Cloud model simulation control method and system based on pre-processing

Abstract

The invention discloses a cloud model simulation control method and system based on preprocessing, and belongs to the technical field of model simulation. A simulation pretreatment scheme in the prior art is low in simulation efficiency and low in precision. According to the cloud model simulation control method based on pre-processing, the model grouping unit, the model pre-processing unit and the model assembling unit are constructed, and the model object is processed, so that the pre-processing difficulty of the complex model object can be effectively reduced, the pre-processing workload is reduced, and the simulation time is shortened; and meanwhile, all geometric features can be captured, and geometric feature information cannot be lost, so that the simulation precision and efficiency can be effectively improved. By constructing the cloud processing unit, online resources and offline operation can be effectively combined without frequently uploading and downloading files, so that the access speed of a simulation object can be remarkably improved, and the synchronous data processing amount of a simulation model is effectively reduced.

Description

Technical Field

[0001] The present invention relates to a cloud model simulation control method and system based on pre - processing, and belongs to the technical field of model simulation. Background Art

[0002] Currently, computer - aided engineering (CAE) simulation is widely used in the manufacturing industry. Among them, the pre - processing stage is a key step in computer - aided engineering (CAE), which lays the foundation for subsequent solving and analysis. However, the existing pre - processing solutions have relatively low processing efficiency.

[0003] Furthermore, a Chinese patent application (publication number: CN107742042A) provides a CAE simulation pre - processing method, including the following steps: detecting the user's operation to enter the CAE finite - element mesh generation operation interface; establishing a finite - element model according to the user's operation; converting the finite - element model into a digital file containing mesh node information; performing parameter setting on the digital file; and generating an operable file from the digital file to complete the pre - processing process.

[0004] Since the product structure to be simulated is generally composed of various components with different shapes and the structural modeling is relatively complex, simply converting the finite - element model directly into a digital file containing mesh node information will result in complex pre - processing work, long time consumption, high computing resources required, and even possible loss of some geometric feature information, affecting the simulation accuracy and efficiency.

[0005] Furthermore, the above - mentioned CAE simulation solution and existing CAE simulation solutions usually adopt an offline method for separate simulation processing, with a slow file synchronization speed. Simulation engineers need to frequently upload and download files, resulting in a very cumbersome data sharing process and a large workload for synchronizing and sharing simulation models.

[0006] The information disclosed in this background art is only used to understand the background of the inventive concept of the present invention, and therefore it may include information that does not constitute the prior art. Summary of the Invention

[0007] In view of the above problems or one of the above problems, one object of the present invention is to provide a cloud model simulation control method and system based on preprocessing, which fully considers the structural complexity of the simulation object. By constructing a model grouping unit, a model preprocessing unit, and a model assembly unit, grouping and geometric topology analysis are performed on a certain model object; and geometric feature recognition is performed on the grouped part data, and meshing processing is carried out to obtain part mesh data; then the assembly relationship between parts is recognized, and the part mesh data is assembled according to the assembly relationship to obtain a mesh simulation object, so that the preprocessing difficulty of complex model objects can be effectively reduced, the preprocessing workload can be reduced, the simulation time can be reduced, and the demand for simulation computing hardware can be reduced; at the same time, all geometric features can be ensured to be captured, and geometric feature information will not be lost, so that the simulation accuracy and efficiency can be effectively improved.

[0008] In view of the above problems or one of the above problems, another object of the present invention is to provide a cloud model simulation control method and system based on preprocessing, which can effectively combine online resources and offline operations, so that simulation engineers can access and edit simulation objects in the cloud on the local client just like operating local files, without frequently uploading and downloading files, so that the access speed of simulation objects can be significantly improved, the data sharing process becomes simple and convenient, and the amount of synchronous data processing of simulation models is effectively reduced.

[0009] To achieve one of the above objects, the first technical solution of the present invention is as follows: A cloud model simulation control method based on preprocessing, comprising the following steps: Based on a pre-constructed model grouping unit, grouping and geometric topology analysis are performed on a certain model object to obtain grouped part data; Using a pre-constructed model preprocessing unit, geometric feature recognition is performed on the grouped part data, and meshing processing is carried out to obtain part mesh data; Using a pre-constructed model assembly unit, the assembly relationship between parts is recognized, and the part mesh data is assembled according to the assembly relationship to obtain a mesh simulation object; Through a pre-constructed cloud processing unit, the request information sent by the user is obtained; and according to the request information, the mesh simulation object is processed, and response feedback information is generated to realize cloud model simulation control based on preprocessing.

[0010] The present invention fully considers the structural complexity of the simulation object. By constructing a model grouping unit, a model preprocessing unit, and a model assembly unit, it groups a certain model object and performs geometric topology analysis; then it identifies the geometric features of the part grouping data and performs meshing processing to obtain the part mesh data; then it identifies the assembly relationship between the parts and assembles the part mesh data according to the assembly relationship to obtain a mesh simulation object, thereby effectively reducing the preprocessing difficulty of complex model objects, reducing the preprocessing workload, shortening the simulation time, and reducing the demand for simulation computing hardware; at the same time, it can ensure that all geometric features are captured and no geometric feature information is lost, so the simulation accuracy and efficiency can be effectively improved.

