Active anti-eavesdropping method and system

Abstract

The invention discloses an active anti-eavesdropping method and system, and the method comprises the steps: deploying a plurality of acoustic sensors in a target region, collecting sound wave signals in real time, and generating an acoustic data matrix; calculating acoustic wave characteristic entropy according to the acoustic data matrix, and judging whether an eavesdropping signal exists or not based on the acoustic wave characteristic entropy; if the eavesdropping signal exists, performing spectral analysis on the sound wave signal, and extracting a potential eavesdropping signal frequency; and for each potential eavesdropping signal frequency, generating and transmitting a corresponding anti-interference signal to form a multi-band anti-interference signal, thereby realizing active anti-eavesdropping comprehensive coverage. According to the embodiment of the invention, the method can achieve the quick recognition and effective protection of an eavesdropping behavior through the real-time monitoring of the sound wave signal and the generation of the multi-band anti-interference signal, thereby remarkably improving the information safety and privacy protection capability.

Description

Technical Field

[0001] The invention belongs to the technical field of signal processing, and in particular to an active anti-eavesdropping method and system. Background Art

[0002] Currently, existing passive anti-eavesdropping technologies usually determine whether there is an eavesdropping device by monitoring abnormal noise or signals in the acoustic environment, but this method often has delays and is difficult to accurately capture potential eavesdropping signals. The increasing popularity of smart devices and software has made eavesdropping technology increasingly intelligent, and traditional passive protection measures have become inadequate. Summary of the invention

[0003] The purpose of the present invention is to provide an active anti-eavesdropping method and system to address the deficiencies in the prior art. The method and system can realize rapid identification and effective protection against eavesdropping by real-time monitoring of acoustic wave signals and generating multi-band anti-interference signals, thereby significantly improving information security and privacy protection capabilities.

[0004] An embodiment of the present application provides an active anti-eavesdropping method, the method comprising: Deploy multiple acoustic sensors in the target area to collect acoustic signals in real time and generate an acoustic data matrix; Calculating the acoustic wave characteristic entropy according to the acoustic data matrix, and judging whether there is a wiretap signal based on the acoustic wave characteristic entropy; If there is an eavesdropping signal, perform spectrum analysis on the sound wave signal to extract the potential eavesdropping signal frequency; For each potential eavesdropping signal frequency, a corresponding anti-interference signal is generated and transmitted to form a multi-band anti-interference signal to achieve comprehensive coverage of active anti-eavesdropping.

[0005] Optionally, calculating the acoustic wave characteristic entropy according to the acoustic data matrix includes: Extract the frequency characteristics of the acoustic signal from the acoustic data matrix and calculate the amplitude spectrum of different frequency components; The frequency range of the calculated amplitude spectrum is divided into several frequency intervals, and the amplitude value in each frequency interval is counted; The probability distribution of each frequency interval is calculated as: Among them, the is the ith frequency interval The probability distribution of is the sum of the amplitude values ​​falling into the i-th frequency interval, is the sum of the amplitude values ​​of all frequency intervals; According to the probability distribution of each frequency interval, the sound wave characteristic entropy is calculated as: Wherein, n is the number of frequency intervals.

[0006] Optionally, judging whether there is a potential eavesdropping signal based on the acoustic wave characteristic entropy includes: It is determined whether the acoustic wave characteristic entropy is less than a preset threshold value. If it is less than the preset threshold value, it is determined that there is a potential eavesdropping signal.

[0007] Optionally, the anti-interference signal is: Among them, the is the anti-interference signal corresponding to the jth potential eavesdropping signal frequency, is a dynamically adjustable amplitude, the is the jth potential eavesdropping signal frequency, For the phase, is random noise, For time.

[0008] Another embodiment of the present application provides an active anti-eavesdropping system, the system comprising: A collection module is used to deploy multiple acoustic sensors in the target area to collect acoustic wave signals in real time and generate an acoustic data matrix; A judgment module, used for calculating the acoustic wave characteristic entropy according to the acoustic data matrix, and judging whether there is a wiretap signal based on the acoustic wave characteristic entropy; An extraction module is used to perform spectrum analysis on the acoustic signal and extract the potential frequency of the eavesdropping signal if there is one; The interference module is used to generate and transmit corresponding anti-interference signals for each potential eavesdropping signal frequency to form a multi-band anti-interference signal to achieve comprehensive coverage of active anti-eavesdropping.

