Closed-loop artificial pancreas drug infusion control system

Abstract

The application discloses a closed-loop artificial pancreas medicine infusion control system, which comprises an infusion module for outputting medicine, a program module, an input end and an output end of the program module, a plurality of electric connection areas of the input end for receiving a current blood glucose value, an algorithm preset in the program module, the output end and the infusion module being electrically connected, the algorithm calculating a required medicine amount of a user according to the received current blood glucose value, the program module controlling the infusion module to output the medicine according to the calculated required medicine amount of the user, and an infusion hose provided with at least two detection electrodes, the infusion hose being a medicine infusion channel, the electrodes being arranged on a pipe wall of the infusion hose, the infusion hose being communicated with the infusion module when the infusion hose is installed to a working position, the medicine flowing to the body through the infusion hose, and the different electrodes being electrically connected with different electric connection areas respectively to input the current blood glucose value into the program module. The algorithm is used to realize accurate control of the closed-loop artificial pancreas medicine infusion system.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit of and priority of the following patent application: PCT patent application filed on October 25, 2021, application number PCT / CN2021 / 126005. Technical Field

[0003] This invention relates primarily to the field of medical devices, and in particular to a closed-loop artificial pancreas drug infusion control system. Background Technology

[0004] In a healthy person, the pancreas automatically secretes the necessary insulin / glucagon based on blood glucose levels, thus maintaining a reasonable range of blood sugar fluctuations. However, in diabetic patients, pancreatic function is abnormal, and the pancreas is unable to secrete the required insulin normally. Diabetes is a metabolic disease and is a lifelong condition. Current medical technology cannot cure diabetes; it can only control the occurrence and development of diabetes and its complications by stabilizing blood sugar levels.

[0005] Diabetic patients need to have their blood glucose levels checked before insulin injection. Current methods allow for continuous glucose monitoring, sending the data in real-time to a display device for user viewing. This method is called Continuous Glucose Monitoring (CGM). The device is attached to the skin, with its probe inserted into the subcutaneous fluid to perform the measurement. Based on the CGM reading, the infusion device delivers the required amount of insulin subcutaneously, creating a closed-loop or semi-closed-loop artificial pancreas.

[0006] Currently, proportional-integral-derivative (PID) and model-predictive-control (MPC) algorithms are widely studied to achieve closed-loop or semi-closed-loop control of artificial pancreas. However, due to the simple structure of the PID algorithm, it is not suitable for scenarios with large and complex disturbances, while the MPC algorithm faces the dilemma of difficulty in establishing accurate models and large computational load, which may lead to infusion deviations in prediction.

[0007] Therefore, there is an urgent need in the current technology for a closed-loop artificial pancreas drug infusion control system that incorporates an optimized artificial pancreas algorithm. Summary of the Invention

[0008] The embodiment of the present application discloses a closed-loop artificial pancreas drug infusion control system, which is provided with one or more of rMPC algorithm, rPID algorithm and composite artificial pancreas algorithm, fully utilizes the advantages of rPID algorithm and rMPC algorithm to face complex situations, so that the artificial pancreas can provide reliable drug type and drug infusion amount for blood glucose control in various situations, so that the blood glucose reaches the ideal level, and the precise control of the closed-loop artificial pancreas drug infusion system is realized.

[0009] The present application discloses a closed-loop artificial pancreas drug infusion control system, comprising: an infusion module for outputting drugs; a program module, the program module comprising an input end and an output end, the input end comprising a plurality of electrical connection areas to receive the current blood glucose value, the program module is also provided with an algorithm, the algorithm is one or more of rMPC algorithm, rPID algorithm or composite artificial pancreas algorithm, after the output end and the infusion module are electrically connected, the algorithm calculates the required drug amount of the user according to the received current blood glucose value, and the program module controls the infusion module to output drugs according to the calculated required drug amount of the user; and an infusion hose provided with at least two detection electrodes, the infusion hose is a drug infusion channel, the electrodes are arranged on the wall of the infusion hose, when the infusion hose is installed to the working position, the infusion hose is in communication with the infusion module, the drugs flow to the body through the infusion hose, and different electrodes are electrically connected with different electrical connection areas to input the current blood glucose value into the program module.

[0010] According to one aspect of the present application, the rMPC algorithm and the rPID algorithm are based on the classical PID algorithm and the classical MPC algorithm respectively, and the blood glucose in the original physical space is converted to the blood glucose risk in the risk space which is approximately symmetrical, and the current required drug infusion amount is calculated according to the blood glucose risk.

[0011] According to one aspect of the present application, the blood glucose risk space conversion method of the rMPC algorithm and the rPID algorithm includes one or more of the following: segmented weighting method, relative value conversion, blood glucose risk index conversion and improved control variability grid analysis conversion.

[0012] According to one aspect of the present application, the blood glucose risk space conversion method of the rMPC algorithm and the rPID algorithm further includes one or more of the following processing methods:

[0013] ①Subtracting a component proportional to the estimated concentration of the plasma insulin or glucagon;

[0014] ②Subtracting the amount of insulin or glucagon that has not yet acted in the body;

[0015] ③Using an autoregressive method to compensate for the sensing delay of interstitial fluid glucose concentration and blood glucose.

[0016] According to one aspect of the present application, the compound artificial pancreas algorithm comprises a first algorithm and a second algorithm, the first algorithm calculates a first insulin infusion amount I1, the second algorithm calculates a second insulin infusion amount I2, the compound artificial pancreas algorithm calculates an optimized calculation of the first insulin infusion amount I1 and the second insulin infusion amount I2 to obtain a final insulin infusion amount I3.

[0017] According to one aspect of the present application, the final insulin infusion amount I3 is optimized by an average value of the first insulin infusion amount I1 and the second insulin infusion amount I2:

[0018] ①Solving the average value of the first insulin infusion amount I1 and the second insulin infusion amount I2

[0019] ②The average value is brought into the first algorithm and the second algorithm, and the algorithm parameters are adjusted;

[0020] 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;

[0021] ④The steps ①-③ are calculated in a loop until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0022] According to one aspect of the present application, the final insulin infusion amount I3 is optimized by a weighted value of the first insulin infusion amount I1 and the second insulin infusion amount I2:

[0023] ①Solving the weighted value 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;

[0024] ②The weighted value is brought into the first algorithm and the second algorithm, and the algorithm parameters are adjusted;

[0025] 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;

[0026] ④The steps ①-③ are calculated in a loop until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0027] According to one aspect of the present application, the final insulin infusion amount I3 is obtained by comparing the first insulin infusion amount I1 and the second insulin infusion amount I2 with the statistical analysis result I4 of historical data:

[0028]

[0029] According to an aspect of the present application, the first algorithm and the second algorithm are a classic PID algorithm, a classic MPC algorithm, an rPID algorithm or an rMPC algorithm.

[0030] According to an aspect of the present application, the infusion tube comprises an inner tube and at least one outer tube, the outer tube is arranged outside the inner tube, and the inner tube is used for infusing the medicine.

[0031] According to an aspect of the present application, at least one electrode is arranged between the outer wall of the inner tube and the outermost outer tube.

[0032] According to an aspect of the present application, the infusion module comprises a plurality of infusion sub-modules, the plurality of infusion sub-modules are electrically connected with the output end respectively, and the program module selects to control the infusion sub-modules to output the medicine according to the calculated amount of medicine required by the user.

[0033] According to an aspect of the present application, the medicine is a hyperglycemic medicine and a hypoglycemic medicine.

[0034] According to an aspect of the present application, the closed-loop artificial pancreas medicine infusion control system is composed of a plurality of parts, the infusion module and the program module are arranged in different parts and are electrically connected through a plurality of electrical contacts.

[0035] Compared with the prior art, the technical scheme of the present application has the following advantages:

[0036] In the closed-loop artificial pancreas medicine infusion control system disclosed by the present application, one or more of the rMPC algorithm, the rPID algorithm and the composite artificial pancreas algorithm are preset in the system, the advantages of the rPID algorithm and the rMPC algorithm are fully utilized to face complex situations, the artificial pancreas can provide reliable medicine types and medicine infusion amounts for controlling blood sugar in various situations, so that the blood sugar reaches an ideal level, and precise control of the closed-loop artificial pancreas medicine infusion system is realized.

[0037] Further, the final output of the composite artificial pancreas algorithm is a consistent result obtained by calculation of the rMPC algorithm and the rPID algorithm, and the result is more feasible and reliable.

[0038] Further, the final output of the composite artificial pancreas algorithm is the same result obtained by averaging or weighted optimization of different results obtained by calculation of the first algorithm and the second algorithm, the two sets of algorithms compensate for each other, and the accuracy of the output result is further improved.

[0039] Further, the final output of the composite artificial pancreas algorithm is obtained by composite processing of the calculation results of the rMPC algorithm and the rPID algorithm, and the processing combines statistical analysis of historical control data, thereby ensuring the reliability of the insulin infusion amount from another aspect.

[0040] Further, the infusion module comprises a plurality of infusion sub-modules, the plurality of infusion sub-modules are electrically connected with the output end respectively, and the program module selects whether the infusion sub-modules output the medicine. Different medicines are placed in the plurality of sub-modules, the program module selects to send the medicine infusion instruction to different infusion sub-modules, and accurate control of blood glucose is realized.

