High-precision monitoring method, device and equipment for unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle monitoring, in particular to a high-precision monitoring method, device and equipment for an unmanned aerial vehicle, and the method comprises the steps: collecting the flight data of all unmanned aerial vehicles in a current supervision region; the flight early warning degree of each unmanned aerial vehicle is calculated through the flight data, the flight area of each unmanned aerial vehicle is obtained, a suspected area for multi-unmanned aerial vehicle cooperation is determined, the suspected area is analyzed and screened to obtain a marked area, and the data sampling rate of each unmanned aerial vehicle is determined based on the marked area; performing data monitoring on each unmanned aerial vehicle according to the data sampling rate; the risk condition of each unmanned aerial vehicle during flight is calculated, the multi-unmanned aerial vehicle cooperative operation condition existing in the current supervision area is determined according to the multi-unmanned aerial vehicle cooperative operation of the unmanned aerial vehicles, comprehensive analysis is performed on the unmanned aerial vehicles, and the data sampling rate of the unmanned aerial vehicles is determined. Therefore, real-time adjustment is carried out according to the real-time flight condition, and the purpose of high-precision monitoring is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle monitoring, and in particular to a high-precision monitoring method, device and equipment for unmanned aerial vehicles. Background Art

[0002] At present, the rapid development of drone technology and the continuous expansion of its application fields have made high-precision monitoring and management of drones particularly important. Drones have shown great potential in many fields such as agricultural plant protection, logistics distribution, film and television shooting, disaster relief, etc., but at the same time have also caused challenges such as airspace management, flight safety and personal privacy protection. Through real-time monitoring and management of drones, potential flight risks such as collisions and crashes can be discovered and prevented in a timely manner, thereby ensuring the safety of people and property, and helping to reasonably allocate airspace resources, ensuring the safe coexistence of drones and traditional aircraft, and maintaining air traffic order.

[0003] When using drones for flight monitoring, generally speaking, the higher the sampling rate of the drone data, the higher the monitoring accuracy. For example, when performing high-risk flight operations or multi-drone collaborative operations, high-precision information is required to avoid collisions, rear-end collisions, and potential flight hazards. However, existing drone sampling rates are usually set manually, or are monitored for single-machine operation, making it difficult to identify high-risk flight events in a timely manner, such as drone collaborative operations, which in turn makes it impossible to adjust the data sampling rate in real time to achieve high-precision monitoring of multiple drones. Summary of the invention

[0004] In order to solve the technical problem that the data sampling rate used for monitoring by existing drones cannot perform high-precision monitoring of multi-machine collaboration, the purpose of the present invention is to provide a high-precision monitoring method, device and equipment for drones. The technical solutions adopted are as follows: Collect flight data of all drones in the current regulatory area; The flight warning degree of each UAV is calculated through flight data, and the flight area of ​​each UAV is obtained to determine the suspected area for multi-UAV coordination. The suspected area is analyzed and screened to obtain the marked area, and the data sampling rate of each UAV is determined based on the marked area. Data is monitored for each drone according to the data sampling rate.

[0005] Preferably, the flight data includes but is not limited to the speed, altitude, direction, duration, flight trajectory and heading data of each drone.

[0006] Preferably, the flight warning degree of each UAV is calculated through flight data, and the corresponding calculation formula is:

[0007] in, Indicates Flight warning level of each drone; Indicates The average flight speed of the drones; Indicates The flight time of each drone; Indicates The total number of data sets of altitude variation of drones; Indicates In the altitude change dataset of UAVs The height of the data; Indicates the standard deviation; +0.1 is used to prevent the denominator from being 0; Represents the maximum and minimum normalization function.

[0008] Preferably, obtaining the flight area of ​​each drone to determine a suspected area for multi-drone collaboration includes: Get the flight trajectory of each currently flying drone to get the corresponding flight area; The area intersection of multiple drones is determined based on the flight area, and combined with the flight area of ​​each drone, it is determined whether the area intersection is a suspected area for multi-drone collaboration. If so, the suspected area is screened out.

