Data analysis method, device, electronic device, and computer-readable storage medium

Abstract

The present invention provides a data analysis method, device, electronic device, and computer-readable storage medium for interface data interaction and analysis in variable-dimensional simulation. A data distribution model interpolates the turbine inlet total pressure and turbine inlet total temperature output by low-dimensional software, and uses these values, along with the turbine inlet flow rate and the flow rate at the interface between the turbine and the air system, as boundary conditions for high-dimensional software to calculate turbine performance. The high-dimensional software then outputs the two-dimensional data distribution to the data distribution model, which performs an average calculation and uses this as a boundary condition for the low-dimensional software to calculate air system performance or overall aircraft engine performance. The data distribution model's data storage structure and format are structured data, and the data from the data distribution model can be used to characterize the distribution of flow fields and physical quantities at the interface. The present invention can more accurately reflect the influence of flow field distribution at the interface between the air system and the mainstream.

Description

Technical Field

[0001] The present invention relates to the technical field of data simulation and analysis, and in particular to a method and device for analyzing interface data for variable-dimensional simulation. Background Art

[0002] With the introduction of numerical simulation technology into the aero-engine development system, the research and design model for aero-engines has undergone a revolutionary shift, shifting from the traditional reliance on experimental methods for research and verification to a research and design model that primarily relies on numerical simulation, supplemented by experimental research and verification. This has significantly shortened the design cycle and reduced research costs. A major cutting-edge area of numerical simulation technology is variable-dimensional simulation technology, which offers additional advantages for engine design and development. It allows for more complete and rapid evaluation of component designs within the engine system, making system-level simulation and optimization more efficient. The accuracy and resolution of engine models better meet analysis requirements. High-resolution simulations are performed only on components of interest, reducing computing resource consumption and improving design efficiency.

[0003] In traditional turbine performance calculations, at the interface between the air system and the mainstream, the mainstream calculation directly uses a given pressure or flow rate. The air system boundary at this point can be directly obtained from the mainstream or manually determined. This calculation fails to consider the mutual influence between the mainstream and the air system at the interface, nor the distribution of flow field parameters at the interface, resulting in inaccurate performance calculations. Furthermore, manual processing is required at the interface, but existing data analysis is inadequate, limited to data feedback from the integrated bus and graphical analysis of numerical simulation post-processing. This results in a lack of sufficient data for observation and analysis during manual processing. Furthermore, the data formats and volumes of one-dimensional and three-dimensional software on both sides of the interface differ significantly, necessitating appropriate analysis methods. Summary of the Invention

[0004] In order to address the deficiencies of the prior art, the present invention provides a data analysis method and device.

[0005] The present invention achieves the above technical objectives through the following technical means.

[0006] A data analysis method for interface data interaction and analysis of variable-dimensional simulation:

[0007] Obtaining the turbine inlet total pressure, turbine inlet total temperature, flow rate at the turbine inlet, and flow rate at the interface between the turbine and the air system output by the first software, interpolating the turbine inlet total pressure and turbine inlet total temperature using a data distribution model, and using these values, along with the flow rate at the turbine inlet and the flow rate at the interface between the turbine and the air system, as boundary conditions for the second software to calculate aircraft engine turbine performance; the second software outputs a two-dimensional data distribution, averaging the two-dimensional data distribution using the data distribution model, and using this average calculation as the boundary condition for the first software to calculate aircraft engine air system performance or overall aircraft engine performance; the two-dimensional data distribution is the distribution of the turbine outlet total pressure, the turbine outlet total temperature, and the static pressure at the interface between the turbine and the air system;

[0008] The data of the data distribution model can be used to characterize the distribution of the flow field and physical quantities at the interface;

[0009] The dimension of the second software is higher than that of the first software.

[0010] Furthermore, the data distribution model is constructed using test data, and the distribution of the test data is a contour map.

[0011] Furthermore, before performing interpolation calculation on the total pressure at the turbine inlet and the total temperature at the turbine inlet, the total pressure at the turbine inlet and the total temperature at the turbine inlet are normalized; after performing interpolation calculation on the total pressure at the turbine inlet and the total temperature at the turbine inlet, a summary calculation is performed; and a summary calculation is performed on the flow at the turbine inlet and the flow at the interface between the turbine and the air system.

