A method and system for joint optimization of multiple unloading properties of loader working device

Abstract

The present invention relates to the technical field of loader optimization design, and specifically to a joint optimization method and system for multiple unloading height attributes of a loader working device, which obtains the coordinates of each hinge point, component mass, center of mass, and parameters such as cylinder stroke, installation distance, and diameter; establishes a mathematical model based on the working device structure, and calculates various performances of the working device through the mathematical model; determines design variables, objective functions, and constraints based on various performance indicators and design inputs of the working device; optimizes based on different unloading height requirements and basic working device hinge points to obtain multiple sets of working device hinge points that meet different unloading heights, and each set of hinge points meets the corresponding unloading height requirements; and the hinge points of different unloading heights obtained can maintain the commonality of some specific structures. Compared with the existing technology, the present invention can more efficiently optimize the hinge points of different unloading heights, and the obtained hinge points have better performance.

Description

Technical Field

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

[0002] The loader completes various operations such as shoveling, lifting, and unloading by controlling a series of actions of the working device. When the basic connecting rod parameters cannot meet the requirements, the hinge point of the working device needs to be optimized according to the new design requirements. At the same time, the hinge points of different unloading heights optimized from the basic hinge point should meet certain structural universality to facilitate actual production and processing.

[0003] In the prior art, when optimizing the design of a loader's working device, the base hinge coordinates are typically optimized first to obtain hinge coordinates that satisfy a certain unloading height constraint. Based on these hinge coordinates, another unloading height hinge is then optimized. The newly optimized hinge coordinates must maintain some structural commonality with the previously optimized hinge coordinates to facilitate actual production. However, optimizing another set of unloading height hinge coordinates based on the first optimized hinge coordinates does not guarantee that the hinge coordinates will satisfy the constraint. Summary of the Invention

[0004] In response to the above problems, the purpose of the present invention is to provide a method and system for joint optimization of multiple unloading height attributes of a loader working device, which can more efficiently optimize the hinge points of different unloading heights and obtain better hinge performance.

[0005] The technical solution adopted in the present invention is as follows:

[0006] The present invention proposes a method for joint optimization of multiple unloading high-attributes of a loader working device, which specifically includes the following steps:

[0007] S1. Obtain the coordinates of each hinge point, component mass, center of mass, and cylinder stroke, installation distance, and diameter parameters;

[0008] S2. Establish a mathematical model based on the structure of the loader working device, and calculate the theoretical values ​​of various performances of the loader working device through the mathematical model;

[0009] S3. Determine design variables, optimization objectives, and constraints based on the performance and design inputs of the loader working device;

[0010] S4. Generate the initial population of the genetic algorithm according to the variation range of the design variables, determine the hinge point coordinate encoding method under different unloading height requirements, and ensure the universality of some structures;

[0011] S5. Select individuals from the initial population for crossover operations, randomly select parent individuals to exchange genetic information, and perform mutation operations on the individuals after crossover to obtain a new population. In this process, it is necessary to ensure that the partial structures of the hinges under different unloading height requirements are universal;

[0012] 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 individuals with small penalty function values ​​to the next generation;

[0013] 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 coordinates that meet the requirements.

[0014] Furthermore, 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, and randomly generating them according to the range of the set design variables using the following formula:

[0015]

[0016] 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;

[0017] The second set of hinge coordinates uses the following strategy when generating the general structure:

[0018]

[0019] in, and are the first coordinates of the common parts of the first and second sets 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 coordinates of the non-common parts generated by the second set of hinge points are the same as those of the first set.

[0020] Furthermore, step S5 specifically includes: selecting three individuals as parents for crossover by random selection during the crossover process, crossovering the first set of hinge points of the three parent individuals, and calculating the differential evolution operator by the following formula:

[0021]

[0022] Among them, F is the cross 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 is crossed, the common structure part crosses in the following way:

[0023]

[0024] in, and are the first coordinates of the common parts of the first and second sets 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. Since the coordinates after crossing according to the above formula may exceed the range of the design variables, when the result is out of range, the parent generation is reselected for crossing.

[0025] Furthermore, in step S5, the mutation operation includes:

[0026] When the first set of hinge point coordinates mutates, the mutated points are regenerated within the design variable range, as shown in the following formula:

[0027]

[0028] 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 mutation point;

[0029] 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 method is used for mutation:

[0030]

[0031] 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.

