Remote load field visualization optimization method and device for lightweight simulation application

Abstract

The application provides a remote load field visualization optimization method and device for lightweight simulation application, and relates to the technical field of engineering simulation, and comprises the following steps: obtaining a simulation result file, and constructing a solution field object according to the simulation result file to parse a target physical quantity set from the solution field object; performing statistical analysis and processing on the target physical quantity set by using an adaptive filtering and dynamic threshold suppression algorithm to determine global distribution characteristics, and performing coupling point data processing on the target physical quantity set according to the global distribution characteristics to obtain a smooth physical field, wherein the coupling point data processing comprises dynamic threshold identification processing, nonlinear outlier suppression processing and Gaussian weighted smoothing processing; performing visualization artifact processing on the smooth physical field to obtain a data file in a target format, and performing encapsulation processing on the data file in the target format to output a target visualization result file. The application can significantly improve the stability and reliability of simulation results.

Description

Technical Field

[0001] This invention relates to the technical field of engineering simulation, and in particular to a method and apparatus for remote load field visualization optimization in lightweight simulation applications. Background Technology

[0002] In the field of structural finite element simulation, the accuracy of post-processing and visualization of simulation results has a significant impact on the accuracy of engineering analysis. Currently, related technologies propose introducing simulation components based on coupling points as fundamental loads. These coupling point components can be linked to other load components to quickly generate specific loads for lightweight simulation applications. However, this approach is prone to issues such as abnormally prominent deformation fields caused by the coupling points themselves, and numerical misalignment and artifacts during the conversion of result files to visualization files. These factors negatively impact the visualization accuracy of the simulation results. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a method and apparatus for remote load field visualization optimization in lightweight simulation applications, which can significantly improve the stability and reliability of simulation results.

[0004] In a first aspect, embodiments of the present invention provide a method for optimizing the visualization of a remote load field in a lightweight simulation application. The method includes: acquiring a simulation result file and constructing a solution field object based on the simulation result file to parse a set of target physical quantities from the solution field object; performing statistical analysis on the set of target physical quantities using an adaptive filtering and dynamic threshold suppression algorithm to determine global distribution characteristics, and performing coupling point data processing on the set of target physical quantities based on the global distribution characteristics to obtain a smooth physical field. The global distribution characteristics include: mean, standard deviation, local fluctuation degree, and quantiles. The coupling point data processing includes: dynamic threshold identification processing, nonlinear outlier suppression processing, and Gaussian weighted smoothing processing; performing visualization artifact processing on the smooth physical field to obtain a data file in a target format, and encapsulating the data file in the target format to output a target visualization result file.

[0005] In one implementation, the step of resolving the target physical quantity set from the solution object includes: obtaining a user-specified field set through a configurable field abstraction table; and performing dynamic parsing processing on the solution object according to the field path corresponding to the field set through a chained reflection access mechanism to automatically identify and collect the target physical quantities and determine the target physical quantity set.

[0006] In one implementation, the step of performing coupling point data processing on the target physical quantity set according to global distribution characteristics to obtain a smooth physical field includes: performing dynamic threshold identification processing on the target physical quantity set according to global distribution characteristics, identifying abnormal nodes in the target physical quantity set, and performing nonlinear outlier suppression processing on the abnormal nodes to adjust the abnormal nodes into normal nodes in order to obtain a smooth physical field.

[0007] In one implementation, the step of performing dynamic threshold identification processing on the target physical quantity set based on global distribution characteristics to determine abnormal nodes in the target physical quantity set includes: performing dynamic threshold identification processing on the target physical quantity set based on global distribution characteristics and a dynamic threshold identification model, and marking abnormal nodes in the target physical quantity set that exceed the dynamic threshold by automatically adjusting the threshold sensitivity in high dynamic load scenarios.

[0008] In one implementation, the step of performing nonlinear outlier suppression processing on abnormal nodes to adjust them into normal nodes includes: using a nonlinear suppression function to perform flexible suppression processing on the node values ​​of abnormal nodes, suppressing the node values ​​in a direction that tends towards a dynamic threshold, so as to restore the node values ​​from outliers to the dynamic threshold range while preserving the physical meaning of the node values, thereby obtaining normal nodes.

[0009] In one implementation, after the step of adjusting the abnormal node to a normal node, the method includes: performing a weighted fusion process on the abnormal node and its neighborhood using a Gaussian weighted local smoothing model to generate a smooth gradient at the abnormal node, thereby obtaining a smooth physical field.

[0010] In one implementation, the step of performing visualization artifact processing on a smoothed physical field to obtain a data file in the target format includes: after extracting the smoothed physical field, sequentially performing consistency constraint verification processing, radial basis function interpolation processing, and adaptive smoothing and normalization processing on the smoothed physical field, aligning the data length in the smoothed physical field, intelligently filling missing and discontinuous regions, removing residual noise through a dynamic smoothing window, and normalizing field values ​​to the standard range to obtain a data file in the target format.

[0011] Secondly, embodiments of the present invention also provide a remote load field visualization optimization device for lightweight simulation applications. The device includes: a preprocessing module, which acquires simulation result files and constructs a solution field object based on the simulation result files to parse a set of target physical quantities from the solution field object; a coupling point data processing module, which performs statistical analysis on the set of target physical quantities using adaptive filtering and dynamic threshold suppression algorithms to determine global distribution characteristics, and performs coupling point data processing on the set of target physical quantities based on the global distribution characteristics to obtain a smooth physical field. The global distribution characteristics include: mean, standard deviation, local fluctuation degree, and quantiles. The coupling point data processing includes: dynamic threshold identification processing, nonlinear outlier suppression processing, and Gaussian weighted smoothing processing; and a visualization artifact processing module, which performs visualization artifact processing on the smooth physical field to obtain a data file in the target format, and encapsulates the data file in the target format to output a target visualization result file.