[0011] The present invention constructs a cloud processing unit to obtain the request information sent by the user; and according to the request information, it processes the mesh simulation object and generates response feedback information, enabling the effective combination of online resources and offline operations. Thus, simulation engineers can access and edit the simulation objects in the cloud on the local client just like operating local files, without the need to frequently upload and download files, so the access speed of the simulation objects can be significantly improved, making the data sharing process simple and convenient, and effectively reducing the amount of synchronous data processing for the simulation model.

[0012] As a preferred technical measure: Based on the pre-constructed model grouping unit, the method for grouping a certain model object and performing geometric topology analysis to obtain part grouping data is as follows: Obtain the image information of a certain model object, which includes several projection views; Adjust the several projection views to a fixed size to obtain a model image in a unified format.

[0013] Extract features from the model image through a convolutional neural network to generate a feature map; According to the feature map, encode and classify the geometric features of the model object to obtain part grouping data.

[0014] As a preferred technical measure: Using the pre-constructed model preprocessing unit, the method for identifying the geometric features of the part grouping data and performing meshing processing to obtain the part mesh data is as follows: Identify the geometric features of the part grouping data to obtain the part face information; the part face information includes the geometric elements of the parts and the topological information between the geometric elements; the geometric elements include vertex information, edge information, and face information; Process the part face information to obtain the face geometric features; Based on facial geometric features, use the mapping grid algorithm or the sweeping grid algorithm or the filling grid algorithm to divide the grid of the part facial information to obtain grid geometric information; Repair the grid geometric information through the geometric optimization algorithm, and then use the topological optimization algorithm to optimize the local area of the grid geometric information to obtain the component grid data.

[0015] As a preferred technical measure: The method for identifying the geometric features of the part grouping data to obtain the part facial information is as follows: Analyze the geometric features of the part grouping data to obtain all vertex information; According to the vertex information, connect the adjacent vertices to form edges, and thus obtain edge information; According to the edge information, connect the adjacent edges to form loops, and thus obtain edge combinations; Based on the edge combination, obtain the closed surface information; Based on the geometric center of a certain model object, process the closed surface information to obtain the mid-plane position, that is, the surface information; Couple and summarize the surface information, edge information, and vertex information to obtain the part facial information.

[0016] As a preferred technical measure: The method for using a pre-constructed model assembly unit to identify the assembly relationship between parts and assemble the component grid data according to the assembly relationship to obtain the grid simulation object is as follows: Based on a certain model object, identify the assembly relationship between parts, which includes bolt connection or / and welding or / and snap connection; according to the assembly relationship, generate the assembly sequence; Optimize the assembly sequence through the dynamic programming method or / and genetic algorithm to generate the final assembly sequence information; According to the final assembly sequence information, assemble the component grid data to obtain the grid simulation object.

[0017] As a preferred technical measure: The method for obtaining the request information sent by the user through a pre-constructed cloud processing unit; and processing the grid simulation object according to the request information and generating the response feedback information is as follows: Obtain the attribute request information sent by the user; According to the requested attribute information, return the attribute response information, and the attribute response information includes the attribute information and operation information supported by the server; Obtain the operation request information sent by the user based on the Hypertext Transfer Protocol and according to the attribute information and operation information supported by the server; the operation request information is a request to synchronously download a certain simulation object, a request to synchronously upload a certain simulation object, a request to delete a certain simulation object, or a request to lock a certain simulation object; Perform corresponding processing on the grid simulation object according to the operation request information, and generate response feedback information.

[0018] As a preferred technical measure: The method for performing corresponding processing on the grid simulation object according to the operation request information and generating response feedback information is as follows: Step 11, the operation request information is a request to synchronously download a certain simulation object; Parse the request to synchronously download a certain simulation object to obtain authentication information, the file path information of the requested download, and the requested host name; Step 12, check the authentication information. When the authentication passes, execute Step 13; When the authentication fails, generate a first response body, and the first response body includes an authentication error status code; and execute Step 14; Step 13, according to the file path information, search for the specified grid simulation object. If the grid simulation object does not exist, generate a first response body, and the first response body includes a file error status code; and execute Step 14; If the grid simulation object exists, generate a second response body, and the second response body includes a completion status code and the grid simulation object; and execute Step 15; Step 14, process the first response body according to the Hypertext Transfer Protocol to obtain response feedback information, and transmit the response feedback information, that is, the first response body, to the user terminal according to the requested host name; Step 15, process the second response body according to the Hypertext Transfer Protocol to obtain response feedback information, and transmit the response feedback information, that is, the second response body, to the user terminal according to the requested host name.

[0019] As a preferred technical measure: The method for processing the grid simulation object according to the operation request information and generating response feedback information is as follows: Step 21, the operation request information is a request to synchronously upload a certain simulation object; Parse the request to synchronously upload a certain simulation object to obtain authentication information, the file path information of the requested upload, and the requested host name; Step 22, check the authentication information. When the authentication passes, execute Step 23; When the authentication fails, generate a third response body, and the third response body includes a creation error status code; and execute Step 24; Step 23: According to the file path information, upload a certain simulation object based on the Hypertext Transfer Protocol; adjust the mesh simulation object based on the certain simulation object; or replace the original mesh simulation object based on the certain simulation object; meanwhile, generate a fourth response body, where the fourth response body includes a creation success status code; and execute Step 25; Step 24: Process the third response body according to the Hypertext Transfer Protocol to obtain response feedback information, and transmit the response feedback information, i.e., the third response body, to the client according to the request host name; Step 25: Process the fourth response body according to the Hypertext Transfer Protocol to obtain response feedback information, and transmit the response feedback information, i.e., the fourth response body, to the client according to the request host name.

