CN117195491A - Cable structure model determination method, device, computer equipment and storage medium - Google Patents
Cable structure model determination method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a method, a device, computer equipment and a storage medium for determining a cable structure model. The method comprises the following steps: acquiring each piece of structure information of a cable, and establishing a structure model of the cable based on each piece of structure information; identifying density information of each piece of structure information corresponding to the structure model, and identifying density error data of each piece of structure information based on the density information of each piece of structure information and a density error analysis strategy; based on the density error data of the structural information and the structural model, an intelligent mesh subdivision model is constructed, mesh subdivision processing is carried out on the structural information based on the intelligent mesh subdivision model, and a mesh subdivision result of nonuniform structural information is obtained; and establishing a cable structure model of the cable based on mesh subdivision results of the structural information. By adopting the method, the simulation accuracy of the cable structure model can be improved.
Description
Technical Field
The present application relates to the field of power cable technologies, and in particular, to a method and apparatus for determining a cable structural model, a computer device, and a storage medium.
Background
With the rapid increase of the demand of cities for cable loads, the voltage class of power cables is also continuously increased. Simulation experiments for large amplitude power cable structures are very difficult and costly, and therefore computer simulation calculations become an important aid. In the cable simulation process, a cable structure model needs to be built to study the current transmission characteristics of each structural information of the cable, so that the difficulty of a cable simulation experiment is reduced, and therefore, how to build the cable structure model is the current research focus.
The traditional cable structure model construction method is to adopt a shape-oriented mesh subdivision method for the accuracy of the physical structure and physical principle of the cable to obtain the structure model of the power cable. However, the mesh subdivision is equal subdivision, and especially for a multi-layer structure such as a cable, the structure with different thickness and different materials of each layer is equal subdivision, so that the simulation accuracy of the cable structure model is lower.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, apparatus, computer device, computer readable storage medium, and computer program product for determining a cable structural model.
In a first aspect, the present application provides a method for determining a cable structural model. The method comprises the following steps:
acquiring each piece of structure information of a cable, and establishing a structure model of the cable based on each piece of structure information;
identifying density information of each piece of structure information corresponding to the structure model, and identifying density error data of each piece of structure information based on the density information of each piece of structure information and a density error analysis strategy;
based on the density error data of the structural information and the structural model, an intelligent mesh subdivision model is constructed, mesh subdivision processing is carried out on the structural information based on the intelligent mesh subdivision model, and a mesh subdivision result of nonuniform structural information is obtained;
and establishing a cable structure model of the cable based on mesh subdivision results of the structural information.
Optionally, the building a structural model of the cable based on each piece of structural information includes:
identifying the structure attribute of each piece of structure information and the structure parameter of each piece of structure information, and carrying out triangulation processing on each piece of structure information to obtain a uniform mesh subdivision result of each piece of structure information;
And establishing a structural model of the cable based on the mesh subdivision result with uniform structural information.
Optionally, the identifying the density information of each piece of structural information corresponding to the structural model includes:
randomly screening target structure information in each structure information in the structure model, and identifying the grid number corresponding to a grid subdivision result of the target structure information, geometric data of grids in the target structure information and geometric data of the target structure information;
calculating the geometric proportion of the grid in the target structure information based on the geometric data of the target structure information and the geometric data of the grid in the target structure information;
and calculating simulation density corresponding to the target structure information based on the geometric proportion of the grid in the target structure information and the grid number corresponding to the grid subdivision result of the target structure information, and taking the simulation density as density information of each structure information corresponding to the structure model.
Optionally, the identifying the density error data of each of the structural information based on the density information of each of the structural information and the density error analysis policy includes:
Identifying structural model parameters of each piece of structural information in the structural model, and carrying out high-density structure subdivision processing on each piece of structural information based on a high-density solving network and the structural model parameters of each piece of structural information to obtain a sample high-density mesh subdivision result with uniform structural information;
based on the uniform mesh division result of each piece of structure information and a linear interpolation algorithm, projecting the mesh division result of each piece of structure information to the high-density solving network to obtain a high-density mesh division result of each piece of structure information, and calculating an error value between the sample high-density mesh division result of each piece of structure information and the high-density mesh division result of each piece of structure information;
and projecting each error value to a low-density solving network to obtain density error data of each structural information.
Optionally, the constructing an intelligent mesh subdivision model based on the density error data of each piece of structural information and the structural model includes:
and training subdivision parameters in an initial intelligent meshing model based on the meshing structure with uniform structure information in the structural model and density error data of the structure information to obtain the intelligent meshing model.
Optionally, the performing mesh division processing on each piece of structural information based on the intelligent mesh division model to obtain a mesh division result of non-uniform structural information includes:
acquiring geometric data of each piece of structural information, boundary data of each piece of structural information and material properties of each piece of structural information, and constructing each low-density structural subdivision grid of the structural information based on the geometric data of the structural information, the boundary data of the structural information and the material properties of the structural information for each piece of structural information;
predicting, by the intelligent meshing model, an upper limit of a mesh area of an adjacent low-density structural meshing mesh of the low-density structural meshing network based on material properties of the structural information for each low-density structural meshing network, and identifying the mesh area of the adjacent low-density structural meshing mesh of the low-density structural meshing network;
and under the condition that the grid area is larger than the grid area upper limit, performing grid subdivision processing on the adjacent low-density structure subdivision grids of the low-density structure subdivision network again based on the area upper limit of the adjacent low-density structure subdivision grids of the low-density structure subdivision network, and returning to execute grid area upper limit steps of predicting the adjacent low-density structure subdivision grids of the low-density structure subdivision network based on the material attribute of the structural information for each low-density structure subdivision network until all grid areas are not larger than the grid area upper limit corresponding to each grid area, and obtaining the grid subdivision result with nonuniform structural information.
