Anti-unmanned aerial vehicle method, anti-unmanned aerial vehicle system, storage medium and program product

Abstract

The invention discloses an anti-unmanned-aerial-vehicle method, an anti-unmanned-aerial-vehicle system, a storage medium and a program product, and relates to the technical field of anti-unmanned-aerial-vehicle, and the method comprises the steps: judging whether a suspected target exists in an airspace range or not according to first monitoring data, collected by a low-power-consumption module, in the preset airspace range; under the condition that the suspected target exists in the airspace range, whether the unmanned aerial vehicle exists in the airspace range or not is judged according to second monitoring data, collected by a middle power consumption module, in the airspace range; under the condition that the unmanned aerial vehicle exists in the airspace range, the unmanned aerial vehicle is interfered through the high-power-consumption module. The standby power consumption of the anti-unmanned aerial vehicle equipment is reduced through a multi-stage wake-up mechanism, so that the cruising ability of the anti-unmanned aerial vehicle equipment is improved.

Description

Technical Field

[0001] This application relates to the field of anti-drone technology, and in particular to anti-drone methods, anti-drone systems, storage media, and program products. Background Technology

[0002] With the rapid development of drone technology, drone applications have penetrated into areas such as logistics and distribution, agricultural plant protection, and urban surveying. However, at the same time, the number of unregistered, unlicensed, or unregulated "black flight" drones is also increasing.

[0003] Currently, common counter-drone technologies typically employ a single sensor (such as radar or electro-optical) that operates continuously, scanning and monitoring a pre-defined airspace. Upon detecting a suspected drone signal, the sensor triggers subsequent response procedures. Because the sensor operates at full power continuously during this process, the standby power consumption of counter-drone equipment is high.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this application is to provide an anti-drone method, anti-drone system, storage medium, and program product, aiming to solve the technical problem of how to improve the endurance of anti-drone equipment.

[0006] To achieve the above objectives, this application proposes an anti-drone method, which is applied to an anti-drone system. The anti-drone system includes a low-power module, a medium-power module, and a high-power module. The method includes: Based on the first monitoring data collected by the low-power module within a preset airspace range, it is determined whether there is a suspected target within the airspace range; If a suspected target is present in the airspace, the presence of a drone in the airspace is determined based on the second monitoring data collected by the medium power consumption module within the airspace. In the event that a drone is present within the airspace, the high-power module is used to interfere with the drone.

[0007] In one embodiment, the high-power module includes a photoelectric sensor and a kill module, and the step of interfering with the drone through the high-power module includes: The photoelectric sensor is used to identify the behavior information of the drone; Based on the behavioral information, determine whether the drone exhibits threatening behavior; In the event of threatening behavior by the drone, the kill module interferes with the drone.

[0008] In one embodiment, the step of interfering with the drone through the kill module includes: The threat level of the drone is determined by using a Bayesian network threat assessment model, the first monitoring data, the second monitoring data, and the behavioral information. The drone is jammed based on the kill decision associated with the threat level and the kill module.

[0009] In one embodiment, the kill decision includes soft kill and hard kill, the kill module includes a jamming module and a laser radiation module, and the step of jamming the UAV according to the kill decision and the kill module includes: In the case where the kill decision is soft kill, the jamming module performs electromagnetic interference on the UAV. In the case where the kill decision is hard kill, the drone is attacked by laser through the laser radiation module.

[0010] In one embodiment, prior to the step of interfering with the drone via the jamming module, the method further includes: The beamforming coefficients of the interference module are determined using a genetic algorithm, the first monitoring data, the second monitoring data, the behavioral information, and the threat level. The beamforming coefficients are used to control the interference effect of the interference module.

[0011] In one embodiment, the step of determining whether a suspected target exists within the airspace range based on first monitoring data collected by the low-power module within a preset airspace range includes: Based on the Bayesian network threat assessment model and the first monitoring data, it is determined whether there are any suspected targets within the airspace. The step of determining whether a drone exists in the airspace based on the second monitoring data collected by the power consumption module within the airspace includes: The presence of drones within the airspace is determined using the Bayesian network threat assessment model, the first monitoring data, and the second monitoring data.

[0012] In one embodiment, the low-power module includes an acoustic sensor and a radio frequency module. The step of determining whether a suspected target exists within the airspace range based on first monitoring data collected by the low-power module within a preset airspace range includes: The confidence level of the voiceprint features is determined based on the voiceprint features collected by the acoustic sensor and the preset voiceprint information of the UAV. If the confidence level of the voiceprint feature is greater than a preset confidence threshold and / or the signal-to-noise ratio of the signal acquired by the radio frequency module is within a preset signal-to-noise ratio range, it is determined that a suspected target exists within the spatial domain. The step of determining whether a drone exists in the airspace based on the second monitoring data collected by the power consumption module within the airspace includes: Based on the second monitoring data, the distance and speed of the drone are determined; If the distance is within a preset distance range and the moving speed is within a preset speed range, it is determined that a drone exists within the airspace.

[0013] Furthermore, to achieve the above objectives, this application also proposes an anti-drone device, which includes: The first monitoring module is used to determine whether there is a suspected target in the airspace based on the first monitoring data collected by the low-power module within a preset airspace range. The second monitoring module is used to determine whether there is a drone in the airspace range when there is a suspected target in the airspace range, based on the second monitoring data in the airspace range collected by the medium power consumption module. The jamming module is used to interfere with the drone in the presence of the drone within the airspace by means of the high-power module.

