A virtual-real combined test data analysis method based on a two-dimensional model

Abstract

The application provides a kind of real and virtual combination test data analysis method based on two-dimensional model, comprising the following steps: S1 design test and plan test content, make two-dimensional model of test piece, determine sensor measuring point position;S2 obtain the original virtual measuring point simulation data of test piece, carry out structured storage;S3 specify complete data transmission protocol, establish the connection of real test data to two-dimensional model transmission data;S4 through data transmission protocol, start test, carry out real-time data transmission;S5 according to test real-time data set, real-time processing simulation data;S6 test piece standard virtual measuring point simulation data and test real-time data carry out real-time difference calculation, visual display curve.Real-time display of key data and position information of two-dimensional test, real-time display and comparison of simulation data and real test data are completed, the intuitiveness and comprehensiveness of test display are improved, the high cost and long time investment required for building complete three-dimensional model test are avoided, and the test efficiency is significantly improved.

Description

Technical Field

[0001] This invention belongs to the field of experimental virtual-real fusion technology, specifically involving a method for analyzing experimental data based on a two-dimensional model. Background Technology

[0002] In scientific research and engineering practice, traditional data analysis methods mainly rely on actual physical experiments. This involves testing test specimens under various working conditions and loads to obtain and analyze the corresponding physical test data. However, this method has many limitations, such as high testing costs and long testing cycles.

[0003] With the rapid development of computer and simulation technologies, virtual simulation experiments have emerged. These experiments simulate the testing process in a computer environment, using the same loading conditions as real physical experiments, thus reproducing the physical test scenario in a virtual environment. This allows for the acquisition of a large amount of simulation test data at a lower cost and in a shorter timeframe.

[0004] Generally, 3D model virtual testing can intuitively and accurately simulate the physical scene of a real test, and allows for comparison between the simulation model and the test specimen model. However, in some cases, due to technological limitations, resource constraints, or time constraints, constructing a complete and accurate 3D model may become impractical or costly, thus preventing the successful implementation of 3D model-based virtual-real tests. Against this backdrop, 2D model testing, as an effective alternative, is gradually demonstrating its unique value.

[0005] Two-dimensional model testing simplifies the complexity of physical models, focusing on key data and experimental procedures, and provides preliminary experimental data and analysis at a lower cost and faster speed. Although two-dimensional models have limitations in reproducing realistic models, they can provide valuable reference information when resources are limited, laying the foundation for subsequent three-dimensional model design and optimization.

[0006] Currently, the application of hybrid virtual-real data analysis methods in two-dimensional model experiments is still in its early stages, and many challenges remain in practical applications. These include accurately mapping the relationship between the two-dimensional model and simulation data, accurately displaying the specific location of experimental data within the two-dimensional model, and effectively comparing two-dimensional experimental data with simulation results. Therefore, researching an efficient and accurate hybrid virtual-real data analysis method for two-dimensional model experiments, combining the advantages of two-dimensional model experiment data with virtual simulation experiments (even in the absence of a complete three-dimensional model), and using algorithms and data processing to achieve comparative and comprehensive analysis of experimental data, is of great significance for advancing scientific research and engineering practice under resource-constrained conditions. Summary of the Invention

[0007] This invention proposes a virtual-real combined experimental data analysis method based on a two-dimensional model. It enables researchers to view experimental data at key locations using a two-dimensional model of the test specimen, even in the absence of a three-dimensional model. This avoids the high costs and time investment required to construct a complete three-dimensional model, significantly improving experimental efficiency. Furthermore, the data from the two-dimensional model experiments provides reference and optimization directions for subsequent three-dimensional model design. The method achieves real-time display of key data and location information from two-dimensional experiments, real-time display and comparison of simulation and real experimental data, and real-time comparison of the differences between simulation and real experimental data, improving the intuitiveness and comprehensiveness of the experimental presentation. It accurately captures key experimental data under resource-constrained conditions or when a complete three-dimensional model is lacking, promoting the development of scientific research and engineering practice.

