Personalized blood glucose management method and system for diabetic patient

Abstract

The invention discloses a personalized blood glucose management method and system for a diabetic patient, and relates to the technical field of personalized management methods, and the method comprises the steps: determining the blood glucose management logic of the diabetic patient according to a plurality of blood glucose control regions and the age of the diabetic patient; the blood glucose management logic is triggered based on the current blood glucose data and the blood glucose variation of the diabetic patient, and the corresponding blood glucose personalized management event is determined according to the blood glucose management level of the diabetic patient and the current event of the diabetic patient, so that the accuracy of the blood glucose personalized management event is ensured. Therefore, the mobile phone worn by the diabetic presents the corresponding blood glucose personalized management content, and records the execution progress of the blood glucose personalized management content; if the execution progress of the personalized blood glucose management content is lower than the preset execution progress threshold value, a blood glucose early warning event built in the mobile phone is triggered, and the diabetic patient is directly informed of an associated early warning contact person, so that the personalized blood glucose management effect of the diabetic patient is further realized.

Description

Technical Field

[0001] The present invention relates to the technical field of personalized management methods, and particularly to a personalized blood glucose management method and system for diabetic patients. Background Art

[0002] With the development of technology, the early onset of diabetes in patients is becoming more and more common. Diabetic patients mainly experience changes in blood glucose data due to poor eating habits. In the later stage, diabetic patients also need to strictly control their diet. In the existing technology, the current blood glucose data of diabetic patients is collected, and the blood glucose level of diabetic patients is determined based on the current blood glucose data of diabetic patients, without considering the personalized blood glucose management events of diabetic patients, which affects the personalized blood glucose management effect of diabetic patients. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art, and the present invention provides a personalized blood glucose management method and system for diabetic patients.

[0004] An embodiment of the present invention provides a personalized blood glucose management method for diabetic patients, including: Determining a blood glucose data distribution map according to the past blood glucose data of diabetic patients; Determining the blood glucose control area of diabetic patients according to the recognition of the blood glucose data distribution map, and determining the blood glucose management logic of diabetic patients according to multiple blood glucose control areas and the age of diabetic patients. The blood glucose management logic covers the blood glucose prevention and control data interval and the blood glucose change amount control interval; Triggering the blood glucose management logic based on the current blood glucose data and the blood glucose change amount of diabetic patients, and determining the corresponding blood glucose personalized management event according to the blood glucose management level of diabetic patients and the current event of diabetic patients; In this blood glucose personalized management event, the mobile phone worn by the diabetic patient presents the corresponding blood glucose personalized management content and records the execution progress of the blood glucose personalized management content; If the execution progress of the blood glucose personalized management content is lower than the preset execution progress threshold, trigger the blood glucose warning event built into the mobile phone and directly inform the warning contacts associated with the diabetic patient.

[0005] An embodiment of the present invention provides a personalized blood glucose management system for diabetic patients. The personalized blood glucose management system for diabetic patients is applied to the above-mentioned personalized blood glucose management method for diabetic patients. The personalized blood glucose management system for diabetic patients includes: A blood glucose data distribution map module, configured to determine a blood glucose data distribution map according to the past blood glucose data of diabetic patients; A blood glucose management logic module, which is used to determine the blood glucose control area of a diabetic patient according to the recognition of the blood glucose data distribution map, and determine the blood glucose management logic of the diabetic patient according to multiple blood glucose control areas and the age of the diabetic patient. This blood glucose management logic covers the blood glucose prevention and control data range and the blood glucose change amount control range; A blood glucose personalized management event module, which is used to trigger this blood glucose management logic based on the current blood glucose data and the blood glucose change amount of the diabetic patient, and determine the corresponding blood glucose personalized management event according to the blood glucose management level of the diabetic patient and the current event of the diabetic patient; A blood glucose personalized management content module, which is used to present the corresponding blood glucose personalized management content on the mobile phone worn by the diabetic patient in this blood glucose personalized management event, and record the execution progress of the blood glucose personalized management content; A blood glucose warning event module, which is used to trigger the built-in blood glucose warning event of the mobile phone if the execution progress of the blood glucose personalized management content is lower than the preset execution progress threshold, and directly inform the warning contacts associated with the diabetic patient.

[0006] Compared with the prior art, the beneficial effects of the present invention are: In the embodiment of the present invention, through the method in the embodiment of the present invention, the blood glucose data distribution map is determined according to the past blood glucose data of the diabetic patient; the blood glucose control area of the diabetic patient is determined according to the recognition of the blood glucose data distribution map, and the blood glucose management logic of the diabetic patient is determined according to multiple blood glucose control areas and the age of the diabetic patient. This blood glucose management logic covers the blood glucose prevention and control data range and the blood glucose change amount control range; this blood glucose management logic is triggered based on the current blood glucose data and the blood glucose change amount of the diabetic patient, and the corresponding blood glucose personalized management event is determined according to the blood glucose management level of the diabetic patient and the current event of the diabetic patient. This blood glucose management logic is introduced, and the accuracy of the blood glucose personalized management event is ensured.

[0007] Therefore, in this blood glucose personalized management event, the mobile phone worn by the diabetic patient presents the corresponding blood glucose personalized management content and records the execution progress of the blood glucose personalized management content; if the execution progress of the blood glucose personalized management content is lower than the preset execution progress threshold, the built-in blood glucose warning event of the mobile phone is triggered, and the warning contacts associated with the diabetic patient are directly informed, realizing the execution of the blood glucose personalized management content and the control of the execution progress of the blood glucose personalized management content, and introducing the built-in blood glucose warning event of the mobile phone, further realizing the personalized blood glucose management effect of the diabetic patient. Description of the Drawings

[0008] Figure 1 It is a schematic flowchart of the personalized blood glucose management method for diabetic patients in the embodiment of the present invention; Figure 2It is a schematic flowchart of step S11 in the personalized blood glucose management method for diabetic patients in an embodiment of the present invention; Figure 3 It is a schematic flowchart of step S12 in the personalized blood glucose management method for diabetic patients in an embodiment of the present invention; Figure 4 It is a schematic flowchart of step S13 in the personalized blood glucose management method for diabetic patients in an embodiment of the present invention; Figure 5 It is a schematic flowchart of step S14 in the personalized blood glucose management method for diabetic patients in an embodiment of the present invention; Figure 6 It is a schematic flowchart of step S15 in the personalized blood glucose management method for diabetic patients in an embodiment of the present invention; Figure 7 It is a schematic diagram of the structural composition of the personalized blood glucose management system for diabetic patients in an embodiment of the present invention. Detailed implementation manners

[0009] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0010] Please refer to Figures 1 to 7 , a personalized blood glucose management method for diabetic patients, which is applied to the personalized blood glucose management scenario of diabetic patients; the personalized blood glucose management method for diabetic patients includes: Step S11: Determine the blood glucose data distribution map according to the past blood glucose data of the diabetic patient; Step S12: Determine the blood glucose control area of the diabetic patient according to the recognition of the blood glucose data distribution map, and determine the blood glucose management logic of the diabetic patient according to multiple blood glucose control areas and the age of the diabetic patient. The blood glucose management logic covers the blood glucose prevention and control data interval and the blood glucose change amount control interval; Step S13: Trigger the blood glucose management logic based on the current blood glucose data and the blood glucose change amount of the diabetic patient, and determine the corresponding blood glucose personalized management event according to the blood glucose management level of the diabetic patient and the current event of the diabetic patient; Step S14: In the blood glucose personalized management event, the mobile phone worn by the diabetic patient presents the corresponding blood glucose personalized management content, and records the execution progress of the blood glucose personalized management content; Step S15: If the execution progress of the blood glucose personalized management content is lower than the preset execution progress threshold, trigger the blood glucose warning event built in the mobile phone and directly inform the warning contact associated with the diabetic patient; Refer to Figure 2 , in step S11, determine the blood glucose data distribution map according to the past blood glucose data of the diabetic patient; In the specific implementation process of the present invention, the specific steps are as follows: S111: Collect the medical record database of diabetic patients, determine the blood glucose data space based on the detection of the medical record database of diabetic patients, and determine the past blood glucose data of diabetic patients based on the blood glucose data space, the current time, and the name of the diabetic patient; S112: Collect the past blood glucose data of diabetic patients and mark the time nodes of the past blood glucose data of diabetic patients; determine the blood glucose data distribution map based on the synthesis of the past blood glucose data of diabetic patients and the corresponding time nodes.

