Active anti-eavesdropping method and system

Abstract

The application discloses an active eavesdropping prevention method and system, the method comprising: deploying multiple acoustic sensors in a target area, collecting acoustic signals in real time and generating an acoustic data matrix; calculating acoustic feature entropy according to the acoustic data matrix, and judging whether there is an eavesdropping signal based on the acoustic feature entropy; if there is an eavesdropping signal, performing frequency spectrum analysis on the acoustic signal, and extracting potential eavesdropping signal frequencies; for each potential eavesdropping signal frequency, generating and emitting a corresponding anti-interference signal to form a multi-frequency anti-interference signal, and realizing comprehensive coverage of active eavesdropping prevention. By using the embodiment of the application, real-time monitoring of acoustic signals and generation of multi-frequency anti-interference signals can be realized, rapid identification and effective protection of eavesdropping behavior can be realized, and therefore the information security and privacy protection capability can be significantly improved.

Description

Technical Field

[0001] This invention belongs to the field of signal processing technology, specifically an active anti-eavesdropping method and system. Background Technology

[0002] Currently, existing passive anti-eavesdropping technologies typically detect the presence of eavesdropping devices by monitoring abnormal noise or signals in the acoustic environment. However, this method often suffers from delays and struggles to accurately capture potential eavesdropping signals. The increasing prevalence of smart devices and software has led to more intelligent eavesdropping techniques, rendering traditional passive protection measures inadequate. Summary of the Invention

[0003] The purpose of this invention is to provide an active anti-eavesdropping method and system to overcome the shortcomings of the prior art. It can quickly identify and effectively protect against eavesdropping by monitoring sound wave signals in real time and generating multi-band anti-interference signals, thereby significantly improving information security and privacy protection capabilities.

[0004] One embodiment of this application provides an active anti-eavesdropping method, the method comprising:

[0005] Multiple acoustic sensors are deployed in the target area to collect acoustic signals in real time and generate an acoustic data matrix;

[0006] The acoustic wave feature entropy is calculated based on the acoustic data matrix, and the presence of eavesdropping signals is determined based on the acoustic wave feature entropy.

[0007] If eavesdropping signals are present, perform spectral analysis on the sound wave signals to extract the frequencies of potential eavesdropping signals;

[0008] For each potential eavesdropping signal frequency, a corresponding anti-interference signal is generated and transmitted to form a multi-band anti-interference signal, achieving comprehensive coverage of active anti-eavesdropping.

[0009] Optionally, calculating the acoustic wave feature entropy based on the acoustic data matrix includes:

[0010] Frequency features of acoustic signals are extracted from acoustic data matrices, and amplitude spectra of different frequency components are calculated.

[0011] The calculated amplitude spectrum frequency range is divided into several frequency intervals, and the amplitude value in each frequency interval is statistically analyzed.

[0012] The probability distribution for each frequency interval is calculated as follows:

[0013] Among them, the For the i-th frequency interval The probability distribution, the a sum of amplitude values falling into the i-th frequency interval, the sum of amplitude values falling into the i-th frequency interval is calculated as: a sum of amplitude values falling into the i-th frequency interval, the sum of amplitude values falling into the i-th frequency interval is calculated as:

[0014] According to the probability distribution of each frequency interval, the acoustic wave feature entropy is calculated as:

[0015] Wherein, n is the number of frequency intervals.

[0016] Optionally, the judgment of whether there is a potential eavesdropping signal based on the acoustic wave feature entropy comprises:

[0017] Judging whether the acoustic wave feature entropy is less than a preset threshold value, if less than the preset threshold value, judging that there is a potential eavesdropping signal.

[0018] Optionally, the anti-interference signal is:

[0019] Wherein, the is the anti-interference signal corresponding to the j-th potential eavesdropping signal frequency, the is a dynamically adjustable amplitude, the is the j-th potential eavesdropping signal frequency, the is a phase, the is random noise, the is time.

[0020] Another embodiment of the application provides an active anti-eavesdropping system, the system comprises:

[0021] A collection module for deploying a plurality of acoustic sensors in a target area, collecting acoustic wave signals in real time and generating an acoustic data matrix;

[0022] A judgment module for calculating the acoustic wave feature entropy according to the acoustic data matrix, and judging whether there is an eavesdropping signal based on the acoustic wave feature entropy;

[0023] An extraction module for performing spectral analysis on the acoustic wave signal if there is an eavesdropping signal, and extracting potential eavesdropping signal frequencies;

[0024] An interference module for generating and emitting corresponding anti-interference signals for each potential eavesdropping signal frequency to form a multi-band anti-interference signal, so as to realize comprehensive coverage of active anti-eavesdropping.

[0025] Another embodiment of the application provides a storage medium, the storage medium stores a computer program, wherein the computer program is set to execute the method described in any one of the above embodiments when running.

[0026] Still another embodiment of the present application provides an electronic device comprising a memory having a computer program stored therein and a processor configured to execute the computer program to perform the method described in any of the above.