[0014] In addition, to achieve the above objectives, this application also proposes an anti-drone device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the anti-drone method as described above.

[0015] In addition, to achieve the above objectives, this application also proposes an anti-drone system, the system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the anti-drone method as described above.

[0016] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and which, when executed by a processor, implements the steps of the anti-drone method described above.

[0017] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the anti-drone method described above.

[0018] The one or more technical solutions proposed in this application have at least the following technical effects: First, based on the first monitoring data collected by the low-power module within a preset airspace range, it is determined whether there are suspected targets within the airspace range. This achieves continuous airspace screening with low energy consumption and resource overhead, thus optimizing power consumption. Second, when a suspected target is determined to exist within the airspace range, based on the second monitoring data collected by the medium-power module within the airspace range, it is determined whether there are drones within the airspace range. This allows for accurate identification of suspected targets while consuming moderate energy, achieving highly accurate judgment of drone targets and avoiding direct triggering of the highest-power interference module, thereby reducing power waste caused by misjudgments by the low-power module. Third, when drones are present within the airspace range, high-power modules are used to interfere with the drones, effectively curbing illegal drone activities and ensuring airspace security. This application, by comprehensively utilizing multiple sensors with different power consumption for monitoring and a graded response mechanism, achieves full-process prevention and control from initial detection to precise strike. While ensuring efficient drone countermeasures, it reduces the standby power consumption of anti-drone equipment, thereby improving the endurance of the anti-drone equipment. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating an embodiment of the anti-drone method of this application. Figure 2 This is a system architecture diagram of the anti-drone method provided in Embodiment 1 of this application; Figure 3 This is a flowchart illustrating the wake-up triggering process of the anti-drone method provided in Embodiment 1 of this application. Figure 4 This is a schematic diagram of the processing flow of the Bayesian network threat assessment model provided in Embodiment 2 of this application; Figure 5 This is a schematic diagram of the module structure of the anti-drone device according to an embodiment of this application; Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the anti-drone method in the embodiments of this application.

[0022] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0024] It should be noted that in the description of this application and the appended claims, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0025] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0026] Currently, mainstream anti-drone technologies typically employ a single sensor (such as radar or electro-optical) that operates continuously, scanning and monitoring a pre-defined airspace. Upon detecting a suspected drone signal, the sensor triggers subsequent response procedures. While this method can effectively detect, identify, track, and even handle unauthorized drone flights, the continuous full-power operation of the sensor results in high standby power consumption and short battery life, making it difficult for anti-drone equipment to operate continuously for extended periods.

[0027] This application provides a solution. First, based on the first monitoring data collected by the low-power module within a preset airspace range, it determines whether there are suspected targets within the airspace. This achieves continuous airspace screening with low energy consumption and resource overhead, optimizing power consumption. Then, if a suspected target is identified within the airspace, based on the second monitoring data collected by the medium-power module within the airspace range, it determines whether a drone is present. This allows for accurate identification of suspected targets while consuming moderate energy, achieving high-accuracy drone target identification and avoiding direct triggering of the highest-power interference module, thus reducing power waste caused by misjudgments by the low-power module. Finally, if a drone is present within the airspace, a high-power module interferes with the drone to effectively curb illegal drone activities and ensure airspace security. This application, by comprehensively utilizing multiple sensors with different power consumption for monitoring and a tiered response mechanism, achieves end-to-end prevention and control from initial detection to precise strike. While ensuring efficient drone countermeasures, it reduces the standby power consumption of anti-drone equipment, thereby improving its endurance.

[0028] It should be noted that the executing entity in this embodiment can be a counter-drone device or system with data processing, network communication and program execution functions. The counter-drone device or system includes functional modules such as acoustic sensors, radio frequency modules, radar, photoelectric sensors, jamming modules, laser radiation modules and main control modules. The main control module is used to realize communication control between the various modules of the counter-drone device or system.

[0029] Based on this, the embodiments of this application provide an anti-drone method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the anti-drone method of this application. In this embodiment, the anti-drone method includes steps S10 to S30: Step S10: Based on the first monitoring data collected by the low-power module within a preset airspace range, determine whether there is a suspected target within the airspace range; A low-power module refers to a hardware set consisting of a low-power sensor and its necessary minimum signal processing circuitry, which may include low-power acoustic sensors and low-power radio frequency modules. An acoustic sensor is a device that converts sound signals (sound pressure waves) into corresponding electrical signals. Common acoustic sensors include microphone arrays, ultrasonic sensors, and fiber optic acoustic sensors. This embodiment does not impose specific limitations on the acoustic sensors used. A radio frequency module is an electronic component capable of receiving and analyzing signals on one or more specific radio frequency bands. It typically consists of an antenna, a radio frequency front-end, filters, and a signal processor. In the initial monitoring state, this radio frequency module only scans commonly used UAV frequency bands (such as 2.4GHz, 5.8GHz) or remote control frequency bands (such as 900MHz, 433MHz) to detect any abnormal radio frequency signal activity.

[0030] The preset airspace range refers to the three-dimensional spatial range that needs to be monitored by UAVs, which is predefined by software or hardware parameters.

[0031] Monitoring data refers to the set of data monitored within a preset airspace range by sensors (such as acoustic sensors, radio frequency modules, radar, and photoelectric sensors), which can be used to perceive the environmental state within the airspace range. For distinction, the data monitored by the low-power module is called the first monitoring data. The first monitoring data usually includes the acoustic characteristics of the sound signal (such as intensity, frequency, and duration) and information such as the frequency, bandwidth, signal strength, and signal-to-noise ratio of the radio frequency signal.

