A non-invasive blood glucose measurement method, a non-invasive blood glucose measurement system and a medium

Abstract

The application provides a non-invasive blood glucose measurement method, a non-invasive blood glucose measurement system and a medium. The method comprises the following steps: acquiring calibration data of a target object, a real-time blood pressure value and a real-time heart rate value of the target object at a target time; if the target time is in a postprandial period, determining a basic blood pressure set, a basic heart rate set and a reference blood glucose set of the target object changing with time in the postprandial period according to the calibration data; determining a basic blood pressure value, a basic heart rate value and a reference blood glucose set of the target time in the basic blood pressure set, the basic heart rate set and the basic blood glucose set respectively; determining a real-time blood pressure reduction net value according to the real-time blood pressure value, the real-time heart rate value, the basic blood pressure value and the basic heart rate value; converting the real-time blood pressure reduction net value into a real-time blood glucose increase net value according to a preset blood pressure net value and blood glucose net value conversion relationship; and adding the reference blood glucose value and the real-time blood glucose increase net value to obtain a real-time blood glucose value, thereby realizing non-invasive real-time and accurate detection of postprandial blood glucose.

Description

Technical Field

[0001] This application relates to the field of non-invasive blood pressure regulation, and more particularly to a non-invasive blood glucose measurement method, a non-invasive blood glucose measurement system, and a medium. Background Technology

[0002] Postprandial blood pressure changes are a normal physiological response to the digestive process. After eating, blood flow to the digestive system increases, while blood flow to other organs (such as the brain and limbs) relatively decreases, leading to a downward trend in blood pressure. Clinically, a drop in blood pressure of more than 20 mmHg within 2 hours after a meal is defined as postprandial hypotension (PPH). Studies have shown that PPH has a high prevalence in different populations: 24%–59% in healthy elderly people, 40%–100% in patients with Parkinson's disease, 18.8%–27% in patients with essential hypertension (as high as 73% in elderly patients with hypertension), and 37%–70% in patients with type 2 diabetes (69% in elderly diabetic patients over 69 years of age). Recent studies have also found that even healthy young people may experience a slight drop in blood pressure and an increase in heart rate after meals.

[0003] Currently, there is no definitive conclusion on the mechanism of PPH. The mainstream theories include: (1) Autonomic neuropathy mechanism: Healthy people increase heart rate and constrict blood vessels to compensate for the drop in blood pressure by sympathetic nerve excitation. However, people with reduced neurological compensatory function (such as the elderly, patients with cardiovascular and metabolic diseases) have a reduced ability to maintain stable blood pressure, which easily induces PPH; (2) Abnormal gastric emptying mechanism: Postprandial gastric distension can stimulate sympathetic nerve activity, but the gastric emptying rate is faster in the elderly and diabetic patients, and the degree of gastric distension is reduced, resulting in a weakening of sympathetic nerve compensatory excitation; (3) Hyperglycemia mechanism: Acute hyperglycemia can cause vasodilation mediated by perivascular adipose tissue, which promotes a drop in blood pressure.

[0004] The mechanisms described above all suggest that the degree of postprandial blood pressure decrease is closely related to food intake: the greater the food intake, the more blood the digestive system requires, and the more significant the reduction in blood supply to other organs; simultaneously, the greater the food intake, the greater the increase in blood glucose, and the more pronounced the vasodilatory effect. Based on the positive correlation between food intake and postprandial blood glucose, it can be inferred that there is a negative correlation between the degree of postprandial blood pressure decrease and the degree of postprandial blood glucose increase. Currently, no literature or patents have disclosed this specific association.

[0005] In the prior art, Chinese patent application CN200910237853.6 discloses a method and device for calculating blood glucose using blood pressure. This method estimates blood glucose levels based on the statistical pattern of the high comorbidity of hypertension and diabetes, using blood pressure parameters. This method is suitable for long-term trend assessment of fasting blood glucose, but it cannot achieve real-time dynamic detection of blood glucose, especially postprandial blood glucose. International patent WO2022 / 144570A1 discloses a device for estimating blood glucose using heart rate variability from electrocardiogram signals; US patent US11653843B2 discloses a method for estimating blood glucose using blood pressure and pulse wave parameters; and European patent EP3721793A1 involves calculating the glycemic index based on pulse wave parameters. Although these patents involve blood pressure or heart rate parameters, none disclose how to quantitatively calculate the dynamic changes in postprandial blood glucose using changes in postprandial blood pressure and heart rate. In fact, because traditional views consider blood glucose and blood pressure to belong to different physiological regulatory pathways, a direct correlation between the two is usually not established.

[0006] Therefore, there is an urgent need for a method that can detect postprandial blood glucose in real time and non-invasively by utilizing changes in postprandial blood pressure and heart rate. Summary of the Invention

[0007] The purpose of this application is to at least solve one of the technical problems existing in the prior art, and to provide a non-invasive blood glucose measurement method, a non-invasive blood glucose measurement system and medium, which can achieve non-invasive, real-time and accurate detection of postprandial blood glucose through changes in blood pressure and heart rate.

[0008] To achieve the above objectives, a first aspect of this application provides a non-invasive blood glucose measurement method, comprising: Acquire the calibration data of the target object, the real-time blood pressure value and real-time heart rate value of the target object at the target time; wherein, the calibration data includes the calibration blood pressure set, calibration heart rate set and calibration blood glucose set of the target object as calibrated over time; If the target time is within the post-meal period, the baseline blood pressure set, baseline heart rate set, and baseline blood glucose set of the target subject are determined based on the calibration data during the post-meal period. The baseline blood pressure value, baseline heart rate value, and baseline blood glucose set of the target time are determined from the baseline blood pressure value set, baseline heart rate set, and baseline blood glucose set, respectively. The real-time blood pressure reduction net value is determined based on the real-time blood pressure value, real-time heart rate value, baseline blood pressure value, and baseline heart rate value. Based on the preset conversion relationship between blood pressure net value and blood glucose net value, the real-time blood pressure reduction net value is converted into the real-time blood glucose increase net value. The baseline blood glucose value and the real-time blood glucose increase net value are added together to obtain the real-time blood glucose value at the target time. The postprandial period is defined as the time from the start of the meal to the trough of postprandial blood glucose. The baseline blood pressure, baseline heart rate, and baseline blood glucose values ​​are obtained after excluding interfering factors.

[0009] Furthermore, in some embodiments, the baseline blood pressure set, baseline heart rate set, and baseline blood glucose set of the target subject are determined over time during the postprandial period based on calibration data, including: In the calibrated blood pressure set, multiple postprandial calibrated blood pressure values ​​of the target subject during the postprandial period and the pre-meal calibrated blood pressure value at the beginning of the postprandial period are obtained. Based on the pre-meal calibrated blood pressure value and multiple postprandial calibrated blood pressure values, the baseline blood pressure set is determined. In the calibrated heart rate set, multiple postprandial calibrated heart rate values ​​of the target object during the postprandial period and the meal-prepared calibrated heart rate value at the start of the postprandial period are obtained. Based on the meal-prepared calibrated heart rate value and multiple postprandial calibrated heart rate values, the baseline heart rate set is determined. In the calibrated blood glucose set, multiple postprandial calibrated blood glucose values ​​of the target object during the postprandial period and the pre-meal calibrated blood glucose value at the beginning of the postprandial period are obtained. Based on the pre-meal calibrated blood glucose value and multiple postprandial calibrated blood glucose values, a baseline blood glucose set is determined.

[0010] Furthermore, in some embodiments, a baseline blood pressure set is determined based on pre-meal calibrated blood pressure values ​​and multiple post-meal calibrated blood pressure values, including: Multiple postprandial calibrated blood pressure values ​​are compared with the pre-meal calibrated blood pressure values ​​to obtain the blood pressure difference corresponding to each postprandial calibrated blood pressure value. Based on the blood pressure difference corresponding to each postprandial calibrated blood pressure value, a compensation calculation is performed on each postprandial calibrated blood pressure value to obtain multiple postprandial compensated blood pressure values. These multiple postprandial compensated blood pressure values ​​are used as the base blood pressure set. Based on pre-meal calibrated heart rate values ​​and multiple post-meal calibrated heart rate values, a baseline heart rate set is determined, including: Multiple postprandial calibrated heart rate values ​​are compared with the pre-meal calibrated heart rate values ​​to obtain the heart rate difference corresponding to each postprandial calibrated heart rate value. Based on the heart rate difference corresponding to each postprandial calibrated blood pressure value, a compensation calculation is performed on each postprandial calibrated heart rate value to obtain multiple postprandial compensated heart rate values. These multiple postprandial compensated heart rate values ​​are used as the base blood pressure set. Based on pre-meal calibrated blood glucose levels and multiple post-meal calibrated blood glucose levels, a baseline blood glucose set is determined, including: Multiple postprandial calibrated blood glucose values ​​are compared with the pre-meal calibrated blood glucose values ​​to obtain the first blood glucose difference value corresponding to each postprandial calibrated blood glucose value. Based on the first blood glucose difference value corresponding to each postprandial calibrated blood glucose value, a compensation calculation is performed on each postprandial calibrated blood glucose value to obtain multiple postprandial compensated blood glucose values. The multiple postprandial compensated blood glucose values ​​are used as the base blood glucose set.

[0011] Furthermore, in some embodiments, the conversion relationship between net blood pressure and net blood glucose is determined through the following steps: Based on multiple postprandial calibrated blood pressure values, multiple postprandial calibrated heart rate values, baseline blood pressure sets, and baseline heart rate sets, the net reduction in postprandial blood pressure over time for the target subjects was determined. Based on multiple postprandial calibrated blood glucose values ​​and a set of baseline blood glucose, determine the net increase in postprandial blood glucose over time for the target subject during the postprandial period; Based on the time sequence of the postprandial period, the net values ​​of multiple postprandial blood pressure decreases and multiple postprandial blood glucose increases were fitted to obtain the conversion relationship between net blood pressure and net blood glucose values.

[0012] Furthermore, in some embodiments, the above-mentioned non-invasive blood glucose measurement method further includes: Determine the first calibrated blood glucose value at the target time from the calibration data; If the target time falls within the first pre-meal period, the theoretical blood glucose value is determined based on the real-time blood pressure and heart rate values ​​in the pre-established blood pressure-heart rate-blood glucose mapping relationship. The second calibrated blood glucose value at the start time of the first pre-meal period in the calibration data, and the theoretical blood glucose value are corrected based on the first and second calibrated blood glucose values ​​to obtain the real-time blood glucose value at the target time. The first pre-meal period is the period from the trough of post-meal blood glucose the previous evening to the start of the morning meal. or, If the target time falls within the second pre-meal period, the third calibrated blood glucose value at the start time of the second pre-meal period is obtained from the calibration data, and the first calibrated blood glucose value is corrected based on the third calibrated blood glucose value to obtain the real-time blood glucose value at the target time. The second pre-meal period is the period from the time when the blood glucose reaches its trough after breakfast on the same day to the start of lunch on the same day, or from the time when the blood glucose reaches its trough after lunch on the same day to the start of dinner on the same day.

[0013] Furthermore, in some embodiments, the mapping relationship between blood pressure, heart rate, and blood glucose is determined through the following steps: In the calibrated blood pressure set, obtain multiple pre-meal calibrated blood pressure values ​​of the target subject during the first pre-meal period, which vary over time; In the calibrated heart rate set, obtain multiple pre-meal calibrated heart rate values ​​of the target object that change over time during the first pre-meal period; In the calibrated blood glucose set, obtain multiple pre-meal calibrated blood glucose values ​​of the target subject that change over time during the first pre-meal period; Based on the time sequence of the first pre-meal period, the correlation between blood pressure, heart rate and blood glucose was obtained by fitting multiple pre-meal calibrated blood pressure values, multiple pre-meal calibrated heart rate values ​​and multiple post-meal blood pressure reduction values.

[0014] Furthermore, in some embodiments, the theoretical blood glucose value is corrected based on the first calibrated blood glucose value and the second calibrated blood glucose value to obtain the real-time blood glucose value at the target time, including: Calculate the second blood glucose difference between the first calibrated blood glucose value and the second calibrated blood glucose value; Based on the second blood glucose difference, the theoretical blood glucose value is corrected to obtain the real-time blood glucose value.

