Feedforward correlation integral optimization method and apparatus

Abstract

A feedforward correlation integral optimization method and apparatus, belonging to the technical field of system optimization. The feedforward correlation integral optimization method comprises: acquiring a measured value of interference and a measured value of an optimization variable, and, on the basis of the measured value of the interference and the measured value of the optimization variable, obtaining an autocorrelation integral matrix of an augmented input vector (S11); acquiring a measured value of an objective function, and, on the basis of the measured value of the interference, the measured value of the optimization variable and the measured value of the objective function, obtaining a cross-correlation integral vector of the augmented input vector and the objective function (S12); on the basis of the autocorrelation integral matrix of the augmented input vector and the cross-correlation integral vector of the augmented input vector and the objective function, obtaining an augmented gradient vector (S13); and, on the basis of the augmented gradient vector, obtaining a gradient vector of the objective function with respect to the optimization variable and a gradient vector of the objective function with respect to the interference (S14). The feedforward correlation integral optimization method improves the accuracy of optimization using gradients of objective functions with respect to optimization variables.

Description

Feedforward correlation integral optimization method and device Technical Field

[0001] The present disclosure relates to the technical field of system optimization, and in particular to a feedforward correlation integral optimization method and device. Background Art

[0002] In the continuous production process, controllable key operating conditions are often used as optimization variables, and the technical indicators of one or more production units associated with the key operating conditions are called objective functions.

[0003] In a real-time optimization system, it is necessary to adjust the optimization variables so that the objective function reaches its maximum or minimum. The optimization variables are generally controlled by a basic controller (such as a PID controller), and the set values ​​of the optimization variables are determined based on the output of the real-time optimization controller. Therefore, the measured values ​​of the optimization variables fluctuate randomly around the set values ​​of the optimization variables, so the mean of the measured values ​​of the optimization variables is controllable. Since the objective function is subject to random interference, and some interference is uncontrollable, the measured values ​​of the objective function fluctuate randomly over time, resulting in noise when obtaining the gradient of the objective function with respect to the optimization variables, and the noise affects the accuracy of obtaining the gradient of the objective function with respect to the optimization variables. In the correlation integral optimization method, since the gradient of the optimization variables needs to be tuned using the objective function, the objective function is subject to random interference that changes over time, resulting in a decrease in the accuracy of the gradient calculation, which in turn causes a decrease in the accuracy of the system optimization.

[0004] Summary of the Invention

[0005] The present disclosure provides a feedforward correlation integral optimization method and device.

[0006] According to a first aspect of the present disclosure, a feedforward correlation integral optimization method is provided, comprising:

[0007] Obtaining a measured value of the interference and a measured value of the optimization variable, and obtaining an autocorrelation integral matrix of the augmented input vector based on the measured value of the interference and the measured value of the optimization variable;

[0008] Obtaining a measured value of an objective function, and obtaining a cross-correlation integral vector between the augmented input vector and the objective function based on the measured value of the interference, the measured value of the optimization variable, and the measured value of the objective function;

[0009] Obtaining an augmented gradient vector based on an autocorrelation integral matrix of the augmented input vector and a cross-correlation integral vector between the augmented input vector and the objective function;

[0010] Based on the augmented gradient vector, a gradient vector of the objective function with respect to the optimization variable and a gradient vector of the objective function with respect to the interference are obtained.

[0011] According to a second aspect of the present disclosure, a feedforward correlation integral optimization device is provided, comprising:

[0012] A first obtaining module is used to obtain a measured value of the interference and a measured value of the optimization variable, and obtain an autocorrelation integral matrix of the augmented input vector based on the measured value of the interference and the measured value of the optimization variable;

[0013] a second obtaining module, configured to obtain a measured value of an objective function, and obtain a cross-correlation integral vector between the augmented input vector and the objective function based on the measured value of the interference, the measured value of the optimization variable, and the measured value of the objective function;

[0014] a third obtaining module, configured to obtain an augmented gradient vector based on an autocorrelation integral matrix of the augmented input vector and a cross-correlation integral vector between the augmented input vector and the objective function;

[0015] A fourth obtaining module is configured to obtain, based on the augmented gradient vector, a gradient vector of the objective function with respect to the optimization variable and a gradient vector of the objective function with respect to the interference.

[0016] According to the feedforward correlation integral optimization method disclosed herein, the measured values ​​of the disturbance and the measured values ​​of the optimization variable are obtained, and based on the measured values ​​of the disturbance and the measured values ​​of the optimization variable, the autocorrelation integral matrix of the augmented input vector is obtained, so that the influence of random disturbance on the optimization variable can be obtained; the measured value of the objective function is obtained, and based on the measured values ​​of the disturbance and the measured values ​​of the objective function, the cross-correlation integral vector of the augmented input vector and the objective function is obtained, so that the cross-correlation relationship between the optimization variable and the objective function can be obtained when the objective function is subjected to random disturbance; the augmented gradient vector is obtained based on the autocorrelation integral matrix of the augmented input vector and the cross-correlation integral vector of the augmented input vector and the objective function, so that the gradient of the objective function to the optimization variable can be obtained when the objective function is subjected to random disturbance; based on the augmented gradient vector, the gradient vector of the objective function to the optimization variable and the gradient vector of the objective function to the disturbance can be obtained respectively, so that the influence of disturbance on the gradient of the objective function to the optimization variable can be reduced. Thus, the accuracy of obtaining the gradient of the objective function to the optimization variable is improved, and the accuracy of gradient optimization using the objective function to the optimization variable is improved.

[0017] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. By describing the detailed exemplary embodiments with reference to the accompanying drawings, the above and other features and advantages will become more apparent to those skilled in the art.

[0019] FIG1 is a flow chart of a feedforward correlation integral optimization method provided by an embodiment of the present disclosure;

[0020] FIG2 is a flow chart of obtaining an autocorrelation integral matrix of an augmented input vector provided by an embodiment of the present disclosure;

[0021] FIG3 is a block diagram of a feedforward correlation integral optimization device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] To enable those skilled in the art to better understand the technical solutions of the present disclosure, exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0023] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0024] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0025] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of" are used in this specification, features, wholes, steps, operations, elements and / or components are specified to exist, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof are not excluded. Similar words such as "connected" or "connected" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0026] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless expressly defined as such herein.

