Patient blood glucose monitoring and risk intervention system based on artificial intelligence

Abstract

The invention discloses a patient blood glucose monitoring and risk intervention system based on artificial intelligence, and the system comprises a blood glucose data monitoring module which records the blood glucose data of a current patient, carries out the datamation according to the individual difference of the patient, and is used for matching other patients having enough high similarity with the blood glucose data of the current patient; the system further comprises a blood glucose data prediction module which further forms a predicted value of the blood glucose data of the current patient according to the blood glucose data characteristics of other patients and makes a corresponding medication suggestion for the current patient according to the predicted value. According to the method, the average blood glucose index, the maximum blood glucose index and the minimum blood glucose index of the current patient are predicted, so that the cognitive level of the blood glucose patient on the own blood glucose data is enhanced, the blood glucose patient can predict the situation of own blood glucose abnormality in time, and the safety of the current blood glucose patient in the illness period is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of blood sugar prediction, and specifically is a patient blood sugar monitoring and risk intervention system based on artificial intelligence. Background Art

[0002] Traditional blood glucose monitoring methods mainly rely on discrete fingertip blood glucose testing, making it difficult for patients to obtain continuous and comprehensive blood glucose data; at the same time, this method cannot effectively combine individual differences for accurate risk assessment, nor can it respond to abnormal blood glucose in real time and provide targeted intervention recommendations.

[0003] In the prior art, the Chinese invention patent with publication number CN118366595A [An integrated comprehensive management system for diabetic patients] records a technical solution for obtaining the blood sugar parameters of each diabetic patient corresponding to the current monitoring period, and intuitively monitoring and targeted analyzing the blood sugar stability states of each diabetic patient in the fasting state, postprandial state and before bedtime state based on the blood sugar parameters of each diabetic patient corresponding to the current monitoring period. However, this technology mainly makes predictions by tracking the periodic data of a single patient; therefore, this solution needs to record a large amount of data of the patient to be tested for a long time in order to make accurate predictions about the patient himself. At the same time, when the patient's health status changes irregularly, it is difficult to make accurate predictions based on the patient's previous health status.

[0004] Therefore, a technical solution for predicting blood glucose values ​​based on big data of similar blood glucose patients is proposed. The characteristics of the short-term health data of the patient to be tested are matched with a large number of patients with the same symptoms, and the matching results are applied to the prediction of the blood glucose values ​​of the patient to be tested, so as to achieve fast and accurate prediction effects. Summary of the invention

[0005] The purpose of the present invention is to solve the above problems, to meet the needs of accurate recording and processing of individual blood glucose data of patients, to try to generate accurate medication recommendations and intervention strategies, to avoid the risk of patients' difficulty in detecting blood glucose, and thus to improve the quality of life of blood glucose patients, and to propose a patient blood glucose monitoring and risk intervention system based on artificial intelligence.

[0006] The technical solution adopted by the present invention is as follows:

[0007] An artificial intelligence-based patient blood glucose monitoring and risk intervention system, comprising at least:

[0008] The blood glucose data monitoring module records the current patient's blood glucose data and constructs a blood glucose curve on the coordinate system. The blood glucose curve includes a normal blood glucose curve and an abnormal blood glucose curve. An abnormal curve set is constructed based on the abnormal blood glucose curve to determine the abnormal normalization circle.

[0009] According to the normal blood glucose curve, a normal curve set is constructed to determine the normal normalized circle, and an abnormal normalized circle and a normal normalized circle are established in the coordinate system to obtain the normalized circle diagram of the current patient, obtain the normalized circle diagrams of other patients, and calculate the similarity α;

[0010] An abnormal ring set and a normal ring set are created according to the abnormal curve set and the normal curve set respectively, a normal ring diagram is obtained based on the normal naturalized ring and the normal ring set, and an abnormal ring diagram is obtained based on the abnormal naturalized ring and the abnormal ring set;

[0011] Obtain normal donut diagrams and abnormal donut diagrams of multiple other patients, and thereby calculate the similarity β between the other patients and the current patient;

[0012] Combine similarity α and similarity β to match other patients with similar blood glucose data to the current patient;

[0013] It also includes a blood sugar data prediction module, which forms a predicted value for the blood sugar data of the current patient based on the blood sugar data of other patients, and performs risk intervention on the current patient based on the predicted value.

