Data processing method and device for unmanned aerial vehicle detection

Abstract

The invention discloses a data processing method and device for unmanned aerial vehicle detection. The method is applied to an unmanned aerial vehicle sound wave detection system so as to detect an unmanned aerial vehicle, and comprises the steps that to-be-processed sound wave data are obtained, and the to-be-processed sound wave data are data used for representing to-be-detected sound waves collected by a sound wave collection device of the unmanned aerial vehicle sound wave detection system; sound wave enhancement processing is carried out on the to-be-processed sound wave data, enhanced to-be-detected sound wave data are obtained, and the enhanced to-be-detected sound wave data are used for representing data obtained after sound wave enhancement of to-be-detected sound waves; and performing identification processing based on sound wave parameters on the enhanced to-be-detected sound wave data to obtain detection result data. By enhancing the sound wave, the accuracy of detecting the unmanned aerial vehicle by the sound wave is improved, the problem that the accuracy of detecting the unmanned aerial vehicle based on the sound wave is low is solved, and the technical effect of improving the accuracy of detecting the unmanned aerial vehicle by the sound wave is achieved.

Description

Technical Field

[0001] The present application relates to the field of drone detection, and more specifically, to a data processing method and device for drone detection. Background Art

[0002] With the development of drone technology, drones are becoming more and more popular. As the technology becomes more mature, drones have been developing towards miniaturization and micro-scale. Micro drones are small in size, simple to operate, low in flight altitude, and have many obstructions from ground objects, making it difficult for drones to be discovered when flying in places where flying is prohibited, and it is more difficult to detect and locate drones. In the existing technology, there are manual observation, electromagnetic wave detection, and image detection for detecting drones. Among them, since manual observation requires high ability of observers, the stability of manual observation is poor; electromagnetic wave detection mainly irradiates the target by emitting electromagnetic waves and receives its echo, thereby obtaining information such as the position of the target to the electromagnetic wave emission point. For drones coated with electromagnetic wave absorbing materials on the surface, electromagnetic waves are difficult to detect effectively; image detection is to collect drone flight images for drone detection. When the environment is poor, the recognition accuracy of drone images is low, and the image recognition computing power is required to be high, resulting in a low accuracy rate for drone detection.

[0003] The inventors have found that although the above-mentioned problems caused by manual observation, electromagnetic wave detection and image detection can be avoided if acoustic wave detection is used to detect drones, it is easy to cause low detection accuracy for drones with relatively low sound.

[0004] Therefore, this application is proposed to address the problem of low accuracy in drone detection based on sound waves. Summary of the invention

[0005] The main purpose of this application is to provide a data processing method and device for drone detection, so as to solve the problem of low accuracy of drone detection based on sound waves, and achieve the technical effect of improving the accuracy of drone detection by sound waves.

[0006] In order to achieve the above-mentioned purpose, in the first aspect of the present application, a data processing method for drone detection is proposed, which is applied to a drone acoustic wave detection system to realize the detection of drones, including:

[0007] Acquire the sound wave data to be processed, wherein the sound wave data to be processed is data for representing the sound wave to be measured collected by the sound wave collection device of the unmanned aerial vehicle sound wave detection system;

[0008] Performing sound wave enhancement processing on the sound wave data to be processed to obtain enhanced sound wave data to be measured, wherein the enhanced sound wave data to be measured is data used to represent the sound wave to be measured after sound wave enhancement;

[0009] The enhanced sound wave data to be measured is subjected to identification processing based on sound wave parameters to obtain detection result data.

[0010] In some optional embodiments of the present application, performing acoustic wave enhancement processing on the acoustic wave data to be processed to obtain enhanced acoustic wave data to be measured includes:

[0011] Performing identification processing on the sound wave data to be processed to obtain first sound wave data to be processed and second sound wave data to be processed, wherein the first sound wave data to be processed is the sound wave data collected by the first acquisition module in the sound wave acquisition device, and the second sound wave data to be processed is the sound wave data collected by the second acquisition module in the sound wave acquisition device, and the first acquisition module and the second acquisition module are in different positions;

[0012] Performing equivalent processing on the first sound wave data to be processed and the second sound wave data to be processed based on the sound source distance to obtain first equivalent sound wave data to be processed and second equivalent sound wave data to be processed;

[0013] The first equivalent sound wave data to be processed and the second equivalent sound wave data to be processed are subjected to sound wave superposition processing to obtain the enhanced sound wave data to be measured.

