CN116020005A - Closed-loop artificial pancreas insulin infusion personalized control system - Google Patents
Closed-loop artificial pancreas insulin infusion personalized control system Download PDFInfo
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention discloses a closed-loop artificial pancreas insulin infusion personalized control system, which comprises: the detection module is used for continuously detecting the current blood glucose value G; program module connected with the detection module, wherein the program module is preset with an algorithm for calculating insulin infusion instructions, an adaptive unit for adjusting gain coefficients of the algorithm according to the body weight BW of a user and a preset target blood sugar value G B The method comprises the steps of carrying out a first treatment on the surface of the And the infusion module is connected with the program module and used for controlling the infusion module to infuse insulin according to the insulin infusion instruction calculated by the algorithm for calculating the insulin infusion instruction. The self-adaptive unit for adjusting the gain coefficient of the response algorithm according to the weight BW of the user can be preset in the system, the gain coefficient of the response algorithm can be adjusted according to the weight of the user, personalized setting can be conveniently carried out according to the actual situation of the user, the real requirement of the user can be met, and the user experience is improved.
Description
Technical Field
The invention mainly relates to the field of medical instruments, in particular to a closed-loop artificial pancreas insulin infusion personalized control system.
Background
The pancreas of normal people can automatically secrete needed insulin/glucagon according to the glucose level in the blood of the human body, so that the reasonable blood sugar fluctuation range is maintained. However, the pancreas function of diabetics is abnormal, and insulin required by human body cannot be normally secreted. Diabetes is a metabolic disease, a life-long disease. The existing medical technology cannot radically cure diabetes, and the occurrence and development of diabetes and complications thereof can be controlled only by stabilizing blood sugar.
Diabetics need to test blood glucose before injecting insulin into the body. The current detection means can continuously detect blood sugar and send blood sugar data to the display device in real time, so that the blood sugar data is convenient for a user to check, and the detection method is called continuous glucose detection (Continuous Glucose Monitoring, CGM). The method needs to attach the detection device to the skin surface, and the probe carried by the detection device is penetrated into subcutaneous tissue fluid to complete detection. According to the blood glucose value detected by CGM, the infusion device inputs the insulin required currently into the skin, thereby forming a closed-loop or semi-closed-loop artificial pancreas.
Currently, given the large differences in physiological characteristics between individuals, there must also be a large difference between each individual parameter and the parameters of the population mean. The use of a unified artificial pancreas model to handle different individuals with wide variation necessarily suffers from human-model mismatch and does not allow optimal glycemic control for each patient.
Accordingly, there is a need in the art for a closed-loop artificial pancreatic insulin infusion personalized control system with personalized settings.
Disclosure of Invention
The embodiment of the invention discloses a closed-loop artificial pancreas insulin infusion personalized control system, wherein an adaptive unit for adjusting the gain coefficient of an algorithm according to the body weight BW of a user is preset in the system, so that the gain coefficient of a response algorithm can be adjusted according to the body weight of the user, personalized setting can be conveniently carried out according to the actual condition of the user, the actual requirement of the user can be met, and the user experience is improved.
The invention discloses a closed-loop artificial pancreas insulin infusion personalized control system, which comprises: the detection module is used for continuously detecting the current blood glucose value G; program module connected with the detection module, wherein the program module is preset with an algorithm for calculating insulin infusion instructions, an adaptive unit for adjusting gain coefficients of the algorithm according to the body weight BW of a user and a preset target blood sugar value G B The method comprises the steps of carrying out a first treatment on the surface of the And an infusion module connected to the program module for calculating insulin infusion according to the algorithm for calculating insulin infusion instructionsThe infusion instructions, the program module controls the infusion module to infuse insulin.
According to one aspect of the invention, the algorithm for calculating insulin infusion is a classical PID algorithm or rPID algorithm, and the relationship between the gain factor Kp of the proportional part and the user body weight BW is:
Kp＝DIR/(BW*m)
wherein ,
DIR represents the daily insulin demand, U;
BW represents the user weight, kg;
m represents the user weight compensation coefficient.
According to one aspect of the invention, the user weight compensation factor m takes a value of 50 to 500.
According to one aspect of the invention, the scale factor of DIR to body weight BW is in the range of 0.6-1.1U/kg.
According to one aspect of the invention, the algorithm for calculating insulin infusion is a classical MPC algorithm or an rmcp algorithm, the gain factor K of which is related to the user's body weight BW is:
wherein ,
c is a safety coefficient;
s is a clinical experience coefficient;
e is the weight adjustment coefficient, U/Kg.
According to one aspect of the invention, the safety factor c has a value of 1.25 to 3.
According to one aspect of the invention, the clinical experience coefficient s takes a value of 1500-2500.
According to one aspect of the present invention, the weight adjustment factor e takes a value of 0.6 to 1.1U/Kg.
According to one aspect of the invention, the gain factor Kp or K may be adjusted by a factor related to basal insulin demand:
K′ P ＝K P *Sb(t)；K′＝K*Sb(t)，
wherein ,
ratio of basal insulin demand B (t) at Sb (t) being t and basal insulin quantity average Ba throughout the day:
Sb(t)＝B(t)/Ba。
according to one aspect of the invention, the average daily basal insulin quantity Ba is:
Ba＝y*DIR/24，
Wherein y is the basal insulin quantity compensation coefficient.
According to one aspect of the invention, the basal insulin quantity compensation coefficient y takes a value of 0.1 to 5.
According to one aspect of the invention, the basal insulin demand B (t) is set in segments:
(1) when time t is 0 to 4 am, B (t) =0.5×dir/48;
(2) when time t is 4 a.m. to 10 a.m., B (t) =1.5 dir/48;
(3) when time t is 10 am to 0 am, B (t) =dir/48.
According to one aspect of the invention, the risk rmcp algorithm and the risk rPID algorithm convert blood glucose that is asymmetric in the original physical space to blood glucose risk that is approximately symmetric in the risk space, based on the classical PID algorithm and the classical MPC algorithm, respectively.
According to one aspect of the invention, the method of conversion of glycemic risk r by the rmcp algorithm or the rPID algorithm includes one or more of piecewise weighting, relative value conversion, glycemic risk index conversion, and improved control variability grid analysis conversion.
According to one aspect of the invention, the blood glucose risk r is calculated as:
according to one aspect of the invention, the blood glucose risk r is calculated as:
according to one aspect of the invention, the blood glucose risk r is calculated as:
wherein ,
r(G)＝10*f(G) 2
wherein f (G) is a transfer function, and the calculation formula is as follows:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]。
according to one aspect of the invention, the blood glucose risk r is calculated as:
according to one aspect of the invention, when the current blood glucose value G is greater than the target blood glucose value G B At the time, the blood sugar risk r meter
The method comprises the following steps:
r＝r(G),ifG＞G B
wherein ,
r(G)＝10*f(G) 2 ，
wherein f (G) is a transfer function, and the calculation formula is as follows:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]；
when the current blood glucose value G is not greater than the target blood glucose value G B When the blood glucose risk r is calculated as:
r＝G-G B ,ifG≤G B 。
according to one aspect of the present invention, when the current blood glucose level G is not greater than the target blood glucose level G B When blood glucose risk r is calculated as:
r＝-r(G),ifG≤G B
wherein ：
r(G)＝10*f(G) 2
the transfer function f (G) is:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]
when the current blood glucose level G is greater than the target blood glucose level G B When the blood glucose risk r is calculated as:
r＝-4.8265*10 4 -4*G 2 +0.45563*G-44.855,ifG＞G B 。
according to one aspect of the present invention, when the current blood glucose level G is not greater than the target blood glucose level G B When blood glucose risk r is calculated as:
r＝-r(G),ifG≤G B
wherein ：
r(G)＝10*f(G) 2
the transfer function f (G) is as follows:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]
when the current blood glucose level G is greater than the target blood glucose level G B When blood glucose risk r is calculated as:
according to one aspect of the present invention, when the current blood glucose level G is not greater than the target blood glucose level G B When blood glucose risk r is calculated as:
r＝-r(G),ifG≤G B
wherein :
r(G)＝10*f(G) 2
the transfer function f (G) is as follows:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]
when the current blood glucose level G is greater than the target blood glucose level G B When blood glucose risk r is calculated as:
r＝100*(G-G B )/G,ifG＞G B 。
According to one aspect of the invention, the rPID algorithm or the rMPC algorithm further comprises one or more of the following processing modes:
(1) subtracting a component proportional to the predicted plasma insulin concentration estimate;
(2) deducting the amount of insulin that has not been acted upon in vivo;
(3) an autoregressive method is used to compensate for the sensing delay of blood glucose and interstitial fluid glucose concentration.
According to one aspect of the invention, two of the detection module, the program module and the infusion module are connected to each other to form a unitary structure and are adhered to different locations of the skin with the third module.
According to one aspect of the invention, the detection module, the program module and the infusion module are connected to form a unitary structure and are adhered to the same location of the skin.
Compared with the prior art, the technical scheme of the invention has the following advantages:
in the closed-loop artificial pancreas insulin infusion personalized control system disclosed by the invention, the self-adaptive unit for adjusting the gain coefficient of the algorithm according to the weight BW of the user is preset in the system, so that the gain coefficient of the response algorithm can be adjusted according to the weight of the user, personalized setting can be conveniently carried out according to the actual situation of the user, the real requirement of the user can be met, and the user experience is improved.
Furthermore, the coefficient Sb (t) related to the basic insulin demand of different time periods is introduced into the gain coefficient of the algorithm, so that the gain coefficient is adjusted along with the change of time, the insulin demands of users in different time periods are met, and the user experience is further improved.
Furthermore, the preset algorithm is an rPID algorithm or an rMPC algorithm for converting the blood sugar which is asymmetric in the original physical space into the blood sugar risk space which is approximately symmetric, so that the accuracy and the reliability of the infusion result are improved.
