Forest and grass resource data acquisition method and system

Abstract

The invention provides a forest and grass resource data acquisition method and system, and belongs to the technical field of forest and grass resource management and image data processing. The method comprises the steps of controlling an unmanned aerial vehicle to arrive at a target forest grass area, obtaining a plurality of point cloud data sets, determining a minimum bounding box of each point cloud cluster, judging whether a cavity scanning area exists in the target forest grass area or not, and if the cavity scanning area exists in the current forest grass area, collecting a local area image corresponding to the cavity scanning area at a first resolution, otherwise, collecting the whole area image of the target forest grass area at the second resolution, and determining the next target forest grass area after storing the local area image or the whole area image. The system comprises a control end and unmanned aerial vehicle equipment in wireless communication with the control end, and the unmanned aerial vehicle equipment is loaded with a laser radar scanning device and a multi-resolution image acquisition device and is used for realizing the method. According to the technical scheme of the invention, efficient forest and grass resource data acquisition and management can be realized by pertinently paying attention to the data abnormal cavity area.

Description

Technical Field

[0001] The present invention belongs to the technical field of forest and grassland resource management and image data processing, and in particular, relates to a forest and grassland resource data collection method, a computer-readable storage medium for implementing the method, a computer program product, and an electronic device. Background Art

[0002] In the forestry and grassland ecological construction and protection work, forest and grassland resource monitoring and data collection provide the basic information such as forest and grassland distribution and current quality required for forestry and grassland ecological management and protection, and provide a basis and technical reference for forest and grassland related decision-making. It is crucial to the smooth development and orderly implementation of various forestry and grassland work.

[0003] In recent years, the amount of information and data in forest and grassland resource monitoring and data collection has increased dramatically, which has put forward higher requirements for practitioners. If traditional monitoring and investigation methods are used, it will inevitably have an adverse impact on the efficiency and quality of monitoring and investigation, and thus affect the effectiveness of decision-making. The popularization of drones, airborne radars, and smart remote sensing GIS technologies has made the forest and grassland resource monitoring and data collection process gradually intelligent, information-based, and automated, significantly reducing the workload of monitoring and investigation, and improving monitoring efficiency and quality. For example, the Chinese invention patent CN118053087B proposed a forest and grassland photovoltaic complementary evaluation and monitoring method based on drones and model analysis, the CN114821056B proposed an automatic interpretation method of forest and grassland resource changes based on AI technology in remote sensing images, and the CN117592759B proposed a grid patrol method for forest and grassland resources based on drones.

[0004] However, the current forest and grassland resource monitoring and data collection solutions lack a certain degree of collection flexibility. The same processing strategy is used for all target areas, which fails to highlight the key points, especially the identification and processing of some data collection blind spots. The existing technology still has certain defects. Summary of the invention

[0005] In response to the above technical problems, the present invention proposes a forest and grassland resource data collection method, a computer-readable storage medium, a computer program product and an electronic device for implementing the method.

[0006] In a first aspect of the present invention, a method for collecting forest and grass resource data is proposed, and the method is implemented based on an electronic device;

[0007] The method comprises the following steps:

[0008] S100: Control the UAV to reach the target forest and grassland area;

[0009] S200: performing laser scanning on the target forest and grassland area by using the laser radar scanning device to obtain multiple point cloud data sets;

[0010] S300: clustering the plurality of point cloud data based on a clustering algorithm to obtain a plurality of point cloud clusters, and determining a minimum bounding box of each point cloud cluster;

[0011] S400: Based on the minimum bounding box of each point cloud cluster, determine whether there is a hollow scanning area in the target forest and grass area;

[0012] If there is a hole scanning area in the current forest and grassland area, the multi-resolution image acquisition device is started to acquire a local area image corresponding to the hole scanning area at a first resolution;

[0013] If there is no empty scanning area in the current forest and grassland area, the multi-resolution image acquisition device is started to acquire the overall area image of the target forest and grassland area at the second resolution;

[0014] Wherein, the first resolution is greater than the second resolution;

[0015] S500: Save the local area image or the whole area image, determine the next target forest and grassland area, and return to step S100.

[0016] Step S300 clusters the plurality of point cloud data based on a clustering algorithm to obtain a plurality of point cloud clusters, specifically including:

[0017] S310: determining a plurality of cluster centers based on a scanning range of the target forest and grassland area laser scanned by the laser radar scanning device;

[0018] S311: Determine the point cloud cluster to which each point cloud data belongs based on the distance between each point cloud data and the cluster center, wherein each point cloud cluster belongs to only one point cloud cluster, and each point cloud cluster includes at least one point cloud data.