[0015] To achieve the above objectives, a second aspect of this application provides a non-invasive blood glucose measurement system, comprising: A wristwatch is used to measure and calibrate a target object to determine calibration data, as well as to measure real-time data of the target object at a target time, including real-time blood pressure and real-time heart rate. The smart terminal connects wirelessly to the watch. The cloud data center is wirelessly connected to smart terminals and wristwatches. The smart terminal is used to receive real-time data from the wristwatch and upload the real-time data to the cloud data center; The cloud data center is configured to receive real-time data measured at the target time. If the target time is within the post-meal period, it determines the baseline blood pressure set, baseline heart rate set, and baseline blood glucose set of the target object during the post-meal period based on the calibration data. It then determines the baseline blood pressure value, baseline heart rate value, and baseline blood glucose set of the target time from the baseline blood pressure value set, baseline heart rate set, and baseline blood glucose set, respectively. Based on the real-time blood pressure value, real-time heart rate value, baseline blood pressure value, and baseline heart rate value, it determines the real-time blood pressure reduction net value. Based on the preset conversion relationship between blood pressure net value and blood glucose net value, it converts the real-time blood pressure reduction net value into a real-time blood glucose increase net value. Finally, it adds the baseline blood glucose value to the real-time blood glucose value at the target time and transmits the real-time blood glucose value back to the smart terminal and / or wristwatch for display.

[0016] Furthermore, in some embodiments, the wristwatch includes a blood pressure sensing module for measuring blood pressure, a heart rate sensing module for measuring heart rate, a skin impedance sensing module for measuring sympathetic nerve excitation, a skin temperature sensing module for measuring skin temperature, an ambient temperature sensing module for measuring ambient temperature, a communication module for communicating with a smart terminal or cloud data center, a data processing module, and a power module. The data processing module is electrically connected to the blood pressure sensing module, heart rate sensing module, skin impedance sensing module, skin temperature sensing module, ambient temperature sensing module, communication module, and power module, respectively.

[0017] The smart terminal has an application installed, which includes a registration interface, a calibration interface, a blood glucose measurement interface, and a data query interface. The registration interface provides a window for inputting physiological parameters for the target object, the calibration interface provides a window for inputting calibration data for the target object, the blood glucose measurement interface provides a control window for measuring blood glucose for the target object, and the data query interface provides a query window for querying data for the target object.

[0018] To achieve the above objectives, a third aspect of the present application provides a storage medium, which is a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the non-invasive blood glucose measurement method of the first aspect embodiment described above.

[0019] According to the embodiments of this application, a non-invasive blood glucose measurement method, system, and medium have at least the following beneficial effects: By establishing an individualized postprandial physiological parameter benchmark for the target subject, real-time, non-invasive detection of postprandial blood glucose using changes in blood pressure and heart rate is achieved. First, this method, based on the inherent negative correlation between the degree of postprandial blood pressure decrease and the degree of blood glucose increase, breaks through the traditional perception that blood glucose and blood pressure are not directly related, providing a new technical path for blood glucose detection. Second, by pre-calibrating and obtaining the individual's baseline blood pressure, baseline heart rate, and benchmark blood glucose changes over time during the postprandial period, interference factors such as eating, exercise, emotions, and environment can be effectively eliminated. This allows the calculated real-time net blood pressure decrease to more accurately reflect the true physiological fluctuations caused by eating. Furthermore, by using a preset conversion relationship, the net increase in blood glucose is quantitatively calculated, significantly improving the signal-to-noise ratio and accuracy of individualized detection, and solving the problem of dynamically tracking postprandial blood glucose changes in existing technologies. Furthermore, this method only requires conventional blood pressure and heart rate sensors to achieve continuous detection. It is completely non-invasive and avoids the pain and infection risks associated with frequent blood sampling. It is especially suitable for diabetic patients, the elderly, and people with autonomic nervous system dysfunction who need to frequently monitor their blood sugar.

[0020] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description and the accompanying drawings. Attached Figure Description

[0021] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0022] The present application will be further described below with reference to the accompanying drawings and embodiments; Figure 1 This is an optional schematic diagram of a full-day blood pressure monitoring procedure for multiple hypertensive patients provided in an embodiment of this application; Figure 2This is an optional schematic diagram of the non-invasive blood glucose measurement method provided in the embodiments of this application; Figure 3 This is an optional schematic diagram provided in an embodiment of this application for determining the baseline blood pressure set, baseline heart rate set, and baseline blood glucose set; Figure 4 This is an optional schematic diagram of a blood pressure survey study provided in an embodiment of this application; Figure 5 This is a schematic diagram of an optional blood pressure and heart rate change curve of the target object B after lunch on the calibration day, provided in the embodiments of this application. Figure 6 This is an optional schematic diagram illustrating continuous blood glucose measurement for different target objects provided in the embodiments of this application; Figure 7 This is a schematic diagram of an optional blood glucose change curve of the target object B after lunch on the calibration day, provided in the embodiments of this application. Figure 8 This is an optional schematic diagram provided in the embodiments of this application for determining the conversion relationship between net blood pressure and net blood glucose; Figure 9 This is an optional schematic curve illustrating the change in the net decrease in blood pressure of target object B after lunch over time, as provided in the embodiments of this application. Figure 10 This is a schematic diagram of an optional change curve of the net increase in blood glucose after lunch for target object B over time, provided in an embodiment of this application. Figure 11 This is an embodiment of the present application providing an optional curve comparison diagram between the blood glucose level of target object B at specific time points after lunch calculated based on a quadratic function and the actual blood glucose level; Figure 12 This is a schematic diagram illustrating the nighttime blood pressure and heart rate variations in normal individuals, provided in an embodiment of this application. Figure 13 This is an optional schematic diagram provided in an embodiment of this application for determining the mapping relationship between blood pressure, heart rate, and blood glucose; Figure 14 This is an optional schematic diagram illustrating how to obtain a real-time blood glucose value from a corrected theoretical blood glucose value, as provided in an embodiment of this application. Figure 15 This is an optional comparison diagram between the finger-prick blood glucose value and the calculated blood glucose value of the target object C provided in the embodiments of this application; Figure 16 This is an optional comparison diagram between the measured value and the calculated blood glucose value of the continuous blood glucose monitoring system provided in this application embodiment; Figure 17 This is an optional schematic diagram of the non-invasive blood glucose measurement system provided in the embodiments of this application; Figure 18 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0023] This section will describe in detail the specific embodiments of this application. Preferred embodiments of this application are shown in the accompanying drawings. The purpose of the drawings is to supplement the textual description with graphics, so that people can intuitively and vividly understand each technical feature and the overall technical solution of this application, but they should not be construed as limiting the scope of protection of this application.

[0024] In the description of this application, the use of "first" and "second" is for the purpose of distinguishing technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of indicated technical features or the order of the indicated technical features. It should be understood that such use of data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0026] Based on in-depth research into the mechanisms of postprandial physiological changes, this application proposes a novel mechanism for postprandial hypotension: the "insulin-mediated vasodilation mechanism." Specifically, after a meal, elevated blood glucose levels stimulate the secretion of insulin by pancreatic β-cells. In addition to its hypoglycemic effect, insulin also possesses vasodilatory activity, primarily achieved by stimulating the release of nitric oxide from vascular endothelial cells. Research published by Chai Weidong et al. in the February 2002 issue of the *Chinese Journal of Endocrinology and Metabolism* (Vol. 18, No. 1) further supports this insulin-mediated vasodilation mechanism, demonstrating a correlation between insulin-mediated endothelium-dependent vasodilation and hyperglycemia. Nitric oxide, as an endogenous vasodilator, inhibits tonic contraction of skeletal muscle blood vessels, leading to a decrease in peripheral vascular resistance and thus a physiological reduction in blood pressure. In physiological hyperinsulinemia, this mechanism helps regulate arterial blood pressure; however, in pathological hyperinsulinemia (such as insulin resistance-related states), this effect is excessively amplified, potentially leading to significant peripheral vasodilation and inducing postprandial hypotension.

[0027] Based on the dynamic response characteristics of postprandial blood pressure and heart rate, the population can be categorized into the following three groups: Group A: Postprandial blood pressure shows no significant decrease or only a slight decrease, but the heart rate increases compensatorily. This group is commonly seen in healthy individuals with sound cardiovascular function and no metabolic diseases, or those whose conditions are effectively controlled. Group B: Postprandial blood pressure decreases significantly, reaching or exceeding the clinical diagnostic criteria for postprandial hypotension, but the heart rate change is not significant. This group corresponds to individuals with typical symptoms of postprandial hypotension. Group C: Postprandial blood pressure decreases, but does not reach the diagnostic criteria for postprandial hypotension, and the heart rate increases to some extent. This group covers most middle-aged and elderly individuals, excluding typical patients.

[0028] Based on clinical research data, such as the study published by Kohara et al. in *Hypertension*, Volume 33, Issue 1, 1999, postprandial hypotension is associated with asymptomatic cerebrovascular injury in patients with essential hypertension, further supporting the objectivity of the above classification. (Refer to...) Figure 1 As shown, Figure 1 This is an optional schematic diagram of a whole-day blood pressure monitoring of multiple hypertensive patients provided in the embodiments of this application. The observation of 34 hypertensive patients (of which 18 had an average postprandial systolic blood pressure drop of more than 20 mmHg, classified as postprandial hypotension-2 group; and 16 had a significant drop but did not exceed the standard, classified as postprandial hypotension-1 group) and 36 healthy controls with normal blood pressure showed that even in healthy people, their postprandial blood pressure also showed a downward trend, only the severity was lower than that of patients with postprandial hypotension.

[0029] Based on the novel mechanism proposed in this application, insulin forms the core bridge connecting blood glucose and blood pressure: elevated blood glucose drives insulin secretion, insulin secretion triggers vasodilation, and vasodilation leads to a decrease in blood pressure. This causal chain is logically clear, laying the theoretical foundation for inverting blood glucose changes through blood pressure changes. From a technical perspective, blood pressure detection technology is relatively mature, and the main factors interfering with blood pressure measurement can be eliminated one by one through data processing, making quantitative conversion between the two main physiological parameters possible.

[0030] This application summarizes several clinical characteristics of postprandial hypotension based on existing literature data. These characteristics are highly consistent with the above-mentioned mechanisms, specifically: (1) the amount of food consumed is positively correlated with the magnitude and duration of blood pressure decrease; (2) the magnitude of blood pressure decrease induced by carbohydrates is significantly greater than that induced by fat and protein; (3) systolic blood pressure begins to decrease immediately after eating, with the maximum decrease usually occurring 30 to 45 minutes after eating, a time window that is highly consistent with the time of postprandial blood glucose peak; (4) blood pressure returns to normal about 2 hours after eating, consistent with the trend of blood glucose returning to normal 2 hours after eating in healthy individuals; (5) blood pressure decreases most significantly after breakfast, followed by after lunch and dinner, which is basically consistent with the differences in the magnitude of blood glucose changes after the three meals of the day; (6) Oral glucose tolerance test can induce postprandial hypotension in patients with type 2 diabetes, which matches the process of rapid increase in blood glucose during the test; (7) Hypoglycemic drugs (such as metformin) can reduce the magnitude of postprandial blood pressure drop while effectively lowering blood glucose; (8) Exercise can improve the symptoms of postprandial hypotension, which is consistent with its effect of lowering blood glucose; (9) Drugs that inhibit glucose absorption (such as acarbose) can reduce postprandial hypotension while lowering postprandial blood glucose; (10) After exogenous insulin injection, the symptoms of postprandial hypotension worsen, which is completely consistent with the vasodilatory mechanism of insulin proposed in this application; (11) Hyperinsulinemia is an important cause of postprandial hypotension in diabetic patients, which also confirms the above mechanism.

[0031] Given the clear causal relationship between postprandial hyperglycemia and hypotension, the onset and end times of their physiological fluctuations should be synchronous. Numerous clinical trials have confirmed that blood glucose begins to rise 15 minutes after a meal, peaks at approximately 30 to 60 minutes, and returns to baseline levels in healthy individuals about 2 hours after a meal. However, in diabetic patients, due to insulin secretion or dysfunction, the recovery time may be prolonged to 3 to 5 hours after a meal. Correspondingly, postprandial hypotension is usually most pronounced 30 to 60 minutes after a meal, and in some patients, the trough in blood pressure may be delayed until 2 hours after a meal, with full recovery requiring 4 to 5 hours. Based on these facts, this application defines the time window for the synchronous changes in postprandial blood glucose and blood pressure as: a minimum of 2 hours and a maximum of 5 hours. Within this time window, changes in blood pressure and heart rate can be used to measure the dynamic changes in blood glucose. Outside this time window (i.e., the pre-meal period), this measurement principle is not applicable.

[0032] Furthermore, it is worth noting that in diabetic patients using insulin therapy, excessive insulin dosage or insufficient food intake can induce hypoglycemia. Hypoglycemia activates the sympathetic-adrenal system, leading to increased heart rate and peripheral vasoconstriction, resulting in elevated blood pressure. This phenomenon also reveals a negative correlation between blood pressure and blood glucose, indicating that the basic principles of this application are applicable in hypoglycemic situations.

[0033] Based on this, embodiments of this application provide a non-invasive blood glucose measurement method, a non-invasive blood glucose measurement system, and a medium, which can achieve non-invasive, real-time, and accurate detection of postprandial blood glucose through changes in blood pressure and heart rate.

