Methods, devices, equipment and storage media for the layout design of standard parts

Abstract

This application relates to the field of automotive manufacturing process design, and discloses a method, apparatus, equipment, and storage medium for the layout design of standard parts. A local coordinate system of the mechanism is established using a coordinate system distance positioning algorithm. The mechanism layout surface of the standard part is identified based on this local coordinate system. The total weight is calculated based on the mechanism layout surface and the envelope size and material density of the standard part to match the standard part model, thus obtaining the target standard part. The initial layout position of the target standard part is calculated according to layout rules. An avoidance algorithm is used to eliminate interference areas of other standard parts, generating multiple initial layout points. The GA-PSO hybrid optimization algorithm is used to optimize these initial layout points to obtain the optimal layout point. Kalman filtering is introduced to predict and correct the layout position in real time, obtaining the target layout point. Adjustments are made to the target layout point using integer rules (e.g., whole numbers of five or ten), enabling intelligent calculation of the screw and pin layout positions.

Description

Technical Field

[0001] This application relates to the field of automotive manufacturing process design technology, and in particular to a method, apparatus, equipment and storage medium for the layout design of standard parts. Background Technology

[0002] In the design of automated and intelligent software for automotive molds, it is necessary to arrange standard components for various mold mechanisms. Among these standard components, screws and pins are indispensable, responsible for fixing, lifting, and rotating the mechanism. Traditionally, screw and pin placement involves manually calculating their positions and then using software to arrange them. However, manual design is time-consuming and labor-intensive. If the positions of other standard components in the mechanism shift, or if the shape and size of the mechanism change, or if the arranged standard components interfere with each other, the screw and pin positions must be recalculated manually, greatly reducing the efficiency of intelligent design. Summary of the Invention

[0003] This application provides a method for the layout design of standard parts, which can store asset registration in a database and review asset change request information through a multi-level review mechanism, thereby ensuring asset security.

[0004] In a first aspect, embodiments of this application provide a method for arranging standard parts, the method comprising the following steps:

[0005] A local coordinate system for the mechanism is established using a coordinate system distance positioning algorithm, and the mechanism layout surface of the standard parts is identified based on the local coordinate system.

[0006] The total weight is calculated based on the arrangement of the mechanism and the envelope size and material density of the standard parts to match the standard part model, thus obtaining the target standard part;

[0007] The initial placement position of the target standard component is calculated according to the placement rules, and the interference area of ​​other standard components is eliminated by the avoidance algorithm to generate multiple initial placement points;

[0008] The GA-PSO hybrid optimization algorithm is used to optimize multiple initial placement points. The GA genetic algorithm is used to generate diverse placement schemes through selection, crossover and mutation operations. The PSO particle swarm algorithm is used to quickly converge based on the historical optimal information of individuals and the population to obtain the optimal placement point.

[0009] The placement process is set as a dynamic system. The optimal placement point is used as the state variable, and the interferometric detection is used as the observation variable. Kalman filtering is introduced to predict and correct the placement position in real time to obtain the target placement point. The target placement point is then adjusted according to the integer five and integer ten rules.

[0010] In one possible implementation, a local coordinate system for the mechanism is established using a coordinate system distance positioning algorithm, and the mechanism layout surface of the standard component is identified based on the local coordinate system, including:

[0011] Based on a coordinate system distance positioning algorithm, the origin and x, y, z axes of the local coordinate system are obtained;

[0012] Transform the global coordinate system into a local coordinate system. in Here are the coordinates of the point to be transformed in the global coordinate system; This represents the position of the origin of the local coordinate system within the global coordinate system. A rotation matrix is ​​the direction in which global coordinates are rotated to the local coordinate system. Let these be the coordinates of the point in the local coordinate system; It involves first shifting the global origin to the local origin.

[0013] To construct the rotation matrix using the Euler angle rotation method, assume that the local coordinate system is rotated relative to the global coordinate system around the three axes X, Y, and Z by the following angles. Then the rotation matrix is ,

[0014]

[0015]

[0016]

[0017] in, This represents the angle of rotation of the local coordinate system relative to the global coordinate system around the X-axis. Indicates the rotation angle around the X-axis rotation matrix, This represents the angle of rotation of the local coordinate system relative to the global coordinate system around the Y-axis. Indicates the rotation angle around the Y-axis rotation matrix, This represents the angle of rotation of the local coordinate system relative to the global coordinate system around the Z-axis. Rotation angle around the Z-axis The rotation matrix.

[0018] In one possible implementation, the total weight is calculated based on the mechanism layout surface and the envelope size and material density of the standard part to match the standard part model, thereby obtaining the target standard part, including:

[0019] Obtain the length of the envelope of the mechanism layout plane and standard parts. ,Width ,high The volume factor is β, and the range of values ​​for β is... Given that the standard part is made of iron, the weight calculation formula is: β 7300, where 7300 is the density of iron;

[0020] The target standard part is obtained by matching the standard part model with the weight of the standard part.

[0021] In one possible implementation, the initial placement position of the target standard component is calculated according to the placement rules, and interference areas of other standard components are eliminated through an avoidance algorithm to generate multiple initial placement points, including:

[0022] Based on the standard parts layout rules, calculate whether there is any interference from standard parts within the screw layout range at both ends of the target standard part.

[0023] The arrangement of screws and pins is within a three-dimensional coordinate range: ; ; ;

[0024] The layout range of other standard parts on the screw layout surface is obtained through an interface in the code:

[0025]

[0026]

[0027] in, express The boundary of the region along the X-axis. express The boundary of the region along the Y-axis. express The region's boundary along the Z-axis; superscript The identifier indicates the remaining standard parts and is used to distinguish different standard parts; Indicates the first The boundary of the range of the remaining standard parts in the X-axis direction Indicates the first The boundary of the range of the remaining standard parts in the Y-axis direction Indicates the first The boundary of the range of the remaining standard parts in the Z-axis direction;

[0028] The coordinate range after the standard part avoids the standard part is ;in It belongs to But not belonging to and ;

[0029] Set coordinate range: The final placement points of the standard parts are obtained as follows: / 2; / 2; / 2;

[0030] in, and This indicates the range of three-dimensional coordinates for the arrangement of other standard parts on the standard parts arrangement surface. express The boundary of the region along the X-axis. express The boundary of the region along the Y-axis. express The boundary of the region along the axial direction.

[0031] In one possible implementation, the optimization of multiple initial placement points using a GA-PSO hybrid optimization algorithm, the generation of diverse placement schemes through selection, crossover, and mutation operations using a GA genetic algorithm, and the rapid convergence of the PSO particle swarm optimization algorithm based on the historical optimal information of individuals and the population to obtain the optimal placement point, includes:

[0032] The optimization objectives for the arrangement of standard parts include minimizing the offset between the overall center of gravity of the arrangement points and the origin of the local coordinate system of the mechanism, optimizing the uniformity of the spacing between the arrangement points, and maximizing the distance from the arrangement points to the interference area.

