Intelligent monitoring and optimized drug delivery system for cancer chemotherapy based on multi-stage Gaussian pseudospectral method

Abstract

The present invention discloses an intelligent monitoring and optimized drug delivery system for cancer chemotherapy based on a multi-stage Gaussian pseudospectral method, comprising: a tumor size monitoring module and a tumor site drug concentration detection module, one end of which is electrically connected to a data indicator display module, and the other end is electrically connected to a tumor growth model fitting module under drug action, which is further connected to a cancer chemotherapy optimized drug delivery model and performance parameter setting module; a drug-resistant cell number detection module and a cardiotoxicity detection module, one end of which is electrically connected to a data indicator display module, and the other end is connected to a cancer chemotherapy optimized drug delivery model and performance parameter setting module; the cancer chemotherapy optimized drug delivery model and performance parameter setting module is connected to a data processing module, which is further connected to an anticancer drug setting module, which is further connected to a data indicator display module. Using this system, doctors can view the corresponding indicator values ​​during chemotherapy in real time, and the system calculates the given values ​​of anticancer drugs.

Description

Technical Field

[0001] The present invention belongs to the field of chemotherapy drug administration optimization control, and relates to a cancer chemotherapy intelligent monitoring and optimized drug administration system based on a multi-stage Gaussian pseudospectral method. Background Art

[0002] With the increasing incidence of cancer, more effective cancer treatments are gaining widespread attention. Chemotherapy, one of the most effective cancer treatments, is a subject of intense research across various fields. A system capable of real-time monitoring of chemotherapy and optimizing drug delivery is crucial for improving its effectiveness and reducing its cost.

[0003] Therefore, studying efficient control algorithms to achieve optimal scheduling of anticancer drugs and monitoring of relevant indicators during chemotherapy has significant theoretical value and effective application value for improving the survival time and quality of life of cancer patients. Summary of the Invention

[0004] In view of this, the present invention relates to an intelligent monitoring and optimized drug delivery system for cancer chemotherapy based on a multi-stage Gaussian pseudospectral method. During the course of cancer chemotherapy, doctors can view the corresponding indicator values ​​in the chemotherapy process in real time. The system optimizes drug delivery based on the given values ​​of anticancer drugs calculated based on the corresponding data, thereby greatly reducing labor and treatment costs, and better assisting doctors in mastering the treatment process and grasping the treatment effect.

[0005] To achieve the above-mentioned purpose, the present invention adopts the following technical scheme: an intelligent monitoring and optimized drug delivery system for cancer chemotherapy based on multi-stage Gaussian pseudospectral method, comprising a tumor size monitoring module, a tumor site drug concentration detection module, a tumor growth model fitting module under drug action, a drug-resistant cell number detection module, a cardiotoxicity detection module, a cancer chemotherapy optimized drug delivery model and performance parameter setting module, a data processing module, an anticancer drug setting module and a data indicator display module, wherein one end of the tumor size monitoring module and the tumor site drug concentration detection module are electrically connected to the data indicator display module, and the other end is electrically connected to the tumor growth model fitting module under drug action, which is further connected to the cancer chemotherapy optimized drug delivery model and performance parameter setting module; one end of the drug-resistant cell number detection module and the cardiotoxicity detection module are electrically connected to the data indicator display module, and the other end is connected to the cancer chemotherapy optimized drug delivery model and performance parameter setting module; the cancer chemotherapy optimized drug delivery model and performance parameter setting module are connected to the data processing module, which is further connected to the anticancer drug setting module, which is further connected to the data indicator display module; wherein,

[0006] The tumor size monitoring module is used to detect the number of tumor cells in real time and input it into the tumor growth model fitting module under the action of drugs;

[0007] The tumor site drug concentration detection module is used to detect the anticancer drug concentration in the tumor site and input it into the tumor growth model fitting module under the action of the drug;

[0008] The tumor growth model fitting module under the action of drugs is used to fit the tumor growth model according to the number of tumor cells and the concentration of anticancer drugs in the tumor site;

[0009] The drug-resistant cell number detection module is used to detect the number of tumor drug-resistant cells during chemotherapy and input it into the cancer chemotherapy optimization drug delivery model and performance parameter setting module;

[0010] The cardiotoxicity detection module is used to detect cardiotoxicity during chemotherapy and input it into the cancer chemotherapy optimization drug delivery model and performance parameter setting module;

[0011] The cancer chemotherapy optimized drug delivery model and performance parameter setting module is used to establish a cancer chemotherapy optimized drug delivery model, set the initial parameters of the chemotherapy optimized drug delivery model, and adjust the parameters during the chemotherapy process;

[0012] The data processing module is used to convert the cancer chemotherapy time into a discrete point sequence with a Gaussian distribution according to the multi-stage Gaussian pseudospectral method, perform discrete approximation on the corresponding variables in the time segment, solve and obtain the anticancer drug control amount, and input the anticancer drug control amount into the anticancer drug setting module;

[0013] The anticancer drug setting module is used to determine the input time and input dosage of the anticancer drug.

