Blood glucose data monitoring, alarming and pushing system based on Internet of Things

Abstract

The invention discloses a blood glucose data monitoring alarm pushing system based on the Internet of Things, which is characterized in that when an evaluation signaling is triggered, diet data and exercise data of a patient within a set time period before the time point of triggering the evaluation signaling are extracted and are respectively analyzed; therefore, the intake evaluation value and the exercise evaluation value of the patient before the time point of triggering the evaluation signaling are obtained, the reason of abnormal blood glucose can be judged according to the intake evaluation value and the exercise evaluation value of the patient, whether the abnormal blood glucose is caused by diet or exercise factors is distinguished, and the abnormal blood glucose is not caused by diet or exercise when the triggering reason is not the diet reason or the exercise reason. And a diagnosis signal is further triggered to inform medical personnel to go to the bed number of the patient to diagnose and record the cause of abnormal blood glucose, so that the problems that in the prior art, blood glucose data of the patient is collected and compared, an alarm is triggered, related data of the patient cannot be further analyzed, and the blood glucose abnormality is caused are solved. Therefore, the alarm reason is determined.

Description

Technical Field

[0002] The present invention relates to the field of medical health, and in particular to a blood sugar data monitoring and alarm push system based on the Internet of Things. Background Art

[0003] In recent years, the rapid development of Internet of Things technology has provided new ideas for blood sugar monitoring management. The blood sugar monitoring system based on the Internet of Things can realize remote and real-time blood sugar data collection and analysis, providing patients and medical staff with more accurate blood sugar control solutions.

[0004] However, the existing IoT-based blood glucose monitoring system still has some shortcomings: The data analysis capability is limited. Most of the data collected are blood sugar data of patients, and then compared to trigger the alarm. The relevant data of patients cannot be further analyzed to determine the reason why the blood sugar triggers the alarm. Most of them only sound the alarm when blood sugar levels are abnormal. There is a lack of preventive analysis, and they are unable to track blood sugar trends when there are no abnormalities, and issue warnings based on blood sugar trends.

[0005] To this end, a blood sugar data monitoring and alarm push system based on the Internet of Things was launched. Summary of the invention

[0006] In view of this, the present invention provides a blood sugar data monitoring alarm push system based on the Internet of Things to solve the problems raised by the above background technology.

[0007] The purpose of the present invention can be achieved through the following technical solutions: A blood sugar data monitoring alarm push system based on the Internet of Things, comprising: Data transmission module: The patient data collected by the blood glucose monitoring device is sent to the intelligent analysis module in real time through the Internet of Things technology; Intelligent analysis module: including blood sugar analysis unit, patient assessment unit and cause determination unit; The blood glucose analysis unit is used to receive the patient's blood glucose data from the data transmission module and preset the safety range corresponding to the blood glucose. If the patient's blood glucose data at a certain time point is higher or lower than the preset safety range, the evaluation signaling is triggered and sent to the patient evaluation unit. If the patient's blood glucose data is within the preset safety range within the set time period, the dynamic analysis signaling is triggered and sent to the dynamic monitoring module. The patient assessment unit is used to extract the patient's diet data and exercise data within a set time period before the time point of triggering the assessment signal when the assessment signal is triggered, and analyze them respectively, thereby obtaining the patient's intake assessment value and exercise assessment value f3 before the time point of triggering the assessment signal; The cause determination unit is used to determine the triggering cause of the current trigger assessment signaling according to the patient's intake assessment value and exercise assessment value f3; the triggering cause includes diet cause and exercise cause; the triggering cause is integrated into an abnormal blood sugar data packet and sent to the alarm push module; if the triggering cause is not diet cause or exercise cause, the diagnostic signaling is triggered to the medical diagnosis module; Dynamic monitoring module: when the dynamic analysis signal is triggered, the patient's blood sugar change data within the set time period is extracted and analyzed, thereby obtaining the patient's blood sugar change index Xt when the dynamic analysis signal is currently triggered; Medical care diagnosis module: receiving the patient's blood sugar change index Xt when the dynamic analysis signal is currently triggered, and executing corresponding steps; also used to receive the triggered diagnosis signal and execute corresponding steps; Alarm push module: receives and parses the integrated blood sugar abnormality data packet, thereby obtaining the patient corresponding to the blood sugar abnormality data packet, extracts the medical staff responsible for the patient and sends an alarm signal to the mobile terminal of the staff.

