A method for modeling distribution parameters of a sector permanent magnet for a solenoid valve

Abstract

The application relates to the technical field of electromagnetic valves, and particularly discloses a fan-shaped permanent magnet distribution parameter modeling method for an electromagnetic valve. The fan-shaped permanent magnet distribution parameter is calculated through a parameter model, and then the calculated fan-shaped permanent magnet distribution parameter is input into a historical fan-shaped permanent magnet distribution parameter model for training to obtain a second fan-shaped permanent magnet distribution value. Whether the first fan-shaped permanent magnet distribution value matches the second fan-shaped permanent magnet distribution value is judged. The accuracy of the first fan-shaped permanent magnet distribution value can be quickly judged. After the first fan-shaped permanent magnet distribution value that matches the second fan-shaped permanent magnet distribution value is obtained, the first fan-shaped permanent magnet distribution value is corrected. The accuracy of the first fan-shaped permanent magnet distribution value after correction can be quickly provided, and the required fan-shaped permanent magnet distribution parameter can be quickly obtained, and the calculation period can be shortened.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic valve technology, and in particular to a method for modeling the distributed parameters of a sector-shaped permanent magnet used in electromagnetic valves. Background Technology

[0002] Solenoid valves are commonly used fluid control components, and the sector-shaped permanent magnet is an important part of them. Introducing a sector-shaped permanent magnet into a solenoid valve can greatly improve space utilization. The analysis and calculation process requires high computational accuracy and efficiency. Researchers typically use the finite element method to calculate the distributed parameters of the sector-shaped permanent magnet. While this method has the advantage of high computational accuracy, the modeling process is complex and the calculation cycle is long. Lumped parameter models are simple to model and have a short calculation cycle, but their computational accuracy is low. Therefore, it is necessary to use distributed parameter modeling methods to shorten the calculation cycle while meeting certain computational accuracy requirements. Summary of the Invention

[0003] The purpose of this invention is to provide a method for modeling the distributed parameters of a sector-shaped permanent magnet for an electromagnetic valve, comprising: The first characteristic information of the distribution of the sector-shaped permanent magnet of the solenoid valve is obtained, wherein the first characteristic information includes magnetic field distribution information, leakage permeability information and magnetic field direction information; Information is extracted from the first feature information to obtain the distribution value of the first sector permanent magnet; The first feature information is used as the first training sample and input into the historical sector permanent magnet distribution parameter model for training to obtain the second sector permanent magnet distribution value; Determine whether the distribution value of the first sector permanent magnet matches the distribution value of the second sector permanent magnet based on the distribution value of the first sector permanent magnet. If a match is found, the distribution value of the first sector permanent magnet is used as the distribution parameter of the first sector permanent magnet to be corrected. If there is a mismatch, the distribution value of the first sector permanent magnet will be recalculated; The distribution parameters of the first sector-shaped permanent magnet to be corrected are corrected to obtain the corrected distribution value; Determine whether the corrected distribution value meets the preset value; If satisfied, the distribution value of the first sector permanent magnet is optimized based on the corrected distribution value to obtain the required distribution parameters of the sector permanent magnet.

[0004] Preferably, after the step of including magnetic field distribution information, leakage permeability information, and magnetic field direction information in the first feature information, the method further includes: The magnetic field distribution information is extracted to obtain the magnetic field distribution value; Obtain the first magnetic field strength, first magnetic field length, first sector area, and first vacuum permeability of the permanent magnet corresponding to the magnetic field distribution information; The magnetic field distribution value is calculated based on the first magnetic field strength, first magnetic field length, first sector area, and first free magnetic permeability of the permanent magnet, wherein the calculation formula is: F = (B*s*) ) / h; Where F is the magnetic field distribution value, Let be the first free permeability, s be the area of ​​the first sector, h be the length of the first magnetic field, and B be the strength of the first magnetic field. Determine whether the first preset value is met based on the magnetic field distribution value; If the condition is met, it indicates that the magnetic field distribution value is the required first parameter, and the required first parameter is calibrated as the first undetermined value. If the condition is not met, it means that the magnetic field distribution value does not meet the selected parameters. Information is extracted from the leakage permeability information corresponding to the first undetermined value to obtain the leakage permeability value, and the leakage permeability value is used to determine whether the magnetic field leakage permeability exceeds the second preset value. If it does not exceed the limit, it indicates that the leakage permeability is the required second parameter, and the required second parameter is calibrated as the second undetermined value; If the value exceeds the specified limit, it indicates that the leakage permeability does not meet the selected parameters. The magnetic field direction information corresponding to the second undetermined value is extracted to obtain the magnetic field direction coordinate value; Determine whether the first feature information meets the preset value based on the magnetic field direction coordinate value; If the conditions are met, information extraction is performed on the first feature information.

