A security protection method, device, system and storage medium based on unmanned aerial vehicles (UAVs)

Abstract

This application discloses a security protection method, device, system, and storage medium based on unmanned aerial vehicles (UAVs). The method includes: base station construction. The solution provided in this application involves: determining risk source information of the protected target; performing spatiotemporal risk modeling based on the risk source information to determine a time risk model and a spatial risk model; determining the potential threat area of ​​the protected target based on the time risk model and the spatial risk model; planning a monitoring arc to fully cover the potential threat area of ​​the protected target; and constructing a UAV base station capable of completely covering the monitoring arc, so that the UAV base station can achieve security protection for the protected target. This solution can perform spatiotemporal risk modeling based on the risk source information of the protected target, dynamically generate monitoring arcs, and construct UAV base stations, achieving comprehensive, blind-spot-free coverage of the protected target and improving the security protection capability of the UAV system.

Description

Technical Field

[0001] This application relates to the field of base station construction technology, and in particular to a security protection method, device, system and storage medium based on unmanned aerial vehicles (UAVs). Background Technology

[0002] In the marine domain, offshore drilling platforms, offshore wind farms, key port facilities, research vessels, and other high-value mobile or fixed platforms play a crucial role, serving as vital support for marine economic development, national defense security, and scientific research. However, these targets are characterized by their wide distribution and open environments, making them extremely difficult to monitor and highly vulnerable to intrusion and threats from unidentified vessels, unmanned surface vessels, low-speed and slow-moving aircraft, and other suspicious moving targets.

[0003] In recent years, with the rapid development of drone technology, people have begun to try to apply drone systems to maritime monitoring and target inspection, such as using pre-programmed paths for drones to conduct regular patrols, or using electro-optical / infrared payloads to observe specific areas. However, existing drone solutions generally have some problems: the monitoring paths are fixed and singular, making it difficult to achieve comprehensive monitoring and creating blind spots; the response mechanism lacks flexibility and cannot dynamically adjust tasks according to real-time threat situations.

[0004] Therefore, how to provide a security protection method based on drones to improve the security protection capability of drone systems has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a security protection method, device, system, and storage medium based on unmanned aerial vehicles (UAVs) to enhance the security protection capabilities of UAV systems.

[0006] This application provides a security protection method based on unmanned aerial vehicles (UAVs), including:

[0007] Identify risk source information for the protected targets;

[0008] Spatiotemporal risk modeling is performed based on risk source information to determine the temporal risk model and the spatial risk model;

[0009] Based on the time risk model and the spatial risk model, the potential threat areas of the protected target are determined;

[0010] Plan a monitoring arc to comprehensively cover the potential threat areas of the protected targets;

[0011] Construct a drone base station capable of fully covering the monitoring arc, so that the drone base station can provide security protection for the protected target.

[0012] The beneficial effects of this application are as follows: It identifies the risk source information of the protected target, performs spatiotemporal risk modeling based on this information to determine a temporal risk model and a spatial risk model, identifies the potential threat area of ​​the protected target based on these models, plans a monitoring arc to fully cover this potential threat area, and constructs a drone base station capable of completely covering the monitoring arc, enabling the drone base station to provide security protection for the protected target. Because this application can perform spatiotemporal risk modeling based on the risk source information of the protected target and dynamically generate a monitoring arc, and construct a drone base station based on this monitoring arc to provide security protection for the protected target, it achieves comprehensive, blind-spot-free coverage of the protected target, thus improving the security protection capability of the drone system.

[0013] In one embodiment, the risk source information for determining the protected target includes:

[0014] Collect at least one type of data, including prior knowledge data, real-time intelligence data, and sensor monitoring data;

[0015] By analyzing the at least one type of data, the starting location, occurrence time, and movement characteristics of risk sources that pose a potential threat to the protected target can be determined as risk source information.

[0016] In one embodiment, spatiotemporal risk modeling is performed based on risk source information to determine a temporal risk model and a spatial risk model, including:

[0017] Calculate the shortest and longest arrival times from the risk source to the protected target based on the risk source information, and define the time window for the occurrence of the threat as a time risk model;

[0018] A spatial risk model incorporating threat ellipse models is constructed, wherein each risk source corresponds to a threat ellipse model. The threat ellipse model is used to depict the possible spatial distribution range of the corresponding risk source. The two foci of the threat ellipse are the protected target and the risk source point, respectively. The length of the major axis is the maximum range of the suspected target, and the focal length is the distance between the protected target and the risk source.

[0019] In one embodiment, determining the potential threat area of ​​the protected target based on the time risk model and the spatial risk model includes:

[0020] Based on the time risk model, all time windows that pose potential threats to the protected target and all risk sources that may pose a threat within those time windows are identified.

[0021] Based on the spatial risk model, the threat areas corresponding to all risk sources that may pose a threat within the time window where a potential threat exists are identified.

[0022] In one embodiment, determining the threat region corresponding to all potential risk sources within a time window of potential threat based on the spatial risk model includes:

[0023] Set operations are used to identify all potential risk sources that may pose a threat within a time window where a potential threat exists.