[0011] In the embodiment of the present application, collecting the medical record database of diabetic patients, determining the blood glucose data space based on the detection of the medical record database of diabetic patients, and determining the past blood glucose data of diabetic patients based on the blood glucose data space, the current time, and the name of the diabetic patient takes into account the overall consideration of the blood glucose data space, the current time, and the name of the diabetic patient, ensuring the accuracy of the past blood glucose data of diabetic patients.

[0012] At this time, obtain the medical record database of the diabetic patient; the medical record database usually includes the personal information, medical history, diagnosis, treatment records, examination results, etc. of the diabetic patient. At this time, cooperate with the medical institution to obtain the medical record database of the specified diabetic patient through a secure data interface or data export function.

[0013] Extract the patient's blood glucose test results from the medical record database and analyze these results to determine a reasonable blood glucose data space; the blood glucose data space defines the normal range, hyperglycemic or hypoglycemic range of the patient's blood glucose value; at the same time, identify the distribution characteristics of the blood glucose test results in the medical record database, such as mean, standard deviation, minimum value, maximum value, etc.; consider the individual differences of the patient, such as age, course of disease, complications, etc., to determine the blood glucose data space suitable for this patient.

[0014] Combine the blood glucose data space, the current time (or query time), and the patient's name to screen out the blood glucose data of the patient for a specific period from the medical record database; at this time, in the medical record database, use the patient's name as the keyword for searching, and then screen according to the time range (such as the past year, the past six months, etc.) and the blood glucose data space (such as the blood glucose value is between 4.0 - 10.0 mmol / L).

[0015] Furthermore, collect the past blood glucose data of diabetic patients and mark the time nodes of the past blood glucose data of diabetic patients; determine the blood glucose data distribution map based on the synthesis of the past blood glucose data of diabetic patients and the corresponding time nodes, taking into account the overall consideration of the synthesis of the past blood glucose data of diabetic patients and the corresponding time nodes, ensuring the accuracy of the blood glucose data distribution map.

[0016] At this time, extract the previous blood glucose data of the specified diabetic patient from the obtained medical record database or dedicated blood glucose monitoring records. These data usually include key information such as blood glucose values and detection times. At the same time, according to the data range and time period determined in step S111, screen the previous blood glucose data of the patient from the medical record database or blood glucose monitoring records, ensuring the integrity and accuracy of the data and avoiding missing or incorrect data points.

[0017] For each set of previously collected blood glucose data, its corresponding time node needs to be marked. The time node is usually accurate to the hour or even minute to reflect the change of blood glucose value over time. At the same time, add a time column to the data table for each set of blood glucose data and fill in the corresponding time node, ensuring the accuracy and consistency of the time node and avoiding time confusion or duplication.

[0018] Visualize the previously collected blood glucose data marked with time nodes through statistical charts (such as histograms, line charts, scatter plots, etc.) to form a blood glucose data distribution map. This blood glucose data distribution map can intuitively reflect the distribution, change trend, and abnormal points of the patient's blood glucose value. At this time, select an appropriate type of statistical chart, input the blood glucose data and time nodes into the chart software, and generate a blood glucose data distribution map.

[0019] In some embodiments of the present application, create a time node matching table to display the collected blood glucose data and its time nodes. The time node matching table is shown in Table 1: Table 1 Time Node Matching Table

[0020] Reference Figure 3 , in step S12, determine the blood glucose control area of the diabetic patient according to the recognition of the blood glucose data distribution map, and determine the blood glucose management logic of the diabetic patient according to multiple blood glucose control areas and the age of the diabetic patient. This blood glucose management logic covers the blood glucose prevention and control data interval and the blood glucose change amount control interval. In the specific implementation process of the present invention, the specific steps are as follows: S121: Determine multiple blood glucose data intervals based on the division of the blood glucose data distribution map, identify multiple blood glucose warning data according to the recognition of the multiple blood glucose data intervals, and mark the time nodes corresponding to the blood glucose warning data. At this time, the blood glucose warning data exceeds the preset blood glucose data threshold. S122: Determine the blood glucose control interval according to the multiple blood glucose warning data and the corresponding time nodes, and determine the blood glucose control area of the diabetic patient according to the blood glucose control interval, the distribution position of the blood glucose warning data, and the blood glucose control events corresponding to the diabetic patient. S123: In the blood glucose control region of a diabetic patient, determine the blood glucose control events matched by the blood glucose warning data based on the traceability of the blood glucose control region of the diabetic patient, determine the blood glucose management logic of the diabetic patient according to the blood glucose control events, the blood glucose change amount of the blood glucose warning data, and the age of the diabetic patient, and perform blood glucose warning for the diabetic patient based on the blood glucose prevention and control data interval and the blood glucose change amount control interval of the blood glucose management logic.

[0021] In the embodiments of the present application, determine multiple blood glucose data intervals based on the division of the blood glucose data distribution map, identify multiple blood glucose warning data according to the identification of the multiple blood glucose data intervals, and mark the time nodes corresponding to the blood glucose warning data; at this time, the blood glucose warning data exceeds the preset blood glucose data threshold, taking into account the overall division of the blood glucose data distribution map, ensuring the accuracy of the multiple blood glucose data intervals.

[0022] At this time, conduct an in-depth analysis of the blood glucose data distribution map to divide it into multiple blood glucose data intervals, which are usually defined based on the range of blood glucose values, such as the hypoglycemia interval, the normal blood glucose interval, the hyperglycemia interval, etc.; the purpose of dividing the intervals is to better identify the blood glucose status of the patient, especially those blood glucose values that exceed the normal range and require special attention; at this time, observe the blood glucose data distribution map, and manually or automatically divide the blood glucose data intervals using statistical software according to the distribution characteristics of the blood glucose values (such as peaks, valleys, fluctuation ranges, etc.) and the density of data points; ensure that the divided intervals are reasonable and accurate, and can truly reflect the blood glucose status of the patient; at the same time, the number of intervals should not be too many to avoid management complexity.

[0023] Identify the blood glucose warning data according to the blood glucose data intervals divided in the previous step. These warning data refer to the data points that fall within a specific interval and indicate an abnormal blood glucose status of the patient; usually, special attention will be paid to those data points that exceed the normal range (such as hypoglycemia or hyperglycemia); at this time, traverse the blood glucose data set and mark the data points that fall within the warning interval as blood glucose warning data; ensure the accurate identification of the warning data and avoid false alarms or missed reports; at the same time, different processing measures need to be taken for different types of warning data (such as hypoglycemia warnings and hyperglycemia warnings).

[0024] Mark the corresponding time nodes for each blood glucose warning data; the time node is an important part of the blood glucose warning data. It provides specific information about the time when the warning occurs, helps to understand the changing trend of the patient's blood glucose status over time, and provides a basis for subsequent analysis and management; at the same time, in the blood glucose data set, add a time column for each warning data (if not added yet) and fill in the corresponding time node information; ensure the accuracy and consistency of the time nodes and avoid time confusion or duplication; at the same time, the accuracy of the time nodes should be determined according to actual needs (such as accurate to hours, minutes, etc.).

[0025] Furthermore, a blood glucose control range is determined based on multiple blood glucose warning data and corresponding time nodes, and a blood glucose control area for the diabetic patient is determined according to the blood glucose control range, the distribution position of the blood glucose warning data, and the blood glucose control events corresponding to the diabetic patient. This comprehensively considers the blood glucose control range, the distribution position of the blood glucose warning data, and the blood glucose control events corresponding to the diabetic patient, ensuring the accuracy of the blood glucose control area for the diabetic patient.

[0026] At this time, in-depth analysis is carried out on the multiple previously identified blood glucose warning data and their corresponding time nodes. The purpose is to determine the blood glucose control range, that is, the time periods during which the blood glucose warning data frequently appear or the blood glucose values fluctuate abnormally. These control ranges are crucial for formulating personalized blood glucose control strategies. At this time, the blood glucose warning data are arranged in chronological order, and their distribution patterns are observed to identify the time periods during which the warning data frequently appear. These time periods are then determined as the blood glucose control ranges, ensuring that the determination of the control ranges is based on sufficient data analysis and avoiding subjective assumptions. At the same time, the number of control ranges should not be too large to avoid management complexity.

[0027] Combined with the blood glucose control range, the distribution position of the warning data (such as before meals, after meals, before bedtime, etc.), and the blood glucose control events encountered by the patient (such as overeating, insufficient exercise, improper drug dosage, etc.), the blood glucose control area for the patient is determined. These areas reflect the difficulties and challenges in blood glucose control for the patient at different time periods. At this time, the warning data within each control range are carefully analyzed, and combined with the patient's daily living habits and the blood glucose control events encountered, the control range is further divided into different blood glucose control areas. For example, if the warning data within a certain control range are mainly concentrated after meals and the patient overeats during this period, then this range is determined as the "post-meal blood glucose control area", ensuring that the determination of the blood glucose control area is accurate and reasonable and can truly reflect the patient's blood glucose status and control difficulties. At the same time, different management strategies need to be adopted for different control areas.

