Method, device and electronic equipment for determining security of target area

Abstract

The application discloses a kind of method for determining target area security, device and electronic equipment. Specific scheme is: obtaining at least one unmanned aerial vehicle to be detected in target area in multiple dimensions under unmanned aerial vehicle data, wherein multiple dimensions include radio signal dimension, unmanned aerial vehicle vibration audio dimension and image acquisition dimension to unmanned aerial vehicle to be detected;Based on the unmanned aerial vehicle data of at least one unmanned aerial vehicle to be detected, determine the unmanned aerial vehicle type of unmanned aerial vehicle to be detected;In response to the event that unmanned aerial vehicle type is not preset type, determine the flight data of target unmanned aerial vehicle, and according to flight data and the position data of preset detection point of target area, determine the security attribute of preset detection point;Wherein, target unmanned aerial vehicle is unmanned aerial vehicle in at least one unmanned aerial vehicle to be detected.The application realizes accurate detection and positioning to unmanned aerial vehicle, determines whether unmanned aerial vehicle will cause threat to preset detection point.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a method and device for determining the safety of a target area and an electronic device. BACKGROUND

[0002] With the continuous progress of unmanned aerial vehicle technology, the scope and number of unmanned aerial vehicles are also increasing. Unmanned aerial vehicles are widely used in commercial photography, agricultural monitoring, emergency rescue and other fields. However, the widespread use of unmanned aerial vehicles also poses potential safety threats.

[0003] Currently, unmanned aerial vehicles are usually positioned by radar or radio wave detection, etc. to determine whether they will pose a threat to a safe area. However, the above-mentioned methods rely only on a single sensor or signal source, and are susceptible to interference during unmanned aerial vehicle positioning, which cannot achieve accurate positioning of unmanned aerial vehicles, and thus cannot accurately assess whether unmanned aerial vehicles pose a potential threat. SUMMARY

[0004] The present application provides a method and device for determining the safety of a target area and an electronic device, which realizes accurate detection and positioning of unmanned aerial vehicles and determines whether they will pose a threat to a preset detection point.

[0005] According to an aspect of the present application, a method for determining the safety of a target area is provided, which comprises:

[0006] obtaining unmanned aerial vehicle data of at least one to-be-detected unmanned aerial vehicle in a target area in multiple dimensions, wherein the multiple dimensions include a radio signal dimension, an unmanned aerial vehicle vibration audio dimension, and an image acquisition dimension of the to-be-detected unmanned aerial vehicle;

[0007] determining the type of the to-be-detected unmanned aerial vehicle based on the unmanned aerial vehicle data of the at least one to-be-detected unmanned aerial vehicle;

[0008] in response to an event that the type of the unmanned aerial vehicle is not a preset type, determining flight data of the target unmanned aerial vehicle, and determining the safety attribute of a preset detection point of the target area based on the flight data and position data of the preset detection point;

[0009] wherein the target unmanned aerial vehicle is an unmanned aerial vehicle in the at least one to-be-detected unmanned aerial vehicle.

[0010] According to another aspect of the present application, a device for determining the safety of a target area is provided, which comprises:

[0011] a data acquisition module for acquiring unmanned aerial vehicle data of at least one to-be-detected unmanned aerial vehicle in a target area in multiple dimensions, wherein the multiple dimensions include a radio signal dimension, an unmanned aerial vehicle vibration audio dimension, and an image acquisition dimension of the to-be-detected unmanned aerial vehicle;

[0012] The UAV type determination module is configured to determine a UAV type of the UAV to be detected based on UAV data of the at least one UAV to be detected.

[0013] The safety attribute determination module is configured to, in response to an event that the UAV type is not the preset type, determine flight data of the target UAV, and determine a safety attribute of a preset detection point of the target area according to the flight data and position data of the preset detection point.

[0014] The target UAV is a UAV in the at least one UAV to be detected.

[0015] According to another aspect of the present application, an electronic device is provided, which comprises:

[0016] at least one processor; and

[0017] a memory connected to the at least one processor in communication; wherein

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the method for determining the safety of the target area according to any one of the embodiments of the present application.

[0019] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the method for determining the safety of the target area according to any one of the embodiments of the present application when executed by the processor.

[0020] According to another aspect of the present application, a computer program product is provided, which comprises a computer program, and the computer program, when executed by a processor, implements the method for determining the safety of the target area according to any one of the embodiments of the present application.

[0021] The technical scheme of the embodiment of the present application comprises the following steps: acquiring unmanned aerial vehicle data of at least one to-be-detected unmanned aerial vehicle in a target region in multiple dimensions, wherein the multiple dimensions comprise a radio signal dimension, an unmanned aerial vehicle vibration audio dimension, and an image acquisition dimension of the to-be-detected unmanned aerial vehicle. By acquiring the unmanned aerial vehicle data in multiple dimensions, the accuracy of subsequent determination of the unmanned aerial vehicle type is improved. According to the unmanned aerial vehicle data of the at least one to-be-detected unmanned aerial vehicle, the unmanned aerial vehicle type of the to-be-detected unmanned aerial vehicle is determined. In response to an event that the unmanned aerial vehicle type is not a preset type, flight data of the target unmanned aerial vehicle is determined, and according to the flight data and position data of a preset detection point of the target region, the safety attribute of the preset detection point is determined. Based on this, it can be determined whether the target unmanned aerial vehicle will pose a threat to the preset detection point of the target region, so as to determine whether to take measures on the target unmanned aerial vehicle according to the safety attribute of the preset detection point, so as to ensure the safety of the preset detection point. The present application solves the problem of inaccurate detection and positioning of the unmanned aerial vehicle caused by detection of the unmanned aerial vehicle by a single sensor or signal source in the prior art, improves the accuracy and reliability of unmanned aerial vehicle detection and positioning by feature analysis and processing of the unmanned aerial vehicle data in multiple dimensions. Moreover, when the unmanned aerial vehicle type is not a preset type, whether the target unmanned aerial vehicle will pose a threat to the preset detection point is determined according to the position of the target unmanned aerial vehicle at a preset time, thereby ensuring the safety of the preset detection point of the target region.

