Model order reduction method and device applied to hydraulic system model

Abstract

The invention relates to the technical field of electric control system testing, in particular to a model order reduction method and device applied to a hydraulic system model. The method comprises the steps that quantitative analysis is conducted on a pre-stored hydraulic system model and actually-measured hydraulic data obtained in advance, and a model analysis data set corresponding to the excavator is obtained; the actually-measured hydraulic data comprises actually-measured piston rod data, actually-measured motor data and actually-measured hydraulic pump data; constructing a simplified model according to the model analysis data set to obtain an initial hydraulic model; performing parameter adjustment on the initial hydraulic model according to the actually measured hydraulic data to obtain a target hydraulic model; the target hydraulic model is a reduced-order model corresponding to the hydraulic system model. Simulation is carried out according to the actually measured data of the key components in the hydraulic system to construct the simplified model, the data demand is small, the acquisition time period is short, and therefore the model order reduction efficiency of the hydraulic system model of the excavator is improved.

Description

Technical Field

[0001] This invention belongs to the field of electronic control system testing technology, specifically relating to a model reduction method and device for hydraulic system models. Background Technology

[0002] Hardware-in-the-loop (HIL) testing is a core simulation step in the development and verification of controllers for complex electronic control systems. For excavators, the hydraulic system is the core of power transmission, and the accuracy and real-time performance of the hydraulic system model are crucial for HIL testing. However, the excavator's hydraulic system model is characterized by strong nonlinearity, high order, rigidity, and multiple operating modes, making HIL simulation highly challenging. Therefore, it is necessary to simplify the hydraulic system model while maintaining a certain level of accuracy to facilitate HIL testing. Currently, the main approach is data-driven order reduction (also known as "neural network order reduction"), which uses collected input and output data from the excavator to build a simplified model and predict its behavior.

[0003] However, practice has shown that the effectiveness of traditional data-driven model reduction depends on the completeness of the collected measured data. If the data is not complete enough, it will affect the generalization ability of the model. Therefore, the collected measured data needs to cover all states of the excavator under all working conditions as much as possible. The large amount of data required and the long acquisition period result in low efficiency of model reduction.

[0004] Therefore, improving the efficiency of model reduction in the hydraulic system model of excavators is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a model reduction method and apparatus for hydraulic system models, aiming to improve the model reduction efficiency of excavator hydraulic system models.

[0006] To address the aforementioned technical problems, the first aspect of this invention discloses a model reduction method applied to a hydraulic system model, wherein the hydraulic system model is a simulation model of the hydraulic system of an excavator, and the method includes: A quantitative analysis is performed on the pre-stored hydraulic system model and the pre-acquired measured hydraulic data to obtain the model analysis dataset corresponding to the excavator; the measured hydraulic data includes measured piston rod data, measured motor data and measured hydraulic pump data; A simplified model is constructed based on the model analysis dataset to obtain an initial hydraulic model; the initial hydraulic model includes piston rod model unit, motor model unit and hydraulic pump model unit. The parameters of the initial hydraulic model are adjusted based on the measured hydraulic data to obtain the target hydraulic model; the target hydraulic model is a reduced-order model corresponding to the hydraulic system model.

[0007] As an optional implementation, in the first aspect of the present invention, the step of quantitatively analyzing a pre-stored hydraulic system model and pre-acquired measured hydraulic data to obtain a model analysis dataset corresponding to the excavator includes: Quantitative analysis is performed on the pre-stored hydraulic system model, the pre-acquired measured piston rod data, and the measured hydraulic pump data to obtain piston rod analysis data; Quantitative analysis is performed on the hydraulic system model, the pre-acquired measured motor data, and the measured hydraulic pump data to obtain motor analysis data; Quantitative analysis was performed on the hydraulic system model and the measured hydraulic pump data to obtain hydraulic pump analysis data. The piston rod analysis data, the motor analysis data, and the hydraulic pump analysis data are integrated to obtain the model analysis dataset corresponding to the excavator.

[0008] As an optional implementation, in a first aspect of the present invention, the piston rod analysis data includes a minimum upward pressure value, a minimum downward pressure value, first working pump pressure mapping data, and motion rate mapping data. The minimum upward pressure value is the minimum pilot pressure value for driving the excavator's bucket piston rod upward, and the minimum downward pressure value is the minimum pilot pressure value for driving the excavator's bucket piston rod downward. The first working pump pressure mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different working states of the excavator's bucket piston rod. The motion rate mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different motion rates of the excavator's bucket piston rod. The motor analysis data includes minimum swing pressure value, maximum stop pressure value, second working pump pressure mapping data, and swing speed mapping data. The minimum swing pressure value is the minimum pilot pressure value for driving the excavator's motor to swing, and the maximum stop pressure value is the maximum pilot pressure value for driving the excavator's motor to stop swinging. The second working pump pressure mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different working states of the excavator's motor. The swing speed mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different swing speeds of the excavator's motor. The hydraulic pump analysis data includes a first pilot pump pressure value, a second pilot pump pressure value, a first overflow pump pressure value, and a second overflow pump pressure value. The first pilot pump pressure value is the pump pressure value when the excavator's hydraulic pump is not in an overflow state and has pilot pressure. The second pilot pump pressure value is the pump pressure value when the excavator's hydraulic pump is not in an overflow state and has no pilot pressure. The first overflow pump pressure value is the pump pressure value when the excavator's hydraulic pump is in an overflow state and has pilot pressure. The second overflow pump pressure value is the pump pressure value when the excavator's hydraulic pump is in an overflow state and has no pilot pressure.

[0009] As an optional implementation, in the first aspect of the present invention, the step of constructing a simplified model based on the model analysis dataset to obtain an initial hydraulic model includes: Based on the piston rod analysis data, a simulation model is constructed to obtain the piston rod model unit. Based on the motor analysis data, a simulation model is constructed to obtain motor model units; Based on the hydraulic pump analysis data, a simulation model is constructed to obtain the hydraulic pump model unit; By integrating the piston rod model unit, the motor model unit, and the hydraulic pump model unit, an initial hydraulic model is obtained.

[0010] As an optional implementation, in the first aspect of the present invention, the step of constructing a simulation model based on the piston rod analysis data to obtain piston rod model units includes: The position setting value of the excavator's bucket piston rod is determined based on the minimum upward pressure value and the minimum downward pressure value; the position setting value includes the initial position coordinate value and the extreme position coordinate value of the excavator's bucket piston rod. Based on the position setting value, the first working pump pressure mapping data, and the motion rate mapping data, a model is constructed to obtain the piston rod model unit.

