Method, device and equipment for improving calculation precision of deformation resistance in plate and strip rolling and medium

Abstract

The invention discloses a method for improving the calculation precision of deformation resistance in plate and strip rolling, and the method comprises the steps: obtaining a training sample which at least comprises an actual rolling force and a set rolling force; an actual deformation resistance value and a calculated deformation resistance value are obtained based on the actual rolling force, the set rolling force and the mapping relation between the rolling force and the deformation resistance; determining an error function based on the actual deformation force value and the calculated deformation resistance value; according to the error function, on the basis of the training set of the training samples, a gradient descent algorithm is adopted to iteratively update corresponding model parameters in the deformation resistance calculation model until iteration stop conditions are met; and testing the deformation resistance calculation model based on the test set of the training samples. According to the method, more actual rolling historical data are considered, and through self-learning, the calculation precision of the deformation resistance of each pass in the rolling schedule is remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of testing technologies, and particularly to a method, device, equipment and medium for improving the calculation accuracy of the deformation resistance in strip rolling. Background Art

[0002] In the rolling of plates or strips, as the market competition of strip products becomes increasingly fierce, higher requirements are put forward for product quality, and the product thickness accuracy is one of the important indicators of product quality. At the same time, in the rolling automation control system, the thickness control accuracy is also an important parameter for evaluating the quality of the system.

[0003] In the rolling thickness control system, the prediction accuracy of the rolling force is the key to thickness control, and the deformation resistance, as the physical property of the material itself, is one of the main factors in the calculation of the rolling force. The commonly used rolling force calculation formula in the industry at present is as follows:

[0004]

[0005] Wherein, p m represents the average unit rolling force, n σ represents influencing factors such as geometry, friction, and tension, and k represents the deformation resistance.

[0006] It can be seen from the calculation formula that when the rolling state and tension of the current pass are known, the accuracy of the deformation resistance calculation directly affects the calculation accuracy of the rolling force. In addition, the above formula is only the theoretically ideal calculation result, but in the actual production process, due to various dimensional errors, measurement errors, and the errors of the simplified mathematical model itself, there is a certain error between the measured rolling force and the calculated rolling force. Therefore, an online self-learning function is usually introduced in engineering to reduce the error, that is and continuously update the value of the ada parameter according to the ratio of the measured rolling force to the calculated rolling force.

[0007] Although the online self-learning function also has a certain corrective effect on the deformation resistance, its effect is limited. By using the online self-learning method, the effect of improving the calculation accuracy of the deformation resistance is limited, and in the rolling process, there will still be a situation where the deviation is relatively large. Summary of the Invention

[0008] In view of the above-mentioned disadvantages of the prior art, the present application provides a method, device, equipment and medium for improving the calculation accuracy of the deformation resistance in strip rolling, which is used to solve at least one defect in the prior art.

[0009] To achieve the above object and other objects, the present application provides a method for improving the calculation accuracy of the deformation resistance in strip rolling, and the calculation method includes:

[0010] Obtain training samples, where the training samples at least include: actual rolling force, set rolling force;

[0011] Based on the actual rolling force, the set rolling force, and the mapping relationship between the rolling force and the deformation resistance, obtain the actual deformation resistance value and the calculated deformation resistance value;

[0012] Based on the actual deformation force value and the calculated deformation resistance value, determine the error function;

[0013] According to the error function, based on the training set of the training samples, use the gradient descent algorithm to iteratively update the corresponding model parameters in the deformation resistance calculation model until the iteration stop condition is met;

[0014] Test the deformation resistance calculation model based on the test set of the training samples.

[0015] In an embodiment of the present application, the error function includes:

[0016]

[0017] where e is the error, k meas,i is the actual deformation resistance value, k calc,i is the calculated deformation resistance value.

[0018] In an embodiment of the present application, the mapping relationship between the rolling force and the deformation resistance is expressed as:

[0019]

[0020] where F is the rolling force, w is the average value of the inlet width and the outlet width, l d is the contact arc length, R is the working roll radius, is the flattened working roll radius, Δh is the reduction per pass, v is the Poisson's ratio, E is the elastic modulus, p m is the unit average rolling force, n σ is the rolling influence factor, and k is the deformation resistance.

