Earthwork modeling method and system based on unmanned aerial vehicle

Abstract

The invention provides an earthwork modeling method and system based on an unmanned aerial vehicle, and relates to the technical field of image data processing. The method comprises the following steps: acquiring target point cloud data of an engineering area, and performing elevation verification analysis based on different measurement heights to form target point cloud verification data; collecting target image data of the engineering area, and performing adjustment analysis in combination with the geographic elevation information and the target point cloud verification data to form target point cloud adjustment data; and performing modeling processing according to the target point cloud adjustment data to form a target earthwork model. According to the method, the modeling accuracy can be further improved in a mode of fully utilizing unmanned aerial vehicle modeling, and more accurate and effective modeling data is provided for engineering evaluation and implementation.

Description

Technical Field

[0001] This invention relates to the field of image data processing technology, and more specifically, to a method and system for modeling earthwork engineering based on unmanned aerial vehicles (UAVs). Background Technology

[0002] Earthwork engineering is one of the major construction trades in building engineering, encompassing all aspects of earthwork (excavation, filling, transportation, drainage, and dewatering). Earthwork engineering involves large volumes of work, complex construction conditions, and is significantly affected by geological, hydrological, and meteorological conditions. Therefore, necessary work must be done in organizing earthwork construction to ensure project quality. For earthwork engineering, the first step is to model the project area to accurately determine the volume of earthwork to be performed.

[0003] Currently, most earthwork engineering modeling uses traditional methods, namely setting up total stations for manual surveying. This method is labor-intensive, costly, and inefficient, failing to provide a rapid and effective response to the project. While new modeling methods are emerging, such as using drones equipped with data acquisition platforms to collect image data of the construction area for modeling, this approach improves efficiency but still cannot guarantee high accuracy.

[0004] Therefore, designing a UAV-based earthwork engineering modeling method and system that can further improve the accuracy of modeling by making full use of UAV modeling methods and provide more accurate and effective modeling data for engineering evaluation and implementation is an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a method for earthwork engineering modeling based on unmanned aerial vehicles (UAVs). By digitizing the image data collected by the UAV into point cloud data, the accuracy of the point cloud data, especially in terms of elevation, is improved by comparing and verifying the results obtained by the UAV at different altitudes. Simultaneously, simple color-based texture analysis is performed on the image data collected by the UAV to achieve reasonable region division. Based on this, discrete ground elevation data is combined to verify the elevation of the point cloud data and adjust the regional divisions, greatly improving the precision and accuracy of the point cloud data. This results in a model built using the adjusted point cloud data with higher precision and accuracy, providing important and accurate basic data for subsequent project evaluation and implementation.

[0006] The present invention also aims to provide an earthwork engineering modeling system based on unmanned aerial vehicles (UAVs). This system uses a data acquisition unit to collect basic data for earthwork engineering modeling, and an elevation processing unit to adjust the elevation of point cloud data acquired by the UAV at different altitudes, combining geographic elevation and color-coded data for elevation optimization, resulting in accurate point cloud adjustment data. Based on this, a modeling processing unit processes the accurate point cloud data to form an accurate model of the earthwork engineering project. The different functional units are interconnected, effectively ensuring efficient information transmission and processing, which is a crucial material foundation for realizing earthwork engineering modeling.

[0007] In a first aspect, the present invention provides a method for modeling earthwork engineering based on unmanned aerial vehicles (UAVs), comprising: collecting target point cloud data of the engineering area, performing elevation verification analysis based on different measurement heights to form target point cloud verification data; collecting target image data of the engineering area, and combining it with geographic elevation information and target point cloud verification data for adjustment analysis to form target point cloud adjustment data; and performing modeling processing based on the target point cloud adjustment data to form a target earthwork engineering model.

[0008] In this invention, the method digitizes image data collected by a UAV into point cloud data. The point cloud data is then compared and verified based on the results obtained by the UAV at different altitudes to improve its accuracy, especially in terms of elevation. Simultaneously, simple color-based texture analysis is performed on the UAV-collected image data to achieve reasonable region division. Based on this, discrete ground elevation data is combined to verify the elevation of the point cloud data and adjust the regional divisions, significantly improving the precision and accuracy of the point cloud data. This results in models built using the adjusted point cloud data possessing higher precision and accuracy, providing crucial and accurate foundational data for subsequent engineering evaluation and implementation.

[0009] One possible approach involves collecting target point cloud data of the engineering area and performing elevation verification analysis based on different measurement heights to form target point cloud verification data. This includes: extracting measurement height point cloud data formed at different measurement heights based on the target point cloud data, and calibrating the measurement height point cloud data corresponding to the lowest measurement height as the reference height point cloud data; using the reference height point cloud data as a reference, performing elevation verification analysis on measurement height point cloud data at other different measurement heights to form corresponding measurement height point cloud verification data; and merging the reference height point cloud data and all measurement height point cloud verification data to form target point cloud verification data.

[0010] In this invention, the elevation verification analysis of point cloud data formed from image data collected by UAVs is mainly due to the fact that the accuracy of point cloud data acquired by UAVs at different measurement altitudes varies. It is understood that as the altitude of the UAV gradually increases, the number of effective point clouds extracted from the collected image data will gradually decrease, and the measurement deviation of the effective point clouds, especially in terms of elevation, will gradually increase. Therefore, UAV image acquisition is generally limited to a suitable altitude range. A single image acquisition cannot meet the data acquisition needs. Multiple data acquisitions can obtain enough point cloud data to provide a sufficient data foundation for subsequent modeling. The measurement altitude given each time the UAV performs image data acquisition is different. The elevation error of the point cloud data at the lowest measurement altitude is relatively small. Therefore, the point cloud data at the lowest measurement altitude can be used as the basis for elevation verification and adjustment of the point cloud data measured at other altitudes. Finally, the point cloud data at all measurement altitudes are combined to form sufficient point cloud foundation data required for modeling. Elevation verification can unify the elevation deviation of point cloud data at different measurement heights to the deviation level of data obtained at the lowest measurement height, providing an important data unification basis for the integration of point cloud data, which is also the basis for realizing data aggregation.

[0011] As one possible implementation, using benchmark height point cloud data as a reference, elevation verification analysis is performed on measurement height point cloud data at other different measurement heights to form corresponding measurement height point cloud verification data. This includes: extracting the elevation of benchmark position points at different locations in the benchmark height point cloud data. Where m represents the number of different location points in the reference height point cloud data; for the measurement height point cloud data at other measurement heights, the elevations of the corresponding measurement height location points at different locations are extracted. Where n represents the number of the point cloud data at different measurement heights, formed in ascending order of measurement height, and k represents the number of different locations within the point cloud data at measurement height n; the elevations of different locations within the different measurement height point cloud data are respectively... Elevation of different reference locations in point cloud data at the same reference height The following elevation verification analysis is performed: the elevation of the measured height points for elevation verification is determined. The corresponding location point is designated as the verification location point, and the elevation of the nearest reference location point to the verification location point in the reference height point cloud data is determined. The corresponding location points are designated as verification reference points. A fixed-step effective average elevation analysis is performed on the verification location points, starting from the verification reference points, to obtain the effective verification average elevation corresponding to each verification location point. Based on the valid verification average elevation Elevation of the measured height point corresponding to the verification location point Adjustments were made to establish the verification elevation of the measurement height points corresponding to the verification location points. ,in, u represents the formation of a valid verification average elevation. The number of corresponding benchmark elevation points; a collection of elevations of all measured elevation points. Elevation verification at corresponding measurement height points The data is then assigned to the corresponding location points to form measurement height point cloud verification data.