Optionally, the method further comprises:
acquiring image data of each piece of structure information of the cable, and converting a mesh subdivision result of each piece of structure information into subdivision image data of each piece of structure information;
analyzing average deviation values between the image data of each piece of structure information and the split image data of each piece of structure information through an image feature recognition network, and returning to execute the steps of constructing an intelligent mesh split model based on the density error data of each piece of structure information and the structure model under the condition that the average deviation values are larger than a deviation threshold value until the average deviation values are not larger than the deviation threshold value;
and taking the mesh subdivision result of subdivision image data corresponding to the average deviation value which is not more than the deviation threshold value as the optimized mesh subdivision result with nonuniform structural information.
In a second aspect, the application further provides a device for determining the cable structure model. The device comprises:
the acquisition module is used for acquiring each piece of structural information of the cable and establishing a structural model of the cable based on each piece of structural information;
the identification module is used for identifying the density information of each piece of structure information corresponding to the structure model and identifying the density error data of each piece of structure information based on the density information of each piece of structure information and a density error analysis strategy;
The processing module is used for constructing an intelligent mesh subdivision model based on the density error data of the structural information and the structural model, and performing mesh subdivision processing on the structural information based on the intelligent mesh subdivision model to obtain a mesh subdivision result of non-uniformity of the structural information;
the building module is used for building a cable structure model of the cable based on mesh subdivision results of the structural information.
Optionally, the acquiring module is specifically configured to:
identifying the structure attribute of each piece of structure information and the structure parameter of each piece of structure information, and carrying out triangulation processing on each piece of structure information to obtain a uniform mesh subdivision result of each piece of structure information;
and establishing a structural model of the cable based on the mesh subdivision result with uniform structural information.
Optionally, the identification module is specifically configured to:
randomly screening target structure information in each structure information in the structure model, and identifying the grid number corresponding to a grid subdivision result of the target structure information, geometric data of grids in the target structure information and geometric data of the target structure information;
Calculating the geometric proportion of the grid in the target structure information based on the geometric data of the target structure information and the geometric data of the grid in the target structure information;
and calculating simulation density corresponding to the target structure information based on the geometric proportion of the grid in the target structure information and the grid number corresponding to the grid subdivision result of the target structure information, and taking the simulation density as density information of each structure information corresponding to the structure model.
Optionally, the identification module is specifically configured to:
identifying structural model parameters of each piece of structural information in the structural model, and carrying out high-density structure subdivision processing on each piece of structural information based on a high-density solving network and the structural model parameters of each piece of structural information to obtain a sample high-density mesh subdivision result with uniform structural information;
based on the uniform mesh division result of each piece of structure information and a linear interpolation algorithm, projecting the mesh division result of each piece of structure information to the high-density solving network to obtain a high-density mesh division result of each piece of structure information, and calculating an error value between the sample high-density mesh division result of each piece of structure information and the high-density mesh division result of each piece of structure information;
And projecting each error value to a low-density solving network to obtain density error data of each structural information.
Optionally, the processing module is specifically configured to:
and training subdivision parameters in an initial intelligent meshing model based on the meshing structure with uniform structure information in the structural model and density error data of the structure information to obtain the intelligent meshing model.
Optionally, the processing module is specifically configured to:
acquiring geometric data of each piece of structural information, boundary data of each piece of structural information and material properties of each piece of structural information, and constructing each low-density structural subdivision grid of the structural information based on the geometric data of the structural information, the boundary data of the structural information and the material properties of the structural information for each piece of structural information;
predicting, by the intelligent meshing model, an upper limit of a mesh area of an adjacent low-density structural meshing mesh of the low-density structural meshing network based on material properties of the structural information for each low-density structural meshing network, and identifying the mesh area of the adjacent low-density structural meshing mesh of the low-density structural meshing network;
And under the condition that the grid area is larger than the grid area upper limit, performing grid subdivision processing on the adjacent low-density structure subdivision grids of the low-density structure subdivision network again based on the area upper limit of the adjacent low-density structure subdivision grids of the low-density structure subdivision network, and returning to execute grid area upper limit steps of predicting the adjacent low-density structure subdivision grids of the low-density structure subdivision network based on the material attribute of the structural information for each low-density structure subdivision network until all grid areas are not larger than the grid area upper limit corresponding to each grid area, and obtaining the grid subdivision result with nonuniform structural information.