[0021] In an embodiment of the present application, during the process of using the gradient descent algorithm to iteratively update the corresponding model parameters in the deformation resistance calculation model, a parameter update formula is used for updating, and the parameter update formula includes:

[0022]

[0023] where, is the updated model parameter, c i is the model parameter before update, learnrate is the learning rate, is the gradient of the error with respect to a certain model parameter.

[0024] In an embodiment of the present application, during the process of testing the deformation resistance calculation model on the test set of training samples, when the difference between the error e after the model parameters are updated new and the error e old before the model parameters are updated is within a set range,

[0025]

[0026] In an embodiment of the present application, according to the method for improving the calculation accuracy of the deformation resistance in strip rolling described in claim 1, it is characterized in that the deformation resistance calculation model:

[0027] k = k(c1, c2, …, c n )

[0028] where c n are each model parameter.

[0029] To achieve the above object and other objects, the present application provides a device for improving the calculation accuracy of the deformation resistance in strip rolling, and the device includes:

[0030] A sample acquisition module, configured to acquire training samples, and the training samples at least include: actual rolling force, set rolling force;

[0031] A deformation resistance value calculation module, configured to obtain an actual deformation resistance value and a calculated deformation resistance value based on the actual rolling force, the set rolling force, and the mapping relationship between the rolling force and the deformation resistance;

[0032] An error function determination module, configured to determine an error function based on the actual deformation force value and the calculated deformation resistance value;

[0033] An update module, configured to iteratively update the corresponding model parameters in the deformation resistance calculation model according to the error function based on the training set of the training samples by using the gradient descent algorithm until the iteration stop condition is satisfied;

[0034] A test module, configured to test the deformation resistance calculation model based on the test set of the training samples.

[0035] To achieve the above object and other objects, the present application provides a deformation resistance calculation method, and the calculation method includes:

[0036] Obtain rolling parameters;

[0037] According to the rolling parameters and the deformation resistance calculation model, obtain a deformation resistance value.

[0038] To achieve the above and other objectives, the present application provides an electronic device, including:

[0039] One or more processors; and

[0040] A memory for storing one or more programs, which, when executed by the one or more processors, cause the memory to implement the method for improving the calculation accuracy of the deformation resistance in strip rolling or the deformation resistance calculation method.

[0041] To achieve the above and other objectives, the present application provides a machine-readable medium having instructions stored thereon, which, when executed by one or more processors, cause the processors to execute the method for improving the calculation accuracy of the deformation resistance in strip rolling or the deformation resistance calculation method.

[0042] Advantages of the present application:

[0043] A method for calculating the deformation resistance in strip rolling according to the present application includes: obtaining training samples, where the training samples at least include: actual rolling force, set rolling force; obtaining the actual deformation resistance value and the calculated deformation resistance value based on the actual rolling force, the set rolling force, and the mapping relationship between the rolling force and the deformation resistance; determining an error function based on the actual deformation force value and the calculated deformation resistance value; according to the error function, based on the training set of the training samples, using the gradient descent algorithm to iteratively update the corresponding model parameters in the deformation resistance calculation model until the iteration stop condition is met; testing the deformation resistance calculation model based on the test set of the training samples. The present application considers more actual rolling historical data, and through self-learning, there is a significant improvement in the calculation accuracy of the deformation resistance for each pass in the rolling schedule.

[0044] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts. In the drawings:

[0046] Figure 1 Is a flowchart of a method for improving the calculation accuracy of the deformation resistance in strip rolling according to an embodiment of the present application;

[0047] Figure 2A comparison diagram of the deformation resistance calculated by the model before parameter update and the actual deformation resistance according to an embodiment of the present application;

[0048] Figure 3 A comparison diagram of the deformation resistance calculated by the model and the actual deformation resistance after updating the model parameters according to an embodiment of the present application;

[0049] Figure 4 A principle block diagram of a device for calculating the deformation resistance in strip rolling according to an embodiment of the present application. Specific embodiments

[0050] The following uses specific specific examples to illustrate the implementation manners of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0051] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner. Therefore, only the components related to the present application are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The types, quantities, and proportions of the components in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0052] Although terms such as "first", "second", "A", and "B" can be used herein to describe various elements, these elements should not be limited by these terms and are only used to distinguish one element from another. For example, without departing from the scope of the following technology, the first element can be called the second element, and similarly, the second element can be called the first element. The term "and / or" includes combinations of multiple related items or any item in multiple related items.