[0034] Therefore, the embodiments of this application will be further described below with reference to the accompanying drawings.

[0035] Reference Figure 2 As shown, Figure 2 This is an optional schematic diagram of a non-invasive blood glucose measurement method provided in the embodiments of this application. The non-invasive blood glucose measurement method may include, but is not limited to, steps S101 to S103.

[0036] Step S101: Obtain the calibration data of the target object, the real-time blood pressure value and real-time heart rate value of the target object at the target time.

[0037] The calibration data includes calibrated blood pressure, heart rate, and blood glucose sets calibrated over time for the target subject. The purpose of calibration is to obtain various physiological parameters (including blood pressure, heart rate, and blood glucose) of the target subject at different times of the day in a resting state. These parameters will form the data basis for subsequent blood glucose measurements. The resting state, as referred to here, is a relatively stable state after excluding physiological activities and daily behaviors such as excretion, exercise, bathing, and internal and external interference factors such as sympathetic nerve excitation and ambient temperature. To ensure the validity of the calibration data, the target subject should choose a day with suitable environmental conditions (e.g., room temperature controlled between 10℃ and 25℃, relative humidity between 20% and 80%), feel well, have no sudden illnesses, and take medication and eat normally as prescribed. Exercise, bathing, work, or other activities should be avoided on the calibration day to ensure the continuity and accuracy of the physiological parameters.

[0038] It should be noted that the calibration process for the target subjects mentioned above should last at least one day. Regardless of the number of meals throughout the day, the start time of each meal should be predetermined (based on the moment of taking the first bite of food, referred to as meal time). Snacks between two main meals should also be predetermined in advance. During the calibration period, multiple parameters need to be measured and recorded, including blood pressure (systolic and diastolic), heart rate, blood glucose, skin resistance, skin temperature, and ambient temperature and humidity. These parameters need to be collected simultaneously at several time points throughout the 24 hours of the day. The more frequent the data collection, the larger the accumulated data volume, and the more closely the blood glucose calculation formula derived from it will reflect the individual's actual situation.

[0039] However, increasing the number of time points and parameters will increase the difficulty of measurement, especially the acquisition of blood glucose values. If the traditional finger capillary blood collection method is used, it will increase the difficulty of blood glucose calibration for the target subjects.

[0040] Therefore, this application provides a non-invasive blood glucose measurement system. The system includes a wristwatch for measurement, which integrates a blood pressure sensor module for measuring blood pressure, a heart rate sensor module for measuring heart rate, a skin impedance sensor module for measuring sympathetic nerve excitation, a skin temperature sensor module for measuring skin temperature, and an ambient temperature sensor module for measuring ambient temperature. After the target subject correctly wears the wristwatch according to the instructions, the system automatically collects and records the above parameters at preset time intervals (e.g., every 5 minutes). For blood glucose value acquisition, a continuous glucose monitoring (CGM) module is used for calibration. The CGM module automatically collects glucose concentration signals from subcutaneous tissue fluid through a blood glucose sensor probe inserted under the skin. By aligning the acquisition time points of this system with those of the wristwatch system, a complete set of parameters from multiple time points throughout the target subject's 24-hour period can be obtained, thus constructing a calibration dataset. This dataset includes a set of calibrated blood pressure values, a set of calibrated heart rate values, and a set of calibrated blood glucose values ​​that vary over time, providing an individualized benchmark for subsequent non-invasive blood glucose calculations.

[0041] Step S102: If the target time is within the post-meal period, determine the baseline blood pressure set, baseline heart rate set, and baseline blood glucose set of the target subject during the post-meal period based on the calibration data. Determine the baseline blood pressure value, baseline heart rate value, and baseline blood glucose set of the target time from the baseline blood pressure value set, baseline heart rate set, and baseline blood glucose set, respectively. Determine the real-time blood pressure reduction net value based on the real-time blood pressure value, real-time heart rate value, baseline blood pressure value, and baseline heart rate value. Convert the real-time blood pressure reduction net value into the real-time blood glucose increase net value according to the preset blood pressure net value and blood glucose net value conversion relationship. Add the baseline blood glucose value and the real-time blood glucose increase net value to obtain the real-time blood glucose value at the target time.

[0042] The postprandial period is defined as the time interval from the start of the meal to the point when postprandial blood glucose levels initially rise, then fall, and finally reach their trough. Basal blood pressure, basal heart rate, and basal blood glucose levels are values ​​obtained after excluding confounding factors. Confounding factors include the target individual's physiological activities, lifestyle behaviors, sympathetic nervous system excitation, and ambient temperature.

[0043] In one specific implementation, when the target time is determined to be in the post-meal period, the system first determines the target object's baseline blood pressure set, baseline heart rate set, and baseline blood glucose set over time during that post-meal period based on pre-acquired calibration data. The baseline blood pressure set, baseline heart rate set, and baseline blood glucose set are established as follows: based on the blood pressure, heart rate, and blood glucose values ​​corresponding to each time point within the post-meal period from the calibration data, after removing various interference factors, interpolation or fitting methods are used to generate continuous change curves or discrete data point sets, thereby obtaining the corresponding baseline blood pressure set, baseline heart rate set, and baseline blood glucose set for any given time.

[0044] Subsequently, the baseline blood pressure, baseline heart rate, and baseline blood glucose values ​​corresponding to the target time are extracted from the aforementioned baseline blood pressure, baseline heart rate, and baseline blood glucose sets, respectively. Based on the real-time blood pressure and real-time heart rate values ​​detected at the target time, combined with the extracted baseline blood pressure and heart rate values, the real-time net blood pressure reduction at the target time is calculated according to a preset net blood pressure reduction calculation rule. This calculation rule could be, for example, that the real-time net blood pressure reduction equals the difference between the real-time blood pressure value and the baseline blood pressure value, further weighted and corrected based on the baseline heart rate value to more accurately reflect the blood pressure decrease caused by the meal itself. Next, the calculated real-time net blood pressure reduction is substituted into a pre-defined conversion relationship between net blood pressure and net blood glucose. This conversion relationship is obtained by mathematically fitting the net blood pressure reduction and net blood glucose increase at each time point within the postprandial period of the calibrated data, and can be expressed using linear regression, polynomial fitting, or other functional forms. Through this conversion relationship, the real-time net blood pressure reduction is mapped to the corresponding real-time net blood glucose increase. Finally, the extracted baseline blood glucose value is added to the net increase in real-time blood glucose to obtain the real-time blood glucose value at the target time. This real-time blood glucose value effectively eliminates interference from the baseline state and truly reflects the dynamic changes in blood glucose caused by meals, realizing non-invasive blood glucose measurement based on blood pressure and heart rate parameters.

[0045] Reference Figure 3 As shown, Figure 3 This is an optional schematic diagram of determining the baseline blood pressure set, baseline heart rate set, and baseline blood glucose set provided in the embodiments of this application. The non-invasive blood glucose measurement method may include, but is not limited to, steps S201 to S203.

[0046] Step S201: In the calibrated blood pressure set, obtain multiple postprandial calibrated blood pressure values ​​of the target object that change over time during the postprandial period, as well as the pre-meal calibrated blood pressure value at the beginning of the postprandial period. Based on the pre-meal calibrated blood pressure value and multiple postprandial calibrated blood pressure values, determine the baseline blood pressure set.

[0047] To construct a baseline blood pressure set that varies over time during the postprandial period, it is first necessary to extract the postprandial calibrated blood pressure values ​​of the target subject at various time points during the postprandial period from the calibrated blood pressure set, and simultaneously obtain the pre-meal calibrated blood pressure value at the start of the postprandial period (i.e., the start of the meal). Based on the dynamic relationship between the pre-meal calibrated blood pressure values ​​and the postprandial calibrated blood pressure values ​​at each time point, and after eliminating interfering factors, the baseline blood pressure set reflecting the physiological changes under resting conditions can be determined.

[0048] It is worth noting that each baseline blood pressure in the baseline blood pressure set defined in this application exhibits a rhythmic variation characteristic with a 24-hour cycle, and its dynamic variation curve is morphologically similar to the clinically known "dipper" blood pressure curve. It should be noted that the conventional dipper blood pressure only roughly reflects the overall trend of daytime blood pressure being higher than nighttime blood pressure, and does not exclude the influence of interfering factors such as physiological activities, lifestyle behaviors, daily activities, psychological factors, and environmental conditions. Due to the presence of these interfering factors, the actually measured dipper blood pressure curve often exhibits multiple peaks and troughs, making it difficult to truly reflect the baseline rhythm of blood pressure. (Refer to...) Figure 4 As shown, Figure 4 This is an optional schematic diagram of a blood pressure survey study provided in an embodiment of this application. Figure 4 It can be clearly observed that various daily activities can cause temporary increases in blood pressure, including using the toilet, eating, walking, smoking, drinking alcohol, changing posture, and even washing the face. These instantaneous fluctuations are superimposed on baseline blood pressure, masking the true pattern of blood pressure changes. Therefore, it is necessary to process the data to remove the blood pressure fluctuations caused by the above-mentioned interfering factors in order to obtain baseline blood pressure values ​​that truly reflect the physiological state of the target subject at different times.

[0049] In a preferred embodiment, a baseline blood pressure set is determined based on pre-meal calibrated blood pressure values ​​and multiple post-meal calibrated blood pressure values. This includes the following steps: performing difference processing on the multiple post-meal calibrated blood pressure values ​​and the pre-meal calibrated blood pressure values ​​respectively to obtain the blood pressure difference value corresponding to each post-meal calibrated blood pressure value; performing compensation calculation on each post-meal calibrated blood pressure value based on the blood pressure difference value corresponding to each post-meal calibrated blood pressure value to obtain multiple post-meal compensated blood pressure values; and using the multiple post-meal compensated blood pressure values ​​as the baseline blood pressure set.

[0050] Specifically, when constructing the basic blood pressure dataset, it is first necessary to extract the postprandial calibrated blood pressure values ​​of the target object at various time points within the postprandial period from the calibration dataset, as well as the pre-meal calibrated blood pressure value at the start of the meal. For ease of calculation, the start time of the meal is denoted as t0, and the corresponding blood pressure value is denoted as P0; the end point of the postprandial period (i.e., the moment when blood glucose drops to its lowest trough) is denoted as t l The blood pressure value at that moment is denoted as P. hIt should be noted that, to improve data reliability, if the non-invasive blood glucose measurement system of this application is used, measurements can be taken at t0 or t... l Blood pressure is automatically measured multiple times before and after a specific time point, and the average value is taken as P0 and P1, respectively. h .

[0051] In determining (t0, P0) and (t l ,P h After that, for any time t within the postprandial period, the baseline blood pressure value P bt According to P0 and P h The magnitude relationships are calculated using different linear interpolation formulas: If P0 is greater than P h This indicates that postprandial blood pressure generally shows a downward trend. At this time: P bt =P0–(t-t0)(P0-P h ) / (t l -t0); If P0 is less than P h This indicates that postprandial blood pressure generally shows an upward trend. At this time: P bt =P0+(t-t0)(P h -P0) / (t l -t0); If P0 is greater than P h This indicates that there is no significant fluctuation in postprandial blood pressure; at this time, P... bt =P0.

[0052] The above calculation process essentially performs a compensation operation on the blood pressure values ​​at each post-meal calibration time: First, the pre-meal calibration blood pressure value P0 is used as a reference point. The measured blood pressure values ​​at each post-meal calibration time are compared with the pre-meal calibration blood pressure value P0 to obtain the blood pressure difference at each time (i.e., the degree of deviation between the measured value and the pre-meal calibration blood pressure value P0). This step is called difference processing. Subsequently, the post-meal end point (t) is used as the reference point. l ,P h Using this as a correction benchmark, linear interpolation is used to compensate for the blood pressure value at each time point, calculating the baseline blood pressure value P that should exist at that time point. bt These calculated P bt This constitutes the baseline blood pressure set for the postprandial period.

[0053] Each value in this baseline blood pressure set represents the baseline blood pressure level that should exist at a given time, after excluding physiological activities and daily behaviors such as eating, exercise, excretion, and bathing, as well as interfering factors such as sympathetic nerve excitation and ambient temperature. Blood pressure values ​​obtained in this way accurately reflect the baseline blood pressure changes of the target individual during that period, providing a reliable baseline basis for subsequent calculations of net blood pressure reduction and, consequently, real-time inversion of blood glucose changes.

