Closed-loop artificial pancreas insulin infusion individualization control system

Abstract

The application discloses a closed-loop artificial pancreas insulin infusion personalized control system, which comprises a detection module, a program module connected with the detection module, an adaptive unit for adjusting a gain coefficient of the algorithm according to a user's body weight BW, and a preset target blood glucose value G B ; and an infusion module connected with the program module, wherein the program module controls the infusion module to infuse insulin according to the insulin infusion indication calculated by the algorithm. The adaptive unit for adjusting the gain coefficient of the algorithm according to the user's body weight BW can adjust the gain coefficient of the response algorithm according to the user's body weight, so that the personalized setting can be realized according to the actual condition of the user, the real demand of the user can be met, and the user experience is improved.

Description

TECHNICAL FIELD

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

[0002] The pancreas of a normal person can automatically secrete the required insulin / glucagon according to the glucose level in the human blood, thereby maintaining a reasonable blood glucose fluctuation range. The pancreas function 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 thereby 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] At present, considering the great difference in physiological characteristics among individuals, there is also a large difference between the parameters of each individual and the population mean. Using a unified artificial pancreas model to process different individuals with wide differences will inevitably cause a mismatch between the human body and the model, and cannot achieve ideal blood glucose control for each patient.

[0005] Therefore, the prior art urgently needs a closed-loop artificial pancreas insulin infusion personalized control system with personalized settings. SUMMARY

[0006] The embodiment of the present application discloses a closed-loop artificial pancreas insulin infusion personalized control system, wherein an adaptive unit for adjusting the gain coefficient of an algorithm according to the body weight BW of a user is pre-set in the system, so that the gain coefficient of the response algorithm can be adjusted according to the body weight of the user, personalized settings can be made according to the actual situation of the user, the real needs of the user can be better met, and the user experience is improved.

[0007] The present application discloses a closed-loop artificial pancreas insulin infusion personalized control system, comprising: a detection module for continuously detecting a current blood glucose value G; a program module connected with the detection module, wherein an algorithm for calculating an insulin infusion indication, an adaptive unit for adjusting the gain coefficient of the algorithm according to the body weight BW of a user, and a pre-set target blood glucose value G are pre-set in the program module B; and an infusion module, connected to the program module, for infusing insulin according to the insulin infusion indication calculated by the algorithm for calculating insulin infusion indication.

[0008] According to an aspect of the present application, the algorithm for calculating insulin infusion amount is a classic PID algorithm or an rPID algorithm, and the relationship between the gain coefficient Kp of the proportional part and the user's body weight BW is:

[0009] Kp = DIR / (BW*m)

[0010] wherein,

[0011] DIR represents the total daily insulin requirement, U;

[0012] BW represents the user's body weight, Kg;

[0013] m represents the user's body weight compensation coefficient.

[0014] According to an aspect of the present application, the user's body weight compensation coefficient m is 50-500.

[0015] According to an aspect of the present application, the proportional coefficient of DIR and body weight BW is 0.6-1.1 U / kg.

[0016] According to an aspect of the present application, the algorithm for calculating insulin infusion amount is a classic MPC algorithm or an rMPC algorithm, and the relationship between the gain coefficient K of the classic MPC algorithm or the risk rMPC algorithm and the user's body weight BW is:

[0017]

[0018] wherein,

[0019] c is a safety coefficient;

[0020] s is a clinical experience coefficient;

[0021] e is a body weight adjustment coefficient, U / Kg.

[0022] According to an aspect of the present application, the safety coefficient c is 1.25-3.

[0023] According to an aspect of the present application, the clinical experience coefficient s is 1500-2500.

[0024] According to an aspect of the present application, the body weight adjustment coefficient e is 0.6-1.1 U / Kg.

[0025] According to an aspect of the present application, the gain coefficient Kp or K can be adjusted by a coefficient related to the basal insulin requirement:

[0026] K′ P= K P Sb(t) ; K' = K * Sb(t),

[0027] wherein,

[0028] Sb(t) is the ratio of the basal insulin requirement B(t) at time t and the average daily basal insulin amount Ba:

[0029] Sb(t) = B(t) / Ba.

[0030] According to an aspect of the present application, the average daily basal insulin amount Ba is:

[0031] Ba = y * DIR / 24,

[0032] wherein, y is a basal insulin amount compensation factor.

[0033] According to an aspect of the present application, the basal insulin amount compensation factor y takes a value of 0.1-5.

[0034] According to an aspect of the present application, the basal insulin requirement B(t) is set in sections:

[0035] ① when the time t is from 0 to 4 a.m., B(t) = 0.5 * DIR / 48;

[0036] ② when the time t is from 4 a.m. to 10 a.m., B(t) = 1.5 * DIR / 48;

[0037] ③ when the time t is from 10 a.m. to 0 a.m., B(t) = DIR / 48.

[0038] According to an aspect of the present application, the risk rMPC algorithm and the risk rPID algorithm respectively on the basis of the classic PID algorithm and the classic MPC algorithm, the blood glucose in the original physical space is asymmetrically converted to the blood glucose risk in the risk space which is approximately symmetric.

[0039] According to an aspect of the present application, the conversion method of the blood glucose risk r of the rMPC algorithm or the rPID algorithm includes one or more of the segmented weighting method, the relative value conversion, the blood glucose risk index conversion and the improved control variability grid analysis conversion.

[0040] According to an aspect of the present application, the blood glucose risk r is calculated as:

[0041]

[0042] According to an aspect of the present application, the blood glucose risk r is calculated as:

[0043]

[0044] According to an aspect of the present application, the glycemic risk r is calculated as:

[0045]

[0046] wherein,

[0047] r(G) = 10*f(G) 2

[0048] wherein f(G) is a conversion function calculated as:

[0049] f(G) = 1.509*[(ln(G)) 1.084 -5.381].

[0050] According to an aspect of the present application, the glycemic risk r is calculated as:

[0051]

[0052] According to an aspect of the present application, when the current glycemic value G B is greater than the target glycemic value G

[0053] the glycemic risk r is calculated as:

[0054] r = r(G), if G > G B

[0055] wherein,

[0056] r(G) = 10*f(G) 2 ,

[0057] wherein f(G) is a conversion function calculated as:

[0058] f(G) = 1.509*[(ln(G)) 1.084 -5.381];

[0059] When the current glycemic value G B is not greater than the target glycemic value G

[0060] r = G - G B , if G < G B .

