Network-connected unmanned aerial vehicle identification method and device, electronic equipment and storage medium

Abstract

The invention provides a network connection unmanned aerial vehicle identification method and device, electronic equipment and a storage medium, and the method comprises the steps: monitoring first information of a target unmanned aerial vehicle and second information of a base station through spectrum detection equipment, and determining the protocol information of the target unmanned aerial vehicle based on the first information and the second information; extracting a spectrum signal of the target unmanned aerial vehicle from the first information according to the protocol information; positioning the target unmanned aerial vehicle according to the frequency spectrum signal to obtain target position information of the target unmanned aerial vehicle; based on the protocol information, the frequency spectrum signal and the target position information, whether the target unmanned aerial vehicle is the network-connected unmanned aerial vehicle is determined, that is, according to the spatial features (position information) and the frequency spectrum features (protocol information and frequency spectrum signal) of the network-connected unmanned aerial vehicle, effective identification of the network-connected unmanned aerial vehicle is realized, and a basis is provided for network-connected unmanned aerial vehicle supervision in the unmanned aerial vehicle airspace. In addition, when the target unmanned aerial vehicle is a network-connected unmanned aerial vehicle, positioning of the network-connected unmanned aerial vehicle is realized.

Description

Technical Field

[0001] The present invention relates to the field of drone control technology, and in particular to a method, device, electronic device, and storage medium for identifying networked drones. Background Art

[0002] With the increasing popularity of connected drones, the number of connected drones is growing rapidly. It is particularly important to strengthen the supervision of connected drones in drone airspace.

[0003] Due to the wide variety of connected drones, regulators lack effective rules and thresholds for managing them. Furthermore, connected drones typically use 4G / 5G signals for communication and control, making them equivalent to mobile communication terminals. Within the same communication frequency band, connected drone signals resemble those of other wireless terminals and lack distinguishing characteristics. Traditional radio detection methods based on signal time-frequency characteristics struggle to effectively identify connected drones, hindering effective regulation of connected drones within drone airspace. Therefore, effectively identifying connected drones is a pressing issue that needs to be addressed. Summary of the Invention

[0004] The present invention provides a method, device, electronic device and storage medium for identifying a networked drone, which can solve the problem of being unable to effectively identify a networked drone.

[0005] According to a first aspect of the present invention, a method for identifying a networked drone is provided, the method comprising: Using a spectrum detection device to listen to first information of a target UAV and second information of a base station, and determining protocol information of the target UAV based on the first information and the second information; extracting the spectrum signal of the target UAV from the first information according to the protocol information; Positioning the target UAV according to the spectrum signal to obtain target position information of the target UAV; Based on the protocol information, the spectrum signal and the target location information, it is determined whether the target drone is a networked drone.

[0006] According to a second aspect of the present invention, a device for identifying a networked drone is provided, the device comprising: An information monitoring module, configured to monitor first information of a target UAV and second information of a base station using a spectrum detection device, and determine protocol information of the target UAV based on the first information and the second information; a signal extraction module, configured to extract the spectrum signal of the target UAV from the first information according to the protocol information; A UAV positioning module is used to locate the target UAV according to the spectrum signal and obtain target position information of the target UAV; A drone identification module is used to determine whether the target drone is a networked drone based on the protocol information, the spectrum signal and the target location information.

[0007] According to a third aspect of the present invention, there is provided an electronic device comprising a processor and a memory, The memory is used to store codes and related data; The processor is configured to execute the code in the memory to implement the method for identifying a networked drone as described in any one of the embodiments of the present invention.

[0008] According to a fourth aspect of the present invention, a storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method for identifying a networked drone as described in any one of the embodiments of the present invention is implemented.