[0005] Preferably, the step of inputting the first feature information as a first training sample into the historical sector permanent magnet distribution parameter model for training to obtain the second sector permanent magnet distribution value further includes: The magnetic field distribution value is used as a second training sample and input into the historical sector permanent magnet distribution parameter model to output the third parameter corresponding to the first feature information; wherein, the function of the third parameter is: F1 = (B1*s1*) 1) / h1; Where F1 is the third parameter, 1 represents the second vacuum permeability, s1 represents the area of ​​the second sector, h1 represents the length of the second magnetic field, and B1 represents the strength of the second magnetic field.

[0006] Preferably, the step of inputting the first feature information as a first training sample into the historical sector permanent magnet distribution parameter model for training to obtain the second sector permanent magnet distribution value further includes: The leakage permeability value is used as the third training sample and input into the historical sector permanent magnet distribution parameter model to output the fourth parameter corresponding to the first feature information; wherein, the function of the fourth parameter is:

[0007] in, is the fourth parameter, g is the magnetic flux value, u is the magnetic pressure, a is the cross-sectional area, h2 is the length of the third magnetic field, and l2 is the strength of the third magnetic field.

[0008] Preferably, the step of extracting the magnetic field direction information corresponding to the second undetermined value to obtain the magnetic field direction coordinate value further includes: The magnetic field direction coordinates are used as the fourth training sample and input into the historical sector permanent magnet distribution parameter model to output the fifth parameter corresponding to the magnetic field direction coordinates; wherein, the function of the fifth parameter is:

[0009] Where z is the fifth parameter, x is the X-axis coordinate of the starting point of the magnetic field direction, x1 is the X-axis coordinate of the ending point of the magnetic field direction, y is the y-axis coordinate of the starting point of the magnetic field direction, y2 is the y-axis coordinate of the ending point of the magnetic field direction, and q is the magnetic field coefficient. The distribution value of the first sector permanent magnet is calculated based on the fifth, fourth, and third parameters, wherein the calculation formula is: E=F1*B2*Z Where E is the distribution value of the first sector permanent magnet.

[0010] Preferably, the step of correcting the distribution parameters of the first sector-shaped permanent magnet to be corrected to obtain the corrected distribution value includes: Obtain the correction base number corresponding to the distribution parameters of the first sector permanent magnet to be corrected; The corrected distribution value is calculated based on the corrected base value and the distribution value of the first sector permanent magnet, wherein the calculation formula is: V=Ej Where v is the corrected distribution value and j is the corrected base number.

[0011] This application also provides a device for modeling the distributed parameters of a sector-shaped permanent magnet for an electromagnetic valve, characterized in that it includes: The first acquisition module is used to acquire first feature information of the distribution of the sector-shaped permanent magnet of the solenoid valve, wherein the first feature information includes magnetic field distribution information, leakage permeability information and magnetic field direction information; The first extraction module is used to extract information from the first feature information to obtain the distribution value of the first sector permanent magnet; The first training module is used to input the first feature information as the first training sample into the historical sector permanent magnet distribution parameter model for training, so as to obtain the second sector permanent magnet distribution value. The first judgment module is used to determine whether the distribution value of the first sector permanent magnet matches the distribution value of the second sector permanent magnet. If a match is found, the distribution value of the first sector permanent magnet is used as the distribution parameter of the first sector permanent magnet to be corrected. If there is a mismatch, the distribution value of the first sector permanent magnet will be recalculated; The first correction module is used to correct the distribution parameters of the first sector permanent magnet to be corrected, and obtain the corrected distribution value; The second judgment module is used to determine whether the corrected distribution value meets the preset value; If satisfied, the distribution value of the first sector permanent magnet is optimized based on the corrected distribution value to obtain the required distribution parameters of the sector permanent magnet.

