CN115310227B - Gear power transmission digital twin model construction method - Google Patents
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Abstract
The invention discloses a method for constructing a gear power transmission digital twin model, which comprises the following steps: step S1: constructing a three-dimensional model of the target gear, and acquiring material properties and working parameters of the target gear; step S2: carrying out multiple finite element simulations by utilizing a three-dimensional model of the target gear in combination with the requirement of the gear performance index, randomly adjusting a grid division scheme in each simulation, randomly selecting working parameters as working condition parameters of the current simulation, and obtaining gear simulation data; step S3: utilizing the gear simulation data of finite element simulation to solve the differential increment of the gear state in time, and constructing a gear dynamic propagation digital twin model; step S4: the parameters of the gear power transmission digital twin model are optimized through the random gradient optimizer, so that the loss is reduced to be within a set threshold value, and the final gear power transmission digital twin model is obtained, can be applied to gear shaping, and can achieve the technical purpose of quickly and accurately shaping the gear in a design stage.
Description
Technical Field
The invention belongs to the technical field of gear design, and particularly relates to a method for constructing a digital twin model for gear power transmission.
Background
The transmission gear of the electric drive system of the new energy automobile has high integrated and optimized design difficulty, high power density performance requirement, stricter NVH (noise, vibration and harshness) index requirement and brings new challenges to the shape modification design of the gear. The traditional gear shaping method is mainly used for carrying out gear shaping in a finite element simulation and experience auxiliary iterative optimization mode, and the method is limited by slow simulation calculation speed, deviation of simulation calculation precision and repeated iteration of the shaping process caused by the defect of a systematic shaping theory method, so that the shaping period is long, the shaping effect is difficult to ensure in a design stage, the targeted shaping can be carried out only aiming at a single performance index of a gear, the coordinated shaping of multiple performance indexes of the gear is difficult to be carried out, and the shaping design requirement corresponding to the performance index of the gear of the current new energy automobile electric drive system cannot be met. The main reasons for the defects of the conventional gear shaping method are concentrated at two points:
1. the complex dynamics partial differential equation in the real running process of the gear is highly nonlinear, an analytical solution cannot be obtained, and the numerical simulation calculation speed is low;
2. the gear shaping quantity and the final target performance index do not have differential relation, and in the design stage, only a relatively good shaping quantity can be found through a plurality of simulations by means of discrete optimization, so that the process is very time-consuming and is not suitable for fine gear shaping design required by a new energy automobile electric drive system.
Disclosure of Invention
In view of the above, the invention aims to provide a method for constructing a gear power transmission digital twin model, and the constructed gear power transmission digital twin model can be applied to gear shaping and can realize the technical purpose of quickly and accurately shaping gears in a design stage.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method for constructing a gear power transmission digital twin model comprises the following steps:
step S1: constructing a three-dimensional model of the target gear, and acquiring material properties and working parameters of the target gear;
step S2: carrying out multiple finite element simulations by utilizing a three-dimensional model of the target gear in combination with the requirement of the gear performance index, randomly adjusting a grid division scheme in each simulation, randomly selecting working parameters as working condition parameters of the current simulation, and obtaining gear simulation data;
step S3: solving the differential increment of the gear state in time by utilizing the gear simulation data of one-time finite element simulation, thereby constructing a gear dynamic propagation digital twin model;
step S4: inputting the gear simulation data into the gear dynamic propagation digital twin model in a random and multi-batch manner to perform forward propagation calculation, comparing the data with data corresponding to a finite element simulation process, and taking the difference value of the data and the data as loss; and optimizing parameters of the gear dynamic propagation digital twin model through a random gradient optimizer to reduce loss to be within a set threshold value, so as to obtain a final gear dynamic propagation digital twin model.
Further, in the step S1, the operating parameters include an operating interval of power, rotation speed, and load.