[0019] Step S300 determines the minimum bounding box of each point cloud cluster, specifically including:

[0020] S320: Obtaining the plane dimension coordinates of the point cloud data included in each point cloud cluster;

[0021] S321: Determine the shape of the minimum bounding box based on the distribution of the plane dimension coordinates of the point cloud data, where the shape of the minimum bounding box includes a circle, an ellipse, and an N-gon, where 6≥N≥3;

[0022] S322: Generate a minimum bounding box corresponding to each point cloud cluster.

[0023] Step S400 determines whether there is a hollow scanning area in the current forest and grass area based on the minimum bounding box of each point cloud cluster, specifically including:

[0024] If the boundary distances between the adjacent first minimum bounding box and the second minimum bounding box are both greater than the preset threshold, it is determined that there is a hole scanning area between the first minimum bounding box and the second minimum bounding box.

[0025] The multi-resolution image acquisition device includes a plurality of image acquisition modes with different resolutions;

[0026] In step S400, if there are multiple hole scanning areas in the current forest and grassland area, the areas of the multiple hole scanning areas are estimated, and the size of the first resolution used by the multi-resolution image acquisition device to acquire the local area image corresponding to the hole scanning area is determined according to the area of ​​each hole scanning area.

[0027] The determining, according to the area of ​​each hole scanning area, the size of the first resolution used by the multi-resolution image acquisition device when acquiring the local area image corresponding to the hole scanning area specifically includes:

[0028] Determine the number of hole scanning areas M; M>1;

[0029] Determine the number K of resolution modes supported by the resolution image acquisition device, K>1;

[0030] If K>M, the size of the first resolution used in each hole scanning area is determined according to the estimated area size of each hole scanning area;

[0031] If K≤M, the M hole scanning areas are divided into K-1 groups according to their area sizes, each group contains at least one hole scanning area, the area sizes of the hole scanning areas contained in each group are the same or close, and each group corresponds to a first resolution;

[0032] Furthermore, the first resolution used by the hole scanning area with a larger area is not greater than the first resolution used by the hole scanning area with a smaller area.

[0033] The multi-resolution image acquisition device includes a plurality of image acquisition modes with different resolutions;

[0034] In step S400, if there are M empty scanning areas in the current forest and grassland area, the area and and the total area of ​​the target forest and grassland areas;

[0035] The high-resolution image acquisition device then acquires the overall area image of the target forest and grassland area at the highest resolution.

[0036] The aforementioned forest and grassland resource data collection method can be automatically implemented through various forms of electronic devices through computer program instructions; the computer program instructions can be stored in different forms of storage media and loaded into computer electronic devices for execution.

[0037] Therefore, in the second aspect of the present invention, a computer-readable storage medium is also provided for storing computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the aforementioned forest and grassland resource data collection method.

[0038] In the third aspect of the present invention, a computer device is also proposed, which includes a processor and a memory, wherein the memory is used to store instructions, and the processor is used to call the instructions in the memory so that the computer device executes the aforementioned forest and grassland resource data collection method.

[0039] In the fourth aspect of the present invention, a computer program product is also proposed, which includes a computer program. When the computer program is executed, the above-mentioned forest and grassland resource data collection method is implemented.

[0040] In order to realize the forest and grassland resource data collection method of the first aspect mentioned above, in the fifth aspect of the present invention, a forest and grassland resource data collection system is also proposed, the system includes a control end and an unmanned aerial vehicle device that wirelessly communicates with the control end, and the unmanned aerial vehicle device is equipped with a laser radar scanning device and a multi-resolution image acquisition device.

[0041] The drone device is also equipped with an edge image processing unit;

[0042] The control end controls the UAV to reach the target forest and grass area;

[0043] The UAV equipment performs laser scanning on the target forest and grass area through the laser radar scanning device to obtain multiple point cloud data sets;

[0044] The UAV device clusters the plurality of point cloud data using the edge image processing unit to obtain NumC point cloud clusters, and determines a minimum bounding box of each point cloud cluster; NumC>1;

[0045] The edge image processing unit determines whether there is a hollow scanning area in the target forest and grass area based on the minimum bounding box of each point cloud cluster;

[0046] If there is a hole scanning area in the current forest and grass area, the edge image processing unit starts the multi-resolution image acquisition device to acquire a local area image corresponding to the hole scanning area at a first resolution;

[0047] If there is no empty scanning area in the current forest and grassland area, the multi-resolution image acquisition device is started to acquire the overall area image of the target forest and grassland area at the second resolution;

[0048] Wherein, the first resolution is greater than the second resolution;

[0049] The UAV device saves the local area image or the whole area image and sends it to the control end, and the control end determines the next target forest and grass area.

