Shock absorber valve system adjusting method based on artificial intelligence technology, product, equipment and storage medium

Abstract

The invention relates to the technical field of image processing, in particular to a damper valve system adjustment method based on an artificial intelligence technology, a product, equipment and a storage medium. The method comprises the steps that parameter combinations of all valve systems of the shock absorber are collected; performing data quantization on the parameter combination to obtain a feature combination; and inputting the feature combination into a neural network model to obtain a change curve of the damping force along with the piston speed predicted by the neural network. According to the method, the external characteristic functions of the passive hydraulic shock absorber under different valve system structures are predicted through the artificial intelligence algorithm, and the number of tests in the chassis adjustment process is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, in particular to a shock absorber valve system tuning method, product, equipment and storage medium based on artificial intelligence technology. BACKGROUND

[0002] The shock absorber is an important component of the vehicle suspension system, and its main function is to alleviate and attenuate the impact vibration caused by road excitation during vehicle driving. In the chassis tuning process, the shock absorber tuning work is carried out after the tire selection, spring and stabilizer bar matching, and is mainly used to balance the handling stability and ride comfort of the vehicle. As the most critical and largest workload part in the chassis tuning, the shock absorber tuning has a crucial influence on improving the quality of the entire chassis.

[0003] The working principle of the shock absorber is based on the oil and gas filled inside. When the car moves up and down, the shock absorber piston reciprocates in the cylinder, driving the internal oil to flow repeatedly, and the damping force output is controlled through the compression valve and the recovery valve. The shock absorber has two working states of stretching and compression, and the damping force curve at different speeds is mainly controlled by adjusting the valve system. According to the speed, the working of the shock absorber can be divided into three levels: the piston speed is low (less than 0.1 m / s), the piston speed is medium (0.1 m / s to 0.6 m / s), and the piston speed is high (greater than 0.6 m / s). The low-speed stage mainly corresponds to the initial roll control of the vehicle, providing softness under small excitation; the medium-speed stage corresponds to the attenuation of the vehicle under medium impact, affecting the vehicle response in the non-central area; the high-speed stage corresponds to the isolation of the vehicle under large impact, controlling the vehicle body posture in extreme control.

[0004] The traditional shock absorber valve system tuning mainly relies on experience tuning and test verification method, and this method has problems such as long tuning period, high cost, and dependence on expert experience. Therefore, the present application is proposed. SUMMARY

[0005] The purpose of the present application is to provide a shock absorber valve system tuning method, product, equipment and storage medium based on artificial intelligence technology, which predicts the external characteristic function of the passive hydraulic shock absorber under different valve system structures through an artificial intelligence algorithm, and reduces the number of tests in the chassis tuning process.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: In a first aspect, the present application provides a shock absorber valve system tuning method based on artificial intelligence technology, comprising: Collecting parameter combinations of each valve system of the shock absorber; Quantizing the parameter combinations to obtain feature combinations; Inputting the feature combination into a neural network model to obtain a curve of the damping force predicted by the neural network versus the piston speed; The loss function Loss of the neural network model during training is as follows: ; in, is the number of training samples, are the loss weights of the physical model and the neural network model respectively, is the change curve predicted by the physical model corresponding to the i-th training sample, is the change curve predicted by the neural network model corresponding to the i-th training sample, The change curve obtained by experimenting with a single training sample; The variation curve predicted by the physical model satisfies the physical constraints of the shock absorber.

[0007] In a second aspect, the present application provides a computer program product, which, when running on a computer, enables the computer to execute the shock absorber valve system calibration method based on artificial intelligence technology described in the first aspect.

[0008] In a third aspect, the present application provides an electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the above-mentioned shock absorber valve system adjustment method based on artificial intelligence technology.

[0009] In a fourth aspect, the present application provides a computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions are used to enable a computer to execute the above-mentioned shock absorber valve system calibration method based on artificial intelligence technology.