Further, the detection module, the program module and the injection molding module are connected to form a whole structure and are adhered to the same position of the skin. The three modules are connected into a whole and stuck at the same position, so that the number of the skin sticking devices of the user is reduced, and the interference of more sticking devices on the movement extension of the user is further reduced; meanwhile, the problem of unsmooth wireless communication between the separation devices is effectively solved, and user experience is further enhanced.
Drawings
FIG. 1 is a schematic diagram of the modular relationship of a closed-loop artificial pancreatic insulin infusion control system in accordance with an embodiment of the invention;
FIG. 2 is a graph comparing the blood glucose of a risk space and an original physical space obtained by a segmentation weighting process and a relative value transformation method according to an embodiment of the present invention;
FIG. 3 is a graph comparing risk space converted by BGRI and CVGA methods to blood glucose in an original physical space according to an embodiment of the present invention;
FIG. 4 is an insulin IOB curve according to one embodiment of the invention;
FIG. 5 is a schematic diagram of four clinically optimal basal rate setting types of a main stream referenced in one embodiment in accordance with the invention;
FIG. 6 is a schematic diagram of the modular relationship of a closed-loop artificial pancreatic insulin infusion control system in accordance with another embodiment of the invention;
FIG. 7 is a schematic diagram of the modular relationship of a closed-loop artificial pancreatic insulin infusion control system in accordance with yet another embodiment of the invention;
FIG. 8 is a schematic diagram of a closed-loop artificial pancreas multi-drug infusion control system module relationship according to another embodiment of the invention;
FIG. 9 is a schematic diagram of a dual drug switch according to one embodiment of the present invention;
fig. 10 is a schematic diagram of the modular relationship of a closed-loop artificial pancreatic insulin infusion control system in accordance with another embodiment of the invention.
Detailed Description
As previously mentioned, there must also be a large variance between parameters of each individual and population mean, taking into account the large variance in physiological characteristics between individuals. The use of a unified artificial pancreas model to handle different individuals with wide variation necessarily suffers from human-model mismatch and does not allow optimal glycemic control for each patient.
In order to solve the problem, the invention provides a closed-loop artificial pancreas insulin infusion personalized control system, which is characterized in that a self-adaptive unit for adjusting the gain coefficient of an algorithm according to the weight BW of a user is preset in the system, the gain coefficient of a response algorithm can be adjusted according to the weight of the user, personalized setting is convenient according to the actual situation of the user, the actual requirement of the user can be met, and the user experience is improved.
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the relative arrangement of parts and steps, numerical expressions and numerical values set forth in these embodiments should not be construed as limiting the scope of the present invention unless it is specifically stated otherwise.
Furthermore, it should be understood that the dimensions of the various elements shown in the figures are not necessarily drawn to actual scale, e.g., the thickness, width, length, or distance of some elements may be exaggerated relative to other structures for ease of description.
The following description of the exemplary embodiment(s) is merely illustrative, and is in no way intended to limit the invention, its application, or uses. Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail herein, but where applicable, should be considered part of the present specification.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined or illustrated in one figure, no further discussion thereof will be necessary in the following figure description.
FIG. 1 is a schematic diagram of the module relationship of a closed-loop artificial pancreatic insulin infusion control system according to an embodiment of the invention.
The embodiment of the invention discloses a closed-loop artificial pancreas insulin infusion control system which mainly comprises a detection module 100, a program module 101 and an infusion module 102.
The detection module 100 is used for continuously detecting the current blood glucose level of the user. Typically, the detection module 100 is a continuous glucose meter (Continuous Glucose Monitoring, CGM) that can detect the current blood glucose level of the user in real time, monitor the change of blood glucose, and send the current blood glucose level to the program module 101.
The program module 101 is used to control the operation of the detection module 100 and the injection molding block 102. Thus, the program module 101 is connected to the detection module 100 and the injection molding block 102, respectively. Here, the connection includes a conventional electrical connection or a wireless connection.
The infusion module 102 contains the mechanical structure necessary for infusing insulin and is controlled by the program module 101. Based on the current insulin infusion data from the program module 101, the infusion module 102 infuses the currently desired insulin into the user. At the same time, the infusion state of the infusion module 102 can also be fed back into the program module 101 in real time.
The embodiment of the present invention is not limited to the specific positions and connection relations of the detection module 100, the program module 101 and the injection molding block 102, as long as the foregoing functional conditions can be satisfied.
In one embodiment of the present invention, the three are electrically connected to each other to form a unitary structure. Therefore, the three are stuck on the same position of the skin of the user. The three modules are connected into a whole and stuck at the same position, so that the number of the skin sticking devices of the user is reduced, and the interference of more sticking devices on the activities of the user is further reduced; meanwhile, the problem of wireless communication reliability between the separation devices is effectively solved, and user experience is further enhanced.
In another embodiment of the present invention, the program module 101 and the injection molding module 102 are interconnected to form a unitary structure, while the detection module 100 is separately disposed in another structure. At this time, the detection module 100 and the program module 101 mutually transmit wireless signals to achieve connection with each other. Thus, the program module 101 and the infusion module 102 are attached to one location on the user's skin, while the detection module 100 is attached to another location on the user's skin.
As in yet another embodiment of the present invention, the program module 101 and the detection module 100 are interconnected to form the same device, while the infusion module 102 is provided separately in another configuration. The infusion module 102 and the program module 101 transmit wireless signals to each other to effect the connection to each other. Thus, the program module 101 and the detection module 100 may be attached to a certain location on the skin of the user, while the infusion module 102 may be attached to another location on the skin of the user.
As in yet another embodiment of the present invention, the three are disposed in different configurations, respectively. Therefore, the three are respectively stuck on different positions of the skin of the user. At this time, the program module 101 transmits wireless signals to each other with the detection module 100 and the injection molding module 102, respectively, to achieve connection with each other.
It should be noted that, the program module 101 according to the embodiment of the present invention further has functions of storing, recording, accessing a database, and the like, and thus, the program module 101 can be reused. Therefore, not only can the physical condition data of the user be stored, but also the production cost and the use cost of the user are saved. As described above, when the lifetime of the detection module 100 or the infusion module 102 is terminated, the program module 101 may be separated from the detection module 100, the infusion module 102, or both the detection module 100 and the infusion module 102.
In general, the detection module 100, the program module 101 and the infusion module 102 have different service lives. Therefore, when the three are electrically connected to form the same device, the three can be separated from each other. If a certain module ends the service life first, the user can only replace the module, and keep the other two modules to continue to use.
Here, it should be noted that the program module 101 according to the embodiment of the present invention may further include a plurality of sub-modules. Depending on the functions of the sub-modules, the different sub-modules may be respectively provided in different configurations, and are not particularly limited herein as long as the control conditions of the program module 101 can be satisfied.
Specifically, a rPID (risk-proportional-integral-derivative) algorithm for converting the blood glucose asymmetric in the original physical space into the blood glucose risk approximately symmetric in the risk space is preset in the program module 101, the rPID algorithm is obtained by performing conversion processing on the basis of a classical PID (proportional-integral-derivative) algorithm, a specific processing manner will be described in detail below, and the program module 101 controls the infusion module 102 to infuse insulin according to a corresponding infusion instruction calculated by the rPID algorithm.
The classical PID algorithm can be expressed by the following formula:
wherein ：
K P gain coefficients that are proportional to the portion;
K I is the gain factor of the integrating part;
K D is the gain coefficient of the derivative part;
g represents the current blood glucose level;
G B indicating a target blood glucose level;
c represents a constant;
PID (t) represents an infusion indication sent to the insulin infusion system.
Considering the actual distribution characteristics of glucose concentration in diabetics, for example, normal blood sugar ranges from 80 to 140mg/dL, and can be relaxed to 70 to 180mg/dL, general hypoglycemia can reach 20 to 40mg/dL, and hyperglycemia can reach 400 to 600mg/dL.
The distribution of high/low blood sugar has obvious asymmetry in the original physical space, the hyperglycemia risk and the hypoglycemia risk corresponding to the same degree of deviation of blood sugar from the normal range in clinical practice are obviously different, for example, the reduction of 70mg/dL from 120mg/dL to 50mg/dL can be regarded as serious hypoglycemia, the high clinical risk is realized, and emergency measures such as carbohydrate supplementation and the like are needed to be adopted; while an increase of 70mg/dL from 120mg/dL to 190mg/dL is just beyond the normal range, the blood glucose level is not so high for diabetics, and is often reached in daily cases, and no treatment is needed.
Aiming at the asymmetric characteristic of clinical risk of glucose concentration, the blood glucose asymmetric in the original physical space is converted into the blood glucose risk approximately symmetric in the risk space, so that the PID algorithm is more robust.
Correspondingly, the rPID algorithm formula is converted into the following form:
wherein ：
rPID (t) represents an infusion instruction sent to the insulin infusion system after risk conversion;
r represents a blood glucose risk;
the meaning of the other symbols is as described above.
To maintain stability of PID integral, in combination with physiological effects of insulin in lowering blood glucose, in one embodiment of the present invention, the input parameter to PID, blood glucose bias ge=g-G B Treatment, e.g. of Ge=G-G B The segmentation weighting process is made as follows:
in another embodiment of the present invention, for a blood glucose G greater than the target blood glucose G B The offset of (2) is converted using the relative value as follows:
FIG. 2 is a graph comparing blood glucose risk space obtained by piecewise weighting and relative value transformation to the original physical space.
In the original PID algorithm, the blood glucose risks (namely Ge) at the two sides of the target blood glucose value show serious asymmetry consistent with the original physical space, and after the blood glucose risk is converted into the blood glucose risk space, the blood glucose risks at the two sides of the blood glucose target value are approximately symmetrical, so that the integral term can be kept stable, and the rPID algorithm is more robust.
