A equipment rack machining accuracy control system and method based on big data

Abstract

The invention discloses a equipment rack processing accuracy control system and method based on big data, and relates to the field of processing accuracy control systems. The invention extracts processing trajectory equations from guide rail design drawings of multiple equipment racks to be processed, thereby providing rich data reference basis for optimizing parameters of laser equipment and processing trajectory when laser processing the guide rails of the racks of the equipment to be processed; adjusting parameters of the trajectory equation and parameters of the laser equipment to meet corresponding processing accuracy requirements; providing relatively accurate reference data for optimizing the processing trajectory and equipment parameters for subsequent processing of the guide rails of the racks of the equipment to be processed; storing the adjusted data in a database for subsequent reference and comparison; finally, optimizing and adjusting the processing trajectory and equipment parameters when processing the guide rails of the racks of the equipment to be processed according to the data in the database, thereby improving the processing accuracy.

Description

Technical Field

[0001] The present invention belongs to the field of machining accuracy control systems, and in particular, relates to a big data-based equipment rack machining accuracy control system and method. Background Art

[0002] Chinese patent CN110007643B discloses an automated online detection device and method for the turnout manufacturing process, which measures the profile of the turnout rail, measures the distance, receives and stores data, and retrieves the turnout rail at the same time; calls the data for processing, compares it with the standard data of the turnout rail, and forms a detection report; performs a purge treatment on the turnout rail; realizes the automatic detection of the geometric dimensions of the CNC milling processing profile, and improves the measurement accuracy.

[0003] In some equipment racks, guide rails are one of the components; the processing of guide rails often has high precision requirements. Laser processing has high processing accuracy. However, laser processing is greatly affected by the processing materials. If fixed laser processing equipment parameters and processing trajectories are used for different materials, it will directly affect the processing accuracy. Summary of the invention

[0004] In view of the problems in the related technology, the present invention proposes an equipment frame processing accuracy control system and method based on big data to overcome the above-mentioned technical problems existing in the existing related technology.

[0005] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is a method for controlling the machining accuracy of equipment racks based on big data, comprising the following steps:

[0007] S1, extracting the processing trajectory equation from the guide rail design drawings of multiple equipment racks to be processed and performing preprocessing operations to obtain a second trajectory equation matrix;

[0008] S2, obtaining initial parameters of the laser device to obtain an initial parameter set; determining whether to optimize the second trajectory equation matrix and the initial parameter set according to the maximum curvature of the trajectory equation in the second trajectory equation matrix; when the determination result shows that optimization is required, optimizing the second trajectory equation matrix and the initial parameter set, and obtaining a final trajectory equation matrix set, a first final parameter matrix, and a second final parameter matrix after the optimization is completed;

[0009] S3, storing the final trajectory equation matrix set, the first final parameter matrix and the second final parameter matrix into a database;

[0010] S4, collecting the material of the rack guide rail of the equipment to be processed, the processing trajectory equation and the parameters of the corresponding laser processing equipment, and obtaining the material to be processed, the trajectory equation set to be processed and the parameter set to be processed; performing a preprocessing operation on the trajectory equation set to be processed to obtain a second trajectory equation set to be processed; judging whether to adjust the second trajectory equation set to be processed and the parameter set to be processed according to the maximum value of the curvature of the trajectory equation in the second trajectory equation set to be processed, and when the judgment result is that adjustment is required, adjusting the second trajectory equation set to be processed and the parameter set to be processed in conjunction with the database;

[0011] In order to increase the richness of the collected data, the processing trajectory equations are extracted from the guide rail design drawings of multiple racks of equipment to be processed, which provides rich data reference basis for optimizing the parameters of the laser equipment and the processing trajectory when the rack rails of the equipment to be processed are finally laser processed; because the processing trajectory cannot be too curved, the processing trajectory equations whose maximum curvature exceeds the set threshold in the collected trajectory process are preliminarily adjusted; then the order of the steps that can be processed continuously is exchanged, and the corresponding trajectory equations are merged, which is beneficial to improve the processing efficiency; when the initial trajectory equations and laser equipment parameters cannot meet the corresponding processing accuracy requirements, it is necessary to adjust the parameters of the trajectory equations and the parameters of the laser equipment to meet the corresponding processing accuracy requirements; for the subsequent processing of the rack rails of the equipment to be processed, it provides more accurate reference data for optimizing the processing trajectory and equipment parameters; then the adjusted data is stored in a database for subsequent reference and comparison; finally, according to the data in the database, the processing trajectory and equipment parameters when processing the rack rails of the equipment to be processed are optimized and adjusted, thereby improving the processing accuracy.

[0012] Preferably, the S1 comprises the following steps:

[0013] S11, collecting multiple guide rail design drawings of the racks of the equipment to be processed to obtain a guide rail design drawing set; extracting a processing trajectory equation from each guide rail design drawing in the guide rail design drawing set to obtain an initial trajectory equation matrix;

[0014] S12, merging the trajectory equations that can be merged and processed in the initial trajectory equation matrix to obtain a first trajectory equation matrix;

[0015] S13, calculating the curvature equation of each trajectory equation in the first trajectory equation matrix to obtain a first trajectory curvature equation matrix; then calculating the maximum value of each trajectory curvature equation in the first trajectory curvature equation matrix to obtain a first curvature maximum value matrix;

[0016] S14, setting a first curvature threshold; when there is a curvature maximum value greater than or equal to the first curvature threshold in the first curvature maximum value matrix b, the corresponding first trajectory equation is adjusted until there is no curvature maximum value greater than or equal to the first curvature threshold in the first curvature maximum value matrix b, and a second trajectory equation matrix is ​​obtained;

[0017] Setting the parameters of each trajectory equation in the second trajectory equation matrix to obtain a second trajectory equation parameter set matrix;

[0018] Since the processing trajectory is relatively complex and a single equation cannot describe it, there are multiple processing trajectory equations extracted from each guide rail design drawing in the guide rail design drawing set; by setting a first curvature threshold, the parameters of the equations in the extracted processing trajectory equations whose curvature exceeds the first curvature threshold are adjusted, thereby ensuring the smoothness of the processing trajectory during processing and ensuring the stability of the processing; wherein, by calculating the maximum curvature of the processing trajectory equation, when the maximum value does not exceed the set threshold, it means that the curvature of all trajectory points does not exceed the set threshold.