[0008] A method for analyzing virtual-real combined experimental data based on a two-dimensional model includes the following steps: S1. Design the experiment and plan the experimental content, make a two-dimensional model of the test piece, and determine the sensor measurement point position; S2. Obtain the original virtual measurement point simulation data of the test piece and store it in a structured manner; S3. Specify a complete data transmission protocol and establish a connection for transmitting real experimental data to the two-dimensional model; S4. Start the experiment and perform real-time data transmission through the data transmission protocol; S5. Process simulation data in real time based on the real-time experimental dataset; S6. The simulation data of the standard virtual measuring points of the test piece and the real-time test data are calculated in real time, and the curve is displayed visually.

[0009] Step S1 specifically includes: S101, Create a two-dimensional model of the test specimen and obtain the two-dimensional model dataset of the test specimen, including: two-dimensional model structure data and sensor measurement point identification data; S102, obtain the names of sensor measurement points and the actual data display locations in the two-dimensional model; automatically obtain the names of sensor measurement points and the actual data display locations in the two-dimensional model through OCR recognition technology. In the case of inaccurate or incomplete OCR recognition, mark the actual data display locations in the two-dimensional model through the two-dimensional model data visualization label annotation method.

[0010] Step S2 specifically includes: S201, Create virtual measurement point simulation data for the test piece and obtain the original simulation dataset of the test piece; S202, the original virtual measurement point simulation data of the test piece is preprocessed by the linear interpolation method to ensure that the sampling rate is consistent with that of the real test data, and the standard virtual simulation dataset of the test piece is obtained.

[0011] Step S3 specifically includes: S301, establishes communication between the data receiving end and the data sending end through the data transmission device; S302, according to the data transmission protocol, detect whether the transmitted data includes the measurement point name, frequency, and data test data information specified in the protocol, thereby determining whether the data transmission is normal.

[0012] Step S4 specifically includes: S401, Start the test and wait to receive real-time data; S402, formally receive real-time experimental data and obtain the real-time experimental dataset.

[0013] Step S5 specifically includes: S501, zero-point alignment between real-time experimental data and simulation data; S502 processes the original virtual simulation dataset of the test piece in real time by linear interpolation or downsampling according to the real-time data transmission frequency of the test, and obtains the test standard virtual simulation dataset in real time.

[0014] Step S6 specifically includes: S601 performs real-time downsampling processing on experimental data and displays the data visually, and compares it in real-time with the experimental standard virtual simulation dataset. S602, through the difference calculation method, calculates the difference between the simulation data of the standard virtual measuring point of the test piece and the real-time test data in real time, and obtains the difference dataset between the simulation data of the measuring point and the real-time test data. S603, real-time plotting of difference data curves.

[0015] The beneficial effects of this invention are as follows: (1) A method for recognizing two-dimensional model files using OCR recognition technology is provided.

[0016] Its beneficial effects are: it can quickly identify the sensor measurement point number and sensor measurement point location in the two-dimensional model file through OCR recognition technology, eliminating the need to manually input a large number of measurement point numbers, effectively reducing the time cost of test personnel, and significantly improving the efficiency and reliability of the test preparation stage.

[0017] (2) A two-dimensional model data visualization labeling method is provided, which is used to identify the location of real data display in the two-dimensional model.

[0018] Its beneficial effects are as follows: This method is used when OCR recognition is inaccurate or incomplete. Through a two-dimensional model data visualization labeling method, the display positions of real experimental data or simulation results are intuitively and accurately marked on the two-dimensional model diagram, solving the problem of the disconnect between data and the spatial position of the two-dimensional model. Experimenters can quickly and accurately understand the specific location of specific data points (such as temperature, strain, and displacement) on the two-dimensional model without having to consult additional drawings or reference tables, greatly improving the efficiency and accuracy of data interpretation, and significantly enhancing the intuitiveness and operability of the experimental process and result analysis.