[0033] A fitness function is constructed based on the aforementioned optimization objectives, and the three optimization objectives are weighted to obtain a fitness evaluation index. :

[0034]

[0035] in, , , These represent the weight coefficients corresponding to the three optimization objectives, used to adjust the importance of the optimization objectives in the fitness. This represents the offset of the overall center of gravity of the arrangement point relative to the origin of the local coordinate system of the mechanism; The coefficient of variation representing the spacing between each arrangement point; The fitness evaluation index represents the average minimum distance from the placement point to the interference region. The value range is [0,1];

[0036] Based on the GA genetic algorithm, multiple initial placement points are used as gene fragments and randomly combined to generate an initial population of a certain size. Each population group corresponds to a set of standard part placement schemes.

[0037] Calculate the fitness value of each individual in the population. The roulette wheel selection method is used to prioritize individuals with high fitness values ​​to enter the next generation of the population, while retaining a small number of individuals with low fitness values ​​but scattered distribution to ensure population diversity.

[0038] Individuals with the highest fitness values ​​in the population are selected as parents. Single-point or multi-point crossover is used, with the crossover point denoted as . father generation Gene father generation Gene The offspring gene expression is as follows:

[0039]

[0040] Among them, offspring This represents the gene sequence of the first offspring individual generated by crossover. This represents the gene sequence of the second offspring generated by the crossover.

[0041] Setting the mutation probability to 0.05-0.1, the coordinates of some offspring placement points are slightly adjusted. Let the coordinates before mutation be... Variable Asynchronous Length If the range is 0.5~2mm, then the coordinate expression after the variation is as follows:

[0042]

[0043] in, Indicates the first before mutation The three-dimensional coordinates of each placement point This represents the variable asynchronous length, with a fixed value of 0.5mm, which is the basic range for adjusting the coordinates of the placement points; A random number representing [0,1] is used to control the random range of coordinate adjustment during mutation; , , Indicates the mutated th The three-dimensional coordinates of each placement point;

[0044] By exchanging the placement point gene fragments of parent individuals, offspring placement schemes are generated. A reasonable mutation probability is set, and the placement point coordinates of some offspring individuals are slightly randomly adjusted to generate multiple sets of diverse placement schemes.

[0045] In one possible implementation, the optimization of multiple initial placement points using a GA-PSO hybrid optimization algorithm, the generation of diverse placement schemes through selection, crossover, and mutation operations using a GA genetic algorithm, and the rapid convergence of the PSO particle swarm optimization algorithm based on the historical optimal information of individuals and the population to obtain the optimal placement point, includes:

[0046] The diverse layout schemes generated by the GA algorithm are used as the initial particles for the PSO algorithm, with each particle corresponding to a set of position coordinates for the layout scheme. At the same time, initialize the particle velocity vector. ;

[0047] Calculate the fitness value of each particle and record the individual best historical position of each particle. and the group's historical best position for the entire particle swarm ;

[0048] Based on the velocity and position update formulas of the PSO algorithm, and combining individual optimal and swarm optimal information, the position of each particle is iteratively adjusted, gradually converging towards the optimal region. The calculation formula is as follows:

[0049]

[0050]

[0051] in, Indicates the current iteration number. Indicates the next iteration. Indicates the first The particle in the first The velocity vector at the next iteration; This represents the inertial weight, with a value range of (0.4~0.9), used to balance the global search capability of particles; Represents the learning factor. Indicates the first The particle in the first Position coordinates at the next iteration; Indicates the first The particle in the first The updated velocity vector at the next iteration; Indicates the first The particle in the first The updated position coordinates at the next iteration Represents a random number in the range [0,1].

[0052] When the change in the optimal fitness value of a population over multiple consecutive generations satisfies the following formula;

[0053]

[0054] in, Indicates the first The optimal fitness value of the population at the next iteration; Indicates the first The optimal fitness value of the population at the next iteration; This represents the absolute value of the difference between the optimal fitness values ​​of the population in two consecutive iterations. This represents the convergence threshold, at which iteration stops, and the optimal placement point is output, corresponding to the optimal position of the current population.

[0055] In one possible implementation, the arrangement process is set as a dynamic system, with the optimal arrangement point as the state variable and interferometry detection as the observation variable. Kalman filtering is then used to predict and correct the arrangement position in real time to obtain the target arrangement point. Adjustments are then made to the target arrangement point using a five-ten rule, including:

[0056] The coordinates of the optimal placement point are used as state variables. Construct the system state equation

[0057] in, Indicates the timing of the arrangement process Indicates the first The state variable at time t, This represents the system process noise, which follows a mean of 0 and a covariance of 0. Gaussian distribution, Represents state variables, Represents the observed variable. Represents state variables One of the components, representing the first The optimal placement point at any given time is in space. Coordinates in direction, Indicates the first The optimal placement point at any given time is in space. Coordinate values ​​in the direction;

[0058] Using interferometric detection results as observation variables Construct observation equations The noise represents the observation noise, which follows a mean of 0 and a covariance of 0. Gaussian distribution, It was obtained through interferometric detection, indicating Observations of direction It was obtained through interferometric detection, indicating Observations of direction It was obtained through interferometric detection, indicating Observations of direction;

[0059] based on The optimal placement point coordinates at time t is determined by prediction. state of time and predict covariance matrix :

[0060] Kalman gain:

[0061] Status Update:

[0062] Covariance update:

[0063] in, Indicates the first Predicted state at any given time Indicates the first The prediction covariance matrix at time 1, The Kalman gain is used to weigh the predictions against the observations and to determine the magnitude of the state update. The covariance matrix representing the observation noise. Indicates the first The status after the update at any time. Represents the identity matrix. Indicates the first The updated covariance matrix at time step;

[0064] The coordinates of the deployment points are optimized through multiple rounds of iterative optimization to eliminate the influence of system noise and observation noise, and the corrected initial coordinates of the target deployment points are obtained.

[0065] The coordinates of the target placement points after Kalman filtering correction are adjusted according to the integer five and ten rule in their local coordinate system: given the value of a coordinate as... The formula for multiples of five and ten is: ,in, This function represents rounding, which rounds the input value to the nearest integer. This indicates the adjusted coordinate values, which are multiples of 5. Represents the original numerical values ​​of the coordinates of the placement points;

[0066] The adjusted target layout points are then subjected to a final interference re-inspection and rule verification to confirm that the adjusted coordinates do not generate new interference and comply with all layout rules, thus obtaining the target layout points for the standard parts.

