CN112402732B - Insulin infusion quantity control method based on self-adaptive control weighting factor strategy - Google Patents
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
- A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
- A61M5/142—Pressure infusion, e.g. using pumps
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M2205/00—General characteristics of the apparatus
- A61M2205/33—Controlling, regulating or measuring
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M2230/00—Measuring parameters of the user
- A61M2230/20—Blood composition characteristics
- A61M2230/201—Glucose concentration
Abstract
The invention discloses an insulin infusion quantity control method based on an adaptive control weighting factor strategy, which utilizes a CARIMA model to predict future blood sugar changes through real-time monitoring of human blood sugar data, and then adopts minimum variance control to adjust the adaptive control weighting factor, so that an insulin pump can accurately control insulin infusion rate, reduce human blood sugar fluctuation and control the blood sugar fluctuation in a preset target interval. Compared with the existing generalized predictive control algorithm, the adaptive control weighting factor adopted in the invention can flexibly adjust the numerical value of the adaptive control weighting factor according to the situation of a user, thereby effectively reducing the occurrence risk of hypoglycemia while ensuring the control effect of the hypoglycemia.
Description
Technical Field
The invention relates to the field of insulin pump infusion quantity estimation, in particular to an insulin infusion quantity control method based on an adaptive control weighting factor strategy.
Background
Diabetes is one of the major chronic diseases threatening the life health of humans, and places a heavy burden on the development of society. At present, the number of Chinese diabetics is about 1.164 hundred million, and the first place in the world. An artificial pancreas, also known as an insulin closed-loop infusion system, is capable of automatically infusing insulin in response to fluctuations in human blood glucose, thereby controlling the blood glucose level of a diabetic patient within a set target interval. As an effective treatment means for diabetes mellitus, artificial pancreas has entered a long-term clinical trial stage in a number of European and American countries, and has achieved good results.
While artificial pancreatic intelligence systems based on generalized predictive control have achieved a profound performance in the glycemic control of a type of diabetic patient, they have a significant problem in that the risk of hypoglycemia is caused by excessive insulin injections. Currently, the hypoglycemic prophylaxis strategy adopted is mainly to introduce insulin metabolism curves and calculate insulin residues in the patient. However, an important disadvantage of this strategy is the large individual variability, and each patient needs to map his own insulin metabolism curve. At the same time, the curve is affected by the patient's diet, exercise and mood, often with large errors.
Patent publication number CN110124151a discloses a generalized predictive control closed-loop insulin infusion system based on an adaptive reference curve strategy. However, the above patent controls blood glucose through an adaptive reference curve, and does not control blood glucose through an adaptive control weighting factor.
Disclosure of Invention
In order to solve the technical problems, the technical scheme of the invention is as follows:
an insulin infusion amount control method based on an adaptive control weighting factor strategy, comprising the following steps:
collecting the current blood sugar value of a user through a blood sugar value detection module;
obtaining a predicted value of the blood glucose change of a user in the future through a CARIMA model and a Dipsilon chart equation according to the current blood glucose value of the user;
calculating the control input increment of the insulin pump through a minimum variance control model according to the predicted value of the blood sugar change of a future user; wherein the control weighting factor used for the minimum variance control is an adaptive control weighting factor, and the adaptive control weighting factor lambda is expressed by the following formula:
λ＝(ξ×u) Δy
wherein, xi represents a preset value; the u represents the deviation degree; the delta y represents the variation of the blood glucose level;
based on the idea of closed-loop control, the control input increment of the insulin pump is iteratively optimized.
The invention has the following beneficial effects:
1. compared with the existing proportional-integral control, fuzzy logic control and model prediction control, the invention has higher robustness, is easier to build and does not need to manually input dining information;
2. the invention adopts a CARIMA prediction model, a minimum variance control model, closed loop feedback correction and parameter rolling optimization, thereby ensuring the accuracy of prediction and the effectiveness of control;
3. the invention adopts the self-adaptive control weighting factor strategy, which can rapidly regulate down the insulin injection rate when the blood sugar of the patient has a descending trend, and prevent the phenomenon of hypoglycemia caused by excessive insulin infusion.
