A control method and system applied to a brake pad shot blasting machine and a medium

Abstract

This invention relates to a control method, system, and medium for a brake pad shot blasting machine. The method is based on a brake pad steel backing conveyor system and a polishing machine unit. The method includes: U1. Acquiring speed data and quantity data of the brake pad steel backing conveyor belt, and acquiring real-time shot output quantity data based on a laser counting sensor on the shot blasting machine; U2. Constructing a predictive model for the shot output quantity based on the speed data, quantity data, and output quantity data of the brake pad steel backing conveyor belt. This invention not only improves energy storage efficiency and reduces energy loss, but also optimizes the input and output of energy storage, further ensuring the rational use of energy and improving energy utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of brake pad precision machining technology, and in particular to a control method, system and medium for brake pad shot blasting machines. Background Technology

[0002] With the continuous development of automation technology, the processing technology for brake pads is also constantly improving. After the initial forming of the brake pad steel backing, further finishing is required. This is achieved by using a shot blasting machine to polish and grind the initially formed brake pad, resulting in a finished brake pad. The number of shot blasts produced affects the finishing of the brake pad; too many shots consume more energy, while too few shots result in substandard processing. Therefore, how to precisely control the shot blasting machine to complete the finishing of the steel backing has become a problem that urgently needs to be solved.

[0003] In the prior art, patent application (application number: 202211618831.6) discloses a feeding mechanism, working method, and shot blasting machine for a shot blasting machine. The feeding mechanism of the shot blasting machine includes: a support assembly, including a support frame, a fixed frame, an extension frame, and a fixed frame. Fixed frames are provided at both ends of the support frame, and an extension frame is provided at one end of each fixed frame. A fixed frame is provided on the extension frame; an inner conveyor belt assembly, including a fixed roller, a positioning roller, and an inner conveyor belt. The fixed roller and the positioning roller are disposed on the fixed frame. The conveyor belts are mounted on the fixed and positioning rollers, and the fixed frame is V-shaped. The inner and outer conveyor belt assemblies are installed inside the fixed cover of the shot blasting assembly. The inner conveyor belt is mounted on the fixed and positioning rollers, and the outer conveyor belt is mounted on the moving roller. By rotating the handwheel, the worm gear on the rotating rod rotates, driving the worm wheel and lifting bolt to rotate. The lifting bolt moves up and down relative to the fixed frame, thereby adjusting the distance between the inner and outer conveyor belts. Then, the shot blasting head and nozzle are used to shot blast the products between the inner and outer conveyor belts. However, this solution does not have precise control over the number of shots blasted, and does not achieve automation, which will affect the finishing of the workpiece. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the present invention provides a control method, system and medium for a brake pad shot blasting machine. It can not only accurately control the shot blasting machine and improve the precision of the finishing of the brake pad steel backing, but also accurately control the number of shot blasts according to the number of brake pad steel backings and the speed of the conveyor belt, thereby reducing energy consumption.

[0005] To achieve the above and other related objectives, the present invention provides the following technical solution:

[0006] A control method for a brake pad shot blasting machine, the method being implemented based on a brake pad steel-backed track conveying system and a polishing machine unit, the method comprising:

[0007] U1. Obtain speed data of the brake pad steel back conveyor belt and quantity data of the brake pad steel back, and obtain real-time data of shot blasting output quantity based on the laser counting sensor on the shot blasting machine;

[0008] U2. Based on the speed data of the brake pad steel back conveyor belt, the quantity data of the brake pad steel back, and the data of the shot blasting output quantity, a prediction model for the shot blasting output quantity is constructed to predict the shot blasting output quantity and obtain the predicted shot blasting output quantity data.

[0009] U3. Based on the predicted shot blasting output quantity data, the shot blasting output quantity of the shot blasting machine is optimized by using the distribution estimation optimization algorithm that integrates the golden sine, and the optimized shot blasting output quantity data of the shot blasting machine is obtained.

[0010] U4. Based on the optimized shot blasting output quantity data of the shot blasting machine, establish the energy loss function G of the shot blasting machine, evaluate the shot blasting output quantity of the shot blasting machine, and output the energy loss assessment data of the shot blasting machine.