[0022] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0024] Figure 1 is a flowchart of a method for determining the safety of a target region provided by an embodiment of the present application;

[0025] Figure 2 is a flowchart of a method for determining the safety of a target region provided by an embodiment of the present application;

[0026] Figure 3 is an example diagram of a signal time-frequency feature map provided by an embodiment of the present application;

[0027] Figure 4 is an example diagram of a sound time-frequency feature map provided by an embodiment of the present application;

[0028] Figure 5 is a structural diagram of a device for determining the security of a target area provided by an embodiment of the present application;

[0029] Figure 6 is a structural diagram of an electronic device for implementing a method for determining the security of a target area according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to only those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to the process, method, product, or device.

[0032] Embodiment one

[0033] Figure 1 is a flowchart of a method for determining the security of a target area provided by an embodiment of the present application. The embodiment can be applicable to detecting the type of a to-be-detected unmanned aerial vehicle in the target area, and evaluating unmanned aerial vehicles that are not of a preset type, to ensure the safety of a preset detection point in the target area. The method can be executed by a device for determining the security of a target area. The device for determining the security of a target area can be implemented in the form of hardware and / or software, and can be configured in an electronic device such as a mobile phone, a computer, or a server. As shown in the figure, the method comprises the following steps. Figure 1

[0034] S110, obtaining unmanned aerial vehicle data of at least one to-be-detected unmanned aerial vehicle in a target area in multiple dimensions, wherein the multiple dimensions include a radio signal dimension, an unmanned aerial vehicle vibration audio dimension, and an image acquisition dimension of the to-be-detected unmanned aerial vehicle. ​

[0035] In the embodiments of the present application, whether the UAV in the target area is a potential threat is determined by detecting the UAV in the target area. The target area can be a pre-divided area. For example, the target area can be a circular area with a radius of three kilometers. The UAV in the target area is the UAV to be detected. In order to improve the accuracy of UAV type detection, UAV data of the UAV to be detected in multiple dimensions can be obtained. The multiple dimensions can be radio signal dimension, UAV vibration audio dimension, and image acquisition dimension. Optionally, the UAV data in the radio signal dimension can be information such as signal frequency and bandwidth of the UAV image transmission signal of the UAV to be detected. The UAV data in the UAV vibration audio dimension can be understood as information such as frequency and intensity of the sound when the UAV to be detected is flying. The UAV data in the image acquisition dimension can be the UAV image obtained by image acquisition of the UAV to be detected.

[0036] Specifically, the UAV data of at least one UAV to be detected in the target area in the radio signal dimension, the UAV vibration audio dimension, and the image acquisition dimension of the UAV to be detected is obtained, so as to determine the UAV type of the UAV to be detected by the UAV data, and determine whether the UAV to be detected is a potential threat based on the UAV type.

[0037] S120, determine the UAV type of the UAV to be detected based on the UAV data of the at least one UAV to be detected.

[0038] The UAV type can be understood as the category of the UAV to be detected. For example, the UAV type of the UAV to be detected can be type A, type B, or type C.

[0039] Specifically, the UAV type of each UAV to be detected is determined according to the UAV data of each UAV to be detected in multiple dimensions, so as to determine whether the UAV to be detected is a UAV type allowed to fly in the target area according to the UAV type of the UAV to be detected.

[0040] S130, in response to the event that the UAV type is not the preset type, determine the flight data of the target UAV, and determine the safety attribute of the preset detection point according to the flight data and the position data of the preset detection point of the target area.

[0041] The target unmanned aerial vehicle can be an unmanned aerial vehicle of the at least one unmanned aerial vehicle to be detected, which is of a type different from the preset type. The flight data can be flight data of the target unmanned aerial vehicle when the target unmanned aerial vehicle is flying in the target area. For example, the flight data can include a flight position, a flight speed, a flight direction, and the like of the target unmanned aerial vehicle. The preset detection point can be a location or an area in the target area, which is required to be protected, and which is set in advance. The position data can be height data of the preset detection point, or three-dimensional coordinate data of the preset detection point in a three-dimensional coordinate system with the center of the earth as an origin. The safety attribute can be used to represent whether the preset detection point is safe.

[0042] Specifically, in response to an event that the unmanned aerial vehicle to be detected is of a type different from the preset type, the unmanned aerial vehicle to be detected, which is of the type different from the preset type, is taken as a target unmanned aerial vehicle, and flight data of the target unmanned aerial vehicle is determined. According to the flight data and position data of a preset detection point in the target area, whether the target unmanned aerial vehicle will pose a threat to the preset detection point, i.e., whether the preset detection point is safe, is determined.

[0043] In the embodiment of the present application, the preset type is a type of unmanned aerial vehicle that is not forbidden to fly in the target area. The preset detection point safety attribute can be determined in the following manner: if the unmanned aerial vehicle to be detected is of a type of unmanned aerial vehicle that is forbidden to fly in the target area, the unmanned aerial vehicle to be detected is determined as a target unmanned aerial vehicle; a control signal of the target unmanned aerial vehicle is intercepted and processed, and the intercepted control signal is analyzed to obtain flight data of the target unmanned aerial vehicle; the flight data is substituted into a preset position determination function to obtain predicted position information of the target unmanned aerial vehicle at a preset time; and the safety attribute of the preset detection point is determined according to the predicted position information and position data of the preset detection point in the target area.

[0044] The target unmanned aerial vehicle can be an unmanned aerial vehicle of the at least one unmanned aerial vehicle to be detected, which is of a type different from the preset type. The flight data can be flight data of the target unmanned aerial vehicle when the target unmanned aerial vehicle is flying in the target area. For example, the flight data can include a flight position, a flight speed, a flight direction, and the like of the target unmanned aerial vehicle. The preset detection point can be a location or an area in the target area, which is required to be protected, and which is set in advance. The position data can be height data of the preset detection point, or three-dimensional coordinate data of the preset detection point in a three-dimensional coordinate system with the center of the earth as an origin. The safety attribute can be used to represent whether the preset detection point is safe.

