Oblique photography earthwork calculation method, device, equipment and readable storage medium

Abstract

The application discloses a kind of earthwork calculation method, device, equipment and readable storage medium of oblique photography, the method comprises: obtaining the input data of two periods of same construction area based on oblique photography;The business scene data of first period and the block terrain data of second period are standardized, and the difference grid structure of two periods is generated, wherein the business scene data of first period includes the terrain three-dimensional grid of two periods, flat field elevation, design elevation, excavation and filling model calculation square;The difference grid structure of two periods is processed using area contour algorithm, and the grid point cloud of two periods is generated;The elevation difference of the grid point cloud of two periods is calculated, and the calculation result of the excavation amount and the filling amount of target calculation area is calculated by the elevation difference of the grid point cloud of two periods.The application realizes high-precision and efficient calculation for kilometer-level scene.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and more specifically to a method, apparatus, device, and readable storage medium for earthwork quantity calculation using oblique photography. Background Technology

[0002] Earthwork volume is one of the key data points in the construction organization design of earthwork projects. It serves as the basis for organizing labor when using manual excavation or calculating machine shifts and construction period when using mechanized construction. Therefore, the estimation of earthwork volume always occupies an important position in construction.

[0003] There are currently two methods for calculating earthwork volume. The first is the traditional method, such as the cross-section method, contour line method, triangular network method, and grid method. Traditional earthwork volume calculation mostly relies on manual data collection, which is time-consuming and inaccurate. In some areas, data collection is also difficult, increasing the manpower and time costs during construction.

[0004] The second method involves acquiring terrain information through drone oblique photography or laser scanning. This new terrain data requires traditional third-party quantity surveying software such as Southern CASS and Feishida for earthwork volume calculation. While this solution supports various quantity surveying needs, it suffers from significant computational bottlenecks with inputs of tens of thousands of point cloud volumes, leading to crashes or extremely high processing times. To address the issue of large-scale data, current methods require segmenting the input terrain or performing sparse sampling. The former sacrifices computational efficiency, and the latter sacrifices accuracy, neither of which meets the quantity surveying requirements for new terrain data. Furthermore, the aforementioned methods currently support relatively limited business scenarios, mostly only supporting cut and fill quantity surveying at fixed elevations, and do not support cut and fill quantity calculations for diverse business needs.

[0005] In addition, point cloud processing software CloudCompare and reverse modeling software Geomagic also provide patch-based volume calculation functions. Although this type of method is not widely recognized in the industry, it can indeed guarantee calculation accuracy to a high extent, but calculation efficiency and computational resource consumption are still difficult issues to avoid.

[0006] There is currently no effective solution to the technical problems of low calculation accuracy and efficiency in existing traditional quantity calculation methods. Summary of the Invention

[0007] The purpose of this invention is to provide a method, apparatus, device, and readable storage medium for earthwork quantity calculation using oblique photography, which can solve the technical problems of low calculation accuracy and efficiency in existing traditional quantity calculation methods.

[0008] One aspect of the present invention provides a method for earthwork volume calculation based on oblique photogrammetry. The method includes: acquiring input data for two periods of the same construction area obtained based on oblique photogrammetry, wherein the input data includes operational scenario data for a first period, segmented terrain data for a second period, and a target volume calculation area; standardizing the operational scenario data for the first period and the segmented terrain data for the second period to generate a differential grid structure for the two periods, wherein the operational scenario data for the first period includes three-dimensional terrain grids, leveling elevations, design elevations, and cut-fill model calculations for the two periods; processing the differential grid structure for the two periods using a regional contour algorithm to generate raster point clouds for the two periods; calculating the elevation difference between the raster point clouds for the two periods, and calculating the cut and fill volumes for the target volume calculation area using the elevation difference between the raster point clouds for the two periods.

[0009] Optionally, the difference in grid structure between the two periods can be processed using a regional contour algorithm to generate raster point clouds for the two periods. This includes: using a regional contour algorithm to convert the difference in grid structure between the two periods into point clouds to generate point cloud data for the two periods; rasterizing the block terrain data of the target computation area to generate a raster network; and calculating the unit raster elevation value of the point cloud data in the raster network for the two periods to obtain the raster point clouds for the two periods.

