A mountainous terrain detection method, system, storage medium and intelligent terminal

Abstract

The application relates to a mountainous terrain detection method and system, a storage medium and an intelligent terminal, and relates to the field of geographic surveying and mapping technology, which comprises the following steps: acquiring global image information of a region to be detected and hovering position information of a UAV; determining tree position information according to the hovering position information and the tree image information; controlling the UAV to move to a preset starting point and move according to a preset flight method to acquire detection image information of a position corresponding to the tree position information; fitting according to the ground image information and the detection image information to determine terrain fitting information, and demarcating a tree coverage area in the region to be detected according to the terrain fitting information; controlling a preset unmanned vehicle to move to the tree coverage area from a preset base station and acquire under-tree image information in the tree coverage area; and fitting according to the terrain fitting information and the under-tree image information to form a mountainous terrain model. The application has the effect of facilitating three-dimensional modeling processing of mountainous geographic structures.

Description

Technical Field

[0001] This application relates to the field of geographic surveying technology, and in particular to a method, system, storage medium and intelligent terminal for detecting mountain terrain. Background Technology

[0002] As comprehensive natural disaster risk surveys are conducted, disaster-prone areas need to undergo risk assessments based on their natural conditions to reduce the likelihood of significant casualties or economic losses due to natural disasters. Mountainous areas, being particularly vulnerable to natural disasters, require risk assessments. Current assessment methods involve geographic mapping of mountainous regions, using the mapped geographical structure to evaluate the risks.

[0003] In related technologies, mountain geographic mapping generally utilizes drones carrying imaging components to photograph the terrain, and then uses the photographed images to create 3D models to obtain the actual geographic situation of the mountainous area.

[0004] Regarding the aforementioned technologies, the inventors believe that there are many trees in mountainous areas. When using the camera to photograph the terrain, some terrain features are obscured by the trees, resulting in incomplete terrain details in the captured images. This makes it difficult to perform 3D modeling of the mountainous geographical structure, and there is still room for improvement. Summary of the Invention

[0005] To facilitate the three-dimensional modeling of mountainous geographical structures, this application provides a method, system, storage medium, and intelligent terminal for mountainous terrain detection.

[0006] Firstly, this application provides a method for detecting mountain terrain, employing the following technical solution:

[0007] A method for detecting mountain terrain, comprising:

[0008] Acquire global image information of the area to be tested and the hovering position information of the preset UAV, wherein the global image information includes ground image information and tree image information;

[0009] The tree's location information is determined based on the hover position information and the tree image information;

[0010] Control the drone to move to the preset starting point and move according to the preset flight method to obtain the detection image information of the location corresponding to the tree location information;

[0011] The terrain fitting information is determined by fitting the ground image information with the detection image information, and the tree coverage area is delineated in the area to be measured based on the terrain fitting information.

[0012] Control the pre-set unmanned vehicle to move from the pre-set base station to the tree-covered area and acquire image information under the trees in the tree-covered area;

[0013] A mountainous terrain model is formed by fitting terrain fitting information with information from images under trees.

[0014] By adopting the above technical solution, global image information of the area that needs to be 3D modeled is first obtained. The location of the trees is distinguished based on the global image. The drone is controlled to move so that it can acquire images of the terrain in the area where the trees are located from multiple angles. When the terrain image cannot be acquired by the drone, the unmanned vehicle is driven to move to the location of the tree to detect the terrain in the tree area, so as to reduce the occurrence of some areas that cannot be detected. Thus, the 3D modeling of the geographical structure of the mountainous area can be effectively performed.

[0015] Optionally, methods for determining the starting point include:

[0016] Connect the locations of each tree with a line, define the area enclosed by the outermost line segment as the detection area, and define the locations of each tree at the edge of the detection area as base points.

[0017] The detection area is expanded outward by a preset reference distance to determine the flight area, and the point on the edge of the flight area corresponding to the base point is defined as the flight point;

[0018] The distance information is determined by calculating the position corresponding to the hovering position information and the flight point;

[0019] The minimum distance information is determined according to the preset sorting rules, and the flight point corresponding to this distance information is defined as the starting point.

[0020] By adopting the above technical solution, the area to be detected by the drone is divided according to the location of the trees, and then the point closest to the drone's hovering position is determined from the detection area to control the drone's movement and operation. This allows the drone to enter the scanning operation state more quickly and improves the overall operation efficiency.

[0021] Optional flight methods include:

[0022] Obtain the initial angle information of the angle at the starting point within the flight area;

[0023] The angle bisector is determined by dividing the angle with the starting point and the starting angle information, and the direction of movement perpendicular to the bisector is determined at the starting point.

[0024] Determine the projection points of each flight point onto the bisecting line, and calculate the straight-line distance information between each projection point and the starting point;

[0025] Based on the sorting rules, determine the straight-line distance information with the largest corresponding value, and define the flight point corresponding to this straight-line distance information as the farthest point;

[0026] Control the drone to move a preset fixed distance away from the starting point and along the edge of the flight area, and after the movement is completed, control the drone to move in the direction corresponding to the movement direction information;

[0027] When the drone moves to the edge of the flight area, it moves a fixed distance away from the starting point and along the edge of the flight area, and the direction corresponding to the movement direction information is reversed to update the movement direction information, until the drone moves to the farthest point and stops moving.

[0028] By adopting the above technical solution, the UAV can effectively and completely acquire images of the required detection area, increase the range of terrain areas that the UAV can acquire, reduce the area that the unmanned vehicle needs to operate in, and further improve the overall operation efficiency.

