Closed-loop artificial pancreas insulin infusion control system

Abstract

The application discloses a closed-loop artificial pancreas insulin infusion control system, which comprises a detection module, an infusion module, a program module and an electronic module.The detection module is used for continuously detecting the current blood glucose value in a user's body; the infusion module comprises an infusion processor, a second processor and a program module; the infusion processor is used for calculating a first insulin infusion amount I1 according to the current blood glucose value by a first algorithm; the second processor is used for calculating a second insulin infusion amount I2 according to the current blood glucose value by a second algorithm; the program module is connected with the detection module and used for controlling the detection module and the infusion module; and the electronic module is used for optimizing and calculating the first insulin infusion amount I1 and the second insulin infusion amount I2 by a third algorithm to obtain a final insulin infusion amount I3, and sending the final insulin infusion amount I3 to the infusion module, so that the infusion module performs insulin infusion according to the final infusion amount I3.Through further processing of the result, the infusion result is more accurate and reliable, and the precise control of the closed-loop artificial pancreas insulin infusion system is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of medical devices, in particular to a closed-loop artificial pancreas insulin infusion control system. BACKGROUND

[0002] The pancreas of a normal person can automatically secrete the required insulin / glucagon according to the glucose level in the blood of the human body, so as to maintain a reasonable blood glucose fluctuation range. The pancreas of a diabetic patient is abnormal and cannot normally secrete the required insulin of 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 glucose.

[0003] A diabetic patient needs to detect blood glucose before injecting insulin into the body. The current detection method can continuously detect blood glucose and send blood glucose data to a display device in real time for the user to view. This detection method is called continuous glucose monitoring (CGM). The detection device is attached to the skin surface, and the probe carried by the detection device is inserted into the subcutaneous tissue fluid to complete the detection. According to the blood glucose value detected by the CGM, the infusion device inputs the required insulin into the subcutaneous tissue, thereby forming a closed-loop or semi-closed-loop artificial pancreas.

[0004] Currently, in order to realize closed-loop or semi-closed-loop control of insulin infusion, proportional-integral-derivative (PID) algorithm and model-predictive-control (MPC) algorithm are widely studied. However, the PID algorithm is not suitable for relatively complex scenarios due to its simple structure, and the MPC algorithm faces the difficulties of difficult establishment of an accurate model and large amount of calculation, which may result in predicted infusion deviation.

[0005] Therefore, there is an urgent need in the prior art for a closed-loop artificial pancreas insulin infusion control system with accurate infusion instructions. SUMMARY

[0006] The embodiments of the present application disclose a closed-loop artificial pancreas insulin infusion control system, which comprises a first algorithm, a second algorithm and a third algorithm. The first algorithm calculates a first insulin infusion amount I1, the second algorithm calculates a second insulin infusion amount I2, and the third algorithm calculates an optimized I1 and I2 to obtain a final insulin infusion amount I3. Through further processing of the results, the infusion results are more accurate and reliable, and the precise control of the closed-loop artificial pancreas insulin infusion system is realized.

[0007] The application discloses a closed-loop artificial pancreas insulin infusion control system, which comprises a detection module, an infusion module, a program module and an electronic module.

[0008] According to an aspect of the application, the first algorithm and the second algorithm are one of a classic PID algorithm, a classic MPC algorithm, an rMPC algorithm or an rPID algorithm.

[0009] According to an aspect of the application, the risk rMPC algorithm and the risk rPID algorithm respectively convert the asymmetric blood glucose in the original physical space to the approximately symmetric blood glucose risk in the risk space on the basis of the classic PID algorithm and the classic MPC algorithm.

[0010] According to an aspect of the application, the blood glucose risk space conversion method of the rMPC algorithm and the rPID algorithm comprises one or more of the following:

[0011] According to an aspect of the application, the blood glucose risk space conversion method of the rMPC algorithm and the rPID algorithm further comprises one or more of the following:

[0012]

[0013] ① deducting a component proportional to the estimated plasma insulin concentration;

[0014] ② deducting the amount of insulin that has not yet taken effect in the body;

[0015] ③ compensating for the sensing delay of blood glucose and interstitial fluid glucose concentration by using an autoregressive method.

[0016] According to an aspect of the application, the final insulin infusion amount I3 is optimized by the average value of the first insulin infusion amount I1 and the second insulin infusion amount I2.

[0017] ① solving the average value of the first insulin infusion amount I1 and the second insulin infusion amount I2

[0018] ② sending the average value to the infusion module​ The weighted mean is brought into the first algorithm and the second algorithm, and the algorithm parameters are adjusted;

[0019] ③The first insulin infusion amount I1 and the second insulin infusion amount I2 are recalculated based on the current blood glucose value, the first algorithm and the second algorithm after the adjustment of the parameters;

[0020] ④The steps ①-③ are cyclically calculated until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0021] According to an aspect of the present application, the final insulin infusion amount I3 is optimized by a weighted mean of the first insulin infusion amount I1 and the second insulin infusion amount I2:

[0022] ①The weighted mean of the first insulin infusion amount I1 and the second insulin infusion amount I2 is solved Wherein, α and β are the weighted coefficients of the first insulin infusion amount I1 and the second insulin infusion amount I2 respectively;

[0023] ②The weighted mean is brought into the first algorithm and the second algorithm, and the algorithm parameters are adjusted;

[0024] ③The first insulin infusion amount I1 and the second insulin infusion amount I2 are recalculated based on the current blood glucose value, the first algorithm and the second algorithm after the adjustment of the parameters;

[0025] ④The steps ①-③ are cyclically calculated until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0026] According to an aspect of the present application, the final insulin infusion amount I3 is obtained by comparing the first insulin infusion amount I1 and the second insulin infusion amount I2 with a statistical analysis result I4 of historical data:

[0027]

[0028] According to an aspect of the present application, the second processor is an electronic processor, which is located in the electronic module.

[0029] According to an aspect of the present application, the second processor is a detection processor, which is located in the detection module.

[0030] According to an aspect of the present application, the detection module and the infusion module are electrically connected to each other to form an integral structure, and are pasted at the same position of the skin.

[0031] According to an aspect of the present application, the detection module and the infusion module are pasted at different positions of the skin respectively, and are connected through wireless connection.

[0032] Compared with the prior art, the technical scheme of the present application has the following advantages:

[0033] The first algorithm calculates a first insulin infusion amount I1, the second algorithm calculates a second insulin infusion amount I2, and the third algorithm calculates an optimized I1 and I2 to obtain a final insulin infusion amount I3.

[0034] Further, the first algorithm and the second algorithm are one of the rMPC algorithm and the rPID algorithm, which converts the asymmetric blood glucose in the original physical space to the approximately symmetric blood glucose risk in the risk space, fully utilizes the advantages of the rPID algorithm and the rMPC algorithm to face complex situations, and enables the artificial pancreas to provide reliable insulin infusion amounts in various situations, so that the blood glucose reaches the ideal level at the expected time, and the precise control of the closed-loop artificial pancreas insulin infusion system is realized.

[0035] Further, the final insulin infusion instruction is the same result obtained by the rMPC algorithm and the rPID algorithm, which makes the result more feasible and reliable.

[0036] Further, the final insulin infusion instruction is the same result obtained by averaging or weighted optimization of different results obtained by the rMPC algorithm and the rPID algorithm, the two sets of algorithms complement each other, and the accuracy of the output structure is further improved.

[0037] Further, the final insulin infusion instruction is obtained by comparing the different results obtained by the rMPC algorithm and the rPID algorithm with the statistical analysis results of historical data, and the reliability of the insulin infusion amount is ensured from another aspect by comparing with the historical data.

[0038] Further, the detection module and the infusion module are connected to form an integral structure and are attached to the same position of the skin. The number of devices attached to the skin of the user will be reduced, thereby weakening the interference of the attachment of more devices on the stretching of the user's activities; at the same time, the problem of poor wireless communication between separated devices is effectively solved, and the user experience is further enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The figure is a schematic diagram of the module relationship of the closed-loop artificial pancreas insulin infusion control system according to an embodiment of the present application.

[0040] Figure 2 The figure is a comparison diagram of the relationship between the risk space and the blood glucose in the original physical space obtained by the segmented weighted processing and the relative value conversion method in an embodiment of the present application.

[0041] Figure 3 a graph showing the relationship between the risk space and the blood glucose of the original physical space obtained by the BGRI and CVGA methods according to one embodiment of the present application;

[0042] Figure 4 an insulin IOB curve according to one embodiment of the present application;

[0043] Figure 5 a schematic diagram of four types of clinical optimal basal rate settings commonly cited according to one embodiment of the present application;

[0044] Figure 6 a schematic diagram of the module relationship of a closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present application;

[0045] Figure 7 a schematic diagram of the module relationship of a closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present application;

[0046] Figure 8 a schematic diagram of the module relationship of a closed-loop artificial pancreas multi-drug infusion control system according to another embodiment of the present application;

[0047] Figure 9 a schematic diagram of a double-drug switching according to one embodiment of the present application;

[0048] Figure 10 a schematic diagram of the module relationship of a closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present application. DETAILED DESCRIPTION

[0049] As described above, since the PID algorithm is simple in structure, it is not suitable for relatively complex scenarios, and the MPC algorithm faces the difficulties of difficult establishment of an accurate model and large amount of calculation, which may result in a predicted infusion deviation.

