Automatic optimizing method and system for operability of excavator

Abstract

The invention discloses an automatic optimization method and system for excavator operability, and the method comprises the steps: obtaining a to-be-optimized excavator original parameter set, and setting a test working condition; based on the excavator original parameter set, performing simulation under a set test working condition, obtaining a cost original value, performing normalization on the cost original value, and performing calculation to obtain a total cost after the cost original value is normalized; respectively constructing an accuracy evaluation cost function, a responsiveness evaluation cost function and a coordination evaluation cost function, presetting the weight of each cost function according to the actual use frequency of the excavator, and constructing a total cost function; and performing iterative optimization on the excavator original parameter set to be optimized based on a particle swarm optimization algorithm, continuously iterating until the total cost function is converged to be minimum, and outputting a global optimal parameter combination. According to the method, automatic optimization of excavator parameters is realized, and the controllability adaptation error is reduced.

Description

Technical Field

[0001] This invention relates to the field of excavator control technology, and in particular to an automatic optimization method and system for excavator operability. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In the field of engineering construction, excavators are core construction equipment, and their handling performance directly determines construction efficiency and operational accuracy. An excavator system is composed of key components such as an engine, hydraulic pump, and control valves. However, unavoidable manufacturing errors, such as dimensional tolerances and performance deviations, inevitably exist in the production of these components. This means that the parameters calibrated for the first excavator cannot be directly adapted to all excavators of the same model. Even excavators produced in the same batch may exhibit inconsistent handling performance due to individual differences in components.

[0004] Currently, the main technical method for excavator parameter calibration is "manual calibration." This involves experienced calibrators adjusting parameters such as pumps, valves, and speeds for each excavator individually. Through repeated operation and testing, these parameters are adjusted until the excavator's handling performance meets the expected standards. The specific process is as follows: The calibrator starts the excavator and manually operates the control levers to perform single actions (such as boom lifting and stick digging) and combined actions (such as boom lifting + swing) under different working conditions, including no-load and full-load conditions. Based on their own experience, they judge whether the parameters are reasonable. If problems such as response delays, action stuttering, or insufficient accuracy exist, the pump control curves, valve control curves, and other parameters are manually modified, and the testing and adjustment are repeated until basic operating requirements are met.

[0005] However, existing manual calibration techniques have significant technical problems: On the one hand, the number of calibration personnel is limited and their professional levels vary. Experienced calibration personnel can quickly find optimal parameters, while novice calibration personnel are prone to parameter adjustment deviations, resulting in inconsistent calibration quality for different equipment. Some excavators may have hidden dangers such as "slow response," "low precision in micro-operations," and "uncoordinated compound movements." On the other hand, manual calibration requires repeated operation tests for each piece of equipment, which not only consumes a lot of time and manpower but also cannot guarantee that the parameters can be adapted to all actual working conditions, making it difficult to achieve optimal matching of excavator control performance. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes an automatic optimization method and system for excavator operability, which enables automatic optimization of excavator parameters and reduces operability adaptation errors.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides an automatic optimization method for the operability of an excavator, comprising: Obtain the set of original parameters of the excavator to be optimized, and set the test conditions; Based on the original set of excavator parameters, simulations were performed under the set test conditions to obtain the original cost values ​​corresponding to responsiveness, accuracy, and coordination. The original cost values ​​were normalized, and the total cost after normalization was calculated. Accuracy assessment cost function, responsiveness assessment cost function and coordination assessment cost function are constructed respectively. The weights of each cost function are preset according to the actual usage frequency of the excavator, and the total cost function is constructed. The particle swarm optimization algorithm iteratively optimizes the original set of excavator parameters to be optimized, and continues to iterate until the total cost function converges to the minimum, outputting the globally optimal parameter combination.

[0008] A further technical solution is that the excavator's original parameter set includes pump control curves, valve control curves, speed, and power; the test conditions are single-action step operation, single-action micro operation, and common compound operation under no-load / full-load conditions, wherein the common compound operation includes the combination of boom lifting and swinging and the combination of boom lifting and stick digging.