[0010] Compared with the prior art, the present invention has the following advantages: The artificial intelligence-based shock absorber valve system calibration method provided in the embodiments of this application can guide chassis calibration in the early stages of vehicle development through parameter combination construction, data quantization, neural network model construction, and physical constraint construction. It is used to predict the shock absorber's external characteristic curve for different valve system combinations, reduce the number of tests during chassis calibration, and significantly improve chassis calibration efficiency. This method is primarily used in performance development areas such as suspension development and chassis calibration, significantly reducing the labor intensity of shock absorber calibration and significantly shortening the development cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the specific embodiments or the prior art of the present application, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0012] Figure 1 is a flow chart of a shock absorber valve system tuning method based on artificial intelligence technology provided by an embodiment of the present application. Figure 2 is a structural schematic diagram of an electronic device provided by the present application. DETAILED DESCRIPTION

[0013] The exemplary embodiments of the present application will be described below in conjunction with the drawings, which include various details of the embodiments of the present application to help understanding. They should be considered only as exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Also, for the sake of clarity and conciseness, the description below omits the description of well-known functions and structures.

[0014] The present application will be further described in detail below in conjunction with the embodiments.

[0015] Figure 1 is a flow chart of a shock absorber valve system tuning method based on artificial intelligence technology provided by an embodiment of the present application. The method can be executed by a computer program and integrated in an electronic device. The embodiment of the present application provides a shock absorber valve system tuning method based on artificial intelligence technology integrated in an electronic device, which predicts the external characteristic function of a passive hydraulic shock absorber under different valve system structures through an artificial intelligence algorithm, and obtains the damping force change curve of the shock absorber with various valve system structures. As shown in Figure 1 The embodiment of the present application provides a shock absorber valve system tuning method based on artificial intelligence technology, which includes the following steps: S110, collecting parameter combinations of each valve system of the shock absorber.

[0016] The specification parameters and quantity parameters of the piston valve system and the bottom valve system of the shock absorber are sorted out to form parameter combinations. The parameter combinations include but are not limited to: the number, length, width and diameter of the orifice of the throttle valve of the piston valve system, the diameter and number of the orifice of the piston valve, the inner diameter, outer diameter, thickness and number of various restoring valve pieces, and the outer diameter and thickness of various gaskets; the number, length, width and diameter of the orifice of the throttle valve of the bottom valve system, the diameter and number of the orifice of the bottom valve body, the outer diameter, thickness and number of various valve pieces, and the outer diameter and thickness of various gaskets.

[0017] S120, data quantization is performed on the parameter combination to obtain a feature combination.

[0018] In practical applications, for the same shock absorber, there are more than 50 data including the piston and the bottom valve train. If directly input into the neural network model, it will cause problems such as difficulty in capturing effective features, increased complexity, and performance decline. To solve this problem, the embodiment performs quantization processing on part of the parameter combination to reduce the number of features, and the obtained feature combination has a significant impact on the change curve. The feature combination includes quantized features and original parameter combinations.

[0019] Optionally, since the throttle valve plate causes the damping force to change through the throttle flow change, the opening area of the throttle valve plate is taken as the feature of the throttle valve plate, and the outer diameter, thickness, number, and other parameters of the throttle valve plate are omitted.

[0020] Optionally, the recovery valve plate, the flow-through valve plate, and the compression valve plate are installed in a stacked manner through multiple different specifications of valve plates, the oil flow of the piston hole is affected by the deformation of the valve plate, the deformation of the valve plate is affected by the stiffness, and the stiffness is related to the equivalent thickness of the valve plate. Therefore, the equivalent thicknesses of the recovery valve plate, the flow-through valve plate, and the compression valve plate are calculated respectively, and the equivalent thicknesses are taken as the features of the recovery valve plate, the flow-through valve plate, and the compression valve plate respectively, and the outer diameter, thickness, number, and other parameters of the valve plate are omitted. Calculating the equivalent thickness of the shock absorber valve plate is essentially equivalent to a group of stacked valve plates into a single valve plate with the same bending stiffness. A single valve plate is too thin to provide sufficient damping force. Therefore, the shock absorber valve train is usually composed of multiple valve plates with different thicknesses and diameters. Directly analyzing the deformation and force relationship of the entire valve plate group is very complex. By calculating the "equivalent thickness", it can be modeled, analyzed and calculated as a single, thicker valve plate to calculate the opening pressure, which greatly simplifies the design process.

[0021] After data quantization, the adjustable parameters of a single throttle valve plate originally have 5, which are converted to 1 after quantization. The adjustable parameters of the recovery valve train and the compression valve train can reach 9-18 according to the number of stacked valve plates, which are converted to 1 after quantization.