[0061] According to an aspect of the present application, when the current glycemic value G B is not greater than the target glycemic value G

[0062] r = -r(G), if G < G B

[0063] wherein:

[0064] r(G) = 10*f(G) 2

[0065] Under the conversion function f(G):

[0066] f(G) = 1.509*[(ln(G)) 1.084 - 5.381]

[0067] When the current blood glucose value G is greater than the target blood glucose value G B , the blood glucose risk r is calculated as:

[0068] r = -4.8265*10 4 - 4*G 2 + 0.45563*G - 44.855, if G > G B .

[0069] According to one aspect of the present application, when the current blood glucose value G is not greater than the target blood glucose value G B , the blood glucose risk r is calculated as:

[0070] r = -r(G), if G < G B

[0071] wherein:

[0072] r(G) = 10*f(G) 2

[0073] The conversion function f(G) is as follows:

[0074] f(G) = 1.509*[(ln(G)) 1.084 - 5.381]

[0075] When the current blood glucose value G is greater than the target blood glucose value G B , the blood glucose risk r is calculated as:

[0076]

[0077] According to one aspect of the present application, when the current blood glucose value G is not greater than the target blood glucose value G B , the blood glucose risk r is calculated as:

[0078] r = -r(G), if G < G B

[0079] wherein:

[0080] r(G) = 10*f(G) 2

[0081] The conversion function f(G) is as follows:

[0082] f(G) = 1.509 * [(ln(G) 1.084 - 5.381]

[0083] When the current blood glucose value G is greater than the target blood glucose value G B , the blood glucose risk r is calculated as:

[0084] r = 100 * (G - G B ) / G, if G > G B .

[0085] According to one aspect of the present application, one or more of the following processing methods is included in the rPID algorithm or the rMPC algorithm:

[0086] ① deducting a component proportional to the estimated plasma insulin concentration;

[0087] ② deducting the amount of insulin that has not yet taken effect in the body;

[0088] ③ compensating for the sensing delay of blood glucose and interstitial fluid glucose concentration using an autoregressive method.

[0089] According to one aspect of the present application, two of the detection module, the program module and the infusion module are connected to form an integral structure, and the third module is attached to different positions on the skin, respectively.

[0090] According to one aspect of the present application, the detection module, the program module and the infusion module are connected to form an integral structure, and are attached to the same position on the skin.

[0091] Compared with the prior art, the technical scheme of the present application has the following advantages:

[0092] In the closed-loop artificial pancreas insulin infusion personalized control system disclosed in the present application, the self-adaptive unit for adjusting the gain coefficient of the algorithm according to the user's body weight BW in the system can adjust the gain coefficient of the response algorithm according to the user's body weight, which facilitates personalized setting according to the actual situation of the user and better meets the real needs of the user, thereby improving the user experience.

[0093] Further, the coefficient Sb(t) related to the basal insulin requirement amount in different time periods is introduced into the gain coefficient of the algorithm, so that the gain coefficient is adjusted with the change of time, meeting the insulin requirements of the user in different time periods and further improving the user experience.

[0094] Further, the preset algorithm is an rPID algorithm or an rMPC algorithm for converting the originally asymmetric blood glucose to approximately symmetric blood glucose risk space, thereby improving the accuracy and reliability of the infusion result.

[0095] Furthermore, the detection module, program module, and infusion module are connected to form a single integrated structure and attached to the same location on the skin. By connecting the three modules into a unified whole and attaching them to the same spot, the number of devices attached to the user's skin is reduced, thus lessening the interference with the user's movements caused by having too many devices. Simultaneously, it effectively solves the problem of poor wireless communication between separate devices, further enhancing the user experience. Attached Figure Description

[0096] Figure 1 This is a schematic diagram of the module relationships of a closed-loop artificial pancreas insulin infusion control system according to an embodiment of the present invention;

[0097] Figure 2 This is a comparison diagram of the relationship between blood glucose levels in the risk space and the original physical space, obtained by segmented weighting and relative value conversion in one embodiment of the present invention.

[0098] Figure 3 This is a comparison chart of the relationship between blood glucose levels in the risk space obtained by conversion using the BGRI and CVGA methods according to an embodiment of the present invention and the original physical space.

[0099] Figure 4 An insulin IOB curve according to an embodiment of the present invention;

[0100] Figure 5 This is a schematic diagram illustrating four mainstream clinically optimal baseline rate settings cited in one embodiment of the present invention;

[0101] Figure 6 This is a schematic diagram of the module relationships of a closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present invention;

[0102] Figure 7 This is a schematic diagram of the module relationships of a closed-loop artificial pancreas insulin infusion control system according to yet another embodiment of the present invention;

[0103] Figure 8 This is a schematic diagram of the module relationships of a closed-loop artificial pancreas multi-drug infusion control system according to another embodiment of the present invention;

[0104] Figure 9 This is a schematic diagram of dual-drug switching according to an embodiment of the present invention;

[0105] Figure 10 This is a schematic diagram of the module relationships of a closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present invention. Detailed Implementation

[0106] As mentioned above, considering the huge difference between physiological characteristics of individuals, there are also great differences between parameters of each individual and population mean. The unified artificial pancreas model is used to process different individuals with wide differences, which inevitably causes the problem of human-model mismatch, and cannot make each patient achieve ideal blood glucose control.

[0107] To solve this problem, the application provides a closed-loop artificial pancreas insulin infusion personalized control system, wherein an adaptive unit for adjusting the gain coefficient of the algorithm according to the body weight BW of the user is arranged in the system, the gain coefficient of the response algorithm can be adjusted according to the body weight of the user, the personalized setting according to the actual situation of the user is facilitated, the real needs of the user are better met, and the user experience is improved.

[0108] 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.

[0109] In addition, it should be understood that, for the convenience of description, the sizes of various components shown in the drawings are not necessarily drawn in accordance with the actual proportional relationship, for example, the thickness, width, length or distance of certain units can be enlarged relative to other structures.

[0110] The following description of the exemplary embodiments is merely illustrative in nature and is in no way intended to limit the application or its application or use. Techniques, methods, and apparatuses known to those of ordinary skill in the relevant art can not be discussed in detail herein, but should be considered part of the present specification when applicable.

[0111] It should be noted that similar reference numbers and letters represent similar items in the following drawings, so once an item is defined or described in one drawing, it will not need to be further discussed in subsequent drawings.

[0112] Figure 1 The application embodiment closed-loop artificial pancreas insulin infusion control system module relationship schematic diagram.

[0113] The closed-loop artificial pancreas insulin infusion control system disclosed by the application embodiment mainly includes a detection module 100, a program module 101 and an infusion module 102.

[0114] The detection module 100 is configured to continuously detect the current blood glucose level of the user. Generally, the detection module 100 is a continuous glucose monitoring (CGM) device, which can detect the current blood glucose level of the user in real time, monitor the change of the blood glucose level, and send the current blood glucose level to the program module 101.