[0032] Suspected targets refer to aerial objects with certain flight characteristics, such as drones, birds, or other unidentified flying objects.

[0033] For example, the low-power module can continuously collect audio data from the surrounding environment using acoustic sensors and extract acoustic feature information (first monitoring data), such as sound frequency and amplitude. Then, it compares this acoustic feature information with known UAV acoustic feature information, such as comparing their frequency ranges. If their frequency ranges overlap, it determines that a suspected target exists in the current airspace. Simultaneously, the radio frequency module periodically scans a preset UAV frequency band to receive radio frequency signals (first monitoring data) within the airspace. If radio frequency information is received from the UAV frequency band, it can also determine that a suspected target exists in the current airspace.

[0034] Understandably, by using low-power modules to monitor the preset airspace range in real time with relatively low power, the standby power consumption of the entire anti-drone equipment can be effectively reduced. At the same time, through the collaboration between multiple sensors, it is possible to determine whether there are potential suspected targets in the airspace in a short time, which improves the efficiency of drone detection and thus maintains the early warning capability for suspicious drone targets while maintaining low power consumption.

[0035] In one feasible implementation, the low-power module includes an acoustic sensor and a radio frequency module, and step S10 includes: Step A11: Determine the confidence level of the voiceprint features based on the voiceprint features collected by the acoustic sensor and the preset UAV voiceprint information. Voiceprint features refer to the characteristic parameters extracted from the audio signal monitored by the acoustic sensor, including but not limited to: Mel frequency cepstral coefficients (MFCC), spectral centroid, zero-crossing rate, frequency bandwidth, etc.

[0036] The preset drone voiceprint information refers to the set of known drone voiceprint features pre-stored in the anti-drone equipment. It can include typical voiceprint feature parameters of different drone models so as to make comprehensive and accurate identification and comparison.

[0037] Confidence level refers to a numerical indicator used to represent the degree of matching between voiceprint features and preset drone voiceprint information. It is usually expressed as a percentage or a value between 0 and 1. The higher the value, the more similar the two are, and the more likely the corresponding audio signal is emitted by the drone.

[0038] For example, algorithms such as cosine similarity and Euclidean distance can be used to calculate the similarity between the voiceprint features in the first monitoring data and the voiceprint features of each drone in the preset drone voiceprint information. Then, by combining the weight parameters of each drone voiceprint feature, the confidence value of the voiceprint features in the first monitoring data matching the preset drone voiceprint information can be determined.

[0039] Understandably, voiceprint feature comparison can effectively distinguish drones from interference sources such as birds and wind noise, effectively reducing the situation of misidentifying other similar objects as drones, thereby reducing the false activation of subsequent high-power sensors.

[0040] Step A12: If the confidence level of the voiceprint feature is greater than a preset confidence threshold and / or the signal-to-noise ratio of the signal acquired by the radio frequency module is within a preset signal-to-noise ratio range, it is determined that there is a suspected target in the airspace.

[0041] The confidence threshold is a decision boundary used to measure the degree of matching between the voiceprint features in the first monitoring data and the voiceprint information of the UAV. When the actual calculated confidence exceeds the threshold, it is considered that the monitored voiceprint features have a sufficiently high probability of belonging to the UAV.

[0042] Signal-to-noise ratio (SNR) refers to the ratio of signal power to noise power of a radio frequency signal received by a radio frequency module at a specific frequency band. It reflects the relative strength of the effective signal and background interference noise in the monitoring data. The preset SNR range refers to the range of SNR values ​​that the UAV signal is usually located in within the specific frequency band monitored by the radio frequency module.

[0043] For example, the main control module of the anti-drone equipment can process the data monitored by the acoustic sensor and radio frequency module. On the one hand, it calculates the confidence level of the acoustic signature and determines whether it is greater than a preset confidence threshold. On the other hand, it simultaneously calculates the signal-to-noise ratio (SNR) of the received radio frequency signal and determines whether it is within a preset SNR range. If either or both conditions are met, it can be determined that a suspected target exists in the airspace. Conversely, if the confidence level of the acoustic signature is less than or equal to the preset confidence threshold, and the SNR is not within the preset SNR range, it can be determined that no suspected target exists in the airspace.

[0044] In this embodiment, a two-factor decision is made by setting the confidence level of the voiceprint feature and the signal-to-noise ratio of the radio frequency signal, rather than relying on a single judgment criterion. Whether in long-distance detection scenarios with poor signal-to-noise ratio or when facing new / silent targets with unclear voiceprint features, an effective early warning capability can be maintained through an alternative path, thereby improving the adaptability and reliability of UAV monitoring.

[0045] In one feasible implementation, step S10 includes: Step B11: Using the Bayesian network threat assessment model and the first monitoring data, determine whether there are any suspected targets within the airspace. A Bayesian network is a model based on probability theory and graph theory used to represent the conditional dependencies between variables. In this embodiment, the Bayesian network includes multiple nodes associated with the first monitoring data such as voiceprint features and signal-to-noise ratio. Each node represents a variable, and the edges between nodes represent the conditional dependencies between variables.