[0019] (3) A method for linear interpolation or downsampling of simulation data is provided.

[0020] Its beneficial effects are as follows: This method provides flexible data processing means. Linear interpolation can smooth data or increase the density of data points in key areas as needed without significantly increasing the amount of computation, thereby improving the smoothness and detail of the resulting curves; downsampling can effectively compress the volume of simulation data, remove redundant information, significantly reduce the burden of subsequent storage, transmission and visualization rendering, improve processing efficiency, and at the same time maintain the integrity of key feature information.

[0021] (4) A method for downsampling experimental data is provided.

[0022] Its beneficial effects are as follows: This method is specifically designed for high-speed acquired real-time experimental data streams, reducing the data rate and total volume through downsampling. Its core value lies in effectively alleviating data storage pressure, reducing network transmission requirements, and accelerating real-time visualization refresh speed without losing core dynamic characteristics (such as peak values ​​and trends). This enables more stable and efficient processing of long-term, high-frequency experimental data streams when conducting virtual-real combined data analysis and comparison, ensuring the smoothness and responsiveness of visualization comparisons.

[0023] (5) A method for calculating the difference between real-time experimental data and experimental simulation data is provided.

[0024] Its beneficial effects are as follows: This method can evaluate the degree of agreement or deviation between the actual experimental results and the simulation prediction in real time and quantitatively, providing a key basis for data analysis and comparison, anomaly diagnosis, model verification and correction during the experimental process, greatly enhancing the real-time performance and effectiveness of the virtual-real combination analysis, and supporting experimental decision-making. Attached Figure Description

[0025] Figure 1 This is a flowchart of a virtual-real combined experimental data analysis method based on a two-dimensional model; Figure 2 This is a flowchart of step S1 of the present invention; Figure 3This is a flowchart of step S2 of the present invention; Figure 4 This is a flowchart of step S3 of the present invention; Figure 5 This is a flowchart of step S4 of the present invention; Figure 6 This is a flowchart of step S5 of the present invention; Figure 7 This is a flowchart of step S6 of the present invention. Detailed Implementation

[0026] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection claimed by the present invention.

[0027] like Figure 1 As shown, a method for analyzing experimental data combining virtual and real data based on a two-dimensional model includes the following steps: S1, designing the experiment and planning the experimental content, fabricating a two-dimensional model of the test specimen, and determining the sensor measurement point positions. Step S1 specifically includes the following steps:

[0028] S101, Create a two-dimensional model of the test specimen and obtain the two-dimensional model dataset of the test specimen, including: two-dimensional model structure data and sensor measurement point identification data.

[0029] S102, Obtain the names of sensor measurement points and the actual data display locations in the 2D model. Using OCR recognition technology, the names of sensor measurement points and the actual data display locations in the 2D model are automatically obtained. For cases where OCR recognition is inaccurate or incomplete, the actual data display locations in the 2D model are identified using a 2D model data visualization labeling method.

[0030] S2. Obtain the original virtual measurement point simulation data of the test specimen and store it in a structured format. Step S2 specifically includes the following steps:

[0031] S201, Create virtual measurement point simulation data for the test piece and obtain the original simulation dataset of the test piece.

[0032] S202, the original virtual measurement point simulation data of the test piece is preprocessed by the linear interpolation method to ensure that the sampling rate is consistent with that of the real test data, and the standard virtual simulation dataset of the test piece is obtained.

[0033] S3. Define a complete data transmission protocol and establish a connection for transmitting real experimental data to the two-dimensional model. Step S3 specifically includes the following steps:

[0034] S301 establishes communication between the data receiving end and the data sending end through the data transmission device.

[0035] S302, according to the data transmission protocol, detect whether the transmitted data includes complete test data information such as the measurement point name, frequency, and data specified in the protocol, thereby determining whether the data transmission is normal.