[0041] Further, the infusion module comprises a plurality of infusion sub-modules, the plurality of infusion sub-modules are electrically connected with the output end respectively, and the program module selects whether the infusion sub-modules output the medicine. Different medicines are placed in the plurality of sub-modules, the program module selects to send the medicine infusion instruction to different infusion sub-modules, and accurate control of blood glucose is realized. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 Workflow diagram of the closed-loop artificial pancreas medicine infusion control system according to one embodiment of the present application;

[0043] Figure 2 Comparison diagram of the relationship between the blood glucose in the risk space and the original physical space obtained by the piecewise weighting processing and the relative value conversion method according to one embodiment of the present application;

[0044] Figure 3 Comparison diagram of the relationship between the blood glucose in the risk space and the original physical space obtained by the BGRI and CVGA method conversion according to one embodiment of the present application;

[0045] Figure 4 Insulin IOB curve according to one embodiment of the present application;

[0046] Figure 5 Schematic diagram of the four kinds of clinical optimal basal rate setting types commonly used according to one embodiment of the present application;

[0047] Figure 6a Cross-sectional schematic diagram of the infusion tube of the closed-loop artificial pancreas medicine infusion control system according to one embodiment of the present application in the installation position;

[0048] Figure 6b Cross-sectional schematic diagram of the infusion tube of the closed-loop artificial pancreas medicine infusion control system according to one embodiment of the present application in the working position;

[0049] Figures 7a-7b Top view schematic diagram of the closed-loop artificial pancreas medicine infusion control system according to another embodiment of the present application;

[0050] Figures 8a-8b Local longitudinal cross-sectional view of the infusion hose provided with two electrodes according to one embodiment of the present application;

[0051] Figures 9a-9c Local longitudinal cross-sectional view of the infusion tube and two electrodes according to another embodiment of the present application;

[0052] Figure 10Local longitudinal section view of three electrodes provided on the infusion tube according to another embodiment of the present application;

[0053] Figure 11 Local longitudinal section view of the infusion tube including inner tube layer and outer tube layer according to another embodiment of the present application. DETAILED DESCRIPTION

[0054] As mentioned above, PID algorithm is not suitable for complex scenarios with large disturbance due to its simple structure, while MPC algorithm faces the difficulty of establishing accurate model and large amount of calculation, which may result in predicted infusion deviation.

[0055] In order to solve the problem, the present application provides a closed-loop artificial pancreas drug infusion control system, which is provided with one or more of rMPC algorithm, rPID algorithm and composite artificial pancreas algorithm, and makes full use of the advantages of rPID algorithm and rMPC algorithm to face complex scenarios, so that the artificial pancreas can provide reliable drug type and drug infusion amount for controlling blood glucose in various situations, so as to make the blood glucose reach the ideal level, and realize precise control of the closed-loop artificial pancreas drug infusion system.

[0056] 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 otherwise specifically stated.

[0057] In addition, it should be understood that 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 some units can be enlarged relative to other structures.

[0058] The following description of 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 devices known to those of ordinary skill in the relevant art can not be discussed in detail here, but in the case of applicable techniques, methods and devices, these techniques, methods and devices should be considered as part of the present specification.

[0059] 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 the subsequent drawing description.

[0060] Figure 1 Workflow diagram of the closed-loop artificial pancreas drug infusion control system according to the embodiment of the present application.

[0061] The closed-loop artificial pancreas drug infusion control system of the embodiment of the present application comprises three basic parts: an electrode, a program module and an infusion module. Blood glucose parameter information is obtained by the electrode and converted into an electrical signal. The electrical signal is transmitted into the program module through the electrode and / or electrode lead wire. The program module reads the current blood glucose value G, and an algorithm and a target blood glucose value G B are preset in the program module. The algorithm calculates the current required drug amount (such as insulin or glucagon amount) of the user through the current blood glucose value G, and the program module sends the calculated current required drug amount of the user to the infusion module to control the infusion module to perform drug infusion, thereby stabilizing blood glucose. The current blood glucose value is detected by the electrode in real time, and the detection and infusion cycle is continuously performed. This process does not require human intervention and is completed directly through program analysis to control the stability of blood glucose.

[0062] Specifically, the algorithm preset in the program module is an rPID (risk-proportion-integral-derivative) algorithm for converting blood glucose that is asymmetric in the original physical space to blood glucose risk that is approximately symmetric in the risk space. The rPID algorithm is obtained by conversion processing based on the classical PID (proportion-integral-derivative) algorithm, and the specific processing manner will be described in detail below. According to the corresponding infusion instruction calculated by the rPID algorithm, the program module controls the infusion module to infuse insulin.

[0063] The classical PID algorithm can be represented by the following formula:

[0064]

[0065] Wherein:

[0066] K P is the gain coefficient of the proportional part;

[0067] K I is the gain coefficient of the integral part;

[0068] K D is the gain coefficient of the derivative part;

[0069] G represents the current blood glucose value;

[0070] G B represents the target blood glucose value;

[0071] C represents a constant;

[0072] PID(t) represents the infusion instruction sent to the insulin infusion system.

[0073] Considering the actual distribution of glucose concentration in diabetic patients, such as normal blood glucose range of 80-140 mg / dL, which can also be relaxed to 70-180 mg / dL, general hypoglycemia can reach 20-40 mg / dL, and hyperglycemia can reach 400-600 mg / dL.

[0074] The distribution of high / low blood glucose has significant asymmetry in the original physical space, and the same degree of deviation of blood glucose from the normal range in clinical practice corresponds to significantly different risks of high blood glucose and low blood glucose, such as a decrease of 70 mg / dL from 120 mg / dL to 50 mg / dL, which is considered as 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 diabetic patients, and it is often reached in daily situations, and basically no treatment measures need to be taken.

[0075] 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 which is approximately symmetric in the risk space, so that the PID algorithm is more robust.

[0076] Correspondingly, the rPID algorithm formula is converted as follows:

[0077]

[0078] Among them:

[0079] rPID(t) represents the infusion instruction sent to the insulin infusion system after risk conversion;

[0080] r represents the blood glucose risk;

[0081] The meanings of other symbols are as described above.

[0082] In order to maintain the stability of the PID integral, combined with the physiological effect of insulin in reducing blood glucose, in an embodiment of the present application, the input parameter of PID, the blood glucose deviation Ge=G-G B is processed, such as the segmented weighted processing on Ge=G-G B , as follows:

[0083]

[0084] In another embodiment of the present application, the relative value is used to convert the deviation greater than the target blood glucose G B , as follows:

[0085]

[0086] Figure 2 A blood glucose risk space obtained by piecewise weighting processing and relative value conversion versus a blood glucose relationship diagram of an original physical space.

[0087] 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, and 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.

[0088] In another embodiment of the present application, there is a fixed zero risk point when converting risk, and data deviating from both sides of the zero risk point is processed. The original parameter corresponding to the zero risk point is positive when converted to the risk space, and the original parameter corresponding to the zero risk point 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:

[0089]

[0090] Wherein:

[0091] r(G)=10*f(G) 2

[0092] The conversion function f(G) is as follows:

[0093] f(G)=1.509*[(ln(G)) 1.084 -5.381]

[0094] In the classic blood glucose risk index method, the blood glucose value corresponding to the zero risk point of this method is 112 mg / dL. In other embodiments of the present application, the zero risk point blood glucose value can also be adjusted in combination with the risk and data trend of clinical practice, which is not specifically limited here. The risk space of the blood glucose value greater than the zero risk point of the blood glucose value is fitted, and the specific fitting method is not specifically limited.

[0095] 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 modified 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 . The polynomial model fitting is performed on the same, and the following risk functions for the two sides of the zero risk point are obtained:

[0096]

[0097] The maximum value is limited:

[0098] |r| = min (|r|, n)

[0099] The value range of the maximum value n is 0-80 mg / dL, and the value of n is preferably 60 mg / dL.

[0100] 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 combination with the real risk and data trend in clinical practice, and the equal risk point is fitted again without specific limitation. The specific value for limiting the maximum value is also not specifically limited.

[0101] Figure 3 The comparison chart of the blood glucose risk converted to the risk space by the BGRI and CVGA methods and the blood glucose in the original physical space.

[0102] Similar to the processing of Zone-MPC, the blood glucose risk converted by the BGRI and CVGA methods is quite 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.

[0103] 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:

[0104] r = -r(G), if G ≤ G B ,

[0105] wherein:

[0106] r(G) = 10*f(G) 2

[0107] The conversion function f(G) is as follows:

[0108] f(G) = 1.509*[(ln(G)) 1.084 -5.381]

[0109] r = -4.8265*10 4 -4*G 2 +0.45563*G - 44.855, if G > G B .

[0110] 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:

[0111] r = r(G), if G > G B ,

[0112] wherein:

[0113] r(G) = 10*f(G) 2

[0114] The conversion function f(G) is as follows:

[0115] f(G) = 1.509*[(ln(G)) 1.084 -5.381]

[0116] r = G - G B , if G ≤ G B .