[0014] In some optional embodiments of the present application, performing equivalent processing on the first sound wave data to be processed and the second sound wave data to be processed based on the sound source distance to obtain the first equivalent sound wave data to be processed and the second equivalent sound wave data to be processed includes:

[0015] Determine equivalent sound source distance data according to the first sound wave data to be processed and the second sound wave data to be processed;

[0016] Performing phase delay adjustment processing on the first sound wave data to be processed based on the equivalent sound source distance data to obtain the first equivalent sound wave data to be processed;

[0017] The second sound wave data to be processed is subjected to phase delay adjustment processing based on the equivalent sound source distance data to obtain the second equivalent sound wave data to be processed.

[0018] In some optional embodiments of the present application, the enhanced sound wave data to be measured is subjected to recognition processing based on sound wave parameters to obtain detection result data including:

[0019] Performing recognition processing based on sound wave parameters on the enhanced sound wave data to be measured to obtain first sound wave parameter data to be identified and second sound wave parameter data to be identified, wherein the first sound wave parameter data to be identified is data used to represent the intensity parameter of the sound wave to be measured, and the second sound wave parameter data to be identified is data used to represent the frequency parameter of the sound wave to be measured;

[0020] Performing identification processing on the first to-be-identified sound wave parameter data based on a preset sound wave intensity parameter rule to obtain first sound wave detection result data;

[0021] Performing identification processing on the second sound wave parameter data to be identified based on a preset sound wave frequency parameter rule to obtain second sound wave detection result data;

[0022] The detection result data is determined according to the first acoustic wave detection result data and the second acoustic wave detection result data.

[0023] In some optional embodiments of the present application, the first to-be-identified sound wave parameter data is subjected to identification processing based on a preset sound wave intensity parameter rule to obtain the first sound wave detection result data, including:

[0024] Compare the sound wave intensity of the first sound wave parameter data to be identified with a preset sound wave intensity threshold,

[0025] If the sound wave intensity of the first sound wave parameter data to be identified is greater than or equal to the preset sound wave intensity threshold, the first sound wave detection result data indicates that the sound wave source is a drone;

[0026] If the sound wave intensity of the first sound wave parameter data to be identified is less than the preset sound wave intensity threshold, the first sound wave detection result data indicates that the sound wave source is not a drone.

[0027] In some optional embodiments of the present application, the second acoustic wave parameter data to be identified is processed based on a preset acoustic wave frequency parameter rule to obtain the second acoustic wave detection result data, including:

[0028] Compare the second to-be-identified sound wave parameter data with a preset frequency threshold to obtain second sound wave detection result data,

[0029] If the second sound wave parameter data to be identified is greater than or equal to the first preset frequency threshold, and the second sound wave parameter data to be identified is less than or equal to the second preset frequency threshold, the second sound wave detection result data indicates that the sound wave source is a drone;

[0030] If the second sound wave parameter data to be identified is less than the first preset frequency threshold or less than the second preset frequency threshold, the second sound wave detection result data indicates that the sound wave source is non-UAV, and the second preset frequency threshold is greater than the first preset frequency threshold.

[0031] According to a second aspect of the present application, a data processing device for drone detection is proposed, which is applied to a drone acoustic wave detection system to achieve detection of drones, including:

[0032] A data acquisition module, used to acquire the sound wave data to be processed, wherein the sound wave data to be processed is data used to represent the sound waves to be measured collected by the sound wave collection device of the unmanned aerial vehicle sound wave detection system;

[0033] An enhancement module, used for performing sound wave enhancement processing on the sound wave data to be processed to obtain enhanced sound wave data to be measured, wherein the enhanced sound wave data to be measured is data used to represent the sound wave to be measured after sound wave enhancement;

[0034] The identification module is used to perform identification processing on the enhanced sound wave data to be measured based on the sound wave parameters to obtain detection result data.