[0020] As a preferred technical measure: The method for processing the mesh simulation object according to the operation request information and generating response feedback information is as follows: Step 31: The operation request information is a request to delete a certain simulation object or a request to lock a certain simulation object; Analyze the operation request information to obtain authentication information, file path information for request processing, and the request host name; Step 32: Check the authentication information. When the authentication is passed, execute Step 33; When the authentication fails, generate a failure status code; and execute Step 34; Step 33: Delete or lock the mesh simulation object according to the file path information and obtain a success status code; then execute Step 35; Step 34: Process the failure status code according to the Hypertext Transfer Protocol to obtain response feedback information, and transmit the response feedback information to the client according to the request host name; Step 35: Process the success status code according to the Hypertext Transfer Protocol to obtain response feedback information, and transmit the response feedback information to the client according to the request host name.

[0021] To achieve one of the above purposes, the second technical solution of the present invention is: A cloud model simulation control system based on preprocessing, which applies the above-mentioned cloud model simulation control method based on preprocessing; it includes a preprocessing module, a cloud control module, an incremental synchronization module, and a real-time file monitoring module; The preprocessing module is provided with a model grouping unit, a model preprocessing unit, and a model assembly unit, and is used for grouping a certain model object and performing geometric topology analysis to obtain a mesh simulation object; Cloud control module, which is provided with a cloud processing unit for obtaining request information sent by a user; and processing grid simulation objects according to the request information and generating response feedback information; Incremental synchronization module, which is constructed by using a block comparison algorithm and is used for transmitting the changed parts in a file; Real-time file monitoring module, which is constructed based on a file event listening mechanism and is used for capturing the save operation, modification operation, and deletion operation of local files in real time to trigger immediate synchronization; meanwhile, it can cooperate with an asynchronous queue for processing to ensure that user operations are not blocked during multi-task concurrency, and realize synchronous editing while the file is being edited.

[0022] By setting up a pre-processing module, a cloud control module, an incremental synchronization module, and a real-time file monitoring module, the present invention enables the effective combination of online resources and offline operations, so that simulation engineers can access and edit simulation objects in the cloud on a local client just like operating local files, without frequently uploading and downloading files. Therefore, the access speed of simulation objects can be significantly improved, the data sharing process becomes simple and convenient, and the amount of synchronous data processing for simulation models is effectively reduced.

[0023] Meanwhile, by migrating the simulation process to the cloud and combining with a synchronization mechanism, the present invention can ensure that local files and cloud files are always kept consistent, reducing the workload of manual synchronization.

[0024] Compared with the prior art solutions, the present invention has the following beneficial effects: The present invention fully considers the structural complexity of simulation objects. By constructing a model grouping unit, a model pre-processing unit, and a model assembly unit, a certain model object is grouped and geometric topology analysis is performed; geometric features of the part grouping data are identified and meshed to obtain part mesh data; then the assembly relationship between parts is identified, and the part mesh data is assembled according to the assembly relationship to obtain a grid simulation object. Thereby, the pre-processing difficulty of complex model objects can be effectively reduced, the pre-processing workload can be lightened, the simulation time can be reduced, and the requirement for simulation computing hardware can be reduced; at the same time, all geometric features can be ensured to be captured without losing geometric feature information, so that the simulation accuracy and efficiency can be effectively improved.

[0025] The present invention can enable the effective combination of online resources and offline operations, so that simulation engineers can access and edit simulation objects in the cloud on a local client just like operating local files, without frequently uploading and downloading files. Therefore, the access speed of simulation objects can be significantly improved, the data sharing process becomes simple and convenient, and the amount of synchronous data processing for simulation models is effectively reduced. Brief Description of the Drawings

[0026] Figure 1It is a schematic flow diagram of a cloud model simulation control method of the present invention; Figure 2 It is a schematic diagram for grid optimization of the present invention. Detailed implementation manners

[0027] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0028] On the contrary, the present invention covers any alternatives, modifications, equivalent methods and solutions made within the spirit and scope of the present invention as defined by the claims. Further, in order to enable the public to better understand the present invention, some specific details are described in detail in the following detailed description of the present invention. Those skilled in the art can fully understand the present invention without the description of these details.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the present invention. The terms used herein are only for the purpose of describing specific embodiments, and are not intended to limit the present invention.

[0030] As Figure 1 shown, the first specific embodiment of the cloud model simulation control method based on preprocessing of the present invention: A cloud model simulation control method based on preprocessing includes the following: Based on a pre-constructed model grouping unit, a certain model object is grouped and geometric topology analysis is performed to obtain part grouping data; Using a pre-constructed model preprocessing unit, geometric feature recognition is performed on the part grouping data, and meshing processing is performed to obtain part mesh data; Using a pre-constructed model assembly unit, the assembly relationship between parts is recognized, and the part mesh data is assembled according to the assembly relationship to obtain a mesh simulation object; Through a pre-constructed cloud processing unit, the request information sent by the user is obtained; and according to the request information, the mesh simulation object is processed, and response feedback information is generated to realize cloud model simulation control based on preprocessing.