[0010] Optionally, the business scenario data of the first period and the segmented terrain data of the second period are standardized to generate differential mesh structures for the two periods, including: parsing the elevation values ​​of the leveling elevation and constructing a planar mesh structure corresponding to the elevation values; parsing the design points of the design elevation, expanding the design points, and constructing a mesh structure through the expanded design points to obtain the design surface mesh structure; parsing the cut-fill model mesh structure in the cut-fill model calculation, obtaining the vertices of the cut-fill model mesh structure, and constructing a rough outer contour based on the vertices of the model mesh structure; integrating the three-dimensional mesh, planar mesh structure, design surface mesh structure, and rough outer contour of the two periods to generate the differential mesh structure of the first period; and parsing the segmented terrain data of the second period to obtain the differential mesh structure of the second period.

[0011] Optionally, the difference between the two time periods is converted into point clouds using a region contour algorithm to generate point cloud data for the two time periods. This includes: performing contour line collision on the difference between the two time periods using a region contour algorithm to obtain the segmented mesh structure to be processed; cropping the segmented mesh structure to be processed using a region contour algorithm to generate a cropped segmented network structure; converting the cropped segmented network structure into point clouds using an adaptive spatial sampling density to obtain segmented point clouds; and merging all segmented point clouds from the first and second time periods to obtain point cloud data for the two time periods.

[0012] Optionally, the excavation and fill volumes of the target volume calculation area are obtained by calculating the elevation difference between the grid point clouds of two periods, including: determining the baseline period and the comparison period of the two grid point clouds; calculating the elevation difference between the grid point clouds of the baseline period and the comparison period; accumulating the elevation differences of all grid point clouds and multiplying them by the area of ​​the unit grid to obtain the excavation and fill volumes of the construction area.

[0013] Optionally, the method further includes: obtaining the number of point clouds required for the system to display, constructing a two-dimensional sampling point matrix using the number of point clouds; determining the three-dimensional coordinates of the nearest neighbor point to the two-dimensional sampling point matrix in the point cloud data of the reference period based on a nearest neighbor search algorithm; determining the elevation difference of the grid to which the nearest neighbor point belongs as an energy value; and constructing the four-dimensional point cloud required for display using the three-dimensional coordinates and energy value.

[0014] Another aspect of the present invention provides an earthwork quantity calculation device based on oblique photogrammetry. The device includes: an acquisition module for acquiring input data of the same construction area in two periods based on oblique photogrammetry, wherein the input data includes operational scenario data of the first period, block terrain data of the second period, and a target quantity calculation area; a first processing module for standardizing the operational scenario data of the first period and the block terrain data of the second period to generate a difference grid structure between the two periods, wherein the operational scenario data of the first period includes three-dimensional terrain grids, leveling elevations, design elevations, and cut-fill model calculations for the two periods; a second processing module for processing the difference grid structure between the two periods using a regional contour algorithm to generate raster point clouds for the two periods; and a quantity calculation module for calculating the elevation difference between the raster point clouds of the two periods and calculating the cut and fill volumes of the target quantity calculation area based on the elevation difference between the raster point clouds of the two periods.

[0015] Optionally, the second processing module is further configured to: use a regional contour algorithm to convert the difference grid structure of the two periods into point clouds to generate point cloud data for the two periods; perform rasterization processing on the block terrain data of the target calculation area to generate a raster network; and calculate the unit raster elevation value of the point cloud data in the raster network for the two periods to obtain the raster point cloud for the two periods.

[0016] Another aspect of the present invention provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the earthwork calculation method of oblique photography according to any of the above embodiments.

[0017] Another aspect of the present invention provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the earthwork calculation method for oblique photography according to any of the above embodiments. Further, the computer-readable storage medium may primarily include a program storage area and a data storage area, wherein the program storage area may store an operating system, at least one application program required for a function, etc.; and the data storage area may store data created based on the use of blockchain nodes, etc.

