An adaptive insulin infusion method and system

Abstract

The present invention discloses an adaptive insulin infusion method and system, which calculates blood glucose estimation based on blood glucose value acquired in a period before blood glucose detection signal is lost and first-order and second-order derivatives of blood glucose value with respect to time, and during the period when blood glucose detection signal is lost, when preset conditions are triggered, calculates insulin correction bolus at the corresponding moment according to blood glucose estimation, and divides insulin correction bolus into trust correction bolus for rapid infusion in a first time period and remaining correction bolus for slow infusion in a subsequent second time period, thereby ensuring the safety of insulin infusion during mealtime for users and improving user experience.

Description

Technical Field

[0001] The present invention mainly relates to the field of medical devices, and in particular to an adaptive insulin infusion method and system. Background Art

[0002] The pancreas of a normal person can automatically secrete the required insulin / glucagon according to the glucose level in the human blood, thereby maintaining a reasonable range of blood sugar fluctuations. However, the pancreatic function of diabetic patients is abnormal and cannot normally secrete the insulin required by the human body. Diabetes is a metabolic disease and a lifelong disease. Current medical technology cannot cure diabetes, and can only control the occurrence and development of diabetes and its complications by stabilizing blood sugar.

[0003] Diabetic patients need to test their blood sugar before injecting insulin into their bodies. Current detection methods can continuously detect blood sugar and send the blood sugar value to a display device in real time for users to view. This detection method is called continuous glucose monitoring (CGM). This method requires the detection device to be attached to the surface of the skin, and the probe it carries is inserted into the subcutaneous tissue fluid to complete the detection. According to the blood sugar value detected by CGM, the infusion device injects the currently required insulin into the subcutaneous tissue, thereby forming a closed-loop or semi-closed-loop artificial pancreas.

[0004] At present, the infusion module of the closed-loop artificial pancreas needs to adjust the insulin infusion volume according to the blood sugar value detected by the CGM device to complete large-dose infusion. However, if the signal of the CGM device is lost during this period, such as sensor replacement, poor communication connection, CGM device power outage, etc., the artificial pancreas will not be able to complete the large-dose infusion, which will bring a bad user experience and even bring safety risks.

[0005] Therefore, the prior art urgently needs an adaptive infusion method and system that can complete large-dose insulin infusion even if a blood glucose value signal cannot be received. Summary of the invention

[0006] The embodiment of the present invention discloses an adaptive insulin infusion method and system, which calculates the blood glucose estimation value based on the blood glucose value obtained in the period before the blood glucose detection signal is lost and the first-order and second-order derivatives of the blood glucose value with respect to time. During the period when the blood glucose detection signal is lost, the insulin correction bolus at the corresponding moment is calculated according to the blood glucose estimation value, and the insulin correction bolus is divided into a trust correction bolus that is rapidly infused in a first time period and a remaining correction bolus that is slowly infused in a subsequent second time period, thereby ensuring the safety of insulin infusion during meals and improving the user experience.

[0007] The present invention discloses an adaptive insulin infusion method, which detects the blood sugar level in a user's body through a detection module, determines at least the first-order derivative and the second-order derivative of the blood sugar level with respect to time based on the blood sugar level obtained a period of time before the blood sugar detection signal is lost, and calculates the blood sugar estimation value based on at least the blood sugar level and the first-order derivative and the second-order derivative of the blood sugar level with respect to time; when a predetermined condition is triggered, calculates a correction bolus of insulin according to the blood sugar estimation value, and completes the infusion by an infusion module; wherein the correction bolus is divided into a trusted correction bolus infused in a first time period and a remaining correction bolus infused in a second time period, and the second time period is longer than the first time period.

[0008] According to one aspect of the present invention, the predetermined condition includes at least one of a blood glucose estimated value exceeding a threshold value, a fingertip blood value exceeding a threshold value, and a meal.

[0009] According to one aspect of the present invention, the blood glucose estimation value is also calculated in combination with the fingertip blood value and the blood glucose confidence factor.

[0010] According to one aspect of the present invention, the blood sugar confidence factor is positively correlated with the duration of loss of the blood sugar detection signal.

[0011] According to one aspect of the invention, the confidence correction bolus is determined by the product of the correction bolus and the infusion confidence factor.

[0012] According to one aspect of the present invention, the infusion confidence factor is negatively correlated with the duration of loss of the blood glucose detection signal.

[0013] According to one aspect of the present invention, the first-order derivative and / or second-order derivative of the blood glucose value with respect to time is processed by a smoothing algorithm and then used to calculate the blood glucose estimation value.

[0014] According to one aspect of the present invention, the blood glucose estimation value also includes a calculated value before the blood glucose detection signal is lost.

[0015] According to one aspect of the present invention, before the infusion module performs a correction bolus infusion, confirmation by the user is required.

[0016] The present invention also discloses an adaptive insulin infusion system, comprising a detection module for obtaining the blood sugar value in the user's body; an infusion module for infusing the currently required insulin into the user's body; and a program module for determining at least the first-order derivative and the second-order derivative of the blood sugar value with respect to time based on the blood sugar value obtained in a recent period of time, and calculating the blood sugar estimation value based at least on the blood sugar value and the first-order derivative and the second-order derivative of the blood sugar value with respect to time; wherein a preset algorithm is also provided in the program module, for calculating a large corrective dose of insulin based on the blood sugar estimation value when a predetermined condition is triggered, and the infusion is completed by the infusion module.

[0017] According to one aspect of the present invention, the predetermined condition includes at least one of a blood glucose estimated value exceeding a threshold value, a fingertip blood value exceeding a threshold value, and a meal.

[0018] According to one aspect of the present invention, the program module is also used to obtain the fingertip blood value, and calculate the blood glucose estimation value by combining the fingertip blood value and the blood glucose confidence factor.

[0019] According to one aspect of the present invention, the blood sugar confidence factor is positively correlated with the duration of loss of the blood sugar detection signal.

[0020] According to one aspect of the invention, the insulin correction bolus is divided into a trust correction bolus infused during a first time period and a remainder correction bolus infused during a second time period, the second time period being longer than the first time period.

[0021] According to one aspect of the invention, the confidence correction bolus is determined by the product of the correction bolus and the infusion confidence factor.

[0022] According to one aspect of the present invention, the infusion confidence factor is negatively correlated with the duration of loss of the blood glucose detection signal.

[0023] According to one aspect of the present invention, the blood glucose estimation value also includes a calculated value before the blood glucose detection signal is lost.

[0024] According to one aspect of the present invention, before the infusion module performs a correction bolus infusion, confirmation by the user is required.

[0025] According to one aspect of the present invention, the preset algorithm is one of the classic MPC algorithm, the rMPC algorithm or the composite algorithm. The rMPC algorithm, based on the classic MPC algorithm, converts the asymmetric blood glucose in the original physical space into an approximately symmetric blood glucose risk in the risk space, and calculates the currently required large dose of insulin correction based on the blood glucose risk.

[0026] According to one aspect of the present invention, the blood glucose risk space conversion method of the rMPC algorithm includes one or more of a piecewise weighted method, a relative value conversion, a blood glucose risk index conversion, and an improved control variability grid analysis conversion.

[0027] According to one aspect of the present invention, the blood glucose risk space conversion method of the rMPC algorithm also includes one or more of the following processing methods:

[0028] ① Subtract a component proportional to the estimated predicted plasma insulin concentration;

[0029] ②Deduct the amount of insulin that has not yet taken effect in the body;

[0030] ③ The autoregressive method is used to compensate for the delay in sensing blood glucose and interstitial fluid glucose concentrations.

[0031] According to one aspect of the present invention, the composite algorithm includes a first algorithm and a second algorithm. The first algorithm is used to calculate the first insulin infusion amount I1, and the second algorithm is used to calculate the second insulin infusion amount I2. The composite algorithm further optimizes the calculation of the first insulin infusion amount I1 and the second insulin infusion amount I2 to obtain a large corrected insulin dose I3.

[0032] According to one aspect of the present invention, the correction bolus insulin dose I3 is optimized by the average of the first insulin infusion amount I1 and the second insulin infusion amount I2:

[0033] ① Solve the average value of the first insulin infusion volume I1 and the second insulin infusion volume I2

[0034] ② The average value Bringing it into the first algorithm and the second algorithm to adjust algorithm parameters;

[0035] ③ Recalculate the first insulin infusion amount I1 and the second insulin infusion amount I2 based on the current blood glucose value, the first algorithm after adjusting the parameters, and the second algorithm;

[0036] ④ Repeat steps ① to ③ until I1=I2 and the insulin correction large dose I3=I1=I2.

[0037] According to one aspect of the present invention, the correction bolus insulin dose I3 is optimized by a weighted average of the first insulin infusion amount I1 and the second insulin infusion amount I2:

[0038] ① Solve the weighted mean of the first insulin infusion volume I1 and the second insulin infusion volume I2 Wherein α and β are weighted coefficients of the first insulin infusion amount I1 and the second insulin infusion amount I2, respectively;

[0039] ②The weighted mean Bring it into the first algorithm and the second algorithm to adjust algorithm parameters;

[0040] ③ Recalculating the first insulin infusion amount I1 and the second insulin infusion amount I2 based on the current blood glucose value, the first algorithm after adjusting the parameters, and the second algorithm;

[0041] ④ Perform a cyclic calculation for steps ① to ③ until I1=I2, and the insulin correction large dose I3=I1=I2.

[0042] According to one aspect of the present invention, the insulin correction bolus I3 is obtained by comparing the first insulin infusion volume I1 and the second insulin infusion volume I2 with the statistical analysis result I4 of the historical data:

[0043]

[0044] Compared with the prior art, the technical solution of the present invention has the following advantages:

[0045] In the adaptive insulin infusion method and system disclosed in the present invention, the first-order derivative and the second-order derivative with respect to time of the blood glucose value obtained some time before the blood glucose detection signal is lost are calculated by a program module, and the blood glucose estimation value is calculated based on the blood glucose value and the first-order derivative and the second-order derivative of the blood glucose value with respect to time. When a predetermined condition is triggered, a large corrective dose of insulin is calculated according to the blood glucose estimation value. Even if the blood glucose detection signal is lost, the infusion module can complete the infusion of a large dose of insulin, thereby ensuring the infusion safety of the user.