Further, in the step S2, the gear simulation data acquisition method is as follows:
21 Saving the node displacement u of each time step node i in each simulation i Node speed v i Node acceleration a i And node force f i And combined into a node state vector x i ；
22 Combining all node state vectors to obtain a gear state matrix of the kth simulation time step
Further, the process data of all time steps of each simulation are obtained as +.>
Wherein K is the total time step number;
23 The final performance index obtained by each simulation is P, P is a fixed vector, and the time t is represented 0 ～t K Each performance index of the middle gear, the simulation data of the gear obtained by the mth simulation is that
All M simulations get gear simulation dataset +.>
Further, in the step S3, the method for solving the differential increment of the gear state in time is as follows:
31 Using a power information encoding network to encode the state vector x of each node i of the gear i Coded to hidden space vector h i ；
32 According to hidden space vector h of all nodes i Constructing a node map G' at an original coordinate position on the gear;
33 A symmetrical normalized Laplacian matrix of a node diagram G' is constructed, the diagram G is sent into a power information forward propagation network, the action rule of a gear under a complex power environment is simulated, and G is output M ；
34 Obtaining differential representation of node state vector at current time by using power information decoding network
Differentiating the node state vector +.>
Node state matrix added to the last moment +.>
And updating node state matrix information according to boundary conditions of the digital twin body to obtain a gear power transmission digital twin model, wherein the gear power transmission digital twin model comprises the following steps:
wherein ,
representing a gear digital model at t k+1 A state matrix of time; t (T) mask and Fmask Are mask operators, and: t (T) mask All coefficients corresponding to boundary condition points in the operator matrix are 0, and coefficients corresponding to non-boundary condition nodes are 1; />
Is T mask Binary negation of F mask The operator represents the amount of change caused by the gear constrained by the external operating condition setting.
Further, in the step 31), the state vector x of each node i of the gear is encoded by using the power information encoding network i Coded to hidden space vector h i The method of (1) is as follows:
311 Randomly sampling nodes from a three-dimensional model of the target gear, and selecting a gear state matrix corresponding to the current working condition and time from a gear simulation data set D'
By means of->
Obtaining the state vector x of each sampling node i by linear or higher-order interpolation i ；
312 To each state vector x) i All are sent into the same shared dynamic information coding network MLP and output hidden space vector h i Hidden space vector h i The original coordinate position of the sampling node i on the gear is recorded.
Further, in the step 32), the node map G' is constructed by:
321 According to the actual geometric shape of the target gear, the distance r is specified, and for each node i, all nodes j in the three-dimensional sphere with i as the sphere center and r as the radius are traversed, if pi p i -p j || 2 ＜r(||·|| 2 2-norm), then create the edges of node i and node j, denoted as e ij ，e ij The value of (2) is
Wherein, gamma is a coefficient; p is p i A position coordinate vector representing node i; p is p j A position coordinate vector representing a node j;
322 Step 321) is circularly executed until all nodes i and adjacent nodes j thereof are traversed, and a node graph G' is constructed.
Further, in the step 33), the method for constructing the symmetric normalized laplace matrix of the node graph G' includes:
331 Through e) ij Constructing an adjacent matrix A of a node diagram G', wherein the adjacent matrix A is an N multiplied by N square matrix, square matrix elements represent the spatial metric relation of two corresponding nodes, and if two nodes i and j are adjacent, a is calculated ij ＝e ij Otherwise a ij ＝0，a ij For contiguous matrix elements, i.e. a ij ∈A；
332 Symmetric normalized laplace matrix L for constructing node map G sys The calculation formula is as follows:
L sys ＝D -1/2 LD -1/2
wherein D is the degree matrix of the graph G ', and L is the Laplacian matrix of the graph G';
the laplace matrix can be found from the adjacency matrix: l=d-a.
