A method and device for optimizing terrace reclamation based on drones

Abstract

The present application relates to the field of terrace reclamation technology, and more particularly to a method and device for optimizing terrace reclamation based on an unmanned aerial vehicle (UAV). The method comprises the following steps: using an unmanned aerial vehicle (UAV) to capture images of a terrace to be reclaimed, obtaining terrace image data of the terrace to be reclaimed; extracting DEM data from the terrace image data to determine reclamation range information of the terrace to be reclaimed; obtaining special condition information of the terrace to be reclaimed, and defining the special condition information as reclamation constraints of the terrace to be reclaimed; constructing and trimming a three-dimensional model of the terrace reclamation to generate a terrace reclamation surface; generating control lines based on the topographic conditions of the terrace to be reclaimed, setting them as control elements of the terrace to be reclaimed, and marking them on a field map; and demarcating rubber planting pits based on the terrace reclamation surface and the control lines of the terrace to be reclaimed, in combination with the plant spacing and planting preset density in the rubber planting technical regulations. The present invention can quickly construct a three-dimensional model of rubber plantation terraces, providing auxiliary decision support for the construction and management of rubber plantation reclamation projects.

Description

Technical Field

[0001] The present application relates to the technical field of terrace reclamation, and in particular to a terrace reclamation optimization method and device based on drones. Background Art

[0002] As rubber planting and cultivation technology continues to improve, new requirements are put forward for rubber plantation reclamation and planting. It is urgent to adopt advanced reclamation planning technology to ensure the scientificity and rationality of rubber plantation reclamation to meet the needs of efficient, precise and environmentally friendly modern agriculture.

[0003] As an emerging surveying and mapping tool, drone-based surveying technology boasts high efficiency, high precision, and high flexibility. First, by equipping them with sensors such as high-definition cameras and lidar, they can rapidly acquire high-resolution ground imagery and three-dimensional spatial data, providing accurate and comprehensive foundational data support for rubber plantation reclamation planning and design. Second, drones can complete large-scale ground surveys and measurements in a short period of time, significantly improving work efficiency. Finally, based on drone-derived data, digital models of rubber plantations can be constructed, enabling intelligent planning, design, and decision-making support.

[0004] Currently, rubber plantation reclamation design is primarily based on CAD-assisted manual calculations and delineation, relying on manual measurements and ground surveys. This process is cumbersome and lengthy, with high costs in terms of manpower, material resources, and time. Traditional methods, particularly in areas with complex terrain and lush vegetation, often suffer from low measurement accuracy, low efficiency, and high costs. Furthermore, rubber plantation planning requires comprehensive consideration of multiple factors, such as topography, soil, climate, and planting methods. Relying solely on manual operations and judgment presents significant limitations. Therefore, developing a rubber plantation reclamation planning and design method and tool based on drone-based measurement has important practical significance and broad market prospects for the development of ecological rubber plantations. Summary of the Invention

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] According to a first aspect of the present invention, the present invention claims protection for a terrace reclamation optimization method based on drones, comprising:

[0007] Using a drone to shoot images of the terraced fields to be reclaimed, and obtaining terraced field image data of the terraced fields to be reclaimed;

[0008] Extracting DEM data from the terrace image data, and determining the reclamation range information of the terrace to be reclaimed based on the slope information of the terrace to be reclaimed in the DEM data;

[0009] Using the drone to obtain special condition information of the terraced fields to be reclaimed, and defining the special condition information as a reclamation constraint condition for the terraced fields to be reclaimed;

[0010] Constructing a three-dimensional model of terrace reclamation according to the reclamation constraint conditions and reclamation range information and trimming the model to generate a terrace reclamation surface;

[0011] generating a control line according to the topographic conditions of the terraced fields to be reclaimed, setting the control line as a control element of the terraced fields to be reclaimed, and marking the control line on a field map;

[0012] Rubber planting pits are delineated based on the terrace reclamation surface and control line of the terrace to be reclaimed, combined with the plant spacing and planting preset density in the rubber planting technical regulations.

