An intelligent unmanned aerial vehicle countermeasure method and system based on distributed acoustic warning

Abstract

This invention discloses an intelligent drone countermeasure method and system based on distributed acoustic early warning. The method includes: collecting sound signals within sub-regions of their respective monitoring ranges by multiple acoustic early warning devices distributed at equal intervals along the boundary of a monitoring area; determining the presence of a drone within the sub-region of the acoustic early warning device's monitoring range and reporting early warning information to a backend control center; determining, based on the received early warning information, whether the drone has entered the sub-region corresponding to the acoustic early warning device and generating an early warning notification containing the sub-region's location information; intelligently matching corresponding countermeasure strategies from a countermeasure strategy library and generating countermeasure commands based on preset decision rules and early warning notifications; and implementing interference countermeasures against the intruding drone based on the countermeasure commands. This invention aims to solve the problems of weak detection capabilities against radio-silent and fiber-optic drones, disconnect between early warning and countermeasures, and high system costs that hinder large-scale deployment in existing drone defense systems.

Description

Technical Field

[0001] This invention relates to the field of drone defense technology, and in particular to an intelligent drone countermeasure method and system based on distributed acoustic early warning. Background Technology

[0002] With the popularization of drone technology, its application in logistics, public safety and other fields is becoming increasingly widespread. However, the security threats such as unauthorized flights and illegal intrusions are also becoming more and more serious. Existing drone detection methods mainly include radar detection, radio spectrum detection, photoelectric detection and acoustic detection.

[0003] However, each of these technologies has significant drawbacks. Radar detection is ineffective against low-altitude, slow-moving, and small drones (low, slow, and small), has blind spots, and is susceptible to ground clutter interference. Radio detection is completely ineffective against drones in radio-silent mode, using fiber optic control, or purely visual navigation. While photoelectric detection (such as visible light / infrared cameras) has mature identification technology, its performance is heavily dependent on good lighting and weather conditions and is easily affected by obstructions. Traditional acoustic detection has good versatility, capable of detecting any drone that generates noise, including silent and fiber optic drones, but generally suffers from short detection range and susceptibility to environmental noise interference. High-performance acoustic arrays offer excellent detection results, but their large size, weight, and high cost make large-scale distributed deployment difficult.

[0004] Existing countermeasures against drones mainly face the following technical bottlenecks: The jamming methods are crude and inefficient: Traditional jamming equipment mostly uses omnidirectional and broadband suppression jamming, which not only consumes a lot of power and is easy to interfere with friendly equipment, but also has limited effect on the remote control link of racing drones that use anti-jamming technologies such as frequency hopping, and lacks the ability of "precise jamming".

[0005] The system has a low degree of closed-loop operation: the early warning, identification, and interference links are isolated and lack linkage. It is impossible to dynamically adjust the interference strategy based on the real-time early warning results, making it difficult to cope with rapidly changing threats.

[0006] Therefore, there is an urgent need for a system that can effectively detect various types of drones, is cost-effective, can be flexibly deployed, and can achieve rapid and intelligent countermeasures. Summary of the Invention

[0007] The embodiments of this application provide an intelligent drone countermeasure method and system, which aims to solve the problems of weak detection capabilities against radio silence and fiber optic drones, disconnect between early warning and countermeasure, and high system cost that makes large-scale deployment difficult in existing drone defense systems.

[0008] This invention provides an intelligent drone countermeasure method based on distributed acoustic early warning, comprising: An intelligent drone countermeasure method based on distributed acoustic early warning includes the following steps: Step S1: Collect sound signals within their respective monitoring sub-areas by distributing multiple sound warning devices at equal intervals along the boundary of the monitoring area, and extract time-frequency features and identify drone voiceprints from the sound signals; wherein, the drone voiceprint identification includes: calculating the signal-to-noise ratio of the sound signal, and when the signal-to-noise ratio exceeds a preset threshold and the time-frequency features match the drone voiceprint, determining that a drone exists within the monitoring sub-area of ​​the sound warning device, and reporting the warning information to the backend control center; Step S2: The backend control center identifies the sound warning device that issued the warning based on the received warning information, determines that the drone has entered the sub-area corresponding to the sound warning device, and generates a warning notification containing the sub-area location information. Step S3: The backend control center intelligently matches the corresponding countermeasure strategy from the countermeasure strategy library and generates countermeasure instructions based on the preset decision rules and early warning notifications; Step S4: Send the countermeasure command to the corresponding intelligent countermeasure device to carry out interference countermeasures against the intruding drone.

