Frequency domain response simulation and measurement data processing method and device

Abstract

The embodiment of the present application provides a method and device for processing frequency domain response simulation and measurement data, the method comprising: selecting a position corresponding to a test measurement point as a selection position of a simulation model node corresponding to a physical three-dimensional model, and when the distance between the simulation finite element and the test measurement point relative to the distance between adjacent points in the simulation model is lower than a threshold, taking the finite element node closest to the test measurement point as a spatial point matching the test measurement point to obtain a corresponding spatial distribution comparison result; performing a modal correlation analysis on the simulation result data and the field measured frequency response data through a preset modal matching algorithm to obtain a modal matching relationship between the simulation result data and the field measured data, and obtaining a frequency domain response comparison result based on the modal matching relationship; correcting the simulation parameters of the physical three-dimensional model based on the spatial distribution comparison result and the frequency domain response comparison result; the present application improves the accuracy of data processing and comparison, and realizes real-time integration and analysis of simulation data and test measurement data.

Description

Technical Field

[0001] The present application relates to the field of simulation testing, and specifically to a method and device for frequency domain response simulation and measurement data processing. Background Art

[0002] In the field of modern engineering and manufacturing, the rapid construction and application of virtual test systems has become an important means to improve product design efficiency and quality. By simulating the actual physical environment and test process, virtual test systems can identify potential problems and optimize them in the early stages of product development. However, current technologies still face many challenges and shortcomings in the construction and application of virtual test systems.

[0003] First, the traditional virtual test system construction process is complicated and time-consuming. In traditional methods, the loading and virtual assembly of physical 3D models require a lot of manual intervention and expertise. The construction of the test virtual scene often requires multiple repeated operations, and the configuration of test parameters and the loading and association of measurement channels also require tedious steps. This complex operation process not only prolongs the time of system construction, but also easily introduces human errors, affecting the accuracy and reliability of the test.

[0004] Secondly, the complexity of data collection and processing is also an important issue. In traditional virtual test systems, the collection of test data and the loading and analysis of simulation data are two independent processes. The collection of real-time data measured on site needs to be carried out under actual test conditions. This process is often affected by many factors such as the environment, equipment and operators, resulting in unstable data quality. The loading and analysis of simulation result data requires matching and comparing the test data with the simulation model. This process requires not only a large amount of computing resources, but also efficient data processing algorithms to ensure the accuracy and integrity of the data.

[0005] In addition, the matching accuracy between the simulation model and the actual test data is also a key issue. In the traditional method, the node selection and parameter output of the simulation model need to accurately match the position of the test measurement point. This process requires high-precision spatial coordinate transformation and data processing technology to ensure the reliability of the comparison results. However, due to the possible spatial deviation between the simulated finite element model and the actual test measurement points, how to accurately select the simulation model nodes corresponding to the test measurement points and perform effective data output and comparison is a challenging task.

[0006] The matching of time step and frequency is also a difficulty in current technology. The output time step of the simulation model is often inconsistent with the sampling time step of the real-time data measured on site. It is necessary to interpolate the simulation result data to the frequency corresponding to the sampling point of the real-time data measured on site through a preset interpolation algorithm. At the same time, it is also necessary to unify the frequency boundaries of the simulation result data and the real-time data measured on site through data interception operations to obtain accurate frequency domain comparison results. Any error in this process will affect the final comparison results and the correction effect of the simulation model.

[0007] The existing technology also has the problem of difficulty in integrating test data and simulation data. Test data and simulation data need to be managed and stored separately, which increases the complexity of data management. In large projects, the amount of data is huge, and how to effectively manage and maintain this data becomes a difficult task. The decentralized storage and management of data not only increases the workload of the team, but also easily leads to data redundancy and loss. Integrating and comparing test data and simulation data requires complex data processing and analysis steps. This not only increases the workload, but also puts higher requirements on the skill level of technicians.

[0008] The problem of poor real-time performance also limits the application of virtual test systems. Traditional methods make it difficult to achieve real-time comparison and analysis of simulation data and test data, which limits their application in rapid iteration and optimization. In actual engineering projects, it is often necessary to quickly adjust the simulation model based on the test results for further simulation and optimization. However, the lag in data processing and comparison means that this process often cannot be carried out in real time, thus delaying the progress of the project.

[0009] In general, the existing virtual test systems have many shortcomings in terms of rapid construction and application, including complex processes, difficult data collection and processing, low matching accuracy, poor real-time performance, and complex data management. These problems not only increase the difficulty of product development and optimization, but also affect the accuracy and efficiency of product performance evaluation. Therefore, how to simplify the construction process of the virtual test system, improve the accuracy of data processing and comparison, and realize the real-time integration and analysis of simulation data and test data are important issues that need to be solved in the current technical field. Summary of the invention

[0010] In response to the problems in the prior art, the present application provides a frequency domain response simulation and measurement data processing method and device to improve the accuracy of data processing and comparison, and realize real-time integration and analysis of simulation data and test data.

[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 frequency domain response simulation and measurement data processing method, 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; on-site measured real-time data is collected in the test virtual scene according to the test scene requirements sent by the user, and simulation result data corresponding to the on-site measured real-time data is loaded and parsed;

[0014] Select the position corresponding to the test measurement point as the selection position of the 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 measured data, and when the distance between the simulation finite element and the test measurement point relative to the distance between adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test measurement point is used as the spatial point matching the test measurement point to obtain the corresponding spatial distribution comparison result; set the frequency excitation step size of the simulation model corresponding to the physical three-dimensional model to be consistent with the above, so that the simulation model outputs data at the same frequency interval as the field measured data, perform modal correlation analysis on the simulation result data and the field measured frequency response data through a preset modal matching algorithm, and obtain the modal matching relationship between the simulation result data and the field measured data, and perform frequency domain response result comparison on the simulation result data with the largest correlation in the modal matching relationship and the field measured frequency response data to obtain the frequency domain comparison result;

[0015] The simulation parameters of the physical three-dimensional model in the test virtual scene are modified according to the spatial distribution comparison result and the frequency domain comparison result, and the simulation test is continued based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0016] Furthermore, the frequency domain response results of the simulation result data with the greatest correlation in the modal matching relationship are compared with the field measured frequency response data to obtain the frequency domain comparison results, including:

[0017] Calculating the modal confidence coefficient for the simulation result data with the greatest correlation in the modal matching relationship and the field measured frequency response data;

[0018] The credibility of each vibration mode is determined according to the modal confidence coefficient.

[0019] Furthermore, it also includes:

[0020] Based on the real-time data measured on site, three-dimensional linear interpolation is performed according to the finite element position in the simulation result number to obtain the corresponding test value;

[0021] A test measurement vibration field is constructed according to the test values, and modal test space matching is performed according to the test measurement vibration field.

[0022] Furthermore, 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, including:

[0023] Obtain a digital 3D file of the physical 3D model to be tested and import it into a preset 3D scene of a test station for virtual assembly and arrangement, thereby constructing a virtual test scene;

[0024] According to the performance index to be verified, corresponding test parameters are set in the test virtual scene, and measurement sensors are set and associated.

[0025] Furthermore, collecting the on-site measured real-time data in the test virtual scene according to the test scene requirements sent by the user, and loading and parsing the simulation result data corresponding to the on-site measured real-time data, includes:

[0026] Run the simulation test of the test virtual scene according to the test scene requirements sent by the user, store and annotate the collected actual test data, and obtain the real-time data of the on-site measurement;

[0027] The various physical quantity data obtained by simulation are loaded and analyzed, and then matched with the real-time data measured on site to obtain simulation result data.

[0028] Furthermore, the step of correcting simulation parameters of the physical three-dimensional model in the test virtual scene according to the spatial distribution comparison result and the frequency domain comparison result includes:

[0029] Correcting the position, size and geometric parameters of the physical three-dimensional model in the test virtual scene according to the spatial distribution comparison result and the frequency domain comparison result;

[0030] The three-dimensional model material parameters and constraint conditions of the physical three-dimensional model in the test virtual scene are modified according to the spatial distribution comparison result and the frequency domain comparison result.

[0031] Furthermore, the simulation test is continued based on the physical three-dimensional model to obtain the test life cycle data and file it, including:

[0032] Carry out long-term dynamic simulation in a virtual test environment with corrected parameters to obtain data for the entire test life cycle;

[0033] The obtained data of the entire life cycle of the test are classified and archived according to different physical quantity categories.