[0022] S130, inputting the feature combination into the neural network model to obtain a change curve of the damping force predicted by the neural network with respect to the piston speed.

[0023] The feature combination is written in vector form and input into a neural network model, which has the mapping capability to obtain the change curve according to the feature combination. The horizontal axis of the change curve is the piston speed, and the vertical axis is the damping force. In an embodiment, the change curve is divided into multiple speed segments, and the trend of the change curve of each speed segment is the same, then the neural network model can output the damping force value of each speed point, and further calculate the linear equation parameters (including slope and intercept) of different speed segments.

[0024] Before using the neural network model to obtain the change curve, the neural network model needs to be selected and trained.

[0025] In this embodiment, a physics informed neural network (PINN) model is used as the model for predicting the change curve of the damper. This model uses a deep learning method that combines basic neural networks and physical equations. Its main feature is to incorporate the physical constraints of the physical system into the training process of the neural network, so that the neural network model can not only learn the input feature combination, but also meet the physical laws. By modifying the traditional loss function and adding physical constraints, it is ensured that the output of the neural network model not only fits the training samples, but also meets the dynamic behavior of the physical system, and reduces the demand for the amount of training set data. Traditional deep learning methods often require a large amount of experimental data, while this method can be trained with a small amount of data plus physical equation constraints.

[0026] The neural network model in this embodiment uses a multi-layer perceptron neural network as the main network, and uses the regularization technique of Dropout to prevent network overfitting, and updates the parameters of the neural network model through the backpropagation algorithm. Table 1 is the hyperparameters of the multi-layer perceptron neural network.

[0027] Table 1 Hyperparameters of the neural network model

[0028] In Table 1, the Adam (Adaptive Moment Estimation) optimizer is a widely used adaptive optimization algorithm in the field of deep learning, which combines the advantages of momentum mechanism and adaptive learning rate adjustment. Leaky ReLU (Leaky ReLU) is an improved version of the ReLU activation function, which aims to solve the "neuron death" problem caused by the zero gradient of ReLU in the negative input interval. The input is a 13-dimensional feature combination, and the output is the damping force value of 10 speed points.‌

[0029] In a multi-layer perceptron neural network, the neurons of each layer are connected to all neurons of the next layer. Information flows from the input layer, through one or more hidden layers, and finally to the output layer, propagating in one direction.

[0030] Dropout refers to randomly and temporarily discarding (setting the activation value to zero) a portion of neurons in the network according to a certain probability p during the training of the neural network. Dropout forces the network not to rely on any specific neuron or feature combination through this random "discarding". Each iteration trains a slightly different "sub-network". This can be seen as a kind of model averaging, effectively reducing the complex co-adaptation between neurons, making the network more generalizable and less likely to overfit to noise in the training data. Throughout the training process, Dropout randomly masks a portion of neurons in each iteration, as if training multiple sub-networks, thereby suppressing overfitting and improving the prediction ability and robustness of the model for unseen positions.

[0031] The PINN in the embodiment combines a neural network and physical constraints, and takes the output of the neural network model as the output of the physical model after passing through the physical constraints (i.e., obtained from the tuning experience of the tuning engineer), that is, the physical model is equivalent to connecting a correction layer after the neural network model. It can be seen that the change curve predicted by the physical model satisfies the physical constraints of the damper, and the change curve predicted by the physical model is obtained by correcting the change curve predicted by the neural network. The predicted change curves of the physical model and the neural network model are respectively obtained, and the prediction error of the two models is weighted and fused as the final error to update the weights of the neural network model. The final error calculation method, that is, the loss function Loss, is as follows: ; Equation 1 Wherein, is the number of training samples, are the loss weights of the physical model and the neural network model, respectively, is the change curve predicted by the physical model corresponding to the i-th training sample, is the change curve predicted by the neural network model corresponding to the i-th training sample, is the change curve obtained by testing a single training sample.

[0032] First, collect training samples. The training samples are feature combinations of each valve system of the damper obtained by manual debugging during the damper tuning process. The damping force of the damper under different motion strokes (stretching, compression) and different speeds (0.05 m / s, 0.1 m / s, 0.3 m / s, 0.6 m / s, 1 m / s) is recorded by testing to form a corresponding point pair of speed-damping force. The corresponding point pair is the true value .

