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

Abstract

The embodiment of the present application provides a scalar field simulation and real-time data processing method and device, the method comprising: performing test time matching of scalar field data according to on-site measured real-time data and simulation result data, performing point-to-point comparison between the obtained test scalar field data and the scalar data in the simulation result data, and determining the corresponding scalar distribution comparison result; 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, performing average scalar processing on similar areas in the simulation model, and using the simulation model after the average scalar processing as the spatial point matching the test measurement point to obtain the corresponding scalar comparison result; correcting the simulation parameters of the physical three-dimensional model in the test virtual scene according to the scalar distribution comparison result and the scalar comparison result; the present application can effectively combine simulation data and real-time data to achieve more accurate product performance evaluation and optimization.

Description

Technical Field

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

[0002] In modern industry and engineering, the combination of simulation technology and test technology has become an important means to improve product design and performance. Through simulation technology, the behavior of real physical systems can be simulated on computers to predict and optimize product performance; while through test technology, the performance and reliability of products can be verified under actual conditions. However, current technology faces many challenges in combining simulation data with real-time data and has obvious limitations.

[0003] In the traditional product development process, simulation data and real-time data often exist independently. Simulation data is usually obtained through numerical simulation methods on computers, while real-time data is collected during actual tests. This independent working method brings several significant problems. First, the problem of data inconsistency is more prominent. Since there may be differences between the simulation model and the actual test conditions, there is often inconsistency between simulation data and real-time data. This inconsistency will directly affect the accuracy of product performance evaluation, and then affect product optimization and improvement.

[0004] Secondly, the complexity of data management is also an important issue. In traditional methods, simulation data and real-time data need to be managed and stored separately, which increases the complexity of data management. Especially 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.

[0005] In addition, the difficulty of data integration cannot be ignored. Integrating and comparing simulation data and real-time data requires complex data processing and analysis steps. This not only increases the workload, but also places higher requirements on the skill level of technicians. Data from different sources and formats need to be converted and matched, which is not only cumbersome, but also easily introduces new errors, affecting the final analysis results.

[0006] Another prominent phenomenon is the problem of poor real-time performance. Traditional methods make it difficult to achieve real-time comparison and analysis of simulation data and real-time data, which limits its 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.

[0007] In actual operation, the acquisition of real-time data and the loading and analysis of simulation data are also complex processes. Real-time data needs to be collected in real time during the actual test process, and these data are often multi-dimensional and multi-channel. The loading and analysis of simulation data requires matching and comparing real-time data with simulation results. 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.

[0008] In the prior art, there are also many challenges in the comparison process of test and simulation data. Point-to-point comparison of test scalar field data and simulation result data requires precise time matching and space matching. Any error in this process will affect the accuracy of the comparison results. In addition, the node selection and parameter output of the simulation model also 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.

[0009] In general, the existing technologies have many problems in the combination and application of simulation data and real-time data, such as inconsistent data, complex management, difficult integration, and poor real-time performance. 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 effectively combine simulation data and real-time data to achieve more accurate product performance evaluation and optimization is an important issue that needs 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 scalar field simulation and real-time data processing method and device, which can effectively combine simulation data and real-time data to achieve more accurate product performance evaluation and optimization.

[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 scalar field simulation and real-time 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] According to the on-site measured real-time data and the simulation result data, the scalar field data test time is matched, and the obtained test scalar field data is compared point-to-point with the scalar data in the simulation result data to determine the corresponding scalar distribution comparison result; the position corresponding to the test measurement point is selected 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 on-site measured real-time data, average scalar processing is performed on similar areas in the simulation model, and the simulation model after the average scalar processing is used as the spatial point matching the test measurement point to obtain the corresponding scalar comparison result;

[0015] The simulation parameters of the physical three-dimensional model in the test virtual scene are corrected according to the scalar distribution comparison result and the scalar 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 scalar field data test time matching is performed based on the field measured real-time data and the simulation result data, including:

[0017] Setting 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 real-time data measured on site;

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

[0019] Furthermore, the obtained experimental scalar field data is compared point-to-point with the scalar data in the simulation result data to determine the corresponding scalar distribution comparison result, including:

[0020] By calculating the root mean square error between the obtained test scalar field data and the scalar data in the simulation result data, an index of the difference between the simulation result data and the field measured real-time data is determined;

[0021] The linear correlation between the simulated scalar field and the test scalar field is calculated by a preset correlation coefficient algorithm, and the maximum difference between the simulated scalar field and the test scalar field is calculated by a maximum deviation algorithm.

[0022] Furthermore, the performing average scalar processing on similar regions in the simulation model includes:

[0023] Constructing a measurement value data set according to the three-dimensional coordinates of the measurement points in the simulation model and the corresponding scalar measurement values, and constructing a simulation result data set according to the three-dimensional coordinates of the simulation finite element nodes in the simulation result data and the corresponding scalar simulation values;

[0024] The spatial range defined by the preset average scalar processing spatial range threshold value and the three-dimensional coordinates of the measuring point is taken as the envelope, the simulation finite element nodes and their corresponding scalar values ​​in the spatial range are selected in the simulation result data set, and the average value of the simulation result data is calculated.

[0025] Furthermore, the method of using the simulation model after the average scalar processing as a spatial point matching the test measurement point to obtain a corresponding scalar comparison result also includes:

[0026] 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;

[0027] A test measurement scalar field is constructed according to the test values, and test space matching of scalar field data is performed according to the test measurement scalar field.

[0028] Furthermore, the three-dimensional linear interpolation is performed according to the finite element position in the simulation result number based on the on-site measured real-time data to obtain the corresponding test value, including:

[0029] Constructing a measurement value data set according to the three-dimensional coordinates of the measurement points in the simulation model and the corresponding scalar measurement values, and constructing a simulation result data set according to the three-dimensional coordinates of the simulation finite element nodes in the simulation result data and the corresponding scalar simulation values;

[0030] Three-dimensional linear interpolation is performed according to the simulation result data set to obtain corresponding test values.

[0031] Further, performing three-dimensional linear interpolation according to the simulation result data set to obtain corresponding test values ​​includes:

[0032] Traversing the left and right finite element nodes in the simulation result data set;

[0033] Extract the node coordinate value, in the coordinate value A preset number of test measurement points are selected in three directions according to the nearest principle as interpolation reference points for three-dimensional linear interpolation.

[0034] In a second aspect, the present application provides a scalar field simulation and real-time data processing 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] An analysis and comparison module is used to match the test time of scalar field data according to the field measured real-time data and the simulation result data, and to determine the corresponding scalar distribution comparison result by performing point-to-point comparison between the obtained test scalar field data and the scalar data in the simulation result data; 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 real-time data, perform average scalar processing on similar areas in the simulation model, and use the simulation model after the average scalar processing as the spatial point matching the test measurement point to obtain the corresponding scalar comparison result;

[0037] The simulation test module is used to correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the scalar distribution comparison result and the scalar comparison result, and continue the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[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 scalar field simulation and real-time 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 scalar field simulation and real-time 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 scalar field simulation and real-time data processing method.