[0117] Meanwhile, the maximum value can also be limited:

[0118] |r| = min(|r|, n)

[0119] wherein the maximum value n is defined in the range of 0-80 mg / dL, preferably, the value of n is 60 mg / dL.

[0120] In other embodiments of the present application, the blood glucose value of the zero risk point can also be set as the target blood glucose value G B , the BGRI method is adopted for data less than or equal to the target blood glucose value G B , and the deviation processing method is adopted for data greater than the target blood glucose value G B , specifically, the segmented weighted processing or the relative value processing.

[0121] When the segmented weighted processing is adopted, at this time:

[0122] r = -r(G), if G ≤ G B ,

[0123] wherein:

[0124] r(G) = 10*f(G) 2

[0125] Under the conversion function f(G):

[0126] f(G) = 1.509*[(ln(G)) 1.084 -5.381]

[0127]

[0128] When the relative value processing is adopted:

[0129] r = -r(G), if G ≤ G B ,

[0130] wherein:

[0131] r(G) = 10*f(G) 2

[0132] Under the fitted symmetrical conversion function f(G):

[0133] f(G) = 1.509*[(ln(G)) 1.084 -5.381]

[0134] r = 100*(G-G B ) / G, if G > G B

[0135] When the blood glucose values corresponding to the zero risk points are all the target blood glucose value G BWhen the data is less than or equal to the target blood glucose value G B , the processing functions of the segmented weighted processing, the relative value processing and the CVGA method are consistent, so when the segmented weighted processing or the relative value processing is adopted for the data less than or equal to the target blood glucose value G B , and the BGRI method is adopted for the data greater than the zero risk point blood glucose value, the processing results are equivalent to the aforementioned CVGA method when the blood glucose value is less than or equal to the target blood glucose value G B , and the BGRI method when the blood glucose value is greater than the target blood glucose value G B . The calculation formula is not described herein again.

[0136] It should be noted that in each embodiment of the present application, the target blood glucose value G B is 80-140 mg / dL, and preferably, the target blood glucose value G B is 110-120 mg / dL.

[0137] The above processing methods can make the rPID algorithm convert the asymmetric blood glucose in the original physical space to the approximately symmetric blood glucose risk in the risk space, so as to retain the simple and robust characteristics of the PID algorithm, and have the targeted and clinically valuable blood glucose risk control function, and realize the precise control of the closed-loop artificial pancreas drug infusion system.

[0138] In the closed-loop artificial pancreas control system, there are three delay effects: insulin absorption delay (about 20 minutes from subcutaneous to blood circulation tissue, and about 100 minutes to reach the liver), insulin onset delay (about 30-100 minutes), and sensing delay of interstitial fluid glucose concentration and blood glucose (about 5-15 minutes). Any attempt to accelerate the responsiveness of the closed-loop system can lead to unstable system behavior and system oscillation. In order to compensate for the insulin absorption delay in the closed-loop artificial pancreas control system, in an embodiment of the present application, an insulin feedback compensation mechanism is introduced. The amount of insulin that has not been absorbed in the body is deducted from the output, and a component proportional to the estimated plasma insulin concentration (the actual human insulin secretion also uses the insulin concentration in the blood as a negative feedback signal). The formula is as follows:

[0139]

[0140] Wherein:

[0141] PID(t) represents the infusion instruction sent to the insulin infusion system;

[0142] PID c (t) represents the infusion instruction with compensation sent to the insulin infusion system;

[0143] 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.

[0144] 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:

[0145]

[0146] wherein:

[0147] represents the estimation of the plasma insulin concentration at the current time;

[0148] PID c (n-1) represents the output with compensation at the last time;

[0149] represents the estimation of the plasma insulin concentration at the last time;

[0150] represents the estimation of the plasma insulin concentration at the time before the last time;

[0151] K0 represents the coefficient of the output part with compensation at the last time;

[0152] K1 represents the coefficient of the estimation part of the plasma insulin concentration at the last time;

[0153] K2 represents the coefficient of the estimation part of the plasma insulin concentration at the time before the last time;

[0154] wherein, the initial value is The time interval between each time can be selected according to actual needs.

[0155] Correspondingly, the compensation output formula after the risk conversion by the foregoing method is as follows:

[0156]

[0157] wherein:

[0158] rPIDc(t) represents the infusion instruction with compensation sent to the insulin infusion system after the risk conversion;

[0159] rPID(t) represents the infusion instruction sent to the insulin infusion system after the risk conversion;

[0160] The meanings of other characters are as described above.

[0161] In order to compensate for the delay of insulin action in the closed-loop artificial pancreas control system, in one embodiment of the present application, insulin on board (IOB) that has not yet acted in the body is introduced, and IOB is deducted from the output of insulin, preventing the accumulation of insulin infusion, excessive amount, and the risk of postprandial hypoglycemia.

[0162] Figure 4 is an insulin IOB curve according to an embodiment of the present application.

[0163] According to the IOB curve shown in FIG. 1, the cumulative residual amount of previously infused insulin can be calculated, and the specific curve can be selected according to the actual insulin action time of the user. Figure 4

[0164] PID'(t) = PID(t) - IOB(t)

[0165] wherein:

[0166] PID'(t) represents the infusion instruction sent to the insulin infusion system after deducting IOB;

[0167] PID(t) represents the infusion instruction sent to the insulin infusion system;

[0168] IOB(t) represents the amount of insulin that has not yet acted in the body at time t.

[0169] Correspondingly, the output formula for deducting the amount of insulin that has not yet acted in the body after risk conversion by the foregoing method is as follows:

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

[0171] wherein:

[0172] rPID'(t) represents the infusion instruction sent to the insulin infusion system after deducting the amount of insulin that has not yet acted in the body after risk conversion;

[0173] rPID(t) represents the infusion instruction sent to the insulin infusion system after risk conversion;

[0174] The meanings of other characters are as described above.

[0175] 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:

[0176] IOB(t) = IOB m,t + IOB o,t ​

[0177] wherein:

[0178]

[0179] wherein:

[0180] IOB m,t represents the amount of meal insulin that has not yet acted in the body at time t;

[0181] IOB o,t represents the amount of non-meal insulin that has not yet acted in the body at time t;

[0182] D i (i = 2-8) represents the respective coefficients of the IOB curve corresponding to the insulin action time of i, respectively;

[0183] I m,t represents the amount of meal insulin;

[0184] I 0,t represents the amount of non-meal insulin;

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

[0186] The meal insulin and non-meal insulin are distinguished in the processing of IOB, which can make the insulin be cleared faster when eating and blood glucose is too high, can obtain greater insulin output, and blood glucose regulation is faster. When close to the target, a longer insulin action time curve is used to make the insulin be cleared slower, and blood glucose regulation is more conservative and stable.

[0187] When PID'(t) > 0 or rPID'(t) > 0, the final amount of insulin infused is PID'(t) or rPID'(t);

[0188] When PID'(t) < 0 or rPID'(t) < 0, the final amount of insulin infused is 0.

[0189] In order to compensate for the sensing delay of tissue glucose concentration and blood glucose in the closed-loop artificial pancreas control system, in an embodiment of the present application, an autoregressive method is used for compensation, and the formula is as follows:

[0190]

[0191] wherein,

[0192] G SC (n) represents the current time interstitial fluid glucose concentration, that is, the measurement value of the sensing system;

[0193] represents the estimated concentration of blood glucose at the last time point;

[0194] G SC (n-1) and G SC (n-2) represent the interstitial fluid glucose concentration at the last time point and the time point before the last time point respectively;

[0195] K0 represents the coefficient of the estimated concentration of blood glucose at the last time point;

[0196] K1 and K2 represent the coefficients of the interstitial fluid glucose concentration at the last time point and the time point before the last time point respectively.

[0197] wherein, at the initial time point,

[0198] By estimating the blood glucose concentration through the interstitial fluid glucose concentration, the sensing delay of the interstitial fluid glucose concentration and the blood glucose is compensated, the PID algorithm is more accurate, and accordingly, the rPID algorithm can more accurately calculate the actual demand of the human body for insulin.

[0199] In the embodiment of the present application, for the insulin absorption delay, the insulin onset delay, the sensing delay of the interstitial fluid glucose concentration and the blood glucose, partial compensation or full compensation can be performed, preferably, all the delay factors are considered for full compensation, so that the rPID algorithm is more accurate.

[0200] In another embodiment of the present application, the program module 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 converting processing on the basis of the classical MPC (model-predictive-control) algorithm, and the program module controls the infusion module to infuse insulin according to the corresponding infusion instruction calculated by the rMPC algorithm.

[0201] 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:

[0202] x t+1 = Ax t + BI t

[0203] G t = Cx t

[0204] wherein:

[0205] x t+1 represents the state parameter at the next time point,

[0206] x ta state parameter representing a current time point,

[0207] I t an insulin infusion amount representing a current time point;

[0208] G t a blood glucose concentration representing a current time point.

[0209] The parameter matrix is as follows:

[0210]

[0211]

[0212] C=[1 0 0]

[0213] b1, b2, b3, K are prior values.

