Grassland spatial data acquisition method based on unmanned aerial vehicle surveying and mapping

Abstract

The invention provides a grassland spatial data acquisition method based on unmanned aerial vehicle surveying and mapping, relates to the technical field of surveying and mapping, and aims to solve the problems that when a grassland is surveyed and mapped, acquired grassland surveying and mapping data are not accurate enough due to an external environment which changes quickly, for example, the unmanned aerial vehicle deviates from a route due to strong wind; in the invention, the unmanned aerial vehicle executes the flight surveying and mapping task corresponding to each surveying and mapping sub-path in sequence, and the surveying and mapping path is planned again according to the data volume of the initial actual surveying and mapping data and the data volume of the predicted surveying and mapping data corresponding to the surveying and mapping sub-path corresponding to the flight surveying and mapping task. Whether the flight surveying and mapping task needs to be executed again is determined to ensure the accuracy of the data, even if the external environment of the surveying and mapping sub-geographic area changes in the process that the unmanned aerial vehicle executes the flight surveying and mapping task, only the surveying and mapping sub-path corresponding to the flight surveying and mapping task needs to be re-flied, unnecessary repeated work is reduced, and the working efficiency is improved. And the overall surveying and mapping efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of surveying and mapping technology, and in particular to a grassland spatial data collection method based on unmanned aerial vehicle surveying and mapping. Background Art

[0002] Through surveying and mapping, maps, terrain models and geographic information system data can be generated, which is crucial for many fields such as urban planning, land management, environmental monitoring, disaster prevention, agricultural development, etc. With the development of drone technology, drone-based surveying and mapping has gradually become an emerging method. Drones can carry a variety of sensors, such as: lidar, camera acquisition devices, which are used to collect high-resolution spatial data during flight. Drone-based surveying and mapping requires planning a surveying and mapping path that can cover the entire surveying and mapping area according to the surveying and mapping area, and letting the drone perform surveying and mapping tasks so that the drone can follow the surveying and mapping path from the starting point of the surveying and mapping path to the end point of the surveying and mapping path. After the drone reaches the end point of the surveying and mapping path, the surveying and mapping is completed according to the data collected by the sensors in the drone.

[0003] However, the above method also has the following technical problems: When mapping areas with fast-changing external environments such as grasslands, environmental factors such as wind speed, light and temperature will have a significant impact on the flight stability of drones and the working effect of sensors. Since grasslands are flat and open, the wind speed is usually high, which is prone to strong winds, which may cause the actual flight path of drones to deviate from the mapping path. In addition, the temperature difference between day and night in grassland areas is large, accompanied by a sharp change in lighting conditions. The sudden increase or decrease in lighting will affect the imaging quality. At the same time, the sensors on drones are more sensitive to temperature, and temperature changes may cause the sensor measurement errors to increase. Therefore, when mapping grassland mapping areas based on drones, the fast-changing external environment may cause the grassland mapping data obtained to be inaccurate. For example, strong winds may cause drones to deviate from their routes, extreme lighting conditions will reduce image quality, and temperature fluctuations will affect the accuracy of sensors. When it is found that the data collected by the sensors in drones is inaccurate, in order to ensure data quality and mapping accuracy, it is often necessary to re-plan the mapping path and let the drone perform the mapping task again, which increases the extra workload and reduces the overall efficiency of mapping. Summary of the invention

[0004] In view of the above technical problems, the technical solution adopted by the present invention is: A grassland spatial data collection method based on UAV mapping includes the following steps: S1. When the drone starts executing A for the first time j The corresponding flight mapping task is performed at the same time as steps S2-S4, A jis the jth mapping sub-path, the value of j ranges from 2 to m, and m is the number of mapping sub-paths.