[0115] The program module 101 is configured 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 a conventional electrical connection or a wireless connection.

[0116] The infusion module 102 includes the mechanical structure necessary for infusion of insulin, and is controlled by the program module 101. According to the current insulin infusion amount data sent by the program module 101, the infusion module 102 infuses the 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 program module 101 in real time.

[0117] 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.

[0118] In one embodiment of the present application, the three are electrically connected to each other to form an integral structure. Therefore, the three are attached to the same position on the user's skin. The three modules are connected to form an integral structure and are attached to the same position, so that the number of devices attached to the user's skin is reduced, thereby reducing the interference with the user's activities caused by the attachment of more devices; at the same time, the problem of the reliability of wireless communication between separate devices is effectively solved, further enhancing the user experience.

[0119] 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. Therefore, the program module 101 and the infusion module 102 are attached to a certain position on the user's skin, and the detection module 100 is attached to another position on the user's skin.

[0120] In another embodiment of the present application, the program module 101 and the detection module 100 are connected to each other to form an integral structure, 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. Therefore, the program module 101 and the detection module 100 can be attached to a certain position on the user's skin, and the infusion module 102 can be attached to another position on the user's skin.

[0121] As in another embodiment of the present application, the three are respectively provided in different structures. Thus, the three are respectively attached to different positions of the user's skin. At this time, the program module 101 transmits wireless signals with the detection module 100 and the infusion module 102 respectively to achieve connection with each other.

[0122] It should be noted that the program module 101 of the embodiment of the present application also has functions of storage, recording and access to databases, etc. Thus, the program module 101 can be reused. In this way, not only can the user's physical condition data be stored, but also production costs and user use costs can be 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.

[0123] 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 one 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 continued use.

[0124] Here, it should be noted that the program module 101 of the embodiment of the present application can also include a plurality of sub-modules. According to the functions of the sub-modules, different sub-modules can be respectively provided in different structures, which are not specifically limited here, as long as the control conditions of the program module 101 can be met.

[0125] Specifically, the program module 101 is pre-provided with an rPID (risk-proportional-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 processing on the basis of a classical PID (proportional-integral-derivative) algorithm, and the specific processing manner will be described in detail below. According to the corresponding infusion instructions calculated by the rPID algorithm, the program module 101 controls the infusion module 102 to infuse insulin.

[0126] The classical PID algorithm can be represented by the following formula:

[0127]

[0128] Among them:

[0129] K P is the gain coefficient of the proportional part;

[0130] K I is the gain coefficient of the integral part;

[0131] K D is the gain coefficient of the differential part;

[0132] G represents the current blood glucose value;

[0133] G B G represents the target blood glucose value;

[0134] C represents a constant;

[0135] PID(t) represents the infusion instruction sent to the insulin infusion system.

[0136] Considering the actual distribution characteristics of the glucose concentration of a diabetic patient, such as the normal blood glucose range of 80-140 mg / dL, which can also be relaxed to 70-180 mg / dL, the general hypoglycemia can reach 20-40 mg / dL, and the hyperglycemia can reach 400-600 mg / dL.

[0137] The distribution of hyper / hypoglycemia has significant asymmetry in the original physical space, and the same degree of deviation of blood glucose from the normal range corresponds to significantly different risks of hyperglycemia and hypoglycemia in clinical practice, such as a decrease of 70 mg / dL from 120 mg / dL to 50 mg / dL, which is considered to be severe hypoglycemia with high clinical risk, and emergency measures such as supplement of carbohydrates need to be taken; while 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 a diabetic patient, and it is often reached in daily situations, and basically no treatment measures need to be taken.

[0138] 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.

[0139] Correspondingly, the rPID algorithm formula is converted into the following form:

[0140]

[0141] Among them:

[0142] rPID(t) represents the infusion instruction sent to the insulin infusion system after risk conversion;

[0143] r represents the blood glucose risk;

[0144] The meanings of other symbols are as described above.

[0145] 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 piecewise weighted processing is made on Ge=G B , as follows:

[0146]

[0147] In another embodiment of the present application, the deviation from the target blood glucose G B is converted using a relative value as follows:

[0148]

[0149] Figure 2 The blood glucose risk space obtained by piecewise weighting processing and relative value conversion is compared with the blood glucose relationship of the original physical space.

[0150] 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.

[0151] In another embodiment of the present application, there is a fixed zero risk point when converting the risk, and data deviating from both sides of the zero risk point is processed. The original parameter corresponding to the greater zero risk point is positive when converted to the risk space, and the original parameter corresponding to the less zero risk 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 . The risk space conversion formula is as follows:

[0152]

[0153] Wherein:

[0154] r(G) = 10 * f(G) 2

[0155] The conversion function f(G) is as follows:

[0156] f(G) = 1.509 * [(ln(G)) 1.084 - 5.381]

[0157] In the classic blood glucose risk index method, the blood glucose value corresponding to the zero risk point 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 is fitted, and the specific fitting method is not specifically limited.

[0158] 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 equal risk data pairs (90 mg / dL, 180 mg / dL; 70 mg / dL, 300 mg / dL; 50 mg / dL, 400 mg / dL) are assumed. In the embodiment of the present application, the equal risk data pair (70 mg / dL, 300 mg / dL) is adjusted to (70 mg / dL, 250 mg / dL) in consideration of the real risk and data trend in clinical practice, and the zero risk point blood glucose value is set as the target blood glucose value G B . A polynomial model is fitted to obtain the following risk function for the risk on both sides of the zero risk point:

[0159]

[0160] The maximum value is limited as follows:

[0161] |r| = min (|r|, n)

[0162] The value of the maximum value n is in the range of 0-80 mg / dL, and preferably 60 mg / dL.

[0163] In other embodiments of the present application, the zero risk point blood glucose value and the equal risk data pair can also be adjusted in consideration of the real risk and data trend in clinical practice, and the equal risk point is fitted. The fitting method is not specifically limited, and the specific value for limiting the maximum value is also not specifically limited.

[0164] Figure 3 The graph is a comparison between the blood glucose risk converted to the risk space by the BGRI and CVGA methods and the blood glucose in the original physical space.

[0165] 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.