[0054] The calculation method for baseline blood pressure is further explained using subject B (male, 45 years old, with a history of hypertension) as an example. (Refer to...) Figure 5 As shown, Figure 5 This is a schematic diagram of an optional blood pressure and heart rate change curve for target subject B after lunch on the calibration day, provided in an embodiment of this application. The target subject's lunch started at 12:45 (let t0=0 for ease of calculation), and the blood pressure (i.e., systolic blood pressure) measured at this time was P0=128mmHg. The end time of this postprandial period was determined to be 150 minutes postprandial (i.e., 15:15, t...) using the postprandial blood glucose curve. l =150), the corresponding blood pressure P at this moment h =109 mmHg. Since P0 is greater than P... h This indicates that postprandial systolic blood pressure generally shows a downward trend. Therefore, taking 50 minutes after a meal (t=50) as an example, the baseline blood pressure at this time is calculated as follows: P b50 =P0–(50-t0)(P0-P h ) / (t l -t0)=128-(50-0)(128-109) / (150-0)=121.67(mmHg).

[0055] Figure 5 The measured curves of blood pressure and heart rate changes over time after lunch for the target subject B are shown. Figure 5 The lieutenant general's meal begins at point a (corresponding to time t0) and ends 150 minutes later at point b (corresponding to time t). l Connect the points (times) with a straight line (shown as a dashed line); this straight line represents the baseline blood pressure for that postprandial period. From Figure 5 It can be observed that the baseline shows a slight downward trend, indicating that after excluding various interfering factors, the target subject B's basal blood pressure after lunch slowly decreases over time.

[0056] Step S202: In the calibrated heart rate set, obtain multiple postprandial calibrated heart rate values ​​of the target object that change over time during the postprandial period, as well as the meal-prepared calibrated heart rate value at the start of the postprandial period. Based on the meal-prepared calibrated heart rate value and multiple postprandial calibrated heart rate values, determine the baseline heart rate set.

[0057] In a preferred embodiment, determining a baseline heart rate set based on pre-meal calibrated heart rate values ​​and multiple post-meal calibrated heart rate values ​​includes: performing difference processing on the multiple post-meal calibrated heart rate values ​​and pre-meal calibrated heart rate values ​​respectively to obtain heart rate difference values ​​corresponding to each post-meal calibrated heart rate value; performing compensation calculation on each post-meal calibrated heart rate value based on the heart rate difference values ​​corresponding to each post-meal calibrated blood pressure value to obtain multiple post-meal compensated heart rate values; and using the multiple post-meal compensated heart rate values ​​as a baseline blood pressure set.

[0058] When constructing the baseline heart rate dataset, it is first necessary to extract the postprandial calibrated heart rate values ​​of the target object at various time points within the postprandial period from the calibration dataset, as well as the pre-meal calibrated heart rate value at the start of the meal. For ease of calculation, the start time of the meal is denoted as t0, and the corresponding calibrated heart rate value is denoted as R0; the end point of the postprandial period (i.e., the moment when blood glucose drops to its lowest trough) is denoted as t l The calibrated heart rate value at that moment is denoted as R. l It should be noted that, to improve data reliability, if the non-invasive dynamic blood glucose monitoring system designed in this application is used, it can be used at t0 or t... l The heart rate is automatically measured multiple times before and after the specified time, and the average value is taken as R0 and R1 respectively. l .

[0059] Given (t0,R0) and (t l, R l After that, for any time t within the postprandial period, the baseline heart rate R... bt According to R0 and R l The magnitude relationships are calculated using linear interpolation formulas: If R0 is less than R l This indicates that the overall heart rate decreases after a meal, therefore R bt =R0–(t-t0)(R0-R h ) / (t l -t0); If R0 is greater than R l This indicates that the overall heart rate tends to increase after a meal, therefore R... bt =R0+(t-t0)(R h -R0) / (t l -t0); If R0 equals R l This indicates that there is no significant fluctuation in heart rate after a meal, then R bt =R0.

[0060] The heart rate data of the aforementioned target subject B after lunch will be used as an example. The target subject's heart rate R0 at the start of lunch (t0=0) was 71 beats / min, and 150 minutes after lunch (t... l =150) heart rate R l The frequency is 78 times per minute. Since R0 is less than R... l Heart rate tends to rise after a meal. Therefore, taking 50 minutes after a meal (t=50) as an example, the baseline heart rate at that time can be calculated as: R b50 =71+(50-0)×(78-71) / (150-0)≈73.33 times / minute.

[0061] The above calculation process essentially performs a compensation operation on the heart rate values ​​at each post-meal calibration time point: First, the pre-meal calibration heart rate value R0 is used as a reference point. The measured heart rate values ​​at each post-meal calibration time point are compared with the pre-meal calibration heart rate value R0 to obtain the heart rate difference at each time point (i.e., the degree of deviation between the measured value and the pre-meal calibration heart rate value R0). This step is called difference processing. Subsequently, the post-meal end point (t) is used as the reference point. l, R l Using this as a calibration benchmark, linear interpolation is used to compensate for the heart rate value at each moment, calculating the baseline heart rate value R that should exist at that moment. bt These calculated R values bt This constitutes the baseline heart rate set for the post-meal period.

[0062] Each value in this baseline heart rate set represents the baseline heart rate level that should exist at a given time, after excluding physiological activities and daily behaviors such as eating, exercise, excretion, and bathing, as well as interfering factors such as sympathetic nerve excitation and ambient temperature. Heart rate values ​​obtained in this way accurately reflect the baseline variation of the target subject's heart rate during that period, providing necessary baseline heart rate data for subsequent calculations of the net reduction in blood pressure (since heart rate is involved in the calculation of the net reduction in blood pressure).

[0063] Step S203: In the calibrated blood glucose set, obtain multiple postprandial calibrated blood glucose values ​​of the target object during the postprandial period and the pre-meal calibrated blood glucose value at the beginning of the postprandial period. Based on the pre-meal calibrated blood glucose value and multiple postprandial calibrated blood glucose values, determine the benchmark blood glucose set.

[0064] It should be noted that after a meal, the concentration of glucose in the blood rises rapidly due to the digestion and absorption of carbohydrates, and exercise accelerates the metabolism and clearance of glucose. Therefore, the postprandial baseline blood glucose level referred to in this application is a blood glucose value measured after excluding the influence of physiological activities and daily behaviors such as eating, excretion, exercise, bathing, as well as factors such as sympathetic nerve excitation and ambient temperature.

[0065] Furthermore, refer to Figure 6 As shown, Figure 6 This is an optional schematic diagram illustrating continuous blood glucose measurement for different target objects provided in the embodiments of this application. Figure 6 The target population included multiple individuals with type 2 diabetes, multiple individuals with type 1 diabetes, and multiple healthy individuals. 24-hour continuous glucose monitoring was performed using a glycated hemoglobin (HbA1c) system. Based on their HbA1c levels, the target population was divided into high, moderate, and low groups, as shown below. Figure 6 The three blood glucose curves shown are for changes over time (LGroup - blood glucose curves of healthy individuals, MGroup - blood glucose curves of individuals with type 1 diabetes, and HGroup - blood glucose curves of individuals with type 2 diabetes). From... Figure 6 As can be observed, all three curves show three distinct peaks and three troughs. The peaks correspond to the highest blood glucose levels reached after each of the three meals, and the troughs correspond to the lowest blood glucose levels reached after each of the three meals. In chronological order, breakfast typically begins around 7:00 AM. Figure 6 At point a), blood glucose levels drop to their lowest trough around 11:30 AM after breakfast, approximately 4 hours after the start of breakfast. This time point can be considered the end point of the post-breakfast period. Figure 6 Point b). Blood sugar rose again after 11:30, indicating that lunch was starting at this time. After lunch, blood sugar dropped to the second trough around 17:00. Figure 6 Point C is designated as the end point of the post-lunch period. Dinner begins at 5:00 PM, and post-dinner blood glucose rises before reaching its lowest point around midnight. Based on the aforementioned upper limit of the time range for synchronized changes in post-dinner blood glucose and blood pressure (not exceeding 5 hours), 6 hours after dinner, i.e., 11:00 PM, is designated as the end point of the post-dinner period. Figure 6 (d point). Due to the different times of each meal, there may be time intervals between the end of the post-breakfast period and the start of lunch, and between the end of the post-lunch period and the start of dinner. There is an even longer time interval between the end of the post-dinner period and the start of breakfast the next day. These time intervals are all defined as the pre-meal period in this application. Among them, the study published by Feng-fei Li et al. in Science Report on May 8, 2017, provides evidence of the changes in blood glucose fluctuations in the pre-meal and post-meal periods of the day in newly diagnosed type 2 diabetes patients with different glycated hemoglobin values.

[0066] from Figure 6 It can be observed that connecting the end point b (after breakfast), the end point c (after lunch), and the end point d (after dinner) sequentially with the adjacent meal start point (such as the breakfast start point a) using straight lines, the resulting broken line reflects the basic fluctuation trend of blood glucose without considering meal disturbances. This shows that, excluding the influence of meal factors, the fluctuation range of the target subject's blood glucose level throughout the day is actually relatively small. Based on this finding, this application defines the line formed by these connection points as the baseline blood glucose line, and the blood glucose value corresponding to each time point on this baseline blood glucose line is the baseline blood glucose value.

[0067] It should be noted that, Figure 6The use of a straight line in the diagram is merely a simplified, exemplary representation intended to intuitively illustrate the concept of baseline blood glucose. In practical applications, the baseline blood glucose line can also be generated using curve fitting, interpolation, or other mathematical methods, depending on individual differences and physiological characteristics, and is not limited to a straight line. Any curve that reflects the basal blood glucose variation after eliminating mealtime interference should be considered an equivalent implementation of this application.

[0068] In a preferred embodiment, a baseline blood glucose set is determined based on the pre-meal calibrated blood glucose value and multiple post-meal calibrated blood glucose values. Specifically, this includes the following steps: performing difference processing on the multiple post-meal calibrated blood glucose values ​​and the pre-meal calibrated blood glucose values ​​respectively to obtain a first blood glucose difference value corresponding to each post-meal calibrated blood glucose value; performing compensation calculation on each post-meal calibrated blood glucose value based on the first blood glucose difference value corresponding to each post-meal calibrated blood glucose value to obtain multiple post-meal compensated blood glucose values; and using the multiple post-meal compensated blood glucose values ​​as the baseline blood glucose set.

[0069] Specifically, when constructing a baseline blood glucose set, it is first necessary to extract the postprandial calibrated blood glucose values ​​of the target object at various time points within the postprandial period from the calibration data, as well as the pre-meal calibrated blood glucose value at the start of the meal. Using the pre-meal calibrated blood glucose value as a benchmark reference point, the difference between each postprandial calibrated blood glucose value and this benchmark is calculated. This difference reflects the amount of blood glucose change caused by the combined effects of eating and other factors; this application refers to this difference as the first blood glucose difference. However, the change in postprandial measured blood glucose values ​​is the result of multiple factors, including physiological blood glucose elevation caused by eating itself, as well as the influence of interfering factors such as exercise, excretion, emotional fluctuations, and ambient temperature. To obtain a blood glucose change pattern that reflects the resting state solely determined by basal metabolism, it is necessary to perform compensation calculations on each postprandial calibrated blood glucose value to eliminate the aforementioned interfering factors. The compensation calculation process is as follows: Starting with the blood glucose level at the beginning of the meal and ending with the blood glucose level at the end of the postprandial period (i.e., the moment when blood glucose drops to its lowest point), a reference line reflecting the basal trend is constructed. For any time t within the postprandial period, the baseline blood glucose value U... bt It can be determined by linear interpolation.

[0070] In one embodiment, the compensation calculation process described above is as follows: First, determine the start time t0 of the meal (t0 can be set to 0 for ease of calculation) and the corresponding calibrated blood glucose value U0. Then, by comparing the blood glucose values ​​at multiple specific time points 120 minutes after the meal, find the lowest blood glucose value U0. l and its corresponding time point t l , t lThis is the end point of the postprandial period. It's worth noting that if the trough in the blood glucose curve is not obvious, the end point can be chosen within the range of 120 to 330 minutes postprandial, depending on the target individual's metabolic characteristics: 120 to 130 minutes postprandial is suitable for individuals with normal blood glucose metabolism, while 180 to 240 minutes is suitable for diabetic patients with delayed insulin secretion peaks. If t l If it exceeds 330 minutes, then take t. l =330 is the end point, but this end point should not be later than the start time of the next meal.

[0071] Given (t0, U0) and (t l U l After that, for any time t within the postprandial period, the baseline blood glucose value U bt According to U0 and U l Calculate the size relationship separately: If U0 is greater than U l This indicates that postprandial blood glucose generally shows a downward trend, then U bt =U0–(t-t0)(U0-U l ) / (t l -t0); If U0 is less than U l This indicates that postprandial blood glucose generally shows an upward trend, then U bt =U0+(t-t0)(U l -U0) / (t l -t0): If U0=U l This indicates that there is no significant fluctuation in postprandial blood glucose, then U bt =U0.

