Quality optimization method, electronic equipment and computer readable storage medium

Abstract

The invention discloses a quality optimization method, electronic equipment and a computer readable storage medium. The method comprises the steps of obtaining first sample data; performing data analysis on the first sample data based on the first quality information and the first constraint parameter information to determine at least one design parameter from the at least two to-be-determined design parameters; acquiring second sample data according to the at least one design parameter; obtaining a response model of the design parameter to the constraint parameter based on the second sample data; determining a plurality of groups of design parameter information based on the response model and the constraint parameters; acquiring third quality information corresponding to each group of design parameter information; and taking at least one group of design parameter information corresponding to the third quality information of which the quality is minimum or smaller than the first threshold value as final design parameter information. In this way, the technical problem that a carrying device is too large in mass is solved.

Description

Technical Field

[0001] The present application relates to the field of design, and in particular to a quality optimization method, an electronic device, and a computer-readable storage medium. Background Art

[0002] In the field of cargo transportation and handling, handling devices play an important role. There are many kinds of handling devices, such as forklifts, robotic arms, and stackers. These handling devices are used as cargo pick-up and placement devices to grab, transport, stack, or pick up and place cargo from high-rise shelves in warehouses, workshops, etc. With the continuous development of technology, the requirements for handling devices are also constantly increasing, and they are required to provide higher work efficiency and work performance. Taking the stacker as an example, the supporting structure of the stacker-the column will bend and deform when subjected to the bending moment generated by various mass components and cargo, and local stress concentration will occur, which is the main obstacle to improving the efficiency and reliability of the stacker. In order to ensure the stiffness and strength requirements of the stacker, the usual method is to increase the plate thickness of the column and add a large number of stiffening plates, but this will lead to mass redundancy of the stacker, resulting in material waste and excessive manufacturing costs. Therefore, how to reduce its own quality while meeting the structural design requirements has become a technical problem that technicians in this field need to solve urgently. Summary of the invention

[0003] The main purpose of this application is to provide a quality optimization method, an electronic device and a computer-readable storage medium, which can solve the technical problem of excessive mass of the handling device.

[0004] In order to solve the above technical problems, the first technical solution adopted by the present application is: to provide a quality optimization method. The method is applied to a support structure, which corresponds to at least two design parameters to be determined. The method includes obtaining first sample data, each sample in the first sample data includes first quality information, at least one first constraint parameter information and at least two design parameter information to be determined; based on the first quality information and the first constraint parameter information, the first sample data is analyzed to determine at least one design parameter from at least two design parameters to be determined; according to at least one design parameter, second sample data is obtained, each second sample data includes second quality information, at least one second constraint parameter information and at least one design parameter information; based on the second sample data, a response model of the design parameter to the constraint parameter is obtained; based on the response model and the constraint parameter, several groups of design parameter information are determined; the third quality information corresponding to each group of design parameter information is obtained; at least one group of design parameter information corresponding to the third quality information with the minimum quality or the quality less than the first threshold is used as the final design parameter information.

[0005] To solve the above technical problem, the second technical solution adopted by the present application is to provide an electronic device. The electronic device includes a memory and a processor, the memory is used to store program data, and the program data can be executed by the processor to implement the method described in the first technical solution.

[0006] To solve the above technical problems, the third technical solution adopted by the present application is to provide a computer-readable storage medium. The computer-readable storage medium stores program data and can be executed by a processor to implement the method described in the first technical solution.

[0007] The beneficial effects of the present application are: by obtaining first sample data including design parameter information to be determined, first quality information and first constraint parameter information, determining the design parameters using the first quality information and the first constraint parameter information, further obtaining second sample data including second quality information, second constraint parameter information and design parameter information according to the design parameters, and obtaining a response model of the design parameters to the constraint parameters based on the second sample data. After determining the design parameter information that meets the threshold requirements of the constraint parameters through the response model, the final design parameter information is further determined according to the quality requirements, thereby achieving quality optimization design that meets the constraint parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0009] Figure 1 It is a flowchart of the first embodiment of the quality optimization method of the present application;

[0010] Figure 2 It is a flow chart of the second embodiment of the quality optimization method of the present application;

[0011] Figure 3 It is a schematic diagram of a column structure;

[0012] Figure 4 It is a simplified structural diagram of a column;

