Model training method, device, pantograph-catenary system simulation method, device, and medium

Abstract

The present application provides a model training method, apparatus, pantograph-catenary system simulation method, apparatus, and medium, relating to the technical field of pantograph-catenary simulation. The method includes obtaining a training sample set, constructing an objective loss function including a physical loss function, constraining the output of the pantograph-catenary simulation model to conform to physical laws through the physical loss function, and finally training the pantograph-catenary simulation model according to the training sample set, and obtaining the trained pantograph-catenary simulation model by minimizing the objective loss function. By adding the physical loss function to the objective loss function, the physical law constraint on the output of the pantograph-catenary simulation model can be realized, and the effect of improving the prediction accuracy of the pantograph-catenary simulation model can be achieved.

Description

Technical Field

[0001] The present application relates to the field of bow-net simulation technology, and specifically to a model training method, device, bow-net system simulation method, device, and medium. Background Art

[0002] In electrified railways, the interaction between the pantograph and catenary is crucial for maintaining a stable current supply. Currently, the finite element method (FEM) is commonly used to model and evaluate the dynamic performance of pantograph-catenary systems. However, this method is difficult to model and requires complex and computationally intensive computations. Summary of the Invention

[0003] The present application provides a model training method, device, bow-net system simulation method, device, and medium, which can reduce the computational difficulty during the simulation process and improve the accuracy of dynamic simulation.

[0004] This application provides a model training method, including:

[0005] Obtain a training sample set; the training sample set includes multiple training samples, and the training samples include input features and sample labels, the input features include initial simulation parameters and pantograph simulation results of multiple consecutive time steps after finite element simulation of the pantograph system, and the sample labels include the pantograph simulation results of one time step after the multiple consecutive time steps;

[0006] Constructing a target loss function for the pantograph-catenary simulation model; the target loss function includes a physical loss function, which is used to constrain the output of the pantograph-catenary simulation model to conform to physical laws;

[0007] The pantograph simulation model is trained based on the training sample set, and the trained pantograph simulation model is obtained by minimizing the target loss function.

[0008] Optionally, the pantograph-catenary simulation results include a state matrix, an external force matrix, a mass matrix, a damping matrix, and a stiffness matrix, and the physical loss function includes:

[0009] ;

[0010] in, is the loss value of the physical loss function, is the total number of training samples, Indicates the training samples, is the physical loss term, , According to the pantograph-catenary simulation model and The state matrix obtained by the input features of the training samples, According to the pantograph-catenary simulation model and The external force matrix obtained from the input features of the training samples, denotes the mass matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample; denotes the damping matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample; denotes the stiffness matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample.

[0011] Optionally, constructing an objective loss function for the pantograph-catenary simulation model, including:

[0012] Determining the weighted sum of the basic loss function and the physical loss function as the objective loss function.

[0013] Optionally, constructing an objective loss function for the pantograph-catenary simulation model, including:

[0014] Determining the sum of the basic loss function and the physical loss function as the objective loss function.

[0015] Optionally, the basic loss function is the mean square error loss function.

[0016] Optionally, the pantograph-catenary simulation model includes a time convolutional network, a long short-term memory network, a multi-head attention mechanism, and a fully connected network connected in sequence.

[0017] A pantograph-catenary system simulation method provided by the present application includes:

[0018] Obtaining the current simulation parameters of the pantograph-catenary system;

[0019] Determining the current pantograph-catenary simulation result according to the current simulation parameters and the pantograph-catenary simulation model; wherein, the pantograph-catenary simulation model is trained based on one or more of the foregoing methods.

[0020] To achieve the above object and other related objects, the present application provides a model training device, including:

[0021] A training data acquisition module, configured to acquire a training sample set; the training sample set includes a plurality of training samples, and each training sample includes initial simulation parameters and the pantograph-catenary simulation result obtained by performing finite element simulation on the pantograph-catenary system according to the initial simulation parameters;

[0022] A loss function construction module, configured to construct an objective loss function for the pantograph-catenary simulation model; the objective loss function includes a physical loss function, and the physical loss function is used to constrain the output of the pantograph-catenary simulation model to conform to physical laws;

[0023] A model training module for training a pantograph-catenary simulation model according to a training sample set, and obtaining a trained pantograph-catenary simulation model by minimizing an objective loss function.

