A method and device for comprehensive assessment of unmanned aerial vehicle threats for urban low-altitude networks

Abstract

The present application relates to the field of anti-non-technical, in particular to a kind of unmanned aerial vehicle threat comprehensive evaluation method and device for urban low-altitude network.The method includes obtaining unmanned aerial vehicle flight data;Obtain spatial data;Obtain unmanned aerial vehicle report data;Based on unmanned aerial vehicle flight data and unmanned aerial vehicle report data, compliance threat assessment is carried out to obtain compliance threat score;Based on unmanned aerial vehicle flight data and spatial data, space threat assessment is carried out to obtain space threat score;Based on unmanned aerial vehicle flight data and spatial data, behavior mode threat assessment is carried out to obtain behavior mode threat score;Based on compliance threat score, space threat score, behavior mode threat score, comprehensive threat assessment is carried out.In this way, the comprehensive determination of unmanned aerial vehicle threat level in wide-area scenario is realized, and the accuracy and advance of low-altitude safety early warning is improved.

Description

Technical Field

[0001] This invention relates to the field of anti-drone technology, specifically to a method and apparatus for comprehensive assessment of drone threats to urban low-altitude networks. Background Technology

[0002] Drone threat assessment is a core decision-making module of anti-drone systems. Existing drone threat assessment methods are mainly geared towards key area protection or single no-fly zone boundary alarm scenarios. The typical approach is to determine whether a drone has intruded into the no-fly zone based on a preset no-fly zone geometry, combined with parameters such as the drone's real-time position, speed, and heading, and to provide a threat level based on distance thresholds or rule thresholds, thus supporting subsequent response decisions.

[0003] Existing drone threat assessment methods have the following shortcomings: 1. Failure to consider multi-no-fly zone scenarios: Urban low-altitude airspace often contains multiple no-fly zones (such as government buildings, substations, and airports). When multiple no-fly zones are adjacent, existing methods only issue warnings for each no-fly zone independently, lacking a global priority assessment of targets simultaneously threatening multiple no-fly zones, which can easily lead to conflicts in the allocation of countermeasure resources. Furthermore, drones may simultaneously approach multiple constrained targets or evade detection by a single no-fly zone through complex cross-regional flight patterns (such as multi-point reconnaissance and probing close-range maneuvers). 2. Ignoring the coupling impact of commercial airways: Commercial airways (eVTOL routes, logistics corridors) are not included in the assessment as dynamic constraints. When a drone approaches an airway, the system cannot quantify the collision risk with normal aircraft and the potential interference with commercial navigation, thus missing such threats with significant safety risks. Summary of the Invention

[0004] In order to address or at least partially address the deficiencies or shortcomings of the prior art, this invention proposes a method and apparatus for comprehensive assessment of drone threats in urban low-altitude networks.

[0005] In a first aspect of the present invention, a method for comprehensive assessment of drone threats to urban low-altitude networks is provided, comprising:

[0006] S1. Acquire drone flight data; acquire spatial data; acquire drone registration data;

[0007] S2. Conduct a compliance threat assessment based on drone flight data and drone registration data to obtain a compliance threat score;

[0008] S3. Conduct a space threat assessment based on UAV flight data and spatial data to obtain a space threat score;

[0009] S4. Conduct behavioral pattern threat assessment based on UAV flight data and spatial data to obtain a behavioral pattern threat score;

[0010] S5. Conduct a comprehensive threat assessment based on compliance threat score, spatial threat score, and behavioral pattern threat score.

[0011] As a preferred embodiment of the present invention, in S1, the UAV flight data includes the UAV's identification, three-dimensional position, and speed.

[0012] As a preferred embodiment of the present invention, in S1, the spatial data includes no-fly zones and commercial airways. Each no-fly zone has a core area boundary, a buffer zone radius, and a threat weight. Each commercial airway has a centerline, width, altitude layer interval, a specified flight direction vector, and a threat weight.

