Vector field simulation and real-time data processing method and device

Abstract

The embodiment of the present application provides a vector field simulation and real-time data processing method and device, the method comprising: performing vector field test time matching and vector field test space matching according to the on-site measured real-time data and the simulation result data, performing point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data, and determining the corresponding displacement distribution comparison results, vector field comparison results, and load-displacement comparison results; correcting the simulation parameters of the physical three-dimensional model in the test virtual scene according to the displacement distribution comparison results, the vector field comparison results, and the load-displacement comparison results, and continuing to perform simulation tests based on the physical three-dimensional model to obtain test full life cycle data and file archiving; the present application can improve the accuracy of data processing and time-space matching, and realize the systematization and automation of parameter correction.

Description

Technical Field

[0001] The present application relates to the field of simulation testing, and in particular to a vector field simulation and real-time data processing method and device. Background Art

[0002] In modern engineering and manufacturing, vector field simulation and real-time data processing methods have become key technologies for improving product design accuracy and reliability. With this method, physical 3D models can be simulated and verified in a virtual environment, thereby comprehensively evaluating product performance. However, current technologies still face some challenges and shortcomings in their implementation.

[0003] First, the initial step of the simulation verification method is to load the physical 3D model into the preset 3D scene of the test station and perform virtual assembly. This process requires the support of high-precision 3D modeling technology and virtual reality technology to ensure the consistency between the virtual scene and the actual test environment. Configuring the test parameters in the test virtual scene and loading and associating the measurement channels requires a detailed test plan and strict parameter settings to ensure a high degree of consistency between the simulation and actual test conditions. However, traditional methods often require a lot of manual intervention and adjustment in this process, which increases the complexity and time cost of the operation.

[0004] Secondly, collecting the real-time data measured on site in the test virtual scene according to the test scene requirements sent by the user, and loading and parsing the simulation result data corresponding to the real-time data measured on site is a complex and error-prone process. The collection of test data needs to be carried out under actual test conditions. This process may be affected by many factors such as the environment, equipment and operators, resulting in unstable data quality. The loading and parsing of simulation result data requires efficient data processing algorithms and powerful computing resources to ensure the accuracy and integrity of the data. The shortcomings of traditional methods in data collection and processing often lead to data deviations and errors, affecting the final simulation verification results.

[0005] When performing time matching and space matching of vector field tests, it is necessary to accurately match the real-time data measured on site with the simulation result data in time and space. This process requires high-precision time synchronization technology and space coordinate conversion technology to ensure that the test data and simulation data are compared under the same time and space coordinates. By performing point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data, the corresponding displacement distribution comparison results, vector field comparison results, and load-displacement comparison results can be determined. However, traditional methods have limited technical means in time and space matching and data comparison, and often cannot achieve high-precision matching and comparison, which affects the accuracy of simulation verification.

[0006] Displacement distribution comparison results, vector field comparison results, and load-displacement comparison results are key evaluation indicators in the simulation verification process. These comparison results directly reflect the difference between the performance of the physical 3D model under actual test conditions and the simulation results. Based on these comparison results, the simulation parameters of the physical 3D model in the test virtual scene can be corrected to further improve the accuracy and reliability of the simulation model. However, traditional methods often rely on experience and repeated experiments when correcting simulation parameters, lack of systematic and automated means, and increase workload and uncertainty.

[0007] Finally, the final step of the simulation verification method is to continue the simulation test based on the corrected physical 3D model, obtain the test life cycle data and archive the files. The test life cycle data includes all simulation and test data from initial design to final verification, which is an important basis for product performance evaluation and optimization. However, traditional methods have limited means for data management and archiving, and often have problems such as data dispersion, poor management and information loss, which affect the integrity and traceability of the data.

[0008] In general, the existing vector field simulation and real-time data processing methods have problems such as complex operation, difficult data collection and processing, low time-space matching accuracy, limited parameter correction methods, and poor data management during implementation. These problems not only increase the difficulty of product development and simulation verification, but also affect the accuracy and reliability of product performance evaluation. Therefore, how to simplify the operation process, improve the accuracy of data processing and time-space matching, realize the systematization and automation of parameter correction, and strengthen data management and archiving are important issues that need to be solved in the current technical field. Summary of the invention

[0009] In view of the problems in the prior art, the present application provides a vector field simulation and real-time data processing method and device, which can improve the accuracy of data processing and time-space matching, and realize the systematization and automation of parameter correction.

[0010] In order to solve at least one of the above problems, the present application provides the following technical solutions:

[0011] In a first aspect, the present application provides a vector field simulation and real-time data processing method, comprising:

[0012] The physical three-dimensional model is loaded into a preset three-dimensional scene of the test station and virtually assembled to construct a test virtual scene, test parameters are configured in the test virtual scene, and measurement channels are loaded and associated; on-site measured real-time data is collected in the test virtual scene according to the test scene requirements sent by the user, and simulation result data corresponding to the on-site measured real-time data is loaded and parsed;

[0013] Performing vector field test time matching and vector field test space matching according to the on-site measured real-time data and the simulation result data, performing point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data, and determining corresponding displacement distribution comparison results, vector field comparison results, and load-displacement comparison results;

[0014] The simulation parameters of the physical three-dimensional model in the test virtual scene are corrected according to the displacement distribution comparison results, the vector field comparison results and the load-displacement comparison results, and the simulation test is continued based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0015] Furthermore, the obtained real-time vector field data is compared point-to-point with the vector field data in the simulation result data to determine the corresponding displacement distribution comparison result, including:

[0016] Constructing a global displacement field through difference processing according to the obtained real-time vector field data, and superimposing the global displacement field constructed by the real-time vector field data and the global displacement field obtained by the simulation result data;

[0017] The matching points of the superimposed global displacement field are numerically compared to calculate the overall accuracy of the simulation result data.

[0018] Furthermore, the obtained real-time vector field data is compared point-to-point with the vector field data in the simulation result data to determine the corresponding displacement distribution comparison result, including:

[0019] Selecting set stress concentration points and load action points in the physical three-dimensional model to construct a local displacement comparison point set;

[0020] According to the residual, relative error and root mean square error of the simulation and test displacement values ​​of each node in the local displacement comparison point set, the corresponding displacement distribution comparison result is obtained.

[0021] Furthermore, performing point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data to determine the corresponding vector field comparison result includes:

[0022] Acquire simulation result data and interpolated test data matching the simulation result data; summarize vector field curves;

[0023] A designated area is selected, average stress and strain values ​​at each position or area are calculated, and regional errors of the vector field are calculated based on the average stress and strain values.

[0024] Furthermore, performing point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data to determine the corresponding vector field comparison result includes:

[0025] Selecting a set yield point or a fracture point in the physical three-dimensional model, and extracting the maximum stress and the maximum strain of the point from the simulation result data and the vector field real-time data;

[0026] The relative error of the maximum value of the vector field is calculated according to the maximum stress and the maximum strain, and it is determined whether the error is within an acceptable range according to a predetermined threshold.

[0027] Furthermore, the real-time vector field data obtained is compared point-to-point with the vector field data in the simulation result data to determine the corresponding load-displacement comparison result, including:

[0028] Extracting the data of the initial elastic region of the vector field curve and the load-displacement curve from the simulation result data and the real-time data of the vector field, and performing linear fitting respectively to obtain the slope of the fitting line;

[0029] The deviation from the initial stiffness is calculated from the slope.

[0030] Furthermore, the real-time vector field data obtained is compared point-to-point with the vector field data in the simulation result data to determine the corresponding load-displacement comparison result, including:

[0031] Determine the yield point in the vector field curve of the simulation result data and the vector field real-time data, and record the yield stress and yield strain in the simulation result data and the vector field real-time data;

[0032] The corresponding yield stress difference and yield strain difference are determined according to the yield stress and yield strain.

[0033] In a second aspect, the present application provides a vector field simulation and real-time data processing device, comprising:

[0034] The test configuration module is used to load the physical three-dimensional model into the preset test station three-dimensional scene and perform virtual assembly to construct a test virtual scene, configure the test parameters in the test virtual scene, and load and associate the measurement channels; collect the on-site measured real-time data in the test virtual scene according to the test scene requirements sent by the user, and load and analyze the simulation result data corresponding to the on-site measured real-time data;

[0035] A vector field comparison module is used to perform vector field test time matching and vector field test space matching according to the on-site measured real-time data and the simulation result data, and to perform point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data to determine the corresponding displacement distribution comparison result, vector field comparison result and load-displacement comparison result;

[0036] The simulation test module is used to correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the displacement distribution comparison results, the vector field comparison results and the load-displacement comparison results, and continue the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0037] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the vector field simulation and real-time data processing method when executing the program.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the vector field simulation and real-time data processing method.

[0039] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the vector field simulation and real-time data processing method.