[0028] Therefore, in the blood glucose control area of the diabetic patient, the blood glucose control events matching the blood glucose warning data are determined based on the traceability of the blood glucose control area of the diabetic patient. According to the blood glucose control events, the blood glucose change amount of the blood glucose warning data, and the age of the diabetic patient, the blood glucose management logic for the diabetic patient is determined. Based on the blood glucose prevention and control data range and the blood glucose change amount control range of the blood glucose management logic, blood glucose warning for the diabetic patient is carried out. This comprehensively considers the blood glucose control events, the blood glucose change amount of the blood glucose warning data, and the age of the diabetic patient, ensuring the accuracy of the blood glucose management logic for the diabetic patient.

[0029] At this time, deeply explore the root causes of blood glucose warning data generated by diabetic patients within a specific blood glucose control range, which usually involves multiple aspects such as the patient's daily living habits, eating habits, exercise volume, drug use situation, etc.; through retrospective analysis, determine which factors lead to the appearance of blood glucose warning data, that is, blood glucose control events; at this time, analyze the blood glucose warning data within the blood glucose control range one by one, combined with the patient's daily living records (such as diet diary, exercise record, drug use record, etc.), and external influencing factors (such as stress events, infections, etc.), trace and determine the specific events that lead to blood glucose warning; ensure the accuracy and comprehensiveness of the retrospective analysis, and avoid missing important information; at the same time, pay attention to distinguishing between direct causes and indirect causes, as well as primary causes and secondary causes.

[0030] Based on the blood glucose control events traced in the previous step, combined with the change amount of blood glucose in the blood glucose warning data (that is, the rising and falling amplitude of blood glucose values) and the patient's age information, formulate personalized blood glucose management logic, which will guide how to adjust the patient's diet, exercise, drug use, etc. to more effectively control blood glucose; at this time, comprehensively consider the nature of the blood glucose control events, the magnitude of the blood glucose change amount, and the patient's age, and formulate a set of targeted blood glucose management strategies; for example, for young patients with blood glucose elevation caused by overeating, it is recommended to increase exercise volume and adjust the diet structure; while for elderly patients with blood glucose fluctuations caused by improper drug dosage, it is necessary to adjust the drug type or dosage; ensure that the formulation of blood glucose management logic is both scientific and practical, taking into account both the effect of blood glucose control and the patient's daily living quality and safety.

[0031] According to the blood glucose management logic formulated in the previous step, set the blood glucose prevention and control data range and the blood glucose change amount control range, which will be used as the benchmark for blood glucose warning, to monitor the patient's blood glucose status in real time and send warning signals when necessary; at this time, according to the target blood glucose value and the allowable blood glucose fluctuation range determined in the blood glucose management logic, set the blood glucose prevention and control data range (that is, the normal blood glucose range) and the blood glucose change amount control range (that is, the limit of the rising and falling amplitude of blood glucose); then, use the blood glucose monitoring device to track the patient's blood glucose data in real time. Once the data exceeds the set range, the warning mechanism is triggered; ensure that the setting of the blood glucose prevention and control data range and the blood glucose change amount control range is both reasonable and effective, being able to detect and handle abnormal blood glucose conditions in a timely manner and avoid frequent false alarms or missed alarms.

[0032] Specifically, suppose there is a 65-year-old diabetic patient who has determined the blood glucose control range after breakfast and dinner through the previous steps, and traced the specific events that led to blood glucose warning (such as overeating after breakfast and insufficient drug dosage after dinner); now, formulate blood glucose management logic for this patient according to step S123 and conduct blood glucose warning.

[0033] Trace the blood glucose control events matched by the blood glucose warning data. At this time, after breakfast: the patient's blood glucose increased due to overeating; after dinner: the patient's blood glucose fluctuated due to insufficient drug dosage. Further, after breakfast: it is recommended that the patient reduce the carbohydrate intake in breakfast, increase the intake of vegetables and high-fiber foods, and it is recommended to do mild exercise (such as taking a walk) within 30 minutes after breakfast; after dinner: it is recommended that the patient adjust the drug dosage, increase the insulin injection amount after dinner, and it is recommended to avoid strenuous exercise after dinner to avoid affecting drug absorption.

[0034] Set the blood glucose prevention and control data range after breakfast to be 4.4 - 7.2 mmol / L, and the control range of blood glucose change amount is an increase not exceeding 2.0 mmol / L; set the blood glucose prevention and control data range after dinner to be 5.0 - 9.0 mmol / L (considering the slightly higher blood glucose tolerance of elderly patients), and the control range of blood glucose change amount is an increase or decrease not exceeding 1.5 mmol / L; use a blood glucose monitoring device to track the patient's blood glucose data in real time. Once the data exceeds the set range, an early warning signal is triggered to remind the patient to take corresponding measures to adjust blood glucose; through such steps and examples, a personalized blood glucose management logic is formulated for diabetic patients, and real-time blood glucose early warning is carried out, so as to more effectively control blood glucose and reduce the risk of complications.

[0035] In an embodiment of the present application, a blood glucose management logic matching table is collected. The blood glucose management logic matching table is used to illustrate how to formulate a blood glucose management logic and set an early warning range according to blood glucose control events and patient characteristics (such as age): The blood glucose management logic matching table is shown in Table 2: Table 2 Blood Glucose Management Logic Matching Table

[0036] Reference Figure 4 , in step S13, trigger this blood glucose management logic based on the current blood glucose data and blood glucose change amount of the diabetic patient, and determine the corresponding blood glucose personalized management event according to the blood glucose management level of the diabetic patient and the current event of the diabetic patient; In the specific implementation process of the present invention, the specific steps are as follows: S131: When the smart watch is worn on the diabetic patient, collect the current blood glucose data of the diabetic patient based on the smart watch, and record the current event of the diabetic patient in real time. Determine the corresponding detection time period based on the traceability of the current event, and determine the blood glucose change amount according to the current blood glucose data and the detection time period; S132: If the current blood glucose data and the blood glucose change amount of a diabetic patient respectively meet the blood glucose prevention and control data range and the blood glucose change amount control range, then trigger this blood glucose management logic; in the blood glucose management logic, determine the blood glucose management level of the diabetic patient based on the current blood glucose data and the blood glucose change amount of the diabetic patient. S133: Determine a first management parameter according to the blood glucose management level of the diabetic patient and the age of the diabetic patient, determine a second management parameter according to the blood glucose management level of the diabetic patient and the current event of the diabetic patient, and determine the corresponding blood glucose personalized management event based on the first management parameter, the second management parameter and the personalized management mapping relationship. At this time, the blood glucose personalized management event includes a diet management event, an exercise management event, a working hour management event or a lifestyle management event.

[0037] In an embodiment of the present application, when the smart watch is worn by a diabetic patient, collect the current blood glucose data of the diabetic patient based on the smart watch, and record the current event of the diabetic patient in real time. Determine the corresponding detection time period based on the traceability of the current event, and determine the blood glucose change amount according to the current blood glucose data and the detection time period, which takes into account both the current blood glucose data and the detection time period as a whole, and ensures the accuracy of the blood glucose change amount.

[0038] At this time, when the smart watch is worn by a diabetic patient, collect the current blood glucose data of the diabetic patient based on the smart watch, and record the current event of the diabetic patient in real time. Determine the corresponding detection time period based on the traceability of the current event, and determine the blood glucose change amount according to the current blood glucose data and the detection time period; at this time, the diabetic patient wears the smart watch, and the watch is built-in or connected to a blood glucose monitoring device, which can collect the patient's blood glucose data in real time and accurately; ensure the stable connection between the smart watch and the blood glucose monitoring device, and the accurate and timely data collection.

[0039] The smart watch or the supporting mobile phone application can record the current activities or events of a diabetic patient in real time, such as dining, exercising, resting, etc.; the patient needs to actively or through a preset method record the current event when using the smart watch for subsequent traceability and analysis. At the same time, the system traces and determines a reasonable detection time period related to the current event according to the recorded current event; for example, if the current event is dining, the detection time period is a period of time before and after dining; at this time, ensure that the traced detection time period is closely related to the current event and can accurately reflect the blood glucose change.

[0040] Within the determined detection time period, the system collects multiple blood glucose data points and calculates the blood glucose change amount according to these data points; the blood glucose change amount is obtained by calculating the difference between the maximum value, the minimum value or the average value of the blood glucose data within the time period and the current blood glucose data; at this time, ensure that the collection of blood glucose data points is dense and accurate enough to accurately calculate the blood glucose change amount.