[0041] It can be seen from the above technical scheme that the present application provides a scalar field simulation and real-time data processing method and device, by matching the test time of scalar field data according to the real-time data measured on site and the simulation result data, and performing point-to-point comparison between the obtained test scalar field data and the scalar data in the simulation result data to determine the corresponding scalar distribution comparison result; 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, performing average scalar processing on similar areas in the simulation model, and using the simulation model after the average scalar processing as the spatial point matching the test measurement point to obtain the corresponding scalar comparison result; correcting the simulation parameters of the physical three-dimensional model in the test virtual scene according to the scalar distribution comparison result and the scalar comparison result, thereby being able to effectively combine the simulation data and the real-time data to achieve more accurate product performance evaluation and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 One of the flowcharts of the scalar field simulation and real-time data processing method in the embodiment of the present application;

[0044] Figure 2 The second flowchart of the scalar field simulation and real-time data processing method in the embodiment of the present application;

[0045] Figure 3 The third flowchart of the scalar field simulation and real-time data processing method in the embodiment of the present application;

[0046] Figure 4 This is a fourth flow chart of the scalar field simulation and real-time data processing method in the embodiment of the present application;

[0047] Figure 5 FIG5 is a flow chart of the scalar field simulation and real-time data processing method in the embodiment of the present application;

[0048] Figure 6 This is the sixth flow chart of the scalar field simulation and real-time data processing method in the embodiment of the present application;

[0049] Figure 7 FIG7 is a flow chart of the scalar field simulation and real-time data processing method in the embodiment of the present application;

[0050] Figure 8is a structural diagram of a scalar field simulation and real-time data processing 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 scalar field simulation and real-time data processing method and device, which matches the test time of scalar field data according to the real-time data measured on site and the simulation result data, and performs point-to-point comparison between the obtained test scalar field data and the scalar data in the simulation result data to determine the corresponding scalar distribution comparison result; 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, performs average scalar processing on similar areas in the simulation model, and uses the simulation model after the average scalar processing as the spatial point matching the test measurement point to obtain the corresponding scalar comparison result; and corrects the simulation parameters of the physical three-dimensional model in the test virtual scene according to the scalar distribution comparison result and the scalar comparison result, thereby effectively combining the simulation data and the real-time data to achieve more accurate product performance evaluation and optimization.

[0057] In order to effectively combine simulation data and real-time data to achieve more accurate product performance evaluation and optimization, the present application provides an embodiment of a scalar field simulation and real-time data processing method, see Figure 1 The scalar field simulation and real-time 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 this process in detail, and includes test parameter configuration, loading and association of measurement channels, and collection of real-time data measured on site and loading and analysis of simulation result data. This process builds a complete and operational virtual test environment through model loading, parameter configuration, data collection and analysis.

[0060] First, the system needs to load the physical 3D model into the 3D scene of the preset test station and perform virtual assembly. In the specific implementation process, the system reads the data files of the physical 3D model from the database or file system. These files are usually stored in standard 3D formats (such as STL, OBJ, FBX, etc.). The loading process involves importing these 3D models into the simulation software and placing them in the 3D scene of the preset test station. Virtual assembly refers to combining various 3D models in a predetermined assembly order and position according to actual test requirements to build a complete test virtual scene. This process can be achieved through the assembly module of the simulation software. Users can accurately assemble various components together through operations such as dragging, rotating and moving.

[0061] In the test virtual scene, the system also needs to configure the test parameters and load and associate the measurement channels. The test parameters include the initial conditions, environmental conditions, operating parameters, etc. of the test, which directly affect the accuracy and effectiveness of the test results. The system allows users to enter or select the corresponding test parameters through the user interface or script configuration tool. Loading and associating measurement channels refers to adding virtual sensors or measuring devices to the test scene and associating them with the corresponding test objects. These measurement channels are used to collect various data generated during the test in real time, such as stress, strain, temperature (scalar), displacement, etc. In this way, the system can monitor and record various key parameters in real time during the virtual test.

[0062] According to the test scenario requirements sent by the user, the system collects real-time data measured on site in the test virtual scene. In the specific implementation process, the system can communicate with the field test equipment through the network interface to obtain the field measured data in real time. These data include physical quantities collected by sensors, operating parameters recorded by the control system, etc. The system loads these data into the test virtual scene and parses them to ensure that the virtual test environment is highly consistent with the actual field conditions.

[0063] At the same time, the system also needs to load and parse the simulation result data corresponding to the real-time data measured on site. The simulation result data is calculated by the simulation model and is used to predict various changes and results during the test. The system analyzes this data, compares and verifies it with the field measured data, and thus evaluates the accuracy and reliability of the simulation model. In the specific implementation process, the system loads the simulation result data into the virtual test environment and displays these results through a graphical interface or data analysis tool. Users can intuitively view and analyze the differences between the simulation results and the actual results.

[0064] This technical process solves the problem of the difficulty in unifying virtual test and field measured data in traditional test systems. By loading the physical 3D model into the virtual test scene and performing detailed parameter configuration and data collection, the system can build a highly realistic virtual test environment. At the same time, by loading and parsing the simulation result data, the system can effectively verify and optimize the virtual test results, improving the accuracy and reliability of the virtual test.

[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. This step involves digitizing the actual physical model (for example, a 3D model of an aircraft wing or a car engine based on a vector field) and accurately reproducing its geometric shape and positional relationship in a virtual environment. Through virtual assembly, the system can build a complete test virtual scene.

[0066] In this test virtual scene, users can configure test parameters such as fluid flow rate, pressure and temperature to simulate different experimental conditions. Next, the system will load and associate measurement channels to ensure that relevant data can be collected and monitored in the virtual scene. These measurement channels may include the location and configuration of velocity, pressure and temperature sensors to accurately collect fluid dynamics 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 wind tunnel experiment, the system collects real-time data on the velocity field, pressure field, and temperature field of the wing surface and the surrounding air. 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 computational fluid dynamics (CFD) simulation 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. In this way, users can optimize test parameters and simulation models to improve the overall effect of experiments and simulations. For example, by comparing the measured and simulated pressure distribution on the wing surface, users can identify errors in the aerodynamic model and make corrections, thereby improving the performance and efficiency of the design.

[0069] In general, step S101 constructs a complete virtual test environment by loading and virtually assembling the physical 3D model, configuring the test parameters, loading and associating the measurement channels, and collecting and analyzing the field measured data and simulation result data. This technical process has significant advantages in improving the accuracy and efficiency of virtual tests, and can help users quickly and accurately perform test configuration and data analysis, thereby improving the overall test effect.

[0070] Step S102: performing test time matching of scalar field data according to the field measured real-time data and the simulation result data, performing point-to-point comparison between the obtained test scalar field data and the scalar data in the simulation result data, and determining the corresponding scalar distribution comparison result; 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 real-time data, performing average scalar processing on similar areas in the simulation model, and using the simulation model after the average scalar processing as a spatial point matching the test measurement point to obtain a corresponding scalar comparison result;

[0071] Optionally, in this embodiment, step S102 describes how to perform scalar field data test time matching based on the field measured real-time data and the simulation result data, and determine the temperature distribution comparison result by comparing the scalar field data. At the same time, the system also needs to select the position corresponding to the test point in the simulation model and perform average temperature processing to obtain the thermal stress comparison result. This process achieves accurate comparison and verification of simulation results and actual test results through time matching, data comparison, node selection and temperature processing.

[0072] First, the system matches the thermal test time based on the field measured real-time data and the simulation result data. The implementation of this process is based on the processing and analysis of time series data. The system reads the temperature time series in the field measured real-time data and the simulation result data, and matches the two in time through timestamps or other synchronization marks. During the matching process, the system may need to perform data interpolation or alignment processing to ensure that the two sets of data are compared on the same time axis. The accuracy of time matching directly affects the reliability of subsequent temperature field data comparison.

[0073] Next, the system compares the obtained test temperature field data with the temperature data in the simulation result data point-to-point, and determines the corresponding temperature distribution comparison results. In the specific implementation process, the system reads the test temperature field data and the simulation temperature field data in the test virtual scene respectively, and compares the temperature values ​​at the same location. The comparison results can be displayed through visualization tools (such as heat maps and color maps), and users can intuitively view the differences in temperature distribution. This comparison process can help users identify the accuracy and deviation of the simulation model in scalar prediction, and provide a basis for model optimization.