[0067] Secondly, this application provides a standard component layout design apparatus, which includes the following modules:

[0068] The local coordinate system establishment module is used to establish a local coordinate system of the mechanism through a coordinate system distance positioning algorithm, and to identify the mechanism layout surface of the standard parts based on the local coordinate system of the mechanism.

[0069] The standard parts determination module is used to calculate the total weight and match the standard parts model based on the mechanism layout surface and the envelope size and material density of the standard parts to obtain the target standard parts;

[0070] The placement point calculation module is used to calculate the initial placement position of the target standard part according to the placement rules, eliminate the interference area of ​​other standard parts through the avoidance algorithm, and generate multiple initial placement points.

[0071] The placement point optimization module is used to optimize multiple initial placement points using the GA-PSO hybrid optimization algorithm. It uses the GA genetic algorithm to perform selection, crossover and mutation operations to generate diverse placement schemes, and uses the PSO particle swarm algorithm to quickly converge based on the historical optimal information of individuals and the population to obtain the optimal placement point.

[0072] The layout design optimization module is used to set the layout process as a dynamic system. The optimal layout point is used as the state variable, and the interferometric detection is used as the observation variable. Kalman filtering is introduced to predict and correct the layout position in real time to obtain the target layout point. The target layout point is then adjusted according to the integer five and integer ten rules.

[0073] Thirdly, this application provides a standard component layout design device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the standard component layout design device to execute the various steps of the standard component layout design method described above.

[0074] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the various steps of the above-described method for arranging and designing standard components.

[0075] Based on the method provided in this application, its beneficial effects are as follows: By establishing a local coordinate system for the mechanism through a coordinate system distance positioning algorithm, it accurately identifies the layout surface that meets the installation requirements, eliminating the subjective errors of traditional manual surface selection from the source. Simultaneously, through quantitative calculation of the interference region, it controls the spatial position deviation of the layout point to a minimal range, laying a high-precision foundation for subsequent standard component installation. Combining the load-bearing capacity of the layout surface, the envelope size of the standard component, and the material density, a multi-dimensional model selection system is constructed. This ensures that the total weight of the standard component matches the lightweight and center-of-gravity balance requirements of the mechanism, and avoids the risk of assembly failure due to improper model selection through secondary constraints such as material compatibility and strength grade. It also reduces the cost losses caused by replacing non-standard components. Through the application of the GA-PSO hybrid optimization algorithm, it not only utilizes the selection, crossover, and mutation operations of the genetic algorithm to achieve diversified generation of layout schemes, but also relies on the rapid convergence characteristics of the particle swarm optimization algorithm to lock the globally optimal layout point in a short time. Compared with a single algorithm, this avoids local optimum traps and improves the efficiency of scheme iteration. The introduction of Kalman filtering enables real-time prediction of placement location and noise elimination. The coordinate adjustment rules of integers five and ten transform the optimal solution of the algorithm into practical coordinates that conform to engineering processing and assembly specifications, which greatly reduces the rework rate of on-site assembly and improves the consistency of placement of different batches of products. Attached Figure Description

[0076] Figure 1 This is a flowchart of an embodiment of the standard component layout design method provided in this application. Figure 1 ;

[0077] Figure 2 This is a flowchart of an embodiment of the standard component layout design method provided in this application. Figure 2 ;

[0078] Figure 3 This is a schematic diagram of the mechanism layout of the standard parts arrangement design method provided in the embodiments of this application;

[0079] Figure 4 This is a schematic diagram of the structure of the standard parts arrangement design device provided in the embodiments of this application;

[0080] Figure 5 This is a structural schematic diagram of the equipment for arranging standard parts provided in the embodiments of this application. Detailed Implementation

[0081] This application provides a standard component layout design method that can register and store assets in a database and review asset change request information through a multi-level review mechanism, thereby solving the problem of poor asset security in daily management.

[0082] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. The terms "first," "second," "third," "fourth," etc., used in the specification, claims, and accompanying drawings of this application, are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "including" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses.

[0083] It is understood that any part of this application concerning data acquisition or collection has been authorized by the user.

[0084] It is understood that the implementing entity of this application can be a standard component layout design device, a mobile terminal, or a server; no specific limitation is made here.

[0085] The specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of a standard component layout design method provided in this application, including:

[0086] 101. Establish a local coordinate system for the mechanism using a coordinate system distance positioning algorithm, and identify the mechanism layout surface of the standard parts based on the local coordinate system;

[0087] Understandably, based on the coordinate system distance positioning algorithm, the origin and x, y, z axes of the local coordinate system are obtained, and the screw and pin (i.e. standard parts) arrangement surface is the surface with the largest z-point in the local coordinate plane;

[0088] Transform the global coordinate system into a local coordinate system. in Here are the coordinates of the point to be transformed in the global coordinate system; This represents the position of the origin of the local coordinate system within the global coordinate system. A rotation matrix is ​​the direction in which global coordinates are rotated to the local coordinate system. Let these be the coordinates of the point in the local coordinate system; It involves first shifting the global origin to the local origin.

[0089] To construct the rotation matrix using the Euler angle rotation method, assume that the local coordinate system is rotated relative to the global coordinate system around the three axes X, Y, and Z by the following angles. Then the rotation matrix is ,

[0090]

[0091]

[0092]

[0093] in, This represents the angle of rotation of the local coordinate system relative to the global coordinate system around the X-axis. Indicates the rotation angle around the X-axis rotation matrix, This represents the angle of rotation of the local coordinate system relative to the global coordinate system around the Y-axis. Indicates the rotation angle around the Y-axis rotation matrix, This represents the angle of rotation of the local coordinate system relative to the global coordinate system around the Z-axis. Rotation angle around the Z-axis The rotation matrix.

[0094] 102. Calculate the total weight and match the standard part model based on the mechanism layout and the envelope size and material density of the standard part to obtain the target standard part;

[0095] It is understandable that obtaining the length of the envelope of the mechanism layout surface and standard parts is necessary. ,Width ,high The volume factor is β, and the range of values ​​for β is... Given that the standard part is made of iron, the weight calculation formula is: β 7300, where 7300 is the density of iron;

[0096] The target standard part is obtained by matching the standard part model with the standard part's weight.

[0097] 103. Calculate the initial placement position of the target standard parts according to the placement rules, eliminate the interference area of ​​other standard parts through the avoidance algorithm, and generate multiple initial placement points;

[0098] Understandably, the calculation involves determining whether there is interference from standard parts within the screw arrangement range at both ends of the target standard part, based on the standard part arrangement rules.