[0009] Preferably, analyzing the suspected region to screen and obtain the marked region comprises: Analyze the speed and heading data of each drone based on the suspected area, mark the speed extreme point, and obtain the flight time corresponding to each speed extreme point; obtain the heading angle of each drone at each moment through the heading data, obtain the heading angle difference between any moment and the previous adjacent moment, and calculate the heading angle change performance of each drone at adjacent moments; Calculate the confidence that there is multi-UAV collaboration in the suspected area, set the screening threshold, and use the confidence to determine whether there is multi-UAV collaboration in the suspected area. If the confidence is greater than and equal to the screening threshold, there is multi-UAV collaboration in the suspected area and it is determined as a marked area.

[0010] Preferably, the heading angle change performance of each UAV at adjacent moments is calculated, and the corresponding calculation formula is:

[0011] in, Indicates the degree of change of heading angle at adjacent moments; Indicates the heading angle difference between any moment and the previous adjacent moment.

[0012] Preferably, the confidence level of the existence of multi-drone collaboration in the suspected area is calculated, and the corresponding calculation formula is:

[0013] in, Indicates The confidence level of the existence of multiple drones working in a suspected area; Indicates The number of drones in the suspected area; Indicates The number of minimum extreme points of the number of drones in each suspected area; Indicates The suspected area The drone and the adjacent drone are in The difference between the heading angle change performance at the moment when the extreme point is located and the adjacent moment; Indicates The suspected area The drone and the adjacent drone are in The flight time difference of the extreme points; represents the maximum and minimum normalized function; and Both represent constants used to prevent the denominator from being zero.

[0014] Preferably, determining the data sampling rate of each drone based on the marked area includes: Based on the confidence of the existence of multi-drone collaboration in the suspected area, the sampling rate adjustment coefficient of each drone in the marked area is determined. The sampling rate adjustment coefficient of each drone in the unmarked area is 0. The data sampling rate of each drone is calculated. The corresponding calculation formula is:

[0015] in, Indicates the number of The data sampling rate of each drone; Indicates Flight warning level of each drone; Indicates The sampling rate adjustment factor of each drone; Indicates the minimum sampling rate of the drone; Indicates the adjustable range of the drone's sampling rate; Represents the maximum and minimum normalization function.

[0016] To solve the above problems, the present application also provides a high-precision monitoring device for drones, which is used to run the high-precision monitoring method for drones as described above, and includes the following modules: The collection module is used to collect the flight data of all drones in the current supervision area; The data analysis module is used to calculate the flight warning degree of each UAV through flight data, obtain the flight area of ​​each UAV to determine the suspected area for multi-UAV coordination, analyze the suspected area to screen and obtain the marked area, and determine the data sampling rate of each UAV based on the marked area; The monitoring module is used to: monitor the data of each drone according to the data sampling rate.

[0017] To solve the above problems, the present application also provides a high-precision monitoring device for drones, wherein the device stores program data, and when the program data is executed, a high-precision monitoring method for drones as described in any of the above items is implemented.

[0018] The present invention has the following beneficial effects: 1. The present application proposes a high-precision monitoring method for drones. By calculating the risk status of each drone during flight, and based on the relatively consistent and coherent flight movements of multiple drones when multiple drones are working together, the method determines the situation of multiple drones working together in the current regulatory area, conducts a comprehensive analysis of the drones, and determines the data sampling rate for the drones. This method can be adjusted in real time according to the real-time flight conditions to achieve the purpose of high-precision monitoring. It avoids the inaccurate positioning caused by the traditional manual setting of the data sampling rate and the inability to adjust the situation of multiple drones in a timely manner, thereby achieving the purpose of high-precision monitoring.

[0019] 2. The high-precision monitoring device and equipment for drones provided by the present invention have the same beneficial effects as the high-precision monitoring method for drones provided by the present invention, and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are 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.

[0021] Figure 1 A flowchart of a high-precision monitoring method for a drone provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the high-precision monitoring method, device and equipment for drones proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0023] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0024] The following is a detailed description of a high-precision monitoring method, device and equipment for unmanned aerial vehicles provided by the present invention in conjunction with the accompanying drawings.