[0050] If the edge image processing unit determines that there is more than one empty scanning area in the target forest and grassland area, the area and and the total area of ​​the target forest and grassland areas;

[0051] The edge image processing unit then starts the multi-resolution image acquisition device to acquire the overall area image of the target forest and grass area at the highest resolution.

[0052] The technical solution of the present invention can focus on data abnormality hole areas to achieve efficient forest and grass resource data collection and management, thereby achieving collection flexibility. The image adaptive processing strategy is adopted for different types of target areas, which can highlight the monitoring focus, especially accurately identify data collection blind spots, making the forest and grass resource data collection method more targeted.

[0053] Further advantages of the present invention will be further reflected in detail in the specific embodiments section in conjunction with the drawings of the specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 It is a schematic diagram of the main flow of a forest and grass resource data collection method according to an embodiment of the present invention;

[0056] Figure 2 yes Figure 1 The method obtains a plurality of point cloud clusters.

[0057] Figure 3 yes Figure 1 The method generates a principle flow chart of the minimum bounding box corresponding to each point cloud cluster;

[0058] Figure 4-Figure 5 It is a schematic diagram of multiple point cloud clusters and the corresponding minimum bounding boxes in two different situations;

[0059] Figure 6 This is a schematic diagram of the distribution of multiple point cloud clusters in another situation;

[0060] Figure 7 It is a schematic diagram of the functional module composition of a forest and grassland resource data collection system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] First of all, it should be pointed out that the embodiments of the forest and grassland resource data collection method mentioned in this section can be implemented through computer programs on electronic devices and systems configured with memory and processors. The electronic devices and systems can be in the form of physical machines, virtual machines, servers, clusters or any combination thereof.

[0062] Preferably, the specific form of the electronic device may also be a human-computer interaction terminal, and the human-computer interaction terminal may be a desktop terminal, a smart handheld terminal, a mobile terminal, etc. with a human-computer interaction interface.

[0063] Figure 1 It is a main flow chart of a method for collecting forest and grass resources data according to an embodiment of the present invention. Figure 1 The method is implemented based on an unmanned aerial vehicle device, which is equipped with a laser radar scanning device and a multi-resolution image acquisition device.

[0064] Figure 1 The execution flow of multiple steps S100-S500 of the method is shown in the form of a computer program flow or branch (for ease of reading, the step numbers are omitted in the drawings), and each step is specifically implemented as follows:

[0065] S100: Control the UAV to reach the target forest and grassland area;

[0066] S200: performing laser scanning on the target forest and grassland area by using the laser radar scanning device to obtain multiple point cloud data sets;

[0067] S300: clustering the plurality of point cloud data based on a clustering algorithm to obtain a plurality of point cloud clusters, and determining a minimum bounding box of each point cloud cluster;

[0068] S400: Based on the minimum bounding box of each point cloud cluster, determine whether there is a hollow scanning area in the target forest and grass area;

[0069] If there is a hole scanning area in the current forest and grassland area, the multi-resolution image acquisition device is started to acquire a local area image corresponding to the hole scanning area at a first resolution;

[0070] If there is no empty scanning area in the current forest and grassland area, the multi-resolution image acquisition device is started to acquire the overall area image of the target forest and grassland area at the second resolution;

[0071] Wherein, the first resolution is greater than the second resolution;

[0072] S500: Save the local area image or the whole area image, determine the next target forest and grassland area, and return to step S100.

[0073] Next, combine Figure 2-Figure 6 ,right Figure 1 Part of the method that involves steps of improvements or advantages of the present invention is further described.

[0074] The implementation of steps S100-S200 belongs to the prior art and will not be elaborated here.

[0075] The improvement of the embodiment starts from step S300.

[0076] Step S300 clusters the plurality of point cloud data based on a clustering algorithm to obtain a plurality of point cloud clusters, specifically including:

[0077] S310: determining a plurality of cluster centers based on a scanning range of the target forest and grassland area laser scanned by the laser radar scanning device;

[0078] S311: Determine the point cloud cluster to which each point cloud data belongs based on the distance between each point cloud data and the cluster center, wherein each point cloud cluster belongs to only one point cloud cluster, and each point cloud cluster includes at least one point cloud data.

[0079] Figure 2 yes Figure 1 The method is a schematic diagram of the principle of obtaining multiple point cloud clusters.

[0080] exist Figure 4 In FIG. 1 , a UAV performs laser scanning on a first target forest and grass area through an airborne laser radar (LiDAR) scanning device, and obtains multiple point cloud data sets. The multiple point cloud data sets can be roughly divided into three groups: Figure 4 dark purple O1, blue O2, and yellow O3).