[0040] It can be seen from the above technical scheme that the present application provides a vector field simulation and real-time data processing method and device, by performing vector field test time matching and vector field test space matching according to the on-site measured real-time data and the simulation result data, and performing point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data, to determine the corresponding displacement distribution comparison results, vector field comparison results and load-displacement comparison results; according to the displacement distribution comparison results, the vector field comparison results and the load-displacement comparison results, the simulation parameters of the physical three-dimensional model in the test virtual scene are corrected, and the simulation test is continued based on the physical three-dimensional model to obtain the test full life cycle data and file archiving, thereby improving the accuracy of data processing and time-space matching, and realizing the systematization and automation of parameter correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 One of the flow charts of the vector field simulation and real-time data processing method in the embodiment of the present application;

[0043] Figure 2 The second flowchart of the vector field simulation and real-time data processing method in the embodiment of the present application;

[0044] Figure 3 The third flowchart of the vector field simulation and real-time data processing method in the embodiment of the present application;

[0045] Figure 4 This is a fourth flow chart of the vector field simulation and real-time data processing method in the embodiment of the present application;

[0046] Figure 5 FIG5 is a flowchart of a vector field simulation and real-time data processing method in an embodiment of the present application;

[0047] Figure 6 6 is a flow chart of the vector field simulation and real-time data processing method in the embodiment of the present application;

[0048] Figure 7 FIG7 is a flow chart of the vector field simulation and real-time data processing method in the embodiment of the present application;

[0049] Figure 8 is a structural diagram of a vector field simulation and real-time data processing device in an embodiment of the present application;

[0050] Fig. 9 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0051] Reference numerals:

[0052] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver program storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0054] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0055] In view of the problems existing in the prior art, the present application provides a vector field simulation and real-time data processing method and device, which performs vector field test time matching and vector field test space matching according to the on-site measured real-time data and the simulation result data, and performs point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data to determine the corresponding displacement distribution comparison results, vector field comparison results and load-displacement comparison results; according to the displacement distribution comparison results, the vector field comparison results and the load-displacement comparison results, the simulation parameters of the physical three-dimensional model in the test virtual scene are corrected, and the simulation test is continued based on the physical three-dimensional model to obtain the test full life cycle data and file it, thereby improving the accuracy of data processing and time-space matching, and realizing the systematization and automation of parameter correction.

[0056] In order to improve the accuracy of data processing and time-space matching and realize the systematization and automation of parameter correction, the present application provides an embodiment of a vector field simulation and real-time data processing method, see Figure 1 The vector field simulation and real-time data processing method specifically includes the following contents:

[0057] Step S101: Load the physical three-dimensional model into the preset three-dimensional scene of the test station and perform virtual assembly to construct a test virtual scene, configure the test parameters in the test virtual scene, and load and associate the measurement channels; collect the on-site measured real-time data in the test virtual scene according to the test scene requirements sent by the user, and load and analyze the simulation result data corresponding to the on-site measured real-time data;

[0058] Optionally, in this embodiment, the main purpose of step S101 is to load the physical three-dimensional model in the preset three-dimensional scene of the test station and perform virtual assembly to build a test virtual scene. Subsequently, the test parameters are configured in the virtual scene, the measurement channels are loaded and associated, and the real-time data measured on site is collected according to user needs, and the corresponding simulation result data is loaded and parsed. This process involves multiple technical means and principles, aiming to build a high-precision, operable virtual test environment.

[0059] First, the technical implementation process begins with loading the physical 3D model into the preset test station 3D scene. The preset test station 3D scene is a virtual environment that predefines various conditions and parameters required for the test, including the test station, equipment layout, environmental conditions, etc. Through computer graphics and virtual reality technology, the physical 3D model can be accurately loaded into the virtual scene for virtual assembly. The virtual assembly process includes operations such as position adjustment, posture adjustment, and component matching of the model to ensure that the configuration of the model in the virtual scene is consistent with the actual test station.

[0060] Next, the system configures the test parameters in the test virtual scene. These parameters include test conditions (such as temperature, humidity, pressure, etc.), operating parameters of the test equipment (such as speed, load, etc.), and control parameters during the test (such as sampling frequency, measurement range, etc.). The purpose of configuring test parameters is to lay the foundation for subsequent simulation and data acquisition and ensure accurate reproduction of test conditions.

[0061] In order to realize data collection, the system needs to load and associate measurement channels. Measurement channels refer to the paths and methods for collecting various physical quantity data, such as temperature sensors, pressure sensors, stress sensors, etc. Loading measurement channels means configuring the location, type and acquisition frequency of these sensors in the virtual scene and associating them with the corresponding parts of the virtual model. In this way, the system can collect various physical quantity data in real time during the virtual test to ensure the integrity and accuracy of the data.

[0062] According to the test scenario requirements sent by the user, the system needs to collect real-time data measured on site in the test virtual scene. User requirements may include specific test conditions, specific measurement points, specific physical quantities, etc. The system adjusts the configuration of the virtual test scene based on these requirements to ensure that the collected data meets the user's requirements. Real-time data measured on site refers to various physical quantity data collected during the actual test process, which usually include temperature, pressure, stress, vibration, etc.

[0063] Finally, the system loads and analyzes the simulation result data corresponding to the real-time data measured on site. Simulation result data refers to various physical quantity data obtained through numerical calculation and physical modeling during the virtual test process. By loading and analyzing this data, the system can compare and analyze the virtual test results with the actual test results, identify the differences between the virtual model and the actual model, and make corresponding adjustments and optimizations.

[0064] In terms of the technical problems solved, step S101 solves the problems of model loading, parameter configuration, data collection and result comparison in the virtual test environment by constructing a test virtual scene, configuring test parameters, loading and associating measurement channels, collecting real-time data measured on site, and loading and parsing simulation result data. These operations ensure the high accuracy and consistency of the virtual test environment, providing a reliable foundation for subsequent simulation and optimization.

[0065] In terms of the technical effects achieved, through step S101, the system can build a high-precision, operational test virtual scene to ensure that the various conditions and parameters in the virtual test process are highly consistent with the actual situation. By configuring the test parameters and loading the measurement channels, the system can collect various physical quantity data in real time during the virtual test and adjust them according to user needs. By loading and parsing the simulation result data, the system can compare and analyze the virtual test results with the actual test results, identify potential problems and differences, and make corresponding adjustments and optimizations.

[0066] For example, in step S101, the physical three-dimensional model is first loaded into the preset test station three-dimensional scene, and virtual assembly is performed to construct a test virtual scene. In the virtual scene, test parameters are configured, including material properties, boundary conditions, load conditions, etc. At the same time, measurement channels are loaded and associated, and these channels are used to collect data of physical quantities such as stress or strain.

[0067] Suppose we are conducting a static test on a cantilever beam. First, the 3D model of the cantilever beam is imported into the virtual test scene through computer-aided design (CAD) software. Then, the test parameters are configured, such as the material properties of the cantilever beam (such as Young's modulus, Poisson's ratio), boundary conditions (fixed end), and applied force (a vertical downward concentrated force is applied at the free end of the cantilever beam).

[0068] In the virtual test scenario, it is also necessary to configure the measurement channels, such as arranging strain gauges at different positions of the cantilever beam to monitor the change of strain. After completing these configurations, the system will collect real-time data measured on site according to the test scenario requirements sent by the user. Suppose in the actual test, the strain data of the cantilever beam at different positions are obtained through strain gauges and data acquisition systems.

[0069] Next, these measured data are loaded into the virtual scene, and the simulation result data corresponding to these data are analyzed. In the virtual scene, the stress and strain distribution of the cantilever beam under the same load conditions are calculated by the finite element analysis (FEA) method. The simulation result data will include the stress and strain values ​​of the cantilever beam at different positions.

[0070] In order to compare the virtual and the real, we compared and analyzed the actual measured strain data with the simulation result data. Assume that in the actual test, we arranged three strain gauges on the cantilever beam, which are located at 1 / 4, 1 / 2 and 3 / 4 of the cantilever beam length. Through the data acquisition system, the strain values ​​at these positions are obtained, for example:

[0071] Measured strain value (1 / 4): 150 microstrain

[0072] Measured strain value (1 / 2): 300 microstrain

[0073] Measured strain value (3 / 4): 450 microstrain

[0074] In the virtual simulation scenario, the simulated strain value calculated by finite element analysis may be:

[0075] Simulation strain value (1 / 4): 145 microstrain

[0076] Simulation strain value (1 / 2): 290 microstrain

[0077] Simulation strain value (3 / 4): 440 microstrain

[0078] Comparing these data, it can be found that the simulated strain value is very close to the measured strain value, and the error is within a reasonable range. This shows that the virtual simulation model reflects the stress and strain distribution of the actual cantilever beam well, verifying the accuracy and reliability of the simulation model.