[0034] In a second aspect, the present application provides a frequency domain response simulation and measurement data processing method and device, comprising:

[0035] A scene building 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 measured real-time data in the test virtual scene according to the test scene requirements sent by the user, and load and parse the simulation result data corresponding to the on-site measured real-time data;

[0036] A comparison and analysis module is used to select 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 measured data; when the distance between the simulation finite element and the test measurement point relative to the distance between adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test measurement point is used as a spatial point matching the test measurement point to obtain a corresponding spatial distribution comparison result; the frequency excitation step length of the simulation model corresponding to the physical three-dimensional model is set to be consistent with the above, so that the simulation model outputs data at the same frequency interval as the field measured data; a modal correlation analysis is performed on the simulation result data and the field measured frequency response data through a preset modal matching algorithm to obtain a modal matching relationship between the simulation result data and the field measured data; a frequency domain response result comparison is performed on the simulation result data with the largest correlation in the modal matching relationship and the field measured frequency response data to obtain a frequency domain comparison result;

[0037] A parameter correction module is used to correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the spatial distribution comparison results and the frequency domain comparison results, and continue the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[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 frequency domain response simulation and measurement data processing method 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 frequency domain response simulation and measurement data processing method.

[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 frequency domain response simulation and measurement data processing method.

[0041] It can be seen from the above technical scheme that the present application provides a frequency domain response simulation and measurement data processing method and device, by selecting the position corresponding to the test measurement point as the selection position of the simulation model node corresponding to the physical three-dimensional model, when the distance between the simulation finite element and the test measurement point relative to the adjacent points in the simulation model is lower than the threshold, the finite element node closest to the test measurement point is used as the spatial point matching the test measurement point to obtain the corresponding spatial distribution comparison result; the simulation result data and the field measured frequency response data are subjected to modal correlation analysis through a preset modal matching algorithm to obtain the modal matching relationship between the simulation result data and the field measured data, and the frequency domain response comparison result is obtained according to the modal matching relationship; the simulation parameters of the physical three-dimensional model are corrected according to the spatial distribution comparison result and the frequency domain response comparison result, thereby improving the accuracy of data processing and comparison, and realizing real-time integration and analysis of simulation data and test data. 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 This is one of the flow charts of the frequency domain response simulation and measurement data processing method in the embodiment of the present application;

[0044] Figure 2 The second flowchart of the frequency domain response simulation and measurement data processing method in the embodiment of the present application;

[0045] Figure 3 The third flowchart of the frequency domain response simulation and measurement data processing method in the embodiment of the present application;

[0046] Figure 4 This is a fourth flow chart of the frequency domain response simulation and measurement data processing method in the embodiment of the present application;

[0047] Figure 5 FIG5 is a flowchart of the frequency domain response simulation and measurement data processing method in the embodiment of the present application;

[0048] Figure 6 FIG6 is a flowchart of the frequency domain response simulation and measurement data processing method in the embodiment of the present application;

[0049] Figure 7 FIG7 is a flow chart of the frequency domain response simulation and measurement data processing method in the embodiment of the present application;

[0050] Figure 8 It is a structural diagram of a frequency domain response simulation and measurement data processing method device 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 method and device for processing frequency domain response simulation and measurement data. By selecting a position corresponding to a test measuring point as a selection position of a simulation model node corresponding to a physical three-dimensional model, when the distance between the simulation finite element and the test measuring point relative to the adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test measuring point is used as a spatial point matching the test measuring point to obtain a corresponding spatial distribution comparison result; a modal correlation analysis is performed on the simulation result data and the field measured frequency response data through a preset modal matching algorithm to obtain a modal matching relationship between the simulation result data and the field measured data, and a frequency domain response comparison result is obtained according to the modal matching relationship; the simulation parameters of the physical three-dimensional model are corrected according to the spatial distribution comparison result and the frequency domain response comparison result, thereby improving the accuracy of data processing and comparison.

[0057] In order to improve the accuracy of data processing and comparison and realize the real-time integration and analysis of simulation data and test data, the present application provides an embodiment of a frequency domain response simulation and measurement data processing method, see Figure 1 The frequency domain response simulation and measurement data processing method specifically includes the following contents:

[0058] Step S101: Load the physical three-dimensional model into the preset three-dimensional scene of the test station and perform virtual assembly to construct a test virtual scene, configure the test parameters in the test virtual scene, and load and associate the measurement channels; collect the on-site measured real-time data in the test virtual scene according to the test scene requirements sent by the user, and load and analyze the simulation result data corresponding to the on-site measured real-time data;

[0059] Optionally, in this embodiment, step S101 describes how to load the physical 3D model into the preset test station 3D scene and perform virtual assembly, build the test virtual scene, configure the test parameters, load and associate the measurement channels, and collect and analyze the measured data and its simulation result data. This process not only realizes the docking of virtual and actual data, but also lays the foundation for subsequent data analysis and simulation model verification.

[0060] First, in step S101, the system loads the physical 3D model into the 3D scene of the preset test station and performs virtual assembly. The implementation of this step requires converting the actual physical model into a digital 3D model and importing it into the simulation software. Virtual assembly refers to installing and combining various components in a virtual environment according to the actual process flow and assembly requirements to form a complete test virtual scene. The technical principle of this process is based on computer-aided design (CAD) and computer-aided manufacturing (CAM) technology, and realizes the virtual reproduction of the physical model through digital means.

[0061] Next, the system configures the test parameters in the test virtual scene and loads and associates the measurement channels. The configuration of test parameters includes but is not limited to test conditions, environmental parameters, operation steps, etc. These parameters are an important basis for simulating the actual test environment and conditions. Loading and associating measurement channels means setting up various measurement points and sensors in the virtual scene and associating them with the corresponding physical measurement equipment. In this way, the system can monitor and record test data in real time in the virtual environment to ensure the accuracy and reliability of the virtual test.

[0062] After constructing the test virtual scene and configuring the test parameters, the system collects real-time data measured on site in the test virtual scene according to the test scene requirements sent by the user. In the specific implementation process, the system first receives the user's test scene requirements, which include specific measurement points, measurement cycles, data types and other information. Then, the system starts the corresponding measurement equipment and collects the measured data on site according to the preset measurement channels and parameters. These data include various physical quantities such as temperature, pressure, and stress, which are important bases for reflecting the actual test situation.

[0063] After data collection is completed, the system loads and analyzes the simulation result data corresponding to the real-time data measured on site. The simulation result data is the result obtained by simulating the test process in a virtual environment through simulation software, and usually includes physical quantities corresponding to the measured data. The system compares and analyzes these simulation data with the measured data to verify the accuracy and effectiveness of the simulation model. The technical principle of this process is based on data mining and big data analysis technology. By comparing the measured data and simulation data, the deficiencies and areas that need improvement in the simulation model are found, thereby improving the accuracy and reliability of the simulation model.

[0064] This technical process solves the problems of low efficiency, large errors, and difficulty in verifying simulation models in traditional tests. By loading the physical 3D model into the virtual scene for virtual assembly, the system can simulate the actual test process in the virtual environment, reducing the cost and time of the actual test. Configuring test parameters and loading measurement channels ensures the accuracy and reliability of data collection. Collecting on-site measured data and loading analytical simulation result data realizes the connection between virtual and actual data, providing data support for the verification and optimization of simulation models.

[0065] For example, in step S101, the system first loads the physical 3D model into the 3D scene of the preset test station and performs virtual assembly. For example, a complex geological model or a 3D model of an engineering component is imported into the virtual environment to ensure that its geometric shape and positional relationship are accurately reproduced. Subsequently, the system constructs a virtual scene of the test, in which the user can configure the test parameters, such as scalar field data such as temperature, pressure or concentration, to simulate different experimental conditions.

[0066] Next, the system loads and associates measurement channels to ensure that relevant data can be collected and monitored in the virtual scene. Measurement channels may include the location and configuration of temperature sensors, pressure sensors, or concentration sensors to accurately collect scalar field data in the virtual environment.

[0067] According to the test scenario requirements sent by the user, the system collects real-time data measured on site in the test virtual scene. For example, in a heat conduction experiment, the system collects real-time temperature distribution data on the surface and inside of the object. These data are transmitted to the system in real time through the network or other data transmission methods.

[0068] Subsequently, the system loads and parses the simulation result data corresponding to the real-time data measured on site. The simulation result data is usually generated by numerical simulation (such as finite element analysis) and reflects the theoretical prediction results under the same experimental conditions. By comparing and analyzing the measured data with the simulation data, the system can evaluate the accuracy and reliability of the simulation model, thereby optimizing the test parameters and simulation model and improving the overall effect of the experiment and simulation. For example, by comparing the measured and simulated temperature distributions, users can identify errors in the heat conduction model and make corrections to improve the performance and efficiency of the system design.