[0024] The union of the threat ellipse models corresponding to all target risk sources is determined as the threat region corresponding to all risk sources that may constitute a threat under the time window in which a potential threat exists.

[0025] In one embodiment, the planning of a monitoring arc to comprehensively cover the potential threat area of ​​the protected target includes:

[0026] Based on the boundary of the potential threat area of ​​the protected target and the preset monitoring forward radius, the arc length, position and angle range of the monitoring arc are determined using geometric calculation methods to ensure that the monitoring arc can completely cover the potential threat area.

[0027] In one embodiment, the construction of a drone base station capable of completely covering the monitoring arc includes:

[0028] Based on the parameters of the monitoring arc, combined with the maximum flight distance and coverage capability of the drone, the layout location and coverage angle of the drone base station are determined to ensure seamless connection between base stations and full coverage of the monitoring area.

[0029] This application also provides a security protection device based on unmanned aerial vehicles, including:

[0030] The first determination module is used to determine the risk source information of the protected target;

[0031] The modeling module is used to perform spatiotemporal risk modeling based on risk source information in order to determine the temporal risk model and the spatial risk model.

[0032] The second determining module is used to determine the potential threat area of ​​the protected target based on the time risk model and the spatial risk model.

[0033] The planning module is used to plan a monitoring arc to comprehensively cover the potential threat areas of the protected target;

[0034] A construction module is used to build a drone base station that can completely cover the monitoring arc, so that the drone base station can achieve security protection for the protected target.

[0035] In one embodiment, the first determining module includes:

[0036] The collection submodule is used to collect at least one type of data, including prior knowledge data, real-time intelligence data, and sensor monitoring data.

[0037] The analysis submodule is used to analyze the at least one type of data to determine the starting location, occurrence time, and movement characteristics of risk sources that pose a potential threat to the protected target as risk source information.

[0038] In one embodiment, the modeling module includes:

[0039] The calculation submodule is used to calculate the shortest and longest arrival times from the risk source to the protected target based on the risk source information, and to define the time window for the occurrence of the threat as a time risk model.

[0040] A construction submodule is used to construct a spatial risk model containing threat ellipse models. Each risk source corresponds to a threat ellipse model, which is used to depict the possible spatial distribution range of the corresponding risk source. The two foci of the threat ellipse are the protected target and the risk source point, respectively. The length of the major axis is the maximum range of the suspected target, and the focal length is the distance between the protected target and the risk source.

[0041] In one embodiment, the second determining module includes:

[0042] The first determination submodule is used to determine, based on the time risk model, all time windows that pose potential threats to the protected target and all risk sources that may pose a threat within those time windows;

[0043] The second determination submodule is used to determine the threat areas corresponding to all risk sources that may pose a threat within a time window where a potential threat exists, based on the spatial risk model.

[0044] In one embodiment, the second determining submodule is further configured to:

[0045] Set operations are used to identify all potential risk sources that may pose a threat within a time window where a potential threat exists.

[0046] The union of the threat ellipse models corresponding to all target risk sources is determined as the threat region corresponding to all risk sources that may constitute a threat under the time window in which a potential threat exists.

[0047] In one embodiment, the planning module includes:

[0048] The third determination submodule is used to determine the arc length, position, and angle range of the monitoring arc using geometric calculation methods based on the boundary of the potential threat area of ​​the protected target and the preset monitoring forward radius, so as to ensure that the monitoring arc can completely cover the potential threat area.

[0049] In one embodiment, the building module is further configured to:

[0050] Based on the parameters of the monitoring arc, combined with the maximum flight distance and coverage capability of the drone, the layout location and coverage angle of the drone base station are determined to ensure seamless connection between base stations and full coverage of the monitoring area.

[0051] This application also provides a security protection system based on unmanned aerial vehicles (UAVs), including:

[0052] At least one processor; and,

[0053] A memory communicatively connected to the at least one processor; wherein,

[0054] The memory stores instructions that can be executed by the at least one processor to implement the UAV-based security protection method described in any of the above embodiments.

[0055] This application also provides a computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor corresponding to a drone-based security protection system, enables the drone-based security protection system to implement the drone-based security protection method described in any of the above embodiments.

[0056] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0057] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0058] The accompanying drawings are provided to further illustrate the present application and form part of the specification. They are used together with the embodiments of the present application to explain the application and do not constitute a limitation thereof. In the drawings:

[0059] Figure 1 This is a flowchart of a security protection method based on a drone in one embodiment of this application;

[0060] Figure 2 This is a schematic diagram illustrating the determination of the monitoring arc in one embodiment of this application;

[0061] Figure 3 This is a schematic diagram of a drone base station in one embodiment of this application;

[0062] Figure 4 This is a schematic diagram of a multi-layer reciprocating patrol mode in one embodiment of this application;

[0063] Figure 5This is a schematic diagram of a fixed-point hovering monitoring mode in one embodiment of this application;

[0064] Figure 6 This is a schematic diagram of the fixed-point hovering monitoring mode in another embodiment of this application;

[0065] Figure 7 This is a schematic diagram of the structure of a drone-based security protection device according to one embodiment of this application;

[0066] Figure 8 This is a schematic diagram of the hardware structure of a security protection system based on a drone in one embodiment of this application. Detailed Implementation

[0067] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application.