[0014] Wherein, the blood sugar data monitoring module records the blood sugar data of the current patient in one cycle and constructs a blood sugar curve on the coordinate system;

[0015] Extract the abnormal curve corresponding to the abnormal state from the abnormal blood glucose curve, connect the endpoints of multiple adjacent abnormal curves to form an abnormal curve set, connect the head and tail endpoints of the abnormal curve set to form a line segment set that runs through the abnormal curve set, and combine the head and tail endpoints of the line segment that runs through the abnormal curve set to form a ring structure that bends into a perfect circle to become an abnormal normalized ring;

[0016] A normal curve corresponding to a normal state is extracted from the normal blood glucose curve, the endpoints of multiple adjacent normal curves are connected to form a normal curve set, the head and tail endpoints of the normal curve set are connected to form a line segment set that runs through the normal curve set, and the head and tail endpoints of the line segment running through the normal curve set are unified to form a ring structure that bends into a perfect circle to become a normalized ring.

[0017] The similarity α between the normalized donut diagrams of the current patient and multiple other patients is calculated according to the following relationship:

[0018]

[0019] Among them, A0 represents the abnormal normalized annulus area of ​​the current patient:

[0020]

[0021] is the outer radius of the normalized donut diagram of the current patient, is the inner radius of the normalized donut diagram of the current patient,

[0022] Represents the abnormal normalized annulus area of ​​the i-th other patient:

[0023]

[0024] in, is the outer radius of the normalized donut chart of the i-th other patient’s blood glucose data, The inner radius of the normalized donut chart for the blood glucose data of the i-th other patient;

[0025] Other patients corresponding to the normalized donut diagram that meet the similarity range α are determined as proportion-matched patients.

[0026] The normal curve set is formed into a normal circle set according to the curvature of the normal normalized circle, and the normal normalized circle and the normal circle set are drawn with the same point as the center in the coordinate system, which is defined as a normal circle diagram of the current patient's blood sugar data;

[0027] In the normal ring diagram, the coordinates of multiple parallel points of the normal ring set relative to the normal normalized ring are recorded as M nor0 、M nor1 、M nor2 ,……,M norn , n represents the number of parallel points in the normal donut diagram, and records the distance R between each parallel point and the corresponding center point E nor0 , R nor1 , R nor2 ,……,R norn .

[0028] The abnormal curve set is bent according to the curvature of the abnormal normalized ring to form an abnormal ring set that fluctuates around the abnormal normalized ring;

[0029] Draw an abnormal normalized circle and an abnormal circle set at the center point F in the coordinate system, which is defined as the abnormal circle diagram of the current patient's blood glucose data. The coordinates of multiple parallel points of the abnormal circle set relative to the abnormal normalized circle in the abnormal circle diagram are recorded as M abn0 、M abn1 、M abn2 ,……,M abnm , m represents the number of parallel points in the abnormal donut diagram, and records the distance R between each parallel point and the corresponding point F abn0 , R abn1 , R abn2 ,……,R abnm .