Optionally, the apparatus further includes:
the image conversion module is used for acquiring image data of each piece of structure information of the cable and converting the mesh subdivision result of each piece of structure information into subdivision image data of each piece of structure information;
the adjustment module is used for analyzing average deviation values between the image data of each piece of structure information and the split image data of each piece of structure information through the image feature recognition network, and returning to execute the steps of constructing the intelligent mesh split model based on the density error data of each piece of structure information and the structure model until the average deviation value is not larger than a deviation threshold value under the condition that the average deviation value is larger than the deviation threshold value;
And the determining module is used for taking the mesh subdivision result of the subdivision image data corresponding to the average deviation value which is not more than the deviation threshold value as the optimized mesh subdivision result with nonuniform structural information.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of any one of the methods of the first aspect when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of the methods of the first aspect.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of the method of any of the first aspects.
The method, the device, the computer equipment, the storage medium and the computer program product for determining the cable structural model are characterized in that each structural information of the cable is obtained, and the structural model of the cable is built based on each structural information; identifying density information of each piece of structure information corresponding to the structure model, and identifying density error data of each piece of structure information based on the density information of each piece of structure information and a density error analysis strategy; based on the density error data of the structural information and the structural model, an intelligent mesh subdivision model is constructed, mesh subdivision processing is carried out on the structural information based on the intelligent mesh subdivision model, and a mesh subdivision result of nonuniform structural information is obtained; and establishing a cable structure model of the cable based on mesh subdivision results of the structural information. The structure model of the cable is obtained by carrying out uniform subdivision on the structure information of the cable, then the density information of the structure model is calculated, and the density error data of the uniformly-dissected structure model is analyzed through a density error analysis strategy. And then, establishing an intelligent mesh subdivision model based on the density error data and the structure model, so as to mesh each piece of structure information, obtain a mesh subdivision result of each piece of structure information, and finally establish a cable structure model of the cable, so that the establishment of the cable structure model is based on the self-defined subdivision of each piece of structure information of the cable, the condition of density error caused by uniform subdivision is avoided, and the simulation accuracy of the cable structure model is improved.
Drawings
FIG. 1 is a flow diagram of a method for determining a model of a cable structure in one embodiment;
FIG. 2 is a flow diagram of an example of a determination of a cable structure model in one embodiment;
FIG. 3 is a block diagram of a cable structure model determination device in one embodiment;
fig. 4 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The method for determining the cable structure model is mainly applied to application environments corresponding to high-voltage cable simulation technologies. The method can be applied to the terminal, the server and a system comprising the terminal and the server, and is realized through interaction of the terminal and the server. The server may be implemented as a stand-alone server or as a server cluster formed by a plurality of servers. The terminal may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, etc. The terminal obtains a structural model of the cable by uniformly splitting each structural information of the cable, then calculates density information of the structural model, and analyzes density error data of the uniformly split structural model through a density error analysis strategy. And then, establishing an intelligent mesh subdivision model based on the density error data and the structure model, so as to mesh each piece of structure information, obtain a mesh subdivision result of each piece of structure information, and finally establish a cable structure model of the cable, so that the establishment of the cable structure model is based on the self-defined subdivision of each piece of structure information of the cable, the condition of density error caused by uniform subdivision is avoided, and the simulation accuracy of the cable structure model is improved.
In one embodiment, as shown in fig. 1, a method for determining a cable structure model is provided, and the method is applied to a terminal for illustration, and includes the following steps:
step S101, each piece of structure information of the cable is acquired, and a structure model of the cable is built based on each piece of structure information.
In this embodiment, the terminal collects structural information between layers of the cable, and then, the terminal performs structural subdivision on each structural information through a conventional structural subdivision model to establish a structural model corresponding to each structural information. The structure information includes, but is not limited to, unit attributes of each structure information, including unit type, distribution real constant, section attribute, distribution material attribute, and the like. Conventional structure subdivision models include, but are not limited to, commercial structure models such as COMSOL (simulation model), ANSYS (simulation model), finite element simulation model, and the like. The specific process of building the structural model will be described in detail later. Specifically, the terminal sets unit attributes of each structural information including unit type, distribution real constant, section attribute, distribution material attribute, and the like according to a CAD model (conventional structure subdivision model), and then the terminal performs Delaunay triangulation processing on each set structural information in triagle software to generate a low-density uniform mesh (LDUM).
Step S102, identifying density information of each piece of structural information corresponding to the structural model, and identifying density error data of each piece of structural information based on the density information of each piece of structural information and a density error analysis strategy.
In this embodiment, the terminal calculates the mesh density of each piece of structure information in the structure model based on each mesh of the mesh division result in each piece of structure information in the structure model, and obtains the density information of each piece of structure information corresponding to the structure model. The specific calculation process will be described in detail later. And then the terminal projects the mesh division result of each structure information through a high density solving network (HDUM) and a low density solving network (LDUM), and calculates error information of the mesh division result in the same density network to obtain density error data of each structure information. The specific projection process will be described in detail later.
Step S103, an intelligent meshing model is constructed based on the density error data of each piece of structural information and the structural model, meshing processing is carried out on each piece of structural information based on the intelligent meshing model, and meshing results with nonuniform structural information are obtained.