[0012] Preferably, the first acquisition module includes: The first judgment unit is used to determine whether the first preset value is met based on the magnetic field distribution value. If the condition is met, it indicates that the magnetic field distribution value is the required first parameter, and the required first parameter is calibrated as the first undetermined value. If the condition is not met, it means that the magnetic field distribution value does not meet the selected parameters. The second judgment unit is used to extract information from the leakage permeability information corresponding to the first undetermined value, obtain the leakage permeability value, and determine whether the magnetic field leakage permeability exceeds the second preset value through the leakage permeability value. If it does not exceed the limit, it indicates that the leakage permeability is the required second parameter, and the required second parameter is calibrated as the second undetermined value; If the value exceeds the specified limit, it indicates that the leakage permeability does not meet the selected parameters. The first extraction unit is used to extract the magnetic field direction information corresponding to the second undetermined value to obtain the magnetic field direction coordinate value; The third judgment unit is used to determine whether the first feature information meets the preset value based on the magnetic field direction coordinate value. If the conditions are met, information extraction is performed on the first feature information.

[0013] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0014] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0015] The beneficial effects of this application are as follows: When it is necessary to obtain the parameters of the distribution of the sector permanent magnet of the solenoid valve, this solution first calculates the distribution parameters of the sector permanent magnet by modeling a parameter model, and then inputs the calculated distribution parameters of the sector permanent magnet into the historical distribution parameter model of the sector permanent magnet for training to obtain the second distribution value of the sector permanent magnet. The first distribution value of the sector permanent magnet is used to determine whether it matches the second distribution value of the sector permanent magnet. This can quickly determine the accuracy of the first distribution value of the sector permanent magnet. After obtaining the first distribution value of the sector permanent magnet that matches the second distribution value of the sector permanent magnet, the first distribution value of the sector permanent magnet is then corrected. This correction can quickly provide the accuracy of the first distribution value of the sector permanent magnet, thereby quickly obtaining the required distribution parameters of the sector permanent magnet and shortening the calculation cycle. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a method flow according to an embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the device structure according to an embodiment of this application.

[0018] Figure 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of this application.

[0019] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0021] like Figure 1-3 As shown, this application provides a method for modeling the distributed parameters of a sector-shaped permanent magnet for an electromagnetic valve, including: S1. Obtain the first characteristic information of the distribution of the sector-shaped permanent magnet of the solenoid valve, wherein the first characteristic information includes magnetic field distribution information, leakage magnetic permeability information and magnetic field direction information; S2. Extract information from the first feature information to obtain the distribution value of the first sector permanent magnet; S3. Input the first feature information as the first training sample into the historical sector permanent magnet distribution parameter model for training, and obtain the second sector permanent magnet distribution value; S4. Determine whether the distribution value of the first sector permanent magnet matches the distribution value of the second sector permanent magnet based on the distribution value of the first sector permanent magnet. S5. If a match is found, the distribution value of the first sector permanent magnet is used as the distribution parameter of the first sector permanent magnet to be corrected. S6. If there is a mismatch, the distribution value of the first sector permanent magnet will be recalculated. S7. Correct the distribution parameters of the first sector-shaped permanent magnet to be corrected to obtain the corrected distribution value; S8. Determine whether the corrected distribution value meets the preset value; S9. If satisfied, the distribution value of the first sector permanent magnet is optimized based on the corrected distribution value to obtain the required distribution parameters of the sector permanent magnet.

