Virtual-reality combined comparative analysis and verification method and device integrating simulation and experiment

Abstract

The embodiment of the present application provides a virtual-reality comparison analysis and verification method and device that integrates simulation and experiment, the method comprising: loading a physical three-dimensional model into a preset three-dimensional scene of a test station and performing virtual assembly to construct a test virtual scene, configuring test parameters in the test virtual scene, and loading and associating measurement channels; collecting on-site actual test data in the test virtual scene according to the test scene requirements sent by the user, loading and parsing simulation result data corresponding to the on-site actual test data; correcting the simulation parameters of the physical three-dimensional model in the test virtual scene according to at least one of the temperature distribution comparison results and the thermal stress comparison results, the spatial distribution comparison results and the vibration comparison results, the displacement distribution comparison results, the stress / strain comparison results and the load-displacement comparison results; the present application can improve the accuracy and reliability of product design and performance evaluation, and promote the development and innovation of engineering technology.

Description

Technical Field

[0001] The present application relates to the field of simulation testing, and specifically to a virtual-reality comparative analysis and verification method and device integrating simulation and experiment. Background Art

[0002] In modern engineering and manufacturing, simulation technology has been widely used in product design, performance evaluation and optimization. Traditional product design and testing methods usually rely on physical samples and laboratory tests, which are not only time-consuming and costly, but also may pose safety risks in some cases. In order to overcome these challenges, simulation technology has gradually become an important tool for engineers and scientists. Through computer simulation, various performance and behaviors of products can be simulated in a virtual environment, so as to identify and solve potential problems at an early stage and significantly reduce the number of physical sample production and testing. However, although simulation technology has significant advantages in many aspects, it still faces some limitations and challenges in practical applications.

[0003] First, simulation technology relies on mathematical models and calculation methods, and its accuracy and reliability depend on the accuracy of the model and the correctness of the input parameters. However, no matter how complex and accurate the model is, it is always an approximation of reality. Simplifications and assumptions in the model may cause differences between the simulation results and the actual situation. For example, when performing thermal simulation, it is very complicated to accurately simulate the thermodynamic behaviors of materials such as heat conduction, convection and radiation, and any simplification may affect the accuracy of the results. In addition, complex geometric shapes, nonlinear material properties, and the diversity of boundary conditions also increase the difficulty of building simulation models.

[0004] Secondly, although the improvement of computing power has made large-scale simulation possible, simulation calculations still require a lot of time and resources. Especially in multi-physics field coupling simulation, such as simulation that simultaneously considers multiple physical effects such as heat, force and vibration, the computational complexity and time requirements are significantly increased. This not only places high demands on computing hardware, but also increases the cost of simulation work. Moreover, long simulation calculations may cause delays in the progress of engineering projects and affect the market competitiveness of products.

[0005] In addition, simulation technology also faces challenges in data acquisition and processing in practical applications. High-quality simulation requires accurate input data, including material properties, geometric dimensions, environmental conditions, etc. However, these data are often difficult to measure or obtain accurately in actual engineering, and may even change over time and with environmental changes. For example, the mechanical properties of materials may vary depending on the manufacturing process, and changes in environmental conditions such as temperature and humidity can also affect the performance of the product. These uncertainties increase the uncertainty of the simulation results, which in turn affects the reliability and effectiveness of the simulation.

[0006] When performing simulation verification, current technology usually relies on comparing simulation results with limited experimental data. However, the acquisition of experimental data also faces many challenges. Experimental testing not only requires specialized equipment and sites, but also consumes a lot of manpower and material resources. In addition, the experimental process may be affected by various factors, such as equipment accuracy, operator proficiency, environmental conditions, etc., resulting in the inability to guarantee the accuracy and consistency of experimental data. Moreover, experimental tests under some extreme conditions may be difficult or impossible to conduct, such as material performance testing under high temperature and high pressure environments, or dynamic response testing involving large-scale complex structures.

[0007] Despite the above challenges, the importance of simulation technology in product design and optimization cannot be ignored. In order to improve the application effect of simulation technology, researchers and engineers are constantly exploring new methods and technologies. For example, virtual assembly technology can detect and solve potential problems in assembly in advance by simulating the assembly process of products in a virtual environment, reducing rework and adjustments in actual assembly. In addition, with the development of sensor technology and data acquisition technology, high-precision experimental data acquisition has become more feasible, which provides more reliable data support for the calibration and verification of simulation models.

[0008] However, current simulation technology still has shortcomings in achieving virtual-real integration and comprehensive verification of product performance. In particular, the accuracy and efficiency of existing technologies need to be further improved in multi-physics field coupling simulation, simulation verification under complex environmental conditions, and dynamic response analysis of large-scale structures. In addition, how to effectively integrate simulation and experimental data and improve the reliability and accuracy of simulation results through multi-source data fusion and intelligent algorithms is also one of the key directions of current technical research.

[0009] In summary, although simulation technology has broad application prospects and significant advantages in modern engineering and manufacturing, it still faces many challenges in practical applications, such as model accuracy, computing resources, data acquisition and processing. In order to solve these problems, further exploring and developing virtual-real combination comparative analysis and verification methods that integrate simulation and experiment has important practical significance and application value. This will help improve the accuracy and reliability of product design and performance evaluation, and promote the development and innovation of engineering technology. Summary of the invention

[0010] In response to the problems in the prior art, the present application provides a virtual-reality comparative analysis and verification method and device that integrates simulation and experiment, which can improve the accuracy and reliability of product design and performance evaluation, and promote the development and innovation of engineering technology.

[0011] In order to solve at least one of the above problems, the present application provides the following technical solutions:

[0012] In a first aspect, the present application provides a virtual-real combination comparative analysis and verification method integrating simulation and experiment, comprising:

[0013] 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; field test 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 field test data is loaded and parsed;

[0014] Performing thermal test time matching and thermal test space matching with the simulation result data according to the field test data, performing point-to-point comparison between the obtained test temperature field data and the temperature data in the simulation result data, and determining the corresponding temperature distribution comparison results and thermal stress comparison results; performing modal test space matching and modal test natural frequency matching with the simulation result data, performing point-to-point comparison between the obtained test vibration field data and the vibration data in the simulation result data, and determining the corresponding spatial distribution comparison results and vibration comparison results; performing static test time matching and static test space matching with the simulation result data according to the field test data, performing point-to-point comparison between the obtained test static field data and the static data in the simulation result data, and determining the corresponding displacement distribution comparison results, stress / strain comparison results, and load-displacement comparison results;

[0015] According to at least one of the temperature distribution comparison result and the thermal stress comparison result, the spatial distribution comparison result and the vibration comparison result, the displacement distribution comparison result, the stress / strain comparison result and the load-displacement comparison result, 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 archive the files.

[0016] Furthermore, the performing of thermal test time matching according to the field test data and the simulation result data includes:

[0017] Setting the output time step of the simulation model corresponding to the physical three-dimensional model to be consistent with the field test data, so that the simulation model outputs data at the same time interval as the field test data;

[0018] The simulation result data is interpolated to the time corresponding to the sampling point of the field test data through a preset interpolation algorithm, and the time boundary of the simulation result data and the field test data is unified through a data interception operation.

[0019] Furthermore, the performing of thermal test space matching based on the field test data and the simulation result data includes:

[0020] Selecting a position corresponding to the test measurement point as a selection position of a simulation model node corresponding to the physical three-dimensional model, so that the simulation model outputs parameter data at the same spatial coordinate point as the field test data;

[0021] In the simulation model, average temperature processing is performed on similar areas, and the simulation model after the average temperature processing is used as a spatial point matching the test measurement point.

[0022] Further, the performing modal test space matching based on the field test data and the simulation result data includes:

[0023] Selecting a position corresponding to the test measurement point as a selection position of a simulation model node corresponding to the physical three-dimensional model, so that the simulation model outputs parameter data at the same spatial coordinate point as the field test data;

[0024] When the distance between the simulation finite element distance test point and the adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test point is used as a spatial point matching the test point.

[0025] Furthermore, the matching of the modal test natural frequencies according to the field test data and the simulation result data includes:

[0026] Setting the output time step of the simulation model corresponding to the physical three-dimensional model to be consistent with the field test data, so that the simulation model and the field test data output data at the same time interval;

[0027] The simulation result data is interpolated to the vibration frequency corresponding to the sampling point of the field test data through a preset interpolation algorithm, and the vibration frequency boundary of the simulation result data and the field test data is unified through a data interception operation.

[0028] Furthermore, the obtained test static field data is compared point-to-point with the static data in the simulation result data to determine the corresponding displacement distribution comparison result, including:

[0029] Constructing a global displacement field by difference processing according to the obtained test static field data, and superimposing the global displacement field constructed by the test static field data and the global displacement field obtained by the simulation result data;

[0030] The matching points of the superimposed global displacement field are numerically compared to calculate the overall accuracy of the simulation result data.

[0031] Furthermore, the obtained test static field data is compared point-to-point with the static data in the simulation result data to determine the corresponding displacement distribution comparison result, including:

[0032] Selecting set stress concentration points and load action points in the physical three-dimensional model to construct a local displacement comparison point set;

[0033] 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.