[0036] S4. Initiate the experiment and perform real-time data transmission via the data transmission protocol. Step S4 specifically includes the following steps:

[0037] S401, start the test and wait to receive real-time data.

[0038] S402, formally receive real-time experimental data and obtain the real-time experimental dataset.

[0039] S5. Process the simulation data in real time based on the real-time experimental dataset. Step S5 specifically includes the following steps:

[0040] S501, zero-point alignment between real-time experimental data and simulation data.

[0041] S502 processes the original virtual simulation dataset of the test piece in real time by linear interpolation or downsampling according to the real-time data transmission frequency of the test, and obtains the test standard virtual simulation dataset in real time.

[0042] S6. Calculate the real-time difference between the simulated data of the standard virtual measuring points on the test piece and the real-time experimental data, and visualize the curve. Step S6 specifically includes the following steps:

[0043] S601 performs real-time downsampling processing on experimental data and displays it visually, comparing it in real-time with the experimental standard virtual simulation dataset.

[0044] S602 uses a difference calculation method to calculate the difference between the simulation data of the standard virtual measuring points of the test piece and the real-time test data in real time, and obtains the difference dataset between the simulation data of the measuring points and the real-time test data.

[0045] S603, real-time plotting of difference data curves.

[0046] Example: The embodiments of the present invention require the collection of comprehensive test-related datasets, including two-dimensional model datasets, original virtual simulation datasets of test specimens, standard virtual simulation datasets of test specimens, real-time test datasets, and difference datasets.

[0047] like Figure 1 As shown, a method for analyzing virtual and real experimental data based on a two-dimensional model specifically includes the following steps: S1. Design the experiment and plan the experimental content, and determine the sensor measurement point location.

[0048] Step S1 requires the collection of data including: a two-dimensional model dataset. For example... Figure 2 Step S1 specifically includes the following steps:

[0049] S101, Create a two-dimensional model of the test specimen and obtain the two-dimensional model dataset of the test specimen, including: two-dimensional model structure data and sensor measurement point identification data.

[0050] Specifically, the two-dimensional model file of the test specimen is obtained, and the model file is organized into folders according to the structure of the test specimen. After the organization is completed, the file is compressed using a compression tool to obtain a two-dimensional model dataset containing test specimen model data and sensor measurement point identification data.

[0051] S102, Obtain the names of sensor measurement points and the actual data display locations in the 2D model. Using OCR recognition technology, the names of sensor measurement points and the actual data display locations in the 2D model are automatically obtained. For cases where OCR recognition is inaccurate or incomplete, the actual data display locations in the 2D model are identified using a 2D model data visualization labeling method.

[0052] Specifically, the model file is first identified using OCR technology. The following is an overview of the identification process: 1. Image preprocessing: Perform preprocessing operations such as denoising and binarization on the prepared model files to improve the accuracy of subsequent recognition; 2. Text Region Detection: Utilizing image processing techniques to locate regions in the model file that may contain text; 3. Character segmentation: The detected text region is segmented into individual characters to prepare for subsequent character recognition; 4. Feature extraction: Extract the characteristic information of each character, such as shape, texture, angle, etc. These features will be used for subsequent character recognition; 5. Character recognition: The extracted character features are compared with a pre-trained character database to identify the content of each character; 6. Information integration: Organize the extracted information into sensor measurement point identification data.

[0053] Secondly, for cases where OCR recognition is inaccurate or incomplete, a second data acquisition process is performed. Specifically, a two-dimensional model data visualization labeling method is used to identify the locations where the actual data is displayed in the two-dimensional model. The following is an overview of the two-dimensional model data visualization labeling method processing procedure:

[0054] 1. Visual display of 2D model files; 2. Create a canvas based on the visual display area; 3. Obtain the two-dimensional object of the canvas; 4. Implement a trigger listener for the canvas; 5. Visualize the area drawn during the triggering process. The area can be drawn repeatedly, with the last drawn area being used as the reference. 6. Finally, record the coordinate values ​​of the visualized label area.