[0008] In some embodiments, the patient's intake assessment value before the time point of triggering the assessment signal is obtained, specifically: Extracting the types of food and the intake of the corresponding food types from the patient's dietary data within a set time period before the time point of triggering the assessment signal; Calculate the glycemic load of each type of food for the patient, that is, calculate the glycemic load of each food using the formula (number of grams of carbohydrates in the food × GI value of the food) / 100, and record it as GLi, where i represents the number of the corresponding type of food, i=1,2...k, k is the total number of food types consumed by the patient; Preset the load value range of each group of blood glucose load GLi, and set each load value range to correspond to an intake score; The glycemic load GLi of each type of food corresponding to the patient is matched with the preset range of load values ​​for each group, so as to obtain the intake score of each type of food for the patient in the set time period before the triggering evaluation signal time point; the intake scores of each group are added up, and the accumulated value is used as the patient's intake evaluation value.

[0009] In some embodiments, the patient's motion assessment value f3 before the time point of triggering the assessment signal is obtained, specifically: Extracting the patient's movement duration and movement frequency from the patient's movement data within a set time period before the triggering evaluation signaling time point; Count the number of times the patient exercises within a set time period before the triggering evaluation signal time point, and calculate the patient's exercise frequency f1 within the set time period, that is, calculate it by the ratio of exercise times / days; The time point at which the patient's single exercise starts is marked as the starting point, and the time point at which the patient ends is marked as the ending point. The time difference between the starting point and the ending point of the single exercise is calculated to obtain the exercise duration of the patient's corresponding single exercise. The exercise duration of each group of the patient is accumulated to obtain the patient's exercise duration f2 within the set time period; According to the formula , weighted calculation is performed on the patient's exercise frequency f1 and exercise duration f2 in the set time period before the triggering evaluation signaling time point, so as to obtain the patient's exercise evaluation value f3 in the set time period before the triggering evaluation signaling time point; wherein a1 and a2 are the influence weight factors of the exercise frequency f1 and the exercise duration f2, respectively, It is a preset correction factor, and its value is set to 0.957. It can be dynamically adjusted according to the actual situation.

[0010] In some embodiments, the triggering reason of the current triggering assessment signaling is determined according to the patient's intake assessment value and the movement assessment value f3, specifically: The normal reference range of the intake assessment value and the exercise assessment value f3 is preset; if the patient's intake assessment value in the set time period before the triggering assessment signal time point is higher or lower than the preset normal reference range, it is determined that the cause of the abnormal blood oxygen is due to diet; If the intake assessment value is higher than the preset normal reference range, the difference between the intake assessment value and the highest value in the normal reference range is calculated as the patient's diet excess index before the time point when the assessment signal is triggered; if the intake assessment value is lower than the preset normal reference range, the difference between the intake assessment value and the lowest value in the normal reference range is calculated as the patient's diet deficiency index before the time point when the assessment signal is triggered; If the patient's motion assessment value f3 within a set time period before the triggering assessment signaling time point is higher or lower than a preset normal reference range, it is determined that the cause of the abnormal blood oxygen is motion; If the motion assessment value f3 is higher than the preset normal reference range, the difference between the motion assessment value f3 and the highest value in the normal reference range is calculated as the patient's motion excess index before the triggering assessment signal time point; if the motion assessment value f3 is lower than the preset normal reference range, the difference between the motion assessment value f3 and the lowest value in the normal reference range is calculated as the patient's motion deficiency index before the triggering assessment signal time point.

