Intelligent explosion pit data acquisition method

Abstract

The invention discloses an intelligent data acquisition method for an explosion pit. The method comprises the following steps: surveying a target area, setting a flight height, a flight area and a flight overlapping rate of an unmanned aerial vehicle, and selecting a take-off point and a landing point; image control point positions are arranged in the target area; recording the geographical location information of the measured photo control point location; acquiring an image with high resolution; distortion correction and uniform light and color processing are carried out on the image; carrying out aerial triangulation control point encryption calculation to generate point cloud data of air-ground fusion; checking and multi-point verification are carried out on the generated point cloud data and the image control point coordinates; generating high-precision live-action three-dimensional model data of the explosion pit in the target area; automatically calculating the geographic information position of the blasting pit, the drop point diameter difference between the blasting pit and the test target, and the soil throwing amount of the blasting pit; and finally recording and storing the data. According to the invention, the geographic information position of the blasting pit, the soil throwing amount of the blasting pit and the diameter difference between the drop point and the target can be fully automatically calculated, and the calculation process is intelligent, high in efficiency and high in reliability.

Description

Technical Field

[0001] The present invention belongs to the technical field of data collection, and in particular relates to an intelligent data collection method for explosion pits. Background Art

[0002] The collection of blast crater data is an important part of weapon and ammunition testing. Measuring the geographic location of the blast crater, the amount of soil thrown into the blast crater, the diameter difference between the impact point and the target, and the azimuth are important data supports for further research on near-ground explosions and penetrating explosions. They can also be used as a criterion for judging the destructive effectiveness of weapons and ammunition.

[0003] At present, the data collection of soil dumping volume, diameter difference between the landing point and the target, and azimuth angle in the blast crater at the shooting range are mainly measured by tape measures, and the data collection of the blast crater location is mainly measured by handheld GPS. In actual explosions, the shape of the blast crater is irregular, and the long and short diameters of the crater and the deepest point of the crater are all manually determined. The diameter difference measurement between the landing point and the target is more susceptible to the influence of the geographical environment of the shooting range. The measurement data has large errors and its measurement efficiency is not high. The electronic evidence of the crater in the shooting range is also sampled by two-dimensional photography using a camera, which is prone to incomplete sampling in actual operations. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the present invention provides an intelligent data collection method for explosion pits, including surveying the target area, setting the flight altitude, flight area, and flight overlap rate of the drone, and selecting the take-off and landing points; arranging image control points in the target area; recording the geographical location information of the measured image control points; obtaining high-resolution images; performing distortion correction and uniform light and color processing on the images; performing encrypted calculation of the control points of aerial triangulation to generate point cloud data of air-ground fusion; checking and multi-point verification of the generated point cloud data and the coordinates of the image control points; generating high-precision real-life three-dimensional model data of the explosion pits in the target area; automatically calculating the geographical information position of the explosion pit, the diameter difference between the crater and the test target, and the amount of soil thrown from the explosion pit; and finally recording and saving the data. The present invention can fully automatically calculate the geographical information position of the explosion pit, the amount of soil thrown from the crater, and the diameter difference between the landing point and the target, and the calculation process is intelligent, efficient, and reliable.

[0005] The technical solution adopted by the present invention to solve the technical problem is as follows:

[0006] Step 1: Survey the target area, set the flight altitude, flight area, flight overlap rate, and select the take-off and landing points;

[0007] Step 2: Deploy image control points in the target area;

[0008] Step 3: Use the real-time kinematic differential equipment RTK to record centimeter-level geographic location information of the measured image control points;

[0009] Step 4: Carry out low-altitude photography by drones and auxiliary photography on the ground to obtain high-resolution images;

[0010] Step 5: Perform distortion correction and light and color homogenization processing on the acquired ground and aerial images, and import the POS data into the corresponding aerial images;

[0011] Step 6: Import the aerial image into ContextCapture Center software to perform encryption calculation of aerial triangulation control points to generate air-ground fused point cloud data;

[0012] Step 7: Check and verify the generated point cloud data and the coordinates of the image control points to ensure that the coordinate system and elevation are within the error range;

[0013] Step 8: Construct a triangular mesh to generate high-precision real-scene 3D model data of the crater in the target area;

[0014] Step 9: Import the real-life 3D model into the Acute 3D Viewer software to automatically calculate the geographic location of the blast crater, the diameter difference between the crater and the test target, and the amount of soil thrown from the blast crater;

[0015] Step 10: Record and save the data.

