Aircraft identification method and device, processing equipment and storage medium

Abstract

The invention discloses an aircraft identification method and device, processing equipment and a storage medium. The method comprises the following steps: acquiring position information of a to-be-identified terminal; wherein the terminal to be identified is a terminal connected with a network; determining whether the to-be-identified terminal is a target aircraft or not based on the position information and spatio-temporal information and / or communication information determined based on the position information; wherein the spatio-temporal information indicates the time feature and / or the spatial feature of the position where the terminal to be identified is located, and the communication information indicates the related features of the communication between the terminal to be identified and the network. According to the method, the recognition precision is higher, and the recognition result is more reliable.

Description

Technical Field

[0001] The present invention relates to and is not limited to the field of information processing, and in particular, to a method, apparatus, processing device, and storage medium for identifying an aircraft. Background Art

[0002] With the gradual maturity of aircraft technology and the increasing prevalence of aircraft application scenarios, airspace regulatory authorities have placed a higher emphasis on the safety defense and countermeasures for low-altitude flights, which is directly related to the progress of low-altitude airspace opening and the development of the low-altitude economy. Major regulatory departments have introduced corresponding policies and regulations, clearly requiring that while gradually opening airspace, it is necessary to strengthen the supervision and countermeasure capabilities for low-altitude flights. However, in the related technologies for detecting aircraft, there are situations such as complex implementation, low detection efficiency, low detection accuracy, and narrow signal coverage range for detection. Summary of the Invention

[0003] In view of this, the present invention discloses a method, apparatus, processing device, and storage medium for identifying an aircraft.

[0004] According to a first aspect of an embodiment of the present disclosure, a method for identifying an aircraft is provided, the method including:

[0005] Obtaining position information of a terminal to be identified; wherein, the terminal to be identified is a terminal connected to a network;

[0006] Based on the position information, and based on spatio-temporal information and / or communication information determined according to the position information, determining whether the terminal to be identified is a target aircraft; wherein, the spatio-temporal information indicates time characteristics and / or space characteristics of the position where the terminal to be identified is located, and the communication information indicates characteristics related to the communication of the terminal to be identified with the network.

[0007] In some embodiments, the obtaining position information of the terminal to be identified includes:

[0008] Obtaining the position information of the terminal to be identified from a network device;

[0009] Wherein, the network device includes at least one of the following: Location Management Function (LMF), Gateway Mobile Location Center (GMLC), and a solution platform device.

[0010] In some embodiments, before determining whether the terminal to be identified is a target aircraft, the method further includes:

[0011] Determining the terminal to be identified based on a first policy, the first policy including a policy determined based on predetermined information, the predetermined information including at least one of the following: the location where the terminal to be identified is located, the moving trajectory of the terminal to be identified, and the ground object environment where the terminal to be identified is located.

[0012] In some embodiments, determining whether the terminal to be identified is the target aircraft based on the position information, and the spatio-temporal information and / or communication information determined based on the position information includes:

[0013] Determining whether the terminal to be identified is the target aircraft based on the grid information of the grid where the terminal to be identified is located and the correlation between the grid information and the operating characteristics of the target aircraft;

[0014] Wherein, the grid is a spatial unit divided for space based on spatial attributes, time attributes, signal characteristics, and / or related accessory information; the operating characteristics are characteristics determined based on the spatial attributes, time attributes, signal characteristics, and / or related accessory information of the target aircraft's operation.

[0015] In some embodiments, the grid information includes at least one of the following:

[0016] Grid type, used to indicate the dimension of the space corresponding to the grid;

[0017] Grid spatial range, used to indicate the size and / or position of the grid;

[0018] Grid flight attribute, used to indicate whether the grid supports the flight of the aircraft;

[0019] Communication signal characteristics, used to indicate the communication signal characteristics in different grids;

[0020] Accessory information, at least used to indicate the geographical and landform characteristics of the ground corresponding to the grid.

[0021] In some embodiments, determining whether the terminal to be identified is the target aircraft based on the grid information of the grid where the terminal to be identified is located and the correlation between the grid information and the operating characteristics of the target aircraft includes at least one of the following:

[0022] If it is determined that the terminal to be identified is in a grid that does not support the flight of the aircraft, it is determined that the terminal to be identified is not the target aircraft;

[0023] If it is determined that the terminal to be identified is in a grid that supports the flight of the aircraft, determine whether the terminal to be identified is the target aircraft based on the accessory information and the movement trajectory of the terminal to be identified;

[0024] If it is determined that the terminal to be identified is in a grid that supports the flight of the aircraft, it is determined that the terminal to be identified is the target aircraft;

[0025] Based on the comparison result between the communication signal characteristics indicated by the grid where the terminal to be identified is located and the signal characteristics of the terminal to be identified obtained from the network, determine that the terminal to be identified is the target aircraft.

[0026] In some embodiments, determining whether the terminal to be identified is a target aircraft based on the position information, and the spatio-temporal information and / or communication information determined based on the position information includes:

[0027] Input the position information, the spatio-temporal information and / or the communication information into a terminal recognition model for recognition to obtain the recognition result of whether the terminal to be identified is a target aircraft.

[0028] In some embodiments, the method further includes:

[0029] In response to determining that the terminal to be identified is the target aircraft, determine whether the target aircraft is a cooperative networked aircraft.

[0030] In some embodiments, the method further includes:

[0031] In response to determining that the target aircraft is not a cooperative networked aircraft, perform an alarm process for the target aircraft.

[0032] In some embodiments, the method further includes:

[0033] In response to determining that the target aircraft is a cooperative networked aircraft, determine whether to perform an alarm process for the target aircraft based on the comparison result between the true flight information of the target aircraft and the flight information reported by the target aircraft.

[0034] According to the second aspect of the embodiments of the present disclosure, there is provided an identification device for an aircraft, the device includes:

[0035] An acquisition module, configured to acquire the position information of a terminal to be identified; wherein, the terminal to be identified is a terminal connected to a network;

[0036] A determination module, configured to: based on the position information, and the spatio-temporal information and / or communication information determined based on the position information, determine whether the terminal to be identified is a target aircraft; wherein, the spatio-temporal information indicates the time characteristics and / or space characteristics of the position where the terminal to be identified is located, and the communication information indicates the characteristics related to the communication of the terminal to be identified with the network.

[0037] According to the third aspect of the embodiments of the present disclosure, there is provided a processing device, the processing device includes:

[0038] A memory, used to store an executable program;

[0039] A processor, when executing the executable program stored in the memory, implements the method described in any one of the embodiments of the present disclosure.

[0040] According to a fourth aspect of the embodiments of the present disclosure, a computer storage medium is provided. The computer storage medium stores an executable program, and when the executable program is executed by a processor, the method described in any one of the embodiments of the present disclosure is implemented.

[0041] In the embodiments of the present disclosure, the location information of the terminal to be identified is obtained; wherein, the terminal to be identified is a terminal connected to the network. In this way, the location information of the terminal to be identified connected to the network can be obtained. Based on the location information, and the spatio-temporal information and / or communication information determined based on the location information, it is determined whether the terminal to be identified is a target aircraft; wherein, the spatio-temporal information indicates the time characteristics and / or spatial characteristics of the location where the terminal to be identified is located, and the communication information indicates the characteristics related to the communication between the terminal to be identified and the network. In this way, it is possible to comprehensively determine whether the terminal to be identified is a target aircraft based on the location information, time characteristics, spatial characteristics, and / or characteristics related to communication with the network, and the identification accuracy will be higher and the identification result will be more reliable. Description of the Drawings

[0042] Figure 1 It is a schematic flow chart of a method for identifying an aircraft shown according to the first embodiment;

[0043] Figure 2 It is a schematic flow chart of a method for identifying an aircraft shown according to the second embodiment;

[0044] Figure 3 It is a schematic flow chart of a method for identifying an aircraft shown according to the third embodiment;

[0045] Figure 4 It is a schematic flow chart of a method for identifying an aircraft shown according to the fourth embodiment;

[0046] Figure 5 It is a schematic flow chart of a method for identifying an aircraft shown according to the fifth embodiment;

[0047] Figure 6 It is a schematic flow chart of a method for identifying an aircraft shown according to the sixth embodiment;

[0048] Figure 7 It is a schematic diagram of a model training method shown according to the seventh embodiment;

[0049] Figure 8 It is a schematic flow chart of a method for identifying an aircraft shown according to the eighth embodiment;

[0050] Figure 9 It is a schematic flow diagram of an identification method for an aircraft shown according to the ninth embodiment;

[0051] Figure 10 It is a schematic flow diagram of an identification method for an aircraft shown according to the tenth embodiment;

[0052] Figure 11 It is a schematic diagram of an identification device for an aircraft shown according to the eleventh embodiment. Detailed implementation manners

[0053] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be construed as limitations on the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0054] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0055] In the following description, the terms "first / second / third" are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first / second / third" can be interchanged with a specific order or sequence when permitted, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.