[0033] The training sample is input into the neural network model to be trained to obtain the output of the neural network model Then After processing by the correction layer, the The , and are input into the Loss function to obtain the Loss value. Along the direction of minimizing the Loss value, the parameters in the neural network model are optimized until the difference between the Loss values of two adjacent optimizations is less than a set threshold, that is, the Loss reaches stability, and the neural network model training is completed.

[0034] The method for adjusting the valve system of a shock absorber based on artificial intelligence technology provided in the embodiments of the present application can guide chassis adjustment work in the early stage of vehicle development through parameter combination construction, data quantization, neural network model construction, and physical constraint construction. The method is used to predict the external characteristic curve of the shock absorber under different valve system combinations, reduces the number of tests in the chassis adjustment process, and greatly improves the efficiency of chassis adjustment. The method is mainly applied to the performance development field of suspension development and chassis adjustment, greatly reduces the labor intensity of shock absorber adjustment, and significantly shortens the development cycle.

[0035] The operation of the physical model (i.e., the correction layer) and how to obtain the change curve predicted by the physical model will be described in detail below. When the neural network model is trained, the change curve predicted by the physical model is obtained in the following manner: First, the constraint rules are obtained by performing statistics on the training sample.

[0036] The constraint rules include the relationship between the thickness of the restoring shim and the speed inflection point in the change curve, the relationship between the throttling flow of the throttle valve and the damping force in the low-speed section of the change curve, the relationship between the number of restoring valve pieces and the damping force in the high-speed section of the change curve, the relationship between the thickness of the restoring valve piece and the damping force in the medium-speed section and the damping force in the high-speed section of the change curve, and the relationship between the equivalent thickness of the restoring valve piece and the slope of the change curve.

[0037] Second, the change curve predicted by the neural network model is corrected by the constraint rules to obtain the change curve predicted by the physical model.

[0038] For the relationship between the thickness of the restoring gasket and the speed inflection point in the change curve, the speed inflection point is the horizontal coordinate value of the point where the slope of the change curve changes obviously, that is, the piston speed value. The thinner the thickness of the restoring gasket is, the greater the damping force at the speed inflection point is; on the contrary, the thicker the thickness of the restoring gasket is, the smaller the damping force at the speed inflection point is. Based on this, in the parameter combination, the relationship between the thickness of the restoring gasket and the damping force at the speed inflection point is fitted, for example, a linear relationship. Then, for the input scene of each training sample, the damping force at the speed inflection point under the current training sample (the thickness of the current restoring gasket) is calculated according to the linear relationship. The calculated damping force at the speed inflection point is replaced with the damping force at the same speed inflection point in the change curve output by the neural network model, to form the change curve predicted by the physical model.

[0039] For the relationship between the throttling flow of the throttle valve and the low-speed damping force in the change curve. The low-speed damping force is the damping force when the piston speed is less than 0.1 m / s. The smaller the throttling flow is, the greater the low-speed damping force is, and vice versa. Based on this, in the parameter combination, the relationship between the throttling flow of the throttle valve and the low-speed damping force in the change curve is fitted, for example, a linear relationship. Then, for the input scene of each training sample, the low-speed damping force under the current training sample (the current throttling flow) is calculated according to the linear relationship. The calculated low-speed damping force is replaced with the low-speed damping force in the change curve output by the neural network model, to form the change curve predicted by the physical model.

[0040] The relationship between the number of restoring valve pieces and the medium-speed damping force and the high-speed damping force in the change curve. The medium-speed damping force is the damping force when the piston speed is 0.1 m / s to 0.6 m / s, and the high-speed damping force is the damping force when the piston speed is greater than 0.6 m / s. The greater the number of restoring valve pieces is, the greater the medium-speed damping force and the high-speed damping force are; on the contrary, the smaller the number of restoring valve pieces is, the smaller the medium-speed damping force and the high-speed damping force are. Based on this, in the parameter combination, the relationship between the number of restoring valve pieces and the medium-speed damping force and the high-speed damping force in the change curve is fitted, for example, a linear relationship. Then, for the input scene of each training sample, the medium-speed damping force and the high-speed damping force under the current training sample (the current number of restoring valve pieces) are calculated according to the linear relationship. The calculated medium-speed damping force and the high-speed damping force are replaced with the medium-speed damping force and the high-speed damping force in the change curve output by the neural network model, to form the change curve predicted by the physical model.

