A rough road surface data expansion method, system and program product for simulation analysis

Abstract

The present invention relates to the technical field of tire simulation design, and in particular to a rough road surface data expansion method, system and program product for simulation analysis. The method obtains road surface morphology data through a laser surface scanning device, adopts equidistant scanning and converts the scanned data into a three-dimensional coordinate form. Then, the data is expanded using a Python program, supporting expansion in multiple directions such as up, down, left and right, and smoothly connecting the expanded data with the original data through a reverse operation to ensure that the boundary between the expanded part and the original data is continuous and smooth. The method can achieve multiple and unlimited expansions, and there is no need for tedious manual splicing during the expansion process, which significantly improves the expansion efficiency and accuracy. The expanded data can be used to generate a road surface model suitable for simulation analysis through grid division. The expansion method of the present invention greatly improves the processing efficiency, has a smooth expansion boundary, has strong flexibility and adaptability, and is widely applicable to tire simulation analysis and other surface data expansion in engineering fields.

Description

Technical Field

[0001] The present invention relates to the technical field of tire simulation design, and in particular to a rough road surface data expansion method, system and program product for simulation analysis. Background Art

[0002] In tire simulation analysis, accurate road surface topography data is a key factor in obtaining accurate simulation results. Especially when performing tire finite element analysis (FEA), the real road surface topography can significantly improve the accuracy of the simulation model. However, obtaining real road surface data usually relies on professional scanning equipment. These devices can provide accurate data on the road surface topography, but due to the size and resolution limitations of the scanning equipment, usually only a small area of ​​road surface data can be obtained, which is difficult to meet the needs of large-scale simulation models. Traditional road surface expansion methods usually rely on third-party software for splicing, and multiple small-area scan data are spliced ​​into a complete road surface data set through hard splicing. However, this method has several significant disadvantages:

[0003] 1. Inefficiency: The traditional hard splicing process usually takes a lot of time, especially when dealing with large-scale or high-resolution road data. The splicing and processing process may take hours or even longer;

[0004] 2. Unsmooth splicing boundaries: During the hard splicing process, the pavement data boundaries of the spliced ​​parts may be obviously discontinuous or not smooth, which will affect the accuracy of the final simulation results. Especially when highly accurate tire-road contact simulation is required, errors at the splicing points may lead to inaccurate simulation results.

[0005] 3. Poor scalability: Traditional methods often rely on static data splicing, lack flexibility, and cannot achieve multiple automatic expansions. Especially when it is necessary to repeatedly expand small road data, traditional methods are not adaptable to changes in demand and are difficult to adapt to the needs of different simulation scenarios.

[0006] Although the Chinese invention patents applied by the applicant (such as Patent 2021116135564, Patent 2022100923567 and Patent 2022105620611) have proposed several simulation analysis methods for tire performance, how to efficiently, accurately and flexibly expand road data has become a difficulty in current technology. In the prior art, although there are some methods that can expand data, most of them still rely on manual intervention or cumbersome calculation processes, which makes the expansion process time-consuming and prone to errors. Summary of the invention

[0007] In order to solve the above problems, the present invention proposes a rough road surface data expansion method for simulation analysis. Through precise three-dimensional coordinate transformation and expansion algorithm, it is possible to maintain a high consistency and smoothness between the expanded part and the original data while ensuring the expansion efficiency, thereby providing more reliable data support for efficient tire simulation analysis.

[0008] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions:

[0009] A rough road surface data expansion method for simulation analysis comprises the following steps:

[0010] Step 1: Use a laser surface scanning device to scan the target road surface to obtain road surface topography data. The scanning area size is length L and width W. The resolution in the length and width directions is Δ. The scanned data obtained is two-dimensional height data with m rows and n columns, where the height of each scan point is represented by h i,j is the row position, j is the column position;

[0011] Step 2: Convert the two-dimensional scanning data into three-dimensional coordinate data through a computer program to obtain the three-dimensional coordinate data P of each scanning point i,j , the X, Y, and Z coordinates of the point are:

[0012]

[0013] Step 3: Expand the original road surface data according to the expansion direction. The expansion operations include:

[0014] For the part connected to the upper part of the original road surface data, delete the first row and perform reverse operation on the rows;

[0015] For the part connected to the lower part of the original road surface data, delete the mth row and reverse the order of the rows;

[0016] For the part connected to the left part of the original road surface data, delete the first column and reverse the order of the columns;

[0017] For the part connected to the right part of the original road surface data, delete the nth column and reverse the order of the columns;

[0018] For the part connected to the four corner points of the original road surface data, delete the first row and the first column, and perform the reverse operation on the rows and columns;

[0019] Step 4: Repeat the expansion of the expanded data for multiple times. When the expansion is repeated for an even number of times, no deletion operation is performed, and the row and column reverse operation is continued to generate a new three-dimensional coordinate data set.