[0011] Furthermore, the method also includes:

[0012] U5. Based on the energy loss assessment data of the shot blasting machine, a preset threshold is set. If the energy loss assessment data of the shot blasting machine is less than the preset threshold, the control data of the shot blasting machine is output. If the energy loss assessment data of the shot blasting machine is greater than the preset threshold, the energy storage optimization is not completed. Repeat steps U3-U4 until the control data of the shot blasting machine is output.

[0013] Furthermore, in step U2, the construction of a prediction model for the shot blasting output quantity, which predicts the energy storage of the shot blasting machine, includes:

[0014] U21. Based on the speed data of the brake pad steel backing conveyor belt, the quantity data of the brake pad steel backing, and the output data of shot blasting, establish a relational mapping function M for the finishing of the brake pad steel backing.

[0015] ,

[0016] Where x1 is the speed data of the conveyor belt for brake pad steel backing, x2 is the quantity data of brake pad steel backing, x3 is the output data of shot blasting, x4 is the precision data of brake pad steel backing finishing, and α, β and γ are the relationship factors of brake pad steel backing shot blasting finishing precision, thus obtaining the relationship data of brake pad steel backing finishing.

[0017] U22. Based on the relationship data information of the brake pad steel backing finishing, establish a shot blasting output quantity prediction function H of the shot blasting machine.

[0018] ,

[0019] Where y is the relational data information of the brake pad steel backing precision machining, and ω1 and ω2 are the shot blasting output quantity prediction constant parameters of the shot blasting machine;

[0020] U23. Based on the shot blasting output quantity prediction function H of the shot blasting machine, the shot blasting output quantity is predicted to obtain the predicted shot blasting output quantity data information.

[0021] Furthermore, based on the speed data of the conveyor belt for the brake pad steel backing, the quantity data of the brake pad steel backing, and the output data of the shot blasting, constraints are applied to the shot blasting accuracy relationship factors α, β, and γ of the brake pad steel backing, and a constraint function f is established.

[0022] .

[0023] Furthermore, the constant parameters ω1 and ω2 for predicting the shot blasting output quantity of the shot blasting machine are,

[0024] ,

[0025] ,

[0026] ,

[0027] Where n is a positive integer, y0 is the relation prediction series of the shot blasting machine, and y is the relation data information of the brake pad steel backing finishing.

[0028] Furthermore, in step U3, the optimization of the shot blasting output quantity of the shot blasting machine using the distribution estimation optimization algorithm based on the fused golden sine wave includes:

[0029] U31. Based on the predicted shot blasting output quantity data information, construct a shot blasting output quantity data population of the shot blasting machine, and initialize it to obtain the initialized shot blasting output quantity data population of the shot blasting machine, and determine the number of cycles L;

[0030] U32. Based on the initialized shot blasting machine's shot blasting output quantity data population, establish a population individual fitness function P that incorporates the golden sine curve.

[0031] ,

[0032] Among them, z jLet z be the j-th individual in the population of shot blasting output data of the initialized shot blasting machine. j+1 The (j+1)th individual in the population of shot blasting output data of the initialized shot blasting machine is given, where ρ is the golden sine factor. The fitness values ​​of individuals in the population are calculated to obtain the fitness value data information of individuals in the population of shot blasting output data of the shot blasting machine.

[0033] U33. Based on the fitness values ​​of individuals in the shot blasting output population of the shot blasting machine, establish a probability function Q for the shot blasting output subpopulation.

[0034] ,

[0035] Among them, a k This refers to the fitness values ​​of individuals in a shot blasting machine population, where k is the sample size and θ is the output quantity. k δ is the adaptive regulator for individuals in the population. k The weight coefficients of individuals in the population are used to obtain the subpopulation probability data information of the dominant shot blasting output quantity of the shot blasting machine;

[0036] U34. Based on the probability data of the shot blasting output quantity subpopulation of the shot blasting machine, random sampling is performed to generate a new population. Steps U32-U33 are repeated until the optimized shot blasting output quantity data of the shot blasting machine is obtained. The shot blasting output quantity of the shot blasting machine is then optimized and controlled to obtain the optimized shot blasting output quantity data of the shot blasting machine.

[0037] Furthermore, the golden sine factor ρ is,

[0038] ,

[0039] Among them, z j Let j be the j-th individual in the population of shot blasting output data of the shot blasting machine after initialization, where c1 and c2 are the golden coefficients and µ is a constant factor.