[0099] The arrangement of screws and pins is within a three-dimensional coordinate range: ; ; ;

[0100] The layout range of other standard parts on the screw layout surface is obtained through an interface in the code:

[0101]

[0102]

[0103] in, express The boundary of the region along the X-axis. express The boundary of the region along the Y-axis. express The region's boundary along the Z-axis; superscript The identifier indicates the remaining standard parts and is used to distinguish different standard parts; Indicates the first The boundary of the range of the remaining standard parts in the X-axis direction Indicates the first The boundary of the range of the remaining standard parts in the Y-axis direction Indicates the first The boundary of the range of the remaining standard parts in the Z-axis direction;

[0104] The coordinate range after the standard part avoids the standard part is ;in It belongs to But not belonging to and ;

[0105] Set coordinate range: The final placement points of the standard parts are obtained as follows: / 2; / 2; / 2;

[0106] in, and This indicates the range of three-dimensional coordinates for the arrangement of other standard parts on the standard parts arrangement surface. express The boundary of the region along the X-axis. express The boundary of the region along the Y-axis. express The boundary of the region along the axial direction.

[0107] 104. The GA-PSO hybrid optimization algorithm is used to optimize multiple initial placement points. The GA genetic algorithm is used to perform selection, crossover and mutation operations to generate diverse placement schemes. The PSO particle swarm algorithm is used to quickly converge based on the historical optimal information of individuals and the population to obtain the optimal placement point.

[0108] Understandably, the optimization objectives for determining the layout of standard components include minimizing the offset between the overall center of gravity of the layout points and the origin of the local coordinate system of the mechanism, optimizing the uniformity of the spacing between each layout point, and maximizing the distance from the layout points to the interference region.

[0109] A fitness function is constructed based on the optimization objectives, and the three optimization objectives are weighted to obtain the fitness evaluation index. :

[0110]

[0111] in, , , These represent the weight coefficients corresponding to the three optimization objectives, used to adjust the importance of the optimization objectives in the fitness. This represents the offset of the overall center of gravity of the arrangement point relative to the origin of the local coordinate system of the mechanism; The coefficient of variation representing the spacing between each arrangement point; The fitness evaluation index represents the average minimum distance from the placement point to the interference region. The value range is [0,1];

[0112] Based on the GA genetic algorithm, multiple initial placement points are used as gene fragments and randomly combined to generate an initial population of a certain size. Each population group corresponds to a set of standard part placement schemes.

[0113] Calculate the fitness value of each individual in the population. The roulette wheel selection method is used to prioritize individuals with high fitness values ​​to enter the next generation of the population, while retaining a small number of individuals with low fitness values ​​but scattered distribution to ensure population diversity.

[0114] Individuals with the highest fitness values ​​in the population are selected as parents. Single-point or multi-point crossover is used, with the crossover point denoted as . father generation Gene father generation Gene The offspring gene expression is as follows:

[0115]

[0116] Among them, offspring This represents the gene sequence of the first offspring individual generated by crossover. This represents the gene sequence of the second offspring generated by the crossover.

[0117] Setting the mutation probability to 0.05-0.1, the coordinates of some offspring placement points are slightly adjusted. Let the coordinates before mutation be... Variable Asynchronous Length (0.5~2mm), then the coordinate expression after the variation is as follows:

[0118]

[0119] in, Indicates the first before mutation The three-dimensional coordinates of each placement point This represents the variable asynchronous length, with a fixed value of 0.5mm, which is the basic range for adjusting the coordinates of the placement points; A random number representing [0,1] is used to control the random range of coordinate adjustment during mutation; , , Indicates the mutated th The three-dimensional coordinates of each placement point;

[0120] By exchanging the placement point gene fragments of parent individuals, offspring placement schemes are generated. A reasonable mutation probability is set, and the placement point coordinates of some offspring individuals are slightly randomly adjusted to generate multiple sets of diverse placement schemes.

[0121] The diverse layout schemes generated by the GA algorithm are used as the initial particles for the PSO algorithm, with each particle corresponding to a set of position coordinates for the layout scheme. At the same time, initialize the particle velocity vector. ;

[0122] Calculate the fitness value of each particle and record the individual best historical position of each particle. and the group's historical best position for the entire particle swarm ;

[0123] Based on the velocity and position update formulas of the PSO algorithm, and combining individual optimal and swarm optimal information, the position of each particle is iteratively adjusted, gradually converging towards the optimal region. The calculation formula is as follows:

[0124]

[0125]

[0126] in, Indicates the current iteration number. Indicates the next iteration. Indicates the first The particle in the first The velocity vector at the next iteration; This represents the inertial weight, with a value range of (0.4~0.9), used to balance the global search capability of particles; Represents the learning factor. Indicates the first The particle in the first Position coordinates at the next iteration; Indicates the first The particle in the first The updated velocity vector at the next iteration; Indicates the first The particle in the first The updated position coordinates at the next iteration Represents a random number in the range [0,1].

[0127] When the change in the optimal fitness value of a population over multiple consecutive generations satisfies the following formula;

[0128]

[0129] in, Indicates the first The optimal fitness value of the population at the next iteration; Indicates the first The optimal fitness value of the population at the next iteration; This represents the absolute value of the difference between the optimal fitness values ​​of the population in two consecutive iterations. This represents the convergence threshold, at which iteration stops, and the optimal placement point is output, corresponding to the optimal position of the current population.

[0130] 105. Set the placement process as a dynamic system, with the optimal placement point as the state variable and interferometry detection as the observation variable. Introduce Kalman filtering to predict and correct the placement position in real time, obtain the target placement point, and adjust the target placement point according to the five-ten rule.

[0131] It is understandable that the coordinates of the optimal placement point will be used as the state variable. Construct the system state equation

[0132] in, Indicates the timing of the arrangement process Indicates the first The state variable at time t, This represents the system process noise, which follows a mean of 0 and a covariance of 0. Gaussian distribution, Represents state variables, Represents the observed variable. Represents state variables One of the components, representing the first The optimal placement point at any given time is in space. Coordinates in direction, Indicates the first The optimal placement point at any given time is in space. Coordinate values ​​in the direction;

[0133] Using interferometric detection results as observation variables Construct observation equations The noise represents the observation noise, which follows a mean of 0 and a covariance of 0. Gaussian distribution, It was obtained through interferometric detection, indicating Observations of direction It was obtained through interferometric detection, indicating Observations of direction It was obtained through interferometric detection, indicating Observations of direction;

[0134] based on The optimal placement point coordinates at time t is determined by prediction. state of time and predict covariance matrix :

[0135] Kalman gain:

[0136] Status Update:

[0137] Covariance update:

[0138] in, Indicates the first Predicted state at any given time Indicates the first The prediction covariance matrix at time 1, The Kalman gain is used to weigh the predictions against the observations and to determine the magnitude of the state update. The covariance matrix representing the observation noise. Indicates the first The status after the update at any time. Represents the identity matrix. Indicates the first The updated covariance matrix at time step;

[0139] The coordinates of the deployment points are optimized through multiple rounds of iterative optimization to eliminate the influence of system noise and observation noise, and the corrected initial coordinates of the target deployment points are obtained.

