Plateau salt lake area earthwork volume measurement method based on unmanned aerial vehicle laser radar

Abstract

The invention relates to the technical field of unmanned aerial vehicle laser point cloud, and discloses a plateau salt lake area earthwork volume measurement method based on an unmanned aerial vehicle laser radar, and the method comprises the steps: obtaining digital elevation models of a target area before and after cleaning; wherein the target area is a plateau salt lake area; and based on the digital elevation model before and after the target area is cleaned, analyzing the terrain model before and after the cleaning through spatial analysis and overlay analysis functions, and calculating the clear salt volume of the Eastern region salt pan sedimentation basin area to obtain the final filling and excavation volume. According to the method, the digital elevation model (DEM) of the area before and after cleaning is taken as the basis, the terrain model before and after cleaning is analyzed through spatial analysis and overlay analysis functions, the salt cleaning volume of the area of the salt pan sedimentation basin in the eastern region is calculated, the final filling and excavation amount is obtained, and the calculation mode can realize the accurate amount of the filling and excavation amount.

Description

Technical Field

[0001] The present application relates to the field of UAV laser point cloud technology, and in particular to a method for measuring earthwork volume in plateau salt lake areas based on UAV laser radar. Background Art

[0002] There are several main methods for measuring earthwork volume:

[0003] Area multiplied by thickness method: Applicable to rectangular or approximately rectangular areas. First measure the area of ​​the area, then measure the average thickness of the soil sample, and multiply the two to get the earthwork volume 1.

[0004] Grid method: Divide the terrain into several grids, with each grid having a side length of about 10 meters. Calculate the fill and excavation volume of each grid based on the average elevation and design elevation of each grid, and finally add up the earthwork volume of the entire area12.

[0005] Contour method: Use the contour lines on the topographic map to calculate the earthwork volume between adjacent contour lines. First, calculate the area between the contour lines, then calculate the fill and cut volume between each contour line based on the average height and design elevation between the contour lines, and finally add up the earthwork volume of the entire area1.

[0006] Average elevation method: Select several measuring points in the measurement area, measure the elevation of these points respectively, and then calculate the average elevation of all measuring points. Subtract the average elevation from the design elevation to get the fill and cut height. Finally, multiply the fill and cut height by the area of ​​the measurement area to get the earthwork volume 1.

[0007] Cone volume method: Applicable to the case of irregular slopes. Select several measuring points in the measurement area, measure the altitude and relative position relationship of these points respectively, and then calculate the volume of these points. Add up the volumes to get the earthwork volume of the entire area1.

[0008] DTM method (digital terrain model method): The earthwork volume is calculated using a digital terrain model (DTM). DTM data includes the three-dimensional coordinate information of each point on the terrain surface. By analyzing the ups and downs of each point on the terrain surface, the earthwork volume at different design elevations can be calculated1.

[0009] Section method: Applicable to areas with complex terrain and large fluctuations. The accuracy of earthwork volume is related to the spacing. The smaller the spacing, the higher the accuracy, but the calculation amount is large23.

[0010] In the prior art, for example, the patent technology with patent number CN105783810B discloses a method for measuring the amount of engineering earthwork based on drone photography technology, which includes the following steps: S1, point cloud data acquisition; S2, preprocessing the acquired point cloud data; S3, using the integral method to calculate the amount of earthwork. The method for measuring the amount of engineering earthwork based on drone photography technology of the present invention has high measurement accuracy and good work efficiency, and can save a lot of labor costs. Specifically, the method for measuring the amount of engineering earthwork collects dense point cloud elevation data of the original surface and calculates the amount of earthwork using the integral method. This method uses a small number of image control points through photogrammetry to generate dense point cloud data covering the measurement area. According to the set earthwork measurement boundary and design elevation, accurate measurement results can be obtained by integration.

[0011] The patented technology with the patent number CN114413851A discloses a method and system for measuring earthwork volume in road engineering, including the following steps: A. Base point control is carried out in the area to be measured, and the coordinate values ​​of the base points are collected by using drones, and the image data around the base points are taken, and the coordinate values ​​and image data are collected in the information collection module. The coordinate values ​​and image data can be collected in the information collection module, and the three-dimensional model and coordinate system can be constructed through the three-dimensional construction module. The three-dimensional model and coordinate system can be corrected through the correction module, and the three-dimensional model and coordinate system can be matched and fused through the matching and fusion module to obtain accurate three-dimensional graphics. The three-dimensional graphics can be analyzed and calculated by the central processing unit to obtain earthwork volume information, which solves the problem that the traditional earthwork volume measurement method for road engineering is not accurate enough, and most of them use manual measurement or laser ranging and marking methods, which are time-consuming and labor-intensive.

[0012] The patent technology with patent number CN106767710A discloses a method and system for measuring the earthwork volume of road engineering, wherein a method for measuring the earthwork volume of road engineering includes the following steps: arranging image control points within the measurement area, and measuring the coordinate values ​​of the arranged image control points; gridding the acquired point cloud data to form a gridded point set array; judging the positional relationship between each point in the point set array and the measurement area, and when the point cloud is inside or on the boundary of any convex polygon, calculating the corresponding cubic volume; integrating and summing the earthwork volume corresponding to the excavation and filling based on the calculation result of the above-mentioned cubic volume, the earthwork volume of the road engineering can be obtained. The present invention has good mobility, flexibility and safety, and can automatically perform flight scanning measurement and imaging of the ground objects to obtain high-precision point cloud and image data of the surface, thereby realizing the measurement of the earthwork volume of the road engineering.