In this embodiment, the terminal trains an initial intelligent mesh generation model based on density error data of each structure information and the structure model, and obtains an intelligent mesh generation model. And then, the terminal performs mesh subdivision processing on each piece of structural information based on the intelligent mesh subdivision model to obtain a mesh subdivision result with uneven structural information. Wherein the smart mesh subdivision model is a structure subdivision network consisting of one fully connected network layer (FCN) and two residual network layers (resiets). The dimensions of the FCN layer are X-32-64-128-128-64-32-8-1, where X represents the dimension of the input. Each hidden layer uses a ReLU function as an activation function. ResNet1 enhances the FCN by adding a connection from the first hidden layer to the output of the last hidden layer. ResNet2 enhances FCNs by adding multiple remaining connections. The specific splitting process will be described in detail later.
Step S104, a cable structure model of the cable is built based on mesh subdivision results of the structural information.
In this embodiment, the terminal performs permutation and combination of mesh division results of each structure information according to a connection relationship between each structure information and a geometric structure relationship of each structure information based on mesh division results of each structure information, to obtain a cable structure model of the cable.
Based on the scheme, the structure model of the cable is obtained by uniformly splitting each structure information of the cable, then the density information of the structure model is calculated, and the density error data of the uniformly split structure model is analyzed through a density error analysis strategy. And then, establishing an intelligent mesh subdivision model based on the density error data and the structure model, so as to mesh each piece of structure information, obtain a mesh subdivision result of each piece of structure information, and finally establish a cable structure model of the cable, so that the establishment of the cable structure model is based on the self-defined subdivision of each piece of structure information of the cable, the condition of density error caused by uniform subdivision is avoided, and the simulation accuracy of the cable structure model is improved.
Optionally, building a structural model of the cable based on each structural information includes: identifying the structure attribute of each structure information and the structure parameter of each structure information, and carrying out triangulation processing on each structure information by the structure attribute of each structure information and the structure parameter of each structure information to obtain a uniform mesh subdivision result of each structure information; and establishing a structural model of the cable based on the mesh subdivision result with uniform structural information.
In this embodiment, the terminal identifies the structure attribute of each structure information and the structure parameter of each structure information in the structure model, performs triangulation processing on each structure information to obtain a mesh division result with uniform structure information, and then, based on the mesh division result with uniform structure information, the terminal performs permutation and combination on the mesh division result of each structure information according to the connection relationship between each structure information and the geometric structure relationship of each structure information, so as to establish the structure model of the cable. The structural parameters are real constant parameters for distributing structural information, section attribute parameters for the structural information, material attribute parameters for distributing the structural information and the like.
Based on the scheme, the structure model of the cable is built by carrying out triangular equal subdivision on the structure attribute of each structure information and the structure parameter of each structure information, so that the built structure model of the cable is high in accuracy, and the accuracy of the follow-up identification density error data is improved.
Optionally, identifying density information of each structural information corresponding to the structural model includes: randomly screening target structure information in each structure information in the structure model, and identifying the grid number corresponding to the grid subdivision result of the target structure information, the geometric data of grids in the target structure information and the geometric data of the target structure information; calculating the geometric proportion of the grid in the target structure information based on the geometric data of the target structure information and the geometric data of the grid in the target structure information; based on the geometric proportion of the grid in the target structure information and the grid number corresponding to the grid subdivision result of the target structure information, calculating the simulation density corresponding to the target structure information, and taking the simulation density as the density information of each structure information corresponding to the structure model.
In this embodiment, the terminal randomly screens the target structure information in each structure information in the structure model, and identifies the number of meshes corresponding to the mesh division result of the target structure information, the geometric data of the meshes in the target structure information, and the geometric data of the target structure information. Then, the terminal calculates a geometric duty of the mesh in the target structure information based on the geometric data of the target structure information and the geometric data of the mesh in the target structure information. Wherein the geometric duty ratio is a ratio between geometric data of the single mesh and geometric data of the structural information. And then, the terminal calculates the simulation density corresponding to the target structure information based on the geometric proportion of the grid in the target structure information and the grid number corresponding to the grid subdivision result of the target structure information. And finally, the terminal takes the simulation density as density information of each piece of structure information corresponding to the structure model. Because the mesh subdivision result corresponding to each structure information is uniform, the simulation density in each structure information is the same, and therefore, the simulation density of all the structure information can be obtained by calculating the simulation density of a single structure information. The density information is a simulation density degree corresponding to the simulation density, and the density information comprises a Low Density Uniform Mesh (LDUM) and a High Density Uniform Mesh (HDUM). The terminal presets a density range corresponding to the low-density uniform grid and a density range corresponding to the high-density uniform grid, and then, the terminal determines density information corresponding to the simulation density based on the density range to which the simulation density belongs.
Based on the scheme, the simulation density corresponding to the single structure information is calculated, so that the density information of each structure information corresponding to the structure model is obtained. The efficiency of calculating the density information of the lattice structure information is improved.
Optionally, identifying the density error data of each structural information based on the density information of each structural information and the density error analysis policy includes: identifying structural model parameters of each piece of structural information in the structural model, and carrying out high-density structure subdivision processing on each piece of structural information based on a high-density solving network and the structural model parameters of each piece of structural information to obtain a sample high-density mesh subdivision result with uniform structural information; based on the uniform mesh division result of each structure information and a linear interpolation algorithm, projecting the mesh division result of each structure information to a high-density solving network to obtain a high-density mesh division result of each structure information, and calculating an error value between a sample high-density mesh division result of each structure information and the high-density mesh division result of each structure information; and projecting each error value to a low-density solving network to obtain density error data of each structural information.