[0011] As an optional implementation, in the first aspect of the present invention, the step of constructing a simulation model based on the motor analysis data to obtain a motor model unit includes: The swing speed setting of the excavator motor is determined based on the minimum swing pressure value and the maximum stop pressure value; the swing speed setting value includes the initial swing speed value and the swing speed limit value of the excavator motor. Based on the rotational speed setting, the second working pump pressure mapping data, and the rotational speed mapping data, a model is constructed to obtain a motor model unit.

[0012] As an optional implementation, in the first aspect of the present invention, the step of constructing a simulation model based on the hydraulic pump analysis data to obtain a hydraulic pump model unit includes: The hydraulic pump pressure setting value of the excavator is determined based on the first pilot pump pressure value, the second pilot pump pressure value, the first overflow pump pressure value, and the second overflow pump pressure value; the pump pressure setting value includes the excavator's initial pump pressure value and pump pressure limit value; The model is constructed based on the pump pressure setting value to obtain the hydraulic pump model unit.

[0013] A second aspect of this invention discloses a model reduction device for a hydraulic system model, wherein the hydraulic system model is a simulation model of the hydraulic system of an excavator, and the device comprises: The quantitative analysis module is used to perform quantitative analysis on the pre-stored hydraulic system model and the pre-acquired measured hydraulic data to obtain the model analysis dataset corresponding to the excavator; the measured hydraulic data includes measured piston rod data, measured motor data and measured hydraulic pump data; The model building module is used to build a simplified model based on the model analysis dataset to obtain an initial hydraulic model; the initial hydraulic model includes piston rod model units, motor model units, and hydraulic pump model units. The parameter adjustment module is used to adjust the parameters of the initial hydraulic model based on the measured hydraulic data to obtain the target hydraulic model; the target hydraulic model is a reduced-order model corresponding to the hydraulic system model.

[0014] As an optional implementation, in the second aspect of the present invention, the quantitative analysis module performs quantitative analysis on a pre-stored hydraulic system model and pre-acquired measured hydraulic data to obtain the model analysis dataset corresponding to the excavator. The specific methods include: Quantitative analysis is performed on the pre-stored hydraulic system model, the pre-acquired measured piston rod data, and the measured hydraulic pump data to obtain piston rod analysis data; Quantitative analysis is performed on the hydraulic system model, the pre-acquired measured motor data, and the measured hydraulic pump data to obtain motor analysis data; Quantitative analysis was performed on the hydraulic system model and the measured hydraulic pump data to obtain hydraulic pump analysis data. The piston rod analysis data, the motor analysis data, and the hydraulic pump analysis data are integrated to obtain the model analysis dataset corresponding to the excavator.

[0015] As an optional implementation, in a second aspect of the present invention, the piston rod analysis data includes a minimum rising pressure value, a minimum falling pressure value, first working pump pressure mapping data, and motion rate mapping data. The minimum rising pressure value is the minimum pilot pressure value that drives the excavator's bucket piston rod to rise, and the minimum falling pressure value is the minimum pilot pressure value that drives the excavator's bucket piston rod to fall. The first working pump pressure mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different working states of the excavator's bucket piston rod. The motion rate mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different motion rates of the excavator's bucket piston rod. The motor analysis data includes minimum swing pressure value, maximum stop pressure value, second working pump pressure mapping data, and swing speed mapping data. The minimum swing pressure value is the minimum pilot pressure value for driving the excavator's motor to swing, and the maximum stop pressure value is the maximum pilot pressure value for driving the excavator's motor to stop swinging. The second working pump pressure mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different working states of the excavator's motor. The swing speed mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different swing speeds of the excavator's motor. The hydraulic pump analysis data includes a first pilot pump pressure value, a second pilot pump pressure value, a first overflow pump pressure value, and a second overflow pump pressure value. The first pilot pump pressure value is the pump pressure value when the excavator's hydraulic pump is not in an overflow state and has pilot pressure. The second pilot pump pressure value is the pump pressure value when the excavator's hydraulic pump is not in an overflow state and has no pilot pressure. The first overflow pump pressure value is the pump pressure value when the excavator's hydraulic pump is in an overflow state and has pilot pressure. The second overflow pump pressure value is the pump pressure value when the excavator's hydraulic pump is in an overflow state and has no pilot pressure.

[0016] As an optional implementation, in the second aspect of the present invention, the specific method by which the model building module constructs a simplified model based on the model analysis dataset to obtain the initial hydraulic model includes: Based on the piston rod analysis data, a simulation model is constructed to obtain the piston rod model unit. Based on the motor analysis data, a simulation model is constructed to obtain motor model units; Based on the hydraulic pump analysis data, a simulation model is constructed to obtain the hydraulic pump model unit; By integrating the piston rod model unit, the motor model unit, and the hydraulic pump model unit, an initial hydraulic model is obtained.

[0017] As an optional implementation, in the second aspect of the present invention, the specific method by which the model building module constructs a simulation model based on the piston rod analysis data to obtain the piston rod model unit includes: The position setting value of the excavator's bucket piston rod is determined based on the minimum upward pressure value and the minimum downward pressure value; the position setting value includes the initial position coordinate value and the extreme position coordinate value of the excavator's bucket piston rod. Based on the position setting value, the first working pump pressure mapping data, and the motion rate mapping data, a model is constructed to obtain the piston rod model unit.

[0018] As an optional implementation, in the second aspect of the present invention, the specific method by which the model building module constructs a simulation model based on the motor analysis data to obtain the motor model unit includes: The swing speed setting of the excavator motor is determined based on the minimum swing pressure value and the maximum stop pressure value; the swing speed setting value includes the initial swing speed value and the swing speed limit value of the excavator motor. Based on the rotational speed setting, the second working pump pressure mapping data, and the rotational speed mapping data, a model is constructed to obtain a motor model unit.

[0019] As an optional implementation, in the second aspect of the present invention, the specific method by which the model building module constructs a simulation model based on the hydraulic pump analysis data to obtain the hydraulic pump model unit includes: The hydraulic pump pressure setting value of the excavator is determined based on the first pilot pump pressure value, the second pilot pump pressure value, the first overflow pump pressure value, and the second overflow pump pressure value; the pump pressure setting value includes the excavator's initial pump pressure value and pump pressure limit value; The model is constructed based on the pump pressure setting value to obtain the hydraulic pump model unit.

