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 of the program module for calculating a required medicine amount of a user, the output end and the infusion module being electrically connected, the program module controlling 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 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, medicine flowing to a 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] This invention discloses a closed-loop artificial pancreas drug infusion control system, which includes a pre-set hybrid artificial pancreas algorithm. The hybrid artificial pancreas algorithm includes a cPID algorithm and / or a cMPC algorithm. 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. By deeply combining the PID and MPC algorithms, the advantages of the two algorithms are fully utilized, enabling the artificial pancreas to provide reliable types and dosages of drugs for blood glucose control under various conditions, thereby achieving ideal blood glucose levels and realizing precise control of the closed-loop artificial pancreas drug infusion system.

[0009] This invention discloses a closed-loop artificial pancreas drug infusion control system, comprising: an infusion module for dispensing drug; a program module including an input terminal and an output terminal, the input terminal including multiple electrical connection areas to receive the current blood glucose value, the program module also having a preset hybrid artificial pancreas algorithm for calculating the amount of insulin required by the user, the hybrid artificial pancreas algorithm including a cPID algorithm and / or a cMPC algorithm, the input of the cPID algorithm being the intermediate value of the MPC algorithm, the input of the cMPC algorithm being the output value of the PID algorithm, the output terminal being electrically connected to the infusion module, the program module controlling the infusion module to dispense drug according to the calculated amount of drug required by the user; and an infusion tubing with at least two detection electrodes, the infusion tubing serving as a drug infusion channel, the electrodes being disposed on the tubing wall, when the infusion tubing is installed in the working position, the infusion tubing being connected to the infusion module, the drug flowing into the body through the infusion tubing, and different electrodes being electrically connected to different electrical connection areas, with the current blood glucose value input to the program module.

[0010] According to one aspect of the present invention, the cPID algorithm is calculated based on the blood glucose value at the current moment predicted by the prediction model of the MPC algorithm, and the calculation formula is as follows:

[0011]

[0012] in:

[0013] K P It is the gain coefficient of the proportional part;

[0014] K I It is the gain coefficient of the integral part;

[0015] K D It is the gain coefficient of the differential part;

[0016] G MPC(t) This represents the blood glucose level at the current moment, predicted by the MPC prediction model.

[0017] G B Indicates the target blood glucose level;

[0018] C represents a constant;

[0019] cPID(t) represents the infusion instruction sent to the insulin infusion system.

[0020] According to one aspect of the present invention, the cPID algorithm is calculated after risk transformation based on the current blood glucose value predicted by the prediction model of the MPC algorithm, and the calculation formula is as follows:

[0021]

[0022] in:

[0023] K P It is the gain coefficient of the proportional part;

[0024] K I It is the gain coefficient of the integral part;

[0025] K D It is the gain coefficient of the differential part;

[0026] r MPC(t) This represents the blood glucose risk after risk conversion based on the current blood glucose value predicted by the MPC prediction model.

[0027] G B Indicates the target blood glucose level;

[0028] C represents a constant;

[0029] rcPID(t) represents the infusion instruction sent to the insulin infusion system.

[0030] According to one aspect of the present invention, the blood glucose risk space transformation method includes one or more of the following: piecewise weighting, relative value transformation, blood glucose risk index transformation, and improved controlled variability grid analysis transformation.

[0031] According to one aspect of the present invention, the blood glucose risk space transformation method further includes one or more of the following processing methods:

[0032] ① The component that is proportional to the estimated plasma insulin concentration is deducted from the predicted plasma insulin concentration;

[0033] ② Subtract the amount of insulin that has not yet taken effect in the body;

[0034] ③ An autoregressive method was used to compensate for the sensing delay of blood glucose and interstitial fluid glucose concentrations.

[0035] According to one aspect of the present invention, the insulin infusion rate at the current time in the prediction model of the cMPC algorithm is calculated using the rPID algorithm, and the prediction model is as follows:

[0036] xt+1 =Ax t +BI PID(t)

[0037] G t =Cx t

[0038] in:

[0039] x t+1 Indicates the state parameters at the next moment.

[0040] x t This represents the state parameters at the current moment.

[0041] I PID(t) This represents the insulin infusion volume at the current moment, calculated using the PID algorithm.

[0042] G t This indicates the blood glucose concentration at the current moment.

[0043] The parameter matrix is ​​as follows:

[0044]

[0045]

[0046] C = [1 0 0]

[0047] b1, b2, b3, and K are prior values.

[0048] According to one aspect of the present invention, the insulin infusion rate at the current time in the prediction model of the cMPC algorithm is calculated using the rPID algorithm, and the prediction model is as follows:

[0049] x t+1 =Ax t +BI rPID(t)

[0050] G t =Cx t

[0051] in:

[0052] x t+1 Indicates the state parameters at the next moment.

[0053] x t This represents the state parameters at the current moment.

[0054] I rPID(t) This represents the insulin infusion volume at the current moment, calculated using the rPID algorithm.

[0055] G t This indicates the blood glucose concentration at the current moment.

[0056] The parameter matrix is ​​as follows:

[0057]

[0058]

[0059] C = [1 0 0]

[0060] b1, b2, b3, and K are prior values.

[0061] According to one aspect of the present invention, the rPID algorithm, based on the PID algorithm, transforms blood glucose that is asymmetric in the original physical space into blood glucose risk that is approximately symmetric in the risk space.

[0062] According to one aspect of the present invention, the blood glucose value in the value function of the cMPC algorithm undergoes risk transformation, and the value function after blood glucose risk transformation is:

[0063]

[0064] in,

[0065] r t+j This represents the blood glucose risk value after step j;

[0066] I' t+j This indicates the change in insulin infusion volume after step j;

[0067] t represents the current time;

[0068] N and P are the number of steps within the control time window and the prediction time window, respectively;

[0069] R is the weighting coefficient for the insulin component.

[0070] According to one aspect of the present invention, the blood glucose risk transformation method includes one or more of the following: piecewise weighting, relative value transformation, blood glucose risk index transformation, and improved controlled variability grid analysis transformation.

[0071] According to one aspect of the present invention, the blood glucose risk conversion method further includes one or more of the following processing methods:

[0072] ① The component that is proportional to the estimated plasma insulin concentration is deducted from the predicted plasma insulin concentration;

[0073] ② Subtract the amount of insulin that has not yet taken effect in the body;

[0074] ③ An autoregressive method was used to compensate for the sensing delay of blood glucose and interstitial fluid glucose concentrations.

[0075] According to one aspect of the present invention, the hybrid artificial pancreas algorithm includes a cPID algorithm and a cMPC algorithm. The cPID algorithm calculates a first insulin infusion volume I1, the cMPC algorithm calculates a second insulin infusion volume I2, and the hybrid artificial pancreas algorithm performs optimized calculations on the first insulin infusion volume I1 and the second insulin infusion volume I2 to obtain a final insulin infusion volume I3.

[0076] According to one aspect of the invention, the final insulin infusion volume I3 is optimized by averaging the first insulin infusion volume I1 and the second insulin infusion volume I2:

[0077] ① Calculate the average value of the first insulin infusion volume I1 and the second insulin infusion volume I2.

[0078] ② Average value Substitute these parameters into the cPID and cMPC algorithms and adjust the algorithm parameters.

[0079] ③ Based on the current blood glucose value, the first insulin infusion volume I1 and the second insulin infusion volume I2 are recalculated using the cPID algorithm and cMPC algorithm after parameter adjustment;

[0080] ④ Repeat steps ① to ③ until I1 = I2, and finally the insulin infusion volume I3 = I1 = I2.

[0081] According to one aspect of the invention, the final insulin infusion volume I3 is optimized by a weighted average of the first insulin infusion volume I1 and the second insulin infusion volume I2:

[0082] 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;

[0083] ②Weighted values Substitute these parameters into the cPID and cMPC algorithms and adjust the algorithm parameters.

[0084] 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;

[0085] ④ Repeat steps ① to ③ until I1 = I2, and finally the insulin infusion volume I3 = I1 = I2.

[0086] According to one aspect of the invention, the final insulin infusion volume I3 is obtained by comparing the first insulin infusion volume I1 and the second insulin infusion volume I2 with the statistical analysis result I4 of historical data:

[0087]

[0088] According to one aspect of the invention, the infusion tubing includes an inner tube and at least one outer tube, the outer tube being disposed outside the inner tube, and the inner tube being used for infusing a drug.

[0089] According to one aspect of the invention, at least one electrode is disposed between the outer wall of the inner tube and the outermost outer tube.

[0090] According to one aspect of the present invention, the infusion module includes a plurality of infusion sub-modules, each of which is electrically connected to an output terminal, and the program module selects and controls the infusion sub-modules to output the drug according to the calculated amount of drug required by the user.

[0091] According to one aspect of the present invention, the drug is a blood glucose-raising drug and a blood glucose-lowering drug.

[0092] According to one aspect of the present invention, a closed-loop artificial pancreas drug infusion control system comprises multiple parts, with the infusion module and the program module located in different parts and electrically connected by multiple electrical contacts.

[0093] Compared with the prior art, the technical solution of the present invention has the following advantages:

[0094] The closed-loop artificial pancreas drug infusion control system disclosed in this invention incorporates a hybrid artificial pancreas algorithm, which includes a cPID algorithm and / or a cMPC algorithm. The input to the cPID algorithm is the intermediate value of the MPC algorithm, and the input to the cMPC algorithm is the output value of the PID algorithm. By deeply integrating the PID and MPC algorithms, the system fully utilizes their advantages, enabling the artificial pancreas to reliably provide the type and amount of drugs for blood glucose control under various conditions, thereby achieving ideal blood glucose levels and realizing precise control of the closed-loop artificial pancreas drug infusion system.

[0095] Furthermore, the cPID algorithm in the hybrid artificial pancreas algorithm further transforms the current blood glucose value predicted by the prediction model of the MPC algorithm into a blood glucose risk conversion, making the hybrid artificial pancreas algorithm more robust.

[0096] Furthermore, the cMPC algorithm in the hybrid artificial pancreas algorithm combines the current insulin infusion volume with the prediction model calculated by the PID algorithm or rPID algorithm and the input with or without risk transformation, flexibly utilizing the advantages of the PID algorithm, MPC algorithm and blood glucose risk transformation to deal with complex scenarios. This enables the artificial pancreas to provide a reliable insulin infusion volume under various conditions, thereby ensuring that blood glucose reaches the ideal level at the expected time, and realizing precise control of the closed-loop artificial pancreas drug infusion control system.

