An intelligent blood glucose monitoring method and system for nephrology

Abstract

The present invention discloses a method and system for intelligent monitoring of blood glucose in the Department of Nephrology, including: collecting and analyzing the blood glucose data of patients in the Department of Nephrology, obtaining the abnormal blood glucose change factor of each data, and using the abnormal factor and the data deviation size to obtain the attention of the data, so as to distinguish the importance of the blood glucose data, and finally obtain the final blood glucose level risk score through the periodic performance and overall attention of the important data, judge whether the patient's blood glucose level is at risk and adjust the treatment plan in time. The present invention can reduce the impact of inaccurate blood glucose level thresholds caused by individual differences, so that the obtained risk score conforms to the patient's own blood glucose change characteristics, and at the same time can mine the patient's blood glucose curve fluctuation through the abnormal factors and periodic performance of the data change, more comprehensively reflect the changes in the patient's blood glucose state, obtain a more accurate real-time blood glucose level monitoring effect, and realize intelligent monitoring of blood glucose in the Department of Nephrology.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and relates to a method and system for intelligent blood glucose monitoring in nephrology. Background Art

[0002] Blood glucose, as an important indicator for evaluating the normality of human glucose metabolism, is usually used for screening, diagnosing, and monitoring various glucose metabolism disorders such as diabetes and hypoglycemia. The monitoring of blood glucose is of great significance in nephrology work, which can help doctors detect and intervene in problems related to blood glucose metabolism in a timely manner, improving the treatment effect and quality of life of patients. In order to accurately understand the changes in the patient's blood glucose level, doctors usually use monitoring devices such as blood glucose meters or externally attached sensors to obtain the patient's real-time blood glucose data, and judge whether there is an abnormal blood glucose situation by the proportion of time that the blood glucose is within the normal range. During the process of monitoring the patient's blood glucose level, doctors usually evaluate the patient's disease risk based on the percentage of blood glucose reaching the standard, that is, the proportion of time that the blood glucose value is within the normal range between the high and low thresholds, and the higher the proportion, the lower the risk. However, the setting of the normal blood glucose threshold is often based on the research data of a large-scale population, and it cannot be well applied to all patients. Moreover, the blood glucose fluctuation of patients is also very important for the evaluation and diagnosis of the disease, while the percentage of blood glucose reaching the standard cannot reflect this fluctuation. Therefore, although the method of using the threshold to judge whether the blood glucose level is normal is simple and easy to implement, it has the defects of inaccuracy and incompleteness in actual application, which may lead to certain deviations in the analysis results and reduce the accuracy and real-time performance of the monitoring. Summary of the Invention

[0003] The purpose of the present invention is to solve the problem that although the method of using the normal blood glucose threshold to judge whether the blood glucose level is normal in the prior art is simple and easy to implement, it has the defects of inaccuracy and incompleteness in actual application, which may lead to certain deviations in the analysis results and reduce the accuracy and real-time performance of the monitoring, and provide a method and system for intelligent blood glucose monitoring in nephrology. To achieve the above purpose, the present invention adopts the following technical solutions: A method for intelligent blood glucose monitoring in nephrology, comprising: collecting continuously changing blood glucose data of a patient for a period of time; obtaining corresponding abnormal blood glucose change factors based on the local change characteristics of the collected blood glucose data; obtaining the relative deviation degree of the blood glucose data based on the change law of the blood glucose data; determining the attention degree of the blood glucose data based on the obtained abnormal blood glucose change factors and the relative deviation degree of the blood glucose data; obtaining the final blood glucose level risk score based on the periodic performance and overall attention degree of the blood glucose data; comparing the set blood glucose score threshold based on the size of the blood glucose level risk score to evaluate whether the patient's blood glucose is abnormal; if so, giving an alarm; if not, indicating that the patient's body is normal.

[0004] A further improvement of the present invention is that: further, the blood sugar data of the patient that changes continuously over a period of time is collected, specifically: a fixed collection frequency for the patient's blood sugar data is set based on the blood sugar meter; at the same time, the patient's daily meal time is recorded and the patient's collection cycle is set.