In a preferred embodiment, the "predictive value of future blood glucose changes for the user is obtained from the current blood glucose level of the user by means of the CARIMA model and the Dipsilon figure equation"
The following equation is obtained through the CARIMA model and the Dipsilon diagram equation:
y(k+j)＝G j (z -1 )Δu(k+j-1)+F j (z -1 )y(k)(j＝1，2...n)
wherein y (k) represents the blood glucose level of the user at time k; the y (k+j) represents a predicted value of the blood glucose level of the user in advance of the j step at the time k; deltau (k+j-1) represents the control input increment of the insulin pump at time k; n represents the maximum predicted length; the G is j (z -1 ) A weight coefficient representing a control input increment of the insulin pump at time k; said F j (z -1 ) A weight coefficient indicating a blood glucose level; said z -1 An operator that is shifted back by 1 step.
In a preferred embodiment, said G j (z -1 ) The expression is carried out by the following formula:
G j (z -1 )＝E j (z -1 )B(z -1 )
said E j (z -1 ) The expression is carried out by the following formula:
E j (z -1 )＝e j0 +e j1 z -1 +…+e j-1 z -j+1
wherein said e j0 ～e j-1 Is an adjustable parameter; said z -1 ～z -j+1 Is an operator shifted backwards by 1-j-1 steps;
said B (z) -1 ) The expression is carried out by the following formula:
B(z -1 )＝b 0 +b 1 z -1 +…+b nb z -nb
wherein, the b 0 ～b nb Is an adjustable parameter; said z -1 ～z -nb Is an operator shifted backward by 1-nb steps.
In a preferred embodiment, said F j (z -1 ) The expression is carried out by the following formula:
F j (z - 1)＝f j0 +f j1 z -1 +…+f jn z -n
wherein said f j0 ～f jn Is an adjustable parameter; said z -1 ～z -n Is an operator shifted backwards by 1-n steps.
In the present preferred embodiment, y (k+j) =g is given j (z -1 )Δu(k+j-1)+F j (z -1 ) Derivation of y (k):
the CRIMA model is described below;
A(z -1 )y(k)＝B(z -1 )u(k-1)+C(z -1 )ξ(k)/Δ
A(z -1 )＝1+a 1 z -1 +…+a na z -na
B(z -1 )＝b 0 +b 1 z -1 +…+b nb z -nb
C(z -1 )＝1+c 1 z -1 +…+c nc z -nc
wherein y (k) represents the blood glucose level of the user at the moment k, and u (k-1) is the insulin injection rate at the moment k-1; ζ (k) is white noise with zero mean; delta= (1-z) -1 ) Representing the integral factor. z -1 For the backward operator, na, nb, nc represent the order of the model. a, a 1 ～a na ，b 1 ～b nb And c 1 ～c nz Are all atModel parameters optimized in real time are given different values according to the acquisition environment.
To predict the advanced j-step output, the Dioaphantine equation for the Diosporanic map is introduced:
1＝E j (z -1 )A(z -1 )Δ+z -j F j (z -1 )
E j (z -1 )＝e j0 +e j1 z -1 +…+e j-1 z -j+1
F j (z -1 )＝f j0 +f j1 z -1 +…+f jn z -n
wherein E is j (z -1 ) And F j (z -1 ) The reason is that the model parameters A (z -1 ) And a polynomial uniquely determined by the prediction step j, wherein e j0 ～e j-1 And f j0 ～c jn All are parameters which can be optimized on line in real time, and different values are given according to the acquisition environment. n represents the maximum predicted length. Prediction step j=1, 2..n.
The following equation is obtained through the CARIMA model and the Dipsilon diagram equation:
y(k+j)＝G j (z -1 )Δu(k+j-1)+F j (z -1 )y(k)(j＝1，2...n)
G j (z -1 )＝E j (z -1 )B(z -1 )
wherein y (k+j) represents a predicted value of the blood glucose level of the user that leads by j steps at time k; deltau (k+j-1) represents the control input increment of the insulin pump at time k.
In a preferred embodiment, the "calculating the control input increment of the insulin pump by the minimum variance control model according to the predicted value of the blood glucose change of the future user" includes the following:
wherein J represents the infusion rate of the insulin pump; n represents a predicted length; the lambda represents an adaptive control weighting factor; m represents a control length; the w (k+j) is expressed by the following formula:
W＝Qy(k)+My r (j＝1，2，...，n)
wherein said y r Representing a reference curve; y (k) represents the current blood glucose level of the user;
the Q is expressed by the following formula:
Q＝[α，α 2 ，...，α n ] T
the alpha represents an adaptive softening factor.