[0029] Optionally, after the UAV has moved a fixed distance along the edge of the flight area and before moving in the direction corresponding to the movement direction information, the flight method further includes:

[0030] The path that the current UAV needs to move along the direction corresponding to the current movement direction information is defined as the path to be moved;

[0031] A detection circle is defined with a preset fixed value as the radius, and the center of the detection circle is controlled to move along the direction corresponding to the moving direction information on the path to be moved.

[0032] After the detection circle has moved the entire path to be moved, determine whether the detection circle contains the position corresponding to the tree position information;

[0033] If the detected circle contains the location information of the tree, then control the drone to move along the path to be moved;

[0034] If the detection circle does not contain the location information corresponding to the tree, the drone is controlled to continue moving a fixed distance away from the starting point along the edge of the flight area.

[0035] By adopting the above technical solution, areas without trees can be filtered out, thereby reducing the ineffective operation time of drones and greatly improving operation efficiency.

[0036] Optionally, before the drone moves a fixed distance along the edge of the flight area, the drone's flight method may also include:

[0037] Obtain the current location information of the drone;

[0038] Calculate the distance between the current location and the farthest point;

[0039] Determine whether the distance value corresponding to the interval distance information is less than a fixed distance;

[0040] If the distance value corresponding to the interval distance information is less than the fixed distance, then control the UAV to move along the edge of the flight area to the distance value corresponding to the interval distance information;

[0041] If the distance value corresponding to the interval distance information is not less than a fixed distance, then control the drone to move a fixed distance along the edge of the flight area.

[0042] By adopting the above technical solution, it is possible to determine whether the drone has approached the farthest point, thereby reducing the need for repeated cyclical detection by the drone and further improving operational efficiency.

[0043] Alternatively, the methods for moving the autonomous vehicle include:

[0044] Obtain the area number information based on the time sequence of the tree-covered area;

[0045] When the first tree-covered area appears, calculate the distance between each point on the edge of the tree-covered area and the base station to determine the travel distance information;

[0046] The sorting rules determine the minimum corresponding moving distance information, and the point on the edge of the tree-covered area corresponding to this moving distance information is defined as the nearest point, so as to control the unmanned vehicle to move to the nearest point;

[0047] When the autonomous vehicle moves to the nearest point, obtain the obstacle distance information of the autonomous vehicle towards the preset center point;

[0048] The starting point is defined as the position where the distance to the obstacle in the direction of the center point is a preset fixed value.

[0049] Control the unmanned vehicle to move to the starting point, and after moving to the starting point, control the unmanned vehicle to move in the preset rotation direction, and determine whether the distance value corresponding to the obstacle distance information is a fixed value during the movement;

[0050] If the distance value corresponding to the obstacle distance information is a fixed value, the unmanned vehicle is controlled to continue moving along the edge of the tree-covered area in the direction of rotation until the unmanned vehicle moves back to the starting point to move to the next tree-covered area according to the area number information to continue detection;

[0051] If the distance value corresponding to the obstacle distance information is not a fixed value, the difference between the obstacle distance information and the fixed value is calculated to determine the difference distance information, and the unmanned vehicle is controlled to move towards the center point by the distance corresponding to the difference distance information.

[0052] By adopting the above technical solution, the unmanned vehicle can be controlled to move around the tree trunk in a circle to obtain the actual terrain conditions at the tree location, so as to facilitate the subsequent three-dimensional modeling of the mountain terrain structure.

[0053] Optional methods for determining the center point include:

[0054] Offset the edge of the tree-covered area inward by a preset simulated distance to obtain offset boundary information;

[0055] Determine whether there are any intersections in the boundary lines corresponding to the offset boundary information;

[0056] If there is no intersection point in the boundary line corresponding to the offset boundary information, the simulation distance is repeatedly corrected by the preset correction distance until the area enclosed by the offset boundary information is smaller than the preset reference area. The area enclosed by the offset boundary information at this time is defined as the center point.

[0057] If there are intersections in the boundary lines corresponding to the offset boundary information, the distance between each intersection and the starting point is calculated, and the closest intersection is defined as the center point.

[0058] By adopting the above technical solution, the center point inside the tree trunk can be determined by offsetting, which facilitates the subsequent control of the unmanned vehicle's movement.

[0059] Secondly, this application provides a mountain terrain detection system, which adopts the following technical solution:

[0060] A mountain terrain detection system, comprising:

[0061] The acquisition module is used to acquire global image information of the area to be tested and the hovering position information of the preset UAV. The global image information includes ground image information and tree image information.

[0062] The processing module, connected to the acquisition module, is used for information storage and processing;

[0063] The processing module determines the tree's location information based on the hover position information and the tree image information;

[0064] The processing module controls the drone to move to a preset starting point and moves according to a preset flight method to obtain the detection image information of the location corresponding to the tree location information;

[0065] The processing module fits the ground image information with the detected image information to determine the terrain fitting information, and delineates the tree coverage area in the area to be measured based on the terrain fitting information;

[0066] The processing module controls a pre-set unmanned vehicle to move from a pre-set base station to a tree-covered area and acquires image information under the trees in the tree-covered area.

[0067] The processing module fits the terrain fitting information with the image information under the tree to form a mountainous terrain model.

[0068] By adopting the above technical solution, the acquisition module first acquires global image information of the area to be 3D modeled, so that the processing module can distinguish the location of trees based on the global image. The processing module controls the movement of the drone so that the drone can acquire images of the terrain of the area where the trees are located from multiple angles. When the processing module determines that the terrain image cannot be acquired by the drone, the processing module drives the unmanned vehicle to move to the location of the tree to detect the terrain of the tree area, so as to reduce the occurrence of some areas that cannot be detected, thereby enabling effective 3D modeling of the geographical structure of mountainous areas.