[0013] Furthermore, the method of using a drone to shoot images of the terraced fields to be reclaimed to obtain terraced field image data of the terraced fields to be reclaimed further includes:

[0014] Analyze all historical terrace feature vectors in the terrace feature database and analyze the feature vectors;

[0015] Analyze the co-occurrence feature standard values ​​of all normal terrace standard feature vectors in the terrace feature library, and perform analysis on the co-occurrence features of the standard feature vectors;

[0016] Analyze the relevant characteristic information of the standard characteristic vector of normal terraces;

[0017] Performing correlation analysis on the relevant contents of the feature vector to be detected and the standard feature vector, wherein the basis of the correlation analysis is the identification engine terrace returned by the feature vector to be detected and the target terrace connected in the standard feature vector;

[0018] Through data learning of normal drone positioning images, the features of normal drone positioning images are analyzed, and positive sample learning is performed on the features of the normal drone positioning images; abnormal drone positioning images generated by drones are learned, relevant features are analyzed, and negative sample learning is performed on the abnormal drone positioning images;

[0019] Perform classification configuration on the normal UAV positioning image features by a standardization method, combined with relevant reclamation equipment and environmental complexity, by a normalization method;

[0020] The terrace decision value data is stored in the drone cache through learning results;

[0021] The calculated terrace decision value data is used to perform feature analysis and terrace value calculation on the relevant UAV positioning image data in the current reclamation equipment. When the result is a negative number, the relevant UAV positioning image is considered to be a normal UAV positioning image; otherwise, the relevant UAV positioning image is considered to be a terrace UAV positioning image. The absolute value of the relevant UAV positioning image is given as the correlation degree to assist the relevant terrace detection results.

[0022] Furthermore, the step of extracting DEM data from the terrace image data and determining the reclamation range information of the terrace to be reclaimed based on the slope information of the terrace to be reclaimed in the DEM data further includes:

[0023] Extracting DEM data from the terraced field image data using an oblique photography model;

[0024] The effective reclamation range of the terrace to be reclaimed is extracted based on the terrace slope and aspect information of the terrace to be reclaimed in the DEM data, and the terrace to be reclaimed is classified by slope aspect.

[0025] Furthermore, the extracting the effective reclamation range of the terrace to be reclaimed based on the terrace slope and aspect information of the terrace to be reclaimed in the DEM data, and classifying the terrace to be reclaimed in terms of aspect, further includes:

[0026] Matrix-transforming the DEM data and performing peripheral expansion;

[0027] Constructing a pixel window, and corresponding each position of the matrix of the DEM data after peripheral expansion to the middle pixel of the pixel window;

[0028] In each pixel, the terrace slope information is calculated based on the pixel's rate of change in different directions;

[0029] Move the pixel window until all cells are traversed;

[0030] The terrace slope information of the terrace to be reclaimed is calculated based on the terrace slope information, and the terrace to be reclaimed is classified into south-facing slope and north-facing slope.

[0031] Furthermore, the using of the drone to obtain special condition information of the terraced fields to be reclaimed, and defining the special condition information as a reclamation constraint condition for the terraced fields to be reclaimed, further includes:

[0032] The special situation information of the terraced fields to be reclaimed shall at least include:

[0033] Boulders, steep slopes, dry ditches, and farmhouse facilities.

[0034] Furthermore, the method further comprises generating a control line according to the topographic conditions of the terraced fields to be reclaimed, setting the control line as a control element of the terraced fields to be reclaimed, and marking the control line on a field map, and further comprising:

[0035] According to the terrain conditions of the terraced fields to be reclaimed, special marking points and control lines are generated at the valley line, ridge line, starting and ending lines, and end points as control elements for terraced field reclamation, and are marked on the ground for reference by construction personnel;

[0036] Among them, a fusion algorithm based on hydrological analysis and curvature discrimination method is used to extract ridge lines and valley lines.