[0012] In this invention, elevation verification is performed on point cloud data at different measurement heights using reference height point cloud data. The main purpose is to correct the deviation of the elevation data of the point clouds acquired at different measurement heights, ensuring that the deviation of the corrected elevation value is at the same level as the elevation deviation of the reference height point cloud data. This verification is possible because, firstly, the reference height point cloud data contains a large number of valid data points at lower measurement heights, allowing for the formation of a reference average elevation value near the fewer valid data points acquired at higher heights. Using this average value as the basis for adjusting the elevation values ​​of corresponding data points ensures the rationality and accuracy of the verification. Secondly, all point cloud data have unified position coordinates, meaning they can be mapped to the same coordinate system. Therefore, regardless of whether the point cloud data was collected at the same height, the relative relationships of the position points are definite, providing the foundation for the position information verification. It is understandable that the reference elevation cloud data points around the location point to be verified, which can represent the average elevation value of the location point, are located in an area where the image data can be considered to be at the same level. That is, the elevation value will not be affected by other environmental factors, especially the effect of vegetation alignment. Therefore, the average elevation value can be used as the average level value of this area. Since the location point is located in this area, the average elevation value of any number of location points is at the same level. Based on this, the elevation of the location point to be verified can be reasonably and accurately verified and adjusted.

[0013] As one possible implementation, a fixed-step effective average elevation analysis is performed on the verification location points, starting from the verification reference location point, to obtain the effective verification average elevation corresponding to the verification location points. This includes: setting an angle step size α, taking the verification reference position point as the starting angle, rotating clockwise by an angle step size α each time, obtaining the position point corresponding to the reference height point cloud data in the direction pointed after the rotation, and extracting the elevation of the reference position point corresponding to the position point. The elevations of the corresponding reference points are extracted sequentially according to the angle step size α. This process continues until the elevations of all extracted benchmark points are collected, circling the verification location point. This forms the elevation set of relevant points of the verification benchmark corresponding to the verification location point. ,in, , where i represents the elevation of different reference points extracted around the verification location point. Numbered according to the extraction order; Elevation set of relevant points for verification benchmark. The following selection is performed based on the elevation of different benchmark locations: according to the set of elevations of relevant points of the verification benchmark. The elevations of different reference points are extracted sequentially according to their numbering order to obtain the corresponding average elevation values. ,in, If the newly extracted benchmark elevation meets the following conditions: If the newly extracted benchmark elevation is not met, then retain the new benchmark elevation. If the newly extracted benchmark points are not found, the extraction of new benchmark point elevations will continue until the set of relevant elevation points for the verification benchmark is complete. The elevation of all benchmark points is determined, and the resulting average elevation value is identified as the valid verification average elevation. ,in, This is the average elevation limit.

[0014] In this invention, the acquisition of the average elevation value near the verification location point considers that the only data that can reflect the elevation level near the verification location point is the elevation data of the location points in the reference height point cloud data. However, due to the irregularity of the point cloud data, these location points are at varying distances from the verification location point. This distance may cause some location points in the reference height point cloud data near the verification location point to be at different levels from the verification location point. These areas at the same level can be understood as the extent of the influence of environmental factors during point cloud data extraction. For example, if the verification location point happens to be in an area with abundant vegetation, then the elevation deviation obtained from the location point also includes the deviation caused by the height of the vegetation. If there is a location point in the surrounding reference height point cloud data that is far away and exceeds the vegetation coverage, then selecting the elevation data of this location point as the basis for verification analysis will cause an error in the average elevation value. Therefore, it is necessary to filter the location points in the benchmark height point cloud data near the verification location point. This application first obtains a certain number of location point elevation data from the benchmark height point cloud data by setting an angle step size. The set angle step size can be determined according to the density of location points in the benchmark height point cloud data to avoid unreasonable extraction of location point elevation data that is too far away, which would affect the efficiency of subsequent analysis. After obtaining the elevation data, it is also necessary to determine whether the location points corresponding to these elevation data are located in the same horizontal area as the verification location point. A quick way is to determine this by using the average elevation value. Of course, it is possible to use image data for partitioning, but it is not as efficient as the average value method. If these location points are located in the same horizontal area, then the average elevation value formed by any number of points will be relatively stable. Therefore, using the average elevation value as the basis for analysis and judgment is reasonable and accurate. The average elevation value can be set according to the actual situation or determined based on big data analysis of the average elevation values ​​of the same horizontal area.

[0015] One possible approach is to collect target image data of the engineering area and combine it with geographic elevation information and target point cloud verification data for adjustment and analysis to form target point cloud adjustment data. This includes: dividing the engineering area into regions based on color according to the target image data to form region division data; adjusting the overall elevation of the target point cloud verification data based on the region division data and geographic elevation information to form unified target point cloud adjustment data; and adjusting the regional elevation of the unified target point cloud adjustment data based on color according to the region division data to form target point cloud adjustment data.

[0016] In this invention, it is understood that although the point cloud data obtained by the UAV at different measurement altitudes has reached the same level of elevation deviation after elevation verification and adjustment, this deviation is still significant and will affect the engineering evaluation. Therefore, further deviation correction is required. This application considers using geographic elevation for correction. Of course, geographic elevation information can be more accurate remote sensing data or data measured manually. The role of geographic elevation is to correct the point cloud data, so the amount does not need to be too large, and the acquisition of data will not have a significant impact on cost and cycle. Geographic elevation can further correct the elevation deviation of the point cloud data at the overall level. However, considering that the construction area is not at the same environmental level, especially the vegetation cover will affect the elevation accuracy of the point cloud data collected by the UAV, it is also necessary to combine the color information of the image data collected by the UAV to further perform elevation correction by region to obtain more accurate and reasonable elevation data, effectively ensuring the accuracy and precision of modeling.

[0017] As one possible implementation, the engineering area is divided into regions based on color according to the target image data to form region division data. This includes: performing grayscale processing on the target image data to form target grayscale image data; using pixels as the unit of analysis, traversing the grayscale values ​​of all pixels from any edge of the image in the analysis direction, and identifying adjacent pixels whose grayscale value difference exceeds the partition grayscale difference threshold; extracting the continuity of the identified adjacent pixels, and determining the boundary between adjacent pixels as the region boundary line.

[0018] In this invention, color-based region segmentation of the target image data is primarily achieved by determining the boundaries of the regions based on the gradient of color changes. Here, the image data is converted to grayscale, and the gradient of grayscale value changes between adjacent pixels is used as the basis for region boundary segmentation. The grayscale difference threshold for each region can be set according to actual conditions or determined based on large-scale data analysis of the grayscale value gradient change patterns of pixels at different region boundaries. Of course, choosing grayscale values ​​as the basic data for color differentiation also facilitates subsequent elevation adjustments based on color information. After all, the relationship between multi-parameter color representations like RGB and elevation data is complex, and the uniformity of grayscale values ​​allows for a one-to-one mapping with elevation numerical changes, facilitating subsequent elevation adjustments. After determining the region boundaries, different regions in the image can be segmented based on these boundaries.