[0022] As described in steps S1-S9 above, researchers typically use the finite element method to calculate the distributed parameters of the sector permanent magnet. While this method offers high computational accuracy, the modeling process is complex and the computation cycle is long. Lumped parameter models are simple to model and have a short computation cycle, but their computational accuracy is low. Therefore, it is necessary to shorten the computation cycle by using distributed parameter modeling while maintaining a certain level of computational accuracy. In this scheme, when it is necessary to obtain the distributed parameters of the sector permanent magnet in the solenoid valve, the distributed parameters of the sector permanent magnet are first simply calculated using a parameter model, and then the calculated distributed parameters of the sector permanent magnet are... The data is input into a historical sector permanent magnet distribution parameter model for training to obtain the second sector permanent magnet distribution value. The distribution value of the first sector permanent magnet is then used to determine whether it matches the second sector permanent magnet distribution value. This allows for a rapid assessment of the accuracy of the first sector permanent magnet distribution value. After obtaining the first sector permanent magnet distribution value that matches the second sector permanent magnet distribution value, the first sector permanent magnet distribution value is then corrected. This correction provides a rapid increase in the accuracy of the first sector permanent magnet distribution value, thereby enabling the rapid acquisition of the required sector permanent magnet distribution parameters and shortening the calculation cycle.

[0023] In one embodiment, after the step of including magnetic field distribution information, leakage permeability information, and magnetic field direction information in the first feature information, the following steps are included: S10. Extract the magnetic field distribution information to obtain the magnetic field distribution value; S11. Obtain the first magnetic field strength, first magnetic field length, first sector area, and first vacuum permeability of the permanent magnet corresponding to the magnetic field distribution information; S12. Calculate the magnetic field distribution value based on the first magnetic field strength, first magnetic field length, first sector area, and first free magnetic permeability of the permanent magnet, wherein the calculation formula is: F = (B*s*) ) / h; Where F is the magnetic field distribution value, Let be the first free permeability, s be the area of ​​the first sector, h be the length of the first magnetic field, and B be the strength of the first magnetic field. S13. Determine whether the first preset value is met based on the magnetic field distribution value; S14. If satisfied, it indicates that the magnetic field distribution value is the required first parameter, and the required first parameter is marked as the first undetermined value; S15. If not satisfied, it means that the magnetic field distribution value does not meet the selected parameters. S16. Extract information from the leakage permeability information corresponding to the first undetermined value to obtain the leakage permeability value, and determine whether the magnetic field leakage permeability exceeds the second preset value through the leakage permeability value. S17. If it does not exceed the limit, it means that the leakage permeability is the required second parameter, and the required second parameter is marked as the second undetermined value; S18. If the value exceeds the limit, it indicates that the leakage permeability value does not meet the selected parameters. S19. Extract the magnetic field direction information corresponding to the second undetermined value to obtain the magnetic field direction coordinate value; S20. Determine whether the first feature information satisfies a preset value based on the magnetic field direction coordinate value; S21. If satisfied, information extraction is performed on the first feature information.

[0024] As described in steps S10-S21 above, when it is necessary to extract information from the first feature information, the obtained magnetic field distribution value is first processed. Then, the magnetic field distribution value is calculated using the magnetic field strength, magnetic field length, sector area, and vacuum permeability included in the magnetic field distribution value. Next, the magnetic field distribution value is judged to determine whether it meets a first preset value. If it does, the required first parameter is marked as a first undetermined value. Then, the leakage permeability information corresponding to the first undetermined value is extracted to obtain the leakage permeability value. Next, it is judged whether the leakage permeability value exceeds a second preset value. If it does not exceed the second preset value, it is indicated that the leakage permeability value is the required second parameter. The required second parameter is calibrated as a second undetermined value. If it exceeds the value, it indicates that the leakage permeability does not meet the selected parameter. The magnetic field direction information corresponding to the second undetermined value is extracted to obtain the magnetic field direction coordinate value. The magnetic field direction coordinate value is used to determine whether the first feature information meets the preset value. If it does, the first feature information is extracted. By progressively decomposing and calculating the first feature information of the solenoid valve sector permanent magnet distribution, it is possible to avoid an error in one parameter affecting the calculation of the entire solenoid valve sector permanent magnet distribution parameter, thereby effectively improving the efficiency of calculating the solenoid valve sector permanent magnet distribution parameter.