In another embodiment of the invention, there is a fixed zero risk point at risk transition, and data on both sides of the zero risk point is processed. The original parameters corresponding to points greater than zero risk are positive values when being converted into a risk space, and the original parameters corresponding to points less than zero risk are negative values when being converted into the risk space. In particular, the classical glycemic risk index (BGRI) method can be consulted, which is based on clinical practice, regarding the imminence of hypoglycemia of 20mg/dL and hyperglycemia of 600mg/dLBed risk is comparable, and blood glucose in the range of 20-600mg/dL is handled overall by logarithmization. Setting the blood glucose value corresponding to the zero risk point of the method as a target blood glucose value G B . The risk space conversion formula is as follows:
wherein :
r(G)＝10*f(G) 2
the transfer function f (G) is as follows:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]
in the classical glycemic risk index method, the blood glucose value corresponding to the zero risk point of the method is 112mg/dL. In other embodiments of the present invention, the blood glucose level at the zero risk point may also be adjusted in combination with the risk and data trend of clinical practice, and is not specifically limited herein. Fitting is performed on a risk space of the blood glucose level with the blood glucose level greater than the zero risk point, and the specific fitting mode is not particularly limited.
In another embodiment of the present invention, the improved controlled variable grid analysis Control Variability Grid Analysis (CVGA) method is used, the original CVGA defined zero risk point blood glucose value is 110mg/dL, and the following equal risk blood glucose value data pairs (90 mg/dL,180mg/dL, 70mg/dL,300mg/dL, 50mg/dL,400 mg/dL) are assumed, in the embodiment of the present invention, the actual risk of clinical practice and trend characteristics of data are combined to consider, the actual risk and data are adjusted, the equal risk data pairs (70 mg/dL,300 mg/dL) are corrected to (70 mg/dL,250 mg/dL), and the zero risk point blood glucose value is set as a target blood glucose value G B . And performing polynomial model fitting on the model to obtain risk functions respectively processed at two sides of the zero risk point as follows:
and limits the maximum value thereof:
|r|＝min(|r|,n)
wherein the maximum value n is defined to be in the range of 0 to 80mg/dL, preferably, n is defined to be 60mg/dL.
In other embodiments of the invention, the blood glucose value and the equal risk data of the zero risk point can also be adjusted by combining the real risk and the data trend of clinical practice, the specific limitation is not made here, the equal risk point is fitted, and the specific fitting mode is not limited; the specific numerical values used to define the maximum values are also not particularly limited.
Fig. 3 is a graph comparing blood glucose risk converted to risk space by BGRI and CVGA methods to blood glucose in the original physical space.
Similar to the treatment with Zone-MPC, the risk of glycemia after conversion by BGRI and CVGA methods was fairly gentle in the euglycemic range, especially in the range of 80-140 mg/dL. Unlike Zone-MPC, which is completely 0 in the range, the further tuning capability is lost, and rPID risk is gentle in the range, but still has stable and slow tuning capability, so that the blood glucose can be further tuned to the target value, and more precise blood glucose control is realized.
In another embodiment of the present invention, a unified processing manner may be adopted for the data on both sides of the zero risk point, and as in the foregoing embodiment, the BGRI or CVGA method may be adopted for the data on both sides of the zero risk point; different processing methods can also be adopted, such as combining BGRI and CVGA methods, and the same zero risk point blood glucose value, such as target blood glucose value G, can be adopted B When the blood glucose level is smaller than the target blood glucose level G B When the BGRI method is adopted, the blood sugar value is larger than the target blood sugar value G B The CVGA method is adopted, and at the moment:
r＝-r(G),ifG≤G B
wherein :
r(G)＝10*f(G) 2
the transfer function f (G) is as follows:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]
r＝-4.8265*10 4 -4*G 2 +0.45563*G-44.855,ifG＞G B 。
similarly, when the blood glucose level is smaller than the target blood glucose level G B When adopting CVGA method, the blood sugar value is larger than the target blood sugar value G B The BGRI method is adopted, and at this time:
r＝r(G),ifG>G B
wherein :
r(G)＝10*f(G) 2
the transfer function f (G) is:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]
r＝G-G B ,ifG≤G B 。
at the same time, the maximum value can be limited:
|r|＝min(|r|,n)
wherein the maximum value n is defined to be in the range of 0 to 80mg/dL, preferably, n is defined to be 60mg/dL.
In other embodiments of the present invention, the blood glucose level at the zero risk point may be set to the target blood glucose level G B For less than or equal to the target blood glucose value G B Adopts BGRI method for data greater than target blood glucose value G B The data of (a) adopts a deviation amount processing method, such as a segmentation weighting process or a relative value process.
When the piecewise weighting process is employed, at this time:
r＝-r(G),ifG≤G B
wherein :
r(G)＝10*f(G) 2
the transfer function f (G) is:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]
when the relative value processing is adopted:
r＝-r(G),ifG≤G B
wherein :
r(G)＝10*f(G) 2
the fitted symmetric transfer function f (G) is:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]
r＝100*(G-G B )/G,ifG＞G B
when the blood sugar values corresponding to the zero risk points are all the target blood sugar values G B In the case of the target blood glucose level G or lower B When the segmentation weighting process, the relative value process and the CVGA method are adopted, the processing functions are consistent, and therefore, when the blood glucose level G is equal to or lower than the target blood glucose level G B The data of (2) is subjected to a sectional weighting process or a relative value process, and when the data of the blood glucose level greater than the zero risk point is subjected to a BGRI method, the processing result is equivalent to the blood glucose level which is less than or equal to the target blood glucose level G B When adopting CVGA method, the blood sugar value is larger than the target blood sugar value G B When the BGRI method is adopted, the calculation formula is not repeated.
In each of the embodiments of the present invention, the target blood glucose level G B It is preferably 80-140 mg/dL, and the target blood glucose level G B 110-120 mg/dL.
Through the processing mode, the blood glucose asymmetric in the original physical space of the rPID algorithm can be converted into the blood glucose risk approximately symmetric in the risk space, so that the characteristics of simplicity and robustness of the PID algorithm can be maintained, the blood glucose risk control function with clinical value is achieved, and the accurate control of the closed-loop artificial insulin gland infusion system is realized.
There are three major delay effects in a closed loop artificial pancreas control system: insulin absorption delay (about 20 minutes from subcutaneous arrival at blood circulation tissue to about 100 minutes at liver), insulin onset delay (about 30-100 minutes), interstitial fluid glucose concentration and blood glucose sensing delay (about 5-15 minutes). Any attempt to accelerate closed loop responsiveness may result in unstable system behavior and system oscillations. To compensate for insulin absorption delays in a closed-loop artificial pancreas control system, in one embodiment of the invention, an insulin feedback compensation mechanism is introduced. Subtracting from the output the amount of insulin not yet absorbed in the body, a fraction proportional to the estimate of plasma insulin concentration
(actual human insulin secretion also signals negative feedback regulation of insulin concentration in plasma). The formula is as follows:
wherein ：
PID (t) represents an infusion indication sent to the insulin infusion system;
PID c (t) represents a compensated infusion indication sent to an insulin infusion system;
gamma represents the compensation coefficient of the estimated plasma insulin concentration to the algorithm output, and a larger coefficient results in a relatively conservative algorithm and a relatively aggressive coefficient, so in the embodiment of the invention, gamma ranges from 0.4 to 0.6, preferably, gamma is 0.5.
wherein ：
PID c (n-1) represents an output of the band offset at the previous time;
K 0 coefficients representing the output portion with compensation at the previous time;
K 1 a coefficient representing an estimated portion of the plasma insulin concentration at the previous time;
K 2 a coefficient representing an estimated portion of plasma insulin concentration at the previous time;
wherein the initial value
The time intervals can be selected according to actual requirements.
Correspondingly, the compensation output formula after risk conversion by the method is as follows:
wherein ：
rPIDc (t) represents a compensated infusion indication sent to the insulin infusion system after risk conversion;
rPID (t) represents an infusion instruction sent to the insulin infusion system after risk conversion;
the meaning of the representation of the other characters is as described above.
To compensate for the delay in insulin onset in a closed-loop artificial pancreatic control system, in one embodiment of the invention, insulin IOB (insulin onboard) is introduced that has not been functional in the body, and IOB is subtracted from the insulin output to prevent the risk of insulin infusion accumulation, excessive amounts, postprandial hypoglycemia, etc.
Fig. 4 is an insulin IOB curve according to an embodiment of the invention.
From the IOB curve shown in fig. 4, the cumulative residual amount of insulin previously infused can be calculated, and the selection of a particular curve can be determined based on the actual insulin action time of the user.
PID′(t)＝PID(t)-IOB(t)
wherein ：
PID' (t) represents an infusion indication sent to the insulin infusion system after subtraction of the IOB;
PID (t) represents an infusion indication sent to the insulin infusion system;
IOB (t) represents the amount of insulin that has not been acted upon in the body at time t.
Correspondingly, the output formula for deducting the amount of insulin which has not been acted in the body after risk conversion by the method is as follows:
rPID′(t)＝rPID(t)-IOB(t)
wherein ：
rPID' (t) represents an infusion indication sent to the insulin infusion system after risk conversion that deducts the amount of insulin that has not been functional in the body;
rPID (t) represents an infusion instruction sent to the insulin infusion system after risk conversion;
the meaning of the representation of the other characters is as described above.
To obtain more ideal control effect, the calculation of the IOB is processed as follows m 、IOB o IOBs corresponding to meal insulin and other insulins than meal, respectively. The formula is as follows:
IOB(t)＝IOB m,t +IOB o,t
wherein ：
wherein ：
IOB m,t indicating the amount of meal insulin that has not been active in the body at time t;
IOB o,t Indicating the amount of non-prandial insulin that has not been active in the body at time t;
D i (i=2-8) means corresponding to insulin, respectivelyThe corresponding coefficient of the IOB curve with time of action i;
I m,t indicating the amount of meal insulin;
I 0,t indicating a non-meal insulin amount;
IOB (t) represents the amount of insulin that has not yet been acted upon in the body at time t.