[0041] For the relationship between the medium-speed section damping force and the high-speed section damping force of the recovery valve plate thickness and the change curve. The medium-speed section damping force and the high-speed section damping force both increase as the recovery valve plate thickness increases; conversely, the medium-speed section damping force and the high-speed section damping force both decrease as the recovery valve plate thickness decreases. Based on this, in the parameter combination, the relationship between the thickness of the recovery valve plate and the medium / high-speed section damping force of the change curve is fitted, for example, a linear relationship. Then, for the input scene of each training sample, the medium-speed section damping force and the high-speed section damping force under the current training sample (the current recovery valve plate thickness) are calculated according to the linear relationship. The calculated medium-speed section damping force and high-speed section damping force replace the medium-speed section damping force and high-speed section damping force in the change curve output by the neural network model to form the change curve predicted by the physical model.

[0042] For the relationship between the equivalent thickness of the recovery valve plate and the slope of the change curve, the following three steps are included: The first step is to obtain the equivalent thickness of the recovery valve plate according to the i-th training sample, and to obtain the slope of each speed section on the change curve according to the equivalent thickness.

[0043] Specifically, by statistically analyzing the training samples, a corresponding point pair of the equivalent thickness and the slope of each speed section is obtained; and a linear fitting is performed on the corresponding point pair to obtain a plurality of relationship formulas with the equivalent thickness as the independent variable and the slope of each speed section as the dependent variable. The speed sections include a low-speed section, a medium-speed section, and a high-speed section, and can also be more than four speed sections divided in detail. The slopes of the damping forces in the same speed section are the same.

[0044] The relationship formula of one speed section is as follows: ; Formula 2 ; Formula 3 Wherein, D is the equivalent thickness of the recovery valve plate, k and b are the coefficients obtained by fitting, K rebound is the slope of the speed section, is the identification of the type of recovery valve plate, is the number of the i-th type of recovery valve plate, d i is the actual thickness of a single recovery valve plate, is the outer diameter of a single recovery valve plate. is the maximum value of the outer diameter of the recovery valve plate.

[0045] The equivalent thickness in the i-th training sample is brought into each relationship formula to obtain the slope of each speed section. For example, the equivalent thickness of the i-th training sample is calculated, and the equivalent thickness is brought into D in Formula 2 to obtain the slope of the speed section that satisfies the physical constraint .

[0046] Second step, extract the damping force of each speed segment from the change curve predicted by the neural network model corresponding to the i-th training sample.

[0047] The i-th training sample is input into the neural network model to obtain a change curve predicted by the neural network model. The change curve is divided into multiple speed segments according to the obvious difference in slope, for example, divided into a low speed (less than 0.1 m / s) segment, a medium speed (0.1 m / s to 0.6 m / s) segment and a high speed (greater than 0.6 m / s) segment. For each speed segment, the damping force of the speed segment is extracted, and the damping force of each speed segment should be linear.

[0048] Third step, according to the extracted damping force and slope of each speed segment, obtain the damping force of each speed segment predicted by the physical model.

[0049] The output of the physical model and the output of the neural network model satisfy the following formula: ; Formula 4 Wherein, and are different speeds in the same speed segment. is the damping force of each speed segment of the physical model to be solved, is the damping force of each speed segment extracted from the output of the neural network model.

[0050] For example, the slope of the low speed segment calculated in formula 2, the two speeds of the low speed segment (referred to as the first speed and the second speed), and the damping force of the first speed output by the neural network model are brought into formula 4 to calculate the damping force of the second speed predicted by the physical model. The operation method of other speed segments is the same, so that the damping force of any speed predicted by the physical model can be obtained.

[0051] The aforementioned constraint rules can be used to modify the change curve predicted by the neural network model simultaneously or at least one of them, and it can be seen that the prediction curve of the physical model changes constantly with the model training.