[0018] This invention constructs a three-dimensional mesh structure through multiple types of input data, samples the three-dimensional mesh structure as a point cloud for computation, and achieves high-precision and high-efficiency computation for oblique photogrammetry by dynamically adapting to kilometer-level scenes and terrain data with millions of facets through rasterization. Attached Figure Description

[0019] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This illustrates an optional flowchart of the earthwork calculation method based on oblique photography provided in Embodiment 1 of the present invention; Figure 2 A structural block diagram of the oblique photography earthwork calculation device provided in Embodiment 2 of the present invention is shown; and Figure 3 A block diagram of a computer device suitable for implementing an oblique photography earthwork quantity calculation method is shown in Embodiment 3 of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0021] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0022] Example 1 This embodiment provides a method for calculating earthwork volume using oblique photography. Figure 1 A flowchart of the earthwork calculation method for this oblique photography is shown, as follows: Figure 1 As shown, the earthwork calculation method for oblique photography may include steps S101 to S104, wherein: Step S101: Obtain input data for the same construction area in two periods based on oblique photography, wherein the input data includes business scenario data for the first period, block terrain data for the second period, and target calculation area; In this embodiment, the input data is a terrain grid extracted from the oblique photogrammetry model. It has the characteristics of a huge number of facets, a large calculation range, and data block division. This terrain grid can be a terrain grid structure with a kilometer-level scene and millions of facets.

[0023] Step S102: Standardize the business scenario data of the first period and the block terrain data of the second period to generate a difference grid structure between the two periods. The business scenario data of the first period includes the terrain three-dimensional grid, leveling elevation, design elevation, and cut-fill model calculation of the two periods. Differential mesh structure is a three-dimensional mesh structure corresponding to the changes in terrain structure in the same terrain area after excavation and / or filling.

[0024] This method standardizes different business input data into a two-period 3D mesh structure for quantity calculation, ensuring algorithm compatibility with diverse business needs and improving user experience. Simultaneously, it effectively recreates the real construction environment, enhancing the accuracy of earthwork quantity calculation.

[0025] Step S103: The difference in the grid structure between the two periods is processed using a region contour algorithm to generate grid point clouds for the two periods. Point cloud refers to the set of points on the surface of a three-dimensional mesh structure at various times.

[0026] In computational tasks involving large scenes at different scales, issues frequently arise such as inconsistent accuracy across scales and excessive computational resource consumption due to the vast scope. Sampling the 3D mesh structure as a point cloud for computation solves the resource consumption problem caused by millions of mesh faces, achieving high-precision and efficient computation for kilometer-scale scenes, while keeping computation time within the second range.

[0027] Step S104: Calculate the elevation difference of the raster point cloud between the two periods, and calculate the excavation and filling volume of the target calculation area using the elevation difference of the raster point cloud between the two periods.

[0028] Traditional methods for obtaining terrain data suffer from two main drawbacks: firstly, the data acquisition methods themselves prevent high-precision calculations; secondly, the calculation methods cannot maintain the accuracy required for large datasets. This embodiment addresses this by using high-precision raster data to largely reconstruct the input terrain data, significantly reducing computational errors.

[0029] Preferably, step S102 may include steps S1021 to S1025, wherein: Step S1021: Analyze and obtain the elevation value of the flat field elevation, and construct a planar grid structure corresponding to the elevation using the elevation value; A mesh structure is a planar mesh structure that uses the elevation value of each flat field as a vertex, determines the lines connecting each vertex to its nearest neighbors, and splices together the triangular patches formed by the connecting lines.

[0030] Step S1022: Analyze and obtain the design points of the design elevation, expand the design points outward, construct a grid structure through the expanded design points, and obtain the design surface grid structure; The design elevation refers to the selected elevation at a specific location relative to a benchmark point in vertical design. The number of design points is determined by actual needs. Expanding the design points takes into account construction errors and better reflects the actual construction terrain structure. Expanding the design points involves generating several points within a predetermined distance from the design point, and finally constructing a grid structure based on these points to obtain the design surface grid structure. The construction process of the design surface grid structure is similar to that of the planar grid structure and will not be elaborated here.

[0031] Step S1023: Analyze and obtain the cut-and-fill model mesh structure in the cut-and-fill model calculation, obtain the vertices of the cut-and-fill model mesh structure, and construct a rough outer contour based on the vertices of the model mesh structure; The principle of constructing a rough outer contour is similar to that of constructing a planar grid structure, and will not be elaborated here.