[0027] In a first aspect, an embodiment of the present disclosure provides a feedforward correlation integral optimization method. FIG1 is a flow chart of the feedforward correlation integral optimization method provided by an embodiment of the present disclosure. As shown in FIG1 , the present disclosure is a feedforward correlation integral optimization method provided by an embodiment, comprising:

[0028] Step S11: Obtain the measured values ​​of the interference and the measured values ​​of the optimization variables, and obtain the autocorrelation integral matrix of the augmented input vector based on the measured values ​​of the interference and the measured values ​​of the optimization variables.

[0029] In some embodiments, the optimization variables are important parameters in the system, and the objective function is the technical indicators of one or more production units associated with the optimization variables. In a real-time optimization system for a catalytic cracking unit, the optimization variables include, but are not limited to, the reaction temperature of the reactor, the amount of new catalyst added, the feed temperature, the pre-lift steam flow rate, the measured value of the reaction temperature controller, the feed temperature controller measured value, and the pre-lift steam flow rate measured value. The mean value of the optimization variables can be determined based on the given value of a basic controller (such as a PID controller). The objective function is gasoline yield, and the optimization variables are adjusted to maximize the gasoline yield.

[0030] In some embodiments, the objective function may be subject to some interference, which is generally uncontrollable and unadjustable. Among them, some interference is measurable. The present disclosure can use the measurable but uncontrollable and unadjustable interference as the feedforward interference variable in the system, and adopt the feedforward method to perform corresponding processing, thereby reducing the impact of the interference on the objective function. In the existing technology, the optimization variable is generally used as the input. The embodiment provided by the present disclosure takes into account the measurable interference, amplifies the input, and uses the interference and the optimization variable as the augmented input vector. The autocorrelation integral matrix of the augmented input vector represents the correlation between the optimization variable and the interference.

[0031] For example, in an online optimization system for a continuous reforming device, the optimization variable is the reaction temperature of the reactor, and the objective function is the yield of aromatics in the reaction liquid after the reforming reaction. The purpose of optimizing the above device is to maximize the objective function, that is, to maximize the yield of aromatics in the reaction liquid. In the above reaction, the yield of aromatics in the reaction liquid is also related to factors such as the composition of the raw materials, the feed amount of the reforming reaction, the reforming reaction pressure, and the hydrogen-to-oil ratio of the reforming reaction. These factors can be measured but are difficult to adjust due to process and other reasons. The present disclosure uses the above-mentioned measurable and uncontrollable factors that affect the objective function as feedforward interference variables, and performs corresponding processing on the feedforward interference variables to obtain better optimization results.

[0032] In some embodiments, a common measurement device may be used to obtain the measured value of the interference and the measured value of the optimization variable. The present disclosure does not limit the manner in which the measured value of the interference and the measured value of the optimization variable are obtained.

[0033] In some embodiments, the optimization variables of the optimization system may not be fully optimized in the system due to certain process reasons. Therefore, a part of the optimization variables that can be put into optimization will be put into optimization and online tuning will be performed, and the other part of the optimization variables that cannot be put into optimization will not be tuned online, as measurable but uncontrollable variables. The embodiments provided in the present disclosure use some optimization variables that cannot be put into optimization as feedforward variables. For example, in a catalytic cracking unit, the objective function is the gasoline yield of the catalytic cracking unit, and the optimization variables include but are not limited to reaction temperature, feed temperature, pre-lift steam flow, atomizing steam flow and new catalyst addition amount. Under normal circumstances, the above-mentioned optimization variables can all be put into optimization and online tuning. However, due to certain reasons in the production process, the feed temperature among the optimization variables is no longer controllable. At this time, the feed temperature needs to be used as a feedforward variable and no longer as an optimization variable for optimization. Among them, the reasons for the feed temperature among the optimization variables no longer being controllable include but are not limited to the failure of the feed temperature control valve.

[0034] FIG2 is a flow chart of obtaining the autocorrelation integral matrix of the augmented input vector provided by an embodiment of the present disclosure. As shown in FIG2 , obtaining the autocorrelation integral matrix of the augmented input vector includes:

[0035] Step S21: Obtain an autocorrelation integral matrix of the optimization variables based on the measured values ​​of the optimization variables.

[0036] In some embodiments, the measured values ​​of the optimization variables can be processed accordingly to obtain an autocorrelation integral matrix of the optimization variables. The autocorrelation integral matrix of the optimization variables represents the correlation between the optimization variables.

[0037] In some embodiments, obtaining an autocorrelation integral matrix of the optimization variables based on the measured values ​​of the optimization variables includes:

[0038] Based on the measured values ​​of the optimization variables, the autocorrelation integral matrix of the optimization variables is obtained using formulas (1) and (2):

[0039] Where i = 1, 2, ..., m; j = 1, 2, ..., m; m means that the measurement value of the optimization variable has m dimensions; T i represents the i-th first integral constant; M i represents the i-th second integral constant; t is the first variable; τ is the second variable; u represents the measured value of the optimized variable; u i Represents the measurement value of the i-th dimension in the optimization variable; u j represents the measurement value of the jth dimension in the optimization variable; u i (t-τ) represents the measurement value of the i-th dimension in the optimization variable with respect to the variable t-τ; u j (t) represents the measured value of the jth dimension in the optimization variable with respect to variable t; k represents the relevant integral value; Represents the correlation integral value of the optimization variables of the i-th dimension and the j-th dimension; Represents the autocorrelation integral matrix of the optimization variables.

[0040] In some embodiments, the measured values ​​of the optimization variables have multiple dimensions, and the correlations between the dimensions in the measured values ​​of the optimization variables are obtained respectively, fully considering the different dimensions in the measured values ​​of the optimization variables, and then the autocorrelation integral matrix of the optimization variables is obtained, so that the autocorrelation integral matrix of the optimization variables is more comprehensive.

[0041] Step S22: Obtain an autocorrelation integral matrix based on the measured value of the interference.

[0042] In some embodiments, the interference measurement values ​​can be processed accordingly to obtain an interference autocorrelation integral matrix. The interference autocorrelation integral matrix represents the correlation between interferences.