[0009] A further technical solution is that the specific method for normalizing the original cost value is as follows: ; ; ; in , , The value of the new particles obtained after iteration. , , The original cost value is obtained after simulating the original set of excavator parameters under test conditions.

[0010] A further technical solution is that the accuracy evaluation cost function includes an accuracy cost function for the step response and an accuracy cost function for micro-operations; the accuracy cost function for the step response is expressed as: ;in, For command current, This is the actual current; The precision cost function of the micro-operation is expressed as: .

[0011] A further technical solution is that the responsiveness evaluation cost function is expressed as: ; in, The time it takes for the main pump current to reach its maximum. This is the time it takes for the pump pressure to start building up.

[0012] A further technical solution is that the coordination evaluation cost function is expressed as: ; in, For single-action time ratio, This refers to the time ratio of compound actions.

[0013] A further technical solution is that the total cost function is expressed as: ;in, , , To pre-determine the weights of each cost function based on the actual usage frequency of the excavator, and + + The value is 1.

[0014] Secondly, the present invention provides an automatic optimization system for the operability of an excavator, comprising the following modules: The parameter acquisition module is configured to: acquire the set of original parameters of the excavator to be optimized and set the test conditions; The parameter processing module is configured to: perform simulation under set test conditions based on the excavator's original parameter set, obtain the original cost values ​​corresponding to responsiveness, accuracy, and coordination, normalize the original cost values, and calculate the total cost after normalization of the original cost values. The cost function construction module is configured to: construct the accuracy evaluation cost function, the responsiveness evaluation cost function, and the coordination evaluation cost function respectively; preset the weights of each cost function according to the actual usage frequency of the excavator; and construct the total cost function. The optimal parameter output module is configured to iteratively optimize the original set of excavator parameters to be optimized based on the particle swarm optimization algorithm, continuously iterating until the total cost function converges to the minimum, and outputting the globally optimal parameter combination.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention first obtains and normalizes the raw cost values ​​for responsiveness, accuracy, and coordination based on simulation. Then, it presets the weights of each cost function according to the actual usage frequency of the excavator and constructs the total cost function. Finally, it iteratively optimizes the cost function using the PSO algorithm. The entire process relies on objective simulation data, normalization calculations, and algorithm iterations, rather than subjective human judgment, completely avoiding calibration deviations caused by differences in the skill level of calibration personnel. This ensures that the parameter calibration standards for all excavators are uniform, improving the consistency and accuracy of the calibration results.

[0016] This invention first obtains the initial set of parameters for the excavator to be optimized and sets the test conditions. Then, it simulates the excavator under the set conditions based on the initial parameters. Subsequently, it iteratively optimizes the initial parameters using the Particle Swarm Optimization (PSO) algorithm until the total cost function converges to a minimum and the globally optimal parameter combination is output. The entire process only requires starting the simulation and optimization process based on the initial parameters and set conditions, eliminating the need for repeated manual adjustments. It can automatically adapt to the component errors of different equipment, allowing each excavator to obtain optimal parameters that suit its own hardware characteristics, thus solving the problem of "the first machine's parameters not being compatible with all machines".

[0017] This invention constructs three types of evaluation cost functions—accuracy, responsiveness, and coordination—to transform key indicators of operational performance into quantifiable mathematical models. The original parameters are then continuously iteratively optimized using the PSO algorithm until the total cost function converges to a minimum. Minimizing the total cost signifies that responsiveness, accuracy, and coordination have reached an optimal balance, effectively avoiding problems such as slow response, low precision in micro-operations, and uncoordinated complex movements. This achieves precise parameter optimization and reduces potential operational performance risks in excavators. Attached Figure Description

[0018] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0019] Figure 1 This is a schematic diagram of the automatic optimization method for excavator operability according to the present invention. Detailed implementation method: The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0022] Example 1 This embodiment provides an automatic optimization method for excavator operability, such as... Figure 1 As shown in the flowchart, the method provided in this embodiment has two objectives: firstly, it can adaptively find the optimal matching curve for each component's error based on only one operation by the operator; secondly, it prevents calibration deviations by calibration personnel. The specific process of the method is as follows: First, define the parameters to be optimized and the test conditions: Obtain the collection of raw parameters of the excavator to be optimized. Collection of excavator raw parameters This includes pump control curves, valve control curves, speed, and power.