[0097] Furthermore, the hybrid artificial pancreas algorithm compensates for delays in insulin absorption, insulin onset, and the sensing delays in blood glucose and interstitial fluid glucose concentrations, making the output calculated by the hybrid artificial pancreas algorithm more reliable.

[0098] Furthermore, the final output of the hybrid artificial pancreas algorithm is a consistent result obtained by calculating using the cMPC and cPID algorithms, which is more feasible and reliable.

[0099] Furthermore, the final output of the composite artificial pancreas algorithm is the same result obtained by averaging or weighting the different results calculated by the cMPC algorithm and the cPID algorithm. The two algorithms compensate for each other, further improving the accuracy of the output results.

[0100] Furthermore, the final output of the hybrid artificial pancreas algorithm is obtained by optimizing the calculation results of the cMPC and cPID algorithms. This optimization, combined with statistical analysis of historical control data, ensures the reliability of insulin infusion from another perspective.

[0101] Furthermore, the infusion module includes multiple infusion sub-modules, each electrically connected to the output terminal. The program module selects and controls whether the infusion sub-modules output medication. Different medications are placed in the multiple sub-modules, and the program module selects and sends medication infusion commands to different infusion sub-modules to achieve precise blood glucose control.

[0102] Furthermore, the infusion volume of each drug is calculated using the same algorithm, ensuring consistency of the underlying conditions during calculation and making the calculation results more stable. Attached Figure Description

[0103] Figure 1 This is a flowchart illustrating the operation of a closed-loop artificial pancreas drug infusion control system according to an embodiment of the present invention.

[0104] Figure 2 This is a comparison diagram of the relationship between blood glucose levels in the risk space and the original physical space, obtained by segmented weighting and relative value conversion in one embodiment of the present invention.

[0105] Figure 3This is a comparison chart of the relationship between blood glucose levels in the risk space obtained by conversion using the BGRI and CVGA methods according to an embodiment of the present invention and the original physical space.

[0106] Figure 4 An insulin IOB curve according to an embodiment of the present invention;

[0107] Figure 5 This is a schematic diagram illustrating four mainstream clinically optimal baseline rate settings cited in one embodiment of the present invention;

[0108] Figure 6a This is a cross-sectional schematic diagram of the infusion tube of a closed-loop artificial pancreas drug infusion control system according to an embodiment of the present invention, located at the installation position.

[0109] Figure 6b This is a cross-sectional schematic diagram of the infusion tube in the working position of a closed-loop artificial pancreas drug infusion control system according to an embodiment of the present invention.

[0110] Figures 7a-7b This is a top view schematic diagram of a closed-loop artificial pancreas drug infusion control system according to another embodiment of the present invention;

[0111] Figures 8a-8b A partial longitudinal cross-sectional view of an infusion tubing having two electrodes provided according to an embodiment of the present invention;

[0112] Figures 9a-9c This is a partial longitudinal cross-sectional view of the infusion tube and two electrodes according to another embodiment of the present invention;

[0113] Figure 10 A partial longitudinal cross-sectional view of an infusion tubing with three electrodes disposed thereon, according to yet another embodiment of the present invention;

[0114] Figure 11 This is a partial longitudinal cross-sectional view of an infusion hose comprising an inner tubing layer and an outer tubing layer, according to yet another embodiment of the present invention. Detailed Implementation

[0115] As mentioned earlier, in order to achieve closed-loop or semi-closed-loop control of artificial pancreas, proportional-integral-derivative (PID) algorithm and model-predictive-control (MPC) algorithm have been widely studied. However, due to the simple structure of PID algorithm, it is not suitable for scenarios with large and complex disturbances, while MPC algorithm faces the dilemma of difficulty in establishing accurate models and large amount of computation, which may lead to infusion deviation in prediction.

[0116] To address this problem, this invention provides a closed-loop artificial pancreas drug infusion control system. This system incorporates a hybrid artificial pancreas algorithm including a cPID algorithm and / or a cMPC algorithm. The input to the cPID algorithm is the intermediate value of the MPC algorithm, and the input to the cMPC algorithm is the output value of the PID algorithm. By deeply integrating the PID and MPC algorithms, the advantages of both algorithms are fully utilized, enabling the artificial pancreas to reliably provide the appropriate types and dosages of drugs for blood glucose control under various conditions, thereby achieving ideal blood glucose levels and realizing precise control of the closed-loop artificial pancreas drug infusion system.

[0117] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that, unless otherwise specifically stated, 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 invention.

[0118] Furthermore, it should be understood that, for ease of description, the dimensions of the various components shown in the accompanying drawings are not necessarily drawn to actual scale; for example, the thickness, width, length, or distance of some units may be enlarged relative to other structures.

[0119] The following description of exemplary embodiments is merely illustrative and is not intended to limit the invention or its application or use in any way. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail herein, but where applicable, such techniques, methods, and apparatus should be considered part of this specification.

[0120] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined or described in a figure, it will not need to be discussed further in the subsequent description of the figures.

[0121] Figure 1 This is a flowchart illustrating the closed-loop artificial pancreas drug infusion control system according to an embodiment of the present invention.

[0122] The closed-loop artificial pancreas drug infusion control system of this invention comprises three basic parts: electrodes, a program module, and an infusion module. Blood glucose parameter information is acquired by the electrodes and converted into electrical signals. These electrical signals are transmitted to the program module via the electrodes and / or electrode wires. The program module reads the current blood glucose value G, and the program module contains a preset algorithm and a target blood glucose value G. BThe algorithm calculates the user's required medication dosage (such as insulin or glucagon) based on the current blood glucose level G. The program module sends this calculated dosage to the infusion module to control the infusion and stabilize blood glucose. The current blood glucose level is monitored in real-time by electrodes, and the infusion cycle is continuously monitored. This process requires no human intervention and is directly accomplished through program analysis to control blood glucose stability.

[0123] Specifically, the preset algorithm in the program module is the rPID (proportional-integral-derivative) algorithm, which converts the asymmetric blood glucose in the original physical space to the approximately symmetric blood glucose risk in the risk space. The rPID algorithm is obtained by transforming the classic PID (proportional-integral-derivative) algorithm. The specific processing method will be detailed below. Based on the corresponding infusion instruction calculated by the rPID algorithm, the program module controls the infusion module to infuse insulin.

[0124] The classic PID algorithm can be expressed by the following formula:

[0125]

[0126] in:

[0127] K P It is the gain coefficient of the proportional part;

[0128] K I It is the gain coefficient of the integral part;

[0129] K D It is the gain coefficient of the differential part;

[0130] G represents the current blood glucose level;

[0131] G B Indicates the target blood glucose level;

[0132] C represents a constant;

[0133] PID(t) represents the infusion instruction sent to the insulin infusion system.

[0134] Considering the actual distribution characteristics of glucose concentration in diabetic patients, such as the normal blood glucose range of 80-140 mg / dL, which can be relaxed to 70-180 mg / dL, hypoglycemia can reach 20-40 mg / dL, while hyperglycemia can reach 400-600 mg / dL.

[0135] The distribution of high / low blood glucose exhibits significant asymmetry in the original physical space. In clinical practice, the risks of high and low blood glucose corresponding to the same degree of deviation from the normal range can be significantly different. For example, a decrease of 70 mg / dL from 120 mg / dL to 50 mg / dL is considered severe hypoglycemia, carrying a high clinical risk and requiring emergency measures such as carbohydrate supplementation. On the other hand, an increase of 70 mg / dL from 120 mg / dL to 190 mg / dL is only slightly above the normal range. For diabetic patients, this degree of hyperglycemia is not serious and is frequently reached in daily life, generally requiring no intervention.

[0136] To address the asymmetric nature of clinical risk associated with glucose concentration, the algorithm transforms the asymmetric blood glucose levels in the original physical space into approximately symmetrical blood glucose risk in the risk space, making the PID algorithm more robust.

[0137] Accordingly, the rPID algorithm formula is transformed into the following form:

[0138]

[0139] in:

[0140] rPID(t) represents the infusion instruction sent to the insulin infusion system after risk conversion;

[0141] r indicates blood sugar risk;

[0142] The meanings of the other symbols are as described above.

[0143] To maintain the stability of the PID integral, and considering the physiological effect of insulin in lowering blood glucose, in one embodiment of the present invention, the input parameter of the PID—the blood glucose deviation Ge = GG B Processing is required, such as setting Ge=GG B Perform segmented weighted processing as follows:

[0144]

[0145] In another embodiment of the invention, for blood glucose levels greater than the target G... B The deviation is converted using relative values, as follows:

[0146]

[0147] Figure 2 This is a comparison diagram of the blood glucose risk space obtained through segmented weighting and relative value transformation with the blood glucose relationship in the original physical space.

[0148] In the original PID algorithm, the blood glucose risk (Ge) on both sides of the target blood glucose value exhibits a severe asymmetry consistent with the original physical space. After being transformed into the blood glucose risk space, the blood glucose risk on both sides of the target blood glucose value is approximately symmetrical, so the integral term can remain stable, making the rPID algorithm more robust.

[0149] In another embodiment of the invention, a fixed zero-risk point exists during risk transformation, and data deviating from the zero-risk point are processed. Original parameters corresponding to values ​​greater than the zero-risk point are positive when transformed into the risk space, while original parameters corresponding to values ​​less than the zero-risk point are negative. Specifically, the classic Blood Glucose Risk Index (BGRI) method can be used as a reference. This method, based on clinical practice, considers hypoglycemia at 20 mg / dL and hyperglycemia at 600 mg / dL to have comparable clinical risk, and uses logarithmic transformation to comprehensively process blood glucose levels within the 20-600 mg / dL range. The blood glucose value corresponding to the zero-risk point of this method is set as the target blood glucose value G. B The risk space transformation formula is as follows:

[0150]

[0151] in:

[0152] r(G) = 10 * f(G) 2

[0153] The transformation function f(G) is as follows:

[0154] f(G) = 1.509 * [(ln(G)) 1.084 -5.381]

[0155] In the classic glycemic risk index method, the blood glucose value corresponding to the zero-risk point is 112 mg / dL. In other embodiments of the present invention, the blood glucose value at the zero-risk point can also be adjusted based on the risk and data trends in clinical practice, and is not specifically limited here. A risk space for blood glucose values ​​greater than the zero-risk point is fitted, and the specific fitting method is also not specifically limited.