[0013] Figure 5 It is a schematic diagram of design parameters and their range of variation;

[0014] Figure 6 It is a schematic diagram of the influence of design parameters on output parameters;

[0015] Figure 7 It is a schematic diagram of a cross section of a column;

[0016] Figure 8 This is the cross-sectional view of the improved column after adding the stiffened plate;

[0017] Fig. 9 is another schematic diagram of design parameters and their range of variation;

[0018] Fig.10 Another cross-sectional view of the improved column with the reinforcement plate added

[0019] Fig.11 It is a schematic diagram of a sample point data obtained;

[0020] Fig.12 is a schematic diagram of the second-order response surface for the maximum deformation;

[0021] Fig.13 is a schematic diagram of the second-order response surface for the maximum stress;

[0022] Fig.14 It is a structural schematic diagram of the first embodiment of the electronic device of the present application;

[0023] Fig.15 It is a structural diagram of the first embodiment of the computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0024] The terms "first", "second", etc. in this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0025] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0026] Reference Figure 1 , Figure 1 This is a flow chart of the first embodiment of the quality optimization method of the present application. The method is applied to a support structure, which corresponds to at least two design parameters to be determined, and includes the following steps:

[0027] S11: Acquire first sample data, where each sample in the first sample data includes first quality information, at least one first constraint parameter information, and at least two pieces of design parameter information to be determined.

[0028] The mass optimization method of the present application is a mass optimization for a structure, which realizes lightweight processing of a target object by changing the structural design parameters of the target object.

[0029] After determining the target object structure that needs to be optimized for quality, the present application simplifies the structure and retains some structural design parameters that will affect the quality of the structure and the degree of influence of the constraint parameters as design parameter information to be determined. Constraint parameters are certain parameters that need to meet preset conditions in numerical value in order for the structure to meet the use standard and can be used for actual use. For example, the maximum deformation of the structure, the maximum stress of the structure, etc. The retained design parameters to be determined can be the length and thickness of each side, the angle between the sides, etc.

[0030] After the quality, at least one constraint parameter and at least two to-be-determined design parameters are determined, first sample data is obtained based on these parameters. Each sample in the first sample data includes the information of the parameters obtained above, including first quality information, at least one first constraint parameter information and at least two to-be-determined design parameter information.

[0031] The method for acquiring these sample point data can be, for example, first designing other structural design parameters that have not been selected as the design parameters to be determined according to conventional specifications, and then designing different types of design parameter information combinations based on the design parameter information to be determined, and further determining the corresponding quality and constraint parameters based on the different types of parameter information combinations, thereby obtaining multiple samples to constitute the first sample data.

[0032] S12: Performing data analysis on the first sample data based on the first quality information and the first constraint parameter information to determine at least one design parameter from at least two design parameters to be determined.

[0033] After obtaining the first sample data, the quality information and constraint parameter information are used as outputs, and each to-be-determined design parameter is used as input, and data analysis is performed to determine at least one design parameter from the to-be-determined design parameters for a subsequent optimization process.

[0034] In order to reduce the amount of calculation and improve the optimization efficiency, the first sample data is analyzed and then the design parameters with higher priority are selected as the design parameters for subsequent quality optimization.

[0035] S13: Acquire second sample data according to at least one design parameter, each second sample data includes second quality information, at least one second constraint parameter information and at least one design parameter information.

[0036] After the design parameters are determined, similar to the process of obtaining the first sample data, the second sample data is obtained based on the quality, at least one constraint parameter and at least one design parameter. Each sample in the second sample data will include the information of the above-mentioned parameters, including the second quality information, at least one second constraint parameter information and at least one design parameter information.

[0037] The method for obtaining these sample point data can be, for example, first designing other structural design parameters that are not selected design parameters according to conventional specifications, and then designing different types of design parameter information combinations based on the design parameter information, and further determining their corresponding quality and constraint parameters based on the different types of parameter information combinations, thereby obtaining multiple samples to form the second sample data.

[0038] S14: Obtaining a response model of the design parameters to the constraint parameters based on the second sample data.

[0039] The response model can be a line model or a surface model. When constructing a response model of a design parameter to a constraint parameter, a line model can be constructed. When constructing a response model of two design parameters to a constraint parameter, a surface model can be constructed. When constructing a response surface model, the model can be fitted using the central composite experimental design (CCD) method.