[0025] The data sampling rate of a drone is usually set manually, or monitored when a single machine is running. It is difficult to identify high-risk flight events in a collaborative operation of multiple people in a timely manner, resulting in the inability to adjust the data sampling rate in real time to achieve high-precision monitoring of multiple machines. In one embodiment of the present invention, a high-precision monitoring method for drones is provided. By calculating the risk status of each drone during flight, the situation of collaborative operation of multiple drones in the current regulatory area is determined, and a comprehensive analysis is performed on the drones to determine the data sampling rate for the drones, so as to adjust the data sampling rate of the drones in real time according to the real-time flight status to achieve the purpose of high-precision monitoring. In order to achieve a high-precision monitoring method for drones, a high-precision monitoring device and equipment for drones are provided. The device and equipment are essentially a software system, which is composed of modules that realize corresponding functions. The specific steps in the method are now introduced in detail.

[0026] See also Figure 1 , which shows a flowchart of a high-precision monitoring method for a drone provided by an embodiment of the present invention, the method comprising: Step S1: Collect flight data of all drones in the current supervision area; Step S2: Calculate the flight warning degree of each UAV through flight data, obtain the flight area of ​​each UAV to determine the suspected area for multi-UAV collaboration, analyze the suspected area to screen and obtain the marked area, and determine the data sampling rate of each UAV based on the marked area; Step S3: Monitor the data of each UAV according to the data sampling rate.

[0027] It is explained that high-precision monitoring of drones is mainly to ensure airspace safety and the legal and compliant use of drones. By analyzing the data, potential safety hazards can be discovered and dealt with in a timely manner to ensure the safe operation of drones in various application scenarios. When regulating drones, the higher the data sampling rate, the more accurate the data positioning. However, existing methods consume a lot of data processing resources and may not meet safety requirements. Therefore, this application comprehensively considers multiple factors to meet regulatory needs while reducing excessive resource consumption.

[0028] Furthermore, the flight data includes, but is not limited to, the speed, altitude, direction, duration, flight trajectory and heading data of each drone.

[0029] It can be explained that speed refers to how fast the drone moves in the air; altitude refers to the vertical position of the drone relative to the ground or sea level; direction refers to the direction the drone is moving in the air; duration refers to the duration of its flight; flight trajectory refers to its actual path in the air; and heading data, that is, the predetermined direction it follows during flight; the acquisition of these flight data not only provides operators with a basis for real-time monitoring and control, but also provides data support for subsequent flight analysis and optimization.

[0030] Specifically, in step S1, the drone is supervised and processed based on the Aeroscope Management System (AMS). The AMS system is the core of the entire drone security system. It adopts advanced information management technology, can realize central monitoring, command investigation and statistics of historical flight data, and provide management departments with complete drone supervision functions to make public airspace safer; that is, in this embodiment, the flight data of each flying drone in the current supervision area is obtained through the AMS system, including but not limited to speed, altitude, direction, duration, flight trajectory and heading data, to facilitate subsequent processing.

[0031] Understandably, from the perspective of single-machine UAV flight control, in different environments such as cities, construction sites, and farmlands, when operators use UAVs to perform continuous high-speed flights, the risks of bombing and collision increase, especially in densely populated areas, where the flight risks will be greater. Therefore, it is necessary to pay attention not only to the duration of the high-speed flight of the UAV, but also to its altitude changes during the process. Since the UAV flies forward, backward, left, and right at the same altitude, the flight field of the UAV operator needs to pay attention to is relatively single, and the flight difficulty is low. If the altitude changes continuously during the process, the flight field of the operator is complex and changeable, and the risk is doubled. Therefore, it is necessary to first evaluate the flight risk of each UAV based on relevant flight data.

[0032] Furthermore, in step S2, the flight warning degree of each UAV is calculated through flight data, and the corresponding calculation formula is:

[0033] in, Indicates Flight warning level of each drone; Indicates The average flight speed of the drones; Indicates The flight time of each drone; Indicates The total number of data sets of altitude variation of drones; Indicates In the altitude change dataset of UAVs The height of the data; Indicates the standard deviation; +0.1 is used to prevent the denominator from being 0; Represents the maximum and minimum normalization function.