[0034] In a second aspect, the present application provides a virtual-real combination comparative analysis and verification device integrating simulation and experiment, comprising:

[0035] A data acquisition module is used to load the physical three-dimensional model into a 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 actual test 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 actual test data;

[0036] A virtual-real comparison module is used to perform thermal test time matching and thermal test space matching according to the field actual test data and the simulation result data, and to determine the corresponding temperature distribution comparison results and thermal stress comparison results by performing point-to-point comparison between the obtained test temperature field data and the temperature data in the simulation result data; to perform modal test space matching and modal test natural frequency matching according to the field actual test data and the simulation result data, and to determine the corresponding spatial distribution comparison results and vibration comparison results by performing point-to-point comparison between the obtained test vibration field data and the vibration data in the simulation result data; to perform static test time matching and static test space matching according to the field actual test data and the simulation result data, and to determine the corresponding displacement distribution comparison results, stress / strain comparison results and load-displacement comparison results by performing point-to-point comparison between the obtained test static field data and the static data in the simulation result data;

[0037] A model updating module is used to correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to at least one of the temperature distribution comparison result and the thermal stress comparison result, the spatial distribution comparison result and the vibration comparison result, the displacement distribution comparison result, the stress / strain comparison result and the load-displacement comparison result, and continue to perform simulation tests based on the physical three-dimensional model to obtain the test full life cycle data and archive the files.

[0038] 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 method for virtual-reality combined comparative analysis and verification of fusion simulation and experiment when executing the program.

[0039] 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 virtual-reality combined comparative analysis and verification method of the fusion simulation and experiment.

[0040] 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 virtual-reality combined comparative analysis and verification method of the fusion simulation and experiment.

[0041] It can be seen from the above technical scheme that the present application provides a virtual-reality comparative analysis and verification method and device integrating simulation and experiment, which constructs a virtual test scene by loading a physical three-dimensional model into a preset three-dimensional scene of a test station and performing virtual assembly, configures test parameters in the virtual test scene, and loads and associates measurement channels; collects field test data in the virtual test scene according to the test scene requirements sent by the user, loads and parses simulation result data corresponding to the field test data; and corrects the simulation parameters of the physical three-dimensional model in the virtual test scene according to at least one of the temperature distribution comparison results and the thermal stress comparison results, the spatial distribution comparison results and the vibration comparison results, the displacement distribution comparison results, the stress / strain comparison results and the load-displacement comparison results, thereby improving the accuracy and reliability of product design and performance evaluation, and promoting the development and innovation of engineering technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] 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.

[0043] Figure 1 One of the flow charts of the virtual-real combination comparative analysis and verification method of the fusion simulation and experiment in the embodiment of the present application;

[0044] Figure 2 The second flowchart of the virtual-real combination comparative analysis and verification method of the fusion simulation and experiment in the embodiment of the present application;

[0045] Figure 3The third flowchart of the virtual-real combination comparative analysis and verification method of the fusion simulation and experiment in the embodiment of the present application;

[0046] Figure 4 This is a fourth flow chart of the virtual-real combination comparative analysis and verification method of the fusion simulation and experiment in the embodiment of the present application;

[0047] Figure 5 This is a fifth flow chart of the virtual-real combination comparative analysis and verification method of the fusion simulation and experiment in the embodiment of the present application;

[0048] Figure 6 This is a sixth flow chart of the virtual-real combination comparative analysis and verification method of the fusion simulation and experiment in the embodiment of the present application;

[0049] Figure 7 This is the seventh flow chart of the virtual-real combination comparative analysis and verification method of the fusion simulation and experiment in the embodiment of the present application;

[0050] Figure 8 A structural diagram of a virtual-real combination comparative analysis and verification device for fusion simulation and experiment in an embodiment of the present application;

[0051] Fig. 9 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0052] Reference numerals:

[0053] 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

[0054] 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.

[0055] 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.

[0056] In view of the problems existing in the prior art, the present application provides a virtual-reality comparative analysis and verification method and device integrating simulation and experiment, which constructs a virtual test scene by loading a physical three-dimensional model into a preset three-dimensional scene of a test station and performing virtual assembly, configures test parameters in the virtual test scene, and loads and associates measurement channels; collects field test data in the virtual test scene according to the test scene requirements sent by the user, loads and parses simulation result data corresponding to the field test data; and corrects the simulation parameters of the physical three-dimensional model in the virtual test scene according to at least one of the temperature distribution comparison results and the thermal stress comparison results, the spatial distribution comparison results and the vibration comparison results, the displacement distribution comparison results, the stress / strain comparison results and the load-displacement comparison results, thereby improving the accuracy and reliability of product design and performance evaluation, and promoting the development and innovation of engineering technology.

[0057] In order to improve the accuracy and reliability of product design and performance evaluation and promote the development and innovation of engineering technology, this application provides an embodiment of a virtual-real combination comparative analysis and verification method integrating simulation and experiment, see Figure 1 The virtual-real combination comparative analysis and verification method of the fusion simulation and experiment specifically includes the following contents:

[0058] Step S101: 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 test data in the test virtual scene according to the test scene requirements sent by the user, and load and parse simulation result data corresponding to the on-site test data;

[0059] Optionally, in this embodiment, in step S101, it is first necessary to load the physical three-dimensional model into a preset three-dimensional scene of the test station and perform virtual assembly to construct a virtual scene for the test. The physical three-dimensional model is usually generated by CAD software and contains the geometric shape and material information of the device or component to be tested. The preset three-dimensional scene of the test station is a virtual environment pre-created according to the layout and environment of the actual test station, which contains various equipment and tools required for the test. By importing the physical three-dimensional model into this preset scene and performing virtual assembly, the real test environment can be simulated to ensure a high degree of consistency between the virtual test and the actual test.

[0060] After building the virtual scene of the test, it is necessary to configure the test parameters in it and load and associate the measurement channels. The test parameters include variables that affect the test results, such as the applied load, boundary conditions, and temperature. These parameters need to be set according to the requirements of the actual test to ensure that the virtual test can accurately reflect the real situation. Loading and associating measurement channels means adding virtual sensors or measurement points to the 3D model and associating them with the corresponding test parameters and results. These virtual sensors can simulate strain gauges, displacement sensors, temperature sensors, etc. in actual tests, and record various data during the virtual test process.

[0061] When the test virtual scene is configured, the system will collect on-site test data in the virtual scene according to the test scene requirements sent by the user. Users can enter or upload specific test requirements through the interface, such as parameters to be measured, test conditions, etc. The system will run simulation tests in the virtual scene according to these requirements and collect corresponding test data. These data can include stress, strain, displacement, temperature and other physical quantities, reflecting the changes in the virtual test process.

[0062] In addition, the system will load and analyze the simulation result data corresponding to the field test data. The simulation result data usually comes from finite element analysis (FEA) or other numerical simulation methods, reflecting the response of the virtual model under the same test conditions. By comparing the field test data with the simulation result data, the accuracy and reliability of the simulation model can be evaluated, the deficiencies in the model can be found, and targeted optimization can be carried out.

[0063] In terms of the technical problems solved, step S101 solves the problem of how to efficiently and accurately simulate the test in a virtual environment by building a virtual test scene, configuring test parameters, collecting actual test data, and analyzing simulation result data. Traditional physical tests require a lot of time and resources, while virtual tests can be carried out quickly in a computer environment, greatly improving test efficiency and cost-effectiveness.

[0064] In terms of the technical effects achieved, through step S101, the system can efficiently perform test simulation in a virtual environment, quickly obtain actual test data and simulation result data, and perform accurate comparative analysis. By loading virtual assembly and measurement channels, the high consistency between virtual tests and actual tests can be ensured, and the accuracy and reliability of test results can be improved. At the same time, users can flexibly configure test parameters according to needs, quickly simulate and analyze a variety of test scenarios, and provide a reliable basis for engineering design and optimization.

[0065] For example, in a car collision test, engineers need to evaluate the deformation and safety performance of the car structure under different collision conditions. Through step S101, the engineer first loads the physical three-dimensional model of the car into the preset collision test three-dimensional scene and performs virtual assembly. Then, configure the test parameters such as collision speed and angle, and load measurement channels in key parts, such as the front end of the car body, the door, and the passenger compartment. According to the collision test requirements input by the user, the system simulates the collision process in the virtual scene and collects actual test data, such as stress, strain, and deformation. At the same time, the system loads and parses the corresponding simulation result data. Through comparative analysis, the performance of the car structure in the collision can be evaluated, the weak links in the design can be found, and optimization can be performed.

[0066] In general, step S101 achieves efficient and accurate test simulation and analysis by constructing a virtual test scene, configuring test parameters, collecting actual test data, and analyzing simulation result data, providing strong support for engineering design and optimization.