[0019] As one possible implementation, based on regional division data and geographic elevation information, the overall elevation of the target point cloud verification data is adjusted to form unified adjusted target point cloud data. This includes: identifying multiple areas without vegetation cover based on regional division data and marking them as elevation coordination areas; randomly extracting two elevation coordination areas from different elevation coordination areas and performing overall elevation adjustment analysis in the following manner: marking the two randomly extracted elevation coordination areas as the adjustment elevation coordination area and the verification elevation coordination area, respectively; and for geographic elevation points in the adjustment elevation coordination area, [the following analysis is performed around the geographic elevation points]. Multiple analytical lines passing through geographic elevation points are determined along the circumference, and these analytical lines satisfy the following conditions: at least one target point cloud verification data point within the elevation adjustment coordination area lies on the analytical line; and the distance from the target point cloud verification data point within the elevation adjustment coordination area to the analytical line is no greater than the effective line distance. Any point whose distance is no greater than the effective line distance is marked as an effective location point, and points located on the analytical lines are marked as online location points. For different analytical lines, the linear function of the analytical line is determined based on the location information of the corresponding effective location points and the location information of the online location points. Where x represents the number of the different analytical lines; based on the linear function corresponding to the analytical line... Determine the geographical elevation analysis elevation value of the geographical elevation location point on the analysis line. Geographic elevation analysis based on different analytical lines Determine the geographic elevation values ​​for point cloud analysis. ,in, Geographic elevation values ​​were analyzed based on point cloud data. The actual elevation value of the geographical elevation location point Determine the overall elevation adjustment value ,in, Adjustment value based on overall elevation The elevation of the target point cloud verification data is adjusted to form the initial adjusted data of the target point cloud. Based on the initial adjusted data of the target point cloud and combined with the geographical elevation information in the verification elevation coordination area, the elevation adjustment verification is performed to form the unified adjusted data of the target point cloud.

[0020] In this invention, elevation adjustment is performed on the target point cloud verification data. Using geographic elevation information as a benchmark, the elevation deviation of the point cloud data collected by the UAV can be reasonably corrected. Of course, geographic elevation information needs to consider two aspects. First, to achieve reasonable data correction, its location information needs to exclude the influence of environmental factors on the elevation data acquired by the UAV. In vegetated areas, the UAV's elevation data will have not only the actual elevation deviation but also deviations caused by vegetation height, leading to inaccurate analysis. Therefore, it is necessary to select areas without vegetation cover to obtain geographic elevation information. Second, due to the discreteness and irregularity of the point cloud data acquired by the UAV, the geographic elevation location may not coincide with the location point in the point cloud data. Therefore, it is necessary to reasonably eliminate the error caused by this possibility of non-coincidence. Here, by obtaining straight lines in multiple directions based on the geographic elevation location, and ensuring that the lines cover the corresponding point cloud data, a functional relationship is established to determine the optimal elevation data for the geographic elevation location point that can be analyzed from the point cloud data, thereby obtaining an accurate and effective elevation adjustment value. The number of straight lines can be set according to the required analysis precision, and the effective straight line distance can also be determined based on the actual situation, as long as the accuracy and reasonableness of the adjustment values ​​are ensured. Of course, since the geographic elevation points may not coincide with the point cloud data points, the analysis may contain some errors. To further ensure the accuracy and reasonableness of the analysis results, a comparative verification of geographic elevation information is performed in another analysis area. The analysis line uses the position coordinates of two points to establish a straight line function, and then uses the geographic elevation information (excluding elevation) to calibrate on the analysis line function, thereby determining the analysis elevation of the geographic elevation point on the analysis line, which serves as comparative analysis data with the actual elevation value of the geographic elevation point.

[0021] As one possible implementation, based on the initial adjustment data of the target point cloud and combined with the geographical elevation information in the verification elevation coordination area, elevation adjustment verification is performed to form unified adjustment data for the target point cloud. This includes: determining the geographical elevation location points in the verification elevation coordination area; determining multiple verification lines passing through the geographical elevation location points in a circular direction around the geographical elevation location points; and the verification lines satisfying the following conditions: at least one location point of the target point cloud verification data in the verification elevation coordination area lies on the verification line; and the distance from the location point of the target point cloud verification data in the verification elevation coordination area to the verification line is not greater than the effective verification distance. Any point not greater than the effective verification distance is marked as a verification location point, and points located on the verification line are marked as collinear location points. For different verification lines, a verification function is determined based on the location information of the corresponding verification location points and the location information of the collinear location points. Where y represents the number of the different verification lines determined; based on the verification function corresponding to the verification line... Determine the geographic elevation verification elevation value of the geographic elevation location point on the verification line. Verification elevation values ​​based on geographical elevations determined by different verification lines. Determine the point cloud to verify the geographic elevation value. ,in, Verify the geographic elevation value based on the point cloud. The actual elevation value of the geographical elevation location point The following verification analysis is performed: If Then the initial adjustment data of the target point cloud is determined as the unified adjustment data of the target point cloud, and Hallow represents the allowable deviation limit of elevation; if Then The results are used as adjustment values ​​to further adjust the elevation of the initial adjustment data of the target point cloud, forming unified adjustment data for the target point cloud.

[0022] In this invention, the verification of the initial adjustment data of the target point cloud is mainly to determine whether the elevation of the point cloud data, after adjustment based on the geographic elevation information of the elevation adjustment coordination area, has reached the received elevation deviation range. It is understood that the verification method for the initial adjustment data of the target point cloud performed in the elevation adjustment coordination area must be the same as the adjustment method used within the elevation adjustment coordination area. This avoids the impact of different methods on the verification results. Of course, the number of verification lines selected can be determined according to actual needs. For the effective verification distance, it should at least be at the same level of accuracy as the effective straight line distance. If it is too far from the effective straight line distance, the verification effect will be insufficient; if it is too small, the verification effect will be impossible due to excessive stringency. Therefore, the effective verification distance can be determined based on the analysis of the error range generated by the straight line using effective location points. The verification method involves obtaining the elevation values ​​of the adjusted point cloud data at geographic elevation locations within the verification elevation coordination area and comparing them with the actual elevations of those locations. If the elevation difference is within the allowable deviation range, the adjustment within the elevation coordination area is considered acceptable. If it exceeds the allowable deviation range, the excess portion is also included in the adjustment data and adjusted again. To improve the accuracy of data adjustment, multiple elevation coordination areas can be selected for repeated verification adjustments. In this case, the adjustments need to be sequential to avoid situations where a large deviation is made in one adjustment area, then disappears in another, only to reappear in a third. Analyzing the sequential deviations of these elevation adjustments is necessary to establish a reasonable adjustment sequence for more efficient and rational adjustments.

[0023] As one possible implementation, color-based regional elevation adjustment is performed on the target point cloud unified adjustment data based on the regional division data to form target point cloud adjustment data. This includes: determining the grayscale values ​​of different locations in the target point cloud unified adjustment data within the vegetation-covered area based on the regional division data. Coordinate the elevation of location points Where r represents the unified adjustment of the numbering of different location points in the target point cloud data within the vegetation-covered area; based on the grayscale value of the location point. Coordinate the elevation of location points Determine the corresponding hue elevation value ,in, , The color height conversion value represents the unit grayscale value. Represents the grayscale baseline value; a collection of hue elevation values ​​at different locations within a vegetation-covered area. The elevation of the location points in the unvegetated areas is coordinated to form target point cloud adjustment data.