[0045]

[0046] The target unmanned aerial vehicle can be an unmanned aerial vehicle of the at least one unmanned aerial vehicle to be detected, which is of a type different from the preset type. The flight data can be flight data of the target unmanned aerial vehicle when the target unmanned aerial vehicle is flying in the target area. For example, the flight data can include a flight position, a flight speed, a flight direction, and the like of the target unmanned aerial vehicle. The preset detection point can be a location or an area in the target area, which is required to be protected, and which is set in advance. The position data can be height data of the preset detection point, or three-dimensional coordinate data of the preset detection point in a three-dimensional coordinate system with the center of the earth as an origin. The safety attribute can be used to represent whether the preset detection point is safe.

[0047] The preset moment can be a moment set in advance for detecting the flight position of the target UAV. The predicted position information can be the flight position of the target UAV in a three-dimensional coordinate system with the earth center as the origin at the preset moment.

[0048] Specifically, if the UAV type of the to-be-detected UAV is not the preset type, it indicates that the UAV type of the to-be-detected UAV is a UAV type that is forbidden to fly in the target area, and the to-be-detected UAV is taken as the target UAV. The control signal of the target UAV is analyzed and processed to obtain flight data of the target UAV, such as the current flight position, the current flight speed, the current flight direction, and the current flight acceleration. The flight data is substituted into the preset position determination function to obtain the predicted position information of the target UAV at the preset moment. According to the three-dimensional coordinate data corresponding to the predicted position information and the three-dimensional coordinate data corresponding to the preset detection point in the target area, it is determined whether the preset detection point in the target area is safe.

[0049] Optionally, the flight data of the target UAV can also be obtained by decoding and analyzing the UAV image transmission signal of the target UAV.

[0050] Exemplarily, the UAV data in the dimension of radio signal is taken as the UAV image transmission signal for illustration. The UAV image transmission signal and the control signal are decoded and processed to extract the global navigation satellite system (GNSS) positioning data and other flight data of the target UAV. The GNSS positioning data includes three coordinates, namely, the latitude coordinate (Latitude, lat), the longitude coordinate (Longitude, lon), and the altitude coordinate (Altitude, alt). The positioning data and other flight data are filtered and denoised to obtain the denoised flight data, so as to determine the predicted position information of the target UAV at the preset moment through the denoised flight data.

[0051] Optionally, the flight data is substituted into the preset position determination function to obtain the position information of the target UAV at multiple moments. According to the position information of the target UAV at multiple moments, the flight trajectory of the target UAV can be determined. Through the flight trajectory of the target UAV, the predicted position information of the target UAV at the preset moment can be determined.

[0052] Optionally, according to the predicted position information and the position data of the preset detection point in the target area, the safety attribute of the preset detection point is determined, including: according to the predicted position information and the position data of the preset detection point, distance information between the predicted position information and the preset detection point is determined; if the distance information does not belong to a preset distance range, it is determined that the safety attribute of the preset detection point is unsafe.

[0053] The distance information can be understood as a distance between the predicted position information and position data of the preset detection point. The distance information can be determined by three-dimensional coordinate data corresponding to the predicted position information and three-dimensional coordinate data corresponding to the preset detection point. The preset distance range can be preset, and the safe distance range between the target UAV and the preset detection point.

[0054] Specifically, the distance information between the target UAV and the preset detection point is determined according to the three-dimensional coordinate data corresponding to the predicted position information and the three-dimensional coordinate data of the predicted detection point. When the distance information is not within the preset distance range, the safety attribute of the preset detection point is determined to be unsafe.

[0055] Optionally, a plurality of distance ranges can be set, and a UAV threat level corresponding to each distance range can be determined. According to the distance information between the target UAV and the preset detection point, the distance range to which the distance information belongs is determined, and the UAV threat level of the target UAV is determined according to the distance range. Based on this, whether to perform interference processing on the target UAV can be determined according to the UAV threat level, so as to ensure the safety of the preset detection point in the target area.

[0056] Optionally, in the case that the safety attribute of the preset detection point is unsafe, interference processing or induced deception processing is performed on the target UAV to update the safety attribute of the preset detection point.

[0057] The interference processing can be radio signal interference processing on the target UAV. The induced deception processing can be sending a false global positioning system (GPS) signal to the target UAV to cause the flight position of the target UAV to deviate, so as to ensure the safety of the predicted detection point.

[0058] Specifically, in the case that the safety attribute of the preset detection point is unsafe, interference processing or induced deception processing is performed on the target UAV to cause the predicted position information of the target UAV to deviate. The distance information between the deviated predicted position information and the preset detection point is determined. When the distance information is within the preset distance range, the safety attribute of the preset detection point is updated to be safe.

[0059] The technical scheme of the embodiment acquires the UAV data of at least one to-be-detected UAV in the target region in multiple dimensions, wherein the multiple dimensions include a radio signal dimension, a UAV vibration audio dimension, and an image acquisition dimension of the to-be-detected UAV. By acquiring the UAV data in multiple dimensions, the accuracy of subsequent determination of the UAV type is improved. According to the UAV data of the at least one to-be-detected UAV, the UAV type of the to-be-detected UAV is determined. In response to an event that the UAV type is not a preset type, flight data of the target UAV is determined, and according to the flight data and position data of a preset detection point of the target region, the safety attribute of the preset detection point is determined. Based on this, it can be determined whether the target UAV will pose a threat to the preset detection point of the target region, so as to determine whether to take measures on the target UAV according to the safety attribute of the preset detection point, so as to ensure the safety of the preset detection point. The present application solves the problem of inaccurate detection and positioning of the UAV in the prior art by detecting the UAV through a single sensor or signal source. By analyzing and processing the UAV data in multiple dimensions, the accuracy and reliability of UAV detection and positioning are improved. Moreover, when the UAV type is not a preset type, according to the position of the target UAV at a preset time, it is determined whether the target UAV will pose a threat to the preset detection point, thereby ensuring the safety of the preset detection point of the target region.