[0041] Specifically, assume that a diabetic patient wears a smartwatch and enables the blood glucose monitoring function. After the diabetic patient wears the smartwatch, the built-in blood glucose monitoring device in the watch starts to collect his blood glucose data in real time. For example, at 10:00 am, the watch collects the blood glucose data of the diabetic patient as 7.2 mmol / L. At the same time, the diabetic patient records his current activity through the smartwatch or the supporting mobile application - he is having breakfast, and this event is automatically or manually recorded in the system.

[0042] Based on the "having breakfast" event recorded by the diabetic patient, the system traces and determines a reasonable detection time period, such as within 30 minutes before and after breakfast. Assume that breakfast starts at 9:30 am and ends at 10:00 am, then the detection time period is from 9:00 am to 10:30 am.

[0043] At the same time, within the determined detection time period, the system collects multiple blood glucose data points of the diabetic patient (for example, at 9:35 it is 6.8 mmol / L, at 9:50 it is 7.0 mmol / L, at 10:15 it is 7.4 mmol / L). The system calculates the difference between these data points and the current blood glucose data (7.2 mmol / L) to obtain the blood glucose change amount. In this example, due to the large number of data points, the system calculates the average value or the difference between the maximum value, minimum value and the current value to approximately represent the blood glucose change amount. Assume that the system calculates that within the detection time period, the average blood glucose change amount of the diabetic patient is +0.4 mmol / L (that is, from the start of breakfast to the current time, the blood glucose has risen by 0.4 mmol / L on average).

[0044] Furthermore, if the current blood glucose data and the blood glucose change amount of the diabetic patient respectively meet the blood glucose prevention and control data range and the blood glucose change amount control range, then this blood glucose management logic is triggered. In the blood glucose management logic, the blood glucose management level of the diabetic patient is determined based on the current blood glucose data and the blood glucose change amount of the diabetic patient, taking into account both the current blood glucose data and the blood glucose change amount of the diabetic patient, ensuring the accuracy of the blood glucose management level of the diabetic patient.

[0045] At this time, if the current blood glucose data and the blood glucose change amount of the diabetic patient respectively meet the blood glucose prevention and control data range and the blood glucose change amount control range, then this blood glucose management logic is triggered. In the blood glucose management logic, the blood glucose management level of the diabetic patient is determined based on the current blood glucose data and the blood glucose change amount of the diabetic patient.

[0046] The system presets blood glucose prevention and control data ranges (such as the normal blood glucose range) and blood glucose change amount control ranges (such as the maximum or minimum allowable blood glucose change amount); the system obtains the current blood glucose data and blood glucose change amount of diabetic patients in real time and compares them with the preset ranges; at this time, ensure the accuracy and rationality of the preset ranges, as well as the accuracy of the real-time data.

[0047] If the current blood glucose data and blood glucose change amount both meet the requirements of the preset ranges, the system triggers the blood glucose management logic; this logic includes a series of algorithms and rules for formulating personalized management strategies according to the patient's blood glucose condition; at this time, ensure that the triggering conditions of the blood glucose management logic are clear and reasonable, as well as the accuracy and effectiveness of the logic itself.

[0048] In the blood glucose management logic, the system determines the patient's blood glucose management level according to the current blood glucose data and blood glucose change amount through preset algorithms or rules; the levels are divided into low risk, medium risk, high risk, etc., which are used to reflect the patient's blood glucose condition and management needs; at this time, ensure that the classification criteria and algorithms of the blood glucose management level are reasonable and accurate, and can truly reflect the patient's blood glucose condition.

[0049] Specifically, assume a diabetic patient with a blood glucose prevention and control data range of 4.4 - 7.8 mmol / L and a blood glucose change amount control range of -1.0 to +1.5 mmol / L (that is, the blood glucose change amount should not be lower than -1.0 mmol / L and should not be higher than +1.5 mmol / L); assume that at a certain moment, the current blood glucose data of the diabetic patient is 6.5 mmol / L and the blood glucose change amount is +0.6 mmol / L (compared with the blood glucose data at the previous detection time point); the system compares the current blood glucose data of 6.5 mmol / L with the blood glucose prevention and control data range of 4.4 - 7.8 mmol / L and finds that it meets the range requirements; at the same time, the system compares the blood glucose change amount of +0.6 mmol / L with the blood glucose change amount control range of -1.0 to +1.5 mmol / L and also finds that it meets the range requirements.

[0050] Since both the current blood glucose data and blood glucose change amount meet the requirements of the preset ranges, the system triggers the blood glucose management logic; in the blood glucose management logic, the system evaluates according to the current blood glucose data of 6.5 mmol / L and the blood glucose change amount of +0.6 mmol / L through preset algorithms or rules. Assume that according to the preset rules, the system classifies patients with current blood glucose data in the range of 6.0 - 7.0 mmol / L and blood glucose change amount between -0.5 and +1.0 mmol / L as low risk level; and classifies patients with current blood glucose data in the range of 7.0 - 7.8 mmol / L and blood glucose change amount between +0.5 and +1.5 mmol / L as medium risk level.

[0051] In this example, the current blood glucose data of the diabetic patient is 6.5 mmol / L, within the range of 6.0 - 7.0 mmol / L. However, the change in blood glucose is +0.6 mmol / L, slightly higher than the upper limit of the blood glucose change for the low-risk level (the lower limit of +1.0 mmol / L is -0.5, but considering it is a value close to the upper limit and the current blood glucose itself is in a relatively low-risk range, there is a slightly flexible handling in this division, and the actual division needs to be based on specific rules). However, since the blood glucose data itself is in a relatively low-risk range and the change, although slightly higher than the low-risk threshold, is still within the controllable range, the system finally classifies the diabetic patient as a level at the boundary between low risk and medium risk (or simplified as "low risk +") to reflect the management requirement that the patient's blood glucose condition is overall good but shows a slightly upward trend. Note that the "low risk +" here is a hypothetical sub-level, and more explicit level divisions are used in actual applications. Through such steps and examples, it can be clearly seen the specific application and implementation process of step S132 in the blood glucose management of diabetic patients, as well as how to determine the blood glucose management level of patients through preset rules and algorithms.

[0052] Therefore, determine the first management parameter based on the blood glucose management level of the diabetic patient and the age of the diabetic patient, determine the second management parameter based on the blood glucose management level of the diabetic patient and the current event of the diabetic patient, and determine the corresponding blood glucose personalized management event based on the first management parameter, the second management parameter, and the personalized management mapping relationship. At this time, the blood glucose personalized management event includes a diet management event, an exercise management event, a working hours management event, or a lifestyle management event, which takes into account the overall consideration of the first management parameter, the second management parameter, and the personalized management mapping relationship, ensuring the accuracy of the corresponding blood glucose personalized management event. At the same time, this blood glucose management logic is introduced, and the accuracy of the blood glucose personalized management event is ensured.

[0053] At this time, determine the first management parameter based on the blood glucose management level of the diabetic patient and the age of the diabetic patient, determine the second management parameter based on the blood glucose management level of the diabetic patient and the current event of the diabetic patient, and determine the corresponding blood glucose personalized management event based on the first management parameter, the second management parameter, and the personalized management mapping relationship; the blood glucose personalized management event includes a diet management event, an exercise management event, a working hours management event, or a lifestyle management event.

[0054] The first management parameter is the result of a comprehensive consideration based on the blood glucose management level and age of the diabetic patient; patients of different age groups have different requirements and tolerances for blood glucose management, so the age factor needs to be taken into account; at this time, ensure the accuracy of the blood glucose management level and the accurate recording of age to accurately determine the first management parameter.

[0055] The second management parameter is the result of comprehensive consideration based on the blood glucose management level of diabetic patients and current events; current events include the patient's daily activities, special physiological states (such as menstruation, pregnancy), psychological states, etc., and these factors all affect the formulation of blood glucose management strategies; at this time, ensure the accuracy of the blood glucose management level and the detailed record of current events to accurately determine the second management parameter.

[0056] Based on the first management parameter, the second management parameter, and the preset personalized management mapping relationship, the system determines the corresponding blood glucose personalized management events, which involve multiple aspects such as diet, exercise, working hours, and lifestyle, aiming to provide personalized management suggestions according to the specific situation of the patient; at the same time, ensure the accuracy and integrity of the personalized management mapping relationship to accurately determine the management events according to the management parameters.

[0057] Specifically, assume a diabetic patient with a medium-risk blood glucose management level, 55 years old, and the current event is the lunch break on a working day.