[0013] As a preferred embodiment of the present invention, in S2, the compliance threat assessment based on UAV flight data and UAV registration data to obtain a compliance threat score specifically includes:

[0014] The compliance threat score is determined using the following formula:

[0015]

[0016] in, Score for compliance threats; As a reporting mark, when At that time, it indicates that the drone has been registered. At that time, it indicated that the drone had not been registered; As an identifier for drones, when When this occurs, it indicates that the drone's identification signal has been received normally. This indicates that the drone's identification signal is missing or abnormal.

[0017] As a preferred embodiment of the present invention, in S3, the spatial threat assessment based on UAV flight data and spatial data to obtain a spatial threat score specifically includes:

[0018] S31. Calculate the drone's progress to the... The shortest distance between the core boundaries of each no-fly zone, where... , This represents the total number of no-fly zones;

[0019] Calculate the number of drones to the Radial velocity of a no-fly zone;

[0020] The drone's position is calculated using the following formula. Minimum intrusion time for each no-fly zone:

[0021]

[0022] in, For drones to the first Minimum intrusion time into a no-fly zone For drones to the first The shortest distance from the core boundary of a no-fly zone, For drones to the first Radial velocity of a no-fly zone;

[0023] The relative position of the drone to the first [unclear] is calculated using the following formula. No-fly zone threat value for each no-fly zone:

[0024]

[0025] in, For drones relative to the first No-fly zone threat value for each no-fly zone For drones to the first The shortest distance from the core boundary of a no-fly zone, For the first The buffer zone radius of each no-fly zone;

[0026] S32. Calculate the drone's progress to the... The shortest distance from the core area boundary of each commercial shipping route, among which... , The total number of commercial shipping routes;

[0027] Calculate the number of drones to the Radial velocity of a commercial shipping route;

[0028] The drone's position is calculated using the following formula. Minimum intrusion time for each commercial shipping lane:

[0029]

[0030] in, For drones to the first Minimum intrusion time for a commercial shipping route For drones to the first The shortest distance from the core area boundary of a commercial shipping route For drones to the first Radial velocity of a commercial shipping route;

[0031] The drone's relative position to the first [unit / item] is calculated using the following formula. The threat value of a commercial shipping route:

[0032]

[0033] in, For drones relative to the first The threat value of a commercial shipping route. For drones to the first The shortest distance from the core area boundary of a commercial shipping route For the first The buffer zone radius of a commercial shipping route;

[0034] S33. The speed threat coefficient is calculated using the following formula:

[0035]

[0036] in, As for the speed threat factor, The current speed of the drone. For reference speed.

[0037] As a preferred embodiment of the present invention, the method of obtaining a space threat score by performing space threat assessment based on UAV flight data and space data further includes:

[0038] S34. A set of no-fly zones and commercial airways whose minimum intrusion time is less than the warning time threshold. , will set No-fly zones and commercial airways are considered as nodes. When the number of nodes is less than 2, the amplification factor is... ;

[0039] Otherwise, amplification factor ,in, For drones to the first Minimum intrusion time into a no-fly zone For drones to the first Minimum intrusion time for a commercial shipping route As the warning time threshold, This is the magnification factor. The dynamic coupling strength;

[0040]

[0041] in, For dynamic coupling strength, The intensity of the no-fly zone approaching its limit. To improve the channel approach intensity, These are the static spatial coupling coefficients;

[0042]

[0043] in, The intensity of the no-fly zone approaching its limit. For drones to the first Radial velocity of a no-fly zone Maximum speed;

[0044]

[0045] in, To improve the channel approach intensity, For drones to the first Radial velocity of a commercial shipping route Maximum speed;

[0046]

[0047] in, The static spatial coupling coefficient, The Euclidean distance between the geometric centers of the no-fly zone and the commercial airway is given. Used as the reference distance;

[0048] S35. The basic space threat level is calculated using the following formula:

[0049]

[0050] in, Based on the level of space threat, As the amplification factor, For set The number of nodes in For the first Threat weight of each node, For the first Threat value of each node;

[0051] The space threat score is calculated using the following formula:

[0052]

[0053] in, Scoring for space threat Based on the level of space threat, This represents the speed threat factor.