[0140] The coordinates of the target placement points after Kalman filtering correction are adjusted according to the integer five and ten rule in their local coordinate system: given the value of a coordinate as... The formula for multiples of five and ten is: ,in, This function represents rounding, which rounds the input value to the nearest integer. This indicates the adjusted coordinate values, which are multiples of 5. Represents the original numerical values ​​of the coordinates of the placement points;

[0141] The adjusted target layout points are then subjected to a final interference re-inspection and rule verification to confirm that the adjusted coordinates do not generate new interference and comply with all layout rules, thus obtaining the target layout points for the standard parts.

[0142] Based on the method provided in this application, its beneficial effects are as follows: By establishing a local coordinate system for the mechanism through a coordinate system distance positioning algorithm, it accurately identifies the layout surface that meets the installation requirements, eliminating the subjective errors of traditional manual surface selection from the source. Simultaneously, through quantitative calculation of the interference region, it controls the spatial position deviation of the layout point to a minimal range, laying a high-precision foundation for subsequent standard component installation. Combining the load-bearing capacity of the layout surface, the envelope size of the standard component, and the material density, a multi-dimensional model selection system is constructed. This ensures that the total weight of the standard component matches the lightweight and center-of-gravity balance requirements of the mechanism, and avoids the risk of assembly failure due to improper model selection through secondary constraints such as material compatibility and strength grade. It also reduces the cost losses caused by replacing non-standard components. Through the application of the GA-PSO hybrid optimization algorithm, it not only utilizes the selection, crossover, and mutation operations of the genetic algorithm to achieve diversified generation of layout schemes, but also relies on the rapid convergence characteristics of the particle swarm optimization algorithm to lock the globally optimal layout point in a short time. Compared with a single algorithm, this avoids local optimum traps and improves the efficiency of scheme iteration. The introduction of Kalman filtering enables real-time prediction of placement location and noise elimination. The coordinate adjustment rules of integers five and ten transform the optimal solution of the algorithm into practical coordinates that conform to engineering processing and assembly specifications, which greatly reduces the rework rate of on-site assembly and improves the consistency of placement of different batches of products.

[0143] Please see Figure 2 , Figure 2 A flowchart illustrating an embodiment of the third standard component layout design method provided in this application includes:

[0144] 201. The optimization objectives for the arrangement of standard parts include minimizing the offset between the overall center of gravity of the arrangement points and the origin of the local coordinate system of the mechanism, optimizing the uniformity of the spacing between the arrangement points, and maximizing the distance from the arrangement points to the interference area.

[0145] 202. Construct a fitness function based on the optimization objectives, and weight the three optimization objectives to obtain the fitness evaluation index. :

[0146]

[0147] in, , , These represent the weight coefficients corresponding to the three optimization objectives, used to adjust the importance of the optimization objectives in the fitness. This represents the offset of the overall center of gravity of the arrangement point relative to the origin of the local coordinate system of the mechanism; The coefficient of variation representing the spacing between each arrangement point; The fitness evaluation index represents the average minimum distance from the placement point to the interference region. The value range is [0,1];

[0148] 203. Based on the GA genetic algorithm, multiple initial placement points are used as gene fragments and randomly combined to generate an initial population of a certain size. Each population corresponds to a set of standard part placement schemes.

[0149] 204. Calculate the fitness value of each individual in the population. The roulette wheel selection method is used to prioritize individuals with high fitness values ​​to enter the next generation of the population, while retaining a small number of individuals with low fitness values ​​but scattered distribution to ensure population diversity.

[0150] Individuals with the highest fitness values ​​in the population are selected as parents. Single-point or multi-point crossover is used, with the crossover point denoted as . father generation Gene father generation Gene The offspring gene expression is as follows:

[0151]

[0152] Among them, offspring This represents the gene sequence of the first offspring individual generated by crossover. This represents the gene sequence of the second offspring generated by the crossover.

[0153] Setting the mutation probability to 0.05-0.1, the coordinates of some offspring placement points are slightly adjusted. Let the coordinates before mutation be... Variable Asynchronous Length If the range is 0.5~2mm, then the coordinate expression after the variation is as follows:

[0154]

[0155] in, Indicates the first before mutation The three-dimensional coordinates of each placement point This represents the variable asynchronous length, with a fixed value of 0.5mm, which is the basic range for adjusting the coordinates of the placement points; A random number representing [0,1] is used to control the random range of coordinate adjustment during mutation; , , Indicates the mutated th The three-dimensional coordinates of each placement point;

[0156] 205. Exchange the placement point gene fragments of parent individuals to generate offspring placement schemes, set reasonable mutation probabilities, and make small random adjustments to the placement point coordinates of some offspring individuals to generate multiple sets of diverse placement schemes.

[0157] Based on the method provided in the embodiments of this application, by applying the GA genetic algorithm, the diverse generation of layout schemes is achieved by using the selection, crossover and mutation operations of the genetic algorithm, and the global optimal layout point is locked in a short time by relying on the fast convergence characteristics of the particle swarm algorithm. Compared with a single algorithm, it avoids the local optimum trap and improves the efficiency of scheme iteration.

[0158] Please see Figure 3 , Figure 3 A schematic diagram of the mechanism layout of the third standard component layout design method provided in this application embodiment includes:

[0159] First, locate the screw's arrangement plane. For different mechanisms, designers will specify the screw's arrangement plane, but computers cannot automatically recognize this. Therefore, an algorithm is needed to allow the computer to automatically find the arrangement plane. Here, a coordinate system distance positioning algorithm is used to find the arrangement plane. When establishing a local coordinate system for the mechanism, we need to proceed in two steps: First, we need to find the origin and x, y, and z axes of the local coordinate system. Their determination can be based on specific circumstances, such as... Figure 3 For the mechanism layout shown, if the screws are specified to be installed at the red circle, then establishing a local coordinate system with the vertex of the cuboid as the origin (yellow circle) and the x, y, z axes as shown in the figure is most appropriate (for convenient coordinate calculation, without needing to calculate angles). The screw layout surface is the surface with the largest z-axis in the local coordinate system. The second step is to transform the global coordinate system to the local coordinate system: Since our modeling software only provides a global coordinate system during modeling, we need to transform the global coordinate system into a local coordinate system. in : The coordinates of the point to be transformed in the global coordinate system; This represents the position of the origin of the local coordinate system within the global coordinate system. A rotation matrix is ​​the direction in which global coordinates are rotated to the local coordinate system. Let be the coordinates of the point in the local coordinate system. It should be noted that the rotation matrix R can be constructed using the Euler angle rotation method: assuming the local coordinate system is rotated relative to the global coordinate system around the three axes X, Y, and Z by angles θ, y, and z respectively. Then the rotation matrix is , in

[0160]

[0161]

[0162]

[0163] The selection of screw and pin types is closely related to the weight of the mechanism and its standard components. Therefore, a reasonable formula is needed to determine the weight of the mechanism and its standard components. First, find the length, width, and height of the envelope of the mechanism with the standard components installed: The volume factor is β, and the range of values ​​for β is: This is because the mechanism is not entirely solid, and considering that the standard parts of the mechanism are made of iron, the weight calculation formula is: β 7300, where 7300 is the density of iron. Screw and pin selection.