[0058] Determine the first management parameter: The blood glucose management level of the diabetic patient is medium risk, and the age is 55 years old; according to the preset rules, the system comprehensively considers the medium-risk level and the age of 55 years old, and determines the first management parameter as "medium risk - middle-aged and elderly", which reflects that as a middle-aged and elderly diabetic patient, the blood glucose management needs of the diabetic patient are relatively strict, and more attention needs to be paid to the stability and control of blood glucose.

[0059] Determine the second management parameter: The current event of the diabetic patient is the lunch break on a working day; according to the preset rules, the system comprehensively considers the medium-risk level and the lunch break event, and determines the second management parameter as "medium risk - lunch break", which reflects that during the lunch break, the diabetic patient needs to appropriately adjust the diet or take a short rest to maintain blood glucose stability.

[0060] Based on the first management parameter "medium risk - middle-aged and elderly" and the second management parameter "medium risk - lunch break", the system makes a match according to the preset personalized management mapping relationship; assume that the mapping relationship stipulates that for patients with "medium risk - middle-aged and elderly" and during the "lunch break" time, the following blood glucose personalized management events are recommended: Diet management event: Choose low-GI (glycemic index) foods for lunch, control the food intake, and avoid a sharp increase in blood glucose after meals; Exercise management event: Take a short walk after the lunch break to promote body metabolism and help lower blood glucose; Working hours management event: It is recommended to appropriately extend the lunch break time to avoid blood glucose fluctuations caused by overwork; Lifestyle management event: Maintain a regular schedule and avoid staying up late, which is beneficial to the stable control of blood glucose; In this example, the system finally determines the following personalized blood glucose management events for diabetic patients: choose low-GI foods and control the portion size for lunch, take a short walk after lunch break, appropriately extend the lunch break, and maintain a regular schedule; through such steps and examples, it can be clearly seen the specific application and implementation process of step S133 in the blood glucose management of diabetic patients, and how to determine personalized blood glucose management events by comprehensively considering the patient's blood glucose management level, age, and current events.

[0061] In an embodiment of the present application, the information of the diabetic patient is as follows: Zhang San, 50 years old, with a medium-risk blood glucose management level, and the current event is after dinner; at this time, the first management parameter is determined: search the matching table, the horizontal axis is "medium risk", the vertical axis is "middle-aged (45 - 60 years old)", and the corresponding first management parameter value is "M1"; the second management parameter is determined: search the matching table, the horizontal axis is "medium risk", the vertical axis is "after dinner", and the corresponding second management parameter value is "N2". According to "M1" and "N2", find the corresponding management event in the personalized management mapping relationship, such as "choose low-GI foods, control the portion size, and take a light walk after dinner".

[0062] Reference Figure 5 , in step S14, in this personalized blood glucose management event, the mobile phone worn by the diabetic patient presents the corresponding personalized blood glucose management content and records the execution progress of the personalized blood glucose management content; In the specific implementation process of the present invention, the specific steps are as follows: S141: In this personalized blood glucose management event, store the personalized blood glucose management event in the mobile phone worn by the diabetic patient, and be able to present the corresponding personalized blood glucose management content of the personalized blood glucose management event on the mobile phone; S142: When the user views the personalized blood glucose management content, based on the camera around the mobile phone management, and collect the user's action images through the camera; S143: Determine the execution content of the user relative to the personalized blood glucose management event based on the analysis of the action images, and determine the execution progress of the personalized blood glucose management content according to the matching of the execution content and the personalized blood glucose management content.

[0063] In an embodiment of the present application, in this personalized blood glucose management event, store the personalized blood glucose management event in the mobile phone worn by the diabetic patient, and be able to present the corresponding personalized blood glucose management content of the personalized blood glucose management event on the mobile phone, introducing the ability to present the corresponding personalized blood glucose management content of the personalized blood glucose management event on the mobile phone.

[0064] At this time, in this blood glucose personalized management event, the blood glucose personalized management event is stored in the mobile phone worn by the diabetic patient, and the specific content of the blood glucose personalized management event is displayed in an intuitive and easy-to-understand manner on the mobile phone interface. This includes various forms such as text descriptions, pictures, charts, or videos, aiming to help the patient clearly understand and follow the management suggestions. At the same time, the presentation method should take into account the patient's reading habits and eyesight conditions to ensure the readability and easy understanding of the information. At the same time, the interface design should be simple and clear to avoid confusion caused by information overload. It is completed through the user interface (UI) module of the mobile phone application. The UI module is responsible for reading the personalized management event data from the storage and converting it into visible elements on the mobile phone screen according to the preset display rules.

[0065] Optionally, Li Si is a patient diagnosed with diabetes. He uses a mobile phone application designed specifically for diabetic patients to track and manage his blood glucose level. Recently, his doctor developed a personalized management plan for him based on his blood glucose data and health condition, including performing mild exercise after meals to lower blood glucose.

[0066] The doctor sends the personalized management event (such as "take a walk 30 minutes after meals") to Li Si through the medical system or email. After receiving this event, Li Si's mobile phone application automatically converts it into a data format suitable for mobile phone storage and stores it in the local database of the application. The application also ensures the synchronization of these data between devices (if cloud storage is enabled) so that Li Si can access his management plan on multiple devices.

[0067] When Li Si opens the mobile phone application, the UI module of the application reads the personalized management event data from the database. The data is converted into an easy-to-understand format, such as a notification with a time reminder and a short description. On the main interface or specific management page of the application, Li Si sees the detailed personalized management content, including the recommended exercise type, duration, and expected blood glucose reduction effect. The interface design is simple and clear, using large fonts and clear icons to ensure that Li Si can easily read and understand the management suggestions. Through this example, it can be clearly seen the specific role of step S141 in the implementation of the blood glucose personalized management event: it ensures the secure storage and intuitive presentation of the management event and its content, providing patients with a convenient and effective management tool to track and manage their blood glucose levels.

[0068] Furthermore, when the user views the blood glucose personalized management content, based on the camera around the mobile phone management, the action images of the user are collected by the camera, and the introduction of collecting the action images of the user by the camera is involved.

[0069] At this time, the user first needs to open the diabetes management application on the mobile phone and navigate to the page containing the personalized blood glucose management content, which is usually achieved by clicking on the notifications, reminders in the application or directly searching on the management page; the user needs to confirm that they have understood the management content and decide to start implementing the relevant management suggestions (such as starting exercise, adjusting diet, etc.).

[0070] After the user decides to start implementing the management suggestions, the application will request access to the camera permission of the mobile phone; once the user grants the permission, the application will activate the camera function and prepare to capture the user's action images; at this time, when using the camera function for the first time, the application usually displays a permission request dialog box to the user, asking the user to confirm whether to allow the application to access the camera; the user needs to agree to this request to continue.

[0071] After the camera is activated, the application will start to capture the user's action images in real time, which involves continuously taking photos or recording videos, depending on the design and management requirements of the application; at the same time, the captured image data will be immediately processed or stored on the device for subsequent analysis and evaluation of the user's implementation situation.

[0072] Specifically, Li Si is a diabetes patient who uses a diabetes management application to track his blood glucose level and implement personalized management suggestions; today, he received a personalized management suggestion, prompting him to take a brisk walk after meals to lower blood glucose; Li Si opened the diabetes management application on his mobile phone and saw this management suggestion in the notification bar; he carefully read the suggestion content and decided to start taking a brisk walk immediately after meals.

[0073] Li Si clicked the "Start Execution" button below the suggestion, and the application immediately requested access to his camera permission; Li Si clicked "Allow" in the permission request dialog box to authorize the application to access his camera; at the same time, after the camera was activated, Li Si started walking in the room; the camera function of the application started to capture his action images in real time to verify whether he took a brisk walk as per the suggestion; to ensure sufficient image data was captured, the application continuously took a series of photos during Li Si's brisk walk and stored them in the local storage of the device.

[0074] After Li Si finishes brisk walking and stops exercising, the application will use image recognition technology to analyze the collected image data to evaluate whether Li Si's brisk walking speed and posture meet the standards of the management recommendations. Based on the analysis results, the application will provide feedback to Li Si, such as "You have completed brisk walking as recommended. Keep it up!" or "Your brisk walking speed is a bit slow. It is recommended to speed up your pace." Through this example, the specific role of step S142 in the process of blood glucose personalized management can be clearly seen: It allows the application to collect real-time action images of the user through the camera function of the mobile phone after the user views the management content and decides to start execution, providing basic data for subsequent analysis and evaluation.

[0075] Therefore, the execution content of the user relative to the blood glucose personalized management event is determined based on the analysis of the action images. According to the matching of this execution content and the blood glucose personalized management content, the execution progress of the blood glucose personalized management content is determined, which is compatible with the overall consideration of the matching of the execution content and the blood glucose personalized management content, ensuring the accuracy of the execution progress of the blood glucose personalized management content.