[0081] Specifically, based on the overall scanning range and the clustering range of each group, three cluster centers can be determined ( Figure 4 Then, based on the distance between each point cloud data and the cluster center, the point cloud cluster to which each point cloud data belongs is determined, and then we can get Figure 4The three point cloud clusters (dark purple point cloud cluster O1, blue point cloud cluster O2 and yellow point cloud cluster O3), each point cloud cluster takes the red attribute point as the cluster center (cluster center).

[0082] Next, step S300 determines the minimum bounding box of each point cloud cluster. Figure 3 yes Figure 1 The method generates a principle flow chart of the minimum bounding box corresponding to each point cloud cluster, which specifically includes:

[0083] S320: Obtaining the plane dimension coordinates of the point cloud data included in each point cloud cluster;

[0084] S321: Determine the shape of the minimum bounding box based on the distribution of the plane dimension coordinates of the point cloud data, where the shape of the minimum bounding box includes a circle, an ellipse, and an N-gon, where 6≥N≥3;

[0085] S322: Generate a minimum bounding box corresponding to each point cloud cluster.

[0086] The minimum bounding box is a planar geometric figure with the smallest area that can enclose a group of objects.

[0087] Therefore, step S300 only needs to perform coordinate recognition and processing in two dimensions (plane dimensions).

[0088] That is to say, while step S300 determines the minimum bounding box corresponding to each point cloud cluster, it also performs data dimensionality reduction processing (from three-dimensional lidar data to two-dimensional plane data), thereby reducing the amount of data processing.

[0089] The minimum bounding box is preferably a square or a circle, and can also be a triangle or a polygon. After reducing the data from three dimensions to two-dimensional plane data, the process of determining the minimum plane bounding box of the data coordinate group (data point) on the plane is very simple. For example, it is only necessary to determine the maximum X coordinate Xmax, the minimum X coordinate Xmin, the maximum Y coordinate Ymax and the minimum Y coordinate Ymin of other coordinate points with the cluster center of each cluster as the center to determine the minimum plane bounding box.

[0090] Taking the minimum bounding box as a rectangle as an example, after obtaining the distribution of the plane dimension coordinates of the point cloud data, the rectangular minimum plane bounding box can be determined by the line segments connecting the coordinate points corresponding to the following four coordinate values ​​in sequence:

[0091] (Xmin, Ymax), (Xmax, Ymax), (Xmax, Ymin), (Xmin, Ymin)

[0092] When the most suitable minimum plane bounding box is determined to be other shapes, other methods may also be used to determine its range coordinates.

[0093] exist Figure 4 In FIG. 5 , it is shown that the minimum plane bounding boxes corresponding to three point cloud clusters (dark purple point cloud cluster O1, blue point cloud cluster O2, and yellow point cloud cluster O3) are respectively rectangular, circular, and rectangular.

[0094] It can be seen that in Figure 4 In the scenario, the three minimum plane bounding boxes determined almost constitute the entire area of ​​the monitoring area. The three minimum plane bounding boxes are closely connected (adjacent), and there is no data blind area or blank area.

[0095] Figure 4 What is shown is a relatively ideal situation, that is, the point cloud data collection of the airborne radar shows that most of the monitoring area is normal, there is no data blind area, or there is no abnormal monitoring area.

[0096] However, in the actual process of forest and grassland resource data collection, the above-mentioned ideal state actually rarely occurs. In most cases, the point cloud data collection of airborne radar shows that there are often data blind spots in the monitoring area, or there are monitoring abnormality areas. In the embodiment of the present application, it is called "hole scanning area", that is, a large number of "blank / hole" areas appear on the output scan image.

[0097] For example Figure 5 In the scenario shown, there is an obvious "hole area" between the point cloud clusters corresponding to O2 and O3.

[0098] The reasons for the occurrence of a hollow scanning area include but are not limited to the following situations:

[0099] (1) The monitoring area includes a large amount of water, photovoltaic panels, glass surfaces and other areas that are prone to specular reflection;

[0100] (2) There is a blind spot during laser scanning;

[0101] (3) There are abnormal phenomena of forest and grass resources in the monitoring area, such as abnormal boundary color mutations caused by fire burn areas and large areas of withered areas;

[0102] (4) Sudden changes in forest and grass vegetation or unreasonable layout in the monitored area may lead to self-occlusion of different objects.

[0103] It can be seen that once a large number or a large proportion of empty scanning areas appear, in addition to foreseeable situations (such as planned forest and grassland photovoltaic complementary projects), it is likely to mean that there are certain abnormalities in the current forest and grassland areas (such as regional collapse to form water areas, scanning blind spots during laser scanning, fire burn areas, large areas of withered areas formed by abnormal boundary color mutation areas, forest and grassland vegetation mutations or unreasonable layout, resulting in self-occlusion of different objects, etc.), and when collecting forest and grassland resource data, it should be paid special attention to in order to adjust the layout of forest and grassland resources later. This is also one of the actual starting points for the inventor to propose the technical solution of this application.