[0005] Furthermore, based on the local variation characteristics of the collected blood sugar data, the corresponding blood sugar variation abnormality factor is obtained, specifically: any blood sugar data is set as reference data, and a fixed-size neighborhood is set for the reference data. for: ,in, For reference data, Represents the first individual data; represents the first-order difference of blood glucose data, where ; and are two adjacent blood sugar change values, is a symbolic function; Indicates the degree of blood sugar change in the neighborhood of the reference data. The larger the value, the more drastic the blood sugar change around the reference data. Also bigger; Indicates the number of blood glucose data in the neighborhood; Represents the number of pairwise combinations of adjacent blood glucose data in the neighborhood; It indicates the frequency of blood sugar fluctuation in the neighborhood of the reference data. If the directions of two adjacent changes are consistent, 1 if the direction is inconsistent, -1; The smaller the value of, the higher the blood sugar fluctuation frequency in the reference data neighborhood, and the higher the corresponding blood sugar abnormality factor The bigger.

[0006] Furthermore, based on the variation pattern of blood sugar data, the relative deviation of blood sugar data is obtained, specifically: ,in, is the mean of all blood sugar data in the sample, is the maximum blood sugar data in the sample, is the minimum blood sugar data in the sample; is the difference between the reference data and the mean, It represents the maximum difference between all the data in the sample and the mean. It is the ratio of the mean deviation of the reference data to the maximum mean deviation. The larger the value is, the greater the relative deviation of the reference data is, and the higher the corresponding attention is.

[0007] Furthermore, the attention level of blood sugar data is determined as follows: , where is the time point of the reference data, represents the th meal time point closest to the reference data, represents the abnormal factor of blood glucose change based on meal time for the reference data, is the abnormal factor of blood glucose change for the reference data, The larger the value, the greater the attention to the reference data. represents the time interval between the reference data and the two meals closest to it. The smaller the interval, the higher the attention.

[0008] Furthermore, based on the periodic performance of blood glucose data, specifically: , where is the intensity of the periodic performance of the blood glucose curve of this patient; represents the th blood glucose data within one day starting from the recording time, is the total number of blood glucose data within one day; represents the th data at the same time point every day in the past u days; represents the attention to the jth blood glucose data on the kth day in the past; is the average value of blood glucose data at the time point in the past u days, is the average value of the attention to blood glucose data at the time point in the past u days; represents the similarity degree of data based on attention at the time point in the blood glucose data sample. The smaller the value, the smaller the difference in blood glucose data at the time point, the greater the intensity of the periodic performance of the blood glucose curve, and the larger the blood glucose level risk score is also; where is the proximity degree of data attention at the time point. The smaller this value, the closer the attention to blood glucose data at the

[0009] time point, and the greater the impact of the difference in blood glucose data at the corresponding time point on the intensity of the periodic performance of the blood glucose curve. Furthermore, based on the periodic performance and overall attention of blood glucose data, the final blood glucose level risk score is obtained, specifically: , where is the final blood glucose level risk score; represents the is the attention variance of all blood glucose data in the sample; Indicates the overall attention level of all blood sugar data in the sample. The larger the value is, the worse the stability of the blood sugar data in the blood sugar data sample is, the lower the blood sugar regulation speed is, and the corresponding blood sugar level risk score is The bigger.

[0010] Further, based on the size of the blood sugar level risk score, the set blood sugar score threshold is compared, specifically: setting the blood sugar score threshold and ;like , it means that the patient's blood sugar level is normal; , it reminds the patient that the blood sugar condition is poor and needs to be adjusted in time; if , it means that the patient's blood sugar condition is abnormal and needs to be treated by a doctor.

[0011] A blood sugar intelligent monitoring system for nephrology comprises: a collection module, the collection module collects blood sugar data of patients that continuously changes over a period of time; a first acquisition module, the first acquisition module acquires corresponding blood sugar change abnormal factors based on the local change characteristics of the collected blood sugar data; a second acquisition module, the second acquisition module acquires the relative deviation of the blood sugar data based on the change law of the blood sugar data; a determination module, the determination module determines the attention of the blood sugar data based on the acquired blood sugar change abnormal factors and the relative deviation of the blood sugar data; a third acquisition module, the third acquisition module derives a final blood sugar level risk score based on the periodic performance and overall attention of the blood sugar data; an evaluation module, the evaluation module evaluates whether the patient's blood sugar is abnormal based on the size of the blood sugar level risk score and the set blood sugar score threshold; if so, an early warning is issued; if not, it indicates that the patient is in good health.

