A spatial flatness measurement device and method suitable for all terrains

Abstract

The present application relates to a spatial flatness measurement device and method applicable to all terrains. The method comprises: obtaining regional visual data and regional spectral data of the area to be measured; identifying the surface material characteristics of the area to be measured based on the regional visual data and the regional spectral data, and obtaining the surface material point cloud data of the area to be measured; using the surface material point cloud data to perform terrain plane fitting on the area to be measured, and obtaining fitting flatness data; and correcting the fitting flatness data based on the regional spectral data, and obtaining the spatial flatness data of the area to be measured. The use of this method can not only improve the measurement accuracy and consistency of the flatness of a specific area or surface, but also adapt to the complex and changeable surface environment, and ensure that the terrain flatness data is highly reliable and accurate under different materials and conditions.

Description

Technical Field

[0001] The present application relates to the field of measurement technology, and in particular to a spatial flatness measurement device, method and computer equipment suitable for all-terrain. Background Art

[0002] With the development of measurement technology, spatial flatness measurement technology has emerged, which refers to the process of evaluating the flatness of a specific area or surface. It is commonly used in construction, civil engineering and manufacturing to ensure that the height difference of the ground or surface in space meets the specified standards. In traditional technology, spatial flatness measurement can be completed by using laser level, flatness detector or other measuring tools to help determine whether the surface is smooth or flat enough. However, traditional technology is greatly affected by environmental factors during the measurement process. For example, temperature changes, vibrations or other external interferences may cause data deviations. Moreover, for surfaces with large areas or complex shapes, different areas have different physical properties, resulting in insufficient measurement accuracy of the flatness of specific areas or surfaces. Summary of the invention

[0003] Based on this, it is necessary to provide a spatial flatness measurement method, device, computer equipment, computer-readable storage medium and computer program product suitable for all terrains that can improve the measurement accuracy of the flatness of a specific area or surface in order to address the above technical problems.

[0004] In a first aspect, the present application provides a spatial flatness measurement method applicable to all terrains. The method comprises:

[0005] Acquire regional visual data and regional spectral data of the area to be measured;

[0006] Identify the surface material characteristics of the area to be measured according to the regional visual data and the regional spectral data, and obtain the surface material point cloud data of the area to be measured;

[0007] Using the surface material point cloud data to perform terrain plane fitting on the area to be measured, to obtain fitting flatness data;

[0008] The fitting flatness data is corrected according to the regional spectral data to obtain the spatial flatness data of the area to be measured.

[0009] In a second aspect, the present application also provides a spatial flatness measurement device applicable to all terrains. The device comprises:

[0010] A regional data acquisition module, used to acquire regional visual data and regional spectral data of the area to be measured;

[0011] A point cloud data generating module, used for identifying the surface material characteristics of the area to be measured according to the regional visual data and the regional spectral data, and obtaining the surface material point cloud data of the area to be measured;

[0012] A flatness calculation module is used to use the surface material point cloud data to perform terrain plane fitting on the area to be measured to obtain fitting flatness data;

[0013] The flatness optimization module is used to correct the fitted flatness data according to the regional spectral data to obtain the spatial flatness data of the area to be measured.

[0014] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0015] Acquire regional visual data and regional spectral data of the area to be measured;

[0016] Identify the surface material characteristics of the area to be measured according to the regional visual data and the regional spectral data, and obtain the surface material point cloud data of the area to be measured;

[0017] Using the surface material point cloud data to perform terrain plane fitting on the area to be measured, to obtain fitting flatness data;

[0018] The fitting flatness data is corrected according to the regional spectral data to obtain the spatial flatness data of the area to be measured.

[0019] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0020] Acquire regional visual data and regional spectral data of the area to be measured;

[0021] Identify the surface material characteristics of the area to be measured according to the regional visual data and the regional spectral data, and obtain the surface material point cloud data of the area to be measured;

[0022] Using the surface material point cloud data to perform terrain plane fitting on the area to be measured, to obtain fitting flatness data;

[0023] The fitting flatness data is corrected according to the regional spectral data to obtain the spatial flatness data of the area to be measured.

[0024] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0025] Acquire regional visual data and regional spectral data of the area to be measured;

[0026] Identify the surface material characteristics of the area to be measured according to the regional visual data and the regional spectral data, and obtain the surface material point cloud data of the area to be measured;

[0027] Using the surface material point cloud data to perform terrain plane fitting on the area to be measured, to obtain fitting flatness data;

[0028] The fitting flatness data is corrected according to the regional spectral data to obtain the spatial flatness data of the area to be measured.

[0029] The above-mentioned spatial flatness measurement method, device, computer equipment, storage medium and computer program product applicable to all terrains obtains regional visual data and regional spectral data of the area to be measured; identifies the surface material characteristics of the area to be measured based on the regional visual data and the regional spectral data, and obtains the surface material point cloud data of the area to be measured; uses the surface material point cloud data to fit the terrain plane of the area to be measured to obtain fitting flatness data; and corrects the fitting flatness data based on the regional spectral data to obtain the spatial flatness data of the area to be measured.

[0030] By acquiring regional visual data and regional spectral data of the area to be measured, the appearance and physical and chemical properties of the area can be captured at the same time. Further use of regional visual data can quickly locate and identify the surface characteristics of the area, while regional spectral data can deeply analyze the composition and characteristics of the material, thereby generating more accurate surface material point cloud data. The terrain plane fitting based on these point cloud data can better reflect the flatness of the actual terrain. Further combining the spectral data to correct the fitting results can eliminate the errors caused by differences in material properties, thereby obtaining more accurate spatial flatness data. This method can not only improve the measurement accuracy and consistency of the flatness of a specific area or surface, but also adapt to the complex and changeable surface environment, ensuring that the terrain flatness data is highly reliable and accurate under different materials and conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A diagram showing an application environment of a spatial flatness measurement method applicable to all terrains in one embodiment;

[0032] Figure 2 A schematic diagram of a flow chart of a spatial flatness measurement method applicable to all terrains in one embodiment;

[0033] Figure 3 A schematic diagram of a flow chart of a method for obtaining spatial flatness data in one embodiment;

[0034] Figure 4 A schematic diagram of a process for obtaining material mechanical data in one embodiment;

[0035] Figure 5 A schematic flow chart of a method for obtaining spatial flatness data in another embodiment;

[0036] Figure 6 A schematic flow chart of a method for obtaining slip-corrected flatness data in one embodiment;

[0037] Figure 7 A schematic diagram of a process for obtaining surface material point cloud data in one embodiment;

[0038] Figure 8 A schematic diagram of a flow chart of a method for generating acquisition device adjustment information in one embodiment;

[0039] Fig. 9 is a structural block diagram of a spatial flatness measurement device applicable to all terrains in one embodiment;

[0040] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] The present application provides a spatial flatness measurement method applicable to all terrains, which can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains the regional visual data and regional spectral data of the area to be measured through the terminal 102; according to the regional visual data and the regional spectral data, the surface material characteristics of the area to be measured are identified to obtain the surface material point cloud data of the area to be measured; the surface material point cloud data is used to fit the terrain plane of the area to be measured to obtain the fitting flatness data; according to the regional spectral data, the fitting flatness data is corrected to obtain the spatial flatness data of the area to be measured. Among them, the server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0043] In one embodiment, Figure 2 As shown, a spatial flatness measurement method suitable for all terrains is provided. Figure 1 The server in the example is used to illustrate the following steps:

[0044] Step 202: Acquire regional visual data and regional spectral data of the area to be measured.