Further, in the step 33), the method for simulating the action rule of the gear in the complex power environment by using the power information forward propagation network includes:
333 The dynamic information propagation network consists of an M-layer GN network based on a graph neural network, wherein the M-layer network is GN m The method comprises the steps of carrying out a first treatment on the surface of the Normalized Laplace matrix L due to the symmetry of graph G sys Is a symmetric array, so GN network is first to L sys And (3) performing characteristic decomposition:
wherein U is L sys Eigenvalue matrix, column vector in U is L sys Is a feature vector of (1), which belongs to the node vector space
λ 1 ～λ n Represents L sys Is a characteristic value of (2);
symmetric normalized Laplace matrix L sys Is:
since the input features are a graph structure with k channels, a set of filter parameters g is designed for each channel θ (Λ)：
wherein ,
is a parameter of a graph filter;
334 Respectively convolving each channel of the multi-channel diagram to obtain a feature matrix after convolution
Carrying out characteristic linear transformation on each channel of nodes in the graph obtained after convolution, wherein a characteristic transformation matrix is +.>
And obtaining output data ++through nonlinear activation function ReLu based on element level>
Namely:
335 Processing inter-node dynamics propagation process and gear internal dynamics propagation process of inter-tooth meshing respectively by utilizing dynamic information forward propagation network, and finally outputting graph G M 。
The invention has the beneficial effects that:
according to the method for constructing the gear power transmission digital twin model, provided by the invention, the three-dimensional model and the working parameters of the target gear are combined, the gear simulation data of the gear operation history or real-time state is utilized by multiple times of finite element simulation, so that potential implicit dynamic representation of the gear can be learned from the gear operation real data, the differential increment of the gear state in time is obtained, the gear power transmission digital twin model can be constructed by utilizing the differential increment, the parameters of the gear power transmission digital twin model are optimized by utilizing the multiple times of finite element simulation data, the simulation precision of the gear power transmission digital twin model is enabled to continuously approximate to the precision of finite element simulation calculation, the gear power transmission digital twin model obtained after optimization can be applied to gear shaping, the gear shaping can be optimized in a design stage, the design time of the gear is shortened, the design capacity and quality are greatly improved, and the gear power transmission digital twin model has practical significance and good application prospect.
Drawings
In order to make the objects, technical solutions and advantageous effects of the present invention more clear, the present invention provides the following drawings for description:
FIG. 1 is a block diagram of a gear power transmission digital twin model constructed by the method of the invention;
FIG. 2 is a schematic diagram of a power information encoder network;
FIG. 3 is a schematic diagram of a node map construction process;
FIG. 4 is a single-layer GN network block diagram in a power information forward propagation network;
fig. 5 is a process diagram of modifying a gear using a digital twin model of gear power transmission.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and specific examples, which are not intended to limit the invention, so that those skilled in the art may better understand the invention and practice it.
The method for constructing the gear power transmission digital twin model of the embodiment comprises the following steps:
s1: and constructing a three-dimensional model of the target gear, and obtaining the material properties and the working parameters of the target gear. The operating parameters include power, rotational speed and the operating interval of the load.
S2: in combination with the requirement of the gear performance index, the three-dimensional model of the target gear is utilized to carry out multiple times of finite element simulation, and in particular, more than 50 times of finite element simulation are generally carried out. Each simulation is carried out on the grid division scheme, working parameters are randomly selected to serve as working condition parameters of the current simulation, namely, the working parameters in the working range of the power, the rotating speed and the load of the gear are randomly selected to serve as the working condition parameters of the current simulation, and the gear simulation data are obtained. In this embodiment, the method for acquiring the gear simulation data is as follows:
21 Saving the node displacement u of each time step node i in each simulation i Node speed v i Node acceleration a i And node force f i And combined into a node state vector x i ；
22 Combining all node state vectors to obtain a gear state matrix of the kth simulation time step
Further, the process data of all time steps of each simulation are obtained as +.>
Wherein K is the total time step number;
23 The final performance index obtained by each simulation is P, P is a fixed vector, and the time t is represented 0 ～t K Each performance index of the middle gear, the simulation data of the gear obtained by the mth simulation is that
All M simulations get gear simulation dataset +.>
S3: solving the differential increment of the gear state in time by using the gear simulation data of one-time finite element simulation so as to construct a gear dynamic propagation digital twin model S θ The constructed gear power transmission digital twin model S θ As shown in fig. 1. Specifically, in the present embodiment, the gear power transmission digital twin model S θ State transition simulator for gears in a single time step, i.e. S θ Gear is set at t k State transition to t for time k+1 Time state:
and θ is an optimization parameter to be learned by the state transition simulator. S is S θ At the heart of (a) is a state differential simulator d θ ，d θ A differential increment in time, which characterizes the gear state, where d, due to the setting to a fixed-length time step θ Characterizing differential increments in gear state over time, by state differential simulator d θ I.e. the state transition procedure S can be determined θ . If the boundary condition is not considered, namely: />
State differential simulator d θ The system comprises a power information coding network, a power information forward propagation network and a power information decoding network. Specifically, in this embodiment, the method for solving the differential increment of the gear state in time is as follows:
31 Using a power information encoding network to encode the state vector x of each node i of the gear i Coded to hidden space vector h i . As shown in fig. 2, the specific method is as follows:
311 Randomly sampling nodes from a three-dimensional model of the target gear, and selecting a gear state matrix corresponding to the current working condition and time from a gear simulation data set D'
By means of->
Obtaining the state vector x of each sampling node i by linear or higher-order interpolation i ；
312 To each state vector x) i All send into the same shared dynamic information coding network MLP and output the hidden spaceVector h i Hidden space vector h i The original coordinate position of the sampling node i on the gear is recorded.