[0074] In order to ensure the accuracy of the comparison, the system needs to select the position corresponding to the test measurement point as the selection position of the simulation model node corresponding to the physical 3D model. In the specific implementation process, the system marks the same spatial coordinate points as the actual test measurement points in the simulation model. In this way, the system can ensure that the parameter data output by the simulation model is compared with the real-time data measured on site at the same spatial position, thereby improving the accuracy of the comparison.

[0075] In the simulation model, averaging the temperature of similar areas is also one of the key steps. The system identifies similar areas corresponding to the actual test points in the simulation model and calculates the average temperature values ​​of these areas. This process can be achieved through the area division algorithm and the weighted average of the temperature data. The simulation model after the average temperature processing can more accurately reflect the temperature distribution under the actual test conditions.

[0076] Finally, the system uses the simulation model after average temperature processing as a spatial point that matches the test measurement point to obtain the corresponding thermal stress comparison results. The system calculates and compares the thermal stress values ​​at these matching points and displays the comparison results through a graphical interface or data report. Based on these comparison results, users can evaluate the accuracy of the simulation model in thermal stress prediction, and optimize the model parameters accordingly to improve the simulation accuracy.

[0077] This technical process solves the complexity of thermal test data matching and temperature field comparison in traditional simulation and test systems. Through time matching, temperature field data comparison, node selection and average temperature processing, the system can achieve accurate comparison between simulation results and actual test results, thereby verifying and optimizing the accuracy and reliability of the simulation model.

[0078] To give a specific example, in the aerospace field, engineers need to simulate and test the stress distribution of wing materials in high temperature environments. Through this technical solution, engineers can time-match and temperature-compare the temperature data measured on site with the simulation results, select the simulation nodes corresponding to the actual measurement points, and perform average temperature processing to finally obtain the thermal stress comparison results. This process helps engineers identify the accuracy of the simulation model, thereby optimizing the design and improving material performance and safety.

[0079] In general, step S102 achieves accurate comparison and verification of simulation results and actual test results through scalar field data test time matching between on-site measured real-time data and simulation result data, scalar field data comparison, simulation model node selection and average scalar processing.

[0080] Step S103: modifying the simulation parameters of the physical three-dimensional model in the test virtual scene according to the temperature distribution comparison result and the thermal stress comparison result, and continuing the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive the files.

[0081] Optionally, in this embodiment, step S103 describes how to correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the temperature distribution comparison results and the thermal stress comparison results, and continue the simulation test based on the corrected model to obtain the test life cycle data and file it. This process achieves the accuracy of the simulation results and the systematic management of data through parameter correction, simulation test and data management.

[0082] First, based on the temperature distribution comparison results and thermal stress comparison results, the system needs to correct the simulation parameters of the physical three-dimensional model in the test virtual scene. In the specific implementation process, the system reads the temperature distribution and thermal stress comparison results and identifies the simulation parameters that need to be adjusted. These parameters may include material properties (such as thermal conductivity, thermal expansion coefficient, etc.), boundary conditions (such as heat flux, ambient temperature, etc.) and load conditions (such as thermal stress distribution, applied force, etc.). The system inputs the corrected parameters into the simulation model through automated algorithms or manual intervention. The correction process needs to ensure the rationality and scientificity of the parameter adjustment to ensure the accuracy of the simulation results.

[0083] Based on the revised physical 3D model, the system continues to conduct simulation tests. The simulation test process involves multiple iterative calculations to simulate the behavior of the test object under different working conditions and time points. The system uses a high-performance computing platform, finite element analysis, thermodynamic simulation and other technologies to perform detailed simulation analysis on the revised model. During the simulation process, the system monitors various key parameters in real time and records simulation data. These data include stress, strain, scalar field distribution, etc., which are displayed through visualization tools, allowing users to intuitively observe and analyze various changes in the simulation process.

[0084] During the simulation test, the system needs to obtain data from the entire life cycle of the test. These data cover the complete time series from the initial state of the test to the end of the test, and record the changes in various physical quantities of the test object throughout its life cycle. The system automatically records and stores these data through the data acquisition module, and performs preliminary data processing and analysis. The acquisition of full life cycle data not only provides a comprehensive basis for the analysis of test results, but also provides valuable data support for subsequent simulation optimization and model improvement.

[0085] Finally, the system archives the data of the entire life cycle of the test. File archiving is an important part of data management, ensuring the long-term preservation and effective management of real-time data. The system stores the data of the entire life cycle in accordance with the preset format and standards through the data management platform. During the archiving process, the system classifies and marks the data to facilitate subsequent query and call. At the same time, the system backs up and encrypts the archived data to ensure the security and reliability of the data. The archived data can be used for subsequent simulation tests, model optimization and technical research, providing continuous data support and decision-making basis.

[0086] This technical process solves the problems of untimely parameter correction and unsystematic data management in traditional simulation systems. Through the feedback of temperature distribution and thermal stress comparison results, the system can correct simulation parameters in time to ensure the accuracy of simulation results. Through the acquisition and filing of data throughout the life cycle, the system realizes the systematic management and long-term preservation of test data.

[0087] For example, in the thermal stress test of nuclear power plant equipment, engineers need to simulate and test the stress distribution of the equipment in a high temperature environment. Through this technical solution, engineers can modify the parameters of the simulation model based on the temperature distribution and thermal stress comparison results, and perform multiple iterative simulation tests to obtain thermal stress data throughout the life cycle of the equipment. The system archives and manages this data, providing strong data support for subsequent equipment optimization design and safety assessment.

[0088] In general, step S103 corrects the simulation parameters through the feedback of the scalar distribution comparison results and the scalar comparison results, and performs simulation experiments based on the corrected model, obtains the data of the entire life cycle of the experiment and archives the files, thereby achieving the accuracy of the simulation results and the systematic management of the data.

[0089] From the above description, it can be seen that the scalar field simulation and real-time data processing method provided in the embodiment of the present application can match the test time of the scalar field data according to the real-time data measured on site and the simulation result data, and determine the corresponding scalar distribution comparison result by performing point-to-point comparison between the obtained test scalar field data and the scalar data in the simulation result data; 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, perform average scalar processing on similar areas in the simulation model, and use the simulation model after the average scalar processing as the spatial point matching the test measurement point to obtain the corresponding scalar comparison result; correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the scalar distribution comparison result and the scalar comparison result, thereby effectively combining the simulation data and the real-time data to achieve more accurate product performance evaluation and optimization.

[0090] In one embodiment of the scalar field simulation and real-time data processing method of the present application, see Figure 2 , and can also include the following:

[0091] Step S201: setting 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 real-time data measured on site;

[0092] Step S202: interpolating the simulation result data to the time corresponding to the sampling point of the field measured real-time data through a preset interpolation algorithm, and unifying the time boundary of the simulation result data and the field measured real-time data through a data interception operation.

[0093] Optionally, in this embodiment, steps S201 and S202 respectively describe how to set the output time step of the simulation model to be consistent with the real-time data measured on site, and how to unify the time boundary of the simulation result data and the real-time data measured on site through interpolation algorithm and data interception operation. These two steps realize efficient comparison and analysis of simulation data and measured data through time step setting, interpolation processing and time boundary unification.

[0094] First, in step S201, the system needs to set the output time step of the simulation model to be consistent with the real-time data measured on site. In the specific implementation process, the system first reads the time step of the real-time data measured on site (i.e., the time interval of data sampling). Then, in the setting of the simulation model, the time step parameter of the output data is adjusted to the same value as the measured data. This adjustment ensures the alignment of the simulation data and the measured data on the time axis, making the subsequent data comparison and analysis more accurate. In this way, the system can avoid data dislocation and comparison errors caused by inconsistent time steps, and improve the credibility of the simulation results.