[0040] S15: Determine several groups of design parameter information based on the response model and the constraint parameters.

[0041] The second sample data is used as original data to generate a response model of the design parameters to the constraint parameters, and each design parameter information is further determined according to the numerical conditions that the constraint parameters need to satisfy and the obtained response model to obtain several groups of design parameter information.

[0042] S16: Obtain third quality information corresponding to each set of design parameter information.

[0043] Since other dimensional parameters in the structure that are not used as design parameters have been designed according to conventional specifications and their parameter information has been fixed, the third quality information corresponding to the structure when the structure is designed according to the design parameter information can be obtained through each set of design parameter information.

[0044] S17: taking at least one set of design parameter information corresponding to the third quality information with the minimum quality or the quality less than the first threshold as the final design parameter information.

[0045] In order to achieve the purpose of quality optimization, quality information with the lowest quality or quality information with quality less than a first threshold is determined from the third quality information, and at least one set of design parameter information corresponding to these quality information is used as final design parameter information.

[0046] The first quality information, the second quality information and the third quality information are all quality information. The first, second and third are used to indicate that the quality information corresponds to different objects. The first quality information corresponds to the design parameter information group to be determined in each sample in the first sample data, the second quality information corresponds to the design parameter information group in each sample in the second sample data, and the third quality information corresponds to several design parameter information groups determined based on the response model and constraint parameters. Similarly, the first constraint parameter information and the second constraint parameter information are both constraint parameter information. The first constraint parameter information corresponds to the design parameter information group to be determined in each sample in the first sample data, and the second constraint parameter information corresponds to the design parameter information group in each sample in the second sample data.

[0047] In this embodiment, by obtaining first sample data including design parameter information to be determined, first quality information, and first constraint parameter information, the design parameters are determined using the first quality information and the first constraint parameter information, and further obtaining second sample data including second quality information, second constraint parameter information, and design parameter information according to the design parameters, and obtaining a response model of the design parameters to the constraint parameters based on the second sample data. After determining the design parameter information that meets the threshold requirements of the constraint parameters through the response model, the final design parameter information is further determined according to the quality requirements, thereby achieving quality optimization design that meets the constraint parameters.

[0048] Since most structures have many design parameters, if all the design parameters are adjusted and optimized at the same time, the workload will be heavy and the calculation efficiency will be too low. Therefore, data analysis is used to screen the design parameters to be determined in order to reduce the number of design parameters. Only the key design parameters are optimized in the subsequent optimization process to improve the optimization efficiency.

[0049] Reference Figure 2 , Figure 2 This is a flow chart of the second embodiment of the quality optimization method of this application. The method is a further extension of step S12, and includes the following steps:

[0050] S21: Performing sensitivity analysis on the first sample data to obtain a first sensitivity of the to-be-determined design parameter to the quality and a second sensitivity to the constraint parameter.

[0051] S22: In response to the first sensitivity and the second sensitivity satisfying a first preset condition, determining to-be-determined design parameters corresponding to the first sensitivity and the second sensitivity satisfying the condition as design parameters.

[0052] In order to reduce the amount of calculation and improve the optimization efficiency, sensitivity analysis is used to screen the design parameters to be determined. First, the design parameters to be determined are taken as input parameters, and the quality and constraint parameters are taken as output parameters. The influence of the input parameters on the output parameters is obtained according to the first sample data. The influence degree is the sensitivity, and the first sensitivity of each design parameter to be determined to the quality and the second sensitivity to the constraint parameter can be obtained. A design parameter to be determined has a first sensitivity to the quality and a second sensitivity to each constraint parameter.

[0053] In one embodiment, the first preset condition includes: the difference between the absolute value of the second sensitivity and the absolute value of the first sensitivity exceeds a second threshold. The sensitivity obtained above is a percentage. By obtaining the difference between the absolute values ​​of the second sensitivity and the first sensitivity, the to-be-determined design parameters whose impact on quality is much smaller than that on the constraint parameters can be screened out and used as design parameters for the subsequent optimization process.

[0054] Each constraint parameter corresponds to a second threshold value, and the second threshold values ​​corresponding to different constraint parameters may be the same or different.