[0104] Therefore, the first problem that needs to be solved in this application is how to identify these empty scanning areas.

[0105] At this time, step S400 can determine whether there is a hollow scanning area in the current forest and grass area based on the minimum bounding box of each point cloud cluster, specifically including:

[0106] If the boundary distances between the adjacent first minimum bounding box and the second minimum bounding box are both greater than the preset threshold, it is determined that there is a hole scanning area between the first minimum bounding box and the second minimum bounding box.

[0107] Since the previous process method has proposed the key technical means to reduce the dimensionality of three-dimensional point cloud data to two-dimensional plane data, the identification of the hole area is also carried out on the plane. Specifically, based on the size of the boundary area of ​​adjacent different minimum bounding boxes, it can be determined whether there is a hole between the two.

[0108] It can be understood that if the first bounding box and the second bounding box have an intersecting or tangent boundary, there is no need to determine whether there is a hole area between the first bounding box and the second bounding box, because at this time the two must not satisfy the condition that "the boundary distance between the first minimum bounding box and the second minimum bounding box is greater than the preset threshold". That is to say, if the first bounding box and the second bounding box have an intersecting or tangent boundary, there is no hole area between the first bounding box and the second bounding box.

[0109] by Figure 4 For example, since the minimum plane bounding box corresponding to O1 and the minimum plane bounding box corresponding to O2 have boundaries that are tangent (or almost adjacent), the minimum plane bounding box corresponding to O2 and the minimum plane bounding box corresponding to O3 have boundaries that are tangent (or almost adjacent), and the minimum plane bounding box corresponding to O3 intersects with the minimum plane bounding box corresponding to O1, there are no hole areas between all the minimum plane bounding boxes.

[0110] If there is no empty scanning area in the current forest and grassland area, the multi-resolution image acquisition device is started to acquire the overall area image of the target forest and grassland area at a lower second resolution.

[0111] If there is no empty scanning area in the current forest and grassland area, the overall image can be obtained in a general way, that is, no special attention is needed, and the acquisition resolution is reduced, thereby reducing the amount of data processing;

[0112] And in Figure 5 In the figure, the minimum plane bounding box corresponding to O3 and the minimum plane bounding box corresponding to O2 neither intersect nor adjoin each other, and the distances between the boundary points on all boundaries of the two bounding boxes are greater than the preset threshold, which means that there is a hole area between the minimum plane bounding box corresponding to O3 and the minimum bounding box corresponding to O2.

[0113] If there is a hole scanning area in the current forest and grassland area, the multi-resolution image acquisition device is started to acquire a local area image corresponding to the hole scanning area at a higher first resolution; the first resolution is greater than the second resolution;

[0114] If there is a hollow scanning area in the current forest and grassland area, it means that the current forest and grassland area is relatively large and there may be certain abnormalities (whether it is data collection abnormalities or actual area abnormalities). At this time, special attention should be paid. Therefore, it is necessary to improve the image acquisition resolution to obtain targeted data analysis results.

[0115] In the actual implementation of this embodiment, drone cruising is used for processing. Therefore, the process of drone cruising and collecting is ongoing. After processing the current area, it is necessary to enter the next area and repeat the above process to complete the processing of all the entire areas, that is, step S500: save the local area image or the overall area image, determine the next target forest and grass area, and return to step S100.

[0116] In the scenario given in the above embodiment, a situation is shown in which there is only one hole scanning area and only two resolution (higher first resolution and lower second resolution) acquisition modes.

[0117] As a further improvement, the multi-resolution image acquisition device includes a plurality of image acquisition modes with different resolutions;

[0118] In step S400, if there are multiple hole scanning areas in the current forest and grassland area, the areas of the multiple hole scanning areas are estimated, and the size of the first resolution used by the multi-resolution image acquisition device to acquire the local area image corresponding to the hole scanning area is determined according to the area of ​​each hole scanning area.

[0119] The determining, according to the area of ​​each hole scanning area, the size of the first resolution used by the multi-resolution image acquisition device when acquiring the local area image corresponding to the hole scanning area specifically includes:

[0120] Determine the number M of void scan areas; M > 1;

[0121] Determine the number K of resolution modes supported by the resolution image acquisition device, K > 1;

[0122] If K > M, determine the size of the first resolution used for each void scan area according to the estimated area size of each void scan area;

[0123] If K ≤ M, divide the M void scan areas into K - 1 groups according to the area size, each group contains at least one void scan area, and the area sizes of the void scan areas included in each group are the same or close, and each grouping corresponds to a first resolution;

[0124] Here, the meaning of "close in area" is that when all void scan areas are arranged in descending or ascending order of area size, two adjacent void scan areas are regarded as close in area.