[0009] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0010] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0011] Compared with the prior art, the present invention provides an active anti-eavesdropping method, which deploys multiple acoustic sensors in a target area, collects sound wave signals in real time and generates an acoustic data matrix; calculates the sound wave characteristic entropy according to the acoustic data matrix, and determines whether there is an eavesdropping signal based on the sound wave characteristic entropy; if there is an eavesdropping signal, performs spectrum analysis on the sound wave signal to extract potential eavesdropping signal frequencies; for each potential eavesdropping signal frequency, generates and transmits a corresponding anti-interference signal to form a multi-band anti-interference signal, thereby achieving comprehensive coverage of active anti-eavesdropping, so that by real-time monitoring of the sound wave signal and generating the multi-band anti-interference signal, rapid identification and effective protection of eavesdropping behavior can be achieved, thereby significantly improving information security and privacy protection capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A hardware structure block diagram of a computer terminal for an active anti-eavesdropping method provided by an embodiment of the present invention; Figure 2 A schematic diagram of a process flow of an active anti-eavesdropping method provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an active anti-eavesdropping system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0014] The embodiment of the present invention firstly provides an active anti-eavesdropping method, which can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0015] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of an active anti-eavesdropping method provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0016] The memory 104 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the active anti-eavesdropping method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0017] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0018] See also Figure 2 The embodiment of the present invention provides an active anti-eavesdropping method, which may include the following steps: S201, deploying multiple acoustic sensors in the target area to collect acoustic wave signals in real time and generate an acoustic data matrix; Deploying multiple acoustic sensors in the target area, collecting acoustic signals in real time and generating an acoustic data matrix is ​​the first and basic step of this active anti-eavesdropping method. This process aims to obtain key information and establish a comprehensive view of acoustic data through multi-point monitoring, providing reliable data support for subsequent eavesdropping signal judgment and interference signal generation. Acoustic sensors have high sensitivity and a wide frequency response capability, and can accurately capture weak acoustic signals under different environmental conditions, including audio that may come from eavesdropping devices. Role and significance: By deploying multiple acoustic sensors, the overall acoustic monitoring capability of the target area is improved. The real-time generation of acoustic data matrix makes the capture of acoustic wave signals more comprehensive and detailed, providing an accurate data basis for subsequent analysis. This process not only helps to detect eavesdropping behavior in a timely manner, but also provides a large amount of acoustic feature data for system analysis, and then determines the presence or absence of secret signals through feature entropy calculation, thereby enhancing the protection capability of information security. One implementation method may include: Step 1: Determine the sensor deployment layout. Based on the size, shape and acoustic environment characteristics of the target area, mathematical modeling methods (such as Voronoi diagram or Delaunay triangulation) are used to design the optimal deployment plan for acoustic sensors. By simulating the sound wave propagation characteristics under different sensor layouts, the optimal sensor position is determined to ensure signal coverage.

[0019] Step 2: Select the right acoustic sensor After determining the layout, select the acoustic sensor that is suitable for the target environment, considering its sensitivity, frequency range and noise rejection ability. At the same time, ensure that the selected sensors can form a network to support real-time data transmission and sharing.

[0020] Step 3: Real-time signal acquisition In the target area, start the acoustic sensor and collect the acoustic wave signal in real time. Each sensor should be equipped with a digital signal processing module to ensure that they can instantly digitize the collected analog signals for subsequent fusion and analysis.

[0021] Step 4: Data matrix generation The acoustic signals collected by multiple sensors are processed synchronously in time and space. According to the coordinate information of the sensor, each collected acoustic signal is assigned a corresponding coordinate position to form an acoustic data matrix with time sequence and spatial information. Specifically, interpolation algorithms (such as bilinear interpolation) can be used to fill in data gaps, making the data matrix more continuous in time and space.