[0019] Preferably, S2 comprises the following steps:

[0020] S21, setting a guide rail material type set; collecting corresponding material samples and characteristic data of each guide rail material type in the guide rail material type set to obtain a material characteristic data matrix;

[0021] S22, setting multiple parameter types of multiple laser devices that affect laser processing accuracy to obtain a laser parameter type set;

[0022] Acquire initial parameters of the laser device according to the laser parameter type set to obtain an initial parameter set;

[0023] S23, calculating the curvature equation of each trajectory equation in the second trajectory equation matrix to obtain a second trajectory curvature equation matrix; and then calculating the maximum value of each trajectory curvature equation in the second trajectory curvature equation matrix to obtain a second curvature maximum value matrix;

[0024] S24, setting a second curvature threshold; when there is a curvature maximum value greater than or equal to the second curvature threshold in the second curvature maximum value matrix, determining whether to optimize the second trajectory equation matrix and the initial parameter set according to the material characteristic data matrix to obtain a final trajectory equation matrix set and a first final parameter matrix;

[0025] When there is no curvature maximum value greater than or equal to the second curvature threshold in the second curvature maximum value matrix, determining whether to optimize the initial parameter set according to the material characteristic data matrix to obtain a second final parameter matrix;

[0026] The types of guide rail materials include steel, aluminum alloy, stainless steel, engineering plastics, composite materials and polymer materials; the characteristic data include thermal conductivity, reflectivity, and pores or cracks caused by the interaction between laser and material; the laser parameter types include power, speed, focal position, etc.; by setting the second curvature threshold, when the maximum curvature in the processing trajectory equation does not reach the second curvature threshold, there is no need to adjust the processing trajectory equation, only the parameters of the laser equipment need to be adjusted, which simplifies the complexity of the adjustment to a certain extent.

[0027] Preferably, determining whether to optimize the second trajectory equation matrix and the initial parameter set according to the material property data matrix in S24 includes the following steps:

[0028] S2411, traversing the material property data matrix, and using laser equipment to process the guide rail raw materials consisting of the guide rail material type corresponding to each row of data in the material property data matrix; after the processing is completed, a first finished guide rail is obtained; a plurality of measurement position points are set on the first finished guide rail to obtain a first position point set; according to the first position point set, a flatness parameter threshold, a flatness parameter error threshold, a size parameter threshold set and a size parameter error threshold set at different position points are set; and then, according to the first position point set, the flatness parameters and size parameters of the first finished guide rail at different position points are measured to obtain a first flatness parameter set and a first size parameter matrix;

[0029] S2412, subtract each flatness parameter in the first flatness parameter set from the flatness parameter threshold and take the absolute value to obtain a first initial flatness parameter error data set; then subtract each row of data in the first size parameter matrix from the size parameter threshold set and take the absolute value to obtain a first initial size parameter error data matrix;

[0030] When there is initial flatness parameter error data in the first initial flatness parameter error data set that is greater than or equal to the flatness parameter error threshold or there is initial size parameter error data in the first initial size parameter error data matrix that is greater than or equal to the corresponding size parameter error threshold, the second trajectory equation matrix and the initial parameter set are optimized; otherwise, there is no need to optimize the second trajectory equation matrix and the initial parameter set, and each initial parameter in the initial parameter set is directly used as the first final parameter corresponding to each guide rail material type, and then the second trajectory equation matrix is ​​directly used as the final trajectory equation matrix corresponding to each guide rail material type;

[0031] By setting multiple measurement locations, the comprehensiveness of the measurement is improved, making the collected flatness parameters and size parameters more accurate. By calculating the error between the collected flatness parameters and size parameters and the standard value, and then comparing the calculated error with the set error threshold, it is possible to determine whether the current processed product meets the processing requirements, thereby providing a data basis for the subsequent construction of the fitness function of the first sparrow population.

[0032] Preferably, optimizing the second trajectory equation matrix and the initial parameter set in S2412 specifically includes the following steps:

[0033] S24121, construct a first sparrow population; set the maximum number of iterations of the first sparrow population to , the current number of iterations is , respectively recorded as the first maximum number of iterations and the first current number of iterations;

[0034] S24122, setting the initial position of each sparrow in the first sparrow population according to the second trajectory equation parameter set matrix and the initial parameter set to obtain a first initial position matrix;

[0035] S24123, setting a fitness function of the first sparrow population according to the first initial flatness parameter error data set, the first initial size parameter error data matrix, the flatness parameter error threshold, and the size parameter error threshold set;

[0036] S24124, start iteration, and set the first current iteration number to 1 before iteration; in each round of iteration, calculate the fitness value of each sparrow according to the fitness function of the first sparrow population to obtain a first fitness value set; take the maximum fitness value in the first fitness value set and the position of the corresponding individual sparrow as the first global optimal fitness and the first global optimal position, respectively, and then update the position of each sparrow in the first sparrow population according to the first global optimal fitness and the first global optimal position; when the position of each sparrow in the first sparrow population is updated, add 1 to the first current iteration number and enter the next round of iteration;

[0037] S24125, when When , the iteration is stopped and the first final position matrix is ​​output; the position with the largest fitness value in the first final position matrix is ​​selected and substituted into the second trajectory equation matrix and the initial parameter set to obtain the first tuned initial parameter set and the first tuned trajectory equation matrix;

[0038] S24126, applying the first optimized initial parameter set and the first optimized trajectory equation matrix to the corresponding laser processing process, and calculating the flatness parameter error data and the size parameter error data according to the flatness parameter threshold and the size parameter threshold set, to obtain an adjusted flatness parameter error data set and an adjusted size parameter error data matrix;

[0039] When there is flatness parameter error data greater than or equal to the flatness parameter error threshold in the adjusted flatness parameter error data set or there is size parameter error data greater than or equal to the corresponding size parameter error threshold in the adjusted size parameter error data matrix, return to S24124 to continue iterating until there is no flatness parameter error data greater than or equal to the flatness parameter error threshold in the adjusted flatness parameter error data set and there is no size parameter error data greater than or equal to the corresponding size parameter error threshold in the adjusted size parameter error data matrix, thereby obtaining a final trajectory equation matrix set and a first final parameter matrix;

[0040] The sparrow optimization algorithm solves the optimization problem through the interaction between individuals; and it includes a variety of different roles, which strengthens the global search and local development capabilities of the algorithm; therefore, in this scheme, the sparrow optimization algorithm is used to perform multiple iterative optimizations on the parameters of each trajectory equation in the second trajectory equation matrix and the initial parameters in the initial parameter set at the same time, and the error values ​​of the processed flatness parameters and size parameters are used as fitness values. Therefore, through continuous iterations, the obtained trajectory equations and equipment parameters are more in line with the processing requirements.