[0125] Moreover, the first resolution used for the void scan area with a larger area is not greater than the first resolution used for the void scan area with a smaller area.

[0126] As an example, assume that the multi - resolution image acquisition device includes 5 image acquisition modes with different resolutions (i.e., K = 5), and the corresponding resolutions from large to small are

[0127] {Pix1, Pix2, Pix3, Pix4, Pix5};

[0128] In one case (K > M), for example, M = 3, that is, there are three void scan areas {Empt1, Empt2, Empt3}, and the corresponding estimated area sizes are {EmptA1, EmptA2, EmptA3};

[0129] Assume EmptA2 > EmptA1 > EmptA3,

[0130] Then the void scan area Empt3 uses the resolution Pix1, the void scan area Empt2 uses the resolution Pix4, and the void scan area Empt1 uses the resolution Pix2 or Pix3.

[0131] In another case (K < M), for example, M = 6, that is, there are 6 void scan areas {Empt1, Empt2, Empt3, Empt4, Empt5, Empt6}. First, arrange the corresponding estimated area sizes in descending order as EmptA1 ≥ EmptA2 ≥ EmptA3 ≥ EmptA4 ≥ EmptA5 ≥ EmptA6;

[0132] Divide {Empt1, Empt2, Empt3, Empt4, Empt5, Empt6} into 4 groups:

[0133] G1={Empt1,Empt2}

[0134] G2={Empt3,Empt4}

[0135] G3=Empt5

[0136] G4=Empt6

[0137] At this time, the hole scanning area Empt6 of the G4 group adopts the resolution Pix1; the hole scanning area Empt5 of the G3 group adopts the resolution Pix2; the hole scanning areas {Empt3, Empt4} in the G2 group adopt the resolution Pix3; and the hole scanning areas {Empt1, Empt2} in the G1 group adopt the resolution Pix4.

[0138] That is, the optional first resolution value is one of {Pix1, Pix2, Pix3, Pix4}, and the smallest Pix5 is used as the second resolution to ensure "higher first resolution and lower second resolution".

[0139] Moreover, when selecting the first resolution, the larger the area of ​​the hole scanning area is, the smaller the selectable first resolution is; and the smaller the area of ​​the hole scanning area is, the larger the first resolution is used. This also meets the actual data acquisition requirements and takes into account the requirements of accuracy and data processing complexity.

[0140] In the actual forest and grass resource collection process, it is also necessary to deal with some extreme situations, that is, there are multiple hollow areas in the target area. Although the area of ​​each hollow area itself is not large, the number of hollow areas is so large that the area of ​​all hollow areas occupies most of the entire current target scanning range. At this time, it means that the current target scanning range is abnormal to a large extent. For this situation, the further improved embodiment of the present invention is as follows:

[0141] In step S400, if there are M empty scanning areas in the current forest and grassland area, the area and and the total area of ​​the target forest and grassland area A, i = 1, 2, ..., M; M ≥ 2, O i is the area value of the i-th hole scanning area;

[0142] like Numc is the number of the minimum bounding boxes; NumC>1;

[0143] The high-resolution image acquisition device then acquires the overall area image of the target forest and grassland area at the highest resolution.

[0144] In this case, excluding rationality (for example, the scanning range is small each time and there are a large number of known photovoltaic panels or lakeside waters in the current scanning area), it is necessary to use the highest priority scanning method, that is, to collect the overall area image of the target forest and grass area with the highest resolution, so as to quickly take relevant measures.

[0145] Figure 6 This is the situation shown, that is, there are multiple hole areas in the target area. Although the area of ​​each hole area itself is not large, the number of holes is so large that the area of ​​all hole areas occupies most of the entire current target scanning range.

[0146] exist Figure 1-Figure 6 Based on the detailed description of the method embodiment, see Figure 7 , briefly introduces the functional module composition diagram of a forest and grassland resource data collection system according to an embodiment of the present invention.

[0147] exist Figure 7 In the figure, a forest and grassland resource data acquisition system is shown, which includes a control end and an unmanned aerial vehicle device that wirelessly communicates with the control end, and the unmanned aerial vehicle device is equipped with a laser radar scanning device and a multi-resolution image acquisition device.