[0069] Thirdly, this application provides a smart terminal, which adopts the following technical solution:

[0070] A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed any of the above-mentioned mountain terrain detection methods.

[0071] By adopting the above technical solution and using a smart terminal, global image information of the area that needs to be 3D modeled is first obtained. The location of trees is distinguished based on the global image. The drone is controlled to move so that it can acquire images of the terrain in the area where the trees are located from multiple angles. When the terrain image cannot be acquired by the drone, the unmanned vehicle is driven to move to the location of the tree to detect the terrain in the tree area, so as to reduce the occurrence of some areas that cannot be detected. Thus, the 3D modeling of the geographical structure of the mountainous area can be effectively performed.

[0072] Fourthly, this application provides a computer storage medium capable of storing corresponding programs, which facilitates three-dimensional modeling of mountainous geographical structures, and adopts the following technical solution:

[0073] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed by any of the above-described mountain terrain detection methods.

[0074] By adopting the above technical solution, the computer program containing the mountain terrain detection method in the storage medium first acquires the global image information of the area that needs to be 3D modeled, distinguishes the location of trees based on the global image, controls the movement of the drone so that the drone can acquire images of the terrain in the area where the trees are located from multiple angles, and drives the unmanned vehicle to move to the location of the tree to detect the terrain in the tree area when the drone cannot acquire the terrain image. This reduces the occurrence of some areas not being detected, thereby enabling effective 3D modeling of the mountain geographical structure.

[0075] In summary, this application includes at least one of the following beneficial technical effects:

[0076] 1. By utilizing the combined use of drones, the terrain conditions of mountainous areas can be obtained relatively completely, which facilitates subsequent 3D modeling of the geographical structure of mountainous areas;

[0077] 2. The drone flight area can be determined based on the actual condition of the trees, reducing the time spent on ineffective drone operations and improving surveying efficiency;

[0078] 3. The approximate center point of the trees can be determined based on the tree cover, which facilitates the control of unmanned vehicles to move and detect the terrain where the trees are located. Attached Figure Description

[0079] Figure 1 This is a flowchart of a method for detecting mountainous terrain.

[0080] Figure 2 This is a schematic diagram of the area to be tested.

[0081] Figure 3 This is a flowchart of the method for determining the starting point.

[0082] Figure 4 This is a flowchart of the flight method.

[0083] Figure 5 This is a diagram illustrating the flight status of the drone.

[0084] Figure 6 This is a flowchart of the method for excluding invalid jobs.

[0085] Figure 7 This is a flowchart of the method for moving to the farthest point.

[0086] Figure 8 This is a flowchart of the unmanned vehicle movement method.

[0087] Figure 9 This is a flowchart of the method for determining the center point.

[0088] Figure 10 This is a flowchart of the modules for mountain terrain detection methods. Detailed Implementation

[0089] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-10 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0090] The embodiments of the present invention will now be described in further detail with reference to the accompanying drawings.

[0091] This application discloses a method for detecting mountain terrain. It utilizes a drone to acquire most of the images of the area to be modeled. When some terrain cannot be acquired by the drone, an unmanned vehicle is driven to acquire the terrain to reduce the occurrence of some terrain not being detected, thereby facilitating subsequent three-dimensional modeling of the mountain geographical structure.

[0092] Reference Figure 1 The methodology for geographic surveying in mountainous areas includes the following steps:

[0093] Step S100: Obtain global image information of the area to be tested and the hovering position information of the preset UAV, wherein the global image information includes ground image information and tree image information.

[0094] The area to be measured is the area planned for the mountainous terrain to be surveyed and modeled. It is delineated by the staff according to the actual situation and will not be elaborated here. The drone is a device equipped with a camera and other shooting equipment and has sufficient power to fly. The shooting equipment can rotate to shoot at different angles. The global image information is obtained by the drone shooting the area to be measured. The size, direction, zoom and other parameters in the global image information are set and adjusted by the drone's altitude. This is common knowledge in the art and will not be elaborated here. In addition, software tools for automatic zoom and adjusting the specific size of the image can also be installed on the camera for adjustment. The staff will set it according to the actual situation and will not be elaborated here. Tree image information is the information on the location of trees in the global image information. Ground image information is the location information in the global image information other than the location of trees. The hovering position information is the position of the drone when it acquires the image corresponding to the global image information. This position is only the projected coordinate on the horizontal plane and does not represent the vertical coordinate in a spatial sense.

[0095] Step S101: Determine the tree location information based on the hover position information and the tree image information.

[0096] The location information of trees corresponds to the location of each tree in the area to be measured. It is determined by the scaling ratio of the image corresponding to the tree image information and the position when the drone takes pictures of the global image information.

[0097] Step S102: Control the drone to move to the preset starting point and move according to the preset flight method to obtain the detection image information of the location corresponding to the tree location information.

[0098] The starting point is the starting point for the drone's movement. It can be set within the area to be measured or outside the area to be measured. The specific location is set by the staff according to the actual situation or the actual situation of the trees, and will not be elaborated further. The flight method is the movement method of the drone when it moves to detect the terrain at the location of the trees. It is set by the staff according to the actual situation, and will not be elaborated further. The image corresponding to the detected image information is the terrain image of the location corresponding to the tree location information collected by the drone during its flight.

[0099] Step S103: Fit the ground image information and the detection image information to determine the terrain fitting information, and delineate the tree coverage area in the area to be tested based on the terrain fitting information.