[0020] A third aspect of this invention discloses another model reduction device applied to a hydraulic system model, wherein the hydraulic system model is a simulation model of the hydraulic system of an excavator, and the device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute a model reduction method for hydraulic system models disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked by a processor, are used to execute a model reduction method for hydraulic system models disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows: First, a quantitative analysis is performed on the pre-stored hydraulic system model and the pre-acquired measured hydraulic data to obtain a model analysis dataset. The measured hydraulic data includes measured piston rod data, measured motor data, and measured hydraulic pump data. Then, a simplified model is constructed based on the model analysis dataset to obtain an initial hydraulic model. Next, the parameters of the initial hydraulic model are adjusted based on the measured hydraulic data to obtain the reduced-order target hydraulic model. Simulations are performed using measured data from key components in the hydraulic system to construct a simplified model. The parameters of the simplified model are then adjusted to enhance its fit with the original hydraulic system model, ensuring the real-time performance and accuracy of the reduced-order model. This approach eliminates the need to collect data covering all states of the excavator under all operating conditions, resulting in smaller data requirements and shorter acquisition times, thus improving the efficiency of model reduction for the excavator's hydraulic system model. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of a model reduction method for hydraulic system models disclosed in an embodiment of the present invention; Figure 2 This is a schematic diagram of a model reduction device for hydraulic system models disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of another model reduction device for hydraulic system models disclosed in an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, or product may include a series of steps or units, or may not be limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or processes.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] Hardware-in-the-loop (HIL) testing is a core simulation step in the development and verification of controllers for complex electronic control systems. For excavators, the hydraulic system is the core of power transmission, and the accuracy and real-time performance of the hydraulic system model are crucial for HIL testing. However, the excavator's hydraulic system model is characterized by strong nonlinearity, high order, rigidity, and multiple operating modes, making HIL simulation highly challenging. Therefore, it is necessary to simplify the hydraulic system model while maintaining a certain level of accuracy to facilitate HIL testing. Currently, the main approach is data-driven order reduction (also known as "neural network order reduction"), which uses collected input and output data from the excavator to build a simplified model and predict its behavior.

[0029] However, practice has shown that the effectiveness of traditional data-driven model reduction depends on the completeness of the collected measured data. If the data is not complete enough, it will affect the generalization ability of the model. Therefore, the collected measured data needs to cover all states of the excavator under all working conditions as much as possible. The large amount of data required and the long acquisition period result in low efficiency of model reduction.

[0030] Therefore, improving the efficiency of model reduction in the hydraulic system model of excavators is a technical problem that urgently needs to be solved.

[0031] To address the aforementioned technical problems, this invention discloses a model reduction method and apparatus for hydraulic system models, aiming to improve the model reduction efficiency of excavator hydraulic system models. Detailed descriptions follow.

[0032] Example 1 Please see Figure 1 , Figure 1This is a schematic flowchart of a model reduction method for hydraulic system models disclosed in an embodiment of the present invention. Figure 1 The method shown can be applied to a model reduction device, which can improve the model reduction efficiency of the excavator's hydraulic system model. Furthermore, this device can be integrated into simulation testing equipment or exist independently of it. The hydraulic system model is a simulation model of the excavator's hydraulic system, such as... Figure 1 As shown, the model reduction method for hydraulic system models disclosed in this embodiment of the invention includes, but is not limited to, the following operations: 101. Perform quantitative analysis on the pre-stored hydraulic system model and the pre-acquired measured hydraulic data to obtain the model analysis dataset corresponding to the excavator; the measured hydraulic data includes measured piston rod data, measured motor data and measured hydraulic pump data.

[0033] 102. Construct a simplified model based on the model analysis dataset to obtain the initial hydraulic model; the initial hydraulic model includes piston rod model unit, motor model unit and hydraulic pump model unit.

[0034] 103. Adjust the parameters of the initial hydraulic model based on the measured hydraulic data to obtain the target hydraulic model; the target hydraulic model is a reduced-order model corresponding to the hydraulic system model.

[0035] Quantitative analysis of the original model and measured data reveals key input-output relationships: 1) the pilot pressure level at which the actuator will activate; 2) the pump pressure change process during pilot activation; 3) the upper limit of pump pressure when the actuator reaches its limit; and 4) the change in actuator pressure per unit time under a given pilot pressure. This invention allows for the construction of a simplified model based on the model analysis dataset within the AMEsim platform through simple logical judgments and integration, and verifies the correctness of the model's basic logic. By referencing the original model and measured hydraulic data, the parameters of the AMEsim model are adjusted and fitted as closely as possible to the original model to ensure the real-time performance and accuracy of the reduced-order model.

[0036] As can be seen, in this embodiment of the invention, firstly, a quantitative analysis is performed on the pre-stored hydraulic system model and the pre-acquired measured hydraulic data to obtain a model analysis dataset. The measured hydraulic data includes measured piston rod data, measured motor data, and measured hydraulic pump data. Then, a simplified model is constructed based on the model analysis dataset to obtain an initial hydraulic model. Next, the parameters of the initial hydraulic model are adjusted based on the measured hydraulic data to obtain the reduced-order target hydraulic model. Simulation is performed using measured data of key components in the hydraulic system to construct a simplified model, and the parameters of the simplified model are adjusted to enhance the fit between the simplified model and the original hydraulic system model, ensuring the real-time performance and accuracy of the reduced-order model. This eliminates the need to collect data covering all states of the excavator under all operating conditions, resulting in low data requirements and short acquisition time, thereby improving the efficiency of model reduction for the excavator's hydraulic system model.

[0037] It should be noted that this invention identifies the changes in key inputs and outputs as critical components move from their initial action to their operational limits during the offline testing phase. Based on the one-to-one correspondence between inputs and outputs in a hydraulic system, a mapping relationship between these inputs and outputs is fitted, while simultaneously focusing on the changes in key variables. A reduced-order model of the response is then established for each component of the hydraulic system. After the model is established, the reduction effect on the input data can be verified through online testing.

[0038] Furthermore, existing common model reduction methods include "physics-based reduction methods" and "model-based reduction methods." Physics-based reduction methods simplify the physical model by simplifying complex physical processes, thus reducing the model's order. However, this requires a high level of domain expertise from the modelers, necessitating reasonable simplification based on the system's dynamic characteristics and operating mechanisms. Model-based reduction methods reduce the model's order by adjusting the number of variables and equations, but are only suitable for small-scale dynamic analyses. They also require in-depth analysis of the coupling relationships between system variables and differential equations, demanding a high level of domain expertise from the modelers. In contrast, this invention requires only a few sets of representative data to obtain a reduced-order model with acceptable simulation error, reducing reliance on the professional experience and theoretical background of engineers. It also considers all operating states of key components, ensuring the simulation accuracy of the model.