[0069] Output parameter data of the spatial coordinate points that are the same as the field measured real-time data; when the distance between the simulation finite element distance test measurement point and the adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test measurement point is used as the spatial point that matches the test measurement point to obtain the corresponding spatial distribution comparison result; set the output time step of the simulation model corresponding to the physical three-dimensional model to be consistent with the above, so that the simulation model outputs data at the same time interval as the field measured real-time data, interpolate the simulation result data to the frequency corresponding to the sampling point of the field measured real-time data through a preset interpolation algorithm, unify the frequency boundaries of the simulation result data and the field measured real-time data through a data interception operation, and obtain the corresponding frequency domain comparison result;

[0070] Step S102: selecting a position corresponding to the test measuring 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 measured data; when the distance between the simulation finite element and the test measuring point relative to the distance between adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test measuring point is used as the spatial point matching the test measuring point to obtain a corresponding spatial distribution comparison result; setting the frequency excitation step length of the simulation model corresponding to the physical three-dimensional model to be consistent with the above, so that the simulation model outputs data at the same frequency interval as the field measured data; performing modal correlation analysis on the simulation result data and the field measured frequency response data through a preset modal matching algorithm to obtain a modal matching relationship between the simulation result data and the field measured data; performing a frequency domain response result comparison on the simulation result data with the largest correlation in the modal matching relationship and the field measured frequency response data to obtain a frequency domain comparison result;

[0071] Optionally, in this embodiment, step S102 describes in detail the process of aligning the simulation model with the measured data in space, time and frequency to ensure that the comparative analysis of the two is consistent and scientific. During the implementation process, the system first selects the position corresponding to the test measurement point as the selection position of the simulation model node. The technical principle of this process is based on spatial coordinate matching. By calculating the distance between the test measurement point and the simulation model node, the node closest to the test point is selected as the matching point. When the distance between the simulation finite element node and the test measurement point is lower than the set threshold, the system uses the node as a node that matches the test measurement point, thereby ensuring that the parameter data output by the simulation model is at the same spatial coordinate point as the measured data. This can effectively solve the problem of data comparison error caused by inconsistent measurement point positions and improve the accuracy of comparative analysis.

[0072] Next, the system sets the output time step of the simulation model to be consistent with the field measured data. Specifically, the time step of the simulation model refers to the time interval of data output. By setting it to be the same as the measured data, the consistency of the two in the time dimension is ensured. The technical principle of this step is based on time synchronization, by adjusting the output frequency of the simulation model to match the sampling frequency of the measured data. In this way, the time comparison error problem caused by different data acquisition frequencies can be solved, ensuring that the data of the two are compared and analyzed at the same time point.

[0073] After the space and time docking is completed, the system interpolates the simulation result data to the frequency corresponding to the sampling point of the measured data through the preset interpolation algorithm. The technical principle of this step is based on the interpolation algorithm. By interpolating the simulation data, it makes it have the same frequency at the sampling point of the measured data. The interpolation algorithm can use linear interpolation, spline interpolation and other methods, and select the appropriate algorithm according to the specific situation of the simulation data and the measured data. The interpolation operation can solve the frequency docking problem caused by different data sampling points, so that the simulation data and the measured data are consistent in frequency.

[0074] Finally, the system unifies the frequency boundaries of the simulation result data and the measured data through data interception. The technical implementation of this step is to intercept the data in the same frequency range for comparison after performing frequency analysis on the simulation data and the measured data. In this way, the comparison error caused by different data frequency ranges can be avoided, and the accuracy and reliability of the comparison results can be improved.

[0075] This technical process has significant advantages in solving the spatial, temporal and frequency inconsistencies in the comparative analysis of simulation data and measured data. By selecting the simulation model nodes corresponding to the test measurement points, the system ensures the consistency of the data in space; by setting the same output time step, the system ensures the consistency of the data in time; through interpolation and data interception operations, the system ensures the consistency of the data in frequency. In this way, the system can achieve accurate docking and comparative analysis of simulation data and measured data, and improve the evaluation accuracy and reliability of the simulation model.

[0076] For example, in a bridge vibration test, engineers need to evaluate the modal response of the bridge at different excitation frequencies. Through step S102, the system first selects the measurement point position on the bridge deck as the selection position of the simulation model node to ensure that the simulation data and the measured data output parameter data at the same spatial point. Then, the system sets the output time step of the simulation model to be consistent with the measured data to ensure that both output data within the same time interval. Next, the system interpolates the simulation result data to the frequency of the measured data through an interpolation algorithm, and unifies the frequency boundaries of the two through a data interception operation. In this way, engineers can evaluate the vibration characteristics of the bridge by comparing the simulation data with the measured data, and discover and improve the deficiencies in the design.

[0077] Specifically, for modal tests, it is necessary to consider data matching in two dimensions: spatial matching and natural frequency matching. Among them, spatial matching is responsible for completing the matching of the test measurement points with the corresponding positions of the simulation finite element, and it is necessary to ensure the similarity between the node positions in the simulation model and the test measurement points. Natural frequency matching requires that the natural frequency selected during the simulation analysis process should be as close as possible to the test data to ensure that the model vibrates within the correct frequency range.

[0078] For example, spatial matching is based on simulation data and calculates the simulation result value corresponding to the coordinates of the test measurement point. For the case where the simulation finite elements are relatively dense (that is, the distance between the simulation finite elements and the test measurement points is closer than the distance between adjacent measurement points), the finite element node closest to the measurement point can be selected as the matching point.

[0079] The comparison of modal test results is mainly achieved through mode shape matching. In terms of comparing the modal shapes of simulation and test, the modes should match as much as possible, including shape and relative displacement. Mode shape matching analysis is mainly performed by calculating the mac value, as follows:

[0080] The modal confidence coefficient MAC is used to measure the credibility of each vibration mode identified. MAC is defined as:

[0081]

[0082] in, and are the simulation value and test value of the vibration mode respectively. The superscript T represents the vector transpose. 0≤MAC≤1. The closer MAC is to 1, the closer the theoretical value is to the test value, and the more accurate the recognition result is.

[0083] For example: In a certain experiment, there are 5 degrees of freedom (mode shape is 5). The simulation results of the first-order mode shape are: 1, 2, 3, 4, 5. The experimental mode shape values ​​of the first-order mode are: 5, 4, 3, 2, 1. It can be seen that the correlation between the two modes is not very good, but the overall shape has a certain similarity. The correlation between the two modes can be expressed by the MAC value, which is calculated as follows:

[0084]

[0085]

[0086] Then MAC=352 / (55×55)=0.4.

[0087] 0.4 indicates that the similarity is not high. Generally, the MAC value of the simulation value and the test value is above 0.9, indicating that the two vibration modes are consistent, and above 0.8 indicates that they are basically consistent.

[0088] In general, step S102 achieves accurate docking of simulation data with measured data in space, time and frequency by selecting simulation model nodes corresponding to test measurement points, setting the same output time step, and unifying the frequency boundaries of data through interpolation and data interception operations. This technical process has significant advantages in improving the evaluation accuracy and reliability of simulation models, and can help users quickly and accurately perform data analysis and model optimization, thereby improving the overall simulation effect and efficiency.

[0089] Step S103: modifying the simulation parameters of the physical three-dimensional model in the test virtual scene according to the spatial distribution comparison result and the frequency domain comparison result, and continuing the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0090] Optionally, in this embodiment, step S103 describes the use of the previous spatial distribution comparison results and frequency domain comparison results to correct the simulation parameters of the physical three-dimensional model, and continue to perform simulation tests based on the corrected model to obtain and archive the full life cycle data of the test. In the specific implementation process, the system first analyzes the spatial distribution comparison results and frequency domain comparison results obtained from step S102, which reflect the differences between the simulation model and the actual measurement data. By identifying and quantifying these differences, the system can determine the simulation parameters that need to be adjusted to improve the accuracy of the model.

[0091] In terms of technical principles, the process of correcting simulation parameters usually involves iterative optimization. The system can use back propagation algorithms or other optimization algorithms to gradually adjust the parameters in the physical three-dimensional model according to the comparison results. These parameters may include material properties, geometric dimensions, boundary conditions, etc. Through repeated adjustments and simulations, the system gradually reduces the error between the simulation results and the measured data, thereby obtaining a more accurate simulation model. This process not only relies on efficient computing power, but also requires a deep understanding of the simulation model and actual physical phenomena to ensure the rationality of the adjustment direction and amplitude.