[0079] This virtual-real comparison analysis can not only verify the accuracy of the simulation model, but also provide a basis for further optimization of the model. For example, if the simulated strain values ​​at certain locations are found to be significantly different from the measured strain values, the test parameter settings in the virtual scene can be further checked, and the material properties, boundary conditions or load conditions can be adjusted to improve the accuracy of the simulation model.

[0080] In summary, based on the technical content, by loading the physical three-dimensional model into the preset test station three-dimensional scene, and performing virtual assembly, constructing the test virtual scene, configuring the test parameters, loading and associating the measurement channels, collecting the on-site measured data, and analyzing the simulation result data, the virtual and real comparison analysis of stress or strain in the static test can be realized. This method can not only improve the efficiency and accuracy of the test, but also provide a basis for the optimization of the simulation model, and has important engineering application value.

[0081] Step S102: performing vector field test time matching and vector field test space matching according to the field measured real-time data and the simulation result data, performing point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data, and determining corresponding displacement distribution comparison results, vector field comparison results, and load-displacement comparison results;

[0082] Optionally, in this embodiment, the main purpose of step S102 is to use the real-time data measured on site and the simulation result data to perform time matching and space matching of the vector field test, and to determine the displacement distribution, stress / strain and load-displacement comparison results by comparing these data. This process involves multiple technical means and principles, aiming to ensure the consistency of virtual simulation and actual test results, and provide a reliable basis for system optimization.

[0083] In the specific implementation process, time matching of the vector field test is first required. Time matching refers to aligning the real-time data measured on site with the simulation result data in the time dimension to ensure that the time axes of the two are consistent. The technical means to achieve this usually include time interpolation, synchronous alignment and other methods. For example, if the sampling frequency of the real-time data measured on site is different from the sampling frequency of the simulation result data, the sampling points of the two can be aligned through the interpolation algorithm to achieve time matching.

[0084] Next is the vector field test space matching, which means aligning the real-time data measured on site with the simulation result data in the spatial dimension to ensure that the measurement positions and directions of the two are consistent. The technical means to achieve this include coordinate transformation, geometric calibration and other methods. For example, if the measurement points of the real-time data measured on site are not completely consistent with the node positions of the simulation model, the measurement points of the two can be aligned through coordinate transformation and geometric calibration to achieve space matching.

[0085] After completing time matching and space matching, the system needs to perform point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data. Point-to-point comparison refers to comparing the field measured real-time data and the simulation result data point by point at the same time and space position to evaluate the consistency between the two. Specifically, the system will compare the data of physical quantities such as displacement, stress / strain, etc. for each measurement point and calculate the difference between the two.

[0086] To further analyze the performance of the system, the system will determine the corresponding displacement distribution comparison results, vector field comparison results, and load-displacement comparison results. The displacement distribution comparison result refers to the difference distribution between the displacement data of each measurement point and the displacement data of the simulation results in the entire measurement area. The vector field comparison result refers to the difference distribution between the stress or strain data of each measurement point and the stress or strain data of the simulation results. The load-displacement comparison result refers to the difference in the load-displacement relationship between the displacement data and the simulation results under different load conditions.

[0087] In terms of the technical problems solved, step S102 solves the consistency verification problem between the real-time data measured on site and the simulation result data through time matching and space matching and point-to-point comparison of the vector field test. Time matching and space matching ensure the alignment of data in time and space dimensions, and point-to-point comparison provides an accurate difference analysis method. These operations ensure the consistency between virtual simulation and actual test results, providing a reliable basis for system optimization and verification.

[0088] In terms of the technical effects achieved, through step S102, the system can achieve accurate matching and comparison of the real-time data measured on site and the simulation result data to ensure the consistency between the two. Through displacement distribution comparison, vector field comparison and load-displacement comparison, the system can identify the differences between the virtual simulation and the actual test results, and provide detailed performance evaluation data. These results provide an important basis for the design optimization and performance improvement of the system.

[0089] For example, in the static test of a bridge structure, engineers need to evaluate the displacement distribution, stress / strain distribution, and load-displacement relationship of the bridge under static load. Through step S102, engineers can match the bridge vector field data measured on site with the simulation result data in time and space to ensure that the data of the two are aligned at the same time and space position. Next, the system will perform a point-to-point comparison, compare the displacement and stress / strain data of each measurement point, and calculate the difference between the two. Through displacement distribution comparison, vector field comparison, and load-displacement comparison, engineers can identify the performance differences of bridge structures in static tests and propose corresponding optimization and improvement plans.

[0090] Specifically, for static tests, it is necessary to consider data matching in two dimensions: time matching and space matching. The comparison of static test results mainly includes three aspects: displacement comparison, vector field comparison and load-displacement comparison. Displacement comparison is also divided into global displacement comparison and local displacement comparison, vector field comparison is divided into maximum value comparison and distribution comparison, and load-displacement comparison is divided into initial stiffness comparison, yield point comparison and ultimate load comparison. The specific comparison methods are as follows:

[0091] Displacement comparison:

[0092] Displacement comparison is divided into global displacement comparison and local displacement comparison. Global displacement comparison compares the deformation of the entire structure, while local displacement comparison analyzes the deformation of a specific node or position.

[0093] Global displacement comparison:

[0094] Firstly, through the data matching process, the global displacement field is constructed based on the test result data by difference processing.

[0095] Second, the global displacement fields obtained from simulation and experiment are displayed through color maps (contour plots) to visually compare the displacement distributions of the two.

[0096] Third, construct an overlay graph to overlay the displacement field graphs of the simulation and the test and observe the difference between the two. You can use colors with different transparency to distinguish between simulation and test data.

[0097] Fourth, perform numerical comparison on all matching points and calculate the error between simulation and test results (if the scales of simulation data and test data are inconsistent, normalization is required). The calculation method is as follows:

[0098] ,

[0099] In the formula, Indicates the degree of deviation between the displacement simulation results and the test results, expressed as a percentage. The simulation results representing the displacement, The displacement is the experimental measurement result.

[0100] Fifth, the average and maximum errors are statistically analyzed to evaluate the overall accuracy of the simulation results.

[0101] ,

[0102] In the formula, Indicates the average value of the degree of deviation, represents the number of measurement points, Indicates The degree of deviation between the simulation results and the test results at each measuring point;

[0103] ,

[0104] In the formula, Indicates the maximum value of the deviation degree, represents the number of measurement points, Indicates The degree of deviation between the simulation results and the test results at each measuring point.

[0105] Local displacement comparison:

[0106] Firstly, according to the characteristics of the structure and the arrangement of the test measurement points, several important nodes (such as stress concentration points, load application points, etc.) are selected to construct a set of local displacement comparison points;

[0107] Second, the simulated and experimental displacement values ​​of each node are tabulated, and the residual, relative error, and root mean square error between them are calculated.

[0108] ,

[0109] ,

[0110] ,

[0111] In the formula, The simulation results representing the displacement, is the experimental measurement result of displacement, represents the residual between the simulation data and the experimental measurement data, Represents the relative error between simulation data and experimental measurement data, represents the root mean square of the deviation, Indicates the number of measurement points.

[0112] Third, draw an error heat map to show the error value of each node and the error space of key points.

[0113] Vector field comparison:

[0114] The stress / strain comparison mainly includes the maximum value comparison and distribution comparison. The maximum stress / strain mainly compares the difference between the maximum stress or strain value of the simulation data and the test data at a specific point in the structure. The distribution mainly compares the difference between the stress or strain distribution of the simulation data and the test data in the entire structure.

[0115] Maximum value comparison:

[0116] First, identify the key points that need to be compared between simulation and experiment, such as the yield point or fracture point of the material.

[0117] Second, based on the selected key points, the maximum stress and maximum strain of the point are extracted from the simulation and test data.

[0118] Third, calculate the relative error of the maximum value of the vector field (taking the stress field as an example).

[0119] Calculate the relative error of stress:

[0120] ,

[0121] Calculate the relative error of strain:

[0122] ,

[0123] In the formula, Indicates the degree of deviation between the maximum stress simulation result and the test result, expressed as a percentage. The simulation results of the maximum stress are shown in Figure 2. is the test measurement result of the maximum stress.

[0124] Fourth, based on the established threshold, analyze whether the error is within an acceptable range.

[0125] Distribution comparison:

[0126] First, simulation data and interpolated test data matched with the simulation data obtained by a data matching module are obtained.

[0127] Second, the vector field curves are summarized and the corresponding simulation data and experimental data are plotted in the same chart, which is convenient for intuitively viewing the shape and trend of the zoom curve.

[0128] Third, select the designated area, calculate the average stress and strain values ​​at each location or area, and compare the differences in the average values.