[0053] As used herein, unless the context indicates otherwise, the singular form is also intended to include the plural form. It will be understood that the term "comprising" means the presence of the described features, quantities, steps, operations, elements, or combinations thereof, but does not exclude the presence or addition of one or more other features, quantities, steps, operations, elements, components, or combinations thereof.

[0054] Before the detailed description, it is intended to clarify that the division of components in this specification is only based on the main functions of each component. That is, two or more of the components described below can be combined into one component, or can be divided into two or more components according to more detailed functions. In addition to the main functions of the components, each of the components described below can also perform some or all of the functions of other components, and some of the main functions of each component can be specifically performed by other components.

[0055] Embodiments of the present application respectively propose a method for improving the calculation accuracy of the deformation resistance in strip rolling, a device for improving the calculation accuracy of the deformation resistance in strip rolling, a calculation method for the deformation resistance in strip rolling, an electronic device, and a computer-readable storage medium. These embodiments will be described in detail below.

[0056] Please refer to Figure 1 , Figure 1 which is a flowchart of a method for improving the calculation accuracy of the deformation resistance in strip rolling according to an embodiment of the present application. Specifically, refer to Figure 1 shown in the figure. The method for improving the calculation accuracy of the deformation resistance in strip rolling at least includes steps S110 - S150:

[0057] Step S110, obtaining training samples, where the training samples at least include: the actual rolling force and the set rolling force;

[0058] Step S120, obtaining the actual deformation resistance value and the calculated deformation resistance value based on the actual rolling force, the set rolling force, and the mapping relationship between the rolling force and the deformation resistance;

[0059] Step S130, determining an error function based on the actual deformation force value and the calculated deformation resistance value;

[0060] Step S140, according to the error function, based on the training set of the training samples, using the gradient descent algorithm to iteratively update the corresponding model parameters in the deformation resistance calculation model until the iteration stop condition is satisfied;

[0061] Step S150, testing the deformation resistance calculation model based on the test set of the training samples.

[0062] Compared with the existing online self-learning of the deformation resistance, this method considers more actual rolling historical data, and at the same time, the learning method of offline fitting and updating the model parameters by these data will obviously have a better correction effect. After introducing the offline self-learning function, there is a significant improvement in the calculation accuracy of the deformation resistance in each pass of the rolling schedule.

[0063] The following will explain each step in detail.

[0064] In step S110, training samples are obtained; the training samples at least include: actual rolling force and set rolling force;

[0065] The training samples can be historical data of the rolling process within a specified time period, which have the characteristics of diversification, that is, data within a wide temperature range, a wide strain range, and a wide rolling rate range.

[0066] Specifically, the training samples include: rolling data such as pass inlet temperature, rolling rate, slab thickness, inlet thickness, inlet width, outlet thickness, outlet width, set rolling force, measured rolling force, etc.; then the training samples are divided into a training set and a test set.

[0067] In one embodiment, the error function includes:

[0068]

[0069] where e is the error, k meas,i is the actual value of the deformation resistance, and k calc,i is the calculated value of the deformation resistance.

[0070] In one embodiment, the mapping relationship between the rolling force and the deformation resistance is expressed as:

[0071]

[0072] where F is the rolling force, w is the average value of the inlet width and the outlet width, l d is the contact arc length, R is the radius of the work roll, is the radius of the flattened work roll, Δh is the reduction per pass, v is the Poisson's ratio, E is the elastic modulus, p m is the unit average rolling force, n σ is the rolling influencing factor, and k is the deformation resistance.