[0012] Furthermore, the data distribution model performs normalization before averaging the two-dimensional data distribution of the turbine outlet total pressure and the turbine outlet total temperature; the data distribution model performs summary calculation after averaging the two-dimensional data distribution of the turbine outlet total pressure and the turbine outlet total temperature; and the static pressure at the interface between the turbine and the air system is averaged and summarized.

[0013] Furthermore, the first software is one-dimensional software or zero-dimensional software, and the one-dimensional software and zero-dimensional software are simulation software for aircraft engine air systems; the second software is three-dimensional software, and the three-dimensional software is commercial software.

[0014] Furthermore, the data storage structure and form of the data distribution model is structured data.

[0015] Furthermore, the structured data is used to draw a two-dimensional graph, a scatter plot and a point-line graph, which are used to analyze the flow field and the distribution of physical quantities at the interface.

[0016] A data analysis device, comprising:

[0017] A data distribution module is used to construct a data distribution model based on the test data, wherein the data distribution model is used to process data from low dimension to high dimension and from high dimension to low dimension at the interface;

[0018] The graphics drawing module is used to draw two-dimensional graphs, scatter plots and point-line graphs based on structured data. The two-dimensional graphs, scatter plots and point-line graphs are used to analyze the flow field and the distribution of physical quantities at the interface.

[0019] An electronic device comprising a memory and a processor;

[0020] The memory is used to store computer programs;

[0021] The processor is configured to execute the computer program and implement the above-mentioned data analysis method when executing the computer program.

[0022] A storage medium stores a computer program, which, when executed by a processor, causes the processor to perform the above-mentioned data analysis method.

[0023] The beneficial effects of the present invention are:

[0024] (1) The interactive interface data distribution model of the present invention couples computing software of different dimensions at the interface and can transmit data in real time, which can more accurately reflect the influence of the flow field distribution at the interface between the air system and the mainstream;

[0025] (2) The data distribution model of the present invention supports data interaction between software of different dimensions, supports simulation data transmission and analysis of no less than 5 key parameters (such as temperature, pressure, speed, etc.), and the interface data transmission error is no more than 1%. By connecting low-dimensional software and high-dimensional software through variable-dimensional simulation, the poor calculation accuracy of the one-dimensional model is avoided.

[0026] (3) The data analysis method of the present invention uses structured data to draw two-dimensional graphs, scatter plots, and point-line graphs. The two-dimensional graphs, scatter plots, and point-line graphs are used to analyze the flow field and the distribution of physical quantities at the interface. When processing data in batches, point-line graphs are selected to observe and filter data. Two-dimensional graphs are helpful in analyzing the correspondence between a certain dimension and a physical quantity. They can also facilitate the observation of the relationship between the physical quantity changes with two dimensions while observing the change of the physical quantity with one dimension. Comparing the compressed scatter plot with the original graph can avoid errors in the averaging algorithm or over-interpretation of data that originally lack regularity.

[0027] (4) The data analysis method of the present invention also supports data interaction between different devices: by using standardized Transmission Control Protocol / Internet Protocol (TCP / IP) communication, information transmission between local area networks is realized, and a general variable dimension process simulation framework is established. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of the data model encapsulation method of the present invention;

[0029] Figure 2 A flowchart of generating a data presentation diagram according to the present invention;

[0030] FIG3( a ) is a two-dimensional graph of data analysis performed by the present invention;

[0031] FIG3( b ) is a scatter plot of data analysis performed by the present invention;

[0032] FIG3( c ) is a dot-line graph of data analysis performed by the present invention. DETAILED DESCRIPTION

[0033] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereto.

[0034] In conventional air system calculations, components (such as turbines) are simplified into one-dimensional models based on empirical formulas, which are often derived from experimental data. However, the accuracy of the one-dimensional calculation results is directly affected by the structural differences between the experimental object and the actual engine structure, as well as the limited scope of experimental data. Furthermore, the limited data volume of one-dimensional calculations prevents the generation of high-dimensional distributions of physical quantities. Variable-dimensional simulation, which connects low-dimensional and high-dimensional software, can mitigate these issues.

