Unmanned aerial vehicle control method for territorial space planning

Abstract

The invention discloses an unmanned aerial vehicle control method for territorial space planning, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: obtaining an attitude angle anomaly coefficient data set, a flight speed data set and a flight height data set of an unmanned aerial vehicle, calculating a flight stability coefficient data set of the unmanned aerial vehicle, and sending out a flight anomaly analysis instruction; after the flight anomaly analysis instruction is received, calculating a flight anomaly index of the unmanned aerial vehicle, and sending out a flight anomaly early warning of the unmanned aerial vehicle; after a flight abnormity early warning of the unmanned aerial vehicle is received, calculating an overlapping degree coefficient and an image quality coefficient of the image data, and calculating a sampling abnormity index of the image data; according to the method, the flight anomaly index of the unmanned aerial vehicle and the sampling anomaly index of the image data are acquired, the land data acquisition anomaly index is calculated, the re-flight position of the unmanned aerial vehicle is marked, and the unmanned aerial vehicle is controlled to perform data acquisition on the re-flight position again, so that the data anomaly position can be accurately positioned, and the accuracy and integrity of land data acquisition are ensured.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle control technology, and in particular to a method for controlling an unmanned aerial vehicle for national land space planning. Background Art

[0002] With the rapid development of drone technology, its application in surveying, monitoring and national space planning is becoming more and more extensive. Traditional national space planning methods often rely on ground measurement and satellite remote sensing data, but these methods have problems such as long measurement cycle, slow data update and limited accuracy. In recent years, the introduction of drone technology has provided new solutions for national space planning. Its high efficiency, flexibility and high precision make drones an important tool in national space planning. Although drone technology has shown great potential in national space planning, there are still some limitations in existing drone control methods. 1. Traditional drone control methods mainly rely on manual operation by operators, which limits the autonomy and intelligence level of drones. 2. Existing drone path planning methods often fly based on preset routes and lack the ability to dynamically adjust the path according to real-time data. 3. The data collected by drones needs to be processed and analyzed complexly before they can be used for national space planning. The existing data processing methods are inefficient and cannot meet the needs of rapid planning.

[0003] In the Chinese invention application with application publication number CN118486196A, a UAV data monitoring system and method are disclosed, including a control module for receiving a connection signal output by a UAV; a data acquisition module for real-time acquisition of flight data and environmental data of the UAV; a communication module for transmitting the flight data and environmental data of the UAV to an integrated management platform; the integrated management platform includes a data receiving unit, a data analysis unit and a data storage unit, the data receiving unit is used to receive the flight data and environmental data of the UAV transmitted by the communication module, the data analysis unit is used to analyze the flight data and environmental data of the UAV, and the data storage unit is used to store the flight data and environmental data of the UAV; an early warning module is used to warn the UAV according to the analysis results of the data analysis unit; and a display module is used to display the parameters of the UAV.

[0004] In the above invention application, the UAV flight data and environmental data are analyzed, and the analysis results are confirmed, and then the UAV is warned based on the analysis results, thereby effectively reducing the occurrence of certain property losses caused by accidents during UAV flight operations. However, in actual use, it is more common that the flight posture of the UAV is affected by various factors such as wind speed, airflow, terrain, etc., which makes the UAV prone to unstable phenomena such as shaking and deviation during flight, thereby affecting the integrity and accuracy of data collection.

[0005] To this end, the present invention provides a drone control method for national land space planning. Summary of the invention

[0006] 1. Technical issues to be solved

[0007] In view of the shortcomings of the prior art, the present invention provides a UAV control method for national land space planning. The present invention obtains the flight anomaly index Fyz of the UAV. n and the sampling anomaly index Cyz of the image data n , calculate the land data collection anomaly index Gty n , marking the UAV's missed-flight position, and controlling the UAV to re-collect data at the missed-flight position, can reflect the problems that the UAV may encounter when collecting land data, such as abnormal flight attitude, poor image quality, etc. The location of data anomalies can be accurately located, thereby ensuring the accuracy and completeness of land data collection, thereby solving the technical problems recorded in the background technology.