[0035] In some optional embodiments of the present application, the enhancement module includes:

[0036] an identification module, used for identifying and processing the acoustic wave data to be processed to obtain first acoustic wave data to be processed and second acoustic wave data to be processed, wherein the first acoustic wave data to be processed is acoustic wave data collected by a first acquisition module in the acoustic wave acquisition device, and the second acoustic wave data to be processed is acoustic wave data collected by a second acquisition module in the acoustic wave acquisition device, and the first acquisition module and the second acquisition module are located at different positions;

[0037] An equivalent module, used for performing equivalent processing on the first sound wave data to be processed and the second sound wave data to be processed based on the sound source distance, to obtain first equivalent sound wave data to be processed and second equivalent sound wave data to be processed;

[0038] The acoustic wave superposition module is used to perform acoustic wave superposition processing on the first equivalent acoustic wave data to be processed and the second equivalent acoustic wave data to be processed to obtain the enhanced acoustic wave data to be measured.

[0039] According to a third aspect of the present application, a computer-readable storage medium is proposed, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned data processing method for drone detection.

[0040] According to the fourth aspect of the present application, an electronic device is proposed, comprising: 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, and the computer program is executed by the at least one processor so that the at least one processor executes the above-mentioned data processing method for drone detection.

[0041] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0042] In the present application, a data processing method for drone detection is applied to a drone acoustic wave detection system to realize drone detection, including: obtaining acoustic wave data to be processed, wherein the acoustic wave data to be processed is data for representing the acoustic wave to be measured collected by the acoustic wave acquisition device of the drone acoustic wave detection system; performing acoustic wave enhancement processing on the acoustic wave data to be processed to obtain enhanced acoustic wave data to be measured, wherein the enhanced acoustic wave data to be measured is data for representing the acoustic wave to be measured after acoustic wave enhancement; performing identification processing based on acoustic wave parameters on the enhanced acoustic wave data to be measured to obtain detection result data. By enhancing the acoustic wave processing to improve the accuracy of drone detection by acoustic waves, the problem of low accuracy of drone detection based on acoustic waves is solved, and the technical effect of improving the accuracy of drone detection by acoustic waves is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other parameters, purposes and advantages of this application become more obvious. The schematic embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0044] Figure 1 A flow chart of a data processing method for drone detection provided in this application;

[0045] Figure 2 A flow chart of a data processing method for drone detection provided in this application;

[0046] Figure 3 A schematic diagram of determining an equivalent sound source distance for this application;

[0047] Figure 4 A schematic diagram of a data processing device for drone detection provided in the present application;

[0048] Figure 5 A schematic diagram of another data processing device for drone detection provided in this application. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0050] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0051] In the present application, the terms "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings. These terms are mainly used to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to have a specific orientation, or to be constructed and operated in a specific orientation.

[0052] In addition, some of the above terms may be used to express other meanings in addition to indicating orientation or positional relationship. For example, the term "on" may also be used to express a certain dependency or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in this application can be understood according to specific circumstances.

[0053] In addition, the terms "installed", "set", "provided with", "connected", "connected", and "socketed" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be an internal connection between two devices, elements, or components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0054] For the detection of drones in areas where drones are prohibited from flying, existing technologies include manual observation, electromagnetic wave detection, and image detection. Electromagnetic wave detection, image detection, etc., electromagnetic wave detection is difficult to effectively detect drones with electromagnetic wave absorbing materials coated on the surface. Image detection has high requirements on the computing power of image recognition, and the accuracy of image recognition in harsh environments is low.

[0055] The inventors have discovered that the problems associated with drone detection in the prior art can be avoided by using acoustic waves to detect drones. However, acoustic wave detection is prone to low detection accuracy for drones with relatively low sound. Therefore, the present application proposes a data processing method and device for drone detection, which uses acoustic waves to detect drones and enhances the accuracy of acoustic waves in detecting drones, thereby solving the problem of low accuracy in drone detection based on acoustic waves.

[0056] In some optional embodiments of the present application, a data processing method for drone detection is proposed. Figure 1 A flowchart of a data processing method for drone detection provided in this application, such as Figure 1 As shown, the method comprises the following steps:

[0057] S101: Acquire sound wave data to be processed;

[0058] The sound wave data to be processed is used to represent the data of the sound waves to be measured collected by the sound wave collection device of the unmanned aerial vehicle sound wave detection system.