[0025] In one embodiment, the step of inputting the first feature information as a first training sample into a historical sector permanent magnet distribution parameter model for training to obtain the second sector permanent magnet distribution value further includes: S22. Input the magnetic field distribution value as a second training sample into the historical sector permanent magnet distribution parameter model, and output the third parameter corresponding to the first feature information; wherein, the function of the third parameter is: F1 = (B1*s1*) 1) / h1; Where F1 is the third parameter, 1 represents the second vacuum permeability, s1 represents the area of ​​the second sector, h1 represents the length of the second magnetic field, and B1 represents the strength of the second magnetic field.

[0026] As described in step S22 above, when it is necessary to calculate the third parameter corresponding to the first feature information, the product obtained by multiplying the second magnetic field strength by the second sector area by the second vacuum permeability and dividing it by the second magnetic field length can be calculated to obtain the third parameter corresponding to the magnetic field distribution value. Calculating the third parameter corresponding to the magnetic field distribution value separately can improve the accuracy of the third parameter.

[0027] In one embodiment, the step of inputting the first feature information as a first training sample into a historical sector permanent magnet distribution parameter model for training to obtain the second sector permanent magnet distribution value further includes: S23. Input the leakage permeability value as the third training sample into the historical sector permanent magnet distribution parameter model, and output the fourth parameter corresponding to the first feature information; wherein, the function of the fourth parameter is:

[0028] in, is the fourth parameter, g is the magnetic flux value, u is the magnetic pressure, a is the cross-sectional area, h2 is the length of the third magnetic field, and l2 is the strength of the third magnetic field.

[0029] As described in step S23 above, when it is necessary to calculate the fourth parameter corresponding to the leakage permeability, the product of the magnetic flux value and the magnetic pressure is obtained by dividing the product by the cross-sectional area. Then, the quotient is multiplied by the length of the third magnetic field and the strength of the third magnetic field. In this way, the fourth parameter corresponding to the leakage permeability can be calculated. Calculating the fourth parameter corresponding to the leakage permeability separately can improve the accuracy of the fourth parameter and avoid the need to recalculate the whole system due to errors in the calculation of the fourth parameter.

[0030] In one embodiment, the step of extracting the magnetic field direction information corresponding to the second undetermined value to obtain the magnetic field direction coordinate value further includes: S24. Input the magnetic field direction coordinates as the fourth training sample into the historical sector permanent magnet distribution parameter model, and output the fifth parameter corresponding to the magnetic field direction coordinates; wherein, the function of the fifth parameter is:

[0031] Where z is the fifth parameter, x is the X-axis coordinate of the starting point of the magnetic field direction, x1 is the X-axis coordinate of the ending point of the magnetic field direction, y is the y-axis coordinate of the starting point of the magnetic field direction, y2 is the y-axis coordinate of the ending point of the magnetic field direction, and q is the magnetic field coefficient. S25. Calculate the distribution value of the first sector-shaped permanent magnet based on the fifth, fourth, and third parameters, wherein the calculation formula is: E=F1*B2*Z Where E is the distribution value of the first sector permanent magnet.

[0032] As described in steps S24-25 above, when it is necessary to calculate the fifth parameter corresponding to the magnetic field direction coordinates, the difference between the X-axis coordinate at the beginning of the magnetic field direction and the X-axis coordinate at the end of the magnetic field direction is obtained by subtracting the X-axis coordinate at the end of the magnetic field direction from the X-axis coordinate at the beginning of the magnetic field direction. The difference is then divided by the difference between the Y-axis coordinate at the beginning and the Y-axis coordinate at the end of the magnetic field direction. The quotient is then multiplied by the magnetic field coefficient. This allows the fifth parameter corresponding to the magnetic field direction coordinates to be calculated separately, avoiding the need to recalculate the whole due to an error in the calculation of the fifth parameter. After obtaining the fifth, fourth, and third parameters, the distribution value of the first sector permanent magnet is calculated using the fifth, fourth, and third parameters. This allows the required distribution value of the first sector permanent magnet to be obtained quickly.