[0043] In some embodiments, obtaining an autocorrelation integral matrix of interference based on the measured value of interference includes:

[0044] Based on the measured value of interference, the autocorrelation integral matrix of interference is obtained using formula (3) and formula (4):

[0045] Where i = 1, 2, ..., g; j = 1, 2, ..., g; g means that the interference measurement value has g dimensions; T m+i represents the m+i first integral constant; M m+irepresents the m+i second integral constant; t is the first variable; τ is the second variable; u m+i represents the measurement value of the i-th dimension in the interference; u m+j represents the measurement value of the jth dimension in the interference; u m+i (t-τ) represents the measurement value of the i-th dimension in the disturbance with respect to the variable t-τ; u m+j (t) represents the measurement value of the jth dimension in the disturbance of variable t; represents the correlation integral value of the interference between the i-th dimension and the j-th dimension; f represents interference; u f represents a measurement of interference; Represents the autocorrelation integral matrix of the interference.

[0046] In some embodiments, the interference measurement values ​​have multiple dimensions, and the correlation between the dimensions in the interference measurement values ​​is obtained separately, fully considering the different dimensions in the interference measurement values, and then the autocorrelation integral matrix of the interference is obtained, making the autocorrelation integral matrix of the interference more comprehensive.

[0047] Step S23: Based on the measured values ​​of the interference and the measured values ​​of the optimization variables, obtain the cross-correlation integral matrix of the optimization variables to the interference and the cross-correlation integral matrix of the interference to the optimization variables.

[0048] In some embodiments, the cross-correlation integral matrix of the optimization variables and the interference represents the correlation of the optimization variables with the interference, and the cross-correlation integral matrix of the interference with the optimization variables represents the correlation of the interference with the optimization variables. The measured values ​​of the interference and the measured values ​​of the optimization variables can be processed accordingly to obtain the cross-correlation integral matrix of the optimization variables and the interference, and the cross-correlation integral matrix of the interference and the optimization variables.

[0049] In some embodiments, based on the measured values ​​of the interference and the measured values ​​of the optimization variables, obtaining a mutual correlation integral matrix of the optimization variables to the interference and a mutual correlation integral matrix of the interference to the optimization variables includes:

[0050] Based on the measured values ​​of the interference and the measured values ​​of the optimization variables, the cross-correlation integral matrix of the optimization variables to the interference is obtained using formulas (5) and (6): Using formula (7) and formula (8) to obtain the cross-correlation integral matrix of the interference to the optimization variable

[0051] Where i = 1, 2, ..., m; j = 1, 2, ..., g; m means that the measured value of the optimization variable has m dimensions; g means that the measured value of the interference has g dimensions; T i represents the i-th first integral constant; Mi represents the i-th second integral constant; T m+j represents the m+jth first integral constant; M m+j represents the m+jth second integral constant; t is the first variable; τ is the second variable; u i represents the measurement value of the i-th dimension in the optimization variable; u m+j represents the measurement value of the jth dimension in the interference; u i (t-τ) represents the measurement value of the i-th dimension in the optimization variable with respect to the variable t-τ; u m+j (t) represents the measurement value of the jth dimension in the disturbance of variable t; u m+j (t-τ) represents the measurement value of the j-th dimension in the disturbance with respect to the variable t-τ; u i (t) represents the measured value of the i-th dimension in the optimization variable with respect to variable t; represents the cross-correlation integral value between the i-th dimension in the optimization variable and the j-th dimension in the interference; represents the cross-correlation integral value between the j-th dimension interference in the interference and the i-th dimension optimization variable in the optimization variable; f represents interference; u f represents a measurement of interference; represents the cross-correlation integral matrix of the optimization variables to the disturbance; Represents the cross-correlation integral matrix of the disturbance on the optimization variables.

[0052] In some embodiments, the measured values ​​of interference and the measured values ​​of optimization variables both include measured values ​​of multiple dimensions, and the measured values ​​of interference of different dimensions and the measured values ​​of optimization variables of different dimensions are obtained respectively, and then the cross-correlation integral matrix of the optimization variables to the interference and the cross-correlation integral matrix of the interference to the optimization variables are obtained, which fully considers the relationship between interference of different dimensions and optimization variables of different dimensions, thereby improving the accuracy of the optimization system.

[0053] Step S24: Obtain an autocorrelation integral matrix of the augmented input vector based on the autocorrelation integral matrix of the optimized variable, the autocorrelation integral matrix of the interference, the cross-correlation integral matrix of the optimized variable to the interference, and the cross-correlation integral matrix of the interference to the optimized variable.

[0054] In some embodiments, the autocorrelation integral matrix of the optimization variables, the autocorrelation integral matrix of the interference, the cross-correlation integral matrix of the optimization variables and the interference, and the cross-correlation integral matrix of the interference and the optimization variables can be transformed accordingly to obtain the autocorrelation integral matrix of the augmented input vector. The autocorrelation integral matrix of the augmented input vector represents the mutual influence between each interference variable and each optimization variable.

[0055] In some embodiments, obtaining an autocorrelation integral matrix of the augmented input vector based on an autocorrelation integral matrix of the optimization variables, an autocorrelation integral matrix of the interference, a cross-correlation integral matrix of the optimization variables versus the interference, and a cross-correlation integral matrix of the interference versus the optimization variables comprises:

[0056] Autocorrelation integral matrix based on optimized variables Interference autocorrelation integral matrix Optimize the cross-correlation integral matrix of variables to disturbance The cross-correlation integral matrix of the optimization variables and the disturbance Using formula (9) to obtain the autocorrelation integral matrix of the augmented input vector

[0057] Where u represents the measured value of the optimization variable; s represents augmentation; u f represents a measurement of interference; represents the autocorrelation integral matrix of the optimization variables; Represents the autocorrelation integral matrix of interference; represents the cross-correlation integral matrix of the optimization variables to the disturbance; represents the cross-correlation integral matrix of the interference on the optimization variables; u s represents the augmented input vector; Represents the autocorrelation integral matrix of the augmented input vector.

[0058] In some examples, the autocorrelation integral matrix of the optimization variables, the autocorrelation integral matrix of the interference, the cross-correlation integral matrix of the optimization variables versus the interference, and the cross-correlation integral matrix of the interference versus the optimization variables are respectively part of the autocorrelation integral matrix of the augmented input vector.

[0059] Step S12: Obtain a measured value of the objective function, and obtain a cross-correlation integral vector between the augmented input vector and the objective function based on the measured value of the interference, the measured value of the optimization variable, and the measured value of the objective function.

[0060] In some embodiments, the objective function is one or more indicators associated with the optimization variables. The objective function may be subject to random interference, so the measured value of the objective function may fluctuate randomly, and the numerical value of the objective function cannot be accurately determined.