[0005] S2. If the drone has only completed A once j-1 Corresponding flight mapping mission, then obtain A j-1 The corresponding initial actual surveying and mapping data volume E 1 j-1 , A j-1 is the j-1th mapping subpath, A j-1 The corresponding initial actual surveying data is the laser radar device in the UAV when the UAV performs A for the first time. j-1 The three-dimensional point cloud data collected during the corresponding flight mapping mission.

[0006] S3, A j-1 The corresponding initial mapping images are input into the target CNN model to obtain the output of the target CNN model A j-1 The corresponding amount of predicted surveying and mapping data E 2 j-1 , A j-1 The corresponding initial mapping image is the image acquisition device in the UAV when the UAV performs A for the first time. j-1 Visible light images collected during the corresponding flight mapping mission.

[0007] S4. If |E 2 j-1 -E 1 j-1 | / E 2 j-1 ≥C 0 , then control the drone to complete A j After the corresponding flight mapping mission, re-execute A j-1 The corresponding flight mapping mission is to make A j-1 The corresponding sub-geographical area is re-surveyed, C 0 It is the preset data volume deviation value.

[0008] The present invention has at least the following beneficial effects: The present invention provides a grassland spatial data collection method based on unmanned aerial vehicle mapping. In the method, a plurality of mapping sub-paths are determined, and the unmanned aerial vehicle is ordered to sequentially execute the flight mapping tasks corresponding to each mapping sub-path. When the unmanned aerial vehicle starts to execute the current flight mapping task, the number of times the previous flight mapping task of the current flight mapping task has been completed is determined. If the task has only been completed once, the data volume of the initial actual mapping data and the data volume of the predicted mapping data corresponding to the mapping sub-path corresponding to the previous flight mapping task are obtained. According to the data volume of the initial actual mapping data, the data volume of the predicted mapping data and the preset data volume deviation degree value, the sensor in the unmanned aerial vehicle is dynamically evaluated before the unmanned aerial vehicle executes the task. Whether the data collected during a flight mapping mission is accurate, and based on the evaluation results, whether it is necessary to re-execute the previous flight mapping mission to ensure the accuracy of the data when the current flight mapping mission is completed. Even if the external environment of the surveying sub-geographic area changes during the drone's execution of the flight mapping mission, resulting in inaccurate data collected by the sensors in the drone during the drone's execution of the flight mapping mission, it is only necessary to re-fly the mapping sub-path corresponding to the flight mapping mission, without re-planning the entire mapping path or repeating all flight mapping missions, thereby reducing unnecessary duplication of work and helping to improve the overall efficiency of surveying. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0010] Figure 1 A flowchart of a grassland spatial data collection method based on drone mapping provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0012] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar tasks, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0013] The embodiment of the present invention provides a grassland spatial data collection method based on drone mapping, the method comprising the following steps: Figure 1 As shown: S1. When the drone starts executing A for the first time j The corresponding flight mapping task is performed at the same time as steps S2-S4, A j is the jth mapping sub-path, the value of j ranges from 2 to m, and m is the number of mapping sub-paths.

[0014] Specifically, the mapping sub-path is the path obtained by dividing the target mapping path corresponding to the UAV according to the preset path distance, wherein the target mapping path corresponding to the UAV can cover the entire grassland mapping area, wherein the preset path distance is the path distance pre-set by technical personnel in this field according to actual needs, and will not be repeated here.

[0015] Specifically, the mapping sub-paths correspond to the mapping sub-geographical areas one by one, and the mapping geographic sub-areas are sub-areas in the grassland mapping area. The mapping sub-geographical areas corresponding to all the mapping sub-paths can cover the entire grassland mapping area.

[0016] Specifically, the starting point of the first surveying sub-path is the starting point of the target surveying sub-path, the end point of the mth surveying sub-path is the end point of the target surveying path, and the end point of the j-1th surveying sub-path is the starting point of the jth surveying sub-path.

[0017] Specifically, the flight surveying and mapping task corresponding to the surveying and mapping sub-path is a task of causing the UAV to follow the surveying and mapping sub-path from the starting point of the surveying and mapping sub-path to the end point of the surveying and mapping sub-path.

