A method and system for improving grid quality in a grid model of an automotive plastic part

Abstract

The application provides a grid quality improvement method and system for a car plastic part grid model, comprising: establishing a mapping relationship between a reinforcing rib-grid element set; comparing the number of grid elements with a pre-set grid number threshold to retain the current grid element set; comparing the area of each grid with a single grid area threshold and judging the effectiveness of the reinforcing rib to delete all grid elements; constructing a set of internal feature lines to be processed, extracting all grid elements bound with the internal feature lines to construct a feature line related grid set, and performing binary determination on each group of internal feature lines and feature line related grid set; performing related grid total area quantification verification through an area quantification verification algorithm, traversing the constructed merging list and deleting invalid feature lines in the list. The application can automatically screen out invalid reinforcing ribs and small feature lines that affect grid quality through rules, and improve the overall grid quality after corresponding processing.

Description

Technical Field

[0001] This application belongs to the field of automotive structure simulation analysis technology, and in particular relates to a method and system for improving mesh quality in automotive plastic part mesh models. Background Technology

[0002] In the field of automotive structural simulation analysis, the quality of the finite element mesh model directly determines the computational accuracy, solution efficiency, and stability. During the mesh generation process for plastic parts such as interior and exterior trim, door panels, and bumpers, a large number of excessively small stiffeners and feature lines are often generated. These tiny features originate from the original CAD model's detailed chamfers, process markings, local fillets, and residual mold features. While they do not participate in structural stress and stiffness transfer, they significantly increase the number of elements, causing mesh distortion and consuming computational resources.

[0003] Currently, the industry mainly relies on engineers to manually screen and measure invalid features and delete them. Traditional manual cleaning is time-consuming and the mesh construction efficiency is insufficient, making it difficult to meet the requirements of high precision, high efficiency and standardization of mesh preprocessing. There is an urgent need for a method that can automatically identify areas where the mesh quality does not meet the standards and automatically optimize the area mesh. Summary of the Invention

[0004] In view of this, this application aims to propose a method and system for improving the mesh quality in automotive plastic part mesh models, in order to solve the problems of low efficiency, inconsistent standards and easy errors in the preprocessing of existing automotive plastic part mesh models by manually adjusting the mesh quality.

[0005] To achieve the above objectives, the technical solution of this application is implemented as follows: Firstly, this application provides a method for improving the mesh quality in a mesh model of automotive plastic parts, including: All stiffener structural entities are extracted by globally filtering the mesh model. By traversing each stiffener structural entity, the mesh cells associated with the stiffener structural entity are collected, and a stiffener-mesh cell set mapping relationship is established. By traversing each set of grid cells and counting the number of grid cells within it, the counted number of grid cells is compared with a pre-set threshold for the number of grid cells to retain the current set of grid cells. By traversing the data structure of the current grid cell set, the area of ​​each grid cell in the grid cell set is determined. The area of ​​each grid cell is compared with a pre-set single grid cell area threshold. The validity of the stiffener is determined by the stiffener grid quantization judgment algorithm. All grid cells are deleted based on the validity of the stiffener. Collect all visible feature line entities in the mesh model and construct a set of internal feature lines to be processed. Construct a feature line associated mesh set by extracting all mesh cells that are collinear with the internal feature lines. By counting the number of associated grid cells for each feature line, a binary decision is made for each group of internal feature lines and the set of associated grid cells based on the number of cells. The total area of ​​the associated grids is quantized and verified using an area quantization verification algorithm to construct a list to be merged. The list to be merged is traversed and invalid feature lines are deleted. The grids after deletion are then reconstructed.