[0009] Furthermore, it also includes: when multiple sound warning devices trigger alarms sequentially in chronological order, the back-end control center determines the movement direction of the drone based on the triggering order, and adjusts the countermeasure strategy based on the movement direction and sub-region location information.

[0010] Furthermore, in step S3, the preset decision rules are based on the distance between the sub-area of ​​the sound warning device's monitoring range that triggers the alarm and the center of the monitoring area, the security protection level of the sub-area of ​​the sound warning device's monitoring range according to the asset sensitivity within the sub-area of ​​the sound warning device's monitoring range and the preset defense requirements, to determine the priority of the countermeasure strategy and the type of countermeasure equipment; the countermeasure strategy library includes: navigation deception, radio interference, physical capture and directed energy strike.

[0011] Furthermore, the sound warning device employs a stereo microphone array, which includes at least three array surfaces, each surface being equipped with two or more microphone elements.

[0012] This invention also provides an intelligent drone countermeasure system based on distributed acoustic early warning, comprising: An intelligent drone countermeasure system based on distributed acoustic warning is provided. The system includes multiple acoustic warning devices, multiple intelligent countermeasure devices, and a back-end control center. Each of the acoustic warning devices monitors a sub-area, collects sound signals within the sub-area, extracts time-frequency features and identifies drone voiceprints, determines whether a drone exists in the sub-area monitored by each acoustic warning device, and reports the warning information to the back-end control center. The backend control center includes a communication module, a region determination module, an intelligent decision-making module, and a countermeasure control module. The communication module receives the early warning information. The region determination module determines the sub-region location of the UAV based on the identifier of the acoustic warning device that sent the early warning information. The intelligent decision-making module intelligently matches the corresponding countermeasure strategy based on the sub-region location information and generates a countermeasure command based on the countermeasure strategy library. The countermeasure control module sends the countermeasure command to the corresponding intelligent countermeasure device. The intelligent countermeasure device is used to receive and execute the countermeasure command to interfere with and counter the intruding drone.

[0013] Furthermore, the sound warning device includes a digital array microphone, an identification processing module, a communication module, and a power management module; The digital array microphone is mainly used for sound signal acquisition and preprocessing. The digital array microphone includes a stereo microphone array and a signal preprocessing module. It continuously acquires sound signals at a fixed sampling rate. The digital array microphone loselessly converts the original PDM signal output by the microphone array into a PCM signal and filters out low-frequency signals and DC components. The time-domain sampled data of the output sound signal is then given to the recognition and processing module. The identification and processing module sequentially performs frame windowing and short-time Fourier transform to obtain the time-frequency power spectrum, extracts time-frequency domain acoustic features based on the time-frequency power spectrum, models them using a machine learning model, and outputs a binary decision of "whether there is a drone or not". The communication module wirelessly transmits the device ID, timestamp, and early warning message indicating the presence or absence of drones of the sound warning device to the backend control center for early warning display in real time. The power management module supplies power to the audible warning device and manages its low power consumption.

[0014] Furthermore, based on the spatial scale characteristics of the protected monitoring area, a core area defense mode or a boundary defense mode is configured for the monitoring area, and the distributed deployment positions of the multiple acoustic early warning devices are determined, thereby ensuring that the monitoring area achieves continuous perception coverage across the entire area. The core area defense mode involves deploying acoustic early warning devices along the outer boundary of the monitoring area with the monitoring area as the center. The monitoring range sub-area of ​​each acoustic early warning device covers different hemisphere areas, forming a 360° continuous omnidirectional defense for the entire monitoring core area boundary through cross-overlap. The boundary defense mode involves deploying acoustic early warning devices in a strip along the boundary, with each acoustic early warning device responsible for a section of linear sub-area.