[0009] Compared with the prior art, the technical solution of the embodiment of the present invention has the following beneficial effects: A spectrum detection device is used to monitor first information from a target drone and second information from a base station, and the protocol information of the target drone is determined based on the first and second information. The spectrum signal of the target drone is extracted from the first information based on the protocol information. The target drone is located based on the spectrum signal to obtain target location information of the target drone. Based on the protocol information, spectrum signal, and target location information, the target drone is determined to be a networked drone. Specifically, the present invention obtains the protocol information and spectrum signal of the target drone by collecting and analyzing communication data (first information and personal information) between the target drone and the base station. Positioning the target drone based on the spectrum signal then obtains the target location information of the target drone, thereby locating the target drone. Furthermore, since networked drones communicate with other wireless terminals via 4G / 5G signals, the protocol information, spectrum signal, and location of the networked drone differ from those of the wireless terminals. Therefore, the present invention effectively identifies networked drones based on the spatial characteristics (location information) and spectrum characteristics (protocol information and spectrum signal) of the networked drones, thereby providing a basis for networked drone regulation in drone airspace. In addition, when the target drone is a networked drone, the positioning of the networked drone is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0011] Figure 1 This is a flow chart of a method for identifying a networked drone provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of device interaction in a method for identifying a networked drone provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of converting spectrum information into trajectory information provided by an embodiment of the present invention; Figure 4 1 is another flow chart of a method for identifying a networked drone provided by an embodiment of the present invention; Figure 5 This is a structural diagram of an identification device for a networked drone provided by an embodiment of the present invention; Figure 6 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0012] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0013] The terms "first," "second," "third," "fourth," and the like (if any) in the description and claims of the present invention and the appended claims are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in orders other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, product, or apparatus.

[0014] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0015] Figure 1 It is a flow chart of the identification method of the networked UAV provided by an embodiment of the present invention. The method can be executed by the identification device of the networked UAV, and the device can be implemented in software and / or hardware. In a specific embodiment, the device can be integrated into an electronic device, such as a computer, a server, a TDOA (Time Difference of Arrival, TDOA) server, etc. The TDOA server can be understood as a software system or platform that provides services for positioning-related services based on the TDOA positioning algorithm. The following embodiments will be described by taking the device integrated into the TDOA server as an example, refer to Figure 1 , the method may specifically include the following steps: Step 101: Use a spectrum detection device to listen to first information of a target UAV and second information of a base station, and determine protocol information of the target UAV based on the first information and the second information.

[0016] The spectrum detection device can be understood as a device used to monitor device communication information. The spectrum detection device can be deployed in a grid-based manner. Using a grid-based deployment, the spectrum detection device can monitor device information on the uplink and downlink channels of a mobile communication system within a specific area, improving the real-time and convenient nature of the spectrum detection device's monitoring of device information. The target drone can be understood as the drone to be identified. The first information can be understood as the downlink signal information sent by the target drone to the base station during the random access process between the target drone and the base station. In a specific embodiment, the first information can be a random access request sent by the target drone to the base station. The second information can be understood as the uplink signal information fed back to the target drone by the base station based on the first information during the random access process between the target drone and the base station. In a specific embodiment, the second information can be a random access response (RAR) sent by the base station to the target drone.

[0017] For example, after the target drone determines the base station with which it wants to communicate, the target drone needs to obtain the information of the base station in order to know how to work correctly on the base station. The base station will also continuously send system information related to the base station (for example, Master Information Block (MIB) and System Information Block (SIB)), and the drone will obtain the system information when needed. After the target drone obtains the system information of the base station, the target drone will initiate a random access process to establish a connection with the base station. During the random access process, the base station and the target drone will agree on the time-frequency resource location for the drone to send signals. For example Figure 2 As shown, the spectrum detection device will monitor the uplink and downlink signals of the target UAV and the base station throughout the process to obtain the first information and the second information. The TDOA server then obtains the parsed information from the first information, and performs protocol parsing on the second information based on the parsed information to obtain the protocol information of the target UAV. In an embodiment of the present invention, the protocol information of the target UAV may include but is not limited to: Radio Network Temporary Identity (RNTI), the start time of the target UAV sending the signal, the end time of the target UAV sending the signal, the start frequency of the target UAV sending the signal, the cutoff frequency of the target UAV sending the signal, and the time-frequency range indication. The time-frequency range indication can be understood as the indication information of the time-frequency range of the target UAV.