[0046] A Bayesian network threat assessment model is a model built on Bayesian networks for assessing threats based on received sensor data (monitoring data). In the main control module of anti-drone equipment, this model runs as an "inference engine". It can construct a joint probability distribution of the existence of a target by quantifying the probabilistic relationship between variables in the Bayesian network, and update the posterior probability using Bayes' theorem, thereby achieving quantitative assessment of threats. For example, features are extracted from the first monitoring data collected by the acoustic sensor and the radio frequency module and input into the Bayesian network threat assessment model; then, the input data is probabilistically calculated by the Bayesian network assessment model, the posterior probability of whether the suspected target exists is output, and the posterior probability is compared with a preset probability threshold to obtain a binary result of whether the suspected target exists.

[0047] In this embodiment, the Bayesian network threat assessment model comprehensively considers the complex correlations and mutual influences between various sensor data. Through probabilistic reasoning, it more comprehensively assesses whether there are suspected targets in the airspace, reducing the possibility of false identification and missed identification. At the same time, by continuously updating the conditional probability parameters and network structure of the model through machine learning, the model can automatically learn and adapt to different monitoring environments and feature changes, thereby better coping with new types of UAVs and complex and ever-changing airspace environments.

[0048] Step S20: In the case of a suspected target in the airspace, determine whether there is a drone in the airspace based on the second monitoring data collected by the medium power consumption module in the airspace. A medium-power module refers to a monitoring device with moderate performance and power consumption. It typically includes a medium-power radio frequency (RF) module with increased monitoring frequency bands and a radar. The monitoring frequency band refers to the range of radio frequencies covered by the RF module during monitoring, i.e., the range of electromagnetic wave frequencies that the RF receiver can scan and receive, usually measured in MHz or GHz. For example, the monitoring frequency band of the RF module can be increased from two bands {2.4 GHz, 5.8 GHz} to multiple bands such as {433 MHz, 915 MHz, 1.2 GHz, 1.5 GHz, 2.4 GHz, 5.8 GHz}, where each band corresponds to a different type of drone's remote control, image transmission, or navigation link. This embodiment does not impose specific limitations on the initial monitoring frequency band range or the subsequently added frequency band range. The radar is an active sensor that uses the principle of electromagnetic wave reflection to measure the target's distance, speed, and angle by emitting pulse signals and receiving target echoes. It can be a band radar or a millimeter-wave radar; this embodiment does not impose specific limitations on this.

[0049] Understandably, the monitoring frequency band is only increased when a suspected target is initially identified. This avoids consuming excessive system resources by using wide-band monitoring throughout the entire monitoring process, thus extending the battery life of the anti-drone equipment.

[0050] The second monitoring data characterization consists of data collected by the power consumption module during airspace monitoring, which typically includes information such as the intensity, angle, and frequency changes of radar reflection signals, as well as the frequency, bandwidth, and signal strength of radio frequency signals.

[0051] In one feasible implementation, step S20 includes: Step A21: Determine the distance and speed of the drone based on the second monitoring data; Distance refers to the straight-line distance between the monitored target detected by radar and radio frequency modules and the anti-drone equipment; moving speed refers to the vector value of the flight speed of the monitored target, which is usually calculated by radar through Doppler frequency shift measurement or continuous frame track differential.

[0052] For example, the distance between the monitored target and the corresponding monitoring device can be calculated using the round-trip time difference of the electromagnetic wave signal emitted by the radar or the arrival time difference of the radio frequency signal received by the radio frequency module in the second monitoring data, thereby determining the distance between the monitored target and the anti-drone device. Simultaneously, the drone's speed can be calculated by analyzing the Doppler frequency shift of the radar reflection signal, or by deducing the signal frequency offset from the received radio frequency signal analyzed by the radio frequency module. When the second monitoring data only includes data collected by the radar or radio frequency module, one set of distance and speed data is calculated and determined as the drone's distance and speed. However, if the second monitoring data includes data collected by both the radar and radio frequency modules, the distance and speed data obtained by the two calculation methods can be cross-checked. If the difference between the two is less than a threshold, the distance and speed data measured by the radar or radio frequency module is determined as the drone's distance and speed.

[0053] Step A22: If the distance is within a preset distance range and the moving speed is within a preset speed range, it is determined that there is a drone in the airspace.

[0054] The preset distance range refers to the distance interval set in advance to determine whether the drone is in a state that requires attention or is dangerous; while the preset speed range refers to the speed interval set in advance to determine whether the drone is in a normal flight state or a suspicious flight state; the settings of both can take into account the drone's normal flight situation and the airspace control requirements of the monitoring area.

[0055] In this embodiment, by adding a monitoring frequency band to the radio frequency module and activating a new sensor, the radar, more accurate detection of drones within the airspace is achieved. At the same time, by introducing dual judgment of distance and speed, the possibility of misidentifying other flying objects as drones is reduced, thereby enabling more accurate determination of whether drones exist within the airspace and reducing false wake-ups of the next level of sensors.

[0056] In one feasible implementation, step S20 includes: Step B21: Determine whether there are drones in the airspace using the Bayesian network threat assessment model, the first monitoring data, and the second monitoring data.

[0057] For example, features are extracted from the voiceprint features (first monitoring data) and second monitoring data (such as radar traces, speed, distance, and radio frequency signals received by the radio frequency module) collected under the synchronous timestamp, and instantiated into the network nodes corresponding to the Bayesian network. Then, the Bayesian network evaluation model is used to backpropagate according to the causal relationship quantity in the network structure, update the posterior probability of the unknown state of whether the drone exists in the network, and compare the posterior probability with the preset probability threshold to obtain a binary result of whether the drone exists.