[0092] In terms of solving technical problems, step S103 improves the accuracy of the model by correcting the simulation parameters, solving the problem of possible deviations between the simulation results and the actual measurement results. These deviations may come from a variety of factors, such as inaccurate initial model assumptions, unreasonable boundary condition settings, inaccurate material properties, etc. By correcting these factors, the system can better reflect the actual situation and ensure that the results of subsequent simulation tests are more reliable and credible.

[0093] The revised simulation model will be used to continue the simulation test to obtain the full life cycle data of the test. This means that the system will be simulated for a longer time and more comprehensively based on the new model, covering the entire process from the initial state to the final state. These data include not only the response data at critical moments, but also the continuous data throughout the test process, providing a comprehensive understanding of the system behavior. These full life cycle data are of great significance for subsequent analysis and decision-making, and can be used to evaluate the system's life, performance stability, fault prediction, etc.

[0094] Once the simulation test is completed, the system will archive all the data. This step ensures the integrity and traceability of the test data, providing a reliable basis for subsequent analysis, verification and improvement. The archived data includes not only the simulation results, but also all relevant simulation parameters, initial conditions, boundary conditions, etc., so that the test process can be reproduced when needed. This data management method helps to preserve and effectively utilize the data for a long time, and supports continuous research and development.

[0095] For example, suppose that in a simulation test of a wind turbine blade, the measured data shows that the frequency of the blade at a specific wind speed is significantly different from the simulation result. Through step S103, engineers can identify possible problems in the simulation model based on the spatial distribution comparison results and the frequency domain comparison results, such as inaccurate material stiffness settings or unreasonable aerodynamic parameters. Then, engineers can adjust these parameters and re-run the simulation test. The corrected simulation results are more consistent with the measured data, thereby improving the accuracy of the model. Finally, the system archives the corrected simulation data and process to provide data support for future research and optimization.

[0096] In general, step S103 improves the accuracy and reliability of the simulation model by correcting the simulation parameters according to the comparison results, continuing the simulation test based on the corrected model, obtaining the full life cycle data and archiving the files. This process not only solves the deviation problem between the initial model and the actual measurement data, but also provides valuable data resources for subsequent research and improvement, supporting the smooth progress of engineering decision-making and optimization work.

[0097] From the above description, it can be seen that the frequency domain response simulation and measurement data processing method provided in the embodiment of the present application can select the position corresponding to the test measurement point as the selection position of the simulation model node corresponding to the physical three-dimensional model. When the distance between the simulation finite element and the test measurement point relative to the adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test measurement point is used as the spatial point matching the test measurement point to obtain the corresponding spatial distribution comparison result; the simulation result data and the field measured frequency response data are subjected to modal correlation analysis through a preset modal matching algorithm to obtain the modal matching relationship between the simulation result data and the field measured data, and the frequency domain response comparison result is obtained according to the modal matching relationship; the simulation parameters of the physical three-dimensional model are corrected according to the spatial distribution comparison result and the frequency domain response comparison result, thereby improving the accuracy of data processing and comparison.

[0098] In one embodiment of the frequency domain response simulation and measurement data processing method of the present application, see Figure 2 , and can also include the following:

[0099] Step S201: Calculate the modal confidence coefficient for the simulation result data and the field measured frequency response data with the greatest correlation in the modal matching relationship:

[0100] Step S202: Calculate the modal confidence coefficient by using the obtained vibration mode test value in the test frequency domain data and the vibration mode simulation value of the vibration data in the simulation result data, and determine the credibility of each vibration mode according to the modal confidence coefficient.

[0101] Optionally, in this embodiment, steps S201 and S202 involve detailed comparison of the test frequency domain data with the simulation result data to determine the matching degree and credibility of the vibration characteristics. The implementation process of these two steps mainly includes point-to-point comparison of vibration data and calculation of modal confidence coefficients to evaluate the credibility of each order vibration mode.

[0102] In step S201, the system first compares the test vector with the simulation result data point-to-point. This means that for each test measurement point, the system compares its corresponding vibration data with the vibration data of the corresponding node in the simulation model. In the specific implementation process, the system reads the vibration information in the test data and the simulation data, and compares them one by one according to the spatial coordinates. Through this detailed comparison, the system can identify the difference between the two and determine the vibration comparison result. In technical principle, point-to-point comparison can most intuitively reflect the degree of match between the simulation model and the actual situation by directly comparing the vibration data at the same spatial position. This process can effectively solve the problem of local errors that may be concealed by data integration or overall comparison, and ensure the accuracy of the comparison results.

[0103] In step S202, the system further calculates the modal confidence coefficient by the test value of the vibration mode in the test vibration field data and the simulation value of the vibration mode in the simulation result data. The vibration mode refers to the vibration form of the structure under a specific mode, and each mode corresponds to a specific vibration frequency and vibration mode. The modal confidence coefficient is an indicator to measure the degree of matching between the simulation model and the test data under a specific mode. In the specific implementation process, the system first extracts the vibration data of each order vibration mode, and then calculates the difference between the test value and the simulation value of each order vibration mode. The modal confidence coefficient is calculated by mathematical methods (such as the modal assurance criterion MAC), and the system can quantitatively evaluate the matching degree of each order vibration mode. In technical principle, the modal confidence coefficient reflects the extent to which the simulation model can accurately predict the real vibration mode of the structure. Through this calculation, the system can identify which modes have a higher degree of credibility and which modes may need further correction.

[0104] In terms of the technical problems solved, steps S201 and S202 solve the problem of matching evaluation between the simulation model and the actual vibration characteristics through precise point-to-point comparison and modal confidence coefficient calculation. Point-to-point comparison ensures consistency in spatial position, while modal confidence coefficient calculation provides matching evaluation on vibration mode. Through these two steps, the system can comprehensively and accurately evaluate the reliability and accuracy of the simulation model and identify the parts that need improvement.

[0105] In terms of the technical effects achieved, through point-to-point comparison, the system can accurately identify the differences between the simulation model and the actual data at each measurement point, and provide detailed vibration comparison results. And through modal confidence coefficient calculation, the system can quantify the credibility of each order vibration mode, providing a basis for further optimization of the simulation model. These two steps combined can significantly improve the accuracy and credibility of the simulation model, ensuring that the vibration characteristics of the model in different modes are consistent with the actual situation.

[0106] For example, in the seismic design of high-rise building structures, engineers need to ensure that the simulation model can accurately predict the vibration characteristics of the building under earthquakes. Through step S201, engineers can compare the measured vibration data with the simulation data in detail to identify the errors of the simulation model at specific locations. On this basis, through step S202, engineers can calculate the modal confidence coefficients of each order of vibration mode and evaluate the matching degree of the simulation model under different modes. If the confidence coefficient of some modes is low, engineers can adjust the model parameters for these modes until the confidence coefficient reaches a satisfactory level. Finally, through this detailed comparison and correction, the simulation model can accurately reflect the vibration characteristics of the building under earthquakes and provide a reliable basis for seismic design.

[0107] In general, steps S201 and S202 comprehensively evaluate the matching and credibility of the simulation model through precise point-to-point comparison and modal confidence coefficient calculation, solve key problems in model verification, and significantly improve the accuracy and reliability of simulation results.

[0108] In one embodiment of the frequency domain response simulation and measurement data processing method of the present application, see Figure 3 , and can also include the following:

[0109] Step S301: performing three-dimensional linear interpolation according to the finite element position in the simulation result number based on the field measured real-time data to obtain the corresponding test value;

[0110] Step S302: constructing a test measurement vibration field according to the test values, and performing modal test space matching according to the test measurement vibration field.

[0111] Optionally, in this embodiment, steps S301 and S302 involve extracting information from the real-time data measured on site, obtaining corresponding test values ​​through three-dimensional linear interpolation, and then constructing the test measurement vibration field and performing modal test space matching. The implementation process of these two steps mainly includes interpolation calculation and vibration field construction, so as to perform modal analysis more accurately.

[0112] In step S301, first, the real-time data measured on site needs to be subjected to three-dimensional linear interpolation according to the finite element position in the simulation result. Specifically, the system will read the data measured on site, which are usually discrete measuring point data distributed in different positions in space. Since the node positions in the finite element model are not completely consistent with the test measuring point positions, it is necessary to map the measured data to the node positions of the finite element model through a three-dimensional linear interpolation method. Three-dimensional linear interpolation refers to estimating the value of any position in three-dimensional space through the data of existing measuring points. In terms of technical principle, three-dimensional linear interpolation uses the known values ​​of the measuring points and the spatial position relationship to calculate the value of the unknown point, so that the value of the interpolation point presents a smooth transition in space. This process ensures the consistency of the test data and the simulation model in spatial position, and provides a basis for subsequent analysis.