[0045] The area to be measured can be a geographical area or physical space where flatness measurement and analysis is required in a specific task or research. This area is usually determined based on project requirements or research objectives, and may involve detailed data collection and analysis of surface characteristics, terrain structure, material composition, etc.

[0046] Among them, regional visual data can be image or video information about a specific area obtained by video equipment (such as cameras, satellites, drones, etc.). These data mainly reflect the appearance characteristics of the area, including visual attributes such as terrain morphology, color, and texture. Regional visual data can intuitively display the structure and shape of the surface.

[0047] Among them, regional spectral data can be data information obtained by spectral sensors or spectral imaging equipment, reflecting the reflection, absorption and emission characteristics of materials in a specific area to light of different wavelengths. This type of data is usually presented in the form of spectral curves, which can reveal the physical and chemical composition characteristics of surface materials, such as the optical properties of soil, vegetation, rocks or water bodies.

[0048] Specifically, remote sensing equipment or drones are used to obtain regional visual data of the area to be measured. The regional visual data usually includes high-resolution images or videos that reflect the morphology and color information of the surface. At the same time, spectral sensors or spectral imaging instruments are used to capture regional spectral data of the area. Regional spectral data records the light reflection characteristics of different wavelengths, thereby obtaining physical and chemical information about the surface materials.

[0049] Step 204 , identifying the surface material characteristics of the area to be measured based on the regional visual data and the regional spectral data, and obtaining the surface material point cloud data of the area to be measured.

[0050] Among them, surface material properties can be the physical and chemical properties of various materials on the earth's surface, including but not limited to the composition, structure, density, texture, reflection spectrum characteristics and other environment-related properties of materials such as soil, rock, vegetation, and water. These properties determine how the material interacts with the external environment, such as reflection, absorption or scattering of light, affecting its performance in remote sensing images.

[0051] Among them, surface material point cloud data can be characteristic data generated by three-dimensional scanning or data processing of the surface, and is a spatial data set composed of a large number of three-dimensional coordinate points. Each point not only contains position coordinates (X, Y, Z), but may also be accompanied by information about the characteristics of the surface material, such as material, reflectivity or spectral properties. These point cloud data can reflect the details and characteristics of the surface by accurately describing the distribution and morphology of the surface material.

[0052] Specifically, a neural network is used to identify and extract the surface morphological features of the area to be measured, such as terrain contours, textures, and other information, from the regional visual data. For the regional spectral data at the same time, the spectral reflectance characteristics of different materials on the surface are analyzed to determine the material composition and physical and chemical properties in the area. On this basis, the visual data and spectral data are fused, and the fused characteristic data is converted into three-dimensional point cloud data of the surface material as the surface material point cloud data through a computer algorithm.

[0053] Step 206: Use the surface material point cloud data to perform terrain plane fitting on the area to be measured to obtain fitting flatness data.

[0054] Among them, terrain plane fitting can be a process of matching the terrain height information in a set of 3D point cloud data with a most suitable plane through a mathematical algorithm. The plane aims to minimize the error between the data points and the plane, reflecting the overall flatness or trend of the area to be measured. Through the fitting process, the local undulations of the terrain can be simplified, thereby obtaining quantitative data of the overall structure or relative flatness of the terrain.

[0055] Among them, the fitting flatness data can be obtained by fitting the terrain plane and is used to quantitatively describe the flatness of the terrain in the area to be measured. These data reflect the deviation between the fitting plane and the actual terrain, that is, the undulation of the surface relative to the ideal plane. The smaller the flatness data, the closer the terrain of the area is to being flat; and the larger the data, the greater the undulation or irregularity of the surface.

[0056] Specifically, the three-dimensional spatial structure of the area to be measured is analyzed using the surface material point cloud data to extract the height information of each point. The distribution of these points is fitted to the terrain plane through mathematical algorithms or fitting models to find the plane that best describes the regional terrain. During the fitting process, the changes in surface materials are taken into account to minimize the impact of local undulations or errors on the overall plane. Finally, the fitting flatness data of the fitting plane is calculated, that is, data describing the smoothness or undulation of the terrain in the area relative to the plane. Among them, the fitting mathematical algorithms or fitting models include the least squares method, RANSAC algorithm (Random Sample Consensus), weighted least squares, smoothing spline, polynomial fitting, and surface fitting, one of which is used alone or multiple of which are used simultaneously.

[0057] Step 208: Correct the fitted flatness data according to the regional spectrum data to obtain the spatial flatness data of the area to be measured.

[0058] Among them, spatial flatness data can be data information that accurately describes the flatness of the surface in three-dimensional space by combining terrain characteristics and material properties. It reflects the degree of undulation of the surface relative to an ideal plane or reference plane, and takes into account the impact of different material properties on the terrain.

[0059] Specifically, after completing the terrain plane fitting and obtaining the preliminary fitting flatness data, the regional spectral data is used to analyze the spectral reflectance characteristics of different materials on the surface to identify the material properties that affect the accuracy of terrain measurement, such as soil type, rock density or vegetation coverage. Then, based on these material properties, the fitting flatness data is corrected to adjust the local deviation caused by material differences. Through this correction, the spatial flatness of the real surface can be more accurately reflected, and finally the spatial flatness data of the area to be measured is generated.

[0060] In the above-mentioned spatial flatness measurement method applicable to all terrains, regional visual data and regional spectral data of the area to be measured are obtained; the surface material characteristics of the area to be measured are identified based on the regional visual data and the regional spectral data, and the surface material point cloud data of the area to be measured are obtained; the surface material point cloud data is used to fit the terrain plane of the area to be measured to obtain fitting flatness data; the fitting flatness data is corrected according to the regional spectral data to obtain the spatial flatness data of the area to be measured.