[0022] Step 5: Dynamic update mechanism Considering the dynamic changes that may exist in the target area (such as human flow, equipment noise, etc.), a dynamic update mechanism is designed. By setting data thresholds and time windows, the system automatically re-collects and updates the acoustic data matrix within a certain time interval to ensure the accuracy and timeliness of the data.

[0023] Step 6: Multi-sensor fusion technology uses multi-sensor fusion technology (such as Kalman filtering or particle filtering) to process data, merge and denoise the sound wave signals from different sensors to improve signal quality and reliability. By analyzing the fused data, a three-dimensional sound wave environment model is constructed, laying a solid foundation for the subsequent sound wave characteristic entropy calculation.

[0024] Through the above steps, a systematic acoustic monitoring network can be formed to collect and analyze the sound wave signals in the target area in real time, laying the foundation for subsequent judgment and interference signal generation. This complex implementation method not only improves the intelligence level of the system, but also enhances the reliability and effectiveness of the anti-eavesdropping effect.

[0025] S202, calculating the acoustic wave characteristic entropy according to the acoustic data matrix, and judging whether there is a wiretap signal based on the acoustic wave characteristic entropy; After obtaining the acoustic data matrix, the next step is to conduct an in-depth analysis of the data. Acoustic characteristic entropy is an indicator that measures the complexity and uncertainty of a signal. By calculating the distribution of acoustic signals in different frequency ranges, a quantitative description of the overall degree of change in the acoustic signal can be obtained. The lower the characteristic entropy, the higher the concentration of the frequency components of the signal, which may indicate the presence of interference signals from a single source, such as signals from eavesdropping devices. By calculating the characteristic entropy of sound waves, we can quickly determine whether the sound wave signals in the environment are normal, and then identify potential eavesdropping behaviors. If the characteristic entropy value changes abnormally, it may indicate the existence of unauthorized sound wave signals, and protective measures can be taken in time to enhance information security and provide necessary support for protecting user privacy and important information.

[0026] Specifically, the frequency characteristics of the acoustic signal can be extracted from the acoustic data matrix, and the amplitude spectra of different frequency components can be calculated; This step aims to analyze the acoustic signal components in the acoustic data matrix and extract the frequency characteristics and corresponding amplitude spectrum information of the signal through spectrum analysis methods such as Fourier transform. By calculating the amplitude spectrum, we can clearly understand the intensity distribution of the acoustic signal in each frequency range, which helps to identify the possible eavesdropping signal frequency.

[0027] The frequency range of the calculated amplitude spectrum is divided into several frequency intervals, and the amplitude value in each frequency interval is counted; This step subdivides the amplitude spectrum into multiple frequency intervals for more in-depth statistical analysis. For example, the frequency range from 0Hz to 1000Hz is evenly divided into 10 intervals. The amplitude in each frequency interval is counted to form a probability distribution of frequency characteristics. The division of frequency intervals provides basic data for the subsequent calculation of acoustic wave characteristic entropy, which helps to capture subtle changes in acoustic wave signals and enhance the detection capability of eavesdropping signals.

[0028] The probability distribution of each frequency interval is calculated as: Among them, the is the ith frequency interval The probability distribution of is the sum of the amplitude values ​​falling into the i-th frequency interval, is the sum of the amplitude values ​​of all frequency intervals; This step calculates the probability distribution of each frequency interval by counting the amplitude values ​​of each frequency interval. The probability distribution provides the necessary input data for the calculation of the acoustic wave characteristic entropy, which can help identify the complexity and abnormality of the signal in the subsequent steps.

[0029] According to the probability distribution of each frequency interval, the sound wave characteristic entropy is calculated as: Where n is the number of frequency intervals. By calculating the probability distribution of each frequency interval, the value of the acoustic characteristic entropy is obtained using the information entropy formula. The calculation of the acoustic characteristic entropy can quantify the complexity of the signal and provide an important basis for the subsequent wiretap signal judgment.

[0030] Specifically, it can be determined whether the acoustic wave characteristic entropy is less than a preset threshold value. If it is less than the preset threshold value, it is determined that there is a potential eavesdropping signal.