[0008] (II) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: A drone control method for national land space planning, comprising the following steps:

[0010] Get the attitude angle anomaly coefficient data set of the drone (Zt1, Zt2, ..., Zt n ), flight speed data set (Fs1, Fs2, ..., Fs n ) and flight altitude datasets (Gd1, Gd2, ..., Gd n ), calculate the flight stability coefficient data set of the UAV (Fw1, Fw2, ..., Fw n ) to issue flight anomaly analysis instructions;

[0011] After receiving the flight anomaly analysis command, the UAV lidar data and the UAV flight data set are aligned according to the timestamp, and the UAV lidar stability coefficient data set (Ld1, Ld2, ..., Ld n ), calculate the flight anomaly index of the drone based on the flight stability coefficient of the drone and the drone lidar stability coefficient, and issue a flight anomaly warning for the drone;

[0012] After receiving the abnormal flight warning of the UAV, the overlap coefficient Cx and the image quality coefficient Tz of the image data are calculated, and the sampling abnormality index Cyz of the image data is calculated based on the overlap coefficient Cx and the image quality coefficient Tz of the image data;

[0013] Get the flight anomaly index Fyz of the drone nand the sampling anomaly index Cyz of the image data n , calculate the land data collection anomaly index Gty n , mark the UAV's go-around position, and control the UAV to re-collect data at the go-around position.

[0014] Furthermore, the attitude angle dataset and flight speed dataset (Fs1, Fs2, ..., Fs n ), and obtain the UAV flight altitude data set (Gd1, Gd2, ..., Gd n ), build a drone flight dataset.

[0015] The attitude angle data set includes the pitch angle data set (Fy1, Fy2, ..., Fy n ), roll angle data set (Hg1, Hg2, ..., Hg n ) and yaw angle datasets (Ph1, Ph2, ..., Ph n ).

[0016] Furthermore, based on the pitch angle dataset of the drone (Fy1, Fy2, ..., Fy n ), roll angle data set (Hg1, Hg2, ..., Hg n ) and yaw angle datasets (Ph1, Ph2, ..., Ph n ), calculate the attitude angle anomaly coefficient data set of the drone (Zt1, Zt2, ..., Zt n ), the calculation formula of the attitude angle anomaly coefficient of the drone is as follows:

[0017]

[0018] Where n represents the total number of timestamp numbers of the drone flight data.

[0019] Furthermore, the attitude angle anomaly coefficient data set (Zt1, Zt2, ..., Zt n ), flight speed data set (Fs1, Fs2, ..., Fs n ) and flight altitude datasets (Gd1, Gd2, ..., Gd n ), calculate the flight stability coefficient data set of the UAV (Fw1, Fw2, ..., Fw n ), the calculation formula of the flight stability coefficient of the UAV is as follows:

[0020]

[0021] When the flight stability coefficient of the drone exceeds When the flight abnormality analysis instruction is issued, Represents the mean of all data in the UAV’s flight stability coefficient dataset.

[0022] Furthermore, after receiving the flight anomaly analysis command, the UAV lidar data is obtained, and the UAV lidar data and the UAV flight data set are aligned according to the timestamp to obtain the UAV lidar distance data set (Ju1, Ju2, ..., Ju n ) and signal strength data sets (Qd1, Qd2, ..., Qd n ).

[0023] Furthermore, based on the UAV LiDAR distance dataset (Ju1, Ju2, ..., Ju n ) and signal strength data sets (Qd1, Qd2, ..., Qd n ), calculate the UAV lidar stability coefficient data set (Ld1, Ld2, ..., Ld n ):

[0024]

[0025] The calculation formula for the corresponding UAV's lidar stability coefficient is as above.

[0026] Furthermore, the flight stability coefficient of the drone and the drone lidar stability coefficient are obtained, and the flight anomaly index of the drone is calculated:

[0027]

[0028] Wherein, i represents the timestamp number of the drone data, and i=1, 2, ..., n;

[0029] When the drone's flight anomaly index exceeds When the UAV is abnormally shaking and tilting, an abnormal flight warning of the UAV is issued. Represents the mean value of the UAV flight anomaly index, σFyz n Represents the variance of the UAV’s flight anomaly index.

[0030] Furthermore, after receiving the abnormal flight warning from the UAV, the aerial survey data processing software is used to analyze the heading overlap Hc and lateral overlap Pc of the image data corresponding to the timestamp of the abnormal flight data, and the overlap coefficient Cx of the image data is calculated:

[0031] Cx=min(Hc,0.6)*min(Pc,0.3)

[0032] In aerial photogrammetry, the heading overlap is 60% to 65%, and the lateral overlap is stipulated to be 30%.