[0023] The test conditions are set as follows: Operating Condition 1: Single-action step operation under no-load / full-load conditions (each single action of the handle suddenly going to the bottom under no-load / full-load conditions); Operating Condition 2: Single-action micro-operation under no-load / full-load conditions (each single action under handle micro-operation under no-load / full-load conditions); Working Condition 3: Commonly used compound actions under no-load / full-load conditions, including the combination of boom lifting and slewing, and the combination of boom lifting and stick digging.

[0024] Then, a simulation is performed, specifically: Based on the original set of excavator parameters, simulations are performed under set test conditions to obtain the original cost values ​​corresponding to responsiveness, accuracy, and coordination, which serve as the "cost benchmark".

[0025] The original cost value is normalized using the following method: ; ; ; in, , , The value of the new particles obtained after iteration. , , The original cost value is obtained after simulating the original set of excavator parameters under test conditions.

[0026] The total cost after normalizing the original cost values ​​is calculated as follows: .

[0027] Then construct the cost function: Accuracy assessment cost function, responsiveness assessment cost function, and coordination assessment cost function are constructed respectively. Specifically: The accuracy evaluation cost function includes the accuracy cost function for the step response and the accuracy cost function for micro-operations. The accuracy cost function for the step response is expressed as: ;in, For command current, The actual current; the objective is to minimize This means ensuring that the actual current reaches the commanded current quickly, smoothly, and without overshoot.

[0028] The cost function for the precision of micro-operations is expressed as: ;in, Main pump pressure, Time; the parameter whose value converges to a minimum.

[0029] The responsiveness evaluation cost function is expressed as: ; in, The time it takes for the main pump current to reach its maximum. For the pump pressure start-up time. Minimize the objective. This means minimizing the machine's response delay.

[0030] The cost function for coordination assessment is expressed as: ; in, For single-action time ratio, This refers to the time ratio of compound actions.

[0031] ;in, , The time required for each individual action; ;in, , The duration of different individual actions within a compound motion; Minimize the objective This ensures that the time ratio in compound movements remains constant, resulting in smooth and uninterrupted movements.

[0032] Based on the actual usage frequency of the excavator, the weights of each cost function are preset, and a total cost function is constructed. The total cost function is expressed as: ;in, , , To pre-determine the weights of each cost function based on the actual usage frequency of the excavator, and + + The value is 1.

[0033] In this embodiment, =0.4; =0.2; =0.4.

[0034] Finally, the particle swarm optimization (PSO) algorithm is used to iteratively optimize the original set of excavator parameters to be optimized, continuing the iteration until the total cost function converges to a minimum, outputting the globally optimal parameter combination. Specifically: Generate N initial particles (each particle corresponds to a set of parameters, with dimensions consistent with the original parameter set: pump control curve, valve control curve, etc.); For each particle, a simulation is performed under test conditions, and the total cost of that particle is calculated using the total cost function. ; contrast The total cost after normalization from the original cost value: If If the total cost is less than the normalized total cost of the original cost, it indicates that the particle parameters are better than the original parameters, and the particle's "individual optimal solution pBesti" is further updated. Among all the pBesti values ​​of all particles, the "global optimal solution gBest" with the minimum total cost is selected, and the particle swarm position / velocity is updated according to the PSO rule. This process is repeated until convergence, and the optimization parameters corresponding to the minimum total cost function are obtained.