[0166] In another embodiment of the present application, uniform processing can be applied to the data deviating from both sides of the zero risk point, as in the foregoing embodiment, the data deviating from both sides of the zero risk point can be processed by BGRI or CVGA method; or different processing can be applied, such as combining BGRI and CVGA method, in which case the same zero risk point blood glucose value, such as target blood glucose value G B , can be used. When the blood glucose value is less than the target blood glucose value G B , the BGRI method is applied, and when the blood glucose value is greater than the target blood glucose value G B , the CVGA method is applied, in which case:

[0167] r = -r(G), if G ≤ G B

[0168] Wherein:

[0169] r(G) = 10*f(G) 2

[0170] The conversion function f(G) is as follows:

[0171] f(G) = 1.509*[(ln(G)) 1.084 - 5.381]

[0172] r = -4.8265*10 4 - 4*G 2 + 0.45563*G - 44.855, if G > G B .

[0173] Similarly, the CVGA method can be applied when the blood glucose value is less than the target blood glucose value G B , and the BGRI method can be applied when the blood glucose value is greater than the target blood glucose value G B , in which case:

[0174] r = r(G), if G > G B

[0175] Wherein:

[0176] r(G) = 10*f(G) 2

[0177] The conversion function f(G) is as follows:

[0178] f(G) = 1.509*[(ln(G)) 1.084 - 5.381]

[0179] r = G - G B , if G ≤ G B .

[0180] Meanwhile, the maximum value can also be limited:

[0181] |r|=min(|r|,n)

[0182] The maximum value n is limited to a range of 0 to 80 mg / dL, and preferably, the value of n is 60 mg / dL.

[0183] In other embodiments of the present invention, the blood glucose value at the zero-risk point can also be set as the target blood glucose value G. B For blood glucose levels less than or equal to the target blood glucose value (G) B The data were obtained using the BGRI method, and for values ​​greater than the target blood glucose level (G... B The data is processed using deviation methods, such as segmented weighting or relative value processing.

[0184] When segmented weighted processing is used, then:

[0185] r = -r(G), if G ≤ G B

[0186] in:

[0187] r(G) = 10 * f(G) 2

[0188] Under the transformation function f(G):

[0189] f(G) = 1.509 * [(ln(G)) 1.084 -5.381]

[0190]

[0191] When using relative values:

[0192] r = -r(G), if G ≤ G B

[0193] in:

[0194] r(G) = 10 * f(G) 2

[0195] Under the fitted symmetric transformation function f(G):

[0196] f(G) = 1.509 * [(ln(G)) 1.084 -5.381]

[0197] r = 100 * (GG B ) / G,ifG>G B

[0198] When the blood glucose value corresponding to the zero risk point is the target blood glucose value (G) B When, for blood glucose levels less than or equal to the target blood glucose value GB The data of the blood glucose value less than or equal to the target blood glucose value G B The data of the blood glucose value less than or equal to the target blood glucose value G B The data of the blood glucose value greater than the target blood glucose value G B The data of the blood glucose value greater than the target blood glucose value G

[0199] It should be noted that in the embodiments of the present application, the target blood glucose value G B is 80-140 mg / dL, preferably, the target blood glucose value G B is 110-120 mg / dL.

[0200] The above processing methods can make the rPID algorithm convert the blood glucose in the original physical space to the blood glucose risk in the risk space which is approximately symmetrical, so as to retain the simple and robust characteristics of the PID algorithm, and also 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.

[0201] In the closed-loop artificial pancreas control system, there are three major 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 the glucose concentration in the interstitial fluid and the 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 concentration of plasma insulin (the actual human insulin secretion also uses the insulin concentration in the blood plasma as a negative feedback signal). The formula is as follows:

[0202]

[0203] Wherein:

[0204] PID(t) represents the infusion instruction sent to the insulin infusion system;

[0205] PID c (t) represents the infusion instruction with compensation sent to the insulin infusion system;

[0206] 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.

[0207] 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:

[0208]

[0209] wherein:

[0210] represents the estimation of the plasma insulin concentration at the current time;

[0211] PID c (n-1) represents the output with compensation at the last time;

[0212] represents the estimation of the plasma insulin concentration at the last time;

[0213] represents the estimation of the plasma insulin concentration at the time before the last time;

[0214] K0 represents the coefficient of the output part with compensation at the last time;

[0215] K1 represents the coefficient of the estimation part of the plasma insulin concentration at the last time;

[0216] K2 represents the coefficient of the estimation part of the plasma insulin concentration at the time before the last time;

[0217] wherein, the initial value is The time interval between each time can be selected according to actual needs.

[0218] Correspondingly, the compensation output formula after the risk conversion by the foregoing method is as follows:

[0219]

[0220] wherein:

[0221] rPIDc(t) represents the infusion instruction with compensation sent to the insulin infusion system after the risk conversion;

[0222] rPID(t) represents the infusion instruction sent to the insulin infusion system after the risk conversion;

[0223] The meanings of other characters are as described above.

[0224] 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, the insulin IOB (insulin onboard) that has not yet acted in vivo is introduced, and the IOB is deducted from the output of insulin, so as to prevent the accumulation and excess of insulin infusion, and to cause the risk of postprandial hypoglycemia.

[0225] Figure 4 is the insulin IOB curve according to the embodiment of the present application.

[0226] According to the IOB curve shown in FIG. 1, the cumulative residual amount of previously infused insulin can be calculated, and the selection of the specific curve can be determined according to the actual insulin action time of the user. Figure 4

[0227] PID'(t) = PID(t) - IOB(t)

[0228] wherein:

[0229] PID'(t) represents the infusion instruction sent to the insulin infusion system after deducting the IOB;

[0230] PID(t) represents the infusion instruction sent to the insulin infusion system;

[0231] IOB(t) represents the amount of insulin that has not yet acted in vivo at time t.

[0232] Correspondingly, the output formula for deducting the amount of insulin that has not yet acted in vivo after risk conversion by the foregoing method is as follows:

[0233] rPID'(t) = rPID(t) - IOB(t)

[0234] wherein:

[0235] rPID'(t) represents the infusion instruction sent to the insulin infusion system after deducting the amount of insulin that has not yet acted in vivo after risk conversion;

[0236] rPID(t) represents the infusion instruction sent to the insulin infusion system after risk conversion;

[0237] The meanings of other characters are as described above.

[0238] 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:

[0239] IOB(t) = IOB m,t + IOB o,t ​

[0240] wherein:

[0241]

[0242] wherein:

[0243] IOB m,t represents the amount of meal insulin that has not yet acted in the body at time t;

[0244] IOB o,t represents the amount of non-meal insulin that has not yet acted in the body at time t;

[0245] D i (i = 2-8) represents the respective coefficients of the IOB curve corresponding to the insulin action time of i, respectively;

[0246] I m,t represents the amount of meal insulin;

[0247] I 0,t represents the amount of non-meal insulin;

[0248] IOB(t) represents the amount of insulin that has not yet acted in the body at time t.