[0058] Step S30: When a drone is present in the airspace, the drone is interfered with by a high-power module.

[0059] High-power modules refer to the most energy-intensive end-effectors in an anti-drone system. They typically include powerful but power-intensive monitoring and jamming devices, such as photoelectric sensors and kill modules. Photoelectric sensors are sensors that convert light signals into electrical signals, and typically include visible light / infrared dual-spectrum cameras. They can accurately locate and track drones by capturing light signals emitted or reflected by the drone. Kill modules are components used to interfere with or disrupt the normal operation of drones. They employ various jamming methods, such as emitting directional electromagnetic pulses, high-intensity lasers, and sound waves, to interfere with or damage the drone's electronic systems, causing the drone to lose control or be unable to continue its mission.

[0060] For example, after continuously detecting the presence of a drone in the airspace through multiple sensors, the photoelectric sensor is immediately activated to accurately detect and track the drone in order to obtain various parameter information of the drone in real time, including parameters such as distance, azimuth angle and altitude. Based on the parameter information, the firing direction and angle of the kill module are controlled to emit electromagnetic waves or lasers at the drone to interfere with the drone's electronic system and prevent it from working properly.

[0061] For example, please refer to Figure 2 , Figure 2 A system architecture diagram for an anti-drone method is provided, which may include a main control module, an energy storage battery, acoustic sensors, a radio frequency module, radar, photoelectric sensors, and a kill module. The main control module is used to implement communication control between the acoustic sensors, radio frequency module, radar, photoelectric sensors, and kill module. The energy storage battery is used to power the other modules. For the processing logic between the acoustic sensors, radio frequency module, radar, photoelectric sensors, and kill module, please refer to [reference needed]. Figure 3 , Figure 3A wake-up trigger flowchart for an anti-drone method is provided. Initially, continuous monitoring is performed through a low-power auxiliary layer, which includes an acoustic sensor and a low-power radio frequency module, corresponding to the first-level sleep state of the anti-drone device. When the acoustic sensor detects an abnormal acoustic signature (i.e., the confidence level of the acoustic signature feature detected by the acoustic sensor is greater than 60%) or the low-power radio frequency module captures a drone signal (i.e., the signal-to-noise ratio of the signal received by the radio frequency module is greater than -85dBm), the main control module wakes up the medium-power auxiliary layer. The low-power and medium-power auxiliary layers simultaneously run, corresponding to the second-level sleep state of the anti-drone device. The medium-power auxiliary layer includes a radar and a medium-power radio frequency module, which is... To monitor the increased frequency bands, the low-power radio frequency module, when the distance and speed of the drone detected by the radar and medium-power radio frequency module are within the preset distance and speed ranges, wakes up the high-power auxiliary layer through the main control module. At this time, the low, medium and high power auxiliary layers operate simultaneously, corresponding to the three-level sleep state of the anti-drone equipment. The high-power auxiliary layer includes photoelectric sensors and kill modules (including jamming modules and laser radiation modules). When the photoelectric sensors identify threatening behavior (such as hovering time greater than 10 seconds or carrying suspicious payloads), the jamming module or laser radiation module is further woken up to control the jamming module or laser radiation module to enter the full-power working state to jam and strike the drone.

[0062] This embodiment provides a counter-drone method. In the initial stage, it uses a low-power acoustic sensor and a radio frequency module with fewer monitoring frequency bands for continuous monitoring. After identifying a suspected target, it activates the radar and increases the monitoring frequency of the radio frequency module for further judgment, overcoming the problem of short battery life caused by the continuous full-power operation of radar and other sensors in traditional equipment. Then, after further confirmation by radar and the enhanced radio frequency module, it activates the photoelectric sensor for visual tracking and controls the standby kill module to interfere with the drone. It can be seen that this application, by constructing a hierarchical wake-up mechanism, can achieve long-term airspace detection with low average power consumption, while ensuring a rapid and accurate fully automatic response from detection to disposal when a threat occurs, effectively solving the security threat posed by "black flight" drones at a low power cost.

[0063] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. Based on this, the high-power module includes a photoelectric sensor and a killing module, and step S30 includes: Step S31: Identify the drone's behavior information using photoelectric sensors; Behavioral information refers to the quantitative features extracted from continuous video frames output by photoelectric sensors through computer vision algorithms that can characterize the target's motion pattern and mission intent. These features include, but are not limited to, flight speed, acceleration, flight direction, altitude change, hovering time, maneuvering actions, whether it carries a payload, and whether there is a flash (suspected photo capture).

[0064] Step S32: Based on the behavioral information, determine whether the drone exhibits threatening behavior; For example, a photoelectric sensor continuously performs optical detection on drones in the airspace. When a drone is detected, the photoelectric sensor converts the collected optical signal into an electrical signal. Then, the electrical signal is analyzed and processed to extract behavioral information such as the drone's flight trajectory, speed, altitude, and attitude. The behavioral information is then compared and analyzed with threat behavior judgment criteria pre-stored in the system. If the behavioral information meets the threat behavior judgment criteria, the drone is determined to have threatening behavior. The threat behavior judgment criteria refer to a set of abnormal flight behaviors defined according to a preset security policy, such as hovering time with a payload greater than 10 seconds.

[0065] For example, if the time during which the photoelectric sensor does not detect the drone exceeds a threshold, or if the drone does not exhibit threatening behavior, the system reverts to a first-level sleep state, keeping the photoelectric sensor and radar in standby mode, and adjusting the monitoring frequency band of the radio frequency module to the initial value, thereby continuously monitoring the preset airspace range through the acoustic sensor and radio frequency module.