[0076] At this time, the application uses image recognition technology (such as computer vision algorithms) to analyze the collected user action images to identify the specific actions of the user, which includes identifying the user's exercise type (such as walking, running, doing exercises, etc.), exercise speed, exercise posture, etc. At this time, image recognition technology usually relies on deep learning models, which are trained with a large amount of data and can accurately identify specific objects or actions in the images. In the diabetes management application, these models are specifically trained to identify actions related to blood glucose management.

[0077] Once the actions of the user are identified, the application will compare these actions with the recommended actions in the blood glucose personalized management event to determine whether the user has performed the corresponding actions as recommended. At this time, the matching logic is based on multiple dimensions such as the similarity, duration, and completion degree of the actions. For example, if the management recommends that the user perform moderate-intensity exercise for 30 minutes, the application will check whether the user has performed continuous exercise for at least 30 minutes and whether the exercise intensity meets the standards of moderate intensity.

[0078] Based on the matching results of the execution content, the application will calculate and determine the execution progress of the blood glucose personalized management content, which usually involves evaluating the percentage of actions completed by the user, the time completion situation, etc. At this time, the execution progress is represented in various ways, such as progress bars, percentages, completion times, etc. These representation methods are designed to provide intuitive feedback to the user to help them understand their progress and completion status in the management plan.

[0079] Specifically, Li Si is a diabetic patient who uses a diabetes management application to track his blood sugar levels and implement a personalized management plan. Today, he received a management advice asking him to do 30 minutes of aerobic exercise after lunch. Li Si opened the mobile application after lunch and clicked the button to start implementing the management advice. The application then activated the camera function and began to collect Li Si's motion images.

[0080] The application uses image recognition technology to analyze the collected images, identify the aerobic exercise (such as brisk walking) that Li Si is performing, further analyze characteristics such as Li Si's exercise speed and posture to ensure that he is performing moderate-intensity aerobic exercise. At the same time, the application compares the identified actions with the recommended actions in the management advice. In this example, the management advice requires Li Si to do 30 minutes of aerobic exercise, and the application identifies that Li Si is performing brisk walking that meets the requirements.

[0081] The application starts timing and continuously monitors Li Si's exercise situation. When Li Si completes 30 minutes of aerobic exercise, the application automatically stops timing. The application calculates and displays Li Si's execution progress as 100%, indicating that he has successfully completed the aerobic exercise requirement in the management advice. Further, the application provides Li Si with a feedback message: "Congratulations on completing today's 30-minute aerobic exercise! Keep up the good work and maintain a healthy lifestyle!" As an incentive, the application also adds points or rewards for Li Si's completion, and these points are used to unlock other functions or rewards in the application.

[0082] In some embodiments of the present application, a table of expected execution content matches is collected, and the table of expected execution content matches is shown in Table 3: Table 3 Table of Expected Execution Content Matches The application identifies that the user is performing a brisk walking action; the user's action matches the recommended action of "brisk walking for 30 minutes". Since the user has just started performing the action and the duration is unknown, the application cannot immediately determine the complete progress. However, it records that the user has started performing the action and updates the progress according to the duration after the user completes it. Assuming the user finally completes 30 minutes of brisk walking, the execution progress is 100%.

[0083] Reference Figure 6 , in step S15, if the execution progress of the blood sugar personalized management content is lower than the preset execution progress threshold, a blood sugar warning event built into the mobile phone is triggered and the warning contacts associated with the diabetic patient are directly informed; In the specific implementation process of the present invention, the specific steps are as follows: S151: Collect the preset execution progress threshold, compare the execution progress of the blood glucose personalized management content with the preset execution progress threshold. If the execution progress of the blood glucose personalized management content is lower than the preset execution progress threshold, trigger the accelerated execution logic for diabetic patients; S152: In the accelerated execution logic for diabetic patients, determine the progress difference amount based on the execution progress of the blood glucose personalized management content and the preset execution progress threshold, and match the corresponding actions to be completed according to this progress difference amount; meanwhile, collect the fatigue coefficient of the diabetic patient, and determine the corresponding substitute actions based on the fatigue coefficient of the diabetic patient, the actions to be completed, and the age of the diabetic patient, so as to accelerate the action progress of the diabetic patient, and collect the optimized execution progress; S153: If the optimized execution progress is lower than the preset execution progress threshold, trigger the blood glucose warning event built into the mobile phone. This blood glucose warning event records the emergency handling matters of the diabetic patient. The diabetic patient conducts autonomous emergency handling along the emergency handling matters and directly informs the warning contacts associated with the diabetic patient through the mobile phone.

[0084] In the embodiment of the present application, by collecting the preset execution progress threshold, comparing the execution progress of the blood glucose personalized management content with the preset execution progress threshold, and triggering the accelerated execution logic for diabetic patients if the execution progress of the blood glucose personalized management content is lower than the preset execution progress threshold, the accelerated execution logic for diabetic patients is introduced.

[0085] At this time, the system first needs to collect the preset execution progress threshold for the current diabetic patient from the preset database or configuration file. This preset execution progress threshold is set according to the specific conditions of the patient (such as age, disease course, blood glucose level, etc.) and the requirements of the management plan, and is used to measure whether the patient has carried out sufficient blood glucose management activities according to the plan; at this time, this preset execution progress threshold will be expressed in the form of a percentage, such as 70% or 80%, and is stored in the system configuration file for quick reading when needed.

[0086] The system needs to evaluate the execution progress of the patient's blood glucose personalized management content in real time or regularly. This usually involves monitoring and recording the patient's daily activities, such as diet, exercise, medication, etc., and comparing these activities with the preset management plan to calculate the actual execution progress; at this time, the system will use multiple data sources such as sensors, application program logs, and user input to collect the patient's activity data and calculate the execution progress through algorithms.

[0087] After obtaining the execution progress and the preset threshold, the system needs to compare these two values to determine whether the patient has carried out sufficient blood glucose management activities according to the plan.

[0088] If the execution progress is lower than the preset threshold, the system needs to trigger a series of operations to accelerate the execution logic, so as to prompt the patient to accelerate the progress of blood glucose management activities, which includes sending reminder notifications, adjusting the management plan, providing additional support resources, etc.; at this time, triggering the acceleration of the execution logic involves multiple operations such as sending text messages, pushing application notifications, and adjusting system configurations, depending on the system design and patient preferences.

[0089] Specifically, Li Si is a diabetic patient who uses a blood glucose management application to track his diet, exercise, and medication intake; the application has developed a personalized blood glucose management plan for him and set a preset execution progress threshold (e.g., 75%); the application reads the preset execution progress threshold of 75% set for Li Si from the configuration file.

[0090] The application calculates his actual execution progress as 60% based on Li Si's daily activity data (such as recorded diet, exercise time, and medication intake); the application compares Li Si's actual execution progress of 60% with the preset threshold of 75% and finds that the execution progress is lower than the threshold; the application sends a reminder notification to Li Si, telling him that his execution progress is lower than the preset threshold and suggesting that he increase his exercise amount or adjust his diet plan to speed up the progress; at the same time, the application also provides him with some additional support resources, such as exercise tutorials and healthy diet suggestions; through this example, it can be clearly seen the important role of step S151 in the process of personalized blood glucose management: by comparing the patient's actual execution progress with the preset threshold, it timely triggers the acceleration of the execution logic to prompt the patient to take necessary measures to accelerate the progress of blood glucose management activities, thereby improving blood glucose levels.

[0091] Furthermore, in the acceleration execution logic of diabetic patients, the progress difference amount is determined based on the execution progress of the personalized blood glucose management content and the preset execution progress threshold, and the corresponding actions to be completed are matched according to this progress difference amount; at the same time, the fatigue coefficient of the diabetic patient is collected, and the corresponding alternative actions are determined based on the fatigue coefficient of the diabetic patient, the actions to be completed, and the age of the diabetic patient, so as to accelerate the action progress of the diabetic patient, and the optimized execution progress is collected, which takes into account the overall consideration of the fatigue coefficient of the diabetic patient, the actions to be completed, and the age of the diabetic patient, ensuring the accuracy of the corresponding alternative actions.

[0092] At this time, the system needs to calculate the difference amount between the execution progress of the personalized blood glucose management content and the preset execution progress threshold. This difference amount represents the amount of additional actions that the patient needs to complete to make up the gap between the current progress and the threshold; at this time, the difference amount is obtained through simple mathematical operations, that is, the preset execution progress threshold minus the actual execution progress; for example, if the preset threshold is 80% and the actual execution progress is 60%, the difference amount is 20%.