[0068] Figure 1 This is a flowchart of a security protection method based on unmanned aerial vehicles (UAVs) according to an embodiment of this application, such as... Figure 1 As shown, the method can be implemented as follows: S101-S105:

[0069] In step S101, the risk source information of the protected target is determined;

[0070] In step S102, spatiotemporal risk modeling is performed based on risk source information to determine the temporal risk model and the spatial risk model;

[0071] In step S103, the potential threat areas of the protected target are determined based on the time risk model and the spatial risk model.

[0072] In step S104, a monitoring arc is planned to comprehensively cover the potential threat area of ​​the protected target;

[0073] In step S105, a drone base station capable of completely covering the monitoring arc is constructed so that the drone base station can provide security protection for the protected target.

[0074] In this application, the risk source information of the protected target is first determined. Specifically, at least one type of data is collected, including prior knowledge data, real-time intelligence data, and sensor monitoring data. By analyzing the at least one type of data, the starting location, occurrence time, and movement characteristic parameters of the risk source that poses a potential threat to the protected target are determined as risk source information.

[0075] Secondly, spatiotemporal risk modeling is performed based on risk source information to determine the temporal risk model and the spatial risk model.

[0076] (1) Time risk model

[0077] Based on risk source information, the shortest and longest arrival times from the risk source to the protected target are calculated, defining the time window for threat occurrence as a time-based risk model. Specifically, the distance between the risk source and the protected target can be calculated based on their latitude and longitude. In the time dimension, for the first... There are several risk sources, and we assume that a suspicious target originating from one of these risk sources departs at time t. i , in t i The suspected target travels in a straight line towards the protected target. Based on the distance between the risk source and the protected target, and the movement characteristics of the suspected target originating from the risk source, the shortest arrival time is obtained. and the longest arrival time is Therefore, the time frame during which a suspicious target originating from this risk source may pose a threat to the protected target is:

[0078] .

[0079] By performing the above calculations on all risk sources, the sources of suspicious targets that need to be defended and their threat time windows can be clearly identified on the timeline, thereby determining the number of risk sources and suspicious targets that the defense system needs to deal with in different time periods.

[0080] In one embodiment of this application, the shortest arrival time from the risk source to the protected target is obtained based on the distance between the risk source and the protected target, and the movement characteristic parameters of the suspected target originating from the risk source. Furthermore, based on at least one of prior knowledge data, real-time intelligence data, and sensor monitoring data, multiple possible travel paths of the risk source are determined; the longest path among these paths is obtained; and the longest arrival time from the risk source to the protected target is obtained based on the longest path and the movement characteristic parameters of the suspected target originating from the risk source. A time window for threat occurrence is then obtained based on the shortest and longest arrival times from the risk source to the protected target.

[0081] (2) Spatial risk model

[0082] A spatial risk model incorporating threat ellipse models is constructed, wherein each risk source corresponds to a threat ellipse model. The threat ellipse model is used to depict the possible spatial distribution range of the corresponding risk source. The two foci of the threat ellipse are the protected target and the risk source point, respectively. The length of the major axis is the maximum range of the suspected target, and the focal length is the distance between the protected target and the risk source.

[0083] In one embodiment, it is assumed that the latitude and longitude of the protected target are... A certain threat source has the following latitude and longitude: The positions of both parties will be converted to a coordinate system with the protected target as the origin and due east as the coordinate system. In a Cartesian coordinate system with the positive axis, the coordinate transformation of this threat source is as follows:

[0084]

[0085]

[0086] in, For the Earth's radius, (x) i ,y i () represents the coordinates of the risk source in a Cartesian coordinate system.

[0087] The threat ellipse model is used to represent the maximum range D of a suspicious target originating from this risk source. max The region that may appear under constraints. It satisfies the following properties:

[0088] ① The two focal points are the protected target O(0,0) and the risk source S(x) respectively. i ,y i );

[0089] ② Major axis length 2a = D max This refers to the maximum range of the suspected target.

[0090] ③ Focal length This refers to the distance between the protected target and the source of risk.

[0091] Therefore, the standard equation of the threat ellipse is obtained as follows:

[0092]

[0093] In this context, the protected target and the risk source are the two foci of the ellipse; the coordinates of the protected target are the origin; and the coordinates of the risk source are (x...). i ,y i The coordinates of the boundary point of the threatening ellipse are (x, y). a To threaten the length of the semi-major axis of the ellipse, 2a = D must be satisfied. max ; b This threatens the length of the minor axis of the ellipse.