[0032] Step S1024: Integrate the three-dimensional meshes of the two periods, the planar mesh structure, the design surface mesh structure, and the rough outer contour to generate the differential mesh structure of the first period; Step S1025: The differential grid structure of the second period is obtained by parsing the block terrain data of the second period.

[0033] The grid structure corresponding to the segmented terrain data from the two periods is not processed.

[0034] By integrating diverse information such as the terrain, leveling elevation, design elevation, and model calculation method of the two periods, and standardizing them into a comparative calculation method of the grid structure of the two periods, a calculation scheme that standardizes multiple different business inputs into two grids is realized, which is compatible with diverse business requirements on the algorithm side.

[0035] Preferably, step S103 may include steps S1031 to S1032, wherein: Step S1031: Use the region contour algorithm to convert the difference grid structure of the two periods into point clouds to generate point cloud data for the two periods. Region contour algorithms can include boundary tracking algorithms, edge detection algorithms, contour analysis algorithms, watershed algorithms, etc.

[0036] Specifically, step S1031 may include steps A1 to A4, wherein: Step A1: Use the region contour algorithm to perform contour line collision on the different mesh structures of the two periods respectively to obtain the block mesh structure to be processed; The pre-defined block information in the region contour algorithm is matched with the difference grid structure of the two periods. If the block information matches the contour line of a certain part of the difference grid structure, the same part of the difference grid structure is taken as the block to be processed for subsequent processing.

[0037] Step A2: Use the region contour algorithm to trim the block mesh structure to be processed to generate the trimmed block network structure; The pre-defined dimensions in the region contour algorithm are used to trim the block mesh structure to be processed, resulting in a trimmed block mesh structure, thus avoiding interference from useless information during the calculation process.

[0038] Step A3: The pruned block network structure is converted into a point cloud by adaptive spatial sampling density to obtain a block point cloud; The adaptive spatial sampling density is determined by the actual scenario requirements and is not limited here.

[0039] Based on adaptive spatial sampling density, a corresponding number of feature points are configured in the cropped block mesh structure to obtain a block point cloud.

[0040] Step A4: Merge all the point cloud blocks from the first and second periods respectively to obtain point cloud data for the two periods.

[0041] The point cloud data for both periods were obtained according to steps A1 to A4. The grid structure was sampled as point cloud for computation, which solved the resource consumption caused by too many grid patches of millions. At the same time, the density of point cloud sampling can be adaptively adjusted according to the computation area, which solved the problem of inconsistent accuracy calculation for large scenes of different scales. It achieved high precision and high efficiency computation for terrain grids with kilometer-scale scenes and millions of patches.

[0042] Step S1032: The block terrain data of the target calculation area is rasterized to generate a raster network. The unit raster elevation value of the point cloud data in the raster network for the two periods is calculated respectively to obtain the raster point cloud for the two periods.

[0043] Specifically, the block terrain data of the target computation area is rasterized using an adaptive spacing to obtain a raster network. The unit raster elevation value of the point cloud data in the corresponding raster network for the two periods is calculated respectively to obtain the raster point cloud for the two periods. The adaptive spacing is determined by the actual scene requirements and is not limited here.

[0044] Adaptive rasterization processing can largely restore the input terrain data with minimal computational error, improving the accuracy of calculations for kilometer-scale scenes. It dynamically supports calculations of terrain data with kilometer-scale scenes and millions of polygons, ensuring computational efficiency in the sub-second range.

[0045] Preferably, step S104 may include steps S1041 to S1043, wherein: Step S1041: Determine the baseline period and the comparison period for the two raster point clouds; The second period was designated as the baseline period, and the first period as the comparison period.

[0046] Step S1042: Calculate the elevation difference of the raster point cloud between the baseline period and the comparison period; Step S1043: Accumulate the elevation differences of all grid point clouds and multiply by the area of ​​the unit grid to obtain the calculated excavation and filling volumes of the construction area.

[0047] The raster point clouds from the two periods are distinguished as baseline and comparison period data, and the difference between the corresponding grids is calculated to obtain the elevation difference raster point cloud. The positive and negative values ​​are accumulated and multiplied by the cell area to obtain the calculation results of the excavation and filling volumes.