[0056] In the following description, reference is made to "greater than" and "less than". It should be noted that in the present disclosure, "greater than" can be used to indicate "greater than" or "equal to"; "less than" can be used to indicate "less than" or "equal to".

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0058] To better understand the embodiments of the present disclosure, the application scenarios of the technical solutions of the present disclosure will be described first:

[0059] The drone (which can be a type of aircraft) industry has witnessed rapid development, with the market scale and application areas constantly expanding. However, the laws and regulations in the drone industry are not perfect, and the regulatory authorities lack effective technical means to supervise the safe operation of drones. At the same time, due to technical factors, drones have problems such as "flying short distances", "flying low altitudes", and "high costs", resulting in a relatively low normal usage rate of drones across the industry. The fifth-generation mobile communication technology (5G) network-connected drones use the 5G network as the drone data link, enabling remote beyond-line-of-sight flight control. Meanwhile, leveraging the characteristics of large bandwidth, high speed, and low latency of the 5G network, real-time collection, real-time transmission, and real-time processing of interactive information can be achieved. It is an inevitable trend for the industry to develop towards the networking and intelligentization of drones through cellular networks.

[0060] In some embodiments, the drone detection technologies mainly include radar detection, radio spectrum detection, optical detection, and acoustic detection, etc. Drones are typical low-altitude, slow-speed, and small targets (abbreviated as low, slow, and small). Low, slow, and small drones are inexpensive, easy to operate, portable, easy to obtain, and have a strong suddenness of takeoff, making them difficult to detect and dispose of. They are likely to be used as tools for carrying explosive items, dispensing biochemical agents, and spreading leaflets. The characteristics of low, slow, and small targets determine the following difficulties in detecting drone targets: First, the "low" characteristic leads to severe interference from background clutter such as ground clutter; second, the "slow" characteristic requires the radar to have excellent low-speed detection performance; finally, the "small" characteristic requires the radar to have a high detection sensitivity and stability.

[0061] In some embodiments, with the evolution of B5G or 6G technology, the integrated communication and sensing technology has seen significant development. By using cellular network communication base stations to achieve the active radar detection function and reusing the existing 5G base station hardware, drone detection can be realized with low cost, high precision, and wide coverage.

[0062] In some embodiments, to detect and counter 4G and 5G network-connected drones, in addition to the above-mentioned radar, wireless spectrum, optoelectronic, and acoustic detections, relevant technologies monitor telecommunication signal emitters with a moving speed greater than a predetermined speed or an abnormal moving path based on 4G or 5G communication signals. An auxiliary detection device is used to obtain the characteristics of suspected drone targets. The control center combines the information of multiple detection devices to detect and determine drone targets and formulate subsequent countermeasure operations.

[0063] In some embodiments, a single technology cannot achieve the full-scenario drone detection function. Multiple technologies need to be jointly deployed, and appropriate technologies should be selected by comprehensively considering coverage, accuracy, cost, and application scenarios.

[0064] In some embodiments, traditional radars perform poorly in detecting low, slow, and small unmanned aerial vehicles (UAVs), and even fail to meet the usage requirements. Therefore, it is necessary to develop UAV detection radars specifically. Radio spectrum detection represented by time difference of arrival (TDOA) has been widely used as a key technology for UAV detection. However, its application scenarios have significant limitations. It can usually only detect UAV spectrum signals in public frequency bands such as 2.4G and 5.8G, and cannot detect networked UAVs using 5G cellular links or UAVs flying in radio silence. The advantage of optoelectronic detection equipment is that it can make up for the detection blind area of radars and provide accurate detection at close range. However, the disadvantage is that it is vulnerable to weather interference (affected by haze, rain, snow, and sandstorm weather). The detection range of acoustic wave detection equipment is limited, and it is easily affected by the environment.

[0065] In some embodiments, the integrated communication and sensing base station system can achieve wide-area detection of low, slow, and small UAVs. To avoid affecting communication users, reducing communication detection interference, and atmospheric duct interference, a large-scale base station needs to remain silent during the detection time slot and cannot carry out communication services, resulting in a large problem of occupying communication resource overhead, usually occupying more than 10% of the air interface resources. At the same time, the communication and sensing radar faces the common problems of traditional radar detection, with strong ground clutter interference. Especially in complex urban environments, the detection performance and accuracy are greatly limited.

[0066] In some embodiments, the method of detecting the target moving speed or moving path based on 4G and 5G communication signals and comprehensively judging unlicensed drones in combination with auxiliary detection devices can meet some detection requirements for networked drones, but there are problems such as high cost, low efficiency, and insufficient coverage scenarios. First, when initially judging a suspected target, this method needs to further track and locate the target in combination with auxiliary detection devices. The control center comprehensively processes radar, optoelectronic, and spectrum information to form a final judgment. For the detection scenario of wide-area and large-scale drones, the dependence on auxiliary detection devices will greatly increase the detection cost, which is not conducive to wide promotion and has the problems of high cost and low efficiency. Second, there are still many detection scenarios that this method cannot solve. For example, if an unlicensed networked drone flies along a normal road and does not fly along abnormal paths such as crossing buildings or rivers, it cannot be identified as abnormal based on speed and path. This method determines the moving path based on the base station interconnection situation generated by the displacement of the target between grid base stations, simply connecting the serving base stations on the flight path, which has the problem of insufficient accuracy and cannot accurately obtain the true flight path of the drone. Therefore, other detection devices are needed to achieve target tracking and path determination. For an unlicensed networked drone hovering somewhere for surveillance, this method fails because the moving speed is 0 and there is no obvious displacement or moving path. Finally, this method does not solve the detection and supervision problems of cooperative networked drones. If criminals use cooperative networked drones to report disguised or modified flight information and avoid supervision, there are potential hazards and risks of causing flight safety accidents.

[0067] The present invention can use the cellular network to detect networked drones as the drone data link, including the risks and supervision problems brought by cooperative and non-cooperative networked drones. The present invention utilizes the native positioning ability of the cellular network and the big data collection and processing ability of the signal characteristics of networked drone terminals. Based on prior information such as network signal characteristics, three-dimensional terrain environment information, and airspace attributes, combined with the location, trajectory, and real-time signal characteristic information of mobile terminals, it realizes the detection, identification, and supervision of cooperative and non-cooperative networked drones. To further improve the detection performance, the detection and sensing ability of a communication-sensing integrated base station can be combined to realize the detection and identification of networked drones. The present invention utilizes the wide-area continuous deployment, positioning, detection, and sensing ability of the cellular network and signal characteristics to realize the detection and identification of cooperative and non-cooperative networked drones, which can reduce the dependence on other detection means or detection devices and provide a low-cost, high-efficiency, and wide-coverage solution for the detection and supervision of networked drones.