[0032] Furthermore, step S6 specifically includes: each set of hinge points corresponds to multiple constraints, the constraint value of each set of hinge points is calculated, and the difference between the constraint value exceeding the constraint boundary and the constraint boundary is used as its penalty function value. In order to make the importance of the constraints equal, the penalty function needs to be normalized.

[0033] Furthermore, the normalization process of the penalty function is performed in the following manner:

[0034]

[0035]

[0036] in, is the constraint value, and are the upper and lower limits of the constraints, is the normalized penalty function value, and all penalty function values ​​are summed up , the final penalty function value as a standard to be retained for the next generation.

[0037] A loader working device multi-unloading high-attribute joint optimization system is used to implement the above-mentioned optimization method, and the system includes a storage medium and a processor;

[0038] The storage medium is used to store instructions;

[0039] The processor is configured to operate according to the instructions to perform the optimization method;

[0040] According to the design requirements, select the corresponding hinge point as the design variable;

[0041] Taking various performance indicators of the loader working device as the objective or constraint function;

[0042] The optimization algorithm is used to iteratively solve the problem. The first iteration needs to execute steps S4-S6, and the subsequent iterations need to execute steps S5-S6.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 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 constraints based on the various performance indicators and design inputs of the working device; optimizes the basic working device hinge points according to different unloading height requirements to obtain multiple sets of working device hinge points that meet different unloading heights, and each set of hinge points meets the corresponding unloading height requirements; and the hinge points with different unloading heights obtained can maintain the commonality of some specific structures, which can effectively improve product development efficiency, and the optimized hinge points have better performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a schematic diagram of the principle of a multi-unloading high-attribute joint optimization method for a loader working device proposed in the present invention;

[0046] Figure 2 A schematic diagram of a hinge point of a loader working device according to an embodiment of the present invention;

[0047] Figure 3 The figure is a schematic diagram of the hinge point optimization process of a loader working device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] See attached Figure 1 The present invention proposes a method and system for joint optimization of multiple unloading properties of a loader working device, which specifically includes the following steps:

[0050] S1. Obtain the coordinates of each hinge point, component mass, center of mass, and cylinder stroke, installation distance, and diameter parameters;

[0051] S2. Establish a mathematical model based on the structure of the loader working device, and calculate the theoretical values ​​of various performances of the loader working device through the mathematical model;

[0052] S3. Determine design variables, optimization objectives, and constraints based on the performance and design inputs of the loader working device;

[0053] S4. Generate the initial population of the genetic algorithm based on the range of design variable variation, determine the hinge coordinate encoding method under different unloading height requirements, and ensure the universality of some structures; specifically include:

[0054] Taking the basic hinge point as the benchmark, two sets of hinge point coordinates that meet different unloading height requirements are optimized and randomly generated according to the range of the set design variables using the following formula:

[0055]

[0056] 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;

[0057] The second set of hinge coordinates uses the following strategy when generating the general structure:

[0058]

[0059] in, and are the first coordinates of the common parts of the first and second sets 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 coordinates of the non-common parts generated by the second set of hinge points are the same as those of the first set.

[0060] S5. Select individuals from the initial population for crossover operations, randomly select parent individuals to exchange genetic information, and perform mutation operations on the individuals after crossover to obtain a new population. In this process, it is necessary to ensure that the partial structures of the hinges under different unloading height requirements are universal; specifically, the following are included:

[0061] In the crossover process, three individuals are randomly selected as parents for crossover, and the first set of hinge points of the three parent individuals are crossed. The differential evolution operator is calculated by the following formula:

[0062]

[0063] Among them, F is the cross 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 is crossed, the common structure part crosses in the following way:

[0064]

[0065] in, and are the first coordinates of the common parts of the first and second sets of hinge points, respectively. and The jth coordinates of the common parts of the first and second sets of hinge points are respectively. The second set of hinge points crosses the non-common parts in the same process as the first set of hinge point coordinates. Since the coordinates after crossing according to the above formula may exceed the range of the design variables, a new parent generation is selected for crossover when the result exceeds the range.

[0066] Mutation operations include:

[0067] When the first set of hinge point coordinates mutates, the mutated points are regenerated within the design variable range, as shown in the following formula:

[0068]

[0069] 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 mutation point;

[0070] 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 method is used for mutation:

[0071]

[0072] 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 mutated coordinates. If the second set of hinge points exceeds the design variable range after mutating according to the first set of hinge points, the first set of hinge points will be mutated again. If the mutated points are not on the common structure, the second set of hinge point coordinates will be regenerated within the design variable range.