[0094] Next, based on the aforementioned time risk model and spatial risk model, the potential threat area of ​​the protected target is determined. The time risk model identifies all time windows where potential threats to the protected target exist, as well as all risk sources that may pose a threat within those time windows. The spatial risk model determines the threat area corresponding to all risk sources that may pose a threat within the time windows where potential threats exist; that is, it uses set operations to determine all target risk sources that may pose a threat within the time windows where potential threats exist. The union of the threat ellipse models corresponding to all target risk sources is determined as the threat area corresponding to all risk sources that may pose a threat within the time windows where potential threats exist. For example, if there are m risk sources within a certain time period, the union of these m ellipses needs to be calculated as the total threat area where the suspected target may appear.

[0095] Next, a monitoring arc is planned to fully cover the potential threat area of ​​the protected target. Based on the boundary of the potential threat area of ​​the protected target and the preset monitoring forward radius, the arc length, position, and angle range of the monitoring arc are determined using geometric calculation methods to ensure that the monitoring arc can completely cover the potential threat area.

[0096] Figure 2 This is a schematic diagram illustrating the determination of the monitoring arc in one embodiment of this application, as shown below. Figure 2 As shown, for the threat ellipse equation in the above embodiment, the intersection point of the threat ellipse and the monitoring arc is first calculated, with the preset forward radius of the monitoring arc being... Therefore, the equation of the circle corresponding to the monitored arc is:

[0097]

[0098] By presetting the forward radius of the monitoring arc, the leading edge position of the drone monitoring is determined.

[0099] By simultaneously solving the equations corresponding to all threat ellipses and the equations corresponding to the monitoring arcs, we can obtain a set of intersection points. For any intersection point in this set... Its polar angle relative to the protected target can be calculated:

[0100] ;

[0101] Note that arctan2(y,x) is a two-parameter arctangent function with a range of (-π, π], and its quadrant can be uniquely determined based on the sign of the coordinates.

[0102] Therefore, the range of the monitored arc angle can be determined. Assume the calculated number of intersection points is... Calculate the minimum and maximum polar angles corresponding to these intersection points:

[0103]

[0104] Then the monitored arc length L can be calculated:

[0105]

[0106] Finally, a drone base station capable of completely covering the monitoring arc is constructed to enable the drone base station to provide security protection for the protected target. Specifically, based on the parameters of the monitoring arc, combined with the maximum flight distance and coverage capability of the drone, the layout location and coverage angle of the drone base station are determined to ensure seamless connection between base stations and full coverage of the monitoring area.

[0107] Figure 3 This is a schematic diagram of a drone base station in one embodiment of this application, as shown below. Figure 3 As shown, to deploy drone base stations along the defense arc for full coverage, the coverage angle of each base station needs to be calculated. Assuming sufficient drones are available, the coverage area of ​​each base station is a circle with a radius equal to the maximum flight distance of the drones. Therefore, when the forward distance of the monitoring arc is determined, the coverage angle of the base station relative to the protected target can be calculated. The specific calculation formula is as follows:

[0108] Let R be the coverage radius of the base station (maximum flight distance of the drone). cover If the outward radius of the monitoring arc is R0, then the coverage angle θ of each base station relative to the protected target is... cover for:

[0109]

[0110] Since the total angle Δθ of the defensive arc is:

[0111]

[0112] Therefore, by dividing the total angle of the defense arc by the coverage angle of the protected target and rounding up, we can obtain the number of UAV base stations n required to achieve full angular coverage. base for:

[0113] .

[0114] Furthermore, drone base stations can be set up at equal intervals on the monitoring arc according to the number of drone base stations.

[0115] In one embodiment, overlapping coverage areas between base stations are considered simultaneously to improve monitoring continuity and fault tolerance. The overlap angle θ of the coverage areas of adjacent drone base stations is calculated. overlap :

[0116]

[0117] Therefore, the polar angle α of all UAV base stations can be obtained. k :

[0118]

[0119] Therefore, the rectangular coordinates of each drone base station are:

[0120]

[0121] In another embodiment of this application, a multi-mode UAV cooperative path planning strategy is designed based on base station layout, including but not limited to reciprocating patrols and fixed-point hovering, to ensure continuous and efficient monitoring of threat areas. Specifically, this includes:

[0122] Mode 1: Multi-layer reciprocating patrol mode

[0123] Figure 4 This is a schematic diagram of a multi-layer reciprocating patrol mode in one embodiment of this application, as shown below. Figure 4 As shown, the patrol mode enables the UAV to conduct multi-layered, reciprocating patrols within a designated area by setting the number of patrol layers, the distance between layers, and the launch time interval. Variables that need to be pre-designed for this mode include: the number of patrol layers N. layer Interlayer distance D interval UAV launch time interval ΔT launch After taking off from the base station, the drone flies forward along a direction perpendicular to the defense arc, and turns back after reaching the preset patrol radius (usually calculated based on the drone's endurance and the threat ellipse boundary), forming a multi-layered, reciprocating patrol strip area. This mode does not require complex real-time scheduling and is suitable for scenarios where the threat direction is clear and large-scale continuous monitoring is required.

[0124] Mode 2: Fixed-point hovering monitoring mode

[0125] The fixed-point hovering mode dispatches drones to key nodes for continuous monitoring, thereby increasing the monitoring density and response speed of local areas.