[0061] By acquiring regional visual data and regional spectral data of the area to be measured, the appearance and physical and chemical properties of the area can be captured at the same time. Further use of regional visual data can quickly locate and identify the surface characteristics of the area, while regional spectral data can deeply analyze the composition and characteristics of the material, thereby generating more accurate surface material point cloud data. The terrain plane fitting based on these point cloud data can better reflect the flatness of the actual terrain. Further combining the spectral data to correct the fitting results can eliminate the errors caused by differences in material properties, thereby obtaining more accurate spatial flatness data. This method can not only improve the measurement accuracy and consistency of the flatness of a specific area or surface, but also adapt to the complex and changeable surface environment, ensuring that the terrain flatness data is highly reliable and accurate under different materials and conditions.

[0062] In one embodiment, Figure 3 As shown, according to the regional spectrum data, the fitting flatness data is corrected to obtain the spatial flatness data of the area to be measured, including:

[0063] Step 302 , detecting the reflection characteristics in the regional spectral data, and identifying the material quality data and spectral characteristic data corresponding to each material in the area to be measured.

[0064] The material quality data may be data information describing the physical, chemical and structural characteristics of a certain material.

[0065] Among them, spectral characteristic data can be data information on the reflection, absorption or emission characteristics of a substance under light of different wavelengths. It can reveal the physical and chemical composition of a material by recording the spectral response of the substance at each wavelength. For example, different materials have different reflection intensities for visible light, infrared light or ultraviolet light, forming a unique spectral curve.

[0066] Specifically, the spectral analysis algorithm is used to decompose and analyze the reflection characteristics of each spectral curve in the regional spectral data, and key features are extracted, such as the reflection intensity and absorption peak position of each material at a specific wavelength. Then, by matching the extracted spectral features with the reference data in the known spectral library (including the generation conditions and physical properties of any spectral curve), different materials in the region are identified and their types are determined. Based on these matching results, detailed material quality data such as density, composition, mineral content, etc., as well as their spectral characteristic data, are generated for each material.

[0067] Step 304: Determine material mechanical data corresponding to each material in the area to be measured according to each material quality data and each spectral characteristic data.

[0068] Among them, material mechanical data can be data information describing the mechanical properties of a material under the action of external forces, including the material's strength, elastic modulus, hardness, shear stress, compressive resistance and fracture toughness.

[0069] Specifically, based on the material quality data (such as density, composition) and spectral characteristic data of each material, combined with the known material physical model, the physical and chemical properties of the material are analyzed. Next, the physical and chemical property data are used to derive the mechanical performance parameters of the material, such as strength, elastic modulus, hardness and shear stress, through the material mechanics model. These calculation results are then compared and calibrated with experimental data or standard databases. After comparison and calibration, the mechanical performance parameters of the material determined to be correct are used as the material mechanics data of each material.

[0070] Step 306: Correct the fitted flatness data according to the mechanical data of each material to obtain the spatial flatness data of the area to be measured.

[0071] Specifically, the mechanical properties of the surface materials in the area to be measured, such as compression resistance and elastic modulus, are analyzed based on the material mechanics data to determine the performance of these materials under external forces or environmental factors (such as settlement and deformation). Based on these mechanical properties, the flatness data obtained by the preliminary fitting is corrected, especially considering the errors in the observed regional spectral data caused by different mechanical properties of materials, such as surface deformation, local unevenness, light refraction, light convergence, light scattering, light diffraction, light interference, etc. Through this correction step, the actual surface flatness condition can be more accurately reflected, and finally the corrected spatial flatness data is generated.

[0072] In this embodiment, by detecting the reflective characteristics of the regional spectral data, the quality and spectral characteristics of each material are accurately identified, and the mechanical data of the material is further derived. This process ensures a comprehensive understanding of the physical properties of the materials in the measured area, and thus is more accurate when correcting the fitted flatness data. The final spatial flatness data not only takes into account the composition of the surface material, but also combines the mechanical properties, which improves the accuracy and reliability of the flatness assessment. This comprehensive analysis method reduces errors and improves the accuracy and efficiency of terrain modeling and engineering design.

[0073] In one embodiment, Figure 4 As shown, the material mechanical data includes the material elastic modulus, material friction coefficient and material roughness; according to the material quality data and the spectral characteristic data, the material mechanical data corresponding to each material in the area to be measured is determined, including:

[0074] Step 402 : identifying the material elastic modulus corresponding to each material according to the data drift of each spectral characteristic data and the characteristic information of each material quality data.

[0075] Specifically, the spectral characteristic data of each material is analyzed, and the spectral data drift in different wavelength ranges is calculated. These spectral data drifts reflect the changes in the deformation of the material under environmental changes or external forces. These spectral drifts are correlated with material quality data (such as density, composition, etc.) to extract key characteristic information. Then, using the known material physical model, these characteristic information are combined with the spectral drift to derive the elastic modulus of the material.

[0076] Step 404 , identifying the material friction coefficient corresponding to each material according to the reflection beam density of each spectral characteristic data and the characteristic information of each material quality data.

[0077] Among them, the reflection bundle density can be the degree of reflection concentration of the material surface to the incident light, reflecting the distribution of light on the material surface. High reflection bundle density means that the material surface is smooth (small friction coefficient) and the reflected light is concentrated, while low reflection bundle density indicates that the surface is relatively rough (large friction coefficient) and the reflected light is scattered.

[0078] Specifically, the reflection beam density in the spectral characteristic data is analyzed to determine the degree of reflection concentration of the material surface to light of different wavelengths, which reflects the roughness and texture characteristics of the material surface. Combined with the characteristic information in the material quality data (such as the composition and surface structure of the material), analyze how these characteristics affect the friction behavior of the material. Based on the physical model, the reflection beam density is combined with the material quality data to derive the material friction coefficient.

[0079] Step 406 , identifying the material roughness corresponding to each material according to the reflection beam divergence of each spectral characteristic data and the characteristic information of each material quality data.

[0080] The reflection beam divergence may be the local reflection degree of light when it is reflected on the surface of an object, and is usually related to the amount of change of the local surface.

[0081] Specifically, the reflection beam divergence in the spectral characteristic data is analyzed to measure the local reflection degree of light when it is reflected on the material surface. The larger the beam divergence, the greater the change in the local surface, and the more dispersed the local reflection of the light. Combined with the characteristic information in the material quality data (such as the composition and structure of the material), the influence of the physical properties of the material on the surface roughness is further analyzed. Using the physical model, the reflection beam divergence is combined with the material quality characteristics to deduce the surface roughness of the material.