[0040] The optimized control function g2 for the shot blasting output quantity of the shot blasting machine is as follows:

[0041] ,

[0042] Where b represents the optimized shot blasting output quantity data of the shot blasting machine, and σ3 and σ4 are the input quantity control optimization factors.

[0043] Furthermore, the energy loss function G of the shot blasting machine is,

[0044] ,

[0045] Wherein, λ1 and λ2 are the energy loss decision factors of the shot blasting machine, h1 is the input data of the shot blasting machine, and h2 is the output data of the optimized shot blasting machine.

[0046] To achieve the above and other related objectives, the present invention also provides a control system for a brake pad shot blasting machine, including a computer device programmed or configured to perform the steps of any of the control methods for a brake pad shot blasting machine described in the present invention.

[0047] To achieve the above and other related objectives, the present invention also provides a computer-readable storage medium storing a computer program programmed or configured to perform any of the control methods for a brake pad shot blasting machine described in the present invention.

[0048] The present invention has the following positive effects:

[0049] 1. This invention constructs a predictive model for the shot blasting output quantity to predict the shot blasting output quantity, and combines it with a distribution estimation optimization algorithm based on the fusion of the golden sine wave to optimize the shot blasting output quantity of the shot blasting machine. This not only improves the efficiency of brake pad steel backing finishing and reduces energy consumption, but also optimizes the shot blasting output quantity of the shot blasting machine based on the quantity of brake pad steel backing and the speed of the conveyor belt, further ensuring the rational use of energy and improving the processing accuracy of brake pad steel backing.

[0050] 2. This invention evaluates the shot blasting output quantity by establishing an energy loss function G for the shot blasting machine. This not only allows for adaptive adjustment of the shot blasting output quantity during the shot blasting process, ensuring the processing efficiency of brake pad steel backing while improving energy utilization, but also enables real-time monitoring of the shot blasting machine, ensuring its normal operation and improving safety. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0052] Figure 2 This is a schematic diagram of the process for constructing a predictive model for shot blasting output quantity according to the present invention;

[0053] Figure 3 This is a flowchart illustrating the distribution estimation optimization algorithm using the fused golden sine curve of the present invention.

[0054] Figure 4 This is a schematic diagram of the brake pad steel back track conveying system and shot blasting machine of the present invention.

[0055] The labels in the diagram are as follows: 1-polishing machine, 2-brake pad steel back conveyor belt, 3-laser counting sensor. Detailed Implementation

[0056] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0057] Example 1: As Figure 1 or Figure 4 As shown, a control method for a brake pad shot blasting machine is described. This method is based on a brake pad steel back track conveying system and a polishing machine unit. The method includes:

[0058] U1. Obtain speed data of the brake pad steel back conveyor belt and quantity data of the brake pad steel back, and obtain real-time data of shot blasting output quantity based on the laser counting sensor on the shot blasting machine;

[0059] U2. Based on the speed data of the brake pad steel back conveyor belt, the quantity data of the brake pad steel back, and the data of the shot blasting output quantity, a prediction model for the shot blasting output quantity is constructed to predict the shot blasting output quantity and obtain the predicted shot blasting output quantity data.

[0060] U3. Based on the predicted shot blasting output quantity data, the shot blasting output quantity of the shot blasting machine is optimized by using the distribution estimation optimization algorithm that integrates the golden sine, and the optimized shot blasting output quantity data of the shot blasting machine is obtained.

[0061] U4. Based on the optimized shot blasting output quantity data of the shot blasting machine, establish the energy loss function G of the shot blasting machine, evaluate the shot blasting output quantity of the shot blasting machine, and output the energy loss assessment data of the shot blasting machine.

[0062] In this embodiment, the method further includes:

[0063] U5. Based on the energy loss assessment data of the shot blasting machine, a preset threshold is set. If the energy loss assessment data of the shot blasting machine is less than the preset threshold, the control data of the shot blasting machine is output. If the energy loss assessment data of the shot blasting machine is greater than the preset threshold, the energy storage optimization is not completed. Repeat steps U3-U4 until the control data of the shot blasting machine is output.

[0064] In this embodiment, as Figure 2 As shown, in step U2, the construction of a prediction model for the shot blasting output quantity, which predicts the energy storage of the shot blasting machine, includes:

[0065] U21. Based on the speed data of the brake pad steel backing conveyor belt, the quantity data of the brake pad steel backing, and the shot blasting output quantity data, establish a relational mapping function M for brake pad finishing.