[0059] In an optional embodiment of the present application, a UAV sound wave collection device collects sound wave data within the test area, wherein the UAV sound wave collection device is provided with a plurality of sound wave collection modules at different positions, and the plurality of sound wave collection modules are arranged in a plane circle on the UAV sound wave collection device, and the sound wave data to be processed includes the sound wave data collected by the plurality of sound wave collection modules.

[0060] S102: performing sound wave enhancement processing on the sound wave data to be processed to obtain enhanced sound wave data to be measured;

[0061] The enhanced sound wave data to be measured is used to represent the data after the sound wave to be measured is enhanced. By setting the sound wave data to be measured to be enhanced, the sound wave intensity of the sound wave to be measured is improved, thereby improving the accuracy of the sound wave detection drone.

[0062] In some optional embodiments of the present application, a data processing method for drone detection is proposed. Figure 2 A flowchart of a data processing method for drone detection provided in this application, such as Figure 2 As shown, the method comprises the following steps:

[0063] S201: performing identification processing on the sound wave data to be processed to obtain first sound wave data to be processed and second sound wave data to be processed;

[0064] The first sound wave data to be processed is the sound wave data collected by the first acquisition module in the sound wave acquisition device, and the second sound wave data to be processed is the sound wave data collected by the second acquisition module in the sound wave acquisition device, and the first acquisition module and the second acquisition module are in different positions;

[0065] S202: performing equivalent processing on the first sound wave data to be processed and the second sound wave data to be processed based on the sound source distance to obtain first equivalent sound wave data to be processed and second equivalent sound wave data to be processed;

[0066] In some optional embodiments of the present application, a data processing method for drone detection is proposed, the method comprising the following steps:

[0067] Determine equivalent sound source distance data based on the first sound wave data to be processed and the second sound wave to be processed; perform phase delay adjustment processing on the first sound wave data to be processed based on the equivalent sound source distance data to obtain first equivalent sound wave data to be processed; perform phase delay adjustment processing on the second sound wave data to be processed based on the equivalent sound source distance data to obtain second equivalent sound wave data to be processed.

[0068] In an optional embodiment of the present application, sound wave enhancement is performed on the sound wave data collected by different acquisition modules. By performing sound source distance equivalence on different sound wave data, taking the sound wave enhancement of sound waves collected by two acquisition modules as an example, the first acquisition module collects first sound wave data to be processed, and the second acquisition module collects second sound wave data to be processed. The sound source position is determined according to the collected sound wave data. The sound source position can be determined by the first sound wave data to be processed, or by the second sound wave data to be processed. The sound source position is different from the sound wave distance between the sound source position and the first acquisition module and the sound wave distance between the sound source module and the second acquisition module. S, the first acquisition module position C1 and the second acquisition module position C2 are set to construct a spatial right triangle. For example, the sound source position S, the first acquisition module position C1 and the second acquisition module position C2 are the three vertices of the right triangle, SC1 is the first right-angled side of the right triangle, the distance is H, C1C2 is the second right-angled side of the right triangle, the distance is D, SC2 is the hypotenuse of the right triangle, and the distance is L. In this case, the equivalent sound source distance is H. Based on the equivalent sound source distance, the phase delay of the sound wave data to be processed is adjusted, and the phase delay of the second sound wave data to be processed is adjusted to obtain the phase delay to be adjusted. The phase delay of the second sound wave data to be processed is adjusted according to the Δt.

[0069] In another optional embodiment of the present application, a method for enhancing sound waves collected by multiple collection modules is provided. Figure 3 This is a schematic diagram of determining the equivalent sound source distance for this application, such as Figure 3As shown, the sound source position S, the first acquisition module position C1, the second acquisition module position C2, and the third acquisition module position C3, the first acquisition module collects the first sound wave data to be processed, the second acquisition module collects the second sound wave data to be processed, and the third acquisition module collects the third sound wave data to be processed, wherein SO is the distance between the sound source position and the plane of the device for detecting the sound wave acquisition device of the drone, the distance is h, the distance between the sound source position S and the position of the first acquisition module C1 is s1, the distance between the sound source position S and the position of the second acquisition module C2 is s2, and the distance between the sound source position S and the position of the third acquisition module C3 is s3. The first acquisition module position C1, the second acquisition module position C2, and the third acquisition module position C3 are arranged at equal distances, C 1O is d, C2O is 2d, C3O is 3d, the equivalent sound source distance can be h, and the phase delays of the first sound wave data to be processed, the second sound wave data to be processed and the third sound wave data to be processed are calculated according to the equivalent sound source distance, and the phase delays of the corresponding sound wave data to be processed are adjusted according to the corresponding phase delays; the equivalent sound source distance can be s1, and the phase delays of the first sound wave data to be processed, the second sound wave data to be processed and the third sound wave data to be processed are calculated according to the equivalent sound source distance, and the phase delays of the corresponding sound wave data to be processed are adjusted according to the corresponding phase delays, and the equivalent receiving positions corresponding to the second acquisition module position C2 and the third acquisition module position C3 are C2' and C3' respectively.