[0033] In one embodiment, the step of correcting the distribution parameters of the first sector-shaped permanent magnet to be corrected to obtain the corrected distribution value includes: S26. Obtain the correction base number corresponding to the distribution parameters of the first sector permanent magnet to be corrected; S27. Calculate the corrected distribution value based on the corrected base number and the distribution value of the first sector permanent magnet, wherein the calculation formula is: V=Ej Where v is the corrected distribution value and j is the corrected base number.

[0034] As described in steps S26-27 above, after calculating the distribution value of the first sector permanent magnet, the distribution value of the first sector permanent magnet needs to be corrected. This can improve the accuracy of the distribution value of the first sector permanent magnet based on the correction base. Specifically, the corrected distribution value is calculated by multiplying the correction base by the distribution value of the first sector permanent magnet. In this way, the accuracy of the distribution value of the first sector permanent magnet can be improved after correction, and the calculation cycle can be shortened.

[0035] This application also provides a device for modeling the distributed parameters of a sector-shaped permanent magnet for an electromagnetic valve, characterized in that it includes: The first acquisition module 1 is used to acquire first feature information of the distribution of the sector-shaped permanent magnet of the solenoid valve, wherein the first feature information includes magnetic field distribution information, leakage magnetic permeability information and magnetic field direction information; The first extraction module 2 is used to extract information from the first feature information to obtain the distribution value of the first sector permanent magnet; The first training module 3 is used to input the first feature information as the first training sample into the historical sector permanent magnet distribution parameter model for training, so as to obtain the second sector permanent magnet distribution value. The first judgment module 4 is used to determine whether the distribution value of the first sector permanent magnet matches the distribution value of the second sector permanent magnet. If a match is found, the distribution value of the first sector permanent magnet is used as the distribution parameter of the first sector permanent magnet to be corrected. If there is a mismatch, the distribution value of the first sector permanent magnet will be recalculated; The first correction module 5 is used to correct the distribution parameters of the first sector permanent magnet to be corrected, and obtain the corrected distribution value; The second judgment module 6 is used to determine whether the corrected distribution value meets the preset value; If satisfied, the distribution value of the first sector permanent magnet is optimized based on the corrected distribution value to obtain the required distribution parameters of the sector permanent magnet.

[0036] In one embodiment, the first acquisition module includes: The first judgment unit is used to determine whether the first preset value is met based on the magnetic field distribution value. If the condition is met, it indicates that the magnetic field distribution value is the required first parameter, and the required first parameter is calibrated as the first undetermined value. If the condition is not met, it means that the magnetic field distribution value does not meet the selected parameters. The second judgment unit is used to extract information from the leakage permeability information corresponding to the first undetermined value, obtain the leakage permeability value, and determine whether the magnetic field leakage permeability exceeds the second preset value through the leakage permeability value. If it does not exceed the limit, it indicates that the leakage permeability is the required second parameter, and the required second parameter is calibrated as the second undetermined value; If the value exceeds the specified limit, it indicates that the leakage permeability does not meet the selected parameters. The first extraction unit is used to extract the magnetic field direction information corresponding to the second undetermined value to obtain the magnetic field direction coordinate value; The third judgment unit is used to determine whether the first feature information meets the preset value based on the magnetic field direction coordinate value. If the conditions are met, information extraction is performed on the first feature information.

[0037] like Figure 3 As shown, this application also provides a computer device, which can be a server, and its internal structure can be as follows: Figure 3As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores all data required for the automatic output power adjustment method of the energy storage inverter. The network interface is used for communication with external terminals via a network connection. When the processor executes the computer program, it implements the automatic output power adjustment method of the energy storage inverter.

[0038] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment on which the present application is applied.

[0039] One embodiment of this application also provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements any of the above-described methods for modeling the distributed parameters of a sector-shaped permanent magnet for an electromagnetic valve. Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0040] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0041] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0042] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components. Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of one or more computer-usable storage media (including but not limited to disk storage, CD-ROMs) containing computer-usable program code. The form of a computer program product implemented on ROM, optical memory, etc.

[0043] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0044] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 The function specified in one or more boxes.