[0100] The terrain fitting information corresponds to the image of the terrain features of the area to be measured, determined by comparing the bottom image information with the detection image information. Figure 2 The image fitting method is a conventional technique for those skilled in the art and will not be described in detail. The tree-covered area is the area where the terrain cannot be detected after the drone's mobile detection. It can be analyzed by dividing the area to be tested into several unit blocks. If there is an unidentified terrain area in the unit block, then the area is designated as a tree-covered area.

[0101] Step S104: Control the preset unmanned vehicle to move from the preset base station to the tree-covered area and acquire image information under the trees in the tree-covered area.

[0102] The unmanned vehicle is a device equipped with cameras and other imaging equipment and has sufficient power to move around. The base station is the location where the unmanned vehicle is placed on a daily basis. The image information under the tree corresponds to the image obtained by the unmanned vehicle moving to the area covered by trees and taking pictures of that area.

[0103] Step S105: Fit the terrain fitting information with the image information under the tree to form a mountainous terrain model.

[0104] The image corresponding to the tree-under image information is fitted to the image of the terrain fitting information so that the terrain that cannot be detected in the terrain fitting information image can be filled, thereby enabling three-dimensional modeling of the mountainous terrain. Three-dimensional modeling can be achieved through modeling software such as SolidWorks and UG. The modeling method is a conventional technique for those skilled in the art and will not be described in detail.

[0105] Reference Figure 3 The methods for determining the starting point include:

[0106] Step S200: Connect the locations corresponding to the location information of each tree with lines, define the area enclosed by the outermost line segment as the detection area, and define the location corresponding to the location information of each tree at the edge of the detection area as the base point.

[0107] Combination Figure 2 By connecting each tree location point with other tree location points, the line segment points in the outermost region can be determined. The area enclosed by the outermost line segment includes all tree location points, which is the area where tree image detection needs to be performed. This area is defined as the detection area for identification, so as to facilitate subsequent control of the drone's movement. At the same time, each tree node on the edge of the detection area is defined as a base point to distinguish different tree nodes, which facilitates further control of the drone's movement.

[0108] Step S201: Expand the detection area outward by a preset reference distance to determine the flight area, and define the point on the edge of the flight area corresponding to the base point as the flight point.

[0109] The reference distance is a fixed value, set by the staff to allow the drone to take good pictures of the trees. The specific value is set by the staff and will not be elaborated here. The outward expansion direction is the direction on the line connecting the drone's hovering position and the reference point, away from the center of the detection area. The flight point is the point after expanding the reference distance outward from the reference point. It is defined to facilitate the subsequent analysis of the flight point.

[0110] Step S202: Calculate the distance information based on the position corresponding to the hovering position information and the flight point.

[0111] The distance corresponding to the distance information is the distance between the hovering position information and the flight point. This distance is the projected distance on the horizontal plane, not the actual distance in a spatial sense. It can be calculated based on the coordinates of both.

[0112] Step S203: Determine the distance information with the smallest corresponding value according to the preset sorting rules, and define the flight point corresponding to the distance information as the starting point.

[0113] The sorting rule is a method that can sort numerical values, such as the bubble sort algorithm. The sorting rule can determine the distance information with the smallest corresponding value among all the distance information. This means that the flight point corresponding to the distance information is closest to the position of the drone when it is hovering. At this time, the flight point is defined as the starting point for the drone to fly.

[0114] Reference Figure 4 Flight methods include:

[0115] Step S300: Obtain the starting angle information of the angle at the starting point within the flight area.

[0116] Reference Figure 5 The included angle information is the included angle formed by the line segments formed by the starting point and the two adjacent flight points, which can be determined by analyzing the coordinates of the three.

[0117] Step S301: Divide the angle bisectors based on the initial included angle information and the starting point to determine the bisector line, and determine the movement direction information perpendicular to the bisector line at the starting point.

[0118] The bisector line is a straight line that bisects the angle corresponding to the initial included angle information. This line passes through the starting point, and the direction corresponding to the movement direction information is the direction perpendicular to the bisector line at the starting point. This direction can be towards the left or right of the bisector line, which is set by the staff according to the actual situation and will not be elaborated further.

[0119] Step S302: Determine the projection points of each flight point on the bisecting line, and calculate the straight-line distance information between each projection point and the starting point.

[0120] The projection point is the perpendicular projection of the flight point onto the bisecting line. The distance corresponding to the straight-line distance information is the distance between the projection point and the starting point. The coordinates of the projection point can be determined based on the bisecting line and the flight point. Then, the distance between the two can be calculated based on the coordinates of the projection point and the starting point.

[0121] Step S303: Determine the straight-line distance information with the largest corresponding value according to the sorting rules, and define the flight point corresponding to the straight-line distance information as the farthest point.

[0122] The straight-line distance information with the largest corresponding value among all the straight-line distance information is determined by sorting rules. This means that the projection point of the flight point corresponding to this straight-line distance information is farthest from the starting point on the bisecting line. This flight point is defined as the farthest point for identification, so as to facilitate subsequent analysis of the UAV flight situation.

[0123] Step S304: Control the drone to move a preset fixed distance away from the starting point and along the edge of the flight area, and after the movement is completed, control the drone to move in the direction corresponding to the movement direction information.

[0124] The fixed distance is a constant value, set by staff according to the actual situation, and will not be elaborated further. The direction away from the starting point can be clockwise or counterclockwise from the edge of the flight area, as set by staff. If the direction corresponding to the movement direction information is to the left of the bisector of the line, the drone should be to the right of the bisector of the line after moving away from the starting point, and vice versa. After controlling the drone to move a fixed distance, it moves in the direction corresponding to the movement direction information to perform image detection on each tree in the flight area.