[0148] The drone device is also equipped with an edge image processing unit;

[0149] The control end controls the UAV to reach the target forest and grass area;

[0150] The UAV equipment performs laser scanning on the target forest and grass area through the laser radar scanning device to obtain multiple point cloud data sets;

[0151] The UAV device clusters the plurality of point cloud data using the edge image processing unit to obtain NumC point cloud clusters, and determines a minimum bounding box of each point cloud cluster; NumC>1;

[0152] The edge image processing unit determines whether there is a hollow scanning area in the target forest and grass area based on the minimum bounding box of each point cloud cluster;

[0153] If there is a hole scanning area in the current forest and grass area, the edge image processing unit starts the multi-resolution image acquisition device to acquire a local area image corresponding to the hole scanning area at a first resolution;

[0154] If there is no empty scanning area in the current forest and grassland area, the multi-resolution image acquisition device is started to acquire the overall area image of the target forest and grassland area at the second resolution;

[0155] Wherein, the first resolution is greater than the second resolution;

[0156] The UAV device saves the local area image or the whole area image and sends it to the control end, and the control end determines the next target forest and grass area.

[0157] If the edge image processing unit determines that there is more than one empty scanning area in the target forest and grassland area, the area and and the total area of ​​the target forest and grassland area A, i = 1, 2, ..., M; M ≥ 2, O i is the area value of the i-th hole scanning area; M is the number of hole scanning areas;

[0158] like

[0159] The edge image processing unit then starts the multi-resolution image acquisition device to acquire the overall area image of the target forest and grassland area at the highest resolution.

[0160] The technical solution of the present invention is to control the UAV to reach the target forest and grassland area, obtain multiple point cloud data sets, determine the minimum bounding box of each point cloud cluster, judge whether there is a hole scanning area in the target forest and grassland area, if there is a hole scanning area in the current forest and grassland area, collect the local area image corresponding to the hole scanning area with a first resolution, otherwise collect the overall area image of the target forest and grassland area with a second resolution, save the local area image or the overall area image and then determine the next target forest and grassland area.

[0161] In the above processing process, key technical means are included, including reducing the dimensionality of three-dimensional point cloud data to two-dimensional plane data, determining the two-dimensional plane minimum bounding box of the point cloud cluster, identifying the hole scanning area based on the two-dimensional plane minimum bounding box, and adopting different image acquisition modes based on factors such as the number / area of ​​different hole scanning areas. As a result, the technical solution of the present application can focus on data abnormality hole areas to achieve efficient forest and grass resource data collection and management, thereby achieving collection flexibility, and adopting image adaptive processing strategies for different types of target areas, which can highlight monitoring priorities, especially accurately identify data collection blind spots, making the forest and grass resource data collection method more targeted.

[0162] For other technologies, principles, algorithms or models not elaborated in detail in this application, please refer to the prior art.

[0163] In the above-mentioned embodiment section, the present invention provides multiple embodiments, each of which can constitute an independent technical solution and may contribute to the prior art and solve corresponding technical problems. However, it should be pointed out that different embodiments can be combined with each other without violating logic; at the same time, each embodiment can solve at least one technical problem, but it is not required that each individual embodiment solves multiple or all technical problems.

[0164] At the same time, in the specific implementation of this application, if user-related data is involved, when the embodiment of this application is applied to a specific product or technology, the user's permission or consent must be obtained, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0165] The above descriptions of various implementations of the present disclosure are exemplary, non-exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The selection of terms used herein is intended to best explain the principles of the implementations, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the various implementations disclosed herein.

Claims

1. A method for collecting forest and grass resource data, the method is implemented based on an unmanned aerial vehicle device, the unmanned aerial vehicle device is equipped with a laser radar scanning device and a multi-resolution image acquisition device, It is characterized in that The method comprises the following steps: S100: Control the UAV to reach the target forest and grassland area; S200: performing laser scanning on the target forest and grassland area by using the laser radar scanning device to obtain multiple point cloud data sets; S300: clustering the plurality of point cloud data based on a clustering algorithm to obtain a plurality of point cloud clusters, and determining a minimum bounding box of each point cloud cluster; S400: Based on the minimum bounding box of each point cloud cluster, determine whether there is a hollow scanning area in the target forest and grass area; If there is a hole scanning area in the current forest and grassland area, the multi-resolution image acquisition device is started to acquire a local area image corresponding to the hole scanning area at a first resolution; If there is no empty scanning area in the current forest and grassland area, the multi-resolution image acquisition device is started to acquire the overall area image of the target forest and grassland area at the second resolution; Wherein, the first resolution is greater than the second resolution; S500: Save the local area image or the whole area image, determine the next target forest and grassland area, and return to step S100.