[0006] Secondly, based on the same inventive concept, this application also provides a mesh quality improvement system for automotive plastic part mesh models, comprising: The stiffener structure entity acquisition module is configured to extract all stiffener structure entities by globally filtering the mesh model, collect the mesh elements associated with each stiffener structure entity by traversing each acquired stiffener structure entity, and establish a stiffener-mesh element set mapping relationship. The grid cell comparison module is configured to iterate through each grid cell set and count the number of grid cells in it, then compare the counted number of grid cells with a pre-set grid cell count threshold to retain the current grid cell set. The invalid stiffener deletion module is configured to determine the area of ​​each grid cell in the current grid cell set by traversing the data structure of the current grid cell set, compare the area of ​​each grid cell with a pre-set single grid cell area threshold, and determine the validity of the stiffener by a stiffener grid quantization judgment algorithm, and delete all grid cells based on the validity of the stiffener. The feature line entity acquisition module is configured to collect all visible feature line entities in the mesh model and construct a set of internal feature lines to be processed. It constructs a feature line associated mesh set by extracting all mesh cells that are collinear with the internal feature lines. The binary decision module is configured to perform binary decision on each set of internal feature lines and feature line associated mesh sets by counting the number of associated mesh units for each feature line and according to the number branch decision algorithm. The invalid feature line deletion module is configured to perform total area quantization verification of associated meshes through an area quantization verification algorithm to construct a list to be merged, traverse the list to be merged and delete invalid feature lines in the list, and reconstruct the mesh after deletion.

[0007] Thirdly, based on the same inventive concept, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the first aspect.

[0008] Fourthly, based on the same inventive concept, this application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions for causing the computer to perform the method as described in the first aspect.

[0009] Compared with existing technologies, the method and system for improving mesh quality in automotive plastic part mesh models described in this application have the following advantages: The method described in this application is implemented based on the secondary development interface of CAE preprocessing software. It uses a program to replace manual screening and correction of common unqualified meshes, which greatly improves the efficiency and accuracy of mesh model cleaning. It effectively solves the problem of insufficient mesh quality caused by small-sized reinforcing rib structures and excessively short feature lines. This method can automatically improve the quality of automotive mesh models. Attached Figure Description

[0010] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating a method for improving mesh quality in a mesh model of an automotive plastic part, as described in an embodiment of this application. Figure 2 This is a schematic diagram of the reinforcing rib mesh described in the embodiments of this application; Figure 3 This is a schematic diagram of a mesh that has no practical engineering significance, as described in the embodiments of this application. Figure 4 This is a schematic diagram of the feature line type and the feature line associated mesh described in the embodiments of this application; Figure 5 This is a schematic diagram of an invalid feature line with an excessively small size as described in an embodiment of this application; Figure 6 This is a diagram showing the effect of deleting feature lines and reconstructing the mesh as described in an embodiment of this application. Figure 7 This is a schematic diagram of a mesh quality improvement system in a mesh model of an automotive plastic part according to an embodiment of this application; Figure 8 This is a schematic diagram of the hardware structure of the electronic device described in an embodiment of this application. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0012] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0013] The embodiments of this application are described in detail below with reference to the accompanying drawings.

[0014] Please see Figure 1 As shown in the figure, this embodiment provides a method for improving the mesh quality in a mesh model of automotive plastic parts, specifically including the following steps: Step S10: Extract all stiffener structural entities through global filtering of the mesh model. By traversing each stiffener structural entity, collect the mesh cells associated with the stiffener structural entity and establish a stiffener-mesh cell set mapping relationship.

[0015] Specifically, in this embodiment, by performing global model entity filtering on the input mesh model in the CAE preprocessing software tool, all structural entities of type stiffener are obtained. Each obtained stiffener structural entity is traversed, and all visible mesh elements associated with that stiffener structure are collected. A dictionary mapping structure MAP is constructed to store each group of mesh elements, with the key being the stiffener structural entity. The value is the corresponding grid cell set. The unified expression can be represented as: ; like Figure 2 As shown in the example, the reinforcing rib consists of five mesh panels. If five grids are associated, then... .

[0016] Loop through each Retrieve associated grid cells one by one and generate subsets Each group is stored in the mapping structure MAP to complete the binding and storage of all stiffeners and meshes.

[0017] Step S20: By traversing each grid cell set and counting the number of grid cells in it, the counted number of grid cells is compared with a pre-set grid cell count threshold to retain the current grid cell set.