[0039] In an optional embodiment, a quantitative analysis is performed on a pre-stored hydraulic system model and pre-acquired measured hydraulic data to obtain a model analysis dataset corresponding to the excavator, including: Quantitative analysis is performed on the pre-stored hydraulic system model, the pre-acquired measured piston rod data, and the measured hydraulic pump data to obtain piston rod analysis data; Quantitative analysis was performed on the hydraulic system model, the pre-acquired measured motor data, and the measured hydraulic pump data to obtain motor analysis data; Quantitative analysis was performed on the hydraulic system model and measured hydraulic pump data to obtain hydraulic pump analysis data. By integrating the analysis data of the piston rod, motor, and hydraulic pump, a model analysis dataset corresponding to the excavator is obtained.

[0040] In this optional embodiment, by identifying the changes in the input and output of key components, the mapping relationship between the input and output can be fitted based on the one-to-one correspondence between the input and output in the hydraulic system.

[0041] In another optional embodiment, quantitative analysis is performed on the pre-stored hydraulic system model and the pre-acquired measured hydraulic data to obtain the model analysis dataset corresponding to the excavator. This also includes: dynamic characteristic decomposition of the hydraulic system model, extraction of key state variables and output responses from the hydraulic system model, and multi-dimensional correlation analysis combined with time-series data from the measured hydraulic data. The multi-dimensional correlation analysis includes a collaborative analysis of the pilot pressure change trend, pump pressure fluctuation cycle, and actuator motion hysteresis to identify the dominant dynamic behavior of the hydraulic system model under different operating modes. Based on the dominant dynamic behavior, the input-output pairs most relevant to the model reduction objective are selected, and redundant data is eliminated, thereby optimizing the data structure and scale of the model analysis dataset and improving the efficiency and accuracy of subsequent model construction. Specifically, the dynamic characteristic decomposition employs a piecewise linearization method based on operating modes, dividing the nonlinear dynamics of the hydraulic system model into multiple linear subsystems. Each subsystem corresponds to a typical operating condition, such as bucket digging, slewing motion, or travel drive. Then, for each linear subsystem, the singular value decomposition of its state space matrix is ​​calculated to determine the key state variables and dominant frequency response of that subsystem. Simultaneously, combined with measured hydraulic data, the boundary conditions and transient behavior of each linear subsystem are verified to ensure that the quantitative analysis covers the entire operating range of the hydraulic system model. Furthermore, the multi-dimensional correlation analysis involves statistical analysis of the measured hydraulic data, calculating the mean, variance, autocorrelation function, and cross-correlation function of each data channel to quantify the dependencies between pilot pressure and pump pressure, and between pump pressure and actuator motion. Based on these statistics, a transfer function model of pilot pressure-pump pressure-actuator motion is established to describe the simplified dynamic characteristics of the hydraulic system model.

[0042] Through this dynamic characteristic decomposition and multi-dimensional correlation analysis, the model analysis dataset not only includes static mapping relationships but also incorporates dynamic response features, thus laying the foundation for building a high-precision reduced-order model.

[0043] In another optional embodiment, the piston rod analysis data includes a minimum rising pressure value, a minimum falling pressure value, first working pump pressure mapping data, and motion rate mapping data. The minimum rising pressure value is the minimum pilot pressure value that drives the excavator's bucket piston rod to rise, and the minimum falling pressure value is the minimum pilot pressure value that drives the excavator's bucket piston rod to fall. The first working pump pressure mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different working states of the excavator's bucket piston rod, and the motion rate mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different motion rates of the excavator's bucket piston rod. The motor analysis data includes minimum swing pressure, maximum stop pressure, second working pump pressure mapping data, and swing speed mapping data. The minimum swing pressure is the minimum pilot pressure value that drives the excavator motor to swing. The maximum stop pressure is the maximum pilot pressure value that drives the excavator motor to stop swinging. The second working pump pressure mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different working states of the excavator's motor. The swing speed mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different swing speeds of the excavator's motor. The hydraulic pump analysis data includes the first pilot pump pressure, the second pilot pump pressure, the first overflow pump pressure, and the second overflow pump pressure. The first pilot pump pressure is the pump pressure when the excavator's hydraulic pump is not in overflow state and has pilot pressure. The second pilot pump pressure is the pump pressure when the excavator's hydraulic pump is not in overflow state and has no pilot pressure. The first overflow pump pressure is the pump pressure when the excavator's hydraulic pump is in overflow state and has pilot pressure. The second overflow pump pressure is the pump pressure when the excavator's hydraulic pump is in overflow state and has no pilot pressure.

[0044] In this optional embodiment, by considering the input-output relationship of key components from the start of movement to the limit of movement, all working states of the components are covered during the order reduction process, thus avoiding the problem of poor simulation accuracy of untrained working conditions due to incomplete consideration of working conditions.

[0045] In another optional embodiment, a simplified model is constructed based on the model analysis dataset to obtain an initial hydraulic model, including: A simulation model is constructed based on the piston rod analysis data to obtain the piston rod model elements; Based on the motor analysis data, a simulation model is constructed to obtain the motor model unit; A simulation model is constructed based on the hydraulic pump analysis data to obtain the hydraulic pump model unit. By integrating the piston rod model unit, motor model unit, and hydraulic pump model unit, an initial hydraulic model is obtained.

[0046] In this optional embodiment, a reduced-order model of the response is established based on the analysis data of each key component of the hydraulic system, thereby simplifying the overall model.

[0047] In another optional embodiment, a simplified model is constructed based on the model analysis dataset to obtain an initial hydraulic model. This further includes: using a modular modeling method to decompose the initial hydraulic model into multiple functionally independent sub-model units, each corresponding to a key component of the hydraulic system. A unified interface specification is defined to enable data exchange and collaborative simulation between the sub-model units. The sub-model units include, but are not limited to, piston rod model units, motor model units, hydraulic pump model units, and newly added valve control model units and load model units. The valve control model unit is used to simulate the control logic and flow characteristics of the pilot valve, and the load model unit is used to simulate the influence of external loads on the actuator's motion. When constructing each sub-model unit, a parameterized description of the input-output relationship is achieved using lookup tables, linear interpolation, or polynomial fitting methods based on the mapping relationships and dynamic characteristics in the model analysis dataset. For example, for the piston rod model unit, a two-dimensional or three-dimensional lookup table is generated based on the first working pump pressure mapping data and motion rate mapping data, where the input is the pilot pressure and pump pressure value, and the output is the piston rod position and motion rate. Similarly, for the motor model unit, a similar lookup table model is constructed based on the second working pump pressure mapping data and rotation speed mapping data. In addition, to enhance the real-time performance of the simplified model, each sub-model unit adopts a state machine mechanism to manage its working mode switching. For example, the hydraulic pump model unit includes normal mode, overflow mode and no-load mode, and automatically triggers mode switching based on the pilot pressure value and pump pressure value. At the same time, an adaptive time step adjustment strategy is introduced, using a smaller simulation step size in the stage of drastic dynamic changes to ensure accuracy, and using a larger step size in the steady state stage to improve computational efficiency.