[0093] Based on the calculated progress difference, the system needs to match corresponding actions to be completed for the patient. These actions should be part of the personalized blood glucose management plan and can help the patient quickly improve the execution progress. At this time, the system will select appropriate actions from the management plan for recommendation according to the size of the difference and the patient's specific situation (such as preferences, abilities, etc.). For example, if the difference is large, the system will recommend some actions with greater intensity or more obvious effects.

[0094] To ensure that the patient can execute the recommended actions safely and effectively, the system needs to collect the patient's fatigue coefficient. The fatigue coefficient reflects the patient's current fatigue level and is an important basis for evaluating whether the patient can execute additional actions. At the same time, the fatigue coefficient is collected through various methods, such as heart rate monitoring, questionnaires, user input, etc. The system will synthesize multiple data sources to obtain a relatively accurate fatigue coefficient value.

[0095] After collecting the fatigue coefficient, the system needs to determine substitute actions suitable for the patient based on factors such as the fatigue coefficient, actions to be completed, and the patient's age. Substitute actions refer to those actions with similar effects to the actions to be completed but with less burden on the patient. At this time, the system will use an algorithm or model to evaluate the impact of different actions on the patient and select the most suitable substitute action according to the evaluation results. For example, if the patient's fatigue coefficient is high, the system will recommend some easy exercises or adjust the diet plan to replace the original exercise plan.

[0096] After determining the substitute actions, the patient needs to execute these actions according to the system's recommendation. The system needs to continuously track the patient's execution situation and collect the optimized execution progress after completion. At the same time, the system will track the patient's execution situation through applications, sensors, etc. and use a similar algorithm to calculate the optimized execution progress.

[0097] Specifically, Li Si is a diabetic patient who uses a blood glucose management application to track his diet, exercise, and medication. The application has developed a personalized blood glucose management plan for him and set a preset execution progress threshold (such as 80%). However, Li Si's actual execution progress is only 65%, which is lower than the threshold. The application calculates that the difference between Li Si's execution progress and the preset threshold is 15%.

[0098] Based on the amount of difference, the application matched some actions to be completed for Li Si, such as adding 30 minutes of aerobic exercise and reducing the intake of high-sugar foods; at the same time, the application collected Li Si's fatigue coefficient through methods such as heart rate monitoring and questionnaires, and found that his current fatigue level was relatively high; according to the fatigue coefficient and the actions to be completed, the application recommended some alternative actions for Li Si, such as taking a relaxing walk and consuming some low-sugar, high-fiber foods to replace the original exercise and diet plan.

[0099] Li Si carried out the alternative actions according to the recommendations of the application; the application continuously tracked his implementation situation and collected the optimized implementation progress after completion; after adjustment, Li Si's implementation progress increased to 75%. Although it was still lower than the preset threshold, there was already obvious progress; through this example, it can be clearly seen the important role of step S152 in the process of personalized blood glucose management: it provides a more flexible and personalized management plan for patients by analyzing the progress difference amount, matching the actions to be completed, collecting the fatigue coefficient, determining the alternative actions, and implementing the alternative actions and collecting the optimized implementation progress, etc., to help them reach the preset implementation progress threshold faster.

[0100] Therefore, if the optimized implementation progress is lower than the preset implementation progress threshold, the built-in blood glucose warning event of the mobile phone is triggered. This blood glucose warning event records the emergency handling matters of diabetic patients. Diabetic patients conduct autonomous emergency handling along the emergency handling matters and directly inform the warning contacts associated with the diabetic patients through the mobile phone. The introduction of directly informing the warning contacts associated with the diabetic patients through the mobile phone, at the same time, realizes the implementation of the personalized blood glucose management content and the control of the implementation progress of the personalized blood glucose management content, and introduces the built-in blood glucose warning event of the mobile phone, further realizing the personalized blood glucose management effect of diabetic patients.

[0101] At this time, the system needs to compare the optimized implementation progress of the diabetic patient with the preset implementation progress threshold; if the optimized implementation progress is still lower than the preset threshold, it indicates that the blood glucose management situation of the patient is relatively critical and emergency measures need to be taken. This step usually involves simple numerical comparison operations and is automatically completed by the system.

[0102] Once it is determined that the optimized implementation progress is lower than the preset threshold, the system needs to immediately trigger the built-in blood glucose warning event of the mobile phone. This warning event is a preset response mechanism for emergencies, aiming to remind patients and associated personnel to pay attention and take actions; at this time, triggering the warning event involves sending specific notifications or alerts to the patient's mobile phone and activating the emergency response function associated with the mobile phone.

[0103] After a blood glucose warning event is triggered, the system needs to display a series of emergency handling matters to the patient. These matters are formulated based on the patient's specific situation and blood glucose management plan, aiming to help the patient quickly stabilize the blood glucose level and prevent the deterioration of the condition. At this time, the emergency handling matters are presented in the form of a list, including specific steps such as immediately measuring blood glucose, ingesting an appropriate amount of sugar or insulin, and avoiding strenuous exercise.

[0104] After receiving the warning event and emergency handling matters, the patient needs to perform autonomous emergency handling according to the instructions. This requires the patient to have a certain self-management ability to quickly identify and respond to emergencies. At this time, the patient uses devices such as a mobile application and a blood glucose meter to assist in completing the emergency handling steps, such as recording blood glucose values and adjusting medication doses.

[0105] While the patient is performing emergency handling, the system needs to automatically notify the warning contacts associated with the patient. These contacts are usually the patient's family members, friends, or medical staff, who provide necessary support and assistance in case of an emergency. At the same time, the notification is sent through multiple methods such as text messages, phone calls, and emails, and the content includes the patient's current condition, the emergency handling measures already taken, and the help needed from the contacts.

[0106] Specifically, Li Si is a diabetic patient who uses a blood glucose management application to track his diet, exercise, and medication intake. The application sets a preset execution progress threshold for him (for example, 80%). However, after a series of optimization measures, Li Si's execution progress is still only 70%, which is lower than the preset threshold. The application detects that Li Si's optimized execution progress is 70%, which is lower than the preset 80% threshold.

[0107] The application immediately triggers a blood glucose warning event built into the mobile phone and sends an emergency notification to Li Si's mobile phone. After the warning event is triggered, the application shows Li Si the following emergency handling matters: immediately measure blood glucose, ingest an appropriate amount of sugar if the blood glucose is below the normal range, avoid strenuous exercise, and contact medical staff as soon as possible.

[0108] After receiving the warning notification, Li Si immediately measured his blood glucose value using a blood glucose meter and found that it was indeed below the normal range. He ingested an appropriate amount of sugar according to the instructions of the application and avoided strenuous exercise. At the same time, the application automatically sent a notification to Li Si's preset warning contact (his wife), informing her of Li Si's current blood glucose condition and the emergency handling measures already taken. After receiving the notification, his wife immediately contacted the medical staff and prepared to go to Li Si's location to provide further support.

[0109] Please refer to Figure 7 , Figure 7It is a schematic structural diagram of the personalized blood glucose management system for diabetic patients in the embodiments of the present invention; the personalized blood glucose management system for diabetic patients includes: A blood glucose data distribution map module 21, configured to determine a blood glucose data distribution map according to the previous blood glucose data of a diabetic patient; A blood glucose management logic module 22, configured to determine the blood glucose control area of a diabetic patient according to the recognition of the blood glucose data distribution map, and determine the blood glucose management logic of the diabetic patient according to multiple blood glucose control areas and the age of the diabetic patient, and this blood glucose management logic covers the blood glucose prevention and control data interval and the blood glucose change amount control interval; A blood glucose personalized management event module 23, configured to trigger this blood glucose management logic based on the current blood glucose data and the blood glucose change amount of a diabetic patient, and determine the corresponding blood glucose personalized management event according to the blood glucose management level of the diabetic patient and the current event of the diabetic patient; A blood glucose personalized management content module 24, configured to present the corresponding blood glucose personalized management content on the mobile phone worn by the diabetic patient in this blood glucose personalized management event, and record the execution progress of the blood glucose personalized management content; A blood glucose warning event module 25, configured to trigger a blood glucose warning event built in the mobile phone and directly inform the warning contacts associated with the diabetic patient if the execution progress of the blood glucose personalized management content is lower than a preset execution progress threshold.

[0110] For any combination of the technical features of the above embodiments, for the sake of concise description, not all combinations of the technical features in the above embodiments are described. However, as long as these combinations of technical features do not conflict, they should all be considered as the scope recorded in this specification.