[0013] A search revealed that there is currently no technology to measure earthwork volume from the perspective of digital elevation models before and after cleaning of plateau salt lake areas. Summary of the invention

[0014] The purpose of this application is to provide a method for measuring earthwork volume in plateau salt lake areas based on drone lidar. The digital elevation model (DEM) of the target area before and after cleaning is used as the basis to analyze the terrain model before and after cleaning through spatial analysis and overlay analysis functions, and statistically calculate the cleared salt volume in the salt field sedimentation tank area in the eastern area to obtain the final filling and excavation volume.

[0015] In order to achieve the above purpose, the technical solutions adopted are as follows:

[0016] A method for measuring earthwork volume in plateau salt lake areas based on unmanned aerial vehicle laser radar, the method comprising:

[0017] Obtaining a digital elevation model of the target area before and after cleaning; wherein the target area is a plateau salt lake area;

[0018] Based on the digital elevation model of the target area before and after cleaning, the terrain model before and after cleaning was analyzed through spatial analysis and overlay analysis functions, and the cleared salt volume in the salt field sedimentation pond area of ​​the eastern area was calculated to obtain the final filling and excavation volume.

[0019] Furthermore, based on the digital elevation model of the target area before and after cleaning, the terrain model before and after cleaning was analyzed through spatial analysis and overlay analysis functions, and the cleared salt volume of the East Salt Field Settling Pool area was calculated to obtain the final filling and excavation volume, including:

[0020] Under the condition that the digital elevation models before and after the cleaning of the target area have the same coordinate system and elevation system, the cut and fill calculation results are obtained through the 3DAnalyst tool → grid surface → cut and fill tool, and the cleaning volume of the East District Salt Field Settling Pond is obtained based on the cut and fill calculation results;

[0021] Based on the extracted digital elevation model data within the excavation or backfill range, the statistical function of ArcGIS is used to calculate the excavation and backfill volume.

[0022] Furthermore, a digital elevation model of the target area before and after cleaning is obtained, including:

[0023] Based on the UAV equipped with LiDAR payload, the target area is flown on the spot to obtain the LiDAR point cloud data before and after the target area is cleaned;

[0024] Based on the laser radar point cloud data before and after cleaning of the target area, point cloud data is solved to generate a digital elevation model.

[0025] Furthermore, based on the UAV equipped with the LiDAR payload, a field flight is conducted over the target area to obtain LiDAR point cloud data before and after the target area is cleaned, including:

[0026] The goal is to obtain the LiDAR point cloud data of the target area before and after cleaning, and design the flight range and route of the UAV;

[0027] Determine the take-off and landing site of the drone according to the requirements of the drone equipment operation manual;

[0028] The UAV is controlled to perform field flight according to a set flight control strategy to obtain laser radar point cloud data.

[0029] Further, the flight control strategy is set, including flight control mode, aviation and route curvature control mode;

[0030] The flight control method includes:

[0031] During the entire flight, the aircraft's turning slope is controlled within 15°;

[0032] During the flight along the survey line, the following requirements must be met:

[0033] Different routes use alternating left and right turns;

[0034] The deviation from the route does not exceed 25m, and the altitude does not deviate by 25m;

[0035] The yaw angle is not greater than 6°;

[0036] The tilt angle of the image is not greater than 2°;

[0037] The curvature of the route is not greater than 3%;

[0038] The aircraft speed is maintained at the designed speed for aerial photography;

[0039] The aviation and route curvature control method includes: real-time monitoring of the flight altitude through a laser radar measurement system, and making corrections when the actual flight altitude value deviates from the designed flight altitude.

[0040] Further, based on the laser radar point cloud data before and after the target area is cleaned, point cloud data is solved to generate a digital elevation model, including:

[0041] Import the laser radar point cloud data into the Qianxunyi laser radar system-point cloud preprocessing module for format conversion, and use the converted IMR data and GNSS data to upload to the Qianxun Cloud Control Console to calculate the trajectory data and radar data;

[0042] Input the solved trajectory data and radar data into the Qianxunyi LiDAR System-Point Cloud Preprocessing Module, input the engineering path, coordinate system, and set the point cloud generation parameters and filtering parameters in turn to solve the point cloud, and automatically generate high-precision point cloud data;

[0043] Based on the high-precision point cloud data, the required data is selected, the resolution and DEM construction are set, a triangulated network is constructed for key points, and after interpolation processing, the grid is transformed into raster data to obtain a digital elevation model.

[0044] Furthermore, the solved trajectory data and radar data are input into the Qianxunyi LiDAR System-Point Cloud Preprocessing Module, and the engineering path, coordinate system, and point cloud generation parameters and filtering parameters are input in turn to perform point cloud solution, and high-precision point cloud data is automatically generated, including:

[0045] Layered inspection;

[0046] Point cloud denoising;

[0047] Point cloud post-processing.