[0050] To solve the problem, the present application provides a closed-loop artificial pancreas insulin infusion control system, which comprises a first algorithm, a second algorithm and a third algorithm, the first algorithm calculates a first insulin infusion amount I1, the second algorithm calculates a second insulin infusion amount I2, and the third algorithm calculates an optimized I1 and I2 to obtain a final insulin infusion amount I3, through further processing of the result, the infusion result is more accurate and reliable, and the precise control of the closed-loop artificial pancreas insulin infusion system is realized.

[0051] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be understood that the relative arrangement, numerical expressions and values of the components and steps set forth in these embodiments should not be construed as limiting the scope of the present application unless specifically stated otherwise.

[0052] It is to be understood that the drawings are to be used only for illustrative purposes and that they are not to be used to limit the present application in any way. In particular, the dimensions of the various elements shown in the figures are not to scale with each other for purposes of illustration.

[0053] The following description of example embodiments is merely illustrative in nature and is in no way intended to limit the application or its application or uses. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art can not be discussed in detail herein. However, where appropriate, such techniques, methods, and apparatus should be considered as falling within the scope of the present description.

[0054] It should be noted that like reference numerals and letters refer to like items in the following drawings, and as a result, once an item is defined or described in one drawing, it need not be discussed further in subsequent drawings.

[0055] Figure 1 A schematic diagram of the relationship between the modules of the closed-loop artificial pancreas insulin infusion control system of embodiments of the present application.

[0056] The closed-loop artificial pancreas insulin infusion control system disclosed in embodiments of the present application mainly comprises a detection module 100, a program module 101, and an infusion module 102.

[0057] The detection module 100 is used to continuously detect the current blood glucose value of the user. Generally, the detection module 100 is a continuous glucose monitor (CGM), which can detect the current blood glucose value of the user in real time and monitor the change in blood glucose, and send the current blood glucose value to the program module 101.

[0058] The program module 101 is used to control the operation 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 conventional electrical connection or wireless connection.

[0059] The infusion module 102 contains the mechanical structure necessary for insulin infusion, and is controlled by the program module 101. According to the current insulin infusion amount data issued by the program module 101, the infusion module 102 infuses the current required insulin into the user's body. At the same time, the infusion state of the infusion module 102 can also be fed back to the program module 101 in real time.

[0060] The embodiments of the present application 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 above-mentioned functional conditions can be met.

[0061] As in one embodiment of the present application, the three are electrically connected to each other to form an integral structure. Thus, the three are pasted at the same position on the user's skin. The three modules are connected into an integral and pasted at the same position, the number of devices pasted on the user's skin will be reduced, thereby weakening the interference to the user's activities caused by pasting more devices; at the same time, the problem of the reliability of wireless communication between the separated devices is effectively solved, further enhancing the user experience.

[0062] As in another embodiment of the present application, 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. At this time, the detection module 100 and the program module 101 transmit wireless signals to each other to realize the connection with each other. Thus, the program module 101 and the infusion module 102 are pasted at a certain position on the user's skin, and the detection module 100 is pasted at another position on the user's skin.

[0063] As in another embodiment of the present application, 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 realize the connection with each other. Thus, the program module 101 and the detection module 100 can be pasted at a certain position on the user's skin, and the infusion module 102 can be pasted at another position on the user's skin.

[0064] As in another embodiment of the present application, the three are separately arranged in different structures. Thus, the three are pasted at different positions on the user's skin. At this time, the program module 101 transmits wireless signals to the detection module 100 and the infusion module 102 respectively to realize the connection with each other.

[0065] It should be noted that the program module 101 of the embodiment of the present application also has the functions of storage, recording and access to the database, and thus the program module 101 can be reused. In this way, not only the user's physical condition data can be stored, but also the production cost and the user's use cost are saved. As described above, when the detection module 100 or the infusion module 102 reaches the end of life, the program module 101 can be separated from the detection module 100, the infusion module 102 or both the detection module 100 and the infusion module 102.

[0066] Generally, the service life of the detection module 100, the program module 101 and the infusion module 102 is different. Thus, when the three are electrically connected to each other to form the same device, the three can also be separated from each other in pairs. For example, when one module reaches the end of life, the user can only replace the module, and keep the other two modules for continuous use.

[0067] It should be noted that the program module 101 of the embodiment of the present application can also include a plurality of sub-modules. Different sub-modules can be arranged in different structures according to their functions, which are not specifically limited here, as long as the control conditions of the program module 101 can be met.

[0068] Specifically, the program module 101 is pre-configured with an rPID (risk-proportion-integral-derivative) algorithm for converting blood glucose that is asymmetric in the original physical space to blood glucose risk that is approximately symmetric in the risk space. The rPID algorithm is obtained by converting the classical PID (proportion-integral-derivative) algorithm, and the specific processing manner will be described in detail below. According to the corresponding infusion instruction calculated by the rPID algorithm, the program module 101 controls the infusion module 102 to infuse insulin.

[0069] The classical PID algorithm can be represented by the following formula:

[0070]

[0071] Among them:

[0072] K P is the gain coefficient of the proportional part;

[0073] K I is the gain coefficient of the integral part;

[0074] K D is the gain coefficient of the derivative part;

[0075] G represents the current blood glucose value;

[0076] G B represents the target blood glucose value;

[0077] C represents a constant;

[0078] PID(t) represents the infusion instruction sent to the insulin infusion system.

[0079] Considering the actual distribution characteristics of glucose concentration of diabetic patients, such as the normal blood glucose range of 80-140 mg / dL, which can also be relaxed to 70-180 mg / dL, the general low blood glucose can reach 20-40 mg / dL, and the high blood glucose can reach 400-600 mg / dL.

[0080] The distribution of high / low blood glucose has significant asymmetry in the original physical space. In clinical practice, the risk of high blood glucose and the risk of low blood glucose corresponding to the same deviation of blood glucose from the normal range are obviously different. For example, a decrease of 70 mg / dL from 120 mg / dL to 50 mg / dL is considered as severe hypoglycemia, which has a high clinical risk and requires emergency measures such as supplement of carbohydrates. However, an increase of 70 mg / dL from 120 mg / dL to 190 mg / dL just exceeds the normal range, and the degree of high blood glucose is not serious for diabetic patients, and the blood glucose is often reached in daily situations, and basically no treatment measures are needed.

[0081] In view of the asymmetric characteristics of the clinical risk of glucose concentration, the asymmetric blood glucose in the original physical space is converted to the blood glucose risk in the risk space which is approximately symmetric, so that the PID algorithm is more robust.

[0082] Correspondingly, the rPID algorithm formula is converted as follows:

[0083]

[0084] Among them:

[0085] rPID(t) represents the infusion instruction sent to the insulin infusion system after the risk conversion;

[0086] r represents the blood glucose risk;

[0087] The meanings of other symbols are as described above.

[0088] In order to maintain the stability of the PID integral, in combination with the physiological effect of insulin in reducing blood glucose, in an embodiment of the present application, the input parameter of the PID, the blood glucose deviation Ge=G-G B is processed, such as the segmented weighted processing on Ge=G-G B , as follows:

[0089]

[0090] In another embodiment of the present application, the relative value is used to convert the deviation greater than the target blood glucose G B , as follows:

[0091]

[0092] Figure 2 The comparison chart of the blood glucose risk space obtained by the segmented weighted processing and the relative value conversion and the blood glucose in the original physical space.

[0093] In the original PID algorithm, the blood glucose risk (i.e. Ge) on both sides of the target blood glucose value presents a serious asymmetry consistent with the original physical space. After conversion to the blood glucose risk space, the blood glucose risk on both sides of the target blood glucose value is approximately symmetrical, so that the integral term can remain stable, making the rPID algorithm more robust.

[0094] In another embodiment of the present application, there is a fixed zero risk point when converting risk, and data deviating from both sides of the zero risk point is processed. The original parameter corresponding to the risk point greater than zero is positive when converted to the risk space, and the original parameter corresponding to the risk point less than zero is negative 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 considers that the clinical risk of hypoglycemia of 20 mg / dL and hyperglycemia of 600 mg / dL is equivalent, and the blood glucose in the range of 20-600 mg / dL is processed as a whole by logarithmization. The blood glucose value corresponding to the zero risk point of this method is set to the target blood glucose value G B . Its risk space conversion formula is as follows:

[0095]

[0096] Wherein:

[0097] r(G) = 10*f(G) 2

[0098] The conversion function f(G) is as follows:

[0099] f(G) = 1.509*[(ln(G)) 1.084 -5.381]

[0100] 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 application, the zero risk point blood glucose value can also be adjusted in combination with the risk and data trend of clinical practice, which is not specifically limited here. The risk space of the blood glucose value greater than the zero risk point of the blood glucose value is fitted, and the specific fitting method is not specifically limited.