[0035] Example 2 This embodiment provides an automatic optimization system for excavator operability, including the following modules: The parameter acquisition module is configured to: acquire the set of original parameters of the excavator to be optimized and set the test conditions; The parameter processing module is configured to: perform simulation under set test conditions based on the excavator's original parameter set, obtain the original cost values ​​corresponding to responsiveness, accuracy, and coordination, normalize the original cost values, and calculate the total cost after normalization of the original cost values. The cost function construction module is configured to: construct the accuracy evaluation cost function, the responsiveness evaluation cost function, and the coordination evaluation cost function respectively; preset the weights of each cost function according to the actual usage frequency of the excavator; and construct the total cost function. The optimal parameter output module is configured to iteratively optimize the original set of excavator parameters to be optimized based on the particle swarm optimization algorithm, continuously iterating until the total cost function converges to the minimum, and outputting the globally optimal parameter combination.

[0036] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0037] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. An automatic optimization method for excavator operability, characterized in that, include: Obtain the set of original parameters of the excavator to be optimized, and set the test conditions; Based on the original set of excavator parameters, simulations were performed under the set test conditions to obtain the original cost values ​​corresponding to responsiveness, accuracy, and coordination. The original cost values ​​were normalized, and the total cost after normalization was calculated. Accuracy assessment cost function, responsiveness assessment cost function and coordination assessment cost function are constructed respectively. The weights of each cost function are preset according to the actual usage frequency of the excavator, and the total cost function is constructed. The particle swarm optimization algorithm iteratively optimizes the original set of excavator parameters to be optimized, and continues to iterate until the total cost function converges to the minimum, outputting the globally optimal parameter combination.

2. The automatic optimization method for excavator operability as described in claim 1, characterized in that, The excavator's original parameter set includes pump control curves, valve control curves, speed, and power; the test conditions are single-action step operation, single-action micro operation, and common compound operation under no-load / full-load conditions, wherein the common compound operation includes the combination of boom lifting and swinging and the combination of boom lifting and stick digging.

3. The automatic optimization method for excavator operability as described in claim 1, characterized in that, The specific method for normalizing the original cost value is as follows: ； ； ； in , , The value of the new particles obtained after iteration. , , The original cost value is obtained after simulating the original set of excavator parameters under test conditions.

4. The automatic optimization method for excavator operability as described in claim 1, characterized in that, The accuracy evaluation cost function includes the accuracy cost function of the step response and the accuracy cost function of the micro-operation; the accuracy cost function of the step response is expressed as: ;in, For command current, This is the actual current; The precision cost function of the micro-operation is expressed as: 。 5. The automatic optimization method for excavator operability as described in claim 1, characterized in that, The responsiveness evaluation cost function is expressed as: ； in, The time it takes for the main pump current to reach its maximum. This is the time it takes for the pump pressure to start building up.

6. The automatic optimization method for excavator operability as described in claim 1, characterized in that, The coordination evaluation cost function is expressed as: ； in, For single-action time ratio, This refers to the time ratio of compound actions.

7. The automatic optimization method for excavator operability as described in claim 1, characterized in that, The total cost function is expressed as: ;in, , , To pre-determine the weights of each cost function based on the actual usage frequency of the excavator, and + + The value is 1.

8. An automatic optimization system for excavator operability, characterized in that, Includes the following modules: The parameter acquisition module is configured to: acquire the set of original parameters of the excavator to be optimized and set the test conditions; The parameter processing module is configured to: perform simulation under set test conditions based on the excavator's original parameter set, obtain the original cost values ​​corresponding to responsiveness, accuracy, and coordination, normalize the original cost values, and calculate the total cost after normalization of the original cost values. The cost function construction module is configured to: construct the accuracy evaluation cost function, the responsiveness evaluation cost function, and the coordination evaluation cost function respectively; preset the weights of each cost function according to the actual usage frequency of the excavator; and construct the total cost function. The optimal parameter output module is configured to iteratively optimize the original set of excavator parameters to be optimized based on the particle swarm optimization algorithm, continuously iterating until the total cost function converges to the minimum, and outputting the globally optimal parameter combination.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the automatic optimization method for excavator operability as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the automatic optimization method for excavator operability as described in any one of claims 1-7.

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