[0014] The beneficial effects of the present invention are:

[0015] First, a tumor growth model is fitted based on the number of tumor cells and the anticancer drug concentration in the tumor site detected in real time by the tumor size monitoring module and the drug concentration detection module. Then, a cancer chemotherapy optimization drug delivery model is established based on the number of tumor-resistant cells and cardiotoxicity detected in real time by the drug-resistant cell number detection module and the cardiotoxicity detection module. With the solution of the Gaussian pseudospectral method, an optimized drug scheduling plan is obtained and the chemotherapy progress is displayed and monitored in real time, making cancer chemotherapy more convenient and intelligent.

[0016] Secondly, the drug administration model established by the present invention takes into account the inhibitory effect of drug-resistant cells on the process of cancer chemotherapy. Therefore, the constraint condition of reducing drug-resistant cells is added to the model, thereby ensuring the efficacy of anti-cancer drugs as much as possible; and the present invention adds the constraint condition of cardiotoxicity to the model, so that the side effects on the patient's heart can be minimized during chemotherapy, thereby prolonging the patient's survival time and ensuring the patient's quality of life.

[0017] Finally, since the model solution involved in the present invention needs to be carried out in real time and efficiently, there are high requirements for the selection of the solution algorithm. The Gaussian pseudospectral method has the advantages of high solution accuracy and fast solution speed. Therefore, in the present invention, the use of the Gaussian pseudospectral method to solve the drug delivery model can better achieve the precise determination of anticancer drugs and real-time monitoring of the drug delivery system.

[0018] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a structural schematic diagram of the present invention;

[0020] Figure 2 This is a structural diagram of the data processing module and the modules connected thereto according to the present invention;

[0021] Figure 3 A given curve graph of anticancer drugs obtained after pseudospectral point control parameter optimization;

[0022] Figure 4 The curves of tumor cell number changes, in vivo drug concentrations, and cumulative drug toxicity were obtained after parameter optimization using pseudo-spectral point matching control. DETAILED DESCRIPTION

[0023] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0024] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0025] Example 1

[0026] like Figure 1 As shown, the present invention provides an intelligent monitoring and optimized drug delivery system for cancer chemotherapy based on a multi-stage Gaussian pseudospectral method, comprising a tumor size monitoring module 1, a tumor site drug concentration detection module 2, a tumor growth model fitting module 3 under drug action, a drug-resistant cell number detection module 4, a cardiotoxicity detection module 5, a cancer chemotherapy optimized drug delivery model and performance parameter setting module 6, a data processing module 7, an anticancer drug setting module 8, and a data indicator display module 9. The tumor size monitoring module 1 and the tumor site drug concentration detection module 2 are electrically connected at one end to the data indicator display module 9, and at the other end to the tumor growth model fitting module 3 under drug action, which is further connected to the cancer chemotherapy optimized drug delivery model and performance parameter setting module 6; the drug-resistant cell number detection module 4 and the cardiotoxicity detection module 5 are electrically connected at one end to the data indicator display module 9, and at the other end to the cancer chemotherapy optimized drug delivery model and performance parameter setting module 6; the cancer chemotherapy optimized drug delivery model and performance parameter setting module 6 is connected to the data processing module 7, which is further connected to the anticancer drug setting module 8, which is further connected to the data indicator display module 9.

[0027] The tumor size monitoring module 1 is used to detect the number of tumor cells in real time and input the number into the tumor growth model fitting module 3 under the action of drugs.

[0028] The tumor site drug concentration detection module 2 is used to detect the anticancer drug concentration in the tumor site and input it into the tumor growth model fitting module 3 under the action of the drug.

[0029] The tumor growth model fitting module 3 under the action of drugs is used to fit the tumor growth model according to the number of tumor cells and the concentration of anticancer drugs in the tumor site.