32 According to hidden space vector h of all nodes i In the original coordinate position on the gear, a node map G 'is constructed, and the construction process of the node map G' is shown in fig. 3. Specifically, the node map G' is constructed by:
321 According to the actual geometric shape of the target gear, the distance r is specified, and for each node i, all nodes j in the three-dimensional sphere with i as the sphere center and r as the radius are traversed, if pi p i -p j || 2 ＜r(||·|| 2 2-norm), then create the edges of node i and node j, denoted as e ij ，e ij The value of (2) is
Wherein, gamma is a coefficient, and is reasonably set according to the size of an actual gear; p is p i A position coordinate vector representing node i; p is p j A position coordinate vector representing a node j;
322 Step 321) is circularly executed until all nodes i and adjacent nodes j thereof are traversed, and a node graph G' is constructed.
33 A symmetrical normalized Laplacian matrix of a node diagram G' is constructed, the diagram G is sent into a power information forward propagation network, the action rule of a gear under a complex power environment is simulated, and G is output M . Specifically, the construction method of the symmetric normalized laplace matrix of the node graph G' comprises the following steps:
331 Through e) ij Constructing an adjacent matrix A of a node diagram G', wherein the adjacent matrix A is an N multiplied by N square matrix, square matrix elements represent the spatial metric relation of two corresponding nodes, and if two nodes i and j are adjacent, a is calculated ij ＝e ij Otherwise a ij ＝0，a ij For contiguous matrix elements, i.e. a ij ∈A；
332 Symmetric normalized laplace matrix L for constructing node map G sys The calculation formula is as follows:
L sys ＝D -1/2 LD -1/2
wherein D is the degree matrix of the graph G ', and L is the Laplacian matrix of the graph G';
the laplace matrix can be found from the adjacency matrix: l=d-a.