The distinguishing treatment of meal insulin and non-meal insulin is carried out on the IOB, so that the insulin can be cleared more quickly when the meal and the blood sugar are too high, the greater insulin output can be obtained, and the blood sugar regulation is faster. And when approaching the target, a longer insulin action time curve is adopted, so that insulin is cleared more slowly, and blood sugar regulation is more conservative and stable.
When PID '(t) >0 or rPID' (t) >0, the amount of insulin finally infused is PID '(t) or rPID' (t);
when PID '(t) <0 or rPID' (t) <0, the amount of insulin finally infused is 0.
To compensate for interstitial fluid glucose concentration and sensing delays of blood glucose in a closed-loop artificial pancreas control system, in one embodiment of the present invention, an autoregressive method is employed to compensate, as follows:
wherein ,
G SC (n) represents the interstitial fluid glucose concentration at the current moment, i.e. the measurement of the sensing system;
G SC (n-1) and G SC (n-2) represents the interstitial fluid glucose concentration at the previous time and the previous time, respectively;
K 0 a coefficient representing an estimated concentration portion of blood glucose at the previous time;
K 1 and K2 The coefficients of interstitial fluid glucose concentration at the previous time and the previous time are shown, respectively.
Wherein, at the initial moment,
the blood glucose concentration is estimated through the interstitial fluid glucose concentration, so that the sensing delay of the interstitial fluid glucose concentration and the blood glucose is compensated, the PID algorithm is more accurate, and the rPID algorithm can calculate the actual requirement of a human body on insulin more accurately correspondingly.
In the embodiment of the invention, for the insulin absorption delay, the insulin onset delay and the tissue fluid glucose concentration and the blood glucose sensing delay can be partially or completely compensated, and preferably, all delay factors are considered for complete compensation, so that the rPID algorithm is more accurate.
In another embodiment of the present invention, a rmc (risk-model-prediction-control) algorithm for converting the blood glucose asymmetrical in the original physical space into the blood glucose risk approximately symmetrical in the risk space is preset in the program module 101, the rmc algorithm is obtained by performing conversion processing on the basis of a classical MPC (model-prediction-control) algorithm, and the program module 101 controls the infusion module 102 to infuse insulin according to a corresponding infusion instruction calculated by the rmc algorithm.
The classical MPC algorithm consists of three elements, a predictive model, a cost function and constraints. The classical MPC prediction model is as follows:
x t+1 ＝Ax t +BI t
G t ＝Cx t
wherein ：
x t+1 a state parameter representing the next moment in time,
x t a state parameter representing the current time of day,
I t indicating the insulin infusion quantity at the current time;
G t indicating the blood glucose concentration at the current time.
The parameter matrix is as follows:
C＝[1 0 0]
b 1 ,b 2 ,b 3 k is an a priori value.
The cost function of MPC consists of the sum of squares of the deviations of the output G (blood glucose level) and the sum of the squares of the changes of the input I (insulin quantity). The MPC needs to obtain the minimum solution of the cost function.
wherein ：
I′ t+j indicating a change in insulin infusion after step j;
t represents the current time;
n, P is the number of steps in the control time window and the prediction time window, respectively;
r is the weighting coefficient of the insulin component therein.
Insulin infusion in step j is I t +I′ t+j 。
In the embodiment of the invention, the time window T is controlled c =30min, prediction time window T p ＝60min, the weighting factor R of the insulin quantity is 11000. Although the control time window used in the calculation was 30min, only the first calculation result of insulin output was used in the actual operation, and the minimum solution of the cost function was recalculated based on the latest blood glucose value obtained after the operation.
In an embodiment of the invention, the infusion time step within the control time window is j n ，j n The value of (2) is 0-30 min, preferably 2min. Step number n=t c /j n J ranges from 0 to N.
In other embodiments of the present invention, the control time window, the prediction time window, and the weighting coefficient of the insulin amount may also be selected as other values, which are not particularly limited herein.
As described above, since the distribution of high/low blood sugar (original physical space) has significant asymmetry, the hyperglycemia risk and the hypoglycemia risk corresponding to the same degree of deviation of blood sugar from the normal range in clinical practice will be significantly different, and the asymmetric blood sugar in the original physical space is converted into the blood sugar risk approximately symmetric in the risk space according to the asymmetric characteristics of the clinical risk of glucose concentration, so that the MPC algorithm is more accurate and flexible. The cost function of the rmc algorithm after risk transformation is as follows:
wherein ,
r t+j indicating the blood glucose risk value after the j-th step;
I′ t+j indicating the change in insulin infusion after step j.
Converting the deviation of the blood glucose value into corresponding blood glucose risk, wherein the specific conversion mode is consistent with the mode in the rPID algorithm, such as sectional weighting processing and relative value processing; further comprising setting a fixed zero risk point in the risk space, the blood glucose concentration of the zero risk point being settable to a target blood glucose value. Processing data on two sides deviating from zero risk points, such as BGRI and improved CVGA methods; and also includes processing the data on both sides of the deviation from the target blood glucose level in different ways.
Specifically, when the piecewise weighting processing is employed:
when the relative value processing is adopted:
when the classical glycemic risk index method is used:
wherein :
r(G t+j )＝10*f(G t+j ) 2
conversion function f (G t+j ) The following are provided:
f(G t+j )＝1.509*[(ln(G t+j )) 1.084 -5.381]
when the control variability grid analysis method is employed:
at the same time, the maximum value is limited:
|r t+j |＝min(|r t+j |,n)
wherein the maximum value n is defined to be in the range of 0 to 80mg/dL, preferably, n is defined to be 60mg/d.
When the blood glucose level is smaller than the target blood glucose level G B When the BGRI method is adopted, the blood sugar value is larger than the target blood sugar value G B When the CVGA method is adopted, the following steps are adopted:
r t+j ＝-r(G t+j ),ifG t+j ≤G B
wherein :
r(G t+j )＝10*f(G t+j ) 2
conversion function f (G t+j ) The following are provided:
f(G t+j )＝1.509*[(ln(G t+j )) 1.084 -5.381]
r t+j ＝-4.8265*10 4 -4*G t+j 2 +0.45563*G t+j -44.855,ifG t+j ＞G B
when the blood glucose level is smaller than the target blood glucose level G B When adopting CVGA method, the blood sugar value is larger than the target blood sugar value G B When the BGRI method is adopted, the following steps are adopted:
r t+j ＝r(G t+j ),ifG t+j ＞G B
wherein :
r(G t+j )＝10*f(G t+j ) 2
conversion function f (G t+j ) The following are provided:
f(G t+j )＝1.509*[(ln(G t+j )) 1.084 -5.381]
r t+j ＝G t+j -G B ,ifG t+j ≤G B 。
at the same time, the maximum value can be limited:
|r t+j |＝min(|r t+j |,n)
wherein the maximum value n is defined to be in the range of 0 to 80mg/dL, preferably, n is defined to be 60mg/dL.
When the blood glucose level is smaller than the target blood glucose level G B When the BGRI method is adopted, the blood sugar value is larger than the target blood sugar value G B When a segmentation weighting method is adopted, the following steps are adopted:
r t+j ＝-r(G t+j ),ifG t+j ≤G B
wherein :
r(G t+j )＝10*f(G t+j ) 2
conversion function f (G t+j ) The following are provided:
f(G t+j )＝1.509*[(ln(G t+j )) 1.084 -5.381]
when the blood glucose level is smaller than the target blood glucose level G B When the BGRI method is adopted, the blood sugar value is larger than the target blood sugar value G B When the relative value conversion is adopted:
r t+j ＝-r(G t+j ),ifG t+j ≤G B
wherein :
r(G t+j )＝10*f(G t+j ) 2
conversion function f (G t+j ) The following are provided:
f(G t+j )＝1.509*[(ln(G t+j )) 1.084 -5.381]
when the pair is less than or equal to the target blood glucose value G B The data of (2) is subjected to a sectional weighting process or a relative value process, and when the data of the blood glucose level greater than the zero risk point is subjected to a BGRI method, the processing result is equivalent to the blood glucose level which is less than or equal to the target blood glucose level G B When adopting CVGA method, the blood sugar value is larger than the target blood sugar value G B When the BGRI method is adopted, the calculation formula is not repeated.
In the above various conversion formulas:
r t+j a blood glucose risk value at step j;
G t+j is the blood glucose level detected in step j.
Target blood glucose level G B It is preferably 80-140 mg/dL, and the target blood glucose level G B 110-120 mg/dL.
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 are not repeated here.
Similarly, to compensate for insulin absorption delay, an insulin feedback compensation mechanism may be used for compensation; to compensate for insulin onset delays, IOB compensation may also be employed; sensing delay of tissue fluid glucose concentration and blood glucose concentration can also adopt autoregressive compensation, and a specific compensation mode is consistent with rPID algorithm, and is specific:
for insulin absorption delay, the compensation formula is as follows:
wherein ：
I t+j an infusion indication to be sent to the insulin infusion system at step j;
rI c(t+j) an infusion instruction sent to the insulin infusion system at step j after risk conversion;
gamma represents the compensation coefficient of the estimated plasma insulin concentration to the algorithm output, and a larger coefficient results in a relatively conservative algorithm and a relatively aggressive coefficient, so in the embodiment of the invention, gamma ranges from 0.4 to 0.6, preferably, gamma is 0.5.
For insulin onset delay, the compensation formula is as follows:
rI′ t+j ＝rI t+j -IOB(t+j)
wherein ：
rI′ t+j indicating an infusion instruction sent to the insulin infusion system after deducting the IOB at step j after risk conversion;
rI t+j an infusion instruction sent to the insulin infusion system at step j after risk conversion;
IOB (t+j (indicating the amount of insulin that has not been active in the body at time t+j).