[0126] Figure 5 This is a schematic diagram of a fixed-point hovering monitoring mode in one embodiment of this application, as shown below. Figure 5 As shown, drones are deployed to various nodes on the defense arc to hover or circle within a small area, continuously monitoring the circular area surrounding each node. This mode requires the establishment of a drone status (endurance, occupancy) monitoring and dynamic replacement mechanism. Variables that need to be pre-designed in this mode include: the number of hovering monitoring points N. point and its coordinates (x) k ,y k Defense Level N layer .

[0127] Figure 6 This is a schematic diagram of the fixed-point hovering monitoring mode in another embodiment of this application, such as... Figure 6 As shown, the entire monitoring arc is divided into several sub-sections, and each sub-section is patrolled collaboratively by a group of drones within its corresponding strip area. This model improves local search frequency and response speed by reducing the area of ​​responsibility of a single drone. The variables that need to be designed in this model include: the number of monitoring sub-sections N. segment Defense Level N layer Whether to adopt staggered deployment (Boolean variable).

[0128] To maximize the overall cost-effectiveness of the monitoring system within a specific time window, key design parameters in the aforementioned multi-layered reciprocating patrol mode and fixed-point hovering monitoring mode are optimized. For example, a simulation optimization framework based on intelligent optimization algorithms (such as particle swarm optimization) is adopted to automatically search for optimal parameter configurations by simulating the entire process of suspicious target movement and UAV monitoring and interception.

[0129] In one embodiment of this application, an intelligent optimization algorithm is used to automatically adjust and optimize path planning parameters, while a dynamic replanning mechanism is established to cope with the emergence of new risk sources or changes in environmental conditions, ensuring the flexibility and adaptability of the protection system. Specifically, this step, based on real-time monitoring data and threat changes, uses an intelligent optimization algorithm to dynamically adjust the location, coverage angle, or number of UAV base stations to cope with newly emerging risk sources or changes in environmental conditions, ensuring the continuous effectiveness and adaptability of the maritime target protection system. The intelligent optimization algorithm can be an advanced algorithm such as particle swarm optimization, which automatically searches for optimal path planning parameters through simulation and iteration. The dynamic replanning mechanism rapidly adjusts the spatiotemporal risk model, monitoring area planning, and UAV path planning when new risk sources emerge or environmental conditions change, ensuring the real-time performance and effectiveness of the protection system.

[0130] Specifically, for different modes, the decision variables are the corresponding combinations of design parameters. For example, for a multi-layer reciprocating patrol mode, the number of patrol layers N layer Interlayer distance D interval UAV launch time interval ΔT launch .

[0131] The fitness function, based on simulation results, comprehensively evaluates monitoring and control effectiveness and resource consumption. In one embodiment, the fitness function is taken as follows:

[0132]

[0133] Where, N totalTo detect the total number of suspicious targets, N controlled To successfully control the number of suspicious targets, N consume To determine the total number of drones consumed, the weighting coefficients for the two are ω1 and ω2 (both positive numbers).

[0134] An intelligent optimization algorithm is used to search within the decision variable space, iteratively evaluating the fitness values ​​of different parameter combinations, and finally outputting the optimal parameter configuration that minimizes the optimal parameter configuration. Based on the optimal parameter configuration, the UAV launch time and path planning results are generated.

[0135] Because the maritime threat situation exhibits significant time- and space-varying characteristics, the system immediately triggers a replanning process when a new risk source emerges.

[0136] 1) Update the time risk model: incorporate the spatiotemporal parameters of new risk sources and recalculate the time windows of all threats.

[0137] 2) Reconstruct the spatial threat model: Based on the updated set of risk sources, regenerate the threat ellipse and calculate its union.

[0138] 3) Adjust the monitoring arc and base station location: Based on the new threat range, recalculate the monitoring arc parameters and optimize the location coordinates of the drone base station.

[0139] 4) Replan the drone path: Based on the new spatiotemporal threat model and optimization algorithm, regenerate or adjust the drone path planning strategy and its parameters to ensure the continued effectiveness of the monitoring system.

[0140] Through the above steps, this invention achieves closed-loop optimization and dynamic adaptation of UAV monitoring and interception strategies, which can effectively cope with complex and ever-changing maritime threat environments.

[0141] In one embodiment, this application also establishes an effectiveness assessment and feedback mechanism. Specifically, this mechanism collects and analyzes actual data during the monitoring process to assess the overall effectiveness of the protection system and feeds the assessment results back to the preceding steps, forming a closed-loop optimization mechanism to continuously improve the capability and level of maritime target protection. For example, based on the feedback data, the actual monitoring coverage is calculated. When the monitoring coverage is lower than a preset value, the number of UAV base stations is increased or decreased, and the patrol mode is redesigned. In addition, the positions of multiple UAVs performing tasks simultaneously can be obtained, and combined with the location of the risk source and the movement characteristic parameters of suspicious targets in the risk source, it can be determined whether the protected target can be effectively protected. If at least one UAV can intercept or interfere with the suspicious target before it reaches the preset position, it is determined that the protected target can be effectively protected. The preset position is a position at a preset distance from the protected target to prevent the suspicious target from getting too close to the protected target and causing a threat. Otherwise, the time interval between UAVs is shortened or more patrol UAVs are added.