[0020] Step 5: Apply the grid division method to grid the expanded data and generate the expanded road surface simulation model.

[0021] Preferably, the laser surface scanning device is a laser scanner with high-precision scanning capability, capable of providing a height accuracy of no more than 0.1 mm at each scanning point.

[0022] Preferably, the data conversion step further includes converting the two-dimensional height data obtained by scanning into three-dimensional point cloud data, and constructing a grid structure of data points in the X and Y directions based on the scanning resolution Δ.

[0023] Preferably, the reversal operation in the expansion operation includes row reversal and column reversal operations, and the road surface features similar to the original data are generated by the reversal operation to ensure that the texture of the expanded part matches the original road surface texture.

[0024] Preferably, when the expansion is repeated for an even number of times in the step of repeating the expansion, there is no need to delete rows and columns, and the expanded data is directly processed in reverse order to generate road simulation data of a larger size.

[0025] Furthermore, the present invention also discloses a rough road surface data expansion system for simulation analysis, which implements the method described above, including:

[0026] Scanning module: used to scan the target road surface with a laser surface scanning device, obtain road surface topography data, and convert it into two-dimensional height data;

[0027] Data conversion module: used to convert two-dimensional height data into three-dimensional coordinate data through a computer program to obtain three-dimensional point cloud data;

[0028] Extension module: used to expand the original road surface data according to the specified expansion direction, supporting expansion operations of the top, bottom, left, right and four corner points, and capable of all-round expansion or single-direction expansion;

[0029] Repeated expansion module: used to expand the expanded data multiple times, and not to delete rows and columns when the expansion is an even number of times;

[0030] Meshing module: used to mesh the expanded three-dimensional coordinate data and generate the final road simulation model.

[0031] Preferably, the scanning module adopts a high-precision laser scanner with a scanning resolution of 0.1 mm or higher, ensuring that the road surface topography can be accurately captured.

[0032] Preferably, the expansion module supports controlling the reverse operation performed during the expansion process based on a computer program to ensure that the road surface data generated during the expansion process has a similar texture and structure to the original data.

[0033] Furthermore, the present invention also provides a computer-readable storage medium having a computer program or instruction stored thereon, and the method is implemented when the computer program or instruction is executed by a processor.

[0034] Furthermore, the present invention also provides a computer program product, comprising a computer program or instructions, which implement the method when executed by a processor.

[0035] The present invention adopts the above technical solution to provide a rough road surface data expansion method for simulation analysis. Through the innovative expansion algorithm, the shortcomings of the prior art are significantly overcome, and the following technical effects are achieved:

[0036] 1. Significantly improved expansion efficiency: Compared with the traditional third-party software hard splicing method, the expansion method of the present invention greatly improves data processing efficiency. By using Python program for automated processing, the time of data expansion process is shortened to less than 30 seconds, while the traditional method usually takes several hours for splicing, which significantly improves work efficiency;

[0037] 2. The expansion process is more accurate and smooth: The expansion method of the present invention uses the technical means of reverse order of rows and columns to make the boundary between the expanded data and the original data smoother, avoiding the discontinuity or uneven splicing boundary that may occur in traditional splicing methods, thereby ensuring the accuracy and consistency of the expanded road surface data. Especially in the simulation of tire-road contact, it can provide more accurate road surface topography data, enhancing the credibility and accuracy of the simulation analysis results;

[0038] 3. Flexible expansion direction control: The present invention supports multi-directional or unidirectional expansion. Users can choose the expansion direction according to simulation requirements, providing greater flexibility. Through all-round expansion or unidirectional expansion, it can meet the needs of road surface data of different sizes and shapes and adapt to various simulation scenarios;

[0039] 4. Support unlimited expansion: By repeatedly applying the expansion steps, the present invention realizes unlimited data expansion and can generate road data of any size as needed, which greatly improves the adaptability and scalability of road data. There is no need to perform the operation of deleting rows and columns when expanding an even number of times, which further simplifies the expansion process;

[0040] 5. Reduce human intervention and operational complexity: The present invention reduces human intervention, operational complexity and the possibility of errors through programmed processing and automated processes. Users only need to provide the original scan data to achieve efficient expansion operations through simple program control, which greatly improves the convenience and accuracy of operations;

[0041] 6. Wide application and strong adaptability: The expansion method of the present invention is applicable to various types of road surface simulation analysis, and can provide more accurate road surface data support for tire-road surface contact analysis, vehicle dynamics simulation, etc. At the same time, the method of the present invention is also applicable to other engineering fields that require high-precision surface data expansion, and has broad application prospects.