[0030] Among them, calculate the preliminary coordinate matching degree and distance matching , as follows;

[0031]

[0032]

[0033] in, , Set as the weights of coordinates and distances in a normal donut diagram, respectively. , They are set as the weights of coordinates and distances in the anomaly donut diagram, respectively;

[0034] Set the normal donut chart and abnormal donut chart of other patients' blood glucose data as the control group C k ;

[0035] Candidate control group C k The matching error between the normal donut diagram of the patient's blood glucose data and the normal donut diagram of the patient's blood glucose data in terms of coordinate features :

[0036]

[0037] Candidate control group C k The position of the 𝑗th parallel point in the normal donut diagram, is the position of the dth parallel point of the normal ring set relative to the normal normalized ring in the normal ring diagram of the current patient;

[0038] Candidate control group C k The matching error between the normal donut diagram of the patient's blood glucose data and the normal donut diagram of the current patient's blood glucose data in terms of distance features :

[0039]

[0040] in, Candidate control group C k The distance between the 𝑗th parallel point and point E in the normal donut diagram, is the distance between the dth parallel point of the normal ring set relative to the normal normalized ring and the corresponding point E in the normal ring diagram of the current patient;

[0041] Candidate control group C k The matching error between the abnormal donut diagram of the patient's blood glucose data and the abnormal donut diagram of the current patient's blood glucose data in terms of coordinate features :

[0042]

[0043] Candidate control group C kThe position of the qth parallel point in the abnormal donut diagram, is the position of the rth parallel point of the abnormal ring set relative to the abnormal normalized ring in the abnormal ring diagram of the current patient;

[0044] Candidate control group C k The matching error between the abnormal donut diagram of the patient's blood glucose data and the abnormal donut diagram of the current patient's blood glucose data in terms of distance features :

[0045]

[0046] in, Candidate control group C k The distance between the qth parallel point and point F in the abnormal donut diagram, is the distance between the rth parallel point of the abnormal ring set relative to the abnormal normalized ring and the corresponding point F in the abnormal ring diagram of the current patient.

[0047] Among them, the control group C k The specific calculation formula for the similarity β between the abnormal donut diagram of the patient and the abnormal donut diagram of the current patient is as follows:

[0048]

[0049] in, and is the weighting coefficient, and ;

[0050] Get the similarity β to the control group C k The corresponding multiple amplitudes match the patient.

[0051] Among them, based on the similarity α and Calculation of the proportion matching patient set and amplitude matched patient set , determine the fluctuation range of the current patient's blood sugar data, and determine the current patient's blood sugar abnormality and the level of medication required;

[0052] Will and The set is re-divided into three sets, namely and The collection is , in and not in The collection is , and in and not in The collection is :

[0053] In the collection , and , calculate the average value of the average blood sugar index respectively:

[0054] , , ;

[0055] Average values ​​of maximum glycemic index:

[0056] , , ;

[0057] And the average of the minimum glycemic index:

[0058] , , ;

[0059] At this time, the average blood sugar index of the current patient is calculated and predicted from the screened matching patient set and combined with the weighted coefficient. , maximum glycemic index and minimum glycemic index :

[0060]

[0061] in, , , Separately , and The weighted index of

[0062] Based on the predicted average blood sugar index, maximum blood sugar index and minimum blood sugar index of the current patient, the fluctuation range of the current patient's blood sugar data is predicted and risk intervention is performed.

[0063] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0064] 1. In the present invention, due to the use of a blood glucose data monitoring module, the blood glucose data of the current patient is recorded, and the similarities α and β between other patients and the current patient are calculated, and other patients with blood glucose data similar to the current patient are matched in combination with the similarity α and the similarity β, and a predicted value of the current patient's blood glucose data is formed based on the blood glucose data of other patients, and risk intervention is performed on the current patient based on the predicted value, so as to achieve the purpose of rapid and accurate prediction.

[0065] 2. In the present invention, since a large number of similar patient groups are matched with the current patient, the further development trend of the current patient's condition can be predicted through the development status of the blood sugar condition of the similar patient groups, thereby assisting the current patient to have a more accurate anticipation of the treatment needs and treatment effects of the blood sugar condition.