[0073] S6. Calculate the penalty function values ​​of the new population individuals based on the mathematical model, normalize the penalty function values ​​of all constraints, and retain individuals with small penalty function values ​​to the next generation; specifically, this includes:

[0074] Each set of hinge points corresponds to multiple constraints. The constraint value of each set of hinge points is calculated, and the difference between the constraint value exceeding the constraint boundary and the constraint boundary is used as its penalty function value. In order to make the importance of the constraints equal, the penalty function needs to be normalized.

[0075] The normalization process of the penalty function is performed in the following manner:

[0076]

[0077]

[0078] in, is the constraint value, and are the upper and lower limits of the constraints, is the normalized penalty function value, and all penalty function values ​​are summed up , the final penalty function value as a standard to be retained for the next generation.

[0079] 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 coordinates that meet the requirements.

[0080] A loader working device multi-unloading high-attribute joint optimization system is used to implement the above-mentioned optimization method, and the system includes a storage medium and a processor;

[0081] The storage medium is used to store instructions;

[0082] The processor is configured to operate according to the instructions to perform the optimization method;

[0083] According to the design requirements, select the corresponding hinge point as the design variable;

[0084] Taking various performance indicators of the loader working device as the objective or constraint function;

[0085] The optimization algorithm is used to iteratively solve the problem. The first iteration needs to execute steps S4-S6, and the subsequent iterations need to execute steps S5-S6.

[0086] The present invention will be further described below by means of specific examples:

[0087] Take the six-link loader working device as an example, see the attached Figure 3 , the specific implementation process of the optimization method is as follows:

[0088] S1 obtains the coordinates of each hinge point, component mass, center of mass, cylinder stroke, installation distance, diameter and other parameters. The hinge point structure of the working device is as follows: Figure 2 As shown;

[0089] S2 establishes a mathematical model based on the structure of the working device and calculates the various performances of the working device through the mathematical model;

[0090] S3 determines the design variables, objective functions, and constraints based on the performance and design inputs of the working device.

[0091] Based on different dumping height requirements and basic working device hinge points, we optimize to obtain multiple sets of working device hinge points that meet different dumping heights. Each set of hinge points meets the corresponding dumping height requirements. Moreover, the hinge points obtained at different dumping heights can maintain the commonality of some specific structures. For example, we optimize both small and large dumping heights, and ensure that the frames and buckets of the two optimized hinge points are common. Specifically, we include:

[0092] S4. Initialize the population and randomly generate hinge coordinates within the design variable range of the small-height unloading. Randomly generate hinge coordinates that meet the requirements of the large-height unloading within the design variable range of the large-height unloading. During the generation of the large-height unloading hinge coordinates, the frame and bucket must be the same as those of the small-height unloading hinge. The formula for randomly generating the small-height unloading hinge coordinates is as follows:

[0093]

[0094] 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 following strategy is used to generate the general structure of the large unloading hinge coordinates:

[0095]

[0096] in, and They are the first coordinates of the small high dump and large high dump frame or bucket parts, and They are the j-th coordinates of the frame or bucket parts of the small high dump and large high dump respectively. The large high dump hinge point generates the same non-universal part coordinates as the small high dump.

[0097] S5. During the crossover process, three individuals are randomly selected as parents for crossover. The small high-unloading hinge point is crossed with the large high-unloading hinge point. The differential evolution operator is calculated using the following formula:

[0098]

[0099] Where F is the crossover factor, ranging from 0.8 to 1.2. , , Represents three parents, i represents the i-th gene position. When the coordinates of the large-scale joint point are crossed, the common structure part crossover adopts the following method:

[0100]

[0101] in, and are the first coordinates of the small high dump and large high dump frame or bucket parts, and The jth coordinate of the small and large high dump frame or bucket parts, respectively. The large high dump hinge point crosses the non-universal part coordinates in the same process as the small high dump hinge point. Since the coordinates after crossing according to the above formula may exceed the range of the design variables, when the result exceeds the range, reselect the parent generation for crossover;

[0102] Then, the mutation operation is performed. When the coordinates of the small high unloading hinge point mutate, the mutation point is regenerated within the design variable range of the small high unloading, as shown in the following formula:

[0103]