[0214] 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.

[0215]

[0216] wherein:

[0217] i′ t+j represents the change of the insulin infusion amount after the jth step;

[0218] represents the difference between the predicted blood glucose concentration and the target blood glucose value after the jth step;

[0219] t represents a current time point;

[0220] N and P are respectively the step number in the control time window and the prediction time window;

[0221] R is the weighted coefficient of the insulin component.

[0222] The insulin infusion amount of the jth step is I t + I′ t+j .

[0223] 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.

[0224] 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.

[0225] 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 limited here.

[0226] As mentioned before, due to the significant asymmetry of the distribution of high / low blood glucose (original physical space), the risk of high blood glucose and the risk of low blood glucose corresponding to the same deviation of blood glucose from the normal range in clinical practice will be significantly different. 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 MPC algorithm is more accurate and flexible. The value function of the rMPC algorithm after the risk conversion is as follows:

[0227]

[0228] Wherein,

[0229] r t+j represents the blood glucose risk value after the jth step;

[0230] I′ t+j represents the change of the insulin infusion amount after the jth step.

[0231] 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 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.

[0232] Specifically, when using segmented weighted processing:

[0233]

[0234] When using relative value processing:

[0235]

[0236] When using the classical blood glucose risk index method:

[0237]

[0238] Wherein:

[0239] r(G t+j ) = 10*f(G t+j ) 2

[0240] The conversion function f(G t+j ) is as follows:

[0241] f(G t+j ) = 1.509*[(ln(G t+j )) 1.084 - 5.381]

[0242] When the control variability grid analysis method is used:

[0243]

[0244] The maximum value thereof is also limited:

[0245] |r t+j | = min(|r t+j |, n)

[0246] The range of the value of the maximum value n is 0-80 mg / dL, and preferably the value of n is 60 mg / d.

[0247] 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 :

[0248] r t+j = -r(G t+j ), if G t+j ≤ G B

[0249] Wherein:

[0250] r(G t+j ) = 10*f(G t+j ) 2

[0251] The conversion function f(G t+j ) is as follows:

[0252] f(G t+j ) = 1.509*[(ln(G t+j )) 1.084 - 5.381]

[0253] r t+j = -4.8265*10 4 - 4*G t+j 2+0.45563*G t+j -44.855,if G t+j >G B

[0254] 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 :

[0255] r t+j = r(G t+j ),if G t+j ≤ G B ,

[0256] wherein:

[0257] r(G t+j ) = 10*f(G t+j ) 2

[0258] The conversion function f(G t+j ) is as follows:

[0259] f(G t+j ) = 1.509*[(ln(G t+j )) 1.084 -5.381]

[0260] r t+j = G t+j -G B ,if G t+j ≤ G B .

[0261] The maximum value can also be limited:

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

[0263] wherein the value range of the maximum value n is 0-80 mg / dL, and preferably the value of n is 60 mg / dL.

[0264] 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 :

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

[0266] wherein:

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

[0268] The conversion function f(G t+j ) is as follows:

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

[0270]

[0271] 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:

[0272] r t+j =-r(G t+j ),if G t+j ≤G B

[0273] Where:

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

[0275] The conversion function f(G t+j ) is as follows:

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

[0277]

[0278] 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.

[0279] It should be noted that in the above various conversion formulas:

[0280] r t+jGj

[0281] Gj t+j Gj

[0282] Gj B Gj B Gj

[0283] The relationship between the beneficial effect after risk conversion and the blood glucose and blood glucose risk is consistent with the rPID algorithm, and is not repeated here.

[0284] Similarly, in order to compensate for the insulin absorption delay, an insulin feedback compensation mechanism can also be used for compensation; in order to make up 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 the rPID algorithm, specifically:

[0285] For insulin absorption delay, the compensation formula is as follows:

[0286]

[0287] Wherein:

[0288] Ij t+j Indicates the infusion instruction sent to the insulin infusion system at the jth step;

[0289] rIj c(t+j) Indicates the infusion instruction sent to the insulin infusion system at the jth step after risk conversion;

[0290] γ represents the compensation coefficient of the estimated plasma insulin concentration to the algorithm output, and the increase of the coefficient will lead to the relative conservatism of the algorithm, and the decrease of the coefficient will lead to the relative aggressiveness, therefore, in the embodiment of the present application, the range of γ is 0.4-0.6, preferably, γ is 0.5. Indicates the estimation of plasma insulin concentration at the jth step.

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

[0292] rIj t+j = rIj t+j - IOB(t+j)

[0293] Wherein:

[0294] rIj t+j Indicates the infusion instruction sent to the insulin infusion system at the jth step after risk conversion and deduction of IOB;

[0295] rIj t+jThis indicates the infusion instruction sent to the insulin infusion system at step j after risk conversion;

[0296] IOB(t+j) represents the amount of insulin in the body that has not yet taken effect at time t+j.

[0297] Similarly, IOB(t+j) can be distinguished between being in a meal and not being in a meal, in which case:

[0298] IOB(t+j)=IOB m，t+j +IOB o，t+j

[0299] in:

[0300]

[0301] in:

[0302] IOB m，t+j This represents the amount of insulin consumed after a meal that has not yet taken effect in the body at time t+j.

[0303] IOB o,t+j This represents the amount of non-meal insulin that has not yet taken effect in the body at time t+j;

[0304] D i (i = 2 - 8) represent the corresponding coefficients of the IOB curves for insulin action time i;

[0305] I m,t+j This represents the insulin level at time t+j after the meal.

[0306] I 0,t+j This represents the non-meal insulin amount at time t+j;

[0307] IOB(t+j) represents the amount of insulin in the body at time t+j that has not yet taken effect.

[0308] When rI′ t+j When >0, the final infused insulin volume is rI′. t+j ;

[0309] When rI′ t+j When <0, the final amount of insulin infused is 0.

[0310] For the sensing delay of tissue fluid glucose concentration and blood glucose concentration, autoregressive compensation can also be used, as shown in the following formula:

[0311]

[0312] in,

[0313] G SC(t+j) represents the interstitial fluid glucose concentration at t+j, i.e. the measurement value of the sensing system;

[0314] G (t+j-1) represents the estimated concentration of blood glucose at t+j-1;

[0315] G (t+j-1) represents the estimated concentration of blood glucose at t+j-1; SC G (t+j-1) represents the estimated concentration of blood glucose at t+j-1; SC G (t+j-1) represents the estimated concentration of blood glucose at t+j-1;

[0316] K0 represents the coefficient of the estimated concentration part of blood glucose at t+j-1;

[0317] K1 and K2 represent the coefficients of the interstitial fluid glucose concentrations at t+j-1 and t+j-2, respectively.

[0318] wherein at the initial time,

[0319] The beneficial effects of various compensation methods are consistent with those in the rPID algorithm, which are not repeated here.

[0320] It should be noted that in the rMPC algorithm, it is preferred to compensate for the delay in the effect of insulin and the sensing delay of the interstitial fluid glucose concentration and the blood glucose concentration.

[0321] In another embodiment of the present application, a composite artificial pancreas algorithm is preset in the program module, the composite artificial pancreas algorithm includes a first algorithm and a second algorithm, when the electrode detects the current blood glucose value and sends the current blood glucose value to the program module, the first algorithm calculates a first insulin infusion amount I1, the second algorithm calculates a second insulin infusion amount I2, the composite artificial pancreas algorithm optimizes the calculation of the first insulin infusion amount I1 and the second insulin infusion amount I2 to obtain a final insulin infusion amount I3, and sends the final insulin infusion amount I3 to the infusion module, and the infusion module performs insulin infusion according to the final infusion amount I3.

[0322] The first algorithm and the second algorithm are one of a classic PID algorithm, a classic MPC algorithm, an rMPC algorithm or an rPID algorithm. The rMPC algorithm or the rPID algorithm is an algorithm for converting blood glucose that is asymmetric in the original physical space to blood glucose risk that is approximately symmetric in the risk space. The conversion method of blood glucose risk in the rMPC algorithm and the rPID algorithm is as described above.

[0323] When I1 = I2, I3 = I1 = I2;

[0324] 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:

[0325] ①Solving the average value of the first insulin infusion amount I1 and the second insulin infusion amount I2

[0326] ②The average value is substituted into the first algorithm and the second algorithm respectively, and the algorithm parameters are adjusted;

[0327] 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;

[0328] ④The steps ①-③ are calculated in a loop until I1=I2, and the final insulin infusion amount I3=I1=I2.

[0329] At this time, when the first algorithm or the second algorithm is PID or rPID algorithm, the algorithm parameters are K P , and K D =T D / K P , T D can be taken as 60min-90min, K I =T I *K P , and T I can be taken as 150min-450min. When the first algorithm or the second algorithm is MPC or rPMC algorithm, the algorithm parameters are K.

