing the key risk factors and corresponding weights by using a risk scoring model to obtain a blood sugar risk score for the user; and, A target warning level corresponding to the blood glucose threshold of the user and the blood glucose risk score is determined from a plurality of preset warning levels, and in response to the target warning level being greater than the preset warning threshold, a corresponding target warning strategy is executed.

2. The method according to claim 1, characterized in that The blood glucose threshold of the user includes an upper threshold and a lower threshold, and determining the blood glucose threshold of the user based on the mean and the standard deviation includes: determining a product of the standard deviation and an adjustable parameter, and determining a sum of the product and the mean as the upper threshold; and, The lower threshold is obtained by subtracting the mean from the product.

3. The method according to claim 2, characterized in that The product of the adjustable parameters is determined based on the historical blood glucose data of the user. In response to a change value of the historical blood glucose data in a preset time period being lower than a preset threshold, the product of the adjustable parameters is determined to be within the range of 1.5 to 2. In response to a change value of the historical blood glucose data in the preset time period being greater than or equal to the preset threshold, the product of the adjustable parameters is determined to be within the range of 2.5 to 3.

4. The method according to claim 1, characterized in that: The step of analyzing the key risk factors and corresponding weights using a risk scoring model to obtain a blood sugar risk score for the user includes: Analyzing the key risk factors through the risk scoring model, identifying the factor value corresponding to each of the key risk factors, and determining the product of the factor value and the corresponding weight; The products corresponding to each of the key risk factors are added together to obtain the blood sugar risk score of the user.

5. The method according to claim 1, characterized in that The preset warning levels include at least a normal level, a mild level, a moderate level, a high level, a severe level and a crisis level, and each of the preset warning levels corresponds to a blood glucose threshold range and a blood glucose risk score range, respectively. Determining the target warning level corresponding to the user's blood glucose threshold and the blood glucose risk score from a plurality of preset warning levels includes: Determine a target blood glucose threshold range where the blood glucose threshold of the user lies and a target blood glucose risk score range where the blood glucose risk score lies; The target warning level is determined based on the target blood glucose threshold range and the target blood glucose risk score range.

6. The method according to claim 1, characterized in that The execution of the corresponding target warning strategy at least includes: displaying abnormal warning information and intervention strategies through a display interface, wherein the intervention strategy is obtained by analyzing the physical indicator data through a deep learning model.

7. The method according to claim 1, characterized in that After determining the blood glucose threshold of the user based on the mean and the standard deviation, the method further includes: In response to the blood glucose threshold being outside a preset range, the detection result is determined to be an abnormal result, and a fault warning strategy is executed, wherein the fault warning strategy at least includes executing data repair and executing a system recovery program.

8. A blood sugar monitoring and early warning device, characterized in that: The blood sugar monitoring and early warning device comprises: The acquisition module is used to obtain the user's historical blood sugar data and body index data; a determination module, configured to determine a mean value and a standard deviation of the historical blood glucose data, and determine a blood glucose threshold value of the user based on the mean value and the standard deviation; The determination module is further used to analyze the physical indicator data through a correlation analysis model, identify key risk factors in the physical indicator data and the weight corresponding to each key risk factor, and analyze the key risk factors and the corresponding weights using a risk scoring model to obtain a blood sugar risk score for the user; An early warning module is used to determine the target early warning level corresponding to the user's blood sugar threshold and the blood sugar risk score from a plurality of preset early warning levels, and in response to the target early warning level being greater than the preset early warning threshold, execute the corresponding target early warning strategy.

9. A computer device, characterized in that: include: A memory and a processor; wherein the memory is connected to the processor for storing a program; the processor is used to implement the steps of the blood glucose monitoring and early warning method as described in any one of claims 1 to 7 by running the program stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the steps of the blood glucose monitoring and early warning method according to any one of claims 1 to 7.

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