[0011] In some embodiments, the blood sugar change data of the patient within a set time period is extracted and analyzed, specifically: Extract the patient's blood sugar data at each time point within a set time period, and construct a line graph of the patient's blood sugar changes within the set time period, draw the value points in the line graph corresponding to the blood sugar data at each time point, and connect adjacent value points to obtain a blood sugar line; Extract the preset safety range corresponding to blood sugar, and draw the threshold range corresponding to the preset safety range in the line graph; Calculate the slope of each blood sugar line and the angle between it and the horizontal line. If the angle between the blood sugar line and the horizontal line is an acute angle, mark the slope of the blood sugar line as a descending slope. If the angle between the blood sugar line and the horizontal line is an obtuse angle, mark the slope of the blood sugar line as an ascending slope. Sum up all the rising slopes and falling slopes respectively to obtain the total rising and falling values ​​of the patient within the set time period; Calculate the ratio between the rising sum and the falling sum, that is, calculate by rising sum / falling sum, so as to obtain the blood sugar ratio of the patient in the set time period; A reference threshold range of the blood glucose ratio is set. If the blood glucose ratio is higher than the set reference threshold range, it is determined that the patient's blood glucose trend is gradually increasing within the set time period; if the blood glucose ratio is lower than the set reference threshold range, it is determined that the patient's blood glucose trend is gradually decreasing within the set time period; If the blood sugar trend is determined to be gradually increasing, the highest value in the blood sugar data at each time point is extracted, and the difference between the highest value and the preset safety range is calculated, which is recorded as the trigger difference R1; If the blood sugar trend is determined to be gradually decreasing, the lowest value in the blood sugar data at each time point is extracted, and the difference between the lowest value and the preset safety range is calculated, which is recorded as the trigger difference R2.

[0012] In some embodiments, the patient's blood glucose change index Xt when the dynamic analysis signaling is currently triggered is obtained, specifically: The standard deviation formula is used to calculate the blood sugar data at each time point, and the calculated result is used as the blood sugar fluctuation value of the patient within the set time period, recorded as De; where e=1 or 2, D1 represents the blood sugar fluctuation value calculated when the blood sugar trend is gradually increasing, and D2 represents the blood sugar fluctuation value calculated when the blood sugar trend is gradually decreasing; Based on the patient's blood sugar trend within a set time period, according to the formula , weighted calculation is performed on various parameters of the patient to obtain the patient's blood sugar change index Xt within the set time period; where S1 and S2 respectively represent that the patient's blood sugar is in a gradually increasing state and a gradually decreasing state; m1 and m2 are the influence weight factors of the trigger difference value R1 and the blood sugar fluctuation value D1 in the gradually increasing state; u1 and u2 are the influence weight factors of the trigger difference value R2 and the blood sugar fluctuation D2 in the gradually decreasing state.

[0013] In some embodiments, the patient's blood glucose change index Xt is received when the dynamic analysis signaling is currently triggered, and corresponding steps are performed, specifically: Analyze the patient's blood sugar change index Xt to determine whether the patient is in a state of gradually increasing blood sugar or gradually decreasing blood sugar; Preset reference threshold indexes of blood sugar change index Xt in a state where blood sugar gradually increases or in a state where blood sugar gradually decreases; Based on the analysis results, the patient's blood sugar change index Xt when the dynamic analysis signal is currently triggered is compared with the corresponding preset reference threshold index. If it is greater than the corresponding preset reference threshold index, an early warning is triggered and sent to the mobile terminal of the medical staff responsible for the patient.

[0014] In some embodiments, receiving the triggered diagnostic signaling and executing corresponding steps are specifically: Obtain the medical staff responsible for the patient and send the triggered diagnostic signaling to the mobile terminal of the staff. After receiving and confirming the triggered diagnostic signaling, the medical staff goes to the patient's bed number to diagnose and record the cause of the abnormal blood sugar. If the medical staff receives and rejects the triggered diagnostic signal, a circle is drawn with the patient's bed number as the center and the distance as the radius. All medical staff within the circle are screened and a diagnostic signal is sent to their mobile terminals. The medical staff with the shortest distance is selected from the medical staff who have confirmed the diagnostic signal, and the patient's bed number is sent to the mobile terminal of the person.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention extracts the patient's diet data and exercise data within a set time period before the triggering evaluation signaling time point when triggering the evaluation signaling, and analyzes them respectively, thereby obtaining the patient's intake evaluation value and exercise evaluation value before the triggering evaluation signaling time point, and can judge the cause of abnormal blood sugar according to the patient's intake evaluation value and exercise evaluation value, distinguish whether it is caused by diet or exercise factors, and further trigger a diagnosis signaling to notify medical staff to go to the patient's bed number to diagnose and record the cause of abnormal blood sugar when the triggering cause is not a diet cause or an exercise cause, thereby solving the problem that most of the prior art collects the patient's blood sugar data and compares it to trigger an alarm, but cannot further analyze the patient's relevant data to determine the cause of the alarm; The present invention extracts and analyzes the blood sugar change data of the patient within a set time period when the dynamic analysis signaling is triggered, thereby obtaining the blood sugar change index of the patient when the dynamic analysis signaling is currently triggered, and compares the blood sugar change index of the patient when the dynamic analysis signaling is currently triggered with the corresponding preset reference threshold index. If it is greater than the corresponding preset reference threshold index, an early warning is triggered and sent to the mobile terminal of the medical staff responsible for the patient, thereby solving the problem that most of the existing technologies only issue an alarm when the blood sugar is abnormal and lack preventive analysis; The present invention helps to reduce the health risks caused by abnormal blood sugar through real-time monitoring and timely alarm. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 It is a principle block diagram of the present invention; Figure 2 It is a schematic diagram of the blood sugar change line graph in the present invention. DETAILED DESCRIPTION