[0015] Furthermore, the back-end control center and the acoustic warning device adopt a basic transmission mode; in the basic transmission mode, only the device ID, timestamp, and warning information of whether or not a drone is present are transmitted.

[0016] Furthermore, the intelligent decision-making module has a built-in decision rule library, which includes decision rules for dynamically matching countermeasure strategies based on the distance between the monitoring sub-area of ​​the alarm-triggered sound warning device and the center of the monitoring area, as well as the triggering timing of multiple sound warning device monitoring sub-areas.

[0017] Furthermore, the intelligent countermeasures device includes at least one of the following: a portable drone jamming gun, a fixed navigation decoy base station, or a mesh-based drone capture system.

[0018] Compared with the prior art, the present invention has at least the following beneficial effects: (1) All-type detection capability: This invention utilizes the universality of acoustic detection to effectively detect UAVs with radio silence, fiber optic and pure visual navigation, filling the blind spots of radar and radio detection.

[0019] (2) Low cost and scalability: The present invention uses a miniaturized, low-cost acoustic warning device to make up for the insufficient detection distance of a single device through distributed deployment, so as to build a large-scale, blind-spot-free warning network in an economical and efficient manner.

[0020] (3) Integrated early warning and countermeasure: This invention deeply integrates sound early warning with intelligent countermeasure decision-making, realizing full-process automation and intelligence from "discovering the area" to "identifying the threat level" and then to "automatic countermeasure", which greatly improves the response speed.

[0021] (4) Flexible deployment mode: This invention supports two modes: core area defense and boundary line defense. It can be flexibly deployed according to actual protection needs and adapt to a variety of application scenarios.

[0022] (5) Low power consumption and long battery life: The sound warning device of the present invention has low power consumption, which significantly extends the working time of field deployment. Attached Figure Description

[0023] Figure 1 A flowchart illustrating an intelligent UAV countermeasure method based on distributed acoustic early warning, provided in one embodiment of this application; Figure 2 This is a schematic diagram of the structure of an acoustic warning device provided in one embodiment of this application; Figure 3 A schematic diagram illustrating the communication transmission method between the acoustic warning device and the back-end control center provided in one embodiment of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.

[0025] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] To address the problems existing in the prior art, this invention provides an intelligent UAV countermeasure method and system based on distributed acoustic early warning. By constructing an early warning network based on "sub-region existence detection" through a low-cost, low-power acoustic early warning device, it solves the problem of detecting "low, slow, small," radio-silent, and fiber optic UAVs.

[0027] Example 1:

[0028] This invention provides an intelligent drone countermeasure method based on distributed acoustic early warning, such as... Figure 1 As shown, it includes the following steps: Step S1 (Sub-area monitoring): Multiple sound warning devices, distributed at equal intervals along the boundary of the monitoring area, collect sound signals within their respective monitoring sub-areas. Time-frequency features of the sound signals are extracted, and drone voiceprints are identified. The identification includes: calculating the signal-to-noise ratio (SNR) of the sound signal; when the SNR exceeds a preset threshold and the time-frequency features match the drone voiceprint, it is determined that a drone exists within the monitoring sub-area of ​​the sound warning device, and a warning is reported to the backend control center. The sound warning device uses a stereo microphone array, which includes at least three array surfaces, each with two or more microphone elements. In this embodiment, preferably, the stereo microphone array has five array surfaces, with four microphone elements on the top surface and five microphone elements on each of the four sides, achieving spatial coverage and gain through reasonable spatial arrangement.

[0029] Specifically, in order to effectively identify the drone signal from the sound signals (drone sound signal and ambient noise) collected by the stereo microphone array, the key indicator is the signal-to-noise ratio (SNR). Considering that both drone noise and ambient noise are mainly concentrated in the low-frequency band, a SNR threshold is used. This means that if the effective sound pressure level of the drone's acoustic signal reaching the stereo microphone array after propagation attenuation exceeds the ambient noise sound pressure level by 6 dB, the drone signal can be effectively identified from the noisy signal collected.