[0045] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

Claims

1. A method for modeling the distributed parameters of a sector-shaped permanent magnet for an electromagnetic valve, characterized in that, include: The first characteristic information of the distribution of the sector-shaped permanent magnet of the solenoid valve is obtained, wherein the first characteristic information includes magnetic field distribution information, leakage permeability information and magnetic field direction information; The magnetic field distribution information is extracted to obtain the magnetic field distribution value; Obtain the first magnetic field strength, first magnetic field length, first sector area, and first vacuum permeability of the permanent magnet corresponding to the magnetic field distribution information; The magnetic field distribution value is calculated based on the first magnetic field strength, first magnetic field length, first sector area, and first free magnetic permeability of the permanent magnet, wherein the calculation formula is: F=(B*s* ) / h; Where F is the magnetic field distribution value, Let be the first free permeability, s be the area of ​​the first sector, h be the length of the first magnetic field, and B be the strength of the first magnetic field. Determine whether the first preset value is met based on the magnetic field distribution value; If the condition is met, it indicates that the magnetic field distribution value is the required first parameter, and the required first parameter is calibrated as the first undetermined value. If the condition is not met, it means that the magnetic field distribution value does not meet the selected parameters. Information is extracted from the leakage permeability information corresponding to the first undetermined value to obtain the leakage permeability value, and the leakage permeability value is used to determine whether the magnetic field leakage permeability exceeds the second preset value. If it does not exceed the limit, it indicates that the leakage permeability is the required second parameter, and the required second parameter is calibrated as the second undetermined value; If the value exceeds the specified limit, it indicates that the leakage permeability does not meet the selected parameters. The magnetic field direction information corresponding to the second undetermined value is extracted to obtain the magnetic field direction coordinate value; Determine whether the first feature information meets the preset value based on the magnetic field direction coordinate value; If the conditions are met, information is extracted from the first feature information to obtain the distribution value of the first sector permanent magnet; The first feature information is used as the first training sample and input into the historical sector permanent magnet distribution parameter model for training to obtain the second sector permanent magnet distribution value; Determine whether the distribution value of the first sector permanent magnet matches the distribution value of the second sector permanent magnet based on the distribution value of the first sector permanent magnet. If a match is found, the distribution value of the first sector permanent magnet is used as the distribution parameter of the first sector permanent magnet to be corrected. If there is a mismatch, the distribution value of the first sector permanent magnet will be recalculated; The distribution parameters of the first sector-shaped permanent magnet to be corrected are corrected to obtain the corrected distribution value; Determine whether the corrected distribution value meets the preset value; If satisfied, the distribution value of the first sector permanent magnet is optimized based on the corrected distribution value to obtain the required distribution parameters of the sector permanent magnet.

2. The method for modeling the distributed parameters of a sector-shaped permanent magnet for an electromagnetic valve according to claim 1, characterized in that, The step of inputting the first feature information as a first training sample into the historical sector permanent magnet distribution parameter model for training to obtain the second sector permanent magnet distribution value further includes: The magnetic field distribution value is used as a second training sample and input into the historical sector permanent magnet distribution parameter model to output the third parameter corresponding to the first feature information; wherein, the function of the third parameter is: F1=(B1*s1* 1) / h1; Where F1 is the third parameter, 1 represents the second vacuum permeability, s1 represents the area of ​​the second sector, h1 represents the length of the second magnetic field, and B1 represents the strength of the second magnetic field.

3. The method for modeling the distributed parameters of a sector-shaped permanent magnet for an electromagnetic valve according to claim 2, characterized in that, The step of inputting the first feature information as a first training sample into the historical sector permanent magnet distribution parameter model for training to obtain the second sector permanent magnet distribution value further includes: The leakage permeability value is used as the third training sample and input into the historical sector permanent magnet distribution parameter model to output the fourth parameter corresponding to the first feature information; wherein, the function of the fourth parameter is: in, is the fourth parameter, g is the magnetic flux value, u is the magnetic pressure, a is the cross-sectional area, h2 is the length of the third magnetic field, and l2 is the strength of the third magnetic field.