[0046] Furthermore, the predetermined condition is at least one of the blood glucose valuation exceeding a threshold, the fingertip blood value exceeding a threshold and eating. When the program module detects that the blood glucose valuation or the fingertip blood value exceeds the threshold or the user is eating, it determines that the user needs a correction large dose infusion of insulin, thereby ensuring the safety of the user.

[0047] Furthermore, the calculated correction bolus of insulin is divided into a trust correction bolus that is quickly infused within a first time period to cope with a rapid rise in the user's blood sugar level during a meal, and a remaining correction bolus that is slowly infused within a second time period. After the blood sugar detection signal is restored, the insulin infusion amount can be dynamically adjusted according to the blood sugar value detected by the detection module to adapt to the amount of insulin required in the user's body, thereby avoiding excessive or insufficient insulin infusion and improving the user experience.

[0048] Furthermore, blood sugar valuation can also be calculated in combination with fingertip blood values. During the period when the blood sugar detection signal is lost, on the one hand, the estimated blood sugar value can be calculated by the algorithm, and on the other hand, the blood sugar valuation can be optimized in combination with the fingertip blood value, thereby improving the accuracy of blood sugar valuation and more accurately calculating the large corrected insulin dose, thereby improving the user experience.

[0049] Furthermore, the trust-corrected bolus is determined by the product of the corrected bolus and the infusion trust factor, and the output trust factor is negatively correlated with the duration of loss of the blood glucose detection signal, so that the trust-corrected bolus is infused in a decreasing manner over time, thereby improving the safety of insulin infusion and enhancing the user experience.

[0050] Furthermore, the first-order derivative and / or second-order derivative of the blood glucose value with respect to time is processed by a smoothing algorithm and then used to calculate the blood glucose valuation, which can eliminate the interference of abnormal blood glucose value points, improve the accuracy of blood glucose valuation, and further improve the accuracy of large doses of insulin correction, thereby improving the user experience.

[0051] Furthermore, the algorithm can be used to calculate the blood sugar estimate before the blood sugar detection signal is lost, and compare and calibrate it with the blood sugar value detected by CGM to obtain a more accurate historical blood sugar value. The adjusted historical blood sugar value can also be displayed to the user for the user to understand. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of the relationship between modules of a closed-loop artificial pancreas insulin infusion control system according to an embodiment of the present invention;

[0053] Figure 2 A comparison diagram of the relationship between blood sugar in the risk space and the original physical space obtained by segmented weighted processing and relative value conversion method according to one embodiment of the present invention;

[0054] Figure 3 A comparison diagram of the relationship between blood glucose in the risk space obtained by conversion using the BGRI and CVGA methods and the original physical space according to one embodiment of the present invention;

[0055] Figure 4 is an insulin IOB curve according to an embodiment of the present invention;

[0056] Figure 5 A schematic diagram of a curve showing blood sugar value calculation based on different parameters according to an embodiment of the present invention;

[0057] Figure 6 A schematic diagram of the relationship between modules of a closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present invention;

[0058] Figure 7 A schematic diagram of the relationship between modules of a closed-loop artificial pancreas insulin infusion control system according to yet another embodiment of the present invention;

[0059] Figure 8 The figure is a schematic diagram of the relationship between modules of a closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present invention. DETAILED DESCRIPTION

[0060] As mentioned above, when the blood sugar detection signal is lost, the insulin infusion module will not be able to perform normal large-dose infusion, affecting the user experience and may even affect the user's personal safety.

[0061] In order to solve this problem, the present invention provides an adaptive insulin infusion method and system, which calculates the blood glucose estimation at the lost and future moments based on the blood glucose value obtained some time before the blood glucose detection signal is lost and the first-order and second-order derivatives of the blood glucose value with respect to time. During the period when the blood glucose detection signal is lost, when a predetermined condition is triggered, the insulin correction bolus at the corresponding moment is calculated according to the blood glucose estimation, and the insulin correction bolus is divided into a trusted correction bolus for rapid infusion in a first time period and a remaining correction bolus for slow infusion in a subsequent second time period, thereby ensuring the safety of insulin infusion during meals and improving the user experience.

[0062] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments should not be construed as limiting the scope of the present invention unless otherwise specifically stated.

[0063] In addition, it should be understood that for ease of description, the sizes of the various components shown in the drawings are not necessarily drawn according to actual proportions. For example, the thickness, width, length or distance of certain units may be enlarged relative to other structures.

[0064] The following description of the exemplary embodiments is merely illustrative and is not intended to limit the present invention and its application or use in any sense. Techniques, methods and devices known to ordinary technicians in the relevant field may not be discussed in detail here, but where applicable, these techniques, methods and devices should be considered as part of this specification.

[0065] It should be noted that like reference numerals and letters denote similar items in the following figures, and thus, once an item is defined or described in one figure, it will not require further discussion in the subsequent figure descriptions.

[0066] Figure 1 Schematic diagram of the relationship between modules of a closed-loop artificial pancreas insulin infusion control system according to an embodiment of the present invention.

[0067] The closed-loop artificial pancreas insulin infusion control system disclosed in the embodiment of the present invention mainly includes a detection module 100 , a program module 101 and an infusion module 102 .

[0068] The detection module 100 is used to continuously detect the current blood sugar level of the user. Generally, the detection module 100 is a continuous glucose monitoring (CGM), which can detect the current blood sugar level of the user in real time, monitor blood sugar changes, and send the current blood sugar level to the program module 101.

[0069] The program module 101 is used to control the work of the detection module 100 and the infusion module 102. Therefore, the program module 101 is connected to the detection module 100 and the infusion module 102 respectively. Here, the connection includes a conventional electrical connection or a wireless connection.

[0070] The infusion module 102 includes a mechanical structure necessary for infusing insulin and is controlled by the program module 101. According to the current insulin infusion amount data sent by the program module 101, the infusion module 102 infuses the currently required insulin into the user's body. At the same time, the infusion status of the infusion module 102 can also be fed back to the program module 101 in real time.

[0071] The embodiments of the present invention do not limit the specific positions and connection relationships of the detection module 100, the program module 101 and the infusion module 102, as long as the aforementioned functional conditions can be met.

[0072] For example, in one embodiment of the present invention, the three are electrically connected to form an integral structure. Therefore, the three are attached to the same position of the user's skin. The three modules are connected as a whole and attached to the same position, and the number of devices attached to the user's skin will be reduced, thereby reducing the interference of more devices attached to the user's activities; at the same time, it also effectively solves the problem of wireless communication reliability between separate devices, further enhancing the user experience.

[0073] For example, in another embodiment of the present invention, the program module 101 and the infusion module 102 are connected to each other to form an integral structure, and the detection module 100 is separately arranged in another structure. In this case, the detection module 100 and the program module 101 transmit wireless signals to each other to achieve mutual connection. Therefore, the program module 101 and the infusion module 102 are attached to a certain position of the user's skin, and the detection module 100 is attached to another position of the user's skin.

[0074] In another embodiment of the present invention, the program module 101 and the detection module 100 are connected to each other to form the same device, and the infusion module 102 is separately arranged in another structure. The infusion module 102 and the program module 101 transmit wireless signals to each other to achieve mutual connection. Therefore, the program module 101 and the detection module 100 can be attached to a certain position of the user's skin, and the infusion module 102 can be attached to another position of the user's skin.

[0075] As in another embodiment of the present invention, the three are respectively arranged in different structures. Therefore, the three are respectively attached to different positions of the user's skin. At this time, the program module 101 transmits wireless signals to the detection module 100 and the infusion module 102 to achieve mutual connection.

[0076] It should be noted that the program module 101 of the embodiment of the present invention also has the functions of storing, recording and accessing the database, so the program module 101 can be reused. This not only can store the user's physical condition data, but also saves production costs and user usage costs. As mentioned above, when the detection module 100 or the infusion module 102 expires, the program module 101 can be separated from the detection module 100, the infusion module 102, or from both the detection module 100 and the infusion module 102.

[0077] Generally, the service life of the detection module 100, the program module 101 and the infusion module 102 are different. Therefore, when the three are electrically connected to form the same device, the three can also be separated from each other in pairs. If one module expires first, the user can only replace the module and keep the other two modules for continued use.

[0078] It should be noted that the program module 101 of the embodiment of the present invention may also include multiple submodules. According to the functions of the submodules, different submodules may be respectively arranged in different structures, which are not specifically limited here, as long as the control conditions of the program module 101 can be met.

[0079] Specifically, the program module 101 is preset with an rPID (risk-proportional-integral-differential) algorithm for converting asymmetric blood sugar in the original physical space to approximately symmetric blood sugar risk in the risk space. The rPID algorithm is obtained by conversion processing based on the classic PID (proportional-integral-differential) algorithm. The specific processing method will be described in detail below. According to the corresponding infusion instructions calculated by the rPID algorithm, the program module 101 controls the infusion module 102 to infuse insulin.

[0080] The classic PID algorithm can be expressed by the following formula:

[0081]

[0082] in:

[0083] K P is the gain factor of the proportional part;

[0084] K I is the gain coefficient of the integral part;

[0085] K D is the gain coefficient of the differential part;

[0086] G represents the current blood sugar value;

[0087] G B Indicates target blood glucose value;

[0088] C represents a constant;

[0089] PID(t) represents the infusion instruction sent to the insulin infusion system.

[0090] Taking into account the actual distribution characteristics of glucose concentration in diabetic patients, for example, the normal blood sugar range is 80-140 mg / dL, which can also be relaxed to 70-180 mg / dL. General hypoglycemia can reach 20-40 mg / dL, and hyperglycemia can reach 400-600 mg / dL.

[0091] The distribution of high / low blood sugar has significant asymmetry in the original physical space. In clinical practice, the risks of hyperglycemia and hypoglycemia corresponding to the same degree of blood sugar deviation from the normal range will be significantly different. For example, a decrease of 70 mg / dL from 120 mg / dL to 50 mg / dL will be considered as severe hypoglycemia, which has a high clinical risk and requires emergency measures such as carbohydrate supplementation. However, an increase of 70 mg / dL from 120 mg / dL to 190 mg / dL is just beyond the normal range. For diabetic patients, the degree of high blood sugar is not serious and is often reached in daily situations, so basically no treatment measures are required.