[0017] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete, and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0018] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless explicitly defined as such herein.

[0019] See also Figure 1-Figure 2 As shown, the blood glucose data monitoring and alarm push system based on the Internet of Things includes a data transmission module, an intelligent analysis module, a dynamic monitoring module, a medical diagnosis module and an alarm push module; The data transmission module is used to send the patient data collected by the blood glucose monitoring device to the intelligent analysis module in real time through the Internet of Things technology; IoT technology, for example, uses the Guihua Network Bluetooth gateway to achieve data transmission between the blood glucose meter and the cloud server; The intelligent analysis module includes a blood sugar analysis unit, a patient assessment unit, and a cause determination unit; The blood glucose analysis unit is used to receive the patient's blood glucose data from the data transmission module and preset the safety range corresponding to the blood glucose. If the patient's blood glucose data at a certain time point is higher or lower than the preset safety range, the evaluation signaling is triggered and sent to the patient evaluation unit. If the patient's blood glucose data is within the preset safety range within the set time period, the dynamic analysis signaling is triggered and sent to the dynamic monitoring module. The patient assessment unit is used to extract the patient's diet data and exercise data within a set time period before the time point of triggering the assessment signal when the assessment signal is triggered, and analyze them respectively, thereby obtaining the patient's intake assessment value and exercise assessment value f3 before the time point of triggering the assessment signal; Specifically: Extracting the types of food and the intake of the corresponding food types from the patient's dietary data within a set time period before the time point of triggering the assessment signal; Calculate the glycemic load of each type of food for the patient, that is, calculate the glycemic load of each food using the formula (number of grams of carbohydrates in the food × GI value of the food) / 100, and record it as GLi, where i represents the number of the corresponding type of food, i=1,2...k, k is the total number of food types consumed by the patient; The GI value of food refers to the glycemic index, which is a measure of the effect of carbohydrates in food on blood sugar concentration; Preset the load value range of each group of blood glucose load GLi, and set each load value range to correspond to an intake score; the technicians will set it according to the actual situation, and it can be dynamically adjusted later; Match the patient's blood glucose load GLi of various types of food with the preset range of load values ​​of each group, so as to obtain the patient's intake score of various types of food in the set time period before the triggering evaluation signal time point; The intake scores of each group were added up, and the accumulated value was used as the patient's intake assessment value; It should be noted that, in summary, by evaluating the patient's dietary intake, it helps patients and medical professionals identify whether the current triggering of the blood sugar alarm is caused by the patient's eating habits.

[0020] Extracting the patient's movement duration and movement frequency from the patient's movement data within a set time period before the triggering evaluation signaling time point; Count the number of times the patient exercises within a set time period before the triggering evaluation signal time point, and calculate the patient's exercise frequency f1 within the set time period, that is, calculate it by the ratio of exercise times / days; The time point at which the patient's single exercise starts is marked as the starting point, and the time point at which the patient ends is marked as the ending point. The time difference between the starting point and the ending point of the single exercise is calculated to obtain the exercise duration of the patient's corresponding single exercise. The exercise duration of each group of the patient is accumulated to obtain the patient's exercise duration f2 within the set time period; According to the formula , weighted calculation is performed on the patient's exercise frequency f1 and exercise duration f2 in the set time period before the triggering evaluation signaling time point, so as to obtain the patient's exercise evaluation value f3 in the set time period before the triggering evaluation signaling time point; wherein a1 and a2 are the influence weight factors of the exercise frequency f1 and the exercise duration f2, respectively, It is a preset correction factor, and its value is set to 0.957. It can be adjusted dynamically according to the actual situation later. It should be noted that, in summary, by evaluating the patient's exercise condition, it is helpful for patients and medical professionals to identify whether the current triggering of the blood sugar alarm is caused by the patient's exercise condition.