[0330] When I1≠I2, I1 and I2 can also be weighted, and the calculated values after the weighted processing are substituted into the first algorithm and the second algorithm 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:

[0331] ①Solving the weighted value 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;

[0332] ②The weighted value The algorithm parameters are adjusted and brought into the rMPC algorithm and the rPID algorithm;

[0333] The first insulin infusion amount I1 and the second insulin infusion amount I2 are recalculated based on the current blood glucose value, the adjusted rMPC algorithm and the rPID algorithm;

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

[0335] Similarly, when the first algorithm or the second algorithm is a PID or RPID algorithm, the algorithm parameters are K P , and K D =T D / K P , T D may be 60min-90min, and K I =T I *K P , T I may be 150min-450min. When the first algorithm or the second algorithm is an MPC or rPMC algorithm, the algorithm parameters are K.

[0336] In the embodiment of the present application, the values of α and β can be adjusted according to the sizes of the first insulin infusion amount I1 and the second insulin infusion amount I2, when I1≥I2, α≤β; when I1≤I2, α≥β; preferably, α+β=1. In other embodiments of the present application, the values of α and β can also be in other ranges, which are not limited here.

[0337] 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 method, each algorithm is mutually referenced, preferably, the rMPC algorithm and the rPID algorithm are mutually referenced, further improving the accuracy of the output result, making the result more feasible and reliable.

[0338] In another embodiment of the present application, the program module 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, and the final insulin infusion amount I3 is calculated. One of I1 and I2 that is closer to the statistical analysis result I4 is selected as the calculation result of the final composite artificial pancreas algorithm, that is, the final insulin infusion amount I3. The program module sends the final insulin infusion amount I3 to the infusion module for infusion; that is:

[0339]

[0340] The reliability of the insulin infusion amount is ensured by comparison with historical data.

[0341] 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 for 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 statistical analysis results.

[0342] 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 module is used to identify whether the user is having a meal or exercising. The meal recognition can be based on the blood glucose change rate and determined by a specific threshold. The blood glucose change rate can be calculated from two time points or obtained by linear regression of multiple time points in a period of time. Specifically, when the change rate of two time points is used for calculation, the calculation formula is:

[0343] dG t / dt=(G t -G t-1 ) / Δt

[0344] wherein:

[0345] G t represents the blood glucose value at the current time point;

[0346] G t-1 represents the blood glucose value at the previous time point;

[0347] Δt represents the time interval between the current time point and the previous time point.

[0348] When the change rate of three time points is used for calculation, the calculation formula is:

[0349] dG t / dt=(3G t -4G t-1 +G t-2 ) / 2Δt

[0350] wherein:

[0351] G t represents the blood glucose value at the current time point;

[0352] G t-1 represents the blood glucose value at the previous time point;

[0353] G t-2 represents the blood glucose value at the time point two time points before;

[0354] Δt represents the time interval between the current time and the last time.

[0355] 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 personalized.

[0356] 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 decline 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 drug 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 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 parameters to the program module, thereby improving the output reliability of the composite artificial pancreas algorithm in the exercise scenario.

[0357] The exercise sensor can be provided in the program module or the infusion module. Preferably, in the embodiment of the present application, the exercise sensor is provided in the program module.

[0358] It should be noted that the number of exercise sensors and the position of the multiple 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.

[0359] 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 of the body. Preferably, in the embodiment of the present application, the exercise sensor is a combination of a three-axis acceleration sensor and a gyroscope.

[0360] 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.

[0361] In another embodiment of the present application, a hybrid artificial pancreas algorithm is preset in the program module, and the hybrid artificial pancreas algorithm includes a cPID algorithm and / or a cMPC algorithm, wherein the input of the cPID algorithm is the intermediate value of the MPC algorithm, and the input of the cMPC algorithm is the output value of the PID algorithm.

[0362] Specifically, the cPID algorithm is calculated according to the blood glucose value at the current time predicted by the MPC prediction model, that is:

[0363]

[0364] wherein:

[0365] K P is a gain coefficient of the proportional part;

[0366] K I is a gain coefficient of the integral part;

[0367] K D is a gain coefficient of the derivative part;

[0368] G MPC(t) represents a blood glucose value at the current time predicted by the MPC prediction model;

[0369] G B represents a target blood glucose value;

[0370] C represents a constant;

[0371] cPID(t) represents an infusion instruction sent to the insulin infusion system.

[0372] Similarly, the cPID algorithm can also be converted in the risk conversion manner as described above, further improving the robustness of the hybrid artificial pancreas algorithm. That is:

[0373]

[0374] wherein:

[0375] K P is a gain coefficient of the proportional part;

[0376] K I is a gain coefficient of the integral part;

[0377] K D is a gain coefficient of the derivative part;

[0378] r MPC(t) represents a blood glucose risk after risk conversion based on the blood glucose value at the current time predicted by the MPC prediction model;

[0379] G B represents a target blood glucose value;

[0380] C represents a constant;

[0381] rcPID(t) represents an infusion instruction sent to the insulin infusion system.

[0382] The insulin infusion amount at the current time in the prediction model of the cMPC algorithm is calculated by the PID algorithm, that is, the prediction model of the cMPC algorithm is:

[0383] x t+1 = Axt +BI PID(t)

[0384] G t =Cx t

[0385] wherein:

[0386] x t+1 denotes the state parameter at the next time instant,

[0387] x t denotes the state parameter at the current time instant,

[0388] I PID(t) denotes the insulin infusion rate at the current time instant calculated by the PID algorithm;

[0389] G t denotes the blood glucose concentration at the current time instant.

[0390] The parameter matrix is as follows:

[0391]

[0392]

[0393] C = [1 0 0]

[0394] b1, b2, b3, K are prior values.

[0395] Similarly, the insulin infusion rate at the current time instant in the prediction model of the cMPC algorithm can also be calculated by the rPID algorithm, and the specific blood glucose risk conversion method is as described above. That is, the cMPC model is:

[0396] x t+1 = Ax t +BI rPID(t)

[0397] G t = Cx t

[0398] wherein:

[0399] x t+1 denotes the state parameter at the next time instant,

[0400] x t denotes the state parameter at the current time instant,

[0401] I rPID(t) denotes the insulin infusion rate at the current time instant calculated by the rPID algorithm;

[0402] G t G represents the blood glucose concentration at the current time.

[0403] The parameter matrix is as follows:

[0404]

[0405]

[0406] C = [1 0 0]

[0407] b1, b2, b3, K are prior values.

[0408] The value function of the cMPC algorithm can be 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.

[0409]

[0410] Wherein:

[0411] I′ t+j I′ represents the change of the insulin infusion amount after the jth step;

[0412] G represents the difference between the predicted blood glucose concentration and the target blood glucose value after the jth step;

[0413] t represents the current time;

[0414] N and P are respectively the step numbers in the control time window and the prediction time window;

[0415] R is the weighted coefficient of the insulin component.

[0416] The insulin infusion amount of the jth step is I t + I′ t+j .

[0417] Similarly, the output G (blood glucose value) in the value function of the cMPC algorithm can also be risk-converted, and the converted value function is as described above:

[0418]

[0419] Wherein,

[0420] r t+j r represents the blood glucose risk value after the jth step;

[0421] I′ t+j I′ represents the change of the insulin infusion amount after the jth step;

[0422] t represents the current time;

[0423] N, P are respectively the step number in the control time window and the prediction time window;

[0424] R is the weighted coefficient of the insulin component.

[0425] In the embodiment of the present application, the cMPC algorithm is a combination of the prediction model calculated by the PID algorithm or rPID at the current time and the value function with or without risk conversion. The advantages of the PID algorithm, the MPC algorithm and the blood glucose risk conversion are flexibly utilized to face complex situations, so that the artificial pancreas can provide reliable insulin infusion under various conditions, so that the blood glucose reaches the ideal level at the expected time, and the precise control of the closed-loop artificial pancreas drug infusion system is realized.

[0426] In the PID algorithm and the MPC algorithm of each stage described above, the risk conversion method is as described above and will not be repeated here. The conversion methods can be the same or different. Similarly, the three delay effects can also be compensated according to the method described above.

[0427] In an embodiment of the present application, the hybrid artificial pancreas algorithm only includes the cPID algorithm or the cMPC algorithm.

[0428] In another embodiment of the present application, the hybrid artificial pancreas algorithm includes the cPID algorithm and the cMPC algorithm, one of which is used to calculate the required insulin of the user, and the other is used as a backup.

[0429] In another embodiment of the present application, the hybrid artificial pancreas algorithm includes the cPID algorithm and the cMPC algorithm, the cPID algorithm is used to calculate the first insulin infusion amount I1, and the cMPC algorithm is used to calculate the second insulin infusion amount I2. The hybrid artificial pancreas algorithm further optimizes the first insulin infusion amount I1 and the second insulin infusion amount I2 to obtain the final insulin infusion amount I3. The specific optimization method is as described above, that is:

[0430] When I1=I2, I3=I1=I2;

[0431] When I1≠I2, the arithmetic mean or the value after weighted processing of the two is substituted into the algorithm to recalculate the current insulin infusion amount I1 and I2. If the data is still not the same, the above process is repeated until I3=I1=I2, that is:

[0432] ①Solving the average value of the first insulin infusion amount I1 and the second insulin infusion amount I2

[0433] ②The average value is substituted into the cPID algorithm and the cMPC algorithm, and the algorithm parameters are adjusted;

[0434] ③ Based on the current blood glucose level, the cPID algorithm and cMPC algorithm with adjusted parameters recalculate the first insulin infusion volume I1 and the second insulin infusion volume I2;

[0435] ④ Repeat steps ① to ③ until I1 = I2, and finally the insulin infusion volume I3 = I1 = I2.