[0018] Specifically, in this embodiment, each grid cell set is traversed and the number of grid cells within it is counted, and a grid number threshold is set. For any set of grid cells Define a function to count the number of grid cells: ; In the formula, Representative grid cell set The total number of inner grid cells.

[0019] Scenario 1: If ,Right now = 1 or = 2. This indicates that the stiffener consists of only a small number of isolated meshes and has no actual engineering significance in terms of load-bearing reinforcement. (See attached diagram.) Figure 3 Typical example: Deleting key-value pairs from a mapping structure MAP Discard this set of data.

[0020] Scenario 2: If If the number of meshes in a concentrated mesh element is greater than or equal to 3, it belongs to a stiffener mesh that requires further evaluation; retain the key-value pairs. Then proceed to the area secondary verification process.

[0021] Step S30: By traversing the data structure of the current grid cell set, determine the area of ​​each grid cell in the grid cell set, compare the area of ​​each grid cell with the pre-set single grid cell area threshold, and determine the effectiveness of the reinforcing ribs by the reinforcing rib grid quantization judgment algorithm. Based on the effectiveness of the reinforcing ribs, delete all grid cells.

[0022] Specifically, in this embodiment, a single grid area threshold is set. Iterate through the data structure in the result of step S20, and perform a grid cell set. Any grid within Grid area is obtained through measurement interface , For grid The unit area value. Subsequently, for each... Comparison of execution area and threshold.

[0023] The invalidity of a stiffener is determined using an invalid stiffener mesh quantization judgment algorithm, the expression of which is: ; In the formula, This is an indicator function: it takes a value of 1 if the condition is true, and 0 if the condition is false. for China satisfies The number of grids.

[0024] In this embodiment, the determination rule is set as follows: if the proportion of small-area meshes exceeds 50%, it is determined to be an invalid reinforcing rib. The expression is: ; Scenario 1: If the above formula is true, then All internal mesh elements are collected into the global mesh set Del_Set to be deleted, and all mesh elements in the resulting mesh set Del_Set are deleted, thus completing the automatic cleanup of stiffening mesh structures with excessively small heights.

[0025] Scenario 2: If the above formula is not true, it means that the reinforcing rib meets the standard, is an effective structure, and the mesh is retained.

[0026] This embodiment addresses the problem of insufficiently tall reinforcing rib mesh structures in automotive mesh models. These structures lack practical engineering significance and increase model complexity, leading to the inability to automatically and accurately identify their height. This embodiment determines whether a reinforcing rib structure is an invalid, small-sized structure by analyzing the area characteristics of the mesh cells associated with it, ultimately achieving automated removal and improving mesh quality.

[0027] Step S40: Collect all visible feature line entities in the mesh model and construct a set of internal feature lines to be processed. Construct a feature line associated mesh set by extracting all mesh cells that are collinear with the internal feature lines.

[0028] Specifically, in this embodiment, after removing stiffening meshes that have no practical engineering significance from the mesh model, some substandard meshes still exist. The root cause of these defects is the constraint imposed by small internal feature lines, which squeeze surrounding mesh cells, leading to mesh fragmentation and mesh cell distortion. This embodiment establishes a quantitative judgment model based on the position of feature lines, the number of associated mesh cells, and the total area of ​​mesh cells. This model automatically filters out and merges invalid small-sized feature lines, achieving secondary optimization of the model mesh quality.

[0029] Specifically, in CAE preprocessing software tools, all visible feature line entities of the input mesh model are collected to construct a set of internal feature lines to be processed. Its expression is: ; In the formula, The set of all visible feature line entities in the model; For the first Single feature line entity, ; This is a list of tasks to be processed.

[0030] Among them, the rule for determining the position attribute of the feature line is as follows: Read each feature line The value of the built-in field Sides distinguishes between two types of feature lines: Characteristic lines Those existing inside the model, which can easily cause local mesh compression, are included in the processing scope. Other cases are represented as follows: Only the outer contour of the model is constrained, without causing internal mesh fragmentation, and is therefore discarded directly. Correspondingly... Figure 4 The green lines represent internal feature lines, while the red lines represent edge feature lines and are not included in the calculation.