[0016] Preferably, the UAV flight altitude is set to 50 meters, the flight area covers the strike target and the explosion crater, the flight heading overlap rate is 80%, and the lateral overlap rate is 70%.

[0017] Preferably, the methods for deploying image control points include: full field deployment, route network deployment and regional network deployment.

[0018] Preferably, the step 3 is specifically:

[0019] The real-time dynamic differential device RTK is composed of at least two devices: a base station and a mobile station; the mobile station continuously solves the data with the base station to obtain the precise coordinates of the point; when measuring the point, make sure that the RTK device is always in a fixed solution state; the number of satellites received by the RTK base station is greater than 15; the number of times each image control point is collected should be greater than 2 times, and finally observe the data. If the difference is greater than the set threshold, re-measure; otherwise, take the average value as the final result.

[0020] Preferably, the step 4 is specifically:

[0021] First, the flight control software of the flight equipment is used to plan the target area, including the flight altitude, flight range, and flight route;

[0022] Design the UAV route setting and determine the route indicators, including altitude, heading overlap rate and lateral overlap rate; the overlap rate is calculated by image point displacement, and its calculation formula is: Where δ is the image point displacement, v is the UAV speed km / h, and t is the camera exposure time;

[0023] The ground close-up data collection uses handheld gimbals, smart phones, and digital camera equipment;

[0024] During the data acquisition process, the camera's five-axis image stabilization and flash functions were turned off, and fixed-focus shooting was used.

[0025] Preferably, the step 5 is specifically:

[0026] First, the previous distortion correction data is imported into the Aerospace Vision MapMatrix software for distortion correction, and then imported into the Photoshop software for uniform light and color processing. Finally, the POS data is written into the aerial photographs. Each group of aerial photographs of each flight corresponds to a group of POS data.

[0027] Preferably, the step 6 is specifically:

[0028] The oblique photography images from aerial and ground data acquisition equipment are imported into the ContextCapture Center real-life 3D imaging software, and then aerial triangulation is performed. The aerial triangulation process will extract feature points from the aerial photos, match the extracted feature points with the same name, and then calculate the spatial position and spatial posture of each aerial photo. After the aerial triangulation is completed, the entire flight situation is evaluated, useless aerial photos are deleted, and finally point cloud data with geographic color texture is output.

[0029] Preferably, the step 7 is specifically:

[0030] After the aerial triangulation is completed, the position and attitude of each aerial photograph are calculated, and the coordinates of the ground image control points are selected for import. After the import is completed, the coordinates of the reference points replace the three-dimensional coordinates of the imaging control points. After the identification is completed, the real-scene 3D modeling imaging software performs encryption operations between the three-dimensional coordinates of the image control points and the reference points to complete the coordinate system conversion and three-dimensional coordinate accuracy correction verification of all point cloud data.

[0031] Preferably, the step 8 is specifically:

[0032] After aerial triangulation, point cloud data of the target area of ​​the high-density target range test is obtained. The software calculates the irregular triangulated network TIN through the aerial triangulation encryption points and generates tiles without mapping. The mapping texture is obtained from the aerial and ground photo data through the position of the tiles, and finally the real-life 3D model is output.

[0033] The beneficial effects of the present invention are as follows:

[0034] Compared with the existing blast crater data collection method, the present invention forms a high-resolution blast crater real-scene three-dimensional model based on high-precision oblique photography combined with air and ground, thereby automatically calculating the blast crater geographic information position, crater soil dumping volume and the diameter difference between the landing point and the target, and the calculation process is intelligent, efficient and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart for implementing the present invention.