Claims

1. A personalized blood glucose management method for diabetic patients, characterized in that, Including: Determine the blood glucose data distribution map according to the past blood glucose data of diabetic patients; Determine the blood glucose control area of diabetic patients according to the recognition of the blood glucose data distribution map, and determine the blood glucose management logic of diabetic patients according to multiple blood glucose control areas and the age of diabetic patients. This blood glucose management logic covers the blood glucose prevention and control data range and the blood glucose change amount control range; Trigger this blood glucose management logic based on the current blood glucose data and blood glucose change amount of diabetic patients, and determine the corresponding blood glucose personalized management event according to the blood glucose management level of diabetic patients and the current event of diabetic patients; In this blood glucose personalized management event, the mobile phone worn by the diabetic patient presents the corresponding blood glucose personalized management content and records the execution progress of the blood glucose personalized management content; If the execution progress of the blood glucose personalized management content is lower than the preset execution progress threshold, trigger the blood glucose warning event built into the mobile phone and directly inform the warning contacts associated with the diabetic patient.

2. The personalized blood glucose management method for diabetic patients according to claim 1, characterized in that, The determining the blood glucose data distribution map according to the past blood glucose data of diabetic patients includes: Collect the medical record database of diabetic patients, and determine the blood glucose data space according to the detection of the medical record database of diabetic patients. Determine the past blood glucose data of diabetic patients based on the blood glucose data space, the current time, and the name of the diabetic patient; Collect the past blood glucose data of diabetic patients and mark the time nodes of the past blood glucose data of diabetic patients; Determine the blood glucose data distribution map based on the synthesis of the past blood glucose data of diabetic patients and the corresponding time nodes.

3. The personalized blood glucose management method for diabetic patients according to claim 1, wherein, The determining the blood glucose control area of diabetic patients according to the recognition of the blood glucose data distribution map, and determining the blood glucose management logic of diabetic patients according to multiple blood glucose control areas and the age of diabetic patients. This blood glucose management logic covers the blood glucose prevention and control data range and the blood glucose change amount control range, includes: Determine multiple blood glucose data ranges based on the division of the blood glucose data distribution map, identify multiple blood glucose warning data according to the recognition of multiple blood glucose data ranges, and mark the time nodes corresponding to the blood glucose warning data; At this time, the blood glucose warning data exceeds the preset blood glucose data threshold; Determine the blood glucose control interval according to multiple blood glucose warning data and the corresponding time nodes, and determine the blood glucose control area of diabetic patients according to the blood glucose control interval, the distribution position of the blood glucose warning data, and the blood glucose control event corresponding to the diabetic patient; In the blood glucose control area of diabetic patients, determine the blood glucose control event matched by the blood glucose warning data based on the traceability of the blood glucose control area of diabetic patients, and determine the blood glucose management logic of diabetic patients according to the blood glucose control event, the blood glucose change amount of the blood glucose warning data, and the age of diabetic patients, and conduct blood glucose warning for diabetic patients based on the blood glucose prevention and control data range and the blood glucose change amount control range of the blood glucose management logic.

4. The personalized blood glucose management method for diabetic patients according to claim 1, characterized in that The triggering this blood glucose management logic based on the current blood glucose data and blood glucose change amount of diabetic patients, and determining the corresponding blood glucose personalized management event according to the blood glucose management level of diabetic patients and the current event of diabetic patients, includes: When the smartwatch is worn by a diabetic patient, the current blood glucose data of the diabetic patient is collected based on the smartwatch, and the current events of the diabetic patient are recorded in real time. The corresponding detection time period is determined based on the traceability of the current events, and the blood glucose change amount is determined according to the current blood glucose data and the detection time period; If the current blood glucose data and the blood glucose change amount of the diabetic patient respectively meet the blood glucose prevention and control data range and the blood glucose change amount control range, then this blood glucose management logic is triggered; in the blood glucose management logic, the blood glucose management level of the diabetic patient is determined based on the current blood glucose data and the blood glucose change amount of the diabetic patient.

5. The personalized blood glucose management method for diabetic patients according to claim 4, characterized in that Triggering this blood glucose management logic based on the current blood glucose data and the blood glucose change amount of the diabetic patient, and determining the corresponding blood glucose personalized management event according to the blood glucose management level of the diabetic patient and the current event of the diabetic patient, further includes: Determining a first management parameter according to the blood glucose management level of the diabetic patient and the age of the diabetic patient, determining a second management parameter according to the blood glucose management level of the diabetic patient and the current event of the diabetic patient, and determining the corresponding blood glucose personalized management event based on the first management parameter, the second management parameter and the personalized management mapping relationship. At this time, the blood glucose personalized management event includes a diet management event, an exercise management event, a working hours management event or a lifestyle management event.

6. The personalized blood glucose management method for diabetic patients according to claim 1, wherein In this blood glucose personalized management event, the mobile phone worn by the diabetic patient presents the corresponding blood glucose personalized management content and records the execution progress of the blood glucose personalized management content, including: In this blood glucose personalized management event, the blood glucose personalized management event is stored in the mobile phone worn by the diabetic patient, and the blood glucose personalized management content corresponding to the blood glucose personalized management event can be presented on the mobile phone.

7. The personalized blood glucose management method for diabetic patients according to claim 6, wherein In this blood glucose personalized management event, the mobile phone worn by the diabetic patient presents the corresponding blood glucose personalized management content and records the execution progress of the blood glucose personalized management content, further includes: When the user views the blood glucose personalized management content, based on the camera around the mobile phone management, and the action image of the user is collected through the camera; Based on the analysis of the action image, the execution content of the user relative to the blood glucose personalized management event is determined, and the execution progress of the blood glucose personalized management content is determined according to the matching of the execution content and the blood glucose personalized management content.

8. The personalized blood glucose management method for diabetic patients according to claim 1, wherein If the execution progress of the blood glucose personalized management content is lower than the preset execution progress threshold, then the blood glucose warning event built in the mobile phone is triggered, and the warning contact person associated with the diabetic patient is directly informed, including: Collecting the preset execution progress threshold, comparing the execution progress of the blood glucose personalized management content with the preset execution progress threshold. If the execution progress of the blood glucose personalized management content is lower than the preset execution progress threshold, then the accelerated execution logic of the diabetic patient is triggered.

9. The personalized blood glucose management method for diabetic patients according to claim 8, characterized in that, If the execution progress of the blood glucose personalized management content is lower than the preset execution progress threshold, then the blood glucose warning event built in the mobile phone is triggered, and the warning contact person associated with the diabetic patient is directly informed, further includes: In the accelerated execution logic for diabetic patients, the progress difference amount is determined based on the execution progress of the personalized blood glucose management content and the preset execution progress threshold, and the corresponding actions to be completed are matched according to this progress difference amount. At the same time, the fatigue coefficient of the diabetic patient is collected, and the corresponding substitute actions are determined based on the fatigue coefficient of the diabetic patient, the actions to be completed, and the age of the diabetic patient to accelerate the action progress of the diabetic patient, and the optimized execution progress is collected. If the optimized execution progress is lower than the preset execution progress threshold, a blood glucose warning event built into the mobile phone is triggered. This blood glucose warning event records the emergency handling matters of the diabetic patient. The diabetic patient conducts autonomous emergency handling along the emergency handling matters and directly notifies the warning contacts associated with the diabetic patient through the mobile phone.

10. A personalized blood glucose management system for diabetic patients, characterized in that, The personalized blood glucose management system for diabetic patients is applied to the personalized blood glucose management method for diabetic patients as described in any one of claims 1-9. The personalized blood glucose management system for diabetic patients includes: A blood glucose data distribution map module for determining a blood glucose data distribution map based on the past blood glucose data of the diabetic patient. A blood glucose management logic module for determining the blood glucose control area of the diabetic patient based on the recognition of the blood glucose data distribution map, and determining the blood glucose management logic of the diabetic patient based on multiple blood glucose control areas and the age of the diabetic patient. This blood glucose management logic covers the blood glucose prevention and control data range and the blood glucose change amount control range. A blood glucose personalized management event module for triggering this blood glucose management logic based on the current blood glucose data and blood glucose change amount of the diabetic patient, and determining the corresponding blood glucose personalized management event based on the blood glucose management level of the diabetic patient and the current event of the diabetic patient. A blood glucose personalized management content module for presenting the corresponding blood glucose personalized management content on the mobile phone worn by the diabetic patient in this blood glucose personalized management event, and recording the execution progress of the blood glucose personalized management content. A blood glucose warning event module for triggering a blood glucose warning event built into the mobile phone if the execution progress of the blood glucose personalized management content is lower than the preset execution progress threshold, and directly notifying the warning contacts associated with the diabetic patient.

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