In a preferred embodiment, M is expressed by the formula:
M＝[1-α，1-α 2 ，...，1-α n ] T 。
in a preferred embodiment, the adaptive control weighting factor includes the following:
manually setting desired blood glucose level
Calculating the deviation u of the current blood sugar value of the user from the expected blood sugar value according to the current blood sugar value y (k) of the user;
calculating the current change delta y of the blood sugar level of the user
The adaptive control weighting factor lambda is calculated from the degree of deviation u and the amount of change deltay.
In a preferred embodiment, the degree of deviation u is expressed by the following formula:
in a preferred embodiment, the variation Δy is expressed by the following formula:
Δy＝y(k)-y(k-1)
and y (k-1) represents the blood glucose level of the user at the previous time.
In a preferred embodiment, the "iterative optimization of the control input increment of the insulin pump based on the idea of closed-loop control" comprises the following sub-steps:
s1: taking the control input increment of the insulin pump as an input value of a CARIMA model, and updating a predicted value of the blood sugar change of a user;
s2: updating the control input increment of the insulin pump according to the updated predicted value of the blood sugar change of the user as the input of the minimum variance control model; the control weighting factor used in the minimum variance control is an adaptive control weighting factor, and the adaptive control weighting factor can adjust the magnitude of the self value according to the change of the blood sugar value;
s3: and S1-S2 are circularly executed, so that iterative optimization of the control input increment of the insulin pump is realized.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
1. compared with the existing proportional-integral control, fuzzy logic control and model prediction control, the invention has higher robustness, is easier to build and does not need to manually input dining information;
2. the invention adopts a CARIMA prediction model, a minimum variance control model, closed loop feedback correction and parameter rolling optimization, thereby ensuring the accuracy of prediction and the effectiveness of control;
3. the invention adopts the self-adaptive control weighting factor strategy, which can rapidly regulate down the insulin injection rate when the blood sugar of the patient has a descending trend, and prevent the phenomenon of hypoglycemia caused by excessive insulin infusion.
Drawings
Fig. 1 is a control schematic of an embodiment.
Fig. 2 is a schematic diagram of a reference curve of an embodiment.
Fig. 3 is a schematic diagram of adaptive control weighting factors according to an embodiment.
Fig. 4 is an experimental result of a conventional generalized predictive control algorithm.
FIG. 5 shows the experimental results of the examples.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
for the purpose of better illustrating the embodiments, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the actual product dimensions;
it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
Examples
As shown in fig. 1, an insulin infusion amount control method based on an adaptive control weighting factor strategy includes the steps of:
s1: obtaining a predicted value of the blood glucose change of a user in the future through a CARIMA model and a Dipsilon chart equation according to the current blood glucose value of the user;
the following equation is obtained through the CARIMA model and the Dipsilon diagram equation:
y(k+j)＝G j (z -1 )Δu(k+j-1)+F j (z -1 )y(k)(j＝1，2...n)
wherein y (k) represents the blood glucose level of the user at time k; y (k+j) represents a predicted value of the blood glucose level of the user that leads by j steps at time k; deltau (k+j-1) represents the control input increment of the insulin pump at time k; n represents the maximum predicted length; g j (z -1 ) A weight coefficient representing a control input increment of the insulin pump at time k; f (F) j (z -1 ) A weight coefficient indicating a blood glucose level; z -1 An operator for 1 step backward;
G j (z -1 ) The expression is carried out by the following formula:
G j (z -1 )＝E j (z -1 )B(z -1 )
E j (z -1 ) The expression is carried out by the following formula:
E j (z -1 )＝e j0 +e j1 z -1 +…+e j-1 z -j+1
in the formula e j0 ～e j-1 Is an adjustable parameter; z -1 ～z -j+1 Is an operator shifted backwards by 1-j-1 steps;
B(z -1 ) The expression is carried out by the following formula:
B(z -1 )＝b 0 +b 1 z -1 +…+b nb z -nb
wherein b is 0 ～b nb Is an adjustable parameter, b 1 ＝0.5，b 2 ＝0.5，b 3 ＝0.5，b 4 ＝0.5，b 5 ＝0.5；z -1 ～z -nb Is an operator shifted backwards by 1-nb steps;
F j (z -1 ) The expression is carried out by the following formula:
F j (z -1 )＝f j0 +f j1 z -1 +…+f jn z -n
wherein f j0 ～f jn Is an adjustable parameter; z -1 ～z -n Is an operator shifted backwards by 1-n steps; n represents the maximum predicted length, n=8.