[0129] ,

[0130] ,

[0131] ,

[0132] In the formula, is the average value of stress simulation results, Indicates The stress simulation results of a finite element are represents the number of finite elements, is the average value of stress test measurement results, Indicates The stress measurement value at each measuring point is Indicates the number of measuring points.

[0133] Fourth, the regional errors of the vector field are calculated, including the root mean square error (RMSE) and the mean absolute error (MAE).

[0134] Comparison of stress regional errors:

[0135] ,

[0136] ,

[0137] In the formula, represents the root mean square error of stress, represents the mean absolute error, Indicates The stress simulation results of a finite element are Indicates The stress measurement value at each measuring point is Indicates the number of measuring points.

[0138] Comparison of regional strain errors:

[0139] ,

[0140] ,

[0141] In the formula, represents the root mean square error of strain, represents the mean absolute error of strain, Indicates The strain simulation results of the finite element are Indicates The strain measurement value at each measuring point is Indicates the number of measuring points.

[0142] Finally, the RMSE and MAE values ​​are compared to analyze the size and distribution of the errors to assess the overall difference between the simulation and experimental data.

[0143] Load-Displacement

[0144] It mainly includes initial stiffness comparison, yield point comparison and ultimate load comparison. The initial stiffness is achieved by comparing the initial slope of the load-displacement curve. The yield point is the turning point of the curve, representing the point where the material or structure begins to yield. The ultimate load describes the maximum load that the structure can withstand.

[0145] Initial stiffness comparison

[0146] First, data from the initial elastic region of the stress-strain or load-displacement curves are extracted from simulation and test data.

[0147] Second, perform linear fitting on the two sets of data (simulation and test) to obtain the slope of the fitting line, which is the initial stiffness.

[0148] Third, calculate the initial stiffness .

[0149] ,

[0150] This formula shows that stiffness is the change in load ( ) and displacement change ( ) ratio. In this context, load usually refers to the force acting on the structure, and displacement is the deformation of the structure caused by the force.

[0151] ,

[0152] This formula is equivalent to the first one, but uses stress and strain Stress is the force per unit area, while strain is the deformation per unit length. This formula shows that stiffness is the ratio of the change in stress to the change in strain.

[0153] Fourth, calculate the initial stiffness deviation .

[0154] ,

[0155] In the formula, represents the simulation stiffness, Measure stiffness for the test.

[0156] Yield point comparison

[0157] First, the yield point is determined from the vector field curves of the simulation and the test. The 0.2% offset method is used, that is, a straight line is drawn parallel to the initial elastic part and shifted 0.2% along the positive direction of the strain axis. The intersection of the straight line and the vector field curve is the yield point.

[0158] Second, record the yield stress in simulation and test and yield strain .

[0159] Third, compare the yield stress and yield strain and calculate the difference.

[0160] Yield stress difference ,

[0161] ,

[0162] In the formula, Yield stress simulation results, Yield stress test results.

[0163] Yield strain difference ,

[0164] ,

[0165] In the formula, Yield strain simulation results, Yield strain test results.

[0166] Ultimate load comparison

[0167] First, find the maximum stress from the vector field curves of the simulation and experiment or the maximum load from the load-displacement curves.

[0168] Second, record the maximum stress in simulation and test , and the corresponding strain .

[0169] Third, the ultimate stress and the corresponding strain are compared and the difference is calculated.

[0170] Ultimate stress difference ,

[0171] ,

[0172] In the formula, represents the simulation result of the ultimate stress, The test results representing the ultimate stress.

[0173] Ultimate strain difference ,

[0174] ,

[0175] In the formula, represents the simulation result of the ultimate strain, The test results representing the ultimate strain.

[0176] In general, step S102 constructs a detailed performance evaluation and difference analysis process through time matching and space matching and point-to-point comparison of vector field tests, ensuring the consistency between the real-time data measured on site and the simulation result data, and providing a reliable basis for the design optimization and performance improvement of the system.

[0177] Step S103: Correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the displacement distribution comparison result, the vector field comparison result and the load-displacement comparison result, and continue the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0178] Optionally, in this embodiment, the main purpose of step S103 is to correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the displacement distribution comparison results, vector field comparison results and load-displacement comparison results obtained in the previous step, and continue the simulation test based on the corrected physical three-dimensional model, and finally obtain the test life cycle data and file it. This process involves multiple technical links, aiming to improve the accuracy and reliability of the simulation results by continuously optimizing the simulation parameters.

[0179] In the specific implementation process, the simulation parameters need to be corrected first. By comparing the results of displacement distribution, stress / strain and load-displacement, the differences between the simulation data and the actual test data can be found. These differences may come from inaccurate parameter settings of the simulation model or deficiencies in the model itself. Based on these comparison results, engineers need to adjust simulation parameters, such as material properties, boundary conditions, load conditions, etc., to make the simulation results closer to the actual test data. For example, if the displacement distribution comparison results show that the displacement of certain parts of the simulation model is too large, the stiffness or boundary conditions of these parts may need to be adjusted.

[0180] After completing the correction of the simulation parameters, the system continues the simulation test based on the corrected physical 3D model. This process involves re-running the simulation software and performing a new simulation calculation using the updated parameters. The simulation test needs to be performed under the same test conditions in order to compare with the previous test data. Through this iterative optimization method, the results of the simulation test will gradually approach the actual test results, thereby improving the accuracy and reliability of the simulation.

[0181] After the simulation test is completed, the system will obtain the full life cycle data of the test. The full life cycle data refers to all the data from the beginning to the end of the simulation test, including initial conditions, dynamic data during the simulation process, final result data, etc. These data cover all aspects of the entire simulation process and provide comprehensive information for subsequent analysis and optimization.

[0182] Finally, the system needs to archive the data of the entire life cycle of the test. File archiving means storing and managing all simulation data in a certain format and standard for subsequent reference and use. The archiving process includes data classification, labeling, storage path setting, etc. Through standardized data archiving, the system can ensure the traceability and reusability of simulation data, and provide reliable data support for subsequent research and development.

[0183] In terms of the technical problems solved, step S103 solves the problem of inconsistency between simulation results and actual test results by correcting simulation parameters and iteratively optimizing simulation tests. Through comparative analysis and parameter correction, the system can gradually approach the actual test results and improve the accuracy and reliability of the simulation model. By archiving data throughout the life cycle, the system can ensure the integrity and standardization of simulation data, which is convenient for subsequent reference and use.

[0184] In terms of the technical effects achieved, through step S103, the system can continuously optimize the simulation parameters to improve the accuracy and reliability of the simulation results. By re-simulating the test, the system can obtain more accurate simulation data, providing a reliable basis for subsequent analysis and optimization. By archiving the data throughout the life cycle, the system can ensure the traceability and reusability of the simulation data, providing long-term data support for subsequent research and development.

[0185] For example, in the durability test of a building structure, engineers need to evaluate the deformation and stress distribution of the building under long-term loads. Through step S102, engineers have obtained the comparison results of the actual test data and the simulation result data. Next, through step S103, engineers can correct the simulation parameters according to the comparison results, such as the elastic modulus of concrete, the yield strength of steel bars, etc. The corrected simulation model will be re-tested for durability simulation to obtain more accurate deformation and stress distribution data. These full life cycle data will be archived to provide reliable data support for the long-term maintenance and management of the building.

[0186] In general, step S103 improves the accuracy and reliability of the simulation results by correcting simulation parameters and iterative optimization.

[0187] From the above description, it can be seen that the vector field simulation and real-time data processing method provided in the embodiment of the present application can perform vector field test time matching and vector field test space matching according to the on-site measured real-time data and the simulation result data, and perform point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data to determine the corresponding displacement distribution comparison results, vector field comparison results, and load-displacement comparison results; according to the displacement distribution comparison results, the vector field comparison results, and the load-displacement comparison results, the simulation parameters of the physical three-dimensional model in the test virtual scene are corrected, and the simulation test is continued based on the physical three-dimensional model to obtain the test full life cycle data and file it, thereby improving the accuracy of data processing and time-space matching, and realizing the systematization and automation of parameter correction.

[0188] In one embodiment of the vector field simulation and real-time data processing method of the present application, see Figure 2, and can also include the following:

[0189] Step S201: constructing a global displacement field through difference processing according to the obtained real-time vector field data, and superimposing the global displacement field constructed by the real-time vector field data and the global displacement field obtained by the simulation result data;

[0190] Step S202: performing numerical comparison of matching points on the superimposed global displacement field to calculate the overall accuracy of the simulation result data.

[0191] Optionally, in this embodiment, step S201 and step S202 are intended to evaluate the accuracy of the simulation results by constructing and comparing the global displacement field. First, the main task of step S201 is to construct the global displacement field based on the real-time data of the vector field, and superimpose it with the global displacement field obtained by the simulation result data. Then, step S202 compares the numerical values ​​of the matching points of the superimposed global displacement field to calculate the overall accuracy of the simulation result data.