[0061] In some embodiments, the measured values ​​of the interference and the measured values ​​of the objective function can be transformed accordingly to obtain a cross-correlation integral vector between the augmented input vector and the objective function. The cross-correlation integral vector between the augmented input vector and the objective function represents the correlation between the optimization variable and the objective function and the correlation between the interference and the objective function.

[0062] In some embodiments, obtaining a cross-correlation integral vector between the augmented input vector and the objective function based on the measured value of the interference and the measured value of the objective function includes:

[0063] Based on the measured values ​​of the interference and the measured values ​​of the objective function, the cross-correlation integral vector of the augmented input vector and the objective function is obtained using formulas (10), (11), (12), (13) and (14):

[0064] Where, i = 1, 2, ..., m; j = 1, 2, ..., g; T i represents the i-th first integral constant; M i represents the i-th second integral constant; T m+j represents the m+jth first integral constant; M m+j represents the m+jth second integral constant; t is the first variable; τ is the second variable; J represents the objective function; J(t) represents the measured value of the objective function with respect to the variable t; u i Represents the optimization variable i The measurement value of the dimension; u m+j represents the measurement value of the jth dimension in the interference; Represents the cross-correlation integral value between the i-th dimension in the optimization variable and the objective function; represents the cross-correlation integral value between the j-th dimension in the interference and the objective function; represents the cross-correlation integral vector between the optimization variable and the objective function; f represents interference; u f represents a measurement of interference; represents the cross-correlation integral vector between the interference and the objective function; s represents augmentation; u s represents the augmented input vector; Represents the cross-correlation integral vector between the augmented input vector and the objective function.

[0065] In some implementations, the interference measurement includes multiple dimensions. Obtaining the interference measurement and the objective function measurement in different dimensions can obtain a more comprehensive correlation between the interference and the objective function.

[0066] Step S13: Obtain an augmented gradient vector based on the autocorrelation integral matrix of the augmented input vector and the cross-correlation integral vector between the augmented input vector and the objective function.

[0067] In some embodiments, the autocorrelation integral matrix of the augmented input vector and the cross-correlation integral vector between the augmented input vector and the objective function can be transformed accordingly to obtain an augmented gradient vector. The augmented gradient vector represents the gradient of the objective function with respect to the optimization variable and the disturbance.

[0068] In some embodiments, obtaining an augmented gradient vector based on an autocorrelation integral matrix of the augmented input vector and a cross-correlation integral vector between the augmented input vector and the objective function includes:

[0069] When the objective function does not include unmeasured interference, the autocorrelation integral matrix based on the augmented input vector and the cross-correlation integral vector of the augmented input vector and the objective function Use formula (15a) to obtain the augmented gradient vector

[0070] Where u represents the measured value of the optimization variable; s represents augmentation; u f represents the measured value of interference; J represents the objective function; u s represents the augmented input vector; represents the cross-correlation integral vector between the augmented input vector and the objective function; represents the autocorrelation integral matrix of the augmented input vector; represents the augmented gradient vector.

[0071] When the objective function includes unmeasured interference, the autocorrelation integral matrix based on the augmented input vector and the cross-correlation integral vector of the augmented input vector and the objective function Use formula (15b) to obtain the augmented gradient vector

[0072] Where ε represents the disturbance caused by unmeasured interference; u represents the measured value of the optimization variable; s represents augmentation; u f represents the measured value of interference; J represents the objective function; u s represents the augmented input vector; represents the cross-correlation integral vector between the augmented input vector and the objective function; represents the autocorrelation integral matrix of the augmented input vector; represents the augmented gradient vector.

[0073] In some embodiments, when the objective function includes unmeasured interference, the augmented gradient vector can be obtained based on the autocorrelation integral matrix of the augmented input vectors of multiple time periods and the cross-correlation integral vectors of the corresponding augmented input vectors and the objective function, and using the least squares method or other mathematical methods.

[0074] For example, when the objective function includes unmeasurable interference, the augmented gradient vector can be obtained based on the cross-correlation integral vector of the augmented input vector of n time periods and the objective function and the autocorrelation integral matrix of the augmented input vector of n time periods using formula (15c) and the least square method or other mathematical methods.

[0075] Among them, ε represents the disturbance caused by unmeasurable interference; represents the cross-correlation integral vector between the augmented input vector and the objective function for n time periods; Represents the cross-correlation integral vector between the augmented input vector and the objective function in the first period; Represents the cross-correlation integral vector between the augmented input vector and the objective function in the second period; represents the cross-correlation integral vector between the augmented input vector and the objective function at the nth time period; represents the autocorrelation integral matrix of the augmented input vector for n periods; Represents the autocorrelation integral matrix of the augmented input vector in the first period; Represents the autocorrelation integral matrix of the augmented input vector in the second period; represents the autocorrelation integral matrix of the augmented input vector of the nth period; n is a positive integer greater than or equal to 2; represents the augmented gradient vector.

[0076] Step S14: Based on the augmented gradient vector, obtain the gradient vector of the objective function with respect to the optimization variable and the gradient vector of the objective function with respect to the interference.

[0077] In some embodiments, obtaining a gradient vector of the objective function with respect to the optimization variable and a gradient vector of the objective function with respect to the interference based on the augmented gradient vector includes:

[0078] Based on the augmented gradient vector Use formula (16) to obtain the gradient vector K of the objective function to the optimization variable p and the gradient vector K of the objective function with respect to the disturbance f ;

[0079] in, represents the augmented gradient vector; K p Represents the gradient vector of the objective function with respect to the optimization variable; K fRepresents the gradient vector of the objective function with respect to the disturbance; m indicates that the gradient vector of the objective function with respect to the optimization variable has m dimensions; g indicates that the gradient vector of the objective function with respect to the disturbance has g dimensions.

[0080] In some embodiments, the augmented gradient vector includes a gradient vector of the objective function with respect to the optimization variable and a gradient vector of the objective function with respect to the interference; wherein the gradient vector of the objective function with respect to the optimization variable is the augmented gradient vector of the first m dimensions, and the gradient vector of the objective function with respect to the interference is the augmented gradient vector of the last g dimensions.