[0164] According to the general arrangement rules for screws and pins, screws and pins in a typical mechanism are usually placed at the two ends or the center of the selected surface. Therefore, according to this rule, we only need to calculate whether there is interference from standard parts within the screw arrangement range at the two ends or the center. First, according to the rule, the range of screw and pin arrangement is a three-dimensional coordinate range: ; ; Then, in the code, we can find the placement range of the remaining standard parts on the screw placement surface (assuming the program interface finds two standard parts) through the official interface:

[0165]

[0166]

[0167] What is the final coordinate range after the screw and pin avoid the standard part? Where C belongs to M but not to N, L,

[0168] Set coordinate range: ,

[0169] Therefore, the final screw and pin placement points are: / 2; / 2; / 2;

[0170] Screw and pin placement points are multiples of five and ten: Due to the requirements of mold processing, the coordinates of each surface of the mechanism are multiples of five and ten in its coordinate system; the length, width, and height are also multiples of five and ten. To ensure processing precision and accuracy, it is also necessary to achieve multiples of five and ten in its local coordinate system (meaning that the coordinates of the placement points are multiples of five and ten): Given the value of a coordinate as n, the formula for multiples of five and ten is: ; in The function returns the rounded integer value of n.

[0171] Based on the above algorithm, the computer can determine the placement of standard screws and pins, and then automatically place these standard parts using the program interface built into the UG design software.

[0172] Based on the method provided in this application, the arrangement positions of screws and pins can be intelligently calculated quickly, greatly saving the efficiency of manual position calculation. A position avoidance algorithm is designed to effectively avoid interference from other standard parts while arranging screws and pins, intelligently solving the problem of unreasonable arrangement positions. It can automatically select screws and pins during arrangement, avoiding the trouble of later adjustments caused by selection errors. It is applicable to the screw and pin arrangement of most mechanisms, such as lifting punch mechanisms, hook punch mechanisms, and lower-position mechanisms, and the screw and pin positions arranged according to this algorithm are accurate.

[0173] The above describes the arrangement design method for standard parts in the embodiments of this application. The following describes the arrangement design apparatus for standard parts in the embodiments of this application. Please refer to [link / reference]. Figure 4 , Figure 4 A schematic diagram of a standard component layout design device provided in this application embodiment includes:

[0174] The local coordinate system establishment module is used to establish the local coordinate system of the mechanism through the coordinate system distance positioning algorithm, and to identify the mechanism layout surface of the standard parts based on the local coordinate system of the mechanism.

[0175] The standard parts determination module is used to calculate the total weight and match the standard parts model based on the mechanism layout surface and the envelope size and material density of the standard parts to obtain the target standard parts;

[0176] The layout point calculation module is used to calculate the initial layout position of the target standard parts according to the layout rules, eliminate the interference area of ​​other standard parts through the avoidance algorithm, and generate multiple initial layout points.

[0177] The placement point optimization module is used to optimize multiple initial placement points using the GA-PSO hybrid optimization algorithm. It uses the GA genetic algorithm to perform selection, crossover and mutation operations to generate diverse placement schemes, and uses the PSO particle swarm algorithm to quickly converge based on the historical optimal information of individuals and the population to obtain the optimal placement point.

[0178] The layout design optimization module is used to set the layout process as a dynamic system. The optimal layout point is used as the state variable, and the interferometric detection is used as the observation variable. Kalman filtering is introduced to predict and correct the layout position in real time to obtain the target layout point, and the target layout point is adjusted according to the integer five and integer ten rules.

[0179] Based on the device provided in this application, its beneficial effects are as follows: By establishing a local coordinate system for the mechanism through a coordinate system distance positioning algorithm in the local coordinate system establishment module, it accurately identifies the layout surface that meets the installation requirements, eliminating the subjective errors of traditional manual surface selection from the source. Simultaneously, through quantitative calculation of the interference region, it controls the spatial position deviation of the layout points to a minimal range, laying a high-precision foundation for subsequent standard component installation. The standard component determination module, combining the load-bearing capacity of the layout surface, the size of the standard component envelope, and the material density, constructs a multi-dimensional model selection system. This ensures that the total weight of the standard components matches the lightweight and center-of-gravity balance requirements of the mechanism, and avoids the risk of assembly failure due to improper model selection through secondary constraints such as material compatibility and strength grade. It also reduces the cost losses caused by replacing non-standard components. The layout point optimization module, through the application of the GA-PSO hybrid optimization algorithm, not only utilizes the selection, crossover, and mutation operations of the genetic algorithm to achieve diversified generation of layout schemes, but also relies on the rapid convergence characteristics of the particle swarm optimization algorithm to lock the globally optimal layout point in a short time. Compared with a single algorithm, this avoids local optimum traps and improves the efficiency of scheme iteration. The layout design optimization module achieves real-time prediction and noise elimination of layout position through the introduction of Kalman filtering. The coordinate adjustment rules of whole numbers five and ten transform the optimal solution of the algorithm into practical coordinates that conform to engineering processing and assembly specifications, which greatly reduces the rework rate of on-site assembly and improves the layout consistency of different batches of products.

[0180] Please see Figure 5 , Figure 5 This is a schematic diagram of a standard component layout design device 500 provided in an embodiment of this application. The standard component layout design device 500 can vary significantly due to different configurations or performance characteristics. It may include one or more processors 510, for example, one or more processors and a memory 520, and one or more storage media 530 storing application programs 533 or data 532. The memory 520 and storage media 530 can be temporary or persistent storage. The program stored in the storage media 530 may include one or more modules (not shown in the diagram), each module may include a series of instruction operations on the standard component layout design device 500. Furthermore, the processor 510 may be configured to communicate with the storage media 5630 and execute the series of instruction operations in the storage media 530 on the standard component layout design device 500.