[0048] Further, layered inspections include:

[0049] The profile and quality inspection tools of the Qianxunyi LiDAR System-Point Cloud Post-Processing Module are used to detect the stratification between routes. If stratification exists, flight strip adjustment is used to reduce the stratification phenomenon.

[0050] Furthermore, point cloud denoising includes removing invalid point clouds and unreasonable point clouds.

[0051] Further, point cloud post-processing includes:

[0052] Point cloud filtering: Construct a triangulated network through the surface point cloud and remove the foot points of the ground objects in the point cloud; classify the ground point cloud based on the iterative morphological filtering method, first divide the survey area into blocks, and the minimum size of the survey area block is larger than the maximum building size in the area, then select the lowest point and the ground points within the set height difference range from the point cloud as ground seed points, establish the initial sparse triangulated network, calculate the angle and distance between the remaining points in the point cloud and the triangulated network, and compare them with the set iteration angle and iteration distance. If it is less than the set iteration value, it is accepted as a ground point, otherwise it is rejected as a ground point, and the above iterations are repeated until no new ground points are added to the triangulated network, thus realizing the process of triangulating network encryption;

[0053] Point cloud editing and classification: remove gross points that are not filtered out after automatic filtering, identify points that are not correctly classified by automatic classification, and add elevation feature points in areas with terrain distortion.

[0054] The beneficial effects of this application are:

[0055] 1) This application uses the digital elevation model (DEM) of the area before and after cleaning as the basis to analyze the terrain model before and after cleaning through spatial analysis and overlay analysis functions, and statistically calculates the cleared salt volume in the East District Salt Field Settling Pond area to obtain the final fill and excavation volume. This calculation method can achieve accurate measurement of the fill and excavation volume.

[0056] 2) This application adopts the technical means of using a drone equipped with a lidar payload to conduct on-site flights over the salt field sedimentation pond area in the eastern area to obtain lidar point cloud data. The instrument integrates a high-precision laser scanner and an inertial navigation system. It has a small overall structure and light weight, and is suitable for data collection on a drone platform. It can quickly and in real time collect high-precision three-dimensional surface data. The internal industry uses the Digital Green Earth LiDAR360 software to solve the point cloud data and generate a digital elevation model (DEM). BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 An overall flow chart of a method for measuring earthwork volume in plateau salt lake areas based on UAV laser radar provided in an embodiment of the present application;

[0058] Figure 2 A schematic diagram of the measurement area provided in the embodiment of the present application;

[0059] Figure 3 QHCORS distribution diagram provided for the embodiments of the present application;

[0060] Figure 4 A schematic diagram of the Matrice 300 RTK UAV platform provided in the embodiments of the present application;

[0061] Figure 5 The laser radar module parameters provided in the embodiment of the present application;

[0062] Figure 6 A flowchart of the laser radar aerial survey operation provided in the embodiment of the present application;

[0063] Figure 7 A schematic diagram of route planning before cleaning the East Salt Field Settling Tank provided in an embodiment of the present application;

[0064] Figure 8 A schematic diagram of route planning after cleaning of the East Salt Field Settling Tank provided in the embodiment of the present application;

[0065] Fig. 9 A schematic diagram of the take-off and landing points of a drone provided in an embodiment of the present application;

[0066] Fig.10 A schematic diagram of a drone field flight provided in an embodiment of the present application;

[0067] Fig.11 A schematic diagram of the point cloud trajectory before cleaning the East Salt Field Settling Tank provided in an embodiment of the present application;

[0068] Fig.12 A schematic diagram of the point cloud trajectory after cleaning the East District Salt Field Settling Tank provided in the embodiment of the present application;

[0069] Fig.13 A point cloud solution flow chart provided in an embodiment of the present application;

[0070] Fig.14 Point cloud layered inspection diagram before cleaning of the East District Salt Field Settling Tank provided in the embodiment of the present application;

[0071] Fig.15 Point cloud layered inspection diagram after cleaning of the East District Salt Field Settling Tank provided in the embodiment of the present application;

[0072] Fig.16 A schematic diagram of point cloud denoising before cleaning the East District Salt Field Settling Tank provided in an embodiment of the present application;

[0073] Fig.17 A schematic diagram of point cloud denoising after cleaning the East District Salt Field Settling Tank provided in an embodiment of the present application;

[0074] Fig.18 A DEM schematic diagram of the East District Salt Field Settling Tank before cleaning provided in the embodiment of the present application;

[0075] Fig.19 A DEM diagram of the East District Salt Field Settling Tank after cleaning provided in the embodiment of the present application;

[0076] Fig. 20 A schematic diagram of point cloud density calculation before cleaning of the East Salt Field Settling Tank provided in an embodiment of the present application;

[0077] Fig.21 A schematic diagram of point cloud density calculation after cleaning of a salt pan sedimentation tank provided in an embodiment of the present application;

[0078] Fig. 22 The elevation accuracy inspection diagram of the East District Salt Field Settling Tank before cleaning provided in the embodiment of the present application;

[0079] Fig.23 The elevation accuracy inspection diagram of the East District Salt Field Settling Tank after cleaning provided in the embodiment of the present application;