[0031] In this embodiment, the method for obtaining the request information sent by the user through a pre-constructed cloud processing unit; and processing the mesh simulation object according to the request information and generating response feedback information is as follows: Obtain the attribute request information sent by the user; Return attribute response information according to the request attribute information, where the attribute response information includes the attribute information and operation information supported by the server; Based on the Hypertext Transfer Protocol, and according to the attribute information and operation information supported by the server, obtain the operation request information sent by the user; the operation request information is a request to synchronously download a simulation object, a request to synchronously upload a simulation object, a request to delete a simulation object, or a request to lock a simulation object; Process the grid simulation object according to the operation request information and generate response feedback information.

[0032] In this embodiment, the first method for processing the grid simulation object according to the operation request information and generating response feedback information is as follows: Step 11, the operation request information is a request to synchronously download a simulation object; Parse the request to synchronously download a simulation object to obtain authentication information, the file path information of the requested download, and the requested host name; Step 12, check the authentication information. When the authentication passes, execute Step 13; When the authentication fails, generate a first response body, where the first response body includes an authentication error status code; and execute Step 14; Step 13, according to the file path information, find the specified grid simulation object. If the grid simulation object does not exist, generate a first response body, where the first response body includes a file error status code; and execute Step 14; If the grid simulation object exists, generate a second response body, where the second response body includes a completion status code and the grid simulation object; and execute Step 15; Step 14, process the first response body according to the Hypertext Transfer Protocol to obtain response feedback information, and based on the requested host name, transmit the response feedback information, that is, the first response body, to the client; Step 15, process the second response body according to the Hypertext Transfer Protocol to obtain response feedback information, and based on the requested host name, transmit the response feedback information, that is, the second response body, to the client.

[0033] In this embodiment, the second method for processing the grid simulation object according to the operation request information and generating response feedback information is as follows: Step 21, the operation request information is a request to synchronously upload a simulation object; Parse the request to synchronously upload a simulation object to obtain authentication information, the file path information of the requested upload, and the requested host name; Step 22, check the authentication information. When the authentication passes, execute Step 23; When the authentication fails, generate a third response body, which includes a created error status code; and execute step 24; Step 23, according to the file path information, upload a certain simulation object according to the Hypertext Transfer Protocol; and adjust the grid simulation object according to the certain simulation object; or replace the original grid simulation object according to the certain simulation object; meanwhile, generate a fourth response body, which includes a created success status code; and execute step 25; Step 24, process the third response body according to the Hypertext Transfer Protocol to obtain response feedback information, and transmit the response feedback information, that is, the third response body, to the client according to the request host name; Step 25, process the fourth response body according to the Hypertext Transfer Protocol to obtain response feedback information, and transmit the response feedback information, that is, the fourth response body, to the client according to the request host name.

[0034] In this embodiment, the third method for processing the grid simulation object according to the operation request information and generating response feedback information is as follows: The operation request information is a request to delete a certain simulation object or a request to lock a certain simulation object; Parse the operation request information to obtain authentication information, file path information for request processing, and the request host name; Step 32, check the authentication information. When the authentication passes, execute step 33; When the authentication fails, generate a failure status code; and execute step 34; Step 33, perform a delete or lock operation on the grid simulation object according to the file path information and obtain a success status code; then execute step 35; Step 34, process the failure status code according to the Hypertext Transfer Protocol to obtain response feedback information, and transmit the response feedback information to the client according to the request host name; Step 35, process the success status code according to the Hypertext Transfer Protocol to obtain response feedback information, and transmit the response feedback information to the client according to the request host name.

[0035] A specific embodiment of the cloud model simulation control system based on preprocessing of the present invention: A cloud model simulation control system based on preprocessing, which applies the above-mentioned cloud model simulation control method based on preprocessing; it includes a preprocessing module, a cloud control module, an incremental synchronization module, a real-time file monitoring module, a breakpoint resumption module, a multi-user collaboration module, and a version control module; The preprocessing module is equipped with a model grouping unit, a model preprocessing unit, and a model assembly unit, which are used to group a certain model object and perform geometric topology analysis to obtain a mesh simulation object, which can be deployed locally or on a cloud server; The cloud control module is equipped with a cloud processing unit, which is used to obtain the request information sent by the user; and according to the request information, process the mesh simulation object and generate response feedback information, which is connected to the preprocessing module through the server; The incremental synchronization module is constructed using a block comparison algorithm, which is used to transfer the changed parts of the file; The real-time file monitoring module is constructed based on the file event listening mechanism, which is used to capture the save operation, modification operation, and deletion operation of local files in real time, triggering instant synchronization; at the same time, it can cooperate with asynchronous queue processing to ensure that user operations are not blocked during multi-task concurrency, realizing simultaneous editing and synchronization of files; The breakpoint resumption module is used to implement block uploading or downloading, and can resume from the breakpoint after interruption; and dynamically compress the transmitted data; The multi-user collaboration module is used to enable multiple users to access and edit files simultaneously, allowing multiple engineers to collaborate on the same project, and supporting a locking mechanism. Only one user is allowed to modify the same file at the same time to ensure data integrity and consistency.