[0113] In step S302, the system constructs a test measurement vibration field according to the test values ​​obtained by interpolation. The vibration field refers to a collection of vibration data of each point in space, which reflects the vibration characteristics of the structure at different positions. Through the test values ​​obtained by interpolation, the system can reconstruct a complete vibration field at the node position of the finite element model. This vibration field not only contains the direct data of the measuring point, but also complements the data of the non-measuring point position through interpolation calculation. Next, the system performs modal test space matching according to the constructed test measurement vibration field. Modal test space matching refers to comparing the vibration mode of the test measurement with the vibration mode of the simulation calculation to evaluate and verify the accuracy of the simulation model. In technical principle, modal test space matching determines the degree of consistency by comparing the vibration morphology of the test and simulation in space. This process usually involves the comparison of modal shapes, frequency matching, etc., and the matching degree is evaluated by quantitative indicators (such as modal assurance criteria MAC).

[0114] In terms of the technical problems solved, steps S301 and S302 solve the consistency problem between the test data and the simulation model in terms of spatial position and vibration mode through three-dimensional linear interpolation and modal test space matching. Three-dimensional linear interpolation ensures the mapping of test data on the nodes of the finite element model, making the two comparable in spatial position. Modal test space matching further verifies the accuracy of the simulation model in terms of vibration mode, ensuring that the simulation results are consistent with the actual situation.

[0115] In terms of the technical effects achieved, through three-dimensional linear interpolation, the system can convert discrete test data into node data that matches the simulation model, providing a complete vibration field. Through modal test space matching, the system can evaluate and verify the accuracy and reliability of the simulation model. The combination of these steps not only improves the accuracy of the simulation model, but also provides a basis for subsequent model optimization and verification.

[0116] For example, in the dynamic analysis of bridge structures, engineers need to ensure that the simulation model can accurately predict the vibration characteristics of the bridge under vehicle-borne vibration. Through step S301, engineers can map the measured vibration data at different locations on the bridge to the node positions of the finite element model through three-dimensional linear interpolation to construct a complete vibration field. On this basis, through step S302, engineers can perform modal matching of the measured vibration field with the simulated vibration field to evaluate the matching degree of each mode. If the matching degree of some modes is low, engineers can further adjust the parameters of the simulation model until the matching degree reaches a satisfactory level. Ultimately, through this fine interpolation and matching, the simulation model can accurately reflect the vibration characteristics of the bridge under actual conditions, providing a reliable basis for the design and safety assessment of the bridge.

[0117] In general, steps S301 and S302 comprehensively evaluate and verify the spatial consistency and vibration mode matching of the simulation model through three-dimensional linear interpolation and modal test space matching, solve the problem of docking between test data and simulation model, and significantly improve the accuracy and reliability of simulation results.

[0118] In one embodiment of the frequency domain response simulation and measurement data processing method of the present application, see Figure 4 , and can also include the following:

[0119] Step S401: obtaining a digital 3D file of the physical 3D model to be tested and importing it into a preset test station 3D scene for virtual assembly and arrangement, thereby constructing a test virtual scene;

[0120] Step S402: setting corresponding test parameters in the test virtual scene according to the performance indicators to be verified, setting measurement sensors and making associations.

[0121] Optionally, in this embodiment, steps S401 and S402 involve digitizing the physical three-dimensional model and importing it into the virtual test scene, and then setting the test parameters and sensors according to the performance indicators to be verified. The implementation process of these two steps mainly includes virtual assembly and test parameter configuration, so as to conduct effective testing and evaluation in a digital environment.

[0122] In step S401, it is first necessary to obtain a digital three-dimensional file of the physical three-dimensional model to be tested. This file is usually generated by three-dimensional scanning, CAD modeling or other digital technologies, and contains detailed geometric information and structural features of the physical object. After obtaining the digital three-dimensional file, the system will import it into the preset three-dimensional scene of the test station. The preset three-dimensional scene of the test station is a virtual environment that simulates the spatial layout and equipment configuration of the actual test station. By importing the digital three-dimensional model into this virtual scene, the system can perform virtual assembly and layout. This process includes correctly positioning and aligning the three-dimensional model to the predetermined position of the test station to ensure that its posture and position in the virtual environment are consistent with the actual test conditions. In terms of technical principles, virtual assembly and layout use computer graphics and simulation technology to seamlessly integrate physical models with virtual environments to build a realistic experimental virtual scene.

[0123] In step S402, the system sets the corresponding test parameters in the test virtual scene according to the set performance indicators to be verified. Performance indicators may include structural strength, vibration characteristics, thermal performance, etc. According to different test purposes, the system needs to configure different test parameters. These parameters may include load conditions, boundary conditions, environmental conditions, etc. Next, the system will set up and associate measurement sensors. Measurement sensors are virtual devices used to collect test data. When setting these sensors in the virtual scene, their type, quantity and location need to be determined according to the test requirements. The system associates sensors and test parameters to ensure that the required data can be collected in real time during the test. In terms of technical principles, sensor settings and associations use virtual simulation technology to simulate the functions of physical sensors in a virtual environment to ensure the accuracy and reliability of test data.

[0124] In terms of the technical problems solved, steps S401 and S402 solve the problem of testing in a virtual environment after the physical model is digitized through virtual assembly and test parameter configuration. Virtual assembly ensures the correct positioning and layout of the digital 3D model in the virtual scene, so that the test conditions are consistent with the actual conditions. Test parameter configuration and sensor association ensure the controllability of the test process and the accuracy of data collection, providing a reliable basis for performance verification.

[0125] In terms of technical effects, by digitizing the physical 3D model and importing it into the virtual test scene, the system can conduct realistic tests and evaluations in a virtual environment. Virtual assembly and layout not only reduce the time and cost of physical tests, but also improve the flexibility and repeatability of the tests. By setting test parameters and sensors, the system can accurately control the test conditions and collect and analyze test data in real time. The combination of these steps not only improves the efficiency and accuracy of the test, but also provides a safe and controllable test platform for performance verification of complex systems.

[0126] For example, in the automotive manufacturing industry, engineers need to verify the performance of new car models under different working conditions. Through step S401, engineers can import the digital three-dimensional model of the car into the virtual test scene for virtual assembly and layout. This process can simulate the actual posture and position of the car on the test station. Next, through step S402, engineers can set corresponding test parameters according to the performance indicators to be verified (such as collision safety, aerodynamic performance, etc.) and arrange sensors in the virtual scene. These sensors can simulate the measuring equipment in the real test and collect test data in real time. Through this virtual test, engineers can obtain detailed performance data and optimize and improve the model without conducting actual physical tests.

[0127] In general, steps S401 and S402 construct a realistic virtual test scene through virtual assembly and test parameter configuration, solve the test difficulties of physical models in a virtual environment, and significantly improve the efficiency and accuracy of the test.

[0128] In one embodiment of the frequency domain response simulation and measurement data processing method of the present application, see Figure 5 , and can also include the following:

[0129] Step S501: running a simulation test of the test virtual scene according to the test scene requirements sent by the user, storing and marking the collected actual test data, and obtaining on-site real-time data;

[0130] Step S502: loading and parsing the various physical quantity data obtained by simulation and making them correspond to the real-time data measured on site to obtain simulation result data.

[0131] Optionally, in this embodiment, steps S501 and S502 involve running a simulation test of the test virtual scene according to user needs, collecting and storing actual test data, and then matching and analyzing these data with the simulation data. The implementation process of these two steps mainly includes simulation test running, data collection and storage, and data analysis and matching, so as to better understand and verify the test results.

[0132] In step S501, it is first necessary to run the simulation test of the test virtual scene according to the test scene requirements sent by the user. User requirements usually include specific test conditions, performance indicators, test environment and other information. The system will set the initial conditions and parameters of the virtual scene according to these requirements, and then start the simulation test. Simulation testing is to simulate the actual test process based on a digital three-dimensional model and preset test parameters using computer simulation technology. During the simulation test, the system will collect various physical quantity data such as stress, strain, temperature, vibration, etc. through virtual sensors. The collected data will be stored and annotated in real time, including the timestamp, collection location, and collection conditions of the data. These data constitute the real-time data measured on site, reflecting the changes of various physical quantities during the simulation test. In terms of technical principles, simulation testing simulates various complex working conditions in actual tests through numerical calculations and physical modeling, and collects data in real time through virtual sensors to ensure the integrity and accuracy of the data.