In this embodiment, the terminal identifies the structure model parameters of each structure information in the structure model, and performs high-density structure subdivision processing on each structure information based on the high-density solving network and the structure model parameters of each structure information, so as to obtain a sample high-density mesh subdivision result with uniform structure information. And then, the terminal projects the mesh division result of each structure information to a high-density solving network based on the mesh division result of each structure information and a linear interpolation algorithm to obtain a high-density mesh division result of each structure information.
And the terminal calculates a sample high-density mesh subdivision result of each structure information and an error value between the sample high-density mesh subdivision result of each structure information and the high-density mesh subdivision result of each structure information, and finally projects each error value to a low-density solving network to obtain density error data of each structure information.
Specifically, the terminal determines model parameter information of each structure information through a subdivision equation corresponding to the structure model based on each structure information. The equation of subdivision is shown below:
F:Γ,B,M,X→A(X)
in the above formula, Γ is the set of geometric structures of the model, B is the set of boundary conditions, M is the set of partial differential equation parameters, X is the geometric position, and a (X) is the upper boundary of the area of the target region.
The terminal performs high-density structure subdivision processing on each piece of structure information based on the high-density solving network and the structure model parameters of each piece of structure information to obtain a high-precision solving result (HAS) (namely a sample high-density mesh subdivision result) based on the high-density solving network (HDUM). Then, the terminal projects a low-precision solution result (LAS) into the high-density grid by using linear interpolation to obtain LAS, wherein LAS HAS the same dimension as HAS. By comparing LAS with HAS, an error E is obtained on the high density solving grid, and then E is projected back to the low density solving grid as the error of the solver (i.e., density error data). The calculation formula of the error E is as follows:
S upper ＝K/(E(x i ) α )
in the above, S upper For the purpose ofUpper boundary of standard area, x i For the center of the ith cell, K and α determine the number of target cells. The degree of refinement of the grid can be adjusted by appropriately changing K to change the number of target units.
Based on the scheme, error data are searched through high-density projection, the accuracy of the searched error data is improved, the high-density projected data are projected back to the low-density network, and the accuracy of determining the density error data of the structural model is improved.
Optionally, constructing the intelligent mesh subdivision model based on the density error data of each structure information and the structure model includes: based on the mesh subdivision structure with uniform structure information in the structure model and the density error data of the structure information, the subdivision parameters in the initial intelligent mesh subdivision model are trained, and the intelligent mesh subdivision model is obtained.
In this embodiment, the terminal trains the subdivision parameters in the initial intelligent mesh subdivision model based on the mesh subdivision structure with uniform structure information in the structure model and the density error data of the structure information, and obtains the intelligent mesh subdivision model.
Based on the scheme, the intelligent mesh subdivision model is trained through the density error data and the structural model, so that the uniform subdivision characteristic of the mesh subdivision model is avoided, and the subdivision accuracy of the intelligent mesh subdivision model is improved.
Optionally, based on the intelligent meshing model, meshing processing is performed on each piece of structural information to obtain a meshing result with non-uniform structural information, including: acquiring geometric data of each structure information, boundary data of each structure information and material properties of each structure information, and constructing each low-density structure subdivision grid of the structure information based on the geometric data of the structure information, the boundary data of the structure information and the material properties of the structure information for each structure information; predicting the upper limit of the grid area of the adjacent low-density structure subdivision grids of the low-density structure subdivision network based on the material property of the structural information by using the intelligent grid subdivision model, and identifying the grid area of the adjacent low-density structure subdivision grids of the low-density structure subdivision network; and under the condition that the grid area is larger than the upper limit of the grid area, performing grid subdivision processing on the adjacent low-density structure subdivision grids of the low-density structure subdivision network again based on the upper limit of the area of the adjacent low-density structure subdivision grids of the low-density structure subdivision network, and returning to execute the step of predicting the upper limit of the grid area of the adjacent low-density structure subdivision grids of the low-density structure subdivision network based on the material property of the structural information for each low-density structure subdivision network until all the grid areas are not larger than the upper limit of the grid area corresponding to each grid area, so as to obtain a grid subdivision result with uneven structural information.
In this embodiment, the terminal acquires geometric data of each structure information, boundary data of each structure information, and material properties of each structure information, and constructs each low-density structure split grid of the structure information based on the geometric data of the structure information, the boundary data of the structure information, and the material properties of the structure information for each structure information. Then, the terminal predicts the upper limit of the mesh area of the adjacent low-density structure subdivision meshes of the low-density structure subdivision network based on the material property of the structural information for each low-density structure subdivision network through the intelligent mesh subdivision model, and identifies the mesh area of the adjacent low-density structure subdivision meshes of the low-density structure subdivision network. The terminal judges whether the grid area is larger than the predicted grid area upper limit. And under the condition that the grid area is larger than the upper limit of the grid area, the terminal performs grid subdivision processing on the adjacent low-density structure subdivision grids of the low-density structure subdivision network again based on the upper limit of the area of the adjacent low-density structure subdivision grids of the low-density structure subdivision network, and returns to execute the step of predicting the upper limit of the grid area of the adjacent low-density structure subdivision grids of the low-density structure subdivision network based on the material attribute of the structural information for each low-density structure subdivision network until all the grid areas are not larger than the upper limit of the grid area corresponding to each grid area, and a grid subdivision result with nonuniform structural information is obtained.