[0024] In this invention, the regional color-based elevation adjustment primarily corrects the elevation deviation caused by vegetation in point cloud data of areas affected by the environment, especially those covered by vegetation. The correction method is simple: the elevation value is adjusted based on the percentage of the grayscale value at a location point relative to a reference grayscale value. It should be noted that the color-to-elevation conversion value and grayscale reference value per unit grayscale value are essentially obtained through big data analysis of the precise elevation of the unvegetated area and the corresponding grayscale value, compared to the actual elevation of location points with different grayscale values ​​obtained from UAV image data. Precise and actual elevations can be obtained experimentally. Of course, the area will vary for each project, and sampling can be performed in the actual project area to ensure the accuracy of the analysis. It should also be noted that this application mainly focuses on elevation adjustment because the main deviation in point cloud data collected by UAVs lies in elevation; therefore, the accuracy of modeling largely depends on the accuracy of the elevation. Thus, the point cloud data formed after elevation adjustment essentially includes both elevation data and planar coordinate data, thus forming the three-dimensional coordinate information of the point cloud data.

[0025] Secondly, the present invention provides an earthwork engineering modeling system based on unmanned aerial vehicles (UAVs), comprising: a data acquisition unit for acquiring target point cloud data, geographic elevation information, and target image data of the engineering area; an elevation processing unit for performing elevation verification analysis on the target point cloud data acquired by the data acquisition unit based on different measurement heights to form target point cloud verification data, and performing adjustment analysis in conjunction with geographic elevation information and target image data to form target point cloud adjustment data; and a modeling processing unit for performing modeling processing on the target point cloud adjustment data formed by the elevation processing unit to form a target earthwork engineering model.

[0026] In this invention, the system completes the basic data collection for earthwork engineering modeling through a data acquisition unit. The elevation processing unit adjusts the elevation of point cloud data acquired by the UAV at different altitudes and optimizes the elevation by combining geographic elevation and color-coded data, forming precise point cloud adjustment data. Based on this, the modeling processing unit processes the accurate point cloud data to create an accurate model of the earthwork engineering. The different functional units are interconnected, effectively ensuring efficient information transmission and processing, which is a crucial material foundation for realizing earthwork engineering modeling.

[0027] The beneficial effects of the unmanned aerial vehicle (UAV)-based earthwork engineering modeling method and system provided by this invention are as follows: This method digitizes image data collected by UAVs into point cloud data, and compares and verifies the point cloud data based on the results obtained by the UAV at different altitudes to improve the accuracy of the point cloud data, especially in terms of elevation. Simultaneously, it utilizes the image data collected by the UAVs to perform simple color-based texture analysis to achieve reasonable region division. Based on this, it combines discrete ground elevation data to verify the elevation of the point cloud data and adjust the regional divisions, significantly improving the precision and accuracy of the point cloud data. This results in models built using the adjusted point cloud data having higher precision and accuracy, providing important and accurate basic data for subsequent engineering evaluation and implementation.

[0028] The system uses a data acquisition unit to collect basic data for earthwork engineering modeling. An elevation processing unit adjusts the elevation of point cloud data acquired by UAVs at different altitudes, and optimizes the elevation by combining geographic elevation and color-coded data, resulting in precise point cloud adjustment data. Based on this, a modeling processing unit processes the accurate point cloud data to create an accurate model of the earthwork engineering project. These interconnected functional units effectively ensure efficient information transmission and processing, forming a crucial material foundation for earthwork engineering modeling. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 A step diagram illustrating an earthwork engineering modeling method based on unmanned aerial vehicles (UAVs) provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of an earthwork engineering modeling system based on unmanned aerial vehicles (UAVs) provided in an embodiment of the present invention. Detailed Implementation

[0031] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention.

[0032] Earthwork engineering is one of the major construction trades in building engineering, encompassing all aspects of earthwork (excavation, filling, transportation, drainage, and dewatering). Earthwork engineering involves large volumes of work, complex construction conditions, and is significantly affected by geological, hydrological, and meteorological conditions. Therefore, necessary work must be done in organizing earthwork construction to ensure project quality. For earthwork engineering, the first step is to model the project area to accurately determine the volume of earthwork to be performed.

[0033] Currently, most earthwork engineering modeling uses traditional methods, namely setting up total stations for manual surveying. This method is labor-intensive, costly, and inefficient, failing to provide a rapid and effective response to the project. While new modeling methods are emerging, such as using drones equipped with data acquisition platforms to collect image data of the construction area for modeling, this approach improves efficiency but still cannot guarantee high accuracy.

[0034] refer to Figures 1-2 This invention provides a method for modeling earthwork engineering based on unmanned aerial vehicles (UAVs). This method digitizes image data collected by the UAV into point cloud data, and compares and verifies the point cloud data based on the results obtained by the UAV at different altitudes to improve the accuracy of the point cloud data, especially in terms of elevation. Simultaneously, it utilizes the image data collected by the UAV to perform simple texture analysis based on color to achieve reasonable region division. Based on this, it combines discrete ground elevation data to verify the elevation of the point cloud data and adjust the regional divisions, greatly improving the precision and accuracy of the point cloud data. This results in a model built using the adjusted point cloud data with higher precision and accuracy, providing important and accurate basic data for subsequent engineering evaluation and implementation.

[0035] A method for modeling earthwork engineering based on unmanned aerial vehicles (UAVs) specifically includes the following steps: S1: Collect target point cloud data of the engineering area, perform elevation verification analysis based on different measurement heights, and form target point cloud verification data.

[0036] Collect target point cloud data of the engineering area, perform elevation verification analysis based on different measurement heights, and form target point cloud verification data. This includes: extracting measurement height point cloud data formed at different measurement heights based on the target point cloud data, and calibrating the measurement height point cloud data corresponding to the lowest measurement height as the reference height point cloud data; using the reference height point cloud data as a reference, performing elevation verification analysis on measurement height point cloud data at other different measurement heights to form corresponding measurement height point cloud verification data; and merging the reference height point cloud data and all measurement height point cloud verification data to form target point cloud verification data.

[0037] The elevation verification analysis of point cloud data formed from image data collected by UAVs is primarily due to the varying accuracy of point cloud data acquired by UAVs at different measurement altitudes. Understandably, as the UAV's altitude increases, the number of effective point clouds extracted from the acquired images gradually decreases, while the measurement deviation of effective point clouds, especially in elevation, gradually increases. Therefore, UAV image acquisition is generally limited to a suitable altitude range. A single image acquisition cannot meet the data acquisition needs; multiple acquisitions can obtain sufficient point cloud data to provide a robust data foundation for subsequent modeling. Since the measurement altitude given during each UAV image acquisition is different, the elevation error of point cloud data at the lowest measurement altitude is relatively small. Therefore, the elevation of point cloud data measured at other altitudes can be verified and adjusted based on the point cloud data at the lowest measurement altitude. Finally, the point cloud data from all measurement altitudes are combined to form sufficient basic point cloud data for modeling. Elevation verification unifies the elevation deviation of point cloud data at different measurement altitudes to the deviation level of data acquired at the lowest measurement altitude, providing an important data unification foundation for point cloud data aggregation, which is also the basis for achieving data collection.