[0041] Preferably, determining whether to optimize the initial parameter set according to the material property data matrix in S24 includes the following steps:

[0042] S2421, traversing the material property data matrix, and using laser equipment to process the guide rail raw materials consisting of the guide rail material type corresponding to each row of data in the material property data matrix; when the processing is completed, a second finished guide rail is obtained;

[0043] A plurality of measurement position points are set on the second finished guide rail to obtain a second position point set; flatness parameters and size parameters of the second finished guide rail at different position points are measured according to the second position point set to obtain a second flatness parameter set and a second size parameter matrix;

[0044] S2422, subtract each flatness parameter in the second flatness parameter set from the flatness parameter threshold and take the absolute value to obtain a second initial flatness parameter error data set; then subtract each row of data in the second size parameter matrix from the size parameter threshold set and take the absolute value to obtain a second initial size parameter error data matrix;

[0045] When there is initial flatness parameter error data in the second initial flatness parameter error data set that is greater than or equal to the flatness parameter error threshold or there is initial size parameter error data in the second initial size parameter error data matrix that is greater than or equal to the corresponding size parameter error threshold, the initial parameter set is optimized; otherwise, there is no need to optimize the initial parameter set, and each initial parameter in the initial parameter set is directly used as the second final parameter corresponding to each guide rail material type;

[0046] The second initial flatness parameter error data set is obtained by subtracting each flatness parameter in the second flatness parameter set from the flatness parameter threshold and taking the absolute value. Then, each row of data in the second size parameter matrix is ​​subtracted from the size parameter threshold set and taking the absolute value to obtain the second initial size parameter error data matrix, which provides a data basis for the subsequent construction of the fitness function of the second sparrow population.

[0047] Preferably, optimizing the initial parameter set in S2422 specifically includes the following steps:

[0048] S24221, construct a second sparrow population; set the maximum number of iterations of the second sparrow population to , the current number of iterations is , respectively recorded as the second maximum number of iterations and the second current number of iterations;

[0049] S24222. Setting the initial position of each sparrow in the second sparrow population according to the initial parameter set to obtain a second initial position matrix;

[0050] S24223, setting a fitness function of the second sparrow population according to the second initial flatness parameter error data set, the second initial size parameter error data matrix, the flatness parameter error threshold, and the size parameter error threshold set;

[0051] S24224, start iteration, and set the second current iteration number to 1 before iteration; in each round of iteration, calculate the fitness value of each sparrow according to the fitness function of the second sparrow population to obtain a second fitness value set; take the maximum fitness value in the second fitness value set and the position of the corresponding individual sparrow as the second global optimal fitness and the second global optimal position, respectively, and then update the position of each sparrow in the second sparrow population according to the second global optimal fitness and the second global optimal position; when the position of each sparrow in the second sparrow population is updated, add 1 to the second current iteration number and enter the next round of iteration;

[0052] S24225, when When , stop iterating and output the second final position matrix; select the position with the largest fitness value in the second final position matrix and substitute it into the initial parameter set to obtain the second optimized initial parameter set;

[0053] S24226, applying the second optimized initial parameter set to the corresponding laser processing process, and calculating the flatness parameter error data and the size parameter error data according to the flatness parameter threshold and the size parameter threshold set;

[0054] When there is flatness parameter error data greater than or equal to the flatness parameter error threshold in the adjusted flatness parameter error data or there is size parameter error data greater than or equal to the corresponding size parameter error threshold in the adjusted size parameter error data, return to S24224 to continue iterating until there is no flatness parameter error data greater than or equal to the flatness parameter error threshold in the adjusted flatness parameter error data and there is no size parameter error data greater than or equal to the corresponding size parameter error threshold in the adjusted size parameter error data, so as to obtain a second final parameter matrix;

[0055] By using the sparrow optimization algorithm to perform multiple iterative optimizations on each initial parameter in the initial parameter set, the error value of the size parameter is used as the fitness value, and through continuous iterations, the obtained equipment parameters are more in line with the processing requirements.

[0056] Preferably, S3 comprises the following steps:

[0057] S31, constructing a trajectory equation database, a first laser device parameter database, and a second laser device parameter database;

[0058] S32, storing the final trajectory equation matrix set, the first final parameter matrix and the second final parameter matrix into a trajectory equation database, a first laser device parameter database and a second laser device parameter database respectively;

[0059] By constructing a trajectory equation database, a first laser equipment parameter database and a second laser equipment parameter database, an optimization direction is provided for the subsequent optimization of the processing parameters of the rack guide rails of the processing equipment.

[0060] Preferably, S4 comprises the following steps:

[0061] S41, setting the rack rail of the equipment to be processed, obtaining the material of the rack rail of the equipment to be processed, the processing trajectory equation and the parameters of the corresponding laser processing equipment according to the requirements, and obtaining the material to be processed, the set of the trajectory equation to be processed and the set of the parameters to be processed;

[0062] The trajectory equations to be processed that can be combined and processed in the trajectory equation set to be processed are combined to obtain a first trajectory equation set to be processed; the curvature equation of each trajectory equation to be processed in the first trajectory equation set to be processed is combined and the maximum value is calculated to obtain a first curvature maximum value set to be processed; when there is a curvature maximum value greater than or equal to a first curvature threshold value in the first curvature maximum value set to be processed, the corresponding trajectory equation to be processed is adjusted until there is no curvature maximum value greater than or equal to the first curvature threshold value in the first curvature maximum value set to be processed, thereby obtaining a second trajectory equation set to be processed;

[0063] S42, calculating the maximum value of the curvature equation of each trajectory equation to be processed in the second trajectory equation set to be processed, to obtain a second curvature maximum value set to be processed;

[0064] S43, when there is no curvature maximum value greater than or equal to the second curvature threshold value in the second curvature maximum value set to be processed, adjusting the parameter set to be processed in accordance with the material to be processed and the second laser equipment parameter database to obtain a first adjusted parameter set to be processed; applying the first adjusted parameter set to be processed to the laser equipment for processing the rack guide rail of the processing equipment;