[0067] Step S102: performing thermal test time matching and thermal test space matching according to the field actual test data and the simulation result data, performing point-to-point comparison between the obtained test temperature field data and the temperature data in the simulation result data, and determining the corresponding temperature distribution comparison results and thermal stress comparison results; performing modal test space matching and modal test natural frequency matching according to the field actual test data and the simulation result data, performing point-to-point comparison between the obtained test vibration field data and the vibration data in the simulation result data, and determining the corresponding spatial distribution comparison results and vibration comparison results; performing static test time matching and static test space matching according to the field actual test data and the simulation result data, performing point-to-point comparison between the obtained test static field data and the static data in the simulation result data, and determining the corresponding displacement distribution comparison results, stress / strain comparison results, and load-displacement comparison results;

[0068] Optionally, in this embodiment, in step S102, it is first necessary to perform thermal test time matching and thermal test space matching based on the field actual test data and the simulation result data. This is to ensure the consistency of the two sets of data in time and space, so that subsequent comparisons can be carried out accurately. Thermal test time matching refers to matching the temperature data recorded during the field actual test with the temperature data at the corresponding time point in the simulation process to ensure that the two are compared at the same time point. Thermal test space matching is to align the measured temperature data with the simulated temperature data in spatial position to ensure that the temperature comparison is performed at the same spatial position.

[0069] After completing the time and space matching, the obtained experimental temperature field data is compared point by point with the temperature data in the simulation result data. This is a point-by-point comparison process. The temperature value of each measured point is compared with the temperature value of the corresponding simulation point to determine the corresponding temperature distribution comparison result and thermal stress comparison result. The temperature distribution comparison result shows the consistency of the temperature field distribution between the two, while the thermal stress comparison result compares the thermal stress calculated based on the temperature distribution to evaluate the accuracy of the simulation model in thermal stress.

[0070] Next, it is necessary to perform modal test space matching and modal test natural frequency matching based on the field test data and the simulation result data. Modal test space matching refers to aligning the measured vibration data with the simulated vibration data in spatial position to ensure vibration comparison at the same position. Modal test natural frequency matching is to match the measured natural frequency with the natural frequency calculated by simulation to ensure frequency consistency. By performing a point-to-point comparison between the obtained test vibration field data and the vibration data in the simulation result data, the vibration value of each measured point is compared with the vibration value of the corresponding simulation point to determine the corresponding spatial distribution comparison results and vibration comparison results. The spatial distribution comparison results show the consistency of the two in the vibration field distribution, while the vibration comparison results directly reflect the accuracy of the vibration response.

[0071] Finally, static test time matching and static test space matching are performed based on the field test data and simulation result data. Static test time matching is to match the measured data with the simulation data at the same time point to ensure time consistency. Static test space matching is to align the measured static field data with the simulated static field data in spatial position to ensure static comparison at the same position. The static field data obtained by the test is compared point-to-point with the static data in the simulation result data to determine the corresponding displacement distribution comparison results, stress / strain comparison results, and load-displacement comparison results. The displacement distribution comparison results show the consistency of the two in the displacement field distribution, the stress / strain comparison results evaluate the accuracy of the simulation model by comparing the stress and strain values, and the load-displacement comparison results evaluate the performance of the simulation model under static loading by comparing the load and displacement relationship.

[0072] In terms of the technical problems solved, step S102 solves the problem of how to accurately evaluate the consistency between the simulation model and the actual test data through detailed time and space matching and point-to-point comparison. Through multi-level and multi-dimensional comparison analysis, the performance of the simulation model can be comprehensively evaluated, deficiencies in the simulation model can be discovered and corrected, and the reliability and accuracy of the simulation results can be ensured.

[0073] In terms of the technical effects achieved, through step S102, the system can conduct a comprehensive and accurate comparison and analysis of the simulation data and the measured data under various test conditions (including thermal tests, modal tests and static tests). The accuracy of the comparison is ensured through time and space matching, and the performance of the simulation model in various physical quantities is evaluated in detail through point-to-point comparison. Ultimately, the system can provide detailed comparison results of temperature distribution, thermal stress, vibration response and static response, providing reliable data support for the verification and optimization of the simulation model.

[0074] For example, in the thermal test of an aircraft engine, engineers need to evaluate the thermal stress and temperature distribution of the engine under high temperature conditions. Through step S102, the engineer first performs time matching and space matching of the thermal test to ensure that the measured temperature data and the simulated temperature data are compared at the same time and space position. Then, through point-to-point comparison, the temperature distribution comparison results and the thermal stress comparison results are determined. Through these comparison results, engineers can evaluate the accuracy of the simulation model under high temperature conditions, discover and correct deficiencies in the model, and ensure the reliability of the simulation results. The same process can be applied to modal tests and static tests. Through detailed comparison and analysis, the performance of the simulation model under different test conditions can be comprehensively evaluated.

[0075] In general, step S102 achieves a comprehensive evaluation and optimization of the simulation model through detailed matching and comparison analysis, ensures the accuracy and reliability of the simulation results, and provides strong support for engineering design and optimization.

[0076] Step S103: Correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to at least one of the temperature distribution comparison result and the thermal stress comparison result, the spatial distribution comparison result and the vibration comparison result, the displacement distribution comparison result, the stress / strain comparison result and the load-displacement comparison result, and continue to perform simulation tests based on the physical three-dimensional model to obtain the test full life cycle data and archive the files.

[0077] Optionally, in this embodiment, in step S103, it is first necessary to correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the various comparison results obtained in the previous step. These comparison results include temperature distribution comparison results, thermal stress comparison results, spatial distribution comparison results, vibration comparison results, displacement distribution comparison results, stress / strain comparison results, and load-displacement comparison results. By combining these comparison results, the difference between the simulation model and the actual experimental data can be fully understood, so as to adjust the simulation parameters in a targeted manner, so that the simulation results are more accurate and reliable.

[0078] The specific technical process includes the following steps: First, analyze various comparison results to find out the main differences between the simulation model and the actual experimental data. For example, the temperature distribution comparison results may show that the temperature deviation in some areas is large, while the thermal stress comparison results may indicate that the stress calculation in some locations is inaccurate. Based on these differences, determine the simulation parameters that need to be corrected, such as material properties, boundary conditions, load conditions, etc.

[0079] Next, the physical 3D model is corrected by adjusting the simulation parameters. For example, if the temperature distribution comparison results show that the temperature in certain areas is too high, the thermal conductivity or boundary conditions in that area may need to be adjusted to reflect a more realistic physical environment. Similarly, if the vibration comparison results show that certain natural frequencies do not match, the stiffness or mass distribution of the structure may need to be adjusted. After completing the parameter correction, the simulation test is continued based on the corrected physical 3D model. By re-simulating, updated simulation results can be obtained, and the comparison is performed again to verify the correction effect. If the comparison results show that the corrected simulation results are more consistent with the actual experimental data, it means that the correction is effective.

[0080] In terms of technical principles, step S103 utilizes the comparison and analysis of simulation and actual experimental data, and continuously optimizes the simulation model by iteratively adjusting the simulation parameters. This process is similar to the feedback mechanism in the optimization algorithm, which gradually approaches the optimal solution through multiple iterations. Each comparison result is used as feedback information to guide the adjustment of the simulation parameters for the next time, and gradually improve the accuracy of the simulation model.

[0081] In terms of the technical problems to be solved, step S103 solves the problem of how to optimize the simulation model based on actual experimental data. Traditional simulation models are often based on theoretical assumptions and preliminary parameter settings, which are difficult to fully reflect the actual situation. By comparing and analyzing with the measured data, the deficiencies in the simulation model can be discovered and targeted corrections can be made, significantly improving the accuracy and reliability of the simulation results.

[0082] In terms of the technical effects achieved, the accuracy and reliability of the simulation model can be significantly improved through the implementation of step S103. The corrected simulation model can more accurately reflect the actual physical process and provide a more reliable basis for engineering design and optimization. Through continuous iterative optimization, the prediction ability of the simulation model will continue to increase, thereby playing a role in a wider range of application scenarios.

[0083] For example, in the thermal stress analysis of aircraft engines, the initial simulation model may be based on theoretical calculations and preliminary parameter settings, and it is difficult to fully and accurately predict the thermal stress distribution of the engine under high temperature conditions. Through step S103, engineers can first conduct thermal tests and simulations, compare the temperature distribution and thermal stress results, and find deficiencies in the simulation model. For example, the temperature deviation in some areas is large, and the thermal stress calculation is inaccurate. Based on these comparison results, engineers can adjust parameters such as thermal conductivity and material properties in the simulation model and re-simulate. Through multiple iterations, the simulation results gradually become more consistent with the actual experimental data, and finally obtain high-precision thermal stress distribution prediction results.

[0084] After completing the correction and simulation, the data of the entire life cycle of the test needs to be archived. This includes recording the parameter settings, comparison results, correction process and final simulation results of each simulation test. Through detailed file archiving, complete data support and reference can be provided for subsequent research and application.

[0085] In general, step S103 optimizes the simulation model and improves its accuracy through comparison analysis and parameter correction, ensuring the reliability and accuracy of the simulation results. Through continuous iterative optimization, the prediction ability of the simulation model is continuously enhanced, providing strong support for engineering design and optimization. At the same time, through detailed data archiving, it also provides important reference and data support for subsequent research and application.

[0086] From the above description, it can be seen that the virtual-reality combined comparative analysis and verification method of integrated simulation and experiment provided in the embodiment of the present application can 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 field test data in the test virtual scene according to the test scene requirements sent by the user, load and parse the simulation result data corresponding to the field test data; correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to at least one of the temperature distribution comparison results and thermal stress comparison results, spatial distribution comparison results and vibration comparison results, displacement distribution comparison results, stress / strain comparison results and load-displacement comparison results, thereby improving the accuracy and reliability of product design and performance evaluation, and promoting the development and innovation of engineering technology.