[0070] S203: Performing acoustic wave superposition processing on the first equivalent acoustic wave data to be processed and the second equivalent acoustic wave data to be processed to obtain enhanced acoustic wave data to be measured.

[0071] After the phase delay adjustment processing is performed on the sound wave data to be processed based on the equivalent sound source distance data, the first equivalent sound wave data to be processed and the second equivalent sound wave data to be processed have the same sound wave waveform and the same phase, and the first equivalent sound wave data to be processed and the second equivalent sound wave data to be processed are superimposed to achieve complete enhancement of the sound wave amplitude.

[0072] S103: performing identification processing based on acoustic wave parameters on the enhanced acoustic wave data to be measured to obtain detection result data.

[0073] In some optional embodiments of the present application, a data processing method for drone detection is proposed, comprising:

[0074] The enhanced sound wave data to be measured are processed for identification based on sound wave parameters to obtain first sound wave parameter data to be identified and second sound wave parameter data to be identified, wherein the first sound wave parameter data to be identified is data used to represent the sound wave intensity parameter to be measured, and the second sound wave parameter data to be identified is data used to represent the sound wave frequency parameter to be measured; the first sound wave parameter data to be identified is processed for identification based on preset sound wave intensity parameter rules to obtain first sound wave detection result data; the second sound wave parameter data to be identified is processed for identification based on preset sound wave frequency parameter rules to obtain second sound wave detection result data; the detection result data is determined according to the first sound wave detection result data and the second sound wave detection result data.

[0075] In an optional embodiment of the present application, a data processing method for drone detection is proposed, which is used to perform recognition processing on first sound wave parameter data to be recognized based on a preset sound wave intensity parameter rule, including:

[0076] The sound wave intensity of the first sound wave parameter data to be identified is compared with the preset sound wave intensity threshold. If the sound wave intensity of the first sound wave parameter data to be identified is greater than or equal to the preset sound wave intensity threshold, the first sound wave detection result data indicates that the sound wave source is a drone; if the sound wave intensity of the first sound wave parameter data to be identified is less than the preset sound wave intensity threshold, the first sound wave detection result data indicates that the sound wave source is not a drone.

[0077] In an optional embodiment of the present application, the enhanced sound wave data to be tested is processed for drone judgment based on intensity parameters, and it is calculated whether the enhanced sound wave intensity to be tested is greater than a preset sound wave intensity threshold. If the enhanced sound wave intensity to be tested is greater than or equal to the preset sound wave intensity threshold, it can be judged as a blade drone; if the enhanced sound wave intensity to be tested is less than the preset sound wave intensity threshold, the enhanced sound wave intensity to be tested is processed for identification of other sound parameters, wherein the preset sound wave intensity threshold is 50*(cos(x / 500)+1) decibels, and x is a distance less than 1.570 kilometers.

[0078] In an optional embodiment of the present application, a data processing method for drone detection is proposed, which performs identification processing on the second sound wave parameter data to be identified based on a preset sound wave frequency parameter rule, including:

[0079] The second sound wave parameter data to be identified is compared with the preset frequency threshold to obtain the second sound wave detection result data. If the second sound wave parameter data to be identified is greater than or equal to the first preset frequency threshold, and the second sound wave parameter data to be identified is less than or equal to the second preset frequency threshold, the second sound wave detection result data indicates that the source of the sound wave is a drone; if the second sound wave parameter data to be identified is less than the first preset frequency threshold or less than the second preset frequency threshold, the second sound wave detection result data indicates that the source of the sound wave is not a drone, and the second preset frequency threshold is greater than the first preset frequency threshold.