[0065] When there is a curvature maximum value greater than or equal to the second curvature threshold value in the second curvature maximum value set to be processed, similarity calculation is performed between the second trajectory equation set to be processed and the second trajectory equation matrix in conjunction with the material to be processed, and the parameters of each trajectory equation in the trajectory equation database corresponding to the trajectory equation with the greatest similarity to the second trajectory equation set to be processed in the second trajectory equation matrix are applied to the second trajectory equation set to be processed, and at the same time, the parameter set to be processed is adjusted in conjunction with the first laser equipment parameter database to obtain a second adjusted parameter set to be processed; and the second adjusted parameter set to be processed is applied to the laser equipment that processes the rack guide rail of the equipment to be processed;

[0066] By applying the parameters of each trajectory equation in the trajectory equation database corresponding to the trajectory equation with the greatest similarity in the second trajectory equation matrix to the second trajectory equation set to be processed to the second trajectory equation set to be processed, the continuity of the second trajectory equation set to be processed and the data in the trajectory equation database is established, and the data in the trajectory equation database are all optimized data, thus providing an adjustment direction for adjusting the parameters of the trajectory equations to be processed in the second trajectory equation set to be processed.

[0067] A big data-based equipment frame processing accuracy control system includes a trajectory equation acquisition module, a first trajectory equation preprocessing module, a laser equipment parameter acquisition module, a first determination module, a first optimization module, a data storage module, a guide rail processing data acquisition module, a second trajectory equation preprocessing module, a second determination module, and a second optimization module.

[0068] The present invention has the following beneficial effects:

[0069] 1. The present invention extracts processing trajectory equations from multiple guide rail design drawings of racks of equipment to be processed, thereby providing abundant data reference basis for optimizing the parameters of laser equipment and processing trajectory when laser processing the rack guide rails of the equipment to be processed is finally performed; when the initial trajectory equations and laser equipment parameters cannot meet the corresponding processing accuracy requirements, it is necessary to adjust the parameters of the trajectory equations and the parameters of the laser equipment to meet the corresponding processing accuracy requirements; for subsequent processing of the rack guide rails of the equipment to be processed, relatively accurate reference data is provided for optimizing the processing trajectory and equipment parameters; the adjusted data is then stored in a database for subsequent reference and comparison; finally, the processing trajectory and equipment parameters when processing the rack guide rails of the equipment to be processed are optimized and adjusted according to the data in the database, thereby improving the processing accuracy.

[0070] 2. In the present invention, by setting a second curvature threshold, when the maximum curvature in the processing trajectory equation does not reach the second curvature threshold, there is no need to adjust the processing trajectory equation, and only the parameters of the laser equipment need to be adjusted, which simplifies the complexity of the adjustment to a certain extent.

[0071] 3. In the present invention, the parameters of each trajectory equation in the second trajectory equation matrix and the initial parameters in the initial parameter set are iteratively optimized multiple times by using the sparrow optimization algorithm, and the error values ​​of the processed flatness parameters and size parameters are used as fitness values. Therefore, through continuous iteration, the obtained trajectory equations and equipment parameters are more in line with the processing requirements.

[0072] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0074] Figure 1The present invention is a flowchart of a device frame processing accuracy control system based on big data for controlling the device frame processing accuracy. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the invention to clearly and completely describe the technical solutions in the embodiments of the invention. Obviously, the described embodiments are only part of the embodiments of the invention, not all of the embodiments. Based on the embodiments in the invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the invention.

[0076] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0077] Embodiment 1

[0078] See also Figure 1 As shown, this embodiment is a method for controlling the machining accuracy of a machine frame based on big data, comprising the following steps:

[0079] S1, extracting the processing trajectory equation from the guide rail design drawings of multiple equipment racks to be processed and performing preprocessing operations to obtain a second trajectory equation matrix;

[0080] The S1 comprises the following steps:

[0081] S11. Collect the guide rail design drawings of multiple racks of equipment to be processed to obtain a guide rail design drawing set , represents the collected guide rail design drawing of the i-th equipment rack to be processed, Indicates the total number of the collected guide rail design drawings of the equipment rack to be processed; from the guide rail design drawing set Extract the machining trajectory equation from each guide rail design drawing and obtain the initial trajectory equation matrix;

[0082] S12, merging the trajectory equations that can be merged in the initial trajectory equation matrix to obtain a first trajectory equation matrix ;as follows,

[0083] ;

[0084] in, represents the jth processing trajectory equation before adjustment extracted from the guide rail design drawing of the i-th equipment rack to be processed; Represents the rail design drawing set from The total number of machining trajectory equations before adjustment extracted from the guide rail design drawings of the i-th equipment rack to be processed;

[0085] S13, calculating the first trajectory equation matrix The curvature equation of each trajectory equation in is obtained to obtain the first trajectory curvature equation matrix; then the maximum value of each trajectory curvature equation in the first trajectory curvature equation matrix is ​​calculated to obtain the first curvature maximum value matrix b; as follows,

[0086] ;

[0087] in, It represents the maximum curvature value of the jth machining trajectory equation before adjustment extracted from the guide rail design drawing of the i-th equipment rack to be processed; the curvature equation calculation formula is as follows:

[0088] ;

[0089] In the formula, represents the curvature equation, represents the original equation, , They represent the first-order derivative and the second-order derivative of the original equation respectively;

[0090] S14, set a first curvature threshold; when there is a curvature maximum value greater than or equal to the first curvature threshold in the first curvature maximum value matrix b, adjust the corresponding first trajectory equation matrix until there is no curvature maximum value greater than or equal to the first curvature threshold in the first curvature maximum value matrix b, and obtain the second trajectory equation matrix ;as follows,

[0091] ;

[0092] in, represents the jth adjusted processing trajectory equation in the guide rail design drawing of the i-th equipment rack to be processed;

[0093] Set the second trajectory equation matrix The parameters of each trajectory equation in get the second trajectory equation parameter set matrix ;as follows,

[0094] ;

[0095] in, express The parameter set, , express The kth parameter in express The total number of parameters in ;

[0096] S2, obtaining initial parameters of the laser device to obtain an initial parameter set; determining whether to optimize the second trajectory equation matrix and the initial parameter set according to the maximum curvature of the trajectory equation in the second trajectory equation matrix; when the determination result shows that optimization is required, optimizing the second trajectory equation matrix and the initial parameter set, and obtaining a final trajectory equation matrix set, a first final parameter matrix, and a second final parameter matrix after the optimization is completed;

