UAV control terminal positioning method, system, electronic device and storage medium

Abstract

The present invention belongs to the field of unmanned aerial vehicles (UAVs), and provides a method, system, electronic device, and storage medium for positioning a UAV control terminal. The method comprises: determining a track point data set when a UAV communication link is not interfered with, and calculating a first position range of the UAV control terminal based on the track point data set when the UAV communication link is not interfered with and using a machine learning algorithm; determining a track point data set when the UAV communication link is interfered with multiple times; when the UAV communication link is interfered with, the UAV returns to its control terminal; using the track point data set when the UAV communication link is interfered with, drawing multiple direction-finding lines of the track points when the UAV returns multiple times; cross-locating the multiple direction-finding lines of the track points of the UAV return to obtain a second position range of the UAV control terminal; determining the intersection of the first and second position ranges of the UAV control terminal, and using it as the position positioning result of the UAV control terminal; the present invention can effectively find the specific position of the UAV control terminal.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicles (UAVs), and more specifically, relates to a positioning method, system, electronic device, and storage medium for a UAV control terminal. Background Art

[0002] In response to the inconvenience caused by illegal and reckless drone flights, which impacts individuals, civil aviation, and society, a growing number of drone countermeasure systems have emerged on the market. Currently, drone monitoring and countermeasure technologies, both domestically and internationally, are primarily categorized into three main types: direct destruction, monitoring and control, and interference and blocking.

[0003] Most current positioning methods default to using the drone's flight trackpoint data, ignoring abnormal trackpoint data, such as when the drone is interfered with. Using all trackpoint data for direction-finding positioning results in low accuracy. Most current AOA algorithms are based on traditional geometric space positioning algorithms, with fixed parameters in the model. This has drawbacks such as an inability to effectively utilize historical data and poor generalization performance. As the number of trackpoints increases, the intersection of direction-finding lines obtained from each trackpoint will generate a large number of false intersections.

[0004] In summary, the existing methods cannot effectively find the specific location of the drone control terminal. They only intercept the flying drone and cannot fundamentally solve the problem of the recurrence of such incidents. Summary of the Invention

[0005] In response to the defects of the existing technology, the purpose of the present invention is to provide a drone control terminal positioning method, system, electronic device and storage medium, aiming to solve the problems that the existing technology cannot effectively find the specific location of the drone control terminal and cannot fundamentally solve the problems caused by illegal and random drone flights.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a method for positioning a drone control terminal, comprising the following steps:

[0007] Determine a track point dataset when the UAV communication link is not interfered with, and calculate a first position range of the UAV control terminal based on the track point dataset when not interfered with and using a machine learning algorithm;

[0008] Determining a data set of waypoints when a communication link of the drone is repeatedly interfered with; when the communication link of the drone is interfered with, the drone returns to its control terminal;

[0009] The track point dataset when the UAV communication link is interfered is used to draw multiple direction-finding lines of the track points when the UAV returns multiple times;

[0010] Cross-locate multiple direction-finding lines of the drone's return track point to obtain the second position range of the drone control terminal;

[0011] Determine the intersection of the first position range and the second position range of the drone control terminal and use it as the position positioning result of the drone control terminal.

[0012] In an optional example, the method of using the track point data set when the drone communication link is interfered to draw multiple direction-finding lines of the track points when the drone returns multiple times is specifically as follows:

[0013] Jam the drone's communication link multiple times and obtain two track points before and after each jamming.

[0014] Connect the two track points before and after each interference to obtain the direction-finding line of the track point corresponding to the interference;

[0015] The track point direction-finding lines corresponding to multiple interferences are summarized to obtain multiple track point direction-finding lines.

[0016] In an optional example, the second position range of the drone control terminal is obtained by cross-locating multiple direction-finding lines of the drone's return track point, specifically:

[0017] Cross-locating a plurality of track point direction-finding lines to determine a plurality of intersection points of the plurality of track point direction-finding lines;

[0018] The additive mixed Gaussian noise distribution or the multiplicative mixed Gaussian noise distribution of the direction-finding lines corresponding to the multiple intersection points is used as the second position range of the UAV control terminal.