[0054] As a preferred embodiment of the present invention, in S5, the comprehensive threat assessment based on compliance threat score, spatial threat score, and behavioral pattern threat score specifically includes:

[0055] The comprehensive threat assessment value is calculated using the following formula:

[0056]

[0057] in, This is a comprehensive threat assessment value. To score for compliance threats, Scoring for space threat Threat score based on behavioral pattern;

[0058] Threat level is determined based on comprehensive threat assessment values:

[0059]

[0060] in, This is the comprehensive threat assessment value.

[0061] In a second aspect of the present invention, a comprehensive drone threat assessment device for urban low-altitude networks is provided, comprising:

[0062] The data acquisition module is configured to acquire UAV flight data; acquire spatial data; and acquire UAV registration data.

[0063] The compliance threat score acquisition module is configured to perform compliance threat assessment based on drone flight data and drone registration data to obtain a compliance threat score;

[0064] The space threat score acquisition module is configured to perform space threat assessment based on UAV flight data and space data to obtain a space threat score.

[0065] The behavioral pattern threat score acquisition module is configured to perform behavioral pattern threat assessment based on UAV flight data and spatial data to obtain a behavioral pattern threat score.

[0066] The integrated threat assessment module is configured to perform a comprehensive threat assessment based on compliance threat scores, spatial threat scores, and behavioral pattern threat scores.

[0067] In a third aspect of the present invention, a computer program product is provided, including a computer program that, when executed by a processor, implements the method provided according to the first aspect.

[0068] In a fourth aspect of the present invention, an electronic device is provided, including one or more processors and a memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the method provided in the first aspect.

[0069] In summary, the present invention has the following beneficial effects:

[0070] The method of this invention performs a compliance threat assessment based on UAV flight data and UAV registration data to obtain a compliance threat score, a spatial threat assessment based on UAV flight data and spatial data to obtain a spatial threat score, a behavioral pattern threat assessment based on UAV flight data and spatial data to obtain a behavioral pattern threat score, and finally performs a comprehensive threat assessment based on the compliance threat score, spatial threat score, and behavioral pattern threat score. This method integrates the UAV's registration / RID compliance status, spatial distance relative to multiple no-fly zones and multiple airways, minimum intrusion time, dynamic coupling amplification factor that approximates multiple constraints, real-time speed, and cross-regional behavioral patterns in historical trajectories to achieve a comprehensive determination of the UAV threat level in a wide-area scenario, thereby improving the accuracy and lead time of low-altitude safety warnings.

[0071] Further or more detailed beneficial effects will be described in conjunction with specific embodiments in the detailed implementation. Attached Figure Description

[0072] Figure 1 A flowchart of the drone threat comprehensive assessment method according to an embodiment of the present invention is shown;

[0073] Figure 2 A block diagram of the drone threat comprehensive assessment device according to an embodiment of the present invention is shown;

[0074] Figure 3 A block diagram of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0075] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0076] In the description of embodiments of the present invention, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0077] This invention proposes a comprehensive threat assessment method for unmanned aerial vehicles (UAVs) in urban low-altitude networks, such as... Figure 1 As shown, the method includes:

[0078] S1. Acquire drone flight data; acquire spatial data; acquire drone registration data;

[0079] In this embodiment, the UAV flight data includes the UAV's identification, three-dimensional position, and velocity vector. Before conducting threat analysis, flight data, including the UAV's identification, is acquired in real time using data from RID ground receiving stations, radar, and electro-optical systems. 3D position and speed .

[0080] In this embodiment, the spatial data includes no-fly zones and commercial airways. Each no-fly zone has a core area boundary, a buffer zone radius, and a threat weight. Each commercial airway has a centerline, width, altitude layer intervals, a specified flight direction vector, and a threat weight. A database of urban low-altitude no-fly zones and airways is obtained, containing: No-fly zone and Commercial shipping routes Each no-fly zone has a core boundary and a buffer zone radius. and threat weight Each channel has a centerline and a width. High-altitude zone , Define the flight direction vector and threat weight All no-fly zones and airways are collectively referred to as constraint nodes / nodes, totaling [number missing]. .