[0012] Thirdly, embodiments of the present invention also provide a server, including a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement any of the methods provided in the first aspect.

[0013] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement any of the methods provided in the first aspect.

[0014] The embodiments of the present invention bring the following beneficial effects:

[0015] This invention provides a method and apparatus for remote load field visualization optimization in lightweight simulation applications. The method acquires simulation result files and constructs a solution field object based on these files. A set of target physical quantities is then extracted from the solution field object. Subsequently, an adaptive filtering and dynamic threshold suppression algorithm is used to perform statistical analysis on the target physical quantity set to determine its global distribution characteristics. Based on these global distribution characteristics, coupling point data processing is performed on the target physical quantity set to obtain a smoothed physical field. Finally, visualization artifact processing is applied to the smoothed physical field to obtain a target format data file. This target format data file is then encapsulated and output as a target visualization result file. This invention significantly improves the stability and reliability of simulation results.

[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a remote load field visualization optimization method for a lightweight simulation application provided in an embodiment of the present invention;

[0020] Figure 2 A schematic diagram illustrating the specific process of a remote load field visualization optimization method for a lightweight simulation application provided in an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of a lightweight graphical interface provided in an embodiment of the present invention;

[0022] Figure 4 A schematic diagram of the structure of a remote load field visualization and optimization device for lightweight simulation applications provided in an embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram of the structure of a server provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Currently, with the widespread application of engineering simulation in product design, performance evaluation, and structural optimization, simulation tools are developing towards lightweight, visualization, and integration. Lightweight simulation applications, based on the finite element method (FEM) kernel and combined with a simple graphical interface and customized process data flow, feature low barriers to entry, embeddability, and cloud display support, becoming an important development form of industrial software. In such applications, simulation contour plot results are typically presented online as deformation, stress, or life contour plots by parsing the result file data and converting it into a visualization format, allowing users to quickly view the result response characteristics in a browser.

[0026] In practical simulation engineering, some simulation conditions introduce special loads associated with coupling points to meet the equivalence requirements of complex conditions. These special loads can affect the result fields during the solution process, often causing anomalies in the post-processing stage. These outliers are retained during the result file conversion process, ultimately leading to the appearance of blotches (local artifacts) in the cloud visualization image, severely affecting the continuity and physical readability of the image. Current lightweight simulation systems lack a universal and automatic mechanism for correcting cloud map anomalies.

[0027] In the field of structural finite element simulation, especially in cases involving non-concentrated loads such as integrated long-range forces or moments, the accuracy of post-processing and visualization of simulation results has a significant impact on the accuracy of engineering analysis. Current simulation systems typically employ a general result extraction framework to read and convert RST-type result files during the result processing stage to generate VTK data for visualization rendering. However, this traditional approach still has technical limitations when dealing with complex field responses caused by long-range loads.

[0028] First, existing data processing workflows are typically based on fixed result field mapping logic, lacking effective mechanisms for identifying and suppressing anomalous node responses (such as abrupt changes, infinity values, or discrete anomalies). Under the wide-area influence of remote forces or moments, the numerical field in local regions is prone to non-physical responses, leading to blob artifacts in the final deformation contour map. These artifacts not only interfere with engineers' judgment of structural response trends but also affect the stability and reproducibility of simulation conclusions.

[0029] Secondly, traditional field transformation schemes often rely on the integrity and numerical consistency of the output structure of the underlying solver, lacking adaptive fault tolerance and correction capabilities. They cannot effectively cope with the result disturbances caused by boundary condition perturbations, and their limitations are even more prominent in lightweight simulation platforms where fast response and universal compatibility are required.

[0030] Furthermore, current mainstream methods still primarily rely on low-level data parsing and static mapping, failing to incorporate dynamic processing based on simulation semantic features or result distribution patterns, and lacking a standardized framework for anomaly field identification and visualization repair. This technical bottleneck results in structural simulation visualization facing multiple risks such as instability, distortion, and uncontrollability under complex load conditions, hindering its further promotion in industrial simulation applications.

[0031] Specifically, as simulation scenarios become more sophisticated, most require importing models with a basic form but complex operating conditions, involving an increasing variety of load types. To improve the ease of use in simulation applications, simulation components based on coupling points as fundamental loads are gradually introduced. Through these coupling point components, other load components can be linked (e.g., in practical applications, coupling points combine to form point mass components that control model mass, rigid body components that change the model's material type to rigid, remote force components that control the force reference position, and torque components), thus quickly forming special loads in lightweight simulation applications. This section details the process by which coupling point components address torque generation in lightweight simulation applications. The torque generation method here uses end-face interaction. By associating the torque component with the coupling point component, selecting the end face during interactive operation locks the geometric center point feature of the end face and sets it as the end-face coupling point. Torque formation requires a complete direction, including the origin of the torque axis and the direction vector. The end-face coupling point serves as the origin of the torque axis, while the direction vector is determined using the outward-facing normal of the end face. Finally, the torque direction around the axis is determined using the right-hand rule. By introducing a point mass component and associating it with the mass assignment component and the coupling point component, and through interactive selection of geometric features, the coupling point is set at the center point feature of the geometry, and the load can be represented in the form of concentrated mass.