[0082] In this embodiment, by combining spectral characteristic data and material quality data, key mechanical and surface characteristics of the material, such as elastic modulus, friction coefficient and roughness, are accurately identified. The mechanical properties, friction characteristics and surface roughness of the material are extracted by analyzing the data drift, reflection beam density and divergence. This comprehensive analysis is beneficial to gain a deeper understanding of the performance of the material under different stress and environmental conditions, and ensures an accurate description of the material properties in multiple dimensions. The final result improves the accuracy of material identification and helps to more reliably carry out terrain modeling, engineering design and material performance evaluation.

[0083] In one embodiment, Figure 5 As shown, according to the mechanical data of each material, the fitting flatness data is corrected to obtain the spatial flatness data of the area to be measured, including:

[0084] Step 502: Determine each deformation correction region in the fitted flatness data according to the elastic modulus of the material.

[0085] The deformation correction area may be a specific area in terrain or structural analysis where adjustment or correction is required due to deformation of the material under external forces or environmental influences.

[0086] Specifically, according to the elastic modulus of the material identified in the early stage, the deformation response characteristics of different materials in the measured area under the action of external force or environment are analyzed. The smaller the elastic modulus of the material, the easier it is to deform, and the larger the elastic modulus, the more difficult it is to deform. The deformation response characteristics of these materials are combined with the fitting flatness data to determine the areas in the fitting plane that are more affected by the elasticity of the material, that is, the deformation correction areas.

[0087] Step 504: According to the first mapping relationship, the deformation correction area corresponding to the elastic modulus correction of each material is retrieved to obtain deformation corrected flatness data.

[0088] The first mapping relationship may be a mapping relationship between the deformation correction area and the corresponding material elastic modulus.

[0089] The deformation-corrected flatness data may be the flatness data obtained by correcting the error introduced by the deformation of the material under the action of external force. Due to the different elastic moduli of different materials, the surface will produce uneven deformation when subjected to force, and the original flatness data may have errors that are ignored in the data collection.

[0090] Specifically, based on the pre-established first mapping relationship, the material elastic modulus of each material is associated with its corresponding deformation correction area, and the error introduced by the deformation of each deformation correction area is calculated using the material elastic modulus, and the original fitting flatness data is corrected. By adjusting the local deformation deviation caused by the mechanical properties of the material, more accurate deformation-corrected flatness data is finally obtained.

[0091] Step 506: Determine each slip correction region in the deformation-corrected flatness data according to the material friction coefficient.

[0092] The slip correction area may be a specific area where there is a risk of slip or displacement due to a low material friction coefficient, thereby affecting the measured or fitted flatness data.

[0093] Specifically, according to the material friction coefficient, the surface slip risk of different materials in the area to be measured is analyzed. Materials with lower friction coefficients are more likely to slip when external forces or terrain changes, while materials with higher friction coefficients are relatively stable. The material friction coefficient is combined with the deformation-corrected flatness data to identify areas with a greater possibility of slip, which are called slip correction areas.

[0094] Step 508: According to the second mapping relationship, retrieve the slip correction area corresponding to the friction coefficient correction of each material to obtain slip correction flatness data.

[0095] The second mapping relationship may be a mapping relationship between the slip correction area and the corresponding material friction coefficient.

[0096] The slip-corrected flatness data may be obtained by correcting the error introduced by the slip effect that may occur in the surface material under low friction coefficient conditions. Since some materials are prone to slip under external force or gravity, the original flatness data may have deviations.

[0097] Specifically, based on the pre-established second mapping relationship, the friction coefficient of each material is associated with its corresponding slip correction area, and the error introduced by the slip degree caused by the low friction coefficient in the slip correction area is calculated according to the material friction coefficient of each material. Based on these slip effects, the original deformation-corrected flatness data is further adjusted to correct the local flatness error caused by slip and generate slip-corrected flatness data.

[0098] Step 510, determining each plane fluctuation correction area in the slip correction flatness data according to the material roughness.

[0099] Among them, the plane fluctuation correction area can be a specific area where slight fluctuations or unevenness appear in the local area due to the large surface roughness of the material. The fluctuations in these areas may affect the accuracy of the overall flatness data, so corrections need to be made according to the roughness of the material.

[0100] Specifically, according to the material roughness of the material, the macroscopic unevenness or texture characteristics of the surface of each area are analyzed. The material surface with large material roughness is prone to cause local plane fluctuations, that is, there is a non-negligible local change in the material surface. These material roughnesses are combined with the flatness data after slip correction to identify the local fluctuation areas that may be caused by roughness, which are called each plane fluctuation correction area.

[0101] Step 512: According to the third mapping relationship, retrieve the plane fluctuation correction area corresponding to the roughness correction of each material to obtain the spatial flatness data.

[0102] The third mapping relationship may be a mapping relationship between the plane fluctuation correction area and the corresponding material roughness.

[0103] Specifically, based on the third mapping relationship established in advance, the roughness of each material is associated with its corresponding plane fluctuation correction area. According to the material roughness, the macro fluctuation of these areas is analyzed, and the influence of the material roughness on the local flatness is calculated, that is, the error introduced by the height error of the material plane caused by the material roughness is calculated. Then, these fluctuation areas are corrected, the error introduced by the local unevenness caused by the material roughness is adjusted, and the error in the slip correction flatness data is corrected to obtain more accurate spatial flatness data.

[0104] In this embodiment, by gradually combining the elastic modulus, friction coefficient and roughness of the material, the deformation, slip and surface fluctuation of the terrain are accurately corrected, and finally more reliable spatial flatness data is obtained. A mapping relationship is established between each characteristic and the corresponding correction area to ensure that the material characteristics are accurately reflected in the flatness calculation. This multi-level correction process not only improves the understanding of the complexity of the surface, but also reduces the errors caused by a single factor, ensuring the accuracy and stability of the terrain flatness assessment, thereby providing more reliable support for applications such as engineering design and topographic mapping.

[0105] In one embodiment, Figure 6 As shown, according to the second mapping relationship, the slip correction area corresponding to the friction coefficient correction of each material is retrieved to obtain the slip correction flatness data, including:

[0106] Step 602: Determine the surface height correction regions in the deformation-corrected flatness data according to the material friction coefficient.

[0107] The ground height correction area can be a specific area where the ground surface slips or settles due to the low friction coefficient of the material or other mechanical properties. The ground surface height in these areas may deviate from the original data due to the slip or settlement effect of the material and needs to be corrected.