[0018] S2. If the drone has only completed A once j-1 Corresponding flight mapping mission, then obtain A j-1 The corresponding initial actual surveying and mapping data volume E 1 j-1 , A j-1is the j-1th mapping subpath, A j-1 The corresponding initial actual surveying data is the laser radar device in the UAV when the UAV performs A for the first time. j-1 The three-dimensional point cloud data collected during the corresponding flight mapping mission.

[0019] Specifically, in step S2, if the drone has completed A twice j-1 If j≠m, the UAV is controlled to complete A j After the corresponding flight mapping mission, execute A j+1 The corresponding flight mapping mission is to make A j+1 The corresponding sub-geographical area is surveyed and mapped. j+1 is the j+1th mapping subpath; if the drone has completed A twice j-1 The corresponding flight mapping task and j = m, then when the UAV completes A j After completing the corresponding flight mapping mission, the mapping is ended.

[0020] In the above steps, for each flight mapping task, if the drone has only completed one flight mapping task, it means that the drone has not re-flown the mapping sub-path corresponding to the flight mapping task. It is necessary to determine whether it is necessary to re-fly the mapping sub-path based on the data collected by the sensors (camera acquisition device and lidar device) in the drone during the execution of the flight mapping task to ensure the accuracy of the data collected by the sensors in the drone. If the drone has completed two flight mapping tasks, it means that the drone has re-flown the mapping sub-path corresponding to the flight mapping task, and the accuracy of the data collected by the sensors in the drone can be guaranteed. There is no need to re-fly the mapping sub-path again. Even if the external environment of the mapping sub-geographical area changes during the execution of the flight mapping task by the drone, resulting in inaccurate data collected by the sensors in the drone during the execution of the flight mapping task, it is only necessary to re-fly the mapping sub-path corresponding to the flight mapping task, without re-planning the entire mapping path or repeating all flight mapping tasks, thereby reducing unnecessary duplication of work and improving the overall efficiency of mapping.

[0021] S3, A j-1 The corresponding initial mapping images are input into the target CNN model to obtain the output of the target CNN model A j-1 The corresponding amount of predicted surveying and mapping data E 2 j-1 , A j-1 The corresponding initial mapping image is the image acquisition device in the UAV when the UAV performs A for the first time. j-1 Visible light images collected during the corresponding flight mapping mission.

[0022] Specifically, the visible light image acquisition frequency of the camera acquisition device in the drone is set by those skilled in the art according to actual needs, for example: collecting a visible light image once per second, collecting a visible light image once per 0.5 seconds, which will not be repeated here.

[0023] Specifically, the target CNN model is a trained CNN model used to extract image features, and the amount of predicted mapping data can be understood as the amount of mapping data collected by the predicted UAV during the performance of the flight mapping mission.

[0024] S4. If |E 2 j-1 -E 1 j-1 | / E 2 j-1 ≥C 0 , then control the drone to complete A j After the corresponding flight mapping mission, re-execute A j-1 The corresponding flight mapping mission is to make A j-1 The corresponding sub-geographical area is re-surveyed, C 0 It is a preset data volume deviation degree value. Those skilled in the art know that the preset data volume deviation degree value is a value pre-set by those skilled in the art according to actual needs, for example: 0.1, 0.15, 0.2, and will not be repeated here.

[0025] Specifically, step S4 also includes: if |E 2 j-1 -E 1 j-1 | / E 2 j-1 <C 0 , then control the drone to complete A j After the corresponding flight mapping mission, execute A j+1 The corresponding flight mapping mission is to make A j+1 The corresponding sub-geographical area is surveyed.

[0026] Specifically, step S4 also includes: j-1 The corresponding C j-1 Stored in the first surveying and mapping database, C j-1 A j-1 The corresponding data volume difference ratio, C j-1 =|E 2 j-1 -E 1 j-1 | / E 2 j-1 .