[0072] Through the above compensation calculation, the measured blood glucose value at each postprandial calibration time is converted into the corresponding compensated blood glucose value U. bt These U bt Together, these constitute the baseline blood glucose set for the postprandial period. Each value in this set represents the baseline blood glucose level that should exist at the corresponding time, after excluding drastic fluctuations caused by the meal itself and various internal and external interference factors. This baseline blood glucose set provides a reliable baseline basis for subsequent calculations of net blood glucose increases and, consequently, real-time inversion of blood glucose changes.

[0073] The calculation method for baseline blood glucose is further explained using subject B (male, 45 years old, with a history of hypertension) as an example. (Refer to...) Figure 7 As shown, Figure 7 This is a schematic diagram of an optional blood glucose change curve of the target object B after lunch on the calibration day, provided in the embodiments of this application. Figure 7Point b corresponds to the start time of lunch (12:45, let t0=0 for ease of calculation), at which time the blood glucose level U0=5.4mmol / L. By observing the blood glucose levels at various time points after the meal, it can be found that the blood glucose level is at its lowest point 150 minutes after the meal. Figure 7 Midpoint C (15:15, t) l =150), the blood glucose level U at that moment l =7.8 mmol / L. Since U0 is less than U... l This indicates that postprandial blood glucose generally shows an upward trend. Therefore, taking 13:00 (i.e., t=15 minutes) as an example, the baseline blood glucose value at this time is calculated as follows: U b15 =5.4+(15-0)(7.8-5.4) / (150-0)=5.64(mmol / L).

[0074] like Figure 7 As shown by the dashed line, connecting points b and c with a straight line yields the baseline blood glucose level for the post-lunch period. This baseline is not a horizontal line but rather exhibits an upward trend, indicating that the target individual's blood glucose level had not yet returned to fasting levels two hours after the meal. This calculation example visually illustrates the process of constructing the baseline blood glucose set: obtaining the baseline blood glucose values ​​at various times within the post-meal period through linear interpolation between the meal start point and the post-meal trough.

[0075] It should be noted that in steps S201 to S203, by establishing baseline blood pressure sets, baseline heart rate sets, and baseline blood glucose sets respectively, individualized physiological benchmarks can be provided for subsequent blood glucose measurement. Specifically, based on the calibration data at the start of the meal and the end of the postprandial period, a linear interpolation method is used to determine the baseline blood pressure, baseline heart rate, and baseline blood glucose at each time point within the postprandial period. This effectively eliminates instantaneous fluctuations caused by interference factors such as eating, exercise, excretion, emotional fluctuations, and ambient temperature, ensuring that the obtained baseline value sequence truly reflects the inherent patterns of changes in various physiological indicators of the target subject over time in a resting state. This processing not only solves the problem of data fluctuations caused by daily activities in conventional measurements but also provides an accurate baseline reference for calculating the net decrease in blood pressure and the net increase in blood glucose, thereby significantly improving the calculation accuracy of blood glucose inversion based on blood pressure and heart rate, and laying a reliable data foundation for achieving high-precision non-invasive dynamic blood glucose monitoring.

[0076] Reference Figure 8 As shown, Figure 8 This is an optional schematic diagram of determining the conversion relationship between net blood pressure and net blood glucose provided in the embodiments of this application. The method for determining the conversion relationship between net blood pressure and net blood glucose may include, but is not limited to, steps S301 to S303.

[0077] Step S301: Based on multiple postprandial calibrated blood pressure values, multiple postprandial calibrated heart rate values, baseline blood pressure set, and baseline heart rate set, determine the net reduction value of multiple postprandial blood pressure values ​​over time for the target subject during the postprandial period.

[0078] When determining the net reduction in postprandial blood pressure, it is necessary to calculate based on the measured blood pressure and heart rate values ​​at each time point after the meal in the calibration dataset, as well as the baseline blood pressure and heart rate sets calculated using the aforementioned method, according to a preset mathematical relationship. This application uses, but is not limited to, the following formula to calculate the net reduction in blood pressure d at any time t between the start of the meal and the postprandial blood glucose trough. Pt (Systolic or diastolic blood pressure): d Pt =P bt -P t -λ(R bt –R t ).

[0079] Among them, P bt P represents the baseline blood pressure value at time t, derived from the baseline blood pressure set; t To calibrate the blood pressure values ​​measured at time t in the dataset; R bt R represents the baseline heart rate at time t, derived from the baseline heart rate set; t λ is the measured heart rate value at time t; λ is an empirical coefficient that is related to the physiological and pathological characteristics of the target object. Under normal circumstances, the value of λ ranges from 0 to 1.

[0080] The physical meaning of the above formula is as follows: First, through the baseline blood pressure value P bt Compared with the measured blood pressure value P t The difference (P) bt –P t This reflects the actual decrease in blood pressure relative to baseline, minus the term λ (P) corrected for heart rate changes. bt –P t The heart rate correction term is introduced because heart rate changes have a compensatory regulatory effect on blood pressure; when the measured heart rate R... t Above baseline heart rate R bt When the heart rate increases due to sympathetic nerve excitation, this effect on blood pressure needs to be removed from the measured blood pressure decrease to obtain the net blood pressure decrease caused solely by eating.

[0081] The following is a specific example using the post-lunch blood pressure data of the aforementioned target subject B. According to the calibrated data of target subject B, the blood pressure P0 at the start of the meal was 128 mmHg, and the blood pressure P150 minutes after the meal was... h =109 mmHg, t l =150 minutes; heart rate R0 = 71 beats / min at the start of the meal, heart rate R150 minutes after the meall =78 beats / min. The baseline blood pressure P calculated using the aforementioned method bt and baseline heart rate R bt Substitution formula d Pt =P bt -P t -λ(R bt –R t From the given information, we can obtain: dPt = (128 - 0.1267t) - Pt - λ(71 + 0.047t - Rt). In one embodiment, using the empirical coefficient λ = 0.85, the net decrease in systolic blood pressure at each time point during the postprandial period can be calculated. Plotting the calculation results at each time point into a curve yields the following result: Figure 9 As shown, Figure 9 This is an optional schematic curve illustrating the change in the net decrease in blood pressure after lunch over time for the target object B, as provided in this application embodiment. The curve visually demonstrates the dynamic change in the magnitude of blood pressure decrease caused solely by eating, after eliminating the influence of heart rate compensation, providing fundamental data for establishing the conversion relationship between the net decrease in blood pressure and the net increase in blood glucose.

[0082] Step S302: Based on multiple postprandial calibrated blood glucose values ​​and a set of baseline blood glucose, determine the net increase in postprandial blood glucose over time for the target subject during the postprandial period.

[0083] When determining the net increase in postprandial blood glucose, it is necessary to calculate based on the measured blood glucose values ​​at each time point after the meal in the calibration dataset, and the baseline blood glucose set calculated by the aforementioned method, according to a preset mathematical relationship. This application uses (but is not limited to) the following formula to calculate the net increase in blood glucose dU at any time t between the start of the meal and the postprandial blood glucose trough. t :dU t =U t –U b .

[0084] Among them, U t To calibrate the blood glucose levels measured at time t after a meal in the dataset; U bt Let U be the baseline blood glucose value at time t, derived from the baseline blood glucose set. The physical meaning of this formula is: Based on the measured blood glucose value U... t Compared with the baseline blood glucose value U bt The difference directly yields the net increase in blood glucose caused by the meal itself. This difference automatically eliminates the background level of blood glucose under basal metabolic conditions, thus accurately reflecting the contribution of food intake to blood glucose levels.

[0085] Let's take the post-lunch data of target subject B as an example for specific explanation. According to the calibrated data of this target subject, the blood glucose U0 at the beginning of lunch was 5.4 mmol / L, and the blood glucose U0 at the end of 150 minutes post-lunch was... l=7.8mmol / L, t l =150 minutes. For the time 60 minutes postprandial (t=60), the measured blood glucose value U... 60 The baseline blood glucose level was 9 mmol / L, and the baseline blood glucose value U at that moment was calculated using the aforementioned method. b60 for: U b60 =5.4+(60-0)×(7.8-5.4) / (150-0)=6.36mmol / L; Will U b60、 Substitute U0 into dU t =U t –U bt This will give you the net increase in blood glucose 60 minutes after a meal: dU 60 =9-6.36=2.64mmol / L.

[0086] By calculating the net increase in blood glucose at each time point within the postprandial period using the method described above, the following results can be obtained: Figure 10 , Figure 10 This is a schematic diagram of an optional curve showing the net increase in blood glucose after lunch over time for the target object B, as provided in this application embodiment. The curve visually illustrates the dynamic change in the net increase in blood glucose caused by eating, providing basic data for establishing the conversion relationship between the net decrease in blood pressure and the net increase in blood glucose.

[0087] Step S303: Based on the time sequence of the postprandial period, fit multiple net postprandial blood pressure reduction values ​​to obtain the conversion relationship between net blood pressure value and net blood glucose value.

[0088] By comparison Figure 9 and Figure 10 A significant positive correlation was observed between the net decrease in postprandial blood pressure and the net increase in postprandial blood glucose. Based on this finding, postprandial blood glucose levels can be estimated using changes in postprandial blood pressure and heart rate. The blood glucose value Ut at time t after a meal can be decomposed into the sum of two parts, namely: U t =U bt +dU t .

[0089] U bt The baseline blood glucose value at time t can be determined by selecting the start time t0 and end time t of the same meal based on the calibration data of the target group. l The corresponding calibrated blood glucose values ​​U0 and U l Calculate and obtain U bt And dU t To determine the net increase in postprandial blood glucose at time t, it is necessary to establish a correlation between the net increase and decrease in blood pressure (dP). tThe conversion relationship between them is used to determine this.

[0090] To obtain this conversion relationship, it is necessary to perform mathematical fitting between multiple net postprandial blood pressure reduction values ​​and multiple net postprandial blood glucose increase values ​​based on the time series of the postprandial period. Specifically, during the calibration process, several specific time points are selected within the postprandial period (e.g., sampling at certain time intervals), and the net blood pressure reduction value dP at each time point is calculated according to the aforementioned method. t And the net increase in blood glucose (dU) at each time point. t Thus, a set of (dP) is obtained. t dU t Data pairs consisting of ) can be used to establish dU using regression analysis. t With dP t The functional relationship between them.

[0091] This application provides multiple fitting methods for the conversion relationship between net blood pressure and net blood glucose, as shown in the following examples (but not limited to these): 1. Linear relationship fitting.

[0092] Assume dU t With dP t The two equations satisfy a linear relationship and are fitted using a univariate linear regression equation: dU t =a+bdP t .

[0093] Where a is the intercept and b is the regression coefficient. Using the n sets of data obtained from calibration (dP) t dU t ), calculate a and b using the least squares method as follows: b=[∑(dU t dP t )–(∑dP t ∑dU t ) / n] / [∑(dP t )²–(∑dP t )² / n]; a=(∑dU t ) / n–b(∑dP t ) / n.

[0094] Taking the calibration process of target subject B within 150 minutes after lunch as an example, a total of 12 specific time points were set (n=12). Referring to Table 1, Table 1 shows the net increase in blood glucose (dU) of target subject B at the 12 specific post-meal time points provided in an embodiment of this application. t and blood pressure reduction net dP t The data table. Where λ = 0.85.

[0095] Table 1

[0096] 12 dU t Values ​​and 12 dP t Substituting these values ​​into the formulas for calculating a and b above, we obtain a = 0.2362 and b = 0.3204. Thus, we obtain the formula for calculating the net increase in blood glucose after lunch for the target subject B (i.e., the conversion relationship between net blood pressure and net blood glucose): dU t =0.2362+0.3204dP t .

[0097] 2. Nonlinear relationship fitting.

[0098] If the accuracy of linear fitting is insufficient, a nonlinear function can be used. For example, assume dU t It is dP t Quadratic function: dU t =α(dP t )²+βdP t +γ.

[0099] In one embodiment, taking target object B as an example, dU is selected at three specific time points (e.g., 13:00, 13:30, and 15:00). t Value and corresponding dP t Substituting the values ​​into the above quadratic function yields a system of three linear equations. Solving this system simultaneously gives the empirical coefficients α, β, and γ. Solving for α, we get α = 0.0065, β = 0.2027, and γ = 0.6474, thus obtaining the quadratic function: dU t =0.0065(dP t )²+0.2027dP t +0.6474(20), and thus obtain the conversion relationship between net blood pressure and net blood glucose. It should be noted that, referring to Table 2 and Figure 11 Table 2 is a data table provided in one embodiment of this application, showing the blood glucose and actual blood glucose (finger-tip capillary whole blood glucose) of target object B at specific time points after lunch calculated according to the above quadratic function, as well as the relative error of the calculation results. Figure 11 This is an embodiment of the present application providing an optional curve comparison diagram between the blood glucose level of target object B at specific time points after lunch calculated based on a quadratic function and the actual blood glucose level.

[0100]

[0101] According to Table 2 and Figure 11 As shown, the actual blood glucose and the calculated blood glucose curves are in good agreement, indicating that there is a relatively close non-linear positive correlation between the net increase in postprandial blood glucose and the net decrease in postprandial blood pressure.