[0018] Step 102: extract the spectrum signal of the target UAV from the first information according to the protocol information.

[0019] In one embodiment, extracting a spectrum signal of a target UAV from first information according to protocol information may include: determining a target time-frequency range of the target UAV according to a time-frequency range indication; and extracting a spectrum signal within the target time-frequency range from the first information using a spectrum sensing device.

[0020] Step 103: Position the target UAV according to the spectrum signal to obtain target position information of the target UAV.

[0021] Among them, the target position information can be understood as the positioning information of the target UAV.

[0022] In one embodiment, the spectrum signal may include a time-frequency resource map. Positioning the target UAV based on the spectrum signal to obtain target location information of the target UAV may include: using a TDOA algorithm to perform positioning based on the time-frequency resource map to obtain target location information of the target UAV.

[0023] In the embodiment of the present invention, since it is necessary to use the TDOA algorithm to perform positioning based on the time-frequency resource map, and the TDOA algorithm needs to perform positioning based on at least two arrival time differences, the number of spectrum detection devices is at least three.

[0024] For example, Figure 3 This is a schematic diagram of converting spectrum information into trajectory information during the TDOA algorithm positioning process provided by an embodiment of the present invention. Figure 3 As shown in the figure, the spectrum detection equipment performs protocol analysis and spectrum perception on the spectrum signal of the received target drone, and obtains Figure 3 The time-frequency resource diagram shown in . Figure 3 It can be seen that there are multiple time-frequency resource maps obtained by multiple spectrum detection devices at the same time. The TDOA algorithm can be used to locate the multiple time-frequency resource maps at the same time to obtain Figure 3 The target location of the target drone is shown in Figure 1. As time passes, the spectrum detection device can obtain the corresponding time-frequency resource map at fixed intervals and use the TDOA algorithm to determine the location coordinates of the target drone based on multiple time-frequency resource maps at the same time.

[0025] For example, take three spectrum detection devices (R1, R2, and R3) as an example. Assuming that the spectrum signal of the target drone extracted from the first information according to the protocol information is s(t), the spectrum signal can be expressed as: Wherein, i represents the number of the spectrum detection device, and i can be a positive integer, such as 1, 2, or 3. t represents time. Represents the spectrum signal. It represents the time delay of the wireless signal reaching the spectrum detection device i. Indicates noise.

[0026] For any two spectrum detection devices, such as R1 and R2, the cross-correlation function between R1 and R2 is: in, It represents the spectrum detection device 1. Indicates the spectrum signal received by the spectrum detection device 1. represents a spectrum detection device 2 . It represents the cross-correlation function between the spectrum signal received by the spectrum detection device R1 and the spectrum signal received by the spectrum detection device R2.

[0027] Substituting the expression of the spectrum signal into the cross-correlation function, we get: in, It represents the cross-correlation between the spectrum signal received by spectrum detection device R1 and the spectrum signal received by spectrum detection device R2. Due to the independence of signal and noise, the TDOA server can decompose the cross-correlation into signal and noise parts: in, It represents the cross-correlation between the spectrum signal received by the spectrum detection device R1 and the spectrum signal received by the spectrum detection device R2. represents the cross-correlation of noise. Expresses the arrival time difference between the spectrum signal and spectrum detection device 2 and spectrum detection device 1. The TDOA server uses the cross-correlation function Search and you can find The maximum value of the arrival time difference between R1 and R2 The signals of the three spectrum detection devices can be calculated using the generalized cross-correlation algorithm (GCC) to obtain the arrival time difference between the three spectrum detection devices. The intersection of the hyperbolas can then be drawn using the TDOA algorithm to locate the target position of the drone.

[0028] Step 104: Determine whether the target drone is a networked drone based on the protocol information, spectrum signal, and target location information.