[0032] In lightweight simulation applications, introducing coupling points and components to apply boundary loads can more realistically simulate physical conditions, but it may have adverse effects in the post-processing stage, resulting in two typical distortion phenomena:

[0033] 1. The problem of abnormally prominent deformation field caused by the influence of coupling points themselves. When coupling points are located in extreme stress areas or have moment coupling, their displacement may be much higher than that of surrounding nodes, forming isolated high-amplitude responses. Specifically, when coupling points produce abnormally large displacement responses due to moment coupling or differences in constraint stiffness, their deformation is often much higher than that of surrounding nodes. Such outliers are usually represented as localized concentrated red areas in the deformation contour map, while other normal areas are compressed into a very small color gamut, appearing as large blue areas. For example, in a typical simulation scenario, the deformation of most nodes is distributed in the range of 0 to 10, while the displacement of coupling points may be as high as 1000. At this time, the color gamut of the entire contour map is forcibly expanded to 0 to 1000, causing other areas except for outliers to tend to be a single blue in the overall color gradation, making it almost impossible to identify the actual deformation gradient and trend distribution. This phenomenon seriously disrupts the visual continuity and readability of the deformation field.

[0034] 2. Numerical misalignment and artifacts during the conversion of RST result files to VTK visualization files. Due to the coupling of multiple physical quantities and the interpolation of step lengths in the result files, there may be node misalignment, inconsistent field lengths, or local numerical overflows. This can cause alternating erroneous values ​​(such as 0 or ∞) in local areas of the converted cloud map, forming obvious blotches, which affects the final visualization accuracy and readability.

[0035] Based on this, the remote load field visualization optimization method and device for lightweight simulation applications provided by this invention can effectively identify and suppress abnormal values ​​caused by load conditions while maintaining the universality and automation of the simulation process, ensuring that the cloud map can be displayed correctly and stably in the cloud under various load conditions, thereby improving the reliability of simulation results and user experience.

[0036] See Figure 1 The diagram shows a flowchart of a remote load field visualization optimization method for a lightweight simulation application. The method mainly includes the following steps S102 to S106:

[0037] Step S102: Obtain the simulation result file and construct a solution field object based on the simulation result file to parse the set of target physical quantities from the solution field object.

[0038] In one implementation, taking Ansys's RST result file as an example, the simulation application designed in this invention aims to efficiently and accurately parse and collect key physical field data from the finite element simulation result file during the data extraction stage. This effectively avoids the node result mapping errors that may occur when traditional reading interfaces handle special loads associated with coupling points (such as remote moments or multibody dynamic equivalent loads). This stage can optimize the automation, accuracy, and scalability of the data extraction process by calling AnsysDPF (DynamicPostprocessingFramework).

[0039] Step S104: Statistical analysis is performed on the target physical quantity set using adaptive filtering and dynamic threshold suppression algorithms to determine global distribution characteristics. Based on these global distribution characteristics, coupling point data processing is performed on the target physical quantity set to obtain a smooth physical field. The global distribution characteristics include mean, standard deviation, local fluctuation, and quantiles. The coupling point data processing includes dynamic threshold identification, nonlinear outlier suppression, and Gaussian weighted smoothing.

[0040] In one implementation, coupling point data processing primarily addresses the prominent deformation field issues arising during the solution process from special loads (such as remote moments or forces) associated with coupling points. By integrating dynamic threshold identification, nonlinear outlier suppression, and Gaussian weighted smoothing, an adaptive filtering and dynamic threshold suppression algorithm can be constructed. This algorithm ensures a smooth transition of outlier regions to surrounding nodes by suppressing the display weight of outliers, achieving a more intuitive, balanced, and physically realistic cloud visualization effect while maintaining the overall trend of the simulation results without distortion.

[0041] Specifically, firstly, in the simulation result import phase, the system performs a comprehensive statistical analysis on the extracted core physical quantities (such as displacement, stress, and strain fields), calculating global distribution characteristics, including: mean, standard deviation, local fluctuation, and quantiles. These statistical data provide the foundation for subsequent modules, ensuring the adaptability and context-dependent nature of the processing. Next, three technical modules are activated sequentially: a dynamic threshold identification module detects and marks abnormal locations; a nonlinear outlier suppression module flexibly adjusts outliers; and a Gaussian weighted smoothing module achieves regional transitions. The processed results are then passed to the subsequent visualization and optimization component.

[0042] Step S106: Perform visualization artifact processing on the smooth physical field to obtain a data file in the target format, and encapsulate the data file in the target format to output the target visualization result file.

[0043] In one implementation, visualization artifact processing is an innovative component designed to address common data misalignment, dimension mismatch, and artifact issues encountered during the conversion of finite element result file data formats to visualization formats (such as VTK format). It aims to eliminate blob patterns or data distortion artifacts in lightweight simulation applications, ensuring the accuracy of the conversion process and the stability of cloud rendering. This processing includes: data extraction, consistency constraint verification, radial basis function interpolation, adaptive smoothing and normalization, and a scalable strategy for deriving multiple physical quantities.

[0044] Specifically, firstly, the raw physical field data (such as displacement and stress components) obtained from the data extraction stage are mapped into a standardized multi-field container. Then, outlier identification, correction, and optimization are performed on these data. Finally, the visualization output is completed through a lightweight VTK file generation module, which achieves smoothing and denoising of the cloud map. It can support parallel computing, is suitable for cloud environments, and ensures end-to-end automation from solution to display.

[0045] The remote load field visualization optimization method for lightweight simulation applications provided in this embodiment of the invention can significantly improve the stability and reliability of simulation results.