[0102] It should be noted that the linear and quadratic functions mentioned above are merely examples. In practical applications, other function forms (such as exponential, logarithmic, and power functions) can be selected for fitting based on data characteristics, and the optimal model can be determined through goodness-of-fit testing. The number of specific time points used in the fitting process should be sufficiently large to ensure statistical significance, and the selection of time points should cover the entire postprandial period. The conversion relationship between net blood pressure and net blood glucose established in this way fully reflects the differences in individual physiological characteristics, providing an accurate mathematical model for subsequent non-invasive blood glucose estimation based on real-time blood pressure and heart rate data.

[0103] It should be noted that in steps S301 to S302, firstly, based on multiple calibrated blood pressure and heart rate values ​​during the postprandial period, as well as the established baseline blood pressure and heart rate sets, multiple net values ​​of postprandial blood pressure reduction over time are calculated. This process effectively isolates the interference of daily activities and environmental factors on blood pressure, extracting the blood pressure change signal solely caused by eating. Simultaneously, based on multiple calibrated blood glucose values ​​and the baseline blood glucose set, multiple net values ​​of postprandial blood glucose increase over time are calculated, similarly eliminating the contribution of basal metabolism to blood glucose and obtaining a pure postprandial blood glucose increment. Based on this, using the temporal information of the postprandial period, mathematical fitting is performed on the multiple net values ​​of blood pressure reduction and multiple net values ​​of blood glucose increase, establishing a functional conversion relationship between the two. This conversion relationship fully reflects individual physiological characteristics, allowing subsequent calculations of blood glucose increase based solely on real-time blood pressure and heart rate measurements to deduce the corresponding net value of blood glucose, which, combined with the baseline blood glucose value, yields accurate real-time blood glucose. This series of processes not only significantly improves the accuracy of personalized blood glucose detection but also provides reliable core algorithmic support for achieving completely non-invasive, continuous, and dynamic blood glucose monitoring.

[0104] Furthermore, the above-mentioned non-invasive blood glucose measurement method may include, but is not limited to, steps S401 to S403: Step S401: Determine the first calibrated blood glucose value at the target time from the calibration data.

[0105] Step S402: If the target time is within the first pre-meal period, the theoretical blood glucose value is determined based on the real-time blood pressure and real-time heart rate values ​​in the pre-established blood pressure, heart rate and blood glucose mapping relationship. The second calibrated blood glucose value at the start time of the first pre-meal period in the calibration data, and the theoretical blood glucose value are corrected based on the first calibrated blood glucose value and the second calibrated blood glucose value to obtain the real-time blood glucose value at the target time.

[0106] The first pre-meal period is the time from the trough of blood glucose after the previous evening meal to the start of the morning meal.

[0107] from Figure 6The three blood glucose curves shown illustrate that, for both diabetic patients and healthy individuals, blood glucose levels gradually decrease from the trough after dinner, reaching their lowest point around 4 AM, then slowly rise before rapidly increasing again after breakfast. This dynamic overnight blood glucose pattern is consistent with... Figure 12 Highly similar Figure 12 This is a schematic diagram illustrating the nighttime blood pressure and heart rate variations in normal individuals, provided in an embodiment of this application. (Comparison) Figure 12 and Figure 6 It can be observed that during this period, the blood glucose curve almost perfectly matches the trends of blood pressure and heart rate curves, which is determined by the body's 24-hour physiological rhythm. When the target subject suddenly wakes up from sleep, blood pressure, heart rate, and blood glucose will rise synchronously under the influence of relevant hormones. If the target subject has poor sleep quality, difficulty falling into deep sleep, or difficulty falling asleep, blood pressure, heart rate, and blood glucose will continue to fluctuate and cannot reach their lowest values. Therefore, it can be concluded that during this period, there is a positive correlation between blood pressure or heart rate and blood glucose.

[0108] Based on the above physiological characteristics, this application adopts the following method for blood glucose measurement during the first pre-meal period: First, during the calibration process, the target subject must wear the device of this application in a resting state to simultaneously detect blood pressure and heart rate during the period, while using a continuous glucose monitoring system to detect blood glucose values ​​during the period, and simultaneously record parameters such as ambient temperature, skin resistance and skin temperature to obtain the calibration dataset for that period.

[0109] Secondly, based on the blood pressure values ​​P at several measurement time points in the calibration dataset... t (Including systolic or diastolic blood pressure), heart rate value R t and blood glucose level U t Establish the mapping relationship between blood pressure, heart rate, and blood glucose at time t within this time period, i.e.: U t = f(P t ,R t This mapping relationship can be established using multiple linear regression, neural networks, or other function fitting methods to reflect the positive correlation between blood pressure, heart rate, and blood glucose during this period.

[0110] In the actual measurement process, when the target subject is in the first pre-meal period, the blood pressure value P at the current target time t is collected in real time. t Heart rate value R tSubstituting the above mapping relationship, the corresponding theoretical blood glucose value can be calculated. However, the theoretical blood glucose value only reflects the general pattern established based on the calibration data, and the actual blood glucose level is also affected by the previous day's dinner intake. For example, if too much carbohydrate was consumed at dinner the previous day, the overall nighttime blood glucose level will be higher; if too little was consumed at dinner, nighttime hypoglycemia may occur. Therefore, the theoretical blood glucose value needs to be corrected. The specific correction method is as follows: Obtain the second calibration blood glucose value corresponding to the starting time of the first pre-meal period (i.e., the trough point of blood glucose after dinner) from the calibration data. On the day of actual measurement, obtain the first calibration blood glucose value at the target time from the calibration data. Finally, correct the theoretical blood glucose value based on the first and second calibration blood glucose values ​​to obtain the corrected real-time blood glucose value. Through the above correction, the influence of the difference in the previous day's dinner intake on nighttime blood glucose can be effectively eliminated, making the calculation result closer to the true value. Thus, the determination of the real-time blood glucose value at the target time within the first pre-meal period is completed.

[0111] Step S403: Alternatively, if the target time is within the second pre-meal period, obtain the third calibrated blood glucose value at the start time of the second pre-meal period from the calibration data, and correct the first calibrated blood glucose value according to the third calibrated blood glucose value to obtain the real-time blood glucose value at the target time.

[0112] The second pre-meal period includes two scenarios: one is the period from the trough of blood glucose after breakfast to the start of lunch; the other is the period from the trough of blood glucose after lunch to the start of dinner. These two periods share similar physiological characteristics: they occur between two meals, and blood glucose levels typically show a slow trend of change, with the subsequent effects of the previous meal gradually weakening.

[0113] To facilitate understanding, we will use the period from the post-lunch blood glucose trough to before dinner as an example. First, during the calibration process, blood glucose values ​​measured at all time points between the end of the post-lunch period (i.e., the blood glucose trough) and the time of dinner are extracted from the calibration dataset. Since sampling points may not be continuous in actual measurements, a linear interpolation method is used to establish the calibrated blood glucose value U at any time t within this period. to The calculation formula for U is: to = U co + (U do -U co (tt) c ) / (t d -t c ).

[0114] Where t, t c t d All times are in minutes, tc is the end time of the lunch break, U cot represents the blood glucose value in the calibration dataset at that moment; d U is the start time for dinner. do The value of blood glucose in the calibration dataset at that moment is t; t is the value of blood glucose at that moment. c With t d At any time between. Through the above U to The calculation formula can calculate the calibrated blood glucose value (U) for each minute within that time period. to This creates a dataset that calibrates how blood glucose changes over time.

[0115] In actual testing, the amount and type of food consumed by the target subject at lunch on the day of the test may differ from those on the calibration day, leading to variations in the end point t of the lunch period. c The actual blood glucose value U at the location c Compared with the calibrated blood glucose value U co The difference is not the same. This difference persists throughout the period from after lunch to before dinner. Therefore, it is necessary to utilize U c The calibrated blood glucose dataset is corrected to obtain the real-time blood glucose value for that specific time period of the day. The correction method is as follows: using U... c with U co The difference is used as a correction factor, which is then added to the blood glucose values ​​at each time point in the calibrated blood glucose dataset to obtain the real-time blood glucose value U at any time t within that time period of the day. t :U t = U to + (Uc- Uco). Where U to Through the above U to The calculation formula can be used to obtain the results, or they can be directly queried from the calibration dataset.

[0116] Let's take target subject B as an example. On a certain day, the lunch break ended at 15:15, and dinner started at 19:15. In the calibration dataset, the blood glucose level at 15:15 was 6.9 mmol / L. (This is based on the above U...) to The calculation formula yielded a standard blood glucose level of 5.6 mmol / L at 18:15. However, due to a larger lunch intake than the standard day, the actual blood glucose level at 15:15 was calculated to be 7.8 mmol / L, which is 0.9 mmol / L higher than the standard blood glucose level at that time. According to U... t = U to After correcting with + (Uc - Uco), the real-time blood glucose level at 18:15 should be 5.6 + 0.9 = 6.5 mmol / L.

[0117] The above method is also applicable to blood glucose measurement from the post-breakfast trough to the pre-lunch period; simply input U... to =U co +(U do -Uco (tt) c ) / (t d -t c ) and U t =U to The parameters in +(Uc-Uco) can be replaced with the data for the end point of the breakfast period and the start point of the lunch period.

[0118] In this way, only the actual blood glucose value U at the end of the current postprandial period needs to be obtained. c (This value can be obtained through the postprandial blood glucose measurement method described above), which can correct the blood glucose dynamics of the entire pre-meal period, thereby obtaining the real-time blood glucose value at any time during this period, without the need for complex blood pressure and heart rate measurements and conversions.

[0119] Reference Figure 13 As shown, Figure 13 This is an optional schematic diagram of determining the mapping relationship between blood pressure, heart rate and blood glucose provided in the embodiments of this application. The method for determining the mapping relationship between blood pressure, heart rate and blood glucose may include, but is not limited to, steps S501 to S504.

[0120] Step S501: In the calibrated blood pressure set, obtain multiple pre-meal calibrated blood pressure values ​​of the target subject during the first pre-meal period. Step S502: In the calibrated heart rate set, obtain multiple pre-meal calibrated heart rate values ​​of the target object that change over time during the first pre-meal period; Step S503: In the calibrated blood glucose set, obtain multiple pre-meal calibrated blood glucose values ​​of the target object that change over time during the first pre-meal period; Step S504: Based on the time sequence of the first pre-meal period, fit multiple pre-meal calibrated blood pressure values, multiple pre-meal calibrated heart rate values ​​and multiple post-meal blood pressure reduction net values ​​to obtain the mapping relationship between blood pressure, heart rate and blood glucose.

[0121] When constructing the mapping relationship between blood pressure, heart rate, and blood glucose during the first pre-meal period, it is first necessary to extract relevant parameters from the calibration dataset for this period. Specifically, multiple pre-meal calibrated blood pressure values ​​of the target object changing over time during the first pre-meal period are obtained from the calibrated blood pressure set, denoted as P. t ; Obtain multiple pre-meal calibrated heart rate values ​​that change over time within this period from the calibrated heart rate set, denoted as R. t ; Obtain multiple pre-meal calibrated blood glucose values ​​that change over time within this period from the calibrated blood glucose set, denoted as U. t All the above parameters must be measured synchronously at the same time point to ensure data consistency and comparability. Then, based on the time sequence of the first pre-meal period, these calibrated data are mathematically fitted to establish a mapping relationship between blood pressure, heart rate, and blood glucose that reflects the physiological characteristics of this period, i.e., a relationship of the form U.t =f(P t ,R t The functional expression of ) can be used. The specific form of this mapping relationship can be flexibly selected according to the data characteristics. For example, the nonlinear form given in equation (19) can be used to construct a polynomial relationship between blood pressure and blood sugar: U t =C1(P t ) n +C2P t n-1 +C3P t n-2 +…+C n .

[0122] U t Let P be the blood glucose level at time t. t The blood pressure value (systolic or diastolic) at time t, C1 to C n These are the empirical coefficients to be determined. By selecting blood pressure and blood glucose values ​​at n calibration times within the first pre-meal period, substituting them into the above polynomial relationship to establish a system of n linear equations, and solving the system simultaneously, the values ​​of each empirical coefficient can be determined, thereby obtaining the blood pressure, heart rate, and blood glucose mapping relationship applicable to the target individual.

[0123] It should be noted that the power polynomial shown in the above polynomial relationship is only one optional form. In practical applications, linear functions, exponential functions, logarithmic functions or other forms of regression models can also be used to fit the data according to the data distribution characteristics. As long as it can accurately describe the intrinsic relationship between blood pressure, heart rate and blood sugar in this period, it falls within the protection scope of this application.