[0066] 3. In the present invention, since the average blood glucose index, maximum blood glucose index and minimum blood glucose index of the current patient are predicted, it is possible to determine the stage to which the blood glucose data of the current patient belongs and whether the blood glucose data has the possibility of changing beyond the normal range, thereby achieving targeted prediction and planning for the blood glucose data of the current patient, enhancing the blood glucose patient's level of cognition of their own blood glucose data, and enabling the blood glucose patient to predict their own blood glucose abnormality in time, thereby improving the safety of the current blood glucose patient during the illness. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0068] Embodiment 1, a patient blood glucose monitoring and risk intervention system based on artificial intelligence, comprising at least:

[0069] The blood sugar data monitoring module records the blood sugar data of the current patient and digitizes it according to the individual differences of the patient, so as to match other patients whose blood sugar data is sufficiently similar to the current patient;

[0070] It also includes a blood sugar data prediction module, which further forms a predicted value for the current patient's blood sugar data based on the blood sugar data characteristics of other patients, and makes corresponding medication recommendations for the current patient based on the predicted value.

[0071] Among them, during the daily exercise and diet of the current patient, the blood sugar data of the current patient is recorded in a cycle (24 hours) and a blood sugar curve is constructed on the coordinate system. The blood sugar curve includes a normal blood sugar curve and an abnormal blood sugar curve;

[0072] The abnormal blood glucose curve and the normal blood glucose curve data are output for comparison with the control group big data corresponding to the existing blood glucose patients, and the groups with high similarity to the abnormal blood glucose curve and the normal blood glucose curve of the current patient are obtained respectively. According to the blood glucose change characteristics of the groups with high similarity, the blood glucose data of the current patient in the next cycle is predicted, and the predicted blood glucose result of the current patient is output;

[0073] Prompting the patient's predicted blood sugar results, as well as the corresponding blood sugar medication and medical advice, can ensure that the patient pays more attention to taking medication on time and in the right amount. When the patient's condition is serious, it can also prompt the patient to call for timely intervention from nursing staff to maintain safety.

[0074] Further, after abnormal fluctuations in the blood sugar data of the current patient are monitored, the abnormal fluctuation curve of the current patient is obtained, including two features of the blood sugar value and the blood sugar change trend, to determine whether the real-time blood sugar value and the blood sugar change trend are in an abnormal state, wherein:

[0075] Abnormal blood sugar level means that the blood sugar level exceeds the safe range. The blood sugar level safety threshold is (3.9mmol / L-6.1mmol / L);

[0076] The abnormal state of blood sugar change trend refers to a sudden increase or decrease in blood sugar value, resulting in a sharp fluctuation, and the fluctuation amplitude exceeds the preset change curvature threshold safety value (the blood sugar value speed change within 1 hour is less than 1.1mmol / L);

[0077] If there are multiple abnormal states (real-time blood glucose values ​​and blood glucose change trends are mixed), and each abnormal state corresponds to an abnormal curve, the abnormal curve of the real-time blood glucose value refers to the time period from when the real-time blood glucose value exceeds the blood glucose value safety threshold to when it returns to the blood glucose value safety threshold, and the abnormal curve of the blood glucose change trend refers to the time period when the curvature of the increase / decrease of the blood glucose value is greater than the change curvature threshold;

[0078] Extracting abnormal curves corresponding to abnormal states from abnormal blood glucose curves, connecting endpoints of multiple adjacent abnormal curves to form an abnormal curve set, connecting the head and tail endpoints of the abnormal curve set to form a line segment set that runs through the abnormal curve set, and combining the head and tail endpoints of the line segment running through the abnormal curve set to form a ring structure, and further bending the ring structure to a perfect circle to become an abnormal normalized ring;

[0079] Similarly, the normal curve corresponding to the normal state is extracted from the normal blood sugar curve.

[0080] The endpoints of multiple normal curves are connected and bent to form a normal normalized ring.