[0046] See Figure 2 The diagram illustrates a specific flowchart of a remote load field visualization optimization method for lightweight simulation applications. This embodiment also provides an implementation method for visualization optimization of simulation result files. This scheme includes: simulation result preprocessing, coupling point data processing, visualization artifact processing, and post-processing component encapsulation. It can realize full-process optimization of lightweight simulation applications from raw simulation results to final cloud maps. The coupling point data processing stage includes three technical modules: dynamic threshold identification, nonlinear outlier suppression, and Gaussian weighted smoothing. The visualization artifact processing stage, associated with the coupling point data processing stage, includes data field extraction strategies, consistency constraint verification, radial basis function interpolation, adaptive smoothing and normalization, and a multi-physical quantity derivation and scalable strategy. See (A) to (D) below for details:

[0047] (A) Simulation Result Preprocessing: First, a unified solution field object is constructed using the `ansys.dpf.post.load_solution` function. This function loads the simulation result file and establishes a high-order abstraction layer, supporting the extraction of various physical quantities using a standardized expression interface. These include structural displacement fields, stress tensor fields, and other related field variables (such as strain or velocity fields). This mechanism is significantly superior to the traditional `ansys.mapdl.reader.rst` interface, which, when using the PyMAPDL library, suffers from mapping errors due to numerical instabilities in the node associations of remote load cases. For example, it may generate abnormally high values ​​or distortions near coupling points, resulting in blob artifacts in subsequent visualizations. The DPF's abstract interface avoids these problems during the data extraction stage, ensuring the physical consistency and integrity of the extracted data.

[0048] In one implementation, to further enhance the modularity and engineering adaptability of the system, a configurable field abstraction table can be introduced in the initialization phase of data extraction. The user-specified field set can be obtained through the configurable field abstraction table, and the solution field object can be dynamically parsed and processed according to the field path corresponding to the field set through a chain reflection access mechanism. The target physical quantity is automatically identified and collected to determine the target physical quantity set.

[0049] In other words, the simulation result file is first read into a solution field object using the ansys.dpf.post.load_solution function, and then a chain of reflections is performed on the solution field object to extract the set of target physical quantities. The field abstract table is used to determine the quantities that the user wants to extract.

[0050] Specifically, the table is defined in dictionary or list form, allowing users or system administrators to pre-specify the required field sets for extraction. This enables rapid mounting and dynamic management of field sets, making the extraction process highly flexible without requiring modification of the underlying code logic. In the actual extraction process, a chained reflection access mechanism is employed; this invention uses chained calls of the `getattr()` function to dynamically resolve field paths. Specific operations include: recursively accessing the specified path from the solution field object, automatically identifying and collecting the target physical quantity set (such as extracting values ​​node-by-node or element-by-element), and efficiently storing them in a unified hierarchical data structure. This design avoids the dependence on static hard-coding of post-processing results data in lightweight simulation applications, ensuring automatic compatibility for future simulation field expansion, such as the extraction of axial physical fields (distinct from global physical fields such as total stress and total deformation).

[0051] (B) Coupling point data processing: Based on the global distribution characteristics, dynamic threshold identification processing is performed on the target physical quantity set to determine the abnormal nodes in the target physical quantity set, and nonlinear outlier suppression processing is performed on the abnormal nodes to adjust the abnormal nodes into normal nodes in order to obtain a smooth physical field. For details, please refer to (1) to (3) below:

[0052] (1) Dynamic threshold identification processing: Based on global distribution characteristics and dynamic threshold identification model, dynamic threshold identification processing is performed on the target physical quantity set. By automatically adjusting the threshold sensitivity in high dynamic load scenarios, abnormal nodes in the target physical quantity set that exceed the dynamic threshold are marked.

[0053] In one implementation, a dynamic threshold identification module is responsible for adaptively detecting the location of anomalous nodes. Based on the aforementioned statistical analysis, this module dynamically calculates thresholds to intelligently distinguish between normal high-gradient regions and genuine anomalies caused by loads (such as numerical mutations near coupling points). Unlike static thresholding methods, this module considers the complexity of the operating conditions; for example, in high-dynamic-load scenarios, it automatically adjusts the threshold sensitivity, identifies potential anomalous node sets, and generates anomaly heatmaps to aid in diagnosis. This ensures the accuracy and robustness of the identification process, avoiding misjudging physically real high-stress areas.

[0054] The formula for dynamic threshold recognition is:

[0055]

[0056] in, For the first The original displacement values ​​of each node; This represents the global average value of the displacement field; The global standard deviation; For dynamic thresholds; This is the threshold adjustment coefficient.

[0057] (2) Nonlinear outlier suppression processing: The node values ​​of abnormal nodes are flexibly suppressed by a nonlinear suppression function, and the node values ​​are suppressed in the direction of the dynamic threshold. In order to retain the physical meaning of the node values, the node values ​​are returned from the outlier to the dynamic threshold range, and normal nodes are obtained.

[0058] In one implementation, the nonlinear outlier suppression module provides flexible suppression for marked outlier nodes. This module introduces a nonlinear suppression function to cap node values ​​exceeding a threshold, progressively suppressing outliers to near the threshold rather than directly truncating or deleting them. This nonlinear design allows for applying different levels of suppression based on the severity of the anomaly; for example, mild attenuation is used for minor anomalies, while stronger suppression is applied for extreme values, thus smoothly bringing outliers back to a reasonable range. The core advantage of this module lies in preserving the physical meaning of the original data; for example, suppressing local peaks caused by load coupling in the stress field while preventing overall trend distortion due to over-smoothing, thereby improving the reliability of the results.

[0059] The formula for suppressing nonlinear outliers is as follows:

[0060]

[0061] in, These are the node values ​​after outlier suppression; This is the nonlinear suppression force coefficient.

[0062] (3) Gaussian weighted smoothing: The abnormal node and its neighborhood are weighted and fused using a Gaussian weighted local smoothing model to generate a smooth gradient at the abnormal node and obtain a smooth physical field.

[0063] In one implementation, a Gaussian weighted smoothing module ensures a seamless transition between processed anomalous regions and neighboring nodes. This module employs a local spatial weighted averaging strategy, using a Gaussian weighting function to weight and fuse anomalous nodes and their neighborhoods. Specifically, it calculates the spatial distance between nodes and assigns attenuation weights, allowing anomalous values ​​to gradually blend into the surrounding distribution, forming a smooth gradient. Each node is assigned a weighting coefficient based on its spatial distance to neighboring nodes, achieving local smoothing through weighted averaging. This effectively weakens excessively prominent regions while maintaining the continuity and visual balance of the deformation field.