[0068] As Figure 1 shown, an identification method for an aircraft is provided in an embodiment of the present disclosure. The method includes:

[0069] Step S101, obtaining the location information of a terminal to be identified; wherein, the terminal to be identified is a terminal connected to the network;

[0070] Step S102: Determine whether the to-be-identified terminal is a target aircraft based on the location information, and the spatio-temporal information and / or communication information determined based on the location information, where the spatio-temporal information indicates the time characteristics and / or spatial characteristics of the location where the to-be-identified terminal is located, and the communication information indicates the characteristics related to the communication of the to-be-identified terminal with the network.

[0071] In the embodiments of the present disclosure, the aircraft identification method can be applied to the management platform and / or device of the aircraft, but is not limited thereto.

[0072] In some embodiments, the aircraft can be a drone. The target aircraft can be a drone terminal.

[0073] In some embodiments, the to-be-identified terminal can be a terminal that establishes a Radio Resource Control (RRC) connection with a base station in the network. The network can be a 4G or 5G network, or other evolved networks with positioning functions.

[0074] In some embodiments, the location information is information obtained based on network positioning.

[0075] In some embodiments, the location information is the location information obtained by the network through positioning the terminals connected to the network based on its own positioning function. The implementation of the positioning process can be achieved by the network based on the positioning management or control function in the network according to its own capabilities. For example, the positioning management or control function can be a Location Management Function (LMF) or a Gateway Mobile Location Center (GMLC).

[0076] Please refer to Figure 2 , step S101 further includes step S201: Obtain the location information of the to-be-identified terminal from a network device.

[0077] In some embodiments, the location information of the terminal to be identified is obtained from a network device; wherein, the network device includes at least one of the following: Location Management Function (LMF), Gateway Mobile Location Center (GMLC), and a solution platform device. The terminal to be identified is a terminal connected to the network, and the location information is information obtained based on network positioning. Based on the location information, and the spatio-temporal information and / or communication information determined based on the location information, it is determined whether the terminal to be identified is a target aircraft; wherein, the spatio-temporal information indicates the time characteristics and / or spatial characteristics of the location where the terminal to be identified is located, and the communication information indicates the characteristics related to the communication between the terminal to be identified and the network. It should be noted that the location information is not limited to being obtained from a network device, and the location information can also be obtained from an entity storing the location information. For example, a separately deployed server that can obtain the location information from a network device.

[0078] In some embodiments, 4G cellular positioning is affected by signal bandwidth, synchronization, and network deployment, and the positioning accuracy is generally about several tens of meters.

[0079] In some embodiments, 5G introduces a variety of positioning enabling technologies, such as: Uplink Time Difference of Arrival (UTDOA), Observed Time Difference of Arrival (OTDOA), Angle-of-Arrival (AOA), RoundTrip Time (RTT), etc. Utilizing the large bandwidth and multi-antenna characteristics of the 5G system, the positioning accuracy is greatly improved, which can meet the positioning requirements of connected aircraft.

[0080] In some embodiments, the 5G positioning capability meets the following minimum requirements: (a) For 90% of the terminals, the horizontal positioning accuracy is better than 1 meter, and the vertical positioning accuracy is better than 3 meters. (b) The end-to-end delay is less than 100 milliseconds. In subsequent B5G / 6G evolved systems, the cellular network positioning accuracy continues to improve, and location capabilities based on the 5G network can be provided, such as real-time location push, electronic fence, map management, location warning, trajectory query, video linkage, location data analysis, and other services.

[0081] In some embodiments, for the 5G system, the location information of the terminal to be identified can be obtained by querying and accessing the LMF or GMLC; for the B5G or 6G system, the real-time location information of the terminal to be identified can be obtained from the corresponding network element or solution platform according to the positioning network architecture. After obtaining the terminal location information, it can be used as the input information for the next step of processing.

[0082] In some embodiments, please refer toFigure 3 Before step S102, step S301 is further included: determining the to-be-identified terminal based on a first policy, where the first policy includes a policy determined based on predetermined information.

[0083] In some embodiments, location information of the to-be-identified terminal is obtained; wherein, the to-be-identified terminal is a terminal connected to a network, and the location information is information obtained based on network positioning. The to-be-identified terminal is determined based on a first policy, where the first policy includes a policy determined based on predetermined information, and the predetermined information includes at least one of the following: the location where the to-be-identified terminal is located, the movement trajectory of the to-be-identified terminal, and the ground object environment where the to-be-identified terminal is located. Based on the location information, and based on the spatio-temporal information and / or communication information determined based on the location information, it is determined whether the to-be-identified terminal is a target aircraft; wherein, the spatio-temporal information indicates the time characteristics and / or spatial characteristics of the location where the to-be-identified terminal is located, and the communication information indicates the characteristics related to the communication of the to-be-identified terminal with the network.

[0084] In some embodiments, factors such as the location (e.g., longitude, latitude, and altitude), trajectory, and ground object environment of the terminal can be screened and compared to determine the to-be-identified terminal.

[0085] In some embodiments, please refer to Figure 4 Step S102 further includes step S401: determining whether the to-be-identified terminal is a target aircraft based on the grid information of the grid where the to-be-identified terminal is located and the association relationship between the grid information and the operation characteristics of the target aircraft.

[0086] In some embodiments, location information of the to-be-identified terminal is obtained; wherein, the to-be-identified terminal is a terminal connected to a network, and the location information is information obtained based on network positioning. Based on the grid information of the grid where the to-be-identified terminal is located and the association relationship between the grid information and the operation characteristics of the target aircraft, it is determined whether the to-be-identified terminal is a target aircraft; wherein, the grid is a spatial unit divided for space based on spatial attributes, time attributes, signal characteristics, and / or relevant accessory information; the operation characteristics are characteristics determined based on the spatial attributes, time attributes, signal characteristics, and / or relevant accessory information of the operation of the target aircraft.

[0087] In some embodiments, the spatial attribute may include an attribute related to location, for example, a location coordinate.

[0088] In some embodiments, a comparison result can be obtained by comparing the grid information with the operation characteristics of the target aircraft; based on the comparison result, the association relationship between the grid information and the operation characteristics of the target aircraft is determined.

[0089] Exemplarily, if the running trajectory of the terminal to be recognized in the grid indicated by the grid information of the grid where the terminal to be recognized is located is the same as the running trajectory feature of the target aircraft, then the grid information of the grid where the terminal to be recognized is located is consistent with the running feature of the target aircraft, and the terminal to be recognized is the target aircraft.

[0090] In some embodiments, the grid information includes at least one of the following:

[0091] Grid type, used to indicate the dimension of the space corresponding to the grid;

[0092] Grid space range, used to indicate the size and / or position of the grid;

[0093] Grid flight attribute, used to indicate whether the grid supports the flight of the aircraft;

[0094] Communication signal feature, used to indicate the communication signal features in different grids;

[0095] Ancillary information, at least used to indicate the geographical and landform features of the ground corresponding to the grid.

[0096] In some embodiments, a four-dimensional spatio-temporal signal grid can be constructed according to the ground object environment and the distribution characteristics of signals over time and space.

[0097] In some embodiments, referring to Table 1, the grid information may include at least one of the following: grid type, grid space range, grid flight attribute, communication signal feature, and ancillary information.

[0098] Table 1:

[0099]

[0100] In the embodiments of the present disclosure, for different scenarios such as cities, rural areas, mountains, lakes, etc., the grid drawing methods are generally the same and follow the same principles and ideas.

[0101] In some embodiments, the grids are first divided into two major categories: ground and space. An aircraft cannot fly on the ground and can only fly in space. The ground grid mainly gives a definite reference to the aircraft from the dimension of communication signal features, and can efficiently distinguish the validity of the terminal height information according to the communication signal features, thereby reducing the dependence on the positioning accuracy of the aircraft.

[0102] In some embodiments, the division of the ground grid can refer to the existing drive test (DT) and network signal big data information.