[0060] Embodiment Two

[0061] Figure 2 is a flowchart of a method for determining the safety of a target region provided by Embodiment Two of the present application. This embodiment is based on the above-mentioned embodiments and refines the step of "determining the UAV type of the to-be-detected UAV based on the UAV data of the at least one to-be-detected UAV". The specific implementation can be referred to the technical scheme of this embodiment. Among them, the same or corresponding technical terms as the above-mentioned embodiments will not be described here. As shown in Figure 2 , the method comprises:

[0062] S210, acquiring UAV data of at least one to-be-detected UAV in the target region in multiple dimensions, wherein the multiple dimensions include a radio signal dimension, a UAV vibration audio dimension, and an image acquisition dimension of the to-be-detected UAV.

[0063] S220, for at least one to-be-detected UAV, acquiring a signal time-frequency feature map in the radio signal dimension from the UAV data of the to-be-detected UAV, and determining at least one signal feature of the to-be-detected UAV based on the signal time-frequency feature map.

[0064] The signal time-frequency characteristic map can be an image determined based on the drone image transmission signal and time of the drone under test. The drone image transmission signal can be understood as the signal used by the drone under test to transmit image data to the receiving device. For example, the signal time-frequency characteristic map can be as follows: Figure 3 As shown, the frequency variation of the UAV image transmission signal over time can be determined by the signal time-frequency characteristic diagram. Signal characteristics can include the spectral characteristics, modulation mode characteristics, instantaneous characteristics, pulse width characteristics, and data transmission interval characteristics of the UAV under test.

[0065] Specifically, at least one radio signal within the airspace corresponding to the target area is acquired using radio wave detection technology. Based on the frequency, modulation method, and time-domain characteristics of the radio signal, the radio signal related to UAV image transmission is determined; that is, the UAV image transmission signal is obtained. Using the UAV image transmission signal, the signal time-frequency characteristic map of the UAV to be detected is determined. This signal time-frequency characteristic map is then used as the UAV data of the current UAV to be detected in the radio signal dimension. For at least one UAV to be detected, the signal time-frequency characteristic map of the current UAV to be detected is determined from the UAV data of the current UAV to be detected. Signal feature extraction is performed on the signal time-frequency characteristic map and the UAV image transmission signal of the UAV to be detected to determine at least one signal feature of the current UAV to be detected.

[0066] S230. Obtain the sound time-frequency feature map in the vibration audio dimension of the drone from the drone data, and determine the voiceprint features of the drone to be detected based on the sound time-frequency feature map.

[0067] The sound time-frequency feature map can be an image determined based on the sound frequency and time of the drone being detected. For example, the sound time-frequency feature map can be as follows: Figure 4 As shown, the time-frequency feature map of the sound can be used to determine how the sound frequency of the drone under test changes over time. The acoustic signature features can be characteristic values ​​used to characterize the sound frequency and intensity of the drone under test.

[0068] Specifically, audio data is collected from audio sensors deployed within the target area. Based on this audio data, a time-frequency characteristic map of the drone's sound under vibration is determined. Feature extraction is then performed on the sound time-frequency characteristic map to identify the drone's acoustic signature.

[0069] It should be noted that different types of drones have different voiceprint characteristics. Voiceprint characteristics can be represented as V = [v1, v2, v3, ..., v m ], where v i This indicates that at the sound frequency f i and time t iThe voiceprint feature value corresponding to the time, i∈[1, m].

[0070] S240, obtaining the to-be-processed image of the unmanned aerial vehicle in the image acquisition dimension from the unmanned aerial vehicle data, and determining at least one image feature of the to-be-detected unmanned aerial vehicle based on the to-be-processed image.

[0071] The to-be-processed image can be an unmanned aerial vehicle image obtained by image acquisition of the to-be-detected unmanned aerial vehicle.

[0072] Specifically, the to-be-processed image of the to-be-detected unmanned aerial vehicle in the image acquisition dimension is obtained from the unmanned aerial vehicle data. A deep learning algorithm, such as a convolutional neural network algorithm (CNN), is used to process the to-be-processed image and extract at least one image feature of the to-be-detected unmanned aerial vehicle. The image feature can be an appearance feature of the to-be-detected unmanned aerial vehicle, such as the shape, color, and texture of the unmanned aerial vehicle.

[0073] It should be noted that before obtaining the to-be-processed image from the unmanned aerial vehicle data, the method further includes: determining the distance difference between the to-be-detected unmanned aerial vehicle and two preset signal receiving points according to the time difference of arrival technology, so as to determine the positioning information of the to-be-detected unmanned aerial vehicle according to the distance difference. According to the positioning information of the to-be-detected unmanned aerial vehicle, an image acquisition device with a long focal length is called to perform image acquisition processing on the to-be-detected unmanned aerial vehicle to obtain the to-be-processed image of the to-be-detected unmanned aerial vehicle. The positioning information is used to represent the position information of the to-be-detected unmanned aerial vehicle.

[0074] The calculation formula of the distance difference can be expressed as follows:

[0075] d ij = c x (t i -t j )

[0076] Wherein, d ij represents the distance difference between the to-be-detected unmanned aerial vehicle and the two preset signal receiving points, c represents the propagation speed of the radio signal of the to-be-detected unmanned aerial vehicle in the air, t i represents the time when the radio signal of the to-be-detected unmanned aerial vehicle arrives at the preset signal receiving point i, and t j represents the time when the radio signal of the to-be-detected unmanned aerial vehicle arrives at the preset signal receiving point j.

[0077] S250, when the at least one signal feature, the voiceprint feature, and the at least one image feature all satisfy the corresponding constraint conditions, determining that the unmanned aerial vehicle type of the to-be-detected unmanned aerial vehicle is the target type.

[0078] The corresponding constraint condition can be a pre-set condition required to be met by the features in different dimensions. Alternatively, the constraint condition can be used to represent the matching degree between the features in different dimensions and the corresponding pre-set features. The target type can be understood as the type of the unmanned aerial vehicle to be detected.

[0079] Specifically, when the at least one signal feature meets the constraint condition in the radio signal dimension, at least one unmanned aerial vehicle type corresponding to the signal feature is determined. When the voiceprint feature meets the constraint condition in the unmanned aerial vehicle vibration audio dimension, at least one unmanned aerial vehicle type corresponding to the voiceprint feature is determined. When the at least one image feature meets the constraint condition in the image acquisition dimension, at least one unmanned aerial vehicle type corresponding to the image feature is determined. According to the plurality of unmanned aerial vehicle types corresponding to the features in different dimensions, the unmanned aerial vehicle type of the unmanned aerial vehicle to be detected is determined as the target type.