[0249] The meal insulin and non-meal insulin are distinguished in the IOB, which can make the insulin be cleared faster when the meal and the blood glucose is too high, and can obtain greater insulin output and faster blood glucose regulation. When close to the target, the longer insulin action time curve is used to make the insulin be cleared slower, and the blood glucose regulation is more conservative and stable.

[0250] When PID'(t) > 0 or rPID'(t) > 0, the final amount of insulin infused is PID'(t) or rPID'(t);

[0251] When PID'(t) < 0 or rPID'(t) < 0, the final amount of insulin infused is 0.

[0252] In order to compensate for the sensing delay of the tissue glucose concentration and the 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:

[0253]

[0254] wherein,

[0255] G SC (n) represents the current time tissue interstitial fluid glucose concentration, that is, the measurement value of the sensing system;

[0256] represents the estimated concentration of blood glucose at the last time point;

[0257] G SC (n-1) and G SC (n-2) represent the tissue fluid glucose concentration at the last time point and the time point before the last time point respectively;

[0258] K0 represents the coefficient of the estimated concentration of blood glucose at the last time point;

[0259] K1 and K2 represent the coefficients of the tissue fluid glucose concentration at the last time point and the time point before the last time point respectively.

[0260] wherein, at the initial time point,

[0261] By estimating the blood glucose concentration through the tissue fluid glucose concentration, the sensing delay of the tissue 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.

[0262] In the embodiment of the present application, the insulin absorption delay, the insulin onset delay, the sensing delay of the tissue fluid glucose concentration and the blood glucose can be partially compensated or fully compensated, preferably, all the delay factors are considered for full compensation, so that the rPID algorithm is more accurate.

[0263] 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.

[0264] 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:

[0265] x t+1 =Ax t +BI t

[0266] G t =Cx t

[0267] wherein:

[0268] x t+1 represents the state parameter at the next time point,

[0269] xt a state parameter representing a current time point,

[0270] I t an insulin infusion amount representing a current time point;

[0271] G t a blood glucose concentration representing a current time point.

[0272] The parameter matrix is as follows:

[0273]

[0274]

[0275] C = [1 0 0]

[0276] b1, b2, b3, K are prior values.

[0277] 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.

[0278]

[0279] wherein:

[0280] I′ t+j represents the change of the insulin infusion amount after the jth step;

[0281] represents the difference between the predicted blood glucose concentration and the target blood glucose value after the jth step;

[0282] t represents a current time point;

[0283] N and P are respectively the step number in the control time window and the prediction time window;

[0284] R is the weighted coefficient of the insulin component.

[0285] The insulin infusion amount of the jth step is I t + I′ t+j .

[0286] 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, and after the operation, the minimum solution of the above value function is recalculated according to the latest blood glucose value obtained.

[0287] 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.

[0288] 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.

[0289] 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:

[0290]

[0291] Wherein,

[0292] r t+j represents the blood glucose risk value after the jth step;

[0293] I′ t+j represents the change of insulin infusion amount after the jth step.

[0294] 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.

[0295] Specifically, when using segmented weighted processing:

[0296]

[0297] When using relative value processing:

[0298]

[0299] When using the classic blood glucose risk index method:

[0300]

[0301] wherein:

[0302] r(G t+j ) = 10*f(G t+j ) 2

[0303] The conversion function f(G t+j ) is as follows:

[0304] f(G t+j ) = 1.509*[(ln(G t+j )) 1.084 - 5.381]

[0305] When the control variability grid analysis method is used:

[0306]

[0307] The maximum value is also limited:

[0308] |r t+j | = min(|r t+j |, n)

[0309] The value of n is limited to 0-80 mg / dL, and preferably n is 60 mg / d.

[0310] 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 :

[0311] r t+j = -r(G t+j ), if G t+j ≤ G B

[0312] wherein:

[0313] r(G t+j ) = 10*f(G t+j ) 2

[0314] The conversion function f(G t+j ) is as follows:

[0315] f(G t+j ) = 1.509*[(ln(G t+j )) 1.084 - 5.381]

[0316] r t+j = -4.8265*10 4 - 4*G t+j2 + 0.45563 * G t+j - 44.855, if G t+j > G B

[0317] 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 :

[0318] r t+j = r(G t+j ), if G t+j > G B

[0319] wherein:

[0320] r(G t+j ) = 10 * f(G t+j ) 2

[0321] The conversion function f(G t+j ) is as follows:

[0322] f(G t+j ) = 1.509 * [(ln(G t+j )) 1.084 - 5.381]

[0323] r t+j = G t+j - G B , if G t+j ≤ G B .

[0324] The maximum value can also be limited:

[0325] |r t+j | = min(|r t+j |, n)

[0326] wherein the value of the maximum value n is in the range of 0-80 mg / dL, and preferably, the value of n is 60 mg / dL.

[0327] 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 :

[0328] r t+j = - r(G t+j ), if G t+j ≤ G B

[0329] wherein:

[0330] r(G t+j )=10*f(G t+j ) 2

[0331] The conversion function f(G t+j ) is as follows:

[0332] f(G t+j )=1.509*[(ln(G t+j )) 1.084 -5.381]

[0333]

[0334] When the BGRI method is used when the blood glucose value is less than the target blood glucose value G B , and the relative value conversion is used when the blood glucose value is greater than the target blood glucose value G B , the following applies:

[0335] r t+j =-r(G t+j ),ifG t+j ≤G B

[0336] Where:

[0337] r(G t+j )=10*f(G t+j ) 2

[0338] The conversion function f(G t+j ) is as follows:

[0339] f(G t+j )=1.509*[(ln(G t+j )) 1.084 -5.381]

[0340]

[0341] When the piecewise weighted processing or the relative value processing is used for data less than or equal to the target blood glucose value G B , and the BGRI method is used for data greater than the zero risk point blood glucose value, the processing result is equivalent to the aforementioned CVGA method used when the blood glucose value is less than or equal to the target blood glucose value G B , and the BGRI method used when the blood glucose value is greater than the target blood glucose value G B , and the calculation formula is not repeated.

[0342] It should be noted that in the above various conversion formulas:

[0343] r t+jGj is the blood glucose risk value at the jth step;

[0344] G t+j Gj is the blood glucose value detected at the jth step.

[0345] G is the target blood glucose value B G is 80-140 mg / dL, preferably, G is 110-120 mg / dL. B G is 110-120 mg / dL.

[0346] 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.

[0347] 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:

[0348] For insulin absorption delay, the compensation formula is as follows:

[0349]

[0350] Wherein:

[0351] I t+j Ij represents the infusion instruction sent to the insulin infusion system at the jth step;

[0352] rI c(t+j) rIj represents the infusion instruction sent to the insulin infusion system after risk conversion at the jth step;

[0353] 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.