[0024] To achieve the above object and other related objects, the present application provides a pantograph-catenary system simulation device, including:

[0025] A current data acquisition module for acquiring current simulation parameters of the pantograph-catenary system;

[0026] A result determination module for determining a current pantograph-catenary simulation result according to the current simulation parameters and the pantograph-catenary simulation model; wherein, the pantograph-catenary simulation model is trained based on one or more of the foregoing methods.

[0027] To achieve the above object and other related objects, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor of a computer, the computer is enabled to execute one or more of the foregoing methods.

[0028] As described above, a model training method, device, pantograph-catenary system simulation method, device, and medium provided by the present application have the following beneficial effects:

[0029] A model training method in the present application. This method obtains a training sample set, constructs an objective loss function including a physical loss function, constrains the output of the pantograph-catenary simulation model to conform to physical laws through the physical loss function, and finally trains the pantograph-catenary simulation model according to the training sample set, and obtains a trained pantograph-catenary simulation model by minimizing the objective loss function. By adding a physical loss function to the objective loss function, physical law constraints on the output of the pantograph-catenary simulation model can be realized, and the effect of improving the prediction accuracy of the pantograph-catenary simulation model can be achieved.

[0030] It should be understood that the above general description and subsequent detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0032] Figure 1 is a flowchart of a model training method shown in an exemplary embodiment of the present application;

[0033] Figure 2It is a schematic structural diagram of the pantograph-catenary simulation model shown in an exemplary embodiment of the present application;

[0034] Figure 3 It is a schematic diagram of the prediction results of the pantograph-catenary simulation model and the finite element simulation model; among them, (a1) is the change of the displacement of the contact wire in the X-axis direction over time, (a2) is the change of the derivative of the displacement of the contact wire in the X-axis direction over time, (b1) is the change of the displacement of the contact wire in the Y-axis direction over time, (b2) is the change of the derivative of the displacement of the contact wire in the Y-axis direction over time, (c1) is the change of the displacement of the contact wire in the Z-axis direction over time, and (c2) is the change of the derivative of the displacement of the contact wire in the Z-axis direction over time;

[0035] Figure 4 It is a schematic diagram of the prediction error of the pantograph-catenary simulation model; among them, (a1) is the change of the displacement of the prediction results of the finite element simulation model and the pantograph-catenary simulation model in the X-axis direction over time, (a2) is the change of the derivative of the displacement of the prediction results of the finite element simulation model and the pantograph-catenary simulation model in the X-axis direction over time, (b1) is the change of the displacement of the prediction results of the finite element simulation model and the pantograph-catenary simulation model in the Y-axis direction over time, (b2) is the change of the derivative of the displacement of the prediction results of the finite element simulation model and the pantograph-catenary simulation model in the Y-axis direction over time, (c1) is the change of the displacement of the prediction results of the finite element simulation model and the pantograph-catenary simulation model in the Z-axis direction over time, and (c2) is the change of the derivative of the displacement of the prediction results of the finite element simulation model and the pantograph-catenary simulation model in the Z-axis direction over time;

[0036] Figure 5 It is a frequency-domain feature diagram of the prediction results of the pantograph-catenary simulation model and the finite element simulation model; among them, (a1) is the amplitude change of the displacement of the contact wire in the X-axis direction in the frequency domain, (a2) is the amplitude change of the derivative of the displacement of the contact wire in the X-axis direction in the frequency domain, (b1) is the amplitude change of the displacement of the contact wire in the Y-axis direction in the frequency domain, (b2) is the amplitude change of the derivative of the displacement of the contact wire in the Y-axis direction in the frequency domain, (c1) is the amplitude change of the displacement of the contact wire in the Z-axis direction in the frequency domain, and (c2) is the amplitude change of the derivative of the displacement of the contact wire in the Z-axis direction in the frequency domain;