[0097] The S2 comprises the following steps:

[0098] S21. Set the rail material type set , Indicates the set i-th guide rail material type, Indicates the total number of set rail material types; collects the rail material type set The corresponding material samples and characteristic data of each guide rail material type are obtained to obtain the material characteristic data matrix ; as follows,

[0099] ;

[0100] in, express The j-th characteristic data of Indicates the total number of characteristic data for each guide rail material type;

[0101] S22, setting multiple parameter types of multiple laser devices that affect laser processing accuracy, and obtaining a laser parameter type set , Indicates the parameter type of the i-th laser equipment that affects the laser processing accuracy. Indicates the total number of parameter types of the laser equipment that affect the laser processing accuracy;

[0102] According to the laser parameter type set Get the initial parameters of the laser device and get the initial parameter set , represents the initial parameter of the i-th type of laser equipment parameter that affects the laser processing accuracy set in the laser equipment;

[0103] S23, calculating the second trajectory equation matrix The curvature equation of each trajectory equation in the second trajectory curvature equation matrix is ​​obtained; the maximum value of each trajectory curvature equation in the second trajectory curvature equation matrix is ​​calculated to obtain the second curvature maximum value matrix ;as follows,

[0104] ;

[0105] in, represents the maximum value of the curvature of the jth adjusted processing trajectory equation in the guide rail design drawing of the i-th equipment rack to be processed;

[0106] S24, setting a second curvature threshold; when the second curvature maximum matrix When there is a maximum curvature value greater than or equal to the second curvature threshold, according to the material property data matrix Determine whether the second trajectory equation matrix And the initial parameter set Optimize and get the final trajectory equation matrix set and the first final parameter matrix ; Represents the final trajectory equation matrix corresponding to the i-th guide rail material type; , They are as follows:

[0107] ; ;

[0108] in, express The kth final trajectory equation matrix in the guide rail design drawing of the jth equipment rack to be processed; The first final parameter representing the jth type of laser equipment parameter affecting laser processing accuracy set corresponding to the i-th guide rail material type;

[0109] When the second curvature maximum matrix When there is no curvature maximum value greater than or equal to the second curvature threshold in the material characteristic data matrix Determine whether the initial parameter set Perform optimization to obtain the second final parameter matrix;

[0110] S24 based on material properties data matrix Determine whether the second trajectory equation matrix And the initial parameter set The optimization process involves the following steps:

[0111] S2411, traversing the material property data matrix , respectively, using laser equipment to measure the material characteristic data matrix The guide rail raw materials composed of the guide rail material type corresponding to each row of data are processed; when the processing is completed, the first finished guide rail is obtained;

[0112] A plurality of measuring position points are set on the first finished guide rail to obtain a first position point set. , represents the i-th measurement position point set on the first finished guide rail, Indicates the total number of measurement position points set on the first finished guide rail;

[0113] According to the first position point set Set the smoothness parameter threshold and smoothness parameter error threshold at different locations , size parameter threshold set and size parameter error threshold set , represents the error threshold of the i-th size parameter at each position point, represents the total number of dimension parameters at each position of the first finished guide rail; and then according to the first position point set Measuring the flatness parameters and size parameters of the first finished guide rail at different positions to obtain a first flatness parameter set and a first size parameter matrix;

[0114] S2412: Subtract each flatness parameter in the first flatness parameter set from the flatness parameter threshold and take the absolute value to obtain a first initial flatness parameter error data set. , represents the initial flatness parameter error data at the i-th measurement position point in the first finished guide rail; then the first initial size parameter error data matrix is ​​obtained by subtracting each row of data in the first size parameter matrix from the size parameter threshold set and taking the absolute value. ;as follows,

[0115] ;

[0116] in, represents the jth initial dimension parameter error data at the i-th position point of the first finished guide rail;

[0117] When the first initial flatness parameter error data set There is an initial flatness parameter error data greater than or equal to the flatness parameter error threshold or the first initial size parameter error data matrix When the initial size parameter error data exists in the matrix greater than or equal to the corresponding size parameter error threshold, the second trajectory equation matrix And the initial parameter set Otherwise, there is no need to optimize the second trajectory equation matrix And the initial parameter set To optimize, directly set the initial parameter set Each initial parameter in is used as the first final parameter corresponding to each guide rail material type, and then the second trajectory equation matrix is ​​directly As the final trajectory equation matrix corresponding to each guide rail material type;

[0118] S2412 for the second trajectory equation matrix And the initial parameter set The optimization process includes the following steps:

[0119] S24121, build the first sparrow population , represents the i-th sparrow in the first sparrow population, represents the size of the first sparrow population; sets the maximum number of iterations of the first sparrow population to , the current number of iterations is , respectively recorded as the first maximum number of iterations and the first current number of iterations; the search space dimension of the first sparrow population is ;

[0120] ;

[0121] S24122, according to the second trajectory equation parameter set matrix And the initial parameter set Set the initial position of each sparrow in the first sparrow population to obtain the first initial position matrix ;as follows,

[0122] ;

[0123] in, represents the component of the initial position of the i-th sparrow in the first sparrow population in the j-th search space dimension;

[0124] S24123, according to the first initial flatness parameter error data set , the first initial size parameter error data matrix , flatness parameter error threshold And the size parameter error threshold set Set the fitness function of the first sparrow population ;as follows,

[0125] ;

[0126] S24124, start iteration, before iteration, set the first current iteration number Set to 1; in each iteration, the fitness function of the first sparrow population is used Calculate the fitness value of each sparrow to obtain a first fitness value set; use the maximum fitness value in the first fitness value set and the position of the corresponding sparrow individual as the first global optimal fitness and the first global optimal position, and then update the position of each sparrow in the first sparrow population according to the first global optimal fitness and the first global optimal position; when the position of each sparrow in the first sparrow population is updated, the first current iteration number is Add 1 and enter the next iteration;

[0127] S24125, when When , stop iterating and output the first final position matrix ;as follows,

[0128] ;

[0129] in, represents the component of the initial position of the i-th sparrow in the first sparrow population in the j-th search space dimension; select the first final position matrix The position with the largest fitness value in the equation matrix And the initial parameter set , obtaining the first optimized initial parameter set and the first optimized trajectory equation matrix;

[0130] S24126, applying the first optimized initial parameter set and the first optimized trajectory equation matrix to the corresponding laser processing process, and calculating the flatness parameter error data and the size parameter error data according to the flatness parameter threshold and the size parameter threshold set to obtain an adjusted flatness parameter error data set and an adjusted size parameter error data matrix;