[0052] The embodiment converts the relationship between the specification parameters of the shock absorber valve plate structure and the change curve into an empirical formula, thereby constructing a physical constraint, i.e. the operation of the modification layer, and realizing the fusion of the physical model and the neural network model.

[0053] As shown in Figure 2 , the embodiment provides an electronic device, comprising: at least one processor; and a memory in communication connection with the at least one processor; wherein The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described above. The at least one processor in the electronic device can perform the method described above, and thus has at least the same advantages as the method described above.

[0054] Optionally, the electronic device further includes an interface for connecting various components, including a high-speed interface and a low-speed interface. Various components are connected to each other by different buses, and can be mounted on a common motherboard or otherwise mounted as needed. The processor can process instructions executed within the electronic device, including instructions stored in the memory or on the memory to display a GUI (Graphical User Interface) on an external input / output device (such as a display device coupled to the interface). In other embodiments, multiple processors can be used with multiple memories, and / or multiple buses can be used with multiple memories, if necessary. Similarly, multiple electronic devices can be connected (e.g., as a server array, a group of blade servers, or a multi-processor system), each device providing part of the necessary operations. Figure 2 The processor 301 in the electronic device is taken as an example.

[0055] The memory 302, as a computer readable storage medium, can be used to store software programs, computer executable programs, and modules, such as program instructions / modules corresponding to the shock absorber valve system tuning method based on artificial intelligence technology in the embodiments of the present application. The processor 301 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 302, that is, implements the shock absorber valve system tuning method based on artificial intelligence technology described above.

[0056] The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and application programs required by at least one function; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory 302 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state memory device. In some examples, the memory 302 can further include a memory remotely arranged with respect to the processor 301, which can be connected to the device through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0057] The electronic device can also include an input device 303 and an output device 304. The processor 301, the memory 302, the input device 303, and the output device 304 can be connected by a bus or other means, Figure 2The bus connection is taken as an example.

[0058] The input device 303 can receive inputted digital or character information, and the output device 304 can include a display device, an auxiliary lighting device (e.g., an LED), a tactile feedback device (e.g., a vibration motor), and the like. The display device can include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, and a plasma display. In some embodiments, the display device can be a touch screen.

[0059] The embodiment provides a computer readable storage medium, and the medium stores computer instructions. The computer instructions are used for causing a computer to execute the method described above. The computer instructions on the computer readable storage medium are used for causing the computer to execute the method, and thus at least have the same advantages as the method.

[0060] The medium in the application can adopt any combination of one or more computer readable media. The medium can be a computer readable signal medium or a computer readable storage medium. The medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of the medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0061] The computer readable signal medium can include a data signal propagated in a baseband or as a part of a carrier wave, in which a computer readable program code is borne. Such a propagated data signal can take multiple forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium, which can send, propagate or transmit a program for use by or in connection with an instruction execution system, device or component.

[0062] The program code contained in the computer readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, optical cable, RF (Radio Frequency, radio frequency), etc., or any suitable combination of the above.

[0063] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0064] In the embodiments described above, all or some of the steps can be implemented by hardware, firmware, software, or any combination thereof. When implemented in software, the software can be stored in one or more computer readable storage medium(s) and executed on one or more computers. The computer readable storage medium(s) can be a volatile or non-volatile storage medium such as, for example, a volatile memory (e.g., a RAM), a non-volatile memory (e.g., a ROM, a flash memory, or the like), or a combination thereof. The computer readable storage medium(s) can be tangible or non-tangible. The computer readable storage medium(s) can be a recording medium or a transmission medium. The computer readable storage medium(s) can be a computer readable recording medium, a computer readable transmission medium, or a combination thereof. The computer readable recording medium can be a recording medium, a computer readable storage medium, or a combination thereof. The computer readable transmission medium can be a transmission medium, a computer readable storage medium, or a combination thereof.

[0065] It should be understood that various forms of flow shown above can be reorganized, reordered, or steps can be added or removed. For example, steps described in the present disclosure can be executed in parallel, in series, or in different orders, as long as the desired results of the disclosed technology are achieved, and the present disclosure is not limited herein.

[0066] The above detailed description does not limit the scope of the application. Various modifications, combinations, sub-combinations and alternatives can be made to the detailed embodiment disclosed herein without departing from the spirit and the principles of the application. Any modification, equivalent replacement or improvement made within the spirit and principles of the application shall fall within the scope of the application.