[0021] The cause determination unit is used to determine the triggering cause of the current trigger assessment signaling according to the patient's intake assessment value and exercise assessment value f3; the triggering cause includes diet cause and exercise cause; the triggering cause is integrated into an abnormal blood sugar data packet and sent to the alarm push module; if the triggering cause is not diet cause or exercise cause, the diagnostic signaling is triggered to the medical diagnosis module; Specifically: The normal reference ranges of the intake assessment value and the exercise assessment value f3 are preset; The normal reference range is set by technicians based on relevant information such as the patient's gender and age; If the patient's intake assessment value in the set time period before the triggering assessment signal time point is higher or lower than the preset normal reference range, it is determined that the cause of the abnormal blood oxygen is due to diet; If the intake assessment value is higher than the preset normal reference range, the difference between the intake assessment value and the highest value in the normal reference range is calculated as the patient's diet excess index before the time point when the assessment signal is triggered; if the intake assessment value is lower than the preset normal reference range, the difference between the intake assessment value and the lowest value in the normal reference range is calculated as the patient's diet deficiency index before the time point when the assessment signal is triggered; Similarly, if the patient's exercise assessment value f3 in the set time period before the triggering assessment signal time point is higher or lower than the preset normal reference range, it is determined that the cause of the abnormal blood oxygen is exercise; If the motion assessment value f3 is higher than the preset normal reference range, the difference between the motion assessment value f3 and the highest value in the normal reference range is calculated as the patient's motion excess index before the time point of triggering the assessment signal; if the motion assessment value f3 is lower than the preset normal reference range, the difference between the motion assessment value f3 and the lowest value in the normal reference range is calculated as the patient's motion deficiency index before the time point of triggering the assessment signal; The abnormal blood sugar data package also includes the patient’s personal information and bed number; The dynamic monitoring module is used to extract the patient's blood sugar change data within a set time period and analyze it when the dynamic analysis signal is triggered, thereby obtaining the patient's blood sugar change index Xt when the dynamic analysis signal is currently triggered; Specifically: Extract the patient's blood sugar data at each time point within a set time period, and construct a line graph of the patient's blood sugar changes within the set time period, draw the value points in the line graph corresponding to the blood sugar data at each time point, and connect adjacent value points to obtain a blood sugar line; Extract the preset safety range corresponding to blood sugar, and draw the threshold range corresponding to the preset safety range in the line graph; Calculate the slope of each blood sugar line and the angle between it and the horizontal line. If the angle between the blood sugar line and the horizontal line is an acute angle, mark the slope of the blood sugar line as a descending slope. If the angle between the blood sugar line and the horizontal line is an obtuse angle, mark the slope of the blood sugar line as an ascending slope. Sum up all the rising slopes and falling slopes respectively to obtain the total rising and falling values ​​of the patient within the set time period; Calculate the ratio between the rising sum and the falling sum, that is, calculate by rising sum / falling sum, so as to obtain the blood sugar ratio of the patient in the set time period; A reference threshold range of the blood glucose ratio is set. If the blood glucose ratio is higher than the set reference threshold range, it is determined that the patient's blood glucose trend is gradually increasing within the set time period; if the blood glucose ratio is lower than the set reference threshold range, it is determined that the patient's blood glucose trend is gradually decreasing within the set time period; If the blood sugar trend is determined to be gradually increasing, the highest value in the blood sugar data at each time point is extracted, and the difference between the highest value and the preset safety range is calculated, which is recorded as the trigger difference R1; If the blood sugar trend is determined to be gradually decreasing, the lowest value in the blood sugar data at each time point is extracted, and the difference between the lowest value and the preset safety range is calculated, which is recorded as the trigger difference 2 R2; The standard deviation formula is used to calculate the blood sugar data at each time point, and the calculated result is used as the blood sugar fluctuation value of the patient within the set time period, recorded as De; where e=1 or 2, D1 represents the blood sugar fluctuation value calculated when the blood sugar trend is gradually increasing, and D2 represents the blood sugar fluctuation value calculated when the blood sugar trend is gradually decreasing; Based on the patient's blood sugar trend within a set time period, according to the formula , weighted calculation is performed on each parameter of the patient, so as to obtain the patient's blood sugar change index Xt within the set time period; wherein S1 and S2 respectively represent the patient's blood sugar in a gradually increasing state and a gradually decreasing state; m1 and m2 are respectively the influence weight factors of the trigger difference value R1 and the blood sugar fluctuation value D1 in the gradually increasing state; u1 and u2 are respectively the influence weight factors of the trigger difference value R2 and the blood sugar fluctuation D2 in the gradually decreasing state; It should be noted that through the dynamic monitoring module, the system can track and analyze the patient's blood sugar changes in real time and detect abnormal blood sugar fluctuations in time.