[0092] In view of the asymmetric characteristics of clinical risk of glucose concentration, the asymmetric blood glucose risk in the original physical space is converted to the approximately symmetric blood glucose risk in the risk space, making the PID algorithm more robust.

[0093] Correspondingly, the rPID algorithm formula is converted into the following form:

[0094]

[0095] in:

[0096] rPID(t) represents the infusion instruction sent to the insulin infusion system after risk conversion;

[0097] r represents glycemic risk;

[0098] The meanings of other symbols are as described above.

[0099] In order to maintain the stability of PID integral, combined with the physiological effect of insulin in lowering blood sugar, in one embodiment of the present invention, the input parameter of PID - blood sugar deviation Ge = GG B For example, Ge=GG B Make segmented weighting processing as follows:

[0100]

[0101] In another embodiment of the present invention, for blood glucose greater than the target blood glucose G B The deviation is converted using relative values, as follows:

[0102]

[0103] Figure 2 This is a comparison diagram of the blood glucose relationship between the blood glucose risk space obtained through segmented weighted processing and relative value conversion and the original physical space.

[0104] In the original PID algorithm, the blood glucose risk (i.e., Ge) on both sides of the target blood glucose value shows severe asymmetry consistent with the original physical space. After conversion to the blood glucose risk space, the blood glucose risk on both sides of the blood glucose target value is approximately symmetrical, so that the integral term can remain stable, making the rPID algorithm more robust.

[0105] In another embodiment of the present invention, there is a fixed zero risk point during risk conversion, and the data on both sides of the zero risk point are processed. The original parameter corresponding to a value greater than the zero risk point is a positive value when converted to the risk space, and the original parameter corresponding to a value less than zero risk is a negative value when converted to the risk space. Specifically, the classic blood glucose risk index (BGRI) method can be used for reference. This method is based on clinical practice and believes that the clinical risks of hypoglycemia of 20 mg / dL and hyperglycemia of 600 mg / dL are equivalent. The blood glucose in the range of 20-600 mg / dL is processed as a whole through logarithmization. The blood glucose value corresponding to the zero risk point of this method is set as the target blood glucose value G B The risk space conversion formula is as follows:

[0106]

[0107] in:

[0108] r(G)=10*f(G) 2

[0109] The conversion function f(G) is as follows:

[0110] f(G)=1.509*[(ln(G)) 1.084 -5.381]

[0111] In the classic blood glucose risk index method, the blood glucose value corresponding to the zero risk point of the method is 112 mg / dL. In other embodiments of the present invention, the blood glucose value at the zero risk point can also be adjusted in combination with the risks and data trends of clinical practice, which is not specifically limited here. The risk space of the blood glucose value greater than the zero risk point is fitted, and the specific fitting method is not specifically limited.

[0112] In another embodiment of the present invention, the improved control variable grid analysis (CVGA) method is used for reference. The zero-risk blood glucose value defined by the original CVGA is 110 mg / dL, and the following equal-risk blood glucose value data pairs are assumed (90 mg / dL, 180 mg / dL; 70 mg / dL, 300 mg / dL; 50 mg / dL, 400 mg / dL). In the embodiment of the present invention, the equal-risk data pairs (70 mg / dL, 300 mg / dL) are adjusted in combination with the actual risks in clinical practice and the trend characteristics of the data, and the equal-risk data pairs (70 mg / dL, 300 mg / dL) are corrected to (70 mg / dL, 250 mg / dL), and the zero-risk blood glucose value is set as the target blood glucose value G B . A polynomial model was fitted to it, and the following risk functions were obtained for both sides of the zero risk point:

[0113]

[0114] And its maximum value is limited:

[0115] |r|=min(|r|,n)

[0116] The maximum value n is limited to a range of 0 to 80 mg / dL, and preferably, the value of n is 60 mg / dL.

[0117] In other embodiments of the present invention, the zero-risk point blood glucose value and equal-risk data pairs can also be adjusted in combination with the actual risks and data trends in clinical practice, without specific limitation herein, and then the equal-risk points are fitted, and the specific fitting method is not specifically limited; the specific numerical value used to limit the maximum value is also not specifically limited.

[0118] Figure 3 This is a comparison chart of the relationship between blood glucose risk converted to risk space by BGRI and CVGA methods and blood glucose in the original physical space.

[0119] Similar to the treatment of Zone-MPC, within the normal blood sugar range, the blood sugar risk after conversion by the BGRI and CVGA methods is quite flat, especially within 80-140 mg / dL. Unlike Zone-MPC, which is completely 0 in this range and loses the ability to further adjust, the risk of rPID is flat in this range, but it still has a stable and slow adjustment ability, which can further adjust blood sugar to the target value and achieve more accurate blood sugar control.

[0120] In another embodiment of the present invention, a unified processing method can be used for the data deviating from the zero risk point on both sides. For example, in the aforementioned embodiment, the data deviating from the zero risk point on both sides can use the BGRI or CVGA method; different processing methods can also be used, such as combining the BGRI and CVGA methods at the same time. In this case, the same zero risk point blood glucose value can be used, such as the target blood glucose value G B When the blood sugar level is lower than the target blood sugar level G B The BGRI method is used when the blood glucose level is greater than the target blood glucose level G B When the CVGA method is used,

[0121] r=-r(G),ifG≤G B

[0122] in:

[0123] r(G)=10*f(G) 2

[0124] The conversion function f(G) is as follows:

[0125] f(G)=1.509*[(ln(G)) 1.084 -5.381]

[0126] r=-4.8265*10 4 -4*G 2 +0.45563*G-44.855,ifG>G B .

[0127] Similarly, when the blood sugar level is lower than the target blood sugar level G B When the CVGA method is used, the blood glucose value is greater than the target blood glucose value G B The BGRI method is used when:

[0128] r=r(G),ifG>G B ,

[0129] in:

[0130] r(G)=10*f(G) 2

[0131] Under the conversion function f(G):

[0132] f(G)=1.509*[(ln(G)) 1.084 -5.381]

[0133] r=GG B ,ifG≤G B .

[0134] You can also set a maximum value limit:

[0135] |r|=min(|r|,n)

[0136] The maximum value n is limited to a range of 0 to 80 mg / dL, and preferably, the value of n is 60 mg / dL.

[0137] In other embodiments of the present invention, the blood glucose value at the zero risk point can also be set as the target blood glucose value G B , for blood glucose levels less than or equal to the target blood glucose level G B The BGRI method is used for data with blood glucose values ​​greater than the target blood glucose value G B The data is processed using deviation methods, such as segmented weighted processing or relative value processing.

[0138] When piecewise weighted processing is used, at this time:

[0139] r=-r(G),if G≤G B

[0140] in:

[0141] r(G)=10*f(G) 2

[0142] Under the conversion function f(G):

[0143] f(G)=1.509*[(ln(G)) 1.084 -5.381]

[0144]

[0145] When relative value processing is used:

[0146] r=-r(G),ifG≤G B ,

[0147] in:

[0148] r(G)=10*f(G) 2

[0149] The fitted symmetric transfer function f(G):

[0150] f(G)=1.509*[(ln(C)) 1.084 -5.381]

[0151] r=100*(GG B ) / G,ifG>G B

[0152] When the blood glucose values ​​corresponding to the zero risk points are all the target blood glucose values ​​G BWhen the target blood glucose value is less than or equal to G B The processing functions of the data are consistent when using the segmented weighted processing, relative value processing and CVGA method. Therefore, when the target blood glucose value G is less than or equal to B The data are processed by segmented weighting or relative value processing. When the BGRI method is used for the data with blood glucose values ​​greater than the zero risk point, the processing result is equivalent to the above-mentioned blood glucose value less than or equal to the target blood glucose value G. B When the CVGA method is used, the blood glucose value is greater than the target blood glucose value G B The BGRI method is used and its calculation formula will not be repeated here.

[0153] It should be noted that in each embodiment of the present invention, the target blood glucose value G B The target blood glucose level is 80 to 140 mg / dL. B It is 110~120mg / dL.

[0154] Through the above processing methods, the rPID algorithm can convert the asymmetric blood sugar risk in the original physical space into an approximately symmetric blood sugar risk in the risk space, thereby retaining the simple and robust characteristics of the PID algorithm while also having targeted and clinically valuable blood sugar risk control functions, thereby achieving precise control of the closed-loop artificial pancreas insulin gland infusion system.

[0155] There are three major delay effects in the closed-loop artificial pancreas control system: insulin absorption delay (about 20 minutes from subcutaneous to blood circulation tissue, about 100 minutes to liver), insulin onset delay (about 30-100 minutes), interstitial fluid glucose concentration and blood glucose sensing delay (about 5-15 minutes). Any attempt to accelerate the closed-loop responsiveness may lead to unstable system behavior and system oscillation. In order to compensate for the insulin absorption delay in the closed-loop artificial pancreas control system, in one embodiment of the present invention, an insulin feedback compensation mechanism is introduced. The amount of insulin that has not been absorbed in the body is deducted from the output, a component proportional to the estimated plasma insulin concentration. (The actual insulin secretion of the human body also uses the insulin concentration in the plasma as a negative feedback regulation signal). The formula is as follows:

[0156]

[0157] in:

[0158] PID(t) represents the infusion instruction sent to the insulin infusion system;

[0159] PID c (t) indicating an infusion instruction with compensation sent to an insulin infusion system;

[0160] γ represents the compensation coefficient of the estimated plasma insulin concentration to the algorithm output. A larger coefficient will make the algorithm relatively conservative, while a smaller coefficient will make the algorithm relatively aggressive. Therefore, in the embodiment of the present invention, the range of γ is 0.4-0.6, and preferably, γ is 0.5.

[0161] represents an estimate of the plasma insulin concentration, which can be obtained by various conventional prediction algorithms, such as directly calculating from the infused insulin according to the pharmacokinetic curve of insulin, or using conventional autoregressive methods:

[0162]

[0163] in:

[0164] represents the estimate of plasma insulin concentration at the current moment;

[0165] PID c (n-1) represents the output with compensation at the previous moment;

[0166] represents the estimate of plasma insulin concentration at the previous moment;

[0167] represents the estimate of plasma insulin concentration at the previous moment;

[0168] K0 represents the coefficient of the output part with compensation at the previous moment;

[0169] K1 represents the coefficient of the estimated part of the plasma insulin concentration at the previous moment;

[0170] K2 represents the coefficient of the estimated part of the plasma insulin concentration at the previous moment;

[0171] Among them, the initial value Each time interval can be selected according to actual needs.