[0037] Figure 6 It is a flowchart of the pantograph-catenary system simulation method shown in an exemplary embodiment of the present application;

[0038] Figure 7 It is a block diagram of the model training device shown in an exemplary embodiment of the present application;

[0039] Figure 8 It is a block diagram of the pantograph-catenary system simulation device shown in an exemplary embodiment of the present application. Detailed implementation manners

[0040] The embodiments of the present application will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application, rather than for limiting the protection scope of the present application.

[0041] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner. Therefore, only the components related to the present application are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0042] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0043] Please refer to Figure 1 , Figure 1 which is a flowchart of a model training method shown in an exemplary embodiment of the present application. Referring to Figure 1 it can be seen that the model training method may include:

[0044] Step S110, obtaining a training sample set.

[0045] Among them, the training sample set includes a plurality of training samples. The training sample includes an input feature and a sample label. The input feature includes initial simulation parameters and the pantograph-catenary simulation results at multiple consecutive time steps after performing a finite element simulation on the pantograph-catenary system. The sample label includes the pantograph-catenary simulation results at the next time step after multiple consecutive time steps.

[0046] In one embodiment of the present application, the process of obtaining a training sample set may include: obtaining initial simulation parameters, which can be input by a user and may include parameters such as span and tension. Initialize the finite element simulation model of the pantograph-catenary system according to each initial simulation parameter, and randomly set the running speed and base uplift force in the finite element simulation model. After simulation using the finite element simulation model, a trajectory is generated, which characterizes the various response states of the system during the operation of the finite element simulation model as the input data changes. The pantograph-catenary simulation results output by the finite element simulation model can be segmented into multiple training samples, that is, a training sample set, with a sliding window of a preset length, and the training sample set is stored in the data set buffer :

[0047] ;

[0048] ;

[0049] wherein, represents the state matrix, represents the external force matrix, represents the th sample segmented from the trajectory, N represents the length of the sliding window, represents the true value. The pantograph-catenary simulation results include a state matrix, an external force matrix, a mass matrix, a damping matrix, and a stiffness matrix. Since the elements in the external force matrix, mass matrix, damping matrix, and stiffness matrix are all constants, when solving the loss function, only the relevant loss function is determined for the state matrix, and the true value here only uses the state matrix.

[0050] Then, the data in the data set buffer is standardized using the Z-score calculation formula to make the basic dimensions of the initial simulation parameters and the pantograph-catenary simulation results match each other. The specific calculation formula is as follows:

[0051] ;

[0052] wherein, is the original element in the matrix , the subscript represents the row index, and the subscript represents the column index. are the mean and standard deviation of the th row respectively. is the result after standardizing the element in the matrix. After standardizing all the elements in the matrix, is obtained.

[0053] It should be noted that by using finite element simulation based on the initial simulation parameters, the pantograph-catenary simulation results can be obtained, and a sufficient number of training samples can be obtained, and the quality of the training samples meets the training requirements. The pantograph-catenary system is a power transmission system composed of a pantograph and a catenary.

[0054] Step S120, construct the objective loss function of the pantograph-catenary simulation model; wherein, the objective loss function includes a physical loss function, and the physical loss function is used to constrain the output of the pantograph-catenary simulation model to conform to physical laws.

[0055] In an embodiment of the present application, the objective loss function can measure the gap between the predicted value and the true value of the pantograph-catenary simulation model, where the predicted value is the pantograph-catenary simulation result obtained by the pantograph-catenary simulation model according to the initial simulation parameters, and the true value is the pantograph-catenary simulation result obtained after finite element simulation according to the initial simulation parameters. After determining the gap between the predicted value and the true value according to the objective loss function, based on the gradient of the loss function, through iterative optimization, the weights of the pantograph-catenary simulation model can be continuously adjusted, and the loss value can be gradually reduced to make the predicted value of the pantograph-catenary simulation model closer to the distribution of the true value.