[0064] The Gaussian weighted local smoothing formula is as follows:

[0065]

[0066] in, The final node value after smoothing; For nodes The set of neighboring nodes; The local smoothing intensity coefficient; The weights are Gaussian, where and They represent the first in the finite element model. With the The three-dimensional coordinate vectors of each node are used to measure the spatial geometric distance between nodes; The smoothing scale parameter is used to control the decay range of the Gaussian weight function. The larger the value, the wider the influence range between nodes. This is a normalization constant used to ensure that the sum of the weights of each node is 1, thereby maintaining the numerical stability of the weighted smoothing result.

[0067] (C) Visual artifact processing: After extracting the smoothed physical field, the smoothed physical field is sequentially processed by consistency constraint verification, radial basis function interpolation, and adaptive smoothing and normalization. The data length in the smoothed physical field is aligned, missing and discontinuous regions are intelligently filled, and residual noise is removed through a dynamic smoothing window. The field values ​​are normalized to the standard range to obtain the data file in the target format. For details, please refer to (1) to (5) below:

[0068] (1) Data field extraction strategy: Use AnsysDPF mechanism to efficiently parse the result file, extract core physical quantities (such as displacement field and stress field), and initially construct data structure to provide a consistent input basis for subsequent modules.

[0069] (2) Consistency Constraint Verification: Before exporting VTK binary data, a maximum length alignment and zero-padding strategy is adopted to scan the spatial dimensions of all field vectors, identify inconsistencies (such as differences in node coordinates or value array lengths), and then align them based on the maximum length, filling missing parts with zero values. This not only eliminates mapping distortion caused by dimension mismatch but also prevents data overflow or loss during the conversion process, ensuring that all physical quantities remain synchronously consistent in the grid space. The automated verification mechanism of this module significantly reduces the risk of human error and supports batch processing of complex working conditions.

[0070] (3) Radial Basis Function Interpolation: To address the challenges of inconsistent node numbers or missing local meshes, the Radial Basis Function (RBF) interpolation module provides spatial completion and data remapping functions. Based on a nonlinear interpolation algorithm using the radial basis function, it intelligently fills in missing or discontinuous regions. Specifically, it calculates the radial distance between known nodes, constructs an interpolation kernel function, and then infers and fills in unknown point values, ensuring physical continuity across meshes. For example, when the mesh is sparse near coupling points, this module can seamlessly reconstruct the stress distribution, avoiding artifacts such as isolated high-value points. This design supports adaptive kernel parameter adjustment, dynamically optimizing interpolation accuracy based on mesh density, thus improving the module's adaptability to non-uniform meshes.

[0071] The RBF interpolation mapping formula is:

[0072]

[0073] in, The coordinates of the source grid nodes; The RBF coefficient; For radial basis functions, such as or ; Solve for the interpolation coefficients of a system of linear equations.

[0074] Compared with traditional linear interpolation methods, RBF interpolation can maintain higher-order continuity between non-conformal grids and avoid oscillations during cross-domain data transfer.

[0075] (4) Adaptive Smoothing and Normalization: The adaptive smoothing and normalization module can further optimize data quality. This module combines adaptive filters (such as smoothing kernels based on local variance) and normalization techniques to suppress noise and unify the scale of the interpolated data. Specific operations include: calculating local gradients, applying dynamic smoothing windows (such as variable radius filtering) to remove residual noise, and normalizing field values ​​to a standard range (such as [0,1]) to ensure the consistency and visual balance of cloud map color mapping. This not only weakens the subtle artifacts induced by the transformation but also preserves the authenticity of the physical gradient, supporting multi-resolution rendering.

[0076] The adaptive smoothing and normalization formulas are:

[0077]

[0078] in, For the first The field, the first The original values ​​of each node, For the average value of the field, Standard deviation To calculate the anomaly detection coefficient, These are the node values ​​after outlier correction.

[0079]

[0080] in, The final node value after smoothing and normalization; This is the smoothing intensity coefficient.

[0081] (5) Multi-physical quantity export scalable strategy: The multi-physical quantity export scalable strategy module is responsible for the final output. This module is designed to be highly scalable, supporting the integration of any number of physical quantities (such as adding temperature or velocity fields), generating lightweight files through the VTK exporter, and embedding metadata tags for cloud parsing. This strategy allows users to customize the export configuration, achieving seamless expansion without refactoring the code, ensuring the system's compatibility with future simulation needs.

[0082] The relevant results are normalized and recalibrated in color levels before being output to VTK, and exported in a binary compressed format with embedded field self-description information to adapt to the fast loading and cross-platform display of lightweight simulation visualization applications. In the visualization output stage, this invention designs a mechanism for batch export and synthesis of multiple fields.

[0083] (D) Post-processing component encapsulation: Traditional VTK file export processes rely on the encapsulated interface provided by pymapdl_reader.read_binary, which suffers from accuracy drift and field missing issues in remote force loading scenarios. This invention utilizes the underlying data connection interface vtk_export operator of DPF to directly couple structured field data with the mesh without relying on the native MAPDL exporter, thus implementing a field pair combination export mechanism.

[0084] Furthermore, since the underlying VTK operator only supports exporting a maximum of two fields at a time, the physical quantity set... Divide into subsets and call them one by one Export a temporary VTK file, and then stack the X, Y, and Z displacement components into a vector field through file merging and data reconstruction. and the von Mises stress field As a scalar field attached to The above scheme can be extended to other physical fields; for example, a temperature field can be incorporated into the results. or energy density field Simply add the corresponding entry in the initial field definition, and it will automatically participate in the above-mentioned outlier correction, array length correction and interpolation steps, thereby significantly improving the visualization accuracy and stability of lightweight simulation applications under multiphysics coupling.