[0031] After calculating the acoustic characteristic entropy, this step compares it with a preset threshold to determine whether the acoustic signal has potential eavesdropping behavior. The set threshold is usually based on empirical values ​​or historical data. Characteristic entropy below this value may indicate that the acoustic signal has become too concentrated, suggesting the presence of potential interference or eavesdropping signals.

[0032] A low value of acoustic feature entropy usually means that the frequency distribution of the signal is relatively concentrated, which may indicate the presence of a single source of signal, such as an audio signal from a wiretap. This logic is based on the basic principles of information theory, where if the complexity of the signal is reduced, it means that it may be emitted by a specific device.

[0033] The main purpose of this judgment process is to timely identify potential eavesdropping signals, thereby providing users with immediate warnings and protective measures. In this way, the information security protection capability can be effectively improved, and the user's privacy and important information can be protected from infringement.

[0034] S203, if there is a wiretap signal, perform spectrum analysis on the sound wave signal to extract the potential wiretap signal frequency; When it is determined that there is a wiretap signal, the purpose of spectrum analysis is to further analyze the frequency components of the sound wave signal to determine the possible source of the wiretap signal. Spectrum analysis technology can effectively identify the components of different frequencies in the signal and extract their amplitude information, which is crucial for locating potential wiretap signals. By using spectrum analysis, complex sound wave signals can be converted into clear frequency information, so that the frequency of potential wiretap signals can be identified and extracted. Role and significance: By deeply analyzing the frequency components of the sound wave signal, the potential eavesdropping signal frequency can be accurately identified, thereby providing a specific frequency reference for the subsequent anti-interference signal generation. This identification can help the security system take more effective actions, enhance information protection, and ensure that the user's privacy is not violated. One implementation method may include: Step 1: Preprocess the sound wave signal Before spectrum analysis, the collected sound wave signal needs to be preprocessed. This includes steps such as denoising, normalization and filtering. Through high-pass and low-pass filters, low-frequency interference and high-frequency noise are removed, and the signal is adjusted to an ideal analysis range.

[0035] Step 2: Fast Fourier Transform (FFT) Apply Fast Fourier Transform (FFT) to the pre-processed sound wave signal to convert the time domain signal into a frequency domain signal. FFT can effectively and quickly calculate the spectrum of the signal and generate the amplitude spectrum and phase spectrum of the frequency components. This conversion allows the amplitude information of different frequency components to be revealed.

[0036] Step 3: Generate a frequency distribution graph Convert the FFT output into a frequency distribution graph to identify the frequency components in the signal and their corresponding amplitudes. By analyzing the frequency distribution graph, it is possible to determine which frequency components in the signal have higher intensity and may indicate the presence of a wiretap.

[0037] Step 4: Set the frequency analysis threshold Based on the characteristics of normal sound wave signals, set the dynamic frequency analysis threshold. This threshold can be set through historical data or machine learning algorithms (such as cluster analysis) to ensure that frequency components with intensity exceeding the threshold are identified in the spectrum as potential eavesdropping signals.

[0038] Step 5: Sorting and selecting frequency components Sort the frequency components with amplitude values ​​higher than the threshold in the frequency distribution diagram. Select the first N frequencies with the highest amplitude values ​​as potential eavesdropping signal frequencies. The credibility of this selection can be further confirmed by calculating the energy spectrum density.

[0039] Step 6: Signal feature verification The selected potential signal frequency is feature verified to check the similarity of its time domain waveform with the known eavesdropping device signal. This can be achieved through matching algorithms (such as correlation coefficient calculation) to ensure that the extracted frequency does correspond to the possible eavesdropping source.

[0040] Step 7: Signal recording and feedback Finally, the extracted potential eavesdropping signal frequency is recorded in the system database and fed back to the anti-eavesdropping system to facilitate the subsequent generation and transmission of anti-interference signals. This recording mechanism also facilitates subsequent data analysis and model optimization.

[0041] Through the above steps, the potential eavesdropping signal frequency can be effectively extracted, providing strong data support and technical basis for active anti-eavesdropping. This implementation method not only improves the accuracy of eavesdropping signal detection, but also provides a practical method for information security protection.

[0042] S204, for each potential eavesdropping signal frequency, a corresponding anti-interference signal is generated and transmitted to form a multi-band anti-interference signal to achieve full coverage of active anti-eavesdropping.