[0034] Specifically, the flight data obtained in the analysis step S1 is monitored and analyzed regularly every minute for each UAV to determine the flight risk assessment, and the corresponding data of the entire flight process during each monitoring is obtained to calculate the flight warning level of each UAV, which is used to indicate the risk status that may occur when the UAV is currently performing a single flight mission.

[0035] To better explain, in real life, most industries usually adopt a multi-machine collaborative operation mode when using drones, that is, multiple drones work together to complete specific flight missions; in the collaborative operation, the drones generally maintain a relatively close distance to better meet the needs. Based on this situation, in order to ensure the accuracy of the data and the accuracy of positioning, a higher data sampling rate is required to prevent accidents during the flight and ensure the safety of the flight.

[0036] Furthermore, in step S2, the flight area of ​​each drone is obtained to determine a suspected area for multi-drone collaboration, including: Step S211: Obtain the flight trajectory of each currently flying UAV to obtain the corresponding flight area.

[0037] To explain, when multiple UAVs work in coordination, they are usually fixed and repeated within a corresponding spatial range. This feature is used to obtain each UAV that is currently flying and performing multi-machine coordination. Specifically, the flight trajectory of each UAV that is currently flying is obtained, and it is used as the flight area of ​​each corresponding UAV, wherein the incomplete flight area, that is, the area that is not fully selected, connects the flight starting point and the current point as the end point, and connects the two points to obtain the flight area. Preferably, in this embodiment, the acquisition of the flight area does not take into account the influence of the flight altitude, but only considers the coverage range.

[0038] Step S212: Determine the area intersection of multiple drones based on the flight area, and determine whether the area intersection is a suspected area for multi-drone collaboration in combination with the flight area of ​​each drone. If so, select the suspected area.

[0039] Specifically, a threshold is set. In this embodiment, the threshold is 0.8. The ratio of the area intersection to the area of ​​each UAV's flight area is obtained and compared with the threshold. If the ratio is greater than and equal to 0.8, it means that there are multiple drones cooperating in the current area, and the area is determined as a suspected area; otherwise, the ratio is less than 0.8, which means that the possibility of multiple drones cooperating in the current area is small, and it is excluded to avoid data redundancy.

[0040] Furthermore, in step S2, the suspected area is analyzed and screened to obtain a marked area, including: Step S221: Analyze the speed and heading data of each UAV based on the suspected area, mark the speed extreme points, and obtain the flight time corresponding to each speed extreme point; obtain the heading angle of each UAV at each moment through the heading data, obtain the heading angle difference between any moment and the previous adjacent moment, and calculate the heading angle change expression of each UAV at adjacent moments.

[0041] To better illustrate, by conducting a detailed analysis of the speed and heading data of the drones, it is possible to identify whether these drones performed the same action at the same time. For example, if two drones changed their heading or adjusted their speed at the same time, this indicates that there is collaborative behavior between the two drones; and collaborative behavior is used to indicate that the two drones are performing a common mission or following a predetermined flight plan.

[0042] It can be explained that the speed extreme point refers to the speed maximum and minimum values ​​are taken into consideration; the flight time obtained based on the speed extreme point refers to the flight time of the drone when it is at the extreme point; the heading angle difference between any moment and the previous adjacent moment is recorded as .

[0043] Furthermore, in step S221, the heading angle change performance of each UAV at adjacent moments is calculated, and the corresponding calculation formula is:

[0044] in, Indicates the degree of change of heading angle at adjacent moments; Indicates the heading angle difference between any moment and the previous adjacent moment.

[0045] To illustrate, the above formula avoids unnecessary large angle jumps by absolute value processing, and Normalization was performed to ensure the accuracy and reliability of the results.