[0048] Through this modular modeling and state machine management, the initial hydraulic model is not only structurally clear and easy to maintain, but also able to accurately reproduce the multi-mode dynamic behavior of the hydraulic system model.

[0049] In another optional embodiment, a simulation model is constructed based on the piston rod analysis data to obtain piston rod model elements, including: The position setting values ​​of the excavator's bucket piston rod are determined based on the minimum upward pressure value and the minimum downward pressure value; the position setting values ​​include the initial position coordinate values ​​and the extreme position coordinate values ​​of the excavator's bucket piston rod. The piston rod model unit is obtained by constructing a model based on the position setting value, the first working pump pressure mapping data, and the motion rate mapping data.

[0050] In another optional embodiment, a simulation model is constructed based on the motor analysis data to obtain motor model units, including: The swing speed setting of the excavator motor is determined based on the minimum swing pressure value and the maximum stop pressure value; the swing speed setting value includes the initial swing speed value and the swing speed limit value of the excavator motor. The motor model unit is obtained by constructing a model based on the rotation speed setpoint, the second working pump pressure mapping data, and the rotation speed mapping data.

[0051] In another optional embodiment, a simulation model is constructed based on the hydraulic pump analysis data to obtain a hydraulic pump model unit, including: The hydraulic pump pressure setting value of the excavator is determined based on the first pilot pump pressure value, the second pilot pump pressure value, the first overflow pump pressure value, and the second overflow pump pressure value; the pump pressure setting value includes the excavator's initial pump pressure value and the pump pressure limit value; The model is constructed based on the pump pressure setpoint to obtain the hydraulic pump model unit.

[0052] In another optional embodiment, the initial hydraulic model is parameter-adjusted based on measured hydraulic data to obtain the target hydraulic model. Specifically, this includes: employing a gradient descent-based optimization algorithm, using measured hydraulic data as a reference, to minimize the error function between the initial hydraulic model output and the measured output; where the error function is defined as root mean square error or mean absolute error, covering multiple working cycles and operating points; the focus of parameter adjustment is calibrating key parameters in the simplified model, such as interpolation coefficients in the mapping table, state machine switching thresholds, and time constants. Specifically, the optimization process is divided into two stages: local calibration and global calibration. In the local calibration stage, parameters are adjusted individually for each sub-model unit using a subset of measured data related to that unit, such as using measured piston rod data to adjust the parameters of the piston rod model unit, to ensure that each unit achieves optimal fit in isolated tests; in the global calibration stage, after integrating all sub-model units, system-level optimization is performed using complete measured hydraulic data to correct errors caused by inter-unit coupling. To accelerate convergence, the optimization algorithm employs a variable learning rate strategy. A larger learning rate is used initially to quickly approximate the optimal solution, while a smaller learning rate is used for fine-tuning in the later stages. Simultaneously, a regularization term is introduced to prevent overfitting, ensuring the target hydraulic model has good generalization ability on unseen data. Furthermore, the parameter tuning process includes sensitivity analysis to identify the parameters that have the greatest impact on the model output and prioritize adjusting these parameters to improve optimization efficiency. After completion, the performance of the target hydraulic model is evaluated through cross-validation. A portion of the measured data is used as the training set, and the remaining data is used as the test set to verify the model's prediction accuracy and stability under different operating conditions.

[0053] Through this systematic parameter adjustment, the target hydraulic model can closely fit the dynamic characteristics of the original hydraulic system model while maintaining low computational complexity.

[0054] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of a model reduction device for hydraulic system models disclosed in an embodiment of the present invention. Figure 2 The device shown can be used to perform the method described in Embodiment 1. This device can improve the model reduction efficiency of the excavator's hydraulic system model. Furthermore, this device can be integrated into simulation testing equipment or exist independently of the simulation testing equipment. The hydraulic system model is a simulation model of the excavator's hydraulic system, such as... Figure 2 As shown, the model reduction device for hydraulic system models disclosed in this embodiment of the invention includes, but is not limited to: The quantitative analysis module 201 is used to perform quantitative analysis on the pre-stored hydraulic system model and the pre-acquired measured hydraulic data to obtain the model analysis dataset corresponding to the excavator; the measured hydraulic data includes measured piston rod data, measured motor data and measured hydraulic pump data; The model building module 202 is used to build a simplified model based on the model analysis dataset to obtain an initial hydraulic model; the initial hydraulic model includes piston rod model units, motor model units, and hydraulic pump model units. The parameter adjustment module 203 is used to adjust the parameters of the initial hydraulic model based on the measured hydraulic data to obtain the target hydraulic model; the target hydraulic model is a reduced-order model corresponding to the hydraulic system model.

[0055] Quantitative analysis of the original model and measured data reveals key input-output relationships: 1) the pilot pressure level at which the actuator will activate; 2) the pump pressure change process during pilot activation; 3) the upper limit of pump pressure when the actuator reaches its limit; and 4) the change in actuator pressure per unit time under a given pilot pressure. This invention allows for the construction of a simplified model based on the model analysis dataset within the AMEsim platform through simple logical judgments and integration, and verifies the correctness of the model's basic logic. By referencing the original model and measured hydraulic data, the parameters of the AMEsim model are adjusted and fitted as closely as possible to the original model to ensure the real-time performance and accuracy of the reduced-order model.

[0056] As can be seen, in this embodiment of the invention, firstly, a quantitative analysis is performed on the pre-stored hydraulic system model and the pre-acquired measured hydraulic data to obtain a model analysis dataset. The measured hydraulic data includes measured piston rod data, measured motor data, and measured hydraulic pump data. Then, a simplified model is constructed based on the model analysis dataset to obtain an initial hydraulic model. Next, the parameters of the initial hydraulic model are adjusted based on the measured hydraulic data to obtain the reduced-order target hydraulic model. Simulation is performed using measured data of key components in the hydraulic system to construct a simplified model, and the parameters of the simplified model are adjusted to enhance the fit between the simplified model and the original hydraulic system model, ensuring the real-time performance and accuracy of the reduced-order model. This eliminates the need to collect data covering all states of the excavator under all operating conditions, resulting in low data requirements and short acquisition time, thereby improving the efficiency of model reduction for the excavator's hydraulic system model.