[0012] Compared with the prior art, the present invention has the following beneficial effects: the present invention obtains the abnormal blood sugar change factor of each data by collecting and analyzing the blood sugar data of patients in the Department of Nephrology, and obtains the attention of the data by using the abnormal factors and the deviation size of the data, so as to distinguish the importance of the blood sugar data, and finally obtains the final blood sugar level risk score by the periodic performance and overall attention of the important data, judges whether the patient's blood sugar level is at risk and adjusts the treatment plan in time. The present invention can reduce the impact of inaccurate blood sugar level thresholds caused by individual differences, so that the obtained risk score conforms to the patient's own blood sugar change characteristics, and at the same time can mine the patient's blood sugar curve fluctuation through the abnormal factors and periodic performance of the data change, more comprehensively reflect the changes in the patient's blood sugar state, obtain a more accurate real-time blood sugar level monitoring effect, and realize the intelligent monitoring of blood sugar in the Department of Nephrology. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0014] Figure 1 It is a schematic flowchart of the intelligent blood glucose monitoring method for the nephrology department of the present invention; Figure 2 It is a schematic structural diagram of the intelligent blood glucose monitoring system for the nephrology department of the present invention. Detailed implementation manners

[0015] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Generally, the components of the embodiments of the present invention described and illustrated in the drawings here can be arranged and designed in various different configurations.

[0016] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0017] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0018] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper", "lower", "horizontal", "inner", etc. are used to indicate the orientation or positional relationship, it is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the invention product is usually placed during use. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, terms such as "first" and "second" are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0019] In addition, if the term "horizontal" appears, it does not mean that the component is required to be absolutely horizontal, but it can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but it can be slightly inclined.

[0020] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and limited, if the terms "set", "installed", "connected", "linked" are used, they should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0021] The following further describes the present invention in detail with reference to the drawings: Refer to Figure 1 , the present invention discloses an intelligent blood glucose monitoring method for nephrology, including: S101, collecting continuously changing blood glucose data of a patient over a period of time; setting a fixed collection frequency for the patient's blood glucose data based on a blood glucose instrument; and simultaneously recording the patient's daily meal time and setting the patient's collection cycle. Specifically, it can be: using a monitoring device such as a blood glucose meter or an externally attached sensor to obtain the continuous u-day blood glucose data of nephrology patients in real time, and the data collection frequency is once every 10 minutes. In addition, the patient's daily meal time also needs to be recorded, and then a corresponding blood glucose data sample is constructed and uploaded to the cloud for real-time monitoring and analysis. Among them, both the collection duration and the collection frequency are set empirically, and the value of u can be set to 15 days; it can be adjusted according to the actual situation in specific applications.

[0022] S102, based on the local change characteristics of the collected blood glucose data, obtaining corresponding blood glucose change abnormal factors; due to individual differences among different patients, the normal blood glucose range for each person has different standards, and the blood glucose monitoring method that uses the percentage of blood glucose reaching the standard time for risk assessment in the conventional method uses a fixed threshold suitable for most people, which is not applicable to all populations and is likely to cause deviations in the analysis results. Moreover, the blood glucose fluctuation situation of the patient is also very important for the assessment and diagnosis of the blood glucose condition, and the percentage of blood glucose reaching the standard time cannot reflect this fluctuation situation, resulting in poor final monitoring effects. Therefore, first, the blood glucose change abnormal factor of each data needs to be calculated according to the fluctuation characteristics and change trend of the blood glucose, and then the attention degree of the data is obtained by using the abnormal factor and the data deviation degree. Finally, the final blood glucose level risk score is obtained according to the periodic performance and overall attention degree of the blood glucose data.

[0023] The conventional method of evaluating blood sugar levels relies more on some special blood sugar data, that is, analyzing whether the patient's blood sugar level is normal by the size of the data exceeding the threshold and the duration of the data. However, the limitations of threshold judgment and the existence of individual differences lead to low accuracy of this method, and it is easy to ignore the blood sugar data that may be abnormal. In practice, there are also large differences in the fluctuation of blood sugar data between normal individuals and abnormal individuals. The blood sugar data of abnormal individuals has poor stability and a relatively large range of variation. Therefore, in order to better mine these data that may be abnormal, the corresponding blood sugar change abnormality factor can be calculated according to the local change characteristics of each data to reflect the fluctuation of the data and distinguish the degree of attention to different data.