[0108] Specifically, according to the material friction coefficient, the sliding or settling characteristics of different materials under external forces are analyzed. Materials with lower friction coefficients are more likely to change height or slide under gravity. The material friction coefficient is combined with the deformation-corrected flatness data to identify areas where surface height changes may occur, which are called surface height correction areas.

[0109] Step 604: According to the fourth mapping relationship, retrieve the surface height correction area corresponding to the correction of the friction coefficient of each material to obtain height-corrected flatness data.

[0110] The fourth mapping relationship may be a mapping relationship between the ground height correction area and the corresponding material friction coefficient.

[0111] The height-corrected flatness data may be flatness data obtained after correcting the error introduced by the change in surface height caused by the friction coefficient of the material.

[0112] Specifically, based on the fourth mapping relationship established in advance, the friction coefficient of each material is associated with its corresponding surface height correction area, and the surface height change in these areas is calculated using the material friction coefficient to evaluate the height deviation caused by the friction characteristics of the material. The deformation-corrected flatness data is corrected to correct the error introduced by the height deviation caused by the friction characteristics of the material, and the height-corrected flatness data is obtained.

[0113] Step 606, determining each slope correction region in the deformation-corrected flatness data according to the material friction coefficient.

[0114] The slope correction area can be a specific area where the slope may slip or deform due to low material friction coefficient or other factors. The slope of these areas may deviate from its actual value due to insufficient friction, resulting in errors in the measurement or analysis results.

[0115] Specifically, the stability of different materials on slopes is analyzed based on the material friction coefficient. Materials with low material friction coefficients are more likely to slip or deform when the slope is steep. The material friction coefficient is combined with the deformation-corrected flatness data to identify areas that may slip or deform when the slope is steep due to insufficient friction, i.e., each slope correction area.

[0116] Step 608: According to the fifth mapping relationship, the slope correction area corresponding to the correction of the friction coefficient of each material is retrieved to obtain the slip correction flatness data.

[0117] The fifth mapping relationship may be a mapping relationship between the slope correction area and the corresponding material friction coefficient.

[0118] Specifically, based on the pre-established fifth mapping relationship, each material friction coefficient is associated with its corresponding slope correction area, and the material friction coefficient is used to evaluate the slope changes in these areas. As a result, materials with lower friction coefficients are more likely to slip or collapse on slopes or in areas with larger inclination angles, thereby affecting the flatness assessment. The material friction coefficient is further used to adjust the slope data of these slope correction areas to correct the slope error caused by the material friction characteristics and obtain slip-corrected flatness data.

[0119] In this embodiment, the surface height and slope are corrected by combining the material friction coefficient to ensure the accuracy of the terrain flatness data. Through the mapping relationship between the material friction coefficient and the surface height and slope correction area, the height and slope changes caused by the friction characteristics are gradually identified and adjusted. This process effectively corrects the errors in the slip and slope changes of the surface, and finally generates accurate slip-corrected flatness data. This method improves the accuracy and integrity of terrain analysis, especially under complex surface conditions, ensuring more stable and reliable surveying and engineering application data.

[0120] In one embodiment, based on the correction steps corresponding to step 502 to step 608, the correction formula for obtaining the spatial flatness data of the area to be measured is:

[0121]

[0122] Among them, the is the spatial flatness data, Correcting the flatness data for the deformation, is the slip-corrected flatness data, To highly correct the flatness data, the z is the fitting flatness data, F is the external force acting on the material, E m is the elastic modulus of the material, A is the area of ​​the force-bearing region, is a coefficient related to the local inhomogeneity of the material, is the influencing factor of local material property changes, is the coefficient related to the strain linear effect, is the local strain, is the friction coefficient correction function, is the friction coefficient of the material, is the local pressure distribution function, is the height variation at different positions, is the correction function of friction coefficient and slope angle, is the initial value of the slope, is the correction factor related to local material slip, is the slope value, is the material roughness, is the fluctuation amplitude function, is the local tilt angle at different positions.

[0123] In one embodiment, Figure 7 As shown, according to the regional visual data and the regional spectral data, the surface material characteristics of the area to be measured are identified, and the surface material point cloud data of the area to be measured is obtained, including:

[0124] Step 702: extract texture features from the regional visual data and the regional spectral data to obtain visual texture feature data and spectral texture feature data.

[0125] Among them, visual texture feature data can be data information describing the surface morphology, structure and pattern distribution extracted by analyzing the visual data in the image or video. These feature data reflect the texture patterns of the object surface at different scales and directions, such as roughness, smoothness, stripes or particles.

[0126] Among them, spectral texture feature data can be the data information reflecting the surface texture and structure extracted by analyzing the spectral response of an object under different wavelengths of light. These features not only include the reflection and absorption characteristics of the spectrum, but also reveal the subtle texture changes of different materials in different bands, such as surface roughness, uniformity and microstructure.

[0127] Specifically, texture analysis algorithms (such as gray-level co-occurrence matrix, Gabor filter, etc.) are used to extract visual texture features from visual data and identify the morphology, structure and texture patterns of different regions. At the same time, texture features are extracted from spectral data, and the changes in spectral response at different wavelengths are analyzed to identify spectral texture features, thereby obtaining visual texture feature data and spectral texture feature data.

[0128] Step 704: According to the pixel positions of the visual texture feature data and the pixel positions of the spectral texture feature data, the visual texture feature data and the spectral texture feature data are fused to obtain fused texture feature data.

[0129] The fused texture feature data may be comprehensive data information formed by integrating data from different sources (such as visual texture features and spectral texture features).

[0130] Specifically, the pixel positions of the visual texture feature data and the spectral texture feature data are extracted to ensure the spatial alignment or correspondence between the two sets of data. The material texture features at the same pixel position are used as a reference, and the visual texture features are fused with the spectral texture features using a feature fusion algorithm (such as weighted fusion, principal component analysis, etc.). In the fusion process, the complementarity of visual and spectral data is comprehensively considered, and their feature information is integrated to improve the accuracy and expression ability of the texture features, and obtain fused texture feature data.

[0131] Step 706: When there is a material texture feature deviation in the fused texture feature data, acquisition device adjustment information is generated according to the fused texture feature data.

[0132] The material texture feature deviation may be caused by equipment error or environmental influence during the data acquisition process, resulting in errors in visual texture feature data and spectral texture feature data.

[0133] The acquisition device adjustment information may be provided to the data acquisition device for the purpose of correcting errors or deviations that occur during the data acquisition process and providing adjustment parameters or setting suggestions.