[0080] The detection result data is determined according to the first sound wave detection result data and the second sound wave detection result data, including: when the first sound wave detection result data and the second sound wave detection result data both indicate that the sound wave source is a drone, the detection result is obtained that the sound wave source is a drone; after performing sound wave enhancement and sound wave parameter identification on the sound wave data to be processed, the source of the enhanced sound wave greater than the preset sound wave intensity within the preset frequency range is the drone; the detection result is obtained by identifying the sound wave frequency and sound wave intensity of the enhanced sound wave, thereby improving the accuracy of the sound wave detection of the drone.

[0081] In some optional embodiments of the present application, a data processing method for drone detection is proposed. After obtaining the detection result data, the method further includes:

[0082] When the detection result data indicates that the sound wave source to be measured is emitted by a drone, the sound wave data to be processed is positioned based on the signal delay to obtain the drone position data; the sound wave data to be processed is identified to obtain the first sound wave data to be processed and the second sound wave data to be processed; the drone state is determined based on the signal delay for the first sound wave data to be processed and the second sound wave data to be processed to obtain the drone state data; the drone detection result data is generated and output according to the drone position data and the drone state data. According to the delay between the first sound wave data to be processed and the second sound wave data to be processed, the drone flight state can be determined, and the drone position and flight state data can be output. The drone sound wave detection can determine the subsequent disposal measures for the drone based on the output.

[0083] In an embodiment of the present application, frequency change parameter identification processing is performed on the sound wave data to be processed to obtain frequency change parameter data, wherein the frequency change parameter data is parameter data used to represent the frequency change of the sound wave to be measured within adjacent time periods; the frequency change parameter data is identified to realize the judgment of the state of the drone, if the frequency change parameter data shows an increase in frequency, the drone is approaching; if the frequency change parameter data shows a decrease in frequency, the drone is moving away; if the frequency change parameter data shows no change, the drone is hovering.

[0084] In some optional embodiments of the present application, a data processing method for drone detection is proposed, comprising:

[0085] The method of performing sound wave enhancement processing on the sound wave data to be processed to obtain enhanced sound wave data to be measured includes: performing denoising-based preprocessing on the sound wave data to obtain preprocessed sound wave data, wherein the preprocessed sound wave data is sound wave data after sound wave denoising; performing sound feature extraction processing on the preprocessed sound wave data to obtain sound feature data; performing sound feature-based classification processing on the preprocessed sound wave data to obtain preprocessed single-source sound wave data, wherein the preprocessed single-source sound wave data is sound wave data of a single sound source; performing sound wave enhancement processing on the preprocessed single-source sound wave data to obtain enhanced sound wave data to be measured.

[0086] In an optional embodiment of the present application, denoising the sound wave to be processed includes: identifying abnormal signals in the sound wave signal to be processed and eliminating the abnormal signals; when there are multiple sound sources to be tested in the area to be tested, classifying and extracting the sound wave to be processed according to the sound characteristics to obtain the sound wave data of a single sound source, wherein the sound wave characteristics include intensity characteristics, frequency characteristics and timbre characteristics, etc., performing sound wave enhancement processing on the sound wave data of the single sound source, so as to perform drone detection and identification on the enhanced single sound source sound wave data.

[0087] In some optional embodiments of the present application, a data processing device for drone detection is proposed. Figure 4 A schematic diagram of a data processing device for drone detection provided in this application, such as Figure 4 As shown, including:

[0088] A data acquisition module 41 is used to acquire the sound wave data to be processed, wherein the sound wave data to be processed is data used to represent the sound waves to be measured collected by the sound wave collection device of the unmanned aerial vehicle sound wave detection system;

[0089] The enhancement module 42 is used to perform sound wave enhancement processing on the sound wave data to be processed to obtain enhanced sound wave data to be measured, wherein the enhanced sound wave data to be measured is data used to represent the sound wave to be measured after the sound wave is enhanced;

[0090] The identification module 43 is used to perform identification processing on the enhanced sound wave data to be detected based on the sound wave parameters to obtain detection result data.