The node diagram G' contains the power information of the current sampling node of the gear and the geometric topology of the gear. And then the graph G' is sent into a power information forward propagation network to simulate the action rule of the gear under the complex power environment. Specifically, the method for simulating the action rule of the gear in the complex power environment by utilizing the power information forward propagation network comprises the following steps:
333 The dynamic information propagation network consists of an M-layer GN network based on a graph neural network, wherein the M-layer network is GN m The multi-layer GN network is connected front and back by adopting a residual structure so as to ensure that gradients in the subsequent training process can be effectively propagated. Specific structure of GN network as shown in fig. 4, the laplace matrix L is normalized due to the symmetry of graph G sys Is a symmetric array, so GN network is first to L sys Feature decomposition (spectral decomposition):
wherein U is L sys Eigenvalue matrix, column vector in U is L sys Is a feature vector of (1), which belongs to the node vector space
λ 1 ～λ n Represents L sys Is a characteristic value of (2); />
Is a feature vector;
symmetric normalized Laplace matrix L sys Is:
since the input features are a graph structure with k channels, a set of filter parameters g is designed for each channel θ (Λ)：
wherein ,
is a parameter of a graph filter;
334 Respectively convolving each channel of the multi-channel diagram to obtain a feature matrix after convolution
Carrying out characteristic linear transformation on each channel of nodes in the graph obtained after convolution, wherein a characteristic transformation matrix is +.>
And obtaining output data ++through nonlinear activation function ReLu based on element level>
Namely:
335 Because the dynamic propagation rule in the gear is different from the dynamic propagation rule in the inter-tooth meshing of the gear, the forward propagation of the dynamic information is two different processing processes, namely the dynamic propagation process only aiming at the inter-node dynamic propagation process of the inter-tooth meshing and the dynamic propagation process of elastic stress in the gear. The forward propagation network of dynamic information is utilized to respectively process the inter-node dynamics propagation process and the internal dynamics propagation process of gears meshed between teeth, and finally a graph G is output M 。
34 Obtaining differential representation of node state vector at current time by using power information decoding network
Differentiating the node state vector +.>
Node state matrix added to the last moment +.>
And updating the node state matrix information according to the boundary conditions of the digital twin body. The boundary condition adopts two mask operators to mask the displacement update of the boundary condition nodes, which are respectively T mask and Fmask Then the gear digital model is at t k+1 The state information of the time is expressed as:
wherein ,
representing a gear digital model at t k+1 A state matrix of time; t (T) mask and Fmask Are mask operators, and: t (T) mask All coefficients corresponding to boundary condition points in the operator matrix are 0, and coefficients corresponding to non-boundary condition nodes are 1; />
Is T mask Binary negation of F mask The operator represents the amount of change caused by the gear constrained by the external operating condition setting.
Obtaining a gear power transmission digital twin model S θ 。
S4: the dynamic propagation digital twin model is distributed and deployed in a multi-GPU computing environment, and gear simulation data are randomly input into the gear dynamic propagation digital twin model S in a plurality of batches θ Forward propagation calculation is carried out, the forward propagation calculation is compared with data corresponding to a finite element simulation process, and the difference value of the forward propagation calculation and the data corresponding to the finite element simulation process is taken as loss; the method comprises the steps of carrying out counter propagation on loss through a counter propagation algorithm, calculating a to-be-optimized parameter pair loss gradient of each layer of a gear power propagation digital twin model, optimizing the parameters of the gear power propagation digital twin model through random gradient optimizers such as Adam and the like to enable the loss to be continuously reduced, enabling simulation precision of the gear power propagation digital twin model to continuously approximate to the precision of finite element simulation calculation until the loss is reduced to be within a set threshold value, optimizing the gear power propagation digital twin model, storing the gear power propagation digital twin model, and obtaining a final gear power propagation digital twin model S θ 。
Specifically, the gear power transmission digital twin model S constructed by adopting the embodiment θ The method of shaping the gear is as follows.
As shown in fig. 5, the gear shaping design method based on digital twin comprises the following steps:
step one: and randomly sampling in a three-dimensional geometric area of the gear to be modified to obtain enough nodes, and constructing a node diagram G. Specifically, the method for constructing the node map G is the same as the method for constructing the node map G' described in step 31) and step 32) in the gear power transmission digital twin model construction method of the present embodiment, and will not be described again.
Step two: and (3) inputting the graph G into a multi-layer graph convolution neural network, adjusting the position of a sampling point by using the multi-layer graph convolution neural network, and outputting the gear after the shape modification.
Step three: inputting the modified gear into a gear power transmission digital twin model S θ The middle step is used for carrying out gear transmission simulation, and a digital twin model S is transmitted by utilizing gear power θ Node diagram of gear after shaping
Performing cyclic iteration to simulate the performance of gear transmission and obtain final performance index +.>
Step four: judging performance index
Whether the target performance index P is reached; if yes, executing a step seven; if not, executing the fifth step.
Step five: performance index to be obtained
And comparing the modified model with the target performance index P, and calculating the difference between the modified model and the target performance index P through the L2-norm to be used as the loss L of the modification optimization.