[0055] S2. Obtain the original virtual measurement point simulation data of the test piece, and store it in a structured manner to obtain the original virtual simulation dataset of the test.

[0056] Step S2 requires the collection of the following data: the original virtual simulation dataset of the test specimen. For example... Figure 3 Step S2 specifically includes the following steps:

[0057] S201, Create virtual measurement point simulation data for the test piece and obtain the original virtual simulation dataset of the test piece.

[0058] S202, the original virtual measurement point simulation data of the test piece is preprocessed by the linear interpolation method to ensure that the sampling rate is consistent with that of the real test data, and the standard virtual simulation dataset of the test piece is obtained.

[0059] S3. Specify a complete data transmission protocol, establish a data connection for transmitting real test data to the virtual two-dimensional model, and check whether the data transmission is normal.

[0060] like Figure 4 Step S3 specifically includes the following steps: S301 establishes communication between the data receiving end and the data sending end through the data transmission device.

[0061] Specifically, by configuring IP addresses and port numbers, connections are established between the client and the data receiver, and between the server and the data sender.

[0062] S302, according to the data transmission protocol, detect whether the transmitted data includes complete test data information such as the measurement point name, frequency, and measurement point data specified in the protocol, thereby determining whether the data transmission is normal.

[0063] Specifically, the test data reception process determines the completeness of the test data information according to the prescribed data transmission protocol, and realizes the real-time reception, processing, storage, and push display of the test data.

[0064] S4. Start the experiment and perform real-time data transmission through the data transmission protocol.

[0065] Step S4 requires the collection of data including: the real-time dataset of the experiment. For example... Figure 5Step S4 specifically includes the following steps:

[0066] S401, start the test and wait to receive real-time data.

[0067] Specifically, the test preparation phase begins, awaiting the receipt of real-time test data.

[0068] S402, formally receive real-time experimental data and obtain the real-time experimental dataset.

[0069] Specifically, when the client receives the data, the experiment enters the start phase, and the client officially begins receiving real-time experimental data.

[0070] S5. Process simulation data in real time based on the real-time experimental dataset.

[0071] Step S5 requires the collection of the following data: the experimental standard virtual simulation dataset. For example... Figure 6 Step S5 specifically includes the following steps:

[0072] S501, zero-point alignment between real-time experimental data and simulation data.

[0073] Specifically, by using digital IO transitions in the data transmission protocol, when the digital IO transitions from 0 to 1 or from 0 to 2, the current time is determined to be the actual start time of the experiment, and this time is aligned with the start time of the simulation data.

[0074] S502, based on the real-time data transmission frequency of the test, performs real-time processing on the original virtual simulation dataset of the test piece using linear interpolation or downsampling methods to obtain the test simulation dataset in real time.

[0075] 1. Linear interpolation method Specifically, the time series of the original virtual measurement point simulation data is processed into time intervals according to the sampling frequency of the actual test data, and the estimated value of each time position is calculated according to the algorithm to form a set of standard virtual measurement point simulation data that can correspond to the actual test data.

[0076] When applied to the processing of raw virtual measurement point data of the test specimen, the first step is to calculate the value of X at each time position. This involves generating a uniformly distributed numerical sequence {Xn} from the time series in the data according to the sampling rate intervals. Specifically, this is done based on the general formula of an arithmetic sequence: Given the nth term an, the first term a1, n, and the common difference d, it can be calculated using the formula: Where n is calculated according to the formula (L is an integer): Where L represents the length of the time series of the original virtual measurement point data of the test specimen, and the sampling rate is the sampling frequency of the actual test data. After calculating the tolerance d, substitute it into the general term formula of the arithmetic sequence to calculate X1…X n The specific numerical composition of the numerical sequence {X n}, (n is an integer and n>0).