[0142] In a specific application example, a cluster of offshore wind farms is taken as the protection target to prevent unidentified vessels or unmanned surface vessels from conducting close-range reconnaissance and sabotage activities.

[0143] Protection target: Central platform of an offshore wind farm (latitude and longitude: λ0, μ0)

[0144] Threat intelligence: It has been learned that at times t1 and t2, there are two potential risk sources, S1 and S2, where suspicious targets may appear.

[0145] Drone specifications: Maximum flight time of 90 minutes per drone, flight speed of 100 km / h, monitoring coverage radius of 10 km.

[0146] Monitoring arc advance radius: 150 kilometers

[0147] First, risk sources are identified and spatiotemporal modeled:

[0148] Input the latitude and longitude of risk sources S1 and S2 and their occurrence times t1 and t2.

[0149] Calculate the shortest and longest times for each risk source to reach the wind farm, and obtain the threat time window accordingly. .

[0150] By using coordinate transformation and threat ellipse modeling, the union of the ellipses corresponding to the two risk sources is obtained, forming the comprehensive threat area for the current period.

[0151] Then, a monitoring arc is generated, and a circle is drawn with the wind farm as the center and R0=20km as the radius. The intersection point of this circle and the threat ellipse is obtained.

[0152] Calculate the polar angles of these intersection points to determine the angular range of the monitoring arc. And calculate the arc length. .

[0153] Finally, deploy drone base stations.

[0154] Calculate the coverage angle of a single base station .

[0155] Calculate the required number of base stations The polar angle and rectangular coordinates of each base station are determined to achieve uniform deployment on the monitoring arc while retaining a certain overlap coverage area.

[0156] In this example, a multi-layer reciprocating patrol is selected, and the initial patrol layer number N is set. layer =3, interlayer spacing D interval =3km, UAV launch interval ΔT launch =20min.

[0157] Drones take off sequentially from the base station and perform reciprocating patrols along the normal direction, achieving hierarchical coverage of threat angles.

[0158] Optimization and dynamic response, using particle swarm optimization algorithm for N layer D interval ΔT launch Parameters were optimized, and the objective function was used to comprehensively balance the number of suspected targets and the amount of drones consumed in successful control.

[0159] When the system detects a new risk source S3, it immediately triggers replanning: updates the threat time window and threat ellipse union; recalculates the monitoring arc and base station location; and adjusts the drone path and launch strategy to ensure uninterrupted monitoring.

[0160] Through the above steps, the system successfully established a dynamic UAV monitoring defense line within a 150-kilometer radius of the wind farm, enabling early detection, continuous tracking, and early warning of unidentified surface targets, significantly improving the safety protection level of the wind farm, and verifying the effectiveness and adaptability of the invention in complex marine environments.

[0161] The beneficial effects of this application are as follows: It identifies the risk source information of the protected target, performs spatiotemporal risk modeling based on this information to determine a temporal risk model and a spatial risk model, identifies the potential threat area of ​​the protected target based on these models, plans a monitoring arc to fully cover this potential threat area, and constructs a drone base station capable of completely covering the monitoring arc, enabling the drone base station to provide security protection for the protected target. Because this application can perform spatiotemporal risk modeling based on the risk source information of the protected target and dynamically generate a monitoring arc, and construct a drone base station based on this monitoring arc to provide security protection for the protected target, it achieves comprehensive, blind-spot-free coverage of the protected target, thus improving the security protection capability of the drone system.

[0162] In one embodiment, step S101 above can be implemented as steps A1-A2 as follows:

[0163] In step A1, at least one type of data is collected, including prior knowledge data, real-time intelligence data, and sensor monitoring data.

[0164] In step A2, the starting location, occurrence time, and movement characteristics of the risk source that poses a potential threat to the protected target are determined by analyzing the at least one piece of data as risk source information.

[0165] In one embodiment, step S102 above can be implemented as steps B1-B2 as follows:

[0166] In step B1, the shortest and longest arrival times from the risk source to the protected target are calculated based on the risk source information, and the time window for the threat to occur is defined as the time risk model;

[0167] In step B2, a spatial risk model containing threat ellipse models is constructed, wherein each risk source corresponds to a threat ellipse model. The threat ellipse model is used to depict the possible spatial distribution range of the corresponding risk source. The two foci of the threat ellipse are the protected target and the risk source point, respectively. The length of the major axis is the maximum range of the suspected target, and the focal length is the distance between the protected target and the risk source.

[0168] In one embodiment, step S103 above can be implemented as steps C1-C2 as follows:

[0169] In step C1, all time windows that pose a potential threat to the protected target and all risk sources that may pose a threat within the time window are determined according to the time risk model;

[0170] In step C2, the threat areas corresponding to all risk sources that may pose a threat within the time window where a potential threat exists are determined based on the spatial risk model.