[0080] In the embodiments of the present application, the manner of determining the unmanned aerial vehicle type can be: determining the to-be-fused feature similarity corresponding to each signal feature according to the at least one signal feature and the pre-set feature in the radio signal dimension, and determining the signal feature similarity based on the weight coefficient of each signal feature and the to-be-fused feature similarity; determining the voiceprint feature similarity corresponding to the voiceprint feature according to the voiceprint feature and the pre-set feature in the unmanned aerial vehicle vibration audio dimension; obtaining at least one feature difference value according to the feature value corresponding to the at least one image feature and the feature value of the pre-set feature in the image acquisition dimension; when the signal feature similarity belongs to a pre-set signal similarity range, the voiceprint feature similarity is greater than a pre-set voice similarity threshold, and the feature difference value is less than a pre-set value, determining the unmanned aerial vehicle type of the unmanned aerial vehicle to be detected as the target type.

[0081] The pre-set feature in the radio signal dimension can be a standard signal feature corresponding to different unmanned aerial vehicle types pre-stored in a database or a corresponding file. The to-be-fused feature similarity can be understood as the feature similarity between the current signal feature and the corresponding pre-set feature. Alternatively, the to-be-fused feature similarity can be determined by at least one of the methods such as Euclidean distance, Manhattan distance, cosine similarity, and spectral similarity measurement method. For example, if the signal feature is a numerical feature, the Euclidean distance or Manhattan distance calculation method can be used. If the signal feature is a vector feature, the cosine similarity method can be used to determine the feature similarity. If the signal feature is a spectral feature, the spectral similarity measurement method can be used to determine the feature similarity. The weight coefficient can be pre-determined to represent the importance of the signal feature. The signal feature similarity can be determined according to the weight coefficient of each signal feature and the to-be-fused feature similarity. Alternatively, the determination method of the signal feature similarity can be:

[0082]

[0083] wherein D represents a signal feature similarity, w i represents a weight coefficient of a signal feature x i , and y i represents a preset feature, represents a similarity of a feature to be fused.

[0084] The preset feature in the dimension of the UAV vibration audio can be a standard voiceprint feature corresponding to different UAV types, which is pre-stored in a database or a corresponding file. The voiceprint feature similarity can be used to represent the similarity between the preset feature and the voiceprint feature. Optionally, the voiceprint feature similarity can be determined in the following manner:

[0085]

[0086] wherein S(V, V') represents a voiceprint feature similarity between a voiceprint feature V and a preset feature V', which is usually in a range of -1 to 1. V represents a voiceprint feature of a UAV to be detected, which is defined as V = [v1, v2,..., vn], wherein vn represents a voiceprint feature value corresponding to a sound frequency fn and a time tn. V' represents a preset feature in the dimension of the UAV vibration audio, which is defined as V' = [v1', v2',..., vn']. V · V' represents a product between the voiceprint feature and the preset feature, i.e., S(V, V') = V · V' / (‖V‖ · ‖V'‖). m i i i m ‖V‖ represents a Euclidean norm of the voiceprint feature V, i.e., ‖V‖ = sqrt(v12+ v22+... + vn2). ‖V'‖ represents a Euclidean norm of the preset feature V'. It should be noted that when the voiceprint feature similarity is determined in the above manner, the closer the voiceprint feature similarity is to 1, the more similar the voiceprint feature and the preset feature are.

[0087] The feature value of the preset feature in the image acquisition dimension can be a feature value corresponding to a standard image feature corresponding to different UAV types, which is pre-stored in a database or a corresponding file. The feature difference can be a difference result between the feature value of the image feature and the feature value of the preset feature. The smaller the feature difference is, the more similar the image feature and the preset feature are.

[0088] The preset signal similarity range can be a standard range pre-set for representing the signal feature similarity. The preset sound similarity threshold value can be a standard value pre-set for the voiceprint feature similarity. The preset value can be a standard value pre-set for the feature difference.

[0089] ​​​​​Specifically, for at least one signal feature, a to-be-fused feature similarity between each signal feature and a preset feature in a radio signal dimension is determined. A signal feature similarity is calculated according to the to-be-fused feature similarity of each signal feature and a weight coefficient. A voiceprint feature similarity between a voiceprint feature and a preset feature in a drone vibration audio dimension is determined. For at least one image feature, a feature difference between each image feature and a preset feature in an image acquisition dimension is calculated. When the signal feature similarity belongs to a preset signal similarity range, the voiceprint feature similarity is greater than a preset voice similarity threshold, and each feature difference is less than a corresponding preset value, the to-be-detected drone is determined to be a target type of drone.

[0090] Optionally, when the signal feature similarity belongs to the preset signal similarity range, the voiceprint feature similarity is greater than the preset voice similarity threshold, and the feature difference is less than the preset value, the drone type of the to-be-detected drone is determined to be the target type, including: when the signal feature similarity belongs to the preset signal similarity range, a first candidate type corresponding to the to-be-detected drone is determined; when the voiceprint feature similarity is greater than the preset voice similarity threshold, a second candidate type corresponding to the to-be-detected drone is determined; when the feature difference is less than the preset value, a third candidate type corresponding to the to-be-detected drone is determined; and the to-be-detected drone is determined to be the target type according to the first candidate type, the second candidate type, and the third candidate type.

[0091] The first candidate type can be a possible drone type of the to-be-detected drone determined according to the signal feature. The second candidate type can be a possible drone type of the to-be-detected drone determined according to the voiceprint feature. The third candidate type can be a possible drone type of the to-be-detected drone determined according to the image feature. For example, the first candidate type is A type, B type, and C type. The second candidate type is B type and C type, and the third candidate type is B type. The target type can be B type.