[0104] in, represents the coordinates after mutation, Represents 0 A random number of 1, and Indicates the upper and lower limits of the mutation point coordinates. When the coordinates of the large high dump hinge point are mutated, the mutation point should be the same as the small high dump mutation point. If the mutation point is on the frame or bucket structure, the mutation is performed in the following way:

[0105]

[0106] in, and Respectively represent the coordinates of the small high unloading hinge point and the large high unloading hinge point before mutation, and Represents the mutated coordinates. If the large unloading hinge exceeds the design variable range after mutating the small unloading hinge, the small unloading hinge will be mutated again. If the mutation point is not on the frame or bucket structure, the large unloading hinge coordinate mutation will also be regenerated within the design variable range.

[0107] S6. Calculate the value of the child penalty function. Calculate the constraint values ​​for both the small and large unloading joints. Subtract the constraint value that exceeds the constraint boundary from the constraint boundary to obtain the penalty function value. To make the importance of the constraints equal, the penalty function needs to be normalized. Penalty function normalization is performed in the following way:

[0108]

[0109]

[0110] in, is the constraint value, and are the upper and lower limits of the constraints, is the normalized penalty function value, and all penalty function values ​​are summed up , the final penalty function value As the criterion for retention in the next generation, the penalty function values ​​of the offspring and the parent are compared, and the individuals with the smaller penalty function value are retained in the next generation.

[0111] S7. When the penalty function value is zero, the algorithm converges and a set of hinge points corresponding to small and large unloading are obtained. The hinge points of small and large unloading can meet the universal requirements of the frame and bucket structure. The specific optimization process is as follows: Figure 3 shown.

[0112] The optimization method proposed in this paper is a genetic algorithm. Drawing on the process of biological evolution, this algorithm simulates mechanisms such as natural selection, inheritance, and mutation to gradually select optimal or near-optimal solutions from an initial population. The algorithm generates hinge coordinates through random generation. The initial hinge coordinates may not necessarily satisfy the constraints. Subsequent crossover and mutation operations allow the hinge coordinates to be modified based on the initial hinge coordinates until they meet the requirements.

[0113] Matters not described in detail in this invention are all known technologies.

[0114] The embodiments described above are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection 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. Establish a mathematical model based on the structure of the loader working device, and calculate the theoretical values ​​of various performances of the loader working device through the mathematical model; S3. Determine design variables, optimization objectives, and constraints based on the performance 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 hinge point coordinate encoding method under different unloading height requirements, and ensure the universality of some structures; S5. Select individuals from the initial population for crossover operations, randomly select parent individuals to exchange genetic information, and perform mutation operations on the individuals after crossover to obtain a new population. In this process, it is necessary to ensure that the partial structures of the hinges under different unloading height requirements are universal; 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 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. 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, and generating random coordinates according to the range of the set design variables using 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 and second sets 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 coordinates of the non-common parts generated by the second set of hinge points are the same as those of the first set.

2. A method for joint optimization of multiple unloading and high-attribute properties of a loader working device according to claim 1, characterized in that: The step S5 specifically includes: selecting three individuals as parents for crossover by random selection during the crossover process, crossovering the first set of hinge points of the three parent individuals, and calculating the differential evolution operator by the following formula: Among them, F is the cross 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 is crossed, the common structure part crosses in the following way: in, and are the first coordinates of the common parts of the first and second sets 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. Since the coordinates after crossing according to the above formula may exceed the range of the design variables, when the result is out of range, the parent generation is reselected for crossing.

3. The multi-unloading high-attribute joint optimization method for a loader working device according to claim 2, characterized in that: In step S5, the mutation operation includes: When the first set of hinge point coordinates mutates, 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 method is 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.

4. The method for joint optimization of multiple unloading and high-attribute properties of a loader working device according to claim 3, 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, and the difference between the constraint value exceeding the constraint boundary and the constraint boundary is used as its penalty function value. In order to make the importance of the constraints equal, the penalty function needs to be normalized.

5. The multi-unloading high-attribute joint optimization method for a loader working device according to claim 4 is 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.

6. A loader working device multi-unloading high-attribute joint optimization system for implementing the optimization method according to any one of claims 1 to 5, characterized in that: The system includes a storage medium and a processor; The storage medium is used to store instructions; The processor is configured 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 iteratively solve the problem. The first iteration needs to execute steps S4-S6, and the subsequent iterations need to execute steps S5-S6.

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