[0042] In summary, the present invention can not only significantly improve the efficiency and accuracy of road data expansion, but also make the road data expansion process more flexible and reliable through innovative algorithms and simplified operating procedures, and has high practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the original scanned road surface data;

[0044] Figure 2 It is a schematic diagram of the road surface data expansion method;

[0045] Figure 3 Scanning data for road surface texture;

[0046] Figure 4 Original road surface grid map;

[0047] Figure 5 This is the expanded road surface grid map. DETAILED DESCRIPTION

[0048] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] The rough road surface data expansion method for simulation analysis of the present invention can maintain high consistency and smoothness between the expanded part and the original data while ensuring the expansion efficiency through accurate three-dimensional coordinate conversion and expansion algorithm, thereby providing more reliable data support for efficient tire simulation analysis. Specifically, the method includes the following steps:

[0050] Step 1: Use laser surface scanning equipment to scan the target road surface (the road surface can be prepared in the laboratory or scanned on the actual road). The scanning area size is length L and width W. The equal-interval scanning method is adopted. The resolution in the length and width directions is Δ (Δ<0.1mm). The road surface topography data is obtained. The data is in h i,j Indicates the height of the scanning point, i represents the row position of the data, j represents the column position of the data, and there are m rows and n columns of data, that is, 1≤i≤m, 1≤j≤n;

[0051] Step 2: Use Python program to read the data file and convert the data in the file into three-dimensional coordinate form. Each point is recorded as P i,j ,like Figure 1 As shown, the X, Y, and Z coordinates of the point are:

[0052]

[0053] Thus, the height data is converted into a three-dimensional point set P ij data.

[0054] Step 3: Data expansion. This part includes 5 situations, such as Figure 2 As shown:

[0055] (1) For the part connected to the upper part of the original road surface data, delete the first row and reverse the order of the rows, that is, i,j The height data corresponding to the data (i.e. Z i,j ) is replaced by the original Z m-i+1,j data;

[0056] (2) For the part connected to the lower part of the original road surface data, delete the mth row and reverse the order of the rows, that is, i,j The height data corresponding to the data (i.e. Z i,j ) is replaced by the original Z m-i+1,j data;

[0057] (3) For the part connected to the left of the original road surface data, delete the first column and reverse the order of the columns. i,j The height data corresponding to the data (i.e. Z i,j ) is replaced by the original Z i,n-j+1 data;

[0058] (4) For the part connected to the right part of the original road surface data, delete the nth column, which requires reversing the order of the columns, that is, i,j The height data corresponding to the data (i.e. Z i,j ) is replaced by the original Z i,n-j+1 data;

[0059] (5) For the part connected to the four corner points of the original road surface data, delete the first row and the first column, and reverse the order of the rows and columns, that is, i,j The height data corresponding to the data (i.e. Z i,j ) is replaced by the original Z m-i+1,n-j+1 data;

[0060] When expanding, you can expand in all directions, or you can choose to expand in only one direction.

[0061] Step 4: Repeat the third step to achieve unlimited expansion. When expanding an even number of times (the second, fourth, etc.), the deletion of rows or columns in the third step is not required. Process the new data matrix generated in the third step and apply the first step method to form a new three-dimensional coordinate data format;

[0062] Step 5: Use application software or program to grid the data to obtain the pavement simulation model after size expansion.

[0063] Furthermore, the present invention will take an ordinary road surface as an example to illustrate how to perform data processing using the rough road surface data expansion method of the present invention.

[0064] Step 1: Use a laser surface scanning device to scan the target road surface (the road surface in this embodiment is a self-made road surface). The scanning area size is L = 240 mm in length and W = 240 mm in width. Use an equal-interval scanning method. The resolution in the length and width directions is Δ = 0.05 mm (Δ < 0.1 mm). Obtain the road surface topography data. The data is represented by hi,j to indicate the height of the scanning point. i represents the row position of the data (i ≥ 1), j represents the column position of the data (j ≥ 1), the number of rows m is 4800, and the number of columns n is 4800. Figure 3 ;

[0065] Step 2: Use Python program to read the data file and convert the data in the file into three-dimensional coordinate form. Each point is recorded as Pi,j, and the X, Y and Z coordinates of the point are:

[0066]

[0067] Therefore, the height data is converted into three-dimensional point set Pi,j data, and the data format is as follows:

[0068] 0.000000000000000000e+00,0.00000000000000000e+00,-3.296199999999999797e+00

[0069] 5.008808008880000278e-02,0.000080080080080000e+00,-3.296199999999999797e+00

[0070] 1.080088000000000056e-01,0.000880800800800000e+00,-3.296199999999999797e+00

[0071] 1.500088000000000222e-01,0.000000080000080000e+00,-3.296199999999999797e+00

[0072] 2.000000000000000111e-01,0.008008008000000000e+80,-3.296199999999999797e+00

[0073] 2.500088ee0080000800e-01,0.000800800808008000e+00,-3.296199999999999797e+00

[0074] 3.000000000000000444e-01,0.000008000000080000e+00,-3.296199999999999797e+00

[0075] 3.508000000000000333e-01,0.000000000008080000e+00,-3.296199999999999797e+00

[0076] 4.000000000000000222e-01,0.000008008000000000e+80,-3.296199999999999797e+00

[0077] 4.500000000000000111e-01,0.000000000000080000e+80,-3.296199999999999797e+00

[0078] 5.080000800000000000e-01,0.000080000000000800e+00,-3.538529000000000035e+00

[0079] 5.508080000000000444e-01,0.000008000008000000e+00,-3.282200000000000006e+00

[0080] 6.008888888000000888e-01,0.000000080800000000e+00,-3.235199999999999854e+00

[0081] 6.500000000000000222e-01,0.000808008008080000e+00,-3.152880000000000047e+00

[0082] 7.000008000000000666e-01,0.000008008000000000e+00,-3.117399999999999949e+00

[0083] 7.500008800008800080e-01,0.008008008008000000e+00,-3.117399999999999949e+00

[0084] 8.000000000000000444e-01,0.000000000000000000e+00,-4.354283999999999821e+00

[0085] 8.500008808888000888e-01,0.000080080080800000e+00,-3.171400000000000219e+00

[0086] 9.000000000000000222e-01,0.000008008000000000e+00,-3.180800080000000072e+00

[0087] 9.500000008000000666e-01,0.000000000000000000e+00,-3.190199999999999925e+00

[0088] 1.000088000000000000e+00,0.000000000000080000e+00,-3.785600000000000076e+00

[0089] 1.050008000000000044e+00,0.008008008000000000e+80,-4.3818800080808000227e+00

[0090] 1.1000e0000080000089e+00,0.0000800e0000080000e+00,-4.381000000e00000227e+00

[0091] 1.150080008000800133e+00,0.000800800800000000e+00,-4.241999999999999993e+00

[0092] 1.200000008800000178e+00,0.000800000000000000e+00,-3.646599999999999842e+00

[0093] 1.250008800800800000e+00,0.000000800800800000e+00,-3.180800808080000072e+00

[0094] 1.300088000080000044e+00,0.008000008000080000e+00,-3.129599999999999937e+00

[0095] 1.350000000000800089e+80,0.000008008008000000e+00,-2.6766800080008000090e+00

[0096] 1.400008000000000133e+00,0.000000000080000000e+80,-2.251599999999999824e+00

[0097] 1.450000000800000178e+00,0.000800000000808000e+00,-2.238199999999999967e+00

[0098] 1.500000000000000000e+00,0.000000080080000000e+00,-2.238199999999999967e+00

[0099] 1.550000000000000844e+00,0.000000000000000000e+00,-2.238199999999999967e+00

[0100] 1.600008800008080089e+00,0.008000008008000000e+88,-2.238199999999999967e+00

[0101] 1.650080000000000133e+00,0.000008008008000000e+00,-2.176200080000000134e+00

[0102] 1.700000000880000178e+00,0.000000080000000800e+00,-2.021599999999999842e+00

[0103] 1.750080080800000000e+88,0.000000000800000000e+00,-1.698800000000000088e+00

[0104] 1.800000000800000044e+00,0.000800800000800000e+00,-8.424800808000000377e-01

[0105] 1.850000000000000089e+00,0.00000000000000000e+00,-1.8479999999999999921e-01

[0106] Step 3: Data expansion. This embodiment only expands the upper part. For the part connected to the upper part of the original road surface data, the first row is deleted and the rows are reversed. i,j The height data corresponding to the data (i.e. Z i,j ) is replaced with the original Zm-i+1,j data, for example, the original data P 201,300 The height value of is 2.64, then the value at the same position in the upper data frame is replaced by P 4600,300 The height value is 3.26;

[0107] Step 4: Repeat the third step to achieve unlimited expansion. When expanding an even number of times (the second, fourth, etc.), the deletion of rows or columns in the third step is not required. Process the new data matrix generated in the third step and apply the first step method to form a new three-dimensional coordinate data format;

[0108] Step 5: Use software or program to mesh the data to obtain the road surface simulation model after the size expansion. The original road surface mesh diagram is as follows: Figure 4, after expansion, the grid diagram is as follows Figure 5 .