[0030] According to the sound pressure level attenuation formula: ; in: For distance from the drone sound source The sound pressure level at a distance of meters, measured in dB; The noise level of the drone is expressed in dB. The propagation distance is expressed in meters (m). That is, the distance the drone's acoustic signal travels... The effective sound pressure level reaching the stereo microphone array surface after propagation attenuation is 100 m. To effectively identify drone signals, it is necessary to ensure -P h >6dB, P h This refers to the ambient noise sound pressure level.

[0031] The system is designed to detect and warn of acoustic signals from medium-sized professional-grade drones. The typical sound source level for a medium-sized professional-grade drone is 85dB, which is... =85dB; Considering that the sound warning device is mainly installed in key locations and relatively quiet areas, the ambient noise sound pressure level P is taken as 85dB; h =45dB; then >Ph+6, under these conditions, the detection range of the acoustic warning device can reach .

[0032] Each array is equipped with multiple microphone elements, and spatial coverage and gain are achieved through reasonable spatial arrangement.

[0033] It should be noted that each distributed acoustic warning device independently collects sound signals within its monitored sub-area, with no need for communication between devices, thus simplifying system design. Furthermore, the use of a stereo microphone array enables a single acoustic warning device to effectively detect medium-sized drones in all directions within a range of 50 meters. When a drone enters the monitored sub-area of ​​an acoustic warning device, the device collects sound signals (including drone sound and ambient noise). The recognition and processing module calculates the real-time signal-to-noise ratio (SNR) and feature matching degree of the collected sound signals. When the real-time SNR of the collected sound signals exceeds a preset SNR threshold and the spectral characteristics match, it determines that a drone is present. The acoustic warning device immediately sends a warning message to the backend control center via the communication module. It should be noted that the warning message is extremely concise, typically containing only the acoustic warning device ID, warning trigger timestamp, and the presence of a drone; transmission time is extremely short, ensuring real-time performance.

[0034] Step S2 (Triggering and Transmission): The backend control center identifies the sound warning device that issued the warning based on the received warning information, determines that the drone has entered the sub-area corresponding to the sound warning device, and generates a warning notification containing the sub-area location information. Step S3 (Area Determination and Early Warning): The communication module of the backend control center receives all early warning information from the front end. The area determination module looks up the geographical sub-area location corresponding to the acoustic warning device based on the ID of the acoustic warning device in the early warning information. The backend control center then determines that a drone has entered the sub-area and generates an early warning notification, which includes the sub-area location information and the trigger time.

[0035] Multi-device timing judgment (enhanced information): When multiple acoustic warning devices issue warnings sequentially in chronological order, the backend control center can roughly determine the drone's movement direction based on the warning sequence and geographical distribution of the devices. This information can be used to further optimize countermeasure decisions, such as activating countermeasure devices ahead of the drone's movement direction in advance.

[0036] Step S4 (Intelligent Countermeasure Decision): The intelligent decision module of the back-end control center dynamically matches the countermeasure strategy and generates countermeasure commands based on the preset decision rules and the sub-area location information (i.e., the early warning notification in this embodiment) and the timing direction information (the UAV movement direction information inferred from the early warning timing of the sound early warning device in this embodiment).

[0037] The preset decision-making rules are based on the distance between the sub-area of ​​the sound warning device's monitoring range that triggers the alarm and the center of the monitoring area, the security protection level of the sub-area of ​​the sound warning device's monitoring range according to the asset sensitivity within the sub-area of ​​the sound warning device's monitoring range and the preset defense requirements, and determine the priority of the countermeasure strategy and the type of countermeasure equipment; the countermeasure strategy library includes: navigation deception, radio interference, physical capture and directed energy strike.

[0038] Example of decision rule application: Example of decision rule 1: If the sub-area that triggers the alarm is located in the outer perimeter of the core area (more than 200 meters away from the core area), it is determined to be a low threat, and the "navigation deception" strategy is matched. A command is generated and sent to the nearby deception base station to guide the drone to fly away.