[0037] Furthermore, the extraction of ridge lines and valley lines using a fusion algorithm based on a hydrological analysis method and a curvature discrimination method further includes:

[0038] Use the created contour curvature calculation model or script to obtain the contour curvature;

[0039] Cut off the upper half of the hydrological analysis model to obtain the storage grid data flow and positive and negative terrain;

[0040] Based on the contour line curvature, the initial ridge line curvature and initial valley line curvature are obtained using the conditional function and threshold;

[0041] Obtain initial ridgeline flow and valleyline flow based on hydrological analysis;

[0042] Based on the initial ridge lines and valley lines, the initial data are divided into 8 levels using the quantile method. The higher the level, the greater the possibility of correctness.

[0043] Add the stretched initial ridgeline curvature to the initial ridgeline flow data, and stretch the result to 0-1. The closer it is to 1, the more likely it is a ridgeline.

[0044] According to a second aspect of the present invention, the present invention seeks protection for a terrace reclamation optimization device based on a drone, comprising:

[0045] one or more processors;

[0046] A memory having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the described terrace reclamation optimization method based on drones.

[0047] The present application relates to the field of terrace reclamation technology, and more particularly to a method and device for optimizing terrace reclamation based on an unmanned aerial vehicle (UAV). The method comprises the following steps: using an unmanned aerial vehicle (UAV) to capture images of a terrace to be reclaimed, obtaining terrace image data of the terrace to be reclaimed; extracting DEM data from the terrace image data to determine reclamation range information of the terrace to be reclaimed; obtaining special condition information of the terrace to be reclaimed, and defining the special condition information as reclamation constraints of the terrace to be reclaimed; constructing and trimming a three-dimensional model of the terrace reclamation to generate a terrace reclamation surface; generating control lines based on the topographic conditions of the terrace to be reclaimed, setting them as control elements of the terrace to be reclaimed, and marking them on a field map; and demarcating rubber planting pits based on the terrace reclamation surface and the control lines of the terrace to be reclaimed, in combination with the plant spacing and planting preset density in the rubber planting technical regulations. The present invention can quickly construct a three-dimensional model of rubber plantation terraces, providing auxiliary decision support for the construction and management of rubber plantation reclamation projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flowchart of a terrace reclamation optimization method based on drones as claimed in an embodiment of the present application;

[0049] Figure 2 A schematic diagram of a geological model before reclamation for a terrace reclamation optimization method based on drones as claimed in an embodiment of the present application;

[0050] Figure 3 A schematic diagram of a step model after reclamation in a terrace reclamation optimization method based on drones as claimed in an embodiment of the present application;

[0051] Figure 4 A schematic diagram of a pixel window for a drone-based terrace reclamation optimization method claimed in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0053] The terms "first", "second" and "third" in this application are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first", "second" and "third" may explicitly or implicitly include at least one of such features. In the description of this application, "multiple" means at least two, for example, two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of this application (such as up, down, left, right, front, back...) are only used to explain the relative positional relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally also include steps or units that are not listed, or may optionally also include other steps or units inherent to these processes, methods, products or devices.

[0054] References to "embodiments" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0055] According to the first embodiment of the present invention, the present invention claims a method for optimizing terrace reclamation based on drones, referring to Figure 1 ,include:

[0056] Using a drone to shoot images of the terraced fields to be reclaimed, and obtaining terraced field image data of the terraced fields to be reclaimed;

[0057] Extracting DEM data from the terrace image data, and determining the reclamation range information of the terrace to be reclaimed based on the slope information of the terrace to be reclaimed in the DEM data;

[0058] Using the drone to obtain special condition information of the terraced fields to be reclaimed, and defining the special condition information as a reclamation constraint condition for the terraced fields to be reclaimed;

[0059] Constructing a three-dimensional model of terrace reclamation according to the reclamation constraint conditions and reclamation range information and trimming the model to generate a terrace reclamation surface;

[0060] generating a control line according to the topographic conditions of the terraced fields to be reclaimed, setting the control line as a control element of the terraced fields to be reclaimed, and marking the control line on a field map;

[0061] Rubber planting pits are delineated based on the terrace reclamation surface and control line of the terrace to be reclaimed, combined with the plant spacing and planting preset density in the rubber planting technical regulations.