[0055] In one embodiment, the constraint parameters corresponding to the first constraint parameter information and the second constraint parameter information are the same. This is because the second constraint parameter information is the original data used to construct the response model, and when determining several sets of design parameter information based on the response model and the constraint parameters, it is necessary to consider the conditions that the constraint parameters need to satisfy. In order to make the obtained several sets of design parameter information more reasonable and accurate, when determining the design parameters, it is selected to determine the design parameters according to the same constraint parameters.

[0056] In one embodiment, the step of determining a plurality of sets of design parameter information based on the response model and the constraint parameters includes: determining a plurality of sets of design parameter information according to the response model and the second preset conditions corresponding to the constraint parameters.

[0057] The constraint parameters include maximum stress and maximum deformation, and the second preset condition includes at least one of the maximum deformation being less than a third threshold and the maximum stress being less than a fourth threshold.

[0058] In one embodiment, before obtaining the second sample data according to at least one design parameter, the angle corresponding to the reinforcing ribs newly added to the support structure is also taken as a design parameter.

[0059] For some support structures, their inertia section moment will have a great influence on their constraint parameters such as deformation or stress. Therefore, in order to improve the performance of the support structure, before obtaining the second sample data, the support structure is improved by adding some stiffening plates to increase its inertia section moment and enhance its support performance, so that the mass can be further reduced. Since new stiffeners are added to the support structure, the corresponding angles need to be used as design parameters to obtain the second sample data for the subsequent quality optimization process. The corresponding angles can be the angles between the stiffeners and the plates.

[0060] A specific example is given below to illustrate the quality optimization method of the present application in more detail.

[0061] Assume that the quality optimization method of the present application is applied to optimize a column structure of a stacker. Figure 3 As shown, Figure 3 The following is a schematic diagram of a column structure. G1 is a column, and G2 is a cargo platform. The cargo platform moves up and down through the column. Due to the mass of the cargo platform, the column will be subjected to pressure and may be deformed.

[0062] The column is mainly composed of I-beams, rectangular plates and reinforcing ribs of varying thicknesses, and contains many dimensional parameters. Simplifying it can result in a wedge-shaped structure, such as Figure 4 As shown, Figure 4 This is a simplified structural diagram of a column. In the actual production process, in order to realize the serial design of the column, the total length of the I-beam is set as a fixed value, and the other parameters are used as design parameters. The seven dimensional parameters such as the width of the I-beam P1, the thickness of the short side of the I-beam P2, the thickness of the long side of the I-beam P3, the length of the middle plate P4, the wedge angle of the middle plate P5, the thickness of the middle plate P6 and the thickness of the left plate P7 are used as design parameters. After determining the design parameters, the range of changes is determined based on the corresponding processing technology, such as Figure 5 The design parameters here are equivalent to the design parameters to be determined.

[0063] Then, based on these design parameters, the experimental design is carried out to obtain sample points, and the influence of each design parameter on the output parameters is determined through sensitivity analysis. The output parameters include the maximum deformation and maximum stress as mass and as constraint parameters.

[0064] like Figure 6 As shown, this is a schematic diagram of the influence of various design parameters on the output parameters.

[0065] In order to achieve lightweight design and complete the purpose of quality optimization, the parameters with a much smaller impact on quality than deformation are given priority for optimization design, and the deformation is greatly reduced at the cost of less mass increase. In the figure, only P4 meets this requirement. Considering that P2 has a small impact on quality and a large impact on maximum stress, a significant reduction in stress value can be achieved at a small mass cost. Therefore, it is decided to retain P2 and P4 as design parameters and exclude the remaining design parameters. The design parameters P2 and P4 retained here are equivalent to the design parameters determined from the design parameters to be determined.

[0066] After determining P2 and P4 as design parameters for quality optimization, it is necessary to determine the values ​​of other dimensional parameters that are not used as design parameters. Considering the feasibility of the processing technology and the purpose of quality optimization, the thickness of other plates is set to 5mm, and the wedge angle of the middle plate is set to 90 degrees, so that the column structure becomes a rectangular structure.

[0067] Based on the rectangular structure, the cross-sectional moment of inertia of the column can be calculated, such as Figure 7 As shown, Figure 7 This is a schematic diagram of the cross section of a column.