[0056] In one embodiment, the pantograph-catenary simulation results include a state matrix, an external force matrix, a mass matrix, a damping matrix, and a stiffness matrix, and the physical loss function can include:

[0057] ;

[0058] Wherein, is the loss value of the physical loss function, is the total number of training samples, represents the th training sample, is the physical loss term, , represents the state matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample, represents the external force matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample, represents the mass matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample, represents the damping matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample, represents the stiffness matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample

[0059] In one embodiment, the basic loss function is the mean square error loss function. The mean square error loss function can include:

[0060] ;

[0061] Wherein, is the loss value of the basic loss function, the state matrix of the pantograph-catenary simulation result in the

[0062] In one embodiment, the process of constructing the objective loss function of the pantograph-catenary simulation model may include: determining the weighted sum of the basic loss function and the physical loss function as the objective loss function; or, determining the sum of the basic loss function and the physical loss function as the objective loss function.

[0063] In a possible implementation manner, the objective loss function may include:

[0064] ;

[0065] Wherein, is the objective loss function, is the first weight corresponding to the basic loss function, is the second weight corresponding to the physical loss function, the first weight and the second weight are in a mutually coupled relationship, and the first weight and the second weight can be set by the operator.

[0066] In another possible implementation manner, the objective loss function may include:

[0067] ;

[0068] Wherein, is the objective loss function.

[0069] In one embodiment, the pantograph-catenary simulation model includes a temporal convolutional network, a long short-term memory network, a multi-head attention mechanism, and a fully connected network connected in sequence.

[0070] It should be noted that by constructing the physical loss function, the interpretability, generalization ability, and prediction accuracy of the model can be enhanced by using physical constraints. The physical constraints can also limit the output within a reasonable physical range and reduce the propagation of uncertainty.

[0071] Exemplarily, the pantograph-catenary simulation model may include:

[0072] 1. The Temporal Convolutional Network (TCN) consists of causal convolution, dilated convolution, and residual blocks. The specific functions of each layer are as follows:

[0073] Causal convolution: Ensure that the output at time Sum filter , the causal convolution is defined as follows:

[0074] ;

[0075] where, represents the convolution kernel size, represents the convolution function.

[0076] Dilated convolution: Expand the receptive field by the dilation factor to extract local features in a long sequence. For the dilated convolution operation of the th layer it is defined as follows:

[0077] ;

[0078] ;

[0079] where, is the output sequence, represents the dilation factor of the th layer, represents the convolution kernel size, represents the past convolution direction, is obtained from the previous causal convolution.

[0080] Residual block: Add the transformation to the identity mapping of the input of the residual block to avoid the vanishing gradient or exploding gradient problem in deep networks, making the network training more stable. It consists of two layers of dilated causal convolution, weight normalization, spatial dropout, and ReLU activation function. The specific formula is as follows:

[0081] ;

[0082] where, represents the original input sequence, represents the features obtained through dilated convolution, represents the output of the residual block.

[0083] 2. Long short-term memory network (LSTM), captures long-term dependencies through a gating mechanism, consists of a forget gate, an input gate, an output gate, and a candidate state, and at the same time introduces a dropout layer to prevent overfitting of the results. The specific formula is as follows:

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] ;

[0089] ;

[0090] Among them, , , , and respectively represent the forget gate, input gate, output gate, cell state, and candidate cell state at the is an element in the matrix, which represents the long-term dependencies captured by the long short-term memory network. and respectively represent the weight matrix and bias vector corresponding to the gate, and respectively represent the sigmoid function and the hyperbolic tangent function, represents the vector dot product.