[0029] Among them, networked drones can be understood as drones that use 4G / 5G signals for communication and control.

[0030] In one embodiment, determining whether a target drone is a networked drone based on protocol information, spectrum signals, and target location information may include: determining the protocol flow of the protocol information; determining the degree of signal variation in the spectrum signals; and determining the location characteristics of the target drone based on the target location information. If the protocol flow, signal variation, and location characteristics meet pre-set conditions, the target drone is determined to be a networked drone. In other words, networked drones can be effectively identified based on the target drone's signal variation, location characteristics, and protocol flow.

[0031] The preset conditions may include: the protocol flow being consistent with the Long Term Evolution (LTE) protocol flow, low signal variance, and the location characteristics matching preset location characteristics. The target location information may include location coordinates at multiple time points. The location characteristics may be understood as the location characteristics of the target drone. These may include, but are not limited to, the target drone's separation distance, target altitude, and target speed. The preset location characteristics may be understood as the preset location characteristics of the connected drone. These may include, but are not limited to, a preset separation distance, a preset altitude range, and a preset speed range.

[0032] Among them, the Long Term Evolution protocol is the long-term evolution of the UMTS technical standard developed by the 3GPP organization and belongs to the fourth generation mobile communication standard. Specifically, the spectrum signal may include multiple time-frequency resource graphs. Determining the degree of signal change of the spectrum signal may include: performing feature comparison on the multiple time-frequency resource graphs to obtain a comparison result; when the comparison result shows that the feature difference is small, determining that the degree of signal change of the spectrum signal is low; when the comparison result shows that the feature difference is large, determining that the degree of signal change of the spectrum signal is high.

[0033] In a specific embodiment, performing feature comparison on multiple time-frequency resource graphs to obtain comparison results may include: using a deep learning model to perform feature extraction and feature comparison on multiple time-frequency resource graphs to obtain comparison results. This can utilize the advantages of the deep learning model to improve the feature extraction accuracy of the time-frequency resource graph, thereby improving the accuracy of the comparison results.

[0034] Determining the position characteristics of the target UAV based on the target position information may include: determining the interval distance between multiple adjacent time points in the multiple time points based on the position coordinates of multiple time points; determining the target speed of the target UAV based on the position coordinates of multiple time points; and determining the target height of the target UAV based on the position coordinates of multiple time points.

[0035] The method for determining whether a location feature matches a preset location feature may include determining that the location feature of the target drone matches the preset location feature when the interval distance between multiple adjacent time points is less than a preset distance, the target speed is within a preset speed range, and the target altitude is within a preset altitude range. Specifically, based on the change in the interval distance, target speed, and target altitude of the target drone, it is determined whether the target drone has similar distance change, speed, and altitude to those of the networked drone, thereby effectively identifying the networked drone.

[0036] The preset speed range can be understood as the preset speed range of the networked drone. The preset altitude range can be understood as the altitude range of the networked drone.

[0037] Specifically, the position coordinates at the multiple time points may include the altitude coordinates of the target drone. Therefore, determining the target altitude of the target drone based on the position coordinates at the multiple time points may include: determining an average altitude of the target drone based on the altitude coordinates corresponding to the multiple time points; and determining the average altitude as the target altitude of the target drone.

[0038] In an embodiment of the present invention, the target drone's protocol information and spectrum signal are collected and analyzed by collecting and analyzing communication data (first information and personal information) between the target drone and a base station. The target drone's target location information is then determined based on the target drone's spectrum signal, thereby locating the target drone. Furthermore, because networked drones communicate with other wireless terminals via 4G / 5G signals, the networked drone's protocol information is similar to that of the wireless terminal's signal, and the networked drone's spectrum signal is similar to that of the wireless terminal's signal. However, the networked drone's location is different from that of the wireless terminal. Therefore, based on the spatial characteristics (location information) and spectrum characteristics (protocol information and spectrum signal) of the networked drone, the present invention effectively identifies the networked drone, thereby providing a basis for monitoring networked drones in drone airspace. Furthermore, when the target drone is a networked drone, the networked drone's location is achieved.