[0156] In another embodiment of the present invention, an improved Control Variability Grid Analysis (CVGA) method is used. The original CVGA defined the zero-risk point blood glucose value as 110 mg / dL and assumed the following equally risky blood glucose value pairs (90 mg / dL, 180 mg / dL; 70 mg / dL, 300 mg / dL; 50 mg / dL, 400 mg / dL). In this embodiment of the present invention, considering the actual risk in clinical practice and the trend characteristics of the data, adjustments are made. The equally risky data pair (70 mg / dL, 300 mg / dL) is corrected to (70 mg / dL, 250 mg / dL), and the zero-risk point blood glucose value is set as the target blood glucose value G. B A multinomial model was then fitted to it, yielding the following risk functions for processing both sides of the zero-risk point:

[0157]

[0158] And its maximum value was limited:

[0159] |r|=min(|r|,n)

[0160] The maximum value n is limited to a range of 0 to 80 mg / dL, and preferably, the value of n is 60 mg / dL.

[0161] In other embodiments of the present invention, the zero-risk point blood glucose value and the equivalent risk data can also be adjusted in combination with the actual risk and data trend in clinical practice, without being specifically limited here. Then, the equivalent risk point is fitted, and the specific fitting method is not specifically limited; the specific value used to limit the maximum value is also not specifically limited.

[0162] Figure 3 This is a comparison chart showing the relationship between blood glucose risk converted to the risk space using the BGRI and CVGA methods and blood glucose levels in the original physical space.

[0163] Similar to Zone-MPC, within the normal blood glucose range, the blood glucose risk converted by BGRI and CVGA methods is quite mild, especially in the 80-140 mg / dL range. Unlike Zone-MPC, which has a risk of zero in this range and loses the ability to be further optimized, rPID, while having a mild risk in this range, still has a stable and slow adjustment ability, allowing blood glucose to be further adjusted towards the target value, achieving more precise blood glucose control.

[0164] In another embodiment of the present invention, data deviating from the zero-risk point can be processed using a uniform method, as in the aforementioned embodiments, where data deviating from the zero-risk point can be processed using either the BGRI or CVGA method; alternatively, different processing methods can be used, such as combining the BGRI and CVGA methods simultaneously. In this case, the same zero-risk point blood glucose value, such as the target blood glucose value G, can be used. B When the blood glucose level is lower than the target blood glucose level (G) B When using the BGRI method, the blood glucose level is greater than the target blood glucose level (G). B When using the CVGA method, at this time:

[0165] r = -r(G), if G ≤ G B ,

[0166] in:

[0167] r(G) = 10 * f(G) 2

[0168] The transformation function f(G) is as follows:

[0169] f(G) = 1.509 * [(ln(G)) 1.084 -5.381]

[0170] r = -4.8265 * 10 4 -4*G 2 +0.45563*G-44.855, if G>G B .

[0171] Similarly, this can also be done when the blood glucose level is lower than the target blood glucose level (G). B When using the CVGA method, the blood glucose level is greater than the target blood glucose level (G). B When using the BGRI method, at this time:

[0172] r = r(G), if G > G B ,

[0173] in:

[0174] r(G) = 10 * f(G) 2

[0175] Under the transformation function f(G):

[0176] f(G) = 1.509 * [(ln(G)) 1.084 -5.381]

[0177] r = GG B if G≤G B .

[0178] It also allows setting a limit on the maximum value:

[0179] |r|=min(|r|,n)

[0180] The maximum value n is limited to a range of 0 to 80 mg / dL, and preferably, the value of n is 60 mg / dL.

[0181] In other embodiments of the present invention, the blood glucose value at the zero-risk point can also be set as the target blood glucose value G. B For blood glucose levels less than or equal to the target blood glucose value (G) B The data were obtained using the BGRI method, and for values ​​greater than the target blood glucose level (G... B The data is processed using deviation methods, such as segmented weighting or relative value processing.

[0182] When segmented weighted processing is used, then:

[0183] r = -r(G), if G ≤ G B ,

[0184] in:

[0185] r(G) = 10 * f(G) 2

[0186] Under the transformation function f(G):

[0187] f(G) = 1.509 * [(ln(G)) 1.084 -5.381]

[0188]

[0189] When using relative values:

[0190] r = -r(G), if G ≤ G B ,

[0191] in:

[0192] r(G) = 10 * f(G) 2

[0193] Under the fitted symmetric transformation function f(G):

[0194] f(G) = 1.509 * [(ln(G)) 1.084 -5.381]

[0195] r = 100 * (GG B ) / G,if G>G B

[0196] When the blood glucose value corresponding to the zero risk point is the target blood glucose value (G) BWhen, for blood glucose levels less than or equal to the target blood glucose value G B The processing function remains consistent when using segmented weighting, relative value processing, and CVGA methods for data. Therefore, when processing data less than or equal to the target blood glucose value G... B The data is processed using segmented weighting or relative value processing. When the BGRI method is used for data with blood glucose values ​​greater than the zero risk point, the processing result is equivalent to the aforementioned processing when the blood glucose value is less than or equal to the target blood glucose value G. B When using the CVGA method, the blood glucose level is greater than the target blood glucose level (G). B The BGRI method is used, and its calculation formula will not be repeated here.

[0197] It should be noted that, in the various embodiments of the present invention, the target blood glucose value G... B The target blood glucose level is 80–140 mg / dL, preferably G. B The concentration is 110–120 mg / dL.

[0198] The above processing methods can transform the asymmetric blood glucose in the original physical space into an approximately symmetric blood glucose risk in the risk space. This retains the simplicity and robustness of the PID algorithm while also providing targeted and clinically valuable blood glucose risk control, thus achieving precise control of the closed-loop artificial pancreas drug infusion system.

[0199] Three major delays exist in closed-loop artificial pancreas control systems: delayed insulin absorption (approximately 20 minutes from subcutaneous tissue to the bloodstream, and approximately 100 minutes to the liver), delayed insulin onset of action (approximately 30-100 minutes), and delayed sensing of interstitial fluid glucose concentration and blood glucose (approximately 5-15 minutes). Any attempt to accelerate closed-loop responsiveness may lead to unstable system behavior and system oscillations. To compensate for the delayed insulin absorption in the closed-loop artificial pancreas control system, an insulin feedback compensation mechanism is introduced in one embodiment of the invention. The amount of insulin not yet absorbed in the body is subtracted from the output, resulting in a component proportional to the estimated plasma insulin concentration. (In reality, human insulin secretion also uses the insulin concentration in the blood plasma as a negative feedback regulatory signal). The formula is as follows:

[0200]

[0201] in:

[0202] PID(t) represents the infusion instruction sent to the insulin infusion system;

[0203] PID c (t) indicates a compensated infusion instruction sent to the insulin infusion system;

[0204] γ represents the compensation coefficient of the estimated plasma insulin concentration to the algorithm output. A larger coefficient will make the algorithm more conservative, while a smaller coefficient will make it more aggressive. Therefore, in the embodiments of the present invention, the range of γ is 0.4-0.6, and preferably, γ is 0.5.

[0205] Estimates of plasma insulin concentration can be obtained using various conventional prediction algorithms, such as direct calculation from the infused insulin based on the insulin pharmacokinetic curve, or by using conventional autoregressive methods.

[0206]

[0207] in:

[0208] This represents an estimate of the current plasma insulin concentration.

[0209] PID c (n-1) represents the compensated output of the previous time step;

[0210] This represents an estimate of the plasma insulin concentration at the previous moment.

[0211] This represents an estimate of the plasma insulin concentration at the previous time step.

[0212] K0 represents the coefficient of the compensated output part in the previous time step;

[0213] K1 represents the coefficient of the estimated portion of the plasma insulin concentration at the previous moment;

[0214] K2 represents the coefficient of the estimated plasma insulin concentration at the previous time step;

[0215] Wherein, initial value The time intervals can be selected according to actual needs.

[0216] Accordingly, the compensation output formula after risk conversion using the aforementioned method is as follows:

[0217]

[0218] in:

[0219] rPIDc(t) represents the compensated infusion instruction sent to the insulin infusion system after risk conversion;

[0220] rPID(t) represents the infusion instruction sent to the insulin infusion system after risk conversion;

[0221] The meanings of the other characters are as described above.

[0222] To compensate for the delayed onset of insulin in the closed-loop artificial pancreas control system, in one embodiment of the present invention, insulin IOB (insulin on board) that has not yet taken effect in the body is introduced. IOB is deducted from the insulin output to prevent insulin accumulation and overdose, which could lead to risks such as postprandial hypoglycemia.

[0223] Figure 4 This is the insulin IOB curve according to an embodiment of the present invention.

[0224] according to Figure 4 The IOB curve shown can be used to calculate the cumulative residual amount of previously infused insulin. The specific curve can be selected based on the user's actual insulin action time.

[0225] PID′(t) = PID(t) - IOB(t)

[0226] in:

[0227] PID'(t) represents the infusion instruction sent to the insulin infusion system after deducting IOB;

[0228] PID(t) represents the infusion instruction sent to the insulin infusion system;

[0229] IOB(t) represents the amount of insulin in the body that has not yet taken effect at time t.

[0230] Accordingly, the output formula after risk conversion using the aforementioned method, minus the amount of insulin that has not yet taken effect in the body, is as follows:

[0231] rPID′(t)=rPID(t)-IOB(t)

[0232] in:

[0233] rPID′(t) represents the infusion instruction sent to the insulin infusion system after risk conversion, excluding the amount of insulin that has not yet taken effect in the body;

[0234] rPID(t) represents the infusion instruction sent to the insulin infusion system after risk conversion;

[0235] The meanings of the other characters are as described above.