[0081] S2. Conduct a compliance threat assessment based on drone flight data and drone registration data to obtain a compliance threat score.

[0082] In this embodiment, the compliance threat assessment based on UAV flight data and UAV registration data to obtain a compliance threat score specifically includes:

[0083] The compliance threat score is determined using the following formula:

[0084]

[0085] in, Score for compliance threats; As a reporting mark, when At that time, it indicates that the drone has been registered. At that time, it indicated that the drone had not been registered; As an identifier for drones, when When this occurs, it indicates that the drone's identification signal has been received normally. This indicates that the drone's identification signal is missing or abnormal.

[0086] This embodiment assumes the drone is flying over a park, and the system can clearly and continuously receive its compliant RID broadcast signal and successfully parse the device serial number. However, after comparing this serial number with the flight reporting database, the system finds no valid flight mission order. That is, at this point... but Therefore, the compliance threat score is 0.6.

[0087] S3. Conduct a space threat assessment based on UAV flight data and spatial data to obtain a space threat score.

[0088] In this embodiment, the space threat assessment based on UAV flight data and spatial data to obtain a space threat score specifically includes:

[0089] S31. Calculate the drone's progress to the... The shortest distance between the core boundaries of each no-fly zone, where... , This represents the total number of no-fly zones.

[0090] This embodiment assumes there are 3 no-fly zones. The shortest distance from the drone to the core boundary of no-fly zone 1 is 200m, the shortest distance from the drone to the core boundary of no-fly zone 2 is 400m, and the shortest distance from the drone to the core boundary of no-fly zone 3 is 800m.

[0091] Calculate the number of drones to the Radial velocity of a no-fly zone.

[0092] In this embodiment, the radial velocity is obtained through the formula... Calculations show that yes (Representing the drone) the closest point to the core area boundary, It is the velocity vector of the drone. .

[0093] This embodiment assumes that the radial velocity of the drone to No-Fly Zone 1 is 16 m / s, the radial velocity of the drone to No-Fly Zone 2 is 10 m / s, and the radial velocity of the drone to No-Fly Zone 3 is -8 m / s.

[0094] The drone's position is calculated using the following formula. Minimum intrusion time for each no-fly zone:

[0095]

[0096] in, For drones to the first Minimum intrusion time into a no-fly zone For drones to the first The shortest distance from the core boundary of a no-fly zone, For drones to the first Radial velocity of a no-fly zone.

[0097] In this embodiment, for No-Fly Zone 1, when the shortest distance is 200m and the radial velocity is 16 m / s, the minimum intrusion time is 12.5s (i.e., the minimum intrusion time for the drone to reach No-Fly Zone 1 is 12.5s). For No-Fly Zone 2, when the shortest distance is 400m and the radial velocity is 10 m / s, the minimum intrusion time is 40s (i.e., the minimum intrusion time for the drone to reach No-Fly Zone 2 is 40s). For No-Fly Zone 3, when the shortest distance is 800m and the radial velocity is -8 m / s, the minimum intrusion time is +∞ (i.e., the minimum intrusion time for the drone to reach No-Fly Zone 3 is +∞).

[0098] The drone's relative position to the first [unit / item] is calculated using the following formula. No-fly zone threat value for each no-fly zone:

[0099]

[0100] in, For drones relative to the first No-fly zone threat value for each no-fly zone For drones to the first The shortest distance from the core boundary of a no-fly zone, For the first The buffer radius of a no-fly zone.

[0101] This embodiment assumes that the buffer zone radius of each of the three no-fly zones is 500m. For no-fly zone 1, when the shortest distance is 200m, the threat value of the drone relative to no-fly zone 1 is 0.6. For no-fly zone 2, when the shortest distance is 400m, the threat value of the drone relative to no-fly zone 2 is 0.2. For no-fly zone 3, when the shortest distance is 800m (greater than the buffer zone radius), the threat value of the drone relative to no-fly zone 3 is 0.

[0102] S32. Calculate the drone's progress to the... The shortest distance from the core area boundary of each commercial shipping route, among which... , This refers to the total number of commercial shipping routes.