[0039] In one embodiment, the method for identifying a networked drone provided by the embodiment of the present invention may further include: Figure 4 Steps 201 to 203 shown: Step 201: Determine the frequency range of the downlink signal of the target UAV based on the protocol information.

[0040] Step 202: Send the frequency range of the downlink signal to the countermeasure device.

[0041] Step 203: Use the countermeasure device to generate a countermeasure signal for countering the target drone according to the frequency range of the downlink signal and user requirements.

[0042] For example, using Figure 2 The countermeasure device generates a countermeasure signal to counter the target drone based on the frequency range of the downlink signal and user needs.

[0043] In an embodiment of the present invention, the frequency range of the target drone's downlink signal is determined based on protocol information; the frequency range of the downlink signal is transmitted to a countermeasure device; and the countermeasure device generates a countermeasure signal to counter the target drone based on the frequency range of the downlink signal and user requirements. Specifically, after determining that the target drone is a networked drone, the frequency range of the target drone's downlink signal can be determined based on the protocol information. The countermeasure device then generates a countermeasure signal to counter the target drone based on the frequency range of the downlink signal and user requirements. Furthermore, because the countermeasure signal is generated based on the frequency range of the target drone's downlink signal, the countermeasure signal can only counter the target drone, precisely interfering with the target drone's downlink, without interfering with the downlink signal transmissions of other drones. This ensures effective countermeasures against networked drones while minimizing the impact on communications of other drones in the legitimate mobile communication system.

[0044] Specifically, determining the frequency range of the downlink signal of the target UAV based on the protocol information can include: querying the frequency information based on the protocol information to obtain the frequency range of the downlink signal of the target UAV, the frequency information including the frequency range of the downlink signal of the UAV corresponding to each protocol information, thereby achieving the purpose of accurately determining the frequency range of the downlink signal of the target UAV based on the protocol information of the target UAV.

[0045] User requirements may include, but are not limited to, target signal strength and target signal coverage. Target signal strength can be understood as the strength of the countermeasure signal. Target signal coverage can be understood as the coverage range of the countermeasure signal.

[0046] In a specific embodiment, using a countermeasure device to generate a countermeasure signal for countering a target drone based on the frequency range of the downlink signal and user requirements may include: using the countermeasure device to generate an initial countermeasure signal with the same frequency range as the downlink signal; and using the countermeasure device to adjust the initial signal strength of the initial countermeasure signal to the target signal strength and the initial signal coverage range of the initial countermeasure signal to the target signal coverage range, thereby obtaining a countermeasure signal for countering the target drone. In other words, by generating a countermeasure signal with a specified signal strength and signal coverage range based on user requirements and the frequency range of the downlink signal, the countermeasure device not only meets user requirements but also enhances the countermeasure effect of the countermeasure signal.

[0047] In a specific embodiment, adjusting the initial signal strength of the initial countermeasure signal to a target signal strength using the countermeasure device may include: adjusting the power of an antenna to a target power using the countermeasure device to adjust the initial signal strength of the initial countermeasure signal to the target signal strength. The target power may be understood as the power of the antenna corresponding to the target signal strength.

[0048] Figure 5 This is a schematic diagram of the structure of the identification device of the networked drone provided by the embodiment of the present invention, which is suitable for executing the identification method of the networked drone provided by the embodiment of the present invention. Figure 5 As shown, the device may specifically include: An information monitoring module 401 is configured to monitor first information of a target UAV and second information of a base station using a spectrum detection device, and determine protocol information of the target UAV based on the first information and the second information; a signal extraction module 402, configured to extract a spectrum signal of the target UAV from the first information according to the protocol information; The UAV positioning module 403 is used to locate the target UAV according to the spectrum signal to obtain target position information of the target UAV; The drone identification module 404 is used to determine whether the target drone is a networked drone based on the protocol information, the spectrum signal and the target location information. Optionally, the drone identification module 404 is specifically configured to: Determining a protocol flow for the protocol information; determining a signal change degree of the spectrum signal; Determining the location characteristics of the target UAV based on the target location information; When the protocol process, the signal change degree, and the location characteristics meet preset conditions, determining that the target drone is the networked drone; The preset conditions are: The protocol process is consistent with the long-term evolution protocol process; The degree of signal change is low; The position feature matches the preset position feature.