Likewise, a meal and a non-meal distinction may also be made for IOB (t+j), where:
IOB(t+j)＝IOB m,t+j +IOB o,t+j
wherein ：
wherein ：
IOB m,t+j indicating the amount of meal insulin that has not been active in the body at time t+j;
IOB o,t+j indicating the amount of non-prandial insulin not yet active in the body at time t+j;
D i (i=2-8) represents the corresponding coefficients of IOB curves corresponding to insulin action times i, respectively;
I m,t+j Indicating the meal insulin quantity at time t+j;
I 0,t+j representing the non-meal insulin quantity at time t+j;
IOB (t+j) represents the amount of insulin that has not yet been acted upon in vivo at time t+j.
When rI' t+j >At 0, the final amount of insulin infused is rI' t+j ；
When rI' t+j <At 0, the final amount of insulin infused is 0.
For sensing delays in interstitial fluid glucose concentration and blood glucose concentration, autoregressive compensation can also be employed, as follows:
wherein ,
G SC (t+j) represents interstitial fluid glucose concentration at time t+j, i.e. the measurement of the sensing system;
G SC (t+j-1) and G SC (t+j-2) represents t respectivelyInterstitial fluid glucose concentrations at times +j-1 and t+j-2;
K 0 a coefficient indicating the estimated concentration portion of blood glucose at time t+j-1;
K 1 and K2 The coefficients of interstitial fluid glucose concentration at times t+j-1 and t+j-2 are shown, respectively.
Wherein, at the initial moment,
the beneficial effects of the various compensation modes are consistent with those of the rPID algorithm and are not repeated here.
In the rmc algorithm, it is preferable to compensate for the delay in insulin action and the delay in sensing of the interstitial fluid glucose concentration and the blood glucose concentration.
In another embodiment of the present invention, a composite artificial pancreas algorithm is preset in the program module 101, the composite artificial pancreas algorithm includes a first algorithm and a second algorithm, and when the detecting module 100 detects the current blood glucose level and sends the current blood glucose level to the program module 101, the first algorithm calculates the first insulin infusion amount I 1 The second algorithm calculates a second insulin infusion amount I 2 First insulin infusion quantity I by composite artificial pancreas algorithm 1 And a second insulin infusion amount I 2 Performing optimization calculation to obtain final insulin infusion quantity I 3 And the final insulin infusion amount I 3 Is sent to the infusion module 102, the infusion module 102 based on the final infusion quantity I 3 Insulin infusion is performed.
The first algorithm and the second algorithm are one of a classical PID algorithm, a classical MPC algorithm, a rMPC algorithm or a rPID algorithm. The rmcp algorithm or the rPID algorithm is an algorithm that converts blood glucose that is asymmetric in the original physical space to blood glucose risk that is approximately symmetric in the risk space. The conversion mode of the blood glucose risk in the rMPC algorithm and the rPID algorithm is as described above.
When I 1 ＝I 2 When I 3 ＝I 1 ＝I 2 ；
When I 1 ≠I 2 When it is, I can be 1 and I2 The arithmetic mean values of the (a) are respectively substituted into the first algorithm and the second algorithm to re-optimize algorithm parameters, the insulin infusion quantity required at the current moment is calculated through the first algorithm and the second algorithm again after parameter optimization, and if I 1 and I2 Still not the same, then fetch I again 1 and I2 Repeating the above process until I 1 and I2 The same, namely:
(1) solving for first insulin infusion quantity I 1 And a second insulin infusion amount I 2 Average value of (2)
(2) Will average the value
Respectively carrying out the algorithm parameters into a first algorithm and a second algorithm, and adjusting the algorithm parameters;
(3) recalculating the first insulin infusion amount I based on the current blood glucose value, the first algorithm after adjusting the parameters, and the second algorithm 1 And a second insulin infusion amount I 2 ；
(4) Performing cyclic calculation on steps (1) - (3) until I 1 ＝I 2 The final insulin infusion amount I 3 ＝I 1 ＝I 2 。
At this time, when the first algorithm or the second algorithm is the PID or rPID algorithm, the algorithm parameter is K P And K is D ＝T D /K P ，T D Can be taken for 60min-90min, K I ＝T I *K P ，T I It can be taken for 150min-450min. When the first algorithm or the second algorithm is an MPC or an rmmc algorithm, the algorithm parameter is K.
When I 1 ≠I 2 In this case, it is also possible to apply to I 1 and I2 Weighting is carried out, calculated values after the weighting are respectively substituted into the first algorithm and the second algorithm to re-optimize algorithm parameters, and the current moment is calculated through the first algorithm and the second algorithm again after the parameter optimizationThe required insulin infusion amount, if I 1 and I2 Still not the same, then pair I again 1 and I2 Weighting, adjusting weighting coefficient, repeating above process until I 1 and I2 The same, namely:
(1) solving for first insulin infusion quantity I 1 And a second insulin infusion amount I 2 Weighted mean of (2)
Wherein alpha and beta are respectively the first insulin infusion quantity I 1 And a second insulin infusion amount I 2 Weighting coefficients of (2);
(2) will weight the mean value
Carrying out the algorithm parameters in a first algorithm and a second algorithm, and adjusting the algorithm parameters;
(3) recalculating the first insulin infusion amount I based on the current blood glucose value, the first algorithm after adjusting the parameters, and the second algorithm 1 And a second insulin infusion amount I 2 ；
(4) Performing cyclic calculation on steps (1) - (3) until I 1 ＝I 2 The final insulin infusion amount I 3 ＝I 1 ＝I 2 。
Similarly, when the first algorithm or the second algorithm is a PID or RPID algorithm, the algorithm parameter is K P And K is D ＝T D /K P ，T D Can be taken for 60min-90min, K I ＝T I *K P ，T I It can be taken for 150min-450min. When the first algorithm or the second algorithm is an MPC or an rmmc algorithm, the algorithm parameter is K.
In embodiments of the invention, alpha and beta may be based on the first insulin infusion amount I 1 And a second insulin infusion amount I 2 Is adjusted in size when I 1 ≥I 2 When alpha is less than or equal to beta; when I 1 ≤I 2 When alpha is more than or equal to beta; preferably, α+β=1. In other embodiments of the present invention, α and β may be other ranges, which are not specifically limited herein.
When the calculation results of the two are the same, i.e. I 3 ＝I 1 ＝I 2 It is considered that the insulin infusion amount at the present time can bring the blood glucose level to a desired level. Through the processing in the mode, the algorithms are mutually referenced, preferably, the first algorithm and the second algorithm are respectively a rMPC algorithm and a rPID algorithm, and the two algorithms are mutually referenced, so that the accuracy of an output result is further improved, and the result is more feasible and reliable.
In another embodiment of the present invention, the program module 101 is further provided with a memory for storing information such as the body state, blood glucose level, insulin infusion amount, etc. of the user history, and statistical analysis can be performed based on the information in the memory to obtain the statistical analysis result I at the current time 4 When I 1 ≠I 2 Respectively compare I 1 、I 2 and I4 Calculating the final insulin infusion quantity I 3 Selecting I 1 and I2 Closer to the statistical analysis result I 4 As a result of the calculation of the final complex artificial pancreas algorithm, i.e. the final insulin infusion I 3 The program module 101 will end the insulin infusion amount I 3 To the infusion device 102 for infusion; namely:
by comparison with the historical data, on the other hand, the reliability of the insulin infusion quantity is ensured.
In another embodiment of the present invention, when I 1 and I2 When the two are inconsistent and have large differences, the blood sugar risk space conversion mode and/or the delay effect compensation mode in the rMPC algorithm and/or the rPID algorithm can be changed to be similar, and then the output result of the composite artificial pancreas algorithm can be finally determined through the arithmetic average value, the weighting treatment or the comparison with the statistical analysis result.
In another embodiment of the invention, the closed-loop artificial pancreas control system further comprises a meal recognition module and a motion recognition module. The usual meal recognition for recognizing whether the user is taking a meal or exercising may be based on the blood glucose change rate and determined by a specific threshold. The blood glucose change rate can be calculated from front and back time points or obtained by linear regression of multiple time points within a period of time, and specifically, when the change rate calculation of the front and back time points is adopted, the calculation formula is as follows:
dG t /dt＝(G t -G t-1 )/△t
wherein ：
G t a blood glucose level indicating the current time;
G t-1 a blood glucose level indicating the last time;
Δt represents the time interval between the current time and the previous time.
When a three-point time change rate calculation formula is adopted, the calculation formula is as follows:
dG t /dt＝(3G t -4G t-1 +G t-2 )/2△t
wherein ：
G t a blood glucose level indicating the current time;
G t-1 a blood glucose level indicating the last time;
G t-2 a blood glucose level indicating the previous time;
Δt represents the time interval between the current time and the previous time.
The raw continuous glucose data may also be filtered or smoothed prior to calculating the blood glucose rate of change. The threshold value can be set to be 1.8mg/mL-3mg/mL, or can be set in a personalized way.
Similar to meal recognition, since exercise causes a rapid decrease in blood glucose, exercise recognition may also be based on the rate of change of blood glucose and determined by a specific threshold. The calculation of the blood glucose rate of change may also be as previously described and the threshold may be personalized. To more quickly determine the occurrence of motion, the closed-loop artificial pancreatic insulin infusion control system also includes a motion sensor (not shown). The motion sensor is used to automatically detect physical activity of the user and the program module 101 may receive physical activity status information. The motion sensor can automatically and accurately sense the physical activity state of the user, and send the activity state parameters to the program module 101, so that the output reliability of the composite artificial pancreas algorithm under the motion scene is improved.