[0066] Step S33: In the event of a threatening behavior by the drone, the drone is interfered with by the kill module.

[0067] In one feasible implementation, step S33, which involves interfering with the drone via the kill module, includes: Step S331: Determine the threat level of the drone using the Bayesian network threat assessment model, the first monitoring data, the second monitoring data, and behavioral information; Threat level refers to a quantitative assessment of the degree of security threat that drones may pose. It is usually divided into multiple levels, such as low threat, medium threat, and high threat.

[0068] Optionally, the threat level probability distribution of the drone belonging to different threat levels can be determined by using a Bayesian network threat assessment model, first monitoring data, second monitoring data, and behavioral information; then, the threat level of the drone can be determined based on the threat level probability distribution.

[0069] For example, the first monitoring data (such as voiceprint features), the second monitoring data (such as distance and speed measured by radar), and behavioral information (such as the drone's flight trajectory and hovering time) are used as evidence and input into a pre-constructed Bayesian network threat assessment model. In this model's Bayesian network, various monitoring data and behavioral information of the drone serve as parent nodes, and threat levels (high, medium, and low) serve as child nodes. Furthermore, the model can, based on the input data and the conditional probability relationships between network nodes, progressively pass on and update the probability information of each node, ultimately calculating the probability distribution of each child node and determining it as the drone's threat level probability distribution. Finally, the level with the highest probability in the threat level probability distribution is output as the final threat level.

[0070] Step S332: Interference with the UAV based on the kill decision and kill module associated with the threat level.

[0071] Kill decision refers to the interference or countermeasures pre-set based on the threat level of the drone.

[0072] For example, different threat levels may correspond to different kill decisions. For instance, a low threat level may only require issuing a warning signal through the kill module, a medium threat level may require the kill module to emit an electromagnetic pulse to interfere, and a high threat level may require more drastic measures such as laser destruction.

[0073] Understandably, by using a Bayesian network threat assessment model to integrate monitoring data and behavioral information collected from multiple sensors, the threat level of drones can be accurately assessed, and different countermeasures can be taken according to the threat level. On the one hand, this improves the flexibility and effectiveness of anti-drone equipment in dealing with drone threats; on the other hand, it can avoid excessive use of interference resources, reduce power consumption, and reduce the impact on the surrounding environment and other legitimate equipment.

[0074] In one feasible implementation, the kill decision includes soft kill and hard kill, the kill module includes a jamming module and a laser radiation module, and the step S332, which involves jamming the UAV based on the kill decision and the kill module, includes: Step S3321: In the case of soft kill decision, electromagnetic interference is carried out on the UAV through the jamming module; Soft kill refers to a non-destructive, reversible countermeasure, including interfering with its communication, navigation, or electronic systems, rendering it unable to complete its mission or causing it to deviate from the target area, manifested as returning to base, landing, or losing control.

[0075] The jamming module is a sub-module in the kill module used to generate and emit specific types of jamming signals. It is usually composed of a signal generator, power amplifier, antenna and other parts. It can generate electromagnetic signals of specific frequency bands and intensities to interfere with the communication or navigation system of the UAV.

[0076] In one feasible implementation, the interference module includes a gallium nitride (GaN) radio frequency power amplifier module for transmitting high-power interference signals.

[0077] For example, the jamming module can determine the specific jamming module, frequency band, jamming parameters, and other jamming information based on the soft kill decision issued by the main control module. Furthermore, it can determine the alignment angle of the antenna beam by combining the flight trajectory and other behavioral information of the UAV provided by the photoelectric sensor. At the same time, it generates a specified jamming waveform (such as Gaussian noise or GPS spoofing signal) through a waveform generator, which is then amplified by a power amplifier and radiated to the UAV through the antenna.

[0078] Optionally, after electromagnetic interference, the subsequent behavior of the drone (such as whether it starts returning to base) can be detected by photoelectric sensors, and the interference parameters (such as increasing power) can be dynamically adjusted according to its subsequent behavior to optimize the interference effect.

[0079] In one feasible implementation, prior to step S3321, the method further includes: Step S3320: The beamforming coefficient of the interference module is determined by using a genetic algorithm, first monitoring data, second monitoring data, behavioral information and threat level. The beamforming coefficient is used to control the interference effect of the interference module.

[0080] Genetic algorithm is an optimization search algorithm that simulates the principles of natural selection and genetics. It encodes the solution to a problem as chromosomes, evaluates the quality of chromosomes using a fitness function, and iterates through operations such as selection, crossover, and mutation to gradually approach the optimal solution to the problem. In this embodiment, the chromosome of the genetic algorithm can be set as a combination of beamforming coefficients.

[0081] Beamforming coefficients are a set of parameters used to control the direction and intensity of interference signals. By adjusting these coefficients, the beam shape and directivity of the interference signal can be changed, thereby improving the energy concentration and interference effect of the interference signal.