[0019] In an optional example, the first position range of the UAV control terminal is calculated based on the track point dataset when not interfered with and using a machine learning algorithm, specifically:

[0020] Determine the installation location of the detection equipment for detecting the UAV track points;

[0021] The spatial distribution between the installation point of the detection equipment and the track point position of the UAV when it is not interfered with is converted into the sample image space;

[0022] The sample image space is modeled using a convolutional neural network trained for positioning to calculate a first position range of the UAV control terminal.

[0023] In a second aspect, the present invention provides a UAV control terminal positioning system, comprising:

[0024] A first position range determining unit is configured to determine a track point dataset when the UAV communication link is not interfered with, and calculate a first position range of the UAV control terminal based on the track point dataset when the UAV communication link is not interfered with and using a machine learning algorithm;

[0025] The second position range determining unit is configured to determine a track point dataset when a communication link of the drone is repeatedly interfered with; when the communication link of the drone is interfered with, the drone returns to its control terminal; use the track point dataset when the communication link of the drone is interfered with to draw multiple direction-finding lines of the track points of the drone when the drone returns multiple times; and cross-locate the multiple direction-finding lines of the track points of the drone when it returns to obtain a second position range of the drone control terminal;

[0026] The control terminal positioning unit is used to determine the intersection of the first position range and the second position range of the drone control terminal and use it as the position positioning result of the drone control terminal.

[0027] In an optional example, the second position range determination unit interferes with the UAV communication link multiple times to obtain two track points before and after each interference; connects the two track points before and after each interference to obtain the track point direction-finding line corresponding to the interference; and summarizes the track point direction-finding lines corresponding to multiple interferences to obtain multiple track point direction-finding lines.

[0028] In an optional example, the second position range determination unit cross-locates multiple track point direction-finding lines to determine multiple intersection points of the multiple track point direction-finding lines; and uses the additive mixed Gaussian noise distribution or multiplicative mixed Gaussian noise distribution of the direction-finding lines corresponding to the multiple intersection points as the second position range of the UAV control terminal.

[0029] In an optional example, the first position range determination unit determines the installation point position of the detection equipment for detecting the UAV track points; converts the spatial distribution between the installation point position of the detection equipment and the track point position when the UAV is not interfered with into a sample image space; and uses the trained convolutional neural network for positioning to model the sample image space to calculate the first position range of the UAV control terminal.

[0030] In a third aspect, the present invention provides an electronic device, comprising: a memory and a processor;

[0031] The memory is used to store computer programs;

[0032] The processor is configured to implement the method provided in the first aspect above when executing the computer program.

[0033] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method provided in the first aspect above is implemented.

[0034] In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:

[0035] The present invention provides a method, system, electronic device and storage medium for positioning a drone control terminal. The method utilizes the characteristic that a drone usually returns to the location of the drone control terminal after being interfered with by a communication link, and uses the track point data of the drone after being interfered with multiple times to perform direction-finding angle positioning of the drone control terminal, thereby improving positioning accuracy. Deep learning technology is applied to the field of positioning based on direction-finding angles to address the shortcomings of current direction-finding positioning algorithms, such as poor generalization performance and inability to effectively utilize historical data. Combined with the key point regression semantic segmentation task in computer vision, deep semantic segmentation neural network modeling is used to achieve detection and positioning, resulting in highly accurate positioning of the drone control terminal. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of a method for positioning a UAV control terminal according to an embodiment of the present invention;

[0037] Figure 2 This is a flowchart of the positioning method for a UAV control terminal provided by an embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of the positioning effect of the UAV control terminal provided by an embodiment of the present invention;

[0039] Figure 4 This is an architecture diagram of the drone control terminal positioning system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0041] In the description of the present invention, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the exemplary expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0042] At present, most positioning methods use the track point data of the UAV flight by default for direction finding and positioning, which has low accuracy. The AOA algorithms are all positioning algorithms based on traditional geometric space, and the parameters in the model are fixed. They have the disadvantages of not being able to effectively utilize historical data and poor generalization performance. As the number of track points increases, the intersection of the direction finding lines obtained by each track point will produce a large number of false intersections. The present invention uses the direction finding angle (AoA) information of the track points before and after the UAV communication link is interfered with to realize the preliminary positioning of the UAV control terminal. Deep learning technology is applied to the field of positioning based on direction finding angle to improve the accuracy of UAV control terminal positioning.