[0103] In some embodiments, currently based on terminal measurement report (MR) data reporting and minimization drive test (MDT) data reporting, the network has accumulated a large amount of data on network signal quality and divided a number of planar grids based on a two-dimensional space. The signal characteristics of each grid tend to be similar. A large amount of existing drive test information can also be used as an input for ground grid division, and its usage is the same as that of MR and MDT data. For ground areas where there are no or few mobile phones or terminals arriving, such as water surfaces and restricted areas, simulation tools can be used to simulate and calculate signal coverage data in combination with network engineering parameters.

[0104] In some embodiments, for spatial grids, from the perspective of flight attributes, they are divided into free flight grids, non-flight grids, and traversable flight grids. When drawing the grids, considering the distribution of the ground object environment and the change of signal characteristics, the space is divided into individual grids, and then the attached information of each grid is added to it.

[0105] In some embodiments, when drawing spatial grids, non-flight grids are divided first, then traversable flight grids are divided, and finally, free flight grids for unmanned aerial vehicles are divided.

[0106] In some embodiments, for non-flight grids, the appearance of the ground object environment determines the grid division (grid position and size). Physical facilities such as buildings and hills physically block the possibility of unmanned aerial vehicle flight. The communication signal characteristics therein can be not considered, and they can be directly divided into a grid, and the geographical and topographical features are attached to the grid. It should be noted that for physical entities such as buildings or hills, their appearance is not necessarily a regular cubic grid. It is necessary to find several circumscribed cubes according to the appearance of these facilities, which exactly enclose these entities while minimizing the gaps.

[0107] In some embodiments, for traversable flight grids, the ground object environment and signal characteristics jointly determine the grid division. Referring to the construction method of non-flight grids, circumscribed cube grids are found based on the ground object environment, and the signal characteristic distribution information is obtained according to the actual measurement (using the air measurement instrument carried by the unmanned aerial vehicle for actual measurement) or simulation data. According to the regions with similar signal characteristics, they are further refined into several grids, and then traversable flight grids are obtained, and the attached information is added to the grids.

[0108] In some embodiments, for a free - flight grid, the variation characteristics of signal features more determine the grid division. After obtaining the non - flyable grids and traversable grids, the remaining free space is divided into grids according to the signal feature changes. Generally speaking, the ground signal features can obtain real data based on MR, MDT, and DT data. The signal distribution in the air needs to be obtained by comprehensively considering signal simulation, signal measurement, and the measured signal data of airborne terminals, especially for areas with complex propagation environments below the highest plane of buildings. It should be noted that the signal features fluctuate over time. For example, during peak traffic periods, there are many users, resulting in a large interference level, and the handover success rate and call drop rate due to network capacity will fluctuate. Therefore, the time dimension needs to be considered when processing grid signal features.

[0109] In some embodiments, the signal features may include at least one of the characteristics of the aircraft data link signal service level, such as service data throughput rate, data packet characteristics, and traffic characteristics.

[0110] Through the above - mentioned method, the ground and air three - dimensional spatio - temporal signal grids of the target area can be obtained.

[0111] In some embodiments, since the ground object environment or communication signal features may both change over time, the spatio - temporal signal grids need to be updated and maintained according to a period or event trigger. The update period can be adjusted according to engineering optimization, and by default, it can be executed according to a 24 - hour period. Event - triggered update and maintenance include, but are not limited to, network changes, such as the addition of new sites or the modification of certain site parameters, or changes in the ground object environment, etc.

[0112] It should be noted that the organization methods of prior information such as communication signal features and three - dimensional ground object environment information of the Geographic Information System (GIS) are not limited to the four - dimensional spatio - temporal signal grids disclosed above, and can also be implemented in other ways. The core idea is to facilitate the arrangement and organization of these prior information to form a signal feature fingerprint library for subsequent comparison and query.

[0113] In some embodiments, please refer to Figure 5 , step S102 further includes step S501: including at least one of the following:

[0114] If it is determined that the terminal to be identified is in a grid that does not support aircraft flight, then it is determined that the terminal to be identified is not the target aircraft;

[0115] If it is determined that the terminal to be identified is in a grid that supports aircraft flight, then based on the accessory information and the movement trajectory of the terminal to be identified, it is determined whether the terminal to be identified is the target aircraft;

[0116] If it is determined that the terminal to be recognized is in a grid that supports the flight of the aircraft, then determine that the terminal to be recognized is the target aircraft;

[0117] Based on the comparison result between the communication signal characteristics indicated by the grid where the terminal to be recognized is located and the signal characteristics of the terminal to be recognized obtained from the network, determine that the terminal to be recognized is the target aircraft.

[0118] In some embodiments, obtain the position information of the terminal to be recognized; wherein, the terminal to be recognized is a terminal connected to the network, and the position information is information obtained based on network positioning. Based on the grid information of the grid where the terminal to be recognized is located and the association relationship between the grid information and the operating characteristics of the target aircraft, determine whether the terminal to be recognized is the target aircraft; wherein, the grid is a spatial unit divided for space based on spatial attributes, temporal attributes, signal characteristics, and / or relevant accessory information; the operating characteristics are characteristics determined based on the spatial attributes, temporal attributes, signal characteristics, and / or relevant accessory information of the operation of the target aircraft. Determine that the terminal to be recognized is in a grid that does not support the flight of the aircraft, and determine that the terminal to be recognized is not the target aircraft.

[0119] In some embodiments, if the terminal is in a non - flyable grid, determine that the terminal to be recognized is not the target aircraft. It is also possible to complete the recognition of this terminal to be recognized by comparing communication signal characteristics and then proceed to the recognition of the next terminal to be recognized.

[0120] In some embodiments, obtain the position information of the terminal to be recognized; wherein, the terminal to be recognized is a terminal connected to the network, and the position information is information obtained based on network positioning. Based on the grid information of the grid where the terminal to be recognized is located and the association relationship between the grid information and the operating characteristics of the target aircraft, determine whether the terminal to be recognized is the target aircraft; wherein, the grid is a spatial unit divided for space based on spatial attributes, temporal attributes, signal characteristics, and / or relevant accessory information; the operating characteristics are characteristics determined based on the spatial attributes, temporal attributes, signal characteristics, and / or relevant accessory information of the operation of the target aircraft. Determine that the terminal to be recognized is in a grid that supports the flight of the aircraft, and determine whether the terminal to be recognized is the target aircraft based on accessory information and the movement trajectory of the terminal to be recognized.

[0121] In some embodiments, if the terminal to be recognized is in a traversable flight grid, it is necessary to combine the accessory information of the grid and the movement trajectory of the terminal to determine whether it is the target aircraft.

[0122] Exemplarily, the grid correspondence is a bridge, and there is a possibility that an aircraft passes through. However, simply judging the moving traces on the bridge cannot lead to the conclusion that the mobile terminal is a pedestrian, vehicle, or aircraft passing through on the bridge. It is necessary to combine the moving trajectories before and after the terminal to be identified to determine whether the terminal is the target aircraft. If the previous trajectory of the terminal to be identified moves from free space, then this terminal should be determined as the target aircraft. The basic principle is to combine position, trajectory, and grid attachment information to comprehensively judge and execute the determination of the moving characteristics of the aircraft.

[0123] In some embodiments, the position information of the terminal to be identified is obtained; wherein, the terminal to be identified is a terminal connected to the network, and the position information is information obtained based on network positioning. Based on the grid information of the grid where the terminal to be identified is located, and the association relationship between the grid information and the operating characteristics of the target aircraft, it is determined whether the terminal to be identified is the target aircraft; wherein, the grid is a spatial unit divided for space based on spatial attributes, time attributes, signal characteristics, and related attachment information; the operating characteristics are characteristics determined based on the spatial attributes, time attributes, signal characteristics, and / or related attachment information of the operation of the target aircraft. It is determined that the grid where the terminal to be identified is located supports the flight of the aircraft, and it is determined that the terminal to be identified is the target aircraft.