[0022] The medical care diagnosis module is used to receive the patient's blood sugar change index Xt when the dynamic analysis signal is currently triggered, and execute the corresponding steps; it is also used to receive the triggered diagnosis signal and execute the corresponding steps; Specifically: Receive the patient's blood sugar change index Xt when the dynamic analysis signaling is currently triggered, and perform corresponding steps, specifically: Analyze the patient's blood sugar change index Xt to determine whether the patient is in a state of gradually increasing blood sugar or gradually decreasing blood sugar; Preset reference threshold indexes of blood sugar change index Xt in a state where blood sugar gradually increases or in a state where blood sugar gradually decreases; Based on the analysis results, the patient's blood sugar change index Xt at the time of the current triggering of the dynamic analysis signaling is compared with the corresponding preset reference threshold index. If it is greater than the corresponding preset reference threshold index, an early warning is triggered and sent to the mobile terminal of the medical staff responsible for the patient; Receive the triggered diagnostic signaling and execute the corresponding steps, specifically: Obtain the medical staff responsible for the patient and send the triggered diagnostic signaling to the mobile terminal of the staff. After receiving and confirming the triggered diagnostic signaling, the medical staff goes to the patient's bed number to diagnose and record the cause of the abnormal blood sugar. The causes of diagnosis include abnormal blood sugar levels caused by patients not taking hypoglycemic drugs as prescribed or improper use of insulin, and patients' mood swings and stress state affecting blood sugar through changes in hormone levels. If the medical staff receives and rejects the triggered diagnosis signaling, a circle is drawn with the patient's bed number as the center and the distance as the radius, and all medical staff within the circle are screened and the diagnosis signaling is sent to their mobile terminals. The medical staff with the shortest distance is selected from the medical staff who confirm the diagnosis signaling, and the patient's bed number is sent to the mobile terminal of the staff; The alarm push module is used to receive and parse the integrated blood sugar abnormality data packet, thereby obtaining the patient corresponding to the blood sugar abnormality data packet, extracting the medical staff responsible for the patient and sending an alarm signal to the mobile terminal of the staff; the alarm signal includes the patient's blood sugar abnormality data packet; Alert information can be delivered in a variety of ways, including SMS, mobile app push, email, or a dedicated medical alert system; The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The influencing weight factor and specific coefficient value in the formula are set by technical personnel in this field according to actual conditions, and can be adjusted and modified later.

[0023] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A blood sugar data monitoring and alarm push system based on the Internet of Things, characterized in that: include: Data transmission module: The patient data collected by the blood glucose monitoring device is sent to the intelligent analysis module in real time through the Internet of Things technology; Intelligent analysis module: including blood sugar analysis unit, patient assessment unit and cause determination unit; The blood glucose analysis unit is used to receive the patient's blood glucose data from the data transmission module and preset the safety range corresponding to the blood glucose. If the patient's blood glucose data at a certain time point is higher or lower than the preset safety range, the evaluation signaling is triggered and sent to the patient evaluation unit. If the patient's blood glucose data is within the preset safety range within the set time period, the dynamic analysis signaling is triggered and sent to the dynamic monitoring module. The patient assessment unit is used to extract the patient's diet data and exercise data within a set time period before the time point of triggering the assessment signal when the assessment signal is triggered, and analyze them respectively, thereby obtaining the patient's intake assessment value and exercise assessment value f3 before the time point of triggering the assessment signal; The cause determination unit is used to determine the triggering cause of the current trigger assessment signaling according to the patient's intake assessment value and exercise assessment value f3; the triggering cause includes diet cause and exercise cause; the triggering cause is integrated into an abnormal blood sugar data packet and sent to the alarm push module; if the triggering cause is not diet cause or exercise cause, the diagnostic signaling is triggered to the medical diagnosis module; Dynamic monitoring module: when the dynamic analysis signal is triggered, the patient's blood sugar change data within the set time period is extracted and analyzed, thereby obtaining the patient's blood sugar change index Xt when the dynamic analysis signal is currently triggered; Medical care diagnosis module: receiving the patient's blood sugar change index Xt when the dynamic analysis signal is currently triggered, and executing corresponding steps; also used to receive the triggered diagnosis signal and execute corresponding steps; Alarm push module: receives and parses the integrated blood sugar abnormality data packet, thereby obtaining the patient corresponding to the blood sugar abnormality data packet, extracts the medical staff responsible for the patient and sends an alarm signal to the mobile terminal of the staff.