[0038] Using the baseline height point cloud data as a reference, elevation verification analysis is performed on the measurement height point cloud data at other different measurement heights to form corresponding measurement height point cloud verification data. This includes: extracting the elevation of the baseline position points at different locations in the baseline height point cloud data. Where m represents the number of different location points in the reference height point cloud data; for the measurement height point cloud data at other measurement heights, the elevations of the corresponding measurement height location points at different locations are extracted. Where n represents the number of the point cloud data at different measurement heights, formed in ascending order of measurement height, and k represents the number of different locations within the point cloud data at measurement height n; the elevations of different locations within the different measurement height point cloud data are respectively... Elevation of different reference locations in point cloud data at the same reference height The following elevation verification analysis is performed: the elevation of the measured height points for elevation verification is determined. The corresponding location point is designated as the verification location point, and the elevation of the nearest reference location point to the verification location point in the reference height point cloud data is determined. The corresponding location points are designated as verification reference points. A fixed-step effective average elevation analysis is performed on the verification location points, starting from the verification reference points, to obtain the effective verification average elevation corresponding to each verification location point. Based on the valid verification average elevation Elevation of the measured height point corresponding to the verification location point Adjustments were made to establish the verification elevation of the measurement height points corresponding to the verification location points. ,in, u represents the formation of a valid verification average elevation. The number of corresponding benchmark elevation points; a collection of elevations of all measured elevation points. Elevation verification at corresponding measurement height points The data is then assigned to the corresponding location points to form measurement height point cloud verification data.

[0039] The purpose of verifying the elevation of point cloud data at different measurement heights using benchmark height point cloud data is to correct the deviation of the elevation data acquired at different measurement heights, ensuring that the deviation of the corrected elevation value is at the same level as the elevation deviation of the benchmark height point cloud data. This verification is possible because, firstly, the benchmark height point cloud data contains a large number of valid data points at lower measurement heights, allowing for the formation of a reference average elevation value near the fewer valid data points acquired at higher heights. Using this average value as the basis for adjusting the elevation values ​​of corresponding data points ensures the rationality and accuracy of the verification. Secondly, all point cloud data share a unified coordinate system, meaning they can be mapped to the same coordinate system. Therefore, regardless of whether the point cloud data was collected at the same height, the relative relationships of the data points are definite, providing the foundation for the location information used in the verification. It is understandable that the reference elevation cloud data points around the location point to be verified, which can represent the average elevation value of the location point, are located in an area where the image data can be considered to be at the same level. That is, the elevation value will not be affected by other environmental factors, especially the effect of vegetation alignment. Therefore, the average elevation value can be used as the average level value of this area. Since the location point is located in this area, the average elevation value of any number of location points is at the same level. Based on this, the elevation of the location point to be verified can be reasonably and accurately verified and adjusted.

[0040] Perform a fixed-step effective average elevation analysis on the verification location points, starting from the verification reference location point, to obtain the effective verification average elevation corresponding to the verification location points. This includes: setting an angle step size α, taking the verification reference position point as the starting angle, rotating clockwise by an angle step size α each time, obtaining the position point corresponding to the reference height point cloud data in the direction pointed after the rotation, and extracting the elevation of the reference position point corresponding to the position point. The elevations of the corresponding reference points are extracted sequentially according to the angle step size α. This process continues until the elevations of all extracted benchmark points are collected, circling the verification location point. This forms the elevation set of relevant points of the verification benchmark corresponding to the verification location point. ,in, , where i represents the elevation of different reference points extracted around the verification location point. Numbered according to the extraction order; Elevation set of relevant points for verification benchmark. The following selection is performed based on the elevation of different benchmark locations: according to the set of elevations of relevant points of the verification benchmark. The elevations of different reference points are extracted sequentially according to their numbering order to obtain the corresponding average elevation values. ,in, If the newly extracted benchmark elevation meets the following conditions: If the newly extracted benchmark elevation is not met, then retain the new benchmark elevation. If the newly extracted benchmark points are not found, the extraction of new benchmark point elevations will continue until the set of relevant elevation points for the verification benchmark is complete. The elevation of all benchmark points is determined, and the resulting average elevation value is identified as the valid verification average elevation. ,in, This is the average elevation limit.

[0041] To obtain the average elevation value near the verification location point, the only data that can reflect the elevation level near the verification location point is the elevation data of the location points in the benchmark height point cloud data. However, due to the irregularity of the point cloud data, these location points are at varying distances from the verification location point. This distance can lead to some location points in the benchmark height point cloud data near the verification location point not being at the same level as the verification location point. These areas at the same level can be understood as the extent of the influence of environmental factors during point cloud data extraction. For example, if the verification location point happens to be in an area with abundant vegetation, then the elevation deviation obtained from the location point also includes the deviation caused by the height of the vegetation. If there is a location point in the surrounding benchmark height point cloud data that is far away and exceeds the vegetation coverage, then selecting the elevation data of this location point as the basis for verification analysis will cause an error in the average elevation value. Therefore, it is necessary to filter the location points in the benchmark height point cloud data near the verification location point. This application first obtains a certain number of location point elevation data from the benchmark height point cloud data by setting an angle step size. The set angle step size can be determined according to the density of location points in the benchmark height point cloud data to avoid unreasonable extraction of location point elevation data that is too far away, which would affect the efficiency of subsequent analysis. After obtaining the elevation data, it is also necessary to determine whether the location points corresponding to these elevation data are located in the same horizontal area as the verification location point. A quick way is to determine this by using the average elevation value. Of course, it is possible to use image data for partitioning, but it is not as efficient as the average value method. If these location points are located in the same horizontal area, then the average elevation value formed by any number of points will be relatively stable. Therefore, using the average elevation value as the basis for analysis and judgment is reasonable and accurate. The average elevation value can be set according to the actual situation or determined based on big data analysis of the average elevation values ​​of the same horizontal area.

[0042] S2: Collect target image data of the engineering area, and combine it with geographic elevation information and target point cloud verification data for adjustment and analysis to form target point cloud adjustment data.

[0043] Collect target image data of the engineering area and combine it with geographic elevation information and target point cloud verification data for adjustment and analysis to form target point cloud adjustment data. This includes: dividing the engineering area into regions based on color according to the target image data to form region division data; adjusting the overall elevation of the target point cloud verification data based on the region division data and geographic elevation information to form unified target point cloud adjustment data; and adjusting the regional elevation of the unified target point cloud adjustment data based on color according to the region division data to form target point cloud adjustment data.

[0044] Understandably, even after adjusting the elevation of point cloud data acquired by UAVs at different measurement altitudes, the resulting point cloud data, while at a similar elevation deviation level, still exhibits a significant deviation that can impact engineering assessments. Therefore, further deviation correction is necessary. This application considers using geographic elevation data for correction. This geographic elevation information can be derived from more precise remote sensing data or manually measured data. The role of geographic elevation is to correct the point cloud data, so the quantity need not be excessive, and the acquisition of such data will not significantly affect cost or timeline. Geographic elevation can further correct the overall elevation deviation of the point cloud data. However, considering that construction areas are not uniformly landscaped, especially since vegetation cover can affect the elevation accuracy of point cloud data acquired by UAVs, it is also necessary to combine color information from UAV-acquired image data for further regional elevation correction to obtain more accurate and reasonable elevation data, effectively ensuring the accuracy and precision of the modeling.

[0045] Based on the target image data, the engineering area is divided into regions based on color to form region division data. This includes: performing grayscale processing on the target image data to form target grayscale image data; using pixels as the unit of analysis, traversing the grayscale values ​​of all pixels from any edge of the image in the analysis direction, and identifying adjacent pixels whose grayscale value difference exceeds the partition grayscale difference threshold; extracting the continuity of the identified adjacent pixels, and determining the boundary between adjacent pixels as the region boundary line.

[0046] Color-based region segmentation of target image data primarily involves determining the boundaries of the regions by assessing the gradient of color changes. Here, the image data is converted to grayscale, and the gradient of grayscale value changes between adjacent pixels is used as the basis for region boundary segmentation. The grayscale difference threshold for each region can be set according to actual conditions or determined based on large-scale data analysis of the grayscale value gradient change patterns of pixels at different region boundaries. Of course, choosing grayscale values ​​as the basic data for color differentiation also facilitates subsequent elevation adjustments based on color information. After all, the relationship between multi-parameter color representations like RGB and elevation data is complex, and the uniformity of grayscale values ​​allows for a one-to-one mapping with elevation numerical changes, simplifying subsequent elevation adjustments. Once the region boundaries are determined, different regions in the image can be segmented based on these boundaries.