[0062] In this embodiment, the following operations are performed in the three-dimensional modeling software ZGIS:

[0063] DEM generates geological model:

[0064] Construct virtual boreholes: simulate stratum structure;

[0065] Parameters: Select the generated DEM data, set the modeling accuracy (consistent with the generated DEM data accuracy: 12.5m), set the modeling depth (bottom truncation parameter), import virtual boreholes, import the range vector data before reclamation (range constraint) Figure 2 The geological model shown;

[0066] The geological model generates a ladder model:

[0067] Reclamation is carried out according to the requirements for terrace reclamation and site factors within the plot. Terraces with different slopes have different widths. For slopes below 15°, the terraces are 2.2m wide and 6° inward. For slopes between 15° and 25°, the terraces are 1.8m wide and 8° inward. For slopes between 26° and 35°, the terraces are 1.4m wide and 8° inward. Excavation is carried out based on the geological model of the wasteland before reclamation. The main manifestation is the changes in the surface DEM. Adjustments are made according to the required height and width of the terrace surface to form the final terrace model.

[0068] Process DEM data according to the distribution range of the step design;

[0069] Formation of steps: Extract the contour lines of the DEM before reclamation and superimpose them with the step design range. Extract the contour lines within the same step according to the two-dimensional distribution of the range. The elevation values ​​of the same step should be consistent. Modify the elevation values ​​of the contour lines within the same range, and so on to generate new contour line data.

[0070] Creation of TIN: Based on the newly generated contour data, select the Kriging interpolation method to generate irregular triangulated grid data (TIN);

[0071] Data conversion: TIN to raster data (digital elevation model DEM);

[0072] Finally got Figure 3 The stepped DEM data shown.

[0073] Furthermore, the method of using a drone to shoot images of the terraced fields to be reclaimed to obtain terraced field image data of the terraced fields to be reclaimed further includes:

[0074] Analyze all historical terrace feature vectors in the terrace feature database and analyze the feature vectors;

[0075] Analyze the co-occurrence feature standard values ​​of all normal terrace standard feature vectors in the terrace feature library, and perform analysis on the co-occurrence features of the standard feature vectors;

[0076] Analyze the relevant characteristic information of the standard characteristic vector of normal terraces;

[0077] Performing correlation analysis on the relevant contents of the feature vector to be detected and the standard feature vector, wherein the basis of the correlation analysis is the identification engine terrace returned by the feature vector to be detected and the target terrace connected in the standard feature vector;

[0078] Through data learning of normal drone positioning images, the features of normal drone positioning images are analyzed, and positive sample learning is performed on the features of the normal drone positioning images; abnormal drone positioning images generated by drones are learned, relevant features are analyzed, and negative sample learning is performed on the abnormal drone positioning images;

[0079] Perform classification configuration on the normal UAV positioning image features by a standardization method, combined with relevant reclamation equipment and environmental complexity, by a normalization method;

[0080] The terrace decision value data is stored in the drone cache through learning results;

[0081] The calculated terrace decision value data is used to perform feature analysis and terrace value calculation on the relevant UAV positioning image data in the current reclamation equipment. When the result is a negative number, the relevant UAV positioning image is considered to be a normal UAV positioning image; otherwise, the relevant UAV positioning image is considered to be a terrace UAV positioning image. The absolute value of the relevant UAV positioning image is given as the correlation degree to assist the relevant terrace detection results.