[0039] Example 2 of decision rule: If the sub-area that triggers the alarm is located in the inner protection circle of the core area (<50 meters away from the core area), it is determined to be a high threat. Immediately match the "strong electromagnetic interference" or "directed energy strike" strategy, generate instructions and send them to the corresponding countermeasures equipment to suppress or destroy it.

[0040] Example 3 of decision rule: If the triggering sequence of multiple sound warning devices indicates that the drone is moving toward the core area, then the jamming devices on the movement path will be activated in advance to achieve "active defense".

[0041] Example of decision rule 4: If multiple sub-regions in different directions around the core area are triggered almost simultaneously, it is determined to be a drone swarm attack, the highest level of countermeasure plan is activated, and all available countermeasure resources are activated.

[0042] Step S5 (Execute Countermeasures): The countermeasure control module sends countermeasure commands via the network to designated intelligent countermeasure devices, such as portable drone jammers, fixed navigation decoy base stations, or drone capture devices. Upon receiving the commands, the intelligent countermeasure devices automatically implement precise countermeasures against the target drone.

[0043] Example 2:

[0044] This invention also provides an intelligent drone countermeasure system based on distributed acoustic early warning, such as... Figure 2 and Figure 3 As shown, the system includes multiple acoustic warning devices, multiple intelligent countermeasure devices, and a back-end control center; Each sound warning device monitors a sub-area, which is used to collect sound signals within the monitored sub-area, extract time and frequency features of the sound signals and identify drone voiceprints, determine whether there are drones in the sub-area monitored by each sound warning device, and report the warning information to the back-end control center. The backend control center is communicatively connected to multiple acoustic warning devices and multiple intelligent countermeasure devices. The backend control center includes a communication module, a region determination module, an intelligent decision-making module, and a countermeasure control module. The communication module receives warning information. The region determination module determines the sub-region location of the UAV based on the identifier of the acoustic warning device sending the warning information. The intelligent decision-making module intelligently matches the corresponding countermeasure strategy based on the sub-region location information and generates a countermeasure command based on a countermeasure strategy library. The countermeasure control module sends the countermeasure command to the corresponding intelligent countermeasure device. The intelligent countermeasure device is used to receive and execute the countermeasure command to carry out precise interference and countermeasures against the intruding drone.

[0045] The sound warning device consists of a digital array microphone, an identification and processing module, a communication module, and a power management module.

[0046] Digital array microphone: The digital array microphone includes a stereo microphone array and a signal preprocessing module. The stereo microphone array can continuously acquire ambient and drone noise sound signals at a fixed sampling rate. The digital array microphone losslessly converts the original PDM signal output by the microphone array into a PCM signal and filters out low-frequency signals and DC components from the time-domain sampled data of the output sound signal. It should be noted that, to achieve a balance between cost, size, and performance, a stereo microphone array is used in this embodiment. The stereo microphone array includes at least 3 array faces, and each array face is equipped with more than 2 microphone elements. In this embodiment, preferably, the stereo microphone array is arranged with 5 array faces, of which 4 microphone elements are arranged on the top surface and 5 microphone elements are arranged on each of the 4 sides. Spatial coverage and gain are achieved through reasonable spatial arrangement.

[0047] The identification and processing module performs frame-by-frame windowing and short-time Fourier transform on the time-domain sampled data to obtain the time-frequency power spectrum. Based on the time-frequency power spectrum, it extracts time-frequency acoustic features and then models them using a machine learning model to output a binary decision of whether or not a drone is present.

[0048] Communication Module: Responsible for data transmission and control command interaction between units within the acoustic warning device and between the device and the backend control center. The communication module wirelessly transmits a warning message containing the device ID, timestamp, and whether a drone is present to the backend control center in real time for warning display. The acoustic warning device and the backend control center use a basic transmission mode (i.e., wireless communication). In this mode, only the device ID, timestamp, and warning information regarding the presence of a drone are transmitted. It should be noted that in basic transmission mode, only the warning trigger signal is transmitted, resulting in a very small data volume and low power consumption and bandwidth requirements.