[0035] The present invention first establishes a data distribution model for processing data from low dimension to high dimension and from high dimension to low dimension at the interface; the data storage structure and form of the data distribution model is structured data.

[0036] The interfaces include two categories: ① The interface between the turbine (as a component) and the aircraft engine as a whole is the turbine inlet and the turbine outlet. This interface is a curved surface in three-dimensional space, which can be approximated as a plane distribution (with two-dimensional characteristics) or a linear distribution (with one-dimensional characteristics) or a number (with zero-dimensional characteristics); ② The interface between the turbine (as a component) and the aircraft engine air system (as a component) is the curved surface at the connection between the two, which can also be approximated as a plane distribution, a linear distribution or a number.

[0037] The data obtained from the aircraft engine test or the numerical simulation data (with high accuracy and relatively completeness) verified by the aircraft engine test is used as the initial data. The distribution of the initial data is a contour map, and the initial data is used to build a data distribution model; the data distribution model summarizes a group of flow rates (turbine inlet total pressure, turbine inlet total temperature, turbine inlet flow) at the interface between the turbine and the air system transmitted by the one-dimensional software (or zero-dimensional software) (the turbine inlet total pressure and the turbine inlet total temperature are normalized, interpolated, and summarized, and the turbine inlet flow is summarized) to form a new data distribution model. The three-dimensional software calculates the turbine performance, and the parameters characterizing the turbine performance: the turbine outlet total pressure, the turbine outlet total temperature, and the static pressure at the interface between the turbine and the air system are transmitted to the data distribution model in the form of two-dimensional data distribution. The data distribution model processes the data (wherein the turbine outlet total pressure and the turbine outlet total temperature are normalized and averaged, and the static pressure at the interface between the turbine and the air system is averaged and summarized) and serves as the boundary conditions of the one-dimensional software (or zero-dimensional software). The one-dimensional software (or zero-dimensional software) calculates the aircraft engine air system performance (or the overall aircraft engine performance).

[0038] Among them, the boundary conditions of the three-dimensional software include the total pressure at the turbine inlet, the total temperature at the turbine inlet, the flow rate at the turbine inlet, and the flow rate at the interface between the turbine and the air system. The above boundary conditions are also used as data for calculating turbine performance. The characterization parameters of turbine performance include the total pressure at the turbine outlet, the total temperature at the turbine outlet, and the static pressure at the interface between the turbine and the air system.

[0039] The boundary conditions of one-dimensional software include the static pressure at the interface between the turbine and the air system, and the boundary conditions of zero-dimensional software include the total pressure at the turbine outlet and the total temperature at the turbine outlet; the parameter used to characterize the performance of the aircraft engine air system is the flow rate at the interface between the turbine and the air system, and the parameters used to characterize the overall performance of the aircraft engine are the total pressure at the turbine inlet, the total temperature at the turbine inlet, and the flow rate at the turbine inlet.

[0040] The one-dimensional software and zero-dimensional software are specifically simulation software for aircraft engine air systems. The simulation software for aircraft engine air systems is commonly used software in the field of aircraft engines. The three-dimensional software is commercial software, such as ANSYS.

[0041] The data structure of the data distribution model is a typical "key-value" model. It is implemented in Python based on Pandas.DataFrame and can be easily exported to CSV format for interaction with external interfaces. External interfaces include 3D software, 1D software (or 0D software) and test data. The data distribution model is encapsulated using object-oriented thinking. The flow chart is as follows: Figure 1 As shown, the top level is the base class InterpforCFX, which includes the following functions:

[0042] (1) Initialize the model (__init__): Define the input parameters and the function execution order. The input parameters include (Name, Source, NumofNodes, listout, Guiyi = False, TabelHeader = True), where Name is the output CSV file name; Source is the input CSV file name; NumofNodes determines the size of the generated matrix, for example, if NumofNodes = 50, a 50*50 matrix is generated with a total of 2500 data points; listout determines which physical quantities are calculated; Guiyi indicates whether normalization is required, which defaults to no; TabelHeader indicates whether the table header exists to determine whether the file needs to be saved later, which defaults to yes. The function execution order is divided into two parts: calculation and interaction. The calculation part includes SumCal, CalInterpolate, CalAverage, and Normal functions, and the interaction part includes Readdata, WriteTabelHeader, and Mergecsv functions.

