Unmanned aerial vehicle detection method, device, equipment, storage medium and program product

Abstract

The invention discloses an unmanned aerial vehicle detection method, device and equipment, a storage medium and a program product, and the specific technical scheme comprises the steps: obtaining to-be-recognized terminal data from a core network, the to-be-recognized terminal data being obtained by collecting the to-be-recognized terminal data through a network data analysis function NWDAF according to a preset collection moment; for each preset acquisition moment, positioning each terminal by using the to-be-identified terminal data to obtain a positioning result of the terminal at each preset acquisition moment; for each terminal, constructing motion track data corresponding to the terminal according to the preset acquisition moment and the positioning result corresponding to each preset acquisition moment; based on the motion track data corresponding to each terminal, calculating a total score corresponding to each terminal according to different preset scoring indexes; and determining the terminal of which the total score is greater than a preset threshold as an unmanned aerial vehicle. Therefore, the unmanned aerial vehicle can be accurately recognized, and the recognition accuracy of the unmanned aerial vehicle is improved.

Description

Technical Field

[0001] The present application belongs to the field of wireless communication technology, and in particular relates to a method, apparatus, device, storage medium, and program product for detecting a drone. Background Art

[0002] With the widespread application of drone technology, drones have shown great potential in many fields. However, the disorderly flight and illegal use of drones have also posed challenges to social security. Therefore, how to effectively manage and monitor drone flights has become an urgent issue.

[0003] Currently, methods for detecting and addressing drone intrusions primarily target drones that rely on private network connections. Specifically, drones establish a private link with their operator's controller for point-to-point communication. Specifically, electronic frequency-scanning equipment is deployed in no-fly zones to scan for signal fluctuations in a specific frequency band to detect communication between the drone and the controller, thereby identifying the drone. Interference signals are then sent on the same frequency band to counter the drone, forcing it to return. However, with the advancement of drone technology, communication between drones and operator controllers has evolved from point-to-point private networks to public network relays. Drone manufacturers are installing mobile communication modules on drones and operator controllers to connect them and exchange information over mobile communication networks. Because both drones and controllers use mobile communication modules for communication, they share the same wireless signal characteristics as other standard mobile communication terminals. In this case, applying a jamming signal in the same frequency band as the drone would affect a large number of normal mobile communication users in the area. Therefore, existing frequency-scanning methods are unable to detect drone intrusions, let alone interfere with them. Summary of the Invention

[0004] The embodiments of the present application provide a method, apparatus, device, storage medium, and program product for detecting drones, which can accurately identify drones.

[0005] In a first aspect, an embodiment of the present application provides a method for detecting a drone, comprising:

[0006] Obtaining the terminal data to be identified from the core network, where the terminal data to be identified is collected by the network data analysis function NWDAF according to a preset collection time;

[0007] For each preset collection moment, each terminal is positioned using the terminal data to be identified to obtain a positioning result of the terminal at each preset collection moment;

[0008] For each terminal, constructing motion trajectory data corresponding to the terminal according to the preset collection time and the positioning result corresponding to each preset collection time;

[0009] Based on the motion trajectory data corresponding to each terminal, the total score corresponding to each terminal is calculated according to different preset scoring indicators;

[0010] The terminal whose total score is greater than a preset threshold is determined to be a drone.

[0011] In a possible implementation, positioning each terminal using the to-be-identified terminal data to obtain a positioning result of the terminal at each preset collection time includes:

[0012] For each terminal, obtain the horizontal angle of arrival VAOA, the timing advance TADV and the vertical angle of arrival HAOA corresponding to the terminal from the terminal data to be identified;

[0013] The positioning result of the terminal at the preset collection time is calculated using the horizontal arrival angle VAOA, the time advance TADV and the vertical arrival angle HAOA.

[0014] In a possible implementation, positioning each terminal using the to-be-identified terminal data to obtain a positioning result of the terminal at each preset collection time includes:

[0015] For each terminal, obtain the horizontal angle of arrival VAOA, the timing advance TADV and the vertical angle of arrival HAOA corresponding to the terminal from the terminal data to be identified;

[0016] Calculating a first positioning result of the terminal at the preset collection time using the horizontal angle of arrival VAOA, the timing advance TADV, and the vertical angle of arrival HAOA;

[0017] If there is a preset positioning method, obtaining a second positioning result according to the preset positioning method;

[0018] The second positioning result is used as the positioning result of the terminal.

[0019] In one possible implementation, the to-be-identified terminal data includes a horizontal angle of arrival (VAOA), a timing advance (TADV), and a vertical angle of arrival (HAOA) between the terminal and a base station; and positioning each terminal using the to-be-identified terminal data to obtain a positioning result of the terminal at each preset collection time includes:

[0020] For each preset acquisition moment, the vertical position of the terminal is calculated using the horizontal angle of arrival VAOA and the timing advance TADV;

[0021] For each preset acquisition moment, the horizontal position of the terminal is calculated using the vertical angle of arrival HAOA and the timing advance TADV;

[0022] For each preset collection moment, a positioning result of the terminal at the collection moment is determined based on the vertical position and the horizontal position.