[0095] Next, in step S202, the system interpolates the simulation result data to the time corresponding to the sampling point of the real-time data measured on site through a preset interpolation algorithm. The interpolation algorithm is a key technology to achieve this process. The system can use linear interpolation, spline interpolation or other advanced interpolation algorithms to interpolate the simulation data to the same time point as the measured data as needed. In the specific implementation process, the system first identifies the time point difference between the simulation data and the measured data, and then applies the interpolation algorithm to calculate the value of the simulation data at the time corresponding to the sampling point of the measured data. The interpolated data can be better compared and analyzed point by point with the measured data to reduce the error on the time axis.

[0096] In addition, the system also needs to unify the time boundaries of the simulation result data and the real-time data measured on site through data interception operations. In the specific implementation process, the system aligns the time boundaries of the simulation data and the measured data to ensure that the two sets of data are compared within the same time range. This operation includes identifying the start time and end time of the data and intercepting the data segments within the same time range. By unifying the time boundaries, the system can ensure that the simulation data and the measured data are analyzed within the same time window, thereby improving the accuracy of data comparison.

[0097] This technical process solves the comparison error problem caused by the inconsistency of time step and data sampling point in traditional simulation systems. Through time step setting, interpolation processing and time boundary unification, the system can achieve efficient alignment and comparison of simulation data and measured data, ensuring the accuracy and reliability of simulation results.

[0098] To give a specific example, in the thermodynamic simulation test of an automobile engine, engineers need to compare the scalar data output by the simulation model with the actual test data. Through step S201, the engineer sets the output time step of the simulation model to be consistent with the actual test to ensure that the data sampling interval is the same. Then, through step S202, the system uses a linear interpolation algorithm to interpolate the simulation result data to the time corresponding to the actual test data sampling point, and unifies the time boundary through a data interception operation. In this case, engineers can accurately compare the simulation data with the measured data, analyze the temperature distribution, find the shortcomings of the simulation model, and make optimization adjustments.

[0099] Specifically, for scalar field data tests, it is necessary to consider data matching in two dimensions: time matching and space matching. Among them, time matching is responsible for ensuring that the data points of the simulation model and real-time data on the time axis correspond to each other, so as to make effective comparison and analysis. Spatial matching is responsible for the comparison of temperature distribution. It is necessary to ensure that the node positions in the simulation model are consistent with the test measurement point positions, or to make regional average comparisons, as follows:

[0100] Time matching: In the process of time matching, there are three matching dimensions: synchronous sampling, time interpolation, and data interception. First, the output time step of the simulation model is set to be consistent with the real-time data as much as possible, so that the simulation model can output data at the same time interval as the real-time data. However, there is no guarantee that the time step of the simulation output will completely match the real-time data. At this time, the interpolation method can be used to interpolate the simulation data to the time corresponding to the real-time data sampling point to obtain a more accurate comparison result. Finally, the data interception operation is used to make the simulation data consistent with the real-time data in time span, unifying the time boundary of data analysis.

[0101] Taking the strain data of a certain point in the scalar field data test as an example, the data difference method used in this process is as follows:

[0102] Suppose we have a set of real-time data points and a set of simulated data points ,in represents the sampling time of real-time data, Represents real-time data measurement values, represents the simulation data sampling time, Represents simulation data results.

[0103] We hope to interpolate the corresponding real-time data at the simulation time point, that is, find all The corresponding time . Then the basic formula for interpolation is:

[0104] ,

[0105] in, .

[0106] In general, steps S201 and S202 achieve efficient alignment and comparison of simulation data and measured data by adjusting the output time step, interpolation processing and time boundary unification of the simulation model. This technical process has significant advantages in improving the accuracy and reliability of the simulation model, and can help users quickly and accurately perform data analysis and model optimization, thereby improving the overall simulation effect and efficiency.

[0107] In one embodiment of the scalar field simulation and real-time data processing method of the present application, see Figure 3 , and can also include the following:

[0108] Step S301: performing root mean square error calculation on the obtained test scalar field data and the scalar data in the simulation result data to determine an index of the difference between the simulation result data and the field measured real-time data;

[0109] Step S302: Calculate the linear correlation between the simulated scalar field and the test scalar field by using a preset correlation coefficient algorithm, and calculate the maximum difference between the simulated scalar field and the test scalar field by using a maximum deviation algorithm.

[0110] Optionally, in this embodiment, steps S301 and S302 describe how to quantify the difference between the simulated scalar field and the test scalar field through root mean square error calculation, correlation coefficient algorithm and maximum deviation algorithm. These steps achieve accurate comparison and evaluation of simulation results and measured data through determination of difference index, linear correlation analysis and maximum difference calculation.

[0111] First, in step S301, the system needs to calculate the root mean square error (RMSE) to determine the index of the difference between the simulation result data and the real-time data measured on site. In the specific implementation process, the system first obtains the simulated temperature field data and the test temperature field data. The calculation process of the root mean square error includes the following steps: First, the system calculates the difference between the simulated data and the measured data at each sampling point, then squares and sums these differences, and finally takes the average and takes the square root. The root mean square error, as a difference indicator, reflects the overall deviation between the simulation results and the measured data. Through this indicator, the user can intuitively understand the degree of consistency between the simulation results and the measured data, thereby evaluating the accuracy of the simulation model.

[0112] Next, in step S302, the system calculates the linear correlation between the simulated temperature field and the test temperature field through a preset correlation coefficient algorithm. The correlation coefficient is an indicator that measures the strength of the linear relationship between two variables, and its value range is between -1 and 1. In the specific implementation process, the system first calculates the mean of the simulated data and the measured data, then calculates the average value of the product of the deviation of each data point from the mean, and finally divides it by the product of the standard deviation of each data to obtain the correlation coefficient. The closer the correlation coefficient is to 1, the stronger the linear correlation between the simulated temperature field and the test temperature field. Through this algorithm, users can evaluate the correlation between the simulated data and the measured data, and further verify the effectiveness of the simulation model.

[0113] In addition, the system needs to calculate the maximum difference between the simulated temperature field and the test temperature field through the maximum deviation algorithm. In the specific implementation process, the system traverses all sampling points of the simulated data and the measured data, calculates the absolute difference at each point, and records the maximum value. As an indicator of the extreme difference between the simulated and measured data, the maximum deviation can reveal the deviation of the simulation model in certain local areas. Through this algorithm, users can identify the shortcomings of the simulation model under specific conditions, so as to carry out targeted optimization and improvement.

[0114] This technical process solves the problem of lack of accurate evaluation methods in traditional simulation systems. Through the calculation of root mean square error, correlation coefficient and maximum deviation, the system can comprehensively quantify the difference between simulation results and measured data, providing a reliable evaluation basis.

[0115] To give a specific example, in the thermodynamic simulation test of aircraft engines, engineers need to evaluate the accuracy of the simulation model in a high temperature environment. Through step S301, the system calculates the root mean square error between the simulated temperature field and the test temperature field, and obtains an overall deviation index. Then, through step S302, the system calculates the correlation coefficient between the simulated temperature field and the test temperature field, and evaluates the linear correlation between the two. At the same time, the system also calculates the maximum deviation between the two and identifies the extreme differences in the simulation model in certain local areas. These evaluation results provide engineers with a comprehensive basis for simulation model performance analysis, helping them to discover and improve the shortcomings of the simulation model.

[0116] Specifically, the temperature field data based on the experiment obtained in the data matching process is compared point by point with the temperature data of the simulation analysis to evaluate the accuracy of the simulation model in temperature prediction. In this process, it is necessary to calculate the comparison and analysis results such as the root mean square error, average deviation, maximum deviation and correlation coefficient. The details are as follows:

[0117] Root mean square error: An indicator that measures the difference between real-time data and the simulation data set, used to evaluate the overall error between the simulated temperature field and the experimental temperature field.