[0043] (2) SumCal: Generate structured coordinates using linspace and meshgrid in numpy. Then, perform iterative calculations on the physical quantities to be calculated based on listout. The specific calculation process calls CalInterpolate or CalAverage. After the calculation is completed, the preliminary calculation results are generated through DataFrame.to_csv. Among them, the total pressure at the turbine inlet, the total temperature at the turbine inlet, the flow rate at the turbine inlet, the flow rate at the interface between the turbine and the air system, the static pressure at the interface between the turbine and the air system, the total pressure at the turbine outlet, and the total temperature at the turbine outlet all need to be summarized and calculated.

[0044] (3) Interpolation calculation (CalInterpolate): The total pressure and temperature at the turbine inlet need to be interpolated. Call Readdata to read the data, and then call normal to normalize the data. The interpolation algorithm uses scipy.Interpolate.Rbf, which belongs to the radial deviation equation (radial basis function approximation / interpolation of n-dimensional scattered data). The algorithm parameter smooth can be adjusted to smooth the interpolation result. After the calculation is completed, the relative error between the interpolated value and the original value is returned.

[0045] (4) Average calculation (CalAverage): The total pressure at the turbine outlet, the total temperature at the turbine outlet, and the static pressure at the interface between the turbine and the air system need to be averaged. For example, when the number of control data points does not exceed 100, the coordinate interval to be averaged is set to [0, 0.01], [0.01, 0.02]...; in actual operation, a for loop is used to traverse all data points and average the data points within a specific interval.

[0046] (5) Normalization: Normalization is a dimensionless processing method that converts the absolute value of the physical system numerical value into a relative value. The purpose is to reduce the error caused by the relative size disparity of data in the data set due to inconsistencies in data magnitude and dimension. The present invention only normalizes the coordinates to facilitate analysis and subsequent data averaging. The normalization method is Min-Max normalization, that is, all data is processed into values within the (0,1) interval. The calculation formula is x^ = (x-x_min) / (x_max-x_min). In the parameter interaction between low-dimensional software and high-dimensional software, the turbine inlet total pressure, turbine inlet total temperature, turbine outlet total pressure, and turbine outlet total temperature need to be normalized according to the above process.

[0047] (6) Read data: Read data using pandas.read_csv, set header to 3, and set the return value to a Numpy array of specific physical quantities. In this embodiment, the radial distribution of the turbine inlet total pressure and temperature, obtained through experiments or simulations, is used to obtain reliable data. The flow rate at the interface between the turbine and the air system and the turbine inlet flow rate are the raw data obtained by the sensor.

[0048] (7) Set the table header (WriteTableHeader): In order to interact with other software later, it is necessary to set a specific table header to form structured data that can be recognized by the software. The main step is to set the name of the interface and the vector direction.

[0049] (8) Output data (Mergecsv): Use the open function to open the corresponding csv and output it.

[0050] Taking the total pressure at the turbine inlet as an example, the interface data model used for variable-dimensional simulation is described in detail. The calculation source of the total pressure at the turbine inlet is zero-dimensional software. The zero-dimensional software only calculates a number (i.e., the average value of the total pressure at the turbine inlet). In fact, due to the rotation effect, the total pressure at the turbine inlet has a radial distribution. To solve this problem, it is necessary to estimate the radial distribution of the total pressure at the turbine inlet, that is, to convert the number calculated by the zero-dimensional software into an approximate radial distribution of the total pressure at the turbine inlet through the interface data model. Specifically: (1) Read the radial distribution of the total pressure at the turbine inlet obtained by experiment or simulation with high credibility; (2) Normalize the distribution; (3) Interpolate the number calculated by the zero-dimensional software to the distribution to form a new distribution; (4) Set the header, summarize and form structured data that can be recognized by the three-dimensional software, and output the data to the three-dimensional software.

[0051] By using the above structured data to analyze the specific flow field characteristics of the interface, a complete and feasible variable-dimensional simulation data analysis tool with engineering practicality was established for different dimensions. In order to be applicable to data of different sizes and dimensions, three data display graphs were developed: two-dimensional graph, scatter graph, and point-line graph. These can be generated in batches through programming, and the applicable scenarios of different graphs are explained. The specific steps include:

[0052] (1) Read structured data: Read data through pandas.read_csv, set header to 3, and set the return value to a Numpy.array of specific physical quantities.