[0046] It can be explained that the calculation formula middle, It indicates the heading angle difference between any moment and the previous adjacent moment, that is, the change; "It means adding 180° to the original heading angle difference;" " represents the modulo operation, and "360°" represents a complete circle angle; for example, suppose ,So In addition, since the heading angle usually cycles between 0-360°, there may be an angle overflow. or When adjusting , by subtracting or adding 360° to ensure that it is within the range of -180°-180°, ensuring It takes into account the change of direction while minimizing the value. -180° or 180° means that the drone turns from one direction to the completely opposite direction, and 0° means that the heading of the drone does not change.

[0047] Step S222: Calculate the confidence level of the existence of multiple drones cooperating in the suspected area, set a screening threshold, and determine whether there is multiple drones cooperating in the suspected area based on the confidence level. If the confidence level is greater than and equal to the screening threshold, there is multiple drones cooperating in the suspected area, and it is determined as a marked area.

[0048] Understandably, when the speed of a drone changes significantly during multi-drone collaboration, it usually means that the drone may need to adjust its heading or perform other operations to adapt to mission requirements. Therefore, when analyzing multi-drone collaboration of drones in a suspected area, we can first compare the flight time difference for the speed extreme points of the same serial number of multiple drones, that is, by comparing the occurrence time of the extreme points to indicate whether the speed change is relatively consistent in time. When the speed extreme points of the drones occur very close in time, it means that there is a certain degree of coordination when performing the task; then verify it through the change of heading angle, that is, for similar heading angle changes at the speed extreme points of the same serial number, pay more attention to the flight time difference; when the drones occur at the speed extreme points, the changes in their heading angles are similar, which supports the assumption that there is coordination when performing the task.

[0049] Furthermore, in step S222, the confidence of the existence of multi-drone collaboration in the suspected area is calculated, and the corresponding calculation formula is:

[0050] in, Indicates The confidence level of the existence of multiple drones working in a suspected area; Indicates The number of drones in the suspected area; Indicates The number of minimum extreme points of the number of drones in each suspected area; Indicates The suspected area The drone and the adjacent drone are in The difference between the heading angle change performance at the moment when the extreme point is located and the adjacent moment; Indicates The suspected area The drone and the adjacent drone are in The flight time difference of the extreme points; represents the maximum and minimum normalized function; and Both represent constants used to prevent the denominator from being zero.

[0051] To explain, Indicates The minimum extreme value points of the number of drones in the suspected area are compared with the minimum extreme value points to meet the requirement that the drones do not cross the boundary as a whole; It means taking the average value, because each drone is compared with the drones with adjacent numbers until the last one, and the last drone is compared with the first drone, so the overall comparison Second-rate.

[0052] It can be explained that Indicates The suspected area The drone and the adjacent drone are in The difference between the heading angle change performance at the moment of the extreme point and the adjacent moment reflects the heading angle change of the drone at a specific moment. The time when the extreme point is located and the adjacent time, There is no significant difference in the heading angle change performance of the UAVs, that is, the heading angle of the UAV remains stable at this moment, and there is no obvious turn or change. The value may be 0, so the " " to prevent the denominator from being 0; Indicates The suspected area The drone and the adjacent drone are in The flight time difference of the extreme points is At the extreme point, The first When the flight time of a UAV is exactly the same as that of other adjacent UAVs, there is no time difference between the two, so the “ " to prevent the denominator from being 0.

[0053] In this embodiment, the screening threshold is 0.65. If the confidence that there are multiple drones working in collaboration in the current suspected area is greater than and equal to the screening threshold, there is multiple drones working in collaboration in the suspected area, and it is screened out and marked to obtain a marked area, indicating that there are multiple drones working in collaboration in the marked area, and the risk is higher and more attention needs to be paid.

[0054] Furthermore, step S3 includes: Based on the confidence of the existence of multi-drone collaboration in the suspected area, the sampling rate adjustment coefficient of each drone in the marked area is determined. The sampling rate adjustment coefficient of each drone in the unmarked area is 0. The data sampling rate of each drone is calculated. The corresponding calculation formula is:

[0055] in, Indicates the number of The data sampling rate of each drone; Indicates Flight warning level of each drone; Indicates The sampling rate adjustment factor of each drone; Indicates the minimum sampling rate of the drone; Indicates the adjustable range of the drone's sampling rate; Represents the maximum and minimum normalization function.