The motion sensor may be provided in the detection module 100, the program module 101 or the injection molding block 102. Preferably, in an embodiment of the present invention, the motion sensor is provided in the program module 101.
It should be noted that, the embodiments of the present invention do not limit the number of motion sensors and the setting positions of the plurality of motion sensors, as long as the conditions that the motion sensors sense the activity status of the user can be satisfied.
The motion sensor includes a three-axis acceleration sensor or a gyroscope. The triaxial acceleration sensor or gyroscope can sense the activity intensity, the activity mode or the body posture of the body more accurately. Preferably, in the embodiment of the present invention, the motion sensor is a combination of a triaxial acceleration sensor and a gyroscope.
It should be noted that, in the calculation process, the blood glucose risk conversion modes adopted by the rmcp algorithm and the rPID algorithm may be the same or different, and the compensation modes about the delay effect may be the same or different, and the calculation process may also be adjusted according to the actual situation.
In another embodiment of the present invention, an adaptive unit for adjusting the gain factor of the algorithm according to the weight of the user is also provided in the program module 101. In some inventive embodiments, the infusion module 102 or the program module 101 may indicate the user's daily insulin demand DIR. In another embodiment of the present invention, the DIR may be calculated from the body weight BW, in particular, the DIR is proportional to BW, i.e. dir=e×bw, where e is the body weight adjustment factor.
For a type-I diabetes patient, the weight adjustment coefficient e can select the average value of the crowd to be 0.53U/kg, and can also select personalized treatment by combining the exercise habit of the patient, such as selecting a lower weight adjustment coefficient for a professional exercise patient, such as 0.4U/kg; patients who are less involved in exercise choose a higher weight adjustment factor, such as 0.6U/kg. For type II diabetics, the pancreatic secretion function and insulin resistance condition can be combined, and the personalized weight adjustment coefficient can be selected in a large range, such as 0.1-1.5U/kg, and the common range is 0.6-1.1U/kg.
In one embodiment of the present invention, the algorithm preset in the program module 101 is a classical PID algorithm or an rPID algorithm, where the gain factor kp=dir/(bw×m) of the proportional part of the algorithm, m is the weight compensation factor of the user, and the value is 50-500, preferably, m is 135.
Gain factor K of integral part in PID algorithm or rPID algorithm I And gain coefficient K of differential part D Can be converted into Kp-related coefficients, e.g. K D ＝T D /K P ，T D Can be taken for 60min-90min, K I ＝T I *K P ，T I It can be taken for 150min-450min. T (T) D 、T I Big then algorithm is aggressive, otherwise relatively conservative. Different coefficient settings may be used during daytime and night sleep, for example, a smaller time parameter may be selected at night.
In another embodiment of the present invention, the algorithm preset in the program module 101 is a classical MPC algorithm or an rmc algorithm, the gain factor K of which is BW dependent:
wherein ：
c is a safety coefficient;
s is a clinical experience coefficient;
e is the weight adjustment coefficient, U/Kg.
The safety factor c can be selected between 1.25 and 3 according to the risk of hypoglycemia at night; the values of the clinical experience coefficients s may be 1500, 1700, 1800, 2000, 2200, 2500, etc., and may be adjusted according to clinical results, without specific limitation. In a preferred embodiment of the invention, the clinical experience coefficient s is 1700. The weight adjustment coefficient e has the value range as described above.
In the two previous embodiments, the gain factor Kp of the PID algorithm or rPID algorithm and the gain factor Kp of the MPC algorithm or rMPC algorithm are increasedThe beneficial factor K can also be adjusted by introducing a factor Sb (t) related to basal insulin demand, corresponding to K' P ＝K P *Sb(t)，K′＝K*Sb(t)。
The coefficient Sb (t) of basal insulin demand correlation is the ratio of basal insulin demand B (t) at t to the average value Ba of basal insulin quantities throughout the day, i.e., sb (t) =b (t)/Ba. Wherein ba=y is DIR/24, y is the basic insulin quantity compensation coefficient, the value is 0.1-5, the crowd mean value of the coefficient is 0.47, children are slightly smaller, for example, 0.3-0.4 can be taken.
The daily basal insulin quantity average value Ba can be calculated according to the actual basal rate setting of the user. the basal insulin demand B (t) at t can be set according to four types of clinical optimal basal rate settings of the main stream. Fig. 5 is a main stream of clinically optimal basal rate setting types, four in number, from the references Holterhus, P.M., J.Bokelmann, et al (2013), "Predicting the Optimal Basal Insulin Infusion Pattern in Children and Adolescents on Insulin samples," Diabetes Care 36 (6): 1507-1511 ], where the horizontal axis is time, 24 hours a day, and the vertical axis is the relative deviation of basal insulin demand from the mean of basal insulin quantity Ba over the day, mostly between 0.5 and 1.5.
B (t) can also refer to a basic rate segmentation setting commonly used in clinic, such as a three-segment setting, as follows:
(1) when time t is 0 to 4 am, B (t) =0.5 DIR/48;
(2) when time t is 4 a.m. to 10 a.m., B (t) =1.5 DIR/48;
(3) when time t is 10 am to 0 am, B (t) =dir/48.
In other inventive embodiments, B (t) may also be calculated based on a base rate setting known and appropriate to the user.
In the embodiment of the present invention, sb (t) ranges from 0.2 to 2, preferably from 0.5 to 1.5. By introducing coefficients Sb (t) related to the basic insulin demands of different time periods, the gain coefficients are adjusted along with the change of time, so that the insulin demands of users in different time periods are met, and the accuracy of closed-loop control is further improved.
In the embodiment of the invention, the conversion mode of the rPID algorithm and the rMPC algorithm for converting the blood glucose which is asymmetric in the original physical space into the blood glucose risk which is approximately symmetric in the risk space, the compensation mode of various delays and the beneficial effects are not repeated here as described above. Meanwhile, the calculation results of each algorithm can be further processed, and the further processing mode, the beneficial effects and the like of the calculation results are not repeated here.
Fig. 6 is a schematic diagram of the modular relationship of a closed-loop artificial pancreatic insulin infusion control system in accordance with another embodiment of the invention.
In the embodiment of the invention, the closed-loop artificial pancreatic insulin infusion control system mainly comprises a detection module 100, an infusion module 102 and an electronic module 103.
The detection module 100 is used for continuously detecting the real-time blood glucose level of the user. Typically, the detection module 100 is a continuous glucose meter (Continuous Glucose Monitoring, CGM) that can detect blood glucose levels in real time and monitor blood glucose changes and send current blood glucose levels to the infusion module 102 and the electronic module 103.
The infusion module 102 contains the necessary mechanical structure for infusing insulin and also includes elements such as an infusion processor 1021 that execute a first algorithm and is controlled by the electronic module 103. The infusion module 102 calculates a first insulin infusion amount I currently required by a first algorithm after receiving the current blood glucose value sent by the detection module 100 1 And calculate the first insulin infusion quantity I 1 To the electronic module 103.
The electronic module 103 is used to control the operation of the detection module 100 and the injection molding block 102. Thus, the electronic module 103 is connected to the detection module 100 and the injection molding block 102, respectively. Here, the electronic module 103 is an external electronic device such as a mobile phone or a handset, and thus the connection means wireless connection. The electronic module 103 includes a second processor, in this embodiment of the present invention, the second processor is an element such as the electronic processor 1031 capable of executing a second algorithm and a third algorithm, and the electronic module 103 calculates a second insulin delivery currently required by the second algorithm after receiving the current blood glucose level sent by the detection module 100Fluence I 2 . Here, the first and second algorithms used by the electronic module 103 and the infusion module 102 to calculate the amount of insulin currently required are not the same.
The electronic module 103 receives the first insulin infusion amount I sent by the infusion module 102 1 Thereafter, the first insulin infusion quantity I is further subjected to a third algorithm 1 And a second insulin infusion amount I 2 Performing optimization calculation to obtain final insulin infusion quantity I 3 And the final insulin infusion amount I 3 Is sent to the infusion module 102, and the infusion module 102 infuses the currently required insulin I into the body of the user 3 . At the same time, the infusion status of the infusion module 102 can also be fed back into the electronic module 103 in real time. The specific optimization is as described above. Namely:
when I 1 ＝I 2 When I 3 ＝I 1 ＝I 2 ；
When I 1 ≠I 2 In this case, the electronic module 103 further substitutes the arithmetic average value or the weighted value of the two values into the algorithm to recalculate the current insulin infusion amount I 1 and I2 If the data are not the same, repeating the above process until I 3 ＝I 1 ＝I 2 The method comprises the following steps:
(1) solving for the first insulin infusion quantity I 1 And said second insulin infusion amount I 2 Average value of (2)
(2) Will average the value
Carrying out the first algorithm and the second algorithm, and adjusting algorithm parameters;
(3) recalculating the first insulin infusion amount I based on the current blood glucose value, the first algorithm after adjusting the parameters, and the second algorithm 1 And a second insulin infusion amount I 2 ；
(4) Performing cyclic calculation on steps (1) - (3) until I 1 ＝I 2 Final isletsQuantity of plain infusion I 3 ＝I 1 ＝I 2 。
Or:
(1) solving for the first insulin infusion quantity I 1 And a second insulin infusion amount I 2 Weighted mean of (2)
Wherein alpha and beta are respectively the first insulin infusion quantity I 1 And said second insulin infusion amount I 2 Weighting coefficients of (2);
(2) will weight the mean value
Carrying out the first algorithm and the second algorithm, and adjusting algorithm parameters;
(3) Recalculating a first insulin infusion amount I based on the current blood glucose value, the first algorithm after adjusting the parameters, and the second algorithm 1 And a second insulin infusion amount I 2 ；
(4) Performing cyclic calculation on steps (1) - (3) until I 1 ＝I 2 The final insulin infusion amount I 3 ＝I 1 ＝I 2 。
When the two are different, the electronic module 103 can also perform statistical analysis on the two and the historical information based on the physical state, blood glucose level, insulin infusion amount and the like of the user at each past moment to obtain a statistical analysis result I at the current moment 4 Comparing and selecting I 1 and I2 Closer to the statistical analysis result I 4 As a final insulin infusion quantity I 3 The electronic module 103 will end the final insulin infusion amount I 3 To the infusion device 102 for infusion; namely:
in the embodiment of the present invention, the history information of the user may be stored in the electronic module 103, or may be stored in a cloud management system (not shown), where the cloud management system is connected to the electronic module 103 wirelessly.