[0036] The version control module is used to implement version control of files, allowing multiple versions of files to coexist, and supporting comparison and restoration of differences between versions.

[0037] The second specific embodiment of the cloud model simulation control method based on preprocessing of the present invention: A cloud model simulation control method based on preprocessing includes a preprocessing stage of the model object and a cloud service interaction stage that combines online and offline.

[0038] In this embodiment, the preprocessing stage of the model object can be completed locally or on the cloud platform, and it includes the following steps: S1: In the part grouping scenario, the model grouping unit uses the feature extraction ability of the convolutional neural network (CNN) to encode the part geometric features to achieve automatic classification of geometric features. The following is a detailed elaboration in combination with the specific data processing flow and mathematical model: Step 1. Input the image data of the three-dimensional model of the part, which is a two-dimensional projection view, including the front view, top view, side view, etc., and the size is usually , and the format is RGB.

[0039] Step 2. Adjust the input image to a fixed size (such as ), unify the input format for network processing. Then, through a convolutional neural network, such as the open-source neural network framework YOLO, extract features from the image to generate a feature map.

[0040] The method for feature extraction from the image is as follows: Based on the convolutional neural network CNN, taking the third-generation open-source neural network framework YOLOv3 as an example, its feature extraction network Darknet-53 contains 53 convolutional layers. The specific steps are as follows: Step 1. Input the original image.

[0041] Step 2. Perform convolution and downsampling on the original image, that is, use multiple convolutional layers and residual blocks to extract the features of the original image; and perform downsampling through a convolutional layer or pooling layer with a stride of 2 to obtain sampled data; The calculation formula for the convolution operation is as follows:

[0042] Among them, is the output feature map, is the input feature map, is the convolution kernel, is the bias.

[0043] Step 3. Perform multi-scale feature fusion on the sampled data, that is, extract feature maps at different levels (such as , , ), and use upsampling and skip connections to fuse features at different scales to obtain feature maps at multiple scales.

[0044] S4. Output feature maps at multiple scales for subsequent object detection.

[0045] Further divide the feature map into cells. If the center of the target falls into a certain cell, then this cell is responsible for detecting this target. The center of the target refers to the center point coordinates of the true bounding box (Ground Truth Bounding, abbreviated as: Box) of the object. For a bounding box, its position is usually represented by four values, which include the upper left corner coordinates and the lower right corner coordinates , or the center point coordinates plus the width and height .

[0046] Each cell predicts B bounding boxes. A bounding box is a rectangular area used to accurately label the target position in the image. In the open-source neural network framework YOLO, each bounding box is represented by a four-dimensional vector where is the center point coordinate of the bounding box, which is valued relative to the position of the cell, and its value range is 0~1; and are the width and height of the bounding box, which are usually valued according to the width-to-height ratio relationship of the entire image, or valued according to the scaling factor of the prior box. Each bounding box outputs position information (x, y, w, h) and confidence. The position information is used to represent the offset and width / height of the bounding box relative to the cell; the confidence is used to reflect the probability that there is a target within the bounding box, that is, the matching degree between the bounding box and the true target. Its calculation formula is as follows:

[0047] Among them, represents the probability (binary classification) that there is a target within the bounding box, represents the intersection over union of the predicted box and the true box, which is used to measure the accuracy of the predicted box.

[0048] If and at this time, then the final confidence is .

[0049] Each cell also predicts C class probabilities. The C class probabilities predicted by each bounding box are conditional probabilities: , which represents the probability that when there is a target within the bounding box, the target belongs to the i-th class, where represents the existence of the currently predicted target, is the i-th target class. Since each bounding box is only responsible for detecting one class, the sum of the C probabilities is 1.

[0050] S2: The model preprocessing unit uses the automatic mid-surface extraction module to conduct in-depth geometric topology analysis on the input model object, including geometric discretization, element recognition, topology construction, checking Euler consistency, thickness analysis, identifying thin-wall regions, mid-surface generation, and interpolating to generate the mid-surface. Geometric discretization includes generating point clouds by triangular meshing; element recognition includes detecting surfaces (planes / surfaces), detecting edges, and detecting key points; topology construction includes establishing adjacency relationships; thickness analysis includes calculating local thickness; mid-surface generation includes matching inner and outer surfaces. Furthermore, geometric elements such as surfaces, edges, and points in the model object are identified, as well as their topological relationships.

[0051] The model preprocessing unit first processes the input model using the boundary representation method to obtain vertex information, edge information, and face information. The vertex information includes all key vertices extracted from the model data. The edge information includes the edges formed by connecting adjacent vertices and records the faces associated with each edge. The face information is composed of looped edges to form the closed surface of the model. Then, according to preset rules or user-defined rules, the position of the midplane is determined. Usually, the midplane is located near the geometric center of the entity. Then, using computer algorithms, based on the results of geometric topology analysis and the definition of the midplane, a two-dimensional midplane is generated.

[0052] S3: Select a mesh generation algorithm, including mapped mesh, structured mesh generation, swept mesh, filled mesh, quality control, adaptive meshing, etc. Each algorithm is suitable for different geometric features. At the same time, set up a mesh quality control tool to ensure that the generated mesh quality meets the requirements of simulation analysis and supports adaptive mesh refinement.