[0080] Fig.24 A schematic diagram of the volume calculation boundary line range of the East District Salt Field Settling Tanks (the first and second blocks) provided in the embodiment of the present application;

[0081] Fig.25 A schematic diagram of the volume calculation boundary line range of the East District Salt Field Sedimentation Tank (third block) provided in the embodiment of the present application;

[0082] Fig.26 A digital elevation model (DEM) map of the ground before cleaning the East District Salt Field Settling Pool provided in the embodiment of the present application;

[0083] Fig. 27 A digital elevation model (DEM) map of the East District Salt Field Settling Tank after cleaning provided in the embodiment of the present application;

[0084] Fig.28 The ARCGIS cut and fill calculation tool dialog box provided in the embodiment of the present application. DETAILED DESCRIPTION

[0085] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0086] The specific implementation of the present application is further described in detail below in conjunction with the drawings and examples.

[0087] See also Figure 1 , is an overall flow chart of a method for measuring earthwork volume in a plateau salt lake area based on a UAV laser radar provided in an embodiment of the present application. The embodiment of the present application provides a method for measuring earthwork volume in a plateau salt lake area based on a UAV laser radar, and the method comprises the following steps:

[0088] S1: Obtaining a digital elevation model of a target area before and after cleaning; wherein the target area is a plateau salt lake area.

[0089] S2: Based on the digital elevation model of the target area before and after cleaning, the terrain model before and after cleaning is analyzed through spatial analysis and overlay analysis functions, and the cleared salt volume in the salt field sedimentation tank area of ​​the eastern area is calculated to obtain the final filling and excavation volume.

[0090] In this embodiment, a specific case is provided. The target area is located in the Golmud City area of ​​Haixi Prefecture, Qinghai Province, in the south-central part of the Qaidam Basin, and is under the jurisdiction of Golmud City, Qinghai Province. Geographic coordinates: 95°16′27″ east longitude, 36°47′50″ north latitude.

[0091] From Golmud, drive 60km along the Chag Expressway to reach Chaerhan, and drive 7.2km northeast along the shortcut to reach the survey area. The traffic is generally convenient. Figure 2 .

[0092] The QHCORS signal covers the entire survey area, using the QHCORS station data (Gaersu Station) of the Qinghai Province Satellite Positioning Continuous Operation Service System. The coordinate system is the CGCS2000 National Geodetic Coordinate System and the 1985 National Height Datum. For details of the QHCORS distribution map, see Figure 3 .

[0093] There are geographical control points buried in years and months around the survey area, see Table 1 for details. After field investigation, YYTG03, YYTG05 and YYTG06 are well preserved and can be used as reference points for verification after inspection. The inspection records are shown in Table 2 for details.

[0094] Table 1 Results of topographic control points

[0095]

[0096]

[0097] Table 2 Field inspection record

[0098]

[0099] The basis for this specific case study is as follows:

[0100] 1. "Urban Surveying Specification" (CJJ / T8-2011);

[0101] 2. “Quality Evaluation Index and Calculation Method of Airborne LiDAR Point Cloud Data” (GB / T36100-2018);

[0102] 3. Technical Specifications for Airborne LiDAR Data Acquisition (CH / T8024-2011);

[0103] 4. Technical Specifications for Airborne LiDAR Data Processing (CH / T8023-2011);

[0104] 5. "Technical Specifications for Real-Time Kinematic Measurement (RTK) of Global Positioning System" CH / T2009-2010;

[0105] 6. Basic geographic information digital achievements: "1:500, 1:1000, 1:2000 digital elevation model" (CH / T9008.2-2010);

[0106] 7. "Quality Inspection and Acceptance of Surveying and Mapping Results" GB / T24356-2009;

[0107] 8. "Regulations on the Preparation of Surveying and Mapping Technical Summary" CH / T 1001-2005;

[0108] 9. "Quality Inspection and Acceptance of Digital Surveying and Mapping Results" GB / T 18316-2008;

[0109] 10. Technical Requirements for UAV Aerial Photography Systems, CH / Z 3002-2010;

[0110] 11. Basic Requirements for Safe Operation of Unmanned Aerial Photography, CH / Z 3001-2010.

[0111] The technical indicators of this specific case are as follows:

[0112] (1) Coordinate system: National 2000 coordinate system;

[0113] (2) Height datum: 1985 National Height Datum;

[0114] (3) Projection mode: Gauss-Krüger 3° zone, zone number 32;

[0115] (4) Central meridian: 96°.

[0116] The configuration of technical personnel and equipment for this specific case is as follows:

[0117] Technical personnel configuration: In order to provide results data in a timely manner, this project has invested 1 working group, with a total of 5 technical management personnel, surveyors, and service personnel participating in this work, including 1 senior surveying and mapping engineer, 2 surveying and mapping engineers, 1 assistant surveying and mapping engineer, and 1 driver.

[0118] The configuration of instruments and equipment is shown in Table 3.