[0087] In one embodiment of the virtual-real combination comparative analysis and verification method of the present application, see Figure 2 , and can also include the following:

[0088] Step S201: setting the output time step of the simulation model corresponding to the physical three-dimensional model to be consistent with the field test data, so that the simulation model outputs data at the same time interval as the field test data;

[0089] Step S202: interpolating the simulation result data to the time corresponding to the sampling point of the field test data through a preset interpolation algorithm, and unifying the time boundary of the simulation result data and the field test data through a data interception operation.

[0090] Optionally, in the present embodiment, in step S201, it is first necessary to set the output time step of the simulation model corresponding to the physical three-dimensional model to be consistent with the field test data. The purpose of this step is to ensure that the simulation model can output data at the same time interval as the actual test, so as to ensure the alignment in time in the subsequent comparison process. The specific technical process of implementation includes: first, obtaining the time step of the field test data, which can usually be obtained through experimental records or data acquisition systems. Then, the same time step is set in the simulation model to ensure that the time resolution of the simulation data matches the measured data.

[0091] In terms of technical principles, this process utilizes the consistency principle of time steps, that is, by unifying the time steps of simulation and measured data, the alignment of the two sets of data on the time axis is ensured. The advantage of this is that the simulation data and measured data at the same time point can be directly compared, avoiding interpolation or data alignment errors caused by different time steps.

[0092] In terms of the technical problems solved, step S201 solves the problem of aligning the simulation data with the measured data on the time axis. Traditional simulation and measured data often have different sampling frequencies, resulting in inconsistent data points in time, which brings difficulties to subsequent comparison and analysis. By unifying the time step, the consistency of the data in time can be ensured, simplifying the subsequent processing steps.

[0093] In terms of the technical effect achieved, through step S201, the output data of the simulation model and the measured data are completely aligned in time, ensuring that the two can be directly compared. This not only improves the accuracy of data comparison, but also reduces the error caused by inconsistent time steps, and improves the reliability of simulation results.

[0094] Next, in step S202, the simulation result data needs to be interpolated to the time corresponding to the sampling point of the field test data through a preset interpolation algorithm. The purpose of this step is to further refine the simulation data so that it completely corresponds to the measured data in time. The specific technical process of implementation includes: first, select a suitable interpolation algorithm, such as linear interpolation, spline interpolation, etc., according to the characteristics of the simulation data and the measured data. Then, the simulation result data is interpolated according to the sampling points of the measured data to generate new simulation data, so that it has a corresponding simulation value at each measured time point.

[0095] In terms of technical principles, the interpolation algorithm uses the relationship between known data points to calculate new data point values, thereby achieving data refinement and alignment. Through the interpolation operation, more simulation data points can be obtained without increasing the actual amount of calculation, so that they are completely consistent with the measured data in time.

[0096] In terms of the technical problems solved, step S202 solves the problem of refining and aligning the simulation data with the measured data at the time point. Even if the time step has been unified in step S201, the specific sampling points of the simulation data and the measured data may still not be completely consistent. Through interpolation, simulation data that completely corresponds to the time point of the measured data can be generated to ensure a more accurate comparison between the two.

[0097] In terms of the technical effect achieved, through step S202, it is possible to ensure that the simulation data and the measured data are completely aligned at the time point, thereby improving the accuracy of data comparison. The interpolation operation not only refines the simulation data, but also enhances the continuity of the data, making the comparison analysis more reliable.

[0098] For example, in a structural vibration experiment, the sampling frequency of the field measured data is 100 Hz, while the output time step of the initial simulation model is 0.02 seconds (ie, 50 Hz). Through step S201, the time step of the simulation model is adjusted to 0.01 seconds (ie, 100 Hz), which is consistent with the measured data. Then, in step S202, the simulation result data is interpolated to the sampling points of the measured data through a linear interpolation algorithm. For example, the measured data has sampling values ​​at time points such as 0.005 seconds and 0.015 seconds. Through interpolation calculation, the corresponding simulation values ​​are generated at these time points. Finally, through the data interception operation, the time boundary of the simulation result data and the measured data is unified, for example, the time period from 0 seconds to 10 seconds is intercepted.

[0099] Through these steps, the simulation data and the measured data are completely aligned on the time axis, and can be directly compared point by point to ensure the accuracy and reliability of the comparison results. In this way, not only the matching degree between the simulation data and the measured data is improved, but also a reliable basis is provided for subsequent model correction and optimization.

[0100] In general, steps S201 and S202 achieve the temporal alignment and refinement of the simulation data and the measured data through the unification and interpolation operations of the time steps, thus solving the problems of time axis alignment and data refinement.

[0101] In one embodiment of the virtual-real combination comparative analysis and verification method of the present application, see Figure 3 , and can also include the following:

[0102] Step S301: selecting a position corresponding to a test measurement point as a selection position of a simulation model node corresponding to the physical three-dimensional model, so that the simulation model outputs parameter data at the same spatial coordinate point as the field test data;

[0103] Step S302: performing average temperature processing on similar areas in the simulation model, and using the simulation model after the average temperature processing as a spatial point matching the test measurement point.

[0104] Optionally, in this embodiment, in step S301, it is first necessary to select a position corresponding to the test measuring point as the selection position of the simulation model node corresponding to the physical three-dimensional model. The purpose of this step is to ensure that the simulation model can output parameter data at the same spatial coordinate point as the field test data, so as to ensure spatial alignment in the subsequent comparison process. The specific technical process includes the following steps: First, determine the measuring point positions of the field test, which are usually key points set according to the experimental design and measurement requirements. Then, find the simulation nodes corresponding to these measuring point positions in the physical three-dimensional model to ensure that the spatial coordinates of these nodes are consistent with the measured measuring points.

[0105] In terms of technical principles, this process utilizes the principle of spatial coordinate consistency, that is, by unifying the spatial positions of simulation nodes and measured points, the two sets of data are aligned in space. The advantage of this is that the simulation data and measured data at the same spatial position can be directly compared, avoiding errors caused by different spatial positions.

[0106] In terms of the technical problems solved, step S301 solves the problem of aligning the simulation data with the measured data in spatial coordinates. Traditional simulation and measured data often have different measurement positions, resulting in inconsistent data points in space, which makes subsequent comparison and analysis difficult. By unifying the spatial coordinates, the consistency of the data in space can be ensured, simplifying the subsequent processing steps.

[0107] In terms of the technical effect achieved, through step S301, the output data of the simulation model and the measured data are completely aligned in space, ensuring that the two can be directly compared. This not only improves the accuracy of data comparison, but also reduces the error caused by inconsistent spatial positions, and improves the reliability of simulation results.

[0108] Next, in step S302, it is necessary to perform average temperature processing on similar areas in the simulation model, and use the simulation model after the average temperature processing as a spatial point that matches the test measurement point. The purpose of this step is to further refine and optimize the simulation data so that it is more consistent with the measured data in space. The specific technical process includes: first, identify similar areas in the simulation model, which usually refer to parts with relatively uniform temperature distribution. Then, perform average temperature processing on these areas, that is, calculate the temperature average value of all nodes in the area, and replace the original node temperature value. In this way, the simulation data after the average temperature processing can better reflect the temperature distribution of the actual measurement point.

[0109] In terms of technical principles, average temperature processing uses the principle of statistical averaging. By averaging similar areas, the impact of local temperature fluctuations on the results is reduced. In this way, a smoother and more stable temperature distribution can be obtained, improving the reliability of simulation data.

[0110] In terms of the technical problems solved, step S302 solves the consistency problem of the temperature distribution between the simulation data and the measured data. Even if the spatial coordinates have been unified in step S301, there may still be local differences in the temperature distribution of the simulation data and the measured data. Through average temperature processing, these differences can be reduced to ensure a more accurate comparison between the two.

[0111] In terms of the technical effect achieved, through step S302, the consistency of the temperature distribution of the simulation data and the measured data can be ensured, and the accuracy of data comparison can be improved. The average temperature processing not only smoothes the simulation data, but also enhances the stability of the data, making the comparison analysis more reliable.

[0112] For example, in a thermal analysis experiment of a complex mechanical device, the field measured data measured the temperature at multiple key locations on the surface and inside of the device. Through step S301, the nodes in the simulation model are selected as positions corresponding to these measurement points to ensure that the simulation data and the measured data are aligned in spatial coordinates. Then, in step S302, average temperature processing is performed on similar areas of the simulation model. For example, the average calculation is performed on areas with relatively uniform temperature inside the device to obtain a smoother temperature distribution. Finally, through these steps, the simulation data and the measured data are completely aligned in space and temperature distribution, and can be directly compared point by point to ensure the accuracy and reliability of the comparison results.

[0113] Through these steps, the simulation data and the measured data are completely aligned in space and temperature distribution, and can be directly compared point by point to ensure the accuracy and reliability of the comparison results. In this way, not only the matching degree between the simulation data and the measured data is improved, but also a reliable basis is provided for subsequent model correction and optimization.

[0114] In general, steps S301 and S302 achieve alignment and refinement of simulation data and measured data in terms of space and temperature distribution through the unification of spatial coordinates and average temperature processing, thus solving the problem of consistency of space and temperature distribution.