[0068] Considering that the stiffness of the column will be affected by the moment of inertia of the cross section, in order to increase the moment of inertia of the cross section of the rectangular structure, a stiffening plate is installed at the end far away from the neutral axis of the cross section of the column. The farther the stiffening plate is from the neutral axis, the greater the increase in the moment of inertia of the cross section. The additional stiffening plate is also made of 5mm thin plate to avoid adding too much mass. Figure 8 As shown, Figure 8 Cross-section diagram of the improved column after adding stiffened panels.

[0069] At this time, the angle corresponding to the stiffened plate is also used as a design parameter for the subsequent quality optimization process. Finally, there are four design parameters determined, namely, the width of the I-beam x1, the length of the middle plate x2, the angle between the upper left stiffened plate and the left plate x3, and the angle between the lower left stiffened plate and the left plate x4. After all the design parameters are determined, their transformation range is determined based on the corresponding processing technology, such as Fig. 9 shown.

[0070] The reinforced plate of the improved column can also be in other shapes, such as Fig.10 As shown, Fig.10 Another cross-sectional view of the improved column with the addition of a stiffener. When the improvement is made with such an "L" or "T" shaped stiffener, the corresponding length of the stiffener can be used as a design parameter for the subsequent quality optimization process. The corresponding length can be the distance between the stiffener and each plate.

[0071] Then, based on these design parameters, the experimental design is performed to obtain sample points to fit the second-order response surface, thereby determining the changing relationship between each design parameter and the constraint parameter. In this embodiment, the CCD experimental design method is used for experimental design. CCD is an experimental design method extended from the two-level experimental design. Compared with the two levels, some axis points and several center points are added to fit the second-order response surface model, which is convenient for evaluating the nonlinear relationship between input and output variables. Sample point data is obtained by the CCD experimental design method, such as Fig.11 As shown, Fig.11 Schematic diagram of sample point data obtained. There are 4 design variables and 25 sample points. The mass, maximum deformation and maximum stress of the column are defined as output parameters.

[0072] After obtaining the sample points, the sample point data can be used as the original data for establishing the response surface. Then establish the second-order response surface of the design parameters to the constraint parameters, such as the maximum deformation response surface and the maximum stress response surface. Fig.12 and 13 As shown, this is the second-order response surface of the design parameters to the constraint parameters obtained based on the above sample data. Fig.12 is a schematic diagram of the second-order response surface for the maximum deformation, Fig.13 It is a schematic diagram of the second-order response surface for the maximum stress. It can be seen from the figure that if the deformation is reduced, x1 and x2 should increase, and x3 and x4 should decrease; and x1 and x2 have a greater impact on the maximum stress than x3 and x4.

[0073] After obtaining these response surfaces, several groups of design parameter information can be determined first according to the preset conditions of the constraint parameters. For example, the constraint parameters may include maximum deformation and maximum stress, and the preset conditions of the constraint parameters may be that the maximum deformation does not exceed a threshold value, and the maximum stress does not exceed another threshold value. Specifically, the maximum deformation does not exceed the value of the column height divided by two thousand, and the maximum stress does not exceed the ultimate stress of the column material. Then, the numerical range for each design parameter can usually be obtained, and several groups of design parameter information can be obtained according to the numerical range of each design parameter. Each group of design parameter information can further determine its corresponding quality information. Therefore, in order to achieve the purpose of quality optimization, with the minimum quality as the goal, or with the quality less than a threshold as the goal, at least one group of design parameter information that meets the quality requirements is selected from several groups of design parameter information as the final design parameter information. When the final design parameter information is used for actual design, it can meet the quality requirements and achieve quality optimization.

[0074] like Fig.14 As shown, Fig.14 This is a schematic structural diagram of the first embodiment of the electronic device of the present application.

[0075] The electronic device includes a processor 110 and a memory 120 .

[0076] The processor 110 controls the operation of the electronic device, and the processor 110 may also be referred to as a CPU (Central Processing Unit). The processor 110 may be an integrated circuit chip having the ability to process a signal sequence. The processor 110 may also be a general-purpose processor, a digital signal sequence processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0077] The memory 120 stores instructions and program data required for the processor 110 to operate.

[0078] The processor 110 is used to execute instructions to implement the method provided by any embodiment and possible combination of the quality optimization method described above in the present application.