[0091] 3. The multi-head attention mechanism, which establishes long-range dependencies by calculating the correlations between different positions in the input sequence, consists of multiple parallel self-attention heads. Each self-attention head independently calculates the attention weights and generates an output, as shown in the following formula:

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] Among them, , and respectively represent the query matrix, key matrix, and value matrix. respectively represent the corresponding learned weight matrices, represents the number of attention heads, represents the dimension of each head, It represents the result output by the multi-head attention mechanism.

[0099] 4. The fully connected network (TNN) is located at the output layer of the network, and multiple TNNs together form a multi-layer perceptron (MLP). The TNN is responsible for mapping the result output by the multi-head attention mechanism to the final pantograph-catenary simulation result , which is defined as follows:

[0100] ;

[0101] where , and respectively represent the weight matrix, bias vector and Relu activation function in the fully connected layer.

[0102] Step S130: Train the pantograph-catenary simulation model according to the training sample set, and obtain the trained pantograph-catenary simulation model by minimizing the objective loss function.

[0103] In an embodiment of the present application, the pantograph-catenary simulation model can be used to obtain the pantograph-catenary simulation result according to the input initial simulation parameters. After obtaining the trained pantograph-catenary simulation model, if it is necessary to simulate the pantograph-catenary system, the initial simulation parameters can be input into the pantograph-catenary simulation model, and the pantograph-catenary simulation model will show the pantograph-catenary simulation result. Compared with the related technology of using the finite element method to simulate the pantograph-catenary system, using the pantograph-catenary simulation model obtained in the embodiment of the present application can reduce the simulation calculation amount, and there is no need to perform finite element simulation modeling on the pantograph-catenary system, which can reduce the complexity of the simulation.

[0104] Exemplarily, the training process may include:

[0105] Sampling a batch of data from the data set buffer to train the pantograph-catenary simulation model. The specific training process is as follows:

[0106] 1. Performing local feature extraction on the input sequence through a temporal convolutional network to obtain .

[0107] 2. Inputting into a long short-term memory network to capture long-term dependencies .

[0108] 3. Introducing a multi-head attention mechanism to the long-term dependencies captured by the long short-term memory network to generate .

[0109] 4. Inputting into the fully connected network to map it to the final dynamic response output .

[0110] 5. Based on After calculating the loss function, the pantograph-catenary simulation model is repeatedly trained using the target loss function, the Adam optimizer, and the gradient descent algorithm until the results meet the expectations.

[0111] Exemplarily, please refer to Figure 2 , which is a schematic structural diagram of the pantograph-catenary simulation model shown in an exemplary embodiment of the present application. Taking the Beijing-Tianjin-Hebei line of the high-speed rail line as an example, the finite element simulation method and the supplier simulation model provided by the embodiments of the present application are used to perform finite element simulations on the Beijing-Tianjin-Hebei line of the high-speed rail line respectively. Figure 3 And Figure 4 is a comparison between the method provided by the embodiments of the present application and the traditional method (finite element method). It can be seen that as the time step progresses, the displacements of the pantograph in the x, y, and z directions predicted by the pantograph-catenary simulation model and the rates of change of the displacements in the x, y, and z directions are all very consistent with the actual results calculated by the finite element simulation model, indicating that the pantograph-catenary simulation model can accurately predict the dynamic response. In Figure 3 , the horizontal axis represents the time step, and the vertical axis represents the displacement, with the unit of m. In Figure 4 , the horizontal axis represents the time step, and the vertical axis represents the prediction error, with the unit of m.

[0112] To fully compare the prediction results of FE (blue line) and the prediction results of the pantograph-catenary simulation model (red line), as Figure 5 shown, which gives the frequency domain characteristics of the node dynamic response. In Figure 5 , the horizontal axis represents the frequency, with the unit of HZ, and the vertical axis represents the amplitude. Through Figure 5 the following conclusions can be drawn:

[0113] I. The degree-of-freedom displacements of the catenary nodes indicate that the prediction results of the pantograph-catenary simulation model are very consistent with the actual results calculated by FE in terms of amplitude and shape, indicating that the pantograph-catenary simulation model can accurately predict the dynamic response.