[0172] Correspondingly, the compensation output formula after risk conversion using the above method is as follows:

[0173]

[0174] in:

[0175] rPIDc(t) represents the infusion instruction with compensation sent to the insulin infusion system after risk conversion;

[0176] rPID(t) represents the infusion instruction sent to the insulin infusion system after risk conversion;

[0177] The meanings of other characters are as described above.

[0178] In order to compensate for the delayed onset of insulin in the closed-loop artificial pancreas control system, in one embodiment of the present invention, insulin on board (IOB) that has not yet taken effect in the body is introduced, and IOB is deducted from the output of insulin to prevent the accumulation and excessive infusion of insulin, which may cause risks such as postprandial hypoglycemia.

[0179] Figure 4 is an insulin IOB curve according to an embodiment of the present invention.

[0180] according to Figure 4 The IOB curve shown can calculate the cumulative residual amount of previously infused insulin. The selection of a specific curve can be determined based on the user's actual insulin action time.

[0181] PID′(t)=PID(t)-IOB(t)

[0182] in:

[0183] PID'(t) represents the infusion instruction sent to the insulin infusion system after deducting IOB;

[0184] PID(t) represents the infusion instruction sent to the insulin infusion system;

[0185] IOB(t) represents the amount of insulin that has not yet taken effect in the body at time t.

[0186] Accordingly, the output formula after risk conversion by the above method minus the amount of insulin that has not yet taken effect in the body is as follows:

[0187] rPID′(t)=rPID(t)-IOB(t)

[0188] in:

[0189] rPID'(t) represents the infusion instruction sent to the insulin infusion system after risk conversion, minus the amount of insulin that has not yet taken effect in the body;

[0190] rPID(t) represents the infusion instruction sent to the insulin infusion system after risk conversion;

[0191] The meanings of other characters are as described above.

[0192] In order to obtain a more ideal control effect, the calculation of IOB is processed as follows: IOB m 、IOB o The IOBs for mealtime insulin and insulin other than mealtime insulin are as follows:

[0193] IOB(t)=IOB m,t +IOB o,t

[0194] in:

[0195]

[0196] in:

[0197] IOB m,t It represents the amount of mealtime insulin that has not yet taken effect in the body at time t;

[0198] IOB o,t It represents the amount of non-meal insulin that has not yet taken effect in the body at time t;

[0199] D i (i=2-8) represents the corresponding coefficient of the IOB curve corresponding to the insulin action time i;

[0200] I m,t Indicates the amount of mealtime insulin;

[0201] I 0,t Indicates the amount of non-meal insulin;

[0202] IOB(t) represents the amount of insulin in the body that has not yet taken effect at time t.

[0203] By distinguishing mealtime insulin from non-mealtime insulin in IOB, insulin can be cleared faster during meals and when blood sugar is too high, resulting in greater insulin output and faster blood sugar regulation. When approaching the target, a longer insulin action time curve is used to allow insulin to be cleared more slowly, making blood sugar regulation more conservative and robust.

[0204] When PID'(t)>0 or rPID'(t)>0, the final amount of insulin infused is PID'(t) or rPID'(t);

[0205] When PID'(t)<0 or rPID'(t)<0, the final amount of insulin infused is 0.

[0206] In order to compensate for the sensing delay of the tissue fluid glucose concentration and blood glucose in the closed-loop artificial pancreas control system, in one embodiment of the present invention, an autoregressive method is used for compensation, and the formula is as follows:

[0207]

[0208] in,

[0209] G SC (n) represents the interstitial fluid glucose concentration at the current moment, i.e., the measurement value of the sensing system;

[0210] Indicates the estimated concentration of blood glucose at the last moment;

[0211] G SC (n-1) and G SC (n-2) represents the interstitial fluid glucose concentration at the previous moment and the moment before that, respectively;

[0212] K0 represents the coefficient of the estimated concentration of blood glucose at the previous moment;

[0213] K1 and K2 represent the coefficients of interstitial fluid glucose concentration at the previous moment and the moment before that, respectively.

[0214] Among them, the initial moment,

[0215] The blood glucose concentration is estimated by the interstitial fluid glucose concentration, which compensates for the sensing delay of the interstitial fluid glucose concentration and blood glucose, making the PID algorithm more accurate. Correspondingly, the rPID algorithm can also more accurately calculate the actual insulin demand of the human body.

[0216] In the embodiment of the present invention, insulin absorption delay, insulin onset delay, tissue fluid glucose concentration and blood glucose sensing delay can be partially or fully compensated. Preferably, all delay factors are considered and fully compensated to make the rPID algorithm more accurate.

[0217] In another embodiment of the present invention, the program module 101 is preset with an rMPC (risk-model-prediction-control) algorithm for converting asymmetric blood sugar in the original physical space to approximately symmetric blood sugar risk in the risk space. The rMPC algorithm is obtained by performing conversion processing on the basis of the classical MPC (model-prediction-control) algorithm. According to the corresponding infusion instructions calculated by the rMPC algorithm, the program module 101 controls the infusion module 102 to infuse insulin.

[0218] The classic MPC algorithm consists of three elements: prediction model, value function and constraints. The classic MPC prediction model is as follows:

[0219] x t+1 =Ax t +BI t

[0220] G t =Cx t

[0221] in:

[0222] x t+1 Indicates the state parameters at the next moment,

[0223] xt Indicates the current state parameters,

[0224] I t Indicates the insulin infusion amount at the current moment;

[0225] G t Indicates the blood sugar concentration at the current moment.

[0226] The parameter matrix is ​​as follows:

[0227]

[0228]

[0229] C=[1 0 0]

[0230] b1, b2, b3, K are prior values.

[0231] The cost function of MPC is composed of the sum of squared deviations of the output G (blood sugar level) and the sum of squared changes of the input I (insulin amount). MPC needs to obtain the minimum solution of the cost function.

[0232]

[0233] in:

[0234] I' t+j represents the change in insulin infusion volume after step j;

[0235] represents the difference between the predicted blood glucose concentration and the target blood glucose value after the jth step;

[0236] t represents the current time;

[0237] N and P are the number of steps in the control time window and prediction time window, respectively;

[0238] R is the weighting coefficient of the insulin component.

[0239] The amount of insulin infused in step j is I t +I' t+j .

[0240] In the embodiment of the present invention, the control time window T c =30min, prediction time window T p =60min, and the weighted coefficient R of insulin amount is 11000. It should be noted that although the control time window used in the calculation is 30min, only the first step calculation result of insulin output is used in the actual operation. After the operation, the minimum solution of the above value function is recalculated according to the latest blood glucose value obtained.

[0241] In the embodiment of the present invention, the infusion time step in the control time window is j n , j n The value range of is 0 to 30 minutes, preferably 2 minutes. Number of steps N = T c / j n , j ranges from 0 to N.

[0242] In other embodiments of the present invention, the weighting coefficients of the control time window, the prediction time window and the insulin amount may also be selected as other values, which are not specifically limited here.

[0243] As mentioned above, due to the significant asymmetry of the distribution of high / low blood sugar (original physical space), in clinical practice, the risk of high blood sugar and low blood sugar corresponding to the same degree of deviation from the normal range will be significantly different. In view of the asymmetric characteristics of clinical risk of glucose concentration, the asymmetric blood sugar in the original physical space is converted to the approximately symmetric blood sugar risk in the risk space, making the MPC algorithm more accurate and flexible. The value function of the rMPC algorithm after risk conversion is as follows:

[0244]

[0245] in,

[0246] r t+j represents the blood glucose risk value after the jth step;

[0247] I' t+j Represents the change in insulin infusion volume after step j.

[0248] The deviation of blood glucose value is converted into the corresponding blood glucose risk. The specific conversion method is consistent with the method in the aforementioned rPID algorithm, such as segmented weighted processing and relative value processing; it also includes setting a fixed zero risk point in the risk space, and the blood glucose concentration at the zero risk point can be set as the target blood glucose value. The data on both sides of the deviation from the zero risk point are processed, such as using the BGRI and improved CVGA methods; it also includes using different methods to process the data on both sides of the deviation from the target blood glucose value.

[0249] Specifically, when piecewise weighted processing is used:

[0250]

[0251] When relative value processing is used:

[0252]

[0253] When using the classic glycemic risk index approach:

[0254]

[0255] in:

[0256] r(G t+j )=10*f(G t+j ) 2

[0257] Conversion function f(G t+j )as follows:

[0258] f(G t+j )=1.509*[(ln(G t+j )) 1.084 -5.381]

[0259] When using the controlled variability grid analysis method:

[0260]

[0261] At the same time, its maximum value is also limited:

[0262] |r t+j |=min(|r t+j |,n)

[0263] The maximum value n is limited to a range of 0 to 80 mg / dL, and preferably, the value of n is 60 mg / d.

[0264] When the blood sugar level is lower than the target blood sugar level G B The BGRI method is used when the blood glucose level is greater than the target blood glucose level G B When using the CVGA method:

[0265] r t+j =-r(G t+j ),if G t+j ≤G B

[0266] in:

[0267] r(G t+j )=10*f(G t+j ) 2

[0268] Conversion function f(G t+j )as follows:

[0269] f(G t+j )=1.509*[(ln(G t+j )) 1.084 -5.381]

[0270] r t+j =-4.8265*10 4 -4*G t+j2 +0.45563*G t+j -44.855,ifG t+j >G B

[0271] When the blood sugar level is lower than the target blood sugar level G B When the CVGA method is used, the blood glucose value is greater than the target blood glucose value G B When using the BGRI method:

[0272] r t+j = r(G t+j ),ifG t+j >G B

[0273] in:

[0274] r(G t+j )=10*f(G t+j ) 2

[0275] Conversion function f(G t+j )as follows:

[0276] f(G t+j )=1.509*[(ln(G t+j )) 1.084 -5.381]

[0277] r t+j =G t+j -G B ,if G t+j ≤G B .

[0278] You can also set a maximum value limit:

[0279] |r t+j |=min(|r t+j |,n)

[0280] The maximum value n is limited to a range of 0 to 80 mg / dL, and preferably, the value of n is 60 mg / dL.