[0103] This embodiment assumes there are two commercial airways. The shortest distance from the UAV to the core area boundary of commercial airway No. 1 is 300m, and the shortest distance from the UAV to the core area boundary of commercial airway No. 2 is 600m.

[0104] Calculate the number of drones to the Radial velocity of a commercial shipping route.

[0105] This embodiment assumes that the radial velocity of the UAV to Commercial Airway 1 is 16 m / s and the radial velocity of the UAV to Commercial Airway 2 is -5 m / s.

[0106] The drone's position is calculated using the following formula. Minimum intrusion time for each commercial shipping lane:

[0107]

[0108] in, For drones to the first Minimum intrusion time for a commercial shipping route For drones to the first The shortest distance from the core area boundary of a commercial shipping route For drones to the first Radial velocity of a commercial shipping route.

[0109] In this embodiment, for Commercial Airway 1, when the shortest distance is 300m and the radial velocity is 16 m / s, the minimum intrusion time is 18.75s (i.e., the minimum intrusion time for the UAV to reach Commercial Airway 1 is 18.75s). For Commercial Airway 2, when the shortest distance is 500m and the radial velocity is -5 m / s, the minimum intrusion time is +∞ (i.e., the minimum intrusion time for the UAV to reach Commercial Airway 2 is +∞).

[0110] The drone's relative position to the first [unit / item] is calculated using the following formula. The threat value of a commercial shipping route:

[0111]

[0112] in, For drones relative to the first The threat value of a commercial shipping route. For drones to the first The shortest distance from the core area boundary of a commercial shipping route For the first The buffer radius of a commercial shipping lane.

[0113] This embodiment assumes that the buffer zone radius of both commercial airways is 500m. For commercial airway 1, when the shortest distance is 300m, the airway threat value of the UAV relative to commercial airway 1 is 0.4. For commercial airway 2, when the shortest distance is 600m (greater than the buffer zone radius), the airway threat value of the UAV relative to commercial airway 2 is 0.

[0114] S33. The speed threat coefficient is calculated using the following formula:

[0115]

[0116] in, As for the speed threat factor, The current speed of the drone. For reference speed.

[0117] In this embodiment, assuming the reference speed is 10 m / s and the current speed of the drone is 16 m / s, the calculated speed threat coefficient is 2.28.

[0118] S34. A set of no-fly zones and commercial airways whose minimum intrusion time is less than the warning time threshold. , will set No-fly zones and commercial airways are considered as nodes. When the number of nodes is less than 2, the amplification factor is... .

[0119] This embodiment assumes that the warning time threshold is 20 seconds, then the set The no-fly zone and commercial airway include No. 1 no-fly zone and No. 1 commercial airway, with a total of 2 nodes, and the number of nodes is greater than 2.

[0120] When the number of nodes is greater than 2, the amplification factor ,in, For drones to the first Minimum intrusion time into a no-fly zone For drones to the first Minimum intrusion time for a commercial shipping route As the warning time threshold, This is the magnification factor. The dynamic coupling strength;

[0121]

[0122] in, For dynamic coupling strength, The intensity of the no-fly zone approaching its limit. To improve the channel approach intensity, These are the static spatial coupling coefficients;

[0123]

[0124] in, The intensity of the no-fly zone approaching its limit. For drones to the first Radial velocity of a no-fly zone This is the maximum speed. In this embodiment, the maximum speed is assumed to be 20 m / s, because the radial speed of No-Fly Zone 1 is 16 m / s, therefore the no-fly zone approach intensity... .

[0125]

[0126] in, To improve the channel approach intensity, For drones to the first Radial velocity of a commercial shipping route The maximum speed is assumed to be 20 m / s in this embodiment, because the radial speed of Commercial Channel 1 is 16 m / s, therefore the channel approach intensity... .

[0127]

[0128] in, The static spatial coupling coefficient, The Euclidean distance between the geometric centers of the no-fly zone and the commercial airway is given. The reference distance is assumed to be 1000m. In this embodiment, the Euclidean distance between the geometric center of No. 1 No-fly zone and No. 1 commercial airway is assumed to be 400m, and the static spatial coupling coefficient is assumed to be... .