[0082] Furthermore, the step of extracting DEM data from the terrace image data and determining the reclamation range information of the terrace to be reclaimed based on the slope information of the terrace to be reclaimed in the DEM data further includes:

[0083] Extracting DEM data from the terraced field image data using an oblique photography model;

[0084] The effective reclamation range of the terrace to be reclaimed is extracted based on the terrace slope and aspect information of the terrace to be reclaimed in the DEM data, and the terrace to be reclaimed is classified by slope aspect.

[0085] Among them, in this embodiment, when the DEM data is extracted from the terraced field image data through the oblique photography model, the oblique photography model data generated by the drone aerial photography will carry elevation value information, and the elevation information is extracted;

[0086] Obtain the coordinate string and the elevation value of the coordinate string point by parsing the geographic information file;

[0087] Coordinate string points generate discrete elevation points;

[0088] Interpolation methods (Kriging, inverse distance, etc.) are used to perform interpolation calculations to generate a digital elevation model with a data accuracy of 1m.

[0089] Furthermore, the extracting the effective reclamation range of the terrace to be reclaimed based on the terrace slope and aspect information of the terrace to be reclaimed in the DEM data, and classifying the terrace to be reclaimed in terms of aspect, further includes:

[0090] Matrix-transforming the DEM data and performing peripheral expansion;

[0091] Constructing a pixel window, and corresponding each position of the matrix of the DEM data after peripheral expansion to the middle pixel of the pixel window;

[0092] In each pixel, the terrace slope information is calculated based on the pixel's rate of change in different directions;

[0093] Move the pixel window until all cells are traversed;

[0094] The terrace slope information of the terrace to be reclaimed is calculated based on the terrace slope information, and the terrace to be reclaimed is classified into south-facing slope and north-facing slope.

[0095] In this embodiment, based on the slope aspects calculated previously, the slope aspects are reclassified into north slope (0-22.5°, 337.5-360°) and south slope (157.5-202.5°) according to the geographical orientation.

[0096] Calculation of slope: Calculated by the maximum mean method (Burrough, 1998).

[0097] Expand the periphery of the DEM data matrix by a circle matrix, whose value is the average value of all elevations;

[0098] Construct a 3×3 window, and the middle pixel corresponding to the window corresponds to the pixel at each position of the DEM.

[0099] Within each window, the maximum average method is used for calculation, as shown in formula (1):

[0100] Slope=arctan(√([dz / dx]2+[dz / dy]2))×180 / π (1).

[0101] Among them, dz / dx is the rate of change of pixel e in the x direction:

[0102] [dz / dx]=((c+2f+i)-(a+2d+g)) / (8×x_cellsize) (2).

[0103] dz / dy is the rate of change of pixel e in the y direction:

[0104] [dz / dy]=((g+2h+i)-(a+2b+c)) / (8×y_cellsize) (3).

[0105] x_cellsize and y_cellsize are the cell resolution in the x direction and the cell resolution in the y direction respectively.

[0106] Move the window until all cells are traversed.

[0107] (2) Calculate the slope aspect:

[0108]

[0109] Reference Figure 4 , the gray area is the outer circle, and the white area is the DEM data range. This step is to process the edge points.

[0110] Pixel, also known as pixel point or pixel point, the cell here is a pixel (after the image is enlarged, it is also a pixel).

[0111] The window refers to constructing a 3×3 window centered on each pixel of the DEM data. Starting from the upper left pixel of the data (that is, cell e), it moves from left to right until all pixels are traversed.

[0112] This window is used to calculate the slope of each pixel. For example, the window composed of abcdefghi can calculate the slope of pixel e using the slope calculation formula.

[0113] The slope calculation is mainly performed using the eight-neighborhood method. For each grid cell in the DEM data, the eight surrounding grid cells (up, down, left, right, and four diagonal directions) are selected with it as the center. The elevation difference of these eight grid cells is calculated, and the slope value is calculated based on the elevation difference and the distance between the grid cells.

[0114] Furthermore, the using of the drone to obtain special condition information of the terraced fields to be reclaimed, and defining the special condition information as a reclamation constraint condition for the terraced fields to be reclaimed, further includes:

[0115] The special situation information of the terraced fields to be reclaimed shall at least include:

[0116] Boulders, steep slopes, dry ditches, and farmhouse facilities.