[0081] According to the feedforward correlation integral optimization method disclosed herein, the measured values ​​of the disturbance and the measured values ​​of the optimization variable are obtained, and based on the measured values ​​of the disturbance and the measured values ​​of the optimization variable, the autocorrelation integral matrix of the augmented input vector is obtained, so that the influence of random disturbance on the optimization variable can be obtained; the measured value of the objective function is obtained, and based on the measured values ​​of the disturbance and the measured values ​​of the objective function, the cross-correlation integral vector of the augmented input vector and the objective function is obtained, so that the cross-correlation relationship between the optimization variable and the objective function can be obtained when the objective function is subjected to random disturbance; the augmented gradient vector is obtained based on the autocorrelation integral matrix of the augmented input vector and the cross-correlation integral vector of the augmented input vector and the objective function, so that the gradient of the objective function to the optimization variable can be obtained when the objective function is subjected to random disturbance; based on the augmented gradient vector, the gradient vector of the objective function to the optimization variable and the gradient vector of the objective function to the disturbance can be obtained respectively, so that the influence of disturbance on the gradient of the objective function to the optimization variable can be reduced. Thus, the accuracy of obtaining the gradient of the objective function to the optimization variable is improved, and the accuracy of gradient optimization using the objective function to the optimization variable is improved.

[0082] The steps of the various methods above are divided only for the purpose of clear description. During implementation, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they contain the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0083] In the second aspect, the embodiment of the present disclosure provides a feedforward correlation integral optimization device. FIG3 is a block diagram of the feedforward correlation integral optimization device provided by the embodiment of the present disclosure. As shown in FIG3, the feedforward correlation integral optimization device 300 includes:

[0084] The first obtaining module 301 is configured to obtain the measured values ​​of the interference and the measured values ​​of the optimization variables, and obtain the autocorrelation integral matrix of the augmented input vector based on the measured values ​​of the interference and the measured values ​​of the optimization variables.

[0085] In some embodiments, when the first obtaining module 301 is used to obtain the autocorrelation integral matrix of the augmented input vector based on the measured value of the interference and the measured value of the optimization variable, it is specifically used to:

[0086] Obtaining an autocorrelation integral matrix of the optimization variables based on the measured values ​​of the optimization variables;

[0087] Obtaining an autocorrelation integral matrix of interference based on the measured values ​​of interference;

[0088] Based on the measured values ​​of the interference and the measured values ​​of the optimization variables, a cross-correlation integral matrix of the optimization variables to the interference and a cross-correlation integral matrix of the interference to the optimization variables are obtained;

[0089] The autocorrelation integral matrix of the augmented input vector is obtained based on the autocorrelation integral matrix of the optimization variable, the autocorrelation integral matrix of the interference, the cross-correlation integral matrix of the optimization variable to the interference, and the cross-correlation integral matrix of the interference to the optimization variable.

[0090] In some embodiments, when the first obtaining module 301 is used to obtain the autocorrelation integral matrix of the optimization variable based on the measured value of the optimization variable, it is specifically used to:

[0091] Based on the measured values ​​of the optimization variables, the autocorrelation integral matrix of the optimization variables is obtained using formulas (1) and (2):

[0092] Where i = 1, 2, ..., m; j = 1, 2, ..., m; m means that the measurement value of the optimization variable has m dimensions; T i represents the i-th first integral constant; M i represents the i-th second integral constant; t is the first variable; τ is the second variable; u represents the measured value of the optimized variable; u i represents the measurement value of the i-th dimension in the optimization variable; u j represents the measurement value of the jth dimension in the optimization variable; u i (t-τ) represents the measurement value of the i-th dimension in the optimization variable with respect to the variable t-τ; u j (t) represents the measured value of the jth dimension in the optimization variable with respect to variable t; Represents the relevant integral value; Represents the correlation integral value of the optimization variables of the i-th dimension and the j-th dimension; represents the correlation integral matrix; Represents the autocorrelation integral matrix of the optimization variables.

[0093] In some embodiments, when the first obtaining module 301 is used to obtain the autocorrelation integral matrix of interference based on the measured value of interference, it is specifically used to:

[0094] Based on the measured value of interference, the autocorrelation integral matrix of interference is obtained using formula (3) and formula (4):

[0095] Where i = 1, 2, ..., g; j = 1, 2, ..., g; g means that the interference measurement value has g dimensions; T m+i represents the m+i first integral constant; M m+i represents the m+i second integral constant; t is the first variable; τ is the second variable; u represents the measured value of the optimized variable; u m+i represents the measurement value of the i-th dimension in the interference; u m+j represents the measurement value of the jth dimension in the interference; u m+i (t-τ) represents the measurement value of the i-th dimension in the disturbance with respect to the variable t-τ; u m+j (t) represents the measurement value of the j-th dimension in the disturbance with respect to the variable t-τ; Represents the relevant integral value; represents the correlation integral value of the interference between the i-th dimension and the j-th dimension; f represents interference; u f represents a measurement of interference; represents the correlation integral matrix; Represents the autocorrelation integral matrix of the interference.

[0096] In some embodiments, when the first obtaining module 301 is used to obtain the cross-correlation integral matrix of the optimized variable to the interference and the cross-correlation integral matrix of the interference to the optimized variable based on the measured value of the interference and the measured value of the optimized variable, it is specifically used to:

[0097] Based on the measured values ​​of the interference and the measured values ​​of the optimization variables, the cross-correlation integral matrix of the optimization variables to the interference is obtained using formulas (5) and (6): Using formula (7) and formula (8) to obtain the cross-correlation integral matrix of the interference to the optimization variable

[0098] Where i = 1, 2, ..., m; j = 1, 2, ..., g; m means that the measured value of the optimization variable has m dimensions; g means that the measured value of the interference has g dimensions; T i represents the i-th first integral constant; M i represents the i-th second integral constant; T m+j represents the m+jth first integral constant; M m+jrepresents the m+jth second integral constant; t is the first variable; τ is the second variable; u i Represents the measurement value of the i-th dimension in the optimization variable; u m+j represents the measurement value of the jth dimension in the interference; u i (t-τ) represents the measurement value of the i-th dimension in the optimization variable with respect to the variable t-τ; u m+j (t) represents the measurement value of the jth dimension in the disturbance of variable t; u m+j (t-τ) represents the measurement value of the j-th dimension in the disturbance with respect to the variable t-τ; u i (t) represents the measured value of the i-th dimension in the optimization variable with respect to variable t; represents the cross-correlation integral value between the i-th dimension in the optimization variable and the j-th dimension in the interference; represents the cross-correlation integral value between the j-th dimension interference in the interference and the i-th dimension optimization variable in the optimization variable; f represents interference; u f represents a measurement of interference; represents the cross-correlation integral matrix of the optimization variables to the disturbance; represents the cross-correlation integral matrix of the disturbance on the optimization variables.