[0101] In another embodiment of the present application, the zero risk point blood glucose value defined by the original CVGA method is 110 mg / dL, and the following pairs of blood glucose values with equal risk 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 application, the real risk and data trend characteristics in clinical practice are considered, and the pair of equal risk (70 mg / dL, 300 mg / dL) is modified to (70 mg / dL, 250 mg / dL), and the zero risk point blood glucose value is set as the target blood glucose value G B . The polynomial model fitting is performed, and the following risk functions for the two sides of the zero risk point are obtained:

[0102]

[0103] The maximum value is limited:

[0104] |r|=min(|r|,n)

[0105] The value range of the maximum value n is 0-80 mg / dL, and the value of n is preferably 60 mg / dL.

[0106] In other embodiments of the present application, the zero risk point blood glucose value and the pair of equal risk data can also be adjusted according to the real risk and data trend in clinical practice, which is not specifically limited herein. The equal risk point is fitted again, and the specific fitting method is not specifically limited; and the specific value for limiting the maximum value is also not specifically limited.

[0107] Figure 3 The comparison chart of the blood glucose risk converted to the risk space by the BGRI and CVGA methods and the blood glucose in the original physical space.

[0108] Similar to the processing of Zone-MPC, the blood glucose risk converted by the BGRI and CVGA methods is relatively flat in the normal range of blood glucose, especially in the range of 80-140 mg / dL. Unlike Zone-MPC, which is completely 0 in this range and loses the ability to further optimize, the risk of rPID is flat in this range, but still has stable and slow adjustment ability, which can further adjust the blood glucose to the target value and achieve more accurate blood glucose control.

[0109] In another embodiment of the present invention, data deviating from the zero-risk point can be processed using a uniform method, as in the aforementioned embodiments, where data deviating from the zero-risk point can be processed using either the BGRI or CVGA method; alternatively, different processing methods can be used, such as combining the BGRI and CVGA methods simultaneously. In this case, the same zero-risk point blood glucose value, such as the target blood glucose value G, can be used. B When the blood glucose level is lower than the target blood glucose level (G) B When using the BGRI method, the blood glucose level is greater than the target blood glucose level (G). B When using the CVGA method, at this time:

[0110] r = -r(G), if G ≤ G B

[0111] in:

[0112] r(G) = 10 * f(G) 2

[0113] The transformation function f(G) is as follows:

[0114] f(G) = 1.509 * [(ln(G)) 1.084 -5.381]

[0115] r = -4.8265 * 10 4 -4*G 2 +0.45563*G-44.855, if G>G B .

[0116] Similarly, this can also be done when the blood glucose level is lower than the target blood glucose level (G). B When using the CVGA method, the blood glucose level is greater than the target blood glucose level (G). B When using the BGRI method, at this time:

[0117] r = r(G), if G > G B

[0118] in:

[0119] r(G) = 10 * f(G) 2

[0120] Under the transformation function f(G):

[0121] f(G) = 1.509 * [(ln(G)) 1.084 -5.381]

[0122] r = GG B if G≤G B .

[0123] It also allows setting a limit on the maximum value:

[0124] |r| = min(|r|, n)

[0125] wherein the maximum value n is defined in the range of 0-80 mg / dL, preferably, the value of n is 60 mg / dL.

[0126] In other embodiments of the present application, the blood glucose value of the zero risk point can also be set as the target blood glucose value G B , the BGRI method is adopted for data less than or equal to the target blood glucose value G B , and the deviation processing method is adopted for data greater than the target blood glucose value G B , specifically, such as piecewise weighted processing or relative value processing.

[0127] When the piecewise weighted processing is adopted, at this time:

[0128] r = -r(G), if G ≤ G B

[0129] wherein:

[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]

[0134] When the relative value processing is adopted:

[0135] r = -r(G), if G ≤ G B

[0136] wherein:

[0137] r(G) = 10*f(G) 2

[0138] Under the fitted symmetrical conversion function f(G):

[0139] f(G) = 1.509*[(ln(G)) 1.084 -5.381]

[0140] r = 100*(G-G B ) / G, if G > G B

[0141] When the blood glucose values corresponding to the zero risk points are all the target blood glucose value G BWhen the data is less than or equal to the target blood glucose value G B , the processing functions of the piecewise weighted processing, the relative value processing and the CVGA method are consistent, so when the piecewise weighted processing or the relative value processing is adopted for the data less than or equal to the target blood glucose value G B , and the BGRI method is adopted for the data greater than the zero risk point blood glucose value, the processing results are equivalent to the aforementioned CVGA method for the blood glucose value less than or equal to the target blood glucose value G B , and the BGRI method for the blood glucose value greater than the target blood glucose value G B , and the calculation formula is not repeated.

[0142] It should be noted that in each embodiment of the present application, the target blood glucose value G B is 80-140 mg / dL, and preferably, the target blood glucose value G B is 110-120 mg / dL.

[0143] The above processing methods can make the rPID algorithm convert the asymmetric blood glucose in the original physical space to the approximately symmetric blood glucose risk in the risk space, so as to retain the simple and robust characteristics of the PID algorithm, and have the targeted and clinically valuable blood glucose risk control function, and realize the precise control of the closed-loop artificial pancreas insulin infusion system.

[0144] In the closed-loop artificial pancreas control system, there are three delay effects: insulin absorption delay (about 20 minutes from subcutaneous to blood circulation tissue, and about 100 minutes to reach the liver), insulin onset delay (about 30-100 minutes), and sensing delay of interstitial fluid glucose concentration and blood glucose (about 5-15 minutes). Any attempt to accelerate the responsiveness of the closed-loop system can 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 an embodiment of the present application, an insulin feedback compensation mechanism is introduced. The amount of insulin that has not been absorbed in the body is deducted from the output, and a component proportional to the estimated plasma insulin concentration (the actual human insulin secretion also uses the insulin concentration in the blood as a negative feedback signal). The formula is as follows:

[0145]

[0146] Wherein:

[0147] PID(t) represents the infusion instruction sent to the insulin infusion system;

[0148] PID c (t) represents the infusion instruction with compensation sent to the insulin infusion system;

[0149] γ represents the compensation coefficient of the estimated plasma insulin concentration to the algorithm output, the coefficient becomes larger, the algorithm becomes relatively conservative, and the coefficient becomes smaller, the algorithm becomes relatively aggressive, therefore, in the embodiment of the application, the range of γ is 0.4-0.6, preferably, γ is 0.5.

[0150] represents the estimation of the plasma insulin concentration, which can be obtained by various conventional prediction algorithms, such as being directly calculated from the infused insulin according to the pharmacokinetic curve of the insulin, or using a conventional autoregressive method:

[0151]

[0152] wherein:

[0153] represents the estimation of the plasma insulin concentration at the current time;

[0154] PID c (n-1) represents the output with compensation at the last time;

[0155] represents the estimation of the plasma insulin concentration at the last time;

[0156] represents the estimation of the plasma insulin concentration at the time before the last time;

[0157] K0 represents the coefficient of the output part with compensation at the last time;

[0158] K1 represents the coefficient of the estimation part of the plasma insulin concentration at the last time;

[0159] K2 represents the coefficient of the estimation part of the plasma insulin concentration at the time before the last time;

[0160] wherein, the initial value is The time interval between each time can be selected according to actual needs.

[0161] Correspondingly, the compensation output formula after the risk conversion by the foregoing method is as follows:

[0162]

[0163] wherein:

[0164] rPIDc(t) represents the infusion instruction with compensation sent to the insulin infusion system after the risk conversion;

[0165] rPID(t) represents the infusion instruction sent to the insulin infusion system after the risk conversion;

[0166] The meanings of other characters are as described above.

[0167] In order to compensate for the delay of insulin action in the closed-loop artificial pancreas control system, in one embodiment of the present application, insulin on board (IOB) is introduced, which is the amount of insulin that has not yet acted in the body, and the IOB is deducted from the output of insulin, preventing the accumulation of insulin infusion, excessive amount, and the risk of postprandial hypoglycemia.

[0168] Figure 4 is the insulin IOB curve according to an embodiment of the present application.

[0169] According to the IOB curve shown in FIG. 1, the cumulative residual amount of previously infused insulin can be calculated, and the specific curve can be selected according to the actual insulin action time of the user. Figure 4

[0170] PID'(t) = PID(t) - IOB(t)

[0171] wherein:

[0172] PID'(t) represents the infusion instruction sent to the insulin infusion system after deducting the IOB;

[0173] PID(t) represents the infusion instruction sent to the insulin infusion system;

[0174] IOB(t) represents the amount of insulin that has not yet acted in the body at time t.

[0175] Correspondingly, the output formula for deducting the amount of insulin that has not yet acted in the body after risk conversion by the foregoing method is as follows:

[0176] rPID'(t) = rPID(t) - IOB(t)

[0177] wherein:

[0178] rPID'(t) represents the infusion instruction sent to the insulin infusion system after deducting the amount of insulin that has not yet acted in the body after risk conversion;

[0179] rPID(t) represents the infusion instruction sent to the insulin infusion system after risk conversion;

[0180] The meanings of other characters are as described above.