[0117] Among them, in this embodiment, boulder identification: through the texture characteristics and color contrast of drone images, combined with the elevation mutation information in the DEM data, the distribution of boulders in the area is identified.

[0118] Steep slope identification: Based on the slope information obtained from the DEM data, a slope threshold is set. When the slope is greater than 35°, it is considered a steep slope, and the steep slope areas in the area are identified.

[0119] Identification of farm buildings and facilities: Identify farm buildings and facilities in the area by using building outlines and color features in drone images, combined with land use data or remote sensing classification results in GIS.

[0120] The identified boulders, steep slopes, and farmhouse facilities are used as constraints for terrace reclamation design and superimposed on the DEM data in the form of GIS layers to form the restricted areas for terrace reclamation design.

[0121] Furthermore, the method further comprises generating a control line according to the topographic conditions of the terraced fields to be reclaimed, setting the control line as a control element of the terraced fields to be reclaimed, and marking the control line on a field map, and further comprising:

[0122] According to the terrain conditions of the terraced fields to be reclaimed, special marking points and control lines are generated at the valley line, ridge line, starting and ending lines, and end points as control elements for terraced field reclamation, and are marked on the ground for reference by construction personnel;

[0123] Among them, a fusion algorithm based on hydrological analysis and curvature discrimination method is used to extract ridge lines and valley lines.

[0124] Furthermore, the extraction of ridge lines and valley lines using a fusion algorithm based on a hydrological analysis method and a curvature discrimination method further includes:

[0125] Use the created contour curvature calculation model or script to obtain the contour curvature;

[0126] Cut off the upper half of the hydrological analysis model to obtain the storage grid data flow and positive and negative terrain;

[0127] Among them, a 3×3 window is selected to perform neighborhood analysis on the DEM data and calculate the average value.

[0128] The original DEM data is subtracted from the results of neighborhood analysis to obtain positive and negative terrain data.

[0129]

[0130] Reclassify the positive and negative terrain data twice.

[0131] Assign a value of 1 to areas greater than 0 (positive terrain) and a value of 0 to areas less than 0.

[0132] Assign a value of 1 to areas less than 0 (negative terrain) and a value of 0 to areas greater than 0.

[0133] Based on the contour line curvature, the initial ridge line curvature and initial valley line curvature are obtained using the conditional function and threshold;

[0134] Obtain initial ridgeline flow and valleyline flow based on hydrological analysis;

[0135] Based on the initial ridge lines and valley lines, the initial data are divided into 8 levels using the quantile method. The higher the level, the greater the possibility of correctness.

[0136] Add the stretched initial ridgeline curvature to the initial ridgeline flow data, and stretch the result to 0-1. The closer it is to 1, the more likely it is a ridgeline.

[0137] Among them, in this embodiment, ridgeline flow: is processed in arcgis.

[0138] Depression filling → flow direction calculation → flow calculation → extraction of zero backflow accumulation → data smoothing → hill shadow auxiliary judgment → reclassification (determine the demarcation threshold as 0.5815) → data binarization (assign 1 to the part close to 1 and 0 to the rest) → multiply the reclassified data with the positive terrain data → reclassification (assign 0 to attribute values ​​not equal to 1) → obtain the final ridgeline.

[0139] Valley line flow: processed in ArcGIS.

[0140] Obtain inverse terrain → Calculate flow direction (no need to fill depressions) → Calculate flow rate → Extract zero-valued backflow accumulation → Data smoothing → Auxiliary judgment of hillshade → Reclassify (determine the demarcation threshold as 0.6142) → Binarize data (assign values ​​close to 1 to 1 and the rest to 0) → Multiply reclassified data with negative terrain data → Reclassify (assign attribute values ​​other than 1 to 0) → Obtain the final ridgeline.