Step six: counter-propagating the loss value L, and sequentially calculating a gear dynamic propagation digital twin model S θ And the gradient of the graph rolling correction network parameter pair L, and updating the gradient of the multi-layer graph rolling network through a gradient descent algorithm, and executing the step I.
Step seven: and stopping shaping to obtain the repaired gear geometric digital model.
In the gear shaping design method based on digital twin, the geometric deformation of the gear is adjusted through a graph convolution network so as to achieve the purpose of shaping, the gear after shaping is rapidly simulated and the performance indexes of the gear are obtained through a gear transmission digital twin model, gradient tracking and maintaining are achieved in the whole calculation process, the differential relation between the gear shaping quantity and the target performance indexes of the gear is established, the model can rapidly and finely modify various performance indexes of the gear through a gradient descent algorithm, the shaping can be optimized in a design stage, the design time of the gear is shortened, the design capacity and quality are greatly improved, and the gear shaping method has practical significance and good application prospect.
The above-described embodiments are merely preferred embodiments for fully explaining the present invention, and the scope of the present invention is not limited thereto. Equivalent substitutions and modifications will occur to those skilled in the art based on the present invention, and are intended to be within the scope of the present invention. The protection scope of the invention is subject to the claims.
Claims (7)
1. A method for constructing a gear power transmission digital twin model is characterized by comprising the following steps of: the method comprises the following steps:
step S1: constructing a three-dimensional model of the target gear, and acquiring material properties and working parameters of the target gear;
step S2: carrying out multiple finite element simulations by utilizing a three-dimensional model of the target gear in combination with the requirement of the gear performance index, randomly adjusting a grid division scheme in each simulation, randomly selecting working parameters as working condition parameters of the current simulation, and obtaining gear simulation data;
step S3: solving the differential increment of the gear state in time by utilizing the gear simulation data of one-time finite element simulation, thereby constructing a gear dynamic propagation digital twin model;
step S4: inputting the gear simulation data into the gear dynamic propagation digital twin model in a random and multi-batch manner to perform forward propagation calculation, comparing the data with data corresponding to a finite element simulation process, and taking the difference value of the data and the data as loss; optimizing parameters of the gear dynamic propagation digital twin model through a random gradient optimizer to reduce loss to be within a set threshold value, so as to obtain a final gear dynamic propagation digital twin model;
in the step S3, the method for solving the differential increment of the gear state in time is as follows:
31 Using a power information encoding network to encode the state vector x of each node i of the gear i Coded to hidden space vector h i ；
32 According to hidden space vector h of all nodes i Constructing a node map G' at an original coordinate position on the gear;
33 A symmetrical normalized Laplacian matrix of a node diagram G' is constructed, the diagram G is sent into a power information forward propagation network, the action rule of a gear under a complex power environment is simulated, and G is output M ；
34 Obtaining differential representation of node state vector at current time by using power information decoding network
Differentiating the node state vector +.>
Node state matrix added to the last moment +.>
And updating node state matrix information according to boundary conditions of the digital twin body to obtain a gear power transmission digital twin model, wherein the gear power transmission digital twin model comprises the following steps:
wherein ,
representing a gear digital model at t k A state matrix of time; />
Representing a gear digital model at t k+1 A state matrix of time; t (T) mask and Fmask Are mask operators, and: t (T) mask All coefficients corresponding to boundary condition points in the operator matrix are 0, and coefficients corresponding to non-boundary condition nodes are 1; />
Is T mask Binary negation of F mask The operator represents the amount of change caused by the gear constrained by the external operating condition setting.
2. The method for constructing a gear power transmission digital twin model according to claim 1, wherein: in the step S1, the working parameters include a working interval of power, rotation speed and load.