[0181] In order to obtain a more ideal control effect, the calculation of IOB is processed as follows, IOB m , IOB o respectively correspond to the IOB of meal insulin and other insulin except meal. The formula is as follows:

[0182] IOB(t) = IOB m,t + IOB o,t ​

[0183] wherein:

[0184]

[0185] wherein:

[0186] IOB m,t represents the amount of meal insulin that has not yet acted in the body at time t;

[0187] IOB o,t represents the amount of non-meal insulin that has not yet acted in the body at time t;

[0188] D i (i = 2-8) represents the respective coefficients of the IOB curve corresponding to the insulin action time of i, respectively;

[0189] I m,t represents the amount of meal insulin;

[0190] I 0,t represents the amount of non-meal insulin;

[0191] IOB(t) represents the amount of insulin that has not yet acted in the body at time t.

[0192] The meal insulin and non-meal insulin are distinguished in the processing of IOB, which can make the insulin be cleared faster when eating and blood glucose is too high, can obtain greater insulin output, and blood glucose regulation is faster. When close to the target, a longer insulin action time curve is used to make the insulin be cleared slower, and blood glucose regulation is more conservative and stable.

[0193] When PID'(t) > 0 or rPID'(t) > 0, the final amount of insulin infused is PID'(t) or rPID'(t);

[0194] When PID'(t) < 0 or rPID'(t) < 0, the final amount of insulin infused is 0.

[0195] In order to compensate for the sensing delay of tissue fluid glucose concentration and blood glucose in the closed-loop artificial pancreas control system, in an embodiment of the present application, an autoregressive method is used for compensation, and the formula is as follows:

[0196]

[0197] wherein,

[0198] G SC (n) represents the current time interstitial fluid glucose concentration, that is, the measurement value of the sensing system;

[0199] represents the estimated concentration of blood glucose at the previous time;

[0200] G SC (n-1) and G SC (n-2) represent the interstitial fluid glucose concentration at the previous time and the time before the previous time respectively;

[0201] K0 represents the coefficient of the estimated concentration of blood glucose at the previous time;

[0202] K1 and K2 represent the coefficients of the interstitial fluid glucose concentration at the previous time and the time before the previous time respectively.

[0203] wherein, at the initial time,

[0204] By estimating the blood glucose concentration through the interstitial fluid glucose concentration, the sensing delay of the interstitial fluid glucose concentration and the blood glucose is compensated, the PID algorithm is more accurate, and accordingly, the rPID algorithm can more accurately calculate the actual demand of the human body for insulin.

[0205] In the embodiment of the present application, for the insulin absorption delay, the insulin onset delay, the sensing delay of the interstitial fluid glucose concentration and the blood glucose, partial compensation or full compensation can be performed, preferably, all the delay factors are considered for full compensation, so that the rPID algorithm is more accurate.

[0206] In another embodiment of the present application, the program module 101 is pre-provided with an rMPC (risk-model-predictive-control) algorithm for converting the blood glucose asymmetric in the original physical space to the blood glucose risk approximately symmetric in the risk space, the rMPC algorithm is obtained by conversion processing on the basis of the classical MPC (model-predictive-control) algorithm, and the corresponding infusion instruction calculated according to the rMPC algorithm is used by the program module 101 to control the infusion module 102 to infuse insulin.

[0207] The classical MPC algorithm is composed of three elements, a prediction model, a value function and a constraint condition. The prediction model of the classical MPC is as follows:

[0208] x t+1 =Ax t +BI t

[0209] G t =Cx t

[0210] wherein:

[0211] x t+1 represents the state parameter at the next time,

[0212] xt a state parameter representing a current time point,

[0213] I t an insulin infusion amount representing a current time point;

[0214] G t a blood glucose concentration representing a current time point.

[0215] The parameter matrix is as follows:

[0216]

[0217]

[0218] C=[1 0 0]

[0219] b1, b2, b3, K are prior values.

[0220] The value function of the MPC is composed of the square sum of the deviation of the output G (blood glucose value) and the square sum of the change of the input I (insulin amount). The MPC needs to obtain the minimum solution of the value function.

[0221]

[0222] wherein:

[0223] I′ t+j represents the change of the insulin infusion amount after the jth step;

[0224] represents the difference between the predicted blood glucose concentration and the target blood glucose value after the jth step;

[0225] t represents a current time point;

[0226] N, P are respectively the step number in the control time window and the prediction time window;

[0227] R is the weighted coefficient of the insulin component.

[0228] The insulin infusion amount of the jth step is I t +I′ t+j .

[0229] In the embodiment of the present application, the control time window T c = 30 min, the prediction time window T p = 60 min, and the weighted coefficient R of the insulin amount is 11000. It should be noted that although the control time window adopted in the calculation is 30 min, only the first step calculation result of the insulin output is adopted 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.

[0230] In the embodiment of the present application, the infusion time step j in the control time window is controlled n , j n The value range of j is 0-30 min, preferably 2 min. The number of steps N = T c / j n The range of j is 0 to N.

[0231] In other embodiments of the present application, the control time window, the prediction time window and the weighted coefficient of the insulin amount can also be selected as other values, which are not specifically limited here.

[0232] As mentioned earlier, due to the significant asymmetry of the distribution of high / low blood glucose (original physical space), the same degree of deviation of blood glucose from the normal range corresponds to significantly different high blood glucose risk and low blood glucose risk in clinical practice. In view of the asymmetric characteristics of the clinical risk of glucose concentration, the asymmetric blood glucose in the original physical space is converted to the approximately symmetric blood glucose risk in the risk space, so that the MPC algorithm is more accurate and flexible. The value function of the rMPC algorithm after the risk conversion is as follows:

[0233]

[0234] Wherein,

[0235] r t+j represents the blood glucose risk value after the jth step;

[0236] I′ t+j represents the change of insulin infusion amount after the jth step.

[0237] The deviation of the blood glucose value is converted into the corresponding blood glucose risk, and the specific conversion method is the same as that 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 of the zero risk point can be set as the target blood glucose value. The data deviating from both sides of the zero risk point is processed, such as using the BGRI and improved CVGA method; it also includes using different methods to process the data deviating from both sides of the target blood glucose value.

[0238] Specifically, when using segmented weighted processing:

[0239]

[0240] When using relative value processing:

[0241]

[0242] When using the classical blood glucose risk index method:

[0243]

[0244] wherein:

[0245] r(G t+j ) = 10*f(G t+j ) 2

[0246] The conversion function f(G t+j ) is as follows:

[0247] f(G t+j ) = 1.509*[(ln(G t+j )) 1.084 - 5.381]

[0248] When the control variability grid analysis method is used:

[0249]

[0250] The maximum value is also limited:

[0251] |r t+j | = min(|r t+j |, n)

[0252] wherein the value range of the maximum value n is 0-80 mg / dL, and preferably the value of n is 60 mg / d.

[0253] When the BGRI method is used when the blood glucose value is less than the target blood glucose value G B , and the CVGA method is used when the blood glucose value is greater than the target blood glucose value G B :

[0254] r t+j = -r(G t+j ), if G t+j ≤ G B

[0255] wherein:

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

[0257] The conversion function f(G t+j ) is as follows:

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

[0259] r t+j = -4.8265*10 4 - 4*G t+j2 +0.45563*G t+j -44.855,if G t+j >G B

[0260] When the CVGA method is used when the blood glucose value is less than the target blood glucose value G B , and the BGRI method is used when the blood glucose value is greater than the target blood glucose value G B :

[0261] r t+j =r(G t+j ),if G t+j >G B

[0262] wherein:

[0263] r(G t+j )=10*f(G t+j ) 2

[0264] The conversion function f(G t+j ) is as follows:

[0265] f(G t+j )=1.509*[(ln(G t+j )) 1.084 -5.381]

[0266] r t+j =G t+j -G B ,if G t+j ≤G B .

[0267] The maximum value can also be limited:

[0268] |r t+j | = min(|r t+j |,n)

[0269] wherein the value range of the maximum value n is 0-80 mg / dL, and preferably the value of n is 60 mg / dL.

[0270] When the BGRI method is used when the blood glucose value is less than the target blood glucose value G B , and the piecewise weighted method is used when the blood glucose value is greater than the target blood glucose value G B :

[0271] r t+j =-r(G t+j ),if G t+j ≤G B

[0272] Wherein:

[0273] r(G t+j ) = 10*f(G t+j ) 2

[0274] The conversion function f(G t+j ) is as follows:

[0275] f(G t+j ) = 1.509*[(ln(G t+j )) 1.084 -5.381]

[0276]

[0277] When the blood glucose value is less than the target blood glucose value G B , the BGRI method is adopted, and when the blood glucose value is greater than the target blood glucose value G B , the relative value conversion is adopted:

[0278] r t+j = -r(G t+j ), if G t+j ≤ G B

[0279] Wherein:

[0280] r(G t+j ) = 10*f(G t+j ) 2

[0281] The conversion function f(G t+j ) is as follows:

[0282] f(G t+j ) = 1.509*[(ln(G t+j )) 1.084 -5.381]

[0283]

[0284] When the data less than or equal to the target blood glucose value G B is processed by the segmented weighted processing or the relative value processing, and the data greater than the zero risk point blood glucose value is processed by the BGRI method, the processing result is equivalent to the aforementioned CVGA method when the blood glucose value is less than or equal to the target blood glucose value G B , and the BGRI method when the blood glucose value is greater than the target blood glucose value G B , and the calculation formula is not repeated.