[0436] or:

[0437] ① Calculate the weighted value of the first insulin infusion volume I1 and the second insulin infusion volume I2. Where α and β are the weighting coefficients of the first insulin infusion volume I1 and the second insulin infusion volume I2, respectively;

[0438] ②Weighted values Substitute these parameters into the cPID and cMPC algorithms and adjust the algorithm parameters.

[0439] ③ Based on the current blood glucose level, the cPID algorithm and cMPC algorithm with adjusted parameters recalculate the first insulin infusion volume I1 and the second insulin infusion volume I2;

[0440] ④ Repeat steps ① to ③ until I1 = I2, and the final insulin infusion volume I3 = I1 = I2.

[0441] When the two differ, they can be statistically analyzed against historical information such as the user's past physical condition, blood glucose levels, and insulin infusion volume to obtain the current statistical analysis result I4. The one that is closer to the statistical analysis result I4 between I1 and I2 is selected as the final insulin infusion volume I3, i.e.:

[0442]

[0443] The beneficial effects of optimizing the first insulin infusion volume I1 and the second insulin infusion volume I2 described above are as previously stated and will not be repeated here.

[0444] Figures 6a-6b These are cross-sectional views of the control system 100 according to an embodiment of the present invention. The control system 100 is an integral structure. Figure 6a With infusion hose 130 in the installation position, Figure 6b The infusion hose 130 is in the working position.

[0445] The program module 120 includes an input end 121 and an output end 122. The input end 121 is used to receive the current blood glucose value. In the embodiment of the present application, the input end 121 includes electric connection areas 121a and 121b. In the working state, the electric connection areas are electrically connected with the electrodes or electrode leads to receive the blood glucose parameter signal. In other embodiments of the present application, the input end 121 can also include more electric connection areas according to the number of electrodes. The output end 122 is electrically connected with the power module to realize the control of the infusion module 110 by the program module 120.

[0446] In the use of the control system in the embodiment of the present application, the infusion tube 130 and the input end 121 slide relative to each other, and therefore the input end 121 is provided as an elastic member. The elastic member is selected to ensure the interference fit between the infusion tube 130 and the input end 121 to avoid poor electrical contact. The elastic member includes a conductive adhesive tape, a conductive silicone with directional conductivity, a conductive ring, a conductive ball, etc. When the number of electrodes is relatively large, the electric connection areas are relatively dense, and in this case, the elastic member can be selected as one or a combination of the above according to different structural designs.

[0447] In the embodiment of the present application, the infusion tube 130 is mounted on the mounting device 150. When the infusion tube 130 is in the mounting position, the mounting device 150 protrudes from the surface of the shell of the control system 100, as shown in Figure 6a When the infusion tube 130 is mounted to the working position, the mounting device 150 enters the control system 100, and the top thereof becomes an integral structure with the shell of the control system 100, as shown in Figure 6b .

[0448] Before use, the user installs the mounting device 150 with the infusion tube 130 in the mounting position. When the user uses the control system 100, the user presses the mounting device 150 to complete the installation operation after the control system 100 is attached to the surface of the human body, and the control system can start normal work. Compared with other infusion tube installation methods, the installation method of the embodiment of the present application reduces the operation steps of the user during installation, makes the installation more convenient and flexible, and improves the user experience.

[0449] The infusion tube 130 can be arranged in the mounting device 150 in various ways, which are not specifically limited here. Specifically, in the embodiment of the present application, the mounting device 150 further protrudes part of the infusion tube 130 on the other side (as shown by the dashed line in Figure 6a and 6b ), which is used to be connected with the outlet of the infusion module 110 to realize the flow of the medicine.

[0450] In other embodiments of the present application, the infusion tube 130 further includes an electric contact area 140 connected with the input end 121. As shown in Figure 6aAs shown, when the infusion tube 130 is in the installation position, the electrical contact area 140 is not electrically connected to the input end 121. Also, the other end of the infusion tube 130 is not in communication with the outlet of the infusion module 110. As shown in FIG. 2B, when the infusion tube 130 is installed in the working position, one end of the infusion tube 130 penetrates the subcutaneous tissue (as shown by the solid line portion of the infusion tube in FIG. 2B), and the other end (as shown by the dashed line portion of the infusion tube in FIG. 2B) is in communication with the outlet of the infusion module 110, thereby establishing a flow path for the drug from the infusion module 110 to the tissue fluid of the human body. At the same time, the electrical contact area 140 reaches the electrical connection area of the input end 121, and electrical connection between the program module 120 and the electrical contact area 140 is achieved. Figure 6b Figure 6b Figure 6b

[0451] It should be noted that even if the infusion tube 130 is in communication with the infusion module 110, and the input end 121 is electrically connected to the electrical contact area 140 of the infusion tube 130, as long as the infusion tube 130 does not penetrate the subcutaneous tissue, the program module 120 will be in a non-working state, and the control system will not detect the blood glucose value, nor will it issue an instruction on whether to perform infusion. Therefore, in other embodiments of the present application, when the infusion tube 130 is in the installation position, the electrical contact area 140 can also be electrically connected to the electrical connection area of the input end 121, or the infusion tube 130 can also be in communication with the outlet of the infusion module 110, and this is not specifically limited herein.

[0452] In an embodiment of the present application, medical adhesive tape 160 is also included for attaching the control system 100 to the surface of the skin, so that the program module 120, the infusion module 110, the electrodes, and the infusion tube 130 are attached as a whole to the skin. When the infusion tube 130 is installed in the working position, the portion of the infusion tube 130 that penetrates the subcutaneous tissue is 13.

[0453] Figure 7a FIG. 4 is a top view of a control system 100 according to another embodiment of the present application.

[0454] In one embodiment of the present application, the control system 100 includes two parts. The program module 120 is disposed in one part, and the infusion module 110 is disposed in the other part, and the two parts are electrically connected by a plurality of electrical contacts 123. Compared with the connection end disposed as a plug, the electrical contacts have a smaller contact area, can be flexibly designed, and effectively reduce the size of the control structure. At the same time, the electrical contacts can be directly electrically connected to internal circuits or electrical elements, or can be directly soldered on a circuit board, thereby optimizing the design of the internal circuit, effectively reducing the complexity of the circuit, saving costs, and reducing the size of the infusion device. The types of the electrical contacts 123 include rigid metal contacts or elastic conductive elements. The elastic conductive elements include conductive springs, conductive silicone, conductive rubber, or conductive elastic sheets, etc. ​​​

[0455] The part where the infusion module 110 is located can be discarded after one-time use, and the part where the program module 120 is located can be reused, thereby saving the cost of the user.

[0456] In other embodiments of the present application, the control system 100 can also be composed of more parts, and the parts that do not need to be electrically connected can be connected by using ordinary waterproof plugs.

[0457] Figure 7b The control system 100 according to another embodiment of the present application is shown in a top view.

[0458] In an embodiment of the present application, the control system 100 includes two parts, and the infusion module 110 includes two infusion sub-modules 110a and 110b. The infusion sub-modules 110a and 110b can be placed with different drugs, such as hypoglycemic drugs such as insulin, hyperglycemic drugs such as glucagon, antibiotics, nutrient solutions, analgesics, morphine, anticoagulants, gene therapy drugs, cardiovascular drugs, or other drugs such as chemotherapy drugs. The infusion sub-modules 110a and 110b are electrically connected with the output terminals 122a and 122b, respectively, to realize the control of the program module 120 on the infusion module 110. The outlets of the infusion sub-modules 110a and 110b are respectively used to communicate with the infusion hose 130a and 130b. The infusion hose 130a and 130b are respectively in communication with the infusion hose 130c. The infusion hose 130c is used to pierce the skin, thereby establishing a channel for the two drugs to flow from the infusion module 110 to the body fluid. That is, the control system still only pierces the skin at one position. In an embodiment of the present application, when the current blood glucose value is transmitted into the program module 120, the preset rMPC algorithm, rPID algorithm, compound artificial pancreas algorithm or hybrid artificial pancreas algorithm in the program module 120 calculates the required drug amount of the user according to the received current blood glucose value. The program module 120 can output different infusion signals to different infusion sub-modules to control whether the drug needs to be infused and the required drug amount, thereby realizing the accurate detection and control of blood glucose and stabilizing the physiological state of the user.

[0459] In an embodiment of the present application, the infusion amount of the hypoglycemic drug and / or the current infusion amount of the hyperglycemic drug is estimated by comparing the blood glucose concentration 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 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, compound artificial pancreas algorithm or hybrid artificial pancreas algorithm. Specifically:

[0460] When G P ≥ GB At that time, the infusion module 110 begins to calculate the hypoglycemic drug infusion data I based on the rMPC algorithm, rPID algorithm, composite artificial pancreas algorithm, or hybrid artificial pancreas algorithm. t Administer hypoglycemic drugs via infusion;

[0461] When G P <G B At that time, the infusion module 110 begins to calculate the blood glucose-raising drug infusion data D based on the rMPC algorithm, rPID algorithm, composite artificial pancreas algorithm, or hybrid artificial pancreas algorithm. t Administer blood glucose-raising medication via infusion.