[0033] Furthermore, the image data corresponding to the flight abnormality data timestamp is imported into the image processing software to obtain the image contrast Db and color saturation Sa, and the image quality coefficient Tz of the image data is calculated:

[0034]

[0035] Among them, Db * Sa is the contrast average of the two images before and after the flight abnormal data timestamp, * It is the average color saturation of the two images before and after the timestamp of the flight abnormality data.

[0036] Furthermore, the overlap coefficient Cx and image quality coefficient Tz of the image data are obtained, and the sampling anomaly index Cyz of the image data is calculated:

[0037]

[0038] The calculation formula of the sampling anomaly index Cyz of the corresponding image data is as above.

[0039] Further, obtain the flight anomaly index Fyz of the drone n and the sampling anomaly index Cyz of the image data n , calculate the land data collection anomaly index Gty n :

[0040]

[0041] Corresponding land data collection anomaly index Gty n The calculation formula is as above.

[0042] Furthermore, when the land data collection anomaly index Gty n Greater than When , it means that the land data collected at this timestamp is unreliable, extract the drone GPS data corresponding to this timestamp, mark it as the drone's missed approach position, and control the drone to re-collect data at the missed approach position; represents the mean value of the anomaly index of historical land data collection, σGty n Represents the variance of the anomaly index of historical land data collection.

[0043] (III) Beneficial effects

[0044] The present invention provides a method for controlling a drone for national land space planning, which has the following beneficial effects:

[0045] 1. Obtain the attitude angle anomaly coefficient data set of the drone (Zt1, Zt2, ..., Zt n), flight speed data set (Fs1, Fs2, ..., Fs n ) and flight altitude datasets (Gd1, Gd2, ..., Gd n ), calculate the flight stability coefficient data set of the UAV (Fw1, Fw2, ..., Fw n ), send out flight abnormality analysis instructions, monitor the flight status of the drone in real time, and issue alarms in time when abnormalities are found. It can more accurately judge the flight safety status of the drone, and then take corresponding measures to ensure the safety and stability of the flight process.

[0046] 2. After receiving the flight anomaly analysis command, align the UAV lidar data and the UAV flight data set according to the timestamp, and calculate the UAV lidar stability coefficient data set (Ld1, Ld2, ..., Ld n ), calculates the flight anomaly index of the drone based on the flight stability coefficient of the drone and the stability coefficient of the drone's lidar, issues a flight anomaly warning for the drone, conducts a comprehensive analysis of the drone's flight status from multiple dimensions, and improves the accuracy of anomaly detection.

[0047] 3. After receiving the abnormal flight warning of the UAV, the overlap coefficient Cx and image quality coefficient Tz of the image data are calculated, and the sampling abnormality index Cyz of the image data is calculated based on the overlap coefficient Cx and image quality coefficient Tz of the image data. This has significant benefits in improving the accuracy of image data analysis, timely discovering image sampling abnormalities, supporting fault troubleshooting and positioning, and optimizing UAV image acquisition strategies.

[0048] 4. Obtain the drone’s flight anomaly index Fyz n and the sampling anomaly index Cyz of the image data n , calculate the land data collection anomaly index Gty n , mark the UAV's missed-flight position, and control the UAV to re-collect data at the missed-flight position, which can reflect the problems that the UAV may encounter when collecting land data, such as abnormal flight attitude, poor image quality, etc. The location of data anomalies can be accurately located, thereby ensuring the accuracy and completeness of land data collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The present invention is a flowchart of a method for controlling a drone for national land space planning. DETAILED DESCRIPTION

[0050] 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] See also Figure 1 The present invention provides a method for controlling a drone for national land space planning, comprising the following steps:

[0052] Step 1: Obtain the attitude angle anomaly coefficient dataset of the drone (Zt1, Zt2, ..., Zt n ), flight speed data set (Fs1, Fs2, ..., Fs n ) and flight altitude datasets (Gd1, Gd2, ..., Gd n ), calculate the flight stability coefficient data set of the UAV (Fw1, Fw2, ..., Fw n ) and issue flight anomaly analysis instructions.

[0053] The step 1 includes the following contents:

[0054] Step 101: extract the attitude angle data set and flight speed data set (Fs1, Fs2, ..., Fs2) of the drone from the inertial measurement unit (IMU) of the drone. n ), and obtain the UAV flight altitude data set (Gd1, Gd2, ..., Gd n ), build a drone flight dataset.