2. The blood sugar data monitoring alarm push system based on the Internet of Things according to claim 1 is characterized in that: The patient's intake assessment value before the triggering assessment signaling time point is obtained, specifically: Extracting the types of food and the intake of the corresponding food types from the patient's dietary data within a set time period before the time point of triggering the assessment signal; Calculate the glycemic load of each type of food for the patient, that is, calculate the glycemic load of each food using the formula (number of grams of carbohydrates in the food × GI value of the food) / 100, and record it as GLi, where i represents the number of the corresponding type of food, i=1,2...k, k is the total number of food types consumed by the patient; Preset the load value range of each group of blood glucose load GLi, and set each load value range to correspond to an intake score; The glycemic load GLi of each type of food corresponding to the patient is matched with the preset range of load values ​​for each group, so as to obtain the intake score of each type of food for the patient in the set time period before the triggering evaluation signal time point; the intake scores of each group are added up, and the accumulated value is used as the patient's intake evaluation value.

3. The blood sugar data monitoring alarm push system based on the Internet of Things according to claim 1 is characterized in that: The patient's motion evaluation value f3 before the triggering evaluation signaling time point is obtained, specifically: Extracting the patient's movement duration and movement frequency from the patient's movement data within a set time period before the triggering evaluation signaling time point; Count the number of times the patient exercises within a set time period before the triggering evaluation signal time point, and calculate the patient's exercise frequency f1 within the set time period, that is, calculate it by the ratio of exercise times / days; The time point at which the patient's single exercise starts is marked as the starting point, and the time point at which the patient ends is marked as the ending point. The time difference between the starting point and the ending point of the single exercise is calculated to obtain the exercise duration of the patient's corresponding single exercise. The exercise duration of each group of the patient is accumulated to obtain the patient's exercise duration f2 within the set time period; According to the formula , weighted calculation is performed on the patient's exercise frequency f1 and exercise duration f2 in the set time period before the triggering evaluation signaling time point, so as to obtain the patient's exercise evaluation value f3 in the set time period before the triggering evaluation signaling time point; wherein a1 and a2 are the influence weight factors of the exercise frequency f1 and the exercise duration f2, respectively, It is a preset correction factor, and its value is set to 0.

957. It can be dynamically adjusted according to the actual situation.

4. The blood sugar data monitoring and alarm push system based on the Internet of Things according to claim 1, characterized in that: According to the patient's intake assessment value and movement assessment value f3, the triggering reason of the current trigger assessment signaling is determined, specifically: The normal reference range of the intake assessment value and the exercise assessment value f3 is preset; if the patient's intake assessment value in the set time period before the triggering assessment signal time point is higher or lower than the preset normal reference range, it is determined that the cause of the abnormal blood oxygen is due to diet; If the intake assessment value is higher than the preset normal reference range, the difference between the intake assessment value and the highest value in the normal reference range is calculated as the patient's diet excess index before the time point when the assessment signal is triggered; if the intake assessment value is lower than the preset normal reference range, the difference between the intake assessment value and the lowest value in the normal reference range is calculated as the patient's diet deficiency index before the time point when the assessment signal is triggered; If the patient's motion assessment value f3 within a set time period before the triggering assessment signaling time point is higher or lower than a preset normal reference range, it is determined that the cause of the abnormal blood oxygen is motion; If the motion assessment value f3 is higher than the preset normal reference range, the difference between the motion assessment value f3 and the highest value in the normal reference range is calculated as the patient's motion excess index before the triggering assessment signal time point; if the motion assessment value f3 is lower than the preset normal reference range, the difference between the motion assessment value f3 and the lowest value in the normal reference range is calculated as the patient's motion deficiency index before the triggering assessment signal time point.