Based on the scheme, the intelligent mesh subdivision model is used for carrying out subdivision processing on each mesh for multiple times, so that the accuracy of mesh subdivision results of the structural information is improved.
Optionally, the method further comprises: acquiring image data of each piece of structure information of the cable, and converting a mesh subdivision result of each piece of structure information into subdivision image data of each piece of structure information; analyzing average deviation values between the image data of each structure information and the subdivision image data of each structure information through an image feature recognition network, and returning to execute the steps of constructing the intelligent mesh subdivision model based on the density error data of each structure information and the structure model under the condition that the average deviation values are larger than a deviation threshold value until the average deviation values are not larger than the deviation threshold value; and taking a mesh subdivision result of subdivision image data corresponding to the average deviation value which is not more than the deviation threshold value as an optimized mesh subdivision result with nonuniform structural information.
In this embodiment, the terminal acquires image data of each structural information of the cable, and converts a mesh division result of each structural information into division image data of each structural information; and analyzing average deviation values between the image data of each structure information and the split image data of each structure information through the image feature recognition network. The image feature recognition network may be, but is not limited to, a deep learning based classification convolutional neural network, such as a VGG convolutional neural network, a Google-net series convolutional neural network, a Resnet series convolutional neural network, and a Transformer (VIT) series convolutional neural network.
The terminal presets a deviation threshold value, and calculates an average value between deviation values as an average deviation value. And under the condition that the average deviation value is larger than the deviation threshold value, the terminal returns to execute the steps of constructing the intelligent mesh subdivision model based on the density error data and the structure model of each piece of structure information, and when the average deviation value is not larger than the deviation threshold value, the terminal takes the mesh subdivision result of subdivision image data corresponding to the average deviation value which is not larger than the deviation threshold value as the optimized mesh subdivision result of each piece of structure information.
Based on the scheme, the reality and the accuracy of the mesh dissection result are improved by optimizing the mesh dissection result based on the actual image data.
The application also provides a determining example of the cable structure model, as shown in fig. 2, the specific processing procedure comprises the following steps:
step S201, each structure information of the cable is acquired.
Step S202, identifying the structure attribute of each structure information and the structure parameter of each structure information, and carrying out triangulation processing on each structure information by the structure attribute of each structure information and the structure parameter of each structure information to obtain a uniform mesh subdivision result of each structure information.
Step S203, a structural model of the cable is built based on the mesh subdivision result with uniform all the structural information.
Step S204, randomly screening the target structure information in each structure information in the structure model, and identifying the grid number corresponding to the grid subdivision result of the target structure information, the geometric data of the grids in the target structure information and the geometric data of the target structure information.
Step S205, calculating the geometric duty of the mesh in the target structure information based on the geometric data of the target structure information and the geometric data of the mesh in the target structure information.
Step S206, calculating simulation density corresponding to the target structure information based on the geometric proportion of the grid in the target structure information and the grid number corresponding to the grid subdivision result of the target structure information, and taking the simulation density as the density information of each structure information corresponding to the structure model.
Step S207, identifying structural model parameters of each piece of structural information in the structural model, and performing high-density structure subdivision processing on each piece of structural information based on the high-density solving network and the structural model parameters of each piece of structural information to obtain a sample high-density mesh subdivision result with uniform structural information.
Step S208, based on the uniform mesh division result of each structure information and the linear interpolation algorithm, the mesh division result of each structure information is projected to a high-density solving network to obtain a high-density mesh division result of each structure information, and error values between the sample high-density mesh division result of each structure information and the high-density mesh division result of each structure information are calculated.
Step S209, projecting each error value to a low-density solving network to obtain density error data of each structural information.
Step S210, training subdivision parameters in an initial intelligent meshing model based on the meshing structures with uniform structural information in the structural model and density error data of the structural information to obtain the intelligent meshing model.
Step S211, obtaining geometric data of each structure information, boundary data of each structure information, and material properties of each structure information, and constructing each low-density structure subdivision grid of the structure information based on the geometric data of the structure information, the boundary data of the structure information, and the material properties of the structure information for each structure information.
Step S212, predicting the upper limit of the grid area of the adjacent low-density structure subdivision grids of the low-density structure subdivision network based on the material property of the structural information by using the intelligent grid subdivision model for each low-density structure subdivision network, and identifying the grid area of the adjacent low-density structure subdivision grids of the low-density structure subdivision network.
Step S213, when the grid area is larger than the upper limit of the grid area, the adjacent low-density structure split grids of the low-density structure split network are subjected to grid split processing again based on the upper limit of the area of the adjacent low-density structure split grids of the low-density structure split network, and the grid area upper limit step of predicting the adjacent low-density structure split grids of the low-density structure split network based on the material property of the structure information is performed for each low-density structure split network, until all the grid areas are not larger than the upper limit of the grid area corresponding to each grid area, and the grid split result with nonuniform structure information is obtained.