S2: calculating a control input increment of the insulin pump through a minimum variance control model according to the predicted value of the blood sugar change of the future user of the S1; wherein the minimum variance control usage control weighting factor is an adaptive control weighting factor;
wherein J represents the infusion rate of the insulin pump; n represents a predicted length; λ represents an adaptive control weighting factor; m represents a control length; w (k+j) is expressed by the following formula:
w(k+j)＝α j y(k)+(1-α j )y r (j＝1，2，...，n)
the above can be further written in vector form
W＝Qy(k)+My r (j＝1，2，...，n)
y r Representing a reference curve, as shown in fig. 2; w is expressed by the following formula:
W＝[w(k+1)，w(k+2)，...，w(k+n)] T
q is expressed by the formula:
Q＝[α，α 2 ，...，α n ] T
α represents a softening factor, α=0.6;
m is expressed by the formula:
M＝[1-α，1-α 2 ，...，1-α n ] T ；
as shown in fig. 3, the adaptive control weighting factors include the following:
setting a desired blood glucose level
Calculating the deviation u of the current blood glucose level of the user from the expected blood glucose level according to the current blood glucose level y (k), wherein the deviation u is expressed by the following formula:
calculating the current change amount delta y of the blood glucose level of the user, wherein the change amount delta y is expressed by the following formula:
Δy＝y(k)-y(k-1)；
calculating an adaptive control weighting factor lambda by the deviation u and the variation deltay, the adaptive control weighting factor lambda being expressed by:
λ＝(3×u) Δy ；
s3: based on the idea of closed-loop control, performing iterative optimization on the control input increment of the insulin pump;
s3.1: taking the control input increment of the insulin pump as an input value of a CARIMA model, and updating a predicted value of the blood sugar change of a user;
s3.2: updating the control input increment of the insulin pump according to the updated predicted value of the blood sugar change of the user as the input of the minimum variance control model; wherein, the control weighting factor used in the minimum variance control is an adaptive control weighting factor, and the adaptive control weighting factor can adjust the value of the adaptive control weighting factor according to the condition of a user;
s3.3: and S3.1-S3.2 are circularly executed, so that iterative optimization of the control input increment of the insulin pump is realized.
Test environment of the present embodiment:
this example was implanted in the U.S. FDA approved diabetes simulated treatment test software T1DMS, which can replace animal experiments, and the algorithm was tested for performance. The software T1DMS is the only diabetes treatment test software approved by the FDA in the united states that can be used to replace animal experiments. The academic version of the software includes 10 virtual diabetic adult patients, 10 adolescent patients and 10 pediatric patient models, and provides virtual CGMS and insulin pumps. In the test process, the blood sugar control effect of the insulin pump can be observed by only implanting a control algorithm into the test platform, selecting a test object and setting a meal plan and monitoring indexes.
Experimental results of this example:
as shown in fig. 3, when a significant rise in blood glucose occurs, embodiments will employ a higher adaptively controlled weighting factor value, thereby rapidly increasing the infusion rate of insulin. When blood glucose has a decreasing trend and changes smoothly, embodiments will rapidly decrease the adaptive control weighting factor value and insulin infusion rate.
As shown in fig. 4 and 5, 10 experimental results (solid line represents mean blood glucose, dashed line represents standard deviation of blood glucose) for adolescents with diabetes. Fig. 5 shows the glycemic control effect (control weighting factor λ=5) based on the conventional generalized predictive control algorithm. Although 10 patients had blood glucose concentrations in the ideal interval of 70mg/dl-180mg/dl for a test time of 87.37%, there was a significant hypoglycemic phenomenon; fig. 5 shows experimental results using an adaptively controlled weighting factor. The blood glucose concentration of 10 patients was in the ideal interval of 70mg/dl-180mg/dl for a test time of 86.12%, and the hypoglycemia phenomenon had been completely eliminated. The test results clearly show that the control weighting factors adopted in the embodiment can prevent the hypoglycemia phenomenon while guaranteeing the blood sugar control effect.
The terms describing the positional relationship in the drawings are merely illustrative, and are not to be construed as limiting the present patent;
it is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. For example, different adaptive optimization factor calculation models, such as an exponential model or a logarithmic model, can be set for different situations, so that the adaptive softening factor is more appropriate for the patient, and is more beneficial for stabilizing the blood glucose level. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.