[0281] When the blood sugar level is lower than the target blood sugar level G B The BGRI method is used when the blood glucose level is greater than the target blood glucose level G B When the piecewise weighted method is used:

[0282] r t+j =-r(G t+j ),if G t+j ≤G B

[0283] in:

[0284] r(G t+j )=10*f(G t+j ) 2

[0285] Conversion function f(G t+j )as follows:

[0286] f(G t+j )=1.509*[(ln(G t+j )) 1.084 -5.381]

[0287]

[0288] When the blood sugar level is lower than the target blood sugar level G B The BGRI method is used when the blood glucose level is greater than the target blood glucose level G B When relative value conversion is used:

[0289] r t+j =-r(G t+j ),if G t+j ≤G B

[0290] in:

[0291] r(G t+j )=10*f(G t+j ) 2

[0292] Conversion function f(G t+j )as follows:

[0293] f(G t+j )=1.509*[(ln(G t+j )) 1.084 -5.381]

[0294]

[0295] When the target blood glucose level is less than or equal to G B The data are processed by segmented weighting or relative value processing. When the BGRI method is used for the data with blood glucose values ​​greater than the zero risk point, the processing result is equivalent to the above-mentioned blood glucose value less than or equal to the target blood glucose value G. B When the CVGA method is used, the blood glucose value is greater than the target blood glucose value G B The BGRI method is used and its calculation formula will not be repeated here.

[0296] It should be noted that in the above conversion formulas:

[0297] r t+jis the blood sugar risk value at step j;

[0298] G t+j is the blood glucose value detected at the jth step.

[0299] Target blood sugar level G B The target blood glucose level is 80 to 140 mg / dL. B It is 110~120mg / dL.

[0300] The comparison of the beneficial effects after risk conversion and the relationship between blood glucose and blood glucose risk are consistent with those in the rPID algorithm and will not be repeated here.

[0301] Similarly, in order to compensate for the delay in insulin absorption, the insulin feedback compensation mechanism can also be used for compensation; in order to compensate for the delay in insulin onset, IOB compensation can also be used; the sensing delay of tissue fluid glucose concentration and blood glucose concentration can also be compensated by autoregression. The specific compensation method is also consistent with the rPID algorithm, specifically:

[0302] For delayed insulin inhalation, the compensation formula is as follows:

[0303]

[0304] in:

[0305] I t+j represents the infusion instruction sent to the insulin infusion system at step j;

[0306] I c(t+j) represents the infusion instruction sent to the insulin infusion system at the jth step after risk conversion;

[0307] γ represents the compensation coefficient of the estimated plasma insulin concentration to the algorithm output. A larger coefficient will make the algorithm relatively conservative, while a smaller coefficient will make the algorithm relatively aggressive. Therefore, in the embodiment of the present invention, the range of γ is 0.4-0.6, and preferably, γ is 0.5.

[0308] represents the estimate of plasma insulin concentration at the jth step.

[0309] For delayed insulin onset, the compensation formula is as follows:

[0310] rI′ t+j = rI t+j -IOB(t+j)

[0311] in:

[0312] I' t+j It represents the infusion instruction sent to the insulin infusion system after deducting IOB at the jth step after risk conversion;

[0313] I t+j represents the infusion instruction sent to the insulin infusion system at the jth step after risk conversion;

[0314] IOB(t+j) represents the amount of insulin that has not yet taken effect in the body at time t+j.

[0315] Similarly, IOB(t+j) can also be distinguished between mealtime and non-mealtime. In this case:

[0316] IOB(t+j)=IOB m,t+j +IOB o,t+j

[0317] in:

[0318]

[0319] in:

[0320] IOB m,t+j It represents the amount of mealtime insulin that has not yet taken effect in the body at time t+j;

[0321] IOB o,t+j It represents the amount of non-meal insulin that has not yet taken effect in the body at time t+j;

[0322] D i (i=2-8) represents the corresponding coefficient of the IOB curve corresponding to the insulin action time i;

[0323] I m,t+j represents the amount of mealtime insulin at time t+j;

[0324] i 0,t+j represents the amount of non-meal insulin at time t+j;

[0325] IOB(t+j) represents the amount of insulin that has not yet taken effect in the body at time t+j.

[0326] When rI' t+j >0, the final amount of insulin infused is rI' t+j ;

[0327] When rI' t+j When <0, the final amount of insulin infused is 0.

[0328] For the sensing delay of tissue fluid glucose concentration and blood glucose concentration, autoregressive compensation can also be used, the formula is as follows:

[0329]

[0330] in,

[0331] GSC (t+j) represents the interstitial fluid glucose concentration at time t+j, i.e., the measurement value of the sensing system;

[0332] represents the estimated blood glucose concentration at time t+j-1;

[0333] G SC (t+j-1) and G SC (t+j-2) represents the interstitial fluid glucose concentration at time t+j-1 and time t+j-2, respectively;

[0334] K0 represents the coefficient of the estimated concentration of blood glucose at time t+j-1;

[0335] K1 and K2 represent the coefficients of interstitial fluid glucose concentration at time t+j-1 and time t+j-2, respectively.

[0336] Among them, the initial moment,

[0337] The beneficial effects produced by various compensation methods are consistent with those in the rPID algorithm and will not be repeated here.

[0338] It should be noted that, in the rMPC algorithm, it is preferred to compensate for the delay in the onset of insulin action and the delay in sensing the interstitial fluid glucose concentration and the blood glucose concentration.

[0339] In another embodiment of the present invention, a composite algorithm is preset in the program module 101, and the composite algorithm includes a first algorithm and a second algorithm. When the detection module 100 detects the current blood sugar value and sends the current blood sugar value to the program module 101, the first algorithm calculates the first insulin infusion amount I1, and the second algorithm calculates the second insulin infusion amount I2. The composite algorithm optimizes the first insulin infusion amount I1 and the second insulin infusion amount I2 to obtain a final insulin infusion amount I3, and sends the final insulin infusion amount I3 to the infusion module 102. The infusion module 102 performs insulin infusion according to the final infusion amount I3.

[0340] The first algorithm and the second algorithm are one of the classic PID algorithm, the classic MPC algorithm, the rMPC algorithm or the rPID algorithm. The rMPC algorithm or the rPID algorithm is an algorithm for converting asymmetric blood glucose in the original physical space to approximately symmetric blood glucose risk in the risk space. The conversion method of blood glucose risk in the rMPC algorithm and the rPID algorithm is as described above.

[0341] When I1=I2, I3=I1=I2;

[0342] When I1≠I2, the arithmetic mean of I1 and I2 can be substituted into the first algorithm and the second algorithm respectively to re-optimize the algorithm parameters. After the parameter optimization, the first algorithm and the second algorithm are used to calculate the insulin infusion required at the current moment. If I1 and I2 are still different, the arithmetic mean of I1 and I2 is taken again and the above process is repeated until I1 and I2 are the same, that is:

[0343] ① Solve the average value of the first insulin infusion volume I1 and the second insulin infusion volume I2

[0344] ② The average value respectively into the first algorithm and the second algorithm to adjust the algorithm parameters;

[0345] ③ Recalculate the first insulin infusion amount I1 and the second insulin infusion amount I2 based on the current blood glucose value, the first algorithm after adjusting the parameters, and the second algorithm;

[0346] ④ Perform a cyclic calculation for steps ① to ③ until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0347] At this time, when the first algorithm or the second algorithm is a PID or rPID algorithm, the algorithm parameter is K P , and K D =T D / K P , T D It can take 60min-90min, K I =T I *K P , T I It can be 150min-450min. When the first algorithm or the second algorithm is the MPC or rPMC algorithm, the algorithm parameter is K.

[0348] When I1≠I2, I1 and I2 can also be weighted, and the calculated values ​​after weighted processing are respectively substituted into the first algorithm and the second algorithm to re-optimize the algorithm parameters. After the parameter optimization, the first algorithm and the second algorithm are used again to calculate the insulin infusion amount required at the current moment. If I1 and I2 are still different, I1 and I2 are weighted again, and the weighted coefficient is adjusted. The above process is repeated until I1 and I2 are the same, that is:

[0349] ① Solve the weighted mean of the first insulin infusion volume I1 and the second insulin infusion volume I2 Where α and β are weighted coefficients of the first insulin infusion amount I1 and the second insulin infusion amount I2, respectively;

[0350] ②The weighted mean Bring it into the first algorithm and the second algorithm to adjust the algorithm parameters;

[0351] ③ Recalculate the first insulin infusion amount I1 and the second insulin infusion amount I2 based on the current blood glucose value, the first algorithm after adjusting the parameters, and the second algorithm;

[0352] ④ Perform a cyclic calculation for steps ① to ③ until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0353] Similarly, when the first algorithm or the second algorithm is a PID or RPID algorithm, the algorithm parameter is K P , and K D =T D / K P , T D It can take 60min-90min, K I =T I *K P , T I It can be 150min-450min. When the first algorithm or the second algorithm is the MPC or rPMC algorithm, the algorithm parameter is K.

[0354] In the embodiment of the present invention, α and β can be adjusted according to the size of the first insulin infusion amount I1 and the second insulin infusion amount I2. When I1≥I2, α≤β; when I1≤I2, α≥β; preferably, α+β=1. In other embodiments of the present invention, α and β can also be other value ranges, which are not specifically limited here.

[0355] When the calculation results of the two are the same, that is, I3=I1=I2, it can be considered that the insulin infusion amount at the current moment can make the blood sugar level reach the ideal level. Through the above-mentioned processing, the algorithms refer to each other. Preferably, the first algorithm and the second algorithm are rMPC algorithm and rPID algorithm respectively, and the two refer to each other, which further improves the accuracy of the output results and makes the results more feasible and reliable.

[0356] In another embodiment of the present invention, the program module 101 is further provided with a memory for storing the user's historical physical state, blood sugar level, insulin infusion amount and other information. Statistical analysis can be performed based on the information in the memory to obtain the statistical analysis result I4 at the current moment. When I1≠I2, I1, I2 and I4 are compared respectively to calculate the final insulin infusion amount I3. The one of I1 and I2 that is closer to the statistical analysis result I4 is selected as the calculation result of the final composite algorithm, that is, the final insulin infusion amount I3. The program module 101 sends the final insulin infusion amount I3 to the infusion device 102 for infusion; that is:

[0357]

[0358] By comparing with historical data, the reliability of insulin infusion volume is ensured from another aspect.