[0066] ,

[0067] Where x1 is the speed data of the conveyor belt for brake pad steel backing, x2 is the quantity data of brake pad steel backing, x3 is the output data of shot blasting, x4 is the precision data of brake pad steel backing finishing, and α, β and γ are the relationship factors of brake pad steel backing shot blasting finishing precision, thus obtaining the relationship data of brake pad steel backing finishing.

[0068] U22. Based on the relationship data information of the brake pad steel backing finishing, establish a shot blasting output quantity prediction function H of the shot blasting machine.

[0069] ,

[0070] Where y is the relational data information of the brake pad steel backing precision machining, and ω1 and ω2 are the shot blasting output quantity prediction constant parameters of the shot blasting machine;

[0071] U23. Based on the shot blasting output quantity prediction function H of the shot blasting machine, the shot blasting output quantity is predicted to obtain the predicted shot blasting output quantity data information.

[0072] In this embodiment, based on the speed data of the brake pad steel backing conveyor belt, the quantity data of the brake pad steel backing, and the output data of shot blasting, constraints are applied to the shot blasting accuracy relationship factors α, β, and γ of the brake pad steel backing, establishing a constraint function f.

[0073] .

[0074] In this embodiment, the constant parameters ω1 and ω2 for predicting the shot blasting output quantity of the shot blasting machine are:

[0075] ,

[0076] ,

[0077] ,

[0078] Where n is a positive integer, y0 is the relation prediction series of the shot blasting machine, and y is the relation data information of the brake pad steel backing finishing.

[0079] Example 2: Based on the control method applied to a brake pad shot blasting machine in Example 1, the present invention will be further explained and described below.

[0080] like Figure 1 or Figure 4 As shown, a control method for a brake pad shot blasting machine is described. This method is based on a brake pad steel back track conveying system and a polishing machine unit. The method includes:

[0081] U1. Obtain speed data and brake pad quantity data of the brake pad steel back conveyor belt, and obtain real-time data of shot blasting output quantity based on the laser counting sensor on the shot blasting machine;

[0082] U2. Based on the speed data of the brake pad steel back conveyor belt, the quantity data of the brake pad steel back, and the data of the shot blasting output quantity, a prediction model for the shot blasting output quantity is constructed to predict the shot blasting output quantity and obtain the predicted shot blasting output quantity data.

[0083] U3. Based on the predicted shot blasting output quantity data, the shot blasting output quantity of the shot blasting machine is optimized by using the distribution estimation optimization algorithm that integrates the golden sine, and the optimized shot blasting output quantity data of the shot blasting machine is obtained.

[0084] U4. Based on the optimized shot blasting output quantity data of the shot blasting machine, establish the energy loss function G of the shot blasting machine, evaluate the shot blasting output quantity of the shot blasting machine, and output the energy loss assessment data of the shot blasting machine.

[0085] In this embodiment, as Figure 3 As shown, in step U3, the optimization of the shot blasting output quantity of the shot blasting machine using the distribution estimation optimization algorithm based on the fused golden sine wave includes:

[0086] U31. Based on the predicted shot blasting output quantity data information, construct a shot blasting output quantity data population of the shot blasting machine, and initialize it to obtain the initialized shot blasting output quantity data population of the shot blasting machine, and determine the number of cycles L;

[0087] U32. Based on the initialized shot blasting machine's shot blasting output quantity data population, establish a population individual fitness function P that incorporates the golden sine curve.

[0088] ,

[0089] Among them, z j Let z be the j-th individual in the population of shot blasting output data of the initialized shot blasting machine. j+1 The (j+1)th individual in the population of shot blasting output data of the initialized shot blasting machine is given, where ρ is the golden sine factor. The fitness values ​​of individuals in the population are calculated to obtain the fitness value data information of individuals in the population of shot blasting output data of the shot blasting machine.

[0090] U33. Based on the fitness values ​​of individuals in the shot blasting output population of the shot blasting machine, establish a probability function Q for the shot blasting output subpopulation.

[0091] ,

[0092] Among them, a k This refers to the fitness values ​​of individuals in a shot blasting machine population, where k is the sample size and θ is the output quantity. k δ is the adaptive regulator for individuals in the population. k The weight coefficients of individuals in the population are used to obtain the subpopulation probability data information of the dominant shot blasting output quantity of the shot blasting machine;

[0093] U34. Based on the probability data of the shot blasting output quantity subpopulation of the shot blasting machine, random sampling is performed to generate a new population. Steps U32-U33 are repeated until the optimized shot blasting output quantity data of the shot blasting machine is obtained. The shot blasting output quantity of the shot blasting machine is then optimized and controlled to obtain the optimized shot blasting output quantity data of the shot blasting machine.