[0031] like (No internal feature lines) The process of this module will be terminated directly.

[0032] Step S50: By counting the number of associated grid cells for each feature line, perform a binary decision on each group of internal feature lines and the associated grid set of feature lines according to the number branch decision algorithm.

[0033] Specifically, in this embodiment, the feature lines are traversed and the number of associated grid cells for each feature line is counted. A counting function is defined, and the result of the counting function is compared with a preset threshold for the number of associated grid cells. If the result of the counting function is greater than or equal to the threshold of the number of associated grids, it is determined to be a valid feature line and no processing is performed; if the result of the counting function is less than the threshold of the number of associated grids, an intermediate result set is constructed, and the internal feature lines and their corresponding grid cells are stored in the intermediate result set for the purpose of performing quantitative verification of the total area of ​​associated grids.

[0034] Specifically, in this embodiment, the threshold for the number of associated grids is set to 6, that is... For any internal feature line Extract all mesh cells that are collinear with the feature line and construct a feature line associated mesh set: ; In the formula, Internal feature lines The associated first Mesh This represents the total number of grid cells.

[0035] Count the number of associated mesh cells for each internal feature line using a counting function. .like Figure 4 As shown, grids 1 to 6 are collinear with the internal feature line; therefore, these six grids are associated grids of this feature line. =6. Finally, the number of branches decision algorithm is executed. The core of this algorithm is to process each group... Perform a binary decision: Scenario 1: If This indicates that the feature line is relatively long. Longer feature lines can accurately reflect the geometric changes of the model and will not cause mesh compression or fragmentation, so no processing is required.

[0036] Scenario 2: If This indicates that the feature line coverage is narrow, which easily leads to strong local constraints and mesh distortion. An intermediate result set is then constructed. And store the internal feature line and the corresponding mesh cell, the corresponding formula can be expressed as: = { }

[0037] Step S60: Perform total area quantization verification of associated grids using the area quantization verification algorithm to construct a list to be merged, traverse the list to be merged and delete invalid feature lines in the list, and reconstruct the grids after deletion.

[0038] Specifically, in this embodiment, a quantitative verification of the total area of ​​associated grids is performed to construct a list to be merged. Its expression is: = { | ; For intermediate result sets any group Mesh cells are obtained through the measurement interface. area Summation calculates the total area of ​​the mesh associated with the feature lines. .

[0039] The preset total area threshold is [0, 10], and at least two associated grids are required. A determination can only be made when the multi-condition joint inequality for invalid feature lines is simultaneously satisfied. These are invalid feature lines that are too small. Figure 5 For invalid feature line samples, store this type of feature line in... .

[0040] Batch deletion operation: If traverse the set The CAE software's entity deletion interface is invoked to remove all feature line entities within the set, one by one. Deletion releases the forced geometric constraints imposed by local, minute features, eliminating the source of mesh fragmentation.

[0041] After deleting invalid feature lines, the local geometric constraints within the model are removed, and the mesh elements are no longer subject to the forced segmentation constraints of narrow feature lines. The CAE software's mesh reconstruction interface is then called, and the reconstructed elements meet a unified mesh quality standard. For example... Figure 6As shown, the reconstructed central triangular mesh is merged, solving the problem that the original triangular mesh was not up to standard due to its small size.

[0042] This embodiment is based on the secondary development interface of CAE preprocessing software. It uses a program to replace manual screening and correction of common unqualified meshes, which greatly improves the efficiency and accuracy of mesh model cleaning. It effectively solves the problem of insufficient mesh quality caused by small-sized reinforcing rib structures and excessively short feature lines. This method can automatically improve the quality of automotive mesh models.

[0043] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0044] Based on the same inventive concept, and corresponding to the methods of any of the above embodiments, the embodiments of this application also provide a mesh quality improvement system in a mesh model of automotive plastic parts.