[0285] It should be noted that in the above various conversion formulas:

[0286] r t+jGj represents the blood glucose risk value at the jth step;

[0287] G t+j Gj represents the blood glucose value detected at the jth step.

[0288] G represents the target blood glucose value B G is 80-140 mg / dL, preferably, G B G is 110-120 mg / dL.

[0289] The beneficial effects after risk conversion and the relationship between blood glucose and blood glucose risk are consistent with those in the rPID algorithm, which are not repeated here.

[0290] Similarly, in order to compensate for the insulin absorption delay, an insulin feedback compensation mechanism can also be used for compensation; in order to compensate for the delay in the effect of insulin, IOB compensation can also be used; the sensing delay of interstitial fluid glucose concentration and blood glucose concentration can also be compensated by autoregressive compensation, and the specific compensation methods are consistent with those in the rPID algorithm, specifically:

[0291] For insulin absorption delay, the compensation formula is as follows:

[0292]

[0293] Wherein:

[0294] I t+j Ij represents the infusion instruction sent to the insulin infusion system at the jth step;

[0295] rI c(t+j) rIj represents the infusion instruction sent to the insulin infusion system at the jth step after risk conversion;

[0296] γ represents the compensation coefficient of the estimated plasma insulin concentration to the algorithm output, and the larger the coefficient, the more conservative the algorithm, and the smaller the coefficient, the more aggressive, therefore, in the embodiment of the present application, the range of γ is 0.4-0.6, preferably, γ is 0.5. Ij represents the estimation of plasma insulin concentration at the jth step.

[0297] For insulin effect delay, the compensation formula is as follows:

[0298] rI′ t+j rIj t+j -IOB(t+j)

[0299] Wherein:

[0300] rI′ t+j rIj represents the infusion instruction sent to the insulin infusion system at the jth step after risk conversion and deduction of IOB;

[0301] rI t+jThis indicates the infusion instruction sent to the insulin infusion system at step j after risk conversion;

[0302] IOB(t+j) represents the amount of insulin in the body that has not yet taken effect at time t+j.

[0303] Similarly, IOB(t+j) can be distinguished between being in a meal and not being in a meal, in which case:

[0304] IOB(t+j)=IOB m,t+j +IOB o,t+j

[0305] in:

[0306]

[0307] in:

[0308] IOB m,t+j This represents the amount of insulin consumed after a meal that has not yet taken effect in the body at time t+j.

[0309] IOB o,t+j This represents the amount of non-meal insulin that has not yet taken effect in the body at time t+j;

[0310] D i (i = 2 - 8) represent the corresponding coefficients of the IOB curves for insulin action time i;

[0311] I m,t+j This represents the insulin level at time t+j after the meal.

[0312] I 0,t+j This represents the non-meal insulin amount at time t+j;

[0313] IOB(t+j) represents the amount of insulin in the body at time t+j that has not yet taken effect.

[0314] When rI′ t+j When >0, the final infused insulin volume is rI′. t+j ;

[0315] When rI′ t+j When <0, the final amount of insulin infused is 0.

[0316] For the sensing delay of tissue fluid glucose concentration and blood glucose concentration, autoregressive compensation can also be used, as shown in the following formula:

[0317]

[0318] in,

[0319] G SC(t+j) represents the interstitial fluid glucose concentration at t+j, i.e. the measurement value of the sensing system;

[0320] G (t+j-1) represents the estimated concentration of blood glucose at t+j-1;

[0321] G (t+j-1) represents the estimated concentration of blood glucose at t+j-1; SC G (t+j-1) represents the estimated concentration of blood glucose at t+j-1; SC G (t+j-2) represents the interstitial fluid glucose concentration at t+j-1 and t+j-2, respectively;

[0322] K0 represents the coefficient of the estimated concentration part of blood glucose at t+j-1;

[0323] K1 and K2 represent the coefficients of the interstitial fluid glucose concentration at t+j-1 and t+j-2, respectively.

[0324] wherein, at the initial time,

[0325] The beneficial effects of various compensation methods are consistent with those in the rPID algorithm, which will not be repeated here.

[0326] It should be noted that in the rMPC algorithm, it is preferred to compensate for the delay in the effect of insulin and the sensing delay of the interstitial fluid glucose concentration and the blood glucose concentration.

[0327] In another embodiment of the present application, the program module 101 is pre-provided with a composite artificial pancreas algorithm, the composite artificial pancreas algorithm includes a first algorithm and a second algorithm, when the detection module 100 detects the current blood glucose value and sends the current blood glucose value to the program module 101, the first algorithm calculates the first insulin infusion amount I1, the second algorithm calculates the second insulin infusion amount I2, the composite artificial pancreas algorithm optimizes the calculation of the first insulin infusion amount I1 and the second insulin infusion amount I2, obtains the final insulin infusion amount I3, and sends the final insulin infusion amount I3 to the infusion module 102, the infusion module 102 infuses insulin according to the final infusion amount I3.

[0328] The first algorithm and the second algorithm are one of a classic PID algorithm, a classic MPC algorithm, an rMPC algorithm or an rPID algorithm. The rMPC algorithm or the rPID algorithm is an algorithm for converting blood glucose that is asymmetric in the original physical space to blood glucose risk that is approximately symmetric in the risk space. The conversion method of blood glucose risk in the rMPC algorithm and the rPID algorithm is as described above.

[0329] When I1=I2, I3=I1=I2;

[0330] 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, and the insulin infusion amount required at the current time is calculated again by the first algorithm and the second algorithm respectively after the parameter optimization, if I1 and I2 are still not the same, the arithmetic mean of I1 and I2 is taken again to repeat the above process until I1 and I2 are the same, that is:

[0331] ①Solving the average of the first insulin infusion amount I1 and the second insulin infusion amount I2

[0332] ②The average value is substituted into the first algorithm and the second algorithm respectively, and the algorithm parameters are adjusted;

[0333] ③The first insulin infusion amount I1 and the second insulin infusion amount I2 are recalculated based on the current blood glucose value, the first algorithm and the second algorithm after adjusting the parameters;

[0334] ④The steps ①-③ are calculated in a loop until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0335] At this time, when the first algorithm or the second algorithm is PID or rPID algorithm, the algorithm parameters are K P , and K D =T D / K P , T D can be taken as 60min-90min, K I =T I *K P , and T I can be taken as 150min-450min. When the first algorithm or the second algorithm is MPC or rPMC algorithm, the algorithm parameters are K.

[0336] When I1≠I2, I1 and I2 can also be weighted, and the calculated values after the weighted processing are substituted into the first algorithm and the second algorithm respectively to re-optimize the algorithm parameters, and the insulin infusion amount required at the current time is calculated again by the first algorithm and the second algorithm respectively after the parameter optimization, if I1 and I2 are still not the same, I1 and I2 are weighted again, the weighting coefficient is adjusted, and the above process is repeated until I1 and I2 are the same, that is:

[0337] ①Solving the weighted average of the first insulin infusion amount I1 and the second insulin infusion amount I2 , wherein α and β are the weighting coefficients of the first insulin infusion amount I1 and the second insulin infusion amount I2 respectively;

[0338]

[0339] ​②the weighted average of the blood glucose value is brought into the first algorithm and the second algorithm, and the algorithm parameters are adjusted;

[0340] ③the first insulin infusion amount I1 is recalculated based on the current blood glucose value, the first algorithm and the second algorithm after the adjustment of the parameters;

[0341] and the second insulin infusion amount I2;

[0342] ④the steps ①-③ are calculated in a loop until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0343] Similarly, when the first algorithm or the second algorithm is a PID or RPID algorithm, the algorithm parameters are K P , and K D =T D / K P , T D may be 60min-90min, and K I =T I *K P , T I may be 150min-450min. When the first algorithm or the second algorithm is an MPC or rPMC algorithm, the algorithm parameters are K.

[0344] In the embodiments of the present application, the 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 application, the α and β can also be in other value ranges, which are not limited here.

[0345] 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 time can make the blood glucose value reach the ideal level. Through the processing of the above-mentioned manner, each algorithm is referenced by each other, preferably, the first algorithm and the second algorithm are respectively an rMPC algorithm and an rPID algorithm, which are referenced by each other, further improving the accuracy of the output result, and making the result more feasible and reliable.

[0346] In another embodiment of the present application, the program module 101 is further provided with a memory for storing the information of the historical body state, blood glucose value and insulin infusion amount of the user, and the statistical analysis result I4 at the current time can be obtained based on the information in the memory. When I1≠I2, I1, I2 and I4 are compared respectively, the final insulin infusion amount I3 is calculated, and one of I1 and I2 which is closer to the statistical analysis result I4 is selected as the calculation result of the final composite artificial pancreas 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:​

[0347]

[0348] The reliability of the insulin infusion amount is ensured from another aspect by comparison with historical data.