[0049] Optionally, the spectrum signal includes a plurality of time-frequency resource graphs, and the drone identification module 404 determines the signal change degree of the spectrum signal, including: Performing feature comparison on the multiple time-frequency resource graphs to obtain a comparison result; When the comparison result shows that the feature difference is small, determining that the signal change degree of the spectrum signal is low; When the comparison result shows that the feature difference is large, it is determined that the signal change degree of the spectrum signal is high.

[0050] Optionally, the target location information includes location coordinates at multiple time points, and the location characteristics include an interval distance, a target height, and a target speed. The drone identification module 404 determines the location characteristics of the target drone based on the target location information, including: According to the position coordinates of the multiple time points, the interval distance between multiple adjacent time points in the multiple time points, the target height of the target drone, and the target speed of the target drone are determined.

[0051] Optionally, the protocol information includes a time-frequency range indication, and the signal extraction module 402 is specifically configured to: Determining a target time-frequency range of the target UAV according to the time-frequency range indication; A spectrum sensing device is used to extract a spectrum signal within the target time-frequency range from the first information.

[0052] Furthermore, the device also includes: A frequency range determination module, configured to determine the frequency range of the downlink signal of the target UAV according to the protocol information; A frequency range sending module, sending the frequency range of the downlink signal to the countermeasure device; The drone countermeasure module is used to generate a countermeasure signal for countering the target drone using the countermeasure device according to the frequency range of the downlink signal and user requirements.

[0053] Optionally, the frequency range determination module is specifically configured to: The frequency information is queried according to the protocol information to obtain the frequency range of the downlink signal of the target drone, and the frequency information includes the frequency range of the downlink signal of the drone corresponding to each protocol information.

[0054] Optionally, the user requirements include target signal strength and target signal coverage, and the drone countermeasure module is specifically used to: generating, by the countermeasure device, an initial countermeasure signal having the same frequency range as the downlink signal; The countermeasure device is used to adjust the initial signal strength of the initial countermeasure signal to the target signal strength and adjust the initial signal coverage range of the initial countermeasure signal to the target signal coverage range to obtain a countermeasure signal for countering the target UAV.

[0055] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional modules is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0056] The networked drone identification device provided in embodiments of the present invention collects and analyzes communication data (first information and personal information) between a target drone and a base station to obtain the target drone's protocol information and spectrum signal. It then locates the target drone based on the target drone's spectrum signal to obtain the target drone's target location information, thereby locating the target drone. Furthermore, because networked drones communicate with other wireless terminals via 4G / 5G signals, the networked drone's protocol information is similar to that of the wireless terminal's signal, and the networked drone's spectrum signal is similar to that of the wireless terminal's signal, while the networked drone's location is different from that of the wireless terminal. Therefore, based on the spatial characteristics (location information) and spectrum characteristics (protocol information and spectrum signal) of networked drones, the present invention effectively identifies networked drones, thereby providing a basis for networked drone monitoring in drone airspace. Furthermore, when the target drone is a networked drone, the networked drone's location is achieved.

[0057] Figure 6It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0058] Please refer to Figure 6 , provides an electronic device 50, including: processor 51; and a memory 52 for storing executable instructions of the processor; The processor 51 is configured to execute the above-mentioned method by executing the executable instructions.

[0059] The processor 51 can communicate with the memory 52 via a bus 53 .

[0060] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned method when executed by a processor.