Fig. 7 is a schematic diagram of the modular relationship of a closed-loop artificial pancreatic insulin infusion control system in accordance with a further embodiment of the invention.
In the embodiment of the invention, the closed-loop artificial pancreatic insulin infusion control system mainly comprises a detection module 100, an infusion module 102 and an electronic module 103.
The detection module 100 is used for continuously detecting the real-time blood glucose level of the user. Typically, the detection module 100 is a continuous glucose meter (Continuous Glucose Monitoring, CGM) that can detect blood glucose levels in real time and monitor blood glucose changes, and the current blood glucose level is only sent to the infusion module 102. The detection module 100 further includes a second processor, in this embodiment of the present invention, the second processor is an element capable of executing a second algorithm, such as the detection processor 1001, and the detection module 100 directly calculates the second insulin infusion amount I through the second algorithm after detecting the real-time blood glucose level 2 And calculate a second insulin infusion quantity I 2 To the electronic module 103.
The infusion module 102 calculates the first insulin infusion amount I by a first algorithm after receiving the current blood glucose value sent by the detection module 100 as previously described 1 And infusing a first insulin infusion quantity I 1 To the electronic module 103. Here, the first algorithm and the second algorithm used by the detection module 103 and the infusion module 102 to calculate the amount of insulin are not identical.
The electronic module 103 receives the first insulin infusion quantity I from the detection module 100 and the infusion module 102 respectively 1 And a second insulin infusion amount I 2 Thereafter, the first insulin infusion quantity I is further subjected to a third algorithm 1 And a second insulin infusion amount I 2 Performing optimization calculation to obtain final insulin infusion quantity I 3 And the final insulin infusion amount I 3 Is sent to the infusion module 102, and the infusion module 102 infuses the currently required insulin I into the body of the user 3 . At the same time, the infusion status of the infusion module 102 can also be fed back into the electronic module 103 in real time. The specific optimization is as described above.
In the above two embodiments of the present inventionAfter the detection module 100 detects the current blood glucose level, the infusion processor 1021 initially calculates the first insulin infusion amount I 1 Preliminary calculation of the second insulin infusion amount I by the second processor (e.g., electronic processor 1031 and detection processor 1001) 2 And will I 1 and I2 Sending the final insulin infusion amount I to the electronic module 103, further optimizing the final insulin infusion amount I by the electronic module 103 3 And is sent to the infusion module 102 for insulin infusion to improve the accuracy of the infusion instructions.
In the above two embodiments of the present invention, the first algorithm and the second algorithm are one of the classical PID algorithm, the classical MPC algorithm, the rmc algorithm or the rmpid algorithm, and the advantages of the calculation using the rmpid or rmc algorithm are as described above, and the advantages of the further optimization method are not repeated here.
The embodiment of the present invention is not limited to the specific location and connection relationship between the detection module 100 and the injection molding block 102, as long as the foregoing functional conditions can be satisfied.
In one embodiment of the invention, the two are electrically connected to each other to form a unitary structure and are adhered to the skin of the user at the same location. The two modules are connected into a whole and stuck at the same position, so that the number of the skin sticking devices of the user is reduced, and the interference of more sticking devices on the movement extension of the user is further reduced; meanwhile, the problem of unsmooth wireless communication between the separation devices is effectively solved, and user experience is further enhanced.
As in yet another embodiment of the present invention, both are separately provided in different structures and separately adhered to different locations of the user's skin. At this time, the detection module 100 and the injection molding block 102 transmit wireless signals to each other to be connected to each other.
Fig. 8 is a schematic diagram of the modular relationship of a closed-loop artificial pancreas multi-drug infusion control system according to another embodiment of the invention.
The closed-loop artificial pancreatic insulin infusion control system in the embodiment of the invention mainly comprises the detection module 100, the program module 101 and the infusion module 102, wherein the infusion module 102 has a multi-drug infusion function, the drug can be a drug combination for adjusting and controlling blood sugar of diabetics, the metabolite is glucose (glucose), the main drug is a hypoglycemic drug such as insulin (insulin) and the like, and other combined drugs are hypoglycemic drugs with opposite effects, such as glucagon (glucagon) and the like, cortisol (cortisol) and the like, auxin (growth hormone) and the like, epinephrine (epinephrine) and the like, glucose and the like, and dextrin analogues (such as pramlinine) and the like with similar effects.
Fig. 9 is a schematic diagram of a dual drug infusion switch according to two embodiments of the present invention.
In one embodiment of the invention, the hypoglycemic drug infusion instructions and/or the current hypoglycemic drug infusion instructions are an estimate of G by comparing blood glucose concentrations P And target blood glucose level G B And the blood glucose concentration estimate G P The estimation can be performed according to a prediction model of the rmc or other suitable blood glucose prediction algorithms; the hypoglycemic drug infusion data and/or the hypoglycemic drug infusion data may be calculated by the rmcp algorithm or the rPID algorithm or the composite artificial pancreas algorithm described previously. Specific:
when G P ≥G B At this time, the infusion module 102 begins to infuse the data I for the hypoglycemic agent calculated according to the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm t Performing blood glucose lowering medicine infusion;
When G P ＜G B At this time, the infusion module 102 begins to infuse the data D of the glycemic agent calculated according to the rMPC algorithm or the rPID algorithm or the composite artificial pancreas algorithm t Performing blood sugar increasing drug infusion;
in the embodiment of the inventionIn, I b Indicating that the blood sugar is controlled to the target blood sugar value G without interference B The amount of the hypoglycemic drug required to be infused when G P ＝G B When I t ＝I b When G P ＞G B At the time, along with the infusion of the hypoglycemic drug, G P Further decrease, I t And also decreases. When the infusion module 102 has only one set of drug infusion lines, when G P ＜G B When, i.e. I t ＜I b At this time, the infusion module 102 begins the blood glucose-increasing drug infusion, data D t The rMPC algorithm, the rPID algorithm or the composite artificial pancreas algorithm can be used for calculating, and simultaneously stopping the infusion of the hypoglycemic medicament, so that the hypoglycemic medicament and the hypoglycemic medicament are prevented from affecting each other due to antagonism. When the infusion module 102 has at least two sets of drug infusion lines, when 0.ltoreq.I t ＜I b When the blood sugar-increasing medicine is infused, the blood sugar-increasing medicine can be infused continuously, and the occurrence of hypoglycemia can be effectively prevented; when I t When less than 0, the infusion of the hypoglycemic agent is stopped and only the hypoglycemic agent is infused.
In another embodiment of the present invention, the hypoglycemic agent infusion instructions and/or the current hypoglycemic agent infusion instructions may be directly by comparing the required amount I of hypoglycemic agent t And target hypoglycemic drug amount I b Is carried out, the demand I of the hypoglycemic medicine t And target hypoglycemic drug amount I b The calculation may be performed by the rMPC algorithm or rPID algorithm described previously or by a compound artificial pancreas algorithm. Specific: when the infusion module 102 has at least two sets of drug infusion lines:
when I t ≥I b At this time, the infusion module 102 begins to infuse the data I for the hypoglycemic agent calculated according to the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm t Performing blood glucose lowering medicine infusion;
when 0 is less than or equal to I t ＜I b When the blood sugar-increasing medicine is infused, the blood sugar-increasing medicine can be infused continuously, the occurrence of hypoglycemia can be effectively prevented, and the blood sugar-increasing medicine infuses data I t And blood glucose increasing drug infusion data D t The calculation can be performed by the rMPC algorithm or the rPID algorithm or the composite artificial pancreas algorithm.
When I t When the blood sugar level is less than 0, stopping the infusion of the blood sugar level reducing medicine and only infusing the blood sugar level increasing medicine, and infusing data D by the blood sugar level increasing medicine t The calculation can be performed by an rMPC algorithm or an rPID algorithm or a composite artificial pancreas algorithm.
When the infusion module 102 has only one set of drug infusion lines:
when I t When not less than 0, the infusion module 102 starts to calculate the blood glucose lowering medicine infusion data I according to the rMPC algorithm or the rPID algorithm or the composite artificial pancreas algorithm t Performing blood glucose lowering medicine infusion;
when I t When less than 0, the infusion of the hypoglycemic agent is stopped and only the hypoglycemic agent is infused.
Preferably, in an embodiment of the present invention, the hypoglycemic agent is insulin and the hypoglycemic agent is glucagon.
It should be noted that in the above embodiment, the calculation modes of the blood glucose lowering drug infusion data and the glucagon infusion data in each stage may be the same or different, and preferably, the same algorithm architecture is adopted to calculate, so as to ensure the consistency of the basic conditions during calculation and make the calculation result more accurate. More preferably, the method adopts a composite artificial pancreas algorithm for calculation, and fully utilizes the advantages of the rPID algorithm and the rMPC algorithm to face complex situations, so that the blood sugar control level is more ideal.
Fig. 10 is a schematic diagram of the closed-loop artificial pancreatic insulin infusion control system module relationship according to another embodiment of the invention.