[0056] It is explained that based on the confidence level of the existence of multiple drones working together in the suspected area determined in the above step S222, it is used as the sampling rate adjustment coefficient of the marked area; the minimum sampling rate refers to the minimum data sampling rate that meets the basic drone monitoring needs, which is used to avoid the drone being unable to meet the basic analysis needs due to the data sampling rate being adjusted too small; the adjustable range of the drone's sampling rate refers to the maximum sampling rate - the minimum sampling rate; the data sampling rate of each drone in the current marked area is determined by the above calculation formula to meet different drone flight safety and regulatory requirements.

[0057] Specifically, in step S3, the data sampling rate of each drone in the current marked area is determined based on the above process. The calculation result is analyzed on a per-minute basis, and the analysis result is used as the data sampling rate for subsequent flights of the drone for data collection, so as to ensure the flight safety of the drone while identifying possible multi-drone collaboration and accurately locating the drone, so as to improve the accuracy of supervision and reduce the risk of security threats; for example, when a drone enters a high-risk area or performs a complex task, the data sampling rate is automatically adjusted by running this method to obtain more intensive data, thereby ensuring flight safety.

[0058] An embodiment of the present invention provides a high-precision monitoring device for a drone, which is used to run a high-precision monitoring method for a drone as described in the above embodiment, and includes the following modules: The collection module is used to collect the flight data of all drones in the current supervision area; The data analysis module is used to calculate the flight warning degree of each UAV through flight data, obtain the flight area of ​​each UAV to determine the suspected area for multi-UAV coordination, analyze the suspected area to screen and obtain the marked area, and determine the data sampling rate of each UAV based on the marked area; The monitoring module is used to: monitor the data of each drone according to the data sampling rate.

[0059] It is explained that the acquisition module is used to realize real-time monitoring and collect flight data information of all drones in the current regulatory area, providing basic data support for subsequent data analysis; the data analysis module is used to conduct in-depth analysis of flight data to ensure the accuracy and timeliness of the data that users need to monitor; the monitoring module is used to determine the data sampling rate, realize real-time data monitoring of each drone, realize real-time monitoring and management of drone flight behavior, and can promptly detect and report any abnormal situation to ensure the flight safety of drones and the stability of the regulatory area.

[0060] As an optional implementation method, the monitoring module can also coordinate and early-warn security threat risks to enhance the intelligence of the entire module.

[0061] It can be understood that when a module of a high-precision monitoring device for drones is in operation, it is necessary to utilize a high-precision monitoring method for drones provided in the aforementioned embodiment. Therefore, whether the acquisition module, the data analysis module and the monitoring module are integrated or different hardware is configured to produce functions similar to the effects achieved by the present invention, they all fall within the scope of protection of the present invention.

[0062] An embodiment of the present invention further proposes a high-precision monitoring device for drones, which stores program data. When the program data is executed, a high-precision monitoring method for drones as described in one of the aforementioned embodiments is implemented; the device has the same beneficial effects as the high-precision monitoring method for drones provided above, which will not be repeated here.

[0063] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0064] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A high-precision monitoring method for unmanned aerial vehicles, characterized in that: The method comprises: Collect flight data of all drones in the current regulatory area; The flight warning degree of each UAV is calculated through flight data, and the flight area of ​​each UAV is obtained to determine the suspected area for multi-UAV coordination. The suspected area is analyzed and screened to obtain the marked area, and the data sampling rate of each UAV is determined based on the marked area. Data is monitored for each drone according to the data sampling rate.

2. A high-precision monitoring method for drones as claimed in claim 1, characterized in that: The flight data includes but is not limited to the speed, altitude, direction, duration, flight trajectory and heading data of each drone.