[0043] Figure 1 This is a flow chart of the method for positioning a UAV control terminal provided by an embodiment of the present invention; Figure 1 As shown, the following steps are included:

[0044] S101, determining a track point dataset when the UAV communication link is not interfered with, and calculating a first position range of the UAV control terminal based on the track point dataset when not interfered with and using a machine learning algorithm;

[0045] S102, determining a track point dataset when a communication link of the UAV is interfered with multiple times; when the communication link of the UAV is interfered with, the UAV returns to its control terminal;

[0046] S103, using the track point data set when the UAV communication link is interfered, drawing multiple direction-finding lines of the track points when the UAV returns multiple times;

[0047] S104, cross-locating multiple direction-finding lines of the return track point of the UAV to obtain a second position range of the UAV control terminal;

[0048] S105: Determine the intersection of the first position range and the second position range of the drone control terminal, and use it as the position positioning result of the drone control terminal.

[0049] In an optional example, the method of using the track point data set when the drone communication link is interfered to draw multiple direction-finding lines of the track points when the drone returns multiple times is specifically as follows:

[0050] Jam the drone's communication link multiple times and obtain two track points before and after each jamming.

[0051] Connect the two track points before and after each interference to obtain the direction-finding line of the track point corresponding to the interference;

[0052] The track point direction-finding lines corresponding to multiple interferences are summarized to obtain multiple track point direction-finding lines.

[0053] In an optional example, the second position range of the drone control terminal is obtained by cross-locating multiple direction-finding lines of the drone's return track point, specifically:

[0054] Cross-locating a plurality of track point direction-finding lines to determine a plurality of intersection points of the plurality of track point direction-finding lines;

[0055] The additive mixed Gaussian noise distribution or the multiplicative mixed Gaussian noise distribution of the direction-finding lines corresponding to the multiple intersection points is used as the second position range of the UAV control terminal.

[0056] In an optional example, the first position range of the UAV control terminal is calculated based on the track point dataset when not interfered with and using a machine learning algorithm, specifically:

[0057] Determine the installation location of the detection equipment for detecting the UAV track points;

[0058] The spatial distribution between the installation point of the detection equipment and the track point position of the UAV when it is not interfered with is converted into the sample image space;

[0059] The sample image space is modeled using a convolutional neural network trained for positioning to calculate a first position range of the UAV control terminal.

[0060] Specifically, the technical solution adopted by the present invention to solve its technical problems is: using the direction-finding angle (AoA) information of the track points before and after the UAV communication link is disturbed to achieve preliminary positioning of the UAV control terminal. Deep learning technology is applied to the field of positioning based on direction-finding angles to improve the accuracy of UAV control terminal positioning. The key technology involves using the direction-finding angle information of the track points before and after the UAV communication link is repeatedly disturbed to perform preliminary cross-positioning methods; adopting the key point regression semantic segmentation task in machine learning, and using deep semantic segmentation neural network modeling to achieve more accurate UAV control terminal positioning.

[0061] Reference Figure 2 The technical solutions of the embodiments of the present invention are described in detail as follows:

[0062] Step 1: Separate the track point datasets before and after the drone is countered;

[0063] Step 2: Use the countered track point dataset to intersect the multiple countered direction finding lines to locate the UAV control terminal;

[0064] Step 3: The spatial distribution between the detection device installation point and the track point before the countermeasure is used is converted into a sample image space. The sample image space includes both the azimuth information of the direction-finding angle and the spatial information superimposed between the installation points of each detection device.

[0065] Step 4: Use the designed positioning convolutional neural network Position Net to model the sample image space to calculate the position range of the drone control terminal;

[0066] Step 5: If the UAV control terminal position calculated in the second step is within the UAV control terminal position range calculated in the fourth step, the result of the second step calculation is the UAV control terminal position;