[0131] When there is flatness parameter error data greater than or equal to the flatness parameter error threshold in the adjusted flatness parameter error data set or there is size parameter error data greater than or equal to the corresponding size parameter error threshold in the adjusted size parameter error data matrix, return to S24124 to continue iterating until there is no flatness parameter error data greater than or equal to the flatness parameter error threshold in the adjusted flatness parameter error data set and there is no size parameter error data greater than or equal to the corresponding size parameter error threshold in the adjusted size parameter error data matrix, and the final trajectory equation matrix set is obtained. and the first final parameter matrix ;

[0132] S24 is based on the material property data matrix Determine whether the initial parameter set The optimization process involves the following steps:

[0133] S2421, traversing the material property data matrix , respectively, using laser equipment to measure the material characteristic data matrix The guide rail raw materials composed of the guide rail material type corresponding to each row of data are processed; when the processing is completed, a second finished guide rail is obtained;

[0134] A plurality of measuring position points are set on the second finished guide rail to obtain a second position point set. , represents the i-th measurement position point set on the second finished guide rail, Indicates the total number of measurement position points set on the second finished guide rail;

[0135] According to the second position point set Measuring the flatness parameters and size parameters of the second finished guide rail at different positions to obtain a second flatness parameter set and a second size parameter matrix;

[0136] S2422: Subtract each flatness parameter in the second flatness parameter set from the flatness parameter threshold and take the absolute value to obtain a second initial flatness parameter error data set. , represents the initial flatness parameter error data at the i-th measurement position point in the second finished guide rail; then the second initial size parameter error data matrix is ​​obtained by subtracting each row of data in the second size parameter matrix from the size parameter threshold set and taking the absolute value. ;as follows,

[0137] ;

[0138] in, represents the jth initial dimension parameter error data at the i-th position point of the second finished guide rail;

[0139] When the second initial flatness parameter error data set There is an initial flatness parameter error data greater than or equal to the flatness parameter error threshold or a second initial size parameter error data matrix When there is an initial size parameter error data greater than or equal to the corresponding size parameter error threshold in the initial parameter set Otherwise, there is no need to optimize the initial parameter set To optimize, directly set the initial parameter set Each initial parameter in is used as the second final parameter corresponding to each guide rail material type;

[0140] In S2422, the initial parameter set The optimization process includes the following steps:

[0141] S24221. Constructing the second sparrow population , represents the i-th sparrow in the second sparrow population, represents the size of the second sparrow population; sets the maximum number of iterations of the second sparrow population to , the current number of iterations is , respectively recorded as the second maximum number of iterations and the second current number of iterations; the search space dimension of the second sparrow population is ;

[0142] S24222, according to the initial parameter set Set the initial position of each sparrow in the second sparrow population to obtain the second initial position matrix ;as follows,

[0143] ;

[0144] in, represents the component of the initial position of the i-th sparrow in the second sparrow population in the j-th search space dimension;

[0145] S24223, according to the second initial flatness parameter error data set , the second initial size parameter error data matrix , flatness parameter error threshold And the size parameter error threshold set Set the fitness function of the second sparrow population ;as follows,

[0146] ;

[0147] S24224, start iteration, before iteration, set the second current iteration number Set to 1; in each iteration, the fitness function of the second sparrow population is used Calculate the fitness value of each sparrow to obtain a second fitness value set; use the maximum fitness value in the second fitness value set and the position of the corresponding individual sparrow as the second global optimal fitness and the second global optimal position, and then update the position of each sparrow in the second sparrow population according to the second global optimal fitness and the second global optimal position; when the position of each sparrow in the second sparrow population is updated, the second current iteration number is Add 1 and enter the next iteration;

[0148] S24225, when When , stop iterating and output the second final position matrix ;as follows,

[0149] ;

[0150] in, represents the component of the initial position of the i-th sparrow in the second sparrow population in the j-th search space dimension; select the second final position matrix The position with the largest fitness value in the initial parameter set , the initial parameter set after the second tuning is obtained;

[0151] S24226, applying the second optimized initial parameter set to the corresponding laser processing process, and calculating the flatness parameter error data and the size parameter error data according to the flatness parameter threshold and the size parameter threshold set;

[0152] When there is flatness parameter error data greater than or equal to the flatness parameter error threshold in the adjusted flatness parameter error data or there is size parameter error data greater than or equal to the corresponding size parameter error threshold in the adjusted size parameter error data, return to S24224 to continue iterating until there is no flatness parameter error data greater than or equal to the flatness parameter error threshold in the adjusted flatness parameter error data and there is no size parameter error data greater than or equal to the corresponding size parameter error threshold in the adjusted size parameter error data, and a second final parameter matrix is ​​obtained. ;

[0153] S3, storing the final trajectory equation matrix set, the first final parameter matrix and the second final parameter matrix into a database;

[0154] The S3 comprises the following steps:

[0155] S31, constructing a trajectory equation database, a first laser device parameter database, and a second laser device parameter database;

[0156] S32, the final trajectory equation matrix set , the first final parameter matrix and the second final parameter matrix The parameters are stored in the trajectory equation database, the first laser device parameter database, and the second laser device parameter database respectively;

[0157] S4, collecting the material of the rack guide rail of the equipment to be processed, the processing trajectory equation and the parameters of the corresponding laser processing equipment, and obtaining the material to be processed, the trajectory equation set to be processed and the parameter set to be processed; performing a preprocessing operation on the trajectory equation set to be processed to obtain a second trajectory equation set to be processed; judging whether to adjust the second trajectory equation set to be processed and the parameter set to be processed according to the maximum value of the curvature of the trajectory equation in the second trajectory equation set to be processed, and when the judgment result is that adjustment is required, adjusting the second trajectory equation set to be processed and the parameter set to be processed in conjunction with the database;

[0158] The S4 comprises the following steps:

[0159] S41, setting the rack rail of the equipment to be processed, obtaining the material of the rack rail of the equipment to be processed, the processing trajectory equation and the parameters of the corresponding laser processing equipment according to the requirements, and obtaining the material to be processed, the set of the trajectory equation to be processed and the set of the parameters to be processed;