[0091] In some optional embodiments of the present application, a data processing device for drone detection is proposed. Figure 5 A schematic diagram of a data processing device for drone detection provided in this application, such as Figure 5 As shown, including:

[0092] An identification module 51 is used to identify and process the acoustic wave data to be processed to obtain first acoustic wave data to be processed and second acoustic wave data to be processed, wherein the first acoustic wave data to be processed is the acoustic wave data collected by the first acquisition module in the acoustic wave acquisition device, and the second acoustic wave data to be processed is the acoustic wave data collected by the second acquisition module in the acoustic wave acquisition device, and the first acquisition module and the second acquisition module are located at different positions;

[0093] An equivalent module 52 is used to perform equivalent processing on the first sound wave data to be processed and the second sound wave data to be processed based on the sound source distance to obtain first equivalent sound wave data to be processed and second equivalent sound wave data to be processed;

[0094] The acoustic wave superposition module 53 is used to perform acoustic wave superposition processing on the first equivalent acoustic wave data to be processed and the second equivalent acoustic wave data to be processed to obtain the enhanced acoustic wave data to be measured.

[0095] The specific manner of executing the operation of each unit in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0096] In summary, in the present application, the data processing method for drone detection is applied to the drone acoustic wave detection system to realize the detection of drones, including: obtaining the acoustic wave data to be processed, wherein the acoustic wave data to be processed is data for representing the acoustic wave to be measured collected by the acoustic wave acquisition device of the drone acoustic wave detection system; performing acoustic wave enhancement processing on the acoustic wave data to be processed to obtain enhanced acoustic wave data to be measured, wherein the enhanced acoustic wave data to be measured is data for representing the acoustic wave to be measured after acoustic wave enhancement; performing identification processing based on acoustic wave parameters on the enhanced acoustic wave data to obtain detection result data. By enhancing the acoustic wave processing to improve the accuracy of drone detection by acoustic waves, the problem of low accuracy of drone detection based on acoustic waves is solved, and the technical effect of improving the accuracy of drone detection by acoustic waves is achieved.

[0097] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0098] Obviously, those skilled in the art should understand that the above-mentioned units or steps of the present application can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the present application is not limited to any specific combination of hardware and software.

[0099] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A data processing method for drone detection, the parameters of which are: Applied to drone acoustic wave detection system to detect drones, including: Acquire the sound wave data to be processed, wherein the sound wave data to be processed is data for representing the sound wave to be measured collected by the sound wave collection device of the unmanned aerial vehicle sound wave detection system; Performing sound wave enhancement processing on the sound wave data to be processed to obtain enhanced sound wave data to be measured, wherein the enhanced sound wave data to be measured is data used to represent the sound wave to be measured after sound wave enhancement; The enhanced sound wave data to be measured is subjected to identification processing based on sound wave parameters to obtain detection result data.

2. The data processing method according to claim 1, wherein the parameters are that performing acoustic wave enhancement processing on the acoustic wave data to be processed to obtain enhanced acoustic wave data to be measured comprises: Performing identification processing on the sound wave data to be processed to obtain first sound wave data to be processed and second sound wave data to be processed, wherein the first sound wave data to be processed is the sound wave data collected by the first acquisition module in the sound wave acquisition device, and the second sound wave data to be processed is the sound wave data collected by the second acquisition module in the sound wave acquisition device, and the first acquisition module and the second acquisition module are in different positions; Performing equivalent processing on the first sound wave data to be processed and the second sound wave data to be processed based on the sound source distance to obtain first equivalent sound wave data to be processed and second equivalent sound wave data to be processed; The first equivalent sound wave data to be processed and the second equivalent sound wave data to be processed are subjected to sound wave superposition processing to obtain the enhanced sound wave data to be measured.

3. The data processing method according to claim 2, wherein the first sound wave data to be processed and the second sound wave data to be processed are subjected to equivalent processing based on the sound source distance, and the first equivalent sound wave data to be processed and the second equivalent sound wave data to be processed are obtained, comprising: Determine equivalent sound source distance data according to the first sound wave data to be processed and the second sound wave data to be processed; Performing phase delay adjustment processing on the first sound wave data to be processed based on the equivalent sound source distance data to obtain the first equivalent sound wave data to be processed; The second sound wave data to be processed is subjected to phase delay adjustment processing based on the equivalent sound source distance data to obtain the second equivalent sound wave data to be processed.