[0027] Specifically, when C j-1 ≥C 0 When A is generated j-1 The corresponding abnormal label is stored in the first surveying and mapping database.

[0028] Specifically, the abnormal label indicates that the initial actual surveying and mapping data and the initial surveying and mapping image corresponding to the surveying and mapping subpath are abnormal and may not be accurate enough.

[0029] In the above steps, based on the data volume of the initial actual surveying and mapping data, the data volume of the predicted surveying and mapping data and the preset data volume deviation degree value, it is evaluated whether the data collected by the sensors in the drone during the drone's execution of the flight surveying and mapping task is accurate. If the data volume difference ratio based on the data volume of the initial actual surveying and mapping data and the data volume of the predicted surveying and mapping data is not less than the preset data volume deviation degree value, it means that the data volume of the initial actual surveying and mapping data and the data volume of the predicted surveying and mapping data differ greatly, and the data collected by the sensors in the drone during the drone's execution of the flight surveying and mapping task corresponding to the initial actual surveying and mapping data are not accurate enough. During the drone's execution of the flight surveying and mapping task, the external environment of the surveying and mapping sub-geographical area may have changed. Therefore, it is necessary to re-execute the flight surveying and mapping task to ensure the accuracy of the data collected by the sensors in the drone. If based on The data volume difference ratio between the data volume of the initial actual surveying and mapping data and the data volume of the predicted surveying and mapping data is smaller than the preset data volume deviation degree value, indicating that the data volume difference between the initial actual surveying and mapping data and the data volume difference between the predicted surveying and mapping data is small, and the data collected by the sensors in the UAV during the process of the UAV performing the flight surveying and mapping task corresponding to the initial actual surveying and mapping data is relatively accurate, and there is no need to re-execute the flight surveying and mapping task. Even if the external environment of the surveying and mapping sub-geographical area changes during the process of the UAV performing the flight surveying and mapping task, resulting in inaccurate data collected by the sensors in the UAV during the process of the UAV performing the flight surveying and mapping task, it is only necessary to re-fly the surveying and mapping sub-path corresponding to the flight surveying and mapping task, without re-planning the entire surveying and mapping path or repeating all flight surveying and mapping tasks, thereby reducing unnecessary duplication of work and improving the overall efficiency of surveying and mapping.

[0030] Specifically, before step S1, the UAV is controlled to start executing the flight surveying and mapping task corresponding to the first surveying and mapping sub-path so as to survey the surveying and mapping sub-geographical area corresponding to the first surveying and mapping sub-path, and after the UAV is controlled to complete the flight surveying and mapping task corresponding to the first surveying and mapping sub-path, it continues to execute the flight surveying and mapping task corresponding to the second surveying and mapping sub-path so as to survey the surveying and mapping sub-geographical area corresponding to the second surveying and mapping sub-path.

[0031] Specifically, when the drone completes A for the first time iWhen the corresponding flight surveying mission is in progress, A i The corresponding initial actual surveying data and A i The corresponding initial mapping image is stored in the first mapping database. i is the i-th mapping subpath, i ranges from 1 to m, A i The corresponding initial actual surveying data is the laser radar device in the UAV when the UAV performs A for the first time. i The 3D point cloud data collected during the corresponding flight mapping mission, A i The corresponding initial mapping image is the image acquisition device in the UAV when the UAV performs A for the first time. i Visible light images collected during the corresponding flight mapping mission.

[0032] Specifically, when the drone completes A for the second time i When the corresponding flight surveying mission is in progress, A i The corresponding key actual surveying and mapping data and A i The corresponding key surveying and mapping images are stored in the second surveying and mapping database. i The corresponding key actual surveying data is the laser radar device in the UAV when the UAV performs A for the second time. i The 3D point cloud data collected during the corresponding flight mapping mission, A i The corresponding key mapping image is the image acquisition device in the UAV when the UAV performs A for the second time. i Visible light images collected during the corresponding flight mapping mission.