[0053] (2) Normalization: Normalization is a dimensionless processing method that converts the absolute values of physical system values into relative values. The purpose is to reduce errors caused by the large relative size of data in the dataset due to inconsistencies in data magnitude and dimension. This paper only normalizes the coordinates to facilitate analysis and subsequent data averaging. The normalization method is Min-Max normalization, which processes all data into values within the interval (0, 1).

[0054] (3) Draw a two-dimensional graph: extract the normalized data, combine the two-dimensional coordinates and the physical quantity of interest into a Pandas.DataFrame format, so that they can be sorted according to specific coordinates. Use data.sort_values to sort, use plt.scatter to draw a two-dimensional graph of the primary coordinate dimension and the physical quantity, and configure a color bar according to the secondary coordinate dimension. Select 'viridis' as the color bar type.

[0055] (4) Average data: To facilitate subsequent data analysis, the data needs to be averaged to reduce the amount of data. With the previous coordinate normalization, the coordinate range is already [0, 1], so it is relatively easy to average the data. For example, if the number of data points does not exceed 100, the coordinates can be set to [0, 0.01, 0.02…0.99]. In actual operation, a for loop is used to traverse all data points and average the data points within a specific interval.

[0056] (5) Draw a scatter plot: Use plt.scatter to draw a scatter plot of the average data.

[0057] (6) Draw a dot-line graph: Use plt.plot to draw a dot-line graph of the average data.

[0058] The supporting data analysis method is: when processing data in batches, the most efficient point-line plot should be studied to understand the distribution patterns of different physical quantities (such as pressure, temperature, and velocity) at the interface. Point-line plots can intuitively display these patterns. When conducting in-depth analysis, two-dimensional plots should be studied first to observe the relationship between the two dimensions. When one dimension is of interest, or when the connection between different dimensions is weak, point-line plots can be studied. When there are doubts about the data, a compressed scatter plot can be compared with the original image to avoid errors in the averaging algorithm or over-interpretation of data that originally lacks regularity.

[0059] The following is an in-depth analysis of the advantages and disadvantages of the three data display plots. Two-dimensional plots: The advantage is that the data is directly derived from numerical simulation post-processing, eliminating programming errors and ensuring data reliability. Furthermore, it allows for observing the relationship between physical quantities varying in two dimensions while simultaneously observing the variation of physical quantities varying in one dimension. For example, Figure 3(a) shows that within the range y = [0.4-0.6], the static pressure distribution is strongly correlated with z, with larger z values increasing the static pressure. This is likely because within the range y = [0.4-0.6], as z increases, the airflow approaches the blades, where it is obstructed, converting kinetic energy into pressure potential energy, causing the pressure to rise. The disadvantage is that the data patterns are less intuitive and the data points are too scattered, requiring careful observation. Scatter plots: The advantage is that the data patterns are relatively intuitive and can be compared with two-dimensional plots to verify averaging. The disadvantage is that the data points are still relatively large, the data patterns are less clear, and it is difficult to compare the relationship between physical quantities varying in two dimensions (see Figure 3(b)). Point-line graph: The advantage is that the data patterns are clear and intuitive, and multiple lines can be compared at the same time. The disadvantage is that the lack of regularity in the two-dimensional graph may be over-interpreted, resulting in regularity in the data that originally lacked regularity. For example, the pattern in Figure 3(c) is a convex parabola shape, but compared with Figure 3(a), it can be found that this pattern does not apply to all data, and it is impossible to compare the relationship between physical quantities as they change in two dimensions.

[0060] The present invention provides a data analysis device, comprising:

[0061] A data distribution module is used to construct a data distribution model based on the test data, wherein the data distribution model is used to process data from low dimensions to high dimensions and from high dimensions to low dimensions at the interface between the air system and the main flow;

[0062] The graphics drawing module is used to draw two-dimensional graphs, scatter plots and point-line graphs based on structured data. The two-dimensional graphs, scatter plots and point-line graphs are used to analyze the flow field and the distribution of physical quantities at the interface.