[0160] The trajectory equations to be processed that can be combined and processed in the trajectory equation set to be processed are merged to obtain a first trajectory equation set to be processed; the curvature equation of each trajectory equation to be processed in the first trajectory equation set to be processed is calculated and the maximum value is obtained to obtain a first curvature maximum value set to be processed; when there is a curvature maximum value greater than or equal to a first curvature threshold value in the first curvature maximum value set to be processed, the corresponding trajectory equation to be processed is adjusted until there is no curvature maximum value greater than or equal to the first curvature threshold value in the first curvature maximum value set to be processed, so as to obtain a second trajectory equation set to be processed , represents the equation of the i-th trajectory to be processed, Represents the total number of trajectory equations to be processed;

[0161] S42, the second trajectory equation set to be processed The curvature equation of each trajectory equation to be processed in is calculated and the maximum value is calculated to obtain the second curvature maximum value set to be processed;

[0162] S43, when there is no curvature maximum value greater than or equal to the second curvature threshold value in the second curvature maximum value set to be processed, adjusting the parameter set to be processed in accordance with the material to be processed and the second laser equipment parameter database to obtain a first adjusted parameter set to be processed; applying the first adjusted parameter set to be processed to the laser equipment for processing the rack guide rail of the processing equipment;

[0163] When the second to-be-processed curvature maximum value set has a curvature maximum value greater than or equal to the second curvature threshold, the second to-be-processed trajectory equation set is adjusted according to the to-be-processed material. With the second trajectory equation matrix Perform similarity calculation and transform the second trajectory equation matrix The second set of trajectory equations to be processed The parameters of each trajectory equation in the trajectory equation database corresponding to the trajectory equation with the greatest similarity are applied to the second trajectory equation set to be processed. In the process, the parameter set to be processed is adjusted in cooperation with the first laser equipment parameter database to obtain a second adjusted parameter set to be processed; and the second adjusted parameter set to be processed is applied to the laser equipment for processing the frame guide rail of the equipment to be processed.

[0164] Embodiment 2

[0165] This embodiment discloses a machine frame machining accuracy control system based on big data, the system can implement the method of the above embodiment, including a trajectory equation acquisition module, a first trajectory equation preprocessing module, a laser equipment parameter acquisition module, a first determination module, a first optimization module, a data storage module, a guide rail machining data acquisition module to be machined, a second trajectory equation preprocessing module, a second determination module, and a second optimization module;

[0166] The trajectory equation acquisition module is used to extract the processing trajectory equation from the guide rail design drawings of multiple equipment racks to be processed to obtain a first trajectory equation matrix;

[0167] The first trajectory equation preprocessing module is used to perform a preprocessing operation on the initial trajectory equation in the first trajectory equation matrix to obtain a second trajectory equation matrix;

[0168] The laser device parameter acquisition module is used to acquire the initial parameters of the laser device to obtain an initial parameter set;

[0169] The first determination module is used to determine whether to optimize the second trajectory equation matrix and the initial parameter set simultaneously or whether to optimize the initial parameter set alone according to the maximum curvature value of the trajectory equation in the second trajectory equation matrix, and obtain a determination result;

[0170] The first optimization module is used to optimize the second trajectory equation matrix and the initial parameter set simultaneously or optimize the initial parameter set separately according to the determination result to obtain the final trajectory equation matrix set, the first final parameter matrix and the second final parameter matrix;

[0171] The data storage module is used to store the final trajectory equation matrix set, the first final parameter matrix and the second final parameter matrix into a database to obtain a trajectory equation database, a first laser device parameter database and a second laser device parameter database;

[0172] The processing data acquisition module for the guide rail to be processed is used to collect the material of the guide rail of the rack of the equipment to be processed, the processing trajectory equation and the parameters of the corresponding laser processing equipment, and obtain the material to be processed, the set of the trajectory equation to be processed and the set of the parameters to be processed;

[0173] The second trajectory equation preprocessing module is used to perform a preprocessing operation on the trajectory equation set to be processed to obtain a second trajectory equation set to be processed;

[0174] The second determination module is used to determine whether to adjust the second set of trajectory equations to be processed and the set of parameters to be processed according to the maximum value of the curvature of the trajectory equation in the second set of trajectory equations to be processed, so as to obtain a second determination result;

[0175] The second optimization module is used to adjust the second trajectory equation set and the second processing parameter set in coordination with the trajectory equation database, the first laser device parameter database and the second laser device parameter database when the second judgment result indicates that adjustment is required.

[0176] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0177] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A method for controlling the machining accuracy of equipment racks based on big data, characterized in that: The following steps are involved: S1, extracting the processing trajectory equation from the guide rail design drawings of multiple equipment racks to be processed and performing preprocessing operations to obtain a second trajectory equation matrix; S2, obtaining initial parameters of the laser device to obtain an initial parameter set; Determining whether to optimize the second trajectory equation matrix and the initial parameter set according to the maximum curvature of the trajectory equation in the second trajectory equation matrix; When the judgment result shows that optimization is needed, the second trajectory equation matrix and the initial parameter set are optimized, and after the optimization is completed, the optimization result data is obtained; S3, storing the optimization result data in a database; S4, collecting the material of the rack guide rail of the equipment to be processed, the processing trajectory equation and the parameters of the corresponding laser processing equipment, to obtain the material to be processed, the set of trajectory equations to be processed and the set of parameters to be processed; preprocessing the set of trajectory equations to be processed to obtain a second set of trajectory equations to be processed; According to the maximum value of the curvature of the trajectory equation in the second trajectory equation set to be processed, it is determined whether to adjust the second trajectory equation set to be processed and the parameter set to be processed in accordance with the database.

2. According to the big data-based equipment rack processing accuracy control method of claim 1, it is characterized in that: The S1 comprises the following steps: S11, collecting the guide rail design drawings of multiple equipment racks to be processed and extracting the processing trajectory equation to obtain an initial trajectory equation matrix; S12, merging the trajectory equations that can be merged and processed in the initial trajectory equation matrix to obtain a first trajectory equation matrix; S13, calculating the maximum value of the curvature equation of each trajectory equation in the first trajectory equation matrix to obtain a first curvature maximum value matrix; S14, setting a first curvature threshold; when there is a curvature maximum value greater than or equal to the first curvature threshold in the first curvature maximum value matrix, adjusting the first trajectory equation matrix to obtain a second trajectory equation matrix; The parameters of each trajectory equation in the second trajectory equation matrix are set to obtain a second trajectory equation parameter set matrix.