[0124] In some embodiments, if the terminal to be identified is in a grid where free flight is possible, then in most cases, the terminal to be identified is a drone-borne terminal. To further reduce the misjudgment probability caused by factors such as positioning accuracy, the communication signal characteristics can be combined for further determination of whether the terminal to be identified is the target aircraft.

[0125] In some embodiments, the position information of the terminal to be identified is obtained; wherein, the terminal to be identified is a terminal connected to the network, and the position information is information obtained based on network positioning. Based on the grid information of the grid where the terminal to be identified is located, and the association relationship between the grid information and the operating characteristics of the target aircraft, it is determined whether the terminal to be identified is the target aircraft; wherein, the grid is a spatial unit divided for space based on spatial attributes, time attributes, signal characteristics, and related attachment information; the operating characteristics are characteristics determined based on the spatial attributes, time attributes, signal characteristics, and / or related attachment information of the operation of the target aircraft. Based on the comparison result of the communication signal characteristics indicated by the grid where the terminal to be identified is located and the signal characteristics of the terminal to be identified obtained from the network, it is determined that the terminal to be identified is the target aircraft.

[0126] In some embodiments, there is a strong correlation between signal coverage characteristics and location. After network planning and optimization, ground terminals usually have good wireless metrics such as reference signal receiving power (RSRP) and signal to interference plus noise ratio (SINR) for the corresponding serving cells. For drone terminals flying in the low-altitude area, the signal propagation model is quite different from that of ground terminals. The probability of signal line-of-sight (LOS) transmission increases, the number of neighboring cells increases, and the co-channel interference increases significantly. The RSRP metric does not decay significantly, but the SINR deteriorates significantly and is strongly correlated with the spatial location and the location of neighboring cells. At the same time, due to high interference and complex neighboring cells, the service rate, handover, and dropped call metrics of low-altitude terminals also differ significantly from those of ground terminals and are strongly correlated with location. Therefore, it is possible to determine whether it is a drone-borne terminal based on the signal characteristics of the mobile terminal.

[0127] In some embodiments, the location and trajectory of the mobile terminal have been compared to obtain a preliminary determination that the mobile terminal is the target aircraft. Further confirmation can be made from the dimension of signal characteristics to improve the accuracy of the determination. Comparing from the dimension of communication signal characteristics mainly involves comparing whether the communication signal characteristics of the terminal at a specific moment are highly similar to the communication signal characteristics of the location where it is located.

[0128] In some embodiments, it is necessary to obtain the signal characteristics of the terminal in real time. For example, wireless metrics such as RSRP, SINR, etc. These wireless metrics can be obtained from the MR and MDT reported by the terminal. The MR contains wireless metrics such as the RSRP, SINR, reference signal receiving power (RSRQ, Reference Signal Receiving Power), and received signal strength indication (RSSI, Received Signal Strength Indication) of the serving cell and neighboring cells measured by the terminal in real time. After obtaining these wireless metrics, query the spatio-temporal signal grid where the terminal is located, compare these signal characteristics, and check the similarity and deviation of the matching degree. If the deviation is large, it is necessary to analyze, consider, and match in combination with multiple signal characteristic indicators. For example, if the RSRP and SINR errors of the same serving cell do not exceed the threshold (the default is 6 dB, which can be configured according to engineering optimization), the signal characteristics are considered to match, and the terminal is confirmed as the target aircraft. If the signal characteristics are significantly different and exceed the confidence threshold (which can be configured according to engineering optimization), for example, the difference in RSRP exceeds 20 dB and the difference in SINR exceeds 10 dB, it is necessary to re-combine the positioning, trajectory information, and signal characteristics in other dimensions for decision-making processing, and combine and consider the signal characteristics on the previous trajectory for decision-making processing. Generally speaking, the scenario where the positioning trajectory information and signal characteristics are contradictory is a small probability event.

[0129] In some embodiments, while comparing the wireless signal RSRP and SINR metrics, it is also possible to extend to signal characteristics in other dimensions, such as the physical cell identifier (PCI, Physical Cell Identifier) of the serving cell at a specific location, the number of detected neighboring cells, the neighboring cell PCI, handover events, handover success rate, call drop rate, throughput, and / or bit error rate, etc. The signal characteristic comparison can also be extended to the characteristics at the service level such as the data link packet characteristics and traffic characteristics of the UAV.

[0130] In some embodiments, for the scenario where the positioning accuracy is insufficient, especially when the altitude information is not accurate enough, it is necessary to search for the signal characteristics of the spatio-temporal signal grids at different altitudes in the nearby two-dimensional space for comparison, obtain more matching signal characteristics and spatio-temporal signal grids, and then make a decision on the detection of the terminal type.

[0131] In some embodiments, for the scenario where the position information including the altitude of the terminal is not obtained, for example, when the aircraft is flying along the road and it is impossible to distinguish whether it is a vehicle, pedestrian, or aircraft from the trajectory, it is also possible to determine whether the terminal is the target aircraft through signal characteristic comparison. Because at the same longitude and latitude, the communication signal characteristics are significantly different at different altitudes.

[0132] In some embodiments, please refer to Figure 6, step S102 further includes step S601: inputting the location information, the spatio-temporal information, and / or the communication information into a terminal recognition model for recognition to obtain a recognition result as to whether the terminal to be recognized is a target aircraft.

[0133] In some embodiments, the terminal recognition model is used to recognize the terminal to be recognized. The recognition model can be obtained by training an initial recognition model based on location samples of the aircraft, spatio-temporal information samples, and / or communication information samples.

[0134] In some embodiments, the above embodiments only relate to the application process of the terminal recognition model, and this application process can be executed on the application product. The training process of the terminal recognition model can be executed on a dedicated training device or on the application product, which is not limited herein. When the training process of the terminal recognition model is executed on a dedicated training device, after the terminal recognition model is trained, the trained model can be transplanted to the application product for execution.

[0135] In some embodiments, the location information of the terminal to be recognized is obtained; wherein, the terminal to be recognized is a terminal connected to the network, and the location information is information obtained based on network positioning. The location information, the spatio-temporal information, and / or the communication information are input into a model for recognizing the terminal to be recognized to obtain a recognition result as to whether the terminal to be recognized is a target aircraft; wherein, the spatio-temporal information indicates the time characteristics and / or space characteristics of the location where the terminal to be recognized is located, and the communication information indicates the characteristics related to the communication of the terminal to be recognized with the network.

[0136] In some embodiments, the recognition of the terminal to be recognized can be performed based on big data of network signal characteristics and an artificial intelligence algorithm model (a model for recognizing the terminal to be recognized), for example, the recognition of the terminal to be recognized can be achieved by training using a machine learning classification algorithm.

[0137] Exemplarily, please refer to Figure 7 , which shows a recognition processing flow of a terminal to be recognized:

[0138] In some embodiments, the training and test sample data is [signal feature data; terminal type]. The signal feature data includes, but is not limited to, various signal features shown in the flowchart and new features formed by combinations of various features; the terminal types are divided into unmanned aerial vehicle terminals (or target aircraft) and non-unmanned aerial vehicle terminal data. Non-unmanned aerial vehicle terminals can include, for example, common terminals (such as mobile phones), Internet of Things terminals, vehicle-mounted terminals, etc. Before training, the data set is divided into a training set and a test set as needed, and optimization adjustable parameters are set, such as the objective function, the regularization weight term coefficient, the penalty term coefficient, the maximum depth of the tree, etc.

[0139] In some embodiments, after preparing the feature data vector, various machine learning algorithms can be used for training the classification model. For example, a Stochastic Gradient Descent (SGD) classifier or the XGBoost algorithm can be used, and then cross-validation is performed to measure the accuracy for parameter tuning. The Mean Squared Error (MSE) or Mean Absolute Error (MAE) of the prediction fitting effect using the test set data is used to judge the quality of the model and whether it meets the standard. If the error does not meet the standard, the parameters are continuously iteratively corrected to find the optimal values.