[0092] Specifically, when the signal feature similarity belongs to the preset signal feature similarity range, a drone type corresponding to the preset feature in the radio signal dimension is taken as a first candidate type of the to-be-detected drone. When the voiceprint feature similarity is greater than the preset voice similarity threshold, a drone type corresponding to the preset feature in the drone vibration audio dimension is taken as a second candidate type of the to-be-detected drone. When each feature difference is less than the preset value, a drone type corresponding to the preset feature in the image acquisition dimension is taken as a third candidate type. The drone type of the to-be-detected drone is determined according to the first candidate type, the second candidate type, and the third candidate type, that is, the to-be-detected drone is determined to be the target type of drone.

[0093] S260, in response to the event that the type of the target UAV is not the preset type, determining flight data of the target UAV, and determining a safety attribute of a preset detection point of the target region according to the flight data and position data of the preset detection point.

[0094] The target UAV is a UAV in the at least one to-be-detected UAV.

[0095] The technical scheme of the embodiment acquires UAV data of at least one to-be-detected UAV in the target region in multiple dimensions, wherein the multiple dimensions include a radio signal dimension, a UAV vibration audio dimension, and an image acquisition dimension of the to-be-detected UAV. By acquiring the UAV data in multiple dimensions, the accuracy of subsequent determination of the type of the UAV is improved. For the at least one to-be-detected UAV, a signal time-frequency feature map, a sound time-frequency feature map, and a to-be-processed image are acquired from the UAV data, so as to obtain at least one signal feature by performing feature extraction on the signal time-frequency feature map, obtain a voiceprint feature by performing feature extraction on the sound time-frequency feature map, and obtain at least one image feature by performing feature extraction on the to-be-processed image. When the at least one signal feature, the voiceprint feature, and the at least one image feature all satisfy corresponding constraint conditions, the type of the to-be-detected UAV is determined to be the target type. Based on this, the type of the to-be-detected UAV can be accurately determined. In response to the event that the type of the UAV is not the preset type, flight data of the target UAV is determined, and a safety attribute of a preset detection point of the target region is determined according to the flight data and position data of the preset detection point. Based on this, it can be determined whether the target UAV will pose a threat to the preset detection point of the target region, so as to determine whether to take measures on the target UAV according to the safety attribute of the preset detection point, so as to ensure the safety of the preset detection point. The present application solves the problem of inaccurate detection and positioning of the UAV in the prior art by detecting the UAV through a single sensor or signal source. By performing feature analysis and processing on the UAV data in multiple dimensions, the accuracy and reliability of UAV detection and positioning are improved. When the type of the UAV is not the preset type, it is determined whether the target UAV will pose a threat to the preset detection point according to the position of the target UAV at the preset time, thereby ensuring the safety of the preset detection point of the target region.

[0096] Embodiment Three

[0097] Figure 5 is a structural schematic diagram of a device for determining the safety of a target region provided by Embodiment Three of the present application. As shown in Figure 5 the device includes a data acquisition module 310, a UAV type determination module 320, and a safety attribute determination module 330.

[0098] The data acquisition module 310 is configured to acquire the UAV data of at least one to-be-detected UAV in the target area in multiple dimensions, wherein the multiple dimensions include a radio signal dimension, a UAV vibration audio dimension, and an image acquisition dimension of the to-be-detected UAV; the UAV type determination module 320 is configured to determine the UAV type of the to-be-detected UAV based on the UAV data of the at least one to-be-detected UAV; the safety attribute determination module 330 is configured to, in response to an event that the UAV type is not a preset type, determine flight data of a target UAV, and determine a safety attribute of a preset detection point according to the flight data and position data of the preset detection point of the target area; wherein the target UAV is a UAV in the at least one to-be-detected UAV.

[0099] The technical scheme of the embodiment acquires the UAV data of at least one to-be-detected UAV in the target area in multiple dimensions, wherein the multiple dimensions include a radio signal dimension, a UAV vibration audio dimension, and an image acquisition dimension of the to-be-detected UAV. By acquiring the UAV data in multiple dimensions, the accuracy of subsequent determination of the UAV type is improved. The UAV type of the to-be-detected UAV is determined according to the UAV data of the at least one to-be-detected UAV. In response to an event that the UAV type is not a preset type, flight data of a target UAV is determined, and a safety attribute of a preset detection point is determined according to the flight data and position data of the preset detection point of the target area. Based on this, it can be determined whether the target UAV will pose a threat to the preset detection point of the target area, so as to determine whether to take measures on the target UAV according to the safety attribute of the preset detection point, to ensure the safety of the preset detection point. The application solves the problem of inaccurate detection and positioning of the UAV in the prior art by using a single sensor or signal source to detect the UAV, improves the accuracy and reliability of the UAV detection and positioning by analyzing and processing the UAV data in multiple dimensions, and ensures the safety of the preset detection point of the target area by determining whether the target UAV will pose a threat to the preset detection point according to the position of the target UAV at a preset time when the UAV type is not a preset type.

[0100] On the basis of the above-mentioned embodiments, optionally, the unmanned aerial vehicle type determination module comprises: a signal feature determination unit, configured to obtain, from the unmanned aerial vehicle data of at least one to-be-detected unmanned aerial vehicle, a signal time-frequency feature map in the radio signal dimension, and determine at least one signal feature of the to-be-detected unmanned aerial vehicle based on the signal time-frequency feature map; a voiceprint feature determination unit, configured to obtain a sound time-frequency feature map in the unmanned aerial vehicle vibration audio dimension from the unmanned aerial vehicle data, and determine a voiceprint feature of the to-be-detected unmanned aerial vehicle based on the sound time-frequency feature map; an image feature determination unit, configured to obtain a to-be-processed image in the image acquisition dimension from the unmanned aerial vehicle data, and determine at least one image feature of the to-be-detected unmanned aerial vehicle based on the to-be-processed image; and an unmanned aerial vehicle type determination unit, configured to determine that the unmanned aerial vehicle type of the to-be-detected unmanned aerial vehicle is the target type when the at least one signal feature, the voiceprint feature, and the at least one image feature all satisfy corresponding constraint conditions.