[0236] To achieve a more ideal control effect, the calculation of IOB was processed as follows: IOB m IOB o These correspond to the IOBs for mealtime insulin and other insulins (excluding mealtime insulin), respectively. The formulas are as follows:

[0237] IOB(t) = IOB m,t +IOB o,t

[0238] in:

[0239]

[0240] in:

[0241] IOB m,t This represents the amount of insulin consumed after a meal that has not yet taken effect in the body at time t.

[0242] IOB o,t This represents the amount of non-meal insulin that has not yet taken effect in the body at time t;

[0243] D i (i = 2 - 8) represent the corresponding coefficients of the IOB curves for insulin action time i;

[0244] I m,t Indicates the amount of insulin consumed after a meal;

[0245] I 0,t This indicates the amount of insulin not consumed during meals;

[0246] IOB(t) represents the amount of insulin in the body at time t that has not yet taken effect.

[0247] Differentiating between mealtime and non-mealtime insulin administration for IOB allows for faster insulin clearance during meals and periods of high blood glucose, resulting in greater insulin output and quicker blood glucose regulation. Conversely, when approaching the target level, a longer insulin action time curve is used, leading to slower insulin clearance and more conservative and stable blood glucose regulation.

[0248] When PID'(t)>0 or rPID'(t)>0, the final amount of insulin infused is PID'(t) or rPID'(t);

[0249] When PID'(t)<0 or rPID'(t)<0, the final amount of insulin infused is 0.

[0250] To compensate for the sensing delay of tissue fluid glucose concentration and blood glucose in the closed-loop artificial pancreas control system, an autoregressive method is used in one embodiment of the present invention, as shown in the following formula:

[0251]

[0252] in,

[0253] G SC (n) represents the glucose concentration in the interstitial fluid at the current moment, i.e., the measurement value of the sensing system;

[0254] This indicates the estimated blood glucose concentration at the previous moment;

[0255] G SC (n-1) and G SC (n-2) represent the glucose concentrations in the interstitial fluid at the previous and previous time points, respectively;

[0256] K0 represents the coefficient of the estimated blood glucose concentration at the previous moment;

[0257] K1 and K2 represent the coefficients of interstitial fluid glucose concentration at the previous and previous time points, respectively.

[0258] At the initial moment,

[0259] By estimating blood glucose concentration using interstitial fluid glucose concentration, the sensing delay between interstitial fluid glucose concentration and blood glucose is compensated, making the PID algorithm more accurate. Correspondingly, the rPID algorithm can also more accurately calculate the actual insulin requirement of the human body.

[0260] In this embodiment of the invention, the delays in insulin absorption, insulin onset, tissue fluid glucose concentration, and blood glucose sensing can be partially or fully compensated. Preferably, all delay factors are considered and fully compensated to make the rPID algorithm more accurate.

[0261] In another embodiment of the present invention, the program module is pre-set with an rMPC (Risk-Model-Prediction-Control) algorithm that converts blood glucose that is asymmetric in the original physical space into blood glucose risk that is approximately symmetric in the risk space. The rMPC algorithm is obtained by conversion processing based on the classic MPC (Model-Prediction-Control) algorithm. According to the corresponding infusion instruction calculated by the rMPC algorithm, the program module controls the infusion module to infuse insulin.

[0262] The classic MPC algorithm consists of three elements: a prediction model, a value function, and constraints. The classic MPC prediction model is as follows:

[0263] x t+1 =Ax t +BI t

[0264] G t =Ct t

[0265] in:

[0266] x t+1 Indicates the state parameters at the next moment.

[0267] x tThis represents the state parameters at the current moment.

[0268] I t This indicates the current insulin infusion volume;

[0269] G t This indicates the blood glucose concentration at the current moment.

[0270] The parameter matrix is ​​as follows:

[0271]

[0272]

[0273] C = [1 0 0]

[0274] b1, b2, b3, and K are prior values.

[0275] The value function of MPC consists of the sum of squares of the deviations in the output G (blood glucose level) and the sum of squares of the changes in the input I (insulin level). MPC aims to find the minimum solution to the value function.

[0276]

[0277] in:

[0278] I' t+j This indicates the change in insulin infusion volume after step j;

[0279] This represents the difference between the predicted blood glucose concentration and the target blood glucose value after step j;

[0280] t represents the current time;

[0281] N and P are the number of steps within the control time window and the prediction time window, respectively;

[0282] R is the weighting coefficient for the insulin component.

[0283] The insulin infusion volume in step j is I. t +I′ t+j .

[0284] In this embodiment of the invention, the control time window T c =30min, prediction time window T p =60min, the weighting coefficient R of insulin amount is 11000. It should be noted that although the control time window used in the calculation is 30min, only the first step calculation result of insulin output is used in actual operation. After running, the minimum solution of the above value function is recalculated based on the latest blood glucose value.

[0285] In this embodiment of the invention, the infusion time step within the control time window is j. n j n The value range is 0–30 min, preferably 2 min. Number of steps N = T c / j n The range of j is from 0 to N.

[0286] In other embodiments of the present invention, the weighting coefficients for the control time window, prediction time window, and insulin amount can be selected as other values, which are not specifically limited here.

[0287] As mentioned earlier, due to the significant asymmetry in the distribution of high / low blood glucose (original physical space), the risks of high and low blood glucose corresponding to the same degree of deviation from the normal range in clinical practice will be significantly different. To address the asymmetry in clinical risk related to glucose concentration, the asymmetric blood glucose risk in the original physical space is transformed into a nearly symmetrical blood glucose risk in the risk space, making the MPC algorithm more accurate and flexible. The value function of the rMPC algorithm after risk transformation is as follows:

[0288]

[0289] in,

[0290] r t+j This represents the blood glucose risk value after step j;

[0291] I' t+j This indicates the change in insulin infusion volume after step j.

[0292] The deviation of blood glucose values ​​is converted into corresponding blood glucose risk, and the specific conversion method is consistent with that in the aforementioned rPID algorithm, such as segmented weighting and relative value processing. It also includes setting a fixed zero-risk point in the risk space, where the blood glucose concentration can be set as the target blood glucose value. Data deviating from the zero-risk point on both sides are processed, such as using BGRI and improved CVGA methods; it also includes using different methods to process data deviating from the target blood glucose value on both sides.

[0293] Specifically, when using segmented weighted processing:

[0294]

[0295] When using relative values:

[0296]

[0297] When using the classic glycemic risk index method:

[0298]

[0299] in:

[0300] r(G t+j )=10*f(G t+j ) 2

[0301] Transformation function f(G) t+j )as follows:

[0302] f(G t+j )=1.509*[(ln(G t+j )) 1.084 -5.381]

[0303] When using a controlled volatile grid analysis method:

[0304]

[0305] At the same time, its maximum value was also limited:

[0306] |r t+j |=min(|r t+j |,n)

[0307] The maximum value n is limited to a range of 0 to 80 mg / dL, and preferably, the value of n is 60 mg / d.

[0308] When blood glucose level is lower than the target blood glucose level (G) B When using the BGRI method, the blood glucose level is greater than the target blood glucose level (G). B When using the CVGA method:

[0309] r t+j =-r(G t+j ), if G t+j ≤G B

[0310] in:

[0311] r(G t+j )=10*f(G t+j ) 2

[0312] Transformation function f(G) t+j )as follows:

[0313] f(G t+j )=1.509*[(ln(G t+j )) 1.084 -5.381]

[0314] 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

[0315] When blood glucose levels are lower than the target blood glucose level (G) B When using the CVGA method, the blood glucose level is greater than the target blood glucose level (G). B When using the BGRI method:

[0316] r t+j =r(G t+j ), if G t+j >G B ,

[0317] in:

[0318] r(G t+j )=10*f(G t+f ) 2

[0319] Transformation function f(G) t+j )as follows:

[0320] f(G t+j )=1.509*[(ln(G t+j )) 1.084 -5.381]

[0321] r t+j =G t+j -G B ,if G t+j ≤G B .

[0322] It also allows setting a limit on the maximum value:

[0323] |r t+j |=min(|r t+j |,n)

[0324] The maximum value n is limited to a range of 0 to 80 mg / dL, and preferably, the value of n is 60 mg / dL.

[0325] When blood glucose level is lower than the target blood glucose level (G) B When using the BGRI method, the blood glucose level is greater than the target blood glucose level (G). B When using a segmented weighted method:

[0326] r t+j =-r(G t+j ), if G t+j ≤G B

[0327] in:

[0328] r(G t+j )=10*f(G t+j ) 2

[0329] Transformation function f(G) t+j )as follows:

[0330] f(G t+j )=1.509*[(ln(G t+j )) 1.084 -5.381]

[0331]

[0332] When blood glucose level is lower than the target blood glucose level (G) B When using the BGRI method, the blood glucose level is greater than the target blood glucose level (G). B When using relative value conversion:

[0333] r t+j =-r(G t+j ), if G t+j ≤G B

[0334] in:

[0335] r(G t+j )=10*f(G t+j ) 2

[0336] Transformation function f(G) t+j )as follows:

[0337] f(G t+j )=1.509*[(ln(G t+j )) 1.084 -5.381]

[0338]

[0339] When the target blood glucose level is less than or equal to G B The data is processed using segmented weighting or relative value processing. When the BGRI method is used for data with blood glucose values ​​greater than the zero risk point, the processing result is equivalent to the aforementioned processing when the blood glucose value is less than or equal to the target blood glucose value G. B When using the CVGA method, the blood glucose level is greater than the target blood glucose level (G). B The BGRI method is used, and its calculation formula will not be repeated here.

[0340] It should be noted that in the above conversion formulas:

[0341] r t+jThis represents the blood glucose risk value at step j.

[0342] G t+j This represents the blood glucose level detected at step j.

[0343] Target blood glucose level G B The target blood glucose level is 80–140 mg / dL, preferably G. B The concentration is 110–120 mg / dL.

[0344] The beneficial effects after risk conversion and the comparison of the relationship between blood glucose and blood glucose risk are consistent with those in the rPID algorithm, and will not be repeated here.

[0345] Similarly, insulin feedback compensation can be used to compensate for insulin uptake delay; IOB compensation can be used to compensate for insulin onset delay; and autoregressive compensation can be used to compensate for the sensing delay of tissue fluid glucose concentration and blood glucose concentration. The specific compensation methods are consistent with those in the rPID algorithm.

