Multi-unloading high-attribute joint optimization method and system for working device of loading machine

Abstract

The invention relates to the technical field of loader optimization design, in particular to a multi-unloading high-attribute joint optimization method and system for a loader working device, and the method comprises the steps: obtaining the coordinates of each hinge point, the mass and mass center of a part, the stroke of an oil cylinder, the installation distance, the diameter and other parameters; establishing a mathematical model according to the structure of the working device, and calculating various performances of the working device through the mathematical model; determining a design variable, a target function and a constraint condition according to each performance index and design input of the working device; optimization is carried out according to different unloading height requirements and hinge points of the basic working device, multiple sets of working device hinge points meeting different unloading heights are obtained, and each set of hinge points meets the corresponding unloading height requirement; and the obtained hinge points with different unloading heights can maintain the universality of some specific structures. Compared with the prior art, the method has the advantages that the hinge points with different unloading heights can be optimized more efficiently, and the performance of the obtained hinge points is better.

Description

Technical Field

[0001] The present invention relates to the technical field of loader optimization design, and particularly relates to a multi-dumping height attribute joint optimization method and system for a loader working device. Background Art

[0002] A loader completes various operations such as shoveling, lifting, and discharging by controlling a series of actions of the working device. When the basic link parameters cannot meet the requirements, it is necessary to optimize the design of the hinge points of the working device according to the new design requirements. At the same time, the hinge points with different dumping heights optimized from the basic hinge points should meet certain structural commonality for actual production and processing.

[0003] In the prior art, when optimizing the design of a loader working device, usually the basic hinge point coordinates are first optimized to obtain the hinge point coordinates that meet the constraint conditions of one dumping height. Then, based on these hinge point coordinates, another hinge point with a different dumping height is optimized, and the hinge point coordinates optimized later need to maintain partial structural commonality with the hinge point coordinates optimized previously for actual production. However, when optimizing another set of hinge points with different dumping heights based on the hinge point coordinates optimized for the first time, it cannot be guaranteed that the hinge point coordinates that meet the constraints can be obtained. Summary of the Invention

[0004] Aiming at the above problems, the purpose of the present invention is to provide a multi-dumping height attribute joint optimization method and system for a loader working device, which can more efficiently optimize the hinge points with different dumping heights and obtain better hinge point performance.

[0005] The technical solution adopted by the present invention is as follows: A multi-dumping height attribute joint optimization method for a loader working device proposed by the present invention specifically includes the following steps: S1. Obtain the coordinates of each hinge point, the mass of components, the centroid, and the parameters of the cylinder stroke, installation distance, and diameter; S2. Establish a mathematical model according to the structure of the loader working device, and calculate the theoretical values of the performance of the loader working device through the mathematical model; S3. Determine the design variables, optimization objectives, and constraint conditions according to the performance of the loader working device and the design input; S4. Generate the initial population of the genetic algorithm according to the variation range of the design variables, determine the coding method of the hinge point coordinates under different dumping height requirements, and ensure the commonality of some structures; S5. Select individuals from the initial population for crossover operation, randomly select parental individuals to exchange genetic information, and perform mutation operation on the individuals after crossover to obtain a new population. During this process, it is necessary to ensure the commonality of some structures of the hinge points under different dumping height requirements; S6. Calculate the penalty function values of the individuals in the new population according to the mathematical model, normalize the penalty function values of all constraints, and retain the individuals with smaller penalty function values for the next generation; S7. Determine whether to converge according to the penalty function values of the individuals in the population. When the penalty function value is zero, the algorithm converges, and the hinge point coordinates that meet the requirements are output.

[0006] Further, the specific steps of step S4 include: taking the basic hinge point as a reference, optimizing two sets of hinge point coordinates that respectively meet different unloading height requirements, and randomly generating them through the following formula according to the set range of design variables:

[0007] where, represents the i-th coordinate, represents 0 1 random number, and represent the upper and lower limits of the i-th coordinate; The following strategy is adopted for the second set of hinge point coordinates when generating the general structure:

[0008] where, and are respectively the first coordinates of the common parts of the first set of hinge points and the second set of hinge points, and are respectively the j-th coordinates of the common parts of the first set of hinge points and the second set of hinge points. When generating the coordinates of the non-common part of the second set of hinge points, it is the same as the first set.