[0125] Step S305: When the UAV moves to the edge of the flight area, it moves a fixed distance away from the starting point and along the edge of the flight area, and the direction corresponding to the movement direction information is reversed to update the movement direction information, until the UAV moves to the farthest point and stops moving.

[0126] When the drone moves to the edge of the flight area, it moves away from the starting point again and reverses the direction corresponding to the movement direction information so that the drone can move to the other side of the flight area. This enables the drone to scan and move within the flight area to acquire the terrain images that can be obtained. The drone continues to move until it reaches the farthest point, indicating that the drone has completed scanning the flight area. At this point, the drone can be stopped.

[0127] Reference Figure 6 After the UAV has moved a fixed distance along the edge of the flight area, and before it moves in the direction corresponding to the movement direction information, the flight method also includes:

[0128] Step S400: Define the path that the current UAV needs to move along the direction corresponding to the movement direction information as the path to be moved.

[0129] The path to be moved is the path that the UAV needs to move to the other side of the flight area after moving a fixed distance along the edge of the flight area, in the direction corresponding to the movement direction information. This path is defined to facilitate subsequent analysis of the UAV's movement.

[0130] Step S401: Define a detection circle with a preset fixed value as the radius, and control the center of the detection circle to move along the direction corresponding to the moving direction information on the path to be moved.

[0131] The fixed value is a constant, representing the furthest distance at which the drone can acquire relatively clear images at the current flight altitude, as determined by the staff. The specific value is set by the staff, and a detection circle is drawn based on the fixed value. The center of the detection circle is then controlled to move along the path to be tested, thereby simulating the drone's flight conditions.

[0132] Step S402: After the detection circle has moved the entire path to be moved, determine whether the detection circle contains the position corresponding to the tree position information.

[0133] The purpose of the assessment is to determine whether the drone can effectively detect the terrain at the location of the trees as it moves along the path to be moved.

[0134] Step S4021: If the detected circle contains the location information corresponding to the tree, then control the drone to move along the path to be moved.

[0135] When the detection circle contains the location corresponding to the tree's location information, it means that the UAV can perform terrain compensation detection at the tree's location when moving along the path to be moved. At this time, control the UAV to move along the path to be moved in order to realize the UAV's detection of the terrain at the tree's location.

[0136] Step S4022: If the detection circle does not contain the location corresponding to the tree location information, control the drone to continue moving a fixed distance away from the starting point along the edge of the flight area.

[0137] When the detection circle does not contain the location corresponding to the tree location information, it means that the UAV cannot compensate for the uncollected terrain when moving in the path to be moved. In other words, the movement of the UAV in the path to be moved is an invalid operation. At this time, the UAV is controlled to continue to move along the edge of the flight area away from the starting point to re-plan the UAV movement path, thereby reducing the invalid operation of the UAV and improving the overall detection operation efficiency.

[0138] Reference Figure 7 Before the drone moves a fixed distance along the edge of the flight area, the drone's flight methods also include:

[0139] Step S500: Obtain the current location information of the drone.

[0140] The current location information corresponds to the position of the drone after it has moved to the edge of the flight area along the direction of movement information. The location can be obtained through the positioning device carried on the drone.

[0141] Step S501: Calculate the distance information between the current location and the farthest point.

[0142] The distance value corresponding to the interval distance information is the distance between the current position of the drone and the farthest point at the edge of the flight area, which can be calculated by considering the flight area conditions and the coordinate positions of both.

[0143] Step S502: Determine whether the distance value corresponding to the interval distance information is less than the fixed distance.

[0144] The purpose of the judgment is to determine whether the drone will pass the farthest point after moving a fixed distance along the edge of the flight area.

[0145] Step S5021: If the distance value corresponding to the interval distance information is less than the fixed distance, control the UAV to move along the edge of the flight area to the distance value corresponding to the interval distance information.

[0146] When the distance value corresponding to the interval distance information is less than the fixed distance, it means that the drone will pass the farthest point after moving a fixed distance along the edge of the flight area. At this time, control the drone to move along the edge of the flight area to the distance value corresponding to the interval distance information so that the drone can stop at the farthest point and reduce the occurrence of the drone repeatedly flying and detecting in some areas.

[0147] Step S5022: If the distance value corresponding to the interval distance information is not less than the fixed distance, then control the UAV to move a fixed distance along the edge of the flight area.

[0148] When the distance value corresponding to the interval distance information is not less than the fixed distance, it means that the drone will not pass the farthest point after moving a fixed distance along the edge of the flight area. At this time, the drone can be controlled to move a fixed distance along the edge of the flight area so that the drone can move and operate normally.

[0149] Reference Figure 8 The methods of movement for driverless vehicles include:

[0150] Step S600: Obtain area number information based on the time before and after the tree-covered area appears.

[0151] The area number information corresponds to the number of the tree-covered area. After fitting the acquired terrain image during the drone's flight, the first tree-covered area is numbered 1, the second tree-covered area is numbered 2, and so on, until the drone moves to the farthest point.

[0152] Step S601: When the first tree-covered area appears, calculate the distance between each point on the edge of the tree-covered area and the base station to determine the mobile distance information.

[0153] The distance value corresponding to the mobile distance information is the distance between each point on the boundary line of the tree-covered area and the base station, which is calculated using coordinates.

[0154] Step S602: Determine the minimum moving distance information according to the sorting rules, and define the point corresponding to the moving distance information on the edge of the tree-covered area as the nearest point, so as to control the unmanned vehicle to move to the nearest point.

[0155] The sorting rules can determine the mobile distance information with the smallest corresponding value among all mobile distance information. This means that the point on the edge of the tree-covered area corresponding to this mobile distance information is closest to the base station. At this time, the point is defined as the nearest point for identification, so as to control the unmanned vehicle to move to the nearest point to perform terrain detection in the tree-covered area.