[0067] Figure 3 : is a schematic diagram of the positioning effect of the UAV control terminal provided by an embodiment of the present invention; wherein, Figure 3 (a) shows a sample image formed by superimposing rays drawn from the direction-finding angles at each reference point. The resulting sample image contains not only the direction-finding angle information obtained at the reference point, but also the spatial information of each direction-finding line. In the sample image space, each point takes a value of 0 or 1, indicating whether the point is within the direction-finding angle ray. Figure 3 Figure (b) shows the sample generated in the original (a). To leverage the prior information that the ambient noise conforms to a Gaussian distribution, a sample image is created by superimposing rays drawn from each reference point within the sample image region using the obtained direction-finding angle as the center line. The points in the sample image are a mixture of Gaussian noise generated from each direction-finding angle, resulting in an additive mixed Gaussian noise distribution. Method (b) incorporates the prior information about the ambient noise level, compared to method (a). Compared to method (a), method (b) generates samples with a denser information density. Figure 3 Unlike methods (b), where the points in the sample space of method (c) satisfy the additive mixed noise distribution, the points in method (c) are composed of a multiplicative noise superposition. Specifically, the direction-finding angles obtained at each reference point are used as the center line to generate a Gaussian distribution, plotting the ray region and forming a sample image using a multiplicative superposition method. Unlike methods (b) and (a), method (c) reduces the target area, making the target more distinct. Sample image spaces generated using additive mixed Gaussian noise are suitable for positioning tasks where direction-finding lines are lost. However, sample image spaces generated using multiplicative mixed Gaussian noise are not suitable for positioning tasks where direction-finding lines are lost.

[0068] Figure 4 This is a diagram of the architecture of the drone control terminal positioning system provided by an embodiment of the present invention. Figure 4 Shown, including:

[0069] A first position range determining unit 410 is configured to determine a track point dataset when the UAV communication link is not interfered with, and calculate a first position range of the UAV control terminal based on the track point dataset when not interfered with and using a machine learning algorithm;

[0070] The second position range determining unit 420 is configured to determine a track point dataset when the UAV communication link is repeatedly interfered with; when the UAV communication link is interfered with, the UAV returns to its control terminal; use the track point dataset when the UAV communication link is interfered with to draw multiple direction-finding lines for the track points of the UAV when the UAV returns multiple times; and cross-locate the multiple direction-finding lines for the track points of the UAV when the UAV returns to obtain a second position range for the UAV control terminal;

[0071] The control terminal positioning unit 430 is used to determine the intersection of the first position range and the second position range of the drone control terminal and use it as the position positioning result of the drone control terminal.

[0072] It is understandable that the detailed functional implementation of each of the above units can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

[0073] This invention can be applied to future drone countermeasure projects to precisely locate the control terminals of illegal drones. Rather than simply intercepting illegal drones, this invention can fundamentally prevent the recurrence of such incidents, improve countermeasure effectiveness, and help maintain our competitiveness and advantages in the field of drone countermeasures.

[0074] In addition, an embodiment of the present invention provides an electronic device, which includes: a memory and a processor;

[0075] The memory is used to store computer programs;

[0076] The processor is configured to implement the method in the above embodiment when executing the computer program.

[0077] In addition, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method in the above embodiment is implemented.

[0078] Based on the method in the above embodiment, an embodiment of the present invention provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0079] Based on the method in the above embodiment, an embodiment of the present invention further provides a chip, including one or more processors and an interface circuit. Optionally, the chip may also include a bus.

[0080] The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, a digital communicator (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The various methods and steps disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0081] The interface circuit can be used to send or receive data, instructions or information. The processor can use the data, instructions or other information received by the interface circuit to process it, and can send the processing completion information through the interface circuit.

[0082] Optionally, the chip further includes a memory, which may include a read-only memory and a random access memory, and provides operating instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory (NVRAM).

[0083] Optionally, the memory stores an executable software module or a data structure, and the processor can perform corresponding operations by calling an operation instruction stored in the memory (the operation instruction may be stored in an operating system).

[0084] Optionally, the interface circuit can be used to output the execution result of the processor.

[0085] It should be noted that the corresponding functions of the processor and the interface circuit can be implemented through hardware design, software design, or a combination of hardware and software, and there is no limitation here.

[0086] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or a software-based instruction in a processor.

[0087] It is understood that the order of execution of the steps in the above embodiments does not necessarily imply a specific order of execution. The order of execution of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. In addition, in some possible implementations, the steps in the above embodiments can be selectively executed according to actual circumstances, and can be executed partially or completely, which is not limited here.