[0101] Optionally, the unmanned aerial vehicle type determination unit comprises: a signal feature similarity determination subunit, configured to determine a to-be-fused feature similarity corresponding to each signal feature according to the at least one signal feature and a preset feature in the radio signal dimension, and determine a signal feature similarity based on a weight coefficient of each signal feature and the to-be-fused feature similarity; a voiceprint feature similarity determination subunit, configured to determine a voiceprint feature similarity corresponding to the voiceprint feature according to the voiceprint feature and a preset feature in the unmanned aerial vehicle vibration audio dimension; a feature difference value determination subunit, configured to obtain at least one feature difference value according to a feature value corresponding to the at least one image feature and a feature value of a preset feature in the image acquisition dimension; and a target type determination subunit, configured to determine that the unmanned aerial vehicle type of the to-be-detected unmanned aerial vehicle is the target type when the signal feature similarity belongs to a preset signal similarity range, the voiceprint feature similarity is greater than a preset sound similarity threshold value, and the feature difference value is less than a preset value.

[0102] Optionally, the target type determination subunit is configured to determine a first candidate type corresponding to the to-be-detected unmanned aerial vehicle when the signal feature similarity belongs to a preset signal similarity range; determine a second candidate type corresponding to the to-be-detected unmanned aerial vehicle when the voiceprint feature similarity is greater than a preset sound similarity threshold value; determine a third candidate type corresponding to the to-be-detected unmanned aerial vehicle when the feature difference value is less than a preset value; and determine that the to-be-detected unmanned aerial vehicle is the target type according to the first candidate type, the second candidate type, and the third candidate type.

[0103] Optionally, the preset type is a type of unmanned aerial vehicle that is not forbidden to fly in the target region, and the security attribute determination module comprises: a target unmanned aerial vehicle determination unit configured to determine the to-be-detected unmanned aerial vehicle as a target unmanned aerial vehicle if the type of the to-be-detected unmanned aerial vehicle is a type of unmanned aerial vehicle that is forbidden to fly in the target region; a flight attribute determination unit configured to intercept a control signal of the target unmanned aerial vehicle, and analyze the intercepted control signal to obtain flight data of the target unmanned aerial vehicle; a position information determination unit configured to substitute the flight data into a preset position determination function to obtain predicted position information of the target unmanned aerial vehicle at a preset time; and a security attribute determination unit configured to determine a security attribute of a preset detection point according to the predicted position information and position data of the preset detection point in the target region.

[0104] Optionally, the flight data at least comprises a current flight position, a current flight speed, a current flight direction and a current flight acceleration of the target unmanned aerial vehicle, and the preset position determination function is:

[0105]

[0106] wherein (x0, y0, z0) represents three-dimensional coordinate data corresponding to the current flight position in a three-dimensional coordinate system with the center of the earth as the origin, v represents the current flight speed, t represents the preset time, θ represents an included angle between the current flight direction and a horizontal plane, a represents the current flight acceleration, and (x, y, z) represents the predicted position information corresponding to the preset time.

[0107] Optionally, the security attribute determination unit is configured to determine distance information between the predicted position information and the preset detection point according to the predicted position information and the position data of the preset detection point, and determine the security attribute of the preset detection point as unsafe if the distance information is not within a preset distance range.

[0108] Optionally, the security attribute determination module further comprises a security attribute updating unit configured to perform interference processing or induced deception processing on the target unmanned aerial vehicle to update the security attribute of the preset detection point if the security attribute of the preset detection point is unsafe.

[0109] The device for determining the security of the target region provided in the embodiments of the present application can perform the method for determining the security of the target region provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of performing the method.

[0110] Embodiment Four

[0111] Figure 6is a structural schematic diagram of an electronic device provided by Embodiment Four of the present application. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0112] As shown in Figure 6 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0113] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, a speaker, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0114] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the method for determining the safety of the target area.

[0115] In some embodiments, the method for determining safety of a target area can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto electronic device 10 via, e.g., ROM 12 and / or communication unit 19. When the computer program is loaded onto RAM 13 and executed by processor 11, one or more of the steps of the method for determining safety of a target area described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for determining safety of a target area by way of other any suitable means, e.g., by way of firmware.

[0116] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0117] Computer programs used to implement the method for determining safety of a target area of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program running on the processor implements the functions / operations specified in the flowcharts and / or the block diagrams. The computer program can be executed entirely on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0118] Embodiment Five

[0119] Embodiment five of the present application also provides a computer readable storage medium, the computer readable storage medium storing computer instructions, the computer instructions being used for causing a processor to execute a method for determining safety of a target area, the method comprising:

[0120] Obtaining unmanned aerial vehicle data of at least one to-be-detected unmanned aerial vehicle in the target region in multiple dimensions, wherein the multiple dimensions include a radio signal dimension, an unmanned aerial vehicle vibration audio dimension, and an image acquisition dimension of the to-be-detected unmanned aerial vehicle; determining an unmanned aerial vehicle type of the to-be-detected unmanned aerial vehicle based on the unmanned aerial vehicle data of the at least one to-be-detected unmanned aerial vehicle; in response to an event that the unmanned aerial vehicle type is not a preset type, determining flight data of a target unmanned aerial vehicle, and determining a safety attribute of a preset detection point according to the flight data and position data of the preset detection point of the target region, wherein the target unmanned aerial vehicle is an unmanned aerial vehicle in the at least one to-be-detected unmanned aerial vehicle.

[0121] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage 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. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0122] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0123] The systems and techniques described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0124] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0125] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be performed in parallel, in series, or in a different order, and the present disclosure is not limited in this regard.

[0126] The specific embodiments described above are not intended to limit the scope of the present disclosure. Those skilled in the art will understand that various modifications, combinations, sub-combinations, and alternatives can be made to the specific embodiments without departing from the spirit and principles of the present disclosure. Any further modifications, equivalents, and / or alternatives come within the scope of the present disclosure as recited by the claims.