[0023] In a possible implementation, the total score corresponding to each terminal is calculated based on the motion trajectory data corresponding to each terminal according to different preset scoring indicators, including:

[0024] For the motion trajectory data corresponding to each terminal, extract different preset indicator features from the motion trajectory data;

[0025] For different preset indicator features corresponding to the terminal, according to the correspondence between the preset features and the preset weights, determining the target weight corresponding to each preset indicator feature;

[0026] The weights corresponding to different preset indicator features are summed to obtain the total score corresponding to the terminal at the preset collection time.

[0027] In a possible implementation, after determining that a terminal having a total score greater than a preset threshold is a drone, the method further includes:

[0028] For each terminal, a target score corresponding to the terminal is displayed on the display interface, where the target score is the highest score among the total scores corresponding to the terminal at each preset collection moment.

[0029] In a second aspect, an embodiment of the present application provides a device for detecting a drone, comprising:

[0030] A collection module is used to obtain the terminal data to be identified from the core network, where the terminal data to be identified is collected by the network data analysis function NWDAF according to a preset collection time;

[0031] A positioning module, configured to locate each terminal using the terminal data to be identified at each preset collection moment, and obtain a positioning result of the terminal at each preset collection moment;

[0032] A construction module, configured to construct, for each terminal, motion trajectory data corresponding to the terminal according to the preset collection time and the positioning result corresponding to each preset collection time;

[0033] A calculation module is used to calculate the total score corresponding to each terminal based on the motion trajectory data corresponding to each terminal according to different preset scoring indicators;

[0034] The determination module is configured to determine a terminal whose total score is greater than a preset threshold as a drone.

[0035] In a third aspect, an embodiment of the present application provides a terminal device, the device comprising: a processor and a memory storing computer program instructions;

[0036] When the processor executes the computer program instructions, the method for detecting a drone according to the first aspect is implemented.

[0037] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method for drone detection as in the first aspect is implemented.

[0038] In a fifth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device performs the method for drone detection as in the first aspect.

[0039] The method, apparatus, device, storage medium, and program product for drone detection according to the embodiments of the present application obtain data of terminals to be identified from the core network, wherein the data of terminals to be identified is collected by the NWDAF according to preset collection times. For each preset collection time, the data of the terminals to be identified is used to locate each terminal at the preset collection time, thereby obtaining the positioning result corresponding to each terminal at the preset collection time. In this way, by calculating the positioning result corresponding to each terminal at each preset collection time, the motion trajectory data corresponding to each terminal can be constructed. Then, for the motion trajectory data corresponding to each terminal, the total score corresponding to each terminal is calculated from multiple dimensions according to different preset scoring indicators, wherein the preset scoring indicators are pre-set based on the characteristics of the drone terminal. Therefore, the above-mentioned total score can represent the possibility that the terminal to be identified is a drone terminal. A terminal with a total score greater than a preset threshold is a confirmed drone. In this way, the drone terminal is identified from multiple dimensions, thereby improving the accuracy of the identification result. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 This is a flow chart of a method for detecting a drone provided in an embodiment of the present application;

[0042] Figure 2 is an exemplary schematic diagram of a positioning result determination method provided in an embodiment of the present application;

[0043] Figure 3This is a flowchart of a method for determining a positioning result provided by an embodiment of the present application;

[0044] Figure 4a is an exemplary schematic diagram of a vertical position determination method provided in an embodiment of the present application;

[0045] Figure 4b is an exemplary schematic diagram of a method for determining a horizontal position provided in an embodiment of the present application;

[0046] Figure 5 This is a flow chart of a total score calculation method provided in an embodiment of the present application;

[0047] Figure 6 This is an exemplary schematic diagram of a total score calculation method provided in an embodiment of the present application;

[0048] Figure 7 This is an exemplary schematic diagram of a total score calculation method provided in the prior art;

[0049] Figure 8 is an exemplary schematic diagram of motion trajectory data provided in an embodiment of the present application;

[0050] Figure 9 is an exemplary schematic diagram of a display interface provided in an embodiment of the present application;

[0051] Figure 10 This is a schematic diagram of the system architecture for applying the drone detection method provided in the embodiments of the present application;

[0052] Figure 11 This is a schematic diagram of the structure of a system architecture using the drone rejection method provided by an embodiment of the present application;

[0053] Figure 12 This is a schematic structural diagram of a device for detecting drones provided in an embodiment of the present application;

[0054] Figure 13 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0056] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0057] In order to solve the problems of the prior art, the embodiments of the present application provide a method, apparatus, device, storage medium and program product for detecting drones. The following first describes the method for detecting drones provided in the embodiments of the present application. Figure 1 As shown, the method is applied to an electronic device, and the method includes:

[0058] S101. Obtain terminal data to be identified from the core network.