[0115] In one embodiment of the virtual-real combination comparative analysis and verification method of the present application, see Figure 4 , and can also include the following:

[0116] Step S401: selecting a position corresponding to a test measurement point as a selection position of a simulation model node corresponding to the physical three-dimensional model, so that the simulation model outputs parameter data at the same spatial coordinate point as the field test data;

[0117] Step S402: when the distance between the simulation finite element distance test point and the adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test point is used as a spatial point matching the test point.

[0118] Optionally, in the present embodiment, in step S401, it is first necessary to select a position corresponding to the test measuring point as the selection position of the simulation model node corresponding to the physical three-dimensional model. The purpose of this step is to ensure that the simulation model can output parameter data at the same spatial coordinate point as the field test data, so as to ensure spatial alignment in the subsequent comparison process. The specific technical process of implementation includes the following steps: First, the measuring point positions of the field test are determined through experimental design and measurement requirements, and these positions are usually key points. Then, the simulation nodes corresponding to these measuring point positions are found in the physical three-dimensional model to ensure that the spatial coordinates of these nodes are consistent with the measured measuring points. This can be achieved by searching the node coordinate data of the simulation model to ensure that the selected simulation nodes are in the same position as the measured measuring points.

[0119] In terms of technical principles, this process utilizes the principle of spatial coordinate consistency, that is, by unifying the spatial positions of simulation nodes and measured points, the two sets of data are aligned in space. The advantage of this is that the simulation data and measured data at the same spatial position can be directly compared to avoid errors caused by different spatial positions.

[0120] In terms of the technical problems solved, step S401 solves the problem of aligning the simulation data with the measured data in spatial coordinates. Traditional simulation and measured data often have different measurement positions, resulting in inconsistent data points in space, which makes subsequent comparison and analysis difficult. By unifying the spatial coordinates, the consistency of the data in space can be ensured, simplifying the subsequent processing steps.

[0121] In terms of the technical effect achieved, through step S401, the output data of the simulation model and the measured data are completely aligned in space, ensuring that the two can be directly compared. This not only improves the accuracy of data comparison, but also reduces the error caused by inconsistent spatial positions, and improves the reliability of simulation results.

[0122] Next, in step S402, when the distance between the simulated finite element node and the test point is lower than a threshold value relative to the distance between adjacent points in the simulation model, the finite element node closest to the test point is used as a spatial point that matches the test point. The purpose of this step is to further optimize the accuracy of the simulation data so that it is more consistent with the measured data in space. The specific technical process includes the following steps: First, calculate the distance between each test point and all finite element nodes in the simulation model to find the nearest finite element node. Next, compare the distance between the nearest node and the test point and the distance between adjacent simulation nodes. If the former is lower than the set threshold, the node is selected as the matching spatial point.

[0123] In terms of technical principles, this process uses the nearest neighbor principle and distance threshold judgment to ensure accurate spatial alignment of simulation data and measured data by selecting the finite element node that is closest to the test point and meets the distance threshold condition. This can minimize the impact of node selection errors and improve the accuracy of data comparison.

[0124] In terms of the technical problems solved, step S402 solves the problem of accurate spatial alignment of simulation data and measured data. Even if the corresponding simulation nodes have been selected in step S401, there may still be certain errors due to the uneven distribution of nodes in the finite element model. Through the nearest neighbor and distance threshold determination, the selection of nodes can be further optimized to ensure accurate alignment of data.

[0125] In terms of the technical effect achieved, through step S402, the simulation data and the measured data can be accurately aligned in spatial position, thereby improving the accuracy of data comparison. This method not only optimizes the spatial distribution of the simulation data, but also enhances the reliability of the data, making the comparison analysis more reliable.

[0126] For example, in a stress test of a large building structure, the field measured data measured stress at multiple key locations on the surface and inside of the structure. Through step S401, the nodes in the simulation model are selected as positions corresponding to these measuring points to ensure that the simulation data and the measured data are aligned in spatial coordinates. Then, in step S402, by calculating the distance between each measuring point and all finite element nodes in the simulation model, the nearest node is found and its distance with the adjacent nodes is compared. If the threshold condition is met, the node is selected as the matching spatial point. Finally, through these steps, the simulation data and the measured data are completely aligned in spatial position, and can be directly compared point by point to ensure the accuracy and reliability of the comparison results.

[0127] Through these steps, the simulation data and the measured data are completely aligned in space, and can be directly compared point by point to ensure the accuracy and reliability of the comparison results. In this way, not only the matching degree between the simulation data and the measured data is improved, but also a reliable basis is provided for subsequent model correction and optimization.

[0128] In general, steps S401 and S402 achieve precise alignment and optimization of the simulation data and the measured data in spatial position through the unification of spatial coordinates and distance threshold determination, thus solving the problem of precise spatial position matching.

[0129] In one embodiment of the virtual-real combination comparative analysis and verification method of the present application, see Figure 5 , and can also include the following:

[0130] Step S501: setting the output time step of the simulation model corresponding to the physical three-dimensional model to be consistent with the field test data, so that the simulation model outputs data at the same time interval as the field test data;

[0131] Step S502: interpolating the simulation result data to the vibration frequency corresponding to the sampling point of the field test data through a preset interpolation algorithm, and unifying the vibration frequency boundary of the simulation result data and the field test data through a data interception operation.

[0132] Optionally, in the present embodiment, in step S501, it is first necessary to set the output time step of the simulation model corresponding to the physical three-dimensional model to be consistent with the field test data. The purpose of this step is to ensure that the simulation model can output data at the same time interval as the field test data, thereby achieving data synchronization in the time dimension. The specific technical process of implementation includes the following steps: First, determine the data sampling time interval of the field test, which is usually set according to the experimental requirements and the sampling capacity of the equipment during the experimental design phase. Then, set the same time step in the simulation model to ensure that the output time point of the simulation data is consistent with the sampling time point of the measured data. This can be achieved by adjusting the time step parameter in the simulation software.

[0133] In terms of technical principles, this process utilizes the principle of time synchronization, that is, by unifying the time steps of the simulation model and the measured data, the two sets of data are aligned in the time dimension. This synchronization is crucial in time series analysis because it allows direct comparison of simulation data and measured data at the same time point, avoiding errors caused by different time steps.

[0134] In terms of the technical problems solved, step S501 solves the problem of aligning the simulation data with the measured data in terms of time step. Traditional simulation and measured data may be inconsistent in time dimension due to different sampling time intervals, which brings difficulties to subsequent comparison and analysis. By unifying the time step, the consistency of data in time can be ensured, simplifying the subsequent data processing steps.

[0135] In terms of the technical effect achieved, through step S501, the output data of the simulation model and the measured data are completely aligned in time, ensuring that the two can be directly compared at a time point. This not only improves the accuracy of data comparison, but also reduces the error caused by inconsistent time steps, and improves the reliability of the simulation results.

[0136] Next, in step S502, it is necessary to interpolate the simulation result data to the vibration frequency corresponding to the sampling point of the field test data through a preset interpolation algorithm, and unify the vibration frequency boundary of the simulation result data and the field test data through a data interception operation. The purpose of this step is to make the simulation data consistent with the measured data in the frequency dimension, and ensure the alignment of the data within the frequency range. The specific technical process of implementation includes the following steps: First, select a suitable interpolation algorithm, such as linear interpolation, spline interpolation, etc., and perform interpolation calculations according to the frequency distribution of the simulation data. Then, interpolate the simulation data to the vibration frequency corresponding to the sampling point of the measured data to ensure that the two sets of data are aligned in the frequency dimension. Finally, through the data interception operation, unify the vibration frequency boundary of the simulation data and the measured data to ensure that the two are compared within the same frequency range.

[0137] In terms of technical principles, this process utilizes interpolation and data truncation techniques to ensure that the frequency ranges of the two sets of data are consistent by interpolating and unifying the boundaries of the simulation data in the frequency dimension. Interpolation technology can estimate new data points between known data points by constructing an interpolation function, thereby achieving a smooth transition and precise alignment of the data. The data truncation operation is used to unify the frequency range of the data and ensure that the frequency boundaries of the compared data are consistent.

[0138] In terms of the technical problems solved, step S502 solves the problem of aligning the simulated data with the measured data in the frequency dimension. Even if time synchronization has been achieved in step S501, there may still be inconsistencies between the simulated data and the measured data in the frequency dimension due to different sampling frequencies and frequency ranges. Through interpolation and data interception, the frequency alignment of the data can be further optimized to ensure that the two are accurately compared within the same frequency range.

[0139] In terms of the technical effects achieved, through step S502, the simulation data and the measured data can be accurately aligned in the frequency dimension, thereby improving the accuracy of data comparison. The interpolation and data interception operations not only optimize the frequency distribution of the simulation data, but also enhance the reliability of the data, making the comparison analysis more reliable.

[0140] For example, in a vibration analysis experiment of a complex mechanical system, the field measured data was used for vibration measurement at different frequency points. Through step S501, the output time step of the simulation model is set to the same time interval as the measured data to ensure that the simulation data and the measured data are aligned in time. Then, in step S502, the simulation result data is interpolated to the vibration frequency corresponding to the sampling point of the measured data through a preset spline interpolation algorithm to ensure that the two are aligned in the frequency dimension. Finally, through the data interception operation, the vibration frequency boundaries of the simulation data and the measured data are unified to ensure that the two are compared within the same frequency range. Finally, through these steps, the simulation data and the measured data are completely aligned in the time and frequency dimensions, and can be directly compared point by point to ensure the accuracy and reliability of the comparison results.