[0114] II. Although the vibration trends of the dynamic response curves are the same, the high-frequency components are slightly different. Figure 5 The frequency domain diagrams in

[0115] In addition, another advantage of the pantograph-catenary simulation model is that both its calculation speed and the complexity of solving have been greatly optimized. In this example, it takes 100 minutes to perform the interactive simulation of a 1-kilometer catenary pantograph system using the finite element simulation model in MATLAB, while it only takes about 10 seconds to perform the simulation using the pantograph-catenary simulation model. The experimental results show that the established pantograph-catenary simulation model can quickly and accurately perform the finite element dynamics simulation of the catenary pantograph system.

[0116] Through the above example, it is illustrated that the pantograph-catenary simulation model provided by the embodiments of the present application improves the previous simulation method and increases the calculation speed of the model, which has practical significance for the further development of high-speed railways.

[0117] Figure 6 It is a flowchart of the pantograph-catenary system simulation method shown in an exemplary embodiment of the present application. Refer to Figure 6 It can be seen that the pantograph-catenary system simulation method may include:

[0118] Step S610, obtaining the current simulation parameters of the pantograph-catenary system.

[0119] In an embodiment of the present application, the current simulation parameters may include parameters such as span and tension.

[0120] Step S620, determining the current pantograph-catenary simulation result according to the current simulation parameters and the pantograph-catenary simulation model. Among them, the pantograph-catenary simulation model is trained based on the Figure 1 method in the embodiment.

[0121] Figure 7 It is a block diagram of the model training device shown in an exemplary embodiment of the present application. As Figure 7 shown, the exemplary model training device 700 includes:

[0122] A training data acquisition module 710, configured to acquire a training sample set; the training sample set includes a plurality of training samples, and each training sample includes initial simulation parameters and the pantograph-catenary simulation result obtained by performing finite element simulation on the pantograph-catenary system according to the initial simulation parameters.

[0123] A loss function construction module 720, configured to construct an objective loss function for the pantograph-catenary simulation model; the objective loss function includes a physical loss function, and the physical loss function is used to constrain the output of the pantograph-catenary simulation model to conform to physical laws.

[0124] A model training module 730, configured to train the pantograph-catenary simulation model according to the training sample set, and obtain the trained pantograph-catenary simulation model by minimizing the objective loss function.

[0125] Figure 8 It is a block diagram of the pantograph-catenary system simulation device shown in an exemplary embodiment of the present application. AsFigure 8 As shown in the figure, the exemplary pantograph-catenary system simulation device 800 includes:

[0126] A current data acquisition module 810, configured to acquire the current simulation parameters of the pantograph-catenary system.

[0127] It should be noted that the power system composed of a pantograph and a catenary is called a pantograph-catenary system.

[0128] A result determination module 820, configured to determine the current pantograph-catenary simulation result according to the current simulation parameters and the pantograph-catenary simulation model. Among them, the pantograph-catenary simulation model is trained based on Figure 1 the method in the embodiment.

[0129] An embodiment of the present application also provides an electronic device, including: one or more processors; a storage device, configured to store one or more programs, when the one or more programs are executed by the one or more processors, enabling the electronic device to implement the methods provided in the above various embodiments.

[0130] On the other hand, the present application also provides a computer-readable storage medium, on which a computer program is stored, when the computer program is executed by a processor of the computer, enabling the computer to execute the methods provided in the above various embodiments. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist alone without being assembled into the electronic device.

[0131] On the other hand, the present application also provides a computer program product or a computer program, the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, enabling the computer device to execute the methods provided in the above various embodiments.

[0132] In the embodiments of the present application, the terms "first" and "second" are only used for descriptive purposes, and cannot be construed as indicating or implying relative importance. The terms "comprising" and "including" mentioned throughout the specification and claims are open-ended terms, and should be interpreted as "including but not limited to".