[0346] The compensation formula for delayed insulin uptake is as follows:

[0347]

[0348] in:

[0349] I t+j This indicates the infusion instruction sent to the insulin infusion system at step j;

[0350] rI c(t+j) This indicates the infusion instruction sent to the insulin infusion system at step j after risk conversion;

[0351] γ represents the compensation coefficient of the estimated plasma insulin concentration to the algorithm output. A larger coefficient will make the algorithm more conservative, while a smaller coefficient will make it more aggressive. Therefore, in the embodiments of the present invention, the range of γ is 0.4-0.6, and preferably, γ is 0.5. This represents the estimated plasma insulin concentration at step j.

[0352] For delayed insulin onset, the compensation formula is as follows:

[0353] rI′ t+j =rI t+j -IOB(t+j)

[0354] in:

[0355] rI′ t+j This indicates the infusion instruction sent to the insulin infusion system after deducting IOB at step j following risk conversion;

[0356] rI t+jThis indicates the infusion instruction sent to the insulin infusion system at step j after risk conversion;

[0357] IOB(t+j) represents the amount of insulin in the body that has not yet taken effect at time t+j.

[0358] Similarly, IOB(t+j) can be distinguished between being in a meal and not being in a meal, in which case:

[0359] IOB(t+j)=IOB m,t+j +IOB o,t+j

[0360] in:

[0361]

[0362] in:

[0363] 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.

[0364] 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;

[0365] D i (i = 2 - 8) represent the corresponding coefficients of the IOB curves for insulin action time i;

[0366] I m,t+j This represents the insulin level at time t+j after the meal.

[0367] I 0,t+j This represents the non-meal insulin amount at time t+j;

[0368] IOB(t+j) represents the amount of insulin in the body at time t+j that has not yet taken effect.

[0369] When rI′ t+j When >0, the final infused insulin volume is rI′. t+j ;

[0370] When rI′ t+j When <0, the final amount of insulin infused is 0.

[0371] 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:

[0372]

[0373] in,

[0374] G SC(t+j) represents the glucose concentration in the interstitial fluid at time t+j, which is the measured value of the sensing system;

[0375] This represents the estimated blood glucose concentration at time t+j-1;

[0376] G SC (t+j-1) and G SC (t+j-2) represent the glucose concentrations in the interstitial fluid at times t+j-1 and t+j-2, respectively;

[0377] K0 represents the coefficient of the estimated blood glucose concentration at time t+j-1;

[0378] K1 and K2 represent the coefficients of interstitial fluid glucose concentration at time t+j-1 and t+j-2, respectively.

[0379] At the initial moment,

[0380] The beneficial effects of various compensation methods are consistent with those of the rPID algorithm, and will not be repeated here.

[0381] It should be noted that, in the rMPC algorithm, it is preferable to compensate for the delay in insulin onset and the sensing delay in tissue fluid glucose concentration and blood glucose concentration.

[0382] In another embodiment of the present invention, the program module is pre-set with a composite artificial pancreas algorithm, which 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 the first insulin infusion volume I1, the second algorithm calculates the second insulin infusion volume I2, and the composite artificial pancreas algorithm optimizes the calculation of the first insulin infusion volume I1 and the second insulin infusion volume I2 to obtain the final insulin infusion volume I3, and sends the final insulin infusion volume I3 to the infusion module. The infusion module performs insulin infusion according to the final infusion volume I3.

[0383] The first and second algorithms are one of the classic PID algorithm, the classic MPC algorithm, the rMPC algorithm, or the rPID algorithm. The rMPC algorithm or the rPID algorithm is an algorithm that transforms blood glucose levels that are asymmetric in the original physical space into blood glucose risk levels that are approximately symmetric in the risk space. The transformation method of blood glucose risk in the rMPC algorithm and the rPID algorithm is as described above.

[0384] When I1 = I2, I3 = I1 = I2;

[0385] When I1 ≠ I2, the arithmetic mean of I1 and I2 can be substituted into the first and second algorithms respectively to re-optimize the algorithm parameters. After parameter optimization, the required insulin infusion volume at the current time is calculated again using the first and second algorithms respectively. If I1 and I2 are still different, the arithmetic mean of I1 and I2 is taken again and the above process is repeated until I1 and I2 are the same, that is:

[0386] ① Calculate the average value of the first insulin infusion volume I1 and the second insulin infusion volume I2.

[0387] ② Average value Substitute these parameters into the first and second algorithms respectively, and adjust the algorithm parameters accordingly;

[0388] ③ Based on the current blood glucose level, the first algorithm and the second algorithm after parameter adjustment, recalculate the first insulin infusion volume I1 and the second insulin infusion volume I2;

[0389] ④ Repeat steps ① to ③ until I1 = I2, and the final insulin infusion volume I3 = I1 = I2.

[0390] At this point, when the first or second algorithm is a PID or rPID algorithm, the algorithm parameter is K. P And K D =T D / K P T D You can take 60-90 minutes, K I =T I *K P T I The timeframe can be 150min-450min. When the first or second algorithm is an MPC or rPMC algorithm, the algorithm parameter is K.

[0391] When I1 ≠ I2, I1 and I2 can be weighted, and the weighted values ​​can be substituted into the first and second algorithms to re-optimize the algorithm parameters. After parameter optimization, the required insulin infusion volume at the current time can be calculated again using the first and second algorithms. If I1 and I2 are still different, I1 and I2 can be weighted again, the weighting coefficients adjusted, and the above process repeated until I1 and I2 are the same.

[0392] 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;

[0393] ②Weighted values Substitute these parameters into the rMPC and rPID algorithms and adjust the algorithm parameters accordingly.

[0394] ③ Based on the current blood glucose level, the rMPC algorithm and rPID algorithm with adjusted parameters recalculate the first insulin infusion volume I1 and the second insulin infusion volume I2;

[0395] ④ Repeat steps ① to ③ until I1 = I2, and the final insulin infusion volume I3 = I1 = I2.

[0396] Similarly, when the first or second algorithm is a PID or RPID algorithm, the algorithm parameter is K. P And K D =T D / K P T D You can take 60-90 minutes, K I =T I *K P T I The timeframe can be 150min-450min. When the first or second algorithm is an MPC or rPMC algorithm, the algorithm parameter is K.

[0397] In this embodiment of the invention, α and β can be adjusted according to the magnitude of the first insulin infusion volume I1 and the second insulin infusion volume I2. When I1≥I2, α≤β; when I1≤I2, α≥β; preferably, α+β=1. In other embodiments of the invention, α and β can also be other ranges of values, which are not specifically limited here.

[0398] When the calculation results of the two algorithms are the same, i.e., I3 = I1 = I2, it can be considered that the insulin infusion volume at the current moment can bring the blood glucose level to the ideal level. Through the above processing, the algorithms refer to each other. Preferably, the rMPC algorithm and the rPID algorithm refer to each other to further improve the accuracy of the output results and make the results more feasible and reliable.

[0399] In another embodiment of the present invention, the program module is further provided with a memory for storing information such as the user's historical physical state, blood glucose level, and insulin infusion volume. Statistical analysis can be performed based on the information in the memory to obtain the current statistical analysis result I4. When I1 ≠ I2, I1, I2, and I4 are compared respectively to calculate the final insulin infusion volume I3. The one between I1 and I2 that is closer to the statistical analysis result I4 is selected as the final calculation result of the composite artificial pancreas algorithm, i.e., the final insulin infusion volume I3. The program module sends the final insulin infusion volume I3 to the infusion module for infusion; that is:

[0400]

[0401] By comparing with historical data, the reliability of insulin infusion volume is ensured from another perspective.

[0402] In another embodiment of the present invention, when I1 and I2 are inconsistent and have a large difference, they can be adjusted to be similar by changing the blood glucose risk space transformation method and / or the compensation method for the delay effect in the rMPC algorithm and / or rPID algorithm. Then, the output result of the composite artificial pancreas algorithm is finally determined by the above arithmetic mean, weighted processing, or comparison with the statistical analysis results.

[0403] In another embodiment of the present invention, the closed-loop artificial pancreas control system further includes a meal recognition module and an exercise recognition module. These modules are used to identify whether the user is eating or exercising. A common method for meal recognition is based on the rate of change in blood glucose levels, determined by a specific threshold. The rate of change in blood glucose levels can be calculated from two consecutive moments or obtained through linear regression over a period of time. Specifically, when using the rate of change from two consecutive moments, the calculation formula is:

[0404] dG t / dt=(G t -G t-1 ) / Δt

[0405] in:

[0406] G t This indicates the current blood glucose level;

[0407] G t-1 This indicates the blood glucose level at the previous moment;

[0408] Δt represents the time interval between the current moment and the previous moment.

[0409] When using the formula for calculating the rate of change at three points, the formula is:

[0410] dG t / dt=(3G t -4G t-1 +G t-2 ) / 2Δt

[0411] in:

[0412] G t This indicates the current blood glucose level;

[0413] G t-1 This indicates the blood glucose level at the previous moment;

[0414] G t-2 This indicates the blood glucose level at the time two hours ago;

[0415] Δt represents the time interval between the current moment and the previous moment.

[0416] Before calculating the rate of change in blood glucose, the raw continuous glucose data can be filtered or smoothed. The threshold can be set between 1.8 mg / mL and 3 mg / mL, or it can be customized.

[0417] Similar to meal recognition, since exercise causes a rapid drop in blood glucose, exercise recognition can also be based on the rate of blood glucose change and determined using a specific threshold. The calculation of the rate of blood glucose change can also be performed as described above, and the threshold can be customized. To more quickly determine the occurrence of exercise, the closed-loop artificial pancreas drug infusion control system also includes a motion sensor (not shown). The motion sensor is used to automatically detect the user's physical activity, and the program module can receive information about the user's physical activity status. The motion sensor can automatically and accurately sense the user's physical activity state and send the activity status parameters to the program module, improving the reliability of the composite artificial pancreas algorithm's output in exercise scenarios.

[0418] The motion sensor can be located in the program module or the infusion module. Preferably, in this embodiment of the invention, the motion sensor is located in the program module.

[0419] It should be noted that the embodiments of the present invention do not limit the number of motion sensors or the placement of multiple motion sensors, as long as the conditions for motion sensors to sense user activity are met.