[0059] The terminal data to be identified is collected by the network data analysis function NWDAF according to a preset collection time.

[0060] The preset collection time is the time when the Network Data Analytics Function (NWDAF) collects terminal data. Specifically, the preset collection time can be preset based on experience.

[0061] It is understandable that the drone can access the public mobile communication network through the Subscriber Identification Module (SIM) card. Therefore, the data of each terminal can be obtained in real time through the NWDAF, and the terminal data obtained in real time includes the above-mentioned terminal data to be identified.

[0062] S102 : For each preset collection moment, each terminal is positioned using the terminal data to be identified to obtain a positioning result of the terminal at each preset collection moment.

[0063] The data of the terminal to be identified includes the horizontal angle of arrival, vertical angle of arrival, and timing advance of the terminal relative to the base station. Therefore, the electronic device can determine the position of each terminal based on the horizontal angle of arrival, vertical angle of arrival, and timing advance, and obtain a positioning result.

[0064] It should be noted that the above-mentioned terminal data to be identified also includes the current preset collection time and the base station to which the terminal is connected. The electronic device obtains the above-mentioned horizontal arrival angle, vertical arrival angle and time advance from the base station to which the current terminal is connected, thereby calculating the positioning result of the terminal.

[0065] S103 : For each terminal, construct motion trajectory data corresponding to the terminal according to the preset collection time and the positioning result corresponding to each preset collection time.

[0066] It can be understood that for each terminal, after the electronic device calculates the positioning result of each preset collection moment, the positioning result sequence corresponding to each terminal can be determined according to the sequence of the preset collection moments, thereby constructing the motion trajectory data corresponding to the terminal.

[0067] S104 : Based on the motion trajectory data corresponding to each terminal, calculate the total score corresponding to each terminal according to different preset scoring indicators.

[0068] The preset scoring indicators are pre-set based on the characteristics of the drone terminal. For example, the preset scoring indicators may include the length of time the terminal resides in each base station service cell, the terminal's motion state, the terminal's signal quality, and whether a service cell handover occurs. Specific preset scoring indicators can be set based on actual business needs and are not limited in this embodiment of the present application.

[0069] S105: Determine a terminal whose total score is greater than a preset threshold as a drone.

[0070] Among them, when the total score is greater than the preset threshold, it means that the corresponding terminal is more likely to be a drone.

[0071] Using the above method, the data of the terminal to be identified is obtained from the core network, wherein the data of the terminal to be identified is collected by the NWDAF according to the preset collection time. For each preset collection time, the data of the terminal to be identified is used to locate each terminal at the preset collection time, thereby obtaining the positioning result corresponding to each terminal at the preset collection time. In this way, by calculating the positioning result corresponding to each terminal at each preset collection time, the motion trajectory data corresponding to each terminal can be constructed. Then, for the motion trajectory data corresponding to each terminal, the total score corresponding to each terminal is calculated from multiple dimensions according to different preset scoring indicators, wherein the preset scoring indicators are pre-set based on the characteristics of the drone terminal. Therefore, the above total score can represent the possibility that the terminal to be identified is a drone terminal. A terminal with a total score greater than a preset threshold is a confirmed drone. In this way, the drone terminal is identified from multiple dimensions, thereby improving the accuracy of the identification result.

[0072] In some embodiments of the present application, the above S102, for each preset collection time, using the terminal data to be identified to locate each terminal, and obtaining the positioning result of the terminal at each preset collection time, has the following two implementation methods:

[0073] The first implementation method: For each terminal, the horizontal angle of arrival (VAOA), timing advance (TADV), distance between the base station and the terminal, and vertical angle of arrival (HAOA) are obtained from the terminal data to be identified. The horizontal angle of arrival (VAOA), timing advance (TADV), distance between the base station and the terminal, and vertical angle of arrival (HAOA) are used to calculate the terminal's positioning result at the preset collection time.

[0074] The data of the terminal to be identified include the horizontal arrival angle, vertical arrival angle, distance between the base station and the terminal, and time advance.

[0075] The method of calculating the positioning result using the horizontal arrival angle, vertical arrival angle, distance between the base station and the terminal, and time advance is described in detail in subsequent embodiments.

[0076] Using the first implementation described above, each terminal can be located using the horizontal angle of arrival (VAOA), timing advance (TADV), and vertical angle of arrival (HAOA) between the base station and the terminal. This method uses the horizontal angle of arrival (VAOA), timing advance (TADV), and vertical angle of arrival (HAOA) of each terminal and a base station to determine the positioning result for each terminal. This reduces the computational effort and improves efficiency.