[0192] In step S201, the test static field data must first be processed. Since the test data is usually discrete point data, and the global displacement field requires a continuous displacement distribution, it is necessary to construct the global displacement field through difference processing. Difference processing refers to estimating the displacement value of the entire area through an interpolation algorithm based on existing discrete data points. Commonly used interpolation algorithms include linear interpolation, spline interpolation, and Kriging interpolation. For example, in the static test of a bridge, the global displacement field of the bridge can be constructed using the spline interpolation method through the measurement point data.

[0193] Next, the global displacement field constructed by the test static field data is superimposed with the global displacement field obtained by the simulation result data. The purpose of superposition is to compare and analyze the two data in the same spatial framework. The superposition method is usually a simple numerical addition or difference, and the specific method depends on the analysis requirements. For example, in order to evaluate the error of the simulation model, the difference field between the two can be calculated to obtain the error distribution.

[0194] Go to step S202, and compare the numerical values ​​of the matching points of the superimposed global displacement field. Matching points refer to the corresponding spatial position points in the test and simulation. The numerical comparison of these points can directly reflect the accuracy of the simulation results. The method of numerical comparison can be to calculate indicators such as absolute error, relative error or root mean square error. For example, absolute error refers to the difference in displacement values ​​of matching points, while relative error is the ratio of absolute error to actual value. Through these error indicators, the difference between simulation results and test data can be quantified.

[0195] Finally, the overall accuracy of the simulation result data is calculated. The overall accuracy refers to the consistency between the simulation results and the actual test results in the entire test area. Usually, the overall accuracy evaluation result can be obtained by statistically analyzing the distribution of error indicators. For example, the mean and standard deviation of the absolute errors of all matching points can be calculated to obtain the overall accuracy of the simulation results.

[0196] In terms of the technical problems solved, steps S201 and S202 solve the problem of how to evaluate the accuracy of simulation results by constructing a global displacement field and numerical comparison. Through difference processing and superposition methods, discrete test data can be converted into a continuous displacement field and compared and analyzed under the same framework. Through numerical comparison and error calculation, the difference between the simulation results and the actual test results can be quantified, thereby evaluating the accuracy of the simulation model.

[0197] In terms of the technical effects achieved, through steps S201 and S202, the system can construct the global displacement field of the test and simulation, and superimpose and compare them in the same spatial framework, so as to quantify the accuracy of the simulation results. Through the accuracy evaluation, the shortcomings of the simulation model can be identified, guiding the subsequent model optimization and improvement.

[0198] For example, in the static test of aviation structures, engineers need to evaluate the displacement distribution of the wing under static load. Through step S201, engineers can use the measured point data on the wing surface to construct a global displacement field of the wing through the spline interpolation method. Then, the global displacement field constructed by the test data is superimposed with the global displacement field of the simulation results to obtain the difference field between the two. In step S202, the numerical comparison of the matching points of the superimposed global displacement field is performed, and indicators such as absolute error and relative error are calculated. Through these error indicators, engineers can evaluate the accuracy of the simulation model, identify the deficiencies of the wing model in certain areas, and thus guide the optimization and improvement of the model.

[0199] In general, steps S201 and S202 achieve quantitative evaluation of the accuracy of simulation results by constructing and comparing the global displacement field, providing a reliable basis for optimization and improvement of the simulation model.

[0200] In one embodiment of the vector field simulation and real-time data processing method of the present application, see Figure 3 , and can also include the following:

[0201] Step S301: Selecting stress concentration points and load action points in the physical three-dimensional model to construct a local displacement comparison point set;

[0202] Step S302: Obtain corresponding displacement distribution comparison results according to the residual, relative error and root mean square error of the simulation and test displacement values ​​of each node in the local displacement comparison point set.

[0203] Optionally, in this embodiment, in step S301, it is first necessary to select stress concentration points and load application points in the physical three-dimensional model to construct a set of local displacement comparison points. The key to this step is to accurately select the most critical points in the stress distribution and load transfer process. The stress concentration point is usually the point in the structure with the greatest stress and the most prone to damage, while the load application point is the location where the external force directly acts. By selecting these key points, the differences between simulation and actual test data can be more effectively analyzed and compared.

[0204] In the specific implementation process, engineers first need to perform stress analysis on the physical 3D model to identify stress concentration points. This can be done using stress analysis software (such as finite element analysis software), which will mark the points with the highest stress in the model. The load application points are determined based on the experimental design. For example, in the bridge model, the wheel load application point is the load application point. After determining these points, engineers group these points together to form a set of local displacement comparison points.

[0205] In step S302, the engineer needs to compare the simulation and test displacement values ​​of each node in the local displacement comparison point set. Specifically, the residual, relative error and root mean square error of each node need to be calculated. The residual refers to the difference between the simulated displacement value and the test displacement value, reflecting the direct difference between the simulation result and the actual result. The relative error is the ratio of the residual to the test displacement value, which provides a standardized error metric to facilitate the comparison of data of different magnitudes. The root mean square error is the square root of the mean of the sum of the squares of the residuals of all nodes, providing a quantitative indicator of the overall error.

[0206] Through these error calculations, engineers can obtain displacement distribution comparison results. The comparison results can be used to evaluate the accuracy and reliability of the simulation model. For example, if the residual and relative errors of some nodes are large, it means that the simulation results of these nodes are very different from the test results, and parameter adjustments or improvements may be needed in the model.

[0207] In terms of the technical problems solved, steps S301 and S302 solve the problem of how to accurately evaluate the difference between simulation results and actual test results in a local range through the selection of stress concentration points and load application points and detailed error analysis. Through the calculation of residuals, relative errors and root mean square errors, engineers can quantify the differences between simulation and test and provide specific guidance for model optimization.

[0208] In terms of the technical effects achieved, through steps S301 and S302, the system can accurately evaluate the accuracy of the simulation results at key locations. Through detailed analysis of the local displacement comparison point set, engineers can identify the deficiencies of the simulation model and make targeted adjustments and optimizations, thereby improving the accuracy and reliability of the overall simulation results.

[0209] For example, in the simulation and testing of automobile suspension systems, engineers need to evaluate the displacement response of suspension systems under different load conditions. Through step S301, engineers can select stress concentration points (such as suspension connection points) and load application points (such as tire contact points) in the suspension system to construct a set of local displacement comparison points. In step S302, engineers compare the simulation and test displacement values ​​of these points and calculate the residual, relative error and root mean square error. Through these error analyses, engineers can find that the simulation results of certain connection points are quite different from the actual test results, which may be due to inaccurate material parameter settings in the model. By adjusting these parameters, engineers can gradually optimize the simulation model and improve the accuracy of the simulation results.

[0210] In general, steps S301 and S302 achieve accurate evaluation and optimization guidance of simulation results by selecting key points and performing detailed error analysis.

[0211] In one embodiment of the vector field simulation and real-time data processing method of the present application, see Figure 4 , and can also include the following:

[0212] Step S401: acquiring simulation result data and interpolated test data matching the simulation result data; summarizing vector field curves;

[0213] Step S402: Select a designated area, calculate the average stress and strain values ​​of each position or area, and calculate the regional error of the vector field according to the average stress and strain values.

[0214] Optionally, in the present embodiment, in step S401, it is first necessary to obtain simulation result data and interpolated test data that match these simulation result data. The core of this process is to ensure that the simulation data and the test data are compared under the same spatial distribution. The simulation result data usually comes from computer simulation, reflecting the stress and strain distribution of the model under various load conditions. The test data comes from actual physical testing. Since the distribution of the test points may be irregular, it is necessary to convert the test data into a form that matches the simulation data through an interpolation method. The interpolation method can use linear interpolation, spline interpolation or other suitable algorithms to ensure the continuity and comparability of the data.

[0215] After acquiring and processing these data, the next step is to summarize the stress / strain curve. This process involves arranging and plotting the stress and strain values ​​of all data points to form a complete stress / strain curve. These curves can intuitively reflect the deformation behavior of materials or structures under different stress conditions and provide important analysis basis.

[0216] The key to step S402 is to select designated areas and calculate the average stress and strain values ​​at each position in these areas. The designated areas can be selected according to the actual needs of the engineering application. For example, in the analysis of bridge structures, different sections of the bridge can be selected for regional analysis. When calculating the average stress and strain values ​​at each position, the comprehensive stress and strain characteristics of the area can be obtained by averaging the stress and strain values ​​of all data points in the area. This method can effectively smooth local data fluctuations and reflect the overall mechanical properties of the area.