[0359] In another embodiment of the present invention, when I1 and I2 are inconsistent and the difference is large, they can also be adjusted by changing the blood glucose risk space conversion method and / or the compensation method for the delayed effect in the rMPC algorithm and / or rPID algorithm to make them similar, and then the output result of the composite algorithm is finally determined by the above-mentioned arithmetic mean, weighted processing, or comparison with the statistical analysis results.

[0360] In another embodiment of the present invention, the closed-loop artificial pancreas control system further includes a meal recognition module and an exercise recognition module. The modules are used to recognize whether the user is eating or exercising. Common meal recognition modules can be based on the blood sugar change rate E v , and is judged by a specific threshold. Blood glucose change rate E v It can be calculated from two moments before and after, or obtained by linear regression of multiple moments within a period of time. Specifically, when the change rate between two moments before and after is used for calculation, the calculation formula is:

[0361] E v =dG t / dt=(G t -G t-1 ) / Δt

[0362] in:

[0363] E v Indicates the blood sugar change rate at the current moment;

[0364] G t Indicates the blood sugar value at the current moment;

[0365] G t-1 Indicates the blood sugar value at the last moment;

[0366] Δt represents the time interval between the current moment and the previous moment.

[0367] When the rate of change calculation formula at three points is used, the calculation formula is:

[0368] E v =dG t / dt=(3G t -4G t-1 +G t-2 ) / 2Δt

[0369] in:

[0370] G t Indicates the blood sugar value at the current moment;

[0371] Gt-1 Indicates the blood sugar value at the last moment;

[0372] G t-2 Indicates the blood sugar value at the last moment;

[0373] Δt represents the time interval between the current moment and the previous moment.

[0374] In calculating the blood sugar change rate E v The raw continuous glucose data can be filtered or smoothed before use. The threshold can be set to 1.8mg / mL-3mg / mL, or it can be customized.

[0375] In other embodiments of the present invention, the blood glucose change rate E v We can also use the weighted average method to obtain:

[0376]

[0377] in:

[0378] E vi Indicates the blood sugar change rate of each test point in the period before the current moment.

[0379] Blood glucose change rate E obtained by weighted average method v It is smoother, eliminates the interference of some singular points, and improves the accuracy of the algorithm.

[0380] The blood glucose change rate is the first-order derivative of the blood glucose value with respect to time, which can be calculated based on the blood glucose change rate E v Estimation of lost or future blood glucose values. For example, when the blood glucose detection signal is lost due to sensor replacement, poor signal connection, power failure of the detection module 100 (CGM), etc., the blood glucose change rate E can be used to estimate the blood glucose value. v and the most recent blood glucose estimate is lost or a future blood glucose estimate is needed:

[0381] G T =E v *T+G T0

[0382] in:

[0383] G T To estimate blood sugar;

[0384] T is the duration of blood glucose detection signal loss;

[0385] G T0 This is the most recent blood sugar value before the blood sugar detection signal was lost.

[0386] The duration T of blood glucose detection signal loss can be set to an intermittent time mode like a CGM device, such as calculating once every 2 minutes, or it can be set to a higher frequency intermittent time mode, such as calculating once every 1 minute, or it can be set to a continuous time mode.

[0387] It can be understood that if the duration time T of the blood glucose detection signal loss is set to a negative value, then the blood glucose estimation value before the blood glucose detection signal is lost can be calculated according to the above formula. Even if the actual blood glucose value in the user's body can be obtained by CGM before the blood glucose detection signal is lost, the blood glucose estimation value obtained by calculation can be compared and calibrated with the actual blood glucose value to obtain a more accurate historical blood glucose value. In some embodiments of the present invention, the calibrated historical blood glucose value can overwrite the blood glucose value previously measured by CGM, so that the user can know a more accurate historical blood glucose value.

[0388] In a preferred embodiment of the present invention, the duration T of the loss of the blood glucose detection signal is limited to a maximum value, for example, 30 minutes. After the blood glucose detection signal is lost for more than 30 minutes, the program module 101 will no longer calculate the blood glucose valuation, because the long loss time of the blood glucose detection signal will seriously affect the accuracy of the blood glucose valuation. During this period, the user may change the behavior, such as changing from a dining state to a sleeping state or an exercise state. Based on this, the blood glucose valuation and the corresponding calculated insulin injection amount will no longer have a reference significance.

[0389] As mentioned above, refer to Figure 5 , Figure 5 This is a curve diagram of blood glucose values ​​calculated based on different parameters according to an embodiment of the present invention. v To estimate the missing or future blood glucose value, the above algorithm may be inaccurate when the actual blood glucose curve is at a turning point. Therefore, a higher-order derivative of the blood glucose value with respect to time can also be introduced to estimate the missing or future blood glucose value, which can improve the accuracy of the algorithm.

[0390] In the preferred embodiment of the present invention, the second-order derivative of the blood glucose value with respect to time, namely, the blood glucose change acceleration E a To estimate lost or future blood sugar levels, it can accurately estimate blood sugar levels even when actual blood sugar levels are at turning points.

[0391] In some embodiments of the present invention, the blood sugar change acceleration E a The calculation formula is:

[0392]

[0393] in:

[0394] E v0 is the blood sugar change rate at the current moment;

[0395] E v1 It is the blood sugar change rate at the previous moment.

[0396] Based on the acceleration of blood sugar changes E a The calculation formula to estimate blood sugar level is:

[0397]

[0398] As mentioned above, when the blood glucose detection loss duration T is set to a negative value, the blood glucose estimation value for a period of time before the blood glucose detection signal is lost can be calculated.

[0399] In other embodiments of the present invention, the weighted average method may be used to obtain:

[0400]

[0401] in:

[0402] E ai Indicates the acceleration of blood sugar change at each test point in the period before the current moment.

[0403] The blood sugar change acceleration E obtained by weighted average method a It is smoother, eliminates the interference of some singular points, and improves the accuracy of the algorithm.

[0404] In other embodiments of the present invention, a smoothing algorithm such as a quadratic curve fitting method may also be used to calculate the blood sugar change rate and / or blood sugar change acceleration:

[0405]

[0406] in:

[0407] T i is the sampling point time;

[0408] G Ti is the blood glucose value at the sampling point;

[0409] c is the curve constant.

[0410] Before using quadratic curve fitting, it is necessary to sample the blood glucose value for a period of time before the blood glucose detection signal is lost, and obtain a series of combinations of blood glucose value at sampling point and sampling time (G Ti , T i), in the embodiment of the present invention, at least three groups should be sampled to complete the quadratic curve fitting. In the preferred embodiment of the present invention, the three blood glucose values ​​before the blood glucose detection signal is lost are taken as sampling points. In other embodiments of the present invention, the quadratic curve fitting can be performed after the abnormal values ​​of the sampling points are eliminated by statistics, so that the influence of singular points can be eliminated to the greatest extent, making the simulated curve smoother, thereby improving the accuracy of the algorithm.

[0411] In other embodiments of the present invention, other curve fitting algorithms can also be used to calculate the blood glucose change rate E v and / or blood sugar change acceleration E a , such as higher order curve fitting, will not be described here.

[0412] In the embodiment of the present invention, the blood sugar change rate E v and / or blood sugar change acceleration E a It is calculated based on the blood glucose value and the corresponding time before the blood glucose detection signal is lost. Therefore, during the calculation of the blood glucose estimation period, the blood glucose change rate E v and / or blood sugar change acceleration E a This may cause the blood glucose estimate to gradually deviate from the actual blood glucose value during the period when the blood glucose detection signal is lost. Therefore, the calculation time of the blood glucose estimate should not be too long. If the blood glucose detection signal is restored, the blood glucose change rate E needs to be recalculated. v and / or blood sugar change acceleration E a , thereby realizing dynamic or iterative calculation of blood glucose estimation, so that the calculated blood glucose estimation will be more in line with the actual blood glucose value.

[0413] In some embodiments of the present invention, when the program module 101 determines that the estimated blood glucose value exceeds a set threshold, it starts calculating the large correction dose of insulin required by the user and instructs the infusion module 102 to complete the infusion.

[0414] In other embodiments of the present invention, after the user measures the fingertip blood value, when the program module 101 determines that the blood sugar value exceeds the set threshold, it starts calculating the large dose of insulin correction required by the user and instructs the infusion module 102 to complete the infusion.

[0415] The above thresholds can be set by the user or his / her medical staff according to the user's physical condition. It can be understood by those skilled in the art that the setting of the thresholds is not fixed.

[0416] In some further embodiments of the present invention, before the user starts to eat, the program module 101 is given a meal instruction, the program module 101 calculates the large corrected insulin dose required by the user according to the meal instruction, and instructs the infusion module 102 to complete the infusion.

[0417] In some embodiments of the present invention, before the infusion module 102 starts to infuse the correction bolus, it is necessary to obtain a confirmation instruction from the user, and the infusion of the correction bolus can only be started after the user confirms.

[0418] In some embodiments of the present invention, the program module 101 calculates the blood glucose change rate E v and / or blood sugar change acceleration E a After that, the blood glucose value G that has been lost or in the future can be estimated by combining the blood glucose value before the blood glucose detection signal is lost. T , and then according to the estimated blood sugar value G T Calculate the insulin correction dose I required by the human body m :

[0419] I m =G T *μ

[0420] in:

[0421] μ is the insulin sensitivity and can be set by the user.

[0422] The meal instruction can be input by the user, or the program module 101 can be configured according to the blood sugar change rate E. a Or the acceleration of blood sugar change E v Judge for yourself.

[0423] In other embodiments of the present invention, the insulin required by the human body to correct the large dose I m You can also estimate blood sugar level G T On the basis of, it is calculated by using one of the aforementioned classical MPC algorithm, rMPC algorithm or composite artificial pancreas algorithm, which is not limited here.

[0424] In some other embodiments of the present invention, the fingertip blood value G f To optimize the estimated blood sugar values:

[0425] G′ T =G f *f+G T *(1-f)

[0426] in:

[0427] G' T It is an optimized blood sugar estimate combined with fingertip blood value;

[0428] f is the blood sugar confidence factor.