[0024] According to the above logic, first calculate the corresponding abnormal blood sugar change factor for all blood sugar data in the sample. Take any blood sugar data as reference data, set a fixed-size neighborhood for the reference data, and the neighborhood contains 5 adjacent blood sugar data before and after. The abnormal blood sugar change factor of this data for: ,in, For reference data, Represents the first individual data; represents the first-order difference of blood glucose data, where ; and are two adjacent blood sugar change values, is a symbolic function; Indicates the degree of blood sugar change in the neighborhood of the reference data. The larger the value, the more drastic the blood sugar change around the reference data. Also bigger; Indicates the number of blood glucose data in the neighborhood; Represents the number of pairwise combinations of adjacent blood glucose data in the neighborhood. The value of is set to 10, which means that the neighborhood contains 5 adjacent blood glucose data before and after. The value of should be 9; It represents the frequency of blood sugar fluctuation in the neighborhood of the reference data. If the directions of two adjacent changes are consistent, then If the direction is inconsistent, it is -1; The smaller the value of, the higher the blood sugar fluctuation frequency in the reference data neighborhood, and the higher the corresponding blood sugar abnormality factor The bigger.

[0025] At this point, the blood sugar abnormality factor of all data in the sample can be obtained through the above steps, and then the next step of calculation can be performed.

[0026] S103. Obtain the relative deviation degree of blood glucose data based on the variation law of blood glucose data. When monitoring the blood glucose data of patients, more attention needs to be paid to the data at some special time nodes. For example, the peak value, valley value of the blood glucose curve or the blood glucose value exceeding the threshold. These special data can better reflect the blood glucose health level of patients. For nephrology patients who may have physical abnormalities, they may have too large an increase or decrease in blood glucose after meals due to the deterioration of the renal reabsorption function. Therefore, the relative deviation size of blood glucose data rather than a fixed threshold can be used to measure the attention degree of data. In addition, the insulin secretion time and total amount of nephrology patients may be different from those of normal people, resulting in abnormal blood glucose regulation within a certain period after meals. Therefore, the blood glucose data before and after meals also have high analysis value.

[0027] Obtain the relative deviation degree of blood glucose data based on the variation law of blood glucose data, specifically: , where is the mean value of all blood glucose data in the sample, is the maximum blood glucose data in the sample, is the minimum blood glucose data in the sample; is the difference between the reference data and the mean value, represents the maximum difference from the mean value among all data in the sample, is the ratio of the mean difference of the reference data to the maximum mean difference. The larger this value is, the greater the relative deviation degree of the reference data, and the higher the corresponding attention degree.

[0028] S104. Determine the attention degree of blood glucose data based on the obtained blood glucose change abnormal factor and the relative deviation degree of blood glucose data. Denote the attention degree of blood glucose data as , and we can get: , where is the time point where the reference data is located, represents the th meal time point closest to the reference data; represents the blood glucose change abnormal factor of the reference data based on the meal time, is the blood glucose change abnormal factor of the reference data, The larger the value, the greater the attention degree of the reference data, represents the time interval between the reference data and the two meals closest to it. The smaller the interval, the higher the attention degree. Thus, the attention degrees of all data in the sample can be obtained through the above steps, and then the next calculation can be carried out.

[0029] S105. Obtain the final blood glucose level risk score based on the periodic performance and overall attention of blood glucose data. Since the blood glucose level of the human body is closely related to the daily life rhythm, the change of blood glucose data has a strong daily periodicity, and the stronger the periodicity, the lower the possibility of risk. However, there are more or less differences in diet, exercise, and environment every day, and the existence of these differences will weaken the periodic performance of blood glucose data, which may lead to deviations in the risk assessment of patients' blood glucose. To solve the above problems, the previously obtained data attention can be used for weighting when calculating the periodic performance of the patient's blood glucose. Data with similar attention is likely to be blood glucose data under similar conditions, and a higher weight is given during calculation, while a lower weight is given otherwise. In addition, the blood glucose level of normal individuals is usually relatively stable and flat throughout the day, only changing significantly before and after meals, and the speed of blood glucose regulation is also relatively fast. For abnormal individuals, not only will there be more unstable changes in blood glucose levels, but also the phenomenon of slower blood glucose regulation speed will occur due to the weakened absorption and metabolism functions of the body. Therefore, abnormal individuals have more blood glucose data with high attention and a higher overall attention compared to normal individuals.