[0181] The standard component layout design device 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input / output interfaces 560, and / or one or more operating systems 531, such as Windows Server, MacOSX, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 5 The arrangement of standard parts shown does not constitute a limitation on the arrangement of standard parts. It may include more or fewer parts than shown, or combine certain parts, or have different arrangements of parts.

[0182] This application also provides a standard component layout design device. The computer device includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor performs each step of the standard component layout design method in the above embodiments.

[0183] This application also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, storing instructions that, when executed on a computer, cause the computer to perform various steps of a method for arranging standard components.

[0184] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0185] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device, such as a personal computer, server, or network device, to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, ROM, random access memory, RAM, magnetic disks, or optical disks.

[0186] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0187] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for arranging standard parts, characterized in that, The arrangement design method for the standard parts includes the following steps: A local coordinate system for the mechanism is established using a coordinate system distance positioning algorithm, and the mechanism layout surface of the standard parts is identified based on the local coordinate system. The total weight is calculated based on the arrangement of the mechanism and the envelope size and material density of the standard parts to match the standard part model, thus obtaining the target standard part; The initial placement position of the target standard component is calculated according to the placement rules, and the interference area of ​​other standard components is eliminated by the avoidance algorithm to generate multiple initial placement points; The optimization objectives for the standard component layout include minimizing the offset between the overall centroid of the layout points and the origin of the local coordinate system of the mechanism, optimizing the uniformity of the spacing between the layout points, and maximizing the distance from the layout points to the interference region. The GA-PSO hybrid optimization algorithm is used to optimize multiple initial layout points. The GA genetic algorithm is used to generate diverse layout schemes through selection, crossover, and mutation operations. The PSO particle swarm algorithm is used to quickly converge based on the historical optimal information of individuals and the population to obtain the optimal layout point. The placement process is set as a dynamic system. The optimal placement point is used as the state variable, and interferometry is used as the observation variable. Kalman filtering is introduced to predict and correct the placement position in real time to obtain the target placement point. The target placement point is then adjusted according to the integer five and integer ten rules.

2. The method for arranging standard parts as described in claim 1, characterized in that, The step of establishing a local coordinate system for the mechanism using a coordinate system distance positioning algorithm, and identifying the mechanism layout surface of the standard component based on the local coordinate system, includes: Based on a coordinate system distance positioning algorithm, the origin and x, y, z axes of the local coordinate system are obtained; Transform the global coordinate system into a local coordinate system. in Here are the coordinates of the point to be transformed in the global coordinate system; This represents the position of the origin of the local coordinate system within the global coordinate system. Let these be the coordinates of the point in the local coordinate system; It involves first shifting the global origin to the local origin. To construct the rotation matrix using the Euler angle rotation method, assume the local coordinate system is rotated relative to the global coordinate system around the three axes X, Y, and Z by the following angles: Then the rotation matrix is , in, This represents the angle of rotation of the local coordinate system relative to the global coordinate system around the X-axis. Indicates the rotation angle around the X-axis rotation matrix, This represents the angle of rotation of the local coordinate system relative to the global coordinate system around the Y-axis. Indicates the rotation angle around the Y-axis rotation matrix, This represents the angle of rotation of the local coordinate system relative to the global coordinate system around the Z-axis. Rotation angle around the Z-axis The rotation matrix.

3. The method for arranging standard parts as described in claim 1, characterized in that, The step of calculating the total weight and matching the standard part model based on the mechanism layout surface and the envelope size and material density of the standard part to obtain the target standard part includes: Obtain the length of the envelope of the mechanism layout plane and standard parts. ,Width ,high The volume factor is β, and the range of values ​​for β is... Given that the standard part is made of iron, the weight calculation formula is: β 7300, where 7300 is the density of iron; The target standard part is obtained by matching the standard part model with the weight of the standard part.

4. The method for arranging standard parts as described in claim 1, characterized in that, The initial placement position of the target standard component is calculated according to the placement rules, and interference areas of other standard components are eliminated through an avoidance algorithm to generate multiple initial placement points, including: Based on the standard parts layout rules, calculate whether there is any interference from standard parts within the screw layout range at both ends of the target standard part. The arrangement of screws and pins is within a three-dimensional coordinate range: ; ; ; The layout range of other standard parts on the screw layout surface is obtained through an interface in the code: in, express The boundary of the region along the X-axis. express The boundary of the region along the Y-axis. express The region's boundary along the Z-axis; superscript The identifier indicates the remaining standard parts and is used to distinguish different standard parts; Indicates the first The boundary of the range of the remaining standard parts in the X-axis direction Indicates the first The boundary of the range of the remaining standard parts in the Y-axis direction Indicates the first The boundary of the range of the remaining standard parts in the Z-axis direction; The coordinate range after the standard part avoids the standard part is ;in It belongs to But not belonging to and ; Set the coordinate range: The final placement points of the standard parts are: / 2; / 2; / 2; in, and This indicates the range of three-dimensional coordinates for the arrangement of other standard parts on the standard parts arrangement plane. express The boundary of the region along the X-axis. express The boundary of the region along the Y-axis. express The boundary of the region along the axial direction.

5. The method for arranging standard parts as described in claim 1, characterized in that, The method involves optimizing multiple initial placement points using a GA-PSO hybrid optimization algorithm, generating diverse placement schemes through selection, crossover, and mutation operations using a GA genetic algorithm, and rapidly converging the optimal placement point using a PSO particle swarm optimization algorithm based on the historical optimal information of individuals and the population. This includes: The optimization objectives for the arrangement of standard parts include minimizing the offset between the overall center of gravity of the arrangement points and the origin of the local coordinate system of the mechanism, optimizing the uniformity of the spacing between the arrangement points, and maximizing the distance from the arrangement points to the interference area. A fitness function is constructed based on the aforementioned optimization objectives, and the three optimization objectives are weighted to obtain a fitness evaluation index. : in, , , These represent the weight coefficients corresponding to the three optimization objectives, used to adjust the importance of the optimization objectives in the fitness; This represents the offset of the overall center of gravity of the arrangement point relative to the origin of the local coordinate system of the mechanism; The coefficient of variation representing the spacing between each arrangement point; The fitness evaluation index represents the average minimum distance from the placement point to the interference region. The value range is [0,1]; Based on the GA genetic algorithm, multiple initial placement points are used as gene fragments and randomly combined to generate an initial population of a certain size. Each population group corresponds to a set of standard part placement schemes. Calculate the fitness value of each individual in the population. The roulette wheel selection method is used to prioritize individuals with high fitness values ​​to enter the next generation of the population, while retaining a small number of individuals with low fitness values ​​but scattered distribution to ensure population diversity. Individuals with the highest fitness values ​​in the population are selected as parents. Single-point or multi-point crossover is used, with the crossover point denoted as . father generation Gene father generation Gene The offspring gene expression is as follows: Among them, offspring This represents the gene sequence of the first offspring individual generated by crossover. This represents the gene sequence of the second offspring generated by the crossover. Setting the mutation probability to 0.05-0.1, the coordinates of some offspring placement points are slightly adjusted. Let the coordinates before mutation be... Variable Asynchronous Length If the range is 0.5~2mm, then the coordinate expression after the variation is as follows: in, Indicates the first before mutation The three-dimensional coordinates of each placement point This represents the variable asynchronous length, with a fixed value of 0.5mm, which is the basic range for adjusting the coordinates of the placement points; A random number representing [0,1] is used to control the random range of coordinate adjustment during mutation; , , Indicates the mutated th The three-dimensional coordinates of each placement point; By exchanging the placement point gene fragments of parent individuals, offspring placement schemes are generated. A reasonable mutation probability is set, and the placement point coordinates of some offspring individuals are slightly and randomly adjusted to generate multiple sets of diverse placement schemes.