[0354] Gj represents the estimation of plasma insulin concentration at the jth step.

[0355] For insulin effect delay, the compensation formula is as follows:

[0356] rI′ t+j rI′j = rIj t+j -IOB(t+j)

[0357] Wherein:

[0358] rI′ t+j rI′j represents the infusion instruction sent to the insulin infusion system after deducting IOB at the jth step after risk conversion;

[0359] rI t+j represents the infusion command sent to the insulin infusion system at step j after risk conversion;

[0360] IOB(t+j) represents the amount of insulin in the body that has not yet acted at time t+j.

[0361] Similarly, IOB(t+j) can also be meal and non-meal differentiated, in which case:

[0362] IOB(t+j) = IOB m,t+j + IOB o,t+j

[0363] where:

[0364]

[0365] where:

[0366] IOB m,t+j represents the amount of meal insulin in the body that has not yet acted at time t+j;

[0367] IOB o,t+j represents the amount of non-meal insulin in the body that has not yet acted at time t+j;

[0368] D i (i = 2-8) represent the respective coefficients of the IOB curve corresponding to the insulin action time i, respectively;

[0369] I m,t+j represents the amount of meal insulin at time t+j;

[0370] I 0,t+j represents the amount of non-meal insulin at time t+j;

[0371] IOB(t+j) represents the amount of insulin in the body that has not yet acted at time t+j.

[0372] When rI' t+j > 0, the final amount of insulin infused is rI' t+j .

[0373] When rI' t+j < 0, the final amount of insulin infused is 0.

[0374] For the sensing delay of the interstitial glucose concentration and the blood glucose concentration, autoregressive compensation can also be used, with the formula as follows:

[0375]

[0376] where,

[0377] GSC (t+j) represents the glucose concentration in the interstitial fluid at time t+j, which is the measured value of the sensing system;

[0378] This represents the estimated blood glucose concentration at time t+j-1;

[0379] G SC (t+j-1) and G SC (t+j-2) represent the glucose concentrations in the interstitial fluid at times t+j-1 and t+j-2, respectively;

[0380] K0 represents the coefficient of the estimated blood glucose concentration at time t+j-1;

[0381] K1 and K2 represent the coefficients of interstitial fluid glucose concentration at time t+j-1 and t+j-2, respectively.

[0382] At the initial moment,

[0383] The beneficial effects of various compensation methods are consistent with those of the rPID algorithm, and will not be repeated here.

[0384] It should be noted that, in the rMPC algorithm, it is preferable to compensate for the delay in insulin onset and the sensing delay in tissue fluid glucose concentration and blood glucose concentration.

[0385] In another embodiment of the present invention, the program module 101 is pre-set with a composite artificial pancreas algorithm, which 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 volume I1, the second algorithm calculates the second insulin infusion volume I2, the composite artificial pancreas algorithm optimizes the calculation of the first insulin infusion volume I1 and the second insulin infusion volume I2 to obtain the final insulin infusion volume I3, and sends the final insulin infusion volume I3 to the infusion module 102. The infusion module 102 performs insulin infusion according to the final infusion volume I3.

[0386] The first and second algorithms are one of the classic PID algorithm, the classic MPC algorithm, the rMPC algorithm, or the rPID algorithm. The rMPC algorithm or the rPID algorithm is an algorithm that transforms blood glucose levels that are asymmetric in the original physical space into blood glucose risk levels that are approximately symmetric in the risk space. The transformation method of blood glucose risk in the rMPC algorithm and the rPID algorithm is as described above.

[0387] When I1 = I2, I3 = I1 = I2;

[0388] When I1≠I2, the arithmetic mean of I1 and I2 can be substituted into the first algorithm and the second algorithm 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 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:

[0389] ①Solving the average of the first insulin infusion amount I1 and the second insulin infusion amount I2

[0390] ②The average value is substituted into the first algorithm and the second algorithm, respectively, to adjust the algorithm parameters;

[0391] 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;

[0392] ④The steps ①-③ are calculated in a loop until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0393] At this time, 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 can be taken as 60min-90min, K I =T I *K P , T I can be taken as 150min-450min. When the first algorithm or the second algorithm is a MPC or rPMC algorithm, the algorithm parameters are K.

[0394] When I1≠I2, I1 and I2 can also be weighted, and the calculated value after the weighted processing is substituted into the first algorithm and the second algorithm 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 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:

[0395] 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;

[0396] ②The weighted average value The algorithm parameters are adjusted and brought into the first algorithm and the second algorithm;

[0397] The 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 algorithm parameters;

[0398] The steps 1-3 are calculated in a loop until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0399] 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 parameter is K.

[0400] In the embodiments of the present application, alpha and beta can be adjusted according to the size of the first insulin infusion amount I1 and the second insulin infusion amount I2, when I1≥I2, alpha≤beta; when I1≤I2, alpha≥beta; preferably, alpha+beta=1. In other embodiments of the present application, alpha and beta can also be in other value ranges, which are not limited here.

[0401] 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 mutually referenced, preferably, the first algorithm and the second algorithm are respectively rMPC algorithm and rPID algorithm, and the two are mutually referenced, further improving the accuracy of the output result, making the result more feasible and reliable.

[0402] In another embodiment of the present application, the program module 101 further has a memory for storing information of the user's historical body state, blood glucose value and insulin infusion amount, etc. Statistical analysis can be performed based on the information in the memory to obtain a statistical analysis result I4. When I1≠I2, I1, I2 and I4 are compared respectively, the final insulin infusion amount I3 is calculated, and one of I1 and I2 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:

[0403]

[0404] By comparison with historical data, the reliability of the insulin infusion amount is ensured from another aspect.

[0405] 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.

[0406] 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:

[0407] dG t / dt=(G t -G t-1 ) / △t

[0408] Wherein:

[0409] G t represents the blood glucose value at the current time point;

[0410] G t-1 represents the blood glucose value at the previous time point;

[0411] △t represents the time interval between the current time point and the previous time point.

[0412] When the change rate calculation formula of three time points is used, the calculation formula is:

[0413] dG t / dt=(3G t -4G t-1 +G t-2 ) / 2△t

[0414] Wherein:

[0415] G t represents the blood glucose value at the current time point;

[0416] G t-1 represents the blood glucose value at the previous time point;

[0417] G t-2 represents the blood glucose value at the time point before the previous time point;

[0418] △t represents the time interval between the current time and the last time.

[0419] Before calculating the blood glucose rate of change, the original continuous glucose data can also be filtered or smoothed. The threshold can be set to 1.8mg / mL-3mg / mL, or can be personalized.