[0061] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying a networked drone, characterized in that: The method comprises: Using a spectrum detection device to listen to first information of a target UAV and second information of a base station, and determining protocol information of the target UAV based on the first information and the second information; extracting the spectrum signal of the target UAV from the first information according to the protocol information; Positioning the target UAV according to the spectrum signal to obtain target position information of the target UAV; Based on the protocol information, the spectrum signal and the target location information, it is determined whether the target drone is a networked drone.

2. The method according to claim 1, characterized in that The determining whether the target drone is a networked drone based on the protocol information, the spectrum signal, and the target location information includes: Determining a protocol flow for the protocol information; determining a signal change degree of the spectrum signal; Determining the location characteristics of the target UAV based on the target location information; When the protocol process, the signal change degree, and the location characteristics meet preset conditions, determining that the target drone is the networked drone; The preset conditions are: The protocol process is consistent with the long-term evolution protocol process; The degree of signal change is low; The position feature matches the preset position feature.

3. The method according to claim 2, characterized in that The spectrum signal includes a plurality of time-frequency resource graphs, and determining the signal change degree of the spectrum signal includes: Performing feature comparison on the multiple time-frequency resource graphs to obtain a comparison result; When the comparison result shows that the feature difference is small, determining that the signal change degree of the spectrum signal is low; When the comparison result shows that the feature difference is large, it is determined that the signal change degree of the spectrum signal is high.

4. The method according to claim 2, characterized in that The target position information includes position coordinates at multiple time points, and the position characteristics include interval distance, target height, and target speed. Determining the position characteristics of the target UAV based on the target position information includes: According to the position coordinates of the multiple time points, the interval distance between multiple adjacent time points in the multiple time points, the target height of the target drone, and the target speed of the target drone are determined.

5. The method according to claim 1, wherein The protocol information includes a time-frequency range indication, and extracting the spectrum signal of the target UAV from the first information according to the protocol information includes: Determining a target time-frequency range of the target UAV according to the time-frequency range indication; A spectrum sensing device is used to extract a spectrum signal within the target time-frequency range from the first information.

6. The method according to claim 1, characterized in that The method further comprises: Determining the frequency range of the downlink signal of the target UAV according to the protocol information; Sending the frequency range of the downlink signal to a countermeasure device; The countermeasure device is used to generate a countermeasure signal for countering the target drone according to the frequency range of the downlink signal and user requirements.

7. The method according to claim 6, characterized in that Determining the frequency range of the downlink signal of the target UAV according to the protocol information includes: The frequency information is queried according to the protocol information to obtain the frequency range of the downlink signal of the target drone, and the frequency information includes the frequency range of the downlink signal of the drone corresponding to each protocol information.

8. The method according to claim 6, characterized in that The user requirements include target signal strength and target signal coverage, and the generating, by the countermeasure device, a countermeasure signal for countering the target drone according to the frequency range of the downlink signal and the user requirements, includes: generating, by the countermeasure device, an initial countermeasure signal having the same frequency range as the downlink signal; The countermeasure device is used to adjust the initial signal strength of the initial countermeasure signal to the target signal strength and adjust the initial signal coverage range of the initial countermeasure signal to the target signal coverage range to obtain a countermeasure signal for countering the target UAV.

9. A device for identifying a networked drone, characterized in that: The device comprises: An information monitoring module, configured to monitor first information of a target UAV and second information of a base station using a spectrum detection device, and determine protocol information of the target UAV based on the first information and the second information; a signal extraction module, configured to extract the spectrum signal of the target UAV from the first information according to the protocol information; A UAV positioning module is used to locate the target UAV according to the spectrum signal and obtain target position information of the target UAV; A drone identification module is used to determine whether the target drone is a networked drone based on the protocol information, the spectrum signal and the target location information.

10. An electronic device, characterized in that: Including processor and memory, The memory is used to store codes and related data; The processor is used to execute the code in the memory to implement the method for identifying a networked drone according to any one of claims 1 to 8.

11. A storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for identifying a networked drone according to any one of claims 1 to 8.

Images