[0063] Based on the same inventive concept as a data analysis method, the present application also provides an electronic device, which includes one or more processors and one or more memories, wherein a computer-readable code is stored in the memory, wherein the computer-readable code, when executed by one or more processors, implements a data analysis method of the present invention. The memory may include a non-volatile storage medium and an internal memory; the non-volatile storage medium may store an operating system and a computer-readable code. The computer-readable code includes program instructions, which, when executed, enable the processor to execute any data analysis method. The processor is used to provide computing and control capabilities to support the operation of the entire electronic device. The memory provides an environment for the operation of the computer-readable code in the non-volatile storage medium, and when the computer-readable code is executed by the processor, the processor can execute any data analysis method.

[0064] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0065] The computer-readable storage medium may be an internal storage unit of the electronic device described in the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the electronic device.

[0066] The embodiments described are preferred implementations of the present invention, but the present invention is not limited to the above implementations. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essence of the present invention are within the scope of protection of the present invention.

Claims

1. A data analysis method, characterized in that: Interface data interaction and analysis for variable-dimensional simulation; The turbine inlet total pressure, turbine inlet total temperature, flow rate at the turbine inlet, and flow rate at the interface between the turbine and the air system, output by the first software, are obtained; the turbine inlet total pressure and turbine inlet total temperature are interpolated using a data distribution model; the interpolation calculation results, together with the flow rate at the turbine inlet and the flow rate at the interface between the turbine and the air system, are used as boundary conditions for the second software to calculate the performance of the aircraft engine turbine; the second software outputs a two-dimensional data distribution, the two-dimensional data distribution is averaged using the data distribution model, and the average calculation is used as the boundary condition for the first software to calculate the performance of the aircraft engine air system or the overall performance of the aircraft engine; the two-dimensional data distribution is the distribution of the turbine outlet total pressure, the turbine outlet total temperature, and the static pressure at the interface between the turbine and the air system; The data of the data distribution model can be used to characterize the distribution of the flow field and physical quantities at the interface; The dimension of the second software is higher than that of the first software.

2. The data analysis method according to claim 1, characterized in that The data distribution model is constructed using test data, and the distribution of the test data is a contour map.

3. The data analysis method according to claim 1, wherein: Before performing interpolation calculation on the total pressure at the turbine inlet and the total temperature at the turbine inlet, the total pressure at the turbine inlet and the total temperature at the turbine inlet are normalized; after performing interpolation calculation on the total pressure at the turbine inlet and the total temperature at the turbine inlet, a summary calculation is performed; and the flow at the turbine inlet and the flow at the interface between the turbine and the air system are summarized and calculated.

4. The data analysis method according to claim 1, wherein: The data distribution model performs normalization before averaging the two-dimensional data distribution of the turbine outlet total pressure and the turbine outlet total temperature; the data distribution model performs summary calculation after averaging the two-dimensional data distribution of the turbine outlet total pressure and the turbine outlet total temperature; and the static pressure at the interface between the turbine and the air system is averaged and summarized.

5. The data analysis method according to claim 1, wherein: The first software is one-dimensional software or zero-dimensional software, and the one-dimensional software and zero-dimensional software are simulation software for aircraft engine air systems; the second software is three-dimensional software, and the three-dimensional software is commercial software.

6. The data analysis method according to claim 1, characterized in that: The data storage structure and form of the data distribution model is structured data.

7. The data analysis method according to claim 6, characterized in that: The structured data is used to draw a two-dimensional graph, a scatter plot and a point-line graph, which are used to analyze the flow field and the distribution of physical quantities at the interface.

8. A device based on the data analysis method according to any one of claims 1 to 7, characterized in that: include: A data distribution module is used to construct a data distribution model based on the test data, wherein the data distribution model is used to process data from low dimension to high dimension and from high dimension to low dimension at the interface; The graphics drawing module is used to draw two-dimensional graphs, scatter plots and point-line graphs based on structured data. The two-dimensional graphs, scatter plots and point-line graphs are used to analyze the flow field and the distribution of physical quantities at the interface.

9. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the data analysis method according to any one of claims 1 to 7 when executing the computer program.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the data analysis method according to any one of claims 1 to 7.

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