[0118] ,

[0119] in, It is the first The temperature of a point, It is the first The temperature of a point, is the total number of data points.

[0120] Mean Deviation: It is used to measure the average error of simulation results.

[0121] ,

[0122] Maximum deviation: Indicates the maximum difference between the simulated temperature field and the experimental temperature field.

[0123] ,

[0124] Correlation coefficient: Measures the linear correlation between the simulated temperature field and the experimental temperature field.

[0125] ,

[0126] in, and are the average values ​​of the simulation and experimental temperature fields, respectively.

[0127] In general, steps S301 and S302 achieve accurate evaluation of the difference between the simulated scalar field and the experimental scalar field through root mean square error calculation, correlation coefficient algorithm and maximum deviation algorithm. This technical process has significant advantages in improving the accuracy and reliability of the simulation model, and can help users quickly and accurately perform data analysis and model optimization, thereby improving the overall simulation effect and efficiency.

[0128] In one embodiment of the scalar field simulation and real-time data processing method of the present application, see Figure 4 , and can also include the following:

[0129] Step S401: constructing a measurement value data set according to the three-dimensional coordinates of the measurement points in the simulation model and the corresponding scalar measurement values, and constructing a simulation result data set according to the three-dimensional coordinates of the simulation finite element nodes in the simulation result data and the corresponding scalar simulation values;

[0130] Step S402: taking the three-dimensional coordinates of the measuring point as the center and the spatial range defined by the preset average scalar processing spatial range threshold as the envelope, selecting the simulation finite element nodes and their corresponding scalar values ​​in the spatial range in the simulation result data set, and calculating the average value of the simulation result data.

[0131] Optionally, in this embodiment, steps S401 and S402 describe how to use the three-dimensional coordinates and scalar measurement values ​​of the measuring points, and the three-dimensional coordinates and simulated scalar values ​​of the simulated finite element nodes to construct data sets respectively, and calculate the average value of the simulation result data by defining the spatial range. The implementation of these two steps can effectively compare the simulation data with the measured data, thereby evaluating the accuracy and reliability of the simulation model.

[0132] First, in step S401, the system needs to construct a measurement value data set based on the three-dimensional coordinates of the measuring points in the simulation model and the corresponding temperature measurement values. At the same time, a simulation result data set is constructed based on the three-dimensional coordinates of the simulation finite element nodes in the simulation result data and the corresponding temperature simulation values. In the specific implementation process, the system first extracts the three-dimensional coordinates and temperature measurement values ​​of the measuring points from the simulation model, and organizes these data into a set. Then, the system extracts the three-dimensional coordinates and temperature simulation values ​​of the finite element nodes from the simulation result data, and also organizes them into a set. Through this step, the system establishes the basic data structure of the actual measurement data and simulation result data, which provides data support for subsequent analysis and comparison.

[0133] Next, in step S402, the system defines a preset spatial range threshold value with the three-dimensional coordinates of the measuring point as the center, as the envelope of the processing spatial range. Within this envelope, the system selects the simulation finite element nodes and their corresponding temperature values ​​within the spatial range from the simulation result data set. In the specific implementation process, the system first determines the three-dimensional coordinates of each measuring point, and then applies the preset spatial range threshold value to calculate the envelope range of the measuring point. Then, the system traverses the simulation result data set to filter out the simulation finite element nodes and their temperature values ​​within the envelope. Finally, the system calculates the average value of the temperature values ​​of these nodes. This step enables the simulation result data to better reflect the local characteristics of the actual measurement data by performing local averaging processing around the measuring point, thereby improving the accuracy of the evaluation.

[0134] This technical process solves the local deviation problem that may be caused by direct comparison of simulated data with measured data in traditional methods. By constructing a data set and defining a spatial range, the system can effectively compare simulated data with measured data locally, reduce the impact of noise and errors, and improve the evaluation accuracy of the simulation model.

[0135] To give a specific example, in the thermodynamic simulation of building structures, engineers need to evaluate the temperature distribution of the simulation model at different locations. Through step S401, the system constructs a data set of three-dimensional coordinates and temperature measurement values ​​of the measuring points, as well as a data set of three-dimensional coordinates and temperature simulation values ​​of the simulation finite element nodes. Then, through step S402, the system defines a spatial range threshold value with the three-dimensional coordinates of the measuring points as the center, screens out the simulation finite element nodes and their temperature values ​​within the range, and calculates the average value of the temperature values ​​of these nodes. In this way, engineers can evaluate the accuracy of the simulation model at each measuring point by comparing the local average temperature values, and discover and improve the shortcomings of the simulation model.

[0136] Specifically, in the spatial matching process, there are three matching dimensions: node position matching, regional average processing, and temperature field interpolation. First, in the process of selecting simulation model nodes, try to select positions corresponding to the test measurement points, so that the simulation model can output parameter data in the same space as the test data. If the test measurement point does not completely match the simulation node position, you can consider averaging the temperature of similar areas in the simulation model to obtain more consistent results, and use the results as the spatial points that match the test measurement points. Finally, based on the actual test data, according to the finite element position in the simulation results, the test data results are three-dimensionally interpolated, estimated as the test value at the measurement point, and the test measurement temperature field is constructed.

[0137] The specific algorithm flow of average scalar processing is as follows:

[0138] First, build a measurement data set , is the three-dimensional coordinate of the measuring point, is the scalar measurement value corresponding to the measurement point ;

[0139] Second, build a simulation result data set , To simulate the 3D coordinates of the finite element nodes, is the scalar simulation value corresponding to the finite element node .

[0140] Third, set the average scalar processing space range threshold ;

[0141] Fourth, based on the measuring point location As the center, The defined spatial range is an envelope, and the simulation finite element nodes and their corresponding scalar values ​​in the spatial range are selected in the simulation result data set;

[0142] Fifth, calculate the average value of the simulation result data obtained in the fourth step, as follows:

[0143]

[0144] ： is the total number of all simulation finite element nodes within the spatial range determined in the fourth step.

[0145] The specific algorithm for scalar field interpolation is as follows:

[0146] First, build a measurement data set , is the three-dimensional coordinate of the measuring point, The temperature measurement value corresponding to the measuring point ;

[0147] Second, build a simulation result data set , To simulate the 3D coordinates of the finite element nodes, is the temperature simulation value corresponding to the finite element node .

[0148] Third, traverse the left and right finite element nodes in the simulation result data set, extract the node coordinate value, and According to the nearest principle, 8 test points are selected in three directions. , , , , , , , , as the interpolation reference point for three-dimensional linear interpolation, the specific method of linear interpolation is as follows:

[0149] Interpolate in the X direction

[0150] ,

[0151] Interpolate in the Y direction

[0152] ,

[0153] in, and They are the Y-axis coordinates corresponding to the interpolation in the X direction.

[0154] Interpolate in the Z direction

[0155]

[0156] Fourth, after completing all the above interpolation processing, construct the test data temperature field corresponding to the finite element position in the simulation result data set.

[0157] In general, steps S401 and S402 achieve accurate comparison between simulation data and measured data by constructing a measurement data set and a simulation result data set, and defining a spatial range for local averaging. 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.

[0158] In one embodiment of the scalar field simulation and real-time data processing method of the present application, see Figure 5 , and can also include the following:

[0159] Step S501: 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;

[0160] Step S502: constructing a test measurement scalar field according to the test value, and performing test space matching of scalar field data according to the test measurement scalar field.