[0349] In another embodiment of the present application, when both I1 and I2 are inconsistent and the difference is large, the blood glucose risk space conversion manner and / or the compensation manner with respect to the delay effect in the rMPC algorithm and / or the rPID algorithm can also be adjusted to be similar, and then the output result of the compound artificial pancreas algorithm is finally determined by the above-mentioned arithmetic mean, weighted processing, or comparison with the statistical analysis result.

[0350] In another embodiment of the present application, the closed-loop artificial pancreas control system further comprises a meal recognition module and a motion recognition module. The meal recognition commonly used to identify whether the user is having a meal or exercising can be based on the blood glucose change rate and judged by a specific threshold. The blood glucose change rate can be calculated from two time points before and after or obtained by linear regression of multiple time points in a period of time. Specifically, when the change rate of two time points before and after is used for calculation, the calculation formula is:

[0351] dG t / dt=(G t -G t-1 ) / △t

[0352] wherein:

[0353] G t represents the blood glucose value at the current time point;

[0354] G t-1 represents the blood glucose value at the previous time point;

[0355] △t represents the time interval between the current time point and the previous time point.

[0356] When a three-point time change rate calculation formula is used, the calculation formula is:

[0357] dG t / dt=(3G t -4G t-1 +G t-2 ) / 2△t

[0358] wherein:

[0359] G t represents the blood glucose value at the current time point;

[0360] G t-1 represents the blood glucose value at the previous time point;

[0361] G t-2a blood glucose value at a time point before the time point;

[0362] △t represents a time interval between the current time point and the previous time point.

[0363] Before calculating the blood glucose rate of change, the original continuous glucose data can also be filtered or smoothed. The threshold value can be set to 1.8 mg / mL-3 mg / mL, or can be personalized.

[0364] Similar to meal recognition, exercise recognition can also be based on the blood glucose rate of change and determined by a specific threshold value, since exercise can cause a rapid decrease in blood glucose. The calculation of the blood glucose rate of change can also be as described above, and the threshold value can be personalized. In order to determine the occurrence of exercise more quickly, the closed-loop artificial pancreas insulin infusion control system also includes an exercise sensor (not shown). The exercise sensor is used to automatically detect the physical activity of the user, and the program module 101 can receive the physical activity information. The exercise sensor can automatically and accurately sense the physical activity state of the user and send the activity state parameter to the program module 101, thereby improving the output reliability of the composite artificial pancreas algorithm in the exercise scenario.

[0365] The exercise sensor can be arranged in the detection module 100, the program module 101, or the infusion module 102. Preferably, in the embodiment of the present application, the exercise sensor is arranged in the program module 101.

[0366] It should be noted that the number of exercise sensors and the arrangement position of the plurality of exercise sensors are not limited in the embodiment of the present application, as long as the exercise sensor can sense the activity state of the user.

[0367] The exercise 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. Preferably, in the embodiment of the present application, the exercise sensor is a combination of a three-axis acceleration sensor and a gyroscope.

[0368] It should be noted that the blood glucose risk conversion methods used by the rMPC algorithm and the rPID algorithm in the calculation process can be the same or different, the compensation methods for the delay effect can be the same or different, and the calculation process can also be adjusted according to the actual situation.

[0369] In another embodiment of the present application, the program module 101 further includes an adaptive unit for adjusting the algorithm gain coefficient according to the body weight of the user. In some embodiments of the present application, the infusion module 102 or the program module 101 can indicate the total daily insulin requirement DIR of the user. In another embodiment of the present application, the DIR can be calculated from the body weight BW, specifically, the DIR is proportional to the BW, that is, DIR = e*BW, wherein e is a body weight adjustment coefficient.

[0370] For type 1 diabetes patients, the body weight adjustment coefficient e can be selected as the population average 0.53 U / kg, and personalized processing can also be combined with their exercise habits, such as selecting a lower body weight adjustment coefficient for professional athletes, such as 0.4 U / kg; and selecting a higher body weight adjustment coefficient for patients with less exercise, such as 0.6 U / kg. For type 2 diabetes patients, the body weight adjustment coefficient can be selected in a larger range in combination with their pancreatic secretion function and insulin resistance, such as 0.1-1.5 U / kg, and the commonly used range is 0.6-1.1 U / kg.

[0371] In an embodiment of the present application, the preset algorithm in the program module 101 is a classic PID algorithm or an rPID algorithm, and the gain coefficient Kp of the proportional part is DIR / (BW*m), m is a user weight compensation coefficient, and the value is 50-500, preferably, m is 135.

[0372] The gain coefficient K I of the integral part and the gain coefficient K D of the differential part in the PID algorithm or the rPID algorithm can be converted into a coefficient related to Kp, such as K D =T D / K P , T D can be 60 min-90 min, K I =T I *K P , T I can be 150 min-450 min. T D , T I is large, the algorithm is aggressive, and vice versa. Different coefficient settings can be used during the day and at night, such as selecting a smaller time parameter at night.

[0373] In another embodiment of the present application, the preset algorithm in the program module 101 is a classic MPC algorithm or an rMPC algorithm, and the gain coefficient K is related to BW:

[0374]

[0375] Wherein:

[0376] c is a safety coefficient;

[0377] s is a clinical experience coefficient;

[0378] e is a body weight adjustment coefficient, U / Kg.

[0379] The safety factor c can be selected between 1.25-3 according to the risk of nocturnal hypoglycemia; the value of the clinical experience factor s can be 1500, 1700, 1800, 2000, 2200, 2500, etc., which can be adjusted according to clinical results, and is not specifically limited here. In the preferred embodiment of the present application, the clinical experience factor s is 1700. The value range of the body weight adjustment factor e is as described above.

[0380] In the foregoing two embodiments, the gain coefficient Kp of the PID algorithm or the rPID algorithm and the gain coefficient K of the MPC algorithm or the rMPC algorithm can also be adjusted by introducing a coefficient Sb(t) related to the basal insulin requirement, and correspondingly, K' = K * Sb(t), K' = K * Sb(t). P P *Sb(t), K' = K * Sb(t).

[0381] The coefficient Sb(t) related to the basal insulin requirement is the ratio of the basal insulin requirement B(t) at time t to the average daily basal insulin amount Ba, i.e., Sb(t) = B(t) / Ba. Wherein, Ba = y * DIR / 24, y is a basal insulin amount compensation factor, with a value of 0.1-5, the population average of this factor is 0.47, and children are slightly smaller, for example, it can be taken as 0.3-0.4.

[0382] The average daily basal insulin amount Ba can be calculated according to the actual basal rate setting of the user. The basal insulin requirement B(t) at time t can be set according to the four types of mainstream clinical optimal basal rate setting. Figure 5 There are four types of mainstream clinical optimal basal rate setting, which come from the reference [Holterhus, P. M., J. Bokelmann, et al. (2013). "Predicting the Optimal Basal Insulin Infusion Pattern in Children and Adolescents on Insulin Pumps." Diabetes Care 36(6): 1507-1511.], wherein the horizontal axis is time, 24h a day, and the vertical axis is the relative deviation of the basal insulin requirement at the corresponding time from the average daily basal insulin amount Ba, which is mostly between 0.5 and 1.5.

[0383] B(t) can also refer to the commonly used basal rate segmentation setting in the clinic, such as adopting three segment settings, as follows:

[0384] ① When the time t is from 0am to 4am, B(t) = 0.5DIR / 48;

[0385] ② When the time t is from 4am to 10am, B(t) = 1.5DIR / 48;​

[0386] When the time t is from 10:00 am to 0:00 am, B(t) = DIR / 48.

[0387] In other embodiments of the application, B(t) can also be calculated according to the basal rate set by the user as known and appropriate.

[0388] In the embodiments of the application, the range of Sb(t) is 0.2-2, preferably 0.5-1.5. By introducing the coefficient Sb(t) related to the basal insulin requirement in different time periods, the gain coefficient is adjusted with the change of time, meeting the insulin requirement of the user in different time periods, and further improving the accuracy of closed-loop control.

[0389] In the embodiments of the application, the conversion mode of rPID algorithm and rMPC algorithm for converting blood glucose from original physical space asymmetry to blood glucose risk approximately symmetrical in risk space, the compensation mode of various delays, and the beneficial effects have been described before and will not be repeated here. Meanwhile, the calculation results of each algorithm can be further processed, and the further processing mode of the calculation results, the beneficial effects, etc. have been described before and will not be repeated here.

[0390] Figure 6 The module relationship schematic diagram of the closed-loop artificial pancreas insulin infusion control system according to another embodiment of the application.

[0391] In the embodiments of the application, the closed-loop artificial pancreas insulin infusion control system mainly includes a detection module 100, an infusion module 102, and an electronic module 103.

[0392] The detection module 100 is used for continuously detecting the real-time blood glucose value of the user. Generally, the detection module 100 is a continuous glucose monitor (CGM), which can detect the blood glucose value in real time and monitor the blood glucose change, and send the current blood glucose value to the infusion module 102 and the electronic module 103.