[0082] For example, the process of determining beamforming coefficients using a genetic algorithm includes: First, integrating first monitoring data, second monitoring data, behavioral information, and threat levels to form a comprehensive dataset, which serves as the input to the genetic algorithm; then, randomly generating a set of beamforming coefficients as an initial population, and calculating the fitness of different coefficient combinations in the initial population based on the comprehensive dataset, where fitness is an evaluation index of interference effectiveness (such as transmit power and interference efficiency); then, selecting coefficient combinations with fitness higher than a preset fitness threshold to enter the next generation population, and performing crossover and mutation operations on the coefficient combinations in the next generation population, and calculating the fitness of different coefficient combinations in the next generation population, repeating the above selection, crossover, mutation, and fitness calculation operations until a preset termination condition is met (such as reaching a preset number of iterations or the fitness reaching a certain threshold), thus determining the optimal beamforming coefficients.

[0083] In this embodiment, the beamforming coefficient of the interference module is determined by combining a genetic algorithm with various monitoring data, thereby dynamically adjusting the interference effect to adapt to different UAV behaviors and threat levels. At the same time, the optimized beamforming coefficient can effectively reduce the transmission power, while increasing the energy concentration of the interference signal, reducing the diffusion and energy loss of the interference signal, and improving the interference efficiency.

[0084] Step S3322: In the case of a kill decision of hard kill, the drone is attacked by laser through the laser radiation module.

[0085] A laser radiation module is a component that can generate and emit high-energy laser beams, including a laser (including air-cooled or water-cooled types) and a heat dissipation component (an active air-cooled or water-cooled machine corresponding to the laser).

[0086] For example, the laser radiation module can control the laser to convert electrical or chemical energy into a high-energy laser beam and accurately shoot it at the drone based on the hard kill decision issued by the main control module. At the same time, the heat dissipation module monitors the temperature of the laser, and if the temperature exceeds the set value, it will start heat dissipation, such as a fan accelerating air circulation to remove heat, or a liquid cooling system circulating coolant to conduct heat.

[0087] For example, please refer to Figure 4 , Figure 4A schematic diagram of the processing flow of a Bayesian network threat assessment model is provided. First, monitoring data collected from multiple sources (including first monitoring data, second monitoring data, behavioral information, etc.) is fused. The fused monitoring data is then input into a Bayesian network threat assessment model accelerated by an FPGA (Field Programmable Gate Array) to determine the threat level of the drone. Based on the drone's threat level, a threat level decision is made to determine the appropriate kill decision to be taken against the drone, including but not limited to: outputting alarm information, soft kill (electromagnetic interference), and hard kill (laser strike). Furthermore, to further optimize power consumption and interference effect, a genetic algorithm power optimization module can be used to perform genetic algorithm reasoning on the fused data and the drone's threat level to determine beamforming coefficients suitable for the current scenario. These beamforming coefficients are then used for electromagnetic interference via the interference module in the soft kill scenario.

[0088] In this embodiment, photoelectric sensors are used to identify the behavior information of the drone. After determining that a threat exists, the kill module is activated for processing, completing the third wake-up step in the overall drone countermeasure process, and the anti-drone equipment enters full-power operation. Furthermore, a Bayesian network threat assessment model combined with multi-sensor data is used to determine the threat level of the drone, and appropriate processing measures are taken accordingly. This avoids excessive countermeasures and optimizes system energy consumption. In addition, a genetic algorithm is used to optimize the beamforming coefficient of the jamming module, thereby optimizing the beam shape and focusing energy on the target to the maximum extent, significantly improving the jamming effect while reducing the total power consumption.

[0089] Experimental verification has shown that anti-drone equipment using the proposed anti-drone method has been deployed and applied in locations such as around airport runways, nuclear power plants, and military bases. Initially, a first-level sleep mode is set, and continuous monitoring is performed through acoustic sensors and radio frequency modules. When the acoustic sensors detect abnormal acoustic signatures (e.g., confidence level greater than 60%) or the radio frequency module captures drone signals (e.g., signal-to-noise ratio > -85dBm), a first-level wake-up is triggered, activating the millimeter-wave radar and increasing the monitoring frequency band of the radio frequency module. Subsequently, when the radar and radio frequency module confirm the presence of the target (e.g., distance < 3km and speed > 10m / s), a second-level wake-up is triggered, activating the electro-optical sensor and kill module. Furthermore, when the electro-optical system identifies threatening behavior (e.g., carrying a suspicious payload or hovering time greater than 10 seconds), a third-level wake-up is triggered. The jamming module in the kill module can enter jamming mode within 2-4 seconds, causing the drone to return or land. The laser radiation module in the kill module enters an attackable state within 4-6 seconds, inflicting hard damage on the drone. Throughout the process, the acoustic sensor can complete anomaly detection within 500ms, the radar can verify the target within 3 seconds of startup, and the optoelectronic system can complete positioning within 1 second. Combined with the kill time of the jamming module or laser radiation module on the drone, the total delay from deep hibernation to wake-up is less than or equal to 10 seconds. Therefore, the anti-drone device of this application possesses rapid recovery capabilities and can quickly respond to intrusions by illegal drones.

[0090] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the anti-drone method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0091] This application also provides an anti-drone device; please refer to... Figure 5 Anti-drone devices include: The first monitoring module 10 is used to determine whether there is a suspected target in the airspace based on the first monitoring data collected by the low-power module within a preset airspace range. The second monitoring module 20 is used to determine whether there is a drone in the airspace based on the second monitoring data collected by the medium power consumption module in the airspace when there is a suspected target in the airspace. The jamming module 30 is used to jam the drone in the presence of the drone in the airspace by means of a high-power module.