[0133] In step S502, the system loads and analyzes the various physical quantity data obtained by simulation, and performs corresponding analysis with the real-time data measured on site. The analysis process includes converting the simulation data from the original format into a readable numerical form, and classifying and sorting it according to the acquisition location, time, etc. Next, the system will correspond the parsed simulation data with the real-time data measured on site. This correspondence process refers to matching the two according to the same physical quantity, the same location and time, etc., for comparison and analysis. Through this correspondence analysis, the system can evaluate the consistency of simulation data and measured data, and verify the accuracy and reliability of the simulation model. In terms of technical principles, data correspondence and analysis use statistical analysis and data mining techniques to compare simulation and measured data, find out the differences and rules between the two, and evaluate the performance of the simulation model.

[0134] In terms of the technical problems solved, steps S501 and S502 solve the problem of verifying user needs and simulation results through simulation testing and data correspondence analysis. The simulation test runs according to user needs to ensure the flexibility and controllability of the test conditions; data collection and storage annotation ensure the integrity and traceability of the data. Through parsing and correspondence analysis, the system can evaluate the consistency of simulation data and measured data and verify the accuracy of the simulation model.

[0135] In terms of the technical effects achieved, through simulation testing and data correspondence analysis, the system can efficiently conduct tests and verifications in a virtual environment. Simulation testing not only reduces the time and cost of physical tests, but also improves the safety and controllability of tests. Data collection and storage annotation ensure the integrity and traceability of test data, providing a reliable basis for subsequent analysis. Through parsing and correspondence analysis, the system can quantitatively evaluate the performance of the simulation model, identify the deficiencies and improvement directions of the model, and further optimize the simulation model.

[0136] For example, in the performance verification of aircraft engines, engineers need to verify the working status of the engine under different working conditions. Through step S501, engineers can set the initial conditions and parameters of the virtual scene according to the user's test scenario requirements and run the simulation test. During the simulation test, the system will collect data such as engine temperature, pressure, and speed through virtual sensors, and store and annotate them. Next, through step S502, the system loads and analyzes the simulation data, and performs corresponding analysis with the real-time data measured on site. By comparing the simulation and measured data, engineers can evaluate the accuracy of the simulation model, find out the differences between the simulation results and the actual situation, and adjust and optimize the simulation model accordingly.

[0137] In general, steps S501 and S502 construct an efficient and reliable test and verification process through simulation testing and data correspondence analysis, solve the problem of verifying user needs and simulation results, and improve the accuracy and reliability of simulation testing.

[0138] In one embodiment of the frequency domain response simulation and measurement data processing method of the present application, see Figure 6 , and can also include the following:

[0139] Step S601: modifying the position, size and geometric parameters of the physical three-dimensional model in the test virtual scene according to the spatial distribution comparison result and the frequency domain comparison result;

[0140] Step S602: modifying the three-dimensional model material parameters and constraint conditions of the physical three-dimensional model in the test virtual scene according to the spatial distribution comparison result and the frequency domain comparison result.

[0141] Optionally, in this embodiment, steps S601 and S602 involve modifying the physical three-dimensional model to ensure that its position, size, geometric parameters, material parameters and constraints in the test virtual scene are consistent with the actual situation. The implementation process of these two steps mainly includes comparison result analysis, model parameter adjustment and verification to improve the accuracy and reliability of the simulation model.

[0142] In step S601, first, the position, size and geometric parameters of the physical three-dimensional model in the test virtual scene need to be corrected according to the spatial distribution comparison results and the frequency domain comparison results. The spatial distribution comparison results refer to the difference results obtained after comparing and analyzing the position and distribution of the actual physical model in space with the virtual model. The frequency domain comparison results refer to the difference results obtained after comparing and analyzing the vibration characteristics of the actual physical model with the virtual model. Based on these comparison results, the system will identify the differences in position, size and geometric parameters between the virtual model and the actual model. During the technical implementation process, the system will use these comparison results to adjust the virtual model, such as correcting the position offset of the model, adjusting the inconsistency of the geometric shape, correcting the dimensional error, etc. These correction operations are based on computer graphics and numerical analysis techniques to ensure that the virtual model is highly consistent with the actual model in terms of spatial distribution and vibration characteristics.

[0143] In step S602, the system corrects the material parameters and constraints of the three-dimensional model of the physical three-dimensional model in the test virtual scene according to the spatial distribution comparison results and the frequency domain comparison results. The material parameters include the density, elastic modulus, Poisson's ratio, etc. of the model, which determine the response characteristics of the model under force and vibration. The constraints include the boundary conditions and loading conditions of the model in the virtual scene, which affect the behavior of the model during the simulation process. According to the comparison results, the system will identify the differences in material properties and constraints between the virtual model and the actual model, and make corresponding corrections. For example, if the comparison results show that the frequency of the virtual model is inconsistent with the actual model, the system will adjust the material parameters to match the actual situation; if the comparison results show that the deformation of the model under certain constraints is inconsistent with the actual situation, the system will adjust the boundary conditions or loading conditions. In the process of technical implementation, the system uses numerical optimization and material modeling technology to accurately correct the material parameters and constraints of the virtual model to ensure the accuracy of the simulation results.

[0144] In terms of the technical problems solved, steps S601 and S602 solved the inconsistency between the virtual model and the actual model in terms of position, size, geometric parameters, material parameters and constraints by comparing the results and correcting the parameters. The spatial distribution comparison and frequency domain comparison results provide accurate difference information, guiding the system to perform targeted correction operations to ensure the accuracy and reliability of the virtual model.

[0145] In terms of technical effects, the system can significantly improve the accuracy and consistency of the virtual model through the analysis and correction of the spatial distribution comparison and frequency domain comparison results. The correction of position, size and geometric parameters ensures the consistency of the model in spatial distribution, and the correction of material parameters and constraints ensures that the response characteristics of the model under force and vibration are consistent with the actual situation. Through these correction operations, the system can conduct more accurate tests and evaluations in the virtual environment, improving the accuracy and reliability of simulation tests.

[0146] For example, in the verification of the seismic performance of building structures, engineers need to ensure that the various parameters of the virtual model are consistent with the actual structure. Through step S601, engineers can correct the position, size and geometric parameters of the virtual model based on the spatial distribution comparison results and vibration comparison results to ensure its consistency with the actual structure in spatial distribution and vibration characteristics. Next, through step S602, engineers can correct the material parameters and constraints of the virtual model based on the comparison results to ensure that its performance in the seismic simulation test is consistent with the actual situation. Through these correction operations, engineers can perform accurate seismic performance evaluation in a virtual environment and provide reliable data support for structural design and optimization.

[0147] In general, steps S601 and S602 construct a high-precision virtual model correction process through comparative result analysis and parameter correction, solve the inconsistency problem between the virtual model and the actual model, and improve the accuracy and reliability of the simulation test.

[0148] In one embodiment of the frequency domain response simulation and measurement data processing method of the present application, see Figure 7 , and can also include the following:

[0149] Step S701: performing long-term dynamic simulation in the virtual test environment after parameter correction to obtain full life cycle data of the test;

[0150] Step S702: Classify and archive the obtained test life cycle data according to different physical quantity categories.

[0151] Optionally, in this embodiment, steps S701 and S702 involve performing long-term dynamic simulation in the modified virtual test environment and classifying and archiving the test life cycle data. The implementation process of these two steps mainly includes long-term simulation testing, data collection and storage, and data classification and archiving, so as to comprehensively evaluate the performance of the system during its life cycle.

[0152] In step S701, it is first necessary to perform long-term dynamic simulation in a virtual test environment after parameter correction. Parameter correction refers to the adjustment of the position, size, geometric parameters, material parameters and constraints of the virtual model in the previous steps to make it as consistent as possible with the actual situation. Long-term dynamic simulation refers to simulating the operating status of the system under various working conditions over a long period of time to evaluate its performance throughout its life cycle. During the simulation process, the system will collect various physical quantity data, such as temperature, pressure, stress, strain, vibration, etc. These data reflect the operating status and response characteristics of the system at different time points and under different working conditions. In terms of technical principles, long-term dynamic simulation simulates various complex working conditions of the system during its life cycle through numerical calculation and physical modeling, and collects data in real time through virtual sensors to ensure the integrity and continuity of the data.

[0153] In step S702, the system will classify and archive the obtained test life cycle data according to different physical quantity categories. Classification and archiving refers to classifying the various types of collected data according to their physical quantity properties and storing them in the corresponding database. For example, temperature data, pressure data, stress data, etc. are archived in different database tables respectively. Through this classification and archiving, data can be easily queried, analyzed and managed. During the technical implementation process, the system will first pre-process the collected raw data, including denoising, interpolation, format conversion, etc., and then classify and store them according to the physical quantity category. The classified storage database includes not only numerical data, but also metadata such as the data's timestamp, collection location, and working conditions, to facilitate subsequent analysis and application.