[0217] Next, the regional error of stress / strain is calculated based on the calculated average stress and strain values. The regional error is calculated by comparing the simulation data with the interpolated test data to obtain the difference between the two. Errors can be expressed in a variety of ways, including absolute error, relative error, and root mean square error. These error indicators can quantify the deviation between the simulation results and the test data and evaluate the accuracy of the simulation model.

[0218] In terms of the technical problems solved, steps S401 and S402 solve the problem of how to compare simulation and actual data in the same framework by acquiring and processing simulation and test data. The comparability and continuity of data are ensured through interpolation processing and data aggregation; the influence of local fluctuations in data is reduced through average value calculation and smoothing processing, providing more stable and reliable analysis results.

[0219] In terms of the technical effects achieved, through steps S401 and S402, the system can effectively integrate simulation data and test data to provide detailed stress / strain analysis results. Through the calculation of regional errors, engineers can identify the accuracy of the simulation model in different areas, guide the improvement and optimization of the model, and improve the reliability and application value of the simulation results.

[0220] For example, in the stress analysis of aircraft engine blades, engineers need to evaluate the stress and strain distribution of the blades under high-speed rotation. Through step S401, engineers first obtain simulation result data, which are obtained through finite element analysis software and reflect the stress and strain distribution of the blades under high-speed rotation. At the same time, through laboratory tests, engineers obtain stress and strain data on the surface of the blades. Due to the irregular distribution of test points, engineers need to convert the test data into a form that matches the simulation data through spline interpolation method. Then, summarize these data and draw a stress / strain curve.

[0221] In step S402, engineers select different areas of the blade for analysis, such as the root, middle and tip of the blade. By calculating the average stress and strain values ​​in these areas, engineers can obtain the mechanical properties of each area. Next, the stress / strain errors in these areas are calculated, and the accuracy of the simulation model in different areas is identified by comparing the differences between the simulation results and the test data after interpolation. Through these analyses, engineers can find that the simulation results of the blade model in some areas are quite different from the actual test results, which may be due to inaccurate material parameter settings or improper boundary conditions. By adjusting these parameters in a targeted manner, engineers can gradually optimize the simulation model and improve the accuracy and reliability of the simulation results.

[0222] In general, steps S401 and S402 achieve accurate evaluation and optimization guidance of stress / strain analysis by acquiring and processing simulation and test data.

[0223] In one embodiment of the vector field simulation and real-time data processing method of the present application, see Figure 5 , and can also include the following:

[0224] Step S501: Select a set yield point or fracture point in the physical three-dimensional model, and extract the maximum stress and maximum strain of the point from the simulation result data and the vector field real-time data;

[0225] Step S502: Calculate the relative error of the maximum value of the vector field according to the maximum stress and the maximum strain, and determine whether the error is within an acceptable range according to a predetermined threshold.

[0226] Optionally, in this embodiment, in step S501, it is necessary to select the yield point or fracture point set in the physical three-dimensional model, and extract the maximum stress and maximum strain of the point from the simulation result data and the experimental static field data. The key to this process is to accurately locate the yield point or fracture point, which are usually the locations in the structure that are most prone to plastic deformation or fracture. The yield point is the stress level at which the material begins to undergo plastic deformation, while the fracture point is the stress level at which the material is completely destroyed. These key points can be identified in the model through simulation techniques such as finite element analysis (FEA). The experimental static field data is obtained through actual testing, for example, by measuring at the corresponding position of the physical model through equipment such as strain gauges and stress sensors. When extracting the maximum stress and maximum strain, it is necessary to ensure the accuracy and consistency of the data for subsequent comparison and analysis.

[0227] In step S502, it is necessary to calculate the relative error of the maximum stress / strain value based on the extracted maximum stress and maximum strain. The calculation of the relative error is to standardize the difference between the simulation results and the test results to simplify the comparison of data of different magnitudes. Specifically, the relative error is the difference between the simulation results and the test results divided by the test results to obtain a dimensionless error index. This process can effectively reflect the degree of deviation between the simulation results and the actual test results.

[0228] Next, determine whether the error is within an acceptable range based on the established threshold. The established threshold is the allowable error range set according to the actual needs of the engineering application, and different engineering projects have different tolerances for errors. Generally speaking, engineering projects with high precision requirements allow a smaller error range, while some projects with lower precision requirements allow a larger error range. By comparing the calculated relative error with the established threshold, it can be determined whether the simulation result is within an acceptable accuracy range. If the error is within an acceptable range, it means that the prediction ability of the simulation model is relatively accurate; if the error exceeds the acceptable range, the simulation model needs to be further optimized and adjusted.

[0229] In terms of the technical problems solved, steps S501 and S502 solve the problem of how to accurately evaluate the difference between simulation results and actual test results at local key points by extracting the maximum stress and strain of key points and performing detailed error analysis. Through the calculation of relative errors and threshold judgment, engineers can quantify the difference between simulation and test, providing specific guidance for model optimization.

[0230] In terms of the technical effects achieved, through steps S501 and S502, the system can accurately evaluate the accuracy of the simulation results at key locations of the structure. Through detailed analysis of the maximum stress and maximum strain, engineers can identify the deficiencies of the simulation model and make targeted adjustments and optimizations, thereby improving the accuracy and reliability of the overall simulation results.

[0231] For example, in the simulation and testing of pressure vessels, engineers need to evaluate the stress and strain distribution of the container under high pressure conditions. Through step S501, the engineer first selects the key points in the physical model where yield or fracture may occur, such as the welds or corners of the pressure vessel. These points are usually stress concentration areas and are also the locations most prone to damage. Through finite element analysis, engineers can identify these key points and extract the simulation results of maximum stress and maximum strain. At the same time, through pressure testing, engineers obtain the experimental static field data of these key points. After extracting the maximum stress and maximum strain in the test data, the engineer compares these data with the simulation results.

[0232] In step S502, the engineer calculates the relative errors of these key points and compares them with the established threshold. For example, if the threshold is set to 5%, if the relative error is within 5%, it means that the simulation results are close to the test results and the simulation model has a strong predictive ability; if the relative error exceeds 5%, it means that there is a large deviation between the simulation results and the test results, and the model needs to be optimized. By adjusting the model parameters and improving the simulation method, engineers can gradually reduce the error and improve the accuracy of the simulation results.

[0233] In one embodiment of the vector field simulation and real-time data processing method of the present application, see Figure 6 , and can also include the following:

[0234] Step S601: extracting data of the initial elastic region of the vector field curve and the load-displacement curve from the simulation result data and the vector field real-time data, and performing linear fitting respectively to obtain the slope of the fitting line;

[0235] Step S602: Calculate the deviation of the initial stiffness according to the slope.

[0236] Optionally, in this embodiment, in step S601, the data of the initial elastic region of the stress-strain curve and the load-displacement curve must first be extracted from the simulation result data and the experimental static field data. The initial elastic region refers to the part of the material that exhibits linear elastic deformation at the initial stage of force. The relationship between stress and strain, load and displacement at this stage is usually linear. In order to extract this part of the data, the simulation and experimental data need to be preprocessed to identify and intercept the initial linear segment of the curve. The simulation result data usually comes from numerical simulation methods such as finite element analysis (FEA), which reflects the response of the material or structure under different load conditions. The experimental static field data is obtained through actual physical testing, such as measurement by equipment such as tensile testing machines and presses.

[0237] After extracting the data of the initial elastic region, it is necessary to perform a linear fit on these data to obtain the slope of the fitted line. Linear fitting is a statistical method that fits data points into a straight line through techniques such as the least squares method, so that the deviation between the straight line and the data points is minimized. The slope of the fitted line is an important indicator of the elastic modulus or initial stiffness, reflecting the stiffness characteristics of the material in the initial elastic stage. For the stress-strain curve, the slope is the Young's modulus; for the load-displacement curve, the slope reflects the initial stiffness of the structure.

[0238] In step S602, the deviation of the initial stiffness needs to be calculated based on the slope obtained in step S601. The deviation of the initial stiffness refers to the difference between the slope of the fitted straight line in the simulation result and the test result, which is usually expressed by relative error or absolute error. Calculating the deviation of the initial stiffness can intuitively reflect the difference between the simulation model and the actual test results, and evaluate the accuracy and reliability of the simulation model.

[0239] In terms of the technical problems solved, steps S601 and S602 solve the problem of how to accurately evaluate the difference between the simulation model and the actual test data in the elastic stage by extracting the initial elastic region data and calculating the deviation of the initial stiffness. Through linear fitting and error calculation, the difference between the simulation results and the test data can be quantified, providing specific guidance for the optimization of the model.

[0240] In terms of the technical effects achieved, through steps S601 and S602, the system can accurately evaluate the accuracy of the simulation results in the initial elastic stage of the material or structure. Through detailed analysis of the stress-strain curve and the load-displacement curve, engineers can identify the deficiencies of the simulation model and make targeted adjustments and optimizations, thereby improving the accuracy and reliability of the overall simulation results.