[0161] Optionally, in this embodiment, steps S501 and S502 describe how to construct a test measurement scalar field and perform matching evaluation through three-dimensional linear interpolation and scalar field data test space matching. These steps achieve accurate comparison of simulation data and measured data through interpolation and matching algorithms, solving the problem of data space inconsistency.

[0162] First, in step S501, the system needs to perform three-dimensional linear interpolation based on the real-time data measured on site and the finite element position in the simulation results to obtain the corresponding test values. In the specific implementation process, the system first obtains the real-time data measured on site, which usually includes the three-dimensional coordinates of the measuring points and the corresponding scalar measurement values. Then, the system applies the three-dimensional linear interpolation algorithm according to the finite element node positions in the simulation results to interpolate the measured data to these nodes. The basic principle of three-dimensional linear interpolation is to use the values ​​of known data points to calculate the values ​​of unknown points through linear relationships. Specifically, based on the known measuring point data, the system uses the data of the surrounding adjacent measuring points to calculate the scalar value of the simulation node position in proportion. This step converts the irregularly distributed measured data into test values ​​that correspond to the simulation data in space through the interpolation algorithm, thereby solving the problem of data space inconsistency.

[0163] Next, in step S502, the system constructs a test measurement scalar field according to the test values ​​obtained in step S501, and performs test space matching of the scalar field data. In the specific implementation process, the system first organizes the test values ​​obtained by interpolation into a complete test measurement scalar field, and the structure of this scalar field is consistent with the simulation scalar field. Then, the system performs spatial matching of the test measurement scalar field and the simulation scalar field. The process of test space matching of scalar field data includes aligning and comparing the two scalar fields and evaluating the scalar differences between the two at each position. The system generates a difference field by calculating the scalar difference at each position, and performs statistical analysis on the difference field, thereby evaluating the overall and local performance of the simulation model. Through this matching process, the user can intuitively see the degree of consistency between the simulation results and the actual measurement, find out the shortcomings of the simulation model and make improvements.

[0164] This technical process solves the problem of difficult comparison caused by inconsistent data space in traditional simulation evaluation. Through three-dimensional linear interpolation and scalar field data test space matching, the system can effectively align and compare measured data with simulation data in space, improving the accuracy and reliability of the evaluation.

[0165] To give a specific example, in the heat dissipation simulation of electronic equipment, engineers need to evaluate the temperature distribution of the simulation model in various parts of the equipment. Through step S501, the system performs three-dimensional linear interpolation based on the temperature data measured on site and according to the finite element position in the simulation results to obtain the test temperature values ​​of various parts of the equipment. Then, through step S502, the system constructs a test measurement temperature field based on these test values ​​and spatially matches it with the simulation temperature field. By comparing the two temperature fields, engineers can intuitively see the temperature differences in various parts of the equipment, evaluate the accuracy of the simulation model, and discover and improve the shortcomings of the simulation model.

[0166] In general, steps S501 and S502 achieve accurate comparison between simulation data and measured data through three-dimensional linear interpolation and scalar field data test space matching. 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.

[0167] In one embodiment of the scalar field simulation and real-time data processing method of the present application, see Figure 6 , and can also include the following:

[0168] Step S601: constructing a measurement value data set according to the three-dimensional coordinates of the measurement points in the simulation model and the corresponding scalar measurement values, and constructing a simulation result data set according to the three-dimensional coordinates of the simulation finite element nodes in the simulation result data and the corresponding scalar simulation values;

[0169] Step S602: performing three-dimensional linear interpolation according to the simulation result data set to obtain corresponding test values.

[0170] Optionally, in this embodiment, steps S601 and S602 describe in detail how to construct a measurement data set and a simulation result data set, and use a three-dimensional linear interpolation method to obtain corresponding test values. This process achieves accurate comparison of simulation data and measured data through the construction of data sets and the application of interpolation algorithms, and solves the problem of data space inconsistency.

[0171] First, in step S601, the system needs to construct a measurement value data set based on the three-dimensional coordinates of the measuring points in the simulation model and the corresponding scalar measurement values. At the same time, the simulation result data set is constructed based on the three-dimensional coordinates of the simulation finite element nodes in the simulation result data and the corresponding scalar simulation values. In the specific implementation process, the system first extracts the three-dimensional coordinates and scalar measurement values ​​of the measuring points from the simulation model, and organizes these data into a set. The measurement value data set contains the spatial position of the measuring points and the corresponding scalar data, which is an important basis for reflecting the actual measurement situation. Then, the system extracts the three-dimensional coordinates and scalar simulation values ​​of the finite element nodes from the simulation result data, and also organizes them into a set. The simulation result data set records the scalar distribution of the simulation model at each finite element node, which is an important data reflecting the simulation results. Through this step, the system establishes the basic data structure of the actual measurement data and the simulation result data, which provides data support for subsequent interpolation and comparative analysis.

[0172] Next, in step S602, the system performs three-dimensional linear interpolation according to the simulation result data set to obtain the corresponding test value. In the specific implementation process, the system first obtains the three-dimensional coordinates and scalar simulation values ​​of the finite element nodes in the simulation result data set. Then, the system applies a three-dimensional linear interpolation algorithm to interpolate the measured point data in the measurement value data set to the finite element node position in the simulation result data set. The principle of three-dimensional linear interpolation is to use the values ​​of known data points to calculate the values ​​of unknown points through a linear relationship. Specifically, based on the known measured point data, the system uses the data of the surrounding adjacent measured points to proportionally calculate the scalar value of the simulation node position. Through this interpolation process, the system converts irregularly distributed measured data into test values ​​that correspond to the simulation data in space, thereby solving the problem of data space inconsistency.

[0173] This technical process solves the problem of difficult comparison caused by inconsistent data space in traditional simulation evaluation. By constructing a measurement data set and a simulation result data set, the system can effectively align and compare the measured data with the simulation data in space, improving the accuracy and reliability of the evaluation. The application of the three-dimensional linear interpolation method makes the interpolation process smoother and more accurate, reducing the errors caused by irregular data distribution.

[0174] For example, in the thermodynamic simulation of aircraft engines, engineers need to evaluate the temperature distribution of the simulation model in different parts of the engine. Through step S601, the system constructs a data set of three-dimensional coordinates and temperature measurement values ​​of the measuring points, as well as a data set of three-dimensional coordinates and temperature simulation values ​​of the simulation finite element nodes. Then, through step S602, the system performs three-dimensional linear interpolation based on the simulation result data set to obtain the test temperature values ​​of various parts of the engine. In this way, engineers can evaluate the accuracy of the simulation model in various parts by comparing these test values ​​with the actual measurement values, and discover and improve the shortcomings of the simulation model.

[0175] In general, steps S601 and S602 achieve accurate comparison between simulation data and measured data by constructing a measurement data set and a simulation result data set, and using a three-dimensional linear interpolation method. This technical process has significant advantages in improving the evaluation accuracy and reliability of the simulation model, and can help users quickly and accurately perform data analysis and model optimization, thereby improving the overall simulation effect and efficiency.

[0176] In one embodiment of the scalar field simulation and real-time data processing method of the present application, see Figure 7 , and can also include the following:

[0177] Step S701: traverse the left and right finite element nodes in the simulation result data set;

[0178] Step S702: Extract the node coordinate value. A preset number of test measurement points are selected in three directions according to the nearest principle as interpolation reference points for three-dimensional linear interpolation.

[0179] Optionally, in this embodiment, steps S701 and S702 describe in detail how to perform three-dimensional linear interpolation calculations by traversing the finite element nodes in the simulation result data set and selecting appropriate test measurement points in each direction as interpolation reference points. This process achieves accurate comparison of simulation data and measured data by accurately selecting interpolation reference points and applying interpolation algorithms, solving the problem of data space inconsistency.