[0099] In some embodiments, when the first obtaining module 301 is used to obtain the autocorrelation integral matrix of the augmented input vector based on the autocorrelation integral matrix of the optimization variable, the autocorrelation integral matrix of the interference, the cross-correlation integral matrix of the optimization variable to the interference, and the cross-correlation integral matrix of the interference to the optimization variable, the first obtaining module 301 is specifically used to:

[0100] Autocorrelation integral matrix based on optimized variables Interference autocorrelation integral matrix Optimize the cross-correlation integral matrix of variables to disturbance The autocorrelation integral matrix of the optimization variables and the disturbance Using formula (9) to obtain the autocorrelation integral matrix of the augmented input vector

[0101] Where u represents the measured value of the optimization variable; s represents augmentation; u f Measurement of interference; represents the autocorrelation integral matrix of the optimization variables; Represents the autocorrelation integral matrix of interference; represents the cross-correlation integral matrix of the optimization variables to the disturbance; Represents the autocorrelation integral matrix of the interference on the optimization variable; u s represents the augmented input vector; Represents the autocorrelation integral matrix of the augmented input vector.

[0102] The second obtaining module 302 is configured to obtain a measurement value of the objective function, and obtain a cross-correlation integral vector between the augmented input vector and the objective function based on the measurement value of the interference and the measurement value of the objective function.

[0103] In some embodiments, when the second obtaining module 302 is used to obtain the cross-correlation integral vector between the augmented input vector and the objective function based on the measured value of the interference and the measured value of the objective function, it is specifically used to:

[0104] Based on the measured values ​​of the interference and the measured values ​​of the objective function, the cross-correlation integral vector of the augmented input vector and the objective function is obtained using formulas (10), (11), (12), (13) and (14):

[0105] Where, i = 1, 2, ..., m; j = 1, 2, ..., g; T i represents the i-th first integral constant; M i represents the i-th second integral constant; T m+j represents the m+jth first integral constant; M m+j represents the m+jth second integral constant; t is the first variable; τ is the second variable; J represents the objective function; J(t) represents the measured value of the objective function with respect to the variable t; u i Indicates the i The measurement value of the dimension optimization variable; u m+j represents the measurement value of the jth dimension in the interference; Represents the cross-correlation integral value between the i-th dimension in the optimization variable and the objective function; represents the cross-correlation integral value between the j-th dimension in the interference and the objective function; represents the cross-correlation integral vector between the optimization variable and the objective function; f represents interference; u f represents a measurement of interference; represents the cross-correlation integral vector between the interference and the objective function; s represents augmentation; u s represents the augmented input vector; Represents the cross-correlation integral vector between the augmented input vector and the objective function.

[0106] The third obtaining module 303 is configured to obtain an augmented gradient vector based on the autocorrelation integral matrix of the augmented input vector and the cross-correlation integral vector between the augmented input vector and the objective function.

[0107] In some embodiments, when the third obtaining module 303 is used to obtain the augmented gradient vector based on the autocorrelation integral matrix of the augmented input vector and the cross-correlation integral vector between the augmented input vector and the objective function, it is specifically used to:

[0108] When the objective function does not include unmeasured interference, the autocorrelation integral matrix based on the augmented input vector and the cross-correlation integral vector of the augmented input vector and the objective function Use formula (15) to obtain the augmented gradient vector

[0109] Where u represents the measured value of the optimization variable; s represents augmentation; u f represents the measured value of interference; J represents the objective function; u s represents the augmented input vector; represents the cross-correlation integral vector between the augmented input vector and the objective function; represents the autocorrelation integral matrix of the augmented input vector; represents the augmented gradient vector.

[0110] When the objective function includes unmeasured interference, the autocorrelation integral matrix based on the augmented input vector and the cross-correlation integral vector of the augmented input vector and the objective function Use formula (15b) to obtain the augmented gradient vector

[0111] Where ε represents the disturbance caused by unmeasured interference; u represents the measured value of the optimization variable; s represents augmentation; u f represents the measured value of interference; J represents the objective function; u s represents the augmented input vector; represents the cross-correlation integral vector between the augmented input vector and the objective function; represents the autocorrelation integral matrix of the augmented input vector; represents the augmented gradient vector.

[0112] In some embodiments, when the objective function includes unmeasured interference, the augmented gradient vector can be obtained based on the autocorrelation integral matrix of the augmented input vectors of multiple time periods and the cross-correlation integral vectors of the corresponding augmented input vectors and the objective function, and using the least squares method or other mathematical methods.

[0113] For example, when the objective function includes unmeasurable interference, the augmented gradient vector can be obtained based on the cross-correlation integral vector of the augmented input vector of n time periods and the objective function and the autocorrelation integral matrix of the augmented input vector of n time periods using formula (15c) and the least square method or other mathematical methods.

[0114] Among them, ε represents the disturbance caused by unmeasurable interference; represents the cross-correlation integral vector between the augmented input vector and the objective function for n time periods; Represents the cross-correlation integral vector between the augmented input vector and the objective function in the first period; Represents the cross-correlation integral vector between the augmented input vector and the objective function in the second period; represents the cross-correlation integral vector between the augmented input vector and the objective function at the nth time period; represents the autocorrelation integral matrix of the augmented input vector for n periods; Represents the autocorrelation integral matrix of the augmented input vector in the first period; Represents the autocorrelation integral matrix of the augmented input vector in the second period; represents the autocorrelation integral matrix of the augmented input vector of the nth period; n is a positive integer greater than or equal to 2; represents the augmented gradient vector.

[0115] The fourth obtaining module 304 is configured to obtain, based on the augmented gradient vector, a gradient vector of the objective function with respect to the optimization variable and a gradient vector of the objective function with respect to the interference.