Step S214, a cable structure model of the cable is built based on mesh subdivision results of the structural information.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a device for determining the cable structure model for realizing the method for determining the cable structure model. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiment of the determining device for one or more cable structure models provided below may be referred to the limitation of the determining method for a cable structure model hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 3, there is provided a determining apparatus for a cable structural model, including: an acquisition module 310, an identification module 320, a processing module 330, and a setup module 340, wherein:
an obtaining module 310, configured to obtain each structural information of a cable, and establish a structural model of the cable based on each structural information;
the identifying module 320 is configured to identify density information of each structural information corresponding to the structural model, and identify density error data of each structural information based on the density information of each structural information and a density error analysis policy;
the processing module 330 is configured to construct an intelligent mesh division model based on the density error data of each piece of the structural information and the structural model, and perform mesh division processing on each piece of the structural information based on the intelligent mesh division model to obtain a mesh division result of non-uniformity of each piece of the structural information;
And the establishing module 340 is configured to establish a cable structure model of the cable based on the mesh division result of each piece of the structure information.
Optionally, the acquiring module 310 is specifically configured to:
identifying the structure attribute of each piece of structure information and the structure parameter of each piece of structure information, and carrying out triangulation processing on each piece of structure information to obtain a uniform mesh subdivision result of each piece of structure information;
and establishing a structural model of the cable based on the mesh subdivision result with uniform structural information.
Optionally, the identifying module 320 is specifically configured to:
randomly screening target structure information in each structure information in the structure model, and identifying the grid number corresponding to a grid subdivision result of the target structure information, geometric data of grids in the target structure information and geometric data of the target structure information;
calculating the geometric proportion of the grid in the target structure information based on the geometric data of the target structure information and the geometric data of the grid in the target structure information;
And calculating simulation density corresponding to the target structure information based on the geometric proportion of the grid in the target structure information and the grid number corresponding to the grid subdivision result of the target structure information, and taking the simulation density as density information of each structure information corresponding to the structure model.
Optionally, the identifying module 320 is specifically configured to:
identifying structural model parameters of each piece of structural information in the structural model, and carrying out high-density structure subdivision processing on each piece of structural information based on a high-density solving network and the structural model parameters of each piece of structural information to obtain a sample high-density mesh subdivision result with uniform structural information;
based on the uniform mesh division result of each piece of structure information and a linear interpolation algorithm, projecting the mesh division result of each piece of structure information to the high-density solving network to obtain a high-density mesh division result of each piece of structure information, and calculating an error value between the sample high-density mesh division result of each piece of structure information and the high-density mesh division result of each piece of structure information;
and projecting each error value to a low-density solving network to obtain density error data of each structural information.
Optionally, the processing module 330 is specifically configured to:
and training subdivision parameters in an initial intelligent meshing model based on the meshing structure with uniform structure information in the structural model and density error data of the structure information to obtain the intelligent meshing model.
Optionally, the processing module 330 is specifically configured to:
acquiring geometric data of each piece of structural information, boundary data of each piece of structural information and material properties of each piece of structural information, and constructing each low-density structural subdivision grid of the structural information based on the geometric data of the structural information, the boundary data of the structural information and the material properties of the structural information for each piece of structural information;
predicting, by the intelligent meshing model, an upper limit of a mesh area of an adjacent low-density structural meshing mesh of the low-density structural meshing network based on material properties of the structural information for each low-density structural meshing network, and identifying the mesh area of the adjacent low-density structural meshing mesh of the low-density structural meshing network;
and under the condition that the grid area is larger than the grid area upper limit, performing grid subdivision processing on the adjacent low-density structure subdivision grids of the low-density structure subdivision network again based on the area upper limit of the adjacent low-density structure subdivision grids of the low-density structure subdivision network, and returning to execute grid area upper limit steps of predicting the adjacent low-density structure subdivision grids of the low-density structure subdivision network based on the material attribute of the structural information for each low-density structure subdivision network until all grid areas are not larger than the grid area upper limit corresponding to each grid area, and obtaining the grid subdivision result with nonuniform structural information.
Optionally, the apparatus further includes:
the image conversion module is used for acquiring image data of each piece of structure information of the cable and converting the mesh subdivision result of each piece of structure information into subdivision image data of each piece of structure information;
the adjustment module is used for analyzing average deviation values between the image data of each piece of structure information and the split image data of each piece of structure information through the image feature recognition network, and returning to execute the steps of constructing the intelligent mesh split model based on the density error data of each piece of structure information and the structure model until the average deviation value is not larger than a deviation threshold value under the condition that the average deviation value is larger than the deviation threshold value;
and the determining module is used for taking the mesh subdivision result of the subdivision image data corresponding to the average deviation value which is not more than the deviation threshold value as the optimized mesh subdivision result with nonuniform structural information.
The respective modules in the above-described determination means of the cable structure model may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of determining a model of a cable structure. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of any one of the methods of the first aspect when the computer program is executed.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method according to any one of the first aspects.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method of any of the first aspects.