[0140] In some embodiments, after training, the optimal parameters Params of the required model are obtained and the model is saved. Then, based on this model and parameters, various signal features can be input to identify the target aircraft.

[0141] It should be noted that due to the existence of multiple binary classifier machine learning algorithms, there is no restriction on the specific machine learning algorithm, but the market value is a method for determining whether the terminal is a drone terminal type by using signal features and machine learning algorithms to train the model.

[0142] In some embodiments, if it is confirmed from the dimensions of the position trajectory and signal features that the terminal to be identified is a drone terminal (target aircraft), the identification task is completed. If there is no match from both the position and signal features, it is determined that the terminal is a ground terminal, not a drone terminal, and the next terminal is continued to be identified without performing the subsequent step of judgment post-processing.

[0143] In some embodiments, for flexible deployment, the connection of the networked drone can be determined based on the position trajectory or signal features alone. For example, if it is already possible to accurately determine that the terminal belongs to a drone terminal based on the position and trajectory, and the cost or difficulty of constructing the signal feature library is high, the terminal can be determined based on the terminal position trajectory without performing signal feature comparison and verification. If the positioning accuracy of the network system is insufficient or the three-dimensional positioning information of the terminal cannot be obtained, analysis and comparison can be performed based on the signal features, two-dimensional position information, and prior information, rather than using the position trajectory to determine whether the terminal is a networked drone terminal.

[0144] In some embodiments, please refer to Figure 8 This method further includes step S801: in response to determining that the terminal to be identified is the target aircraft, determining whether the target aircraft is a cooperative networked aircraft.

[0145] In some embodiments, obtain the location information of the terminal to be identified; wherein, the terminal to be identified is a terminal connected to the network, and the location information is information obtained based on network positioning. Based on the location information, and the spatio-temporal information and / or communication information determined based on the location information, determine whether the terminal to be identified is the target aircraft; wherein, the spatio-temporal information indicates the time characteristics and / or spatial characteristics of the location where the terminal to be identified is located, and the communication information indicates the characteristics related to the communication of the terminal to be identified with the network. In response to determining that the terminal to be identified is the target aircraft, determine whether the target aircraft is a cooperative connected aircraft.

[0146] In some embodiments, determine whether a terminal belongs to a connected aircraft and confirm whether the terminal is a cooperative connected aircraft.

[0147] In some embodiments, based on the terminal identifier, query from relevant core network network elements whether the terminal is a registered compliant connected drone terminal (target aircraft), such as the network function (NF) or network exposure function (NEF) network element of the unmanned aerial vehicle system (UAS) in the 5G system. It is also possible to query from relevant regulatory platforms (such as the unmanned aerial system traffic management (UTM) or the regulatory platform of the UAS service supplier (USS)) whether the terminal is a cooperative drone terminal.

[0148] In some embodiments, please refer to Figure 9 that the method further includes step S901: In response to determining that the target aircraft is a cooperative connected aircraft, based on the comparison result between the true flight information of the target aircraft and the flight information reported by the target aircraft, determine whether to perform an alarm process for the target aircraft.

[0149] In some embodiments, obtain the location information of the terminal to be identified; wherein, the terminal to be identified is a terminal connected to the network, and the location information is information obtained based on network positioning. Based on the location information, and the spatio-temporal information and / or communication information determined based on the location information, determine whether the terminal to be identified is the target aircraft; wherein, the spatio-temporal information indicates the time characteristics and / or spatial characteristics of the location where the terminal to be identified is located, and the communication information indicates the characteristics related to the communication of the terminal to be identified with the network. In response to determining that the target aircraft is not a cooperative connected aircraft, perform an alarm process for the target aircraft.

[0150] In some embodiments, the terminal is determined to be a connected drone terminal based on its location trajectory and signal characteristics. After querying the supervision platform, it is found that there is no registered flight mission and it is under the control of the supervision platform, posing a potential safety hazard. Thus, it is determined to be a non-cooperative "unauthorized flight" connected drone, and corresponding warning information needs to be generated and reported to the supervision platform for subsequent processing.

[0151] In some embodiments, for non-cooperative connected drones, after querying the supervision platform and determining that they are not cooperative targets, additional detection means can be used as a supplement to confirm whether they are drones. For example, the detection result information of the integrated communication and sensing base station can be used for multiple confirmations.

[0152] In some embodiments, obtain the location information of the terminal to be identified, where the terminal to be identified is a terminal connected to the network, and the location information is obtained based on network positioning. Based on the location information, as well as the spatio-temporal information and / or communication information determined based on the location information, determine whether the terminal to be identified is a target aircraft, where the spatio-temporal information indicates the time characteristics and / or spatial characteristics of the location where the terminal to be identified is located, and the communication information indicates the characteristics related to the communication between the terminal to be identified and the network. In response to determining that the target aircraft is a cooperative connected aircraft, based on the comparison result between the actual flight information of the target aircraft and the flight information reported by the target aircraft, determine whether to perform warning processing for the target aircraft.

[0153] In some embodiments, verify the flight information of cooperative connected drones to check whether there is a risk of inconsistency between the actual flight information and the reported flight information of the cooperative drones, and then perform corresponding subsequent processing.

[0154] In some embodiments, after it is determined that the terminal is a cooperative connected drone terminal, compare the detected location and trajectory information of the drone with the reported location and trajectory and other flight information to analyze whether the drone is flying legally or there is a risk of "unauthorized flight" with tampered flight information.

[0155] In some embodiments, if the verification is okay, it is considered that the cooperative connected drone is flying legally; if the verification fails and it is found that the difference between the actually detected location trajectory and the location trajectory reported to the supervision platform is very large, or even not in the same area, then enter the process of handling unauthorized flight of cooperative drones.

[0156] In some embodiments, when it is determined that the terminal to be identified is a cooperative drone with unauthorized flight, it is necessary to trigger a warning for the cooperative connected drone with unauthorized flight and report it to the platform to execute subsequent supervision and countermeasure operations.

[0157] In some embodiments, for legally flying connected drones, no additional processing is required, and the flight information of the drones is directly reported to the platform as trustworthy.

[0158] In some embodiments, the detection results (recognition results) of networked UAVs are reported to the platform, and the platform performs subsequent supervision operations.

[0159] To better understand the embodiments of the present disclosure, the present disclosure will be further described below through one exemplary embodiment:

[0160] Example 1:

[0161] Please refer to Figure 10 to provide a method for identifying an aircraft, the method comprising:

[0162] Step S1001, perform prior processing.

[0163] In some embodiments, this step mainly constructs a four-dimensional spatio-temporal signal grid based on the ground object environment and the distribution characteristics of signals over time and space for use in subsequent steps. For the specific definition process of the grid, please refer to the foregoing embodiment section and will not be elaborated here.

[0164] Step S1002, perform positioning of the service terminal.

[0165] In some embodiments, the positioning of the terminal to be identified is performed based on the network positioning function. For the positioning scheme, please refer to the foregoing embodiment section and will not be elaborated here.

[0166] Step S1003, perform comparison of position and trajectory information.

[0167] For the comparison process, please refer to the foregoing embodiment section and will not be elaborated here.

[0168] Step S1004, perform comparison of signal characteristics.

[0169] Step S1005, determine whether the terminal to be identified is a networked UAV terminal. If so, perform step S1006.

[0170] Step S1006, query whether the networked UAV terminal is a cooperative UAV. If so, perform step S1007, otherwise perform step S1011.

[0171] Step S1007, verify the reported flight information.

[0172] Step S1008, determine whether the verification is successful. If successful, perform step S1009, otherwise perform step 1010.

[0173] Step S1009, determine compliance flight and perform step S1012.

[0174] Step S1010, perform warning handling for cooperative black non-risk and perform step S1012.

[0175] Step S1011: Perform non - cooperative black flight risk warning handling.