2. A method for collecting forest and grass resource data as claimed in claim 1, characterized in that: Step S300 clusters the plurality of point cloud data based on a clustering algorithm to obtain a plurality of point cloud clusters, specifically including: S310: determining a plurality of cluster centers based on a scanning range of the target forest and grassland area laser scanned by the laser radar scanning device; S311: Determine the point cloud cluster to which each point cloud data belongs based on the distance between each point cloud data and the cluster center, wherein each point cloud cluster belongs to only one point cloud cluster, and each point cloud cluster includes at least one point cloud data.

3. A method for collecting forest and grass resource data as claimed in claim 1, characterized in that: Step S300 determines the minimum bounding box of each point cloud cluster, specifically including: S320: Obtaining the plane dimension coordinates of the point cloud data included in each point cloud cluster; S321: Determine the shape of the minimum bounding box based on the distribution of the plane dimension coordinates of the point cloud data, where the shape of the minimum bounding box includes a circle, an ellipse, and an N-gon, where 6≥N≥3; S322: Generate a minimum bounding box corresponding to each point cloud cluster.

4. A method for collecting forest and grass resource data as claimed in claim 1, characterized in that: Step S400 determines whether there is a hollow scanning area in the current forest and grass area based on the minimum bounding box of each point cloud cluster, specifically including: If the boundary distances between the adjacent first minimum bounding box and the second minimum bounding box are both greater than the preset threshold, it is determined that there is a hole scanning area between the first minimum bounding box and the second minimum bounding box.

5. The method for collecting forest and grass resource data according to claim 1, characterized in that: The multi-resolution image acquisition device includes a plurality of image acquisition modes with different resolutions; In step S400, if there are multiple hole scanning areas in the current forest and grassland area, the areas of the multiple hole scanning areas are estimated, and the size of the first resolution used by the multi-resolution image acquisition device to acquire the local area image corresponding to the hole scanning area is determined according to the area of ​​each hole scanning area.

6. A method for collecting forest and grass resource data as claimed in claim 5, characterized in that: The determining, according to the area of ​​each hole scanning area, the size of the first resolution used by the multi-resolution image acquisition device when acquiring the local area image corresponding to the hole scanning area specifically includes: Determine the number of hole scanning areas M; M>1; Determine the number K of resolution modes supported by the resolution image acquisition device, K>1; If K>M, the size of the first resolution used in each hole scanning area is determined according to the estimated area size of each hole scanning area; If K≤M, the M hole scanning areas are divided into K-1 groups according to their area sizes, each group contains at least one hole scanning area, the area sizes of the hole scanning areas contained in each group are the same or close, and each group corresponds to a first resolution; Furthermore, the first resolution used by the hole scanning area with a larger area is not greater than the first resolution used by the hole scanning area with a smaller area.

7. A method for collecting forest and grass resource data as claimed in claim 1, characterized in that: The multi-resolution image acquisition device includes a plurality of image acquisition modes with different resolutions; In step S400, if there are M empty scanning areas in the current forest and grassland area, the area and And the total area of ​​the target forest and grassland area is A,i=1,2,…,M; M≥2.

8. A forest and grass resource data collection system, the system comprising a control terminal and an unmanned aerial vehicle device that wirelessly communicates with the control terminal, the unmanned aerial vehicle device being equipped with a laser radar scanning device and a multi-resolution image collection device, It is characterized in that The drone device is also equipped with an edge image processing unit; The control end controls the UAV to reach the target forest and grass area; The UAV equipment performs laser scanning on the target forest and grass area through the laser radar scanning device to obtain multiple point cloud data sets; The UAV device clusters the plurality of point cloud data using the edge image processing unit to obtain NumC point cloud clusters, and determines a minimum bounding box of each point cloud cluster; NumC>1; The edge image processing unit determines whether there is a hollow scanning area in the target forest and grass area based on the minimum bounding box of each point cloud cluster; If there is a hole scanning area in the current forest and grass area, the edge image processing unit starts the multi-resolution image acquisition device to acquire a local area image corresponding to the hole scanning area at a first resolution; If there is no empty scanning area in the current forest and grassland area, the multi-resolution image acquisition device is started to acquire the overall area image of the target forest and grassland area at the second resolution; Wherein, the first resolution is greater than the second resolution; The UAV device saves the local area image or the whole area image and sends it to the control end, and the control end determines the next target forest and grass area.

9. A forest and grass resource data collection system as claimed in claim 8, characterized in that: If the edge image processing unit determines that there is more than one empty scanning area in the target forest and grassland area, the area and And the total area of ​​the target forest and grassland area is A,i=1,2,…,M; M≥2.

10. A computer program product, comprising a computer program or computer executable instructions, which, when executed by a processor, implements a forest and grassland resource data collection method as described in any one of claims 1 to 7.

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