[0045] like Figure 7 As shown, the mesh quality improvement system in the automotive plastic part mesh model includes: The stiffener structure entity acquisition module 11 is configured to extract all stiffener structure entities by globally filtering the mesh model, collect the mesh elements associated with each stiffener structure entity by traversing each acquired stiffener structure entity, and establish a stiffener-mesh element set mapping relationship. The grid cell comparison module 12 is configured to traverse each grid cell set and count the number of grid cells in it, and compare the counted number of grid cells with a preset grid number threshold in order to retain the current grid cell set. The invalid stiffener deletion module 13 is configured to determine the area of ​​each grid in the current grid cell set by traversing the data structure of the current grid cell set, compare the area of ​​each grid with a pre-set single grid area threshold, and determine the validity of the stiffener by a stiffener grid quantization judgment algorithm, and delete all grid cells based on the validity of the stiffener. The feature line entity acquisition module 14 is configured to collect all visible feature line entities in the mesh model and construct a set of internal feature lines to be processed. It constructs a feature line associated mesh set by extracting all mesh cells that are collinear with the internal feature lines. The binary decision module 15 is configured to perform binary decision on each set of internal feature lines and feature line associated mesh sets by counting the number of associated mesh units of each feature line and according to the number branch decision algorithm. The invalid feature line deletion module 16 is configured to perform total area quantization verification of associated meshes through an area quantization verification algorithm to construct a list to be merged, traverse the list to be merged and delete invalid feature lines in the list, and reconstruct the mesh after deletion.

[0046] For ease of description, the above system is described by dividing it into various modules based on their functions. Of course, in implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware.

[0047] The system described in the above embodiments is used to implement the corresponding method in any of the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0048] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the methods described in any of the above embodiments.

[0049] Figure 8 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0050] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0051] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0052] The input / output interface 1030 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0053] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0054] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0055] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0056] The electronic devices described above are used to implement the corresponding methods in any of the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0057] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to perform the methods described in any of the above embodiments.

[0058] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0059] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to perform the methods described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0060] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0061] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the claims of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A method for improving mesh quality in a mesh model of automotive plastic parts, characterized in that, include: All stiffener structural entities are extracted by globally filtering the mesh model. By traversing each stiffener structural entity, the mesh cells associated with the stiffener structural entity are collected, and a stiffener-mesh cell set mapping relationship is established. By traversing each set of grid cells and counting the number of grid cells within it, the counted number of grid cells is compared with a pre-set threshold for the number of grid cells to retain the current set of grid cells. By traversing the data structure of the current grid cell set, the area of ​​each grid cell in the grid cell set is determined. The area of ​​each grid cell is compared with a pre-set single grid cell area threshold. The validity of the stiffener is determined by the stiffener grid quantization judgment algorithm. All grid cells are deleted based on the validity of the stiffener. Collect all visible feature line entities in the mesh model and construct a set of internal feature lines to be processed. Construct a feature line associated mesh set by extracting all mesh cells that are collinear with the internal feature lines. By counting the number of associated grid cells for each feature line, a binary decision is made for each group of internal feature lines and the set of associated grid cells based on the number of cells. The total area of ​​the associated grids is quantized and verified using an area quantization verification algorithm to construct a list to be merged. The list to be merged is traversed and invalid feature lines are deleted. The grids after deletion are then reconstructed.

2. The method according to claim 1, characterized in that, The process involves extracting all stiffener structural entities through a global mesh model, iterating through each stiffener structural entity, collecting the mesh elements associated with each stiffener structural entity, and establishing a stiffener-mesh element set mapping relationship, including: The input network model is subjected to global model entity screening using CAE preprocessing software tools, and all structural entities of the type of stiffener are extracted. Iterate through each obtained stiffener structure entity, collect all visible mesh cells associated with the stiffener structure entity, and construct a dictionary mapping structure to store each mesh cell; where the key is the stiffener structure entity and the value is the corresponding mesh cell set; Iterate through each stiffener structure entity, retrieve associated mesh elements one by one and generate subsets, then store each subset into the mapping structure.