[0036] Figure 2 The figure is a schematic diagram of the outdoor flight route of the UAV according to an embodiment of the present invention.

[0037] Figure 3 This is a schematic diagram of crater data calculation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0039] The purpose of the present invention is to overcome the shortcomings of large errors and low efficiency in manually collecting blast crater data, and to provide an intelligent blast crater data collection method combined with unmanned aerial vehicle oblique photography three-dimensional modeling.

[0040] The implementation of the present invention is as follows:

[0041] Step 1: Survey the target area, set the flight altitude, flight area, flight overlap rate, and select the take-off and landing points;

[0042] Step 2: Arrange image control points in the target area;

[0043] Step 3: Use RTK to record centimeter-level geographic location information of the measured image control points;

[0044] Step 4: Carry out low-altitude photography by drones and auxiliary photography on the ground to obtain high-resolution images;

[0045] Step 5: Perform distortion correction and light and color uniformity processing on the acquired ground and aerial images, and import the POS data into the corresponding aerial images;

[0046] Step 6: Import the image into ContextCapture Center software to perform encryption calculation of aerial triangulation control points to generate high-precision point cloud data with air-ground fusion;

[0047] Step 7: Check and verify the generated point cloud data and image control point coordinates to ensure that the coordinate system and elevation are within the error range;

[0048] Step 8: construct a triangular mesh to generate high-precision real-scene 3D model data of the crater in the target area;

[0049] Step 9: Import the real-life 3D model into the Acute 3D Viewer software to automatically calculate the geographic location of the blast crater, the diameter difference between the crater and the test target, and the amount of soil thrown from the blast crater;

[0050] Step 10. Record and save the data.

[0051] Example:

[0052] Step 1: Survey the target area, set the drone flight altitude, flight area, flight overlap rate, and select the take-off and landing points. To ensure high resolution of the collected images, the flight altitude is generally set to 50 meters, the flight area covers the strike target and the blast crater, the flight heading overlap rate is 80%, the lateral overlap rate is 70%, and a flat area is selected as the take-off and landing point.

[0053] Step 2: Deploy image control points in the target area. The methods of deploying image control points include: full field deployment, route network deployment and regional network deployment. The high-precision oblique photography image control point deployment method mainly adopts regional network deployment, following the aerial triangulation accuracy criteria. Deploy image control points in the area according to the following location optimization principles: the selected image control points should be clearly identifiable and the points can be clearly seen in the aerial photograph; the image control points should be deployed on flat ground as much as possible, and the elevation difference should not exceed 1m within 2m around the plane of the deployment points; the image control points at the edge of the flight area should be deployed within 40m-100m of the edge of the flight area, and should not be deployed on the edge line; the image control points should be selected as close as possible to the center line of the lateral overlapping aerial photograph (referring to the front aerial photograph).

[0054] Step 3: Use RTK to record centimeter-level geographic location information of the test targets and measurement image control points in the shooting range. The image control points are measured by ground GPS or Beidou positioning equipment to obtain accurate coordinate positions. Currently, the real-time dynamic differential equipment (RTK) has higher measurement accuracy. The system consists of at least two devices, a base station and a mobile station. The mobile station continuously calculates data with the base station to obtain the precise coordinates of the points. When measuring points, it is necessary to ensure that the RTK equipment is always in a fixed solution state; the number of satellites received by the RTK base station is greater than 15; the number of times each image control point is collected should be greater than 2 times, and finally observe the data. If the difference is large, re-measure; if the difference is not large, take the average value as the final result.