[0057] It should be noted that this invention identifies the changes in key inputs and outputs as critical components move from their initial action to their operational limits during the offline testing phase. Based on the one-to-one correspondence between inputs and outputs in a hydraulic system, a mapping relationship between these inputs and outputs is fitted, while simultaneously focusing on the changes in key variables. A reduced-order model of the response is then established for each component of the hydraulic system. After the model is established, the reduction effect on the input data can be verified through online testing.

[0058] Furthermore, existing common model reduction methods include "physics-based reduction methods" and "model-based reduction methods." Physics-based reduction methods simplify the physical model by simplifying complex physical processes, thus reducing the model's order. However, this requires a high level of domain expertise from the modelers, necessitating reasonable simplification based on the system's dynamic characteristics and operating mechanisms. Model-based reduction methods reduce the model's order by adjusting the number of variables and equations, but are only suitable for small-scale dynamic analyses. They also require in-depth analysis of the coupling relationships between system variables and differential equations, demanding a high level of domain expertise from the modelers. In contrast, this invention requires only a few sets of representative data to obtain a reduced-order model with acceptable simulation error, reducing reliance on the professional experience and theoretical background of engineers. It also considers all operating states of key components, ensuring the simulation accuracy of the model.

[0059] In an optional embodiment, the quantitative analysis module 201 performs quantitative analysis on the pre-stored hydraulic system model and the pre-acquired measured hydraulic data to obtain the model analysis dataset corresponding to the excavator. The specific methods include: Quantitative analysis is performed on the pre-stored hydraulic system model, the pre-acquired measured piston rod data, and the measured hydraulic pump data to obtain piston rod analysis data; Quantitative analysis was performed on the hydraulic system model, the pre-acquired measured motor data, and the measured hydraulic pump data to obtain motor analysis data; Quantitative analysis was performed on the hydraulic system model and measured hydraulic pump data to obtain hydraulic pump analysis data. By integrating the analysis data of the piston rod, motor, and hydraulic pump, a model analysis dataset corresponding to the excavator is obtained.

[0060] In this optional embodiment, by identifying the changes in the input and output of key components, the mapping relationship between the input and output can be fitted based on the one-to-one correspondence between the input and output in the hydraulic system.

[0061] In another optional embodiment, the quantitative analysis module 201 performs quantitative analysis on the pre-stored hydraulic system model and the pre-acquired measured hydraulic data to obtain the model analysis dataset corresponding to the excavator. The specific method further includes: performing dynamic characteristic decomposition on the hydraulic system model, extracting key state variables and output responses from the hydraulic system model, and combining this with time-series data from the measured hydraulic data to perform multi-dimensional correlation analysis. This multi-dimensional correlation analysis includes a collaborative analysis of the pilot pressure change trend, pump pressure fluctuation cycle, and actuator motion hysteresis to identify the dominant dynamic behavior of the hydraulic system model under different operating modes. Based on the dominant dynamic behavior, the input-output pairs most relevant to the model reduction objective are selected, and redundant data is eliminated, thereby optimizing the data structure and scale of the model analysis dataset and improving the efficiency and accuracy of subsequent model construction. Specifically, the dynamic characteristic decomposition employs a piecewise linearization method based on operating modes, dividing the nonlinear dynamics of the hydraulic system model into multiple linear subsystems. Each subsystem corresponds to a typical operating condition, such as bucket digging, slewing motion, or travel drive. Then, for each linear subsystem, the singular value decomposition of its state space matrix is ​​calculated to determine the key state variables and dominant frequency response of that subsystem. Simultaneously, combined with measured hydraulic data, the boundary conditions and transient behavior of each linear subsystem are verified to ensure that the quantitative analysis covers the entire operating range of the hydraulic system model. Furthermore, the multi-dimensional correlation analysis involves statistical analysis of the measured hydraulic data, calculating the mean, variance, autocorrelation function, and cross-correlation function of each data channel to quantify the dependencies between pilot pressure and pump pressure, and between pump pressure and actuator motion. Based on these statistics, a transfer function model of pilot pressure-pump pressure-actuator motion is established to describe the simplified dynamic characteristics of the hydraulic system model.

[0062] Through this dynamic characteristic decomposition and multi-dimensional correlation analysis, the model analysis dataset not only includes static mapping relationships but also incorporates dynamic response features, thus laying the foundation for building a high-precision reduced-order model.

[0063] In another optional embodiment, the piston rod analysis data includes a minimum rising pressure value, a minimum falling pressure value, first working pump pressure mapping data, and motion rate mapping data. The minimum rising pressure value is the minimum pilot pressure value that drives the excavator's bucket piston rod to rise, and the minimum falling pressure value is the minimum pilot pressure value that drives the excavator's bucket piston rod to fall. The first working pump pressure mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different working states of the excavator's bucket piston rod, and the motion rate mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different motion rates of the excavator's bucket piston rod. The motor analysis data includes minimum swing pressure, maximum stop pressure, second working pump pressure mapping data, and swing speed mapping data. The minimum swing pressure is the minimum pilot pressure value that drives the excavator motor to swing. The maximum stop pressure is the maximum pilot pressure value that drives the excavator motor to stop swinging. The second working pump pressure mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different working states of the excavator's motor. The swing speed mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different swing speeds of the excavator's motor. The hydraulic pump analysis data includes the first pilot pump pressure, the second pilot pump pressure, the first overflow pump pressure, and the second overflow pump pressure. The first pilot pump pressure is the pump pressure when the excavator's hydraulic pump is not in overflow state and has pilot pressure. The second pilot pump pressure is the pump pressure when the excavator's hydraulic pump is not in overflow state and has no pilot pressure. The first overflow pump pressure is the pump pressure when the excavator's hydraulic pump is in overflow state and has pilot pressure. The second overflow pump pressure is the pump pressure when the excavator's hydraulic pump is in overflow state and has no pilot pressure.

[0064] In this optional embodiment, by considering the input-output relationship of key components from the start of movement to the limit of movement, all working states of the components are covered during the order reduction process, thus avoiding the problem of poor simulation accuracy of untrained working conditions due to incomplete consideration of working conditions.

[0065] In yet another optional embodiment, the model building module 202 constructs a simplified model based on the model analysis dataset to obtain the initial hydraulic model in the following ways: A simulation model is constructed based on the piston rod analysis data to obtain the piston rod model elements; Based on the motor analysis data, a simulation model is constructed to obtain the motor model unit; A simulation model is constructed based on the hydraulic pump analysis data to obtain the hydraulic pump model unit. By integrating the piston rod model unit, motor model unit, and hydraulic pump model unit, an initial hydraulic model is obtained.