[0085] Furthermore, regarding data storage and transmission, the generated VTK file adopts a binary compression format, significantly reducing data redundancy and lowering the latency of the lightweight simulation application during front-end loading. Through this mechanism, models with millions of nodes can be loaded and interactively displayed within seconds. In addition, the VTK file contains built-in self-describing field information, eliminating the need for additional metadata files for subsequent post-processing and visualization expansion.

[0086] In one implementation, the above method realizes a complete optimization link from node anomaly suppression to cross-field smooth reconstruction, which can effectively improve the visualization quality of remote load field simulation results, eliminate local anomalies and artifacts, make the cloud map more consistent with the actual structural response characteristics, and reduce the post-processing and rendering computational burden.

[0087] The post-processing component design and packaging stage, as the final phase of the overall process, is responsible for integrating the outputs of simulation result preprocessing, coupling point data processing, and visualization artifact processing into reusable, modular software components. This supports the rapid deployment and cloud integration of lightweight simulation applications. This stage focuses on standardization, scalability, and compatibility, ensuring end-to-end automation from raw results to cloud map optimization, and enabling low-barrier integration into the industrial software ecosystem.

[0088] Furthermore, in terms of component design, a microservice architecture is adopted, modularizing core functions into independent units: the coupling point data processing component encapsulates dynamic threshold recognition, nonlinear outlier suppression, and Gaussian weighted smoothing modules as a single service; the visualization artifact processing component integrates data field extraction, consistency constraint verification, radial basis function interpolation, adaptive smoothing and normalization, and multi-physical quantity derivation strategies, forming another service layer. These units are interconnected through a unified interface, supporting parameterized configuration (such as threshold adjustment or field selection), and have built-in error handling mechanisms (such as exception logging and rollback).

[0089] In the encapsulation phase, the system employs containerization technology combined with scripted scheduling to achieve high portability and deployment consistency of post-processing components. Specifically, a complete post-processing computation pipeline is encapsulated using Docker containers. A unified entry script sequentially calls each functional module in a pipeline manner: the input RST simulation result file is preprocessed and then passed to the coupling point data processing and artifact suppression module, ultimately generating an optimized VTK visualization result file. This encapsulation structure supports a plug-in module extension mechanism, allowing users to add physical quantity calculation or visualization modules by modifying configuration files (YAML format) without recompiling the main program, significantly improving the system's flexibility and maintainability. Furthermore, the containerized encapsulation has cross-cloud platform deployment capabilities, running directly in cloud environments such as AWS and Azure. Through a built-in WebSocket real-time communication interface, the system enables real-time data interaction with the browser, supporting encrypted communication and concurrent access by multiple users, ensuring the security and efficiency of remote operations.

[0090] This design enhances the modularity and reusability of the system, shortens the development cycle, supports seamless integration into existing simulation tools, ultimately ensures stable display of cloud maps under various load conditions and optimizes user experience, and supports the post-processing stage of large-scale simulation applications.

[0091] In practical applications, the remote load field visualization optimization method for lightweight simulation applications provided in this embodiment of the invention is essentially a design for a simulation application. This method can be applied to various lightweight simulation scenarios. (See also...) Figure 3The diagram illustrates a lightweight graphical interface, using universal joint torsion analysis as an example. As a mechanical transmission component, the universal joint requires evaluation of its deformation and stress distribution under torsion conditions to optimize its design and performance. This application, based on a finite element method (FEM) kernel and integrated with cloud computing, achieves end-to-end optimization from load application to result visualization. The overall methodology is implemented step-by-step, including simulation result preprocessing, coupling point data processing, visualization artifact processing, and post-processing component encapsulation.

[0092] First, in the simulation result preprocessing stage, the user applies torsional torque through a lightweight graphical interface. Specifically, the universal joint end face is selected as the load application area, and a special load associated with the coupling point is introduced: the geometric center point of the end face is used as the coupling point, and the end face normal is used as the axial torque application. This mode is compatible with complex interaction forms, realizes the equivalent loading of remote torque, and ensures the physical accuracy of the simulation conditions. The model is uploaded to the cloud supercomputing platform for finite element solution, generates result files (such as rst format), and returns them to the local or cloud server. Subsequently, data extraction is performed using the AnsysDPF framework: the solution field object is constructed using the ansys.dpf.post.load_solution function to extract the total deformation field and equivalent stress field. At the same time, comprehensive statistical analysis is performed to calculate global distribution characteristics, including indicators such as mean, standard deviation, local fluctuation, and quantiles. These statistical data provide an adaptive basis for subsequent processing, ensuring context relevance.

[0093] The unprocessed raw contour maps exhibit obvious anomalies: the total deformation contour map appears as a speckled pattern, with uneven overall displacement due to the influence of coupling points; the main body of the model appears opaque and bluish, and high-deformation spots appear in random areas; the upper and lower limits of the stress contour map are abnormally zero, failing to reflect the true response characteristics. This stems from the retention of nodal anomalies caused by load coupling during the transformation, resulting in visualization artifacts.

[0094] Next, the coupling point data processing stage is entered. This stage activates three technical modules to address deformation field anomalies near the coupling points. First, the dynamic threshold identification module, based on preprocessed statistical data and combined with global standard deviation and local volatility, sets multi-level threshold boundaries to detect and mark anomalous nodes and regions of numerical abrupt change. Second, the nonlinear outlier suppression module applies a suppression function to the marked nodes for capping: progressively suppressing outliers to near the threshold, using different intensities of attenuation based on the severity of the anomaly to ensure a smooth return of values ​​to a reasonable range while preserving the overall deformation trend. Finally, the Gaussian weighted smoothing module calculates the spatial distance between nodes and performs weighted fusion on the anomalous region and its neighborhood to form a smooth gradient. Through these modules, coupling point anomalies are effectively suppressed; for example, the deformation of the gimbal end face changes from a speckled pattern to a uniform distribution.