In an embodiment of the present invention, the closed-loop artificial pancreatic insulin infusion control system disclosed in the embodiment of the present invention mainly includes a detection module 200 and an infusion module 202. The detection module 200 is used for continuously detecting the current blood glucose level of the user. In general, the detection module 100 is a continuous glucose detector (Continuous Glucose Monitoring, CGM) that can detect the current blood glucose level of a user in real time and monitor the change of blood glucose; the detection module 200 further includes a detection processing unit 2001, an algorithm for calculating an insulin infusion amount is preset in the detection processing unit 2001, and when the detection module 200 detects a current blood glucose level of a user, the detection processing unit 2001 calculates an insulin amount required by the user through the preset algorithm and sends the insulin amount required by the user to the infusion module 202.
The infusion module 202 contains the mechanical structure necessary to infuse insulin and an electronic transceiver that receives the user's insulin amount information from the detection module 200. Based on the current insulin infusion data from the detection module 200, the infusion module 202 infuses the currently desired insulin into the user. At the same time, the infusion status of the infusion module 102 can also be fed back into the detection module 200 in real time.
In the embodiment of the present invention, the algorithm for calculating insulin infusion preset in the detection processing unit 2001 is one of a classical PID algorithm, a classical MPC algorithm, a rmc algorithm, a rmpid algorithm or a composite artificial pancreas algorithm, and the method and the beneficial effects of calculating by using the rmpid algorithm, the rmc algorithm or the composite artificial pancreas algorithm are not repeated here as described above.
The embodiment of the present invention does not limit the specific location and connection relationship between the detection module 2100 and the injection molding block 202, as long as the aforementioned functional conditions can be satisfied.
In one embodiment of the invention, the two are electrically connected to each other to form a unitary structure and are adhered to the skin of the user at the same location. The two modules are connected into a whole and stuck at the same position, so that the number of the skin sticking devices of the user is reduced, and the interference of more sticking devices on the movement extension of the user is further reduced; meanwhile, the problem of unsmooth wireless communication between the separation devices is effectively solved, and user experience is further enhanced.
As in yet another embodiment of the present invention, both are separately provided in different structures and separately adhered to different locations of the user's skin. At this time, the detection module 200 and the injection molding block 202 transmit wireless signals to each other to achieve connection with each other.
In summary, the invention discloses a closed-loop artificial pancreas insulin infusion personalized control system, wherein an adaptive unit for adjusting the gain coefficient of an algorithm according to the body weight BW of a user is preset in the system, so that the gain coefficient of a response algorithm can be adjusted according to the body weight of the user, personalized setting can be conveniently performed according to the actual situation of the user, the actual requirement of the user can be met, and the user experience is improved.
While certain specific embodiments of the invention have been described in detail by way of example, it will be appreciated by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the invention. It will be appreciated by those skilled in the art that modifications may 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 (24)
1. A closed-loop artificial pancreatic insulin infusion personalized control system, comprising:
The detection module is used for continuously detecting the current blood glucose value G;
the program module is connected with the detection module, and is preset with an algorithm for calculating insulin infusion instructions, an adaptive unit for adjusting gain coefficients of the algorithm according to the body weight BW of a user and a preset target blood sugar value G B； and
and the infusion module is connected with the program module and used for controlling the infusion module to infuse insulin according to the insulin infusion instruction calculated by the algorithm for calculating the insulin infusion instruction.
2. The closed-loop artificial pancreatic insulin infusion personalized control system according to claim 1, wherein the algorithm for calculating insulin infusion amount is a classical PID algorithm or a risk rPID algorithm, and the relationship between the gain factor Kp of the proportional part and the user body weight BW is:
Kp＝DIR/(BW*m)
wherein ,
DIR represents the daily insulin demand, U;
BW represents the user weight, kg;
m represents the user weight compensation coefficient.
3. The closed-loop artificial pancreatic insulin infusion personalized control system of claim 2 wherein said user weight compensation factor m has a value of 50-500.
4. The closed-loop artificial pancreatic insulin infusion personalized control system according to claim 1, wherein the algorithm for calculating insulin infusion is a classical MPC algorithm or a risk rmcp algorithm, and wherein a gain factor K of the classical MPC algorithm or the risk rmcp algorithm is related to a user body weight BW:
wherein ,
c is a safety coefficient;
s is a clinical experience coefficient;
e is the weight adjustment coefficient, U/Kg.
5. The closed-loop artificial pancreatic insulin infusion personalized control system of claim 4 wherein said safety factor c has a value of 1.25-3.
6. The closed-loop artificial pancreatic insulin infusion personalized control system of claim 4 wherein said clinical experience coefficient s has a value of 1500-2500.
7. The closed-loop artificial pancreatic insulin infusion personalized control system according to claim 4, wherein said weight adjustment factor e has a value of 0.6-1.1U/Kg.
8. The closed loop artificial pancreatic insulin infusion personalized control system according to claim 2 or 4, wherein the gain factor Kp or K is adjustable by a factor related to basal insulin demand: k'. P ＝K P *Sb(t)，K′＝K*Sb(t)
wherein ,
The ratio of the basal insulin demand B (t) when Sb (t) is t to the average value Ba of the basal insulin quantity on the whole day;
Sb(t)＝B(t)/Ba。
9. the closed-loop artificial pancreatic insulin infusion personalized control system of claim 8 wherein said daily basal insulin quantity mean Ba is:
Ba＝y*DIR/24
wherein ,
y is the basal insulin quantity compensation coefficient.
10. The closed-loop artificial pancreatic insulin infusion personalized control system of claim 9 wherein said basal insulin quantity compensation coefficient y has a value of 0.1 to 5.
11. The closed-loop artificial pancreatic insulin infusion personalized control system of claim 8 wherein said basal insulin demand B (t) is set in segments:
(1) when time t is 0 to 4 am, B (t) =0.5×dir/48;
(2) when time t is 4 a.m. to 10 a.m., B (t) =1.5 dir/48;
(3) when time t is 10 am to 0 am, B (t) =dir/48.
12. The closed-loop artificial pancreatic insulin infusion personalized control system according to claim 2 or 4, wherein the risk rmcp algorithm and the risk rPID algorithm convert blood glucose asymmetric in an original physical space to blood glucose risk approximately symmetric in a risk space based on a classical PID algorithm and a classical MPC algorithm, respectively.
13. The closed-loop artificial pancreatic insulin infusion personalized control system of claim 3, wherein said rmcp algorithm or said method of conversion of glycemic risk r of said rmcp algorithm comprises one or more of a piecewise weighting method, a relative value conversion, a glycemic risk index conversion, and a modified control variability grid analysis conversion.
14. The closed-loop artificial pancreatic insulin infusion personalized control system of claim 13 wherein said blood glucose risk r is calculated as:
15. the closed-loop artificial pancreatic insulin infusion personalized-infusion control system of claim 13, wherein said blood glucose risk r is calculated as:
16. the closed-loop artificial pancreatic insulin infusion personalized control system of claim 13 wherein said blood glucose risk r is calculated as:
wherein ,
r(G)＝10*f(G) 2
wherein f (G) is a transfer function, and the calculation formula is as follows:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]。
17. the closed-loop artificial pancreatic insulin infusion personalized control system of claim 13 wherein said blood glucose risk r is calculated as:
18. the closed-loop artificial pancreatic insulin infusion personalized control system of claim 13, wherein when said current blood glucose value G is not greater than a target blood glucose value G B When the blood glucose risk r is calculated as:
r＝r(G)，if G＞G B
wherein ：
r(G)＝10*f(G) 2
the transfer function f (G) is:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]
when the current blood glucose value G is greater than the target blood glucose value G B When the blood glucose risk r is calculated as:
r＝G-G B ，if G≤G B 。
19. the closed-loop artificial pancreatic insulin infusion personalized control system of claim 13, wherein when said current blood glucose value G is not greater than a target blood glucose value G B When the blood glucose risk r is calculated as:
r＝-r(G)，if G≤G B
wherein ：
r(G)＝10*f(G) 2
the transfer function f (G) is as follows:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]
when the current blood glucose value G is greater than the target blood glucose value G B When the blood glucose risk r is calculated as: r= -4.8265 x 10 4 -4*G 2 +0.45563*G-44.855，if G＞G B 。
20. The closed-loop artificial pancreatic insulin infusion personalized control system of claim 13, wherein when said current blood glucose value G is not greater than a target blood glucose value G B When the blood glucose risk r is calculated as:
r＝-r(G)，if G≤G B
wherein ：
r(G)＝10*f(G) 2
the transfer function f (G) is:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]
when the current blood glucose value G is greater than the target blood glucose value G B When the blood glucose risk r is calculated as:
21. the closed-loop artificial pancreatic insulin infusion personalized control system of claim 13, wherein when said current blood glucose value G is not greater than a target blood glucose value G B When the blood glucose risk r is calculated as:
r＝-r(G)，if G≤G B
wherein ：
r(G)＝10*f(G) 2
the transfer function f (G) is:
f(G)＝1.509*[(ln(G)) 1.084 -5.381]
When the current blood glucose value G is greater than the target blood glucose value G B When the blood glucose risk r is calculated as:
r＝100*(G-G B )/G，if G＞G B 。
22. the closed-loop artificial pancreatic insulin infusion personalized control system according to any one of claims 12-21, wherein the rppid algorithm or rmc algorithm further comprises one or more of the following:
(1) subtracting a component proportional to the predicted plasma insulin concentration estimate;
(2) deducting the amount of insulin that has not been acted upon in vivo;
(3) an autoregressive method is used to compensate for the sensing delay of blood glucose and interstitial fluid glucose concentration.
23. The personalized closed-loop artificial pancreatic insulin infusion control system according to claim 1, wherein two of the detection module, the program module and the infusion module are connected to each other to form a unitary structure and are adhered to different locations of the skin with a third module.
24. The closed-loop artificial pancreatic insulin infusion control personalization system according to claim 1, wherein said detection module, said program module, and said infusion module are connected to form a unitary structure and are affixed to the same location of the skin.
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