[0009] Further, the specific steps of step S5 include: in the crossover process, randomly select three individuals as parents for crossover, crossover the first set of hinge points of the three parent individuals, and calculate the differential evolution operator through the following formula:

[0010] where, F is the crossover factor, and its value range is between 0.8 and 1.2, , , represent the three parents, and i represents the i-th gene position; when crossing the second set of coordinates, the following method is adopted for crossing the common structure part:

[0011] where, and are respectively the first coordinates of the common parts of the first set of hinge points and the second set of hinge points, and They are the j-th coordinates of the common parts of the first set of hinge points and the second set of hinge points respectively. The coordinate crossing process of the second set of hinge points is the same as that of the first set of hinge points when the coordinates are in the cross non-common part; since the coordinates after crossing according to the above formula may exceed the range of the design variables, when the result exceeds the range, the parent generation is reselected for crossing.

[0012] Further, in the step S5, the mutation operation includes: When the coordinates of the first set of hinge points mutate, the mutation points are regenerated within the range of the design variables, as shown in the following formula:

[0013] Where represents the coordinates after mutation, represents a random number between 0 and 1, and represent the upper and lower limits of the coordinates of the mutation points; When the coordinates of the second set of hinge points mutate, the mutation points should be the same as those of the first set of hinge points. If the mutation points are on the common structure, the following method is used for mutation:

[0014] Where and represent the coordinates of the first set of hinge points and the second set of hinge points before mutation respectively, and represent the coordinates after mutation. If the second set of hinge points exceeds the range of the design variables after mutating according to the first set of hinge points, the first set of hinge points is mutated again; if the mutation points are not on the common structure, the coordinates of the second set of hinge points are also regenerated within the range of the design variables during mutation.

[0015] Further, the step S6 specifically includes: Each set of hinge points corresponds to multiple constraint conditions. Calculate the constraint values for each set of hinge points, and take the difference between the constraint values that exceed the constraint boundary and the constraint boundary as its penalty function value. To make the importance of the constraint conditions equal, the penalty function needs to be normalized.

[0016] Further, the normalization of the penalty function is carried out in the following way:

[0017]

[0018] Where, is the constraint value, and are the upper and lower limits of the constraint respectively, is the normalized penalty function value, and sum all the penalty function values , take the final penalty function value as the criterion to be retained for the next generation.

[0019] A multi-unloading height attribute joint optimization system for a loader working device is used to implement the above optimization method. The system includes a storage medium and a processor; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the optimization method; According to the design requirements, select the corresponding hinge points as design variables; Take the performance indicators of the loader working device as the objective or constraint functions; Through iterative solution by an optimization algorithm, the first iteration needs to execute steps S4 - S6, and subsequent iterations need to execute steps S5 - S6.

[0020] The present invention has the following beneficial effects compared with the prior art: The present invention constructs a mathematical model for the working device, and calculates the various performances of the working device through the mathematical model; determines the design variables, objective functions, and constraint conditions according to the various performance indicators and design inputs of the working device; optimizes according to different unloading height requirements and the hinge points of the basic working device to obtain multiple sets of hinge points of the working device that meet different unloading heights, and each set of hinge points meets the corresponding unloading height requirements; and the obtained hinge points with different unloading heights can maintain the generality of some specific structures, which can effectively improve the product development efficiency, and the performances of the optimized hinge points are better. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic diagram of the principle of a multi-unloading height attribute joint optimization method for a loader working device proposed by the present invention; Figure 2 is a schematic diagram of the hinge points of a loader working device according to an embodiment of the present invention; Figure 3 is a schematic diagram of the optimization process of the hinge points of a loader working device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0023] Refer to the attached Figure 1 , a multi-unloading height attribute joint optimization method and system for a loader working device proposed by the present invention specifically includes the following steps: S1. Obtain the coordinates of each hinge point, the mass of the component, the centroid, as well as the parameters of the cylinder stroke, installation distance, and diameter; S2. According to the structure of the loader working device, establish a mathematical model, and calculate the theoretical values of various performances of the loader working device through the mathematical model; S3. Determine the design variables, optimization objectives, and constraint conditions based on the various performances and design inputs of the loader working device; S4. Generate the initial population of the genetic algorithm according to the variation range of the design variables, determine the coding method of the hinge point coordinates under different unloading height requirements, and ensure the generality of some structures; specifically including: Based on the basic hinge point, optimize two sets of hinge point coordinates that meet different unloading height requirements respectively, and randomly generate them through the following formula according to the set range of the design variables:

[0024] Among them, represents the i-th coordinate, represents 0 1 random number, and represent the upper and lower limits of the i-th coordinate; The following strategy is adopted for the second set of hinge point coordinates when generating the general structure:

[0025] Among them, and are the first coordinates of the common part of the first set of hinge points and the second set of hinge points respectively, and are the j-th coordinates of the common part of the first set of hinge points and the second set of hinge points respectively. When generating the coordinates of the non-common part of the second set of hinge points, it is the same as the first set.