[0077] Second implementation method: Step 1: For each terminal, obtain the horizontal angle of arrival VAOA, timing advance TADV, distance between the base station and the terminal, and vertical angle of arrival HAOA corresponding to the terminal from the terminal data to be identified.

[0078] Step 2: Calculate the first positioning result of the terminal at the preset collection time using the horizontal angle of arrival VAOA, the time advance TADV, the distance between the base station and the terminal, and the vertical angle of arrival HAOA.

[0079] Step 3: If a preset positioning method exists, obtain a second positioning result according to the preset positioning method.

[0080] Among them, the preset positioning method can be to determine the location information of each terminal through phased array radar or laser radar, that is, the above-mentioned second positioning result.

[0081] It should be noted that the embodiment of the present application does not limit the number of preset positioning methods, that is, there can be multiple preset positioning methods. In this way, the electronic device can obtain the positioning results corresponding to different preset positioning methods, and then perform a fusion calculation on the first positioning result and the second positioning result to finally determine the positioning result corresponding to each terminal.

[0082] The electronic device can receive access to third-party positioning technology through an open API interface. The third-party positioning technology is the aforementioned preset positioning method. In this way, if the accuracy of the second positioning result determined by the third-party positioning technology is greater than the accuracy of the first positioning result calculated by the base station based on the horizontal angle of arrival, vertical angle of arrival, and time advance, the second positioning result is obtained.

[0083] Step 4: Use the second positioning result as the positioning result of the terminal.

[0084] It should be noted that, when there are multiple preset positioning methods, multiple second positioning results can be fused and calculated. For example, multiple second positioning results can be fused and calculated by weighting or other methods to finally obtain one second positioning result.

[0085] Specifically, such as Figure 2 As shown, Figure 2 a is an exemplary schematic diagram of fusing positioning results of multiple systems to obtain the positioning result corresponding to the terminal. Figure 2 b is a flow chart of determining the terminal positioning result.

[0086] against Figure 2 a. After the terminal positioning results are determined by the 5G base station, lidar, and phased array radar respectively, the terminal positioning results corresponding to each system are synchronized and error-attenuated. Then, the positioning results corresponding to each system are integrated and transmitted back via 5G wireless data, thereby displaying the terminal trajectory data on the electronic device.

[0087] against Figure 2 b. By introducing interference coordination technology, system errors can be reduced. Then, the positioning results of multiple systems are integrated to obtain the terminal positioning results. Through 5G wireless data backhaul, the positioning results are combined with the application to obtain the total score corresponding to each terminal.

[0088] With the second implementation described above, after calculating the first positioning result of the terminal at the preset collection time using the horizontal angle of arrival (VAOA), the timing advance (TADV), and the vertical angle of arrival (HAOA), a second positioning result can be obtained using a preset positioning method. This means that the terminal can be more accurately positioned using other positioning methods, ultimately obtaining a positioning result corresponding to each terminal. This improves the accuracy of the final positioning result.

[0089] Among them, for the above-mentioned calculation of the positioning result of the terminal at the preset collection time using the horizontal arrival angle VAOA, the time advance TADV and the vertical arrival angle HAOA, as shown in FIG. Figure 3 As shown, it can be implemented as follows:

[0090] S301: For each preset collection moment, the vertical position of the terminal is calculated using the horizontal angle of arrival VAOA, the distance between the terminal and the base station, and the timing advance TADV.

[0091] S302: For each preset collection moment, the horizontal position of the terminal is calculated using the vertical angle of arrival HAOA, the distance between the terminal and the base station, and the timing advance TADV.

[0092] S303: For each preset collection moment, determine a positioning result of the terminal at the collection moment based on the vertical position and the horizontal position.

[0093] Using the method provided in the embodiment of the present application, the vertical position of the terminal is calculated by the horizontal angle of arrival VAOA, the distance between the terminal and the base station, and the time advance TADV, and the horizontal position of the terminal is calculated using the vertical angle of arrival HAOA, the distance between the terminal and the base station, and the time advance TADV. In this way, the positioning result of each terminal can be calculated using the horizontal angle of arrival VAOA, the vertical angle of arrival HAOA, the distance between the terminal and the base station, and the time advance TADV corresponding to a base station, reducing the amount of calculation and improving calculation efficiency.

[0094] Specifically, Figure 4a This is a front view of the location of the terminal in the building. Figure 4b A top view of the location of the terminals in the building.

[0095] The vertical position of each terminal relative to the ground, or the height of each terminal, is determined based on the horizontal angle of arrival (VAOA) between each terminal and the base station in the building, the distance between base stations, and the antenna height. Specifically, a terminal with a horizontal angle of arrival of 19 degrees is located on the 1st floor; a terminal with a horizontal angle of arrival of 16 degrees is located on the 3rd floor; and a terminal with a horizontal angle of arrival of 336 degrees is located on the 17th floor.