[0030] Based on the periodic performance of blood glucose data, specifically: , where is the intensity of the periodic performance of the patient's blood glucose curve; represents the th blood glucose data within one day starting from the recording time, is the total number of blood glucose data within one day; here, the value of u is 15 days; represents the th data at the same time point every day in the past 15 days; represents the attention of the th blood glucose data at the same time point on the past kth day; is the average value of blood glucose data at the time point in the past 15 days, is the average value of the attention of blood glucose data at the time point in the past 15 days; represents the degree of data similarity based on attention at the time point in the blood glucose data sample. The smaller the value of , the smaller the difference in blood glucose data at the time point, the greater the intensity of the blood glucose curve periodic performance, and the greater the blood glucose level risk score ; among them, is the degree of proximity of data attention at the time point. The smaller this value is, the closer the attention of blood glucose data at the The greater the difference in blood glucose data at time points, the greater the impact on the intensity of the periodic performance of the blood glucose curve.

[0031] Based on the periodic performance and overall attention of blood glucose data, the final blood glucose level risk score is obtained, specifically: , where is the final blood glucose level risk score; represents the th data in the entire sample, represents the total number of data in the sample; is the variance of attention for all blood glucose data in the sample; represents the overall attention level of all blood glucose data in the sample. The larger the value of , the worse the stability of blood glucose data and the lower the blood glucose regulation speed in the blood glucose data sample, and the corresponding blood glucose level risk score is larger.

[0032] Thus, the corresponding blood glucose level risk score can be obtained through the blood glucose data of the patient in the past 15 days. This score can reflect the blood glucose health status of the patient in the recent 15 days. The higher the score, the worse the health status, and the more likely there is a risk of onset.

[0033] S106. Based on the magnitude of the blood glucose level risk score, compare with the set blood glucose score threshold to evaluate whether the patient's blood glucose is abnormal; if so, give an alarm; if not, it means the patient's body is normal.

[0034] Set the blood glucose score threshold and ; is 0.6, is 0.8. If , that is , it means the patient's blood glucose condition is normal; , that is , it means the patient's blood glucose condition is poor and self-adjustment is needed in a timely manner; if , that is , it means the patient's blood glucose condition is abnormal and medical treatment by a doctor is required.

[0035] See Figure 2, the present invention discloses a smart blood glucose monitoring system for nephrology department, including: a collection module, which collects continuously changing blood glucose data of a patient over a period of time; a first acquisition module, which acquires corresponding abnormal blood glucose change factors based on the local change characteristics of the collected blood glucose data; a second acquisition module, which acquires the relative deviation degree of the blood glucose data based on the change rule of the blood glucose data; a determination module, which determines the attention degree of the blood glucose data based on the acquired abnormal blood glucose change factors and the relative deviation degree of the blood glucose data; a third acquisition module, which obtains the final blood glucose level risk score based on the periodic performance and overall attention degree of the blood glucose data; an evaluation module, which evaluates whether the patient's blood glucose is abnormal by comparing the set blood glucose score threshold based on the size of the blood glucose level risk score; if so, a warning is issued; if not, it indicates that the patient's body is normal.

[0036] The terminal device provided by an embodiment of the present invention. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Or, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0037] The computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention.

[0038] The terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0039] The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0040] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory.