[0393] The infusion module 102 contains the mechanical structure necessary for insulin infusion, and also includes an infusion processor 1021 and other elements capable of executing the first algorithm, and is controlled by the electronic module 103. After receiving the current blood glucose value sent by the detection module 100, the infusion module 102 calculates the first insulin infusion amount I1 required at present by the first algorithm, and sends the calculated first insulin infusion amount I1 to the electronic module 103.

[0394] Electronic module 103 is used to control the operation of detection module 100 and infusion module 102. Therefore, electronic module 103 is connected to both detection module 100 and infusion module 102. Here, electronic module 103 is an external electronic device such as a mobile phone or handheld device; therefore, the connection refers to a wireless connection. Electronic module 103 includes a second processor. In this embodiment of the invention, the second processor is an electronic processor 1031 or similar element capable of executing a second algorithm and a third algorithm. After receiving the current blood glucose value sent by detection module 100, electronic module 103 calculates the required second insulin infusion amount I2 using the second algorithm. Here, the first and second algorithms used by electronic module 103 and infusion module 102 to calculate the required insulin amount are different.

[0395] After receiving the first insulin infusion volume I1 from the infusion module 102, the electronic module 103 further optimizes and calculates the first insulin infusion volume I1 and the second insulin infusion volume I2 using a third algorithm to obtain the final insulin infusion volume I3. This final insulin infusion volume I3 is then sent to the infusion module 102, which infuses the required insulin I3 into the user's body. Simultaneously, the infusion status of the infusion module 102 is also fed back to the electronic module 103 in real time. The specific optimization method is as described above. That is:

[0396] When I1 = I2, I3 = I1 = I2;

[0397] 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 volume I1 and I2. If the data are still different, the above process is repeated until I3 = I1 = I2, that is:

[0398] ① Calculate the average value of the first insulin infusion volume I1 and the second insulin infusion volume I2.

[0399] ② Average value Substitute these parameters into the first and second algorithms and adjust the algorithm parameters accordingly.

[0400] ③ Based on the current blood glucose level, the first algorithm and the second algorithm after parameter adjustment, recalculate the first insulin infusion volume I1 and the second insulin infusion volume I2;

[0401] ④ Repeat steps ① to ③ until I1 = I2, and finally the insulin infusion volume I3 = I1 = I2.

[0402] or:

[0403] ① Calculate the weighted average of the first insulin infusion volume I1 and the second insulin infusion volume I2. wherein a and b are weighting factors of the first insulin infusion amount I1 and the second insulin infusion amount I2 respectively;

[0404] ②The weighted mean value is brought into the first algorithm and the second algorithm, and the algorithm parameters are adjusted;

[0405] ③The first insulin infusion amount I1 and the second insulin infusion amount I2 are recalculated based on the current blood glucose value, the first algorithm and the second algorithm after the adjustment of the parameters;

[0406] ④The steps ①-③ are calculated in a loop until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0407] When the two are different, the electronic module 103 can also compare them with the statistical analysis result I4 of the current time based on the statistical analysis of the historical information such as the body state, blood glucose value and insulin infusion amount of the user at each time in the past, and select 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:

[0408]

[0409] In the embodiment of the application, the historical information of the user can be stored in the electronic module 103, or can be stored in a cloud management system (not shown), and the cloud management system is connected with the electronic module 103 through wireless connection.

[0410] Figure 7 The figure shows the relationship between the modules of the closed-loop artificial pancreas insulin infusion control system according to another embodiment of the application.

[0411] In the embodiment of the application, the closed-loop artificial pancreas insulin infusion control system mainly comprises a detection module 100, an infusion module 102 and an electronic module 103.

[0412] The detection module 100 is used for continuously detecting the real-time blood glucose value of the user. Generally, the detection module 100 is a continuous glucose monitor (CGM), which can detect the blood glucose value in real time and monitor the change of the blood glucose value, and the current blood glucose value is only sent to the infusion module 102. The detection module 100 also comprises a second processor, which is a detection processor 1001 and the like capable of executing a second algorithm in the embodiment of the application. After the detection module 100 detects the real-time blood glucose value, the second insulin infusion amount I2 is directly calculated through the second algorithm, and the calculated second insulin infusion amount I2 is sent to the electronic module 103.

[0413] ​The infusion module 102 receives the current blood glucose value sent by the detection module 100, and calculates the first insulin infusion amount I1 by 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 not the same.

[0414] After the electronic module 103 receives the first insulin infusion amount I1 and the second insulin infusion amount I2 sent by the detection module 100 and the infusion module 102 respectively, the electronic module 103 further optimizes the first insulin infusion amount I1 and the second insulin infusion amount I2 by a third algorithm to obtain a 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 current required insulin I3 into the user. 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.

[0415] In the above two embodiments of the present application, after the detection module 100 detects the current blood glucose value, the first insulin infusion amount I1 is initially calculated by the infusion processor 1021, the second insulin infusion amount I2 is initially calculated by the second processor (such as the electronic processor 1031 and the detection processor 1001), and I1 and I2 are sent to the electronic module 103, the electronic module 103 is further optimized, and the optimized final insulin infusion amount I3 is sent to the infusion module 102 for insulin infusion, thereby improving the accuracy of the infusion instruction.

[0416] In the above two embodiments of the present application, 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, and the advantages of using the rPID or rMPC algorithm are as described above, and the beneficial effects of the further optimization method are also as described above, which are not repeated here.

[0417] The embodiments of the present application do not limit the specific positions and connection relationships of the detection module 100 and the infusion module 102, as long as the above-mentioned functional conditions can be met.

[0418] As in an embodiment of the present application, the two are electrically connected to each other to form a whole structure and are attached to the same position of the user's skin. The two modules are connected to form a whole and are attached to the same position, the number of devices attached to the user's skin will be reduced, thereby weakening the interference of attaching more devices on the user's stretching activities; at the same time, the problem of poor wireless communication between separated devices is also effectively solved, further enhancing the user experience.

[0419] As in another embodiment of the present application, the two are respectively provided 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 connection with each other.

[0420] Figure 8 The schematic diagram of the module relationship of the closed-loop artificial pancreas multi-drug infusion control system according to another embodiment of the present application.

[0421] The closed-loop artificial pancreas insulin infusion control system in the embodiment of the present application is as previously described, mainly including the detection module 100, the program module 101 and the infusion module 102, the infusion module 102 having a multi-drug infusion function, the drugs can be a combination of drugs for regulating blood glucose control for diabetic patients, the metabolite thereof being glucose, the main drug being insulin and its analogs and other hypoglycemic drugs, and other combination drugs being hyperglycemic drugs having opposite effects, such as glucagon and its analogs, cortisol and its analogs, growth hormone and its analogs, epinephrine and its analogs, glucose, and the like, and amylin analogs (such as pramlintide) having similar effects.

[0422] The infusion module 102 can infuse hypoglycemic drugs and / or hyperglycemic drugs into the user's body according to the hypoglycemic drug infusion instructions and / or hyperglycemic drug infusion instructions issued by the program module 101. The hypoglycemic drugs and the hyperglycemic drugs can be infused through different drug pipelines respectively, or can be infused at different times through the same drug pipeline, and the specific design of the drug pipeline is not limited herein.

[0423] Figure 9 The schematic diagram of the dual-drug infusion switching according to the two embodiments of the present application.

[0424] In one embodiment of the present application, the hypoglycemic drug infusion instruction and / or the current hyperglycemic drug infusion instruction is obtained by comparing the blood glucose concentration estimate G P with the target blood glucose value G B , and the blood glucose concentration estimate G P can be estimated according to the prediction model of the rMPC or other suitable blood glucose prediction algorithm; the hypoglycemic drug infusion data and / or the hyperglycemic drug infusion data can be calculated by the aforementioned rMPC algorithm or rPID algorithm or the compound artificial pancreas algorithm. Specifically:

[0425] When G P ≥ G BWhen G t , the infusion module 102 starts to infuse the hypoglycemic drug according to the hypoglycemic drug infusion data I P calculated by the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm B .

[0426] When G t , the infusion module 102 starts to infuse the hypoglycemic drug according to the hypoglycemic drug infusion data I b calculated by the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm B .

[0427] It should be noted that in the embodiments of the present application, I P represents the amount of hypoglycemic drug needed to control the blood glucose at the target blood glucose value G B in the absence of interference, when G t = G b , I P = I B , when G P > G t , with the infusion of the hypoglycemic drug, G P further decreases, and I B also decreases. When the infusion module 102 has only one set of drug infusion pipeline, when G t < G b , that is, I t < I t , the infusion module 102 starts to infuse the hyperglycemic drug, and the hyperglycemic drug infusion data D b can be calculated by the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm, and at the same time, the infusion of the hypoglycemic drug is stopped to prevent the hypoglycemic drug and the hyperglycemic drug from affecting each other due to antagonism. When the infusion module 102 has at least two sets of drug infusion pipelines, when 0≤I t < I t , the infusion of the hyperglycemic drug can be started while the infusion of the hypoglycemic drug is continued, which can effectively prevent the occurrence of hypoglycemia; when I b < 0, the infusion of the hypoglycemic drug is stopped and only the hyperglycemic drug is infused.