[0096] Then the electronic device calculates the horizontal position of each terminal based on the vertical arrival angle HAOA, the distance between base stations and the time advance TADV. The distribution of each terminal on the projection plane of the building is as follows: Figure 4b In this way, the electronic device can determine the specific location of each terminal in the building and obtain the positioning result corresponding to each terminal at the current preset collection time.

[0097] In some embodiments of the present application, the above S104 calculates the total score corresponding to each terminal based on the motion trajectory data corresponding to each terminal according to different preset scoring indicators, such as Figure 5 As shown, it can be implemented as follows:

[0098] S1041 : For the motion trajectory data corresponding to each terminal, extract different preset scoring index features from the motion trajectory data.

[0099] For each terminal, the motion trajectory data includes a feature vector corresponding to each preset collection moment, and the feature vector includes different preset scoring index features.

[0100] S1042: For different preset scoring indicator features corresponding to the terminal, determine a target weight corresponding to each preset scoring indicator feature according to a correspondence between the preset scoring indicator features and the preset weights.

[0101] S1043: Sum the weights corresponding to different preset scoring indicator features to obtain a total score corresponding to the terminal at the preset collection time.

[0102] Specifically, the method for calculating the total score corresponding to the terminal at the preset collection time is as follows: Figure 6 As shown, after acquiring the terminal's trajectory data, the system determines whether the terminal's trajectory meets the following criteria: Dongle or Module type, average dwell time per day or number of shutdowns within X days, whether the terminal's speed is greater than X kilometers per hour, the road network and cell handover relationship within the area, and the terminal's corresponding horizontal angle of arrival. Corresponding indicator features are extracted from the trajectory data, and the extracted indicator features and the above criteria are input into a scoring system. The scoring system determines whether the extracted indicator features meet the above criteria, determines the target weight corresponding to each preset scoring indicator feature based on the weight corresponding to each judgment criterion, and outputs a detailed list of drone users.

[0103] The total score corresponding to each terminal can be calculated according to the scoring method shown in Table 1.

[0104] Table 1

[0105]

[0106] The electronic device obtains the SIM card power-on / off status according to the preset scoring indicator Evt_POWER_ON. If the terminal is powered on, the target weight of the terminal in the above preset scoring indicator is 20.

[0107] Obtain the SIM card power on / off status based on the preset scoring indicator Evt_POWER_OFF. If the terminal is in the power off state, the target weight of the terminal in the above preset scoring indicator is 10.

[0108] According to the preset scoring indicator Evt_HO, the terminal is queried to see whether it is in a handover state. If the terminal has a cell handover, the target weight of the terminal in the above preset scoring indicator is 10;

[0109] According to the preset scoring index Evt_HTTP_genera, the terminal accesses the Internet Protocol (IP) address. If the IP address accessed by the terminal is the target IP address, the target weight of the terminal in the above preset scoring index is 10;

[0110] According to the preset scoring indicator Evt_DNS, the terminal accesses the domain name. If the domain name accessed by the terminal is the target domain name, the target weight of the terminal in the above preset scoring indicator is 10;

[0111] The terminal positioning result is queried according to the preset scoring indicator Evt_UEMRTAOA. If the terminal positioning result has not changed, the target weight of the terminal in the above preset scoring indicator is 10;

[0112] Whether the terminal is on the whitelist is determined according to the preset scoring indicator IMSI_TYPE. If the terminal is a whitelist user, the target weight of the terminal in the above preset scoring indicator is 30.

[0113] It should be noted that the above-mentioned preset scoring indicators are only used as examples. In actual implementation, the preset scoring indicators can be set according to actual business needs, and this application does not impose any restrictions.

[0114] Among them, the existing technology usually adopts the serial recognition method to determine whether the terminal data to be identified has the terminal data corresponding to the drone, such as Figure 7 As shown, the existing serial recognition method is as follows:

[0115] S701: Obtain details of users residing in a specific cell.

[0116] The details of users residing in a specific cell are the terminal data to be identified in the above embodiment.

[0117] S702: Determine whether the type is Dongle or Module.

[0118] If yes, execute S703; if no, discard the data.

[0119] Among them, the drone terminal types are mainly concentrated in Dongle and Module types.

[0120] S703: Perform residence time analysis.

[0121] The dwell time analysis is to determine whether the terminal data to be identified meets the conditions in S704.

[0122] S704: Determine whether the average dwell time within X days is less than n hours or whether the number of power on and off times is greater than m times.

[0123] If yes, execute S705; if no, discard the data.

[0124] S705: Analyze the user's motion status.

[0125] The user motion state analysis is to determine whether the terminal data to be identified meets the conditions in S706.

[0126] S706: Determine whether the terminal's moving speed is greater than X km / hour.

[0127] If yes, execute S707; if no, discard the data.

[0128] S707: Perform cell handover relationship analysis.