[0134] Specifically, the fused texture feature data is analyzed to detect whether there are material texture feature deviations. These material texture feature deviations reflect the errors in the visual texture feature data and spectral texture feature data caused by equipment errors or environmental influences during the data acquisition process. According to the type and degree of material texture feature deviations, the relationship between them and the acquisition equipment settings, such as acquisition angle, resolution, lighting conditions, etc., is determined. The algorithm is used to calculate the equipment adjustment information required to correct these material texture feature deviations, including adjustment parameters or setting suggestions, and generate acquisition equipment adjustment information.

[0135] Step 708, controlling each acquisition device to adjust the spatial information according to the acquisition device adjustment information.

[0136] Specifically, the acquisition device adjustment information is parsed to determine the acquisition device parameters corresponding to each acquisition device that needs to be adjusted, such as acquisition angle, resolution, focal length, lighting conditions, etc. These adjustment information are transmitted to each acquisition device, and the device performs automatic adjustment according to the received instructions, such as changing the position, angle or other settings of the sensor or camera to optimize the data acquisition process. The adjustment effect is then verified through the feedback mechanism of the device to ensure that the acquisition parameters reach the expected optimization target.

[0137] Step 710, based on controlling the adjusted acquisition devices to perform data acquisition, return to the step of acquiring regional visual data and regional spectral data of the area to be measured until there is no material texture feature deviation in the fused texture feature data.

[0138] Specifically, the adjusted acquisition device is controlled to re-acquire data to obtain updated regional visual data and regional spectral data. The updated regional visual data and regional spectral data are processed and analyzed to generate new fused texture feature data. Then, the fused texture feature data is subjected to deviation detection to determine whether there is still a material texture feature deviation. If the deviation still exists, the device is further adjusted according to the acquisition device adjustment information and the data is re-acquired. This process is continuously cycled until the material texture feature deviation in the fused texture feature data is eliminated.

[0139] Step 712: Identify the surface material characteristics of the area to be measured based on the fused texture feature data, and obtain the surface material point cloud data of the area to be measured.

[0140] Specifically, based on the fusion of texture feature data, the visual and spectral features in the data are analyzed to identify the types and distribution of different surface materials in the area to be measured. Through classification algorithms or models, these material property information is classified and extracted, and the identified material properties are mapped into three-dimensional space to generate three-dimensional point cloud data containing the position and morphology of surface materials. The three-dimensional point cloud data is further used to accurately describe the spatial distribution of surface materials through multiple three-dimensional coordinate points, and finally a complete surface material point cloud data of the area to be measured is formed.

[0141] In this embodiment, the texture features of regional visual data and spectral data are extracted and fused to ensure that the features of different data sources correspond accurately. If there is a deviation in the fused texture feature data, the data acquisition process is optimized by generating equipment adjustment information to ensure the accuracy of the acquisition equipment. The data is continuously adjusted and re-collected until the texture feature deviation is eliminated, and finally the surface material characteristics are identified and high-precision surface material point cloud data is generated. This cyclic optimization method improves the accuracy of the data, reduces errors, ensures the accuracy of surface characteristic analysis and three-dimensional modeling, and is helpful for terrain mapping and engineering applications in complex environments.

[0142] In one embodiment, Figure 8 As shown, according to the regional visual data and the regional spectral data, the surface material characteristics of the area to be measured are identified, and the surface material point cloud data of the area to be measured is obtained, including:

[0143] Step 802, calculating a set of texture feature deviation values ​​between different texture feature data of the fused texture feature data.

[0144] The texture feature deviation value set may be a numerical set of all texture feature differences calculated when comparing texture feature data from different sources (such as visual texture features and spectral texture features) at different locations of the material.

[0145] Specifically, different texture feature data in the fused texture feature data, such as visual texture features and spectral texture features, are extracted, and the differences between these texture feature data are compared using a difference calculation algorithm (such as Euclidean distance, cosine similarity, etc.), and the texture feature deviation values ​​between them are calculated point by point or region by region. The deviation values ​​of each feature data at different positions of the material are aggregated to form a texture feature deviation value set containing different texture features.

[0146] Step 804, calculating the device posture adjustment amount, device position adjustment amount and device terrain correction amount corresponding to each acquisition device according to the texture feature deviation value set.

[0147] The equipment terrain correction amount may be an adjustment amount made to the position or angle of the acquisition equipment in order to adapt to terrain changes and improve data acquisition accuracy.

[0148] Specifically, based on the texture feature deviation value set, the deviations between texture data collected by different acquisition devices are analyzed to determine whether these deviations may be caused by the device posture, position or terrain influence. Then, the correction algorithm is used to calculate the posture adjustment amount (such as angle adjustment), position adjustment amount (such as distance or height adjustment) of each acquisition device, and the device terrain correction amount required due to terrain influence.

[0149] Step 806, calculating the six-axis adjustment information of the acquisition device corresponding to each acquisition device according to the device posture adjustment amount, the device position adjustment amount and the device terrain correction amount.

[0150] Among them, the six-axis adjustment information of the acquisition device can be the adjustment amount of the six degrees of freedom of the data acquisition device in space (ie, three translation directions of X, Y, and Z, and three rotation directions around X, Y, and Z axes).

[0151] Specifically, according to the device posture adjustment amount, device position adjustment amount and device terrain correction amount, the current state of each acquisition device in space and the required adjustment are analyzed. Through the six-axis adjustment algorithm, the adjustment information of the acquisition device in six degrees of freedom is calculated respectively, including three translation directions (X, Y, Z axes) and three rotation directions (rotation around X, Y, Z axes), and the six-axis adjustment information of the acquisition device is obtained.

[0152] Step 808: Generate acquisition device adjustment information according to the six-axis adjustment information of each acquisition device.

[0153] Specifically, based on the six-axis adjustment information of each acquisition device, the specific adjustment requirements of the device in the three translation directions (X, Y, and Z axes) and the three rotation directions (around the X, Y, and Z axes) are determined respectively, and these six-axis adjustment information are integrated to generate specific device adjustment parameters as the acquisition device adjustment information, such as the device's moving distance, rotation angle, etc.

[0154] In this embodiment, by calculating the deviation value set of the fused texture feature data, the attitude, position and terrain correction of the acquisition device are gradually derived, thereby generating six-axis adjustment information and finally generating device adjustment information. This process ensures that the acquisition device can accurately adjust its position and angle in space, optimize data acquisition accuracy, and reduce errors caused by changes in device attitude, position or terrain. By continuously adjusting device parameters, this method improves the accuracy and consistency of data acquisition, ensures high-quality surface feature data, and provides reliable support for subsequent analysis and application.