[0141] The extraction of ridge lines and valley lines adopts a fusion algorithm based on hydrological analysis and curvature discrimination: the two algorithms are used for weighted evaluation. The higher the score, the more accurate the extraction results are recognized by both algorithms, and the lower the score, the opposite is true.

[0142] Use the created contour curvature calculation model or script to obtain the contour curvature. The formula is as follows:

[0143]

[0144] Where c_paln is the mean curvature:

[0145]

[0146] Cut off the upper half of the hydrological analysis model to obtain the storage grid data (flow) and positive and negative terrain;

[0147] Based on the contour line curvature, the conditional function and threshold are used to obtain the initial ridge line curvature and initial valley line curvature.

[0148] Obtain initial ridgeline flow and valleyline flow based on hydrological analysis;

[0149] Based on the initial ridge lines and valley lines in the previous step, the quantile method is used to divide the above initial data into 8 levels. The higher the level, the greater the possibility of correctness.

[0150] The stretched initial ridgeline curvature is added to the initial ridgeline flow data, and the result is stretched between 0 and 1. The closer it is to 1, the more likely it is a ridgeline. The same is true for valley lines.

[0151] In this embodiment, based on the design results of rubber plantation terrace reclamation, combined with the rubber planting technical regulations, the plant spacing is 2.5m-3m, the row spacing is 8m-10m, and the planting density is an average of 400 plants / hm2. 2 -500 plants / hm 2 Rubber planting pits are demarcated according to the requirements.

[0152] According to a second embodiment of the present invention, the present invention claims protection for a terrace reclamation optimization device based on a drone, comprising:

[0153] one or more processors;

[0154] A memory having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the described terrace reclamation optimization method based on drones.

[0155] Compared with the existing technology, the present invention applies drone measurement technology to rubber plantation reclamation design, which can fully perceive complex scenes in a large-scale, high-precision and high-definition manner. Combined with the constraints of terrace reclamation design, it can efficiently and automatically generate three-dimensional spatial scenes of terraces, improve the accuracy of terrace modeling, standardize rubber plantation reclamation design, and provide auxiliary decision-making support for the early planning and design of terrace reclamation.

[0156] Drone-based surveying technology can rapidly acquire large-scale geospatial data. Its high-precision sensors and image processing technology generate highly accurate digital elevation models. Compared to traditional manual surveying methods, drone surveying significantly improves the efficiency and quality of data acquisition. Furthermore, drone surveying can capture three-dimensional spatial information about rubber plantation reclamation areas, including topography, landforms, and vegetation cover, providing data support for comprehensive planning and assessment of rubber plantation reclamation designs.

[0157] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0158] In addition, the functional units in the various embodiments of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the description and drawings of this application, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

[0159] The above detailed description of the specific embodiments of the invention is intended only as an example, and the present application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions of the invention are also within the scope of the present application. Therefore, equivalent changes, modifications, and improvements made without departing from the spirit and scope of the present application should be included within the scope of the present application.

Claims

1. A terrace reclamation optimization method based on drones, characterized in that: include: Using a drone to shoot images of the terraced fields to be reclaimed, and obtaining terraced field image data of the terraced fields to be reclaimed; Extracting DEM data from the terrace image data, and determining the reclamation range information of the terrace to be reclaimed based on the slope information of the terrace to be reclaimed in the DEM data; Using the drone to obtain special condition information of the terraced fields to be reclaimed, and defining the special condition information as a reclamation constraint condition for the terraced fields to be reclaimed; The special condition information of the terraced fields to be reclaimed at least includes: boulders, steep slopes, and farmhouse facilities; Constructing a three-dimensional model of terrace reclamation according to the reclamation constraint conditions and reclamation range information and trimming the model to generate a terrace reclamation surface; According to the terrain conditions of the terraced fields to be reclaimed, special marking points and control lines are generated at the valley line, ridge line, starting and ending lines, and end points as control elements for terraced field reclamation, and are marked on the ground for reference by construction personnel; Use the created contour curvature calculation model or script to obtain the contour curvature; Cut off the upper half of the hydrological analysis model to obtain the storage grid data flow and positive and negative terrain; Based on the contour line curvature, the initial ridge line curvature and initial valley line curvature are obtained using the conditional function and threshold; Obtain initial ridgeline flow and valleyline flow based on hydrological analysis; Based on the initial ridge lines and valley lines, the initial data are divided into 8 levels using the quantile method. The higher the level, the greater the possibility of correctness. Add the stretched initial ridgeline curvature to the initial ridgeline flow data and stretch the result to 0-1. The closer it is to 1, the more likely it is a ridgeline. Rubber planting pits are delineated based on the terrace reclamation surface and control line of the terrace to be reclaimed, combined with the plant spacing and planting preset density in the rubber planting technical regulations.