[0081] Draw an abnormal naturalized circle and a normal naturalized circle through the same center in the coordinate system;

[0082] Thus, a normalized donut chart of the current patient's blood sugar data is obtained;

[0083] The known historical blood sugar data of multiple other patients are also divided into a period of 24 hours to form multiple periodic data, thereby obtaining a normalized donut chart of the blood sugar data of multiple other patients;

[0084] According to the obtained normalized donut charts of multiple other patients, the normalized donut charts of the blood glucose data of the current patient are compared one by one, the similarity α is calculated, and other patients with similarity α close to the current patient data are selected as subsequent reference objects. By comparing the blood glucose data of the reference objects with the current patient, the blood glucose change trend of the current patient is predicted, and the intervention plan is adjusted;

[0085] Calculate the similarity α between the normalized donut graphs of the current patient and multiple other patients. The specific formula is as follows:

[0086]

[0087] Among them, the abnormal normalized annulus area A0 of the current patient is:

[0088]

[0089] is the outer radius of the normalized donut diagram of the current patient, is the inner radius of the normalized donut diagram of the current patient,

[0090] The abnormal normalized annulus area of ​​the i-th other patient :

[0091]

[0092] in, is the outer radius of the normalized donut chart of the i-th other patient’s blood glucose data, The inner radius of the normalized donut chart for the blood glucose data of the i-th other patient;

[0093] Thus, the similarity α between the normalized donut diagram of the current patient and the normalized donut diagram of each other patient is obtained, and the similarity range is set to determine other patients corresponding to the normalized donut diagrams corresponding to the similarity range α as proportion matching patients (similarity 1>α≥0.9);

[0094] The abnormal curve set is further bent according to the curvature of the abnormal normalized ring, and combined with the abnormal normalized ring to form an abnormal ring set that fluctuates around the abnormal normalized ring;

[0095] Similarly, the normal curve set is bent according to the normal normalized ring to form a normal ring set.

[0096] Set points E and F in the coordinate system, and draw a normal naturalized circle and a normal circle set with point E as the center;

[0097] Similarly, at point F, draw the abnormal naturalized ring and the abnormal ring set at the same scale;

[0098] Thus, the normal donut chart () and abnormal donut chart () of the current patient's blood sugar data are obtained;

[0099] In the normal ring diagram, the coordinates of multiple parallel points of the normal ring set relative to the normal normalized ring are recorded as M nor0 、M nor1 、M nor2 ,……,M norn , n represents the number of parallel points in the normal donut diagram, and records the distance R between each parallel point and the corresponding point E nor0 , R nor1 , R nor2 ,……,R norn ;

[0100] Correspondingly, the coordinates of multiple parallel points of the abnormal ring set relative to the abnormal normalized ring in the abnormal ring diagram are recorded as M abn0 、M abn1 、M abn2 ,……,M abnm , m represents the number of parallel points in the abnormal donut diagram, and records the distance R between each parallel point and the corresponding point F abn0 , R abn1 , R abn2 ,……,R abnm ;

[0101] The parallel points are set as inflection points in the curves on the normal ring set and the abnormal ring set that are parallel to the arcs of the corresponding normal naturalized ring and the abnormal naturalized ring, that is, the line between the parallel point and the center of the circle in the normal ring diagram / abnormal ring diagram, and the line between the parallel point and the center of the circle is perpendicular to the intersection of the normal naturalized ring / abnormal naturalized ring;

[0102] Similarly, according to the known normalized donut diagrams of multiple other patients, the normal donut diagrams and abnormal donut diagrams of the corresponding blood glucose data of other patients are obtained, and thus the normal donut diagrams and abnormal donut diagrams of the blood glucose data of other patients are set as the control group C. k , respectively obtain the coordinate features and distance features of the control group and the current patient's blood glucose data, and calculate the preliminary coordinate matching degree S k And the distance matching degree T k ;

[0103]

[0104]

[0105] in, , Set them as the weights of coordinates and distances in the normal donut diagram, respectively, to satisfy ;

[0106] , Set as the weights of coordinates and distances in the anomaly donut diagram respectively, satisfying ;