[0155] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0156] Based on the same inventive concept, the embodiment of the present application also provides a spatial flatness measuring device applicable to all terrains for implementing the spatial flatness measuring method applicable to all terrains mentioned above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more spatial flatness measuring devices applicable to all terrains provided below can refer to the above limitations on a spatial flatness measuring method applicable to all terrains, and will not be repeated here.

[0157] In one embodiment, Fig. 9 As shown, a spatial flatness measurement device suitable for all terrains is provided, including: a regional data acquisition module 902, a point cloud data generation module 904, a flatness calculation module 906 and a flatness optimization module 908, wherein:

[0158] A regional data acquisition module 902 is used to acquire regional visual data and regional spectral data of the area to be measured;

[0159] The point cloud data generating module 904 is used to identify the surface material characteristics of the area to be measured based on the regional visual data and the regional spectral data, and obtain the surface material point cloud data of the area to be measured;

[0160] The flatness calculation module 906 is used to use the surface material point cloud data to perform terrain plane fitting on the area to be measured to obtain fitting flatness data;

[0161] The flatness optimization module 908 is used to correct the fitted flatness data according to the regional spectrum data to obtain the spatial flatness data of the area to be measured.

[0162] In one embodiment, the flatness optimization module 908 is also used to detect the reflection characteristics in the regional spectral data, identify the material quality data and spectral characteristic data corresponding to each material in the area to be measured; determine the material mechanical data corresponding to each material in the area to be measured based on the material quality data and the spectral characteristic data; and correct the fitted flatness data based on the material mechanical data to obtain the spatial flatness data of the area to be measured.

[0163] In one embodiment, the flatness optimization module 908 is also used to identify the material elastic modulus corresponding to each material based on the data drift of each spectral characteristic data and the characteristic information of each material quality data; identify the material friction coefficient corresponding to each material based on the reflection beam density of each spectral characteristic data and the characteristic information of each material quality data; identify the material roughness corresponding to each material based on the reflection beam divergence of each spectral characteristic data and the characteristic information of each material quality data.

[0164] In one embodiment, the flatness optimization module 908 is also used to determine each deformation correction area in the fitting flatness data according to the material elastic modulus; each deformation correction area has a first mapping relationship with the corresponding material elastic modulus; according to the first mapping relationship, the deformation correction area corresponding to each material elastic modulus correction is retrieved to obtain deformation corrected flatness data; according to the material friction coefficient, each slip correction area in the deformation corrected flatness data is determined; each slip correction area has a second mapping relationship with the corresponding material friction coefficient; according to the second mapping relationship, the slip correction area corresponding to each material friction coefficient correction is retrieved to obtain slip corrected flatness data; according to the material roughness, each plane fluctuation correction area in the slip corrected flatness data is determined; each plane fluctuation correction area has a third mapping relationship with the corresponding material roughness; according to the third mapping relationship, the plane fluctuation correction area corresponding to each material roughness correction is retrieved to obtain spatial flatness data.

[0165] In one embodiment, the flatness optimization module 908 is also used to determine each surface height correction area in the deformation-corrected flatness data according to the material friction coefficient; each surface height correction area has a fourth mapping relationship with the corresponding material friction coefficient; according to the fourth mapping relationship, the surface height correction area corresponding to each material friction coefficient correction is retrieved to obtain the height-corrected flatness data; according to the material friction coefficient, each slope correction area in the deformation-corrected flatness data is determined; each slope correction area has a fifth mapping relationship with the corresponding material friction coefficient; according to the fifth mapping relationship, the slope correction area corresponding to each material friction coefficient correction is retrieved to obtain the slip-corrected flatness data.

[0166] In one embodiment, the point cloud data generation module 904 is also used to extract texture features from regional visual data and regional spectral data to obtain visual texture feature data and spectral texture feature data; according to the pixel positions of the visual texture feature data and the pixel positions of the spectral texture feature data, the visual texture feature data and the spectral texture feature data are fused to obtain fused texture feature data; when there is a material texture feature deviation in the fused texture feature data, acquisition device adjustment information is generated according to the fused texture feature data; according to the acquisition device adjustment information, each acquisition device is controlled to adjust spatial information; based on the control of each adjusted acquisition device to perform data acquisition, the step of obtaining regional visual data and regional spectral data of the area to be measured is returned until there is no material texture feature deviation in the fused texture feature data; according to the fused texture feature data, the surface material characteristics of the area to be measured are identified to obtain the surface material point cloud data of the area to be measured.

[0167] In one embodiment, the point cloud data generation module 904 is also used to calculate a set of texture feature deviation values ​​between different texture feature data of the fused texture feature data; calculate the device posture adjustment amount, device position adjustment amount and device terrain correction amount corresponding to each acquisition device based on the texture feature deviation value set; calculate the six-axis adjustment information of the acquisition device corresponding to each acquisition device based on the device posture adjustment amount, device position adjustment amount and device terrain correction amount; generate acquisition device adjustment information based on the six-axis adjustment information of each acquisition device.

[0168] Each module in the above-mentioned spatial flatness measurement device applicable to all terrains can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0169] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig.10 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store server data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a spatial flatness measurement method suitable for all terrains is implemented.

[0170] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0171] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0172] In one embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0173] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-mentioned method embodiments.

[0174] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0175] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0176] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0177] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A spatial flatness measurement method applicable to all terrains, characterized in that: The method comprises: Acquire regional visual data and regional spectral data of the area to be measured; Identify the surface material characteristics of the area to be measured according to the regional visual data and the regional spectral data, and obtain the surface material point cloud data of the area to be measured; Using the surface material point cloud data to perform terrain plane fitting on the area to be measured, to obtain fitting flatness data; According to the regional spectral data, the fitting flatness data is corrected to obtain the spatial flatness data of the area to be measured; The step of correcting the fitting flatness data according to the regional spectral data to obtain the spatial flatness data of the area to be measured includes: Detecting the reflection characteristics in the regional spectral data, and identifying the material quality data and spectral characteristic data corresponding to each of the materials in the area to be measured; Determining material mechanical data corresponding to each of the materials in the area to be measured according to each of the material quality data and each of the spectral characteristic data; According to the mechanical data of each material, the fitting flatness data is corrected to obtain the spatial flatness data of the area to be measured; Wherein, the fitting flatness data is corrected according to the mechanical data of each material, and the correction formula for obtaining the spatial flatness data of the area to be measured is: Among them, the is the spatial flatness data, To correct the flatness data for deformation, The slip-corrected flatness data is To highly correct the flatness data, the z is the fitting flatness data, F is the external force acting on the material, E m is the elastic modulus of the material, A is the area of ​​the force-bearing region, is a coefficient related to the local inhomogeneity of the material, is the influencing factor of local material property changes, is the coefficient related to the strain linear effect, is the local strain, is the friction coefficient correction function, is the material friction coefficient, is the local pressure distribution function, is the height variation at different positions, is the correction function of friction coefficient and slope angle, is the initial value of the slope, is the correction factor related to local material slip, is the slope value, is the material roughness, is the fluctuation amplitude function, is the local tilt angle at different positions.