[0420] Similar to meal recognition, exercise recognition can also be based on the blood glucose rate of change and determined by a specific threshold value due to the rapid decrease in blood glucose caused by exercise. 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.

[0421] The exercise sensor can be provided 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 provided in the program module 101.

[0422] It should be noted that the number of exercise sensors and the 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.

[0423] 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.

[0424] It should be noted that the blood glucose risk conversion method used by the rMPC algorithm and the rPID algorithm in the calculation process can be the same or different, the compensation method for the delay effect can be the same or different, and the calculation process can also be adjusted according to the actual situation.

[0425] 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 user's body weight. In some embodiments of the present application, the infusion module 102 or the program module 101 can indicate the user's total daily insulin requirement DIR. 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, i.e. DIR = e*BW, wherein e is the body weight adjustment coefficient.

[0426] 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 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.

[0427] 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.

[0428] 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.

[0429] 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:

[0430]

[0431] Wherein:

[0432] c is a safety coefficient;

[0433] s is a clinical experience coefficient;

[0434] e is a body weight adjustment coefficient, U / Kg.

[0435] 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.

[0436] 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, P P K' = K * Sb(t), K' = K * Sb(t).

[0437] 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.

[0438] 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.

[0439] B(t) can also refer to the commonly used basal rate segmentation setting in the clinic, such as using three segment settings, as follows:

[0440] ① When the time t is from 0am to 4am, B(t) = 0.5DIR / 48;

[0441] ② When the time t is from 4am to 10am, B(t) = 1.5DIR / 48;​

[0442] When the time t is from 10:00 am to 0:00 am, B(t) = DIR / 48.

[0443] 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.

[0444] 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.

[0445] In the embodiments of the application, the conversion mode of the rPID algorithm and the rMPC algorithm for converting the blood glucose that is asymmetric in the original physical space to the blood glucose risk that is approximately symmetric in the risk space, the compensation mode of various delays, and the beneficial effects have been described above 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 above and will not be repeated here.

[0446] Figure 6 A schematic diagram of the module relationship of the closed-loop artificial pancreas insulin infusion control system according to another embodiment of the application.

[0447] 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.

[0448] 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) that can detect the blood glucose value in real time and monitor the change of blood glucose, and send the current blood glucose value to the infusion module 102 and the electronic module 103.

[0449] 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.

[0450] The electronic module 103 is used to control the operation of the detection module 100 and the infusion module 102. Therefore, the electronic module 103 is connected with the detection module 100 and the infusion module 102 respectively. Here, the electronic module 103 is an external electronic device such as a mobile phone or a handset, and therefore the connection means wireless connection. The electronic module 103 comprises a second processor, which in the embodiment of the present application is an electronic processor 1031 or the like capable of executing a second algorithm and a third algorithm. After receiving the current blood glucose value sent by the detection module 100, the electronic module 103 calculates the second insulin infusion amount I2 required at present by the second algorithm. Here, the first algorithm and the second algorithm used by the electronic module 103 and the infusion module 102 to calculate the insulin amount required at present are not the same.

[0451] After receiving the first insulin infusion amount II sent by the infusion module 102, the electronic module 103 further calculates the first insulin infusion amount II and the second insulin infusion amount I2 by the third algorithm to obtain the final insulin infusion amount I3, and sends the final insulin infusion amount I3 to the infusion module 102, which infuses the insulin I3 required at present 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. That is:

[0452] When II = I2, I3 = II = I2;

[0453] When II ≠ I2, the electronic module 103 further calculates the current insulin infusion amount II and I2 by putting the arithmetic mean or the value after weighted processing of the two into the algorithm, and if the data are still not the same, the above process is repeated until I3 = II = I2, that is:

[0454] ①Solving the mean value of the first insulin infusion amount II and the second insulin infusion amount I2

[0455] ②Putting the mean value into the first algorithm and the second algorithm, and adjusting the algorithm parameters;

[0456] ③Re-calculating the first insulin infusion amount II and the second insulin infusion amount I2 based on the current blood glucose value, the first algorithm and the second algorithm after adjusting the parameters;

[0457] ④Carrying out loop calculation on steps ①-③ until II = I2, and the final insulin infusion amount I3 = II = I2.

[0458] Or:

[0459] ①Solving the weighted mean value of the first insulin infusion amount II and the second insulin infusion amount I2 wherein a and b are weighting factors of the first insulin infusion amount I1 and the second insulin infusion amount I2 respectively;

[0460] ②The weighted mean value is brought into the first algorithm and the second algorithm, and the algorithm parameters are adjusted;

[0461] 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;

[0462] ④The steps ①-③ are calculated in a loop until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0463] 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:

[0464]

[0465] 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 to the electronic module 103 through wireless connection.

[0466] 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.

[0467] 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.

[0468] 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) that 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 further comprises a second processor, which is a detection processor 1001 or 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 processor directly calculates the second insulin infusion amount I2 through the second algorithm and sends the calculated second insulin infusion amount I2 to the electronic module 103.

[0469] ​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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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.

[0474] As in an 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 user's skin. The two modules are connected to form an integral structure and are attached to the same position, the number of devices attached to the user's skin is 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.

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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 here.

[0479] Figure 9 The schematic diagram of the dual-drug infusion switching according to the two embodiments of the present application.

[0480] 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:

[0481] 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 .

[0482] 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 .

[0483] 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 , i.e. 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.

[0484] 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:

[0485] When I t ≥ I b , the infusion module 102 starts to perform the hypoglycemic drug infusion according to the hypoglycemic drug infusion data I t calculated by the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm.

[0486] When 0 ≤ I t <I b , 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 aforementioned rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm.

[0487] 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.

[0488] When the infusion module 102 has only one set of drug infusion pipeline:

[0489] When I t ≥ 0, the infusion module 102 starts to perform the hypoglycemic drug infusion according to the hypoglycemic drug infusion data I t calculated by the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm.

[0490] When I t < 0, the infusion of the hypoglycemic drug is stopped and only the hyperglycemic drug is infused.

[0491] Preferably, in the embodiments of the present application, the hypoglycemic drug is insulin and the hyperglycemic drug is glucagon.

[0492] 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.

[0493] Figure 10 is 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.

[0494] 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.

[0495] 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.

[0496] 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.

[0497] 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.

[0498] 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.

[0499] 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.

[0500] To sum up, the application discloses a closed-loop artificial pancreas insulin infusion personalized control system, the adaptive unit for adjusting the gain coefficient of the response algorithm according to the body weight of the user is preset in the system, the gain coefficient of the response algorithm can be adjusted according to the body weight of the user, individualized setting can be conveniently conducted according to the actual condition of the user, the real demand of the user can be better met, and user experience is improved.