[0116] In some embodiments, when the fourth obtaining module 304 is used to obtain the gradient vector of the objective function with respect to the optimization variable and the gradient vector of the objective function with respect to the interference based on the augmented gradient vector, it is specifically used to:

[0117] Based on the augmented gradient vector Use formula (16) to obtain the gradient vector K of the objective function to the optimization variable p and the gradient vector K of the objective function with respect to the disturbance f ;

[0118] in, represents the augmented gradient vector; K p Represents the gradient vector of the objective function with respect to the optimization variable; K fRepresents the gradient vector of the objective function with respect to the disturbance; m indicates that the gradient vector of the objective function with respect to the optimization variable has m dimensions; g indicates that the gradient vector of the objective function with respect to the disturbance has g dimensions.

[0119] The present disclosure provides a feedforward correlation integral optimization device, wherein a first acquisition module acquires the measured value of the interference and the measured value of the optimization variable, and based on the measured value of the interference and the measured value of the optimization variable, obtains the autocorrelation integral matrix of the augmented input vector, which can obtain the influence of random interference on the optimization variable; the second acquisition module acquires the measured value of the objective function, and based on the measured value of the interference and the measured value of the objective function, obtains the cross-correlation integral vector between the augmented input vector and the objective function, which can obtain the cross-correlation relationship between the optimization variable and the objective function when the objective function is subject to random interference; the third acquisition module acquires the augmented gradient vector based on the autocorrelation integral matrix of the augmented input vector and the cross-correlation integral vector between the augmented input vector and the objective function; the fourth acquisition module can respectively obtain the gradient vector of the objective function for the optimization variable and the gradient vector of the objective function for the interference based on the augmented gradient vector, which can reduce the influence of interference on the gradient of the objective function for the optimization variable, thereby improving the accuracy of obtaining the gradient of the objective function for the optimization variable, and further improving the accuracy of gradient optimization of the optimization variable using the objective function.

[0120] The functions or modules included in the device provided in the embodiments of the present disclosure can be used to execute the method described in the first aspect of the method embodiment above. Its specific implementation and technical effects can be referred to the description of the above method embodiment. For the sake of brevity, they will not be repeated here.

[0121] It should be noted that all modules involved in this embodiment are logical modules. In actual applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovations of this disclosure, this embodiment does not include units that are not closely related to solving the technical problems proposed by this disclosure. However, this does not mean that other units do not exist in this embodiment.

[0122] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0123] Those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present embodiment and to form different embodiments.

[0124] It is understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present disclosure, and the present disclosure is not limited thereto. Those skilled in the art may make various modifications and improvements without departing from the spirit and substance of the present disclosure, and such modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A feedforward correlation integral optimization method, characterized in that: include: Obtaining a measured value of the interference and a measured value of the optimization variable, and obtaining an autocorrelation integral matrix of the augmented input vector based on the measured value of the interference and the measured value of the optimization variable; Obtaining a measured value of an objective function, and obtaining a cross-correlation integral vector between the augmented input vector and the objective function based on the measured value of the interference, the measured value of the optimization variable, and the measured value of the objective function; Obtaining an augmented gradient vector based on an autocorrelation integral matrix of the augmented input vector and a cross-correlation integral vector between the augmented input vector and the objective function; Based on the augmented gradient vector, a gradient vector of the objective function with respect to the optimization variable and a gradient vector of the objective function with respect to the interference are obtained.

2. The method according to claim 1, characterized in that The step of obtaining an autocorrelation integral matrix of an augmented input vector based on the measured value of the interference and the measured value of the optimization variable comprises: Obtaining an autocorrelation integral matrix of the optimization variables based on the measured values ​​of the optimization variables; Obtaining an autocorrelation integral matrix of the interference based on the measured value of the interference; Based on the measured values ​​of the interference and the measured values ​​of the optimization variables, obtaining a mutual correlation integral matrix of the optimization variables with respect to the interference and a mutual correlation integral matrix of the interference with the optimization variables; An autocorrelation integral matrix of the augmented input vector is obtained based on the autocorrelation integral matrix of the optimization variables, the autocorrelation integral matrix of the interference, the cross-correlation integral matrix of the optimization variables to the interference, and the cross-correlation integral matrix of the interference to the optimization variables.

3. The method according to claim 2, characterized in that The step of obtaining an autocorrelation integral matrix of the optimization variables based on the measured values ​​of the optimization variables comprises: Based on the measured values ​​of the optimization variables, the autocorrelation integral matrix of the optimization variables is obtained using formula (1) and formula (2): in, m indicates that the measurement value of the optimization variable has m dimensions; T i represents the i-th first integral constant; M i represents the i-th second integral constant; t is the first variable; τ is the second variable; u i represents the measured value of the i-th dimension in the optimization variable; u j represents the measured value of the jth dimension in the optimization variable; u i (t-τ) represents the measured value of the i-th dimension in the optimization variable with respect to the variable t-τ; u j (t) represents the measured value of the j-th dimension in the optimization variable with respect to variable t; Represents the correlation integral value of the optimization variables of the i-th dimension and the j-th dimension; represents the autocorrelation integral matrix of the optimization variables.

4. The method according to claim 3, characterized in that The obtaining of the autocorrelation integral matrix of the interference based on the measured value of the interference includes: Based on the measured value of the interference, the autocorrelation integral matrix of the interference is obtained using formula (3) and formula (4): Wherein, i=1,2,...,g; j=1,2,...,g; g indicates that the measured value of the interference has g dimensions; T m+i represents the m+i first integral constant; M m+i represents the m+i second integral constant; t is the first variable; τ is the second variable; u m+i represents the measured value of the i-th dimension in the interference; u m+j represents the measurement value of the jth dimension in the interference; u m+i (t-τ) represents the measurement value of the i-th dimension in the disturbance with respect to the variable t-τ; u m+j (t) represents the measured value of the j-th dimension in the disturbance with respect to variable t; represents the correlation integral value of the interference between the i-th dimension and the j-th dimension; f represents the interference; u f a measurement representing said interference; represents the autocorrelation integral matrix of the interference.