[0047] Based on regional division data and geographic elevation information, the overall elevation of the target point cloud verification data is adjusted to form unified adjusted target point cloud data. This includes: identifying multiple areas without vegetation cover based on regional division data and marking them as elevation coordination areas; randomly selecting two elevation coordination areas from different elevation coordination areas and performing overall elevation adjustment analysis in the following manner: marking the two randomly selected elevation coordination areas as the adjustment elevation coordination area and the verification elevation coordination area, respectively; for geographic elevation location points in the adjustment elevation coordination area, determining multiple analysis lines passing through the geographic elevation location points in a circular direction around the geographic elevation location points, and the analysis lines satisfying the following conditions: at least one target point cloud verification data location point in the adjustment elevation coordination area lies on the analysis line, and the distance from the target point cloud verification data location point in the adjustment elevation coordination area to the analysis line is not greater than the effective line distance, and any point not greater than the effective line distance is marked as an effective location point, and points located on the analysis line are marked as online location points; for different analysis lines, determining the linear function of the analysis line based on the location information of the corresponding effective location points and the location information of the online location points. Where x represents the number of the different analytical lines; based on the linear function corresponding to the analytical line... Determine the geographical elevation analysis elevation value of the geographical elevation location point on the analysis line. Geographic elevation analysis based on different analytical lines Determine the geographic elevation values ​​for point cloud analysis. ,in, Geographic elevation values ​​were analyzed based on point cloud data. The actual elevation value of the geographical elevation location point Determine the overall elevation adjustment value ,in, Adjustment value based on overall elevation The elevation of the target point cloud verification data is adjusted to form the initial adjusted data of the target point cloud. Based on the initial adjusted data of the target point cloud and combined with the geographical elevation information in the verification elevation coordination area, the elevation adjustment verification is performed to form the unified adjusted data of the target point cloud.

[0048] Elevation adjustment of target point cloud verification data, using geographic elevation information as a benchmark, can reasonably correct elevation deviations in the point cloud data collected by UAVs. Of course, geographic elevation information needs to consider two aspects. First, to achieve reasonable data correction, its location information needs to exclude the influence of environmental factors on the elevation data acquired by the UAV. In vegetated areas, the UAV's elevation data will have not only actual elevation deviations but also deviations caused by vegetation height, leading to inaccurate analysis. Therefore, it is necessary to select areas without vegetation cover to obtain geographic elevation information. Second, due to the discreteness and irregularity of the point cloud data acquired by the UAV, geographic elevation locations may not coincide with the locations in the point cloud data. Therefore, it is necessary to reasonably eliminate the errors caused by this possibility of non-coincidence. Here, by obtaining straight lines in multiple directions based on geographic elevation locations, and ensuring that these lines cover the corresponding point cloud data, a functional relationship is established to determine the optimal elevation data for the geographic elevation locations that can be analyzed from the point cloud data, thereby obtaining accurate and effective elevation adjustment values. The number of straight lines can be set according to the required analysis precision, and the effective straight line distance can also be determined based on actual conditions, as long as the accuracy and reasonableness of the adjustment value are guaranteed. Of course, since the geographic elevation points may not coincide with the point cloud data points, the analysis may contain some errors. To further ensure the accuracy and reasonableness of the analysis results, a comparative verification of geographic elevation information is performed in another analysis area. The analysis line uses the position coordinates of two points to establish a linear function. Then, based on the geographic elevation information excluding the elevation, the line is calibrated on the analysis line function to determine the analysis elevation of the geographic elevation point on the analysis line, which serves as comparative analysis data with the actual elevation value of the geographic elevation point.

[0049] Based on the initial adjustment data of the target point cloud and combined with the geographical elevation information in the verification elevation coordination area, elevation adjustment verification is performed to form unified adjustment data for the target point cloud. This includes: determining the geographical elevation location points in the verification elevation coordination area; determining multiple verification lines passing through the geographical elevation location points in a circular direction around the geographical elevation location points; and the verification lines satisfying the following conditions: at least one location point of the target point cloud verification data in the verification elevation coordination area lies on the verification line; and the distance from the location point of the target point cloud verification data in the verification elevation coordination area to the verification line is not greater than the effective verification distance. Any point not greater than the effective verification distance is marked as a verification location point, and points located on the verification line are marked as collinear location points. For different verification lines, a verification function is determined based on the location information of the corresponding verification location points and the location information of the collinear location points. Where y represents the number of the different verification lines determined; based on the verification function corresponding to the verification line... Determine the geographic elevation verification elevation value of the geographic elevation location point on the verification line. Verification elevation values ​​based on geographical elevations determined by different verification lines. Determine the point cloud to verify the geographic elevation value. ,in, Verify the geographic elevation value based on the point cloud. The actual elevation value of the geographical elevation location point The following verification analysis is performed: If Then the initial adjustment data of the target point cloud is determined as the unified adjustment data of the target point cloud, and Hallow represents the allowable deviation limit of elevation; if Then The results are used as adjustment values ​​to further adjust the elevation of the initial adjustment data of the target point cloud, forming unified adjustment data for the target point cloud.

[0050] The verification of the initial adjustment data of the target point cloud is mainly to determine whether the elevation of the point cloud data, after adjustment based on the geographic elevation information of the elevation adjustment coordination area, has reached the received elevation deviation range. Understandably, the verification method for the initial adjustment data of the target point cloud conducted within the elevation adjustment coordination area should be the same as the adjustment method used within the elevation adjustment coordination area. This avoids the impact of different methods on the verification results. Of course, the number of verification lines selected can be determined according to actual needs. For the effective verification distance, it should at least be at the same level of accuracy as the effective straight line distance. If it is too far from the effective straight line distance, the verification effect will be insufficient; if it is too small, the verification effect will be too stringent. Therefore, the effective verification distance can be determined based on the analysis of the error range generated by the straight line using effective location points. The verification method involves obtaining the elevation values ​​of the adjusted point cloud data at geographic elevation locations within the verification elevation coordination area and comparing them with the actual elevations of those locations. If the elevation difference is within the allowable deviation range, the adjustment within the elevation coordination area is considered acceptable. If it exceeds the allowable deviation range, the excess portion is also included in the adjustment data and adjusted again. To improve the accuracy of data adjustment, multiple elevation coordination areas can be selected for repeated verification adjustments. In this case, the adjustments need to be sequential to avoid situations where a large deviation is made in one adjustment area, then disappears in another, only to reappear in a third. Analyzing the sequential deviations of these elevation adjustments is necessary to establish a reasonable adjustment sequence for more efficient and rational adjustments.

[0051] Based on the regional division data, color-based regional elevation adjustments are performed on the unified adjustment data of the target point cloud to form the target point cloud adjustment data. This includes: determining the grayscale values ​​of different locations in the unified adjustment data of the target point cloud within the vegetation-covered area based on the regional division data. Coordinate the elevation of location points Where r represents the unified adjustment of the numbering of different location points in the target point cloud data within the vegetation-covered area; based on the grayscale value of the location point. Coordinate the elevation of location points Determine the corresponding hue elevation value ,in, , The color height conversion value represents the unit grayscale value. Represents the grayscale baseline value; a collection of hue elevation values ​​at different locations within a vegetation-covered area. The elevation of the location points in the unvegetated areas is coordinated to form target point cloud adjustment data.