[0030] The drug-resistant cell number detection module 4 is used to detect the number of tumor drug-resistant cells during chemotherapy and input the number into the cancer chemotherapy optimization drug administration model and performance parameter setting module 6.

[0031] The cardiotoxicity detection module 5 is used to detect cardiotoxicity during chemotherapy and input it into the cancer chemotherapy optimization drug delivery model and performance parameter setting module 6.

[0032] The cancer chemotherapy optimized drug delivery model and performance parameter setting module 6 is used to establish a cancer chemotherapy optimized drug delivery model, set initial parameters of the chemotherapy optimized drug delivery model, and adjust parameters during the chemotherapy process;

[0033] The data processing module 7 is used to convert the cancer chemotherapy time into a discrete point sequence with a Gaussian distribution according to the multi-stage Gaussian pseudospectral method, perform discrete approximation on the corresponding variables in the time segment, solve and obtain the anticancer drug control amount, and input the anticancer drug control amount into the anticancer drug setting module 8.

[0034] The anticancer drug setting module 8 is used to determine the input time and input dosage of the anticancer drug.

[0035] The data indicator display module 9 is used to display information such as the number of tumor cells during chemotherapy, the concentration of anticancer drugs in the tumor site, the number of tumor resistant cells, cardiac toxicity, the input time and input dose of anticancer drugs, etc.

[0036] The process of fitting a tumor growth model based on tumor cell count and anticancer drug concentration at the tumor site is performed within the nonlinear fitting module of the 1stOpt software, based on the tumor growth kinetics equation and the corresponding pharmacokinetic equation. This process derives the required parameter values ​​to generate a mathematical model of tumor growth under the given anticancer drug effect. This generated tumor growth model complements the overall cancer chemotherapy dosing model, enabling dynamic management of tumor growth inhibition, thus enabling real-time monitoring of tumor growth and real-time adjustment of the prescribed anticancer drug dosage and duration.

[0037] like Figure 2 As shown, the data processing module includes a Gaussian pseudo-spectral method point control parameterization module and a nonlinear programming problem solving module. Figure 2 The processing of each module of the system of the present invention is described below;

[0038] The information collection module collects the number of tumor cells at the beginning and during chemotherapy, the concentration of anticancer drugs at the tumor site, the number of tumor-resistant cells, and cardiac toxicity through the tumor size monitoring module, the tumor site drug concentration detection module, the drug-resistant cell number detection module, and the cardiotoxicity detection module, and inputs the collected information into the initialization module;

[0039] The initialization module (i.e. the aforementioned performance parameter setting module) is used to set the initial drug control amount u (0) (t), and set the optimization accuracy tol, and the number of iterations D to zero;

[0040] The Gaussian pseudo-spectral method point control parameterization module is used to convert the cancer chemotherapy time [t0,t f ] is converted into a discrete point sequence with Gaussian distribution, and the corresponding variables in the time segment are discretely approximated;

[0041] The nonlinear programming problem solving module is used to obtain the drug control amount u that meets the convergence requirements by calculation (D) (t) and output to the control signal output module;

[0042] The control signal output module controls the drug amount u (D) (t) Transmitted to the anticancer drug given module and the data indicator display module.

[0043] The working steps of the Gaussian pseudospectral point control parameterization module are as follows:

[0044] Step 1: Divide the chemotherapy cycle into equal time intervals, and set the initial time t0 and the end time t f and I internal time nodes t1, t2,…, t I Divide the entire interval into I+1 segments, and the length of each segment is recorded as a1, a2, ..., a I+1 , the time interval corresponding to the jth segment is [t j-1 ,t j ], a new time variable ζ is introduced to transform the time scale of the control domain:

[0045]

[0046] The transformation of each subinterval is the same, and the LG point is in the time domain t∈[t j-1 ,t j ]The corresponding moment requires the following inverse transformation:

[0047]

[0048] By transforming formula (13), the general form OCP (with respect to the time domain t∈[t0,t f ]) in the state vector differential term Then it is transformed into the time domain ζ∈[ζ0,ζ f ] in the form of:

[0049]

[0050] Step 2: Let the number of points used by the vector of segment j be K j , the state vector x of this segment j (ζ) and control vector u j (ζ) are respectively expressed as:

[0051]

[0052]