[0043] In order to effectively counteract the presence of eavesdropping signals, it is necessary to generate and transmit anti-interference signals for each extracted potential eavesdropping signal frequency. Anti-interference signals are generated by specific mathematical models and physical methods, corresponding to the frequencies of potential eavesdropping signals. These anti-interference signals are designed to interfere with or mask the signals received by the eavesdropping device by superimposing them on the frequencies of the eavesdropping signals, thereby reducing their effectiveness or rendering them ineffective.

[0044] Specifically, the generated anti-interference signal may be: Among them, the is the anti-interference signal corresponding to the jth potential eavesdropping signal frequency, The amplitude of the anti-jamming signal can be dynamically adjusted to ensure that the coverage and strength are sufficient to interfere with the reception of the eavesdropping signal. Appropriate amplitude design can optimize the signal countermeasure effect and prevent signal loss in the target area. is the jth potential eavesdropping signal frequency. Each anti-interference signal corresponds to a specific eavesdropping signal frequency one by one, and can accurately counter eavesdropping signals of specific frequencies. The selection of frequencies is based on the potential eavesdropping signal frequencies extracted from the analysis, so that the anti-interference signal can play the maximum effect in the corresponding frequency band. The phase is the phase. By adjusting the phase, the waveform of the anti-interference signal can be controlled so that the best interference effect can be achieved under specific receiving conditions. The phase adjustment helps to optimize the interference effect between the interference signal and the wiretap signal. The introduction of random noise can increase the complexity of the anti-interference signal, making the signal decoding process of the eavesdropping device difficult. This randomness can improve the effectiveness of the anti-interference signal and prevent it from being identified or parsed by the eavesdropping device, thereby further enhancing information protection. Through the reasonable design and adjustment of these parameters, the anti-interference signal can form an effective barrier to potential eavesdropping signals and ensure the information security in the target area.

[0045] The design of the anti-jamming signal aims to generate a complex waveform by matching the frequency of the potential eavesdropping signal, so that the signal can effectively counter the receiving ability of the eavesdropping device. This design not only enhances the coverage of the signal, but also ensures the diversity of the signal, making it more difficult for the eavesdropping equipment to interpret the signal. By introducing changes in phase, amplitude and noise, the eavesdropping device can be effectively confused, thereby achieving the purpose of protecting information.

[0046] The principle of anti-interference signal is to utilize the interference effect and the principle of volatility. When the anti-interference signal overlaps with the eavesdropping signal in space, the waveforms of the two will interfere with each other, which can offset each other or significantly reduce the effect of one side. By creating a strong interference field relative to the frequency of the eavesdropping signal, the eavesdropping device cannot effectively resolve the clear signal, thereby improving the effectiveness of protection. This strategy allows the system to maintain the privacy of information even in an environment with multiple eavesdropping devices.

[0047] By generating and transmitting multi-band anti-interference signals, comprehensive coverage of anti-eavesdropping signals can be achieved in the entire target area. This not only effectively prevents eavesdropping, but also provides an active information protection mechanism to ensure that users' privacy and sensitive information are not leaked. In addition, the ability to dynamically generate anti-interference signals allows the system to adapt to different eavesdropping environments and signal interference situations, improving the flexibility and effectiveness of the anti-eavesdropping system.

[0048] It can be seen that multiple acoustic sensors are deployed in the target area to collect sound wave signals in real time and generate an acoustic data matrix; the sound wave characteristic entropy is calculated according to the acoustic data matrix, and based on the sound wave characteristic entropy, it is determined whether there is an eavesdropping signal; if there is an eavesdropping signal, the sound wave signal is subjected to spectrum analysis to extract potential eavesdropping signal frequencies; for each potential eavesdropping signal frequency, a corresponding anti-interference signal is generated and emitted to form a multi-band anti-interference signal, thereby achieving comprehensive coverage of active anti-eavesdropping, so that the sound wave signal can be monitored in real time and a multi-band anti-interference signal can be generated, so as to realize rapid identification and effective protection against eavesdropping behaviors, thereby significantly improving information security and privacy protection capabilities.