3. The method for constructing a gear power transmission digital twin model according to claim 1, wherein: in the step S2, the gear simulation data acquisition method is as follows:
21 Saving the node displacement u of each time step node i in each simulation i Node speed v i Node acceleration a i And node force f i And combined into a node state vector x i ；
22 Combining all node state vectors to obtain a gear state matrix of the kth simulation time step
Further, the process data of all time steps of each simulation are obtained as +.>
Wherein K is the total time step number;
23 The final performance index obtained by each simulation is P, P is a fixed vector, and the time t is represented 0 ～t K Each performance index of the middle gear, the simulation data of the gear obtained by the mth simulation is that
All M simulations get gear simulation dataset +.>
4. The method for constructing a gear power transmission digital twin model according to claim 1, wherein: in said step 31), the state vector x of each node i of the gear is encoded by the power information encoding network i Coded to hidden space vector h i The method of (1) is as follows:
311 Randomly sampling nodes from a three-dimensional model of the target gear, and selecting a gear state matrix corresponding to the current working condition and time from a gear simulation data set D'
By means of->
Obtaining the state vector x of each sampling node i by linear or higher-order interpolation i ；
312 To each state vector x) i All are sent into the same shared dynamic information coding network MLP and output hidden space vector h i Hidden space vector h i The original coordinate position of the sampling node i on the gear is recorded.
5. The method for constructing the digital twin model for gear power transmission according to claim 4, wherein the method comprises the following steps: in the step 32), the node map G is constructed by:
321 According to the actual geometric shape of the target gear, the distance r is specified, and for each node i, all nodes j in the three-dimensional sphere with i as the sphere center and r as the radius are traversed, if pi p i -p j || 2 ＜r(||·|| 2 2-norm), then create the edges of node i and node j, denoted as e ij ，e ij The value of (2) is
Wherein, gamma is a coefficient; p is p i A position coordinate vector representing node i; p is p j A position coordinate vector representing a node j;
322 Step 321) is circularly executed until all nodes i and adjacent nodes j thereof are traversed, and a node diagram G is constructed.
6. The method for constructing the digital twin model for gear power transmission according to claim 5, wherein the method comprises the following steps: in the step 33), the method for constructing the symmetric normalized laplace matrix of the node graph G is as follows:
331 Through e) ij Constructing an adjacent matrix A of a node diagram G, wherein the adjacent matrix A is an N multiplied by N square matrix, square matrix elements represent the spatial metric relation of two corresponding nodes, and if two nodes i and j are adjacent, a is calculated ij ＝e ij Otherwise a ij ＝0，a ij For contiguous matrix elements, i.e. a ij ∈A；
332 Symmetric normalized laplacian matrix L for constructing node map G sys The calculation formula is as follows:
L sys ＝D -1/2 LD -1/2
wherein D is the degree matrix of the graph G, and L is the Laplacian matrix of the graph G;
the laplace matrix can be found from the adjacency matrix: l=d-a.
7. The method for constructing the digital twin model for gear power transmission according to claim 6, wherein: in the step 33), the method for simulating the action rule of the gear in the complex power environment by utilizing the power information forward propagation network comprises the following steps:
333 The dynamic information propagation network consists of an M-layer GN network based on a graph neural network, wherein the M-layer network is GN m The method comprises the steps of carrying out a first treatment on the surface of the Normalized Laplace matrix L due to the symmetry of graph G sys Is a symmetric array, so GN network is first to L sys And (3) performing characteristic decomposition:
wherein U is L sys Eigenvalue matrix, column vector in U is L sys Is a feature vector of (1), which belongs to the node vector space
λ 1 ～λ n Represents L sys Is a characteristic value of (2);
symmetric normalized Laplace matrix L sys Is:
due to input featuresIs a graph structure with k channels, for each of which a set of filter parameters g is designed θ (Λ)：
wherein ,
is a parameter of a graph filter;
334 Respectively convolving each channel of the multi-channel diagram to obtain a feature matrix after convolution
Carrying out characteristic linear transformation on each channel of nodes in the graph obtained after convolution, wherein a characteristic transformation matrix is +.>
And obtaining output data ++through nonlinear activation function ReLu based on element level>
Namely:
335 Processing inter-node dynamics propagation process and gear internal dynamics propagation process of inter-tooth meshing respectively by utilizing dynamic information forward propagation network, and finally outputting graph G M 。
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