[0055] Step 4: Carry out low-altitude photography by aircraft and auxiliary photography on the ground to obtain high-resolution aerial survey images. To carry out aerial photography by flight equipment, first use the flight control software of the flight equipment to plan the target area. The planning content includes flight altitude, flight range, and flight route. It is necessary to design the UAV route setting and determine the route indicators, including altitude, heading overlap rate, and lateral overlap rate. The overlap rate calculation needs to be determined by image point displacement to ensure the clarity of the image. The calculation formula is: Where δ is the image point displacement; v is the speed of the drone in km / h; t is the camera exposure time in s. Ground close-up data collection can be done using handheld gimbals, smart phones, digital cameras and other devices. During the data acquisition process, the camera's five-axis anti-shake, flash and other functions must be turned off, and fixed-focus shooting must be used.

[0056] Step 5: Perform distortion correction and uniform light and color processing on the acquired ground and aerial images, and import the POS data into the corresponding aerial images. First, import the previous distortion correction data into the Aerospace Vision MapMatrix software for distortion correction, then import it into the Photoshop software for uniform light and color processing, and finally write the POS data into the aerial photographs. Each set of aerial photographs of each flight corresponds to a set of POS data.

[0057] Step 6: Generate high-precision point cloud data of the target area of ​​the real-life shooting range test for air-ground fusion. Import the oblique photography images of the aerial and ground data acquisition equipment into the ContextCapture Center real-life 3D imaging software, and then perform aerial triangulation encryption. The aerial triangulation encryption process will extract feature points from the aerial photos, match the extracted feature points with the same name, and then calculate the spatial position and spatial attitude of each aerial photo. After the aerial triangulation encryption is completed, the entire flight situation can be evaluated, useless aerial photos can be deleted, and finally point cloud data with geographic color texture can be output.

[0058] Step 7: Correct and verify the generated point cloud data. After the aerial triangulation is completed, the position and attitude of each aerial photograph will be calculated, so that the position of the image control point can be found in the aerial photograph more quickly. The coordinates of the ground image control point are selected for import. After the import is completed, the coordinates of the reference point are replaced with the three-dimensional coordinates of the imaging control point. After the identification is completed, the real-scene 3D modeling imaging software performs encryption operations between the three-dimensional coordinates of the image control point and the reference point, completing the coordinate system conversion and three-dimensional coordinate accuracy correction verification of all point cloud data.

[0059] Step 8: Generate high-precision real-life 3D model data of the target area of ​​the range test. After aerial triangulation, a large amount of high-density point cloud data of the target area of ​​the range test was obtained. The software calculated the irregular triangulated network TIN through the aerial triangulation points and generated tiles without textures. The textures were obtained from the aerial and ground-based photo data based on the position of the tiles, and finally the real-life 3D model was output.

[0060] Step 9: Collect the geographic information of the crater in the shooting range. Import the high-precision real-life 3D model of the shooting range into Acute3DViewer, and you can directly extract the high-precision geographic location information of the test area and the crater, such as accuracy, latitude, and elevation information. After collecting the required geographic location information, you can determine the azimuth of the crater landing point and the diameter difference between the crater landing point and the target through the position distribution in the coordinate space. Perform a filling simulation on the 3D model of the crater in the target area of ​​the shooting range test, and calculate the volume of the filled 3D model.

[0061] Step 10. Record and save the geographic location of the blast crater, the amount of soil thrown from the blast crater, the diameter difference between the impact point and the target, and the amount of soil thrown from the blast crater.

Claims

1. A method for intelligent data collection of explosion pits, characterized in that: The steps include: Step 1: Survey the target area, set the flight altitude, flight area, flight overlap rate, and select the take-off and landing points; Step 2: Deploy image control points in the target area; Step 3: Use the real-time kinematic differential equipment RTK to record centimeter-level geographic location information of the measured image control points; Step 4: Carry out low-altitude photography by drones and auxiliary photography on the ground to obtain high-resolution images; Step 5: Perform distortion correction and light and color homogenization processing on the acquired ground and aerial images, and import the POS data into the corresponding aerial images; Step 6: Import the aerial image into ContextCapture Center software to perform encryption calculation of aerial triangulation control points to generate air-ground fused point cloud data; Step 7: Check and verify the generated point cloud data and the coordinates of the image control points to ensure that the coordinate system and elevation are within the error range; Step 8: Construct a triangular mesh to generate high-precision real-scene 3D model data of the crater in the target area; Step 9: Import the real-life 3D model into the Acute 3D Viewer software to automatically calculate the geographic location of the blast crater, the diameter difference between the crater and the test target, and the amount of soil thrown from the blast crater; Step 10: Record and save the data.