[0119] Table 3 Main hardware configuration table

[0120] Serial number Device Name quantity model Origin use 1 Rotary Wing UAV 1 Matrice 300RTK China Field flight 2 LiDAR 1 QX-200XT China Field flight 3 GPS Receiver 1 STORAGE China Data collection 4 desktop computer 1 unit Assembly Machine China Internal data processing 5 Laptop 1 unit Millet China Field flight 6 External Hard Drive 1 4T China Data backup 7 digital camera 1 unit 8 megapixels China Take work photos 8 Transportation 1 car Off-road vehicles China 9 Track Master 1 DJI China Field flight 10 Digital Green Earth 1 LiDAR360 China Internal data processing 11 Thousand Patrol Wings 1 LiDAR System China Internal data processing 12 ArcGIS Software 1 ArcGIS 10.5 USA Earthwork calculation

[0121] The above instruments and equipment have been inspected, tested and verified in accordance with the regulations and are all within the valid time. UAV and sensor equipment: The Matrice 300 RTK quad-rotor operating platform and the Qianxun QX-200XT laser radar module of Shenzhen DJI Innovations Technology Co., Ltd. are used. Figure 4 Its parameters are as follows: Figure 5 shown.

[0122] This specific case uses the technical means of the Jingwei M300 RTK drone equipped with the QX-200XT laser radar payload to conduct field flights in the East District Salt Field Settlement Pool area to obtain laser radar point cloud data. The instrument integrates a high-precision laser scanner and an inertial navigation system. The overall structure is small and light, and it is suitable for data collection on a drone platform. It can quickly collect high-precision three-dimensional surface data in real time. The internal industry uses the Digital Green Earth LiDAR360 software to solve the point cloud data and generate a digital elevation model (DEM). ARCGIS10.5 software is used to analyze the terrain model before and after the cleaning of the area through spatial analysis and overlay analysis functions, and the cleared salt volume of the East District Salt Field Settlement Pool area is statistically calculated to obtain the final filling and excavation volume.

[0123] LiDAR aerial survey operations such as Figure 6 As shown, the following steps are included:

[0124] Step 1: Flight design.

[0125] In order to obtain image data of the East District Salt Field Sedimentation Basin before and after cleaning, the flight range and route were designed in accordance with the field aerial photogrammetry specifications and volume measurement requirements. Figure 7 and Figure 8 shown.

[0126] Step 2: Field data collection.

[0127] According to the requirements of the UAV equipment operation manual, find a place suitable for UAV take-off and landing, that is, try to find a place with flat terrain, wide vision, and unobstructed take-off and landing points. The take-off and landing point selected for the study is located in the open space on the northeast side of the East District Salt Field Sedimentation Pool area. The specific location is as follows Fig. 9 shown.

[0128] After the take-off and landing site is determined, the relevant equipment is debugged, the flight speed is set to 10 meters per second, and the relative altitude is 80 meters. Fig.10 shown.

[0129] Step 3: Determine quality assurance measures.

[0130] LiDAR production quality management runs through the entire process from task acceptance, organizational preparation, technical design, production operations, process inspection to product delivery and use.

[0131] This embodiment implements a two-level inspection, namely process inspection and final inspection; the process inspection is further divided into self-inspection and mutual inspection; the inspection personnel are composed of the project technical person in charge, the quality inspector of the Geoscience Center and the project person in charge, and the final inspection is reviewed by experts organized by the institute-level technical department.

[0132] Step 4, flight quality assurance.

[0133] (1) Flight control measures

[0134] During the entire flight, the aircraft's turning slope was controlled within 15° (standard turning), avoiding the loss of GPS satellite signal lock. During the flight along the survey line, the following requirements were met:

[0135] Different routes use alternating left and right turns;

[0136] The deviation from the route does not exceed 25m, and the altitude does not deviate by 25m;

[0137] The yaw angle is not greater than 6°;

[0138] The tilt angle of the image is not greater than 2°;

[0139] The curvature of the route is not greater than 3%;

[0140] The aircraft speed is maintained at the designed speed for aerial photography.

[0141] (2) Pre-flight ground testing

[0142] In accordance with strict instrument operating specifications, ground power-on tests and related preparations are carefully performed before each takeoff to ensure that the entire aerial photography system is in normal condition.

[0143] (3) Flight altitude and route curvature control

[0144] Aerial photography operators monitor the flight altitude in real time through the lidar measurement system. When the actual flight altitude value deviates from the designed flight altitude, they communicate with the captain in a timely manner to make corrections, ensuring that the flight altitude meets the design requirements.

[0145] (4) Heading and lateral overlap control

[0146] Since the LiDAR measurement system integrates an advanced aerial photography control system to achieve GPS fixed-point exposure, it ensures that the heading and lateral overlap of the aerial photographs are effectively guaranteed after the aircraft flies along the set route.

[0147] Step 5, internal data processing, includes the following steps:

[0148] Step 51: point cloud data preprocessing.

[0149] Copy the original point cloud, IMU data, GNSS data and other necessary data for internal work obtained in the field to the computer and confirm the integrity of the data. The first step of internal processing is trajectory solution, and its solution accuracy is directly related to the accuracy of the final result.

[0150] First, import the IMU data into the Qianxunyi LiDAR system-point cloud preprocessing module for format conversion. Use the converted IMR data and GNSS data to upload to the Qianxun Cloud Console to calculate the trajectory data, such as Fig.11 and Fig.12 As shown in the figure, after calculation, the trajectory accuracy is less than 0.02 meters, the position calculation meets the specification requirements, the attitude angle range is within 0.06 degrees, the heading angle range is within 0.03 degrees, and the attitude calculation is qualified. It can be used as effective data for point cloud generation.