[0124] Reference Figure 14 As shown, Figure 14 This is an optional schematic diagram of obtaining a real-time blood glucose value from a corrected theoretical blood glucose value provided in the embodiments of this application. The above-mentioned non-invasive blood glucose measurement method may include, but is not limited to, steps S601 to S602.

[0125] Step S601: Calculate the second blood glucose difference between the first calibrated blood glucose value and the second calibrated blood glucose value.

[0126] Step S602: Based on the second blood glucose difference, correct the theoretical blood glucose value to obtain the real-time blood glucose value.

[0127] In the blood glucose measurement during the pre-meal period (i.e., from the trough of blood glucose after dinner to the time before breakfast the next day), this application employs a linear decay correction method based on the starting point deviation. Specifically, the second calibrated blood glucose value U corresponding to the starting time of the first pre-meal period (i.e., the trough of blood glucose after dinner) is first obtained from the calibration data. ao And obtain the measured blood glucose value U at this starting moment. aThe difference between the two is calculated as the second blood glucose difference. U, that is U=U a -U ao This difference reflects the overall deviation of the actual blood glucose level that evening from the calibrated baseline level.

[0128] Considering that the impact of this offset on subsequent moments gradually weakens over time until it decays to zero at the start of the next meal (breakfast), this application introduces a time decay factor to correct for the theoretical blood glucose value. Let t be the time of the post-dinner blood glucose trough. a The start time for breakfast the next day is t. b Then the total duration of the first pre-meal period is L=t b -t a (in minutes). For t a For any time t between t_b and t_t, define l = tt a That is, the length of time from the start of the first pre-meal period to that moment.

[0129] Based on the above parameters, the theoretical blood glucose value Ut' is corrected according to the following formula: U t =U t '+ U(Ll) / L.

[0130] Among them, U t 'U represents the theoretical blood glucose value at time t, calculated using the mapping relationship between blood pressure, heart rate, and blood glucose;' t This is the corrected real-time blood glucose value at time t; U represents the second blood glucose difference at the starting point; (Ll) / L is the linear decay factor, whose value gradually decreases from 1 at the starting point (where l=0, and the correction is at its maximum) to 0 at breakfast time (where l=L, and the correction is zero). Through the above correction, the measured deviation information at the starting point is utilized, and the change law of physiological parameters gradually returning to the baseline rhythm over time is followed, making the calculated real-time blood glucose value more accurate and reliable.

[0131] It's important to note that skin resistance is a sensitive physiological parameter reflecting changes in sympathetic nerve excitability. It indirectly assesses the activity of the sympathetic nervous system by measuring the degree of sweat secretion. When the body is stimulated by stress, the sympathetic nervous system is activated, mobilizing various emergency functions to put the body into a state of high readiness. This manifests as an increased pulse, a rapid rise in blood pressure, and a slight increase in blood glucose levels to meet the body's blood and glucose supply needs. During this process, sweating leads to a decrease in skin resistance, and skin temperature also changes accordingly. When the stress is relieved, the parasympathetic nervous system regains its dominant position, and pulse, blood pressure, and blood glucose gradually decrease, with skin resistance and skin temperature returning to normal levels. Furthermore, environmental temperatures exceeding the normal range, bathing, and exercise can also cause abnormal fluctuations in blood pressure and heart rate.

[0132] The aforementioned method for calculating blood glucose based on changes in blood pressure and heart rate before and after meals is suitable for subjects in a calm state dominated by the parasympathetic nervous system. When sympathetic nerve excitability is increased, blood pressure and heart rate are significantly elevated, or other interfering factors are present, this method may produce large errors in blood glucose calculation. Therefore, the following measures need to be taken to identify and eliminate interfering states: If at any given time, if at least one of the following conditions is met, the blood pressure and heart rate measurements at that time are determined to be affected by increased sympathetic nerve excitability, changes in ambient temperature, or the target subject's lifestyle behavior. In this case, blood glucose measurement should be paused and resumed only after all parameters have returned to normal: (1) The skin resistance is significantly reduced, and the difference between the skin resistance value at the same time in the calibration dataset exceeds the preset threshold (e.g., the relative error is less than -25%). (2) The ambient temperature exceeds the set range (e.g., above 35°C or below 5°C); (3) The skin temperature differs from the skin temperature at the same time in the calibration dataset by more than a preset threshold (e.g., the relative error exceeds ±10%). (4) Less than 30 minutes have passed since the last bath or shower; (5) Less than 30 minutes have passed since the last exercise or physical labor.

[0133] Furthermore, sudden hypoglycemia, especially nocturnal hypoglycemia, can be life-threatening. Utilizing the multi-sensor fusion advantage of the device described in this application, timely hypoglycemia alarms can be provided. If the following conditions are met simultaneously at any given time, it is determined that the target individual may be in a hypoglycemic state, and it is recommended to immediately verify this using an invasive blood glucose meter. Upon confirmation, appropriate countermeasures should be taken promptly: (1) The skin resistance is significantly reduced, and the difference between the skin resistance value at the same time in the calibration dataset exceeds the preset threshold (e.g., the relative error is less than -25%). (2) The ambient temperature is within the normal range (e.g., not higher than 35℃ and not lower than 5℃); (3) The skin temperature is significantly reduced, and the difference between the skin temperature at the same time in the calibration dataset exceeds the preset threshold (e.g., the relative error is less than -10%). (4) The heart rate is significantly elevated and the difference between the heart rate value at the same time in the calibration dataset exceeds the preset threshold (e.g., the relative error is higher than 15%). (5) Blood pressure is significantly elevated and the difference exceeds the preset threshold value (e.g., relative error is higher than 10%) compared with the blood pressure value at the same time in the calibration dataset.

[0134] To verify the measurement accuracy of the method in this application, actual comparative tests were conducted on target subjects with different types of diabetes.

[0135] Subject C was a patient with type 2 diabetes. Continuous blood glucose monitoring was performed by comparing the finger-prick blood glucose level with a calculated value every half hour. This subject's breakfast and lunch times were 8:00 AM and 1:00 PM, respectively. Figure 15 The results shown indicate that Figure 15 This is an optional comparison diagram between the finger-prick blood glucose value and the calculated blood glucose value of the target object C provided in this application embodiment. The mean absolute value of the relative error (MARD) between the finger-prick blood glucose value and the calculated blood glucose value of the target object C is 9.7%, which is close to the MARD value measured by existing invasive devices. This result confirms that the method of this application has considerable accuracy and operability.

[0136] Target subject D was a patient with type 2 prediabetes. Blood glucose levels were measured and calculated using a continuous glucose monitoring system. Breakfast and lunch times for this subject were 12:10 and 20:30, respectively. Figure 16 The results shown indicate that Figure 16 This is an optional comparison diagram between the measured value and the calculated blood glucose value provided by the continuous glucose monitoring system in this application embodiment. The mean absolute value (MARD) of the relative error between the measured value (CGM value) and the calculated value of the continuous glucose monitoring system is 8.6%, which further verifies the applicability and accuracy of the method in different disease stages.

[0137] Reference Figure 17 As shown, Figure 17 This is an optional schematic diagram of a non-invasive blood glucose measurement system provided in an embodiment of this application. The non-invasive blood glucose measurement system 1700 includes: The watch 1701 is used to measure and calibrate a target object to determine calibration data, as well as to measure real-time data of the target object at a target time, including real-time blood pressure and real-time heart rate values.

[0138] Smart terminal 1702, which wirelessly connects with watch 1701; Cloud data center 1703 is wirelessly connected to smart terminal 1702 and wristwatch 1701 respectively. The smart terminal 1702 is used to receive real-time data from the wristwatch 1701 and upload the real-time data to the cloud data center 1703; The cloud data center 1703 is configured to receive real-time data measured at the target time. If the target time is within the post-meal period, it determines the baseline blood pressure set, baseline heart rate set, and baseline blood glucose set of the target object during the post-meal period based on the calibration data. It then determines the baseline blood pressure value, baseline heart rate value, and baseline blood glucose set of the target time from the baseline blood pressure value set, baseline heart rate set, and baseline blood glucose set, respectively. Based on the real-time blood pressure value, real-time heart rate value, baseline blood pressure value, and baseline heart rate value, it determines the real-time blood pressure reduction net value. Based on the preset conversion relationship between blood pressure net value and blood glucose net value, it converts the real-time blood pressure reduction net value into a real-time blood glucose increase net value. It adds the baseline blood glucose value and the real-time blood glucose increase net value to obtain the real-time blood glucose value at the target time and transmits the real-time blood glucose value back to the smart terminal 1702 and / or wristwatch 1701 for display.

[0139] The aforementioned non-invasive blood glucose measurement system 1700 and non-invasive blood glucose measurement method are based on the same inventive concept. By establishing individualized postprandial physiological parameter benchmarks for the target individual, it achieves real-time, non-invasive detection of postprandial blood glucose using changes in blood pressure and heart rate. First, this method, based on the inherent negative correlation between the degree of postprandial blood pressure decrease and the degree of blood glucose increase, breaks through the traditional perception that blood glucose and blood pressure are not directly related, providing a new technical path for blood glucose detection. Second, by pre-calibrating and obtaining the individual's baseline blood pressure, baseline heart rate, and baseline blood glucose changes over time during the postprandial period, it can effectively eliminate interference factors such as eating, exercise, emotions, and environment. This makes the calculated real-time net blood pressure decrease more accurately reflect the true physiological fluctuations caused by eating. Furthermore, by using a preset conversion relationship, the net increase in blood glucose is quantitatively calculated, significantly improving the signal-to-noise ratio and accuracy of individualized detection, and solving the problem of dynamically tracking postprandial blood glucose changes in existing technologies. Furthermore, this method only requires conventional blood pressure and heart rate sensors to achieve continuous detection. It is completely non-invasive and avoids the pain and infection risks associated with frequent blood sampling. It is especially suitable for diabetic patients, the elderly, and people with autonomic nervous system dysfunction who need to frequently monitor their blood sugar.

[0140] Furthermore, in some embodiments, the wristwatch 1701 includes a blood pressure sensing module for measuring blood pressure, a heart rate sensing module for measuring heart rate, a skin impedance sensing module for measuring sympathetic nerve excitation, a skin temperature sensing module for measuring skin temperature, an ambient temperature sensing module for measuring ambient temperature, a communication module for communicating with the smart terminal 1702 or the cloud data center 1703, a data processing module, and a power module.

[0141] It should be noted that the blood pressure and heart rate sensing module is used for 24-hour dynamic monitoring of the target subject's systolic blood pressure, diastolic blood pressure, and heart rate. This module can be implemented using either cuff-type or cuffless technology. Cuff-type technology calculates blood pressure by converting cuff pressure values ​​at specific time points during radial artery occlusion and recovery. Cuffless technology includes two types: one based on the principle that pulse wave propagation velocity is positively correlated with systolic blood pressure, utilizing the time difference between the electrocardiogram and the fingertip photoplethysmography wave; the other analyzes the pulse waveform. This application can choose either of these technical solutions based on considerations of measurement accuracy and anti-interference capability.

[0142] Secondly, the skin resistance sensor module is used to detect the degree of abnormal sympathetic nerve excitability in the target subject, thus eliminating the influence of psychological factors on the accuracy of blood pressure measurement. This module can measure changes in human skin resistance using an external current excitation method. For example, the HKR-11C+ skin resistance sensor can be used, which has a built-in precision operational amplifier and can output high-precision skin resistance data. The sensor's technical parameters are: resistance range 100K-2.5MΩ, measurement accuracy 2.5KΩ, relative error ±2%, sampling frequency 50Hz, and communication baud rate 115200. Since the skin resistance signal is only used to exclude abnormal conditions and is not directly involved in postprandial blood glucose calculation, the accuracy requirements for the sensor are relatively relaxed.

[0143] Furthermore, the skin temperature sensing module is used to detect the target subject's skin temperature. When the skin temperature exceeds the normal range (e.g., above 35°C or below 15°C), it can be determined that the target subject is in an abnormal state unsuitable for measurement, such as having just completed a hot or cold water bath, after strenuous exercise, or experiencing a sudden change in ambient temperature. In such cases, a certain period of time must be waited before blood glucose measurement can be repeated. Skin temperature measurement can employ non-contact infrared sensing or contact sensing. Considering that skin temperature is not directly involved in blood glucose calculation and has lower resolution requirements, infrared sensing is more advantageous because it is not affected by the tightness of the garment.

[0144] Furthermore, the ambient temperature sensor module is used to detect the temperature of the wearing environment. When the ambient temperature exceeds the normal range (e.g., above 30°C or below 10°C), the target's blood pressure may fluctuate significantly, and blood glucose can only be recalculated after the environmental conditions return to normal.

[0145] In addition to the aforementioned sensing modules, the data processing module is responsible for receiving the data collected by each sensing module and performing preliminary processing such as amplification, noise filtering, and analog-to-digital conversion; the communication module (e.g., Bluetooth module) is used for data transmission and reception with the smart terminal 1702 or the cloud data center 1703; and the rechargeable power supply module provides power support for each module.