3. A high-precision monitoring method for unmanned aerial vehicles as claimed in claim 2, characterized in that: The flight warning degree of each UAV is calculated through flight data. The corresponding calculation formula is: in, Indicates Flight warning level of each drone; Indicates The average flight speed of the drones; Indicates The flight time of each drone; Indicates The total number of data sets of altitude variation of drones; Indicates In the altitude change dataset of UAVs The height of the data; Indicates the standard deviation; +0.1 is used to prevent the denominator from being 0; Represents the maximum and minimum normalization function.

4. A high-precision monitoring method for drones as claimed in claim 3, characterized in that: Obtain the flight area of ​​each drone to determine the suspected area for multi-drone coordination, including: Get the flight trajectory of each currently flying drone to get the corresponding flight area; The area intersection of multiple drones is determined based on the flight area, and combined with the flight area of ​​each drone, it is determined whether the area intersection is a suspected area for multi-drone collaboration. If so, the suspected area is screened out.

5. A high-precision monitoring method for unmanned aerial vehicles as claimed in claim 4, characterized in that: Analyze the suspected areas and screen to obtain marked areas, including: Analyze the speed and heading data of each drone based on the suspected area, mark the speed extreme point, and obtain the flight time corresponding to each speed extreme point; obtain the heading angle of each drone at each moment through the heading data, obtain the heading angle difference between any moment and the previous adjacent moment, and calculate the heading angle change performance of each drone at adjacent moments; Calculate the confidence that there is multi-UAV collaboration in the suspected area, set the screening threshold, and use the confidence to determine whether there is multi-UAV collaboration in the suspected area. If the confidence is greater than and equal to the screening threshold, there is multi-UAV collaboration in the suspected area and it is determined as a marked area.

6. A high-precision monitoring method for unmanned aerial vehicles as claimed in claim 5, characterized in that: Calculate the heading angle change performance of each UAV at adjacent moments. The corresponding calculation formula is: in, Indicates the degree of change of heading angle at adjacent moments; Indicates the heading angle difference between any moment and the previous adjacent moment.

7. A high-precision monitoring method for unmanned aerial vehicles as claimed in claim 5, characterized in that: Calculate the confidence of the existence of multi-drone collaboration in the suspected area. The corresponding calculation formula is: in, Indicates The confidence level of the existence of multiple drones working in a suspected area; Indicates The number of drones in the suspected area; Indicates The number of minimum extreme points of the number of drones in each suspected area; Indicates The suspected area The drone and the adjacent drone are in The difference between the heading angle change performance at the moment when the extreme point is located and the adjacent moment; Indicates The suspected area The drone and the adjacent drone are in The flight time difference of the extreme points; represents the maximum and minimum normalized function; and Both represent constants used to prevent the denominator from being zero.

8. A high-precision monitoring method for unmanned aerial vehicles as claimed in claim 5, characterized in that: Determine the data sampling rate for each drone based on the marked area, including: Based on the confidence of the existence of multi-drone collaboration in the suspected area, the sampling rate adjustment coefficient of each drone in the marked area is determined. The sampling rate adjustment coefficient of each drone in the unmarked area is 0. The data sampling rate of each drone is calculated. The corresponding calculation formula is: in, Indicates the number of The data sampling rate of each drone; Indicates Flight warning level of each drone; Indicates The sampling rate adjustment factor of each drone; Indicates the minimum sampling rate of the drone; Indicates the adjustable range of the drone's sampling rate; Represents the maximum and minimum normalization function.

9. A high-precision monitoring device for drones, characterized in that: The method for executing the high-precision monitoring method for a drone as claimed in claim 1 comprises the following modules: The collection module is used to collect the flight data of all drones in the current supervision area; The data analysis module is used to calculate the flight warning degree of each UAV through flight data, obtain the flight area of ​​each UAV to determine the suspected area for multi-UAV coordination, analyze the suspected area to screen and obtain the marked area, and determine the data sampling rate of each UAV based on the marked area; The monitoring module is used to: monitor the data of each drone according to the data sampling rate.

10. A high-precision monitoring device for drones, characterized in that: The device stores program data, and when the program data is executed, a high-precision monitoring method for a drone as described in any one of claims 1 to 8 is implemented.

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