3. According to the big data-based equipment rack processing accuracy control method of claim 2, it is characterized in that: The S2 comprises the following steps: S21, collecting material samples and characteristic data corresponding to various guide rail material types to obtain a material characteristic data matrix; S22, setting multiple parameter types of multiple laser devices that affect laser processing accuracy and obtaining initial parameters of the laser devices to obtain a laser parameter type set and an initial parameter set; S23, calculating the maximum value of the curvature equation of each trajectory equation in the second trajectory equation matrix to obtain a second curvature maximum value matrix; S24, setting a second curvature threshold; when there is a curvature maximum value in the second curvature maximum matrix that is greater than or equal to the second curvature threshold, determine whether to optimize the second trajectory equation matrix and the initial parameter set according to the material characteristic data matrix to obtain the final trajectory equation matrix set and the first final parameter matrix; otherwise, determine whether to optimize the initial parameter set to obtain the second final parameter matrix.

4. According to the big data-based equipment rack processing accuracy control method of claim 3, it is characterized in that: Determining whether to optimize the second trajectory equation matrix and the initial parameter set according to the material characteristic data matrix in S24 includes the following steps: S2411, processing the guide rail raw material corresponding to the material property data matrix; after the processing is completed, obtaining a first finished guide rail; setting a flatness parameter threshold, a flatness parameter error threshold, a size parameter threshold set and a size parameter error threshold set at different positions on the first finished guide rail; and then measuring the flatness parameters and size parameters of the first finished guide rail at different positions to obtain a first flatness parameter set and a first size parameter matrix; S2412. Subtract the first flatness parameter set from the flatness parameter threshold and from the first size parameter matrix from the size parameter threshold set and take the absolute value to obtain a first initial flatness parameter error data set and a first initial size parameter error data matrix; when there is initial flatness parameter error data greater than or equal to the flatness parameter error threshold or there is initial size parameter error data greater than or equal to the corresponding size parameter error threshold, optimize the second trajectory equation matrix and the initial parameter set; otherwise, there is no need to optimize the second trajectory equation matrix and the initial parameter set.

5. The method for controlling the machining accuracy of equipment racks based on big data according to claim 4 is characterized in that: Optimizing the second trajectory equation matrix and the initial parameter set in S2412 specifically includes the following steps: S24121, construct a first sparrow population; set the maximum number of iterations of the first sparrow population to , the current number of iterations is ; S24122, setting the initial position of each sparrow in the first sparrow population according to the second trajectory equation parameter set matrix and the initial parameter set to obtain a first initial position matrix; S24123, setting a fitness function of the first sparrow population according to the first initial flatness parameter error data set, the first initial size parameter error data matrix, the flatness parameter error threshold, and the size parameter error threshold set; S24124, start iteration; in each round of iteration, calculate the fitness value of each sparrow according to the fitness function of the first sparrow population and update the position of each sparrow in the first sparrow population; S24125, when When , the iteration is stopped and the first final position matrix is ​​output; the position with the largest fitness value in the first final position matrix is ​​selected and substituted into the second trajectory equation matrix and the initial parameter set; S24126. Calculate the flatness parameter error data and the size parameter error data according to the flatness parameter threshold and the size parameter threshold set; if there is flatness parameter error data greater than or equal to the flatness parameter error threshold or there is size parameter error data greater than or equal to the corresponding size parameter error threshold, return to S24124 to continue iterating until there is no flatness parameter error data greater than or equal to the flatness parameter error threshold and there is no size parameter error data greater than or equal to the corresponding size parameter error threshold, so as to obtain the final trajectory equation matrix set and the first final parameter matrix.

6. The method for controlling the machining accuracy of equipment racks based on big data according to claim 4 is characterized in that: Determining whether to optimize the initial parameter set according to the material characteristic data matrix in S24 includes the following steps: S2421, processing the guide rail raw material consisting of the guide rail material type corresponding to each row of data in the material characteristic data matrix to obtain a second finished guide rail; measuring the flatness parameters and size parameters of the second finished guide rail at different positions to obtain a second flatness parameter set and a second size parameter matrix; S2422. Subtract the second flatness parameter set from the flatness parameter threshold and from the second size parameter matrix from the size parameter threshold set and take the absolute value to obtain a second initial flatness parameter error data set and a second initial size parameter error data matrix; when there is initial flatness parameter error data greater than or equal to the flatness parameter error threshold or there is initial size parameter error data greater than or equal to the corresponding size parameter error threshold, optimize the initial parameter set; otherwise, there is no need to optimize the initial parameter set.

7. The method for controlling the machining accuracy of equipment racks based on big data according to claim 6 is characterized in that: The S3 comprises the following steps: S31, constructing a trajectory equation database, a first laser device parameter database, and a second laser device parameter database; S32, storing the final trajectory equation matrix set, the first final parameter matrix and the second final parameter matrix into a trajectory equation database, a first laser device parameter database and a second laser device parameter database respectively.

8. The method for controlling the machining accuracy of equipment racks based on big data according to claim 7 is characterized in that: The S4 comprises the following steps: S41, setting the frame rail of the equipment to be processed, obtaining the parameters of the frame rail of the equipment to be processed, obtaining the material to be processed, the set of trajectory equations to be processed and the set of parameters to be processed; preprocessing the trajectory equations to be processed in the set of trajectory equations to be processed to obtain a second set of trajectory equations to be processed; S42, calculating the maximum value of the curvature equation of each trajectory equation to be processed in the second trajectory equation set to be processed, to obtain a second curvature maximum value set to be processed; S43. When there is no curvature maximum greater than or equal to the second curvature threshold in the second curvature maximum set to be processed, the parameter set to be processed is adjusted in combination with the material to be processed and the second laser equipment parameter database to obtain a first adjusted parameter set to be processed; when there is a curvature maximum greater than or equal to the second curvature threshold in the second curvature maximum set to be processed, similarity is calculated between the second trajectory equation set to be processed and the second trajectory equation matrix in combination with the material to be processed, and the parameters of each trajectory equation in the trajectory equation database corresponding to the trajectory equation in the second trajectory equation matrix that has the greatest similarity to the second trajectory equation set to be processed are applied to the second trajectory equation set to be processed, and at the same time, the parameter set to be processed is adjusted in combination with the first laser equipment parameter database to obtain a second adjusted parameter set to be processed.

9. A system for implementing the equipment frame processing accuracy control method based on big data as described in any one of claims 1 to 8.

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