[0048] Preferably, the method further includes steps B1 to B4, wherein: Step B1: Obtain the number of point clouds required for the system to display, and construct a two-dimensional sampling point matrix using the number of point clouds; Step B2: Based on the nearest neighbor search algorithm, determine the three-dimensional coordinates of the nearest neighbor point in the point cloud data of the reference period that is in the two-dimensional sampling point matrix; The nearest neighbor search algorithm can be a kdtree.

[0049] Step B3: Determine the energy value as the elevation difference of the grid to which the nearest neighbor belongs; Step B4: Construct the required four-dimensional point cloud using the three-dimensional coordinates and the energy value.

[0050] Based on the number of point clouds required for display, a sparse two-dimensional sampling point matrix is ​​constructed. Using kdtree nearest neighbor search, the three-dimensional coordinates of the nearest neighbor point in the baseline point cloud and the elevation difference of the current point's grid are used as the energy value, thereby constructing a four-dimensional point cloud for display.

[0051] Four-dimensional point clouds provide a visual display effect, which helps designers to accurately judge the construction environment and improves the user experience.

[0052] This embodiment constructs a three-dimensional mesh structure through multiple types of input data, samples the three-dimensional mesh structure as a point cloud for computation, and achieves high-precision and high-efficiency computation for oblique photogrammetry by dynamically adapting to kilometer-level scenes and terrain data with millions of facets through rasterization.

[0053] Example 2 Embodiment 2 of the present invention also provides an oblique photography earthwork calculation device, which corresponds to the oblique photography earthwork calculation method provided in Embodiment 1 above. The corresponding technical features and effects are not detailed in this embodiment; relevant aspects can be referred to in Embodiment 1 above. Specifically, Figure 2 A structural block diagram of the earthwork calculation device for this oblique photography is shown. Figure 2 As shown, the earthwork calculation device 200 for oblique photography includes an acquisition module 201, a first processing module 202, a second processing module 203, and a calculation module 204, wherein: The acquisition module 201 is used to acquire input data of the same construction area in two periods based on oblique photography. The input data includes business scenario data of the first period, block terrain data of the second period, and target calculation area. The first processing module 202, connected to the acquisition module 201, is used to standardize the business scenario data of the first period and the block terrain data of the second period to generate a difference grid structure between the two periods. The business scenario data of the first period includes the terrain three-dimensional grid, leveling elevation, design elevation, and cut-fill model calculation of the two periods. The second processing module 203 is connected to the first processing module 202 and is used to process the difference grid structure of the two periods using a region contour algorithm to generate grid point clouds of the two periods. The quantity calculation module 204, connected to the second processing module 203, is used to calculate the elevation difference of the raster point cloud in the two periods, and to calculate the excavation and filling volume of the target quantity calculation area through the elevation difference of the raster point cloud in the two periods.

[0054] Optionally, the second processing module is further configured to: use a regional contour algorithm to convert the difference grid structure of the two periods into point clouds to generate point cloud data for the two periods; perform rasterization processing on the block terrain data of the target computation area to generate a raster network; and calculate the unit raster elevation value of the point cloud data of the two periods in the raster network to obtain the raster point cloud for the two periods.

[0055] Optionally, the first processing module is further configured to: parse and obtain the elevation value of the leveling elevation, and construct a planar grid structure corresponding to the elevation through the elevation value; parse and obtain the design point of the design elevation, expand the design point outward, construct a grid structure through the expanded design point, and obtain the design surface grid structure; parse and obtain the cut-and-fill model grid structure in the cut-and-fill model calculation, obtain the vertices of the cut-and-fill model grid structure, and construct a rough outer contour based on the vertices of the model grid structure; integrate the three-dimensional grids of the two periods, the planar grid structure, the design surface grid structure, and the rough outer contour to generate the differential grid structure of the first period; and parse the differential grid structure of the second period from the block terrain data of the second period.

[0056] Optionally, the second processing module is further configured to: perform contour line collision on the differential mesh structures of the two periods using a region contour algorithm to obtain a segmented mesh structure to be processed; trim the segmented mesh structure to be processed using a region contour algorithm to generate a trimmed segmented network structure; convert the trimmed segmented network structure into a point cloud using an adaptive spatial sampling density to obtain a segmented point cloud; and merge all segmented point clouds of the first and second periods to obtain point cloud data for the two periods.