[0107] Candidate control group C k The matching error between the normal donut diagram of the patient's blood glucose data and the normal donut diagram of the patient's blood glucose data in terms of coordinate features :

[0108]

[0109] Candidate control group C k The position of the 𝑗th parallel point in the normal donut diagram, is the position of the dth parallel point of the normal ring set relative to the normal normalized ring in the normal ring diagram of the current patient;

[0110] Candidate control group C k The matching error between the normal donut diagram of the patient's blood glucose data and the normal donut diagram of the current patient's blood glucose data in terms of distance features :

[0111]

[0112] in, Candidate control group C k The distance between the 𝑗th parallel point and point E in the normal donut diagram, is the distance between the dth parallel point of the normal ring set relative to the normal normalized ring and the corresponding point E in the normal ring diagram of the current patient;

[0113] Candidate control group C k The matching error between the abnormal donut diagram of the patient's blood glucose data and the abnormal donut diagram of the current patient's blood glucose data in terms of coordinate features :

[0114]

[0115] Candidate control group C k The position of the qth parallel point in the abnormal donut diagram, is the position of the rth parallel point of the abnormal ring set relative to the abnormal normalized ring in the abnormal ring diagram of the current patient;

[0116] Candidate control group C k The matching error between the abnormal donut diagram of the patient's blood glucose data and the abnormal donut diagram of the current patient's blood glucose data in terms of distance features :

[0117]

[0118] in, Candidate control group Ck The distance between the qth parallel point and point F in the abnormal donut diagram, is the distance between the rth parallel point of the abnormal ring set relative to the abnormal normalized ring and the corresponding point F in the abnormal ring diagram of the current patient;

[0119] The coordinate matching degree S k And the distance matching degree T k Multiply the product to get the control group C k The similarity β between the abnormal donut diagram of the patient's blood glucose data and the abnormal donut diagram of the current patient's blood glucose data is as follows:

[0120]

[0121] in, and is the weighting coefficient, and ;

[0122] In the above, what needs to be compared is that by comparing the normal donut chart and abnormal donut chart of the current patient's blood sugar data with the control group C k , to obtain the control group C k Multiple amplitude matching patients (similarity 1> ≥0.9).

[0123] In this way, by comparing the similarity α and Calculation of the proportion matching patient set and amplitude matched patient set , through which and The aggregated blood sugar characteristics are used to determine the fluctuation range of the current patient's blood sugar data, and further determine the current patient's blood sugar abnormality and the level of medication required;

[0124] In this, the and The set is re-divided into three sets, namely and The collection is , in and not in The collection is , and in and not in The collection is :

[0125] In the collection , and , calculate the average value of the average blood sugar index respectively:

[0126] , , ;

[0127] Average values ​​of maximum glycemic index:

[0128] , , ;

[0129] And the average of the minimum glycemic index:

[0130] , , ;

[0131] At this time, the average blood sugar index of the current patient is calculated and predicted from the screened matching patient set and combined with the weighted coefficient. , maximum glycemic index and minimum glycemic index :

[0132]

[0133] in, , , Separately , and The weighted index of > + , + + =1.

[0134] Based on the predicted average blood sugar index, maximum blood sugar index and minimum blood sugar index of the current patient, it is possible to determine the stage to which the current patient's blood sugar data belongs and whether the blood sugar data has the possibility of changing beyond the normal range, so as to make targeted predictions and plans for the current patient's blood sugar data, enhance the blood sugar patient's level of cognition of their own blood sugar data, and enable the blood sugar patient to predict their own abnormal blood sugar situation in time, thereby improving the safety of the current blood sugar patient during the illness.

[0135] Finally, it is worth mentioning that if the above current patient's sample prediction provided by big data within the current date is successful, the blood glucose data of the current patient within the current date can be trained as the closest sample and recorded, thereby further improving the subsequent prediction accuracy.