[0429] Fingertip blood value G f It can be used to compensate for the calculation amount deviation that may occur during the calculation process to optimize the calculation accuracy.

[0430] In the embodiment of the present invention, the blood sugar confidence factor f can be set to be adjusted according to the length of time t when the blood sugar detection signal is lost:

[0431]

[0432] in:

[0433] T max The maximum allowed duration of blood glucose detection signal loss.

[0434] As mentioned above, blood glucose estimation will become meaningless after exceeding the maximum allowed time of blood glucose detection signal loss.

[0435] In the embodiment of the present invention, the blood sugar trust factor f is positively correlated with the blood sugar detection signal loss time, that is, the longer the blood sugar detection signal is lost, the more trust is placed on the fingertip blood value, thereby weakening the influence of the blood sugar value estimated by the algorithm and improving the calculation accuracy. This is because as the blood sugar detection signal loss time increases, the blood sugar value estimated by the algorithm will gradually deviate from the actual blood sugar value in the user's body.

[0436] In the embodiment of the present invention, after the blood sugar detection signal is lost, the user can measure the fingertip blood value by himself and enter the measurement result into the program module 101, so that the program module 101 can estimate the blood sugar value after the loss and in the future, and then calculate the corrected insulin bolus dose I that needs to be infused. m .

[0437] After calculating the corrected insulin bolus I m Afterwards, the infusion module 102 performs infusion according to the infusion instruction to alleviate the increase in the user's blood sugar level during meals.

[0438] Considering the blood sugar value G estimated by the algorithm T and insulin correction bolus I m It may not be the user's actual blood sugar value and the actual required insulin bolus. In order to further ensure the user's infusion safety, the insulin correction bolus can be divided into two parts for input, that is, the trust correction bolus I infused in a shorter first time. mx and the remainder of the correction bolus I infused over a subsequent longer second period of time ms ,Right now:

[0439] I m =I mx +I ms

[0440] The duration of the first time is shorter than the duration of the second time. Trust Correction Large Dose I mxThe infusion can be completed in a short time, such as 15 minutes, to alleviate the user's blood sugar level surge during and after meals, but it cannot completely lower the user's blood sugar level. After a period of time, such as 2 hours, the infusion module 102 continues to infuse the remaining correction large dose I at a slower rate. ms , in order to reduce the blood sugar in the user to a safe range. ms During this period, the user may have resumed blood sugar testing. At this time, the blood sugar test signal can be obtained and the remaining correction large dose I can be infused. ms Adjustments can be made based on the regained blood glucose detection signal to further ensure the user's infusion safety.

[0441] In the embodiment of the present invention, the trust correction large dose I mx Corrected high dose I m The product of the infusion trust factor g is determined as:

[0442] I mx =I m *g

[0443] in:

[0444] The infusion confidence factor g is negatively correlated with the duration t of the loss of the blood glucose detection signal, and decreases as the loss duration t increases:

[0445]

[0446] The above formula shows that as the blood glucose detection signal loss time t increases, the infusion module 102 will gradually reduce the trust correction large dose I mx infusion.

[0447] Similar to meal recognition, since exercise can cause a rapid drop in blood sugar, exercise recognition can also be based on the rate of change of blood sugar and judged by a specific threshold. The calculation of the rate of change of blood sugar can also be as described above, and the threshold can be personalized. In order to determine the occurrence of exercise more quickly, the closed-loop artificial pancreas insulin infusion control system also includes a motion sensor (not shown). The motion sensor is used to automatically detect the user's physical activity, and the program module 101 can receive physical activity status information. The motion sensor can automatically and accurately sense the user's physical activity state, and send the activity state parameters to the program module 101, thereby improving the output reliability of the composite algorithm in the exercise scenario.

[0448] The motion sensor may be disposed in the detection module 100, the program module 101 or the infusion module 102. Preferably, in the embodiment of the present invention, the motion sensor is disposed in the program module 101.

[0449] It should be noted that the embodiment of the present invention does not limit the number of motion sensors and the locations where the multiple motion sensors are arranged, as long as the conditions for the motion sensors to sense the user's activity status are met.

[0450] The motion sensor includes a three-axis acceleration sensor or a gyroscope. The three-axis acceleration sensor or the gyroscope can more accurately sense the activity intensity, activity mode or body posture of the body. Preferably, in an embodiment of the present invention, the motion sensor is a combination of a three-axis acceleration sensor and a gyroscope.

[0451] It should be noted that, during the calculation process, the blood glucose risk conversion methods used by the rMPC algorithm and the rPID algorithm may be the same or different, the compensation methods for delayed effects may be the same or different, and the calculation process may also be adjusted according to actual conditions.

[0452] Figure 6 FIG. 4 is a schematic diagram showing the relationship between modules of a closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present invention.

[0453] In the embodiment of the present invention, the closed-loop artificial pancreas insulin infusion control system mainly includes a detection module 100 , an infusion module 102 and an electronic module 103 .

[0454] The detection module 100 is used to continuously detect the user's real-time blood sugar value. Generally, the detection module 100 is a continuous glucose monitoring (CGM), which can detect the blood sugar value in real time, monitor the blood sugar change, and send the current blood sugar value to the infusion module 102 and the electronic module 103.

[0455] The infusion module 102 includes a mechanical structure necessary for infusion of insulin, and also includes an infusion processor 1021 and other components capable of executing the first algorithm, and is controlled by the electronic module 103. After receiving the current blood sugar value sent by the detection module 100, the infusion module 102 calculates the first insulin infusion amount I1 required currently through the first algorithm, and sends the calculated first insulin infusion amount I1 to the electronic module 103.

[0456] The electronic module 103 is used to control the work of the detection module 100 and the infusion module 102. Therefore, the electronic module 103 is connected to the detection module 100 and the infusion module 102 respectively. Here, the electronic module 103 is an external electronic device such as a mobile phone or a handheld device, so the connection refers to a wireless connection. The electronic module 103 includes a second processor. In an embodiment of the present invention, the second processor is an electronic processor 1031 or other element that can execute the second algorithm and the third algorithm. After receiving the current blood sugar value sent by the detection module 100, the electronic module 103 calculates the second insulin infusion amount I2 currently required by the second algorithm. Here, the first algorithm and the second algorithm used by the electronic module 103 and the infusion module 102 to calculate the currently required insulin amount are different.

[0457] After receiving the first insulin infusion amount I1 sent by the infusion module 102, the electronic module 103 further optimizes and calculates the first insulin infusion amount I1 and the second insulin infusion amount I2 through the third algorithm to obtain the final insulin infusion amount I3, and sends the final insulin infusion amount I3 to the infusion module 102, and the infusion module 102 infuses the currently required insulin I3 into the user's body. At the same time, the infusion status of the infusion module 102 can also be fed back to the electronic module 103 in real time. The specific optimization method is as described above. That is:

[0458] When I1=I2, I3=I1=I2;

[0459] When I1≠I2, the electronic module 103 further substitutes the arithmetic mean or weighted value of the two into the algorithm to recalculate the current insulin infusion amounts I1 and I2. If the data are still different, the above process is repeated until I3=I1=I2, that is:

[0460] ② Solve for the average value of the first insulin infusion volume I1 and the second insulin infusion volume I2

[0461] ② The average value Bring it into the first algorithm and the second algorithm to adjust algorithm parameters;

[0462] ③ Recalculate the first insulin infusion amount I1 and the second insulin infusion amount I2 based on the current blood glucose value, the first algorithm after adjusting the parameters, and the second algorithm;

[0463] ④ Repeat steps ① to ③ until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0464] or:

[0465] ① Solve the weighted mean of the first insulin infusion volume I1 and the second insulin infusion volume I2 Wherein α and β are weighted coefficients of the first insulin infusion amount I1 and the second insulin infusion amount I2, respectively;

[0466] ②The weighted mean Bring it into the first algorithm and the second algorithm to adjust algorithm parameters;

[0467] ③ Recalculating the first insulin infusion amount I1 and the second insulin infusion amount I2 based on the current blood glucose value, the first algorithm after adjusting the parameters, and the second algorithm;

[0468] ④ Perform a cyclic calculation for steps ① to ③ until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0469] When the two are different, the electronic module 103 can also perform statistical analysis on the two based on historical information such as the user's physical condition, blood sugar level and insulin infusion amount at various times in the past, obtain the statistical analysis result I4 at the current moment, and compare them, select the one of I1 and I2 that is closer to the statistical analysis result I4 as the final insulin infusion amount I3, and the electronic module 103 sends the final insulin infusion amount I3 to the infusion device 102 for infusion; that is:

[0470]

[0471] In the embodiment of the present invention, the user's historical information may be stored in the electronic module 103 or in a cloud management system (not shown), and the cloud management system is connected to the electronic module 103 via a wireless connection.

[0472] Figure 7 FIG. 4 is a schematic diagram showing the relationship between modules of a closed-loop artificial pancreas insulin infusion control system according to yet another embodiment of the present invention.

[0473] In the embodiment of the present invention, the closed-loop artificial pancreas insulin infusion control system mainly includes a detection module 100 , an infusion module 102 and an electronic module 103 .

[0474] The detection module 100 is used to continuously detect the real-time blood glucose value of the user. Generally, the detection module 100 is a continuous glucose monitoring (CGM), which can detect the blood glucose value in real time and monitor the blood glucose change. The current blood glucose value is only sent to the infusion module 102. The detection module 100 also includes a second processor. In the embodiment of the present invention, the second processor is a detection processor 1001 or other element that can execute the second algorithm. After the detection module 100 detects the real-time blood glucose value, it directly calculates the second insulin infusion amount I2 through the second algorithm, and sends the calculated second insulin infusion amount I2 to the electronic module 103.

[0475] As described above, the infusion module 102 receives the current blood sugar value sent by the detection module 100, calculates the first insulin infusion amount I1 through the first algorithm, and sends the first insulin infusion amount I1 to the electronic module 103. Here, the first algorithm and the second algorithm used by the detection module 103 and the infusion module 102 to calculate the insulin amount are different.

[0476] After receiving the first insulin infusion amount I1 and the second insulin infusion amount I2 respectively sent by the detection module 100 and the infusion module 102, the electronic module 103 further optimizes and calculates the first insulin infusion amount I1 and the second insulin infusion amount I2 through the third algorithm to obtain the final insulin infusion amount I3, and sends the final insulin infusion amount I3 to the infusion module 102, and the infusion module 102 infuses the currently required insulin I3 into the user's body. At the same time, the infusion state of the infusion module 102 can also be fed back to the electronic module 103 in real time. The specific optimization method is as described above.