[0055] The attitude angle data set includes the pitch angle data set (Fy1, Fy2, ..., Fy n ), roll angle data set (Hg1, Hg2, ..., Hg n ) and yaw angle datasets (Ph1, Ph2, ..., Ph n ).

[0056] Step 102: Based on the pitch angle dataset (Fy1, Fy2, ..., Fy n ), roll angle data set (Hg1, Hg2, ..., Hg n ) and yaw angle datasets (Ph1, Ph2, ..., Ph n ), calculate the attitude angle anomaly coefficient data set of the drone (Zt1, Zt2, ..., Zt n ), the calculation formula of the attitude angle anomaly coefficient of the drone is as follows:

[0057]

[0058] Where n represents the total number of timestamp numbers of the drone flight data.

[0059] Step 103: Obtain the attitude angle anomaly coefficient data set (Zt1, Zt2, ..., Zt n ), flight speed data set (Fs1, Fs2, ..., Fs n ) and flight altitude datasets (Gd1, Gd2, ..., Gd n ), calculate the flight stability coefficient data set of the UAV (Fw1, Fw2, ..., Fw n ), the calculation formula of the flight stability coefficient of the UAV is as follows:

[0060]

[0061] When the flight stability coefficient of the drone exceeds When the flight abnormality analysis instruction is issued, Represents the mean of all data in the UAV’s flight stability coefficient dataset.

[0062] When using, combine the contents in steps 101 to 103:

[0063] Get the attitude angle anomaly coefficient data set of the drone (Zt1, Zt2, ..., Zt n ), flight speed data set (Fs1, Fs2, ..., Fs n ) and flight altitude datasets (Gd1, Gd2, ..., Gd n ), calculate the flight stability coefficient data set of the UAV (Fw1, Fw2, ..., Fw n ), send out flight abnormality analysis instructions, monitor the flight status of the drone in real time, and issue alarms in time when abnormalities are found. It can more accurately judge the flight safety status of the drone, and then take corresponding measures to ensure the safety and stability of the flight process.

[0064] Step 2: After receiving the flight anomaly analysis command, align the UAV lidar data and the UAV flight data set according to the timestamp, and calculate the UAV lidar stability coefficient data set (Ld1, Ld2, ..., Ld n ), calculate the UAV's flight anomaly index based on the UAV's flight stability coefficient and the UAV's lidar stability coefficient, and issue a flight anomaly warning for the UAV.

[0065] The step 2 includes the following contents:

[0066] Step 201: After receiving the flight anomaly analysis instruction, obtain the UAV laser radar data, align the UAV laser radar data with the UAV flight data set according to the timestamp, and obtain the UAV laser radar distance data set (Ju1, Ju2, ..., Ju n ) and signal strength data sets (Qd1, Qd2, ..., Qd n ).

[0067] Step 202: Based on the UAV laser radar distance data set (Ju1, Ju2, ..., Ju n ) and signal strength data sets (Qd1, Qd2, ..., Qd n ), calculate the UAV lidar stability coefficient data set (Ld1, Ld2, ..., Ld n ):

[0068]

[0069] The calculation formula for the corresponding UAV's lidar stability coefficient is as above.

[0070] Step 203: Obtain the flight stability coefficient of the drone and the drone laser radar stability coefficient, and calculate the flight anomaly index of the drone:

[0071]

[0072] Wherein, i represents the timestamp number of the drone data, and i=1, 2, ..., n.

[0073] When the drone's flight anomaly index exceeds When the UAV is abnormally shaking or tilting, an abnormal flight warning of the UAV is issued. Represents the mean value of the UAV flight anomaly index, σFyz n Represents the variance of the UAV’s flight anomaly index.

[0074] When using, combine the contents in steps 201 to 203:

[0075] After receiving the flight anomaly analysis command, the UAV lidar data and the UAV flight data set are aligned according to the timestamp, and the UAV lidar stability coefficient data set (Ld1, Ld2, ..., Ld n ), calculates the flight anomaly index of the drone based on the flight stability coefficient of the drone and the stability coefficient of the drone's lidar, issues a flight anomaly warning for the drone, conducts a comprehensive analysis of the drone's flight status from multiple dimensions, and improves the accuracy of anomaly detection.