[0154] In terms of the technical problems solved, steps S701 and S702 solve the problems of system life cycle performance evaluation and data management in the virtual test environment through long-term dynamic simulation and data classification and archiving. Long-term dynamic simulation can comprehensively evaluate the performance of the system during its life cycle and identify potential performance bottlenecks and potential faults; data classification and archiving ensure the orderly storage and management of data, providing a reliable foundation for subsequent analysis and application.

[0155] In terms of technical effects, through long-term dynamic simulation and data classification and archiving, the system can conduct comprehensive performance evaluation and data management in a virtual environment. Long-term dynamic simulation can not only simulate the operating status of the system under various working conditions, but also identify the performance change patterns and potential problems of the system throughout its life cycle. Data classification and archiving ensures the orderly storage and management of data and improves the efficiency of data query and analysis.

[0156] For example, in the life assessment of a wind turbine, engineers need to evaluate the performance changes and failure risks of the generator during long-term operation. Through step S701, engineers can perform long-term dynamic simulation in a virtual test environment after parameter correction, simulate the operating status of the generator under different wind speeds, loads, temperatures and other working conditions, and collect various physical quantity data. Next, through step S702, the system classifies and archives the collected full life cycle data according to the physical quantity category, such as storing temperature data, vibration data, stress data, etc. in different database tables. Through this classification and archiving, engineers can easily query and analyze data of different physical quantities, evaluate the performance change patterns and potential failure risks of the generator, and formulate corresponding maintenance and optimization plans.

[0157] In general, steps S701 and S702 build a comprehensive performance evaluation and data management process through long-term dynamic simulation and data classification and archiving, solving the problem of system life cycle performance evaluation and data management in the virtual test environment. Through these steps, engineers can conduct comprehensive performance evaluation and data analysis in the virtual environment, providing strong support for product design and optimization.

[0158] In order to improve the accuracy of data processing and comparison and realize the real-time integration and analysis of simulation data and test data, the present application provides an embodiment of a frequency domain response simulation and measurement data processing method device for realizing all or part of the contents of the frequency domain response simulation and measurement data processing method, see Figure 8 The frequency domain response simulation and measurement data processing method and device specifically include the following contents:

[0159] The scene building 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 the test parameters in the test virtual scene, and load and associate the measurement channels; collect the on-site measured real-time data in the test virtual scene according to the test scene requirements sent by the user, and load and analyze the simulation result data corresponding to the on-site measured real-time data;

[0160] The comparison and analysis module 20 is used to select the position corresponding to the test measurement point as the selection position of the 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 measured data. When the distance between the simulation finite element and the test measurement point relative to the distance between adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test measurement point is used as the spatial point matching the test measurement point to obtain the corresponding spatial distribution comparison result; the frequency excitation step length of the simulation model corresponding to the physical three-dimensional model is set to be consistent with the above, so that the simulation model outputs data at the same frequency interval as the field measured data, and a modal correlation analysis is performed on the simulation result data and the field measured frequency response data through a preset modal matching algorithm to obtain a modal matching relationship between the simulation result data and the field measured data, and a frequency domain response result comparison is performed on the simulation result data with the largest correlation in the modal matching relationship and the field measured frequency response data to obtain a frequency domain comparison result;

[0161] The parameter correction module 30 is used to correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the spatial distribution comparison results and the frequency domain comparison results, and continue the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0162] From the above description, it can be seen that the frequency domain response simulation and measurement data processing method and device provided in the embodiment of the present application can select the position corresponding to the test measurement point as the selection position of the simulation model node corresponding to the physical three-dimensional model. When the distance between the simulation finite element and the test measurement point relative to the adjacent points in the simulation model is lower than the threshold, the finite element node closest to the test measurement point is used as the spatial point matching the test measurement point to obtain the corresponding spatial distribution comparison result; the simulation result data and the field measured frequency response data are subjected to modal correlation analysis through a preset modal matching algorithm to obtain the modal matching relationship between the simulation result data and the field measured data, and the frequency domain response comparison result is obtained according to the modal matching relationship; the simulation parameters of the physical three-dimensional model are corrected according to the spatial distribution comparison result and the frequency domain response comparison result, thereby improving the accuracy of data processing and comparison.

[0163] From the hardware level, in order to improve the accuracy of data processing and comparison and realize the real-time integration and analysis of simulation data and test data, the present application provides an embodiment of an electronic device for realizing all or part of the contents of the frequency domain response simulation and measurement data processing method, and the electronic device specifically includes the following contents:

[0164] 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 frequency domain response simulation and measurement data processing method device and the core business system, user terminal and related database and other related equipment; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., but the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented with reference to the embodiment of the frequency domain response simulation and measurement data processing method in the embodiment, and the embodiment of the frequency domain response simulation and measurement data processing method device, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0165] 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.

[0166] In practical applications, part of the frequency domain response simulation and measurement data processing method 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.

[0167] 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.

[0168] 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.

[0169] In one embodiment, the frequency domain response simulation and measurement data processing method functions may be integrated into the central processing unit 9100. The central processing unit 9100 may be configured to perform the following control:

[0170] Step S101: Load the physical three-dimensional model into the preset three-dimensional scene of the test station and perform virtual assembly to construct a test virtual scene, configure the test parameters in the test virtual scene, and load and associate the measurement channels; collect the on-site measured real-time data in the test virtual scene according to the test scene requirements sent by the user, and load and analyze the simulation result data corresponding to the on-site measured real-time data;

[0171] Step S102: selecting a position corresponding to the test measuring 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 measured data; when the distance between the simulation finite element and the test measuring point relative to the distance between adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test measuring point is used as the spatial point matching the test measuring point to obtain a corresponding spatial distribution comparison result; setting the frequency excitation step length of the simulation model corresponding to the physical three-dimensional model to be consistent with the above, so that the simulation model outputs data at the same frequency interval as the field measured data; performing modal correlation analysis on the simulation result data and the field measured frequency response data through a preset modal matching algorithm to obtain a modal matching relationship between the simulation result data and the field measured data; performing a frequency domain response result comparison on the simulation result data with the largest correlation in the modal matching relationship and the field measured frequency response data to obtain a frequency domain comparison result;

[0172] Step S103: modifying the simulation parameters of the physical three-dimensional model in the test virtual scene according to the spatial distribution comparison result and the frequency domain comparison result, and continuing the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0173] From the above description, it can be seen that the electronic device provided in the embodiment of the present application selects the position corresponding to the test measurement point as the selection position of the simulation model node corresponding to the physical three-dimensional model. When the distance between the simulation finite element distance test measurement point and the adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test measurement point is used as the spatial point matching the test measurement point to obtain the corresponding spatial distribution comparison result; a modal correlation analysis is performed on the simulation result data and the field measured frequency response data through a preset modal matching algorithm to obtain the modal matching relationship between the simulation result data and the field measured data, and a frequency domain response comparison result is obtained according to the modal matching relationship; the simulation parameters of the physical three-dimensional model are corrected according to the spatial distribution comparison result and the frequency domain response comparison result, thereby improving the accuracy of data processing and comparison.

[0174] In another embodiment, the frequency domain response simulation and measurement data processing method device can be configured separately from the central processing unit 9100. For example, the frequency domain response simulation and measurement data processing method device can be configured as a chip connected to the central processing unit 9100, and the frequency domain response simulation and measurement data processing method functions are realized through the control of the central processing unit.

[0175] like Fig. 9 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Fig. 9 In addition, the electronic device 9600 may also include Fig. 9 For components not shown, reference may be made to the prior art.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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, etc. 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.