The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. A method of determining a model of a cable structure, the method comprising:
acquiring each piece of structure information of a cable, and establishing a structure model of the cable based on each piece of structure information;
identifying density information of each piece of structure information corresponding to the structure model, and identifying density error data of each piece of structure information based on the density information of each piece of structure information and a density error analysis strategy;
Based on the density error data of the structural information and the structural model, an intelligent mesh subdivision model is constructed, mesh subdivision processing is carried out on the structural information based on the intelligent mesh subdivision model, and a mesh subdivision result of nonuniform structural information is obtained;
and establishing a cable structure model of the cable based on mesh subdivision results of the structural information.
2. The method of claim 1, wherein said building a structural model of the cable based on each of the structural information comprises:
identifying the structure attribute of each piece of structure information and the structure parameter of each piece of structure information, and carrying out triangulation processing on each piece of structure information to obtain a uniform mesh subdivision result of each piece of structure information;
and establishing a structural model of the cable based on the mesh subdivision result with uniform structural information.
3. The method according to claim 2, wherein the identifying the density information of each structural information corresponding to the structural model includes:
randomly screening target structure information in each structure information in the structure model, and identifying the grid number corresponding to a grid subdivision result of the target structure information, geometric data of grids in the target structure information and geometric data of the target structure information;
Calculating the geometric proportion of the grid in the target structure information based on the geometric data of the target structure information and the geometric data of the grid in the target structure information;
and calculating simulation density corresponding to the target structure information based on the geometric proportion of the grid in the target structure information and the grid number corresponding to the grid subdivision result of the target structure information, and taking the simulation density as density information of each structure information corresponding to the structure model.
4. The method of claim 1, wherein the identifying the density error data for each of the structural information based on the density information for each of the structural information and a density error analysis policy comprises:
identifying structural model parameters of each piece of structural information in the structural model, and carrying out high-density structure subdivision processing on each piece of structural information based on a high-density solving network and the structural model parameters of each piece of structural information to obtain a sample high-density mesh subdivision result with uniform structural information;
based on the uniform mesh division result of each piece of structure information and a linear interpolation algorithm, projecting the mesh division result of each piece of structure information to the high-density solving network to obtain a high-density mesh division result of each piece of structure information, and calculating an error value between the sample high-density mesh division result of each piece of structure information and the high-density mesh division result of each piece of structure information;
And projecting each error value to a low-density solving network to obtain density error data of each structural information.
5. A method according to claim 3, wherein said constructing an intelligent meshing model based on the density error data of each of said structural information and said structural model comprises:
and training subdivision parameters in an initial intelligent meshing model based on the meshing structure with uniform structure information in the structural model and density error data of the structure information to obtain the intelligent meshing model.
6. The method according to claim 1, wherein performing mesh subdivision processing on each piece of the structural information based on the intelligent mesh subdivision model to obtain a mesh subdivision result of each piece of the structural information non-uniformity, comprises:
acquiring geometric data of each piece of structural information, boundary data of each piece of structural information and material properties of each piece of structural information, and constructing each low-density structural subdivision grid of the structural information based on the geometric data of the structural information, the boundary data of the structural information and the material properties of the structural information for each piece of structural information;
Predicting, by the intelligent meshing model, an upper limit of a mesh area of an adjacent low-density structural meshing mesh of the low-density structural meshing network based on material properties of the structural information for each low-density structural meshing network, and identifying the mesh area of the adjacent low-density structural meshing mesh of the low-density structural meshing network;
and under the condition that the grid area is larger than the grid area upper limit, performing grid subdivision processing on the adjacent low-density structure subdivision grids of the low-density structure subdivision network again based on the area upper limit of the adjacent low-density structure subdivision grids of the low-density structure subdivision network, and returning to execute grid area upper limit steps of predicting the adjacent low-density structure subdivision grids of the low-density structure subdivision network based on the material attribute of the structural information for each low-density structure subdivision network until all grid areas are not larger than the grid area upper limit corresponding to each grid area, and obtaining the grid subdivision result with nonuniform structural information.
7. The method according to claim 4, wherein the method further comprises:
acquiring image data of each piece of structure information of the cable, and converting a mesh subdivision result of each piece of structure information into subdivision image data of each piece of structure information;
Analyzing average deviation values between the image data of each piece of structure information and the split image data of each piece of structure information through an image feature recognition network, and returning to execute the steps of constructing an intelligent mesh split model based on the density error data of each piece of structure information and the structure model under the condition that the average deviation values are larger than a deviation threshold value until the average deviation values are not larger than the deviation threshold value;
and taking the mesh subdivision result of subdivision image data corresponding to the average deviation value which is not more than the deviation threshold value as the optimized mesh subdivision result with nonuniform structural information.
8. A device for determining a model of a cable structure, the device comprising:
the acquisition module is used for acquiring each piece of structural information of the cable and establishing a structural model of the cable based on each piece of structural information;
the identification module is used for identifying the density information of each piece of structure information corresponding to the structure model and identifying the density error data of each piece of structure information based on the density information of each piece of structure information and a density error analysis strategy;
the processing module is used for constructing an intelligent mesh subdivision model based on the density error data of the structural information and the structural model, and performing mesh subdivision processing on the structural information based on the intelligent mesh subdivision model to obtain a mesh subdivision result of non-uniformity of the structural information;
The building module is used for building a cable structure model of the cable based on mesh subdivision results of the structural information.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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