[0033] In the above steps, when the UAV completes the flight mapping task for the first time, the initial actual mapping data and the initial mapping images corresponding to the mapping sub-path corresponding to the flight mapping task are stored in the first mapping database. When the UAV completes the flight mapping task for the second time, the key actual mapping data and the key mapping images corresponding to the mapping sub-path corresponding to the flight mapping task are stored in the second mapping database. Separate storage can ensure that the data will not be confused, which is convenient for subsequent data analysis and processing.

[0034] In a specific embodiment, in step S2, if the drone has completed two A j-1 The corresponding flight mapping mission and j=m, then get C j , C j A j The corresponding data volume difference ratio, if C j ≥C 0 , then control the drone to complete A j After the corresponding flight mapping mission, re-execute A j The corresponding flight mapping mission is to make A jThe corresponding sub-geographical area is re-surveyed. If C j <C 0 , then when the drone completes A j After the corresponding flight mapping mission, the mapping is completed, C j Meet the following conditions: C j =|E 2 j -E 1 j | / E 2 j , E 2 j A j The corresponding initial actual surveying and mapping data volume, E 1 j To A j The corresponding initial mapping images are input into the target CNN model to obtain the output of the target CNN model A j The corresponding amount of predicted surveying and mapping data, A j The corresponding initial actual surveying data is the laser radar device in the UAV when the UAV performs A for the first time. j The 3D point cloud data collected during the corresponding flight mapping mission, A j The corresponding initial mapping image is the image acquisition device in the UAV when the UAV performs A for the first time. j Visible light images collected during the corresponding flight mapping mission.

[0035] In the above steps, if the current flight mapping task is the last flight mapping task, and the previous flight mapping task of the current flight mapping task has been completed twice, it means that after completing the current flight mapping task, there is no need to re-execute the previous flight mapping task, and there is no next flight mapping task to execute. At this time, the data volume difference ratio corresponding to the current flight mapping task can be obtained. If the data volume difference ratio is not less than the preset data volume deviation degree value, it means that in the process of executing the current flight mapping task, the data volume of the initial actual mapping data collected by the sensor in the drone and the data volume of the predicted mapping data are quite different, and the data collected by the sensor in the drone in the process of the drone executing the current flight mapping task It is not accurate enough. During the process of the UAV executing the current flight mapping task, the external environment of the mapping sub-geographic area may have changed. Therefore, it is necessary to re-execute the current flight mapping task to ensure the accuracy of the data collected by the sensors in the UAV. If the data volume difference ratio is less than the preset data volume deviation degree value, it means that the difference between the data volume of the initial actual mapping data collected by the sensors in the UAV during the process of the UAV executing the current flight mapping task and the data volume of the predicted mapping data is small. The data collected by the sensors in the UAV during the process of the UAV executing the current flight mapping task is relatively accurate. There is no need to re-execute the current flight mapping task. The mapping can be ended after the UAV completes the current flight mapping task.

[0036] Specifically, after step S4, the following steps S5-S9 are also included: S5. When the drone starts executing A for the second time e The corresponding flight mapping task is performed at the same time as steps S6-S9, A e is the e-th mapping subpath, and the value of e ranges from 1 to m-1.

[0037] S6. If the current drone has completed A e+1 The corresponding flight mapping task then goes to step S7, A e+1 is the e+1th mapping subpath; if the current drone has never executed A e+1 For the corresponding flight surveying task, the drone is controlled to complete A for the second time. e After the corresponding flight mapping mission, execute A e+1 The corresponding flight prediction task is to make A e+1 The corresponding sub-geographical area is surveyed.

[0038] S7. Get A e+1 The corresponding initial actual surveying and mapping data volume E 1 e+1 , A e+1The corresponding initial actual surveying data is the laser radar device in the UAV when the UAV performs A for the first time. e+1 The three-dimensional point cloud data collected during the corresponding flight mapping mission.