[0420] The motion sensor includes a three-axis accelerometer or a gyroscope. A three-axis accelerometer or gyroscope can more accurately sense the intensity, pattern, or posture of body activity. Preferably, in this embodiment of the invention, the motion sensor is a combination of a three-axis accelerometer and a gyroscope.

[0421] It should be noted that during the calculation process, the blood glucose risk conversion methods used by the rMPC algorithm and the rPID algorithm can be the same or different, and the compensation methods for the delay effect can also be the same or different. Adjustments can also be made during the calculation process according to the actual situation.

[0422] In another embodiment of the present invention, the program module is pre-set with a hybrid artificial pancreas algorithm, which 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.

[0423] Specifically: The cPID algorithm is calculated based on the current blood glucose level predicted by the MPC prediction model, that is:

[0424]

[0425] in:

[0426] K P It is the gain coefficient of the proportional part;

[0427] K I It is the gain coefficient of the integral part;

[0428] K D It is the gain coefficient of the differential part;

[0429] G MPC(t) This represents the blood glucose level at the current moment, predicted by the MPC prediction model.

[0430] G B Indicates the target blood glucose level;

[0431] C represents a constant;

[0432] cPID(t) represents the infusion instruction sent to the insulin infusion system.

[0433] Similarly, the cPID algorithm can be transformed using the risk transformation method described above to further improve the robustness of the hybrid artificial pancreas algorithm. That is:

[0434]

[0435] in:

[0436] K P It is the gain coefficient of the proportional part;

[0437] K I It is the gain coefficient of the integral part;

[0438] K D It is the gain coefficient of the differential part;

[0439] r MPC(t) This represents the blood glucose risk after risk conversion based on the current blood glucose value predicted by the MPC prediction model.

[0440] G B Indicates the target blood glucose level;

[0441] C represents a constant;

[0442] rcPID(t) represents the infusion instruction sent to the insulin infusion system.

[0443] The current insulin infusion rate in the cMPC algorithm's prediction model is calculated using the PID algorithm; that is, the cMPC algorithm's prediction model is:

[0444] x t+1 =Axt +BI PID(t)

[0445] G t =Cx t

[0446] in:

[0447] x t+1 Indicates the state parameters at the next moment.

[0448] x t This represents the state parameters at the current moment.

[0449] I PID(t) This represents the insulin infusion volume at the current moment, calculated using the PID algorithm.

[0450] G t This indicates the blood glucose concentration at the current moment.

[0451] The parameter matrix is ​​as follows:

[0452]

[0453]

[0454] C = [1 0 0]

[0455] b1, b2, b3, and K are prior values.

[0456] Similarly, the current insulin infusion rate in the cMPC algorithm's prediction model can also be calculated using the rPID algorithm, with the specific blood glucose risk conversion method described above. That is, the cMPC model is:

[0457] x t+1 =Ax t +BI rpID(t)

[0458] G t =Cx t

[0459] in:

[0460] x t+1 Indicates the state parameters at the next moment.

[0461] x t This represents the state parameters at the current moment.

[0462] I rPID(t) This represents the insulin infusion volume at the current moment, calculated using the rPID algorithm.

[0463] G t This indicates the blood glucose concentration at the current moment.

[0464] The parameter matrix is ​​as follows:

[0465]

[0466]

[0467] C = [1 0 0]

[0468] b1, b2, b3, and K are prior values.

[0469] The value function of the cMPC algorithm can be composed of the sum of squares of the deviations in the output G (blood glucose level) and the sum of squares of the changes in the input I (insulin level). MPC aims to find the minimum solution of the value function.

[0470]

[0471] in:

[0472] I' t+j This indicates the change in insulin infusion volume after step j;

[0473] This represents the difference between the predicted blood glucose concentration and the target blood glucose value after step j;

[0474] t represents the current time;

[0475] N and P are the number of steps within the control time window and the prediction time window, respectively;

[0476] R is the weighting coefficient for the insulin component.

[0477] The insulin infusion volume in step j is I. t +I′ t+j .

[0478] Similarly, the output G (blood glucose level) in the value function of the cMPC algorithm can also undergo risk transformation. The transformed value function is as described above:

[0479]

[0480] in,

[0481] r t+j This represents the blood glucose risk value after step j;

[0482] I' t+j This indicates the change in insulin infusion volume after step j;

[0483] t represents the current time;

[0484] N and P are the number of steps within the control time window and the prediction time window, respectively;

[0485] R is the weighting coefficient for the insulin component.

[0486] In this embodiment of the invention, the cMPC algorithm combines the current insulin infusion volume with a prediction model calculated by the PID algorithm or rPID algorithm and the input with or without risk transformation, and flexibly utilizes the advantages of the PID algorithm, MPC algorithm and blood glucose risk transformation to deal with complex scenarios, so that the artificial pancreas can provide a reliable insulin infusion volume under various conditions, thereby enabling blood glucose to reach the ideal level at the expected time and realizing the precise control of the closed-loop artificial pancreas drug infusion system.

[0487] In the PID and MPC algorithms at each stage mentioned above, the risk conversion methods are as described above and will not be repeated here. The conversion methods can be the same or different. Similarly, the three major delay effects can also be compensated in the aforementioned manner.

[0488] In one embodiment of the present invention, the hybrid artificial pancreas algorithm includes only the cPID algorithm or the cMPC algorithm.

[0489] In another embodiment of the present invention, the hybrid artificial pancreas algorithm includes a cPID algorithm and a cMPC algorithm, one of which is used to calculate the insulin required by the user, and the other algorithm is reserved.

[0490] In another embodiment of the present invention, the hybrid artificial pancreas algorithm includes a cPID algorithm and a cMPC algorithm. The cPID algorithm is used to calculate the first insulin infusion volume I1, and the cMPC algorithm is used to calculate the second insulin infusion volume I2. The hybrid artificial pancreas algorithm then optimizes the first insulin infusion volume I1 and the second insulin infusion volume I2 to obtain the final insulin infusion volume I3. The specific optimization method is as described above, namely:

[0491] When I1 = I2, I3 = I1 = I2;

[0492] When I1 ≠ I2, substitute the arithmetic mean or weighted value of the two into the algorithm to recalculate the current insulin infusion volume I1 and I2. If the data are still different, repeat the above process until I3 = I1 = I2, that is:

[0493] ① Calculate the average value of the first insulin infusion volume I1 and the second insulin infusion volume I2.

[0494] ② Average value Substitute these parameters into the cPID and cMPC algorithms and adjust the algorithm parameters.

[0495] 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;

[0496] ④ Repeat steps ① to ③ until I1 = I2, and finally the insulin infusion volume I3 = I1 = I2.

[0497] or:

[0498] 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;

[0499] ②Weighted values Substitute these parameters into the cPID and cMPC algorithms and adjust the algorithm parameters.

[0500] 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;

[0501] ④ Repeat steps ① to ③ until I1 = I2, and the final insulin infusion volume I3 = I1 = I2.

[0502] 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.:

[0503]

[0504] 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.

[0505] 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.

[0506] The program module 120 includes an input terminal 121 and an output terminal 122. The input terminal 121 receives the current blood glucose value. In this embodiment, the input terminal 121 includes electrical connection areas 121a and 121b. In operation, the electrical connection areas are electrically connected to electrodes or electrode wires to receive blood glucose parameter signals. In other embodiments, depending on the number of electrodes, the input terminal 121 may include more electrical connection areas. The output terminal 122 is electrically connected to the power module to enable the program module 120 to control the infusion module 110.

[0507] During the use of the control system in this embodiment of the invention, the infusion tubing 130 and the input end 121 will slide relative to each other; therefore, the input end 121 is configured as an elastic element. The selection of an elastic element ensures an interference fit between the infusion tubing 130 and the input end 121 to avoid poor electrical contact. The elastic element includes: conductive rubber strips, directional conductive silicone, conductive rings, conductive balls, etc. When the number of electrodes is relatively large, the electrical connection area is relatively dense; in this case, depending on different structural designs, the elastic element can be selected from one or more combinations of the above.

[0508] In this embodiment of the invention, the infusion tubing 130 is mounted on the mounting device 150. When the infusion tubing 130 is in the mounting position, the mounting device 150 protrudes from the surface of the housing of the control system 100, as shown below. Figure 6a As shown. When the infusion tubing 130 is installed in the working position, the mounting device 150 enters the control system 100, and its top becomes an integral structure with the housing of the control system 100, as shown. Figure 6b As shown.

[0509] Before use, the installation device 150, carrying the infusion tubing 130, is in the installation position. During use, the user places the control system 100 against the body surface and presses the installation device 150 to complete the installation. The control system then begins normal operation. Compared to other infusion tubing installation methods, the installation method of this embodiment reduces the number of steps required for installation, making it more convenient and flexible, and improving the user experience.

[0510] There are various ways in which the infusion tubing 130 can be arranged in the mounting device 150, and no specific limitation is made here. Specifically, in this embodiment of the invention, a portion of the infusion tubing 130 protrudes from the other side of the mounting device 150. Figure 6a and 6b (As shown by the dashed line in the middle), it is used for subsequent connection with the outlet of the infusion module 110 to realize drug flow.

[0511] In other embodiments of the invention, the infusion tubing 130 further includes an electrical contact area 140 connected to the input end 121. For example... Figure 6aAs shown, when the infusion hose 130 is in the installation position, the electrical contact area 140 is not electrically connected to the input terminal 121. Furthermore, the other end of the infusion hose 130 is not connected to the outlet of the infusion module 110. Figure 6b As shown, when the infusion tubing 130 is installed in the working position, one end of the infusion tubing 130 is inserted subcutaneously. Figure 6b (Screenshot of the solid line portion of the infusion tubing), the other end ( Figure 6b (The dotted line in the infusion tubing is schematically shown) is connected to the outlet of the infusion module 110, thus establishing a flow channel for the drug from the infusion module 110 to the human tissue fluid. At the same time, the electrical contact area 140 reaches the electrical connection area of ​​the input end 121, realizing the electrical connection between the program module 120 and the electrical contact area 140.