[0151] Step 52, point cloud solution.

[0152] Input the solved trajectory data and radar data into the Qianxunyi LiDAR System-Point Cloud Preprocessing Module, and input the project path, coordinate system, and set the necessary information such as point cloud generation parameters and filter parameters according to the prompts. After confirming that the input information is correct, click Point Cloud Solution, and the software will automatically generate high-precision point cloud data, such as Fig.13 As shown, the solution process is as follows:

[0153] Step 521, layered inspection.

[0154] In order to verify whether there is stratification in the solved point cloud data, the profile and quality inspection tools of the Qianxunyi LiDAR System-Point Cloud Post-Processing Module can be used to quickly detect the stratification between routes (the darker the color, the more obvious the stratification). If there is a more obvious stratification, you can use flight adjustment to weaken the stratification phenomenon and improve data quality. The point cloud data solved this time has no stratification. In accordance with the requirements of the standard, the quality of the radar acquired data is good and can be used for subsequent processing and production. The quality inspection diagram is shown below. See details. Fig.14 and Fig.15 .

[0155] Step 522, point cloud denoising.

[0156] Remove invalid and unreasonable point clouds. Fig.16 and Fig.17 .

[0157] Step 53, point cloud post-processing.

[0158] Point cloud post-processing uses the Qianxunyi LiDAR system-point cloud post-processing module.

[0159] Step 531: point cloud filtering.

[0160] The triangulated network is constructed through the surface point cloud, and the foot points of the objects in the point cloud are removed. This process is called point cloud filtering. The ground point cloud is classified using iterative morphological filtering. This method first divides the survey area into blocks. The minimum size of the survey area block is larger than the size of the largest building in the area. Then, the lowest point and ground points within a certain height difference range are selected from the point cloud as ground seed points to establish the initial sparse triangulated network. The angle, distance and other information of the remaining points in the point cloud to the triangulated network are calculated and compared with the set iteration angle and iteration distance. If it is less than the set iteration value, it is accepted as a ground point. Otherwise, it is rejected as a ground point. The above iterations are repeated until no new ground points are added to the triangulated network, thus realizing the process of triangulating network encryption.

[0161] Step 532, point cloud editing and classification.

[0162] After filtering, most buildings have been separated from the ground point cloud, but some buildings are still mixed in the ground point cloud. In addition, due to building occlusion, the densely built area and the ground point cloud are too sparse, resulting in terrain distortion, which requires manual editing and classification. The main purpose of manual editing is to remove a small number of gross points that are not filtered out after automatic filtering, identify points that are not correctly classified by automatic classification, and appropriately increase the elevation feature points in the terrain distortion area, so as to achieve the purpose of controlling the accuracy of DEM results.

[0163] Step 54: Digital Elevation Model (DEM) Production

[0164] Use the Digital Green Earth LiDAR360 software to output DEM, select the required data, set the resolution and DEM construction, construct a triangulated network for key points, perform interpolation processing, and then transform the grid to raster data to obtain DEM. For details, see Fig.18 and Fig.19 .

[0165] This embodiment performs quality inspection and accuracy evaluation on the two constructed DEMs.

[0166] Specifically, according to the technical requirements of "Airborne LiDAR Point Cloud Data Quality Evaluation Indicators and Calculation Methods" GB / T 36100-2018, "Airborne LiDAR Data Acquisition Technical Specifications" CH / T8024-2011, and "Airborne LiDAR Data Processing Technical Specifications" CH / T 8023-2011, point cloud density, elevation accuracy, and digital elevation model accuracy are selected as evaluation indicators of data quality.

[0167] For point cloud density, the point cloud data density is counted separately according to the area. The statistical results are shown in Fig. 20 and Fig.21 According to the requirements of the Technical Specifications for Airborne LiDAR Data Acquisition (CH / T 8024-2011), see Table 4 and Table 5 for details. The point cloud data density is greater than 4 / m2, which can meet the 1:1000 scale requirement.

[0168] Table 4 Statistics of point cloud density in each region

[0169] Region Name <![CDATA[Point cloud density (points / m 2 )]]> Remark Cleaning of East District Salt Field Sedimentation Tank 107 Cleaning of East District Salt Field Sedimentation Tank 106

[0170] Table 5 Point cloud density requirements

[0171] scale Digital elevation model grid spacing / meter <![CDATA[Point cloud density (points / m 2 )]]> 1：1000 1.0 ≥4

[0172] For the elevation error, this embodiment uses the QHCORS station to adopt the network RTK method to collect check points before and after the cleaning of the salt field sedimentation pond area in the eastern area; as the elevation error judgment point of the point cloud data, the elevation error statistics are shown in Tables 6 and 7.

[0173] Table 6 Statistics of elevation errors before cleaning of East District salt field sedimentation tank (in meters)

[0174]

[0175]

[0176]

[0177] Table 7 Statistics of elevation errors after cleaning of East District salt field sedimentation tank (in meters)

[0178]

[0179]

[0180]

[0181] Check the process Fig. 22 and Fig.23 .