[0241] For example, in the simulation and testing of the automobile suspension system, engineers need to evaluate the stiffness characteristics of the suspension spring under small loads. Through step S601, the engineer first extracts the initial elastic area of ​​the stress-strain curve and load-displacement curve of the suspension spring from the simulation results and the test static field data. The simulation data is obtained through finite element analysis, and the test data is measured through the suspension test bench. After extracting the data of the initial elastic area, the engineer performs linear fitting on these data to obtain the slope of the fitting line, which is the stiffness of the spring in the initial elastic stage.

[0242] In step S602, the engineer calculates the deviation of the initial stiffness based on the slope of the fitted straight line in the simulation and the test. For example, if the slope in the simulation result is 1000 N / mm and the slope in the test result is 950 N / mm, the relative error is 5.26%. Through this calculation, the engineer can determine whether the simulation result is within the acceptable error range. If the error is large, it means that there is a problem with the simulation model in the initial elastic stage, and it may be necessary to adjust the material parameters or improve the simulation method.

[0243] In general, steps S601 and S602 achieve accurate evaluation and optimization guidance of simulation results by extracting initial elastic region data and calculating the deviation of initial stiffness.

[0244] In one embodiment of the vector field simulation and real-time data processing method of the present application, see Figure 7 , and can also include the following:

[0245] Step S701: determining the yield point in the stress-strain curve of the simulation result data and the test static field data, and recording the yield stress and yield strain in the simulation result data and the test static field data;

[0246] Step S702: determining corresponding yield stress difference and yield strain difference according to the yield stress and yield strain.

[0247] Optionally, in the present embodiment, in step S701, it is first necessary to determine the yield point of the stress-strain curve from the simulation result data and the test static field data, and record the yield stress and yield strain in the simulation result data and the test static field data. The yield point is the stress level position at which the material begins to undergo plastic deformation and no longer fully recovers, which has a crucial impact on the design of engineering materials and structures. In order to determine the yield point, a graphical analysis method, such as a bilinear method or a deviation method, is usually used to identify the yield point by the morphological change of the stress-strain curve. In the simulation results, finite element analysis can provide detailed stress-strain data, and by analyzing these data, the yield point of the simulation model can be determined. The test static field data is obtained by actual testing, such as applying a gradually increasing load by equipment such as a tensile testing machine, and recording stress and strain values ​​at the same time, until the material undergoes obvious nonlinear deformation.

[0248] After determining the yield point, the corresponding yield stress and yield strain need to be recorded. The yield stress is the stress value of the material at the yield point, and the yield strain is the strain value of the material at the yield point. These data are important for evaluating material properties and the accuracy of the simulation model.

[0249] In step S702, the corresponding yield stress difference and yield strain difference are determined based on the recorded yield stress and yield strain. The yield stress difference refers to the difference between the yield stress in the simulation result and the yield stress in the test result, which can be expressed by an absolute difference or a relative error. Similarly, the yield strain difference refers to the difference between the yield strain in the simulation result and the yield strain in the test result. By calculating these differences, the accuracy of the simulation model on the yield point can be quantified and its prediction ability and reliability can be evaluated.

[0250] In terms of the technical problems solved, steps S701 and S702 solve the problem of how to accurately evaluate the performance of the simulation model in the material yield stage by determining and analyzing the stress and strain data of the yield point. Through detailed recording and difference analysis of the yield stress and yield strain, the difference between the simulation model and the actual test data can be identified, providing a specific basis for model optimization.

[0251] In terms of the technical effects achieved, through steps S701 and S702, the system can accurately evaluate the accuracy of the simulation results at the yield stage of the material or structure. Through detailed analysis of the yield point, engineers can identify the deficiencies of the simulation model and make targeted adjustments and optimizations, thereby improving the accuracy and reliability of the overall simulation results.

[0252] For example, in the simulation and testing of aviation materials, engineers need to evaluate the yield characteristics of materials under high stress conditions. Through step S701, engineers first determine the yield point of the vector field curve from the simulation results and the real-time data of the vector field. The simulation data is obtained through finite element analysis, and the test data is measured by a tensile testing machine. Through methods such as the two-wire method, engineers can accurately identify the yield point and record the yield stress and yield strain in simulation and testing.

[0253] In step S702, the engineer calculates the yield stress difference and the yield strain difference based on the recorded yield stress and yield strain. For example, if the yield stress in the simulation result is 600 MPa and the yield stress in the test result is 580 MPa, the yield stress difference is 20 MPa. Similarly, the yield strain difference can be calculated. Through these calculations, the engineer can determine whether the simulation result is within an acceptable error range. If the error is large, the simulation model needs to be optimized, such as adjusting material parameters or improving simulation methods.

[0254] In general, steps S701 and S702 achieve accurate evaluation of the simulation results in the yield stage by determining the yield point and calculating the yield stress and yield strain differences. Through these steps, the system can identify the deficiencies of the simulation model and make targeted improvements, thereby improving the accuracy and reliability of the simulation results and providing a reliable basis for engineering design and optimization.

[0255] In order to improve the accuracy of data processing and time-space matching and realize the systematization and automation of parameter correction, the present application provides an embodiment of a vector field simulation and real-time data processing device for realizing all or part of the contents of the vector field simulation and real-time data processing method, see Figure 8 The vector field simulation and real-time data processing device specifically includes the following contents:

[0256] The test configuration module 10 is used to load the physical three-dimensional model into the preset test station three-dimensional scene and perform virtual assembly to construct a test virtual scene, configure test parameters in the test virtual scene, and load and associate measurement channels; collect on-site measured real-time data in the test virtual scene according to the test scene requirements sent by the user, and load and analyze the simulation result data corresponding to the on-site measured real-time data;

[0257] A vector field comparison module 20 is used to perform vector field test time matching and vector field test space matching according to the on-site measured real-time data and the simulation result data, and to perform point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data to determine the corresponding displacement distribution comparison result, vector field comparison result and load-displacement comparison result;

[0258] The simulation test module 30 is used to correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the displacement distribution comparison results, the vector field comparison results and the load-displacement comparison results, and continue the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0259] From the above description, it can be seen that the vector field simulation and real-time data processing device provided in the embodiment of the present application can perform vector field test time matching and vector field test space matching according to the on-site measured real-time data and the simulation result data, and perform point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data to determine the corresponding displacement distribution comparison results, vector field comparison results, and load-displacement comparison results; according to the displacement distribution comparison results, the vector field comparison results, and the load-displacement comparison results, the simulation parameters of the physical three-dimensional model in the test virtual scene are corrected, and the simulation test is continued based on the physical three-dimensional model to obtain the test full life cycle data and file it, thereby improving the accuracy of data processing and time-space matching, and realizing the systematization and automation of parameter correction.

[0260] From the hardware level, in order to improve the accuracy of data processing and time-space matching and realize the systematization and automation of parameter correction, the present application provides an embodiment of an electronic device for realizing all or part of the contents of the vector field simulation and real-time data processing method, and the electronic device specifically includes the following contents:

[0261] Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between the vector field simulation and real-time data processing device and related devices such as the core business system, user terminal and related database; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., but the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented with reference to the embodiment of the vector field simulation and real-time data processing method and the embodiment of the vector field simulation and real-time data processing device in the embodiment, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0262] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0263] In practical applications, part of the vector field simulation and real-time data processing method can be executed on the electronic device side as described above, or all operations can be completed in the client device. The selection can be made based on the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0264] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0265] Fig. 9 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig. 9As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig. 9 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0266] In one embodiment, the vector field simulation and real-time data processing method functions may be integrated into the central processing unit 9100. The central processing unit 9100 may be configured to perform the following control:

[0267] Step S101: Load the physical three-dimensional model into the preset three-dimensional scene of the test station and perform virtual assembly to construct a test virtual scene, configure the test parameters in the test virtual scene, and load and associate the measurement channels; collect the on-site measured real-time data in the test virtual scene according to the test scene requirements sent by the user, and load and analyze the simulation result data corresponding to the on-site measured real-time data;

[0268] Step S102: performing vector field test time matching and vector field test space matching according to the field measured real-time data and the simulation result data, performing point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data, and determining corresponding displacement distribution comparison results, vector field comparison results, and load-displacement comparison results;

[0269] Step S103: correcting the simulation parameters of the physical three-dimensional model in the test virtual scene according to the displacement distribution comparison result, the vector field comparison result and the load-displacement comparison result, and continuing the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0270] From the above description, it can be seen that the electronic device provided in the embodiment of the present application performs vector field test time matching and vector field test space matching according to the on-site measured real-time data and the simulation result data, and performs point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data to determine the corresponding displacement distribution comparison results, vector field comparison results, and load-displacement comparison results; according to the displacement distribution comparison results, the vector field comparison results, and the load-displacement comparison results, the simulation parameters of the physical three-dimensional model in the test virtual scene are corrected, and the simulation test is continued based on the physical three-dimensional model to obtain the test full life cycle data and file archiving, thereby improving the accuracy of data processing and time-space matching, and realizing the systematization and automation of parameter correction.