[0176] Step S1012: Report to the platform for post - processing after execution.

[0177] In some embodiments, a four - dimensional spatio - temporal signal grid for drones and corresponding spatio - temporal signal features are constructed based on the ground object environment and the spatio - temporal distribution characteristics of drone signals. The position trajectories and signal features of each terminal are compared with the spatio - temporal signal features of the grid where the terminal is located to make a drone terminal judgment, and then whether the flight of cooperative / non - cooperative drone terminals is compliant is judged according to the real - time flight information of the drone terminals. In this way, a four - dimensional spatio - temporal signal grid is constructed by combining prior data such as signal features and ground object environment, forming a signal feature fingerprint library for networked drones, and it is updated and maintained periodically and triggered based on events. The four - dimensional spatio - temporal signal grid is used for cellular networked drone detection, which can reduce the computational complexity and deployment difficulty of the entire detection process.

[0178] In some embodiments, the drone terminals, non - drone terminals, and their corresponding four - dimensional spatio - temporal signal annotation information are used as training samples to train a terminal classification model, and this terminal classification model is called to make a drone terminal classification judgment on all terminals in a certain area. That is, by using the positioning ability and signal feature collection ability of the cellular network itself, combined with prior data and the real - time signal features of the network and terminals, forward signal feature comparison or prediction and judgment based on machine learning algorithm models are performed to achieve comprehensive detection and supervision of cooperative and non - cooperative networked drones. For non - cooperative network drones, an alarm is directly reported; for cooperative networked drones, flight information verification is performed to identify "black - flying" drones that have tampered with and reported flight information. At the same time, networked drone detection can be completed separately through the terminal position trajectory or signal feature, further reducing the usage threshold and system deployment complexity.

[0179] In some embodiments, when related technologies solve drone detection, multiple detection technologies are comprehensively used, such as low - altitude and slow - speed radars, radio monitoring devices, optoelectronic devices, and acoustic detection devices. These devices have high deployment costs and great difficulties in networking and installation during continuous wide - area detection. The present invention can complete the detection and supervision of networked drones without additionally deploying detection hardware devices, using the communication network itself and the corresponding data processing platform, which has advantages in terms of cost and deployment and is more easily implemented.

[0180] In some embodiments, the related integrated communication and sensing technology can achieve low-altitude detection of unmanned aerial vehicles (UAVs). By reusing base station equipment, low-altitude UAV detection can be completed. However, a considerable proportion of air interface resources need to be allocated to implement radar detection functions. To reduce interference, the base stations in a contiguous area need to be synchronized in transmission and reception during radar detection, further increasing the air interface resource overhead. At the same time, when the communication and sensing base station detects low, slow, and small targets, the detection performance will degrade due to factors such as a large amount of ground clutter and discontinuous coverage. The present invention reuses base station network equipment but can complete the detection of networked UAVs without occupying air interface resources, having advantages in terms of cost and detection performance and not affecting network communication functions.

[0181] In some embodiments, related 4G and 5G networked UAV detection and countermeasure technologies monitor telecommunications signal sources with a moving speed greater than a predetermined speed or an abnormal moving path based on 4G or 5G communication signals. An auxiliary detection device is used to obtain the characteristics of suspected UAV targets, and the control center combines information from various detection devices to achieve the detection and determination of UAV targets. To implement the detection of networked UAVs using this method, in addition to monitoring abnormal telecommunications signal sources, other auxiliary detection devices are also required to complete the detection function, resulting in high costs and difficulties in implementation and deployment. The present invention utilizes the communication network itself and can efficiently, accurately, and low-costly complete the detection of networked UAVs without auxiliary detection devices, fully leveraging the positioning capabilities of the cellular network and the ability to collect and process signal feature data, having the advantages of low cost and convenient implementation and deployment.

[0182] In some embodiments, the present invention can detect and supervise networked unmanned aircraft without relying on other detection systems. By reusing the existing software and hardware resources of the cellular network, it does not affect communication services, does not occupy air interface resources, and utilizes the network's native positioning capabilities and various wireless signal feature data collected. Combining prior information such as three-dimensional signal feature data, ground object environment information, and airspace management information collected or simulated in the past, it realizes the full-scale control of UAV terminals in a specified area. The main work of this proposal is the collection and processing of data. Signal feature data can be processed by comparing with positive algorithms or using AI algorithm models, with low requirements for hardware and flexible deployment according to the network networking scheme.

[0183] As Figure 11 shown, an embodiment of the present disclosure provides an identification device for an aircraft, the device including:

[0184] An acquisition module 111, configured to acquire the location information of a terminal to be identified; wherein, the terminal to be identified is a terminal connected to the network;

[0185] A determination module 112, configured to: determine whether the terminal to be identified is a target aircraft based on the location information, and spatio-temporal information and / or communication information determined based on the location information; wherein the spatio-temporal information indicates temporal characteristics and / or spatial characteristics of the location where the terminal to be identified is located, and the communication information indicates characteristics related to the communication of the terminal to be identified with a network.

[0186] In some embodiments, the obtaining module 111 is further configured to:

[0187] Obtain the location information of the terminal to be identified from a network device;

[0188] wherein the network device includes at least one of the following: a Location Management Function (LMF), a Gateway Mobile Location Center (GMLC), and a solution platform device.

[0189] In some embodiments, the determination module 112 is further configured to:

[0190] Determine the terminal to be identified based on a first policy, where the first policy includes a policy determined based on predetermined information, and the predetermined information includes at least one of the following: the location where the terminal to be identified is located, the movement trajectory of the terminal to be identified, and the ground object environment where the terminal to be identified is located.

[0191] In some embodiments, the determination module 112 is further configured to:

[0192] Determine whether the terminal to be identified is a target aircraft based on the grid information of the grid where the terminal to be identified is located, and the association relationship between the grid information and the operation characteristics of the target aircraft;

[0193] wherein the grid is a spatial unit divided for space based on spatial attributes, temporal attributes, signal characteristics, and related accessory information; and the operation characteristics are characteristics determined based on spatial attributes, temporal attributes, signal characteristics, and / or related accessory information of the operation of the target aircraft.

[0194] In some embodiments, the determination module 112 is further configured that the grid information includes at least one of the following:

[0195] Grid type, used to indicate the dimension of the space corresponding to the grid;

[0196] Grid spatial range, used to indicate the size and / or location of the grid;

[0197] Grid flight attribute, used to indicate whether the grid supports the flight of an aircraft;

[0198] Communication signal characteristics, used to indicate communication signal characteristics in different grids;

[0199] Ancillary information, at least used to indicate the geographical and geomorphic features of the ground corresponding to the grid.

[0200] In some embodiments, the determining module 112 is further configured to perform at least one of the following:

[0201] If it is determined that the terminal to be recognized is in a grid that does not support the flight of the aircraft, it is determined that the terminal to be recognized is not the target aircraft;

[0202] If it is determined that the terminal to be recognized is in a grid that supports the flight of the aircraft, it is determined whether the terminal to be recognized is the target aircraft based on the ancillary information and the movement trajectory of the terminal to be recognized;

[0203] If it is determined that the terminal to be recognized is in a grid that supports the flight of the aircraft, it is determined that the terminal to be recognized is the target aircraft;

[0204] Based on the comparison result between the communication signal characteristics indicated by the grid where the terminal to be recognized is located and the signal characteristics of the terminal to be recognized obtained from the network, it is determined that the terminal to be recognized is the target aircraft.

[0205] In some embodiments, the determining module 112 is further configured to:

[0206] Input the location information, the spatio-temporal information, and / or the communication information into the terminal recognition model to obtain the recognition result of whether the terminal to be recognized is the target aircraft.

[0207] In some embodiments, the determining module 112 is further configured to:

[0208] In response to determining that the terminal to be recognized is the target aircraft, determine whether the target aircraft is a cooperative connected aircraft.