2. The terrace reclamation optimization method based on drone according to claim 1 is characterized in that: The method of using a drone to shoot images of the terraced fields to be reclaimed to obtain image data of the terraced fields to be reclaimed further includes: Analyze all historical terrace feature vectors in the terrace feature database and analyze the feature vectors; Analyze the co-occurrence feature standard values ​​of all normal terrace standard feature vectors in the terrace feature library, and perform analysis on the co-occurrence features of the standard feature vectors; Analyze the relevant characteristic information of the standard characteristic vector of normal terraces; Performing correlation analysis on the relevant contents of the feature vector to be detected and the standard feature vector, wherein the basis for the correlation analysis is the identification target terrace returned by the test and the target terrace connected in the standard feature vector; Through data learning of normal drone positioning images, the features of normal drone positioning images are analyzed, and positive sample learning is performed on the features of the normal drone positioning images; abnormal drone positioning images generated by drones are learned, relevant features are analyzed, and negative sample learning is performed on the abnormal drone positioning images; Performing classification configuration on the normal UAV positioning image features by a normalization method, taking into account relevant reclamation equipment and environmental complexity; The terrace decision value data is stored in the drone cache through learning results; The calculated terrace decision value data is used to perform feature analysis and terrace value calculation on the relevant UAV positioning image data in the current reclamation equipment. When the result is a negative number, the relevant UAV positioning image is considered to be a normal UAV positioning image; otherwise, the relevant UAV positioning image is considered to be an abnormal UAV positioning image. The absolute value of the terrace decision value of the relevant UAV positioning image is given as the correlation to assist the relevant terrace detection results.

3. The terrace reclamation optimization method based on drone according to claim 1 is characterized in that: Extracting DEM data from the terrace image data, and determining the reclamation range information of the terrace to be reclaimed based on the slope information of the terrace to be reclaimed in the DEM data, further comprising: Extracting DEM data from the terraced field image data using an oblique photography model; The effective reclamation range of the terrace to be reclaimed is extracted based on the terrace slope and aspect information of the terrace to be reclaimed in the DEM data, and the terrace to be reclaimed is classified by slope aspect.

4. The terrace reclamation optimization method based on drone according to claim 3 is characterized in that: The method further comprises extracting the effective reclamation range of the terraced fields to be reclaimed based on the terraced field slope and aspect information of the terraced fields to be reclaimed in the DEM data, and classifying the terraced fields to be reclaimed by aspect. Matrix-transforming the DEM data and performing peripheral expansion; Constructing a pixel window, and corresponding each position of the matrix of the DEM data after peripheral expansion to the middle pixel of the pixel window; In each pixel, the terrace slope information is calculated based on the pixel's rate of change in different directions; Move the pixel window until all cells are traversed; The terrace slope information of the terrace to be reclaimed is calculated based on the terrace slope information, and the terrace to be reclaimed is classified into south-facing slope and north-facing slope.

5. A terrace reclamation optimization device based on drone, characterized in that: include: one or more processors; A memory having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement a terrace reclamation optimization method based on a drone according to any one of claims 1 to 4.

Images