[0026] S5. Select individuals from the initial population for crossover operation, randomly select parent individuals to exchange genetic information, and perform mutation operation on the crossed individuals to obtain a new population. In this process, ensure the generality of some structures of the hinge points under different unloading height requirements; specifically including: During the crossover process, select three individuals as parents for crossover by random selection, cross the first set of hinge points of the three parent individuals, and calculate the differential evolution operator through the following formula:

[0027] Among them, F is the crossover factor, and its value range is between 0.8 and 1.2, , , It represents three parents, and i represents the i-th gene position. When the second set of coordinate crossover occurs, the general structure part crossover adopts the following method:

[0028] Among them, and are respectively the first coordinates of the general part of the first set of hinge points and the second set of hinge points, and are respectively the j-th coordinates of the general part of the first set of hinge points and the second set of hinge points. The coordinate crossover process of the second set of hinge points in the non-general part of the crossover is the same as that of the first set of hinge points. Since the coordinates after crossover according to the above formula may exceed the range of design variables, when the result exceeds the range, new parents are reselected for crossover.

[0029] The mutation operation includes: When the coordinates of the first set of hinge points mutate, the mutation point is regenerated within the range of design variables, as shown in the following formula:

[0030] Where represents the coordinates after mutation, represents 0 1 random number, and represent the upper and lower limits of the coordinates of the mutation point; When the coordinates of the second set of hinge points mutate, the mutation point should be the same as that of the first set of hinge points. If the mutation point is on the general structure, the following method is used for mutation:

[0031] Where and respectively represent the coordinates of the first set of hinge points and the second set of hinge points before mutation, and represent the coordinates after mutation. If the second set of hinge points exceeds the range of design variables after mutating according to the first set of hinge points, the first set of hinge points is mutated again. If the mutation point is not on the general structure, the coordinate mutation of the second set of hinge points is also regenerated within the range of design variables.

[0032] S6. Calculate the penalty function values of the individuals in the new population according to the mathematical model, normalize the penalty function values of all constraints, and retain the individuals with smaller penalty function values to the next generation; specifically include: Each set of hinge points corresponds to multiple constraint conditions. Calculate the constraint values for each set of hinge points, and take the difference between the constraint value that exceeds the constraint boundary and the constraint boundary as its penalty function value. To make the importance of the constraint conditions equal, it is necessary to normalize the penalty function.

[0033] The normalization process of the penalty function is carried out in the following way:

[0034]

[0035] where is the constraint value, and are the upper and lower limits of the constraint respectively, is the value of the penalty function after normalization, sum all the penalty function values and use the final penalty function value as the standard to be retained for the next generation.

[0036] S7. According to the penalty function values of the individuals in the population, judge whether to converge. When the penalty function value is zero, the algorithm converges and outputs the hinge point coordinates that meet the requirements.

[0037] A multi-dumping height attribute joint optimization system for a loader working device is used to implement the above optimization method. The system includes a storage medium and a processor; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the optimization method; Select the corresponding hinge point as the design variable according to the design requirements; Regard the performance indicators of the loader working device as the objective or constraint function; Iteratively solve through the optimization algorithm. The first iteration needs to execute steps S4 - S6, and subsequent iterations need to execute steps S5 - S6.

[0038] The following further illustrates the present invention through specific examples: Taking the six-link loader working device as an example, see the appendix Figure 3 The specific implementation process of the optimization method is as follows: S1. Obtain the coordinates of each hinge point, the mass of the components, the centroid, and parameters such as the cylinder stroke, installation distance, diameter, etc. The hinge point structure of the working device is as Figure 2 shown; S2. Establish a mathematical model according to the structure of the working device, and calculate the various performances of the working device through the mathematical model; S3. Determine the design variables, objective function, and constraint conditions according to the various performances of the working device and the design input.

[0039] Optimize according to different unloading height requirements and the hinge points of the basic working device to obtain multiple sets of hinge points for the working device that meet different unloading heights. Each set of hinge points meets the corresponding unloading height requirements; and the obtained hinge points with different unloading heights can maintain the generality of some specific structures. Taking the simultaneous optimization of small-height unloading and large-height unloading as an example, and ensuring that the vehicle frames and buckets of the two sets of optimized hinge points can be made universal, specifically including: S4. Initialize the population. Randomly generate hinge point coordinates within the design variable range of small-height unloading, and randomly generate hinge point coordinates that meet the requirements of large-height unloading within the design variable range of large-height unloading. During the generation process of the large-height unloading hinge point coordinates, keep the vehicle frame and bucket the same as those of the small-height unloading hinge point. The random generation formula for the small-height unloading hinge point coordinates is as follows:

[0040] Among them, represents the i-th coordinate, represents 0 1 random number, and represent the upper and lower limits of the i-th coordinate. The following strategy is adopted for generating the large-height unloading hinge point coordinates when generating the universal structure:

[0041] Among them, and are respectively the first coordinates of the vehicle frame or bucket parts of small-height unloading and large-height unloading, and are respectively the j-th coordinates of the vehicle frame or bucket parts of small-height unloading and large-height unloading. When generating the non-universal part coordinates of the large-height unloading hinge point, it is the same as that of the small-height unloading.

[0042] S5. During the crossover process, randomly select three individuals as parents for crossover. The small-height unloading hinge point is crossed with the large-height unloading hinge point. The differential evolution operator is calculated by the following formula:

[0043] Among them. F is the crossover factor, and its value range is between 0.8 and 1.2, , , represent the three parents, and i represents the i-th gene position. When crossing the large-height unloading hinge point coordinates, the following method is adopted for crossing the universal structure part:

[0044] Among them, and are respectively the first coordinates of the vehicle frame or bucket parts of small-height unloading and large-height unloading, and They are the j-th coordinates of the small high-dump and large high-dump vehicle frames or bucket parts respectively. When the coordinates of the large high-dump hinge point cross those of the small high-dump hinge point during the cross process in the cross non-universal part, the process is the same. Since the coordinates after crossing according to the above formula may exceed the range of design variables, when the result exceeds the range, reselect the parent generation for crossing; Then, perform the mutation operation. When mutating the coordinates of the small high-dump hinge point, the mutation point is regenerated within the range of the design variables of the small high-dump, as shown in the following formula:

[0045] Among them, represents the coordinates after mutation, represents 0 a random number between 0 and 1, and represent the upper and lower limits of the coordinates of the mutation point. When mutating the coordinates of the large high-dump hinge point, the mutation point should be the same as that of the small high-dump hinge point. If the mutation point is on the vehicle frame or bucket structure, the following method is used for mutation:

[0046] Among them, and represent the coordinates of the small high-dump hinge point and the large high-dump hinge point before mutation respectively, and represent the coordinates after mutation. If the coordinates of the large high-dump hinge point exceed the range of design variables after mutating according to the coordinates of the small high-dump hinge point, the coordinates of the small high-dump hinge point are mutated again. If the mutation point is not on the vehicle frame or bucket structure, the coordinates of the large high-dump hinge point are also regenerated within the range of design variables.

[0047] S6. Calculate the penalty function values of the offspring. Calculate the constraint values for both the small high-dump and large high-dump hinge points. The difference between the constraint value that exceeds the constraint boundary and the constraint boundary is its penalty function value. To make the importance of the constraint conditions equal, it is necessary to normalize the penalty function. The penalty function normalization is carried out in the following way:

[0048]

[0049] Among them, is the constraint value, and are the upper and lower limits of the constraint respectively, is the normalized penalty function value. Sum all the penalty function values , and use the final penalty function value as the standard for retaining the next generation. Compare the penalty function values of the offspring and the parent generation, and retain the individual with the smaller penalty function value in the next generation.

[0050] S7. When the penalty function value is zero, the algorithm converges to obtain a set of hinge points corresponding to the small high-dump and the large high-dump respectively, and the hinge points of the small high-dump and the large high-dump can meet the universality of the frame and the bucket structure. The specific optimization process is as follows Figure 3 shown.

[0051] The optimization method proposed by the present invention is a genetic algorithm. This algorithm draws on the process of biological evolution and gradually selects the optimal solution or a solution close to the optimal solution from an initial population by simulating mechanisms such as "natural selection", "inheritance", and "mutation". In this invention, the algorithm generates hinge point coordinates in a random manner. The initial hinge point coordinates may not satisfy the constraints. Through subsequent crossover and mutation operations, the hinge point coordinates are changed based on the initial hinge points until the hinge point coordinates meet the conditions.

[0052] Matters not detailed in the present invention are all well-known technologies.