[0501] 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 intended to limit the scope of the application. It will be appreciated by persons skilled in the art that modifications can be made to the embodiments described above 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 personalized control system, characterized in that, include: The detection module is used to continuously detect the current blood glucose value G; The program module connected to the detection module includes a preset algorithm for calculating insulin infusion instructions, an adaptive unit for adjusting the gain coefficient of the algorithm based on the user's weight (BW), and a preset target blood glucose value (G). B ; and An infusion module is connected to the program module. The program module controls the infusion module to infuse insulin according to the insulin infusion indication calculated by the algorithm for calculating the insulin infusion indication. The algorithm for calculating the insulin infusion indication includes a risk rPID algorithm or a risk rMPC algorithm. The risk rPID algorithm is expressed by the following formula: Where rPID(t) represents the infusion instruction sent to the insulin infusion system after risk conversion, C represents a constant, and K p This is the gain coefficient of the proportional portion, where r represents the glycemic risk, and K... I K is the gain coefficient of the integral part. D It is the gain coefficient of the differential part; The value function of the risk rMPC algorithm is expressed by the following formula: Where, r t+j I′ represents the blood glucose risk value after step j; t+j This represents the change in insulin infusion volume after step j, where R is the weighting coefficient of the insulin component, and N and P are the number of steps within the control time window and the prediction time window, respectively. When the algorithm for calculating insulin infusion volume is the risk rPID algorithm, the relationship between the gain coefficient Kp of the proportional component and the user's weight BW is as follows: Kp = DIR / (BW*m) in, DIR represents the total daily insulin requirement, expressed in units (U). BW represents the user's weight, in kg; m represents the user's weight compensation coefficient; When the algorithm for calculating insulin infusion volume is the risk rMPC algorithm, the relationship between the gain coefficient K of the risk rMPC algorithm and the user's weight BW is as follows: in, c is the safety factor; s is the clinical experience coefficient; e is the weight adjustment factor, in units of U / Kg; The blood glucose risk r is calculated as follows:

2. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 1, characterized in that, The user weight compensation coefficient m is set to a value of 50 to 500.

3. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 1, characterized in that, The safety factor c is between 1.25 and 3.

4. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 1, characterized in that, The clinical experience coefficient s is set to a value of 1500–2500.

5. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 1, characterized in that, The weight adjustment factor e ranges from 0.6 to 1.1 U / Kg.

6. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 1, characterized in that, The gain coefficient Kp or K can be adjusted using a coefficient related to basal insulin requirements: K′ P =K P *Sb(t), K′=K*Sb(t) in, Sb(t) is the ratio of the basal insulin requirement B(t) at time t to the average daily basal insulin intake Ba. Sb(t)=B(t) / Ba.

7. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 6, characterized in that, The average daily basal insulin dose, Ba, is: Ba = y * DIR / 24 in, y is the basal insulin compensation coefficient.

8. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 7, characterized in that, The basal insulin compensation coefficient y ranges from 0.1 to 5.

9. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 6, characterized in that, The basal insulin requirement B(t) is set in segments: ① When time t is from 0:00 to 4:00 AM, B(t) = 0.5 * DIR / 48; ② When time t is from 4 a.m. to 10 a.m., B(t) = 1.5 * DIR / 48; ③ When time t is from 10:00 AM to 0:00 AM, B(t) = DIR / 48.

10. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 1, characterized in that, The risk rMPC algorithm and the risk rPID algorithm, based on the classic PID algorithm and the classic MPC algorithm respectively, transform blood glucose that is asymmetric in the original physical space into blood glucose risk that is approximately symmetric in the risk space.

11. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 1, characterized in that, The method for converting the blood glucose risk r in the rMPC algorithm or the rPID algorithm includes one or more of the following: piecewise weighting, relative value conversion, blood glucose risk index conversion, and improved control variability grid analysis conversion.

12. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 11, characterized in that, When the current blood glucose value G is greater than the target blood glucose value G B At that time, the blood glucose risk r is calculated as follows: r=r(G),if G>G B in: r(G)=10*f(G) 2 The transformation function f(G) is as follows: f(G)=1.509*[(ln(G)) 1.084 -5.381] When the current blood glucose value G is not greater than the target blood glucose value G B At that time, the blood glucose risk r is calculated as follows: r=G-G B ,if G≤G B 。 13. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 11, characterized in that, When the current blood glucose value G is not greater than the target blood glucose value G B At that time, the blood glucose risk r is calculated as follows: r=-r(G),if G≤G B in: r(G)=10*f(G) 2 The transformation function f(G) is as follows: f(G)=1.509*[(ln(G)) 1.084 -5.381] When the current blood glucose value G is greater than the target blood glucose value G B At that time, the blood glucose risk r was calculated as: r = -4.8265 * 10 4 -4*G 2 +0.45563*G-44.855, if G>G B .

14. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 11, characterized in that, When the current blood glucose value G is not greater than the target blood glucose value G B At that time, the blood glucose risk r is calculated as follows: r=-r(G),if G≤G B in: r(G)=10*f(G) 2 The transformation function f(G) is as follows: f(G)=1.509*[(ln(G)) 1.084 -5.381] When the current blood glucose value G is greater than the target blood glucose value G B At that time, the blood glucose risk r is calculated as follows:

15. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 11, characterized in that, When the current blood glucose value G is not greater than the target blood glucose value G B At that time, the blood glucose risk r is calculated as follows: r=-r(G),if G≤G B in: r(G)=10*f(G) 2 The transformation function f(G) is as follows: f(G)=1.509*[(ln(G)) 1.084 -5.381] When the current blood glucose value G is greater than the target blood glucose value G B At that time, the blood glucose risk r is calculated as follows: r=100*(G-G B ) / G,if G>G B 。 16. The closed-loop artificial pancreas insulin infusion personalized control system according to any one of claims 11-15, characterized in that, The rPID algorithm or rMPC algorithm also includes one or more of the following processing methods: ① The component that is proportional to the estimated plasma insulin concentration is deducted from the predicted plasma insulin concentration; ② Subtract the amount of insulin that has not yet taken effect in the body; ③ An autoregressive method was used to compensate for the sensing delay of blood glucose and interstitial fluid glucose concentrations.

17. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 1, characterized in that, Two of the detection module, the program module, and the infusion module are interconnected to form an integrated structure, and are respectively attached to different locations on the skin along with the third module.

18. The closed-loop artificial pancreas insulin infusion personalized control system according to claim 1, characterized in that, The detection module, the program module, and the infusion module are connected to form an integrated structure and are attached to the same location on the skin.

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