[0039] S8, A e+1 The corresponding initial mapping images are input into the target CNN model to obtain the output of the target CNN model A e+1 The corresponding amount of predicted surveying and mapping data E 2 e+1 , A e+1 The corresponding initial mapping image is the image acquisition device in the UAV when the UAV performs A for the first time. e+1 Visible light images collected during the corresponding flight mapping mission.

[0040] S9. Get C e+1 And control the drone to complete A for the second time e After the corresponding flight mapping mission, re-execute A e+1 The corresponding flight prediction task is to make A e+1 The corresponding sub-geographical area is re-surveyed, C e+1 A e+1 The corresponding data volume difference ratio, C e+1 Meet the following conditions: C e+1 =|E 2 e+1 -E 1 e+1 | / E 2 e+1 .

[0041] Specifically, step S9 also includes: e+1 Stored in the first surveying and mapping database.

[0042] Specifically, when C e+1 ≥C 0 When C e+1 The corresponding abnormal label is stored in the first surveying and mapping database.

[0043] Through the above steps, when the drone starts to execute the current flight mapping task for the second time, it is determined whether the drone has completed the next flight mapping task of the current flight mapping task. If the drone has never executed the next flight mapping task, it means that after the drone executed the current flight mapping task for the first time, it re-executed the previous flight mapping task of the current flight mapping task, and after re-completing the previous flight mapping task, it re-executed the current flight mapping task. At this time, it is only necessary to control the drone to continue to execute the next flight mapping task of the current flight mapping task after completing the current flight mapping task; if the drone has completed the next flight mapping task once, it means that after the drone executed the next flight mapping task for the first time, it re-executed the current flight mapping task. At this time, the data volume difference ratio corresponding to the next flight mapping task can be obtained, and the data volume difference ratio can be stored in the first mapping database to improve data processing efficiency.

[0044] In a specific embodiment, after step S4, steps S5-S10 are also included, wherein step S9 includes: When C e+1 ≥C 0 When the drone is controlled to complete A for the second time e After the corresponding flight mapping mission, re-execute A e+1 The corresponding flight prediction task is to make A e+1 The corresponding sub-geographical area is re-surveyed; when C e+1 <C 0 When setting A e+1 The corresponding preset task execution time goes to step S10.

[0045] S10, control the drone to complete A for the second time e After the corresponding flight mapping mission, re-execute A e+1 The corresponding flight prediction task is in A e+1 Complete A within the corresponding preset task execution time e+1 The corresponding flight mapping mission can be understood as speeding up the UAV to perform A e+1 The corresponding flight speed during the flight mapping mission.

[0046] In the above steps, if the data volume difference ratio corresponding to the next flight mapping task of the current flight mapping task is not less than the preset data volume deviation degree value, it means that in the process of executing the next flight mapping task of the current flight mapping task, the data volume of the initial actual mapping data collected by the sensor in the drone is much different from the data volume of the predicted mapping data, and the data collected by the sensor in the drone in the process of the drone executing the next flight mapping task of the current flight mapping task is not accurate enough. In the process of the drone executing the next flight mapping task of the current flight mapping task, the external environment of the mapping sub-geographical area may have changed. Therefore, it is necessary to re-execute the next flight mapping task of the current flight mapping task. Flight mapping tasks are performed to ensure the accuracy of the data collected by the sensors in the drone. Otherwise, it means that the difference between the amount of initial actual mapping data collected by the sensors in the drone during the next flight mapping task after the current flight mapping task and the amount of predicted mapping data is small. The data collected by the sensors in the drone during the next flight mapping task after the current flight mapping task is relatively accurate and there is no need to collect data again. At this time, setting the preset task execution time allows the drone to execute the next flight mapping task after the current flight mapping task and complete the flight mapping task within the preset task execution time, which helps to reduce the overall mapping time and improve the overall mapping efficiency.