[0095] Subsequently, the visualization artifact processing stage addresses misalignments and artifacts during data transformation. First, the data field extraction strategy maps displacement and stress components from the aforementioned output to a standardized container. Then, before exporting to VTK, the consistency constraint verification module employs a maximum length alignment plus zero-padding strategy to ensure consistent dimensionality across all field vector spaces, eliminating dimensional mismatch distortions. For areas with missing mesh regions, the radial basis function interpolation module constructs an interpolation kernel function for spatial completion and remapping, ensuring physical continuity. The adaptive smoothing and normalization module applies a dynamic filtering window to remove noise and normalizes values ​​to a standard range, improving visual balance. Finally, a multi-physical quantity export scalable strategy generates a lightweight VTK file, supporting the addition of additional fields (such as temperature fields) for cloud parsing. This stage optimizes the original anomaly cloud map into a smooth, noise-free version.

[0096] Finally, the post-processing component encapsulation stage adopts a microservice architecture, integrating the above modules into independent units. This encapsulation is compatible with cloud platforms and enables real-time interaction with the browser via WebSocket, ensuring secure transmission. After implementation, the gimbal torsion cloud map is significantly improved: the deformation field is uniform and continuous, the stress distribution is physically realistic, and there are no speckle artifacts. Users can quickly view the response characteristics in the browser. This solution enhances the post-processing display effect of lightweight simulation applications.

[0097] In summary, this invention employs global statistical generation of dynamic thresholds and Gaussian weighted smoothing to eliminate steps, making the cloud map colors and deformation gradients clearly visible, thereby eliminating red spot distortion at coupling points. Through maximum length alignment, zero padding, RBF interpolation to fill missing nodes, and adaptive normalization denoising, the conversion process can achieve zero misalignment and zero overflow, thus avoiding numerical misalignment and artifact problems during the conversion of the result file to the VTK visualization file. Furthermore, by calling AnsysDPF for unified solution, configurable field tables, and utilizing Docker containers to encapsulate a complete post-processing calculation pipeline, the compatibility and automation of the lightweight platform can be significantly improved.

[0098] Regarding the remote load field visualization optimization method for lightweight simulation applications provided in the foregoing embodiments, this invention provides a remote load field visualization optimization device for lightweight simulation applications. (See attached image.) Figure 4 The diagram shows a structural schematic of a remote load field visualization and optimization device for lightweight simulation applications. The device includes the following components:

[0099] The preprocessing module 402 acquires the simulation result file and constructs a solution field object based on the simulation result file to parse the set of target physical quantities from the solution field object;

[0100] The coupling point data processing module 404 performs statistical analysis on the target physical quantity set through adaptive filtering and dynamic threshold suppression algorithms to determine the global distribution characteristics. Based on the global distribution characteristics, it performs coupling point data processing on the target physical quantity set to obtain a smooth physical field. The global distribution characteristics include: mean, standard deviation, local fluctuation degree and quantile. The coupling point data processing includes: dynamic threshold identification processing, nonlinear outlier suppression processing and Gaussian weighted smoothing processing.

[0101] The visualization artifact processing module 406 performs visualization artifact processing on the smooth physical field to obtain a data file in the target format, and then encapsulates the data file in the target format to output the target visualization result file.

[0102] The remote load field visualization optimization device for lightweight simulation applications provided in this application embodiment can significantly improve the stability and reliability of simulation results.

[0103] In one embodiment, when performing the step of parsing the target physical quantity set from the solution object, the preprocessing module 402 is further configured to: obtain the user-specified field set through a configurable field abstraction table; and, through a chained reflection access mechanism, dynamically parse the solution object according to the field path corresponding to the field set, automatically identify and collect the target physical quantities, and determine the target physical quantity set.

[0104] In one embodiment, when performing the step of processing coupling point data of the target physical quantity set according to global distribution characteristics to obtain a smooth physical field, the coupling point data processing module 404 is further configured to: perform dynamic threshold identification processing on the target physical quantity set according to global distribution characteristics, determine abnormal nodes in the target physical quantity set, and perform nonlinear outlier suppression processing on the abnormal nodes to adjust the abnormal nodes into normal nodes in order to obtain a smooth physical field.

[0105] In one embodiment, when performing the step of dynamic threshold identification processing of the target physical quantity set based on global distribution characteristics to determine abnormal nodes in the target physical quantity set, the coupling point data processing module 404 is further configured to: perform dynamic threshold identification processing on the target physical quantity set based on global distribution characteristics and dynamic threshold identification model, and mark abnormal nodes in the target physical quantity set that exceed the dynamic threshold by automatically adjusting the threshold sensitivity in high dynamic load scenarios.

[0106] In one embodiment, when performing nonlinear outlier suppression processing on abnormal nodes to adjust them into normal nodes, the coupling point data processing module 404 is further configured to: perform flexible suppression processing on the node values ​​of abnormal nodes through a nonlinear suppression function, suppressing the node values ​​in a direction that tends towards a dynamic threshold, so as to retain the physical meaning of the node values ​​while allowing the node values ​​to return from outliers to the dynamic threshold range, thereby obtaining normal nodes.

[0107] In one embodiment, after performing the step of adjusting the abnormal node to a normal node, the coupling point data processing module 404 is further configured to: perform weighted fusion processing on the abnormal node and its neighborhood using a Gaussian weighted local smoothing model to generate a smooth gradient at the abnormal node and obtain a smooth physical field.