[0088] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0089] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0090] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)).

[0091] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for positioning a UAV control terminal, characterized in that: The following steps are involved: Determine a track point dataset when the UAV communication link is not interfered with, and calculate a first position range of the UAV control terminal based on the track point dataset when not interfered with and using a machine learning algorithm; Determining a data set of waypoints when a communication link of the drone is repeatedly interfered with; when the communication link of the drone is interfered with, the drone returns to its control terminal; The track point dataset when the UAV communication link is interfered is used to draw multiple direction-finding lines of the track points when the UAV returns multiple times; Cross-locate multiple direction-finding lines of the drone's return track point to obtain the second position range of the drone control terminal; Determine the intersection of the first position range and the second position range of the drone control terminal and use it as the position positioning result of the drone control terminal.

2. The method according to claim 1, characterized in that The method of using the track point data set when the drone communication link is interfered to draw multiple direction-finding lines of the track points when the drone returns multiple times is specifically as follows: Jam the drone's communication link multiple times and obtain two track points before and after each jamming. Connect the two track points before and after each interference to obtain the direction-finding line of the track point corresponding to the interference; The track point direction-finding lines corresponding to multiple interferences are summarized to obtain multiple track point direction-finding lines.

3. The method according to claim 1 or 2, characterized in that The second position range of the UAV control terminal is obtained by cross-locating multiple direction-finding lines of the UAV return track point, specifically: Cross-locating a plurality of track point direction-finding lines to determine a plurality of intersection points of the plurality of track point direction-finding lines; The additive mixed Gaussian noise distribution or the multiplicative mixed Gaussian noise distribution of the direction-finding lines corresponding to the multiple intersection points is used as the second position range of the UAV control terminal.

4. The method according to claim 1, wherein The first position range of the UAV control terminal is calculated based on the track point data set when it is not interfered with and using a machine learning algorithm, specifically: Determine the installation location of the detection equipment for detecting the UAV track points; The spatial distribution between the installation point of the detection equipment and the track point position of the UAV when it is not interfered with is converted into the sample image space; The sample image space is modeled using a convolutional neural network trained for positioning to calculate a first position range of the UAV control terminal.

5. A UAV control terminal positioning system, characterized in that: include: A first position range determining unit is configured to determine a track point dataset when the UAV communication link is not interfered with, and calculate a first position range of the UAV control terminal based on the track point dataset when the UAV communication link is not interfered with and using a machine learning algorithm; The second position range determining unit is configured to determine a track point dataset when a communication link of the drone is repeatedly interfered with; when the communication link of the drone is interfered with, the drone returns to its control terminal; use the track point dataset when the communication link of the drone is interfered with to draw multiple direction-finding lines of the track points of the drone when the drone returns multiple times; and cross-locate the multiple direction-finding lines of the track points of the drone when it returns to obtain a second position range of the drone control terminal; The control terminal positioning unit is used to determine the intersection of the first position range and the second position range of the drone control terminal and use it as the position positioning result of the drone control terminal.

6. The system according to claim 5, characterized in that The second position range determination unit interferes with the UAV communication link multiple times to obtain two track points before and after each interference; Connect the two track points before and after each interference to obtain the direction-finding line of the track point corresponding to the interference; And the track point direction-finding lines corresponding to multiple interferences are summarized to obtain multiple track point direction-finding lines.

7. The system according to claim 5 or 6, characterized in that The second position range determination unit cross-locates multiple track point direction-finding lines to determine multiple intersections of the multiple track point direction-finding lines; and uses the additive mixed Gaussian noise distribution or multiplicative mixed Gaussian noise distribution of the direction-finding lines corresponding to the multiple intersections as the second position range of the UAV control terminal.

8. The system according to claim 5, characterized in that The first position range determination unit determines the location of the installation point of the detection equipment for detecting the track points of the UAV; converts the spatial distribution between the location of the installation point of the detection equipment and the location of the track points of the UAV when it is not interfered with into a sample image space; And the sample image space is modeled using a convolutional neural network trained for positioning to calculate a first position range of the drone control terminal.

9. An electronic device, characterized in that: include: memory and processor; The memory is used to store computer programs; The processor is configured to implement the method according to any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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