[0156] Step S603: When the unmanned vehicle moves to the nearest point, obtain the obstacle distance information of the unmanned vehicle towards the preset center point.

[0157] The center point is the central location of the tree-covered area. The distance value corresponding to the obstacle distance information is the distance between the unmanned vehicle and the tree trunk facing the center point. This can be achieved by installing a laser rangefinder on the side wall of the unmanned vehicle. The laser rangefinder can adjust its angle according to the position of the unmanned vehicle relative to the center point so that the laser rangefinder is always facing the center point.

[0158] Step S604: Define the position where the distance value corresponding to the obstacle distance information in the direction of the center point is a preset fixed value as the starting point.

[0159] The fixed value is a constant, which is the minimum distance that the unmanned vehicle needs to maintain between itself and the tree trunk, as set by the staff. The starting point is a point on the line connecting the nearest point and the center point that is a fixed distance away from the tree trunk. This point is defined to facilitate the subsequent movement of the unmanned vehicle.

[0160] Step S605: Control the unmanned vehicle to move to the starting point, and after moving to the starting point, control the unmanned vehicle to move in the preset rotation direction, and determine whether the distance value corresponding to the obstacle distance information is a fixed value during the movement.

[0161] The rotation direction is set by the staff to move clockwise or counterclockwise around the tree trunk. The specific direction is set by the staff and will not be elaborated here. The unmanned vehicle is controlled to move to the starting point and move within the tree-covered area to detect the terrain where the tree trunk is located. The purpose of the judgment is to know whether the unmanned vehicle is too close to or too far away from the tree trunk during the movement, so that the terrain image detection result is poor.

[0162] Step S6051: If the distance value corresponding to the obstacle distance information is a fixed value, then control the unmanned vehicle to continue moving along the edge of the tree-covered area in the rotation direction until the unmanned vehicle moves back to the starting point to move to the next tree-covered area according to the area number information to continue detection.

[0163] When the distance value corresponding to the obstacle distance information is a fixed value, it indicates that the current movement of the unmanned vehicle is relatively stable. At this time, the unmanned vehicle can be controlled to continue moving along the edge of the coverage area until the unmanned vehicle moves back to the starting point, indicating that the unmanned vehicle has completed the detection of the current tree coverage area. At this time, the next tree coverage area is detected from front to back according to the corresponding number of the area number information, so as to obtain the terrain conditions of all tree coverage areas.

[0164] Step S6052: If the distance value corresponding to the obstacle distance information is not a fixed value, calculate the difference between the obstacle distance information and the fixed value to determine the difference distance information, and control the unmanned vehicle to move the distance corresponding to the difference distance information towards the center point.

[0165] When the distance value corresponding to the obstacle distance information is not a fixed value, it means that the images acquired by the unmanned vehicle are intersecting, and the position of the unmanned vehicle needs to be adjusted. The distance value corresponding to the difference distance information is the distance that the unmanned vehicle needs to adjust at this time. The calculation method is to subtract the fixed value from the distance corresponding to the obstacle distance information, so as to control the unmanned vehicle to move the distance corresponding to the difference distance information towards the center point, so that the unmanned vehicle can acquire a better image at this time.

[0166] Reference Figure 9 The methods for determining the center point include:

[0167] Step S700: Offset the edge line of the tree-covered area inward by a preset simulated distance to obtain offset boundary information.

[0168] The simulated distance is a fixed value set by the staff. The inward direction is the direction towards the center of the tree-covered area. The offset is the way in which all points on the graphic move by the same distance and can be connected to form a new graphic. The boundary line corresponding to the offset boundary information is the result of the offset edge line of the tree-covered area.

[0169] Step S701: Determine whether there is an intersection point in the boundary line corresponding to the offset boundary information.

[0170] The purpose of the judgment is to determine whether a point that is relatively close to the actual center point of the tree trunk has appeared.

[0171] Step S7011: If there is no intersection point in the boundary line corresponding to the offset boundary information, the simulation distance is repeatedly corrected by the preset correction distance until the area enclosed by the offset boundary information is less than the preset reference area. The area enclosed by the offset boundary information at this time is defined as the center point.

[0172] When there is no intersection point in the boundary line corresponding to the offset boundary information, it means that the offset has not yet reached a position close to the middle of the tree trunk, and it needs to be rescaled. The correction distance is a fixed value set by the staff to correct the simulated distance. The method of correcting the simulated distance is to add the correction distance to the original simulated distance and control the update of the simulated distance to achieve different offset situations. The reference area is an area that can be defined as a point and is set by the staff. The simulated distance is continuously corrected so that the area enclosed by the boundary line after the offset is continuously reduced. At this time, the point defined by this area is closer to the actual center point of the tree trunk. At this time, the enclosed area is defined as the center point to facilitate the movement control of the unmanned vehicle.

[0173] Step S7012: If there are intersections in the boundary lines corresponding to the offset boundary information, calculate the distance between each intersection and the starting point to define the closest intersection as the center point.

[0174] When there is an intersection point in the boundary line corresponding to the offset boundary information, it indicates that a point relatively close to the actual center of the tree trunk has appeared. At this time, the intersection point closest to the starting point is determined and defined as the center point for the unmanned vehicle to move and control.

[0175] Reference Figure 10 Based on the same inventive concept, embodiments of the present invention provide a mountain terrain detection system, comprising:

[0176] The acquisition module is used to acquire global image information of the area to be tested and the hovering position information of the preset UAV. The global image information includes ground image information and tree image information.