[0052] Color-based elevation adjustment for different regions primarily corrects elevation deviations caused by vegetation cover in point cloud data, particularly in areas affected by environmental factors. The correction method is simple: elevation values ​​are adjusted based on the percentage of grayscale value at a given location relative to a reference grayscale value. It's important to note that the color-to-elevation conversion value and grayscale reference value per unit grayscale value are essentially obtained through big data analysis of the precise elevation of undisturbed areas and their corresponding grayscale values, compared to the actual elevations of locations with different grayscale values ​​acquired from UAV image data. Precise and actual elevations can be obtained experimentally. Since the area covered in each project will vary, sampling can be performed in the actual project area to ensure accuracy. It's also crucial to emphasize that this application focuses on elevation adjustment because the main deviation in point cloud data collected by UAVs lies in elevation; therefore, the accuracy of modeling largely depends on the accuracy of the elevation. Thus, the point cloud data resulting from elevation adjustment essentially includes both elevation data and planar coordinate data, forming the three-dimensional coordinate information of the point cloud data.

[0053] S3: Adjust the data based on the target point cloud and perform modeling to form the target earthwork engineering model.

[0054] After obtaining more accurate target point cloud adjustment data for elevation, the point cloud data can be used to model earthwork engineering. The process of converting point cloud data into a model is a relatively mature modeling process. Methods such as fitting can be used to form a complete surface map and then optimize the model.

[0055] This invention also provides an earthwork engineering modeling system based on unmanned aerial vehicles (UAVs). The system includes: a data acquisition unit for acquiring target point cloud data, geographic elevation information, and target image data of the engineering area; an elevation processing unit for performing elevation verification analysis on the target point cloud data acquired by the data acquisition unit based on different measurement heights to form target point cloud verification data, and combining it with geographic elevation information and target image data for adjustment analysis to form target point cloud adjustment data; and a modeling processing unit for performing modeling processing on the target point cloud adjustment data formed by the elevation processing unit to form a target earthwork engineering model.

[0056] The system uses a data acquisition unit to collect basic data for earthwork engineering modeling. An elevation processing unit adjusts the elevation of point cloud data acquired by UAVs at different altitudes, and optimizes the elevation by combining geographic elevation and color-coded data, resulting in precise point cloud adjustment data. Based on this, a modeling processing unit processes the accurate point cloud data to create an accurate model of the earthwork engineering project. These interconnected functional units effectively ensure efficient information transmission and processing, forming a crucial material foundation for earthwork engineering modeling.

[0057] In summary, the beneficial effects of the UAV-based earthwork engineering modeling method and system provided by the embodiments of the present invention are as follows: This method digitizes image data collected by UAVs into point cloud data, and compares and verifies the point cloud data based on the results obtained by the UAV at different altitudes to improve the accuracy of the point cloud data, especially in terms of elevation. Simultaneously, it utilizes the image data collected by the UAVs to perform simple color-based texture analysis to achieve reasonable region division. Based on this, it combines discrete ground elevation data to verify the elevation of the point cloud data and adjust the regional divisions, significantly improving the precision and accuracy of the point cloud data. This results in models built using the adjusted point cloud data having higher precision and accuracy, providing important and accurate basic data for subsequent engineering evaluation and implementation.

[0058] The system uses a data acquisition unit to collect basic data for earthwork engineering modeling. An elevation processing unit adjusts the elevation of point cloud data acquired by UAVs at different altitudes, and optimizes the elevation by combining geographic elevation and color-coded data, resulting in precise point cloud adjustment data. Based on this, a modeling processing unit processes the accurate point cloud data to create an accurate model of the earthwork engineering project. These interconnected functional units effectively ensure efficient information transmission and processing, forming a crucial material foundation for earthwork engineering modeling.

[0059] In the embodiments of this application, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In the specific implementation process, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a relationship between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. At the same time, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0060] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be repeated here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In the specific implementation process, the required indication method can be selected according to specific needs. This application embodiment does not limit the selected indication method; therefore, the indication methods involved in this application embodiment should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0061] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this application embodiment. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0062] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This application does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or communication device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or communication device. The type of memory can be any form of storage medium, and this application does not limit this.

[0063] The “protocol” mentioned in the embodiments of this application may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol applied to future communication systems. The embodiments of this application do not specifically limit this.

[0064] In the embodiments of this application, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0065] In the description of the embodiments of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0066] It should be understood that the processor in the embodiments of this application can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0067] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0068] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0069] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0070] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0071] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0072] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0073] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0077] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for modeling earthwork engineering based on unmanned aerial vehicles (UAVs), characterized in that, include: Collect target point cloud data of the engineering area, perform elevation verification analysis based on different measurement heights, and form target point cloud verification data; Collect target image data of the project area, and combine it with geographic elevation information and target point cloud verification data for adjustment and analysis to form target point cloud adjustment data; The target point cloud data is adjusted and modeled to form a target earthwork engineering model.

2. The method for modeling earthwork engineering based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The target point cloud data of the collected engineering area is subjected to elevation verification analysis based on different measurement heights to form target point cloud verification data, including: Based on the target point cloud data, extract the measurement height point cloud data formed at different measurement heights, and calibrate the measurement height point cloud data corresponding to the lowest measurement height as the reference height point cloud data; Using the reference height point cloud data as a reference, elevation verification analysis is performed on the measurement height point cloud data at other different measurement heights to form corresponding measurement height point cloud verification data; The reference height point cloud data and all the measured height point cloud verification data are merged to form the target point cloud verification data.

3. The method for modeling earthwork engineering based on unmanned aerial vehicles (UAVs) according to claim 2, characterized in that, The step of using the reference height point cloud data as a reference to perform elevation verification analysis on the measurement height point cloud data at other different measurement heights, forming corresponding measurement height point cloud verification data, includes: Extract the elevation of the reference position points at different locations from the reference height point cloud data. Where m represents the number of different points in the reference height point cloud data; For the point cloud data at other measurement heights, extract the elevation of the corresponding measurement height points at different locations. Where n represents the number of the different measurement height point cloud data formed in order from low to high measurement height, and k represents the number of different position points in the measurement height point cloud data with number n; The elevations of different measurement height locations in the point cloud data at different measurement heights are respectively... Elevation of different reference location points in the same reference height point cloud data Perform elevation verification analysis in the following manner: The elevation of the measured height location point to be verified. The corresponding location point is designated as the verification location point, and the elevation of the reference location point closest to the verification location point in the reference height point cloud data is determined. The corresponding location point is designated as the verification reference location point; Perform a fixed-step effective average elevation analysis on the verification location points, starting from the verification reference location point, to obtain the effective verification average elevation corresponding to the verification location points. ; According to the effective verification average elevation Elevation of the measured height position point corresponding to the verification position point Adjustments are made to establish the verification elevation of the measurement height point corresponding to the verification location point. ,in, u represents the effective verification average elevation. The number of corresponding reference location point elevations; Elevation of all measured height locations The corresponding measured height point is used to verify the elevation. The data is then assigned to the corresponding location points to form the measured height point cloud verification data.