[0141] Through these steps, the simulation data and the measured data are completely aligned in the time and frequency dimensions, and can be directly compared point by point to ensure the accuracy and reliability of the comparison results. In this way, not only the matching degree between the simulation data and the measured data is improved, but also a reliable basis is provided for subsequent model correction and optimization.

[0142] In general, steps S501 and S502 achieve precise alignment and optimization of simulation data and measured data in time and frequency dimensions by unifying the time step and interpolating and intercepting the frequency dimension, thus solving the consistency problem in time and frequency dimensions.

[0143] In one embodiment of the virtual-real combination comparative analysis and verification method of the present application, see Figure 6 , and can also include the following:

[0144] Step S601: constructing a global displacement field through difference processing according to the obtained test static field data, and superimposing the global displacement field constructed by the test static field data and the global displacement field obtained by the simulation result data;

[0145] Step S602: performing numerical comparison of matching points on the superimposed global displacement field to calculate the overall accuracy of the simulation result data.

[0146] Optionally, in the present embodiment, in step S601, it is first necessary to construct a global displacement field through difference processing according to the obtained test static field data. The purpose of this step is to use the static field data measured on site to establish a displacement field covering the entire study area, and to superimpose it with the global displacement field obtained by the simulation result data for further analysis and comparison. The specific technical process of implementation includes the following steps: first, static field data are obtained through experiment, and these data usually include the displacement values ​​of each measuring point. Then, the difference processing technology is used to convert the discrete measuring point data into a continuous global displacement field. Commonly used difference methods include linear difference, spline difference and Kriging difference, etc., and suitable methods are selected according to the characteristics of the data. Then, the global displacement field obtained by the simulation result data is superimposed with the global displacement field constructed by difference processing. This step can be realized by the method of numerical superposition, that is, the numerical values ​​of the two displacement fields at the same position are added or combined according to a certain weight to reflect the actual situation.

[0147] In terms of technical principles, this process utilizes the basic principles of difference processing and numerical superposition. Difference processing is a method of constructing a continuous field from discrete data points. Through the interpolation function, the data is smoothly transitioned in space to obtain a continuous displacement field. Numerical superposition is a weighted combination of the values ​​of the two fields to reflect the comprehensive effect. In this way, the measured data and the simulation data can be effectively combined to obtain a more comprehensive global displacement field.

[0148] In terms of the technical problems solved, step S601 solves the problem of how to convert discrete measured data into a continuous global displacement field and combine it with simulation data. Traditional methods are usually difficult to process large-scale discrete data and it is difficult to effectively combine measured data and simulation data. Through difference processing, discrete data can be converted into a continuous field, and through numerical superposition, measured and simulated data can be effectively combined to obtain a more comprehensive displacement field.

[0149] In terms of the technical effects achieved, through step S601, the global displacement field obtained not only contains the real information of the measured data, but also combines the predicted information of the simulation data. This combination makes the global displacement field more comprehensive and accurate, laying the foundation for subsequent data analysis and comparison. Through this method, the utilization rate and accuracy of the data can be effectively improved, and the reliability of the simulation results can be improved.

[0150] Next, in step S602, it is necessary to compare the numerical values ​​of the matching points of the superimposed global displacement field to calculate the overall accuracy of the simulation result data. The purpose of this step is to evaluate the accuracy of the simulation results by comparing the numerical differences between the measured data and the simulated data at the matching points in the superimposed global displacement field. The specific technical process includes the following steps: First, select the matching points in the global displacement field, which are usually points where both the measured data and the simulated data exist. Then, compare the displacement values ​​of each matching point numerically and calculate the difference between them. Then, use these differences to calculate the overall accuracy indicators, such as the root mean square error (RMSE), the average absolute error, etc., to quantify the accuracy of the simulation results.

[0151] In terms of technical principles, this process utilizes the basic principles of numerical comparison and error analysis. By performing numerical comparisons at matching points, the difference between measured data and simulated data can be directly evaluated, thereby quantifying the accuracy of the simulation model. Error analysis calculates error indicators through statistical methods to reflect the overall accuracy level.

[0152] In terms of the technical problems to be solved, step S602 solves the problem of how to quantitatively evaluate the accuracy of the simulation results. Traditional methods can usually only qualitatively compare the simulation results and measured data, and it is difficult to quantitatively evaluate the accuracy. However, through numerical comparison and error analysis, the accuracy index of the simulation results can be accurately calculated, providing a basis for model verification and optimization.

[0153] In terms of the technical effects achieved, through step S602, the overall accuracy index of the simulation results can be obtained. These accuracy indexes not only reflect the reliability of the simulation model, but also provide guidance for the improvement of the model. Through the accuracy evaluation, the error range and distribution of the simulation results can be clarified, and the possible problems in the model can be identified, so as to optimize and improve them in a targeted manner.

[0154] For example, in a static test of a bridge structure, the field measured data were used to measure displacements at multiple key locations. Through step S601, a global displacement field is constructed by difference processing using these measurement point data. Then, the global displacement field obtained from the simulation result data is superimposed on the global displacement field constructed by difference processing to obtain a comprehensive global displacement field. Next, in step S602, matching points in the global displacement field are selected, the displacement value differences of each matching point are calculated, and the overall accuracy index, such as the root mean square error, is calculated using these differences. Finally, through these steps, the overall accuracy index of the simulation results is obtained, which effectively evaluates the reliability and accuracy of the simulation model.

[0155] Through these steps, the simulation data and measured data are effectively combined and compared in the global displacement field, which not only improves the utilization of data, but also provides a reliable basis for model verification and optimization. This method has broad application prospects and important significance in various engineering simulations and experimental comparisons.

[0156] In one embodiment of the virtual-real combination comparative analysis and verification method of the present application, see Figure 7 , and can also include the following:

[0157] Step S701: Selecting stress concentration points and load action points in the physical three-dimensional model to construct a local displacement comparison point set;

[0158] Step S702: 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.

[0159] Optionally, in this embodiment, in step S701, it is first necessary to select the stress concentration points and load application points set in the physical three-dimensional model to construct a local displacement comparison point set. The purpose of this step is to select key positions in the model for detailed analysis and comparison to ensure that the displacement data at these positions can reflect the stress and deformation of the overall structure. The specific technical process includes the following steps: First, determine the stress concentration points and load application points of the physical three-dimensional model. These points are usually the locations with the greatest stress or the most dramatic changes in the structure, such as the supporting points of the bridge, the load-bearing corners of the building, etc. Then, set comparison points at these key positions and record their displacement values. At this point, the local displacement comparison point set contains the data of these key points. This step can be automatically selected by finite element analysis software, or it can be manually selected according to engineering experience and actual needs.

[0160] In terms of technical principles, this process utilizes the basic principles of stress concentration and load distribution. Stress concentration points are usually areas in the structure where stress distribution is uneven. Detailed analysis in these areas can capture the weak links and potential problems of the structure. The load application points are the locations where the structure is directly subjected to force. Analyzing the displacement of these points can more accurately reflect the impact of the load on the structure. By selecting these key points to construct a set of local displacement comparison points, the simulation and measured data can be compared at a more detailed level to improve the accuracy of the analysis.

[0161] In terms of the technical problems solved, step S701 solves the problem of how to effectively select key points in a complex three-dimensional model for detailed comparison. The traditional overall analysis method may ignore the details of local key positions, but by selecting stress concentration points and load application points, more targeted analysis can be performed to capture more detailed displacement change information. This not only improves the accuracy of the comparison analysis, but also can discover potential structural problems.

[0162] In terms of the technical effects achieved, through step S701, a set of local displacement comparison points including key positions is constructed, making the subsequent comparison analysis more targeted and accurate. This method not only improves the depth of data analysis, but also provides a reliable data basis for subsequent error analysis and model optimization. By selecting key positions for detailed comparison, the stress and deformation of the structure can be more accurately evaluated, thereby improving the reliability and practical application value of the simulation results.

[0163] Next, in step S702, it is necessary to obtain the corresponding displacement distribution comparison result based on 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 purpose of this step is to evaluate the degree of matching between the simulation results and the measured data at key positions through detailed error analysis, and to quantify the accuracy of the simulation results. The specific technical process includes the following steps: First, obtain the simulation displacement value and the measured displacement value of each node in the local displacement comparison point set. Then, calculate the residual of each node, that is, the difference between the simulation displacement value and the measured displacement value. Then, calculate the relative error, that is, the ratio of the residual to the measured value, to reflect the relative size of the error. Finally, calculate the root mean square error (RMSE) to comprehensively reflect the error level of all comparison points, so as to obtain the overall displacement distribution comparison result.

[0164] In terms of technical principles, this process utilizes the basic principles of error analysis. Residual is a direct error indicator, reflecting the absolute difference between the simulation results and the measured data. Relative error takes into account the relative size of the error and better reflects the error ratio of different data points. Root mean square error (RMSE) is a comprehensive error indicator, which is obtained by averaging the square of the error of each node and then taking the square root, reflecting the size of the overall error. Through these error analyses, the accuracy and reliability of the simulation results can be fully evaluated.