[0177] The processing module, connected to the acquisition module, is used for information storage and processing;

[0178] The processing module determines the tree's location information based on the hover position information and the tree image information;

[0179] The processing module controls the drone to move to a preset starting point and moves according to a preset flight method to obtain the detection image information of the location corresponding to the tree location information;

[0180] The processing module fits the ground image information with the detected image information to determine the terrain fitting information, and delineates the tree coverage area in the area to be measured based on the terrain fitting information;

[0181] The processing module controls a pre-set unmanned vehicle to move from a pre-set base station to a tree-covered area and acquires image information under the trees in the tree-covered area.

[0182] The processing module fits the terrain fitting information with the image information under the tree to form a mountainous terrain model;

[0183] The starting point determination module determines the starting point location based on the actual conditions of the trees, which facilitates the control of the drone's movement and operation.

[0184] The flight method determination module controls the drone to move and operate within the area to be detected based on the starting point and the tree conditions.

[0185] The invalid area identification module determines whether a drone needs to fly based on the tree conditions, thereby reducing the occurrence of invalid drone operations.

[0186] The flight end determination module determines whether the drone should end its flight based on the distance between the drone and the farthest point.

[0187] The unmanned vehicle movement determination module controls the movement of the unmanned vehicle based on the actual situation of the tree-covered area, so that the unmanned vehicle can acquire a more complete terrain image.

[0188] The center point determination is used to determine the center point of the tree-covered area.

[0189] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0190] This invention provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as a method for detecting mountain terrain.

[0191] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0192] Based on the same inventive concept, embodiments of the present invention provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as a mountain terrain detection method.

[0193] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0194] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for detecting mountain terrain, characterized in that, include: Acquire global image information of the area to be tested and the hovering position information of the preset UAV, wherein the global image information includes ground image information and tree image information; The tree location information is determined based on the hovering position information and tree image information; the drone is controlled to move to a preset starting point and move according to a preset flight method to obtain the detection image information of the location corresponding to the tree location information; the terrain fitting information is determined by fitting the ground image information and the detection image information, and the tree coverage area is delineated in the area to be tested based on the terrain fitting information; the preset unmanned vehicle is controlled to move from the preset base station to the tree coverage area and obtain the image information under the tree in the tree coverage area. A mountainous terrain model is formed by fitting terrain fitting information with information from images under trees. The unmanned vehicle's movement method includes: obtaining area number information based on the time of appearance of tree-covered areas; calculating the distance between each point on the edge of the first tree-covered area and the base station when the first tree-covered area appears to determine the movement distance information; determining the movement distance information with the smallest corresponding value according to a sorting rule, and defining the point on the edge of the tree-covered area corresponding to this movement distance information as the nearest point, thereby controlling the unmanned vehicle to move to the nearest point; obtaining obstacle distance information of the unmanned vehicle towards a preset center point when it moves to the nearest point; defining the position where the distance value corresponding to the obstacle distance information in the direction of the center point is a preset fixed value as the starting point; and controlling the movement of the unmanned vehicle to the nearest point. The unmanned vehicle is moved to the starting point, and after reaching the starting point, it is controlled to move in a preset rotation direction. During the movement, it is determined whether the distance value corresponding to the obstacle distance information is a fixed value. If the distance value corresponding to the obstacle distance information is a fixed value, the unmanned vehicle is controlled to continue moving along the edge of the tree-covered area in the rotation direction until the unmanned vehicle moves back to the starting point to move to the next tree-covered area according to the area number information to continue detection. If the distance value corresponding to the obstacle distance information is not a fixed value, the difference between the obstacle distance information and the fixed value is calculated to determine the difference distance information, and the unmanned vehicle is controlled to move towards the center point by the distance corresponding to the difference distance information. The method for determining the center point includes: offsetting the edge line of the tree-covered area inward by a preset simulated distance to obtain offset boundary information; determining whether there is an intersection point among the boundary lines corresponding to the offset boundary information; if there is no intersection point among the boundary lines corresponding to the offset boundary information, then repeatedly correcting the simulated distance by a preset correction distance until the area enclosed by the offset boundary information is less than the preset reference area, and defining the area enclosed by the offset boundary information at this time as the center point; if there is an intersection point among the boundary lines corresponding to the offset boundary information, then calculating the distance between each intersection point and the starting point to define the closest intersection point as the center point. The method for determining the starting point includes: connecting the corresponding positions of each tree location information with lines, defining the area enclosed by the outermost line segment as the detection area, and defining the corresponding positions of each tree location information at the edge of the detection area as base points; expanding the detection area outward by a preset reference distance to determine the flight area, and defining the points on the edge of the flight area corresponding to the base points as flight points; calculating the distance information based on the position corresponding to the hovering position information and the flight points; determining the distance information with the smallest corresponding value according to a preset sorting rule, and defining the flight point corresponding to this distance information as the starting point; The flight method includes: acquiring the initial angle information of the angle between the starting point and the starting point within the flight area; dividing the angle into bisectors based on the initial angle information and the starting point to determine the bisector line, and determining the movement direction information perpendicular to the bisector line at the starting point; determining the projection points of each flight point on the bisector line, and calculating the straight-line distance information between each projection point and the starting point; determining the straight-line distance information with the largest corresponding value according to the sorting rules, and defining the flight point corresponding to this straight-line distance information as the farthest point; controlling the UAV to move a preset fixed distance away from the starting point along the edge of the flight area, and controlling the UAV to move along the direction corresponding to the movement direction information after the movement ends; when the UAV moves to the edge of the flight area, moving a fixed distance away from the starting point again along the edge of the flight area, and reversing the direction corresponding to the movement direction information to update the movement direction information, until the UAV moves to the farthest point and stops moving.