[0133] The above embodiments only exemplarily illustrate the principles and effects of the present application, rather than limiting the present application. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed in the present application should still be covered by the claims of the present application.

Claims

1. A model training method, characterized in that, Including: Obtain a training sample set; The training sample set includes multiple training samples. A training sample includes input features and a sample label. The input features include initial simulation parameters and pantograph-catenary simulation results at multiple consecutive time steps after performing finite element simulation on the pantograph-catenary system. The sample label includes the pantograph-catenary simulation results at the next time step after multiple consecutive time steps; Construct an objective loss function for the pantograph-catenary simulation model; The objective loss function includes a physical loss function, and the physical loss function is used to constrain the output of the pantograph-catenary simulation model to conform to physical laws; Train the pantograph-catenary simulation model according to the training sample set, and obtain the trained pantograph-catenary simulation model by minimizing the objective loss function; The pantograph-catenary simulation results include a state matrix, an external force matrix, a mass matrix, a damping matrix, and a stiffness matrix. The physical loss function includes: ； Among them, is the loss value of the physical loss function, is the total number of training samples, represents the th training sample, is the physical loss term, , represents the state matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample, represents the external force matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample, represents the mass matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample, represents the damping matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample, represents the stiffness matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample; The pantograph-catenary simulation model includes a time convolutional network, a long short-term memory network, a multi-head attention mechanism, and a fully connected network connected in sequence.

2. The model training method according to claim 1, wherein Construct an objective loss function for the pantograph-catenary simulation model, including: Determine the weighted sum of the basic loss function and the physical loss function as the objective loss function.

3. The model training method according to claim 1, wherein Construct an objective loss function for the pantograph-catenary simulation model, including: Determine the sum of the basic loss function and the physical loss function as the objective loss function.

4. The model training method according to claim 2 or 3, characterized in that, The basic loss function is the mean square error loss function.

5. A pantograph-catenary system simulation method, characterized in that, Including: Obtain the current simulation parameters of the pantograph-catenary system; Determine the current pantograph-catenary simulation results according to the current simulation parameters and the pantograph-catenary simulation model; wherein, the pantograph-catenary simulation model is trained by the method described in any one of claims 1-4.

6. A model training device, characterized in that, Including: A training data acquisition module, used to obtain a training sample set; The training sample set includes multiple training samples. A training sample includes initial simulation parameters and pantograph-catenary simulation results obtained after performing finite element simulation on the pantograph-catenary system according to the initial simulation parameters; A loss function construction module, used to construct an objective loss function for the pantograph-catenary simulation model; The objective loss function includes a physical loss function, and the physical loss function is used to constrain the output of the pantograph-catenary simulation model to conform to physical laws; A model training module, used to train the pantograph-catenary simulation model according to the training sample set, and obtain the trained pantograph-catenary simulation model by minimizing the objective loss function; The pantograph-catenary simulation results include a state matrix, an external force matrix, a mass matrix, a damping matrix, and a stiffness matrix. The physical loss function includes: ； Among them, is the loss value of the physical loss function, is the total number of training samples, represents the th training sample, is the physical loss term, , represents the state matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample, represents the external force matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample, represents the mass matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample, represents the damping matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample, represents the stiffness matrix obtained according to the pantograph-catenary simulation model and the input features of the th training sample; The pantograph-catenary simulation model includes a time convolutional network, a long short-term memory network, a multi-head attention mechanism, and a fully connected network connected in sequence.

7. An pantograph-catenary system simulation device, characterized in that, Including: A current data acquisition module, used to obtain the current simulation parameters of the pantograph-catenary system; A result determination module, used to determine the current pantograph-catenary simulation results according to the current simulation parameters and the pantograph-catenary simulation model; wherein, the pantograph-catenary simulation model is trained by the method described in any one of claims 1-4.

8. A computer-readable storage medium, characterized in that, A computer program is stored thereon. When the computer program is executed by a processor of the computer, the computer is made to execute the method described in any one of claims 1 to 4.

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