[0077] Get {X n After the sequence, judge in turn. If the condition is true, then estimate Y using linear interpolation. n If the condition is not met, compare it with the condition in the next interval, and so on, until the estimated value sequence {Y} is obtained. n}

[0078] Specifically, linear interpolation is based on linear interpolation between two consistent data points, assuming that the function changes linearly between these two data points. Its calculation method is as follows: For known data points (x) n y n ) and (x n+1 y n+1 Find a position X between these two points. n The estimated value Y n (n is an integer and n>0).

[0079] First, calculate X. n Relative to x n and x n+1 Scale factor: Then use the scaling factor t n For y n and y n+1 Perform linear interpolation calculations: Among them, Y n Indicates position X n The estimated value at that location.

[0080] Ultimately, X n Location data and estimated value Y n The data values ​​constitute the processed test specimen standard virtual simulation dataset, with a data length of n.

[0081] 2. Downsampling method Specifically, the principle of data downsampling is to reduce the sampling rate by sampling the original data at intervals, thereby reducing the amount of data.

[0082] First, calculate the downsampling factor of the data. The formula for calculating the downsampling factor is: Among them, F s It is the sampling frequency of the original signal, F y M is the desired sampling frequency after downsampling, and M is the downsampling factor.

[0083] Assuming the original data is x[n], the downsampled data y[n] can be calculated using the following formula: Where n is the sample index after downsampling, and M is the downsampling factor, which is to select one sample from every M samples.

[0084] S6. The simulation data of the standard virtual measuring points of the test piece and the real-time test data are calculated in real time, and the curve is displayed visually.

[0085] Step S6 requires the collection of the following data: the difference dataset. For example... Figure 7 Step S6 specifically includes the following steps:

[0086] S601 performs real-time downsampling processing on experimental data and displays it visually, comparing it in real-time with the experimental standard virtual simulation dataset.

[0087] Specifically, due to the high frequency of real-time data transmission and the large amount of data collected, the real-time data is downsampled using the maximum triangle triple-bucket algorithm to facilitate visualization.

[0088] The maximum triangle triplet algorithm is an effective method for downsampling time series data. Its core idea is to retain important information while reducing the amount of data by selecting points that maximize local data variation (i.e., form the maximum triangle).

[0089] The specific processing steps are as follows: 1. Initialization: Divide the time series data into several buckets (intervals) based on the number of buckets. The calculation formula is as follows: Where L represents the length of the time series data, num represents the number of buckets, and size represents the length of the data interval.

[0090] 2. Calculate representative points for each bucket: Select three points in each bucket: the start point, the end point, and the third point that forms the largest triangle within the data range. The formula for calculating the start point of the data interval is as follows:

[0091] The formula for calculating the endpoint of a data range is as follows: Where size is the data interval obtained in step 1, startn represents the starting point of the data interval, endn represents the ending point of the data interval, and num represents the number of buckets.

[0092] Based on the above calculation results, a set of data intervals is obtained. n [start n end n (data is time series data, such as [(x1, y1), (x2, y2), ...]). If the number of data points in the data interval is less than or equal to 3, all data are retained; if it is greater than 3, the next calculation is performed.

[0093] 3. Select the largest triangle: Based on the cross product of vectors, given the three vertices of the triangle (x1, y1), (x2, y2), (x3, y4), (x5, y5), (x6, y6), (x7, y7), (x8, y8), (x9, y9), (x1, y1), (x2, y2), (x1, y1), (x2, y2), (x1, y1), (x2, y2), (x3 ... n y n Calculate the area of ​​the triangles, and select the third point of the triangle with the largest area as the representative point of the bucket. The calculation formula is as follows:

[0094] Where (x1, y1) are the data points obtained in step 2 (data[start)). n ][0],data[start n [1]), (x2, y2) are the data points obtained in 2. (data[end n ][0],data[end n [1],(x n y n The data range obtained from step 2 is data. n Data points in S n Let the area be the triangle. Iterate through all possible third points (data) within the bucket. n According to area S n By comparing the results, the largest triangle is found, and the starting point, the largest triangle point, and the ending point are preserved.