2. The method according to claim 1, characterized in that The material mechanical data includes material elastic modulus, material friction coefficient and material roughness; the material mechanical data corresponding to each material in the area to be measured is determined according to each material quality data and each spectral characteristic data, including: Identifying the material elastic modulus corresponding to each of the materials according to the data drift of each of the spectral characteristic data and the characteristic information of each of the material quality data; Identifying the material friction coefficient corresponding to each of the materials according to the reflection beam density of each of the spectral characteristic data and the characteristic information of each of the material quality data; The material roughness corresponding to each of the materials is identified according to the reflection beam divergence of each of the spectral characteristic data and the characteristic information of each of the material quality data.

3. The method according to claim 2, characterized in that The step of correcting the fitting flatness data according to the material mechanical data to obtain the spatial flatness data of the area to be measured includes: Determining each deformation correction region in the fitting flatness data according to the elastic modulus of the material; each deformation correction region has a first mapping relationship with the corresponding material elastic modulus; According to the first mapping relationship, the deformation correction area corresponding to the elastic modulus correction of each material is retrieved to obtain deformation correction flatness data; Determining each slip correction region in the deformation-corrected flatness data according to the material friction coefficient; each slip correction region has a second mapping relationship with the corresponding material friction coefficient; According to the second mapping relationship, the slip correction area corresponding to each material friction coefficient correction is retrieved to obtain slip correction flatness data; Determining each plane fluctuation correction region in the slip correction flatness data according to the material roughness; each plane fluctuation correction region has a third mapping relationship with the corresponding material roughness; According to the third mapping relationship, the plane fluctuation correction area corresponding to each material roughness correction is retrieved to obtain the spatial flatness data.

4. The method according to claim 3, characterized in that The step of retrieving the slip correction area corresponding to each material friction coefficient correction according to the second mapping relationship to obtain slip correction flatness data includes: According to the material friction coefficient, determining each surface height correction area in the deformation-corrected flatness data; each surface height correction area has a fourth mapping relationship with the corresponding material friction coefficient; According to the fourth mapping relationship, the ground height correction area corresponding to each material friction coefficient correction is retrieved to obtain height-corrected flatness data; According to the material friction coefficient, determining each slope correction area in the deformation-corrected flatness data; each slope correction area has a fifth mapping relationship with the corresponding material friction coefficient; According to the fifth mapping relationship, the slope correction area corresponding to each material friction coefficient correction is retrieved to obtain the slip correction flatness data.

5. The method according to claim 1, characterized in that The method of identifying the surface material characteristics of the area to be measured based on the regional visual data and the regional spectral data to obtain the surface material point cloud data of the area to be measured includes: Extracting texture features from the regional visual data and the regional spectral data to obtain visual texture feature data and spectral texture feature data; According to the pixel positions of the visual texture feature data and the pixel positions of the spectral texture feature data, the visual texture feature data and the spectral texture feature data are fused to obtain fused texture feature data; In the case where the fused texture feature data has a material texture feature deviation, generating acquisition device adjustment information according to the fused texture feature data; According to the acquisition device adjustment information, control each acquisition device to adjust the spatial information; Based on controlling the adjusted acquisition devices to perform data acquisition, returning to the step of acquiring regional visual data and regional spectral data of the area to be measured, until the fused texture feature data does not have material texture feature deviation; The surface material characteristics of the area to be measured are identified based on the fused texture feature data, and the surface material point cloud data of the area to be measured is obtained.

6. The method according to claim 5, characterized in that The step of generating acquisition device adjustment information according to the fused texture feature data includes: Calculate a set of texture feature deviation values ​​between different texture feature data of the fused texture feature data; Calculate the device posture adjustment amount, device position adjustment amount and device terrain correction amount corresponding to each of the acquisition devices according to the texture feature deviation value set; Calculate the six-axis adjustment information of the acquisition device corresponding to each of the acquisition devices according to the device posture adjustment amount, the device position adjustment amount and the device terrain correction amount; The acquisition device adjustment information is generated according to the six-axis adjustment information of each acquisition device.

7. A spatial flatness measuring device suitable for all terrains, characterized in that: The device comprises: A regional data acquisition module, used to acquire regional visual data and regional spectral data of the area to be measured; A point cloud data generating module, used for identifying the surface material characteristics of the area to be measured according to the regional visual data and the regional spectral data, and obtaining the surface material point cloud data of the area to be measured; A flatness calculation module is used to use the surface material point cloud data to perform terrain plane fitting on the area to be measured to obtain fitting flatness data; A flatness optimization module, used to correct the fitted flatness data according to the regional spectral data to obtain spatial flatness data of the area to be measured; The step of correcting the fitting flatness data according to the regional spectral data to obtain the spatial flatness data of the area to be measured includes: Detecting the reflection characteristics in the regional spectral data, and identifying the material quality data and spectral characteristic data corresponding to each of the materials in the area to be measured; Determining material mechanical data corresponding to each of the materials in the area to be measured according to each of the material quality data and each of the spectral characteristic data; According to the mechanical data of each material, the fitting flatness data is corrected to obtain the spatial flatness data of the area to be measured; Wherein, the fitting flatness data is corrected according to the mechanical data of each material, and the correction formula for obtaining the spatial flatness data of the area to be measured is: Among them, the is the spatial flatness data, To correct the flatness data for deformation, The slip-corrected flatness data is To highly correct the flatness data, the z is the fitting flatness data, F is the external force acting on the material, E m is the elastic modulus of the material, A is the area of ​​the force-bearing region, is a coefficient related to the local inhomogeneity of the material, is the influencing factor of local material property changes, is the coefficient related to the strain linear effect, is the local strain, is the friction coefficient correction function, is the material friction coefficient, is the local pressure distribution function, is the height variation at different positions, is the correction function of friction coefficient and slope angle, is the initial value of the slope, is the correction factor related to local material slip, is the slope value, is the material roughness, is the fluctuation amplitude function, is the local tilt angle at different positions.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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