4. The data processing method according to claim 1, wherein the parameter is: The enhanced sound wave data to be measured is subjected to identification processing based on sound wave parameters to obtain detection result data including: Performing recognition processing based on sound wave parameters on the enhanced sound wave data to be measured to obtain first sound wave parameter data to be identified and second sound wave parameter data to be identified, wherein the first sound wave parameter data to be identified is data used to represent the intensity parameter of the sound wave to be measured, and the second sound wave parameter data to be identified is data used to represent the frequency parameter of the sound wave to be measured; Performing identification processing on the first to-be-identified sound wave parameter data based on a preset sound wave intensity parameter rule to obtain first sound wave detection result data; Performing identification processing on the second sound wave parameter data to be identified based on a preset sound wave frequency parameter rule to obtain second sound wave detection result data; The detection result data is determined according to the first acoustic wave detection result data and the second acoustic wave detection result data.

5. The data processing method according to claim 4, wherein the first acoustic wave parameter data to be identified is subjected to identification processing based on a preset acoustic wave intensity parameter rule, and the first acoustic wave detection result data obtained comprises: Compare the sound wave intensity of the first sound wave parameter data to be identified with a preset sound wave intensity threshold, If the sound wave intensity of the first sound wave parameter data to be identified is greater than or equal to the preset sound wave intensity threshold, the first sound wave detection result data indicates that the sound wave source is a drone; If the sound wave intensity of the first sound wave parameter data to be identified is less than the preset sound wave intensity threshold, the first sound wave detection result data indicates that the sound wave source is not a drone.

6. The data processing method according to claim 4, wherein the second acoustic wave parameter data to be identified is subjected to identification processing based on a preset acoustic wave frequency parameter rule, and the second acoustic wave detection result data obtained comprises: Compare the second to-be-identified sound wave parameter data with a preset frequency threshold to obtain second sound wave detection result data, If the second sound wave parameter data to be identified is greater than or equal to the first preset frequency threshold, and the second sound wave parameter data to be identified is less than or equal to the second preset frequency threshold, the second sound wave detection result data indicates that the sound wave source is a drone; If the second sound wave parameter data to be identified is less than the first preset frequency threshold or less than the second preset frequency threshold, the second sound wave detection result data indicates that the sound wave source is non-UAV, and the second preset frequency threshold is greater than the first preset frequency threshold.

7. A data processing device for drone detection, the parameters of which are: Applied to drone acoustic wave detection system to detect drones, including: A data acquisition module, used to acquire the sound wave data to be processed, wherein the sound wave data to be processed is data used to represent the sound waves to be measured collected by the sound wave collection device of the unmanned aerial vehicle sound wave detection system; An enhancement module, used for performing sound wave enhancement processing on the sound wave data to be processed to obtain enhanced sound wave data to be measured, wherein the enhanced sound wave data to be measured is data used to represent the sound wave to be measured after sound wave enhancement; The identification module is used to perform identification processing on the enhanced sound wave data to be measured based on the sound wave parameters to obtain detection result data.

8. The data processing device according to claim 7, wherein the parameter is: Enhanced modules include: an identification module, used for identifying and processing the acoustic wave data to be processed to obtain first acoustic wave data to be processed and second acoustic wave data to be processed, wherein the first acoustic wave data to be processed is acoustic wave data collected by a first acquisition module in the acoustic wave acquisition device, and the second acoustic wave data to be processed is acoustic wave data collected by a second acquisition module in the acoustic wave acquisition device, and the first acquisition module and the second acquisition module are located at different positions; An equivalent module, used for performing equivalent processing on the first sound wave data to be processed and the second sound wave data to be processed based on the sound source distance, to obtain first equivalent sound wave data to be processed and second equivalent sound wave data to be processed; The acoustic wave superposition module is used to perform acoustic wave superposition processing on the first equivalent acoustic wave data to be processed and the second equivalent acoustic wave data to be processed to obtain the enhanced acoustic wave data to be measured.

9. A computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the data processing method for drone detection as described in any one of claims 1 to 6.

10. An electronic device, the parameters of which include: 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, and the computer program is executed by the at least one processor so that the at least one processor executes the data processing method for drone detection described in any one of claims 1-6.

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