[0165] In terms of the technical problems solved, step S702 solves the problem of how to quantitatively evaluate the matching degree between the simulation results and the measured data at key positions. Traditional methods may lack detailed error analysis and it is difficult to accurately evaluate the accuracy of the simulation results. However, through the calculation of residuals, relative errors and root mean square errors, the error level can be fully quantified and a more detailed accuracy evaluation can be provided. This not only helps to discover problems in the simulation model, but also provides a basis for model optimization.

[0166] In terms of the technical effects achieved, through step S702, detailed displacement distribution comparison results can be obtained, and the accuracy and reliability of the simulation results can be quantitatively evaluated. This method not only improves the meticulousness of error analysis, but also provides detailed data support for the verification and optimization of the simulation model. Through error analysis, the error range and distribution of the simulation results can be clarified, and possible problems in the model can be identified, so as to optimize and improve them in a targeted manner.

[0167] For example, in the force analysis of a large mechanical equipment, through step S701, the stress concentration points and load application points of the key parts of the equipment are selected to construct a set of local displacement comparison points. Then, in step S702, the simulated displacement values ​​and measured displacement values ​​of these comparison points are obtained, and the residual, relative error and root mean square error of each node are calculated. Through these error analyses, detailed displacement distribution comparison results are obtained, and the accuracy and reliability of the simulation results are quantitatively evaluated.

[0168] Through these steps, the simulation data and measured data were analyzed and compared in detail at key locations, which not only improved the depth of data analysis, but also provided a reliable data basis for model verification and optimization. This method has broad application prospects and important significance in various types of engineering simulation and experimental comparison. Through detailed error analysis, the accuracy of simulation results can be accurately evaluated, problems in the model can be discovered and solved, thereby improving the reliability of simulation results and practical application value.

[0169] In order to improve the accuracy and reliability of product design and performance evaluation and promote the development and innovation of engineering technology, the present application provides an embodiment of a fusion simulation and test virtual-real combination comparison analysis and verification device for realizing all or part of the contents of the fusion simulation and test virtual-real combination comparison analysis and verification method, see Figure 8 The virtual-real combination comparative analysis and verification device of the fusion simulation and experiment specifically includes the following contents:

[0170] The data acquisition 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 field test 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 field test data;

[0171] The virtual-real comparison module 20 is used to perform thermal test time matching and thermal test space matching according to the field actual test data and the simulation result data, and to determine the corresponding temperature distribution comparison results and thermal stress comparison results by performing point-to-point comparison between the obtained test temperature field data and the temperature data in the simulation result data; to perform modal test space matching and modal test natural frequency matching according to the field actual test data and the simulation result data, and to determine the corresponding spatial distribution comparison results and vibration comparison results by performing point-to-point comparison between the obtained test vibration field data and the vibration data in the simulation result data; to perform static test time matching and static test space matching according to the field actual test data and the simulation result data, and to determine the corresponding displacement distribution comparison results, stress / strain comparison results and load-displacement comparison results by performing point-to-point comparison between the obtained test static field data and the static data in the simulation result data;

[0172] The model updating module 30 is used to correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to at least one of the temperature distribution comparison result and the thermal stress comparison result, the spatial distribution comparison result and the vibration comparison result, the displacement distribution comparison result, the stress / strain comparison result and the load-displacement comparison result, and continue to perform simulation tests based on the physical three-dimensional model to obtain the test full life cycle data and archive the files.

[0173] From the above description, it can be seen that the virtual-reality comparison analysis and verification device that integrates simulation and experiment provided in the embodiment of the present application can load the physical three-dimensional model into the three-dimensional scene of the preset 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 field test data in the test virtual scene according to the test scene requirements sent by the user, load and parse the simulation result data corresponding to the field test data; correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to at least one of the temperature distribution comparison results and the thermal stress comparison results, the spatial distribution comparison results and the vibration comparison results, the displacement distribution comparison results, the stress / strain comparison results and the load-displacement comparison results, thereby improving the accuracy and reliability of product design and performance evaluation, and promoting the development and innovation of engineering technology.

[0174] From the hardware level, in order to improve the accuracy and reliability of product design and performance evaluation and promote the development and innovation of engineering technology, the present application provides an embodiment of an electronic device for implementing all or part of the contents of the virtual-real combination comparative analysis and verification method of fusion simulation and experiment, and the electronic device specifically includes the following contents:

[0175] 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 virtual-real combination comparison analysis and verification device of fusion simulation and experiment and related equipment such as core business system, user terminal and related database; the logic controller can be a desktop computer, tablet computer and 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 virtual-real combination comparison analysis and verification method of fusion simulation and experiment, and the embodiment of the virtual-real combination comparison analysis and verification device of fusion simulation and experiment, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0176] 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.

[0177] In practical applications, part of the virtual-real combination comparative analysis and verification method of fusion simulation and experiment can be performed on the electronic device side as described above, or all operations can be completed in the client device. The specific selection can be 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.

[0178] 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.

[0179] 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. 9 As 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.

[0180] In one embodiment, the virtual-real combination comparative analysis and verification method function of fusion simulation and experiment can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control:

[0181] Step S101: 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 test data in the test virtual scene according to the test scene requirements sent by the user, and load and parse simulation result data corresponding to the on-site test data;

[0182] Step S102: performing thermal test time matching and thermal test space matching according to the field actual test data and the simulation result data, performing point-to-point comparison between the obtained test temperature field data and the temperature data in the simulation result data, and determining the corresponding temperature distribution comparison results and thermal stress comparison results; performing modal test space matching and modal test natural frequency matching according to the field actual test data and the simulation result data, performing point-to-point comparison between the obtained test vibration field data and the vibration data in the simulation result data, and determining the corresponding spatial distribution comparison results and vibration comparison results; performing static test time matching and static test space matching according to the field actual test data and the simulation result data, performing point-to-point comparison between the obtained test static field data and the static data in the simulation result data, and determining the corresponding displacement distribution comparison results, stress / strain comparison results, and load-displacement comparison results;

[0183] Step S103: Correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to at least one of the temperature distribution comparison result and the thermal stress comparison result, the spatial distribution comparison result and the vibration comparison result, the displacement distribution comparison result, the stress / strain comparison result and the load-displacement comparison result, and continue to perform simulation tests based on the physical three-dimensional model to obtain the test full life cycle data and archive the files.

[0184] From the above description, it can be seen that the electronic device provided in the embodiment of the present application constructs a test virtual scene by loading a physical three-dimensional model into a preset test station three-dimensional scene and performing virtual assembly, configures test parameters in the test virtual scene, and loads and associates measurement channels; collects field test data in the test virtual scene according to the test scene requirements sent by the user, loads and parses simulation result data corresponding to the field test data; and corrects the simulation parameters of the physical three-dimensional model in the test virtual scene according to at least one of the temperature distribution comparison results and thermal stress comparison results, spatial distribution comparison results and vibration comparison results, displacement distribution comparison results, stress / strain comparison results, and load-displacement comparison results, thereby improving the accuracy and reliability of product design and performance evaluation, and promoting the development and innovation of engineering technology.

[0185] In another embodiment, the device for comparing, analyzing and verifying the virtual and the real that integrates the simulation and the experiment can be configured separately from the central processing unit 9100. For example, the device for comparing, analyzing and verifying the virtual and the real that integrates the simulation and the experiment can be configured as a chip connected to the central processing unit 9100, and the function of the method for comparing, analyzing and verifying the virtual and the real that integrates the simulation and the experiment can be realized through the control of the central processing unit.

[0186] like Fig. 9As 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.).

[0192] 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.

[0193] 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.

[0194] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the virtual-real combination comparative analysis and verification method of the fusion simulation and experiment in the above-mentioned embodiment, where 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 virtual-real combination comparative analysis and verification method of the fusion simulation and experiment in the above-mentioned embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0195] Step S101: 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 test data in the test virtual scene according to the test scene requirements sent by the user, and load and parse simulation result data corresponding to the on-site test data;

[0196] Step S102: performing thermal test time matching and thermal test space matching according to the field actual test data and the simulation result data, performing point-to-point comparison between the obtained test temperature field data and the temperature data in the simulation result data, and determining the corresponding temperature distribution comparison results and thermal stress comparison results; performing modal test space matching and modal test natural frequency matching according to the field actual test data and the simulation result data, performing point-to-point comparison between the obtained test vibration field data and the vibration data in the simulation result data, and determining the corresponding spatial distribution comparison results and vibration comparison results; performing static test time matching and static test space matching according to the field actual test data and the simulation result data, performing point-to-point comparison between the obtained test static field data and the static data in the simulation result data, and determining the corresponding displacement distribution comparison results, stress / strain comparison results, and load-displacement comparison results;

[0197] Step S103: Correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to at least one of the temperature distribution comparison result and the thermal stress comparison result, the spatial distribution comparison result and the vibration comparison result, the displacement distribution comparison result, the stress / strain comparison result and the load-displacement comparison result, and continue to perform simulation tests based on the physical three-dimensional model to obtain the test full life cycle data and archive the files.