Claims

1. A method for determining the security of a target area, characterized in that, include: Acquire drone data of at least one drone to be detected within a target area in multiple dimensions, wherein the multiple dimensions include radio signal dimension, drone vibration audio dimension, and image acquisition dimension of the drone to be detected; Based on the drone data of the at least one drone to be detected, determine the drone type of the drone to be detected; In response to an event that the drone type is not a preset type, the flight data of the target drone is determined, and the security attributes of the preset detection point are determined based on the flight data and the location data of the preset detection point in the target area. The target drone is one of the at least one drones to be detected; Determining the drone type of the drone to be detected based on the drone data of the at least one drone to be detected includes: For at least one of the drones to be detected, a signal time-frequency feature map in the radio signal dimension is obtained from the drone data of the drone to be detected, and at least one signal feature of the drone to be detected is determined based on the signal time-frequency feature map; a sound time-frequency feature map in the drone vibration audio dimension is obtained from the drone data, and the voiceprint feature of the drone to be detected is determined based on the sound time-frequency feature map; an image to be processed in the image acquisition dimension is obtained from the drone data, and at least one image feature of the drone to be detected is determined based on the image to be processed; when the at least one signal feature, the voiceprint feature, and the at least one image feature all satisfy the corresponding constraints, the drone type of the drone to be detected is determined to be the target type; The preset type is a type of drone that is not prohibited from flying within the target area. In response to an event where the drone type is not the preset type, the flight data of the target drone is determined, and based on the flight data and the location data of preset detection points in the target area, the security attributes of the preset detection points are determined, including: If the drone to be detected is a drone type prohibited from flying within the target area, then the drone to be detected is determined to be the target drone; the control signal of the target drone is intercepted and processed, and the intercepted control signal is parsed to obtain the flight data of the target drone; the flight data is substituted into a preset position determination function to obtain the predicted position information of the target drone at a preset time; based on the predicted position information and the position data of preset detection points in the target area, the security attribute of the preset detection points is determined.

2. The method according to claim 1, characterized in that, The step of determining the drone type of the drone to be detected as the target type when the at least one signal feature, the voiceprint feature, and the at least one image feature all satisfy the corresponding constraints includes: Based on at least one of the signal features and preset features under the radio signal dimension, determine the similarity of the features to be fused corresponding to each of the signal features, and determine the signal feature similarity based on the weight coefficient of each of the signal features and the similarity of the features to be fused; Based on the voiceprint features and the preset features under the drone vibration audio dimension, the voiceprint feature similarity corresponding to the voiceprint features is determined; At least one feature difference is obtained based on the feature value corresponding to at least one of the image features and the feature value of the preset feature under the image acquisition dimension; When the signal feature similarity falls within a preset signal similarity range, the voiceprint feature similarity is greater than a preset sound similarity threshold, and the feature difference is less than a preset value, the drone type of the drone to be detected is determined to be the target type.

3. The method according to claim 2, characterized in that, The step of determining the drone type of the drone to be detected as the target type when the signal feature similarity falls within a preset signal similarity range, the voiceprint feature similarity is greater than a preset sound similarity threshold, and the feature difference is less than a preset value includes: When the signal feature similarity falls within a preset signal similarity range, the first candidate type corresponding to the drone to be detected is determined; When the voiceprint feature similarity is greater than a preset voice similarity threshold, the second candidate type corresponding to the drone to be detected is determined; When the feature difference is less than a preset value, the third candidate type corresponding to the drone to be detected is determined; Based on the first candidate type, the second candidate type, and the third candidate type, the drone to be detected is determined to be the target type.

4. The method according to claim 1, characterized in that, The flight data includes at least the target UAV's current flight position, current flight speed, current flight direction, and current flight acceleration. The preset position determination function is: ； in, This represents the three-dimensional coordinate data corresponding to the current flight position in a three-dimensional coordinate system with the Earth's center as the origin. This indicates the current flight speed. Indicates the preset time. This indicates the angle between the current flight direction and the horizontal plane. This indicates the current flight acceleration. This indicates the predicted location information corresponding to the preset time.

5. The method according to claim 1, characterized in that, The step of determining the security attributes of the preset detection points based on the predicted location information and the location data of preset detection points in the target area includes: Based on the predicted location information and the location data of the preset detection point, the distance information between the predicted location information and the preset detection point is determined; If the distance information does not fall within the preset distance range, then the security attribute of the preset detection point is determined to be unsafe.

6. The method according to claim 5, characterized in that, Also includes: If the security attribute of the preset detection point is unsafe, the target drone will be subjected to interference or deception to update the security attribute of the preset detection point.

7. An apparatus for determining the security of a target area, characterized in that, include: The data acquisition module is used to acquire drone data of at least one drone to be detected in the target area in multiple dimensions, wherein the multiple dimensions include radio signal dimension, drone vibration audio dimension and image acquisition dimension of the drone to be detected. A drone type determination module is used to determine the drone type of the drone to be detected based on drone data of the at least one drone to be detected. A security attribute determination module is used to determine the flight data of the target drone in response to an event that the drone type is not a preset type, and to determine the security attribute of the preset detection point based on the flight data and the location data of the preset detection point in the target area. The target drone is one of the at least one drones to be detected; The drone type determination module includes: a signal feature determination unit, configured to, for at least one drone to be detected, obtain a signal time-frequency feature map in the radio signal dimension from the drone data of the drone to be detected, and determine at least one signal feature of the drone to be detected based on the signal time-frequency feature map; a voiceprint feature determination unit, configured to, obtain a sound time-frequency feature map in the drone vibration audio dimension from the drone data, and determine the voiceprint feature of the drone to be detected based on the sound time-frequency feature map; an image feature determination unit, configured to, obtain a to-be-processed image in the image acquisition dimension from the drone data, and determine at least one image feature of the drone to be detected based on the to-be-processed image; and a drone type determination unit, configured to, when the at least one signal feature, the voiceprint feature, and the at least one image feature all satisfy corresponding constraints, determine the drone type of the drone to be detected as the target type; The preset type is a type of drone that is not prohibited from flying within the target area. The security attribute determination module includes: a target drone determination unit, used to determine the drone to be detected as a target drone if the drone type of the drone to be detected is a type of drone prohibited from flying within the target area; a flight attribute determination unit, used to intercept and process the control signal of the target drone and parse the intercepted control signal to obtain the flight data of the target drone; a position information determination unit, used to substitute the flight data into a preset position determination function to obtain the predicted position information of the target drone at a preset time; and a security attribute determination unit, used to determine the security attribute of the preset detection point based on the predicted position information and the position data of the preset detection point in the target area.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, which enables the at least one processor to perform the method for determining the security of a target area as described in any one of claims 1-6.

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