[0049] Another embodiment of the present invention provides an active anti-eavesdropping system. Figure 3 , the system may include: The collection module 301 is used to deploy multiple acoustic sensors in the target area, collect acoustic wave signals in real time and generate an acoustic data matrix; A judgment module 302 is used to calculate the acoustic wave characteristic entropy according to the acoustic data matrix, and judge whether there is a wiretap signal based on the acoustic wave characteristic entropy; The extraction module 303 is used to perform spectrum analysis on the sound wave signal to extract the potential frequency of the wiretap signal if there is a wiretap signal; The interference module 304 is used to generate and transmit a corresponding anti-interference signal for each potential eavesdropping signal frequency to form a multi-band anti-interference signal to achieve full coverage of active anti-eavesdropping.

[0050] It can be seen that multiple acoustic sensors are deployed in the target area to collect sound wave signals in real time and generate an acoustic data matrix; the sound wave characteristic entropy is calculated according to the acoustic data matrix, and based on the sound wave characteristic entropy, it is determined whether there is an eavesdropping signal; if there is an eavesdropping signal, the sound wave signal is subjected to spectrum analysis to extract potential eavesdropping signal frequencies; for each potential eavesdropping signal frequency, a corresponding anti-interference signal is generated and emitted to form a multi-band anti-interference signal, thereby achieving comprehensive coverage of active anti-eavesdropping, so that the sound wave signal can be monitored in real time and a multi-band anti-interference signal can be generated, so as to realize rapid identification and effective protection against eavesdropping behaviors, thereby significantly improving information security and privacy protection capabilities.

[0051] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0052] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, deploying multiple acoustic sensors in the target area to collect acoustic wave signals in real time and generate an acoustic data matrix; S202, calculating the acoustic wave characteristic entropy according to the acoustic data matrix, and judging whether there is a wiretap signal based on the acoustic wave characteristic entropy; S203, if there is a wiretap signal, perform spectrum analysis on the sound wave signal to extract the potential wiretap signal frequency; S204, for each potential eavesdropping signal frequency, a corresponding anti-interference signal is generated and transmitted to form a multi-band anti-interference signal to achieve full coverage of active anti-eavesdropping.

[0053] It can be seen that multiple acoustic sensors are deployed in the target area to collect sound wave signals in real time and generate an acoustic data matrix; the sound wave characteristic entropy is calculated according to the acoustic data matrix, and based on the sound wave characteristic entropy, it is determined whether there is an eavesdropping signal; if there is an eavesdropping signal, the sound wave signal is subjected to spectrum analysis to extract potential eavesdropping signal frequencies; for each potential eavesdropping signal frequency, a corresponding anti-interference signal is generated and emitted to form a multi-band anti-interference signal, thereby achieving comprehensive coverage of active anti-eavesdropping, so that the sound wave signal can be monitored in real time and a multi-band anti-interference signal can be generated, so as to realize rapid identification and effective protection against eavesdropping behaviors, thereby significantly improving information security and privacy protection capabilities.

[0054] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0055] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0056] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, deploying multiple acoustic sensors in the target area to collect acoustic wave signals in real time and generate an acoustic data matrix; S202, calculating the acoustic wave characteristic entropy according to the acoustic data matrix, and judging whether there is a wiretap signal based on the acoustic wave characteristic entropy; S203, if there is a wiretap signal, perform spectrum analysis on the sound wave signal to extract the potential wiretap signal frequency; S204, for each potential eavesdropping signal frequency, a corresponding anti-interference signal is generated and transmitted to form a multi-band anti-interference signal to achieve full coverage of active anti-eavesdropping.

[0057] It can be seen that multiple acoustic sensors are deployed in the target area to collect sound wave signals in real time and generate an acoustic data matrix; the sound wave characteristic entropy is calculated according to the acoustic data matrix, and based on the sound wave characteristic entropy, it is determined whether there is an eavesdropping signal; if there is an eavesdropping signal, the sound wave signal is subjected to spectrum analysis to extract potential eavesdropping signal frequencies; for each potential eavesdropping signal frequency, a corresponding anti-interference signal is generated and emitted to form a multi-band anti-interference signal, thereby achieving comprehensive coverage of active anti-eavesdropping, so that the sound wave signal can be monitored in real time and a multi-band anti-interference signal can be generated, so as to realize rapid identification and effective protection against eavesdropping behaviors, thereby significantly improving information security and privacy protection capabilities.