[0477] In the above two embodiments of the present invention, after the detection module 100 detects the current blood glucose value, the infusion processor 1021 preliminarily calculates the first insulin infusion amount I1, and the second processor (such as the electronic processor 1031 and the detection processor 1001) preliminarily calculates the second insulin infusion amount I2, and sends I1 and I2 to the electronic module 103, the electronic module 103 performs further optimization, and then sends the optimized final insulin infusion amount I3 to the infusion module 102 for insulin infusion, thereby improving the accuracy of the infusion instruction.

[0478] In the above two embodiments of the present invention, the first algorithm and the second algorithm are one of the classical PID algorithm, the classical MPC algorithm, the rMPC algorithm or the rPID algorithm. The advantages of using the rPID or rMPC algorithm for calculation are as described above, and the beneficial effects of the further optimization method are also as described above, which will not be repeated here.

[0479] The embodiments of the present invention do not limit the specific location and connection relationship between the detection module 100 and the infusion module 102, as long as the aforementioned functional conditions can be met.

[0480] For example, in one embodiment of the present invention, the two are electrically connected to form an integral structure and are attached to the same position on the user's skin. The two modules are connected as a whole and attached to the same position, which reduces the number of devices attached to the user's skin, thereby reducing the interference of more devices attached to the user's activities and stretching; at the same time, it also effectively solves the problem of poor wireless communication between separate devices, further enhancing the user experience.

[0481] As in another embodiment of the present invention, the two are respectively arranged in different structures and respectively attached to different positions of the user's skin. At this time, the detection module 100 and the infusion module 102 transmit wireless signals to each other to achieve mutual connection.

[0482] Figure 8 Schematic diagram of the relationship between modules of a closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present invention.

[0483] In an embodiment of the present invention, the closed-loop artificial pancreas insulin infusion control system disclosed in the embodiment of the present invention mainly includes a detection module 200 and an infusion module 202. The detection module 200 is used to continuously detect the current blood sugar value of the user. Generally, the detection module 100 is a continuous glucose monitoring (Continuous Glucose Monitoring, CGM), which can detect the current blood sugar value of the user in real time and monitor blood sugar changes; the detection module 200 also includes a detection processing unit 2001, and the detection processing unit 2001 is preset with an algorithm for calculating the amount of insulin infusion. When the detection module 200 detects the current blood sugar value of the user, the detection processing unit 2001 calculates the amount of insulin required by the user through a preset algorithm, and sends the amount of insulin required by the user to the infusion module 202.

[0484] The infusion module 202 includes a mechanical structure necessary for infusing insulin and an electronic transceiver that receives the user's insulin amount information from the detection module 200. According to the current insulin infusion amount data sent by the detection module 200, the infusion module 202 infuses the currently required insulin into the user's body. At the same time, the infusion status of the infusion module 102 can also be fed back to the detection module 200 in real time.

[0485] In an embodiment of the present invention, the algorithm for calculating the insulin infusion amount preset in the detection processing unit 2001 is one of the classic PID algorithm, the classic MPC algorithm, the rMPC algorithm, the rPID algorithm or the composite algorithm. The method and beneficial effects of calculation using the rPID, rMPC algorithm or the composite algorithm are as described above and will not be repeated here.

[0486] The embodiments of the present invention do not limit the specific location and connection relationship between the detection module 2100 and the infusion module 202, as long as the aforementioned functional conditions can be met.

[0487] For example, in one embodiment of the present invention, the two are electrically connected to form an integral structure and are attached to the same position on the user's skin. The two modules are connected as a whole and attached to the same position, which reduces the number of devices attached to the user's skin, thereby reducing the interference of more devices attached to the user's activities and stretching; at the same time, it also effectively solves the problem of poor wireless communication between separate devices, further enhancing the user experience.

[0488] As in another embodiment of the present invention, the two are respectively arranged in different structures and respectively attached to different positions of the user's skin. At this time, the detection module 200 and the infusion module 202 transmit wireless signals to each other to achieve mutual connection.

[0489] In summary, the present invention discloses an adaptive insulin infusion method and system, which calculates the blood glucose estimates at the lost and future moments based on the blood glucose value obtained some time before the blood glucose detection signal is lost and the first-order and second-order derivatives of the blood glucose value with respect to time. During the period when the blood glucose detection signal is lost, when a preset condition is triggered, the insulin correction bolus at the corresponding moment is calculated according to the blood glucose estimate, and the insulin correction bolus is divided into a trust correction bolus for rapid infusion in a first time period and a remaining correction bolus for slow infusion in a subsequent second time period, thereby ensuring the safety of insulin infusion during meals and improving the user experience.

[0490] Although some specific embodiments of the present invention have been described in detail by way of example, it will be appreciated by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It will be appreciated by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. An adaptive insulin infusion system, characterized in that: include: A detection module, the detection module is used to obtain the blood sugar value in the user; An infusion module, which is used to infuse currently required insulin into the user's body; and A program module, the program module is used to determine at least a first-order derivative and a second-order derivative of the blood glucose value with respect to time based on the blood glucose value obtained a period of time before the blood glucose detection signal is lost, and calculate a blood glucose valuation based on at least the blood glucose value and the first-order derivative and the second-order derivative of the blood glucose value with respect to time; obtain a fingertip blood value, and optimize the blood glucose valuation within a maximum allowed time in combination with the fingertip blood value and a blood glucose trust factor to update the blood glucose valuation, the blood glucose trust factor is positively correlated with the blood glucose detection signal loss time, and in the blood glucose valuation optimization process, the longer the blood glucose detection signal is lost, the more trust is placed in the fingertip blood value; Among them, a preset algorithm is also provided in the program module, and the preset algorithm is used to calculate the large correction dose of insulin according to the blood glucose valuation when a predetermined condition is triggered. The program module is also used to send the large correction dose of insulin to the infusion module, and the infusion module completes the infusion of insulin corresponding to the large correction dose of insulin.

2. The adaptive insulin infusion system according to claim 1, characterized in that: The predetermined condition includes at least one of the estimated blood sugar value exceeding a threshold, the fingertip blood value exceeding a threshold, and eating.

3. The adaptive insulin infusion system according to claim 1, characterized in that: The insulin correction bolus is divided into a trust correction bolus infused during a first time period and a remainder correction bolus infused during a second time period, the second time period being longer than the first time period.

4. The adaptive insulin infusion system according to claim 3, characterized in that: The confidence-corrected bolus is determined by the product of the corrected bolus and the infusion confidence factor.

5. The adaptive insulin infusion system according to claim 4, characterized in that: The infusion confidence factor is negatively correlated with the duration of loss of the blood glucose detection signal.

6. The adaptive insulin infusion system according to claim 1, characterized in that: The blood glucose estimation value also includes a calculated value before the blood glucose detection signal is lost.

7. The adaptive insulin infusion system according to claim 1, characterized in that: Before the infusion module performs a correction large-dose infusion, user confirmation is required.

8. The adaptive insulin infusion system according to claim 1, characterized in that: The preset algorithm is one of the classic MPC algorithm, the rMPC algorithm or the composite algorithm. The rMPC algorithm, based on the classic MPC algorithm, converts the asymmetric blood sugar in the original physical space into an approximately symmetric blood sugar risk in the risk space, and calculates the currently required large dose of insulin correction based on the blood sugar risk.

9. The adaptive insulin infusion system according to claim 8, characterized in that: The blood sugar risk space conversion method of the rMPC algorithm includes one or more of a piecewise weighted method, a relative value conversion, a blood sugar risk index conversion and an improved control variability grid analysis conversion.

10. The adaptive insulin infusion system according to claim 9, characterized in that: The blood glucose risk space conversion method of the rMPC algorithm also includes one or more of the following processing methods: ① Subtract a component proportional to the estimated predicted plasma insulin concentration; ②Deduct the amount of insulin that has not yet taken effect in the body; ③ The autoregressive method is used to compensate for the delay in sensing blood glucose and interstitial fluid glucose concentrations.

11. The adaptive insulin infusion system according to claim 8, characterized in that: The composite algorithm includes a first algorithm and a second algorithm. The first algorithm is used to calculate the first insulin infusion amount I1, and the second algorithm is used to calculate the second insulin infusion amount I2. The composite algorithm further optimizes the calculation of the first insulin infusion amount I1 and the second insulin infusion amount I2 to obtain the insulin correction large dose I3.

12. The adaptive insulin infusion system according to claim 11, characterized in that: The insulin correction bolus I3 is optimized by the average of the first insulin infusion amount I1 and the second insulin infusion amount I2: ① Solve the average value of the first insulin infusion volume I1 and the second insulin infusion volume I2 ② The average value Bring it into the first algorithm and the second algorithm to adjust algorithm parameters; ③ recalculating the first insulin infusion amount I1 and the second insulin infusion amount I2 based on the current blood glucose value, the first algorithm after adjusting the parameters, and the second algorithm; ④ Perform a cyclic calculation for steps ① to ③ until I1=I2, and the insulin correction large dose I3=I1=I2.

13. The adaptive insulin infusion system according to claim 11, characterized in that: The insulin correction bolus I3 is optimized by the weighted average of the first insulin infusion amount I1 and the second insulin infusion amount I2: ① Solve the weighted mean of the first insulin infusion volume I1 and the second insulin infusion volume I2 Wherein α and β are weighted coefficients of the first insulin infusion amount I1 and the second insulin infusion amount I2, respectively; ②The weighted mean Bring it into the first algorithm and the second algorithm to adjust algorithm parameters; ③ Recalculating the first insulin infusion amount I1 and the second insulin infusion amount I2 based on the current blood glucose value, the first algorithm after adjusting the parameters, and the second algorithm; ④ Perform a cyclic calculation for steps ① to ③ until I1=I2, and the insulin correction large dose I3=I1=I2.

14. The adaptive insulin infusion system according to claim 11, characterized in that: The insulin correction large dose I3 is obtained by comparing the first insulin infusion amount I1 and the second insulin infusion amount I2 with the statistical analysis result I4 of the historical data:

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