[0182] According to the requirements of the Technical Specifications for Airborne LiDAR Data Processing (CH / T 8023-2011), see Table 8 for details. The mathematical accuracy of the elevation of the point cloud data obtained this time is better than 1 / 3 of the mean error (terrain category: flat land), meeting the accuracy requirements of the 1:1000 scale.

[0183] Table 8 Point cloud data elevation accuracy requirements

[0184]

[0185] In view of the elevation error of the digital elevation model results, the network RTK method was used to collect check points using the QHCORS station as the elevation error judgment points of the digital elevation model. The elevation error statistics are shown in Tables 9 and 10.

[0186] Table 9 Statistics of elevation errors of the digital elevation model before cleaning of the East District salt field sedimentation tank (in meters)

[0187]

[0188]

[0189]

[0190] Table 10 Statistics of elevation errors of the digital elevation model after cleaning of the East District Salt Field Sedimentation Tank (in meters)

[0191]

[0192]

[0193]

[0194] According to the requirements of the Technical Specifications for Airborne LiDAR Data Acquisition (CH / T 8024-2011), see Table 11 for details. The mathematical accuracy of the digital elevation model acquired this time is better than 1 / 4 mean error (terrain category: flat land), which can meet the 1:1000 scale requirement.

[0195] Table 11 Digital elevation model accuracy requirements

[0196]

[0197]

[0198] After obtaining the digital elevation model of the target area before and after cleaning, the earthwork volume is calculated through the following steps:

[0199] Step 10: Extract the volume calculation boundary line.

[0200] Based on the high-precision 3D model obtained by oblique photogrammetry, the EPS geographic information work platform was used to measure the boundary line of the East District Salt Field Sedimentation Pool area and collect the excavation boundary range, such as Fig.24 and Fig.25 shown.

[0201] Step 20: Acquisition of digital elevation model (DEM) before / after cleaning.

[0202] The digital elevation model (DEM) before the survey area was cleaned (Phase I) was obtained through on-site aerial survey on June 6, 2023, and then point cloud processing was performed. The digital elevation model (DEM) after the survey area (first and second blocks) was cleaned (Phase II) was obtained through on-site aerial survey on November 30, 2023, and then point cloud processing was performed. The digital elevation model (DEM) after the survey area (third block) was cleaned (Phase II) was obtained through on-site aerial survey on February 1, 2024, and then point cloud processing was performed. Fig.26 and Fig. 27 shown.

[0203] Step 30: Calculate the volume of two phases of DEM (before and after cleaning).

[0204] ARCGIS volume calculation uses the digital elevation model (DEM) as the basis to analyze the terrain model before and after cleaning through spatial analysis and overlay analysis functions, and uses the statistical analysis module of ARCGIS software to calculate the volume of the fill and cut area to obtain the final fill and cut volume. The specific work can be summarized into the following steps.

[0205] Step 301: Data preparation.

[0206] The digital elevation model DEM data before and after cleaning obtained through point cloud measurement.

[0207] Step 302: data processing.

[0208] Check the consistency of the data coordinate system to ensure that the data has the same coordinate system and elevation system before and after cleaning, and that the coordinate systems are all projection plane coordinates.

[0209] Step 303: Volume calculation.

[0210] Calculate the volume using the 3DAnalyst Tools → Raster Surface → Cut Fill tool. Fig.28 In the dialog window shown, select to input two phases of digital elevation model (DEM) data, and the output raster is the DEM data storage location within the final extracted excavation or backfill range.

[0211] Step 304: Volume statistics.

[0212] Based on the digital elevation model (DEM) data within the excavation or backfill range finally extracted, the statistical function of ArcGIS was used to calculate the excavation and backfill volume. See Table 12 below for details.

[0213] Table 12 Measurement volume statistics

[0214]

[0215] The above implementation modes are only used to illustrate the present application, and are not intended to limit the present application. Ordinary technicians in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present application. Therefore, all equivalent technical solutions also belong to the scope of the present application, and the scope of patent protection of the present application shall be limited by the claims.

Claims

1. A method for measuring earthwork volume in plateau salt lake areas based on UAV laser radar, characterized in that: The method comprises: Obtaining a digital elevation model of the target area before and after cleaning; wherein the target area is a plateau salt lake area; Based on the digital elevation model of the target area before and after cleaning, the terrain model before and after cleaning was analyzed through spatial analysis and overlay analysis functions, and the cleared salt volume in the salt field sedimentation pond area of ​​the eastern area was calculated to obtain the final filling and excavation volume.

2. The method for measuring earthwork volume in plateau salt lake areas based on unmanned aerial vehicle laser radar as claimed in claim 1 is characterized in that: Based on the digital elevation model of the target area before and after cleaning, the terrain model before and after cleaning was analyzed through spatial analysis and overlay analysis functions to calculate the cleared salt volume in the East Salt Field Sedimentation Pool area, and the final fill and cut volume was obtained, including: Under the condition that the digital elevation models before and after the cleaning of the target area have the same coordinate system and elevation system, the cut and fill calculation results are obtained through the 3DAnalyst tool → grid surface → cut and fill tool, and the cleaning volume of the East District Salt Field Settling Pond is obtained based on the cut and fill calculation results; Based on the extracted digital elevation model data within the excavation or backfill range, the statistical function of ArcGIS is used to calculate the excavation and backfill volume.