[0129] The cell handover relationship analysis is to determine whether the terminal data to be identified meets the conditions in S708.

[0130] S708: Determine the handover relationship between the road network and the cell within the area, generate a handover sequence, and make a judgment based on sequence characteristics.

[0131] If yes, execute S709; if no, discard the data.

[0132] S709: Perform cell user measurement report feature analysis.

[0133] The feature analysis of the cell user measurement report is to determine whether the data of the terminal to be identified meets the conditions in S710.

[0134] S710. Determine whether it is a low-altitude device based on the distribution of VAOA angle values of the cell occupied by the 5G radio (Mobile Radio, MR).

[0135] If yes, execute S711; if no, discard the data.

[0136] S711. Output the drone terminal user details.

[0137] So, using Figure 7The method shown in the figure uses a serial mechanism to determine whether the terminal to be identified is a drone for the terminal data corresponding to each terminal. When the terminal data to be identified does not meet any of the above judgment conditions, the terminal data to be identified will be discarded, resulting in a low accuracy in identifying drones. Therefore, the serial mechanism is changed to Figure 6 The parallel mechanism shown can avoid the problem of being unable to accurately identify drones when the data is unstable, and improve the robustness of the recognition system.

[0138] It should be noted that, in the embodiment of the present application, the electronic device may further provide a display interface, on which the target score corresponding to each terminal is displayed on the display interface.

[0139] The target score is the highest score of the total score corresponding to each preset collection moment of the terminal.

[0140] The display interface also includes an overview list, which is used to display the suspected drone trajectory details, where the suspected drone trajectory details are the trajectory data of the terminals whose total score is greater than the preset threshold.

[0141] The display interface can also be refreshed in real time, and the data in the overview list can be refreshed according to the preset saving time threshold. In addition, the display interface also supports the export of trajectory data. When the user clicks on a piece of data in the overview list, the interface pops up the terminal motion trajectory data corresponding to the data, and the terminal motion trajectory data can be exported. Figure 8 As shown, Figure 8 The motion trajectory data corresponding to a certain data.

[0142] like Figure 9 As shown, Figure 9 A display interface is provided for an embodiment of the present application, wherein the lower left corner of the display interface is an overview list of suspected drone trajectory details, and the right side of the display interface is the monitored drone management area.

[0143] Among them, the overview list is shown in Table 2:

[0144] Table 2

[0145]

[0146] Among them, the intercept field provides a corresponding intercept switch, which supports users to intercept a single number through the display interface. After the user clicks intercept, a "Do you want to intercept" prompt box pops up. After the user clicks "Yes", the number is intercepted.

[0147] It should be noted that in the embodiment of the present application, the overview list is refreshed every 30 seconds, and the interception status is filled in according to the sending status of the number in the power on / off interface sending log, and is judged according to the rule of the latest time plus 5 minutes of the intrusion time. If the instruction is intercepted, then the above interception status is filled in with "intercepted", and if there is no interception instruction, then the above interception status is filled in with "not intercepted". The terminal number that has been invaded most recently is at the top of the overview list. When the same number invades multiple times within the same retention threshold, the signaling time intervals are grouped according to 5 minutes, that is, no continuous signaling is generated within 5 minutes, it is considered the next intrusion. The electronic device can read the SampleTime field in the terminal data to be identified corresponding to the intrusion number to obtain the intrusion time, and the service cell accessed by the terminal corresponding to the intrusion number is the corresponding service cell in the drone management area.

[0148] The following combination Figure 10 The complete process of the drone detection method provided in the embodiment of the present application is introduced as follows: Figure 10 As shown, Figure 10 A system architecture diagram of a method for applying drone detection provided in an embodiment of the present application, wherein the system architecture includes a three-level drone detection architecture.

[0149] Specifically, after the drone connects to the public mobile communication network through the SIM card, the NWDAF in the core network can obtain the terminal data to be identified of any terminal in the network in real time. After the NWDAF obtains the terminal data to be identified, it sends the terminal data to be identified to the drone defense platform through the capability exposure platform. The drone defense platform is deployed on the electronic equipment. The drone defense platform further obtains the terminal positioning results detected by the laser radar and phased array radar from the base station, and fuses the terminal positioning results corresponding to the laser radar and phased array radar to finally obtain the terminal positioning result. Based on this positioning result, the total score corresponding to each terminal is calculated, and the total score corresponding to each terminal at each preset collection time is displayed on the display interface of the drone defense platform.

[0150] For terminals whose total score is greater than the preset threshold, the terminal is determined to be a suspected drone, such as Figure 11 As shown in the figure, after receiving the interception command triggered by the user, the core network sends a signal to the base station, and the base station adjusts its strategy to restrict drones from entering the network, thereby completing the network access control of drones.