[0079] like Fig.15 As shown, Fig.15 This is a schematic diagram of the structure of the first embodiment of the computer-readable storage medium of the present application.

[0080] An embodiment of a readable storage medium of the present application includes a memory 210, and the memory 210 stores program data. When the program data is executed, the method provided by any embodiment and possible combination of the quality optimization method of the present application is implemented.

[0081] The memory 210 may include a medium that can store program instructions, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or it may be a server that stores the program instructions. The server may send the stored program instructions to other devices for execution, or it may execute the stored program instructions itself.

[0082] In summary, by obtaining the first sample data including the design parameter information to be determined, the first quality information and the first constraint parameter information, the design parameters are determined using the first quality information and the first constraint parameter information, and further obtaining the second sample data including the second quality information, the second constraint parameter information and the design parameter information according to the design parameters, and obtaining the response model of the design parameters to the constraint parameters based on the second sample data. After determining the design parameter information that meets the threshold requirements of the constraint parameters through the response model, the final design parameter information is further determined according to the quality requirements, thereby achieving quality optimization design that meets the constraint parameters.

[0083] Since most structures have many design parameters, if all the design parameters are adjusted and optimized at the same time, the workload will be heavy and the calculation efficiency will be too low. Therefore, data analysis is used to screen the design parameters to be determined in order to reduce the number of design parameters. Only the key design parameters are optimized in the subsequent optimization process to improve the optimization efficiency.

[0084] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device implementation described above is only illustrative, for example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0085] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0086] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0087] If the integrated units in the above other embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), disk or optical disk and other media that can store program codes.

[0088] The above descriptions are merely embodiments of the present application and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A quality optimization method, applied to a support structure, the support structure corresponding to at least two to-be-determined design parameters, characterized in that: The method comprises: Acquire first sample data, wherein each sample in the first sample data includes first quality information, at least one first constraint parameter information, and the at least two to-be-determined design parameter information; Performing data analysis on the first sample data based on the first quality information and the first constraint parameter information to determine at least one design parameter from the at least two design parameters to be determined; Acquire second sample data according to the at least one design parameter, each of the second sample data includes second quality information, at least one second constraint parameter information and the at least one design parameter information; Acquire a response model of the design parameter to the constraint parameter based on the second sample data; Determine a plurality of sets of design parameter information based on the response model and the threshold requirements corresponding to the constraint parameters; Obtaining third quality information corresponding to each set of design parameter information; At least one set of design parameter information corresponding to the third quality information with the minimum quality or the quality less than the first threshold is used as the final design parameter information.

2. The method according to claim 1, characterized in that The performing data analysis on the first sample data based on the first quality information and the first constraint parameter information to determine at least one design parameter from the at least two design parameters to be determined includes: Performing sensitivity analysis on the first sample data to obtain a first sensitivity of the to-be-determined design parameter to quality and a second sensitivity to a constraint parameter; In response to the first sensitivity and the second sensitivity satisfying a first preset condition, the to-be-determined design parameter corresponding to the first sensitivity and the second sensitivity satisfying the condition is determined as the design parameter.

3. The method according to claim 2, characterized in that The second preset condition includes: a difference between an absolute value of the second sensitivity and an absolute value of the first sensitivity exceeds a second threshold.

4. The method according to claim 3, characterized in that Each constraint parameter corresponds to a second threshold.

5. The method according to claim 1, characterized in that The constraint parameters corresponding to the first constraint parameter information and the second constraint parameter information are the same.

6. The method according to claim 1, characterized in that The determining of several groups of design parameter information based on the response model and the constraint parameters includes: The plurality of groups of design parameter information are determined according to the response model and the second preset condition corresponding to the constraint parameter.

7. The method according to claim 6, characterized in that The constraint parameters include a maximum stress and a maximum deformation, and the second preset condition includes at least one of the maximum deformation being less than a third threshold and the maximum stress being less than a fourth threshold.

8. The method according to claim 1, characterized in that Before acquiring the second sample data according to the at least one design parameter, the method includes taking the angle corresponding to the reinforcing rib newly added to the support structure as the design parameter.

9. An electronic device, characterized in that: The invention comprises a memory and a processor, wherein the memory is used to store program data, and the program data can be executed by the processor to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: Program data is stored and can be executed by a processor to implement the method according to any one of claims 1 to 8.

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