[0066] In this optional embodiment, a reduced-order model of the response is established based on the analysis data of each key component of the hydraulic system, thereby simplifying the overall model.

[0067] In another optional embodiment, the model building module 202 constructs a simplified model based on the model analysis dataset to obtain the initial hydraulic model. The specific method further includes: using a modular modeling method to decompose the initial hydraulic model into multiple functionally independent sub-model units, each sub-model unit corresponding to a key component of the hydraulic system, and realizing data exchange and collaborative simulation between sub-model units by defining a unified interface specification; wherein, the sub-model units include, but are not limited to, piston rod model units, motor model units, hydraulic pump model units, as well as newly added valve control model units and load model units; the valve control model unit is used to simulate the control logic and flow characteristics of the pilot valve, and the load model unit is used to simulate the influence of external load on the movement of the actuator. When constructing each sub-model unit, based on the mapping relationships and dynamic characteristics in the model analysis dataset, a parameterized description of the input-output relationship is achieved using lookup table methods, linear interpolation, or polynomial fitting. For example, for the piston rod model unit, a two-dimensional or three-dimensional lookup table is generated based on the first working pump pressure mapping data and motion rate mapping data, where the input is the pilot pressure and pump pressure value, and the output is the position and motion rate of the piston rod. Similarly, for the motor model unit, a similar lookup table model is constructed based on the second working pump pressure mapping data and rotation speed mapping data. In addition, to enhance the real-time performance of the simplified model, each sub-model unit uses a state machine mechanism to manage its working mode switching. For example, the hydraulic pump model unit includes normal mode, overflow mode, and no-load mode, and automatically triggers mode switching based on the pilot pressure value and pump pressure value. At the same time, an adaptive time step adjustment strategy is introduced, using a smaller simulation step size to ensure accuracy during periods of rapid dynamic change, and a larger step size to improve computational efficiency during steady-state phases.

[0068] Through this modular modeling and state machine management, the initial hydraulic model is not only structurally clear and easy to maintain, but also able to accurately reproduce the multi-mode dynamic behavior of the hydraulic system model.

[0069] In yet another optional embodiment, the model building module 202 constructs a simulation model based on the piston rod analysis data, and the specific methods for obtaining the piston rod model elements include: The position setting values ​​of the excavator's bucket piston rod are determined based on the minimum upward pressure value and the minimum downward pressure value; the position setting values ​​include the initial position coordinate values ​​and the extreme position coordinate values ​​of the excavator's bucket piston rod. The piston rod model unit is obtained by constructing a model based on the position setting value, the first working pump pressure mapping data, and the motion rate mapping data.

[0070] In yet another optional embodiment, the model building module 202 constructs a simulation model based on the motor analysis data, and the specific methods for obtaining the motor model unit include: The swing speed setting of the excavator motor is determined based on the minimum swing pressure value and the maximum stop pressure value; the swing speed setting value includes the initial swing speed value and the swing speed limit value of the excavator motor. The motor model unit is obtained by constructing a model based on the rotation speed setpoint, the second working pump pressure mapping data, and the rotation speed mapping data.

[0071] In another optional embodiment, the model building module 202 constructs a simulation model based on the hydraulic pump analysis data, and the specific methods for obtaining the hydraulic pump model unit include: The hydraulic pump pressure setting value of the excavator is determined based on the first pilot pump pressure value, the second pilot pump pressure value, the first overflow pump pressure value, and the second overflow pump pressure value; the pump pressure setting value includes the excavator's initial pump pressure value and the pump pressure limit value; The model is constructed based on the pump pressure setpoint to obtain the hydraulic pump model unit.

[0072] In another optional embodiment, the parameter adjustment module 203 adjusts the parameters of the initial hydraulic model based on measured hydraulic data to obtain the target hydraulic model. The specific method includes: employing a gradient descent-based optimization algorithm, using measured hydraulic data as a reference, to minimize the error function between the initial hydraulic model output and the measured output; wherein the error function is defined as root mean square error or mean absolute error, covering multiple working cycles and operating points; the focus of parameter adjustment is to calibrate key parameters in the simplified model, such as interpolation coefficients in the mapping table, state machine switching thresholds, and time constants. Specifically, the optimization process is divided into two stages: local calibration and global calibration. In the local calibration stage, parameters are adjusted individually for each sub-model unit using a subset of measured data related to that unit, such as using measured piston rod data to adjust the parameters of the piston rod model unit, to ensure that each unit achieves the best fit in isolated tests; in the global calibration stage, after integrating all sub-model units, system-level optimization is performed using complete measured hydraulic data to correct errors caused by inter-unit coupling. To accelerate convergence, the optimization algorithm employs a variable learning rate strategy. A larger learning rate is used initially to quickly approximate the optimal solution, while a smaller learning rate is used for fine-tuning in the later stages. Simultaneously, a regularization term is introduced to prevent overfitting, ensuring the target hydraulic model has good generalization ability on unseen data. Furthermore, the parameter tuning process includes sensitivity analysis to identify the parameters that have the greatest impact on the model output and prioritize adjusting these parameters to improve optimization efficiency. After completion, the performance of the target hydraulic model is evaluated through cross-validation. A portion of the measured data is used as the training set, and the remaining data is used as the test set to verify the model's prediction accuracy and stability under different operating conditions.

[0073] Through this systematic parameter adjustment, the target hydraulic model can closely fit the dynamic characteristics of the original hydraulic system model while maintaining low computational complexity.

[0074] Example 3 Please see Figure 3 , Figure 3 This is a schematic diagram of another model reduction device for hydraulic system models disclosed in an embodiment of the present invention. Figure 3 The device shown can be used to perform the method described in Embodiment 1. This device can improve the model reduction efficiency of the excavator's hydraulic system model. Furthermore, this device can be integrated into simulation testing equipment or exist independently of the simulation testing equipment. The hydraulic system model is a simulation model of the excavator's hydraulic system, such as... Figure 3 As shown, the model reduction device for hydraulic system models disclosed in this embodiment of the invention includes, but is not limited to: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute some or all of the steps in the model reduction method for hydraulic system models described in Embodiment 1 of the present invention.

[0075] Example 4 This invention discloses a computer storage medium storing computer instructions. When the computer instructions are invoked by a processor, they are used to execute some or all of the steps in the model reduction method for hydraulic system models described in Embodiment 1 of this invention.