[0053] Mapped mesh is mainly used for regular quadrilateral / hexahedron to generate structured meshes; swept mesh can map cross-section meshes to a volume along a path and is suitable for pipes; filled mesh is used to automatically generate tetrahedrons / triangles for complex geometries; quality control is used to check indicators such as aspect ratio and Jacobian matrix; the adaptive meshing algorithm automatically refines high-gradient regions based on the solution results.

[0054] S4: Through geometric optimization algorithms, by removing small features (such as chamfers, small holes), surface smoothing (B-spline fitting), gap filling (based on curvature matching), and topological simplification (edge / face merging), combined with curvature analysis to identify key regions, after optimization, ensure geometric continuity and mesh quality, which is suitable for complex model preprocessing. Repair the mesh geometric information, and then use the topological optimization algorithm to perform mesh optimization processing on local regions, thereby significantly improving the overall quality and computational efficiency of the mesh and meeting the requirements of high-precision numerical simulation. The mesh optimization effect can be seen in Figure 2 。

[0055] S5: Construct an assembly recognition algorithm for identifying the assembly relationship between parts, which includes the following: Identify the connection types between parts through geometric feature extraction (such as bolt holes, welds, snap structures) and topological relationship analysis. Utilize the three-dimensional information of the CAD model, combine with the rule library to match connection features, determine the assembly relationships such as bolts, welding, snaps, etc., and provide a basis for generating the assembly sequence. The assembly relationships include bolt connections, welding connections, snap connections, etc., and generate a reasonable assembly sequence according to these relationships. At the same time, assembly sequence optimization technology is introduced. Through methods such as dynamic programming and genetic algorithms, use dynamic programming to decompose assembly sub-problems, construct the optimal path with the goal of minimizing conflicts; use genetic algorithms to encode and evolve the assembly sequence, and optimize the sequence through selection, crossover, and mutation; thus, it can take into account the constraints of multi-component collaborative assembly, reduce interference and assembly conflicts, improve assembly efficiency and stability, and therefore can ensure the coordination and stability between components when dealing with the simultaneous assembly of multiple parts. And provide a user interactive adjustment function, allowing users to fine-tune the assembly parameters according to actual needs to further improve the assembly quality.

[0056] In this embodiment, in the cloud service interaction stage combining online and offline, through the Hypertext Transfer Protocol HTTP, a series of new methods and header fields are introduced, allowing users to conveniently manage and edit the pre-processing data on the remote server through a Web browser or other clients (user side), just like operating local files. The related operations include mounting the cloud storage path, synchronously downloading simulation objects, synchronously uploading simulation objects, deleting simulation objects, and locking simulation objects. The simulation object is a CAE model.

[0057] S1: The protocol interaction process for mounting the cloud storage path is as follows: Step 1. The client sends a property information request to the server to obtain the properties and methods supported by the server. This request is similar to the client "detecting" the capabilities of the server.

[0058] Step 2. After receiving the property information request for obtaining resources, the server returns a property information response, which contains a list of the properties and methods supported by the server. The client determines which operations can be performed on the server based on this information.

[0059] Step 3. If the client needs to create a new directory on the server, it will send a request for creating a new collection. This request is used to create a new collection, that is, a new directory.

[0060] Step 4. The client sends the content of the file to the server through an upload request. The upload request is used to create a new resource or replace one.

[0061] S2: The protocol interaction process for synchronously downloading simulation objects is as follows: Step 1. The client sends an HTTP GET request to request the download of a file at a specified path. The request contains necessary header information such as the hostname and authentication information.

[0062] Step 2. After receiving the request, the server locates the specified file. If the file exists and the authentication is passed, the server returns a status code of "completed", which can be represented by the number 200, and includes the file content in the response body.

[0063] Step 3. The client receives the file content returned by the server and saves it to the local file system.

[0064] S3: The protocol interaction process for synchronously uploading simulation objects is as follows: Step 1. The client sends an HTTP upload request to request the upload of a file at a specified path. The request contains necessary header information such as the hostname, authentication information, and file content.

[0065] Step 2. After receiving the request, the server verifies the authentication information and processes the file upload. If the file upload is successful, the server returns a status code of "created", which can be represented by the number 201, indicating that the resource has been successfully created.

[0066] S4: The protocol interaction process for deleting simulation objects is as follows: Step 1. The client sends an HTTP delete request to request the deletion of a file at a specified path. The request contains necessary header information such as the hostname and authentication information.

[0067] Step 2. After receiving the request, the server verifies the authentication information and processes the file deletion. If the file deletion is successful, the server returns a status code of "no content", which can be represented by the number 204, indicating that the request was successful and there is no response body.

[0068] S5: The protocol interaction process for locking simulation objects is as follows: Step 1. The client sends an HTTP lock request to request the locking of a file at a specified path. The request contains necessary header information such as the hostname, authentication information, and locking information.

[0069] Step 2. After receiving the request, the server verifies the authentication information and processes the file locking. If the file locking is successful, the server returns a status code of "locked", which can be represented by the number 200, and includes the lock token and other locking information in the response body.

[0070] Meanwhile, the present invention can ensure the efficiency and real-time nature of file synchronization through an incremental synchronization mechanism, a real-time file monitoring mechanism, a breakpoint resumption and compression mechanism, and a multi-user operation mechanism.