[0073] After obtaining the rolling force F, the average value w of the inlet width and the outlet width, the radius R of the work roll, and the radius of the work roll considering flattening the reduction per pass Δh, the Poisson's ratio v, and the elastic modulus E of the pass, the unit average rolling force p m is calculated, and then based on p m = n σ * k, the actual deformation resistance k can be calculated. It should be noted that the rolling influencing factor n σ affecting the value of the deformation resistance includes multiple factors, such as geometric, friction, tension and other influencing factors, which can be represented by n σ The calculation depends on different rolling force models and adopts different calculation methods, which are not restricted here.

[0074] In one embodiment, the deformation resistance calculation model:

[0075] k = k(c1, c2, …, c n ), where c n are each model parameter.

[0076]

[0077] It should be noted that the reverse calculation process of the actual deformation resistance can be implemented during rolling and saved in the database. In this way, when performing the actual deformation resistance calculation, the actual deformation resistance value can be directly read from the database.

[0078] In one embodiment, during the process of iteratively updating the corresponding model parameters in the deformation resistance calculation model using the gradient descent algorithm, a parameter update formula is used for the update. The parameter update formula includes:

[0079]

[0080] where is the updated model parameter, c i is the model parameter before update, learnrate is the learning rate, an initial learning rate is set at the beginning of the iteration. When the error becomes larger and does not converge, the learning rate is decreased; is the gradient of the error with respect to a certain model parameter, and its value can be calculated by analytical or numerical methods.

[0081] In one embodiment, during the process of testing the deformation resistance calculation model on the test set of the training samples, the square error method can also be used to evaluate the accuracy of the deformation resistance model after updating the parameters, that is When the difference between the error e new after the model parameter update and the error e old before the model parameter update is within a set range, the iteration stop condition is satisfied, it is determined that the model update parameter meets the requirements, and it is considered that the model update parameter this time is acceptable, and the parameter value is saved in the database for subsequent calculation of the deformation resistance during rolling.

[0082] Of course, the iteration stop condition can also be that the number of iterations reaches the specified number of iterations.

[0083] In one embodiment, in the training dataset, the deformation resistance calculation model is used to calculate the deformation resistance value: k calc，i = k i (c1, c2, c3, c4, c5, c6), where the model parameter c iThe values are: c1 = 170, c2 = 0.0825, c3 = 0.14, c4 = 0.0491, c5 = -0.08071, c6 = 0.03376.

[0084] In one embodiment, when using gradient descent to update the model parameters, the initial learning rates of each model parameter are initialized, learnrate1 = 1×10 -4 , learnrate2 = 1×10 -12 , learnrate3 = 1×10 -9 , learnrate4 = 1×10 -9 , learnrate5 = 1×10 -9 , learnrate6 = 1×10 -10 . It should be noted that the learning rate is only initialized in the first iteration.

[0085] In one embodiment, during the process of updating the model parameters of the deformation resistance calculation model, when the number of iterations reaches 100 times and the error function does not converge, that is, e j > e j-1 , (where j is the number of iterations), at this time, the learning rate is reduced to half of the original. After the iteration is completed, the updated model parameters of the deformation resistance calculation model are: c1 = 124.72, c2 = 0.002480, c3 = 0.1628, c4 = 0.00331, c5 = -0.08878, c6 = 0.01744. In the test dataset, the deformation resistance is calculated using the model parameters before and after the update, and the mean squared error is calculated:

[0086]

[0087] It is calculated that: e old = 13648935, e new = 227467. That is, e new < e old , it can be considered that the parameter update of this model is effective, and the parameter values are saved in the database for the calculation of the deformation resistance in subsequent rolling.

[0088] Figure 2 For the comparison chart of the deformation resistance calculated by the original model parameters and the actual deformation resistance in the test dataset, it can be seen that a large number of data points deviate from the center line. Figure 3 For the comparison chart of the deformation resistance calculated by the updated model parameters and the actual deformation resistance in the test dataset, it can be seen from the figure that the error of the model calculation decreases, and a large number of data points are distributed near the center line.