[0462] In another embodiment of the present invention, the infusion volume of the hypoglycemic drug and / or the current infusion volume of the hyperglycemic drug can be directly calculated by determining the required amount of the hypoglycemic drug I. t To determine the required amount of blood sugar-lowering medication I t The calculation can be performed using the aforementioned rMPC algorithm, rPID algorithm, composite artificial pancreas algorithm, or hybrid artificial pancreas algorithm. Specifically:

[0463] When I t When the blood glucose level is ≥0, the infusion module 110 starts calculating the blood glucose-lowering drug infusion data I based on the rMPC algorithm, rPID algorithm, composite artificial pancreas algorithm, or hybrid artificial pancreas algorithm. t Administer hypoglycemic drugs via infusion;

[0464] When I t When <0, the infusion module 110 starts calculating the blood glucose-raising drug infusion data D based on the rMPC algorithm, rPID algorithm, composite artificial pancreas algorithm, or hybrid artificial pancreas algorithm. t Administer blood glucose-raising medication via infusion.

[0465] It should be noted that in the above embodiments, the calculation methods for the hypoglycemic drug infusion data and glucagon infusion data at 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 and to make the calculation results more accurate. More preferably, a composite artificial pancreas algorithm or a hybrid artificial pancreas algorithm is used for calculation to fully utilize the advantages of PID algorithm, MPC algorithm and blood glucose risk conversion to deal with complex scenarios and achieve a more ideal blood glucose control level.

[0466] In other embodiments of the present invention, more infusion sub-modules may be provided according to actual needs, and multiple infusion sub-modules may be set in different parts of the control system 100, without specific limitations.

[0467] Figures 8a-8b A partial longitudinal cross-sectional view of the infusion tubing 130 including two electrodes.

[0468] In an embodiment of the present application, the control system 100 comprises at least two electrodes for detecting blood glucose parameters, and the electrodes are arranged on the wall of the infusion tube 130, as shown in Figure 8a The different electrodes are electrically connected to the electrical connection area at the position of the dashed line frame 140. The lumen 131 of the infusion tube 130 is used for infusing medicine.

[0469] In an embodiment of the present application, the electrodes are arranged on the outer surface of the wall of the infusion tube 130, such as the electrode 171 and the electrode 172. Generally, the electrode 171 and the electrode 172 are insulated from each other. The electrode 171 and the electrode 172 are respectively directly electrically connected to the electrical connection areas 121a and 121b of the input end, and the current blood glucose value is transmitted to the program module 120 in the form of an electrical signal, as shown in Figure 8b This design reduces the positions of the control system piercing the skin, and once the same position is pierced, the blood glucose detection and the medicine infusion can be completed, thereby reducing the risk of infection of the user.

[0470] It should be noted that in the embodiment of the present application, when the infusion tube 130 is installed to the working position, part of the electrode 171 and the electrode 172 is located in the subcutaneous tissue fluid, and part is located outside the body, so that the electrical signal is directly transmitted on the electrode. The similar electrode arrangement in other embodiments below has the same function, and will not be described in detail hereinafter.

[0471] In an embodiment of the present application, the control system 100 has only two electrodes, the electrode 171 is a working electrode, and the electrode 172 is an auxiliary electrode. In another embodiment of the present application, the electrode 171 is an auxiliary electrode, and the electrode 172 is a working electrode. The auxiliary electrode is a counter electrode.

[0472] In other embodiments of the present application, more electrodes can be arranged on the surface of the infusion tube 130, and the multiple electrodes are electrically insulated from each other.

[0473] Figures 9a-9c A partial longitudinal sectional view of the infusion tube 130 of another embodiment of the present application.

[0474] It should be noted that the electrodes or electrode leads in all embodiments of the present application are coated or plated on the infusion tube 130, but in order to facilitate marking and description, the electrode leads or electrodes will be shown separately from the infusion tube in the drawings, and the related structure diagrams hereinafter are the same as the mode here, and will not be described in detail hereinafter.

[0475] In an embodiment of the present application, the outer surface of the wall 132 of the infusion tube 130 is provided with the electrodes 271 and 272. The electrode 271 is directly electrically connected to the electrical connection area 121a, and the electrode 272 is directly electrically connected to the electrical connection area 121b, as shown in Figure 8aThe electrode 272 is arranged at the front end of the infusion tube 130, and is electrically connected with the electrical connection area 121b through the electrode lead 2720. When the infusion tube 130 is arranged at the working position, the electrode 272 is located at the outer surface of the tube wall of the subcutaneous part of the infusion tube 130, and a part of the electrode 271 is located in the tissue fluid and another part is located outside the body. At this time, the electrode 272 is indirectly electrically connected with the electrical connection area 121b, and sends the blood glucose information to the program module.

[0476] The shape of the electrode 272 is not limited in the embodiment of the present application. For example, the electrode 272 can be ring-shaped, and the electrode 272 is arranged around the front end of the infusion tube 130, as shown in Fig. 6. At this time, an insulating layer is arranged between the electrode 272 and the electrode 271. Figure 9b Figure 9c As shown in Fig. 7, in another embodiment of the present application, the electrode 271 and the electrode 272 are both arranged at the front end of the infusion tube 130, i.e. at the outer surface of the tube wall of the subcutaneous part. The outer surface of the tube wall 132 is further provided with the electrode lead 2710 and the electrode lead 2720 which are electrically connected with the electrode 271 and the electrode 272 respectively. When the infusion tube 130 is arranged at the working position, the electrical connection areas 121a, 121b of the input end are electrically connected with the electrode lead 2710 and the electrode lead 2720 respectively. Therefore, the electrode 271 and the electrode 272 are indirectly electrically connected with the input end, and the blood glucose value can also be transmitted to the program module. During the detection, the electrode 271 and the electrode 272 are both located in the tissue fluid of the subcutaneous tissue.

[0477] Figure 9c The electrode 272 is arranged in a ring shape around the outer surface of the tube wall 132. The electrode 271 and the electrode 272 can also have other shapes, which are not limited here.

[0478] Figure 10 A partial longitudinal sectional view of the infusion tube 130 provided with three electrodes in another embodiment of the present application is shown in Fig. 8.

[0479] In the embodiment of the present application, three electrodes are arranged on the infusion tube 130, i.e. the electrode 371, the electrode 372 and the electrode 373. The electrode 371, the electrode 372 and the electrode 373 are arranged on the outer surface of the tube wall 132. Similarly, the outer surface of the tube wall 132 is further provided with the electrode lead 3720 and the electrode lead 3730 which are electrically connected with the electrode 372 and the electrode 373 respectively. Similarly, the outer surface of the tube wall 132 is also provided with the electrode lead which is electrically connected with the electrode 371, but is not shown in order to simplify the marking. When the infusion tube 130 is arranged at the working position, the electrode lead of the electrode 371, the electrode lead 3720 and the electrode lead 3730 are electrically connected with the electrical connection areas 121a, 121b, 121c of the input end respectively, so as to realize the electrical connection between the input end and the electrodes. The shapes of the three electrodes can be various, which are not limited here.​

[0480] In the embodiment of the present application, in order to simplify the design of the electrical connection area, the elastic member of the input end is conductive silica gel or a conductive ring. Different elements are doped in the silica gel to realize directional conduction, such as horizontal direction conduction and vertical direction non-conduction. In this way, even if 121a and 121c contact each other, they are still insulated from each other. The electrical connection area 121b can use conductive adhesive strips or conductive balls, which are not specifically limited here.

[0481] In the embodiment of the present application, the electrode 371 is a working electrode, and the electrodes 372 and 373 are auxiliary electrodes. At this time, the electrode 371 and the electrode 372 or the electrode 373 can form different electrode combinations, that is, two electrode combinations share one electrode, such as the electrode 371. The program module 120 can select different electrode combinations to detect the current blood glucose value. After the electrode combination is formed, on the one hand, when a working electrode combination fails, the program module 120 can select other electrode combinations for detection according to the situation, to ensure that the blood glucose detection process is uninterrupted. On the other hand, the program module 120 can select multiple electrode combinations to work at the same time, and statistically analyze multiple sets of data of the same parameter at the same time, to improve the accuracy of the blood glucose value, and then output a more accurate drug infusion signal.

[0482] In another embodiment of the present application, one auxiliary electrode and two working electrodes are included in the electrodes 371, 372 and 373, which can also be arbitrarily selected according to actual needs, and are not specifically limited here.

[0483] In one embodiment of the present application, the electrode 371 is a working electrode, and the electrodes 372 and 373 are auxiliary electrodes, and the auxiliary electrodes 372 and 373 are used as counter electrodes and reference electrodes respectively, thereby forming a three-electrode system. Similarly, the three electrodes can be arbitrarily selected according to actual needs, and are not specifically limited here.