[0151] Based on the same concept, the embodiment of the present application also provides a device for detecting drones, such as Figure 12 As shown, the device includes:

[0152] The collection module 1201 is used to obtain the terminal data to be identified from the core network. The terminal data to be identified is collected by the network data analysis function NWDAF according to a preset collection time;

[0153] The positioning module 1202 is configured to locate each terminal using the terminal data to be identified at each preset collection time, and obtain a positioning result of the terminal at each preset collection time;

[0154] A construction module 1203 is configured to construct, for each terminal, motion trajectory data corresponding to the terminal according to the preset collection time and the positioning result corresponding to each preset collection time;

[0155] A calculation module 1204 is configured to calculate a total score corresponding to each terminal based on the motion trajectory data corresponding to each terminal and according to different preset scoring indicators;

[0156] The determination module 1205 is configured to determine a terminal whose total score is greater than a preset threshold as a drone.

[0157] In a possible implementation, the positioning module 1202 is specifically configured to:

[0158] For each terminal, obtain, from the terminal data to be identified, the horizontal angle of arrival VAOA, the timing advance TADV, the distance between the base station and the terminal, and the vertical angle of arrival HAOA corresponding to the terminal;

[0159] The positioning result of the terminal at the preset collection time is calculated using the horizontal arrival angle VAOA, the time advance TADV, the distance between the base station and the terminal, and the vertical arrival angle HAOA.

[0160] In a possible implementation, the positioning module 1202 is specifically configured to:

[0161] For each terminal, obtain, from the terminal data to be identified, the horizontal angle of arrival VAOA, the timing advance TADV, the distance between the base station and the terminal, and the vertical angle of arrival HAOA corresponding to the terminal;

[0162] Calculating a first positioning result of the terminal at the preset collection time by using the horizontal angle of arrival VAOA, the timing advance TADV, the distance between the base station and the terminal, and the vertical angle of arrival HAOA;

[0163] If there is a preset positioning method, obtaining a second positioning result according to the preset positioning method;

[0164] The second positioning result is used as the positioning result of the terminal.

[0165] In a possible implementation, the terminal data to be identified includes a horizontal angle of arrival VAOA, a timing advance TADV, and a vertical angle of arrival HAOA between the terminal and the base station; the positioning module 1302 is specifically configured to:

[0166] For each preset collection moment, calculate the vertical position of the terminal using the horizontal angle of arrival VAOA, the distance between the terminal and the base station, and the timing advance TADV;

[0167] For each preset collection moment, calculate the horizontal position of the terminal using the vertical angle of arrival HAOA, the distance between the terminal and the base station, and the timing advance TADV;

[0168] For each preset collection moment, a positioning result of the terminal at the collection moment is determined based on the vertical position and the horizontal position.

[0169] In one possible implementation, the calculation module 1204 is specifically configured to:

[0170] For the motion trajectory data corresponding to each terminal, extract different preset scoring index features from the motion trajectory data;

[0171] For different preset scoring indicator features corresponding to the terminal, determining a target weight corresponding to each preset scoring indicator feature according to a correspondence between the preset scoring indicator features and the preset weights;

[0172] The weights corresponding to different preset scoring indicator features are summed to obtain the total score corresponding to the terminal at the preset collection time.

[0173] In a possible implementation, the device further includes:

[0174] The display module is configured to display, for each terminal, a target score corresponding to the terminal on a display interface, where the target score is the highest score among the total scores corresponding to the terminal at each preset collection moment.

[0175] It should be noted that the device for detecting drones is a device corresponding to the above-mentioned method for detecting drones. All implementation methods in the above-mentioned method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0176] Figure 13 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0177] The electronic device may include a processor 1301 and a memory 1302 storing computer program instructions.

[0178] Specifically, the processor 1301 may include a central processing unit (CPU) or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0179] Memory 1302 may include a large capacity memory for data or instructions. By way of example and not limitation, memory 1302 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1302 may include removable or non-removable (or fixed) media. Where appropriate, memory 1302 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, memory 1302 is a non-volatile solid-state memory.

[0180] In certain embodiments, the memory 1302 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0181] The processor 1301 implements any one of the drone detection methods in the above embodiments by reading and executing computer program instructions stored in the memory 1302.

[0182] In one example, the electronic device may further include a communication interface 1303 and a bus 1304. Figure 13 As shown, the processor 1301 , the memory 1302 , and the communication interface 1303 are connected via a bus 1304 and communicate with each other.

[0183] The communication interface 1303 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0184] The bus 1304 includes hardware, software, or both that couples components of the electronic device to each other. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Super Transmission (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, the bus 1304 may include one or more buses. Although embodiments herein describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.

[0185] In addition, in conjunction with the drone detection method in the above embodiments, embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any of the drone detection methods in the above embodiments is implemented.

[0186] An embodiment of the present application also provides a computer program product, including a computer program, which, when processed and executed, implements any one of the drone detection methods in the above embodiments.