Claims

1. A shock absorber valve system calibration method based on artificial intelligence technology, characterized in that: include: Collect parameter combinations of each valve system of the shock absorber; quantizing the parameter combination to obtain a feature combination; Inputting the feature combination into a neural network model to obtain a curve of the damping force predicted by the neural network versus the piston speed; The loss function Loss of the neural network model during training is as follows: ； in, is the number of training samples, are the loss weights of the physical model and the neural network model respectively, is the change curve predicted by the physical model corresponding to the i-th training sample, is the change curve predicted by the neural network model corresponding to the i-th training sample, The change curve obtained by experimenting with a single training sample; The variation curve predicted by the physical model satisfies the physical constraints of the shock absorber.

2. The shock absorber valve system calibration method based on artificial intelligence technology according to claim 1 is characterized in that: The change curve predicted by the physical model is obtained by correcting the change curve predicted by the neural network.

3. The shock absorber valve system calibration method based on artificial intelligence technology according to claim 1 is characterized in that: The parameter combination is subjected to data quantization to obtain a feature combination, including: Taking the opening area of ​​the throttle valve plate as the characteristic of the throttle valve plate; The equivalent thicknesses of the restoration valve sheet, the circulation valve sheet, and the compression valve sheet are calculated respectively, and the equivalent thicknesses are used as the characteristics of the restoration valve sheet, the circulation valve sheet, and the compression valve sheet respectively.

4. The shock absorber valve system calibration method based on artificial intelligence technology according to claim 1 is characterized in that: The neural network model adopts a multi-layer perceptron neural network as the main network, and adopts regularization technology to prevent the network from overfitting.

5. The shock absorber valve system calibration method based on artificial intelligence technology according to claim 2 is characterized in that: When the neural network model is trained, the change curve predicted by the physical model is obtained in the following manner: Obtaining the equivalent thickness of the restored valve plate according to the i-th training sample, and obtaining the slope of each speed segment on the change curve according to the equivalent thickness; Extract the damping force of each speed segment from the change curve predicted by the neural network model corresponding to the i-th training sample; According to the extracted damping force and slope of each speed segment, the damping force of each speed segment predicted by the physical model is obtained.

6. The shock absorber valve system calibration method based on artificial intelligence technology according to claim 5 is characterized in that: The slope of each speed segment on the change curve is obtained according to the equivalent thickness, including: By statistically analyzing the training samples, the corresponding point pairs of equivalent thickness and slope of each velocity segment are obtained; Performing linear fitting on the corresponding point pairs to obtain multiple relationship equations with equivalent thickness as an independent variable and slopes of each speed segment as dependent variables; Substitute the equivalent thickness into each relational equation to obtain the slope of each speed segment.

7. The shock absorber valve system calibration method based on artificial intelligence technology according to claim 5 is characterized in that: When the neural network model is trained, the change curve predicted by the physical model is obtained in the following manner: By statistically analyzing the training samples, we can obtain the constraint rules; Correcting the change curve predicted by the neural network model using the constraint rules to obtain the change curve predicted by the physical model; Among them, the constraint rules include the relationship between the thickness of the restoration gasket and the speed inflection point in the change curve; the relationship between the throttling flow of the throttle valve plate and the damping force of the low-speed section of the change curve; the relationship between the number of restoration valve plates and the damping force of the medium-speed section and the damping force of the high-speed section of the change curve; the relationship between the thickness of the restoration valve plate and the damping force of the medium-speed section and the damping force of the high-speed section of the change curve; the relationship between the equivalent thickness of the restoration valve plate and the slope of the change curve.

8. A computer program product, characterized in that include: The computer program product stores computer instructions, which, when executed by a processor, implement the steps of the shock absorber valve system calibration method based on artificial intelligence technology according to any one of claims 1 to 7.

9. An electronic device, characterized in that: include: at least one processor, and a memory communicatively coupled to the at least one processor; Wherein, the memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the shock absorber valve system calibration method based on artificial intelligence technology as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that The medium stores computer instructions, which are used to enable a computer to execute the shock absorber valve system calibration method based on artificial intelligence technology according to any one of claims 1 to 7.

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