4. The method for modeling the distributed parameters of a sector-shaped permanent magnet for an electromagnetic valve according to claim 3, characterized in that, The step of extracting the magnetic field direction information corresponding to the second undetermined value to obtain the magnetic field direction coordinate value further includes: The magnetic field direction coordinates are used as the fourth training sample and input into the historical sector permanent magnet distribution parameter model to output the fifth parameter corresponding to the magnetic field direction coordinates; wherein, the function of the fifth parameter is: Where z is the fifth parameter, x is the X-axis coordinate of the starting point of the magnetic field direction, x1 is the X-axis coordinate of the ending point of the magnetic field direction, y is the y-axis coordinate of the starting point of the magnetic field direction, y2 is the y-axis coordinate of the ending point of the magnetic field direction, and q is the magnetic field coefficient. The distribution value of the first sector permanent magnet is calculated based on the fifth, fourth, and third parameters, wherein the calculation formula is: E=F1*B2*Z Where E is the distribution value of the first sector permanent magnet.

5. The method for modeling the distributed parameters of a sector-shaped permanent magnet for an electromagnetic valve according to claim 4, characterized in that, The step of correcting the distribution parameters of the first sector-shaped permanent magnet to be corrected to obtain the corrected distribution value includes: Obtain the correction base number corresponding to the distribution parameters of the first sector permanent magnet to be corrected; The corrected distribution value is calculated based on the corrected base value and the distribution value of the first sector permanent magnet, wherein the calculation formula is: V=Ej Where v is the corrected distribution value and j is the corrected base number.

6. A device for modeling the distributed parameters of a sector-shaped permanent magnet for an electromagnetic valve, used to execute the method for modeling the distributed parameters of a sector-shaped permanent magnet for an electromagnetic valve as described in any one of claims 1-5, characterized in that, include: The first acquisition module is used to acquire first feature information of the distribution of the sector-shaped permanent magnet of the solenoid valve, wherein the first feature information includes magnetic field distribution information, leakage permeability information and magnetic field direction information; The first extraction module is used to extract information from the first feature information to obtain the distribution value of the first sector permanent magnet; The first training module is used to input the first feature information as the first training sample into the historical sector permanent magnet distribution parameter model for training, so as to obtain the second sector permanent magnet distribution value. The first judgment module is used to determine whether the distribution value of the first sector permanent magnet matches the distribution value of the second sector permanent magnet. If a match is found, the distribution value of the first sector permanent magnet is used as the distribution parameter of the first sector permanent magnet to be corrected. If there is a mismatch, the distribution value of the first sector permanent magnet will be recalculated; The first correction module is used to correct the distribution parameters of the first sector permanent magnet to be corrected, and obtain the corrected distribution value; The second judgment module is used to determine whether the corrected distribution value meets the preset value; If satisfied, the distribution value of the first sector permanent magnet is optimized based on the corrected distribution value to obtain the required distribution parameters of the sector permanent magnet.

7. The device for modeling the distributed parameters of a sector-shaped permanent magnet for an electromagnetic valve according to claim 6, characterized in that, The first acquisition module includes: The first judgment unit is used to determine whether the first preset value is met based on the magnetic field distribution value. If the condition is met, it indicates that the magnetic field distribution value is the required first parameter, and the required first parameter is calibrated as the first undetermined value. If the condition is not met, it means that the magnetic field distribution value does not meet the selected parameters. The second judgment unit is used to extract information from the leakage permeability information corresponding to the first undetermined value, obtain the leakage permeability value, and determine whether the magnetic field leakage permeability exceeds the second preset value through the leakage permeability value. If it does not exceed the limit, it indicates that the leakage permeability is the required second parameter, and the required second parameter is calibrated as the second undetermined value; If the value exceeds the specified limit, it indicates that the leakage permeability does not meet the selected parameters. The first extraction unit is used to extract the magnetic field direction information corresponding to the second undetermined value to obtain the magnetic field direction coordinate value; The third judgment unit is used to determine whether the first feature information meets the preset value based on the magnetic field direction coordinate value. If the conditions are met, information extraction is performed on the first feature information.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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