[0053] At this time, X represents the state vector, U represents the control vector, and X j (ζ) is based on the time domain ζ∈[ζ0,ζ f ]The state vector of the jth segment, U j (ζ) is based on the time domain ζ∈[ζ0,ζ f ]The state vector of the jth segment. At the same time, X j , m =X j (ζj ,m) is the value of the state vector at the initial moment of the jth segment (m = 0) or the value at the LG configuration point (m = 1, ..., K j ), U j,m =U j (ζ j,m ) is the value of the control vector at the LG collocation point, Z j,m (ζ) and are the basis functions of the Lagrange interpolation polynomial:

[0054]

[0055] Among them, j,m (m=1,…,K j ) is the mth collocation point of the jth segment, ζ j,0 is the initial moment of the jth segment, and ζ j,0 =ζ0=-1, (j=1,…,I+1). Taking the derivative of both sides of formula (16) with respect to ζ yields:

[0056]

[0057] Combining (3) and (7) we can get:

[0058]

[0059] in, is the derivative of the basis function of the Lagrange interpolation polynomial, X j,m is the value of the state vector at the initial moment of segment j (m = 0) or the value at the LG configuration point (m = 1, ..., K j ), is the value of the derivative of the basis function of the Lagrange interpolation polynomial at the initial moment of the jth segment (m = 0), X j,0 is the value of the state vector at the initial moment of segment j (m=0), is the state vector differential term

[0060] Step 3: K j Substituting the LG points into equation (20), we get the discretized nonlinear equation system:

[0061]

[0062] in, is the state differential matrix P j The first column of the vector is formed, By P j The remaining columns form a square matrix, namely:

[0063]

[0064] in, is the discretized n x The state vector of dimension K j The value at the matching point, is a discrete control parameter;

[0065] Step 4: Determine X j,0 When j = 1, X 1,0 =x0 is the given initial value; when 1<j<I+1, X j,0 Calculated using the following formula:

[0066] X j,0 =X j-1 (ζ f ),j=2,…,I+1 (11)

[0067] And X j-1 (ζ f ) is calculated using the numerical integration formula:

[0068]

[0069] is the differential term of the state vector, which is integrated here to obtain the state vector X corresponding to the j-1th time interval j-1 (ζ f ), a j-1 Indicates the time interval corresponding to the j-1th segment.

[0070] Step 5: After the multi-stage Gaussian collocation discretization process, the original f ] is transformed into a time domain ζ∈[ζ0,ζ f ] is a nonlinear programming problem.

[0071] Step 6: The nonlinear programming problem solving module calls the GPOPS solver to solve the problem.

[0072] Example 2:

[0073] This example uses tumor cell growth dynamics and related theories to develop a cancer chemotherapy drug delivery optimization model:

[0074]

[0075] Among them, J and x1 are the number of tumor cells, u1 is the concentration of anticancer drugs, x2 is the drug concentration in the body, and x3 is the cumulative drug concentration. is the derivative of the tumor cell number, is the derivative of the drug concentration in the body,

[0076] is the derivative of cumulative drug toxicity, t is time, T is the treatment cycle, λ is the tumor growth factor, ρ is the maximum value of natural tumor growth under the Gompertz growth model, k is the anticancer drug killing fraction, α is the drug effective concentration, β is the anticancer drug half-life factor, N0 is the initial tumor cell number, v0 is the initial in vivo drug concentration, is the initial cumulative drug toxicity, η1, η2, η3 are the concentration constraints, v max , u max are the upper limit of drug concentration in vivo, the upper limit constraint of cumulative drug toxicity and the upper limit of drug administration rate, respectively. H(x2(t)-α) is a step function. When the drug concentration in vivo is greater than the drug onset concentration, its value is 1; when the drug concentration in vivo is less than the drug onset concentration, its value is 0.

[0077] The cancer chemotherapy intelligent monitoring and optimized drug delivery system runs an internal pseudo-spectral matching point control parameterization algorithm. The specific steps are as follows:

[0078] E1: During the cancer chemotherapy phase, the information collection module is turned on and the cancer chemotherapy optimization drug delivery model is input;

[0079] E2: The initialization module starts running and sets the initial anticancer drug given amount μ during chemotherapy. (0) (t) = 0, the precision is set to tol = 10 -6 , set the number of iterations D to zero;

[0080] E3: Gaussian pseudo-spectral method matching point control parameterization module is run, and the Gaussian pseudo-spectral method matching point control parameterization algorithm is used to convert the cancer chemotherapy time [t0,t f ] is converted into a discrete point sequence with Gaussian distribution, and the corresponding variables in the time segment are discretely approximated to transform the problem into a new nonlinear programming problem;

[0081] E4: The nonlinear programming problem solving module runs and calculates the required anticancer drug control amount μ through the nonlinear programming problem solving algorithm. (D) (t) and output to the control signal output module;

[0082] E5: Control the signal output module to run, and control the amount of anticancer drugs μ (D) (t) Transmitted to the anticancer drug given module through the control signal output module.