[0129] Therefore, the dynamic coupling strength can be obtained. .

[0130] In this embodiment, the warning time threshold is assumed to be 20 seconds and the amplification factor is 2. Therefore, the amplification factor can be calculated. .

[0131] S35. The basic space threat level is calculated using the following formula:

[0132]

[0133] in, Based on the level of space threat, As the amplification factor, For set The number of nodes in For the first Threat weight of each node, For the first Threat value of each node.

[0134] In this embodiment, the set There are two nodes. The first node is No-Fly Zone 1. Since the threat value of the drone relative to No-Fly Zone 1 is 0.6, the threat value of the first node is also 0.6. Assuming the threat weight of No-Fly Zone 1 is 0.3, the threat weight of the first node is also 0.3. The second node is Commercial Airway 1. Since the threat value of the drone relative to Commercial Airway 1 is 0.4, the threat value of the second node is also 0.4. Assuming the threat weight of Commercial Airway 2 is 0.2, the threat weight of the second node is also 0.2. Therefore, the basic spatial threat level can be calculated to be 0.438.

[0135] The space threat score is calculated using the following formula:

[0136]

[0137] in, Scoring for space threat Based on the level of space threat, This represents the speed threat factor.

[0138] In this embodiment, when the basic space threat level is 0.438 and the speed threat coefficient is 2.28, the calculated space threat score is 0.999.

[0139] S4. Conduct behavioral pattern threat assessment based on UAV flight data and spatial data to obtain behavioral pattern threat scores.

[0140] After acquiring UAV flight and spatial data, the system utilizes deep learning models to accurately identify the UAV's motion state. For example, by inputting fused data into a target neural network (such as Transformer or LSTM models), the system identifies the UAV's current specific behavior pattern. The system extracts key dynamic features, such as flight trajectory, altitude changes, sudden speed changes, and formation patterns, to determine its mission attributes. For instance, circling is typically identified as "reconnaissance" intent, straight-line penetration as "attack" intent, and random maneuvers as "harassment." After identifying the basic behavior pattern, the system combines current sensor data with prior knowledge (such as common enemy tactics and no-fly zone rules) to calculate the probability distribution of candidate behaviors corresponding to that behavior pattern. The system highlights anomalous behaviors, such as loitering around sensitive areas or flying at low altitudes, attempting to evade detection (e.g., suddenly decreasing altitude), and maneuvers exhibiting swarm coordination or probing gaps in defense. Finally, a behavior pattern threat score is output. In this embodiment, the specific method of obtaining the behavioral pattern threat score can be understood as existing technology. It is only necessary to input the obtained UAV flight data and spatial data into the relevant model to directly obtain the corresponding behavioral pattern threat score.

[0141] S5. Conduct a comprehensive threat assessment based on compliance threat score, spatial threat score, and behavioral pattern threat score.

[0142] In this embodiment, the comprehensive threat assessment based on compliance threat score, spatial threat score, and behavioral pattern threat score specifically includes:

[0143] The comprehensive threat assessment value is calculated using the following formula:

[0144]

[0145] in, This is a comprehensive threat assessment value. To score for compliance threats, Scoring for space threat Threat score for behavioral patterns.

[0146] In this embodiment, it is assumed that the drone's compliance threat score is 0.6, its space threat score is 0.999, and its behavioral pattern threat score is 0.2. Therefore, the overall threat assessment value is 0.99968.

[0147] Threat level is determined based on comprehensive threat assessment values:

[0148]

[0149] in, This is the comprehensive threat assessment value.

[0150] In this embodiment, L1 represents a safe state, L2 a low-risk state, L3 a medium-risk state, L4 a high-risk state, and L5 an extremely high-risk state. When the comprehensive threat assessment value is 0.99968, the threat level is L5.

[0151] This invention proposes a comprehensive drone threat assessment device for urban low-altitude networks, such as... Figure 2 As shown, the device includes:

[0152] The data acquisition module is configured to acquire UAV flight data; acquire spatial data; and acquire UAV registration data.