[0047] Specifically, when the UAV needs to complete a flight mapping mission within a preset mission execution time, it is not necessary to turn on the data collection functions of the camera acquisition device and the lidar device when performing the flight mapping mission, which helps to reduce resource consumption.

[0048] In a specific embodiment, when the UAV needs to complete a flight mapping mission within a preset mission execution time, if the camera acquisition device and the lidar device in the UAV collect data during the UAV's execution of the flight mapping mission, there is no need to store the collected data in the second mapping database, which helps save storage space.

[0049] In a specific embodiment, when there are abnormal labels corresponding to surveying and mapping sub-paths in the first surveying and mapping database, technicians in this field can process the initial actual surveying and mapping data and initial surveying and mapping images corresponding to the surveying and mapping sub-paths in the first surveying and mapping database and the key actual surveying and mapping data and key surveying and mapping images corresponding to the surveying and mapping sub-paths in the second surveying and mapping database to obtain the target surveying and mapping data corresponding to the surveying and mapping sub-path, so as to complete the surveying and mapping based on the target surveying and mapping data, which helps to improve the accuracy of the surveying and mapping. The processing method in the steps is determined by technicians in this field according to actual needs, such as fusion processing, which will not be repeated here.

[0050] The present invention provides a grassland spatial data collection method based on unmanned aerial vehicle mapping. In the method, a plurality of mapping sub-paths are determined, and the unmanned aerial vehicle is ordered to sequentially execute the flight mapping tasks corresponding to each mapping sub-path. When the unmanned aerial vehicle starts to execute the current flight mapping task, the number of times the previous flight mapping task of the current flight mapping task has been completed is determined. If the task has only been completed once, the data volume of the initial actual mapping data and the data volume of the predicted mapping data corresponding to the mapping sub-path corresponding to the previous flight mapping task are obtained. According to the data volume of the initial actual mapping data, the data volume of the predicted mapping data and the preset data volume deviation degree value, the sensor in the unmanned aerial vehicle is dynamically evaluated before the unmanned aerial vehicle executes the task. Whether the data collected during a flight mapping mission is accurate, and based on the evaluation results, whether it is necessary to re-execute the previous flight mapping mission to ensure the accuracy of the data when the current flight mapping mission is completed. Even if the external environment of the surveying sub-geographic area changes during the drone's execution of the flight mapping mission, resulting in inaccurate data collected by the sensors in the drone during the drone's execution of the flight mapping mission, it is only necessary to re-fly the mapping sub-path corresponding to the flight mapping mission, without re-planning the entire mapping path or repeating all flight mapping missions, thereby reducing unnecessary duplication of work and helping to improve the overall efficiency of surveying.

[0051] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store a computer program related to a method in a method embodiment, and the computer program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0052] An embodiment of the present invention further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided in the above embodiment when executing the computer program.

[0053] An embodiment of the present invention further provides a computer program product, which includes program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0054] Although some specific embodiments of the present invention have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are only for illustration, not for limiting the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A grassland spatial data collection method based on drone mapping, characterized in that: The method comprises the following steps: S1. When the drone starts executing A for the first time j The corresponding flight mapping task is performed at the same time as steps S2-S4, A j is the jth mapping subpath, the value of j ranges from 2 to m, and m is the number of mapping subpaths; S2. If the drone has only completed A once j-1 Corresponding flight mapping mission, then obtain A j-1 The corresponding initial actual surveying and mapping data volume E 1 j-1 , A j-1 is the j-1th mapping subpath, A j-1 The corresponding initial actual surveying data is the laser radar device in the UAV when the UAV performs A for the first time. j-1 The three-dimensional point cloud data collected during the corresponding flight mapping mission; S3, A j-1 The corresponding initial mapping images are input into the target CNN model to obtain the output of the target CNN model A j-1 The corresponding amount of predicted surveying and mapping data E 2 j-1 , A j-1 The corresponding initial mapping image is the image acquisition device in the UAV when the UAV performs A for the first time. j-1 The visible light images collected during the corresponding flight mapping mission; S4. If |E 2 j-1 -E 1 j-1 | / E 2 j-1 ≥C 0 , then control the drone to complete A j After the corresponding flight mapping mission, re-execute A j-1 The corresponding flight mapping mission is to make A j-1 The corresponding sub-geographical area is re-surveyed, C 0 It is the preset data volume deviation value.