[0083] Figure 3 and Figure 4The results, obtained through pseudospectral point-controlled parameter optimization, show a given anticancer drug curve, a tumor cell population curve, an in vivo drug concentration curve, and a cumulative drug toxicity curve. These curves can be used to obtain corresponding data at any time during treatment, enabling doctors to monitor chemotherapy progress in real time for reference.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent cancer chemotherapy monitoring and optimized drug delivery system based on a multi-stage Gaussian pseudospectral method, characterized by: The invention comprises a tumor size monitoring module (1), a tumor site drug concentration detection module (2), a drug-induced tumor growth model fitting module (3), a drug-resistant cell number detection module (4), a cardiotoxicity detection module (5), a cancer chemotherapy optimized drug administration model and performance parameter setting module (6), a data processing module (7), an anticancer drug setting module (8) and a data indicator display module (9). One end of the tumor size monitoring module (1) and the tumor site drug concentration detection module (2) is electrically connected to the data indicator display module (9), and the other end is electrically connected to the drug-induced tumor growth model fitting module (3). The tumor growth model fitting module (3) under drug action is further connected to the cancer chemotherapy optimized drug administration model and performance parameter setting module (6); one end of the drug-resistant cell number detection module (4) and the cardiotoxicity detection module (5) is electrically connected to the data indicator display module (9), and the other end is connected to the cancer chemotherapy optimized drug administration model and performance parameter setting module (6); the cancer chemotherapy optimized drug administration model and performance parameter setting module (6) is connected to the data processing module (7), and the data processing module (7) is further connected to the anticancer drug setting module (8), and the anticancer drug setting module (8) is connected to the data indicator display module (9); wherein, The tumor size monitoring module (1) is used to detect the number of tumor cells in real time and input the data into the tumor growth model fitting module (3) under the action of drugs; The tumor site drug concentration detection module (2) is used to detect the anticancer drug concentration at the tumor site and input it into the tumor growth model fitting module (3) under the action of the drug; The tumor growth model fitting module (3) under the action of the drug is used to fit the tumor growth model according to the number of tumor cells and the concentration of anticancer drugs in the tumor site; The drug-resistant cell number detection module (4) is used to detect the number of tumor drug-resistant cells during chemotherapy and input it into the cancer chemotherapy optimization drug delivery model and performance parameter setting module (6); The cardiotoxicity detection module (5) is used to detect cardiotoxicity during chemotherapy and input it into the cancer chemotherapy optimization drug delivery model and performance parameter setting module (6); The cancer chemotherapy optimized drug delivery model and performance parameter setting module (6) is used to establish a cancer chemotherapy optimized drug delivery model, set initial parameters of the chemotherapy optimized drug delivery model, and adjust parameters during the chemotherapy process; The data processing module (7) is used to convert the cancer chemotherapy time into a discrete point sequence with a Gaussian distribution according to the multi-stage Gaussian pseudo-spectral method, perform discrete approximation on the corresponding variables in the time segment, solve and obtain the anticancer drug control amount, and input the anticancer drug control amount into the anticancer drug setting module (8); The anticancer drug setting module (8) is used to determine the input time and input dosage of the anticancer drug.

2. The cancer chemotherapy intelligent monitoring and optimized drug delivery system based on multi-stage Gaussian pseudospectral method according to claim 1, characterized in that: The cancer chemotherapy optimized drug delivery model is min J(u1(t))=x1(T) x1(0)=N0,x2(0)=v0, x1(t1)≤η1,x1(t2)≤η2,x1(t3)≤η3 0≤x2(t)≤v max , for all t∈[0,T] 0≤u1(t)≤u max for all t∈[0,T] Among them, J and x1 are the number of tumor cells, u1 is the concentration of anticancer drugs, x2 is the drug concentration in the body, and x3 is the cumulative drug concentration. is the derivative of the tumor cell number, is the derivative of the drug concentration in the body, is the derivative of cumulative drug toxicity, t is time, T is the treatment cycle, λ is the tumor growth factor, ρ is the maximum value of natural tumor growth under the Gompertz growth model, k is the anticancer drug killing fraction, α is the drug effective concentration, β is the anticancer drug half-life factor, N0 is the initial tumor cell number, v0 is the initial in vivo drug concentration, is the initial cumulative drug toxicity, η1, η2, η3 are the concentration constraints, v max , u max are the upper limit of drug concentration in vivo, the upper limit constraint of cumulative drug toxicity and the upper limit of drug administration rate, respectively. H(x2(t)-α) is a step function. When the drug concentration in vivo is greater than the drug onset concentration, its value is 1; when the drug concentration in vivo is less than the drug onset concentration, its value is 0.