3. The method according to claim 2, characterized in that, The step of traversing each grid cell set and counting the number of grid cells within it, then comparing the counted number of grid cells with a pre-set grid cell count threshold to determine which grid cell set to retain, includes: Iterate through each set of grid cells and count the number of grid cells in it. Define a grid cell count function for any set of grid cells and compare the counted number of grid cells with a set grid cell count threshold. If the number of grid cells is greater than or equal to the grid number threshold, the key-value pair is retained and a second area verification is performed. If the number of grid cells is less than the grid number threshold, then the key-value pair is deleted from the mapping structure.

4. The method according to claim 1, characterized in that, The step of determining the effectiveness of reinforcing ribs through a reinforcing rib mesh quantization judgment algorithm and deleting all mesh elements based on the effectiveness of the reinforcing ribs includes: If the proportion of grids with a grid area smaller than the single grid area threshold exceeds a preset threshold, it is determined to be an invalid reinforcing rib. All grid cells in the grid cell set are then aggregated into the global result grid set, and all grid cells in the global result grid set are deleted. If the percentage of grids with a grid area smaller than the single grid area threshold does not exceed a preset threshold, the reinforcing rib is determined to be an effective structure.

5. The method according to claim 1, characterized in that, The process involves counting the number of associated grid cells for each feature line and performing a binary decision on each group of internal feature lines and their associated grid sets using a branch decision algorithm based on the number of cells. This includes: Traverse the feature lines and count the number of associated grid cells for each feature line, define a counting function, and compare the result of the counting function with a preset threshold for the number of associated grid cells; If the result of the counting function is greater than or equal to the threshold of the number of associated grids, it is determined to be a valid feature line and no further processing is performed. If the result of the counting function is less than the threshold of the number of associated grids, an intermediate result set is constructed, and the internal feature lines and corresponding grid cells are stored in the intermediate result set to perform quantitative verification of the total area of ​​the associated grids.

6. The method according to claim 5, characterized in that, The step of performing total area quantization verification of associated grids using an area quantization verification algorithm to construct a list to be merged includes: For any set of internal feature lines and feature line associated meshes in the intermediate result set, the total area of ​​the feature line associated meshes is obtained by acquiring the area of ​​the mesh cells associated with the internal feature lines and summing them. The total area of ​​the feature line associated grid is compared with a preset total area interval threshold, and the result of the counting function is limited to be greater than or equal to a preset value; If the condition is met, the internal feature line is determined to be an invalid feature line and stored in the list to be merged. If the list to be merged is a non-empty set, the list to be merged is traversed and all invalid feature lines in the list are deleted one by one.

7. The method according to claim 1, characterized in that, Also includes: After deleting invalid feature lines, the mesh is reconstructed by calling the CAE software mesh reconstruction interface, and the reconstructed central triangle mesh is merged.

8. A mesh quality improvement system for automotive plastic part mesh models, characterized in that, include: The stiffener structure entity acquisition module is configured to extract all stiffener structure entities by globally filtering the mesh model, collect the mesh elements associated with each stiffener structure entity by traversing each acquired stiffener structure entity, and establish a stiffener-mesh element set mapping relationship. The grid cell comparison module is configured to iterate through each grid cell set and count the number of grid cells in it, then compare the counted number of grid cells with a pre-set grid cell count threshold to retain the current grid cell set. The invalid stiffener deletion module is configured to determine the area of ​​each grid cell in the current grid cell set by traversing the data structure of the current grid cell set, compare the area of ​​each grid cell with a pre-set single grid cell area threshold, and determine the validity of the stiffener by a stiffener grid quantization judgment algorithm, and delete all grid cells based on the validity of the stiffener. The feature line entity acquisition module is configured to collect all visible feature line entities in the mesh model and construct a set of internal feature lines to be processed. It constructs a feature line associated mesh set by extracting all mesh cells that are collinear with the internal feature lines. The binary decision module is configured to perform binary decision on each set of internal feature lines and feature line associated mesh sets by counting the number of associated mesh units for each feature line and according to the number branch decision algorithm. The invalid feature line deletion module is configured to perform total area quantization verification of associated meshes through an area quantization verification algorithm to construct a list to be merged, traverse the list to be merged and delete invalid feature lines in the list, and reconstruct the mesh after deletion.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as claimed in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that, in, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to perform the method described in any one of claims 1-7.

Images