[0512] It should be noted that even when the infusion tubing 130 and the infusion module 110 are connected, and the input terminal 121 is electrically connected to the electrical contact area 140 of the infusion tubing 130, the program module 120 will be in a non-working state as long as the infusion tubing 130 is not inserted subcutaneously. In this state, the control system will not detect blood glucose levels or issue a command to initiate infusion. Therefore, in other embodiments of the present invention, when the infusion tubing 130 is in the installation position, the electrical contact area 140 can also be electrically connected to the electrical connection area of ​​the input terminal 121, or the infusion tubing 130 can also be connected to the outlet of the infusion module 110; no specific limitations are imposed here.

[0513] In this embodiment of the invention, a medical adhesive tape 160 is also included for attaching the control system 100 to the skin surface, so as to attach the program module 120, infusion module 110, electrode and infusion tubing 130 as a whole to the skin. When the infusion tubing 130 is installed in the working position, the portion of the infusion tubing 130 inserted under the skin is 13.

[0514] Figure 7a This is a top view of a control system 100 according to another embodiment of the present invention.

[0515] In one embodiment of the present invention, the control system 100 includes two parts. A program module 120 is disposed in one part, and an infusion module 110 is disposed in the other part. The two parts are electrically connected by a plurality of electrical contacts 123. Compared with connectors configured as plug-in terminals, electrical contacts have a smaller contact area, allowing for flexible design and effectively reducing the volume of the control structure. Simultaneously, the electrical contacts can be directly electrically connected to internal circuits or electrical components, or can be directly soldered onto a circuit board, optimizing the design of the internal circuit and effectively reducing circuit complexity, thus saving costs and reducing the size of the infusion device. The types of electrical contacts 123 include rigid metal contacts or elastic conductive elements. Elastic conductive elements include conductive springs, conductive silicone, conductive rubber, or conductive sheets, etc.

[0516] The part containing the infusion module 110 can be discarded after one use, while the part containing the program module 120 can be reused, saving users costs.

[0517] In other embodiments of the present invention, the control system 100 may be composed of more parts, and the parts that do not require electrical connection can be connected by ordinary waterproof plugs.

[0518] Figure 7b This is a top view of a control system 100 according to another embodiment of the present invention.

[0519] In this embodiment of the invention, the control system 100 comprises two parts, and the infusion module 110 includes two infusion sub-modules 110a and 110b. Infusion sub-modules 110a and 110b can hold different medications, such as hypoglycemic drugs like insulin, hypoglycemic drugs like glucagon, antibiotics, nutritional solutions, analgesics, morphine, anticoagulants, gene therapy drugs, cardiovascular drugs, or chemotherapy drugs, and other medications. Infusion sub-modules 110a and 110b are electrically connected to output terminals 122a and 122b, respectively, enabling the program module 120 to control the infusion module 110. The outlets of infusion sub-modules 110a and 110b are respectively connected to portions of infusion tubing 130a and 130b. Portions of infusion tubing 130a and 130b are respectively connected to portion of infusion tubing 130c. Portion of infusion tubing 130c is used for subcutaneous insertion, thereby establishing a channel for the two medications to flow from the infusion module 110 into the body fluids. That is, the control system still inserts the needle subcutaneously at only one location. In this embodiment of the invention, when the current blood glucose value is transmitted to the program module 120, the program module 120 uses a preset rMPC algorithm, rPID algorithm, composite artificial pancreas algorithm, or hybrid artificial pancreas algorithm to calculate the amount of medication required by the user based on the received current blood glucose value. The program module 120 can output different infusion signals to different infusion submodules to control whether medication needs to be infused and the required amount of medication, thereby achieving accurate detection and control of blood glucose to stabilize the user's physiological state.

[0520] In one embodiment of the invention, the infusion volume of a hypoglycemic drug and / or the current infusion volume of a hypoglycemic drug is estimated by comparing blood glucose concentrations. P With target blood glucose level G B The resulting blood glucose concentration estimate G P Blood glucose levels can be estimated using the rMPC prediction model or other suitable blood glucose prediction algorithms; blood glucose-lowering drug infusion data and / or blood glucose-raising drug infusion data can be calculated using the aforementioned rMPC algorithm, rPID algorithm, composite artificial pancreas algorithm, or hybrid artificial pancreas algorithm. Specifically:

[0521] 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;

[0522] 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.

[0523] 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:

[0524] 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;

[0525] 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.

[0526] 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.

[0527] 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.

[0528] Figures 8a-8b A partial longitudinal cross-sectional view of the infusion tubing 130 including two electrodes.

[0529] In an embodiment of the present invention, the control system 100 includes at least two electrodes for detecting blood glucose parameters, and the electrodes are disposed on the wall of the infusion tubing 130, such as... Figure 8a As shown. Different electrodes are electrically connected to the electrical connection area at the position indicated by the dashed box 140. The lumen 131 of the infusion tubing 130 is used for drug infusion.

[0530] In this embodiment of the invention, electrodes, such as electrodes 171 and 172, are disposed on the outer surface of the infusion tubing 130. Generally, electrodes 171 and 172 are insulated from each other. Electrodes 171 and 172 are directly electrically connected to the electrical connection areas 121a and 121b of the input terminal, respectively, transmitting the current blood glucose value to the program module 120 in the form of an electrical signal. Figure 8b As shown in the image. This design reduces the number of skin puncture sites for the control system, allowing for blood glucose testing and drug infusion to be completed in a single puncture at the same location, thus reducing the risk of infection for the user.

[0531] It should be noted that, in this embodiment of the invention, when the infusion tubing 130 is installed in the working position, a portion of electrodes 171 and 172 is located in the subcutaneous tissue fluid, while a portion is located outside the body, enabling direct transmission of electrical signals on the electrodes. Similar electrode arrangements in other embodiments described below have the same function and will not be detailed further.

[0532] In one embodiment of the invention, the control system 100 has only two electrodes: electrode 171 is the working electrode, and electrode 172 is the auxiliary electrode. In another embodiment of the invention, electrode 171 is the auxiliary electrode, and electrode 172 is the working electrode. The auxiliary electrode is a counter electrode.

[0533] In other embodiments of the present invention, more electrodes may be provided on the surface of the infusion tubing 130, and the multiple electrodes are electrically insulated from each other.

[0534] Figures 9a-9c This is a partial longitudinal cross-sectional view of an infusion tubing 130 according to another embodiment of the present invention.

[0535] It should be noted that in all embodiments of the present invention, the electrodes or electrode wires are coated or plated on the infusion tubing 130. However, for ease of marking and description, the electrode wires or electrodes and the infusion tubing will be shown separately in the figures. The related structural illustrations below are the same as those here, and will not be repeated hereafter.

[0536] In this embodiment of the invention, electrodes 271 and 272 are disposed on the outer surface of the wall 132 of the infusion tubing 130. Electrode 271 is directly electrically connected to the electrical connection area 121a, similar to... Figure 8aElectrode 171 is located in the infusion tubing. Electrode 272 is disposed at the front end of the infusion tubing 130 and is electrically connected to the electrical connection area 121b via electrode wire 2720. When the infusion tubing 130 is in the working position, electrode 272 is located on the outer surface of the subcutaneous portion of the tubing wall, while part of electrode 271 is located in the tissue fluid and the other part is located outside the body. At this time, electrode 272 is indirectly electrically connected to the electrical connection area 121b and sends blood glucose information to the program module.

[0537] The shape of electrode 272 is not specifically limited in this embodiment of the invention. For example, electrode 272 can be annular, and electrode 272 can be wrapped around the front end of infusion tubing 130, such as... Figure 9b As shown. At this time, an insulating layer is provided between electrode 272 and electrode 271. For example... Figure 9c As shown, in another embodiment of the present invention, electrodes 271 and 272 are both disposed at the front end of the infusion tubing 130, i.e., on the outer surface of the tubing wall in the subcutaneous portion. The outer surface of the tubing wall 132 is also provided with electrode wires 2710 and 2720, which are electrically connected to electrodes 271 and 272, respectively. When the infusion tubing 130 is installed in the working position, the electrical connection areas 121a and 121b at the input end are electrically connected to electrode wires 2710 and 2720, respectively. Therefore, electrodes 271 and 272 are indirectly electrically connected to the input end, and blood glucose values ​​can still be transmitted to the program module. During detection, electrodes 271 and 272 are both located in the subcutaneous tissue fluid.

[0538] Figure 9c Electrode 272 is configured as a ring and surrounds part of the outer surface of tube wall 132. Electrodes 271 and 272 can also have other shapes, which are not specifically limited here.

[0539] Figure 10 A partial longitudinal cross-sectional view of an infusion tubing 130 with three electrodes, according to another embodiment of the present invention.

[0540] In this embodiment of the invention, three electrodes are provided on the infusion tubing 130: electrode 371, electrode 372, and electrode 373. Electrodes 371, 372, and 373 are respectively disposed on the outer surface of the tubing wall 132. Similarly, electrode wires 3720 and 3730, respectively electrically connected to electrodes 372 and 373, are also provided on the surface of the tubing wall 132. Similarly, electrode wires electrically connected to electrode 371 are also provided on the outer surface of the tubing wall 132, but are not shown for simplicity. When the infusion tubing 130 is installed in the working position, the electrode wires 371, 3720, and 3730 are electrically connected to the input terminal electrical connection areas 121a, 121b, and 121c, respectively, thereby realizing the electrical connection between the input terminal and each electrode. The shapes of the three electrodes can vary, and no specific limitation is made here.

[0541] In this embodiment of the invention, to simplify the design of the electrical connection area, the elastic element at the input end is conductive silicone or a conductive ring. Doping the silicone with different elements can achieve directional conductivity, such as horizontal conductivity and vertical non-conductivity. With this design, even if 121a and 121c are in contact and adjacent, they are still mutually insulated. The electrical connection area 121b can use conductive adhesive strips or conductive balls, etc., without specific limitations.

[0542] In this embodiment of the invention, electrode 371 is the working electrode, and electrodes 372 and 373 are both auxiliary electrodes. Electrodes 371 and 372 or 373 can form different electrode combinations, meaning two electrode combinations share one electrode, such as sharing electrode 371. The program module 120 can select different electrode combinations to detect the current blood glucose value. After forming an electrode combination, on the one hand, if one working electrode combination malfunctions, the program module 120 can select other electrode combinations for detection as needed, ensuring uninterrupted blood glucose detection. On the other hand, the program module 120 can select multiple electrode combinations to work simultaneously, statistically analyzing multiple sets of data with the same parameters at the same time, improving the accuracy of blood glucose values, and thus outputting a more accurate drug infusion signal.