[0089] The present invention collects historical rolling data of a certain steel grade in the offline automatic control system of the rolling line in a recent period of time, performs regression fitting on the mathematical model formula for calculating the deformation resistance, updates the model parameters, so as to improve the calculation accuracy of the deformation resistance, and then combines with the original online self-learning function, thereby improving the prediction accuracy of the rolling force. Compared with the existing online self-learning of the deformation resistance, this application considers more actual rolling historical data, and at the same time, the learning method of offline fitting and updating the model parameters by these data will obviously have a better correction effect. After introducing the offline self-learning function, there is a significant improvement in the calculation accuracy of the deformation resistance for each pass in the rolling schedule.

[0090] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution is prior or posterior, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0091] As Figure 4 shown, a device for improving the calculation accuracy of the deformation resistance in strip rolling includes:

[0092] A sample acquisition module 410, configured to acquire training samples, where the training samples at least include: actual rolling force, set rolling force;

[0093] A deformation resistance value calculation module 420, configured to obtain an actual deformation resistance value and a calculated deformation resistance value based on the actual rolling force, the set rolling force, and the mapping relationship between the rolling force and the deformation resistance;

[0094] An error function determination module 430, configured to determine an error function based on the actual deformation force value and the calculated deformation resistance value;

[0095] An update module 440, configured to iteratively update the corresponding model parameters in the deformation resistance calculation model according to the error function, based on the training set of the training samples, using the gradient descent algorithm until the iteration stop condition is met;

[0096] A test module 450, configured to test the deformation resistance calculation model based on the test set of the training samples.

[0097] It should be noted that the device for improving the calculation accuracy of the deformation resistance in strip rolling provided in the above embodiment and the method for improving the calculation accuracy of the deformation resistance in strip rolling provided in the above embodiment belong to the same concept. The specific ways in which each module and unit perform operations have been described in detail in the method embodiment, and will not be repeated here. In practical applications, the device for improving the calculation accuracy of the deformation resistance in strip rolling provided in the above embodiment can, according to needs, allocate the above functions to different functional modules, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this will not be limited here either.

[0098] An embodiment of the present application also provides a method for calculating the deformation resistance. The calculation method includes:

[0099] Obtain rolling parameters;

[0100] According to the rolling parameters and the deformation resistance calculation model, obtain the deformation resistance value.

[0101] An embodiment of the present application also provides an electronic device, including:

[0102] One or more processors;

[0103] A memory for storing one or more programs, which, when executed by the one or more processors, cause the memory to implement the method for improving the calculation accuracy of the deformation resistance in strip rolling or the method for calculating the deformation resistance.

[0104] An embodiment of the present application also provides one or more machine-readable media that cause a processor to execute the method for improving the calculation accuracy of the deformation resistance in strip rolling or the method for calculating the deformation resistance.

[0105] An embodiment of the present application also provides a computer system suitable for implementing the memory of the embodiment of the present application. The computer system includes a central processing unit (CPU), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) or the program loaded from the storage part into the random access memory (RAM), such as executing the method in the above embodiment. In the RAM, various programs and data required for system operation are also stored. The CPU, ROM, and RAM are connected to each other through a bus. The input / output (I / O) interface is also connected to the bus.

[0106] The following components are connected to the I / O interface: an input section including a keyboard, a mouse, etc.; an output section including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section including a hard disk, etc.; and a communication section including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as required. A removable medium such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive as required so that a computer program read therefrom is installed into the storage section as required.

[0107] Specifically, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product including a computer program carried on a computer-readable medium, the computer program including a computer program for performing the calculation method of the deformation resistance in the strip rolling described in the foregoing embodiment. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), various functions defined in the system of the present application are executed.

[0108] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0109] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0110] The units involved in the embodiments described in this application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not constitute a limitation to the units themselves in some cases.

[0111] Another aspect of this application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor of a computer, the computer is caused to execute the method for improving the calculation accuracy of the deformation resistance in strip rolling as described above. The computer-readable storage medium may be included in the memory described in the above embodiments, or may exist alone without being assembled into the memory.

[0112] Another aspect of this application also provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method for improving the calculation accuracy of the deformation resistance in strip rolling provided in the above various embodiments.