[0094] In this embodiment, the golden sine factor ρ is,

[0095] ,

[0096] Among them, z j Let j be the j-th individual in the population of shot blasting output data of the shot blasting machine after initialization, where c1 and c2 are the golden coefficients and µ is a constant factor.

[0097] The input optimization control function g1 of the shot blasting machine is:

[0098] ,

[0099] Where b represents the optimized shot blasting output quantity data of the shot blasting machine, and σ1 and σ2 are the input quantity control optimization factors;

[0100] The output optimization control function g2 of the shot blasting machine is:

[0101] ,

[0102] Where b represents the optimized shot blasting output quantity data of the shot blasting machine, and σ3 and σ4 are the input quantity control optimization factors.

[0103] In this embodiment, the energy loss function G of the shot blasting machine is,

[0104] ,

[0105] Wherein, λ1 and λ2 are the energy loss decision factors of the shot blasting machine, h1 is the input data of the shot blasting machine, and h2 is the output data of the optimized shot blasting machine.

[0106] In this embodiment, as Figure 4 As shown, the steel back is conveyed to the shot blasting machine 1 via the brake pad steel back conveyor belt 3, and then enters the shot blasting machine 1 for precision processing. The shot blasting machine 1 is equipped with a laser counting sensor 2 to complete the operation.

[0107] In this embodiment, the present invention provides a control system for a brake pad shot blasting machine, including a computer device that is programmed or configured to perform the steps of any of the control methods for a brake pad shot blasting machine described above.

[0108] In this embodiment, the present invention provides a computer-readable storage medium storing a computer program programmed or configured to perform any of the control methods for a brake pad shot blasting machine described in the present invention.

[0109] Any references to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may 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 data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0110] In summary, this invention not only improves energy storage efficiency and reduces energy loss, but also optimizes the input and output of energy storage, further ensuring the rational use of energy and improving energy utilization rate.

[0111] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A control method for a brake pad shot blasting machine, the method being based on a brake pad steel-backed track conveyor system and a polishing machine unit, characterized in that... The method includes: U1. Obtain speed data of the brake pad steel back conveyor belt and quantity data of the brake pad steel back, and obtain real-time data of shot blasting output quantity based on the laser counting sensor on the shot blasting machine; U2. Based on the speed data of the brake pad steel back conveyor belt, the quantity data of the brake pad steel back, and the data of the shot blasting output quantity, a prediction model for the shot blasting output quantity is constructed to predict the shot blasting output quantity and obtain the predicted shot blasting output quantity data. U3. Based on the predicted shot blasting output quantity data, the shot blasting output quantity of the shot blasting machine is optimized by using the distribution estimation optimization algorithm that integrates the golden sine, and the optimized shot blasting output quantity data of the shot blasting machine is obtained. U4. Based on the optimized shot blasting output quantity data of the shot blasting machine, establish the energy loss function G of the shot blasting machine, evaluate the shot blasting output quantity of the shot blasting machine, and output the energy loss assessment data of the shot blasting machine. The method further includes: U5. Based on the energy loss assessment data of the shot blasting machine, a preset threshold is set. If the energy loss assessment data of the shot blasting machine is less than the preset threshold, the control data of the shot blasting machine is output. If the energy loss assessment data of the shot blasting machine is greater than the preset threshold, the energy storage optimization is not completed. Repeat steps U3-U4 until the control data of the shot blasting machine is output. In step U2, the construction of a prediction model for the shot blasting output quantity, which predicts the energy storage of the shot blasting machine, includes: U21. Based on the speed data of the brake pad steel backing conveyor belt, the quantity data of the brake pad steel backing, and the output data of shot blasting, establish a relational mapping function M for the finishing of the brake pad steel backing. ， The relationship data information of brake pad steel backing precision machining is obtained, where x1 is the speed data information of the brake pad steel backing conveyor belt, x2 is the quantity data information of brake pad steel backing, x3 is the data information of the shot blasting output quantity, x4 is the precision data information of brake pad steel backing precision machining, and α, β and γ are the relationship factors of brake pad steel backing shot blasting machining precision. U22. Based on the relationship data information of the brake pad steel backing finishing, establish a shot blasting output quantity prediction function H of the shot blasting machine. ， Where y is the relational data information of the brake pad steel backing precision machining, and ω1 and ω2 are the shot blasting output quantity prediction constant parameters of the shot blasting machine; U23. Based on the shot blasting output quantity prediction function H of the shot blasting machine, the shot blasting output quantity is predicted to obtain the predicted shot blasting output quantity data information.