Claims (6)
1. An insulin infusion amount control method based on an adaptive control weighting factor strategy, comprising the steps of:
collecting the current blood sugar value of a user through a blood sugar value detection module;
obtaining a predicted value of the blood glucose change of a user in the future through a CARIMA model and a Dipsilon chart equation according to the current blood glucose value of the user;
calculating the control input increment of the insulin pump through a minimum variance control model according to the predicted value of the blood sugar change of a future user; wherein the control weighting factor used for the minimum variance control is an adaptive control weighting factor, and the adaptive control weighting factor lambda is expressed by the following formula:
λ＝(ξ×u) Δy
wherein, xi represents a preset value; the u represents the deviation degree; the delta y represents the variation of the blood glucose level;
based on the idea of closed-loop control, performing iterative optimization on the control input increment of the insulin pump;
the self-adaptive control weighting factors comprise the following contents:
manually setting desired blood glucose level
Calculating the deviation u of the current blood sugar value of the user from the expected blood sugar value according to the current blood sugar value y (k) of the user;
calculating the current change delta y of the blood sugar level of the user
Calculating an adaptive control weighting factor lambda through the deviation u and the variation deltay;
the degree of deviation u is expressed by the following formula:
the variation deltay is expressed by the following formula:
Δy＝y(k)-y(k-1)
wherein y (k-1) represents the blood glucose level of the user at the previous time;
based on the idea of closed-loop control, the iterative optimization of the control input increment of the insulin pump comprises the following sub-steps:
s1: taking the control input increment of the insulin pump as an input value of a CARIMA model, and updating a predicted value of the blood sugar change of a user;
s2: updating the control input increment of the insulin pump according to the updated predicted value of the blood sugar change of the user as the input of the minimum variance control model; the control weighting factor used in the minimum variance control is an adaptive control weighting factor, and the adaptive control weighting factor can adjust the magnitude of the self value according to the change of the blood sugar value;
s3: and S1-S2 are circularly executed, so that iterative optimization of the control input increment of the insulin pump is realized.
2. The method of claim 1, wherein the predicted value of the future blood glucose level of the user is obtained from the blood glucose level of the current user by using a CARIMA model and a cartographic equation:
the following equation is obtained through the CARIMA model and the Dipsilon diagram equation:
y(k+j)＝G j (z -1 )Δu(k+j-1)+F j (z -1 )y(k)(j＝1,2…n)
wherein y (k) represents the blood glucose level of the user at time k; the y (k+j) represents a predicted value of the blood glucose level of the user in advance of the j step at the time k; deltau (k+j-1) represents the control input increment of the insulin pump at time k; n represents the maximum predicted length; the G is j (z -1 ) A weight coefficient representing a control input increment of the insulin pump at time k; said F j (z -1 ) A weight coefficient indicating a blood glucose level; said z -1 An operator that is shifted back by 1 step.
3. The method of controlling insulin infusion amount according to claim 2, wherein said G j (z -1 ) The expression is carried out by the following formula:
G j (z -1 )＝E j (z -1 )B(z -1 )
said E j (z -1 ) The expression is carried out by the following formula:
E j (z -1 )＝e j0 +e j1 z -1 +…+e j-1 z -j+1
wherein said e j0 ～e j-1 Is an adjustable parameter; said z -1 ～z -j+1 Is an operator shifted backwards by 1-j-1 steps;
said B (z) -1 ) The expression is carried out by the following formula:
B(z -1 )＝b 0 +b 1 z -1 +…+b nb z -nb
wherein, the b 0 ～b nb Is an adjustable parameter; said z -1 ～z -nb Is an operator shifted backward by 1-nb steps.
4. The method of controlling insulin infusion amount according to claim 2, wherein said F j (z -1 ) The expression is carried out by the following formula:
F j (z -1 )＝f j0 +f j1 z -1 +…+f jn z -n
wherein said f j0 ～f jn Is an adjustable parameter; said z -1 ～z -n Is an operator shifted backwards by 1-n steps.
5. The insulin infusion amount control method according to any one of claims 1 to 4, wherein calculating the control input increment of the insulin pump by the minimum variance control model according to the predicted value of the blood glucose change of the future user comprises:
wherein J represents the infusion rate of the insulin pump; n represents a predicted length; the lambda represents an adaptive control weighting factor; m represents a control length; the w (k+j) is expressed by the following formula:
W＝Qy(k)+My r (j＝1,2,…,n)
wherein said y r Representing a reference curve; y (k) represents the current blood glucose level of the user;
the Q is expressed by the following formula:
Q＝[α,α 2 ,…,α n ] T
the alpha represents an adaptive softening factor.
6. The method of controlling insulin infusion amount according to claim 5, wherein M is expressed by the following formula:
M＝[1-α,1-α 2 ,…,1-α n ] T 。
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