5. The blood sugar data monitoring alarm push system based on the Internet of Things according to claim 1, characterized in that: Extract the patient's blood sugar change data within a set time period and analyze it, specifically: Extract the patient's blood sugar data at each time point within a set time period, and construct a line graph of the patient's blood sugar changes within the set time period, draw the value points in the line graph corresponding to the blood sugar data at each time point, and connect adjacent value points to obtain a blood sugar line; Extract the preset safety range corresponding to blood sugar, and draw the threshold range corresponding to the preset safety range in the line graph; Calculate the slope of each blood sugar line and the angle between it and the horizontal line. If the angle between the blood sugar line and the horizontal line is an acute angle, mark the slope of the blood sugar line as a descending slope. If the angle between the blood sugar line and the horizontal line is an obtuse angle, mark the slope of the blood sugar line as an ascending slope. Sum up all the rising slopes and falling slopes respectively to obtain the total rising and falling values ​​of the patient within the set time period; Calculate the ratio between the rising sum and the falling sum, that is, calculate by rising sum / falling sum, so as to obtain the blood sugar ratio of the patient in the set time period; A reference threshold range of the blood glucose ratio is set. If the blood glucose ratio is higher than the set reference threshold range, it is determined that the patient's blood glucose trend is gradually increasing within the set time period; if the blood glucose ratio is lower than the set reference threshold range, it is determined that the patient's blood glucose trend is gradually decreasing within the set time period; If the blood sugar trend is determined to be gradually increasing, the highest value in the blood sugar data at each time point is extracted, and the difference between the highest value and the preset safety range is calculated, which is recorded as the trigger difference R1; If the blood sugar trend is determined to be gradually decreasing, the lowest value in the blood sugar data at each time point is extracted, and the difference between the lowest value and the preset safety range is calculated, which is recorded as the trigger difference R2.

6. The blood sugar data monitoring alarm push system based on the Internet of Things according to claim 1, characterized in that: The patient's blood sugar change index Xt when the dynamic analysis signaling is currently triggered is obtained, specifically: The standard deviation formula is used to calculate the blood sugar data at each time point, and the calculated result is used as the blood sugar fluctuation value of the patient within the set time period, recorded as De; where e=1 or 2, D1 represents the blood sugar fluctuation value calculated when the blood sugar trend is gradually increasing, and D2 represents the blood sugar fluctuation value calculated when the blood sugar trend is gradually decreasing; Based on the patient's blood sugar trend within a set time period, according to the formula , weighted calculation is performed on various parameters of the patient to obtain the patient's blood sugar change index Xt within the set time period; where S1 and S2 respectively represent that the patient's blood sugar is in a gradually increasing state and a gradually decreasing state; m1 and m2 are the influence weight factors of the trigger difference value R1 and the blood sugar fluctuation value D1 in the gradually increasing state; u1 and u2 are the influence weight factors of the trigger difference value R2 and the blood sugar fluctuation D2 in the gradually decreasing state.

7. The blood sugar data monitoring and alarm push system based on the Internet of Things according to claim 1, characterized in that: Receive the patient's blood sugar change index Xt when the dynamic analysis signaling is currently triggered, and perform corresponding steps, specifically: Analyze the patient's blood sugar change index Xt to determine whether the patient is in a state of gradually increasing blood sugar or gradually decreasing blood sugar; Preset reference threshold indexes of blood sugar change index Xt in a state where blood sugar gradually increases or in a state where blood sugar gradually decreases; Based on the analysis results, the patient's blood sugar change index Xt when the dynamic analysis signal is currently triggered is compared with the corresponding preset reference threshold index. If it is greater than the corresponding preset reference threshold index, an early warning is triggered and sent to the mobile terminal of the medical staff responsible for the patient.

8. The blood sugar data monitoring alarm push system based on the Internet of Things according to claim 1, characterized in that: Receive the triggered diagnostic signaling and execute the corresponding steps, specifically: Obtain the medical staff responsible for the patient and send the triggered diagnostic signaling to the mobile terminal of the staff. After receiving and confirming the triggered diagnostic signaling, the medical staff goes to the patient's bed number to diagnose and record the cause of the abnormal blood sugar. If the medical staff receives and rejects the triggered diagnostic signal, a circle is drawn with the patient's bed number as the center and the distance as the radius. All medical staff within the circle are screened and a diagnostic signal is sent to their mobile terminals. The medical staff with the shortest distance is selected from the medical staff who have confirmed the diagnostic signal, and the patient's bed number is sent to the mobile terminal of the person.

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