3. The method for measuring earthwork volume in plateau salt lake areas based on unmanned aerial vehicle laser radar as claimed in claim 1 is characterized in that: Obtain digital elevation models of the target area before and after cleaning, including: Based on the UAV equipped with LiDAR payload, the target area is flown on the spot to obtain the LiDAR point cloud data before and after the target area is cleaned; Based on the laser radar point cloud data before and after cleaning of the target area, point cloud data is solved to generate a digital elevation model.

4. The method for measuring earthwork volume in plateau salt lake areas based on unmanned aerial vehicle laser radar as claimed in claim 1 is characterized in that: Based on the UAV equipped with LiDAR payload, the target area is flown on the spot to obtain LiDAR point cloud data before and after the target area is cleaned, including: The goal is to obtain the LiDAR point cloud data of the target area before and after cleaning, and design the flight range and route of the UAV; Determine the take-off and landing site of the drone according to the requirements of the drone equipment operation manual; The UAV is controlled to perform field flight according to a set flight control strategy to obtain laser radar point cloud data.

5. The method for measuring earthwork volume in plateau salt lake areas based on unmanned aerial vehicle laser radar as claimed in claim 4 is characterized in that: The set flight control strategy, including flight control mode, aviation and route curvature control mode; The flight control method includes: During the entire flight, the aircraft's turning slope is controlled within 15°; During the flight along the survey line, the following requirements must be met: Different routes use alternating left and right turns; The deviation from the route does not exceed 25m, and the altitude does not deviate by 25m; The yaw angle is not greater than 6°; The tilt angle of the image is not greater than 2°; The curvature of the route is not greater than 3%; The aircraft speed is maintained at the designed speed for aerial photography; The aviation and route curvature control method includes: real-time monitoring of the flight altitude through a laser radar measurement system, and making corrections when the actual flight altitude value deviates from the designed flight altitude.

6. The method for measuring earthwork volume in plateau salt lake areas based on unmanned aerial vehicle laser radar as claimed in claim 3 is characterized in that: Based on the laser radar point cloud data before and after cleaning the target area, point cloud data is solved to generate a digital elevation model, including: Import the laser radar point cloud data into the Qianxunyi laser radar system-point cloud preprocessing module for format conversion, and use the converted IMR data and GNSS data to upload to the Qianxun Cloud Control Console to calculate the trajectory data and radar data; Input the solved trajectory data and radar data into the Qianxunyi LiDAR System-Point Cloud Preprocessing Module, input the engineering path, coordinate system, and set the point cloud generation parameters and filtering parameters in turn to solve the point cloud, and automatically generate high-precision point cloud data; Based on the high-precision point cloud data, the required data is selected, the resolution and DEM construction are set, a triangulated network is constructed for key points, and after interpolation processing, the grid is transformed into raster data to obtain a digital elevation model.

7. The method for measuring earthwork volume in plateau salt lake areas based on unmanned aerial vehicle laser radar as claimed in claim 6 is characterized in that: Input the solved trajectory data and radar data into the Qianxunyi LiDAR System-Point Cloud Preprocessing Module, input the engineering path, coordinate system, and set the point cloud generation parameters and filtering parameters in turn to solve the point cloud, and automatically generate high-precision point cloud data, including: Layered inspection; Point cloud denoising; Point cloud post-processing.

8. The method for measuring earthwork volume in plateau salt lake areas based on unmanned aerial vehicle laser radar as claimed in claim 7 is characterized in that: Layered inspection, including: The profile and quality inspection tools of the Qianxunyi LiDAR System-Point Cloud Post-Processing Module are used to detect the stratification between routes. If stratification exists, flight strip adjustment is used to reduce the stratification phenomenon.

9. The method for measuring earthwork volume in plateau salt lake areas based on unmanned aerial vehicle laser radar as claimed in claim 8, characterized in that: Point cloud denoising, including: removing invalid point clouds and unreasonable point clouds.

10. The method for measuring earthwork volume in plateau salt lake areas based on unmanned aerial vehicle laser radar as claimed in claim 9, characterized in that: Point cloud post-processing, including: Point cloud filtering: Construct a triangulated network through the surface point cloud and remove the foot points of the ground objects in the point cloud; classify the ground point cloud based on the iterative morphological filtering method, first divide the survey area into blocks, and the minimum size of the survey area block is larger than the maximum building size in the area, then select the lowest point and the ground points within the set height difference range from the point cloud as ground seed points, establish the initial sparse triangulated network, calculate the angle and distance between the remaining points in the point cloud and the triangulated network, and compare them with the set iteration angle and iteration distance. If it is less than the set iteration value, it is accepted as a ground point, otherwise it is rejected as a ground point, and the above iterations are repeated until no new ground points are added to the triangulated network, thus realizing the process of triangulating network encryption; Point cloud editing and classification: remove gross points that are not filtered out after automatic filtering, identify points that are not correctly classified by automatic classification, and add elevation feature points in areas with terrain distortion.

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