[0180] First, in step S701, the system needs to traverse all finite element nodes in the simulation result data set. In the specific implementation process, the system first obtains the simulation result data set, which contains the three-dimensional coordinates of all finite element nodes and the corresponding simulation scalar values. The traversal process refers to the system visiting these nodes one by one, preparing to perform further interpolation calculations on each node. Traversal is an important step to ensure that all nodes are processed. It ensures the comprehensiveness and accuracy of subsequent interpolation calculations and lays the foundation for finding a suitable interpolation reference point for each node.

[0181] Next, in step S702, the system extracts the coordinate value of the current node, and selects a preset number of test points as interpolation reference points in the three directions of the coordinate value x, y, and z according to the nearest principle to perform three-dimensional linear interpolation. In the specific implementation process, the system first obtains the three-dimensional coordinates of the current node, and then searches for the test points closest to the node in the measurement value data set. The nearest principle here refers to the closest measurement point in spatial distance. The system will select a preset number of measurement points in the three directions of x, y, and z, respectively, and these measurement points will be used as reference points for interpolation. The principle of three-dimensional linear interpolation is to calculate the scalar value of the current node using the known scalar values ​​of these reference points and the linear relationship. During the interpolation process, the system calculates the interpolation scalar value of the current node according to the spatial position and scalar value of each reference point by weighted average or other linear methods. This step converts the irregularly distributed measured data into interpolation scalar values ​​corresponding to the simulation data in space by accurately selecting the interpolation reference point and performing interpolation calculation, thereby solving the problem of data space inconsistency.

[0182] This technical process solves the problem of difficult comparison caused by inconsistent data space in traditional simulation evaluation. By traversing all finite element nodes in the simulation result data set and selecting appropriate test points in each direction as interpolation reference points, the system can effectively align and compare the measured data with the simulation data in space, improving the accuracy and reliability of the evaluation. The application of the three-dimensional linear interpolation method makes the interpolation process smoother and more accurate, reducing the errors caused by irregular data distribution.

[0183] For example, in the temperature field simulation of a building, engineers need to evaluate the temperature distribution of the simulation model in various parts of the building. Through step S701, the system traverses all finite element nodes in the simulation result data set and extracts the three-dimensional coordinates of the nodes one by one. Then, through step S702, the system selects a preset number of test points as interpolation reference points in the x, y, and z directions of each node according to the nearest principle, and performs three-dimensional linear interpolation calculations. In this way, engineers can evaluate the accuracy of the simulation model in various parts by comparing these interpolated temperature values ​​with the actual measured values, and discover and improve the shortcomings of the simulation model.

[0184] In general, steps S701 and S702 perform three-dimensional linear interpolation calculations by traversing all finite element nodes in the simulation result data set and selecting appropriate test points in each direction as interpolation reference points, thereby achieving an accurate comparison between simulation data and measured data. 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.

[0185] In order to effectively combine simulation data and real-time data to achieve more accurate product performance evaluation and optimization, the present application provides an embodiment of a scalar field simulation and real-time data processing device for implementing all or part of the contents of the scalar field simulation and real-time data processing method, see Figure 8 The scalar field simulation and real-time data processing device specifically includes the following contents:

[0186] 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;

[0187] The analysis and comparison module 20 is used to match the test time of the scalar field data according to the field measured real-time data and the simulation result data, and to determine the corresponding scalar distribution comparison result by performing point-to-point comparison between the obtained test scalar field data and the scalar data in the simulation result data; 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 real-time data, perform average scalar processing on similar areas in the simulation model, and use the simulation model after the average scalar processing as the spatial point matching the test measurement point to obtain the corresponding scalar comparison result;

[0188] The simulation test module 30 is used to correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the scalar distribution comparison result and the scalar comparison result, and continue the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0189] From the above description, it can be seen that the scalar field simulation and real-time data processing device provided in the embodiment of the present application can match the test time of the scalar field data according to the real-time data measured on site and the simulation result data, and determine the corresponding scalar distribution comparison result by performing point-to-point comparison between the obtained test scalar field data and the scalar data in the simulation result data; 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, perform average scalar processing on similar areas in the simulation model, and use the simulation model after the average scalar processing as the spatial point matching the test measurement point to obtain the corresponding scalar comparison result; correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the scalar distribution comparison result and the scalar comparison result, thereby effectively combining the simulation data and the real-time data to achieve more accurate product performance evaluation and optimization.

[0190] From the hardware level, in order to effectively combine simulation data and real-time data to achieve more accurate product performance evaluation and optimization, the present application provides an embodiment of an electronic device for implementing all or part of the contents of the scalar field simulation and real-time data processing method, and the electronic device specifically includes the following contents:

[0191] 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 scalar field simulation and real-time data processing 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 scalar field simulation and real-time data processing method and the embodiment of the scalar field simulation and real-time data processing device in the embodiment, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0192] 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.

[0193] In practical applications, part of the scalar field simulation and real-time data processing method can be executed on the electronic device side as described above, or all operations can be completed in the client device. The 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.

[0194] 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.

[0195] 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.

[0196] In one embodiment, the scalar field simulation and real-time data processing method functions may be integrated into the central processing unit 9100. The central processing unit 9100 may be configured to perform the following control:

[0197] 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;

[0198] Step S102: performing test time matching of scalar field data according to the field measured real-time data and the simulation result data, performing point-to-point comparison between the obtained test scalar field data and the scalar data in the simulation result data, and determining the corresponding scalar distribution comparison result; 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 real-time data, performing average scalar processing on similar areas in the simulation model, and using the simulation model after the average scalar processing as a spatial point matching the test measurement point to obtain a corresponding scalar comparison result;

[0199] Step S103: Correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the scalar distribution comparison result and the scalar comparison result, and continue the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0200] From the above description, it can be seen that the electronic device provided in the embodiment of the present application matches the test time of scalar field data according to the real-time data measured on site and the simulation result data, and determines the corresponding scalar distribution comparison result by performing point-to-point comparison between the obtained test scalar field data and the scalar data in the simulation result data; 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, performs average scalar processing on similar areas in the simulation model, and uses the simulation model after the average scalar processing as the spatial point matching the test measurement point to obtain the corresponding scalar comparison result; and corrects the simulation parameters of the physical three-dimensional model in the test virtual scene according to the scalar distribution comparison result and the scalar comparison result, thereby being able to effectively combine the simulation data and the real-time data to achieve more accurate product performance evaluation and optimization.

[0201] In another embodiment, the scalar field simulation and real-time data processing device can be configured separately from the central processing unit 9100. For example, the scalar field simulation and real-time data processing device can be configured as a chip connected to the central processing unit 9100, and the functions of the scalar field simulation and real-time data processing method can be realized through the control of the central processing unit.

[0202] 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.

[0203] 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, which receives input and controls the operation of various components of the electronic device 9600.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.).

[0208] 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.

[0209] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0210] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the scalar field simulation and real-time 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 of the scalar field simulation and real-time data processing method in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0211] 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;

[0212] Step S102: performing test time matching of scalar field data according to the field measured real-time data and the simulation result data, performing point-to-point comparison between the obtained test scalar field data and the scalar data in the simulation result data, and determining the corresponding scalar distribution comparison result; 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 real-time data, performing average scalar processing on similar areas in the simulation model, and using the simulation model after the average scalar processing as a spatial point matching the test measurement point to obtain a corresponding scalar comparison result;

[0213] Step S103: Correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the scalar distribution comparison result and the scalar comparison result, and continue the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0214] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application matches the test time of scalar field data according to the real-time data measured on site and the simulation result data, and determines the corresponding scalar distribution comparison result by performing point-to-point comparison between the obtained test scalar field data and the scalar data in the simulation result data; 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, performs average scalar processing on similar areas in the simulation model, and uses the simulation model after the average scalar processing as the spatial point matching the test measurement point to obtain the corresponding scalar comparison result; and corrects the simulation parameters of the physical three-dimensional model in the test virtual scene according to the scalar distribution comparison result and the scalar comparison result, thereby being able to effectively combine the simulation data and the real-time data to achieve more accurate product performance evaluation and optimization.