2. The method for intelligent data collection of explosion pits according to claim 1 is characterized in that: The UAV flight altitude is set to 50 meters, the flight area covers the strike target and the explosion crater, the flight heading overlap rate is 80%, and the lateral overlap rate is 70%.

3. The method for intelligent data collection of explosion pits according to claim 1 is characterized in that: The methods for arranging image control points include: full field point arrangement, route network point arrangement and regional network point arrangement.

4. The method for intelligent data collection of explosion pits according to claim 1 is characterized in that: The step 3 is specifically as follows: The real-time dynamic differential device RTK is composed of at least two devices: a base station and a mobile station; the mobile station continuously solves the data with the base station to obtain the precise coordinates of the point; when measuring the point, make sure that the RTK device is always in a fixed solution state; the number of satellites received by the RTK base station is greater than 15; the number of times each image control point is collected should be greater than 2 times, and finally observe the data. If the difference is greater than the set threshold, re-measure; otherwise, take the average value as the final result.

5. The method for intelligent data collection of explosion pits according to claim 4 is characterized in that: The step 4 is specifically as follows: First, the flight control software of the flight equipment is used to plan the target area, including the flight altitude, flight range, and flight route; Design the UAV route setting and determine the route indicators, including altitude, heading overlap rate and lateral overlap rate; the overlap rate is calculated by image point displacement, and its calculation formula is: Where δ is the image point displacement, v is the UAV speed km / h, and t is the camera exposure time; The ground close-up data collection uses handheld gimbals, smart phones, and digital camera equipment; During the data acquisition process, the camera's five-axis image stabilization and flash functions were turned off, and fixed-focus shooting was used.

6. The method for intelligent data collection of explosion pits according to claim 5 is characterized in that: The step 5 is specifically as follows: First, the previous distortion correction data is imported into the Aerospace Vision MapMatrix software for distortion correction, and then imported into the Photoshop software for uniform light and color processing. Finally, the POS data is written into the aerial photographs. Each group of aerial photographs of each flight corresponds to a group of POS data.

7. The method for intelligent data collection of explosion pits according to claim 6 is characterized in that: The step 6 is specifically as follows: The oblique photography images from aerial and ground data acquisition equipment are imported into the ContextCapture Center real-life 3D imaging software, and then aerial triangulation is performed. The aerial triangulation process will extract feature points from the aerial photos, match the extracted feature points with the same name, and then calculate the spatial position and spatial posture of each aerial photo. After the aerial triangulation is completed, the entire flight situation is evaluated, useless aerial photos are deleted, and finally point cloud data with geographic color texture is output.

8. The method for intelligent data collection of explosion pits according to claim 7 is characterized in that: The step 7 is specifically as follows: After the aerial triangulation is completed, the position and attitude of each aerial photograph are calculated, and the coordinates of the ground image control points are selected for import. After the import is completed, the coordinates of the reference points replace the three-dimensional coordinates of the imaging control points. After the identification is completed, the real-scene 3D modeling imaging software performs encryption operations between the three-dimensional coordinates of the image control points and the reference points to complete the coordinate system conversion and three-dimensional coordinate accuracy correction verification of all point cloud data.

9. The method for intelligent data collection of explosion pits according to claim 8, characterized in that: The step 8 is specifically as follows: After aerial triangulation, point cloud data of the target area of ​​the high-density target range test is obtained. The software calculates the irregular triangulated network TIN through the aerial triangulation encryption points and generates tiles without mapping. The mapping texture is obtained from the aerial and ground photo data through the position of the tiles, and finally the real-life 3D model is output.

Images