[0057] Optionally, the quantity calculation module is also used to: determine the baseline period and the comparison period of two grid point clouds; calculate the elevation difference between the grid point clouds in the baseline period and the comparison period; sum up the elevation differences of all grid point clouds and multiply by the area of ​​the unit grid to obtain the quantity calculation results of the excavation and filling volume of the construction area.

[0058] Optionally, the device further includes a visualization module for: obtaining the number of point clouds required to be displayed by the system; constructing a two-dimensional sampling point matrix using the number of point clouds; determining the three-dimensional coordinates of the nearest neighbor point in the point cloud data of the reference period based on a nearest neighbor search algorithm; determining the elevation difference of the grid to which the nearest neighbor point belongs as an energy value; and constructing a four-dimensional point cloud required for display using the three-dimensional coordinates and the energy value.

[0059] Example 3 Figure 3 A block diagram of a computer device suitable for implementing an oblique photogrammetry earthwork calculation method, as provided in Embodiment 3 of the present invention, is shown. In this embodiment, the computer device 300 can be a smartphone, tablet computer, laptop computer, desktop computer, rack server, blade server, tower server, or cabinet server (including a standalone server or a server cluster composed of multiple servers), etc., that executes a program. Figure 3 As shown, the computer device 300 in this embodiment includes, but is not limited to, a memory 301, a processor 302, and a network interface 303 that are communicatively connected to each other via a system bus. It should be noted that... Figure 3 Only a computer device 300 with components 301-303 is shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0060] In this embodiment, the memory 303 includes at least one type of computer-readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 301 may be an internal storage unit of the computer device 300, such as the hard disk or memory of the computer device 300. In other embodiments, the memory 301 may also be an external storage device of the computer device 300, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 300. Of course, the memory 301 may also include both the internal storage unit and the external storage device of the computer device 300. In this embodiment, the memory 301 is typically used to store the operating system and various application software installed on the computer device 300, such as the program code for the earthwork calculation method of oblique photography.

[0061] In some embodiments, processor 302 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. This processor 302 is typically used to control the overall operation of computer device 300. For example, it performs control and processing related to data interaction or communication with computer device 300. In this embodiment, processor 302 is used to run program code containing the steps of the oblique photogrammetry earthwork calculation method stored in memory 301.

[0062] In this embodiment, the earthwork calculation method of oblique photography stored in memory 301 can be further divided into one or more program modules and executed by one or more processors (processor 302 in this embodiment) to complete the present invention.

[0063] Network interface 303 may include a wireless network interface or a wired network interface, which is typically used to establish a communication link between computer device 300 and other computer devices. For example, network interface 303 is used to connect computer device 300 to an external terminal via a network, establishing a data transmission channel and communication link between computer device 300 and the external terminal. The network may be an intranet, the Internet, Global System for Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), 4G network, 5G network, Bluetooth, Wi-Fi, or other wireless or wired networks.

[0064] Example 4 This embodiment also provides a computer-readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, App application store, etc., which stores a computer program. When the computer program is executed by a processor, it implements the steps of the earthwork calculation method of oblique photography.

[0065] Obviously, those skilled in the art should understand that the modules or steps of the above-described embodiments of the present invention can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of the present invention are not limited to any particular hardware and software combination.

[0066] It should be noted that the sequence numbers of the embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0067] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0068] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for calculating earthwork volume using oblique photography, characterized in that, The method includes: The input data for the same construction area in two periods, obtained from oblique photography, are obtained. The input data includes business scenario data for the first period, block terrain data for the second period, and target calculation area. The business scenario data of the first period and the block terrain data of the second period are standardized to generate a difference grid structure between the two periods. The business scenario data of the first period includes the terrain three-dimensional grid, leveling elevation, design elevation and cut-fill model calculation of the two periods. The difference in the grid structure between the two periods is processed using a region contour algorithm to generate raster point clouds for the two periods. Calculate the elevation difference between the raster point clouds in the two periods, and use the elevation difference between the raster point clouds in the two periods to calculate the excavation and filling volumes of the target volume calculation area.