3. The cancer chemotherapy intelligent monitoring and optimized drug delivery system based on multi-stage Gaussian pseudospectral method according to claim 1, characterized in that: The processing steps of the multi-stage Gaussian pseudospectral method are as follows: Step 1: Divide the chemotherapy cycle into equal time intervals, and set the initial time t0 and the end time t f and I internal time nodes t1, t2,…, t I Divide the entire interval into I+1 segments, and the length of each segment is recorded as a1, a2, ..., a I+1 , the time interval corresponding to the jth segment is [t j-1 ,t j ], a new time variable ζ is introduced to perform time scale transformation on the control time domain: The transformation of each subinterval is the same, and the LG point is in the time domain t∈[t j-1 ,t j ]The corresponding moment requires the following inverse transformation: By transforming formula (1), the general form OCP (with respect to the time domain t∈[t0,t f ]) in the state vector differential term Then it is transformed into the time domain ζ∈[ζ0,ζ f ] in the form of: Step 2: Let the number of points used by the vector of segment j be K j , the state vector x of this segment j (ζ) and control vector u j (ζ) are respectively expressed as: At this time, X represents the state vector, U represents the control vector, and X j (ζ) is based on the time domain ζ∈[ζ0,ζ f ]The state vector of the jth segment, U j (ζ) is based on the time domain ζ∈[ζ0,ζ f ]The state vector of the jth segment, at the same time, X j,m =X j (ζ j,m ) is the value of the state vector at the initial moment of the jth segment (m = 0) or the value at the LG configuration point (m = 1, ..., K j ), U j,m =U j (ζ j,m ) is the value of the control vector at the LG collocation point, Z j,m (ζ) and are the basis functions of the Lagrange interpolation polynomial: Among them, j,m (m=1,…,K j ) is the mth collocation point of the jth segment, ζ j,0 is the initial moment of the jth segment, and ζ j,0 =ζ0=-1, (j=1,…,I+1), and taking the derivative of both sides of formula (3) with respect to ζ yields: Among them, X j,m is the value of the state vector at the initial moment of segment j (m = 0) or the value at the LG configuration point (m = 1, ..., K j ); Combining (3) and (7) we can get: in, is the derivative of the basis function of the Lagrange interpolation polynomial, X j,m is the value of the state vector at the initial moment of segment j (m = 0) or the value at the LG configuration point (m = 1, ..., K j ), is the value of the derivative of the basis function of the Lagrange interpolation polynomial at the initial moment of the jth segment (m = 0), X j,0 is the value of the state vector at the initial moment of segment j (m=0), is the state vector differential term Step 3: K j Substituting the LG points into equation (8), we get the discretized nonlinear equation system: in, is the state differential matrix P j The first column of is the vector, By P j The remaining columns form a square matrix, namely: in, is the discretized n x The state vector of dimension K j The value at the matching point, is a discrete control parameter; Step 4: Determine X j,0 When j = 1, X 1,0 =x0 is the given initial value; when 1<j<I+1, X j,0 Calculated using the following formula: X j,0 =X j-1 (g) f ),j=2,…,I+1 (11) And X j-1 (ζ f ) is calculated by numerical integration formula: is the differential term of the state vector, which is integrated here to obtain the state vector X corresponding to the j-1th time interval j-1 (ζ f ), a j-1 Indicates the time interval corresponding to the j-1th segment.

4. The cancer chemotherapy intelligent monitoring and optimized drug delivery system based on multi-stage Gaussian pseudospectral method according to claim 3, characterized in that: This includes calling the GPOPS solver to solve the nonlinear programming problem obtained in step 4 to obtain the controlled amount of anticancer drugs that meets the requirements.

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