[0108] In one embodiment, when performing visualization artifact processing on a smooth physical field to obtain a data file in the target format, the visualization artifact processing module 406 is further configured to: after extracting the smooth physical field, sequentially perform consistency constraint verification processing, radial basis function interpolation processing, and adaptive smoothing and normalization processing on the smooth physical field, align the data length in the smooth physical field, intelligently fill missing and discontinuous regions, remove residual noise through a dynamic smoothing window, and normalize field values ​​to the standard range to obtain a data file in the target format.

[0109] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0110] This invention provides a server, specifically, the server includes a processor and a storage device; the storage device stores a computer program, which, when run by the processor, executes the method described in any of the above embodiments.

[0111] Figure 5 This is a schematic diagram of the structure of a server provided in an embodiment of the present invention. The server 100 includes: a processor 50, a memory 51, a bus 52 and a communication interface 53. The processor 50, the communication interface 53 and the memory 51 are connected through the bus 52. The processor 50 is used to execute executable modules, such as computer programs, stored in the memory 51.

[0112] The memory 51 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 53 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0113] Bus 52 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0114] The memory 51 is used to store programs. After receiving an execution instruction, the processor 50 executes the programs. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 50 or implemented by the processor 50.

[0115] Processor 50 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 50 or by instructions in software form. Processor 50 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 51. The processor 50 reads the information in memory 51 and, in conjunction with its hardware, completes the steps of the above method.

[0116] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0117] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A remote load field visualization optimization method for lightweight simulation applications, characterized in that, The method includes: Obtain the simulation result file and construct a solution field object based on the simulation result file to parse the target physical quantity set from the solution field object; The target physical quantity set is statistically analyzed using an adaptive filtering and dynamic threshold suppression algorithm to determine its global distribution characteristics. Based on these global distribution characteristics, the target physical quantity set is then subjected to coupling point data processing to obtain a smooth physical field. The global distribution characteristics include mean, standard deviation, local fluctuation, and quantiles. The coupling point data processing includes dynamic threshold identification, nonlinear outlier suppression, and Gaussian weighted smoothing. Visualization artifact processing is performed on the smoothed physical field to obtain a data file in the target format. The data file in the target format is then encapsulated to output the target visualization result file.

2. The remote load field visualization optimization method for lightweight simulation applications according to claim 1, characterized in that, The step of resolving the target physical quantity set from the solution field object includes: Retrieve the user-specified set of fields through a configurable field abstraction table; By using a chained reflection access mechanism, the solution object is dynamically parsed based on the field path corresponding to the field set, the target physical quantity is automatically identified and collected, and the target physical quantity set is determined.

3. The remote load field visualization optimization method for lightweight simulation applications according to claim 1, characterized in that, The step of performing coupling point data processing on the target physical quantity set based on the global distribution characteristics to obtain a smooth physical field includes: Based on the global distribution characteristics, dynamic threshold identification processing is performed on the target physical quantity set to determine abnormal nodes in the target physical quantity set. Nonlinear outlier suppression processing is then performed on the abnormal nodes to adjust them into normal nodes, thereby obtaining the smooth physical field.

4. The remote load field visualization optimization method for lightweight simulation applications according to claim 3, characterized in that, The step of performing dynamic threshold identification processing on the target physical quantity set based on the global distribution characteristics to determine abnormal nodes in the target physical quantity set includes: Based on the global distribution characteristics and dynamic threshold identification model, dynamic threshold identification processing is performed on the target physical quantity set. By automatically adjusting the threshold sensitivity in high dynamic load scenarios, abnormal nodes in the target physical quantity set that exceed the dynamic threshold are marked.

5. The remote load field visualization optimization method for lightweight simulation applications according to claim 3, characterized in that, The step of performing nonlinear outlier suppression processing on the abnormal nodes to adjust them into normal nodes includes: By using a nonlinear suppression function, the node values ​​of the abnormal nodes are flexibly suppressed, and the node values ​​are suppressed in a direction that tends towards the dynamic threshold. This process preserves the physical meaning of the node values ​​while allowing them to return from abnormal values ​​to the dynamic threshold range, thus obtaining the normal nodes.

6. The remote load field visualization optimization method for lightweight simulation applications according to claim 3, characterized in that, After the step of adjusting the abnormal node to a normal node, the following is included: A Gaussian weighted local smoothing model is used to perform weighted fusion processing on the abnormal node and its neighborhood to generate a smooth gradient at the abnormal node, thereby obtaining the smooth physical field.

7. The remote load field visualization optimization method for lightweight simulation applications according to claim 1, characterized in that, The step of performing visualization artifact processing on the smoothed physical field to obtain a data file in the target format includes: After extracting the smoothed physical field, the smoothed physical field is sequentially subjected to consistency constraint verification, radial basis function interpolation, and adaptive smoothing and normalization. The data length in the smoothed physical field is aligned, missing and discontinuous regions are intelligently filled, and residual noise is removed through a dynamic smoothing window. The field values ​​are normalized to the standard range to obtain the data file in the target format.

8. A remote load field visualization and optimization device for lightweight simulation applications, characterized in that, The device includes: The preprocessing module acquires the simulation result file and constructs a solution field object based on the simulation result file to parse the set of target physical quantities from the solution field object; The coupling point data processing module performs statistical analysis on the target physical quantity set through adaptive filtering and dynamic threshold suppression algorithms to determine the global distribution characteristics. Based on the global distribution characteristics, it performs coupling point data processing on the target physical quantity set to obtain a smooth physical field. The global distribution characteristics include: mean, standard deviation, local fluctuation degree, and quantile. The coupling point data processing includes: dynamic threshold identification processing, nonlinear outlier suppression processing, and Gaussian weighted smoothing processing. The visualization artifact processing module performs visualization artifact processing on the smoothed physical field to obtain a data file in the target format, and encapsulates the data file in the target format to output the target visualization result file.

9. A server, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 7.

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