[0215] The embodiments of the present application also provide a computer program product capable of implementing all the steps of the scalar field simulation and real-time data processing method in the above embodiments, where the execution subject is a server or a client. When the computer program / instruction is executed by a processor, the steps of the scalar field simulation and real-time data processing method are implemented. For example, the computer program / instruction implements the following steps:

[0216] 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;

[0217] Step S102: performing test time matching of scalar field data according to the field measured real-time data and the simulation result data, performing point-to-point comparison between the obtained test scalar field data and the scalar data in the simulation result data, and determining the corresponding scalar distribution comparison result; 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 real-time data, performing average scalar processing on similar areas in the simulation model, and using the simulation model after the average scalar processing as a spatial point matching the test measurement point to obtain a corresponding scalar comparison result;

[0218] Step S103: Correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the scalar distribution comparison result and the scalar comparison result, and continue the simulation test based on the physical three-dimensional model to obtain the test full life cycle data and archive it.

[0219] From the above description, it can be seen that the computer program product provided in the embodiment of the present application matches the test time of scalar field data according to the real-time data measured on site and the simulation result data, and determines the corresponding scalar distribution comparison result by performing point-to-point comparison between the obtained test scalar field data and the scalar data in the simulation result data; 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, performs average scalar processing on similar areas in the simulation model, and uses the simulation model after the average scalar processing as the spatial point matching the test measurement point to obtain the corresponding scalar comparison result; and corrects the simulation parameters of the physical three-dimensional model in the test virtual scene according to the scalar distribution comparison result and the scalar comparison result, thereby being able to effectively combine the simulation data and the real-time data to achieve more accurate product performance evaluation and optimization.

[0220] It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0221] 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.

[0222] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0223] 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 in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0224] 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 scalar field simulation and real-time data processing method, characterized in that: The method comprises: The physical three-dimensional model is loaded into a preset three-dimensional scene of the test station and virtually assembled to construct a test virtual scene, test parameters are configured in the test virtual scene, and measurement channels are loaded and associated; on-site measured real-time data is collected in the test virtual scene according to the test scene requirements sent by the user, and simulation result data corresponding to the on-site measured real-time data is loaded and parsed; According to the field measured real-time data and the simulation result data, the scalar field data test time matching is the thermal test time matching of the simulated temperature field data and the test temperature field data. The root mean square error is calculated between the obtained test scalar field data and the scalar data in the simulation result data to determine the index of the difference between the simulation result data and the field measured real-time data, wherein the index is the index of the difference between the simulated temperature field data and the test temperature field data. The linear correlation between the simulated scalar field and the test scalar field is calculated by a preset correlation coefficient algorithm, wherein the linear correlation is the linear correlation between the simulated temperature field and the test temperature field, and the maximum difference between the simulated scalar field and the test scalar field is calculated by a maximum deviation algorithm; select a point relative to the test measurement point The corresponding position is used 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 real-time data actually measured on site, a measurement value data set is constructed according to the three-dimensional coordinates of the measuring point in the simulation model and the corresponding scalar measurement value, a simulation result data set is constructed according to the three-dimensional coordinates of the simulation finite element nodes in the simulation result data and the corresponding scalar simulation value, a spatial range defined by the three-dimensional coordinates of the measuring point and the preset average scalar processing spatial range threshold value is used as the envelope, simulation finite element nodes and their corresponding scalar values ​​in the simulation result data set are selected, and the average value of the simulation result data is calculated, and the simulation model after the average scalar processing is used as the spatial point matching the test measuring point to obtain the corresponding scalar comparison result; The simulation parameters of the physical three-dimensional model in the test virtual scene are corrected according to the scalar distribution comparison result and the scalar 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.

2. The scalar field simulation and real-time data processing method according to claim 1, characterized in that: The performing scalar field data test time matching according to the field measured real-time data and the simulation result data comprises: Setting the output time step of the simulation model corresponding to the physical three-dimensional model to be consistent with the on-site measured real-time data, so that the simulation model outputs data at the same time interval as the on-site measured real-time data; The simulation result data is interpolated to the time corresponding to the sampling point of the field measured real-time data through a preset interpolation algorithm, and the time boundary of the simulation result data and the field measured real-time data is unified through a data interception operation.

3. The scalar field simulation and real-time data processing method according to claim 1, characterized in that: The step of using the simulation model processed by the average scalar as a spatial point matching the test measurement point to obtain a corresponding scalar comparison result 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 scalar field is constructed according to the test values, and test space matching of scalar field data is performed according to the test measurement scalar field.

4. The scalar field simulation and real-time data processing method according to claim 3, characterized in that: The method of performing three-dimensional linear interpolation based on the on-site measured real-time data according to the finite element position in the simulation result number to obtain the corresponding test value includes: Constructing a measurement value data set according to the three-dimensional coordinates of the measurement points in the simulation model and the corresponding scalar measurement values, and constructing a simulation result data set according to the three-dimensional coordinates of the simulation finite element nodes in the simulation result data and the corresponding scalar simulation values; Three-dimensional linear interpolation is performed according to the simulation result data set to obtain corresponding test values.

5. The scalar field simulation and real-time data processing method according to claim 4, characterized in that: The performing three-dimensional linear interpolation according to the simulation result data set to obtain corresponding test values ​​includes: Traversing the left and right finite element nodes in the simulation result data set; The node coordinate value is extracted, and a preset number of test measurement points are selected as interpolation reference points in the x, y, and z directions of the coordinate value according to the nearest principle for three-dimensional linear interpolation.

6. A scalar field simulation and real-time data processing 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 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; an analysis and comparison module, for performing test time matching of scalar field data according to the field measured real-time data and the simulation result data, wherein the test time matching of scalar field data is thermal test time matching of simulated temperature field data and test temperature field data, performing root mean square error calculation between the obtained test scalar field data and the scalar data in the simulation result data, determining an index of the difference between the simulation result data and the field measured real-time data, wherein the index is an index of the difference between the simulated temperature field data and the test temperature field data, calculating the linear correlation between the simulated scalar field and the test scalar field by a preset correlation coefficient algorithm, wherein the linear correlation is the linear correlation between the simulated temperature field and the test temperature field, and calculating the maximum difference between the simulated scalar field and the test scalar field by a maximum deviation algorithm; selecting and comparing the test scalar field with the test scalar field; and The position corresponding to the test point is used 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 real-time data measured on site, and a measurement value data set is constructed according to the three-dimensional coordinates of the measurement point in the simulation model and the corresponding scalar measurement value, and a simulation result data set is constructed according to the three-dimensional coordinates of the simulation finite element node in the simulation result data and the corresponding scalar simulation value, and the spatial range defined by the three-dimensional coordinates of the measurement point and the preset average scalar processing spatial range threshold value is used as the envelope, and the simulation finite element nodes and their corresponding scalar values ​​in the spatial range are selected from the simulation result data set, and the average value of the simulation result data is calculated, and the simulation model after the average scalar processing is used as the spatial point matching the test measurement point to obtain the corresponding scalar comparison result; The simulation test module is used to correct the simulation parameters of the physical three-dimensional model in the test virtual scene according to the scalar distribution comparison results and the scalar 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.

7. 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 scalar field simulation and real-time data processing method described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the scalar field simulation and real-time data processing method described in any one of claims 1 to 5 are implemented.

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