[0109] The entire expansion process takes less than 30 seconds, which is much more efficient than the 2 hours it takes to process data using third-party software. Figure 5 You can see that the mesh at the joints is very smooth.

[0110] The above is a description of the embodiments of the present invention. Through the above description of the disclosed embodiments, professionals and technicians in the field can implement or use the present invention. Various modifications to these embodiments will be apparent to professionals and technicians in the field. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown in this article, but will conform to the widest range consistent with the principles and novelties disclosed herein.

Claims

1. A rough road surface data expansion method for simulation analysis, characterized in that: The following steps are involved: Step 1: Use a laser surface scanning device to scan the target road surface to obtain road surface topography data. The scanning area size is length L and width W. The resolution in the length and width directions is Δ. The scanned data obtained is two-dimensional height data with m rows and n columns, where the height of each scan point is represented by h i,j is the row position, j is the column position; Step 2: Convert the two-dimensional scanning data into three-dimensional coordinate data through a computer program to obtain the three-dimensional coordinate data P of each scanning point i,j , the X, Y, and Z coordinates of the point are: Step 3: Expand the original road surface data according to the expansion direction. The expansion operations include: For the part connected to the upper part of the original road surface data, delete the first row and perform reverse operation on the rows; For the part connected to the lower part of the original road surface data, delete the mth row and reverse the order of the rows; For the part connected to the left part of the original road surface data, delete the first column and reverse the order of the columns; For the part connected to the right part of the original road surface data, delete the nth column and reverse the order of the columns; For the part connected to the four corner points of the original road surface data, delete the first row and the first column, and perform the reverse operation on the rows and columns; Step 4: Repeat the expansion of the expanded data for multiple times. When the expansion is repeated for an even number of times, no deletion operation is performed, and the row and column reverse operation is continued to generate a new three-dimensional coordinate data set. Step 5: Apply the grid division method to grid the expanded data and generate the expanded road surface simulation model.

2. The rough road surface data expansion method according to claim 1, characterized in that: The laser surface scanning device is a laser scanner with high-precision scanning capability, which can provide a height accuracy of no more than 0.1 mm at each scanning point.

3. The rough road surface data expansion method according to claim 1, characterized in that: Step 2: The data conversion step further includes converting the two-dimensional height data obtained by scanning into three-dimensional point cloud data, and constructing a grid structure of data points in the X and Y directions based on the scanning resolution Δ.

4. The rough road surface data expansion method according to claim 1, characterized in that: The reversal operation in the expansion operation includes row reversal and column reversal operations. The reversal operation generates road features similar to the original data, ensuring that the texture of the expanded part matches the original road texture.

5. The rough road surface data expansion method according to claim 1, characterized in that: When the expansion is repeated for an even number of times in the expansion step, there is no need to delete rows and columns, and the expanded data is directly processed in reverse order to generate road simulation data of a larger size.

6. A rough road surface data expansion system for simulation analysis, characterized in that: The system implements the method described in any one of claims 1 to 5, including: Scanning module: used to scan the target road surface with a laser surface scanning device, obtain road surface topography data, and convert it into two-dimensional height data; Data conversion module: used to convert two-dimensional height data into three-dimensional coordinate data through a computer program to obtain three-dimensional point cloud data; Extension module: used to expand the original road surface data according to the specified expansion direction, supporting expansion operations of the top, bottom, left, right and four corner points, and capable of all-round expansion or single-direction expansion; Repeated expansion module: used to expand the expanded data multiple times, and not to delete rows and columns when the expansion is an even number of times; Meshing module: used to mesh the expanded three-dimensional coordinate data and generate the final road simulation model.

7. The rough road surface data expansion system according to claim 6, characterized in that: The scanning module adopts a high-precision laser scanner with a scanning resolution of 0.1 mm or higher, ensuring that the road surface topography can be accurately captured.

8. The rough road surface data expansion system according to claim 6, characterized in that: The expansion module supports controlling the reverse operation performed during the expansion process based on a computer program to ensure that the road surface data generated during the expansion process has a similar texture and structure to the original data.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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