[0198] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application constructs a test virtual scene by loading a physical three-dimensional model into a preset test station three-dimensional scene and performing virtual assembly, configures test parameters in the test virtual scene, and loads and associates measurement channels; collects field test data in the test virtual scene according to the test scene requirements sent by the user, loads and parses simulation result data corresponding to the field test data; and corrects the simulation parameters of the physical three-dimensional model in the test virtual scene according to at least one of the temperature distribution comparison results and thermal stress comparison results, spatial distribution comparison results and vibration comparison results, displacement distribution comparison results, stress / strain comparison results, and load-displacement comparison results, thereby improving the accuracy and reliability of product design and performance evaluation, and promoting the development and innovation of engineering technology.

[0199] The embodiments of the present application also provide a computer program product capable of implementing all the steps of the virtual-real combination comparative analysis and verification method of fusion simulation and experiment in the above-mentioned embodiment, the execution subject of which is a server or a client. When the computer program / instruction is executed by a processor, the steps of the virtual-real combination comparative analysis and verification method of fusion simulation and experiment are implemented. For example, the computer program / instruction implements the following steps:

[0200] Step S101: 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 test data in the test virtual scene according to the test scene requirements sent by the user, and load and parse simulation result data corresponding to the on-site test data;

[0201] Step S102: performing thermal test time matching and thermal test space matching according to the field actual test data and the simulation result data, performing point-to-point comparison between the obtained test temperature field data and the temperature data in the simulation result data, and determining the corresponding temperature distribution comparison results and thermal stress comparison results; performing modal test space matching and modal test natural frequency matching according to the field actual test data and the simulation result data, performing point-to-point comparison between the obtained test vibration field data and the vibration data in the simulation result data, and determining the corresponding spatial distribution comparison results and vibration comparison results; performing static test time matching and static test space matching according to the field actual test data and the simulation result data, performing point-to-point comparison between the obtained test static field data and the static data in the simulation result data, and determining the corresponding displacement distribution comparison results, stress / strain comparison results, and load-displacement comparison results;

[0202] Step S103: Correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to at least one of the temperature distribution comparison result and the thermal stress comparison result, the spatial distribution comparison result and the vibration comparison result, the displacement distribution comparison result, the stress / strain comparison result and the load-displacement comparison result, and continue to perform simulation tests based on the physical three-dimensional model to obtain the test full life cycle data and archive the files.

[0203] From the above description, it can be seen that the computer program product provided in the embodiment of the present application constructs a test virtual scene by loading a physical three-dimensional model into a preset test station three-dimensional scene and performing virtual assembly, configures test parameters in the test virtual scene, and loads and associates measurement channels; collects field test data in the test virtual scene according to the test scene requirements sent by the user, loads and parses simulation result data corresponding to the field test data; and corrects the simulation parameters of the physical three-dimensional model in the test virtual scene according to at least one of the temperature distribution comparison results and thermal stress comparison results, spatial distribution comparison results and vibration comparison results, displacement distribution comparison results, stress / strain comparison results, and load-displacement comparison results, thereby improving the accuracy and reliability of product design and performance evaluation, and promoting the development and innovation of engineering technology.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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 A step that specifies a function in one or more boxes.

[0208] 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 virtual-real combination comparative analysis and verification method integrating simulation and experiment, characterized in that: The method comprises: The physical three-dimensional model is loaded into a preset three-dimensional scene of a 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. Field test 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 field test data is loaded and parsed. Perform thermal test time matching and thermal test space matching based on the field test data and the simulation result data, perform point-to-point comparison between the obtained test temperature field data and the temperature data in the simulation result data, and determine the corresponding temperature distribution comparison results and thermal stress comparison results; perform modal test space matching and modal test natural frequency matching based on the field test data and the simulation result data, perform point-to-point comparison between the obtained test vibration field data and the vibration data in the simulation result data, and determine the corresponding spatial distribution comparison results and vibration comparison results; perform static test time matching and static test space matching based on the field test data and the simulation result data, perform point-to-point comparison between the obtained test static field data and the static data in the simulation result data, and determine the corresponding displacement distribution comparison results, stress / strain comparison results, and load-displacement comparison results; The thermal test spatial matching based on the field test data and the simulation result data includes: selecting a position corresponding to the test measurement point as a selection position of a simulation model node corresponding to the physical three-dimensional model, so that the simulation model outputs parameter data at the same spatial coordinate point as the field test data; performing average temperature processing on similar areas in the simulation model, and using the simulation model after the average temperature processing as a spatial point matching the test measurement point; The point-to-point comparison between the obtained experimental static field data and the static data in the simulation result data to determine the corresponding displacement distribution comparison result includes: selecting stress concentration points and load application points in the physical three-dimensional model to construct a local displacement comparison point set; and obtaining the corresponding displacement distribution comparison result based on the residual, relative error, and root mean square error of the simulation and experimental displacement values ​​of each node in the local displacement comparison point set; According to at least one of the temperature distribution comparison result and the thermal stress comparison result, the spatial distribution comparison result and the vibration comparison result, the displacement distribution comparison result, the stress / strain comparison result, and the load-displacement comparison result, 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 archive it.

2. The virtual-real combination comparative analysis and verification method of fusion simulation and experiment according to claim 1 is characterized in that: The performing of thermal test time matching based on the field test data and the simulation result data includes: Setting the output time step of the simulation model corresponding to the physical three-dimensional model to be consistent with the field test data, so that the simulation model and the field test data output data at the same time interval; The simulation result data is interpolated to the time corresponding to the sampling point of the field test data through a preset interpolation algorithm, and the time boundary of the simulation result data and the field test data is unified through a data interception operation.

3. The virtual-real combination comparative analysis and verification method of fusion simulation and experiment according to claim 1 is characterized in that: The performing modal test space matching based on the field test data and the simulation result data includes: Selecting a position corresponding to the test measurement point as a selection position of a simulation model node corresponding to the physical three-dimensional model, so that the simulation model outputs parameter data at the same spatial coordinate point as the field test data; When the distance between the simulation finite element distance test point and the adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test point is used as the spatial point matching the test point.

4. The virtual-real combination comparative analysis and verification method of fusion simulation and experiment according to claim 1 is characterized in that: The performing modal test natural frequency matching based on the field test data and the simulation result data includes: Setting the output time step of the simulation model corresponding to the physical three-dimensional model to be consistent with the field test data, so that the simulation model and the field test data output data at the same time interval; The simulation result data is interpolated to the vibration frequency corresponding to the sampling point of the field test data through a preset interpolation algorithm, and the vibration frequency boundary of the simulation result data and the field test data is unified through a data interception operation.

5. The virtual-real combination comparative analysis and verification method of fusion simulation and experiment according to claim 1 is characterized in that: The point-to-point comparison of the obtained test static field data with the static data in the simulation result data to determine the corresponding displacement distribution comparison result includes: constructing a global displacement field by difference processing based on the obtained experimental static field data, and superimposing the global displacement field constructed by the experimental static field data and the global displacement field obtained by the simulation result data; The matching points of the superimposed global displacement field are numerically compared to calculate the overall accuracy of the simulation result data.

6. A virtual-real combination comparative analysis and verification device integrating simulation and experiment, characterized in that: The device comprises: A data acquisition module is used to load the physical 3D model into a preset test station 3D 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 test 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 test data; The virtual-real comparison module is used to perform thermal test time matching and thermal test space matching according to the on-site actual test data and the simulation result data, and to determine the corresponding temperature distribution comparison results and thermal stress comparison results by performing point-to-point comparison between the obtained test temperature field data and the temperature data in the simulation result data; to perform modal test space matching and modal test natural frequency matching according to the on-site actual test data and the simulation result data, and to determine the corresponding spatial distribution comparison results and vibration comparison results by performing point-to-point comparison between the obtained test vibration field data and the vibration data in the simulation result data; to perform static test time matching and static test space matching according to the on-site actual test data and the simulation result data, and to determine the corresponding displacement distribution comparison results, stress / strain comparison results and load-displacement comparison results by performing point-to-point comparison between the obtained test static field data and the static data in the simulation result data. The result; performing thermal test space matching based on the field test data and the simulation result data, including: selecting a position corresponding to the test measurement point as a selection position of a simulation model node corresponding to the physical three-dimensional model, so that the simulation model outputs parameter data at the same spatial coordinate point as the field test data; performing average temperature processing on similar areas in the simulation model, and using the simulation model after the average temperature processing as a spatial point matching the test measurement point; performing point-to-point comparison between the obtained test static field data and the static data in the simulation result data to determine the corresponding displacement distribution comparison result, including: selecting set stress concentration points and load application points in the physical three-dimensional model to construct a local displacement comparison point set; obtaining the 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; A model updating module is used to correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to at least one of the temperature distribution comparison result and the thermal stress comparison result, the spatial distribution comparison result and the vibration comparison result, the displacement distribution comparison result, the stress / strain comparison result, and the load-displacement comparison result, and to continue the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the virtual-real combination comparative analysis and verification method of fusion simulation and experiment described in any one of claims 1 to 5 are implemented.

8. 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 method for comparative analysis and verification of virtual-reality integration of fusion simulation and experiment as described in any one of claims 1 to 5 are implemented.

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