[0049] Power Management Module: Powers the entire sound alarm device and performs intelligent low-power management. In quiet environments with no abnormal sounds for extended periods, the sound alarm device remains in a low-power "listening" state; once a suspicious sound is triggered, it immediately switches to a high-power "analysis and warning" state, effectively extending the battery life.

[0050] Furthermore, based on the spatial scale characteristics of the protected monitoring area, a core area defense mode or a boundary defense mode is configured for the monitoring area, and the distributed deployment positions of the multiple acoustic early warning devices are determined, thereby ensuring that the monitoring area achieves continuous perception coverage across the entire area. The core area defense mode involves deploying acoustic early warning devices along the outer boundary of the monitoring area with the monitoring area as the center. The monitoring range sub-area of ​​each acoustic early warning device covers different hemisphere areas, forming a 360° continuous omnidirectional defense for the entire monitoring core area boundary through cross-over overlap. The boundary defense mode involves deploying acoustic early warning devices in a strip along the boundary, with each acoustic early warning device responsible for a section of linear sub-area.

[0051] It should be noted that the core area defense mode of this invention involves deploying acoustic warning devices along the boundaries of key locations, with each device responsible for a sub-region of a hemisphere. In this mode, the overlapping deployment of these devices achieves 360° omnidirectional coverage, ensuring that any drone intrusion from any direction will be detected by at least one acoustic warning device.

[0052] The intelligent decision-making module has a built-in decision rule base, which contains decision rules that dynamically match countermeasure strategies based on the distance between the monitoring sub-area of ​​the audible alarm device that triggers the alarm and the center of the monitoring area, as well as the triggering timing of multiple audible alarm device monitoring sub-areas. Intelligent countermeasures include at least one of the following: a portable drone jamming gun, a fixed navigation decoy base station, or a mesh-based drone capture system.

[0053] In summary, the intelligent UAV countermeasure method and system provided in this application solves the detection challenges of "low, slow, small," radio-silent, and fiber-optic UAVs by constructing an early warning network based on "sub-region presence detection" through a low-cost, low-power distributed acoustic early warning device. The system does not rely on high-precision speed and heading measurements; it triggers intelligent countermeasure decisions simply by identifying "which device alarms." The solution is simple, reliable, low-cost, and easy to deploy, and, combined with an intelligent decision-making module, achieves a rapid closed loop from early warning to countermeasure.

[0054] Although this application has disclosed preferred embodiments above, it is not intended to limit this application. Any person skilled in the art can make many possible variations and modifications to the technical solutions of this application, or modify them into equivalent embodiments, without departing from the scope of the technical solutions of this application. Therefore, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of this application, without departing from the content of the technical solutions of this application, should fall within the protection scope of the technical solutions of this application.

Claims

1. An intelligent UAV countermeasure method based on distributed acoustic early warning, characterized in that, Including the following steps: Step S1: Collect sound signals within their respective monitoring sub-areas by distributing multiple sound warning devices at equal intervals along the boundary of the monitoring area, and extract time-frequency features and identify drone voiceprints from the sound signals; wherein, the drone voiceprint identification includes: calculating the signal-to-noise ratio of the sound signal, and when the signal-to-noise ratio exceeds a preset threshold and the time-frequency features match the drone voiceprint, determining that a drone exists within the monitoring sub-area of ​​the sound warning device, and reporting the warning information to the backend control center; Step S2: The backend control center identifies the sound warning device that issued the warning based on the received warning information, determines that the drone has entered the sub-area corresponding to the sound warning device, and generates a warning notification containing the sub-area location information. Step S3: The backend control center intelligently matches the corresponding countermeasure strategy from the countermeasure strategy library and generates countermeasure instructions based on the preset decision rules and early warning notifications; Step S4: Send the countermeasure command to the corresponding intelligent countermeasure device to carry out interference countermeasures against the intruding drone.

2. The method according to claim 1, characterized in that, Also includes: When multiple sound warning devices trigger alarms sequentially in chronological order, the back-end control center determines the drone's movement direction based on the triggering order and adjusts the countermeasure strategy based on the movement direction and sub-region location information.