[0271] In another embodiment, the vector field simulation and real-time data processing device can be configured separately from the central processing unit 9100. For example, the vector field simulation and real-time data processing device can be configured as a chip connected to the central processing unit 9100, and the vector field simulation and real-time data processing method functions can be implemented under the control of the central processing unit.

[0272] like Fig. 9 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Fig. 9 In addition, the electronic device 9600 may also include Fig. 9 For components not shown, reference may be made to the prior art.

[0273] like Fig. 9 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0274] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0275] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0276] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0277] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0278] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0279] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0280] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the vector field simulation and real-time data processing method in the above embodiments, wherein the execution subject is a server or a client. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, all the steps of the vector field simulation and real-time data processing method in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0281] Step S101: Load the physical three-dimensional model into the preset three-dimensional scene of the test station and perform virtual assembly to construct a test virtual scene, configure the test parameters in the test virtual scene, and load and associate the measurement channels; collect the on-site measured real-time data in the test virtual scene according to the test scene requirements sent by the user, and load and analyze the simulation result data corresponding to the on-site measured real-time data;

[0282] Step S102: performing vector field test time matching and vector field test space matching according to the field measured real-time data and the simulation result data, performing point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data, and determining corresponding displacement distribution comparison results, vector field comparison results, and load-displacement comparison results;

[0283] Step S103: correcting the simulation parameters of the physical three-dimensional model in the test virtual scene according to the displacement distribution comparison result, the vector field comparison result and the load-displacement comparison result, and continuing the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0284] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application performs vector field test time matching and vector field test space matching according to the on-site measured real-time data and the simulation result data, and performs point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data to determine the corresponding displacement distribution comparison results, vector field comparison results, and load-displacement comparison results; according to the displacement distribution comparison results, the vector field comparison results, and the load-displacement comparison results, the simulation parameters of the physical three-dimensional model in the test virtual scene are corrected, and the simulation test is continued based on the physical three-dimensional model to obtain the test full life cycle data and file archiving, thereby improving the accuracy of data processing and time-space matching, and realizing the systematization and automation of parameter correction.

[0285] The embodiments of the present application also provide a computer program product capable of implementing all steps of the vector field simulation and real-time data processing method in the above embodiments, where the execution subject is a server or a client. When the computer program / instruction is executed by a processor, the steps of the vector field simulation and real-time data processing method are implemented. For example, the computer program / instruction implements the following steps:

[0286] Step S101: Load the physical three-dimensional model into the preset three-dimensional scene of the test station and perform virtual assembly to construct a test virtual scene, configure the test parameters in the test virtual scene, and load and associate the measurement channels; collect the on-site measured real-time data in the test virtual scene according to the test scene requirements sent by the user, and load and analyze the simulation result data corresponding to the on-site measured real-time data;

[0287] Step S102: performing vector field test time matching and vector field test space matching according to the field measured real-time data and the simulation result data, performing point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data, and determining corresponding displacement distribution comparison results, vector field comparison results, and load-displacement comparison results;

[0288] Step S103: correcting the simulation parameters of the physical three-dimensional model in the test virtual scene according to the displacement distribution comparison result, the vector field comparison result and the load-displacement comparison result, and continuing the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0289] From the above description, it can be seen that the computer program product provided in the embodiment of the present application performs vector field test time matching and vector field test space matching according to the on-site measured real-time data and the simulation result data, and performs point-to-point comparison between the obtained vector field real-time data and the vector field data in the simulation result data to determine the corresponding displacement distribution comparison results, vector field comparison results, and load-displacement comparison results; according to the displacement distribution comparison results, the vector field comparison results, and the load-displacement comparison results, the simulation parameters of the physical three-dimensional model in the test virtual scene are corrected, and the simulation test is continued based on the physical three-dimensional model to obtain the test full life cycle data and file archiving, thereby improving the accuracy of data processing and time-space matching, and realizing the systematization and automation of parameter correction.

[0290] It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0291] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0292] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0293] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0294] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A vector field simulation and real-time data processing method, characterized in that: The method comprises: The physical three-dimensional model is loaded into a preset three-dimensional scene of the test station and virtually assembled to construct a test virtual scene, test parameters are configured in the test virtual scene, and measurement channels are loaded and associated; on-site measured real-time data is collected in the test virtual scene according to the test scene requirements sent by the user, and simulation result data corresponding to the on-site measured real-time data is loaded and parsed; Performing vector field test time matching and vector field test space matching according to the field measured real-time data and the simulation result data; Constructing a global displacement field through difference processing according to the obtained real-time vector field data, superimposing the global displacement field constructed by the real-time vector field data and the global displacement field obtained by the simulation result data, comparing the numerical values ​​of the matching points of the superimposed global displacement field, and calculating the overall accuracy of the simulation result data; Selecting set stress concentration points and load action points in the physical three-dimensional model to construct a local displacement comparison point set, and obtaining a corresponding displacement distribution comparison result according to the residual, relative error and root mean square error of the simulation and test displacement values ​​of each node in the local displacement comparison point set; Acquire simulation result data and interpolated test data matching the simulation result data; summarize vector field curves, select designated areas, calculate average stress and strain values ​​at various positions or areas, and calculate regional errors of the vector field based on the average stress and strain values; Selecting a set yield point or fracture point in the physical three-dimensional model, extracting the maximum stress and maximum strain of the point from the simulation result data and the vector field real-time data; calculating the relative error of the maximum value of the vector field according to the maximum stress and maximum strain, and judging whether the error is within an acceptable range according to a predetermined threshold; Extracting data of the initial elastic region of the vector field curve and the load-displacement curve from the simulation result data and the vector field real-time data, and performing linear fitting respectively to obtain the slope of the fitting line, and calculating the deviation of the initial stiffness according to the slope; Determine the yield point in the vector field curve of the simulation result data and the vector field real-time data, record the yield stress and yield strain in the simulation result data and the vector field real-time data, and determine the corresponding yield stress difference and yield strain difference according to the yield stress and yield strain; The simulation parameters of the physical three-dimensional model in the test virtual scene are corrected according to the displacement distribution comparison results, the vector field comparison results and the load-displacement comparison results, and the simulation test is continued based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

2. A vector field simulation and real-time data processing device, characterized in that: The device comprises: The test configuration module is used to load the physical three-dimensional model into the preset test station three-dimensional scene and perform virtual assembly to construct a test virtual scene, configure the test parameters in the test virtual scene, and load and associate the measurement channels; collect the on-site measured real-time data in the test virtual scene according to the test scene requirements sent by the user, and load and analyze the simulation result data corresponding to the on-site measured real-time data; Vector field comparison module for: Performing vector field test time matching and vector field test space matching according to the field measured real-time data and the simulation result data; Constructing a global displacement field through difference processing according to the obtained real-time vector field data, superimposing the global displacement field constructed by the real-time vector field data and the global displacement field obtained by the simulation result data, comparing the numerical values ​​of the matching points of the superimposed global displacement field, and calculating the overall accuracy of the simulation result data; Selecting set stress concentration points and load action points in the physical three-dimensional model to construct a local displacement comparison point set, and obtaining a corresponding displacement distribution comparison result according to the residual, relative error and root mean square error of the simulation and test displacement values ​​of each node in the local displacement comparison point set; Acquire simulation result data and interpolated test data matching the simulation result data; summarize vector field curves, select designated areas, calculate average stress and strain values ​​at various positions or areas, and calculate regional errors of the vector field based on the average stress and strain values; Selecting a set yield point or fracture point in the physical three-dimensional model, extracting the maximum stress and maximum strain of the point from the simulation result data and the vector field real-time data; calculating the relative error of the maximum value of the vector field according to the maximum stress and maximum strain, and judging whether the error is within an acceptable range according to a predetermined threshold; Extracting data of the initial elastic region of the vector field curve and the load-displacement curve from the simulation result data and the vector field real-time data, and performing linear fitting respectively to obtain the slope of the fitting line, and calculating the deviation of the initial stiffness according to the slope; Determine the yield point in the vector field curve of the simulation result data and the vector field real-time data, record the yield stress and yield strain in the simulation result data and the vector field real-time data, and determine the corresponding yield stress difference and yield strain difference according to the yield stress and yield strain; The simulation test module is used to correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the displacement distribution comparison results, the vector field comparison results and the load-displacement comparison results, and continue the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

3. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the vector field simulation and real-time data processing method according to claim 1 are implemented.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the vector field simulation and real-time data processing method according to claim 1 are implemented.

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