[0076] Step 3: After receiving the abnormal flight warning of the UAV, the overlap coefficient Cx and the image quality coefficient Tz of the image data are calculated, and the sampling abnormality index Cyz of the image data is calculated based on the overlap coefficient Cx and the image quality coefficient Tz of the image data.

[0077] The step three includes the following contents:

[0078] Step 301: After receiving the abnormal flight warning of the UAV, use the aerial survey data processing software to analyze the heading overlap Hc and lateral overlap Pc of the image data corresponding to the abnormal flight data timestamp, and calculate the overlap coefficient Cx of the image data:

[0079] Cx=min(Hc, 0.6)*min(Pc, 0.3)

[0080] In aerial photogrammetry, the heading overlap should generally be 60% to 65%, and the lateral overlap is generally stipulated to be 30%.

[0081] Step 302: Import the image data corresponding to the flight abnormality data timestamp into the image processing software to obtain the image contrast Db and color saturation Sa, and calculate the image quality coefficient Tz of the image data:

[0082]

[0083] Among them, Db * Sa is the contrast average of the two images before and after the flight abnormal data timestamp, * It is the average color saturation of the two images before and after the timestamp of the flight abnormality data.

[0084] Step 303: Obtain the overlap coefficient Cx and image quality coefficient Tz of the image data, and calculate the sampling anomaly index Cyz of the image data:

[0085]

[0086] The calculation formula of the sampling anomaly index Cyz of the corresponding image data is as above.

[0087] When using, combine the contents in steps 301 to 303:

[0088] After receiving the abnormal flight warning of the UAV, the overlap coefficient Cx and the image quality coefficient Tz of the image data are calculated, and the sampling anomaly index Cyz of the image data is calculated based on the overlap coefficient Cx and the image quality coefficient Tz of the image data. This has significant benefits in improving the accuracy of image data analysis, timely discovering image sampling anomalies, supporting fault troubleshooting and positioning, and optimizing UAV image acquisition strategies.

[0089] Step 4: Obtain the drone's flight anomaly index Fyz n and the sampling anomaly index Cyz of the image data n , calculate the land data collection anomaly index Gty n , mark the UAV's go-around position, and control the UAV to re-collect data at the go-around position.

[0090] The step 4 includes the following contents:

[0091] Step 401: Obtain the flight anomaly index Fyz of the drone n and the sampling anomaly index Cyz of the image data n , calculate the land data collection anomaly index Gty n :

[0092]

[0093] Corresponding land data collection anomaly index Gty n The calculation formula is as above.

[0094] Step 402: When the land data collection abnormality index Gty n Greater than When , it means that the land data collected at this timestamp is unreliable. The drone GPS data corresponding to this timestamp is extracted and marked as the drone's missed approach position. The drone is controlled to re-collect data at the missed approach position. represents the mean value of the anomaly index of historical land data collection, σGty n Represents the variance of the anomaly index of historical land data collection.

[0095] When using, combine the contents in steps 401 and 402:

[0096] Get the flight anomaly index Fyz of the drone n and the sampling anomaly index Cyz of the image data n , calculate the land data collection anomaly index Gty n , mark the UAV's missed-flight position, and control the UAV to re-collect data at the missed-flight position, which can reflect the problems that the UAV may encounter when collecting land data, such as abnormal flight attitude, poor image quality, etc. The location of data anomalies can be accurately located, thereby ensuring the accuracy and completeness of land data collection.

[0097] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0098] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0099] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A drone control method for national land space planning, characterized in that: The steps include: Get the attitude angle anomaly coefficient data set of the drone (Zt1, Zt2, ..., Zt n ), flight speed data set (Fs1, Fs2, ..., Fs n ) and flight altitude datasets (Gd1, Gd2, …, Gd n ), calculate the flight stability coefficient data set of the UAV (Fw1, Fw2, ..., Fw n ) to issue flight anomaly analysis instructions; After receiving the flight anomaly analysis command, the UAV lidar data and the UAV flight data set are aligned according to the timestamp, and the UAV lidar stability coefficient data set (Ld1, Ld2, ..., Ld n ), calculate the flight anomaly index of the drone based on the flight stability coefficient of the drone and the drone lidar stability coefficient, and issue a flight anomaly warning for the drone; After receiving the abnormal flight warning of the UAV, the overlap coefficient Cx and the image quality coefficient Tz of the image data are calculated, and the sampling abnormality index Cyz of the image data is calculated based on the overlap coefficient Cx and the image quality coefficient Tz of the image data; Get the flight anomaly index Fyz of the drone n and the sampling anomaly index Gtz of the image data n , calculate the land data collection anomaly index Gty n , mark the UAV's go-around position, and control the UAV to re-collect data at the go-around position.