2. The mountain terrain detection method according to claim 1, characterized in that, After the UAV moves a fixed distance along the edge of the flight area, before moving in the direction corresponding to the movement direction information, the flight method further includes: defining the path that the current UAV needs to move in the direction corresponding to the movement direction information as the path to be moved; drawing a detection circle with a preset fixed value as the radius, and controlling the center of the detection circle to move in the direction corresponding to the movement direction information on the path to be moved; after the detection circle has moved the entire path to be moved, determining whether the detection circle contains the position corresponding to the tree position information; if the detection circle contains the position corresponding to the tree position information, controlling the UAV to move along the path to be moved; if the detection circle does not contain the position corresponding to the tree position information, controlling the UAV to continue moving a fixed distance along the edge of the flight area in a direction away from the starting point.

3. The mountain terrain detection method according to claim 1, characterized in that, Before the UAV moves a fixed distance along the edge of the flight area, the UAV's flight method further includes: acquiring the UAV's current position information; calculating the interval distance information between the current position and the farthest point; determining whether the distance value corresponding to the interval distance information is less than a fixed distance; if the distance value corresponding to the interval distance information is less than the fixed distance, controlling the UAV to move along the edge of the flight area by the distance value corresponding to the interval distance information; if the distance value corresponding to the interval distance information is not less than the fixed distance, controlling the UAV to move along the edge of the flight area by a fixed distance.

4. A mountain terrain detection system, characterized in that, include: The acquisition module is used to acquire global image information of the area to be tested and the hovering position information of the preset UAV. The global image information includes ground image information and tree image information. The processing module, connected to the acquisition module, is used for information storage and processing; The processing module determines the tree's location information based on the hover position information and the tree image information; The processing module controls the drone to move to a preset starting point and moves according to a preset flight method to obtain the detection image information of the location corresponding to the tree location information; The processing module fits the ground image information with the detected image information to determine the terrain fitting information, and delineates the tree coverage area in the area to be measured based on the terrain fitting information; The processing module controls a pre-set unmanned vehicle to move from a pre-set base station to a tree-covered area and acquires image information under the trees in the tree-covered area. The processing module fits the terrain fitting information with the image information under the tree to form a mountainous terrain model; The unmanned vehicle's movement method includes: obtaining area number information based on the time of appearance of tree-covered areas; calculating the distance between each point on the edge of the first tree-covered area and the base station when the first tree-covered area appears to determine the movement distance information; determining the movement distance information with the smallest corresponding value according to a sorting rule, and defining the point on the edge of the tree-covered area corresponding to this movement distance information as the nearest point, thereby controlling the unmanned vehicle to move to the nearest point; obtaining obstacle distance information of the unmanned vehicle towards a preset center point when it moves to the nearest point; and defining the position where the distance value corresponding to the obstacle distance information in the direction of the center point is a preset fixed value as the starting point. The system controls the unmanned vehicle to move to the starting point, and after reaching the starting point, controls the unmanned vehicle to move in a preset rotation direction. During the movement, it determines whether the distance value corresponding to the obstacle distance information is a fixed value. If the distance value corresponding to the obstacle distance information is a fixed value, the system controls the unmanned vehicle to continue moving along the edge of the tree-covered area in the rotation direction until the unmanned vehicle moves back to the starting point to move to the next tree-covered area to continue detection according to the area number information. If the distance value corresponding to the obstacle distance information is not a fixed value, the system calculates the difference between the obstacle distance information and the fixed value to determine the difference distance information, and controls the unmanned vehicle to move the distance corresponding to the difference distance information towards the center point. The method for determining the center point includes: offsetting the edge line of the tree-covered area inward by a preset simulated distance to obtain offset boundary information; determining whether there is an intersection point among the boundary lines corresponding to the offset boundary information; if there is no intersection point among the boundary lines corresponding to the offset boundary information, then repeatedly correcting the simulated distance by a preset correction distance until the area enclosed by the offset boundary information is less than the preset reference area, and defining the area enclosed by the offset boundary information at this time as the center point; if there is an intersection point among the boundary lines corresponding to the offset boundary information, then calculating the distance between each intersection point and the starting point to define the closest intersection point as the center point. The method for determining the starting point includes: connecting the corresponding positions of each tree location information with lines, defining the area enclosed by the outermost line segment as the detection area, and defining the corresponding positions of each tree location information at the edge of the detection area as base points; expanding the detection area outward by a preset reference distance to determine the flight area, and defining the points on the edge of the flight area corresponding to the base points as flight points; calculating the distance information based on the position corresponding to the hovering position information and the flight points; determining the distance information with the smallest corresponding value according to a preset sorting rule, and defining the flight point corresponding to this distance information as the starting point; The flight method includes: acquiring the initial angle information of the angle between the starting point and the starting point within the flight area; dividing the angle into bisectors based on the initial angle information and the starting point to determine the bisector line, and determining the movement direction information perpendicular to the bisector line at the starting point; determining the projection points of each flight point on the bisector line, and calculating the straight-line distance information between each projection point and the starting point; determining the straight-line distance information with the largest corresponding value according to the sorting rules, and defining the flight point corresponding to this straight-line distance information as the farthest point; controlling the UAV to move a preset fixed distance away from the starting point along the edge of the flight area, and controlling the UAV to move along the direction corresponding to the movement direction information after the movement ends; when the UAV moves to the edge of the flight area, moving a fixed distance away from the starting point again along the edge of the flight area, and reversing the direction corresponding to the movement direction information to update the movement direction information, until the UAV moves to the farthest point and stops moving.

5. A smart terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed according to any one of claims 1 to 3.

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