[0071] Incremental synchronization mechanism, including the following: Adopt a block comparison algorithm to only transfer the parts of the file that actually change (such as modified mesh data, parameter configurations), avoiding full-scale transfer. For example, for a 1GB CAE model, only about 10% of the differential data needs to be transferred, significantly reducing the network load.

[0072] Real-time monitoring mechanism, including the following: Through operating system-level file event listening (such as the file change notification function in Windows), real-time capture operations such as saving and modifying local files, triggering immediate synchronization. Combined with asynchronous queue processing, it ensures that user operations are not blocked during multi-task concurrency, achieving a seamless experience of "editing while synchronizing".

[0073] Breakpoint resume and compression mechanism, including the following: Support block-by-block upload and download, and can resume from the breakpoint after interruption; dynamically compress the transmitted data to further improve the transmission speed and still maintain stability in a weak network environment.

[0074] Multi-user operation mechanism, including the following: Support multi-user simultaneous access and editing of files, allowing multiple engineers to collaborate on the same project, improving the work efficiency of the team. Support a locking mechanism to prevent multiple users from modifying the same file simultaneously, ensuring data integrity and consistency. Transmit data through the Hypertext Transfer Protocol HTTPS to ensure the security of data transmission. Support file version control, allowing multiple versions of files to coexist, and support comparison and restoration of differences between versions. The client can configure the automatic synchronization function. Through real-time monitoring and incremental transmission technology, ensure that the local CAE pre-processing data and the cloud CAE pre-processing data are always consistent, reducing the workload of manual synchronization. Ensure seamless use by users on different platforms.

[0075] In summary, the present invention enables users to access and edit model files in the cloud on a local computer as if they were operating local files, without the need to frequently upload and download files, improving the efficiency of data access.

[0076] An apparatus embodiment applying the method of the present invention: An electronic device, which includes: One or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method for cloud model simulation control based on pre-processing.

[0077] A computer medium embodiment applying the method of the present invention: A computer-readable storage medium stores a computer program thereon, and when the program is executed by a processor, it implements the above-described cloud model simulation control method based on preprocessing.

[0078] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0079] The units in the present application are objects that constitute an objective description of the morphological structure by means of physical or virtual representations. The objects are not equal to objects and are not limited to physical and virtual. They can be data processing functions, software programs, processing modes, usage methods, operation modes, work processes, application processes, electronic hardware, circuit modules, processing systems, system imitations, or simulation objects.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art can still modify or equivalently replace the specific embodiments of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A cloud model simulation control method based on pre - processing, characterized in that: It includes the following: Based on a pre - constructed model grouping unit, group a certain model object and perform geometric topology analysis to obtain part grouping data; Use a pre - constructed model pre - processing unit to identify geometric features of the part grouping data and perform meshing to obtain part - component mesh data; Use a pre - constructed model assembly unit to identify the assembly relationship between parts, and assemble the part - component mesh data according to the assembly relationship to obtain a mesh simulation object; Through a pre - constructed cloud processing unit, obtain the request information sent by the user; And according to the request information, process the mesh simulation object and generate response feedback information to achieve cloud model simulation control based on pre - processing.

2. The cloud model simulation control method based on pre - processing according to claim 1, characterized in that: The method of grouping a certain model object and performing geometric topology analysis based on a pre - constructed model grouping unit to obtain part grouping data is as follows: Obtain the image information of a certain model object, which includes several orthographic views; Adjust several orthographic views to a fixed size to obtain a model image in a unified format; Extract features from the model image through a convolutional neural network to generate a feature map; According to the feature map, encode and classify the geometric features of the model object to obtain part grouping data.

3. The cloud model simulation control method based on pre - processing according to claim 1, characterized in that: The method of using a pre - constructed model pre - processing unit to identify geometric features of the part grouping data and perform meshing to obtain part - component mesh data is as follows: Identify geometric features of the part grouping data to obtain part face information; the part face information includes the geometric elements of the part - component and the topological information between the geometric elements; the geometric elements include vertex information, edge information, and face information; Process the part face information to obtain face geometric features; Based on the face geometric features, use a mapped grid algorithm or a swept grid algorithm or a filled grid algorithm to perform grid division on the part face information to obtain grid geometric information; Repair the grid geometric information through a geometric optimization algorithm, and then use a topological optimization algorithm to optimize the local area of the grid geometric information to obtain part - component mesh data.

4. The cloud model simulation control method based on pre - processing according to claim 3, characterized in that: The method of identifying geometric features of the part grouping data to obtain part face information is as follows: Perform geometric feature analysis on the part grouping data to obtain all vertex information; According to the vertex information, connect adjacent vertices to form edges, thereby obtaining edge information; According to the edge information, connect adjacent edges to form loops, thereby obtaining edge combinations; Based on the edge combinations, obtain closed - surface information; Based on the geometric center of a certain model object, process the closed - surface information to obtain the mid - surface position, that is, face information; Couple and summarize the face information, edge information, and vertex information to obtain part face information.