[0146] The data processing module is electrically connected to the blood pressure and heart rate sensing module, skin resistance sensing module, skin temperature sensing module, ambient temperature sensing module, communication module, and power supply module, respectively. It receives instructions from the matching smart terminal 1702 (such as a smartphone), controls each sensing module to detect physiological parameters at preset time intervals before and after meals, and sends the processed data to the smart terminal 1702 in real time for subsequent analysis.

[0147] Furthermore, in some embodiments, the smart terminal 1702 is equipped with a dedicated application that provides a user interface and specifically includes the following functional modules; The registration interface is a window for inputting physiological parameters for the target individual. The target individual can enter their basic personal information and physiological parameters on this interface, including gender, age, height, weight, blood pressure, fasting blood glucose level, and medication status. These parameters will serve as the basis for subsequent calibration and blood glucose calculation.

[0148] The calibration interface serves as a data entry window for the target individual. Following the on-screen prompts, the target individual measures and enters calibration parameters at specific time points before and after each meal on the calibration day. Based on the entered calibration data, the application automatically generates personalized baseline blood pressure, baseline heart rate, and baseline blood glucose datasets for the target individual. It also calculates the net decrease in blood pressure and the net increase in blood glucose at specific time points after each meal on the calibration day, thereby determining the appropriate blood glucose calculation formula for that individual.

[0149] The blood glucose measurement interface provides a control window for measuring blood glucose levels. The target user follows the on-screen instructions to set up and activate the watch's monitoring mode. The watch automatically calculates the target user's blood pressure, heart rate, and blood glucose levels throughout the day. The measurement results are displayed in real-time on the blood glucose measurement interface and can also be simultaneously transmitted back to the watch for display.

[0150] The data query interface provides a window for the target audience to query data. Following the interface prompts, the target audience can review historical monitoring data, including measurements of blood pressure, heart rate, blood glucose levels, and calorie intake, facilitating long-term health management and tracking.

[0151] Please see Figure 18 , Figure 18This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of this application. The electronic device includes: The processor 1801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the non-invasive blood glucose measurement method provided in the embodiments of this application. The memory 1802 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1802 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1802 and is called and executed by the processor 1801 to implement the non-invasive blood glucose measurement method provided in the embodiments of this application. The input / output interface 1803 is used to implement information input and output; The communication interface 1804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 18018 transmits information between various components of the device (e.g., processor 1801, memory 1802, input / output interface 1803, and communication interface 1804); The processor 1801, memory 1802, input / output interface 1803 and communication interface 1804 are connected to each other within the device via bus 18018.

[0152] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, provides the non-invasive blood glucose measurement method of this application.

[0153] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0154] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0155] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0156] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0157] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0158] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0159] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0160] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0161] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0162] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0163] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0164] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A non-invasive blood glucose measurement method, characterized in that, include: Acquire calibration data of the target object, real-time blood pressure value and real-time heart rate value of the target object at the target time; wherein, the calibration data includes the calibration blood pressure set, calibration heart rate set and calibration blood glucose set of the target object as calibrated over time; If the target time falls within the post-meal period, then based on the calibration data, the target object's baseline blood pressure set, baseline heart rate set, and baseline blood glucose set are determined over time during the post-meal period. The baseline blood pressure value, baseline heart rate value, and baseline blood glucose set for the target time are determined from the baseline blood pressure value set, the baseline heart rate set, and the baseline blood glucose set, respectively. A real-time blood pressure reduction net value is determined based on the real-time blood pressure value, the real-time heart rate value, the baseline blood pressure value, and the baseline heart rate value. Based on a preset conversion relationship between blood pressure net value and blood glucose net value, the real-time blood pressure reduction net value is converted into a real-time blood glucose increase net value. The baseline blood glucose value is then added to the real-time blood glucose value at the target time. The postprandial period is defined as the time from the start of the meal to the trough of postprandial blood glucose. The baseline blood pressure, baseline heart rate, and baseline blood glucose values ​​are obtained after excluding interfering factors.

2. The non-invasive blood glucose measurement method according to claim 1, characterized in that, The step of determining the target object's baseline blood pressure set, baseline heart rate set, and baseline blood glucose set over time during the postprandial period based on the calibration data includes: In the calibrated blood pressure set, multiple postprandial calibrated blood pressure values ​​of the target object during the postprandial period and the pre-meal calibrated blood pressure value at the start of the postprandial period are obtained. Based on the pre-meal calibrated blood pressure value and multiple postprandial calibrated blood pressure values, the baseline blood pressure set is determined. In the calibrated heart rate set, multiple postprandial calibrated heart rate values ​​of the target object that change over time during the postprandial period and the meal-prepared calibrated heart rate value at the start of the postprandial period are obtained. Based on the meal-prepared calibrated heart rate value and multiple postprandial calibrated heart rate values, the baseline heart rate set is determined. In the calibrated blood glucose set, multiple postprandial calibrated blood glucose values ​​of the target object during the postprandial period and the pre-meal calibrated blood glucose value at the start of the postprandial period are obtained. Based on the pre-meal calibrated blood glucose value and the multiple postprandial calibrated blood glucose values, the benchmark blood glucose set is determined.

3. The non-invasive blood glucose measurement method according to claim 2, characterized in that, The step of determining the baseline blood pressure set based on the pre-meal calibrated blood pressure value and multiple post-meal calibrated blood pressure values ​​includes: The multiple postprandial calibrated blood pressure values ​​are respectively processed with the pre-meal calibrated blood pressure value to obtain the blood pressure difference value corresponding to each postprandial calibrated blood pressure value. Based on the blood pressure difference value corresponding to each postprandial calibrated blood pressure value, a compensation operation is performed on each postprandial calibrated blood pressure value to obtain multiple postprandial compensated blood pressure values. The multiple postprandial compensated blood pressure values ​​are used as the basic blood pressure set. The step of determining the baseline heart rate set based on the pre-meal calibrated heart rate value and multiple post-meal calibrated heart rate values ​​includes: The postprandial calibrated heart rate values ​​are respectively compared with the pre-meal calibrated heart rate value to obtain the heart rate difference value corresponding to each postprandial calibrated heart rate value. Based on the heart rate difference value corresponding to each postprandial calibrated blood pressure value, a compensation calculation is performed on each postprandial calibrated heart rate value to obtain multiple postprandial compensated heart rate values. The multiple postprandial compensated heart rate values ​​are used as the base blood pressure set. The step of determining the benchmark blood glucose set based on the pre-meal calibrated blood glucose value and multiple post-meal calibrated blood glucose values ​​includes: The postprandial calibrated blood glucose values ​​are respectively compared with the pre-meal calibrated blood glucose values ​​to obtain a first blood glucose difference value corresponding to each postprandial calibrated blood glucose value. Based on the first blood glucose difference value corresponding to each postprandial calibrated blood glucose value, a compensation operation is performed on each postprandial calibrated blood glucose value to obtain multiple postprandial compensated blood glucose values. The multiple postprandial compensated blood glucose values ​​are used as the basic blood glucose set.

4. The non-invasive blood glucose measurement method according to claim 3, characterized in that, The conversion relationship between net blood pressure and net blood glucose was determined through the following steps: Based on the multiple postprandial calibrated blood pressure values, the multiple postprandial calibrated heart rate values, the baseline blood pressure set, and the baseline heart rate set, determine the net reduction value of multiple postprandial blood pressure values ​​of the target subject over time during the postprandial period; Based on the multiple postprandial calibrated blood glucose values ​​and the benchmark blood glucose set, determine the net increase in postprandial blood glucose of the target object over time during the postprandial period; Based on the time sequence of the postprandial period, the net decrease in blood pressure and the net increase in blood glucose after multiple meals are fitted to obtain the conversion relationship between the net blood pressure value and the net blood glucose value.

5. The non-invasive blood glucose measurement method according to claim 1, characterized in that, Also includes: Determine the first calibrated blood glucose value at the target time from the calibration data; If the target time falls within the first pre-meal period, the theoretical blood glucose value is determined based on the real-time blood pressure and real-time heart rate values ​​in a pre-established mapping relationship between blood pressure, heart rate, and blood glucose. The second calibrated blood glucose value at the start time of the first pre-meal period is then used in the calibration data, and the theoretical blood glucose value is corrected based on the first calibrated blood glucose value and the second calibrated blood glucose value to obtain the real-time blood glucose value at the target time. The first pre-meal period is the period from the trough of post-meal blood glucose the previous evening to the start of the morning meal. or, If the target time falls within the second pre-meal period, the third calibrated blood glucose value at the start time of the second pre-meal period is obtained from the calibration data, and the first calibrated blood glucose value is corrected based on the third calibrated blood glucose value to obtain the real-time blood glucose value at the target time; wherein, the second pre-meal period is the period from the time when the blood glucose reaches its trough after breakfast on the same day to the time when lunch is started, or from the time when the blood glucose reaches its trough after lunch on the same day to the time when dinner is started.

6. The non-invasive blood glucose measurement method according to claim 5, characterized in that, The mapping relationship between blood pressure, heart rate, and blood glucose was determined through the following steps: In the calibrated blood pressure set, multiple pre-meal calibrated blood pressure values ​​of the target object as a function of time during the first pre-meal period are obtained; In the calibrated heart rate set, obtain multiple pre-meal calibrated heart rate values ​​of the target object that change over time during the first pre-meal period; In the calibrated blood glucose set, multiple pre-meal calibrated blood glucose values ​​of the target object as a function of time during the first pre-meal period are obtained; Based on the time sequence of the first pre-meal period, the multiple pre-meal calibrated blood pressure values, the multiple pre-meal calibrated heart rate values, and the multiple post-meal blood pressure reduction net values ​​are fitted to obtain the mapping relationship between blood pressure, heart rate, and blood glucose.

7. The non-invasive blood glucose measurement method according to claim 5, characterized in that, The step of correcting the theoretical blood glucose value based on the first calibrated blood glucose value and the second calibrated blood glucose value to obtain the real-time blood glucose value at the target time includes: Calculate the second blood glucose difference between the first calibrated blood glucose value and the second calibrated blood glucose value; The theoretical blood glucose value is corrected based on the second blood glucose difference to obtain the real-time blood glucose value.

8. A non-invasive blood glucose measurement system, characterized in that, include: A wristwatch, the wristwatch being used to measure and calibrate a target object to determine calibration data, and to measure real-time data of the target object at a target time, the real-time data including real-time blood pressure and real-time heart rate; The smart terminal is wirelessly connected to the wristwatch; A cloud data center, which is wirelessly connected to the smart terminal and the wristwatch respectively; The smart terminal is used to receive the real-time data from the wristwatch and upload the real-time data to the cloud data center; The cloud data center is configured to receive the real-time data measured at the target time. If the target time is within the post-meal period, it determines the target object's baseline blood pressure set, baseline heart rate set, and baseline blood glucose set as a function of time during the post-meal period based on the calibration data. It then determines the baseline blood pressure value, baseline heart rate value, and baseline blood glucose set at the target time from the baseline blood pressure value set, the baseline heart rate set, and the baseline blood glucose set, respectively. Based on the real-time blood pressure value, the real-time heart rate value, the baseline blood pressure value, and the baseline heart rate value, it determines the real-time blood pressure reduction net value. Based on a preset conversion relationship between blood pressure net value and blood glucose net value, it converts the real-time blood pressure reduction net value into a real-time blood glucose increase net value. It adds the baseline blood glucose value and the real-time blood glucose increase net value to obtain the real-time blood glucose value at the target time and transmits the real-time blood glucose value back to the smart terminal and / or the wristwatch for display.

9. The non-invasive blood glucose measurement system according to claim 8, characterized in that: The wristwatch includes a blood pressure sensing module for measuring blood pressure, a heart rate sensing module for measuring heart rate, a skin impedance sensing module for measuring sympathetic nerve excitation, a skin temperature sensing module for measuring skin temperature, an ambient temperature sensing module for measuring ambient temperature, a communication module for communicating with the smart terminal or the cloud data center, a data processing module, and a power module. The data processing module is electrically connected to the blood pressure sensing module, the heart rate sensing module, the skin impedance sensing module, the skin temperature sensing module, the ambient temperature sensing module, the communication module, and the power module. A smart terminal, wherein the smart terminal is equipped with an application, and the application has a registration interface, a calibration interface, a blood glucose measurement interface and a data query interface; The registration interface provides a window for inputting physiological parameters for the target object, the calibration interface provides a window for inputting calibration data for the target object, the blood glucose measurement interface provides a control window for measuring blood glucose for the target object, and the data query interface provides a query window for querying data for the target object.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a processor-executable program, which, when executed by a processor, is used to implement the non-invasive blood glucose measurement method as described in any one of claims 1 to 7.

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