[0183] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps in the frequency domain response simulation and measurement data processing method in the above embodiments, 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 in the frequency domain response simulation and measurement data processing method in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0184] Step S101: Load the physical three-dimensional model into the preset three-dimensional scene of the test station and perform virtual assembly to construct a test virtual scene, configure the test parameters in the test virtual scene, and load and associate the measurement channels; collect the on-site measured real-time data in the test virtual scene according to the test scene requirements sent by the user, and load and analyze the simulation result data corresponding to the on-site measured real-time data;

[0185] Step S102: selecting a position corresponding to the test measuring 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 measured data; when the distance between the simulation finite element and the test measuring point relative to the distance between adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test measuring point is used as the spatial point matching the test measuring point to obtain a corresponding spatial distribution comparison result; setting the frequency excitation step length of the simulation model corresponding to the physical three-dimensional model to be consistent with the above, so that the simulation model outputs data at the same frequency interval as the field measured data; performing modal correlation analysis on the simulation result data and the field measured frequency response data through a preset modal matching algorithm to obtain a modal matching relationship between the simulation result data and the field measured data; performing a frequency domain response result comparison on the simulation result data with the largest correlation in the modal matching relationship and the field measured frequency response data to obtain a frequency domain comparison result;

[0186] Step S103: modifying the simulation parameters of the physical three-dimensional model in the test virtual scene according to the spatial distribution comparison result and the frequency domain comparison result, and continuing the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0187] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application selects the position corresponding to the test measurement point as the selection position of the simulation model node corresponding to the physical three-dimensional model. When the distance between the simulation finite element distance test measurement point and the adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test measurement point is used as the spatial point matching the test measurement point to obtain the corresponding spatial distribution comparison result; a modal correlation analysis is performed on the simulation result data and the field measured frequency response data through a preset modal matching algorithm to obtain the modal matching relationship between the simulation result data and the field measured data, and a frequency domain response comparison result is obtained according to the modal matching relationship; the simulation parameters of the physical three-dimensional model are corrected according to the spatial distribution comparison result and the frequency domain response comparison result, thereby improving the accuracy of data processing and comparison.

[0188] The embodiments of the present application also provide a computer program product capable of implementing all the steps of the frequency domain response simulation and measurement data processing method in the above embodiments, where the execution subject is a server or a client. When the computer program / instruction is executed by a processor, the steps of the frequency domain response simulation and measurement data processing method are implemented. For example, the computer program / instruction implements the following steps:

[0189] Step S101: Load the physical three-dimensional model into the preset three-dimensional scene of the test station and perform virtual assembly to construct a test virtual scene, configure the test parameters in the test virtual scene, and load and associate the measurement channels; collect the on-site measured real-time data in the test virtual scene according to the test scene requirements sent by the user, and load and analyze the simulation result data corresponding to the on-site measured real-time data;

[0190] Step S102: selecting a position corresponding to the test measuring 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 measured data; when the distance between the simulation finite element and the test measuring point relative to the distance between adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test measuring point is used as the spatial point matching the test measuring point to obtain a corresponding spatial distribution comparison result; setting the frequency excitation step length of the simulation model corresponding to the physical three-dimensional model to be consistent with the above, so that the simulation model outputs data at the same frequency interval as the field measured data; performing modal correlation analysis on the simulation result data and the field measured frequency response data through a preset modal matching algorithm to obtain a modal matching relationship between the simulation result data and the field measured data; performing a frequency domain response result comparison on the simulation result data with the largest correlation in the modal matching relationship and the field measured frequency response data to obtain a frequency domain comparison result;

[0191] Step S103: modifying the simulation parameters of the physical three-dimensional model in the test virtual scene according to the spatial distribution comparison result and the frequency domain comparison result, and continuing the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0192] From the above description, it can be seen that the computer program product provided in the embodiment of the present application selects the position corresponding to the test measurement point as the selection position of the simulation model node corresponding to the physical three-dimensional model. When the distance between the simulation finite element distance test measurement point and the adjacent points in the simulation model is lower than a threshold, the finite element node closest to the test measurement point is used as the spatial point matching the test measurement point to obtain the corresponding spatial distribution comparison result; a modal correlation analysis is performed on the simulation result data and the field measured frequency response data through a preset modal matching algorithm to obtain the modal matching relationship between the simulation result data and the field measured data, and a frequency domain response comparison result is obtained according to the modal matching relationship; the simulation parameters of the physical three-dimensional model are corrected according to the spatial distribution comparison result and the frequency domain response comparison result, thereby improving the accuracy of data processing and comparison.

[0193] It will be appreciated 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. 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.

[0194] 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.

[0195] 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 including an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0197] 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 frequency domain response simulation and measurement data processing method, characterized in that: The method comprises: The physical three-dimensional model is loaded into a preset three-dimensional scene of the test station and virtually assembled to construct a test virtual scene, test parameters are configured in the test virtual scene, and measurement channels are loaded and associated; field measured 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 measured data is loaded and parsed; 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 measured data; in the simulation model, when the distance between the simulation finite element and the test measurement point relative to the distance between adjacent points is lower than a threshold, the finite element node closest to the test measurement point is used as a spatial point matching the test measurement point to obtain a corresponding spatial distribution comparison result; setting the frequency excitation step of the simulation model corresponding to the physical three-dimensional model to be consistent with the field measured data, so that the simulation model outputs data at the same frequency interval as the field measured data; performing modal correlation analysis on the simulation result data and the field measured frequency response data through a preset modal matching algorithm to obtain a modal matching relationship between the simulation result data and the field measured data; performing a frequency domain response result comparison between the simulation result data with the greatest correlation in the modal matching relationship and the field measured frequency response data to obtain a frequency domain comparison result; The position size and geometric parameters of the physical three-dimensional model in the test virtual scene are corrected according to the spatial distribution comparison results and the frequency domain comparison results. The three-dimensional model material parameters and constraint conditions of the physical three-dimensional model in the test virtual scene are corrected according to the spatial distribution comparison results and the frequency domain comparison results. Simulation tests are continued based on the physical three-dimensional model to obtain the test full life cycle data and archive them.

2. The frequency domain response simulation and measurement data processing method according to claim 1, characterized in that: The frequency domain response result comparison between the simulation result data with the greatest correlation in the modal matching relationship and the field measured frequency response data is performed to obtain the frequency domain comparison result, including: Calculating the modal confidence coefficient for the simulation result data with the greatest correlation in the modal matching relationship and the field measured frequency response data; The credibility of each vibration mode is determined according to the modal confidence coefficient.

3. The frequency domain response simulation and measurement data processing method according to claim 1, characterized in that: Also includes: Based on the real-time data measured on site, three-dimensional linear interpolation is performed according to the finite element position in the simulation result number to obtain the corresponding test value; A test measurement vibration field is constructed according to the test values, and modal test space matching is performed according to the test measurement vibration field.

4. The frequency domain response simulation and measurement data processing method according to claim 1, characterized in that: 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, including: Obtain a digital 3D file of the physical 3D model to be tested and import it into a preset 3D scene of a test station for virtual assembly and arrangement, thereby constructing a virtual test scene; According to the performance index to be verified, corresponding test parameters are set in the test virtual scene, and measurement sensors are set and associated.

5. The frequency domain response simulation and measurement data processing method according to claim 1, characterized in that: The collecting of field measured data in the test virtual scene according to the test scene requirements sent by the user, and loading and parsing simulation result data corresponding to the field measured data, include: Run the simulation test of the test virtual scene according to the test scene requirements sent by the user, store and annotate the collected actual test data, and obtain the on-site measured data; After loading and parsing the various physical quantity data obtained by simulation, a corresponding analysis is performed with the field measured data to obtain simulation result data.

6. The frequency domain response simulation and measurement data processing method according to claim 1, characterized in that: The simulation test is continued based on the physical three-dimensional model to obtain the test life cycle data and file it, including: Carry out long-term dynamic simulation in a virtual test environment with corrected parameters to obtain data for the entire test life cycle; The obtained data of the entire life cycle of the test are classified and archived according to different physical quantity categories.

7. A frequency domain response simulation and measurement data processing method and device, characterized in that: The device comprises: A scene building 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 field measured 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 measured data; A comparison and analysis module is used to select 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 measured data. In the simulation model, when the distance between the simulation finite element and the test measurement point relative to the distance between adjacent points is lower than a threshold, the finite element node closest to the test measurement point is used as a spatial point matching the test measurement point to obtain a corresponding spatial distribution comparison result; the frequency excitation step length of the simulation model corresponding to the physical three-dimensional model is set to be consistent with the field measured data, so that the simulation model outputs data at the same frequency interval as the field measured data, and a modal correlation analysis is performed on the simulation result data and the field measured frequency response data through a preset modal matching algorithm to obtain a modal matching relationship between the simulation result data and the field measured data, and the simulation result data with the largest correlation in the modal matching relationship is compared with the field measured frequency response data for frequency domain response results to obtain a frequency domain comparison result; A parameter correction module is used to correct the position size and geometric parameters of the physical three-dimensional model in the test virtual scene according to the spatial distribution comparison results and the frequency domain comparison results, to correct the three-dimensional model material parameters and constraints of the physical three-dimensional model in the test virtual scene according to the spatial distribution comparison results and the frequency domain comparison results, and to continue the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the frequency domain response simulation and measurement data processing method described in any one of claims 1 to 6 are implemented.

9. 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 frequency domain response simulation and measurement data processing method described in any one of claims 1 to 6 are implemented.

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