[0543] In another embodiment of the present invention, electrodes 371, 372 and 373 include an auxiliary electrode and two working electrodes, which can also be arbitrarily selected according to actual needs, and no specific limitation is made here.

[0544] In one embodiment of the present invention, electrode 371 is the working electrode, and electrodes 372 and 373 are both auxiliary electrodes. Auxiliary electrodes 372 and 373 are used as the counter electrode and reference electrode, respectively, thus forming a three-electrode system. Similarly, the three electrodes can be arbitrarily selected according to actual needs, and no specific limitations are imposed here.

[0545] Other embodiments of the present invention may include more electrodes. Each electrode includes multiple working electrodes and multiple auxiliary electrodes. In this case, each electrode combination includes both a working electrode and an auxiliary electrode, thus multiple electrodes can form multiple electrode combinations. Depending on the requirements, the program module 120 can select one or more electrode combinations to detect blood glucose.

[0546] Figure 11 A partial longitudinal cross-sectional view of the infusion tubing 130, which is another embodiment of the present invention, including an inner tube 170 and an outer tube 180.

[0547] 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.

[0548] 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.

[0549] 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.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] In summary, this invention discloses a closed-loop artificial pancreas drug infusion control system. The system incorporates a hybrid artificial pancreas algorithm including cPID and / or cMPC. The input to the cPID algorithm is the intermediate value of the MPC algorithm, and the input to the cMPC algorithm is the output value of the PID algorithm. Through the deep integration of the PID and MPC algorithms, the advantages of both algorithms are fully utilized, enabling the artificial pancreas to reliably provide the appropriate types and dosages of drugs for blood glucose control under various conditions, thereby achieving ideal blood glucose levels and realizing precise control of the closed-loop artificial pancreas drug infusion system.

[0554] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are for illustrative purposes only and are not intended to limit the scope of the invention. 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 invention. The scope of the invention is defined by the appended claims.

Claims

1. A closed-loop artificial pancreas drug infusion control system, characterized in that, include: Infusion module, the infusion module being used to dispense drugs; The program module includes an input terminal and an output terminal. The input terminal includes multiple electrical connection areas to receive the current blood glucose value. The program module also has a preset hybrid artificial pancreas algorithm for calculating the amount of medication required by the user. The hybrid artificial pancreas algorithm includes a cPID algorithm and a cMPC algorithm. 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. After the output terminal is electrically connected to the infusion module, the program module controls the infusion module to output the medication according to the calculated amount of medication required by the user. and An infusion tubing equipped with at least two detection electrodes serves as a drug infusion channel. The electrodes are disposed on the wall of the infusion tubing. When the infusion tubing is installed in the working position, it is connected to the infusion module, and the drug flows into the body through the infusion tubing. Different electrodes are electrically connected to different electrical connection areas to input the current blood glucose value into the program module. The blood glucose value in the value function of the cMPC algorithm is risk-transformed, and the value function after blood glucose risk transformation is: in, r t+j This represents the blood glucose risk value after step j; I' t+j This indicates the change in insulin infusion volume after step j; t represents the current time; N and P are the number of steps within the control time window and the prediction time window, respectively; R is the weighting coefficient for the insulin component; The blood glucose risk transformation method is an improved controllable variability grid analysis transformation; Among them G B Indicates the target blood glucose level; G t+j This represents the blood glucose value at step j; At the same time, its maximum value is limited: |r t+j |=min(|r t+j |,n) The maximum value n is limited to a range of 0–80 mg / dL.

2. The closed-loop artificial pancreas drug infusion control system according to claim 1, characterized in that, The cPID algorithm is calculated based on the current blood glucose value predicted by the prediction model of the MPC algorithm, and the calculation formula is as follows: in: K P It is the gain coefficient of the proportional part; K I It is the gain coefficient of the integral part; K D It is the gain coefficient of the differential part; G MPC(t) This represents the blood glucose level at the current moment, predicted by the MPC prediction model. G B Indicates the target blood glucose level; C represents a constant; cPID(t) represents the infusion instruction sent to the insulin infusion system.

3. The closed-loop artificial pancreas drug infusion control system according to claim 2, characterized in that, The cPID algorithm is calculated by performing risk transformation on the current blood glucose value predicted by the prediction model of the MPC algorithm, and the calculation formula is as follows: in: K P It is the gain coefficient of the proportional part; K I It is the gain coefficient of the integral part; K D It is the gain coefficient of the differential part; r MPC(t) This represents the risk of blood glucose after risk conversion based on the current blood glucose value predicted by the MPC prediction model; C represents a constant. rcPID(t) represents the infusion instruction sent to the insulin infusion system.

4. The closed-loop artificial pancreas drug infusion control system according to claim 3, characterized in that, The blood glucose risk conversion method also includes one or more of the following processing methods: ① The component that is proportional to the estimated plasma insulin concentration is deducted from the predicted plasma insulin concentration; ② Subtract the amount of insulin that has not yet taken effect in the body; ③ An autoregressive method was used to compensate for the sensing delay of blood glucose and interstitial fluid glucose concentrations.

5. The closed-loop artificial pancreas drug infusion control system according to claim 1, characterized in that, The current insulin infusion rate in the prediction model of the cMPC algorithm is calculated using the PID algorithm, and the prediction model is as follows: x t+1 =Ash t +BI PID(t) G t =Cx t in: x t+1 Indicates the state parameters at the next moment. x t This represents the state parameters at the current moment. I PID(t) This represents the insulin infusion volume at the current moment, calculated using the PID algorithm. G t This indicates the current blood glucose concentration; The parameter matrix is ​​as follows: C＝[1 0 0] b1,b2,b 3, K is the prior value.

6. The closed-loop artificial pancreas drug infusion control system according to claim 1, characterized in that, The current insulin infusion rate in the prediction model of the cMPC algorithm is calculated using the rPID algorithm, and the prediction model is as follows: x t+1 =Ash t +BI rPID(t) G t =Cx t in: x t+1 Indicates the state parameters at the next moment. x t This represents the state parameters at the current moment. I rPID(t) This represents the insulin infusion volume at the current moment, calculated using the rPID algorithm. G t This indicates the current blood glucose concentration; The parameter matrix is ​​as follows: C＝[1 0 0] b1,b2,b 3, K is the prior value.

7. The closed-loop artificial pancreas drug infusion control system according to claim 6, characterized in that, The rPID algorithm, based on the PID algorithm, transforms the asymmetric blood glucose in the original physical space into a blood glucose risk that is approximately symmetric in the risk space.

8. The closed-loop artificial pancreas drug infusion control system according to claim 7, characterized in that, The blood glucose risk conversion method also includes one or more of the following processing methods: ① The component that is proportional to the estimated plasma insulin concentration is deducted from the predicted plasma insulin concentration; ② Subtract the amount of insulin that has not yet taken effect in the body; ③ An autoregressive method was used to compensate for the sensing delay of blood glucose and interstitial fluid glucose concentrations.

9. The closed-loop artificial pancreas drug infusion control system according to claim 1, characterized in that, The hybrid artificial pancreas algorithm includes a cPID algorithm and a cMPC algorithm. The cPID algorithm calculates the first insulin infusion volume I1, and the cMPC algorithm calculates the second insulin infusion volume I2. The hybrid artificial pancreas algorithm optimizes the calculation of the first insulin infusion volume I1 and the second insulin infusion volume I2 to obtain the final insulin infusion volume I3.

10. The closed-loop artificial pancreas drug infusion control system according to claim 9, characterized in that, The final insulin infusion volume I3 is optimized by averaging the first insulin infusion volume I1 and the second insulin infusion volume I2: ① Calculate the average value of the first insulin infusion volume I1 and the second insulin infusion volume I2. ② Average value The parameters are then incorporated into the cPID and cMPC algorithms and adjusted accordingly. ③ Based on the current blood glucose level, the cPID algorithm and the cMPC algorithm after parameter adjustment, recalculate the first insulin infusion volume I1 and the second insulin infusion volume I2; ④ Repeat steps ① to ③ until I1 = I2, and the final insulin infusion volume I3 = I1 = I2.

11. The closed-loop artificial pancreas drug infusion control system according to claim 9, characterized in that, The final insulin infusion volume I3 is optimized using a weighted average of the first insulin infusion volume I1 and the second insulin infusion volume I2: 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; ②Weighted values The parameters are then incorporated into the cPID and cMPC algorithms and adjusted accordingly. ③ Based on the current blood glucose value, the cPID algorithm and the cMPC algorithm after parameter adjustment, recalculate the first insulin infusion volume I1 and the second insulin infusion volume I2; ④ Repeat steps ① to ③ until I1 = I2, and the final insulin infusion volume I3 = I1 = I2.

12. The closed-loop artificial pancreas drug infusion control system according to claim 9, characterized in that, The final insulin infusion volume I3 is obtained by comparing the first insulin infusion volume I1 and the second insulin infusion volume I2 with the statistical analysis result I4 of historical data:

13. The closed-loop artificial pancreas drug infusion control system according to claim 1, characterized in that, The infusion tubing includes an inner tube and at least one outer tube, the outer tube being disposed outside the inner tube, and the inner tube being used for drug infusion.

14. The closed-loop artificial pancreas drug infusion control system according to claim 13, characterized in that, At least one of the electrodes is disposed between the inner tube wall and the outermost tube wall.

15. The closed-loop artificial pancreas drug infusion control system according to claim 1, characterized in that, The infusion module includes multiple infusion sub-modules, each of which is electrically connected to the output terminal. The program module selects and controls the infusion sub-modules to output the drug according to the calculated amount of drug required by the user.

16. The closed-loop artificial pancreas drug infusion control system according to claim 15, characterized in that, The drugs mentioned are blood glucose-raising drugs and blood glucose-lowering drugs.

17. The closed-loop artificial pancreas drug infusion control system according to claim 1, characterized in that, The closed-loop artificial pancreas drug infusion control system consists of multiple parts, with the infusion module and the program module located in different parts and electrically connected through multiple electrical contacts.

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