2. The grassland spatial data collection method based on drone mapping according to claim 1 is characterized in that: The mapping sub-path is a path obtained by dividing the target mapping path corresponding to the UAV according to the preset path distance, wherein the target mapping path corresponding to the UAV can cover the entire grassland mapping area.

3. The grassland spatial data collection method based on drone mapping according to claim 2 is characterized in that: The mapping sub-paths correspond one-to-one to the mapping sub-geographical areas, and the mapping geographic sub-areas are sub-areas in the grassland mapping area.

4. The grassland spatial data collection method based on drone mapping according to claim 2 is characterized in that: The flight mapping task corresponding to the mapping sub-path is a task of causing the UAV to follow the mapping sub-path from the starting point of the mapping sub-path to the end point of the mapping sub-path.

5. The grassland spatial data collection method based on drone mapping according to claim 1 is characterized in that: In step S2, if the drone has completed A twice j-1 If the corresponding flight mapping task is j≠m, the drone is controlled to complete A j After the corresponding flight mapping mission, execute A j+1 The corresponding flight mapping mission is to make A j+1 The corresponding sub-geographical area is surveyed and mapped. j+1 is the j+1th mapping subpath; if the drone has completed A twice j-1 The corresponding flight mapping task and j = m, then when the UAV completes A j After completing the corresponding flight mapping mission, the mapping is ended.

6. The grassland spatial data collection method based on drone mapping according to claim 5 is characterized in that: Step S4 also includes: If |E 2 j-1 -E 1 j-1 | / E 2 j-1 <C 0 , then control the drone to complete A j After the corresponding flight mapping mission, execute A j+1 The corresponding flight mapping mission is to make A j+1 The corresponding sub-geographical area is surveyed.

7. The grassland spatial data collection method based on drone mapping according to claim 1 is characterized in that: In step S4, A j-1 The corresponding C j-1 Stored in the first surveying and mapping database, C j-1 A j-1 The corresponding data volume difference ratio, where C j-1 =|E 2 j-1 -E 1 j-1 | / E 2 j-1 .

8. The grassland spatial data collection method based on drone mapping according to claim 7 is characterized in that: When C j-1 ≥C 0 When A is generated j-1 The corresponding abnormal label is stored in the first surveying and mapping database.

9. The grassland spatial data collection method based on drone mapping according to claim 1 is characterized in that: When the drone completes A for the first time i When the corresponding flight surveying mission is in progress, A i The corresponding initial actual surveying data and A i The corresponding initial mapping image is stored in the first mapping database. i is the i-th mapping subpath, i ranges from 1 to m, A i The corresponding initial actual surveying data is the laser radar device in the UAV when the UAV performs A for the first time. i The 3D point cloud data collected during the corresponding flight mapping mission, A i The corresponding initial mapping image is the image acquisition device in the UAV when the UAV performs A for the first time. i Visible light images collected during the corresponding flight mapping mission.

10. The grassland spatial data collection method based on drone mapping according to claim 9 is characterized in that: When the drone completes A for the second time i When the corresponding flight surveying mission is in progress, A i The corresponding key actual surveying and mapping data and A i The corresponding key surveying and mapping images are stored in the second surveying and mapping database. i The corresponding key actual surveying data is the laser radar device in the UAV when the UAV performs A for the second time. i The 3D point cloud data collected during the corresponding flight mapping mission, A i The corresponding key mapping image is the image acquisition device in the UAV when the UAV performs A for the second time. i Visible light images collected during the corresponding flight mapping mission.

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