5. The method according to claim 4, characterized in that The step of obtaining, based on the measured value of the interference and the measured value of the optimization variable, a mutual correlation integral matrix of the optimization variable with respect to the interference and a mutual correlation integral matrix of the interference with respect to the optimization variable comprises: Based on the measured values ​​of the interference and the measured values ​​of the optimization variables, the cross-correlation integral matrix of the optimization variables to the interference is obtained using formulas (5) and (6): Using formula (7) and formula (8), the cross-correlation integral matrix of the interference to the optimization variable is obtained: in, m indicates that the measured value of the optimization variable has m dimensions; g indicates that the measured value of the interference has g dimensions; T i represents the i-th first integral constant; M i represents the i-th second integral constant; T m+j represents the m+jth first integral constant; M m+j represents the m+jth second integral constant; t is the first variable; τ is the second variable; u i represents the measured value of the i-th dimension in the optimization variable; u m+j represents the measurement value of the jth dimension in the interference; u i (t-τ) represents the measured value of the i-th dimension in the optimization variable with respect to the variable t-τ; u m+j (t) represents the measurement value of the jth dimension in the interference with respect to variable t; u m+j (t-τ) represents the measurement value of the j-th dimension in the disturbance with respect to the variable t-τ; u i (t) represents the measured value of the i-th dimension in the optimization variable with respect to variable t; represents the cross-correlation integral value between the i-th dimension of the optimization variable and the j-th dimension of the interference; represents the cross-correlation integral value between the j-th dimension interference in the interference and the i-th dimension optimization variable in the optimization variable; f represents the interference; u f a measurement representing said interference; A cross-correlation integral matrix representing the optimized variables to the interference; A cross-correlation integral matrix representing the interference on the optimization variables.

6. The method according to claim 5, characterized in that The obtaining of the autocorrelation integral matrix of the augmented input vector based on the autocorrelation integral matrix of the optimization variable, the autocorrelation integral matrix of the interference, the cross-correlation integral matrix of the optimization variable to the interference, and the cross-correlation integral matrix of the interference to the optimization variable comprises: Based on the autocorrelation integral matrix of the optimization variables The autocorrelation integral of the interference matrix The cross-correlation integral matrix of the optimization variables to the disturbance and the cross-correlation integral matrix of the disturbance on the optimization variable The autocorrelation integral matrix of the augmented input vector is obtained using formula (9): Wherein, u represents the measured value of the optimization variable; s represents augmentation; u f a measurement representing said interference; An autocorrelation integral matrix representing the optimization variables; An autocorrelation integral matrix representing the interference; A cross-correlation integral matrix representing the optimized variables to the interference; represents the cross-correlation integral matrix of the interference to the optimization variables; u s represents the augmented input vector; represents the autocorrelation integral matrix of the augmented input vector.

7. The method according to claim 6, characterized in that The step of obtaining a cross-correlation integral vector between the augmented input vector and the objective function based on the measured value of the interference, the measured value of the optimization variable, and the measured value of the objective function comprises: Based on the measured value of the interference, the measured value of the optimization variable and the measured value of the objective function, the cross-correlation integral vector of the augmented input vector and the objective function is obtained using formula (10), formula (11), formula (12), formula (13) and formula (14): Where, i = 1, 2, ..., m; j = 1, 2, ..., g; T i represents the i-th first integral constant; M i represents the i-th second integral constant; T m+j represents the m+jth first integral constant; M m+j represents the m+jth second integral constant; t is the first variable; τ is the second variable; J represents the objective function; J(t) represents the measured value of the objective function with respect to the variable t; u i Indicates the optimization variable i The measurement value of the dimension; u m+j represents the measurement value of the j-th dimension in the interference; represents the cross-correlation integral value between the i-th dimension in the optimization variable and the objective function; represents the cross-correlation integral value between the j-th dimension in the interference and the objective function; represents the cross-correlation integral vector between the optimization variable and the objective function; f represents the interference; u f a measurement representing said interference; represents the cross-correlation integral vector between the interference and the objective function; s represents augmentation; u s represents the augmented input vector; represents the cross-correlation integral vector between the augmented input vector and the objective function.

8. The method according to claim 7, characterized in that The obtaining of the augmented gradient vector based on the autocorrelation integral matrix of the augmented input vector and the cross-correlation integral vector between the augmented input vector and the objective function comprises: When the objective function does not include unmeasured interference, the autocorrelation integral matrix based on the augmented input vector and the cross-correlation integral vector of the augmented input vector and the objective function The augmented gradient vector is obtained using formula (15a) Wherein, u represents the measured value of the optimization variable; s represents augmentation; u f represents the measured value of the interference; J represents the objective function; u s represents the augmented input vector; A cross-correlation integral vector representing the augmented input vector and the objective function; An autocorrelation integral matrix representing the augmented input vector; represents the augmented gradient vector; In the case where the objective function includes the unmeasurable interference, the autocorrelation integral matrix based on the augmented input vector and the cross-correlation integral vector of the augmented input vector and the objective function The augmented gradient vector is obtained using formula (15b) Wherein, ε represents the disturbance caused by the unmeasurable interference; u represents the measured value of the optimization variable; s represents augmentation; u f represents the measured value of the interference; J represents the objective function; u s represents the augmented input vector; A cross-correlation integral vector representing the augmented input vector and the objective function; An autocorrelation integral matrix representing the augmented input vector; represents the augmented gradient vector.

9. The method according to claim 8, characterized in that The obtaining, based on the augmented gradient vector, a gradient vector of the objective function with respect to the optimization variable and a gradient vector of the objective function with respect to the interference includes: Based on the augmented gradient vector Using formula (16), formula (17) and formula (18), the gradient vector K of the objective function to the optimization variable is obtained: p and the gradient vector K of the objective function with respect to the disturbance f ; in, represents the augmented gradient vector; K p represents the gradient vector of the objective function with respect to the optimization variable; K f Represents the gradient vector of the objective function to the interference; m represents that the gradient vector of the objective function to the optimization variable has m dimensions; g represents that the gradient vector of the objective function to the interference has g dimensions.

10. A feedforward correlation integral optimization device, characterized in that: include: A first obtaining module is used to obtain a measured value of the interference and a measured value of the optimization variable, and obtain an autocorrelation integral matrix of the augmented input vector based on the measured value of the interference and the measured value of the optimization variable; a second obtaining module, configured to obtain a measured value of an objective function, and obtain a cross-correlation integral vector between the augmented input vector and the objective function based on the measured value of the interference, the measured value of the optimization variable, and the measured value of the objective function; a third obtaining module, configured to obtain an augmented gradient vector based on an autocorrelation integral matrix of the augmented input vector and a cross-correlation integral vector between the augmented input vector and the objective function; A fourth obtaining module is configured to obtain, based on the augmented gradient vector, a gradient vector of the objective function with respect to the optimization variable and a gradient vector of the objective function with respect to the interference.

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