[0136] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A patient blood glucose monitoring and risk intervention system based on artificial intelligence, characterized in that: At least: The blood glucose data monitoring module records the current patient's blood glucose data and constructs a blood glucose curve on the coordinate system. The blood glucose curve includes a normal blood glucose curve and an abnormal blood glucose curve. An abnormal curve set is constructed based on the abnormal blood glucose curve to determine the abnormal normalization circle. According to the normal blood glucose curve, a normal curve set is constructed to determine the normal normalized circle, and an abnormal normalized circle and a normal normalized circle are established in the coordinate system to obtain the normalized circle diagram of the current patient, obtain the normalized circle diagrams of other patients, and calculate the similarity α; An abnormal ring set and a normal ring set are created according to the abnormal curve set and the normal curve set respectively, a normal ring diagram is obtained based on the normal naturalized ring and the normal ring set, and an abnormal ring diagram is obtained based on the abnormal naturalized ring and the abnormal ring set; Obtain normal donut diagrams and abnormal donut diagrams of multiple other patients, and thereby calculate the similarity β between the other patients and the current patient; Combine similarity α and similarity β to match other patients with similar blood glucose data to the current patient; It also includes a blood sugar data prediction module, which forms a predicted value for the blood sugar data of the current patient based on the blood sugar data of other patients, and performs risk intervention on the current patient based on the predicted value.

2. The artificial intelligence-based patient blood glucose monitoring and risk intervention system according to claim 1, characterized in that: The blood sugar data monitoring module records the blood sugar data of the current patient in one cycle and constructs a blood sugar curve on the coordinate system; Extract the abnormal curve corresponding to the abnormal state from the abnormal blood glucose curve, connect the endpoints of multiple adjacent abnormal curves to form an abnormal curve set, connect the head and tail endpoints of the abnormal curve set to form a line segment set that runs through the abnormal curve set, and combine the head and tail endpoints of the line segment that runs through the abnormal curve set to form a ring structure that bends into a perfect circle to become an abnormal normalized ring; A normal curve corresponding to a normal state is extracted from the normal blood glucose curve, the endpoints of multiple adjacent normal curves are connected to form a normal curve set, the head and tail endpoints of the normal curve set are connected to form a line segment set that runs through the normal curve set, and the head and tail endpoints of the line segment running through the normal curve set are unified to form a ring structure that bends into a perfect circle to become a normalized ring.

3. The patient blood glucose monitoring and risk intervention system based on artificial intelligence as claimed in claim 2, characterized in that: The similarity α between the normalized donut diagrams of the current patient and multiple other patients is calculated according to the following relationship: Among them, A0 represents the abnormal normalized annulus area of ​​the current patient: , is the outer radius of the normalized donut diagram of the current patient, is the inner radius of the normalized donut diagram of the current patient, Represents the abnormal normalized annulus area of ​​the i-th other patient: in, is the outer radius of the normalized donut chart of the i-th other patient’s blood glucose data, The inner radius of the normalized donut chart for the blood glucose data of the i-th other patient; Other patients corresponding to the normalized donut diagram that meet the similarity range α are determined as proportion-matched patients.

4. The artificial intelligence-based patient blood glucose monitoring and risk intervention system according to claim 3, characterized in that: The normal curve set is bent according to the curvature of the normal normalized circle to form a normal circle set, and a normal normalized circle and a normal circle set are drawn with the same point as the center in the coordinate system, which is defined as a normal circle diagram of the current patient's blood sugar data; In the normal ring diagram, the coordinates of multiple parallel points of the normal ring set relative to the normal normalized ring are recorded as M nor0 、M nor1 、M nor2 ,……,M norn , n represents the number of parallel points in the normal donut diagram, and records the distance R between each parallel point and the corresponding center point E nor0 , R nor1 , R nor2 ,……,R norn .