[0113] The above embodiments are only used to exemplarily illustrate the principles and effects of this application, rather than to limit this application. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for improving the calculation accuracy of deformation resistance in strip rolling, characterized in that: The calculation method includes: Acquire a training sample, wherein the training sample at least includes: an actual rolling force and a set rolling force; Obtaining an actual deformation resistance value and a calculated deformation resistance value based on the mapping relationship between the actual rolling force and the set rolling force and between the rolling force and the deformation resistance; determining an error function based on the actual deformation force value and the calculated deformation resistance value; According to the error function, based on the training set of the training samples, a gradient descent algorithm is used to iteratively update the corresponding model parameters in the deformation resistance calculation model until an iteration stop condition is met; The deformation resistance calculation model is tested based on the test set of the training samples.

2. The method for improving the calculation accuracy of deformation resistance in strip rolling according to claim 1, characterized in that: The error function includes: Among them, e is the error, k meas,i is the actual deformation resistance value, k calc,i is the calculated deformation resistance value.

3. The method for improving the calculation accuracy of deformation resistance during strip rolling according to claim 1, characterized in that: The mapping relationship between rolling force and deformation resistance is expressed as: Where F is the rolling force, w is the average of the inlet width and the outlet width, l d is the contact arc length, R is the radius of the working roll, is the radius of the flattened work roll, Δh is the amount of reduction per pass, v is the Poisson's ratio, E is the elastic modulus, p m is the unit average rolling force, n σ is the influencing factor of rolling, and k is the deformation resistance.

4. The method for improving the calculation accuracy of deformation resistance during strip rolling according to claim 1, characterized in that: In the process of iteratively updating the corresponding model parameters in the deformation resistance calculation model using the gradient descent algorithm, the parameter updating formula is used for updating, and the parameter updating formula includes: in, is the updated model parameter, c i is the model parameter before updating, learnrate is the learning rate, is the gradient of the error with respect to a model parameter.

5. The method for improving the calculation accuracy of deformation resistance during strip rolling according to claim 2, characterized in that: In the process of testing the deformation resistance calculation model in the test set of training samples, when the error e after the model parameters are updated new Before updating the model parameters e old When the difference between the errors is within the set range, it is determined that the model update parameters meet the requirements; 6. The method for improving the calculation accuracy of deformation resistance during strip rolling according to claim 1, characterized in that: The deformation resistance calculation model described is: k=k(c1,c2,…,c n ) Among them, c n are the parameters of each model.

7. A device for improving the calculation accuracy of deformation resistance during strip rolling, characterized in that: The device comprises: A sample acquisition module, used to acquire training samples, wherein the training samples at least include: actual rolling force and set rolling force; A deformation resistance value calculation module, used for obtaining an actual deformation resistance value and a calculated deformation resistance value based on the actual rolling force and the set rolling force and a mapping relationship between the rolling force and the deformation resistance; an error function determination module, configured to determine an error function based on the actual deformation force value and the calculated deformation resistance value; An updating module, configured to iteratively update corresponding model parameters in the deformation resistance calculation model using a gradient descent algorithm according to the error function and based on a training set of the training samples until an iteration stop condition is met; A testing module is used to test the deformation resistance calculation model based on the test set of the training samples.

8. A deformation resistance calculation method, characterized in that: The calculation method includes: Get rolling parameters; According to the rolling parameters and the deformation resistance calculation model described in any one of claims 1 to 7, a deformation resistance value is obtained.

9. An electronic device, characterized in that: include: one or more processors; and A memory for storing one or more programs. When the one or more programs are executed by the one or more processors, the memory implements the method for improving the calculation accuracy of deformation resistance in plate and strip rolling as described in any one of claims 1 to 6 or the deformation resistance calculation method as described in claim 8.

10. A machine-readable medium, characterized in that Instructions are stored thereon, which, when executed by one or more processors, enable the processors to execute the method for improving the calculation accuracy of deformation resistance in plate and strip rolling as described in any one of claims 1-6 or the deformation resistance calculation method as described in claim 8.

Images