[0153] The compliance threat score acquisition module is configured to perform compliance threat assessment based on drone flight data and drone registration data to obtain a compliance threat score;

[0154] The space threat score acquisition module is configured to perform space threat assessment based on UAV flight data and space data to obtain a space threat score.

[0155] The behavioral pattern threat score acquisition module is configured to perform behavioral pattern threat assessment based on UAV flight data and spatial data to obtain a behavioral pattern threat score.

[0156] The integrated threat assessment module is configured to perform a comprehensive threat assessment based on compliance threat scores, spatial threat scores, and behavioral pattern threat scores.

[0157] Figure 3 A block diagram of an electronic device 300 according to some embodiments of the present invention is shown. The device 300 includes a processor 301, which performs various appropriate actions and processes based on computer program instructions loaded into random access memory (RAM) 303 according to computer program instructions stored in read-only memory (ROM) 302. Various programs and data required for the operation of the device 300 may also be stored in RAM 303. The processor 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0158] The various processes and procedures described above, such as method 100, can be executed by processor 301. For example, in some embodiments, method 100 may be implemented as a software program tangibly contained in a machine-readable medium. In some embodiments, part or all of the software program may be loaded and / or installed on device 300 via ROM 302. When the software program is loaded into RAM 303 and executed by processor 301, one or more actions of method 100 described above may be performed.

[0159] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0160] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0161] This invention can be a method, apparatus, system, and / or program product. The program product may include a machine-readable storage medium on which machine-readable program instructions for performing various aspects of the invention are loaded. The machine-readable program instructions described herein can be downloaded from the machine-readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the machine-readable program instructions from the network and forwards them for storage in the machine-readable storage medium of the respective computing / processing device.

[0162] Machine program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The machine-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the machine-readable program instructions. This electronic circuitry can execute the machine-readable program instructions to implement various aspects of the invention.

[0163] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. Furthermore, although the operations are depicted in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0164] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A comprehensive threat assessment method for unmanned aerial vehicles (UAVs) in urban low-altitude networks, characterized in that, include: S1. Acquire drone flight data; Acquire spatial data; Obtain drone registration data; S2. Conduct a compliance threat assessment based on drone flight data and drone registration data to obtain a compliance threat score; S3. Conduct a space threat assessment based on UAV flight data and spatial data to obtain a space threat score; S4. Conduct behavioral pattern threat assessment based on UAV flight data and spatial data to obtain a behavioral pattern threat score; S5. Conduct a comprehensive threat assessment based on compliance threat score, spatial threat score, and behavioral pattern threat score.

2. The method according to claim 1, characterized in that, In S1, the UAV flight data includes the UAV's identification, three-dimensional position, and speed.

3. The method according to claim 1, characterized in that, In S1, the spatial data includes no-fly zones and commercial airways. Each no-fly zone has a core area boundary, a buffer zone radius, and a threat weight. Each commercial airway has a centerline, width, altitude layer interval, a specified flight direction vector, and a threat weight.

4. The method according to claim 1, characterized in that, In step S2, the compliance threat assessment based on UAV flight data and UAV registration data to obtain a compliance threat score specifically includes: The compliance threat score is determined using the following formula: ; in, Score for compliance threats; As a reporting mark, when At that time, it indicates that the drone has been registered. At that time, it indicated that the drone had not been registered; As an identifier for drones, when When this occurs, it indicates that the drone's identification signal has been received normally. This indicates that the drone's identification signal is missing or abnormal.