2. The method for controlling a drone for national land space planning according to claim 1, characterized in that: According to the pitch angle dataset of the UAV (Fy1, Fy2, ..., Fy n ), roll angle data set (Hg1, Hg2, ..., Hg n ) and yaw angle datasets (Ph1, Ph2, ..., Ph n ), calculate the attitude angle anomaly coefficient data set of the drone (Zt1, Zt2, ..., Zt n ), the calculation formula of the attitude angle anomaly coefficient of the drone is as follows: Where n represents the total number of timestamp numbers of the drone flight data.

3. The method for controlling a drone for national land space planning according to claim 2, characterized in that: Get the attitude angle anomaly coefficient data set of the drone (Zt1, Zt2, ..., Zt n ), flight speed data set (Fs1, Fs2, ..., Fs n ) and flight altitude datasets (Gd1, Gd2, …, Gd n ), calculate the flight stability coefficient data set of the UAV (Fw1, Fw2, ..., Fw n ), the calculation formula of the flight stability coefficient of the UAV is as follows: When the flight stability coefficient of the drone exceeds When the flight abnormality analysis instruction is issued, Represents the mean of all data in the UAV’s flight stability coefficient dataset.

4. The method for controlling a drone for national land space planning according to claim 1, characterized in that: Based on the UAV LiDAR distance dataset (Ju1, Ju2, ..., Ju n ) and signal strength data sets (Qd1, Qd2, …, Qd n ), calculate the UAV lidar stability coefficient data set (Ld1, Ld2, ..., Ld n ): The calculation formula for the corresponding UAV's lidar stability coefficient is as above.

5. The method for controlling a drone for national land space planning according to claim 4, characterized in that: Get the flight stability coefficient of the drone and the stability coefficient of the drone's lidar, and calculate the flight anomaly index of the drone: Wherein, i represents the timestamp number of the drone data, and i=1, 2, ..., n; When the drone's flight anomaly index exceeds When the UAV is abnormally shaking and tilting, an abnormal flight warning of the UAV is issued. Represents the mean value of the UAV flight anomaly index, σFyz n Represents the variance of the UAV’s flight anomaly index.

6. The method for controlling a drone for national land space planning according to claim 1, characterized in that: After receiving the abnormal flight warning from the UAV, the aerial survey data processing software is used to analyze the heading overlap Hc and lateral overlap Pc of the image data corresponding to the timestamp of the abnormal flight data, and the overlap coefficient Cx of the image data is calculated: Cx=min(Hc,0.6)*min(Pc,0.3) In aerial photogrammetry, the heading overlap is 60% to 65%, and the lateral overlap is stipulated to be 30%.

7. The method for controlling a drone for national land space planning according to claim 1, characterized in that: Import the image data corresponding to the flight abnormality data timestamp into the image processing software to obtain the image contrast Db and color saturation Sa, and calculate the image quality coefficient Tz of the image data: Among them, Db * Sa is the contrast average of the two images before and after the flight abnormal data timestamp, * It is the average color saturation of the two images before and after the timestamp of the flight abnormality data.

8. The method for controlling a drone for national land space planning according to claim 7, characterized in that: Obtain the overlap coefficient Cx and image quality coefficient Tz of the image data, and calculate the sampling anomaly index Cyz of the image data: The calculation formula of the sampling anomaly index Cyz of the corresponding image data is as above.

9. The method for controlling a drone for national land space planning according to claim 8, characterized in that: Get the flight anomaly index Fyz of the drone n and the sampling anomaly index Gtz of the image data n , calculate the land data collection anomaly index Gty n : Corresponding land data collection anomaly index Gty n The calculation formula is as above.

10. The method for controlling a drone for national land space planning according to claim 9, characterized in that: When the land data collection anomaly index Gty n Greater than When , it means that the land data collected at this timestamp is unreliable, extract the drone GPS data corresponding to this timestamp, mark it as the drone's missed approach position, and control the drone to re-collect data at the missed approach position; represents the mean value of the anomaly index of historical land data collection, σGty n Represents the variance of the anomaly index of historical land data collection.

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