[0095] 4. Generate downsampled data: A new downsampled time series is formed by representative points from each bucket.

[0096] S602 uses a difference calculation method to calculate the difference between the simulation data of the standard virtual measuring points of the test piece and the real-time test data in real time, and obtains the difference dataset between the virtual simulation data of the measuring points and the real-time test data.

[0097] Specifically, according to the difference calculation formula: Where, x n Represents real-time experimental data, y n Represents the virtual measurement point simulation data, Δ n This is the difference between the two.

[0098] Finally, the difference dataset is obtained.

[0099] S603, real-time plotting of difference data curves.

[0100] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing virtual and real experimental data based on a two-dimensional model, characterized in that, Includes the following steps: S1. Design the experiment and plan the experimental content, make a two-dimensional model of the test piece, and determine the sensor measurement point position; S2. Obtain the original virtual measurement point simulation data of the test piece and store it in a structured manner; S3. Specify a complete data transmission protocol and establish a connection for transmitting real experimental data to the two-dimensional model; S4. Start the experiment and perform real-time data transmission through the data transmission protocol; S5. Process simulation data in real time based on the real-time experimental dataset; S6. The simulation data of the standard virtual measuring points of the test piece and the real-time test data are calculated in real time, and the curve is displayed visually.

2. The method for analyzing virtual and real experimental data based on a two-dimensional model according to claim 1, characterized in that, Step S1 specifically includes: S101, Create a two-dimensional model of the test specimen and obtain the two-dimensional model dataset of the test specimen, including: two-dimensional model structure data and sensor measurement point identification data; S102, obtain the names of sensor measurement points and the actual data display locations in the two-dimensional model; automatically obtain the names of sensor measurement points and the actual data display locations in the two-dimensional model through OCR recognition technology. In the case of inaccurate or incomplete OCR recognition, mark the actual data display locations in the two-dimensional model through the two-dimensional model data visualization label annotation method.

3. The method for analyzing virtual and real experimental data based on a two-dimensional model according to claim 1, characterized in that, Step S2 specifically includes: S201, Create virtual measurement point simulation data for the test piece and obtain the original simulation dataset of the test piece; S202, the original virtual measurement point simulation data of the test piece is preprocessed by the linear interpolation method to ensure that the sampling rate is consistent with that of the real test data, and the standard virtual simulation dataset of the test piece is obtained.

4. The method for analyzing virtual and real experimental data based on a two-dimensional model according to claim 1, characterized in that, Step S3 specifically includes: S301, establishes communication between the data receiving end and the data sending end through the data transmission device; S302, according to the data transmission protocol, detect whether the transmitted data includes the measurement point name, frequency, and data test data information specified in the protocol, thereby determining whether the data transmission is normal.

5. The method for analyzing virtual and real experimental data based on a two-dimensional model according to claim 1, characterized in that, Step S4 specifically includes: S401, Start the test and wait to receive real-time data; S402, formally receive real-time experimental data and obtain the real-time experimental dataset.

6. The method for analyzing virtual and real experimental data based on a two-dimensional model according to claim 1, characterized in that, Step S5 specifically includes: S501, zero-point alignment between real-time experimental data and simulation data; S502 processes the original virtual simulation dataset of the test piece in real time by linear interpolation or downsampling according to the real-time data transmission frequency of the test, and obtains the test standard virtual simulation dataset in real time.

7. The method for analyzing virtual and real experimental data based on a two-dimensional model according to claim 1, characterized in that, Step S6 specifically includes: S601 performs real-time downsampling processing on experimental data and displays the data visually, and compares it in real-time with the experimental standard virtual simulation dataset. S602, through the difference calculation method, calculates the difference between the simulation data of the standard virtual measuring point of the test piece and the real-time test data in real time, and obtains the difference dataset between the simulation data of the measuring point and the real-time test data. S603, real-time plotting of difference data curves.

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