5. The method according to claim 1, characterized in that, In S3, the spatial threat assessment based on UAV flight data and spatial data to obtain a spatial threat score specifically includes: S31. Calculate the drone's progress to the... The shortest distance between the core boundaries of each no-fly zone, where... , This represents the total number of no-fly zones; Calculate the number of drones to the Radial velocity of a no-fly zone; The drone's position is calculated using the following formula. Minimum intrusion time for each no-fly zone: ; in, For drones to the first Minimum intrusion time into a no-fly zone For drones to the first The shortest distance from the core boundary of a no-fly zone, For drones to the first Radial velocity of a no-fly zone; The drone's relative position to the first [unit / item] is calculated using the following formula. No-fly zone threat value for each no-fly zone: ; in, For drones relative to the first No-fly zone threat value for each no-fly zone For drones to the first The shortest distance from the core boundary of a no-fly zone, For the first The buffer zone radius of each no-fly zone; S32. Calculate the drone's progress to the... The shortest distance from the core area boundary of each commercial shipping route, among which... , The total number of commercial shipping routes; Calculate the number of drones to the Radial velocity of a commercial shipping route; The drone's position is calculated using the following formula. Minimum intrusion time for each commercial shipping lane: ; in, For drones to the first Minimum intrusion time for a commercial shipping route For drones to the first The shortest distance from the core area boundary of a commercial shipping route For drones to the first Radial velocity of a commercial shipping route; The drone's relative position to the first [unit / item] is calculated using the following formula. The threat value of a commercial shipping route: ; in, For drones relative to the first The threat value of a commercial shipping route. For drones to the first The shortest distance from the core area boundary of a commercial shipping route For the first The buffer zone radius of a commercial shipping route; S33. The speed threat coefficient is calculated using the following formula: ; in, As for the speed threat factor, The current speed of the drone. For reference speed.

6. The method according to claim 1, characterized in that, Space threat assessment based on UAV flight data and spatial data to obtain a space threat score also includes: S34. A set of no-fly zones and commercial airways whose minimum intrusion time is less than the warning time threshold. , will set No-fly zones and commercial airways are considered as nodes. When the number of nodes is less than 2, the amplification factor is... ; Otherwise, amplification factor ,in, For drones to the first Minimum intrusion time into a no-fly zone For drones to the first Minimum intrusion time for a commercial shipping route As the warning time threshold, This is the magnification factor. The dynamic coupling strength; ; in, For dynamic coupling strength, The intensity of the no-fly zone approaching its limit. To improve the channel approach intensity, These are the static spatial coupling coefficients; ; in, The intensity of the no-fly zone approaching its limit. For drones to the first Radial velocity of a no-fly zone Maximum speed; ; in, To improve the channel approach intensity, For drones to the first Radial velocity of a commercial shipping route Maximum speed; ; in, The static spatial coupling coefficient, The Euclidean distance between the geometric centers of the no-fly zone and the commercial airway is given. Used as the reference distance; S35. The basic space threat level is calculated using the following formula: ; in, Based on the level of space threat, As the amplification factor, For set The number of nodes in For the first Threat weight of each node, For the first Threat value of each node; The space threat score is calculated using the following formula: ; in, Scoring for space threat Based on the level of space threat, This represents the speed threat factor.

7. The method according to claim 1, characterized in that, In S5, the comprehensive threat assessment based on compliance threat score, spatial threat score, and behavioral pattern threat score specifically includes: The comprehensive threat assessment value is calculated using the following formula: ; in, This is a comprehensive threat assessment value. To score for compliance threats, Scoring for space threat Threat score based on behavioral pattern; Threat level is determined based on comprehensive threat assessment values: ; in, This is the comprehensive threat assessment value.

8. A comprehensive threat assessment device for unmanned aerial vehicles (UAVs) in urban low-altitude networks, characterized in that, include: The data acquisition module is configured to acquire UAV flight data and spatial data. Obtain drone registration data; The compliance threat score acquisition module is configured to perform compliance threat assessment based on drone flight data and drone registration data to obtain a compliance threat score; The space threat score acquisition module is configured to perform space threat assessment based on UAV flight data and space data to obtain a space threat score. The behavioral pattern threat score acquisition module is configured to perform behavioral pattern threat assessment based on UAV flight data and spatial data to obtain a behavioral pattern threat score. The integrated threat assessment module is configured to perform a comprehensive threat assessment based on compliance threat scores, spatial threat scores, and behavioral pattern threat scores.

9. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-7.

10. An electronic device, characterized in that, It includes one or more processors and memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method according to any one of claims 1-7.

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