[0053] The embodiments described above are only used to describe the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for joint optimization of multiple unloading properties of a loader working device, characterized in that: The method comprises the following steps: S1, obtain the coordinates of each hinge point, component mass, center of mass, and cylinder stroke, installation distance and diameter parameters; S2. According to the structure of the loader working device, a mathematical model is established, and the theoretical values ​​of various performances of the loader working device are calculated through the mathematical model; S3. Determine design variables, optimization objectives and constraints based on the performance and design input of the loader working device; S4. Generate the initial population of the genetic algorithm according to the variation range of the design variables, determine the encoding method of the hinge coordinates under different unloading height requirements, and ensure the universality of some structures; S5. Select individuals from the initial population for crossover operation, randomly select parent individuals to exchange genetic information, perform mutation operation on the individuals after crossover, and obtain a new population. In this process, it is necessary to ensure the commonality of the partial structures of the hinges under different unloading height requirements; S6. Calculate the penalty function values ​​of the new population individuals according to the mathematical model, normalize the penalty function values ​​of all constraints, and retain the individuals with small penalty function values ​​to the next generation; S7. Determine whether the algorithm has converged based on the individual penalty function values ​​in the population. When the penalty function value is zero, the algorithm converges and outputs the hinge point coordinates that meet the requirements.

2. A method for joint optimization of multiple high-attributes of a loader working device according to claim 1, characterized in that: The step S4 specifically includes: taking the basic hinge point as a reference, optimizing two sets of hinge point coordinates that meet different unloading height requirements respectively, and generating them randomly according to the range of the set design variables by the following formula: in, represents the i-th coordinate, Represents 0 A random number of 1, and Indicates the upper and lower limits of the i-th coordinate; The second set of hinge coordinates uses the following strategy when generating the general structure: in, and are the first coordinates of the common parts of the first set of hinge points and the second set of hinge points, respectively. and They are the j-th coordinates of the common parts of the first set of hinge points and the second set of hinge points respectively. The coordinates of the non-common parts generated by the second set of hinge points are the same as those of the first set.

3. A method for joint optimization of multiple high-attributes of a loader working device according to claim 2, characterized in that: The step S5 specifically includes: in the crossover process, three individuals are selected as parents for crossover by random selection, the first set of hinge points of the three parent individuals are crossovered, and the differential evolution operator is calculated by the following formula: Among them, F is the crossover factor, which ranges from 0.8 to 1.

2. , , represents three parents, i represents the i-th gene position; when the second set of coordinates are crossed, the common structure part is crossed in the following way: in, and are the first coordinates of the common parts of the first set of hinge points and the second set of hinge points, respectively. and are the j-th coordinates of the common parts of the first and second sets of hinge points, respectively. The crossing process of the second set of hinge points is the same as that of the first set of hinge points when crossing the coordinates of the non-common parts. As the coordinates after crossing according to the above formula may exceed the range of the design variables, the parent generation is reselected for crossing when the result exceeds the range.

4. A method for joint optimization of multiple high-attributes of a loader working device according to claim 3, characterized in that: In step S5, the mutation operation includes: When the first set of hinge point coordinates mutate, the mutated points are regenerated within the design variable range, as shown in the following formula: in represents the coordinates after mutation, Represents 0 A random number of 1, and Indicates the upper and lower limits of the coordinates of the variation point; When the second set of hinge coordinates are mutated, the mutation points must be the same as the first set of hinge mutation points. If the mutation points are on the common structure, the following methods are used for mutation: in and They represent the coordinates of the first set of hinge points and the second set of hinge points before mutation, and Represents the coordinates after mutation. If the second set of hinge points exceeds the range of design variables after mutation according to the first set of hinge points, the first set of hinge points will be mutated again. If the mutation points are not on the common structure, the second set of hinge point coordinate mutations will also be regenerated within the range of design variables.

5. A method for joint optimization of multiple unloading high attributes of a loader working device according to claim 4, characterized in that: The step S6 specifically includes: each set of hinge points corresponds to multiple constraints, the constraint value of each set of hinge points is calculated, the difference between the constraint value exceeding the constraint boundary and the constraint boundary is used as the penalty function value, and in order to make the importance of the constraints equal, the penalty function needs to be normalized.

6. A method for joint optimization of multiple high-attributes of a loader working device according to claim 5, characterized in that: The normalization process of the penalty function is performed in the following manner: in, is the constraint value, and are the upper and lower limits of the constraints, is the normalized penalty function value, summing up all penalty function values , the final penalty function value as a standard to be retained for the next generation.

7. A loader working device multi-unloading high-attribute joint optimization system, used to implement the optimization method according to any one of claims 1 to 6, characterized in that: The system includes a storage medium and a processor; The storage medium is used to store instructions; The processor is used to operate according to the instructions to perform the optimization method; According to the design requirements, select the corresponding hinge point as the design variable; Taking various performance indicators of the loader working device as the objective or constraint function; The optimization algorithm is used to iterate the solution. The first iteration needs to execute steps S4-S6, and the subsequent iterations need to execute steps S5-S6.

Images