[0092] The anti-drone device provided in this application, employing the anti-drone method described in the above embodiments, can solve the technical problem of how to improve the endurance of anti-drone equipment. Compared with the prior art, the beneficial effects of the anti-drone device provided in this application are the same as those of the anti-drone method provided in the above embodiments, and other technical features in the anti-drone device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0093] This application provides an anti-drone device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the anti-drone method in the first embodiment described above.

[0094] The following is for reference. Figure 6 The diagram illustrates a structural schematic suitable for implementing the anti-drone device embodiments of this application. The anti-drone device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The anti-drone device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0095] like Figure 6As shown, the anti-drone device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the anti-drone device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the anti-drone equipment to communicate wirelessly or wiredly with other devices to exchange data. While the figure shows anti-drone equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0096] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0097] The anti-drone device provided in this application, employing the anti-drone method described in the above embodiments, can solve the technical problem of how to improve the endurance of the anti-drone device. Compared with the prior art, the beneficial effects of the anti-drone device provided in this application are the same as those of the anti-drone method provided in the above embodiments, and other technical features of this anti-drone device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0098] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0099] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0100] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the anti-drone method in the above embodiments.

[0101] The computer-readable storage medium provided in this application embodiment may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0102] The aforementioned computer-readable storage medium may be included in the anti-drone equipment; or it may exist independently and not be assembled into the anti-drone equipment.

[0103] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the anti-drone device, the anti-drone device causes the following: based on first monitoring data collected by the low-power module within a preset airspace range, it determines whether there is a suspected target within the airspace range; if a suspected target exists within the airspace range, it determines whether there is a drone within the airspace range based on second monitoring data collected by the medium-power module within the airspace range; if a drone exists within the airspace range, it interferes with the drone through the high-power module.

[0104] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0105] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0106] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0107] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the above-described anti-drone method, thereby solving the technical problem of how to improve the endurance of anti-drone equipment. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the anti-drone method provided in the above embodiments, and will not be repeated here.

[0108] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the anti-drone method described above.

[0109] The computer program product provided in this application can solve the technical problem of how to improve the endurance of anti-drone equipment. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the anti-drone method provided in the above embodiments, and will not be repeated here.

[0110] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for countering unmanned aerial vehicles (UAVs), characterized in that, The anti-drone method is applied to an anti-drone system, which includes a low-power module, a medium-power module, and a high-power module. The method includes: Based on the first monitoring data collected by the low-power module within a preset airspace range, it is determined whether there is a suspected target within the airspace range; If a suspected target is present in the airspace, the presence of a drone in the airspace is determined based on the second monitoring data collected by the medium power consumption module within the airspace. In the event that a drone is present within the airspace, the high-power module is used to interfere with the drone.

2. The anti-drone method as described in claim 1, characterized in that, The high-power module includes a photoelectric sensor and a kill module. The step of interfering with the UAV through the high-power module includes: The photoelectric sensor is used to identify the behavior information of the drone; Based on the behavioral information, determine whether the drone exhibits threatening behavior; In the event of threatening behavior by the drone, the kill module interferes with the drone.

3. The anti-drone method as described in claim 2, characterized in that, The step of interfering with the drone through the kill module includes: The threat level of the drone is determined by using a Bayesian network threat assessment model, the first monitoring data, the second monitoring data, and the behavioral information. The drone is jammed based on the kill decision associated with the threat level and the kill module.

4. The anti-drone method as described in claim 3, characterized in that, The kill decision includes soft kill and hard kill, the kill module includes a jamming module and a laser radiation module, and the step of jamming the UAV according to the kill decision and the kill module includes: In the case where the kill decision is soft kill, the jamming module performs electromagnetic interference on the UAV. In the case where the kill decision is hard kill, the drone is attacked by laser through the laser radiation module.

5. The anti-drone method as described in claim 4, characterized in that, Prior to the step of interfering with the drone via the jamming module, the method further includes: The beamforming coefficients of the interference module are determined using a genetic algorithm, the first monitoring data, the second monitoring data, the behavioral information, and the threat level. The beamforming coefficients are used to control the interference effect of the interference module.

6. The anti-drone method as described in claim 3, characterized in that, The step of determining whether there is a suspected target in the airspace based on the first monitoring data collected by the low-power module within a preset airspace range includes: Based on the Bayesian network threat assessment model and the first monitoring data, it is determined whether there are any suspected targets within the airspace. The step of determining whether a drone exists in the airspace based on the second monitoring data collected by the power consumption module within the airspace includes: The presence of drones within the airspace is determined using the Bayesian network threat assessment model, the first monitoring data, and the second monitoring data.

7. The anti-drone method as described in claim 1, characterized in that, The low-power module includes an acoustic sensor and a radio frequency module. The step of determining whether a suspected target exists in the airspace based on the first monitoring data collected by the low-power module within a preset airspace range includes: The confidence level of the voiceprint features is determined based on the voiceprint features collected by the acoustic sensor and the preset voiceprint information of the UAV. If the confidence level of the voiceprint feature is greater than a preset confidence threshold and / or the signal-to-noise ratio of the signal acquired by the radio frequency module is within a preset signal-to-noise ratio range, it is determined that a suspected target exists within the spatial domain. The step of determining whether a drone exists in the airspace based on the second monitoring data collected by the power consumption module within the airspace includes: Based on the second monitoring data, the distance and speed of the drone are determined; If the distance is within a preset distance range and the moving speed is within a preset speed range, it is determined that a drone exists within the airspace.

8. An anti-drone system, characterized in that, The system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the anti-drone method as described in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the anti-drone method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the anti-drone method as described in any one of claims 1 to 7.

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