5. A cloud model simulation control method based on preprocessing as claimed in claim 1, wherein: The method of using a pre-constructed model assembly unit to identify the assembly relationships between parts and assembling the component grid data according to the assembly relationships to obtain a grid simulation object is as follows: Based on a certain model object, identify the assembly relationships between parts, which include bolt connection and / or welding and / or snap connection; generate an assembly sequence according to the assembly relationships; Optimize the assembly sequence through dynamic programming method and / or genetic algorithm to generate the final assembly sequence information; Assemble the component grid data according to the final assembly sequence information to obtain a grid simulation object.

6. A cloud model simulation control method based on preprocessing as claimed in claim 1, wherein: The method of obtaining the request information sent by the user through a pre-constructed cloud processing unit; and processing the grid simulation object according to the request information and generating a response feedback information is as follows: Obtain the attribute request information sent by the user; According to the request attribute information, return the attribute response information, and the attribute response information includes the attribute information and operation information supported by the server; Based on the Hypertext Transfer Protocol, and according to the attribute information and operation information supported by the server, obtain the operation request information sent by the user; The operation request information is to request synchronous download of a certain simulation object, request synchronous upload of a certain simulation object, request deletion of a certain simulation object or request locking of a certain simulation object; According to the operation request information, perform corresponding processing on the grid simulation object and generate a response feedback information.

7. A cloud model simulation control method based on preprocessing as claimed in claim 6, wherein: The method of performing corresponding processing on the grid simulation object according to the operation request information and generating a response feedback information is as follows: Step 11, the operation request information is to request synchronous download of a certain simulation object; Parse the request for synchronous download of a certain simulation object to obtain authentication information, the file path information of the requested download and the requested host name; Step 12, check the authentication information, and when the authentication is passed, execute step 13; When the authentication fails, generate a first response body, and the first response body includes an authentication error status code; And execute step 14; Step 13, according to the file path information, search for the specified grid simulation object, if the grid simulation object does not exist, generate a first response body, and the first response body includes a file error status code; And execute step 14; If the grid simulation object exists, generate a second response body, and the second response body includes a completion status code and the grid simulation object; And execute step 15; Step 14, process the first response body according to the Hypertext Transfer Protocol to obtain a response feedback information, and transmit the response feedback information, that is, the first response body, to the user side according to the requested host name; Step 15, process the second response body according to the Hypertext Transfer Protocol to obtain a response feedback information, and transmit the response feedback information, that is, the second response body, to the user side according to the requested host name.

8. A cloud model simulation control method based on preprocessing as claimed in claim 6, wherein: The method for processing a grid simulation object according to operation request information and generating response feedback information is as follows: Step 21, the operation request information is a request to synchronously upload a certain simulation object; Parse the request to synchronously upload a certain simulation object to obtain authentication information, the file path information of the requested upload, and the requested host name; Step 22, check the authentication information. When the authentication passes, execute Step 23; When the authentication fails, generate a third response body, and the third response body includes a creation error status code; And execute Step 24; Step 23, according to the file path information, upload a certain simulation object according to the Hypertext Transfer Protocol; And adjust the grid simulation object according to a certain simulation object; or replace the original grid simulation object according to a certain simulation object; at the same time, generate a fourth response body, and the fourth response body includes a creation success status code; And execute Step 25; Step 24, process the third response body according to the Hypertext Transfer Protocol to obtain response feedback information, and transmit the response feedback information, that is, the third response body, to the client according to the requested host name; Step 25, process the fourth response body according to the Hypertext Transfer Protocol to obtain response feedback information, and transmit the response feedback information, that is, the fourth response body, to the client according to the requested host name.

9. A preprocessing-based cloud model simulation control method according to claim 6, characterized in that: The method for processing a grid simulation object according to operation request information and generating response feedback information is as follows: Step 31, the operation request information is a request to delete a certain simulation object or a request to lock a certain simulation object; Parse the operation request information to obtain authentication information, the file path information of the requested processing, and the requested host name; Step 32, check the authentication information. When the authentication passes, execute Step 33; When the authentication fails, generate a failure status code; And execute Step 34; Step 33, perform a delete or lock operation on the grid simulation object according to the file path information and obtain a success status code; Then execute Step 35; Step 34, process the failure status code according to the Hypertext Transfer Protocol to obtain response feedback information, and transmit the response feedback information to the client according to the requested host name; Step 35, process the success status code according to the Hypertext Transfer Protocol to obtain response feedback information, and transmit the response feedback information to the client according to the requested host name.

10. A preprocessing-based cloud model simulation control system, characterized in that: Apply a preprocessing-based cloud model simulation control method according to any one of claims 1-9; it includes a preprocessing module, a cloud control module, an incremental synchronization module, and a real-time file monitoring module; The preprocessing module is provided with a model grouping unit, a model preprocessing unit, and a model assembly unit for grouping a certain model object and performing geometric topology analysis to obtain a grid simulation object; The cloud control module is provided with a cloud processing unit for obtaining the request information sent by the user; And process the grid simulation object according to the request information and generate response feedback information; The incremental synchronization module is constructed using a block comparison algorithm and is used to transfer the changed parts of a file; The real-time file monitoring module is constructed based on a file event listening mechanism and is used to capture in real time the save operation, modification operation, and deletion operation of local files, triggering immediate synchronization; At the same time, it can cooperate with asynchronous queue processing to ensure that user operations are not blocked during multi-task concurrency, realizing simultaneous editing and synchronization of files.

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