5. The patient blood glucose monitoring and risk intervention system based on artificial intelligence as claimed in claim 4, characterized in that: The abnormal curve set is bent according to the curvature of the abnormal normalized ring to form an abnormal ring set that fluctuates around the abnormal normalized ring; Draw an abnormal normalized circle and an abnormal circle set at the center point F in the coordinate system, which is defined as the abnormal circle diagram of the current patient's blood glucose data. The coordinates of multiple parallel points of the abnormal circle set relative to the abnormal normalized circle in the abnormal circle diagram are recorded as M abn0 、M abn1 、M abn2 ,……,M abnm , m represents the number of parallel points in the abnormal donut diagram, and records the distance R between each parallel point and the corresponding point F abn0 , R abn1 , R abn2 ,……,R abnm .

6. The patient blood glucose monitoring and risk intervention system based on artificial intelligence as claimed in claim 5, characterized in that: Calculate the initial coordinate matching And distance matching , as follows; , in, , Set as the weights of coordinates and distances in a normal donut diagram, respectively. , They are set as the weights of coordinates and distances in the anomaly donut diagram, respectively; Set the normal donut chart and abnormal donut chart of other patients' blood glucose data as the control group C k ; Candidate control group C k The matching error between the normal donut diagram of the patient's blood glucose data and the normal donut diagram of the patient's blood glucose data in terms of coordinate features : , Candidate control group C k The position of the 𝑗th parallel point in the normal donut diagram, is the position of the dth parallel point of the normal ring set relative to the normal normalized ring in the normal ring diagram of the current patient; Candidate control group C k The matching error between the normal donut diagram of the patient's blood glucose data and the normal donut diagram of the current patient's blood glucose data in terms of distance features : in, Candidate control group C k The distance between the 𝑗th parallel point and point E in the normal donut diagram, is the distance between the dth parallel point of the normal ring set relative to the normal normalized ring and the corresponding point E in the normal ring diagram of the current patient; Candidate control group C k The matching error between the abnormal donut diagram of the patient's blood glucose data and the abnormal donut diagram of the current patient's blood glucose data in terms of coordinate features : , Candidate control group C k The position of the qth parallel point in the abnormal donut diagram, is the position of the rth parallel point of the abnormal ring set relative to the abnormal normalized ring in the abnormal ring diagram of the current patient; Candidate control group C k The matching error between the abnormal donut diagram of the patient's blood glucose data and the abnormal donut diagram of the current patient's blood glucose data in terms of distance features : in, Candidate control group C k The distance between the qth parallel point and point F in the abnormal donut diagram, is the distance between the rth parallel point of the abnormal ring set relative to the abnormal normalized ring and the corresponding point F in the abnormal ring diagram of the current patient.

7. The patient blood glucose monitoring and risk intervention system based on artificial intelligence as claimed in claim 6, characterized in that: The control group C k The specific calculation formula for the similarity β between the abnormal donut diagram of the patient and the abnormal donut diagram of the current patient is as follows: in, and is the weighting coefficient, and ; Get the similarity β to the control group C k The corresponding multiple amplitudes match the patient.

8. The patient blood glucose monitoring and risk intervention system based on artificial intelligence as claimed in claim 7, characterized in that: Based on the similarity α and Calculation of the proportion matching patient set and amplitude matched patient set , determine the fluctuation range of the current patient's blood sugar data, and determine the current patient's blood sugar abnormality and the level of medication required; Will and The set is re-divided into three sets, namely and The collection is , in and not in The collection is , and in and not in The collection is : In the collection , and , calculate the average value of the average blood sugar index respectively: 、 、 ； Average values ​​of maximum glycemic index: 、 、 ； And the average of the minimum glycemic index: 、 、 ； At this time, the average blood sugar index of the current patient is calculated and predicted from the screened matching patient set and combined with the weighted coefficient. , maximum glycemic index and minimum glycemic index : in, , , Separately , and The weighted index of Based on the predicted average blood sugar index, maximum blood sugar index and minimum blood sugar index of the current patient, the fluctuation range of the current patient's blood sugar data is predicted and risk intervention is performed.

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