[0058] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. An active anti-eavesdropping method, characterized in that: The method comprises: Deploy multiple acoustic sensors in the target area to collect acoustic signals in real time and generate an acoustic data matrix; Calculating the acoustic wave characteristic entropy according to the acoustic data matrix, and judging whether there is a wiretap signal based on the acoustic wave characteristic entropy; If there is an eavesdropping signal, perform spectrum analysis on the sound wave signal to extract the potential eavesdropping signal frequency; For each potential eavesdropping signal frequency, a corresponding anti-interference signal is generated and transmitted to form a multi-band anti-interference signal to achieve comprehensive coverage of active anti-eavesdropping.

2. The method according to claim 1, characterized in that The step of calculating the acoustic wave characteristic entropy according to the acoustic data matrix comprises: Extract the frequency characteristics of the acoustic signal from the acoustic data matrix and calculate the amplitude spectrum of different frequency components; The frequency range of the calculated amplitude spectrum is divided into several frequency intervals, and the amplitude value in each frequency interval is counted; The probability distribution of each frequency interval is calculated as: Among them, the is the ith frequency interval The probability distribution of is the sum of the amplitude values ​​falling into the i-th frequency interval, is the sum of the amplitude values ​​of all frequency intervals; According to the probability distribution of each frequency interval, the sound wave characteristic entropy is calculated as: Wherein, n is the number of frequency intervals.

3. The method according to claim 2, characterized in that The determining whether there is a potential eavesdropping signal based on the acoustic wave characteristic entropy includes: It is determined whether the acoustic wave characteristic entropy is less than a preset threshold value. If it is less than the preset threshold value, it is determined that there is a potential eavesdropping signal.

4. The method according to claim 3, characterized in that The anti-interference signal is: Among them, the is the anti-interference signal corresponding to the jth potential eavesdropping signal frequency, is a dynamically adjustable amplitude, the is the jth potential eavesdropping signal frequency, For the phase, is random noise, For time.

5. An active anti-eavesdropping system, characterized in that: The system comprises: A collection module is used to deploy multiple acoustic sensors in the target area to collect acoustic wave signals in real time and generate an acoustic data matrix; A judgment module, used for calculating the acoustic wave characteristic entropy according to the acoustic data matrix, and judging whether there is a wiretap signal based on the acoustic wave characteristic entropy; An extraction module is used to perform spectrum analysis on the acoustic signal and extract the potential frequency of the eavesdropping signal if there is one; The interference module is used to generate and transmit corresponding anti-interference signals for each potential eavesdropping signal frequency to form a multi-band anti-interference signal to achieve comprehensive coverage of active anti-eavesdropping.

6. The system according to claim 5, characterized in that The judgment module is specifically used for: Extract the frequency characteristics of the acoustic signal from the acoustic data matrix and calculate the amplitude spectrum of different frequency components; The frequency range of the calculated amplitude spectrum is divided into several frequency intervals, and the amplitude value in each frequency interval is counted; The probability distribution of each frequency interval is calculated as: Among them, the is the ith frequency interval The probability distribution of is the sum of the amplitude values ​​falling into the i-th frequency interval, is the sum of the amplitude values ​​of all frequency intervals; According to the probability distribution of each frequency interval, the sound wave characteristic entropy is calculated as: Wherein, n is the number of frequency intervals.

7. The system according to claim 6, characterized in that The judgment module is specifically used for: It is determined whether the acoustic wave characteristic entropy is less than a preset threshold value. If it is less than the preset threshold value, it is determined that there is a potential eavesdropping signal.

8. The system according to claim 7, characterized in that The anti-interference signal is: Among them, the is the anti-interference signal corresponding to the jth potential eavesdropping signal frequency, is a dynamically adjustable amplitude, the is the jth potential eavesdropping signal frequency, For the phase, is random noise, For time.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

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