[0171] In one embodiment, step C2 above can be implemented as steps C21-C22:

[0172] In step C21, set operations are used to determine all target risk sources that may pose a threat within the time window in which a potential threat exists;

[0173] In step C22, the union of the threat ellipse models corresponding to all target risk sources is determined to be the threat region corresponding to all risk sources that may constitute a threat under the time window in which a potential threat exists.

[0174] In one embodiment, step S104 above can be implemented as follows:

[0175] Based on the boundary of the potential threat area of ​​the protected target and the preset monitoring forward radius, the arc length, position and angle range of the monitoring arc are determined using geometric calculation methods to ensure that the monitoring arc can completely cover the potential threat area.

[0176] In one embodiment, step S105 above can be implemented as follows:

[0177] Based on the parameters of the monitoring arc, combined with the maximum flight distance and coverage capability of the drone, the layout location and coverage angle of the drone base station are determined to ensure seamless connection between base stations and full coverage of the monitoring area.

[0178] Figure 7This is a schematic diagram of the structure of a drone-based security protection device according to one embodiment of this application, as shown below. Figure 7 As shown, the device includes:

[0179] The first determining module 701 is used to determine the risk source information of the protected target;

[0180] Modeling module 702 is used to perform spatiotemporal risk modeling based on risk source information in order to determine the temporal risk model and the spatial risk model.

[0181] The second determining module 703 is used to determine the potential threat area of ​​the protected target based on the time risk model and the spatial risk model.

[0182] Planning module 704 is used to plan a monitoring arc for comprehensive coverage of potential threat areas of the protected target;

[0183] The construction module 705 is used to construct a drone base station that can completely cover the monitoring arc, so that the drone base station can achieve security protection for the protected target.

[0184] In one embodiment, the first determining module includes:

[0185] The collection submodule is used to collect at least one type of data, including prior knowledge data, real-time intelligence data, and sensor monitoring data.

[0186] The analysis submodule is used to analyze the at least one type of data to determine the starting location, occurrence time, and movement characteristics of risk sources that pose a potential threat to the protected target as risk source information.

[0187] In one embodiment, the modeling module includes:

[0188] The calculation submodule is used to calculate the shortest and longest arrival times from the risk source to the protected target based on the risk source information, and to define the time window for the occurrence of the threat as a time risk model.

[0189] A construction submodule is used to construct a spatial risk model containing threat ellipse models. Each risk source corresponds to a threat ellipse model, which is used to depict the possible spatial distribution range of the corresponding risk source. The two foci of the threat ellipse are the protected target and the risk source point, respectively. The length of the major axis is the maximum range of the suspected target, and the focal length is the distance between the protected target and the risk source.

[0190] In one embodiment, the second determining module includes:

[0191] The first determination submodule is used to determine, based on the time risk model, all time windows that pose potential threats to the protected target and all risk sources that may pose a threat within those time windows;

[0192] The second determination submodule is used to determine the threat areas corresponding to all risk sources that may pose a threat within a time window where a potential threat exists, based on the spatial risk model.

[0193] In one embodiment, the second determining submodule is further configured to:

[0194] Set operations are used to identify all potential risk sources that may pose a threat within a time window where a potential threat exists.

[0195] The union of the threat ellipse models corresponding to all target risk sources is determined as the threat region corresponding to all risk sources that may constitute a threat under the time window in which a potential threat exists.

[0196] In one embodiment, the planning module includes:

[0197] The third determination submodule is used to determine the arc length, position, and angle range of the monitoring arc using geometric calculation methods based on the boundary of the potential threat area of ​​the protected target and the preset monitoring forward radius, so as to ensure that the monitoring arc can completely cover the potential threat area.

[0198] In one embodiment, the building module is further configured to:

[0199] Based on the parameters of the monitoring arc, combined with the maximum flight distance and coverage capability of the drone, the layout location and coverage angle of the drone base station are determined to ensure seamless connection between base stations and full coverage of the monitoring area.

[0200] Figure 8 This is a schematic diagram of the hardware structure of a drone-based security protection system according to one embodiment of this application, as shown below. Figure 8 As shown, the drone-based security system includes:

[0201] At least one processor 820; and,

[0202] Memory 804 communicatively connected to the at least one processor 820; wherein,

[0203] The memory 804 stores instructions that can be executed by the at least one processor 820 to implement the UAV-based security protection method described in any of the above embodiments.

[0204] Reference Figure 8The drone-based security system 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0205] Processing component 802 typically controls the overall operation of the UAV-based security system 800. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0206] Memory 804 is configured to store various types of data to support the operation of the drone-based security system 800. Examples of this data include instructions for any application or method operating on the drone-based security system 800, such as text, images, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0207] Power supply component 806 provides power to various components of the unmanned aerial vehicle (UAV)-based safety protection system 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the vehicle control system 800.