[0076] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules. They may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0077] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0078] Finally, it should be noted that the technical content disclosed in the embodiments of the present invention is only a preferred embodiment of the present invention and is only used to illustrate the technical solutions of the present invention, and not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A model order reduction method applied to hydraulic system models, characterized in that, The hydraulic system model is a simulation model of the hydraulic system of an excavator, and the method includes: A quantitative analysis is performed on the pre-stored hydraulic system model and the pre-acquired measured hydraulic data to obtain the model analysis dataset corresponding to the excavator; the measured hydraulic data includes measured piston rod data, measured motor data and measured hydraulic pump data; A simplified model is constructed based on the model analysis dataset to obtain an initial hydraulic model; the initial hydraulic model includes piston rod model units, motor model units, and hydraulic pump model units. The parameters of the initial hydraulic model are adjusted based on the measured hydraulic data to obtain the target hydraulic model; the target hydraulic model is a reduced-order model corresponding to the hydraulic system model.

2. The model order reduction method applied to hydraulic system models according to claim 1, characterized in that, The quantitative analysis of the pre-stored hydraulic system model and the pre-acquired measured hydraulic data yields the model analysis dataset corresponding to the excavator, including: Quantitative analysis is performed on the pre-stored hydraulic system model, the pre-acquired measured piston rod data, and the measured hydraulic pump data to obtain piston rod analysis data; Quantitative analysis is performed on the hydraulic system model, the pre-acquired measured motor data, and the measured hydraulic pump data to obtain motor analysis data; Quantitative analysis was performed on the hydraulic system model and the measured hydraulic pump data to obtain hydraulic pump analysis data. The piston rod analysis data, the motor analysis data, and the hydraulic pump analysis data are integrated to obtain the model analysis dataset corresponding to the excavator.

3. The model order reduction method applied to hydraulic system models according to claim 2, characterized in that, The piston rod analysis data includes minimum upward pressure value, minimum downward pressure value, first working pump pressure mapping data, and motion rate mapping data. The minimum upward pressure value is the minimum pilot pressure value that drives the excavator's bucket piston rod to rise, and the minimum downward pressure value is the minimum pilot pressure value that drives the excavator's bucket piston rod to fall. The first working pump pressure mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different working states of the excavator's bucket piston rod. The motion rate mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different motion rates of the excavator's bucket piston rod. The motor analysis data includes minimum swing pressure value, maximum stop pressure value, second working pump pressure mapping data, and swing speed mapping data. The minimum swing pressure value is the minimum pilot pressure value for driving the excavator's motor to swing, and the maximum stop pressure value is the maximum pilot pressure value for driving the excavator's motor to stop swinging. The second working pump pressure mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different working states of the excavator's motor. The swing speed mapping data is used to characterize the mapping relationship between the pump pressure value of the excavator's hydraulic pump and different swing speeds of the excavator's motor. The hydraulic pump analysis data includes a first pilot pump pressure value, a second pilot pump pressure value, a first overflow pump pressure value, and a second overflow pump pressure value. The first pilot pump pressure value is the pump pressure value when the excavator's hydraulic pump is not in an overflow state and has pilot pressure. The second pilot pump pressure value is the pump pressure value when the excavator's hydraulic pump is not in an overflow state and has no pilot pressure. The first overflow pump pressure value is the pump pressure value when the excavator's hydraulic pump is in an overflow state and has pilot pressure. The second overflow pump pressure value is the pump pressure value when the excavator's hydraulic pump is in an overflow state and has no pilot pressure.

4. The model order reduction method applied to hydraulic system models according to claim 3, characterized in that, The step of constructing a simplified model based on the model analysis dataset to obtain an initial hydraulic model includes: Based on the piston rod analysis data, a simulation model is constructed to obtain the piston rod model unit. Based on the motor analysis data, a simulation model is constructed to obtain motor model units; Based on the hydraulic pump analysis data, a simulation model is constructed to obtain the hydraulic pump model unit; By integrating the piston rod model unit, the motor model unit, and the hydraulic pump model unit, an initial hydraulic model is obtained.

5. The model order reduction method applied to hydraulic system models according to claim 4, characterized in that, The step of constructing a simulation model based on the piston rod analysis data to obtain piston rod model elements includes: The position setting value of the excavator's bucket piston rod is determined based on the minimum upward pressure value and the minimum downward pressure value; the position setting value includes the initial position coordinate value and the extreme position coordinate value of the excavator's bucket piston rod. Based on the position setting value, the first working pump pressure mapping data, and the motion rate mapping data, a model is constructed to obtain the piston rod model unit.

6. The model order reduction method applied to hydraulic system models according to claim 4, characterized in that, The step of constructing a simulation model based on the motor analysis data to obtain motor model units includes: The swing speed setting of the excavator motor is determined based on the minimum swing pressure value and the maximum stop pressure value; the swing speed setting value includes the initial swing speed value and the swing speed limit value of the excavator motor. Based on the rotational speed setting, the second working pump pressure mapping data, and the rotational speed mapping data, a model is constructed to obtain a motor model unit.

7. The model order reduction method applied to hydraulic system models according to claim 4, characterized in that, The step of constructing a simulation model based on the hydraulic pump analysis data to obtain hydraulic pump model units includes: The hydraulic pump pressure setting value of the excavator is determined based on the first pilot pump pressure value, the second pilot pump pressure value, the first overflow pump pressure value, and the second overflow pump pressure value; the pump pressure setting value includes the excavator's initial pump pressure value and pump pressure limit value; The model is constructed based on the pump pressure setting value to obtain the hydraulic pump model unit.

8. A model reduction device for hydraulic system models, characterized in that, The hydraulic system model is a simulation model of the hydraulic system of an excavator, and the device includes: The quantitative analysis module is used to perform quantitative analysis on the pre-stored hydraulic system model and the pre-acquired measured hydraulic data to obtain the model analysis dataset corresponding to the excavator; the measured hydraulic data includes measured piston rod data, measured motor data and measured hydraulic pump data; The model building module is used to construct a simplified model based on the model analysis dataset to obtain an initial hydraulic model; the initial hydraulic model includes piston rod model units, motor model units, and hydraulic pump model units. The parameter adjustment module is used to adjust the parameters of the initial hydraulic model based on the measured hydraulic data to obtain the target hydraulic model; the target hydraulic model is a reduced-order model corresponding to the hydraulic system model.

9. A model reduction device applied to hydraulic system models, characterized in that, The hydraulic system model is a simulation model of the hydraulic system of an excavator, and the device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the model reduction method for hydraulic system models as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked by the processor, are used to execute the model reduction method for hydraulic system models as described in any one of claims 1 to 7.

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