2. The control method for a brake pad shot blasting machine according to claim 1, characterized in that: Based on the speed data of the conveyor belt for the brake pad steel backing, the quantity data of the brake pad steel backing, and the output data of the shot blasting, constraints are applied to the shot blasting accuracy factors α, β, and γ of the brake pad steel backing, and a constraint function f is established. ； The precision data information x4 of the brake pad steel backing machining is as follows: ， Among them, x1 is the speed data of the brake pad steel back conveyor belt, x2 is the quantity data of the brake pad steel back, and x3 is the output data of the shot blasting.

3. The control method for a brake pad shot blasting machine according to claim 1, characterized in that: The constant parameters ω1 and ω2 for predicting the shot blasting output quantity of the shot blasting machine are: ， ， ， Where n is a positive integer, y0 is the relation prediction series of the shot blasting machine, and y is the relation data information of the brake pad steel backing finishing.

4. The control method for a brake pad shot blasting machine according to claim 1, wherein In step U3, the optimization of the shot blasting output quantity of the shot blasting machine using the distribution estimation optimization algorithm based on the fused golden sine wave includes: U31. Based on the predicted shot blasting output quantity data information, construct a shot blasting output quantity data population of the shot blasting machine, and initialize it to obtain the initialized shot blasting output quantity data population of the shot blasting machine, and determine the number of cycles L; U32. Based on the initialized shot blasting machine's shot blasting output quantity data population, establish a population individual fitness function P that incorporates the golden sine curve. ， Among them, z j Let z be the j-th individual in the population of shot blasting output data of the initialized shot blasting machine. j+1 The (j+1)th individual in the population of shot blasting output data of the initialized shot blasting machine is given, where ρ is the golden sine factor. The fitness values ​​of individuals in the population are calculated to obtain the fitness value data information of individuals in the population of shot blasting output data of the shot blasting machine. U33. Based on the fitness values ​​of individuals in the shot blasting output population of the shot blasting machine, establish a probability function Q for the shot blasting output subpopulation. ， Among them, a k This refers to the fitness values ​​of individuals in a shot blasting machine population, where k is the sample size and θ is the output quantity. k δ is the adaptive regulator for individuals in the population. k The weight coefficients of individuals in the population are used to obtain the subpopulation probability data information of the dominant shot blasting output quantity of the shot blasting machine; U34. Based on the probability data of the shot blasting output quantity subpopulation of the shot blasting machine, random sampling is performed to generate a new population. Steps U32-U33 are repeated until the optimized shot blasting output quantity data of the shot blasting machine is obtained. The shot blasting output quantity of the shot blasting machine is then optimized and controlled to obtain the optimized shot blasting output quantity data of the shot blasting machine.

5. The control method for a brake pad shot blasting machine according to claim 4, characterized in that: The golden sinusoidal factor ρ is, ， Among them, z j Let j be the j-th individual in the population of shot blasting output data of the shot blasting machine after initialization, where c1 and c2 are the golden coefficients and µ is a constant factor. The optimal control function g2 for the shot blasting output quantity of the shot blasting machine is: ， Where b represents the optimized shot blasting output quantity data of the shot blasting machine, and σ3 and σ4 are the input quantity control optimization factors.

6. The control method for a brake pad shot blasting machine according to claim 1, characterized in that: The energy loss function G of the shot blasting machine is: ， Wherein, λ1 and λ2 are the energy loss decision factors of the shot blasting machine, h1 is the input data of the shot blasting machine, and h2 is the output data of the optimized shot blasting machine.

7. A control system applied to a brake pad shot blasting machine, comprising a computer device, characterized in that, The computer device is programmed or configured to perform the steps of the control method for a brake pad shot blasting machine as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is programmed or configured to perform the control method for a brake pad shot blasting machine as described in any one of claims 1 to 6.

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