2. The method according to claim 1, characterized in that, The process of using a region contour algorithm to process the difference in the mesh structure between the two periods to generate raster point clouds for the two periods includes: The difference in the grid structure between the two periods was converted into point cloud data using a region contour algorithm to generate point cloud data for the two periods. The block terrain data of the target computation area is rasterized to generate a raster network. The unit raster elevation value of the point cloud data in the raster network for the two periods is calculated to obtain the raster point cloud for the two periods.

3. The method according to claim 1, characterized in that, The standardization process for the business scenario data of the first period and the segmented terrain data of the second period, generating a differential grid structure for the two periods, includes: The elevation value of the flat field is obtained by analysis, and a planar grid structure corresponding to the elevation is constructed based on the elevation value; The design points of the design elevation are obtained by analysis. The design points are expanded outward. A grid structure is constructed through the expanded design points to obtain the design surface grid structure. The cut-and-fill model mesh structure in the cut-and-fill model calculation is obtained by parsing, the vertices of the cut-and-fill model mesh structure are obtained, and a rough outer contour is constructed based on the vertices of the model mesh structure. The three-dimensional meshes from the two periods, the planar mesh structure, the design surface mesh structure, and the rough outer contour are integrated to generate the differential mesh structure of the first period; The differential grid structure of the second period was obtained by parsing the patch terrain data of the second period.

4. The method according to claim 2, characterized in that, The step of using a region contour algorithm to perform point cloud generation on the difference in the mesh structure between the two periods, generating point cloud data for the two periods, includes: The region contour algorithm is used to perform contour line collision on the different mesh structures of the two periods to obtain the block mesh structure to be processed. The segmented mesh structure to be processed is trimmed using a region contour algorithm to generate a trimmed segmented network structure. The pruned block network structure is converted into a point cloud by adaptive spatial sampling density to obtain a block point cloud; All point cloud segments from the first and second periods are merged to obtain point cloud data for the two periods.

5. The method according to claim 1, characterized in that, The calculation results of the excavation and fill volumes of the target calculation area obtained by calculating the elevation difference of the raster point clouds in the two periods include: Determine the baseline and comparison periods for the two raster point clouds; Calculate the elevation difference of the raster point cloud between the baseline period and the comparison period; The elevation differences of all grid point clouds are summed and multiplied by the area of ​​each grid cell to obtain the calculated excavation and filling volumes for the construction area.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Obtain the number of point clouds required for the system to display, and construct a two-dimensional sampling point matrix using the number of point clouds; Based on the nearest neighbor search algorithm, the three-dimensional coordinates of the nearest neighbor point to the two-dimensional sampling point matrix are determined in the point cloud data of the reference period; The elevation difference of the grid cells to which the nearest neighbor belongs is determined to be the energy value; The required four-dimensional point cloud is constructed and displayed using the three-dimensional coordinates and the energy values.

7. An earthwork quantity calculation device based on oblique photography, characterized in that, The device includes: The acquisition module is used to acquire input data of the same construction area in two periods based on oblique photography. The input data includes business scenario data of the first period, block terrain data of the second period, and target calculation area. The first processing module is used to standardize the business scenario data of the first period and the block terrain data of the second period to generate a difference grid structure between the two periods. The business scenario data of the first period includes the terrain three-dimensional grid, leveling elevation, design elevation, and cut-fill model calculation of the two periods. The second processing module is used to process the difference in the grid structure of the two periods using a region contour algorithm to generate grid point clouds for the two periods. The quantity calculation module is used to calculate the elevation difference of the raster point cloud between the two periods, and to calculate the excavation and filling volume of the target quantity calculation area based on the elevation difference of the raster point cloud between the two periods.

8. The apparatus according to claim 7, characterized in that, The second processing module is also used for: The difference in the grid structure between the two periods was converted into point cloud data using a region contour algorithm to generate point cloud data for the two periods. The block terrain data of the target computation area is rasterized to generate a raster network. The unit raster elevation value of the point cloud data in the raster network for the two periods is calculated to obtain the raster point cloud for the two periods.

9. A computer device, the computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 6.

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