[0187] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0188] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier wave. "Machine-readable medium" can include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (erasable read-only memory, EROM), floppy disks, compact disc read-only memory (Compact Disc Read-Only Memory, CD-ROM), optical discs, hard disks, optical fiber media, radio frequency (Radio Frequency, RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0189] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0190] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or flowchart and the combination of the boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0191] The above is only a specific implementation method of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited to this. Any technician familiar with this technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the scope of protection of this application.

Claims

1. A method for detecting a drone, characterized in that: include: Obtaining the terminal data to be identified from the core network, where the terminal data to be identified is collected by the network data analysis function NWDAF according to a preset collection time; For each preset collection moment, each terminal is positioned using the terminal data to be identified to obtain a positioning result of the terminal at each preset collection moment; For each terminal, constructing motion trajectory data corresponding to the terminal according to the preset collection time and the positioning result corresponding to each preset collection time; Based on the motion trajectory data corresponding to each terminal, the total score corresponding to each terminal is calculated according to different preset scoring indicators; The terminal whose total score is greater than a preset threshold is determined to be a drone.

2. The method according to claim 1, characterized in that Positioning each terminal using the terminal data to be identified to obtain a positioning result of the terminal at each preset collection time includes: For each terminal, obtain the horizontal angle of arrival VAOA, the timing advance TADV, the distance between the base station and the terminal, and the vertical angle of arrival HAOA corresponding to the terminal from the terminal data to be identified; The positioning result of the terminal at the preset collection time is calculated using the horizontal arrival angle VAOA, the time advance TADV, the distance between the base station and the terminal, and the vertical arrival angle HAOA.

3. The method according to claim 1, characterized in that The method of positioning each terminal using the terminal data to be identified to obtain a positioning result of the terminal at each preset collection time includes: For each terminal, obtain the horizontal angle of arrival VAOA, the timing advance TADV, the distance between the base station and the terminal, and the vertical angle of arrival HAOA corresponding to the terminal from the terminal data to be identified; Calculating a first positioning result of the terminal at the preset collection time by using the horizontal angle of arrival VAOA, the timing advance TADV, the distance between the base station and the terminal, and the vertical angle of arrival HAOA; If there is a preset positioning method, obtaining a second positioning result according to the preset positioning method; The second positioning result is used as the positioning result of the terminal.

4. The method according to claim 2, characterized in that The terminal data to be identified includes a horizontal angle of arrival VAOA, a timing advance TADV, and a vertical angle of arrival HAOA between the terminal and the base station; and calculating a positioning result of the terminal at the preset collection time by using the horizontal angle of arrival VAOA, the timing advance TADV, the distance between the base station and the terminal, and the vertical angle of arrival HAOA includes: For each preset collection moment, calculate the vertical position of the terminal using the horizontal angle of arrival VAOA, the distance between the terminal and the base station, and the timing advance TADV; For each preset collection moment, calculate the horizontal position of the terminal using the vertical angle of arrival HAOA, the distance between the terminal and the base station, and the timing advance TADV; For each preset collection moment, a positioning result of the terminal at the collection moment is determined based on the vertical position and the horizontal position.

5. The method according to claim 1, wherein The method of calculating the total score corresponding to each terminal based on the motion trajectory data corresponding to each terminal according to different preset scoring indicators includes: For the motion trajectory data corresponding to each terminal, extract different preset scoring index features from the motion trajectory data; For different preset scoring indicator features corresponding to the terminal, determining a target weight corresponding to each preset scoring indicator feature according to a correspondence between the preset scoring indicator features and the preset weights; The weights corresponding to different preset scoring indicator features are summed to obtain the total score corresponding to the terminal at the preset collection time.

6. The method according to claim 1, characterized in that After determining the terminal having the total score greater than the preset threshold as a drone, the method further includes: For each terminal, a target score corresponding to the terminal is displayed on the display interface, where the target score is the highest score among the total scores corresponding to the terminal at each preset collection moment.

7. A device for detecting drones, characterized in that: include: A collection module is used to obtain the terminal data to be identified from the core network, where the terminal data to be identified is collected by the network data analysis function NWDAF according to a preset collection time; A positioning module, configured to locate each terminal using the terminal data to be identified at each preset collection moment, and obtain a positioning result of the terminal at each preset collection moment; A construction module, configured to construct, for each terminal, motion trajectory data corresponding to the terminal according to the preset collection time and the positioning result corresponding to each preset collection time; A calculation module is used to calculate the total score corresponding to each terminal based on the motion trajectory data corresponding to each terminal according to different preset scoring indicators; The determination module is configured to determine a terminal whose total score is greater than a preset threshold as a drone.

8. An electronic device, characterized in that: The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the method for detecting a drone according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for detecting a drone according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device performs the method for detecting a drone as described in any one of claims 1 to 6.

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