[0484] Other embodiments of the present application can also provide more electrodes. The electrodes include multiple working electrodes and multiple auxiliary electrodes. At this time, each electrode combination includes a working electrode and an auxiliary electrode, so multiple electrodes can form multiple electrode combinations. According to the needs, the program module 120 can select one or more electrode combinations to detect blood glucose.

[0485] Figure 11 The infusion tube 130 of another embodiment of the present application includes a partial longitudinal sectional view of the inner tube 170 and the outer tube 180.

[0486] In this embodiment of the invention, the infusion tubing 130 includes an inner tube 170 and an outer tube 180 sleeved on the outer wall of the inner tube 170. The multi-layered tube wall increases the strength of the infusion tubing 130 and facilitates puncture. Furthermore, the wall material of the outer tube 180 can be selected as needed; for example, its wall can only allow specific blood glucose levels to pass through, reducing interference from other substances and improving the accuracy of blood glucose detection.

[0487] The lumen 131 of the inner tube 170 serves as a drug infusion channel, and the wall of the infusion tubing 130 includes the inner tube wall and the outer tube wall. Electrode 472 is disposed on the outer side of the inner tube 170 wall. Electrode 471 is disposed on the outer surface of the outer tube 180 wall. In this case, electrode 472 is disposed within the wall of the infusion tubing 130, i.e., electrode 472 is embedded between the outer tube 180 and the inner tube 170.

[0488] In this embodiment of the invention, electrode 472 may be partially covered by outer tube 180 (e.g., Figure 11 As shown in the diagram, electrode 472 is electrically connected to electrical connection area 121b via electrode wire 4720. Electrode 471 is electrically connected to electrical connection area 121a via electrode wire 4710. When electrode 472 is partially or completely covered by outer tube 180, the wall material of outer tube 180 is a permeable membrane or a semi-permeable membrane. This choice facilitates the permeation of blood glucose through the wall of outer tube 180 and its detection by the electrode, thereby increasing the flexibility of electrode placement design without affecting detection.

[0489] In another embodiment of the present invention, electrodes 471 and 472 are both disposed within the wall of the infusion tubing 130, that is, electrodes 471 and 472 are embedded between the inner tube 170 and the outer tube 180, and are completely covered by the outer tube 180. In this case, the material of the outer tube 180 is as described above, and blood glucose can be detected by the electrodes through the outer tube 180.

[0490] It should be noted that in other embodiments of the present invention, more outer tubes may be provided outside the inner tube 170. And as mentioned above, more electrodes may be provided on the infusion tubing 130. Depending on actual needs, different electrodes may be disposed between different outer tubes. At least one electrode may be disposed between the wall of the inner tube and the wall of the outermost outer tube.

[0491] In addition to embedding the electrode within the wall of the infusion tubing 130, some embodiments of the present invention can also reduce... Figure 11 The length of the outer tube 180 is adjusted so that the electrode 472, which is disposed on the outer surface of the inner tube 170, is directly exposed to the tissue fluid. At this time, the distances that the front ends of the outer tube 180 and the front ends of the inner tube 170 enter the tissue fluid are different.

[0492] To sum up, the application discloses a closed-loop artificial pancreas drug infusion control system, one or more of rMPC algorithm, rPID algorithm and composite artificial pancreas algorithm are preset in the system, the advantages of rPID algorithm and rMPC algorithm are fully utilized to face complex scenes, so that the artificial pancreas can provide reliable drug type and drug infusion amount for blood glucose control in various conditions, so that the blood glucose reaches the ideal level, and precise control of the closed-loop artificial pancreas drug infusion system is realized.

[0493] 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. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of the application. The scope of the application is defined by the appended claims.

Claims

1. A closed loop artificial pancreas drug infusion control system, characterized by, Comprise: an infusion module for outputting medicine; a program module comprising an input end and an output end, the input end comprising a plurality of electrical connection areas to receive current blood glucose value, the program module further being preset with an algorithm, the algorithm being one or more of rMPC algorithm, rPID algorithm or composite artificial pancreas algorithm, the rMPC algorithm and the rPID algorithm respectively being based on classic PID algorithm and classic MPC algorithm, converting blood glucose risk which is asymmetric in original physical space to blood glucose risk which is approximately symmetric in risk space, and calculating current required medicine infusion amount according to the blood glucose risk, the composite artificial pancreas algorithm comprising a first algorithm and a second algorithm, the first algorithm calculating a first insulin infusion amount I1, the second algorithm calculating a second insulin infusion amount I2, the composite artificial pancreas algorithm calculating a final insulin infusion amount I3 by optimizing calculation of the first insulin infusion amount I1 and the second insulin infusion amount I2; after the output end and the infusion module are electrically connected, the algorithm calculates required medicine amount of a user according to the received current blood glucose value, and the program module controls the infusion module to output medicine according to the calculated required medicine amount of the user; and an infusion hose provided with at least two detection electrodes, the infusion hose being a medicine infusion channel, the electrodes being arranged on the wall of the infusion hose, the infusion hose being in communication with the infusion module when the infusion hose is installed to a working position, medicine flowing to the body through the infusion hose, and different electrodes being electrically connected with different electrical connection areas to input current blood glucose value to the program module. The value function of the rMPC algorithm after risk conversion is as follows: The blood glucose risk space conversion method of the rMPC algorithm and the rPID algorithm is improved control variability grid analysis conversion: wherein r t+j represents the blood glucose risk value after the jth step, I' t+j represents the change of insulin infusion amount after the jth step, t represents the current time, R is a weighted coefficient of the insulin component, N and P are the step numbers in the control time window and the prediction time window, respectively; At the same time, the maximum value is limited: wherein G B represents a target blood glucose value; G t+j represents a blood glucose value at the jth step of detection; The value range of the maximum value n is 0-80mg / dL. r t+j | = min(|r t+j |,n) The blood glucose risk space conversion method of the rMPC algorithm and the rPID algorithm further comprises one or more of the following processing methods:

2. The closed loop artificial pancreas medication infusion control system of claim 1, wherein, ①Subtracting a component proportional to the estimated concentration of plasma insulin or glucagon; ②Subtracting the amount of insulin or glucagon that has not yet taken effect in or in the body; ③Using an autoregressive method to compensate for the sensing delay of interstitial fluid glucose concentration and blood glucose. The final insulin infusion amount I3 is optimized by the average value of the first insulin infusion amount I1 and the second insulin infusion amount I2:

3. The closed loop artificial pancreas medication infusion control system of claim 1, wherein, ③Recalculating the first insulin infusion amount I1 and the second insulin infusion amount I2 based on the current blood glucose value, the first algorithm and the second algorithm after adjustment of parameters; solving for the first insulin infusion amount I1and the second insulin infusion amount I2 ② Average value Substitute these parameters into the first and second algorithms and adjust the algorithm parameters accordingly. ④Cyclically calculating steps ①-③ until I1=I2, and the final insulin infusion amount I3=I1=I2. The final insulin infusion amount I3 is optimized by the weighted value of the first insulin infusion amount I1 and the second insulin infusion amount I2:

4. The closed loop artificial pancreas medication infusion control system of claim 1, wherein, ​ solving the first insulin infusion amount I1and the second insulin infusion amount I2 wherein a and β are weighting factors for the first insulin infusion amount I1and the second insulin infusion amount I2, respectively; ②Weighted values Substitute these parameters into the first and second algorithms and adjust the algorithm parameters accordingly. ③recomputing the first insulin infusion amount I1 and the second insulin infusion amount I2 based on the current blood glucose value, the first algorithm and the second algorithm after adjusting the parameters; ④carrying out loop calculation on steps ①-③ until I1=I2, and the final insulin infusion amount I3=I1=I2.

5. The closed loop artificial pancreas medication infusion control system of claim 1, wherein, The final insulin infusion amount I3 is obtained by comparing the first insulin infusion amount I1 and the second insulin infusion amount I2 with the statistical analysis result I4 of historical data:

6. The closed loop artificial pancreas drug infusion control system according to any one of claims 3-5, wherein, The first algorithm and the second algorithm are rPID algorithm or rMPC algorithm.

7. The closed loop artificial pancreas medication infusion control system of claim 1, wherein, The infusion tube comprises an inner tube and at least one outer tube, the outer tube is arranged outside the inner tube, and the inner tube is used for infusing drugs.

8. The closed loop artificial pancreas medication infusion control system of claim 7, wherein, At least one electrode is arranged between the inner tube wall and the outer tube wall of the outermost layer.

9. The closed loop artificial pancreas medication infusion control system of claim 1, wherein, The infusion module comprises a plurality of infusion sub-modules, a plurality of the infusion sub-modules are respectively electrically connected with the output end, and the program module selectively controls the infusion sub-modules to output drugs according to the calculated drug amount required by the user.

10. The closed loop artificial pancreas medication infusion control system of claim 9, wherein, The drugs are hyperglycemic drugs and hypoglycemic drugs.

11. The closed loop artificial pancreas medication infusion control system of claim 1, wherein, The closed-loop artificial pancreas drug infusion control system is composed of multiple parts, the infusion module and the program module are arranged in different parts, and are electrically connected through multiple electrical contacts.

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