3. The method according to claim 1, characterized in that, In step S3, the preset decision rules are based on the distance between the sub-area of ​​the sound warning device's monitoring range that triggers the alarm and the center of the monitoring area, the security protection level of the sub-area of ​​the sound warning device's monitoring range according to the asset sensitivity within the sub-area of ​​the sound warning device's monitoring range and the preset defense requirements, and determine the priority of the countermeasure strategy and the type of countermeasure equipment. The countermeasures library includes: navigation deception, radio jamming, physical capture, and directed energy strikes.

4. The method according to claim 1, characterized in that, The sound warning device uses a stereo microphone array, which includes at least three array surfaces, with two or more microphone elements deployed on each array surface.

5. An intelligent UAV countermeasure system based on distributed acoustic early warning, characterized in that, The system includes multiple acoustic warning devices, multiple intelligent countermeasure devices, and a back-end control center. Each acoustic warning device monitors a sub-area, collects sound signals within the sub-area, extracts time-frequency features and identifies drone voiceprints, determines whether a drone exists in the sub-area monitored by each acoustic warning device, and reports warning information to the back-end control center. The backend control center includes a communication module, a region determination module, an intelligent decision-making module, and a countermeasure control module. The communication module receives the early warning information. The region determination module determines the sub-region location of the UAV based on the identifier of the acoustic warning device that sent the early warning information. The intelligent decision-making module intelligently matches the corresponding countermeasure strategy based on the sub-region location information and generates a countermeasure command based on the countermeasure strategy library. The countermeasure control module sends the countermeasure command to the corresponding intelligent countermeasure device. The intelligent countermeasure device is used to receive and execute the countermeasure command to interfere with and counter the intruding drone.

6. The system according to claim 5, characterized in that, The sound warning device includes a digital array microphone, an identification and processing module, a communication module, and a power management module; The digital array microphone is mainly used for sound signal acquisition and preprocessing. The digital array microphone includes a stereo microphone array and a signal preprocessing module. It continuously acquires sound signals at a fixed sampling rate. The digital array microphone loselessly converts the original PDM signal output by the microphone array into a PCM signal and filters out low-frequency signals and DC components. The time-domain sampled data of the output sound signal is then given to the recognition and processing module. The identification and processing module sequentially performs frame windowing and short-time Fourier transform to obtain the time-frequency power spectrum, extracts time-frequency domain acoustic features based on the time-frequency power spectrum, models them using a machine learning model, and outputs a binary decision of "whether there is a drone or not". The communication module wirelessly transmits the device ID, timestamp, and early warning message indicating the presence or absence of drones of the sound warning device to the backend control center for early warning display in real time. The power management module supplies power to the audible warning device and manages its low power consumption.

7. The system according to claim 5, characterized in that, Based on the spatial scale characteristics of the protected monitoring area, a core area defense mode or a boundary defense mode is configured for the monitoring area, and the distributed deployment positions of the multiple acoustic early warning devices are determined to ensure that the monitoring area achieves continuous perception coverage across the entire area. The core area defense mode involves deploying acoustic early warning devices along the outer boundary of the monitoring area with the monitoring area as the center. The monitoring range of each acoustic early warning device covers a different hemisphere region, and through cross-overlap, a 360° continuous omnidirectional defense is formed on the entire monitoring core area boundary. The boundary defense mode involves deploying acoustic early warning devices in a strip along the boundary, with each acoustic early warning device responsible for a section of linear sub-area.

8. The system according to claim 5, characterized in that, The back-end control center and the acoustic warning device use a basic transmission mode; in the basic transmission mode, only the device ID, timestamp, and warning information of whether or not a drone is present are transmitted.

9. The system according to claim 5, characterized in that, The intelligent decision-making module has a built-in decision rule library, which contains decision rules that dynamically match countermeasure strategies based on the distance between the monitoring sub-area of ​​the sound alarm device that triggers the alarm and the center of the monitoring area, as well as the triggering sequence of multiple sound alarm device monitoring sub-areas.

10. The system according to claim 5, characterized in that, The intelligent countermeasures device includes at least one of the following: a portable drone jamming gun, a fixed navigation decoy base station, or a mesh-based drone capture system.

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