4. The method for modeling earthwork engineering based on unmanned aerial vehicles (UAVs) according to claim 3, characterized in that, The effective average elevation of the verification location points is obtained by performing a fixed-step-size analysis with the verification reference location point as the starting direction. ,include: Set an angle step size α, and using the verification reference position point as the starting angle, rotate clockwise by the angle step size α each time to obtain the position point corresponding to the reference height point cloud data in the direction pointed after rotation, and extract the elevation of the reference position point corresponding to the position point. ; The elevations of the corresponding reference points are extracted sequentially according to the angle step α. This continues until the elevations of all extracted reference points are collected around the verification location point. This forms the elevation set of the verification benchmark related points corresponding to the verification location points. ,in, i represents the elevation of different reference points extracted around the verification location point. Numbers generated according to the extraction order; The elevation set of relevant points of the verification benchmark The following selection is performed based on the elevation of different benchmark locations: According to the aforementioned set of elevation points related to the verification benchmark The elevations of the reference points are extracted sequentially according to their numbering order to obtain the corresponding average elevation values. ,in, If the newly extracted elevation of the reference point satisfies If the newly extracted benchmark elevation is not satisfied, then the newly extracted benchmark elevation is retained. If the newly extracted benchmark locations are not found, the newly extracted benchmark locations and elevations will be filtered out, and the process of extracting new benchmark locations and elevations will continue until the set of relevant elevations for the verification benchmarks is fully extracted. The elevation of all the aforementioned reference points is used to determine the average elevation value formed as the valid verification average elevation. ,in, This is the average elevation limit.

5. The method for modeling earthwork engineering based on unmanned aerial vehicles (UAVs) according to claim 4, characterized in that, The target image data of the engineering area is collected and adjusted and analyzed in combination with geographic elevation information and the target point cloud verification data to form target point cloud adjustment data, including: Based on the target image data, the engineering area is divided into regions based on color to form region division data; Based on the regional division data and the geographic elevation information, the overall elevation of the target point cloud verification data is adjusted to form unified adjustment data for the target point cloud. Based on the region division data, the target point cloud unified adjustment data is adjusted by color-based regional elevation adjustment to form the target point cloud adjustment data.

6. The method for modeling earthwork engineering based on unmanned aerial vehicles (UAVs) according to claim 5, characterized in that, The step of dividing the engineering area into regions based on color according to the target image data to form region division data includes: The target image data is processed to grayscale to form target grayscale image data; Using pixels as the unit of analysis, the gray values ​​of all pixels are traversed from any edge of the image in the direction of analysis, and adjacent pixels whose gray value difference exceeds the partition gray value difference threshold are identified. The continuity of adjacent pixels is extracted, and the boundary between adjacent pixels is determined as the region boundary line.

7. The method for modeling earthwork engineering based on unmanned aerial vehicles (UAVs) according to claim 6, characterized in that, The step of adjusting the overall elevation of the target point cloud verification data based on the regional division data and the geographic elevation information to form unified adjustment data for the target point cloud includes: Based on the aforementioned regional division data, several areas not covered by vegetation were identified and marked as elevation-coordinated areas; Two of the aforementioned elevation coordination regions are randomly selected and subjected to overall elevation adjustment analysis in the following manner: The two extracted elevation coordination regions are respectively marked as the adjusted elevation coordination region and the verified elevation coordination region; For the geographical elevation points in the overall elevation adjustment area, multiple analytical lines passing through the geographical elevation points are determined along the circumference of the geographical elevation points, and the analytical lines satisfy the following: Within the overall elevation adjustment area, at least one location point of the target point cloud verification data is located on the analysis line, and within the overall elevation adjustment area, there exists a location point of the target point cloud verification data whose distance to the analysis line is not greater than the effective line distance. Any point whose distance is not greater than the effective line distance is marked as an effective location point, and points located on the analysis line are marked as online location points. For different analytical lines, the linear function of the analytical line is determined based on the position information of the corresponding effective position points and the position information of the online position points. , where x represents the number of the different analytical lines determined; Based on the analysis, the line function corresponding to the line is... The geographical elevation analysis elevation value of the geographical elevation location point on the analysis line is determined. ; The geographical elevation analysis elevation value determined based on the different analytical lines. Determine the geographic elevation values ​​for point cloud analysis. ,in, ; Based on the point cloud analysis, the geographical elevation value is... and the actual elevation value of the geographical elevation location point Determine the overall elevation adjustment value ,in, ; Based on the overall elevation adjustment value The target point cloud verification data is then subjected to elevation adjustment to form initial adjustment data for the target point cloud. Based on the initial adjustment data of the target point cloud, and combined with the geographical elevation information in the verification elevation coordination area, the elevation adjustment verification is performed to form the unified adjustment data of the target point cloud.

8. The method for modeling earthwork engineering based on unmanned aerial vehicles according to claim 7, characterized in that, The step of performing elevation adjustment verification based on the initial adjustment data of the target point cloud and the geographic elevation information in the verification elevation coordination area to form unified adjustment data for the target point cloud includes: Determine the geographical elevation location points within the overall verification elevation area, and determine multiple verification straight lines passing through the geographical elevation location points along a circular direction around them, wherein the verification straight lines satisfy the following: Within the overall verification elevation area, at least one location point of the target point cloud verification data is located on the verification line, and within the overall verification elevation area, there exists a location point of the target point cloud verification data whose distance to the verification line is not greater than the effective verification distance. Any point whose distance is not greater than the effective verification distance is marked as a verification location point, and points located on the verification line are marked as collinear location points. For different verification lines, the verification function of the verification line is determined based on the position information of the corresponding verification point and the position information of the collinear point. , where y represents the number of the different verification lines determined; According to the verification function corresponding to the verification line The geographical elevation verification elevation value of the geographical elevation location point on the verification line is determined. ; The geographical elevation verification elevation value determined based on the different verification lines. Determine the point cloud to verify the geographic elevation value. ,in, ; Verify the geographic elevation value based on the point cloud. and the actual elevation value of the geographical elevation location point The following verification analysis was performed: like Then the initial adjustment data of the target point cloud is determined as the unified adjustment data of the target point cloud, and Hallow represents the allowable deviation limit of elevation; like Then The result is used as an adjustment value to adjust the elevation of the initial adjustment data of the target point cloud again, forming the unified adjustment data of the target point cloud.

9. The method for modeling earthwork engineering based on unmanned aerial vehicles (UAVs) according to claim 8, characterized in that, The step of performing color-based regional elevation adjustment on the target point cloud unified adjustment data according to the region division data to form the target point cloud adjustment data includes: Based on the region division data, the grayscale values ​​of different locations in the target point cloud within the vegetation-covered area are uniformly adjusted. Coordinate the elevation of location points , where r represents the number of different location points in the target point cloud unified adjustment data within the vegetation-covered area; Based on the gray value of the location point Elevation of the location points Determine the corresponding hue elevation value ,in, , The color height conversion value represents the unit grayscale value. Indicates the grayscale reference value; The hue elevation values ​​of different locations within a vegetation-covered area are collected. The elevation of the location points in the unvegetated areas is used to form the target point cloud adjustment data.

10. A UAV-based earthwork engineering modeling system, employing the UAV-based earthwork engineering modeling method according to any one of claims 1-9, characterized in that, include: The data acquisition unit is used to acquire target point cloud data, geographic elevation information, and target image data of the engineering area; The elevation processing unit is used to perform elevation verification analysis on the point cloud data of the target acquired by the data acquisition unit based on different measurement heights to form target point cloud verification data, and to perform adjustment analysis in combination with geographic elevation information and target image data to form target point cloud adjustment data; The modeling processing unit is used to process the target point cloud adjustment data generated by the elevation processing unit into a model to form a target earthwork engineering model.

Images