[0428] In another embodiment of the present application, the hypoglycemic drug infusion instruction and / or the current hyperglycemic drug infusion instruction can be directly performed by comparing the required amount of hypoglycemic drug I t and the target hypoglycemic drug amount I b , which can be calculated by the aforementioned rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm. Specifically, when the infusion module 102 has at least two sets of drug infusion pipelines:

[0429] When I t ≥ I b , the infusion module 102 starts to infuse the hypoglycemic drug according to the hypoglycemic drug infusion data I t calculated by the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm, and the hyperglycemic drug infusion data D t calculated by the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm. b When 0 ≤ I t < I t , the infusion of the hypoglycemic drug can be continued while the infusion of the hyperglycemic drug is started, which can effectively prevent the occurrence of hypoglycemia, and the hypoglycemic drug infusion data I t and the hyperglycemic drug infusion data D t can be calculated by the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm.

[0431] When I t < 0, the infusion of the hypoglycemic drug is stopped and only the hyperglycemic drug is infused, and the hyperglycemic drug infusion data D t can be calculated by the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm.

[0432] When the infusion module 102 has only one set of drug infusion pipeline:

[0433] When I t ≥ 0, the infusion module 102 starts to infuse the hypoglycemic drug according to the hypoglycemic drug infusion data I calculated by the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm.

[0434] When I

[0001] < 0, the infusion of the hypoglycemic drug is stopped and only the hyperglycemic drug is infused.

[0435] Preferably, in the embodiments of the present application, the hypoglycemic drug is insulin and the hyperglycemic drug is glucagon.

[0436] It should be noted that in the above embodiments, the calculation methods of the hypoglycemic drug infusion data and the glucagon infusion data in each stage can be the same or different, preferably, the same algorithm architecture is used for calculation to ensure the consistency of the basic conditions during calculation, so that the calculation result is more accurate. More preferably, the compound artificial pancreas algorithm is used for calculation, which fully utilizes the advantages of the rPID algorithm and the rMPC algorithm to face complex situations, so that the blood glucose control level is more ideal.

[0437] Figure 10 It is a schematic diagram of the module relationship of the closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present application.

[0438] In the embodiments of the present application, the closed-loop artificial pancreas insulin infusion control system disclosed in the embodiments of the present application mainly comprises a detection module 200 and an infusion module 202. The detection module 200 is used for continuously detecting the current blood glucose value of a user. Generally, the detection module 100 is a continuous glucose monitoring (CGM) device, which can detect the current blood glucose value of the user in real time and monitor the blood glucose change; the detection module 200 further comprises a detection processing unit 2001, and the detection processing unit 2001 is preconfigured with an algorithm for calculating the insulin infusion amount. When the detection module 200 detects the current blood glucose value of the user, the detection processing unit 2001 calculates the required insulin amount of the user by using the preconfigured algorithm, and sends the required insulin amount of the user to the infusion module 202.

[0439] The infusion module 202 comprises a mechanical structure necessary for infusion of insulin and an electronic transceiver for receiving the insulin amount information of the user 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 current required insulin into the user. At the same time, the infusion state of the infusion module 102 can also be fed back to the detection module 200 in real time.

[0440] In the embodiments of the present application, the algorithm preconfigured in the detection processing unit 2001 for calculating the insulin infusion amount is one of a classic PID algorithm, a classic MPC algorithm, an rMPC algorithm, an rPID algorithm or a composite artificial pancreas algorithm. The method and beneficial effects of using the rPID, rMPC algorithm or the composite artificial pancreas algorithm have been described above, and will not be repeated here.

[0441] The embodiments of the present application do not limit the specific positions and connection relationships of the detection module 2100 and the infusion module 202, as long as the above-mentioned functional conditions can be met.

[0442] In one embodiment of the present application, the two are electrically connected to each other to form an integral structure and are attached to the same position of the skin of the user. The two modules are connected to form an integral structure and are attached to the same position, so that the number of devices attached to the skin of the user is reduced, thereby weakening the interference of the attachment of a large number of devices on the stretching of the user; at the same time, the problem of poor wireless communication between separated devices is effectively solved, and the user experience is further enhanced.

[0443] In another embodiment of the present application, the two are arranged in different structures and are attached to different positions of the skin of the user. At this time, the detection module 200 and the infusion module 202 transmit wireless signals to each other to realize the connection with each other.

[0444] In conclusion, the application discloses a closed-loop artificial pancreas insulin infusion control system, comprising a first algorithm, a second algorithm and a third algorithm, the first algorithm calculates a first insulin infusion amount I1, the second algorithm calculates a second insulin infusion amount I2, and the third algorithm calculates an optimized calculation of I1 and I2 to obtain a final insulin infusion amount I3, through further processing of the result, the infusion result is more accurate and reliable, and precise control of the closed-loop artificial pancreas insulin infusion system is realized.

[0445] Although some specific embodiments of the application have been described in detail by way of example with reference to the drawings, it is to be understood that the examples are for illustrative purposes only and are not to be construed as limiting the scope of the application. It is to be understood that modifications can be made to the above embodiments without departing from the scope and spirit of the application. The scope of the application is defined by the appended claims.

Claims

1. A closed loop artificial pancreas insulin infusion control system, characterized by, The application relates to a blood glucose control system, comprising: a detection module for continuously detecting the current blood glucose value in a user's body; an infusion module comprising an infusion processor for calculating a first insulin infusion amount I1 according to the current blood glucose value by a first algorithm; a second processor for calculating a second insulin infusion amount I2 according to the current blood glucose value by a second algorithm; and an electronic module for calculating the first insulin infusion amount I1 and the second insulin infusion amount I2 by a third algorithm to obtain a final insulin infusion amount I3, and sending the final insulin infusion amount I3 to the infusion module, which performs insulin infusion according to the final insulin infusion amount I3; wherein the first algorithm and the second algorithm are one of an rMPC algorithm and an rPID algorithm, and the rMPC algorithm and the rPID algorithm respectively convert the blood glucose risk in the original physical space which is asymmetric to the blood glucose risk in the risk space which is approximately symmetric on the basis of a classical PID algorithm and a classical MPC algorithm; the value function of the rMPC algorithm after the risk conversion is as follows: the blood glucose risk space conversion method of the rMPC algorithm and the rPID algorithm comprises an improved control variability grid analysis conversion, wherein: wherein r t+j represents the blood glucose risk value after the jth step; I' t+j represents the change of insulin infusion amount after the jth step; R is a weighted coefficient of the insulin component; N and P are the step numbers in the control time window and the prediction time window, respectively; the blood glucose risk space conversion method of the rMPC algorithm and the rPID algorithm further comprises one or more of the following processing modes: r t+j | = min(|r t+j |,n) wherein the value of the maximum value n is defined in the range of 0 to 80 mg / dL, G B represents a target blood glucose value, G t+j is a blood glucose value detected at the jth step.

2. The closed loop artificial pancreas insulin infusion control system of claim 1, wherein, ① deducting a component proportional to the estimated plasma insulin concentration; ② deducting the amount of insulin which has not yet played its role in the body; ③ compensating for the blood glucose and interstitial fluid glucose concentration sensing delay by using an autoregressive method. the final insulin infusion amount I3 is optimized by the average value of the first insulin infusion amount I1 and the second insulin infusion amount I2:

3. The closed loop artificial pancreas infusion control system of claim 1, wherein, 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 and the second algorithm after adjusting parameters; solving for the average of the first insulin infusion amount I1 and the second insulin infusion amount I2 ② Average value Substitute these parameters into the first and second algorithms and adjust the algorithm parameters accordingly. ④ performing cyclic calculation on steps ①-③ until I1=I2, and the final insulin infusion amount I3=I1=I2. the final insulin infusion amount I3 is optimized by the weighted average value of the first insulin infusion amount I1 and the second insulin infusion amount I2:

4. The closed loop artificial pancreas infusion control system of claim 1, wherein, 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 and the second algorithm after adjusting parameters; solving a weighted mean of the first insulin infusion amount I1 and the second insulin infusion amount I2 wherein a and β are weighting factors for the first insulin infusion amount I1 and the second insulin infusion amount I2, respectively; ii) the weighted mean is brought into the first and second algorithms, adjusting the algorithm parameters; ④ performing cyclic calculation on steps ①-③ until I1=I2, and the final insulin infusion amount I3=I1=I2. the final insulin infusion amount 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 historical data:

5. The closed loop artificial pancreas infusion control system of claim 1, wherein, the second processor is an electronic processor and is located in the electronic module.

6. The closed loop artificial pancreas infusion control system of claim 1, wherein, the second processor is a detection processor and is located in the detection module.

7. The closed loop artificial pancreas infusion control system of claim 1, wherein, the detection module and the infusion module are electrically connected to each other to form an integral structure and are attached to the same position of the skin.

8. The closed loop artificial pancreas infusion control system of claim 1, wherein, ​ 9. The closed loop artificial pancreas infusion control system of claim 1, wherein, The detection module and the infusion module are respectively pasted on different positions of the skin and are connected wirelessly.

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