[0041] If the modules / units integrated in the terminal device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0042] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent blood glucose monitoring method for nephrology department, characterized in that Including: Collecting continuously changing blood glucose data of a patient over a period of time; Based on the local change characteristics of the collected blood glucose data, obtaining corresponding blood glucose change abnormal factors; Based on the change law of the blood glucose data, obtaining the relative deviation degree of the blood glucose data; based on the obtained blood glucose change abnormal factors and the relative deviation degree of the blood glucose data, determining the attention degree of the blood glucose data; based on the periodic performance and overall attention degree of the blood glucose data, obtaining the final blood glucose level risk score; Based on the size of the blood sugar level risk score, the set blood sugar score threshold is compared to evaluate whether the patient's blood sugar is abnormal; if so, an early warning is issued; if not, it means that the patient is in good health; the collection of the patient's blood sugar data that changes continuously over a period of time is specifically: based on the blood sugar meter, a fixed collection frequency is set for the patient's blood sugar data; at the same time, the patient's daily meal time is recorded and the patient's collection cycle is set; based on the local change characteristics of the collected blood sugar data, the corresponding blood sugar change abnormality factor is obtained, specifically: any blood sugar data is set as reference data, and a fixed-size neighborhood is set for the reference data, and the blood sugar change abnormality factor of the blood sugar data is obtained. for: in, For reference data, Represents the first individual data; represents the first-order difference of blood glucose data, where ; and are two adjacent blood sugar change values, is a symbolic function; Indicates the degree of blood sugar change within the reference data neighborhood, The larger the value, the more drastic the blood sugar changes around the reference data. Also bigger; Indicates the number of blood glucose data in the neighborhood; Represents the number of pairwise combinations of adjacent blood glucose data in the neighborhood; It indicates the frequency of blood sugar fluctuation in the neighborhood of the reference data. If the directions of two adjacent changes are consistent, 1 if the direction is inconsistent, -1; The smaller the value of, the higher the blood sugar fluctuation frequency in the reference data neighborhood, and the higher the corresponding blood sugar abnormality factor The larger the blood sugar data, the relative deviation of the blood sugar data is obtained based on the change law of the blood sugar data, specifically: in, is the mean of all blood sugar data in the sample, is the maximum blood sugar data in the sample, is the minimum blood sugar data in the sample; is the difference between the reference data and the mean, It represents the maximum difference between all the data in the sample and the mean. is the ratio of the mean difference of the reference data to the maximum mean difference. The larger the value, the greater the relative deviation of the reference data, and the higher the corresponding attention. The degree of attention of the blood glucose data is determined as follows: in, is the time point of the reference data, Indicates the th meal time point closest to the reference data, Indicates the abnormal factor of blood glucose change based on meal time for the reference data, Is the abnormal factor of blood glucose change for the reference data, The larger the value, the greater the attention to the reference data, Indicates the time interval between the two meals closest to the reference data. The smaller the interval, the higher the attention; The periodic performance based on blood glucose data is specifically: Among them, Is the periodic performance intensity of the blood glucose curve of this patient; Indicates the th blood glucose data within one day starting from the recording time, Is the total number of blood glucose data within one day; Indicates the th data at the same time point every day in the past u days; Indicates the attention to the jth blood glucose data on the kth day in the past; Is the mean value of blood glucose data at the time point in the past u days, In the past u days mean value of the attention to blood glucose data at the time point; Indicates the similarity degree of data based on attention at the time point in the blood glucose data sample, The smaller the value, the smaller the difference in blood glucose data at the time point, the greater the periodic performance intensity of the blood glucose curve the greater, and the higher the blood glucose level risk score; Among them, Is the similarity degree of data attention at the time point. The smaller this value, the closer the attention to blood glucose data at the time point, and the greater the impact of the difference in blood glucose data at the corresponding time point on the periodic performance intensity of the blood glucose curve; The final blood glucose level risk score is obtained based on the periodic performance and overall attention of blood glucose data, specifically: Among them, Is the final blood glucose level risk score; Indicates the th data in the entire sample, Indicates the total number of data in the sample; Is the variance of the attention to all blood glucose data in the sample; Indicates the overall attention level of all blood glucose data in the sample, The larger the value, the worse the stability of blood glucose data in the blood glucose data sample and the lower the blood glucose regulation speed, and the corresponding blood glucose level risk score is larger; based on the magnitude of the blood glucose level risk score, compare it with the set blood glucose score threshold. Specifically: set the blood glucose score threshold and ; if , it indicates that the patient's blood glucose condition is normal; , it reminds the patient that the blood glucose condition is poor and self-adjustment is needed in a timely manner; if , it indicates that the patient's blood glucose condition is abnormal and medical treatment is required.

2. A nephrology blood glucose intelligent monitoring system using the nephrology blood glucose intelligent monitoring method as described in claim 1, characterized in that, Including: A collection module that collects continuously changing blood glucose data of a patient over a period of time; A first acquisition module that obtains corresponding blood glucose change abnormal factors based on the local change characteristics of the collected blood glucose data; A second acquisition module that obtains the relative deviation degree of the blood glucose data based on the change law of the blood glucose data; a determination module that determines the attention degree of the blood glucose data based on the obtained blood glucose change abnormal factors and the relative deviation degree of the blood glucose data; A third acquisition module that obtains the final blood glucose level risk score based on the periodic performance and overall attention degree of the blood glucose data; An evaluation module that compares the set blood glucose score threshold based on the magnitude of the blood glucose level risk score to evaluate whether there is an abnormality in the patient's blood glucose; If so, give an early warning; if not, it means the patient's body is normal.

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