[0208] The multimedia component 808 includes a screen that provides an output interface between the drone-based security system 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 808 may also include a front-facing camera and / or a rear-facing camera. When the drone-based security system 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0209] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when the drone-based security system 800 is in an operating mode, such as alarm mode, recording mode, voice recognition mode, and voice output mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0210] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0211] Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of the drone-based security system 800. For example, sensor assembly 814 may include a sound sensor. Additionally, sensor assembly 814 can detect the on / off state of the drone-based security system 800, the relative positioning of components (e.g., the display and keypad of the drone-based security system 800), and the operational status of the drone-based security system 800 or one of its components. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0212] Communication component 816 is configured to enable the drone-based security system 800 to provide wired or wireless communication capabilities with other devices and cloud platforms. The drone-based security system 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0213] In an exemplary embodiment, the drone-based security protection system 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the drone-based security protection method described in any of the above embodiments.

[0214] This application also provides a computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor corresponding to a drone-based security protection system, enables the drone-based security protection system to implement the drone-based security protection method described in any of the above embodiments.

[0215] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0216] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0217] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0218] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0219] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A security protection method based on unmanned aerial vehicles (UAVs), characterized in that, include: Identify risk source information for the protected targets; Spatiotemporal risk modeling is performed based on risk source information to determine the temporal risk model and the spatial risk model; Based on the time risk model and the spatial risk model, the potential threat areas of the protected target are determined; Plan a monitoring arc to comprehensively cover the potential threat areas of the protected targets; Construct a drone base station capable of completely covering the monitoring arc, so that the drone base station can achieve security protection for the protected target; Spatiotemporal risk modeling is performed based on risk source information to determine the temporal risk model and the spatial risk model, including: Calculate the shortest and longest arrival times from the risk source to the protected target based on the risk source information, and define the time window for the occurrence of the threat as a time risk model; A spatial risk model incorporating threat ellipse models is constructed, wherein each risk source corresponds to a threat ellipse model. The threat ellipse model is used to depict the possible spatial distribution range of the corresponding risk source. The two foci of the threat ellipse are the protected target and the risk source point, respectively. The length of the major axis is the maximum range of the suspected target, and the focal length is the distance between the protected target and the risk source.

2. The method as described in claim 1, characterized in that, The risk source information for determining the protection target includes: Collect at least one type of data, including prior knowledge data, real-time intelligence data, and sensor monitoring data; By analyzing the at least one type of data, the starting location, occurrence time, and movement characteristics of risk sources that pose a potential threat to the protected target can be determined as risk source information.

3. The method as described in claim 1, characterized in that, The step of determining the potential threat area of ​​the protected target based on the time risk model and the spatial risk model includes: Based on the time risk model, all time windows that pose potential threats to the protected target and all risk sources that may pose a threat within those time windows are identified. Based on the spatial risk model, the threat areas corresponding to all risk sources that may pose a threat within the time window where a potential threat exists are identified.

4. The method as described in claim 3, characterized in that, The threat areas corresponding to all potential threat sources within a time window determined by the spatial risk model include: Set operations are used to identify all potential risk sources that may pose a threat within a time window where a potential threat exists. The union of the threat ellipse models corresponding to all target risk sources is determined as the threat region corresponding to all risk sources that may constitute a threat under the time window in which a potential threat exists.

5. The method as described in claim 1, characterized in that, The planned monitoring arc, used to comprehensively cover the potential threat areas of the protected target, includes: Based on the boundary of the potential threat area of ​​the protected target and the preset monitoring forward radius, the arc length, position and angle range of the monitoring arc are determined using geometric calculation methods to ensure that the monitoring arc can completely cover the potential threat area.

6. The method as described in claim 1, characterized in that, The construction of the drone base station capable of completely covering the monitoring arc includes: Based on the parameters of the monitoring arc, combined with the maximum flight distance and coverage capability of the drone, the layout location and coverage angle of the drone base station are determined to ensure seamless connection between base stations and full coverage of the monitoring area.

7. A security protection device based on unmanned aerial vehicles (UAVs), characterized in that, include: The first determination module is used to determine the risk source information of the protected target; The modeling module is used to perform spatiotemporal risk modeling based on risk source information in order to determine the temporal risk model and the spatial risk model. The second determining module is used to determine the potential threat area of ​​the protected target based on the time risk model and the spatial risk model. The planning module is used to plan a monitoring arc to comprehensively cover the potential threat areas of the protected target; A construction module is used to build a drone base station that can completely cover the monitoring arc, so that the drone base station can achieve security protection for the protected target; The modeling module includes: The calculation submodule is used to calculate the shortest and longest arrival times from the risk source to the protected target based on the risk source information, and to define the time window for the occurrence of the threat as a time risk model. A construction submodule is used to construct a spatial risk model containing threat ellipse models. Each risk source corresponds to a threat ellipse model, which is used to depict the possible spatial distribution range of the corresponding risk source. The two foci of the threat ellipse are the protected target and the risk source point, respectively. The length of the major axis is the maximum range of the suspected target, and the focal length is the distance between the protected target and the risk source.

8. A security protection system based on unmanned aerial vehicles (UAVs), characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to implement the drone-based security protection method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor corresponding to the UAV-based security protection system, the UAV-based security protection system is able to implement the UAV-based security protection method as described in any one of claims 1-6.

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