[0209] In some embodiments, the apparatus further includes an execution module 113, which is configured to:

[0210] In response to determining that the target aircraft is not a cooperative connected aircraft, perform an alarm process for the target aircraft.

[0211] In some embodiments, the execution module 113 is further configured to:

[0212] In response to determining that the target aircraft is a cooperative connected aircraft, determine whether to perform an alarm process for the target aircraft based on the comparison result between the true flight information of the target aircraft and the flight information reported by the target aircraft.

[0213] Embodiments of the present disclosure provide a processing device, and the processing device includes:

[0214] A memory for storing an executable program;

[0215] A processor for implementing the method according to any one of the embodiments of the present disclosure when executing the executable program stored in the memory.

[0216] It can be understood that the memory can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM, Read Only Memory), a programmable read-only memory (PROM, Programmable Read-Only Memory), an erasable programmable read-only memory (EPROM, Erasable Programmable Read-Only Memory), an electrically erasable programmable read-only memory (EEPROM, Electrically Erasable Programmable Read-Only Memory), a ferromagnetic random access memory (FRAM, ferromagnetic random access memory), a flash memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM, Compact Disc Read-Only Memory); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM, Random Access Memory), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as a static random access memory (SRAM, Static Random Access Memory), a synchronous static random access memory (SSRAM, Synchronous Static Random Access Memory), a dynamic random access memory (DRAM, Dynamic Random Access Memory), a synchronous dynamic random access memory (SDRAM, Synchronous Dynamic Random Access Memory), a double data rate synchronous dynamic random access memory (DDR SDRAM, Double Data Rate Synchronous Dynamic Random Access Memory), an enhanced synchronous dynamic random access memory (ESDRAM, Enhanced Synchronous Dynamic Random Access Memory), a sync link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and a direct rambus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present application are intended to include, but are not limited to, these and any other suitable types of memories.

[0217] Among them, the method for determining the topological structure disclosed in the present invention can be applied to or implemented by the processor. The processor can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the method for determining the topological structure can be completed by the integrated logic circuit in the hardware of the processor or by instructions in the form of software. The above-mentioned processor can be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can implement or execute each method, step, and logic block diagram disclosed in the present invention. The general-purpose processor can be a microprocessor or any conventional processor, etc. Combining the steps of the method disclosed in the present invention, it can be directly embodied as being executed and completed by the hardware decoding processor, or by a combination of the hardware and software modules in the decoding processor. The software module can be located in the storage medium, and this storage medium is located in the memory. The processor reads the information in the memory and combines its hardware to complete the steps of the method for determining the topological structure provided in the embodiments of the present application.

[0218] The present invention also provides a computer storage medium. The computer storage medium stores an executable program. When the executable program is executed by a processor, it implements the method for determining the topological structure as described in any one of the embodiments of the present disclosure. Specifically, it can be a computer-readable storage medium, for example, including a memory storing a computer program. The above-mentioned computer program can be executed by the processor of the processing device to complete the steps of the method in the embodiments of the present application. The computer-readable storage medium can be a ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM, etc.

[0219] As mentioned above, the above are only the specific implementation manners of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for identifying an aircraft, characterized in that, The method includes: Obtaining the location information of the terminal to be identified; wherein, the terminal to be identified is a terminal connected to the network; Based on the location information, and the spatio-temporal information and / or communication information determined based on the location information, determining whether the terminal to be identified is a target aircraft; wherein, the spatio-temporal information indicates the time characteristics and / or space characteristics of the location where the terminal to be identified is located, and the communication information indicates the characteristics related to the communication of the terminal to be identified with the network.

2. The method according to claim 1, wherein The obtaining the location information of the terminal to be identified includes: Obtaining the location information of the terminal to be identified from a network device; Wherein, the network device includes at least one of the following: Location Management Function (LMF), Gateway Mobile Location Center (GMLC), and solution platform device.

3. The method according to claim 1, wherein Before determining whether the terminal to be identified is a target aircraft, the method further includes: Determining the terminal to be identified based on a first policy, the first policy including a policy determined based on predetermined information, the predetermined information including at least one of the following: the location where the terminal to be identified is located, the movement trajectory of the terminal to be identified, and the ground object environment where the terminal to be identified is located.

4. The method according to claim 1, characterized in that, The determining whether the terminal to be identified is a target aircraft based on the location information, and the spatio-temporal information and / or communication information determined based on the location information includes: Based on the grid information of the grid where the terminal to be identified is located, and the association relationship between the grid information and the operation characteristics of the target aircraft, determining whether the terminal to be identified is a target aircraft; Wherein, the grid is a spatial unit divided for space based on spatial attributes, time attributes, signal characteristics, and / or relevant supplementary information; the operation characteristics are characteristics determined based on the spatial attributes, time attributes, signal characteristics, and / or relevant supplementary information of the operation of the target aircraft.

5. The method according to claim 4, characterized in that The grid information includes at least one of the following: Grid type, used to indicate the dimension of the space corresponding to the grid; Grid space range, used to indicate the size and / or location of the grid; Grid flight attribute, used to indicate whether the grid supports the flight of the aircraft; Communication signal characteristics, used to indicate the communication signal characteristics in different grids; Supplementary information, at least used to indicate the geographical and landform characteristics of the ground corresponding to the grid.

6. The method according to claim 4, wherein The determining whether the terminal to be identified is a target aircraft based on the grid information of the grid where the terminal to be identified is located, and the association relationship between the grid information and the operation characteristics of the target aircraft includes at least one of the following: If it is determined that the terminal to be identified is in a grid that does not support the flight of the aircraft, determining that the terminal to be identified is not the target aircraft; If it is determined that the terminal to be identified is in a grid that supports the flight of the aircraft, determining whether the terminal to be identified is the target aircraft based on the supplementary information and the movement trajectory of the terminal to be identified; If it is determined that the terminal to be identified is in a grid that supports the flight of the aircraft, determining that the terminal to be identified is the target aircraft; Based on the comparison result between the communication signal characteristics indicated by the grid where the terminal to be identified is located and the signal characteristics of the terminal to be identified obtained from the network, determining that the terminal to be identified is the target aircraft.

7. The method according to claim 6, characterized in that, Determining whether the to-be-identified terminal is a target aircraft based on the position information, and spatio-temporal information and / or communication information determined based on the position information includes: Inputting the position information, the spatio-temporal information, and / or the communication information into a terminal identification model for identification to obtain an identification result as to whether the to-be-identified terminal is a target aircraft.

8. The method according to claim 1, characterized in that The method further includes: In response to determining that the to-be-identified terminal is the target aircraft, determining whether the target aircraft is a cooperative connected aircraft.

9. The method according to claim 8, wherein The method further includes: In response to determining that the target aircraft is not a cooperative connected aircraft, performing an alarm process for the target aircraft.

10. The method according to claim 8, wherein The method further includes: In response to determining that the target aircraft is a cooperative connected aircraft, determining whether to perform an alarm process for the target aircraft based on a comparison result between the true flight information of the target aircraft and the flight information reported by the target aircraft.

11. An identification device for an aircraft, characterized in that, The device includes: An acquisition module configured to acquire the position information of a to-be-identified terminal; wherein the to-be-identified terminal is a terminal connected to a network. A determination module configured to: based on the position information, and spatio-temporal information and / or communication information determined based on the position information, determine whether the to-be-identified terminal is a target aircraft; wherein the spatio-temporal information indicates temporal characteristics and / or spatial characteristics of the location where the to-be-identified terminal is located, and the communication information indicates characteristics related to the communication of the to-be-identified terminal with the network.

12. A processing device, characterized in that, The processing device includes: A memory for storing an executable program. A processor for, when executing the executable program stored in the memory, implementing the method according to any one of claims 1 to 10.

13. A computer storage medium, characterized in that, The computer storage medium stores an executable program which, when executed by a processor, implements the method according to any one of claims 1 to 10.

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