6. The method for arranging standard parts as described in claim 1, characterized in that, The method involves optimizing multiple initial placement points using a GA-PSO hybrid optimization algorithm, generating diverse placement schemes through selection, crossover, and mutation operations using a GA genetic algorithm, and rapidly converging the optimal placement point using a PSO particle swarm optimization algorithm based on the historical optimal information of individuals and the population. This includes: The diverse layout schemes generated by the GA algorithm are used as the initial particles for the PSO algorithm, with each particle corresponding to a set of position coordinates for the layout scheme. At the same time, initialize the particle velocity vector. ; Calculate the fitness value of each particle and record the individual best historical position of each particle. and the group's historical best position for the entire particle swarm ; Based on the velocity and position update formulas of the PSO algorithm, and combining individual optimal and swarm optimal information, the position of each particle is iteratively adjusted, gradually converging towards the optimal region. The calculation formula is as follows: in, Indicates the current iteration number. Indicates the next iteration. Indicates the first The particle in the first The velocity vector at the next iteration; This represents the inertial weight, with a value range of (0.4~0.9), used to balance the global search capability of particles; Represents the learning factor. Indicates the first The particle in the first Position coordinates at the next iteration; Indicates the first The particle in the first The updated velocity vector at the next iteration; Indicates the first The particle in the first The updated position coordinates at the next iteration Represents a random number in the range [0,1]. When the change in the optimal fitness value of a population over multiple consecutive generations satisfies the following formula; in, Indicates the first The optimal fitness value of the population at the next iteration; Indicates the first The optimal fitness value of the population at the next iteration; This represents the absolute value of the difference between the optimal fitness values ​​of the population in two consecutive iterations. This represents the convergence threshold, at which iteration stops, and the optimal placement point is output, corresponding to the optimal position of the current population.

7. The method for arranging standard parts as described in claim 1, characterized in that, The process of arranging the placement is set as a dynamic system, with the optimal placement point as the state variable and interferometric detection as the observation variable. Kalman filtering is introduced to predict and correct the placement position in real time, obtaining the target placement point. Adjustments are then made to the target placement point using a five-ten rule, including: The coordinates of the optimal placement point are used as state variables. Construct the system state equation ; in, Indicates the timing of the setup process. Indicates the first The state variable at time t, This represents the system process noise, which follows a mean of 0 and a covariance of 0. Gaussian distribution, Represents state variables, Represents the observed variable. Represents state variables One of the components, representing the first The optimal placement point in space at any given time Coordinates in direction, Indicates the first The optimal placement point in space at any given time Coordinate values ​​in the direction; Using interferometric detection results as observation variables Construct observation equations ,in, The noise represents the observation noise, which follows a mean of 0 and a covariance of 0. Gaussian distribution, It was obtained through interferometric detection, indicating Observations of direction It was obtained through interferometric detection, indicating Observations of direction It was obtained through interferometric detection, indicating Observations of direction; based on The optimal placement point coordinates at time t is determined by prediction. state of time and the predicted covariance matrix : Kalman gain: Status Update: Covariance update: in, Indicates the first Predicted state at any given time Indicates the first The prediction covariance matrix at time 1, The Kalman gain is used to weigh the predictions against the observations and to determine the magnitude of the state update. The covariance matrix representing the observation noise. Indicates the first The status after the update at any time. Represents the identity matrix. Indicates the first The updated covariance matrix at time step; The coordinates of the deployment points are optimized through multiple rounds of iterative optimization to eliminate the influence of system noise and observation noise, and the corrected initial coordinates of the target deployment points are obtained. The coordinates of the target placement points after Kalman filtering correction are adjusted according to the integer five and ten rule in their local coordinate system: given the value of a coordinate as... The formula for multiples of five and ten is: ,in, This function represents rounding, which rounds the input value to the nearest integer. This indicates the adjusted coordinate values, which are multiples of 5. Represents the original numerical values ​​of the coordinates of the placement points; The adjusted target layout points are then subjected to a final interference re-inspection and rule verification to confirm that the adjusted coordinates do not generate new interference and comply with all layout rules, thus obtaining the target layout points for the standard parts.

8. A standard component arrangement design device, characterized in that, The standard component layout design device includes the following modules: The local coordinate system establishment module is used to establish a local coordinate system of the mechanism through a coordinate system distance positioning algorithm, and to identify the mechanism layout surface of the standard parts based on the local coordinate system of the mechanism. The standard parts determination module is used to calculate the total weight and match the standard parts model based on the mechanism layout surface and the envelope size and material density of the standard parts to obtain the target standard parts; The placement point calculation module is used to calculate the initial placement position of the target standard part according to the placement rules, eliminate the interference area of ​​other standard parts through the avoidance algorithm, and generate multiple initial placement points. The layout optimization module is used to determine the optimization objectives of the standard component layout, including minimizing the offset between the overall centroid of the layout points and the origin of the local coordinate system of the mechanism, optimizing the uniformity of the spacing between the layout points, and maximizing the distance from the layout points to the interference region. The GA-PSO hybrid optimization algorithm is used to optimize multiple initial layout points. The GA genetic algorithm is used to generate diverse layout schemes through selection, crossover and mutation operations. The PSO particle swarm algorithm is used to quickly converge based on the historical optimal information of individuals and groups to obtain the optimal layout point. The layout design optimization module is used to set the layout process as a dynamic system. The optimal layout point is used as the state variable, and the interferometric detection is used as the observation variable. Kalman filtering is introduced to predict and correct the layout position in real time to obtain the target layout point. The target layout point is then adjusted according to the integer five and integer ten rules.

9. A standard parts layout design device, characterized in that, The standard component layout design device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to cause the standard component layout design device to perform the steps of the standard component layout design method as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the various steps of the arrangement design method for the standard component as described in any one of claims 1-7.

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