[0027] Compared with the prior art, the active eavesdropping prevention method provided by the present application can realize comprehensive coverage of active eavesdropping prevention by deploying multiple acoustic sensors in a target area, collecting acoustic wave signals in real time and generating an acoustic data matrix, calculating acoustic wave feature entropy according to the acoustic data matrix, judging whether there is an eavesdropping signal based on the acoustic wave feature entropy, performing frequency spectrum analysis on the acoustic wave signals if there is an eavesdropping signal, extracting potential eavesdropping signal frequencies, generating and emitting corresponding anti-interference signals for each potential eavesdropping signal frequency to form a multi-band anti-interference signal, and realizing rapid identification and effective protection of eavesdropping behavior, thereby significantly improving information security and privacy protection capability. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 A hardware structure block diagram of a computer terminal of an active eavesdropping prevention method provided by the embodiment of the present application is shown in

[0029] Figure 2 A flowchart of an active eavesdropping prevention method provided by the embodiment of the present application is shown in

[0030] Figure 3 A structure diagram of an active eavesdropping prevention system provided by the embodiment of the present application is shown in DETAILED DESCRIPTION

[0031] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be explained as a limitation of the present application.

[0032] The embodiment of the present application first provides an active eavesdropping prevention method, which can be applied to an electronic device, such as a computer terminal, specifically, a general computer, etc.

[0033] The following will be described in detail taking a computer terminal as an example. Figure 1 A hardware structure block diagram of a computer terminal of an active eavesdropping prevention method provided by the embodiment of the present application is shown in Figure 1 As shown in the figure, the computer terminal can include one or more ( Figure 1The computer terminal shown in FIG. 1 includes only one processor 102 (the processor 102 can 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 can further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art can understand that Figure 1 The structure shown in FIG. 1 is only illustrative and does not limit the structure of the computer terminal. For example, the computer terminal can include more or fewer components than those shown in FIG. 1, or have a different configuration than that shown in FIG. 1. Figure 1 Figure 1 The structure shown in FIG. 1 is only illustrative and does not limit the structure of the computer terminal. For example, the computer terminal can include more or fewer components than those shown in FIG. 1, or have a different configuration than that shown in FIG. 1.

[0034] The memory 104 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the active eavesdropping prevention method in the embodiments 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, i.e., implements the method described above. The memory 104 can include a high-speed random access memory, and can further include a non-volatile memory such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory remotely disposed with respect to the processor 102, which can be connected to the computer terminal through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0035] The transmission device 106 is used to receive or send data via a network. Specific examples of the network can include a wireless network provided by a communication provider of the 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 to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet in a wireless manner.

[0036] Referring to Figure 2 The embodiments of the present application provide an active eavesdropping prevention method, which can include the following steps:

[0037] S201, deploying a plurality of acoustic sensors in a target area, collecting acoustic wave signals in real time and generating an acoustic data matrix;

[0038] ​Deploying multiple acoustic sensors in the target area, collecting acoustic signals in real time and generating acoustic data matrix is the first step and the basic step of this active eavesdropping prevention 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 wide frequency response capability, which can accurately capture weak acoustic signals in different environmental conditions, including audio that may come from eavesdropping devices.

[0039] By deploying multiple acoustic sensors, the overall acoustic monitoring capability of the target area is improved. Real-time generation of acoustic data matrix makes the capture of acoustic signals more comprehensive and detailed, providing accurate data basis for subsequent analysis. This process not only helps to discover eavesdropping behavior in time, but also provides a large amount of acoustic feature data for system analysis, and then judges the existence of secret signals through feature entropy calculation, thereby enhancing the protection capability of information security. One implementation can include:

[0040] Step 1: Determine sensor deployment layout Based on the size, shape and acoustic environment characteristics of the target area, use mathematical modeling methods (such as Voronoi diagram or Delaunay triangulation) to design the best deployment scheme of acoustic sensors. By simulating the acoustic propagation characteristics under different sensor layouts, determine the optimal sensor position to ensure signal coverage.

[0041] Step 2: Select appropriate acoustic sensors After determining the layout, select acoustic sensors suitable for the target environment, considering their sensitivity, frequency range and noise rejection capability. At the same time, ensure that the selected sensors can form a network to support real-time data transmission and sharing.

[0042] Step 3: Real-time signal acquisition In the target area, start the acoustic sensors and collect acoustic signals in real time. Each sensor should be equipped with a digital signal processing module to ensure that they can immediately digitize the collected analog signals for subsequent fusion and analysis.

[0043] Step 4: Data matrix generation Perform time and space synchronization processing on the acoustic signals collected by multiple sensors. According to the coordinate information of the sensors, assign each collected acoustic signal to the corresponding coordinate position to form an acoustic data matrix with time and space 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.

[0044] Step 5: Dynamic updating mechanism To consider the possible dynamic changes in the target area (such as human flow, equipment noise, etc.), a dynamic updating 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, ensuring the accuracy and timeliness of the data.

[0045] Step 6: Multi-sensor fusion technology Use multi-sensor fusion technology (such as Kalman filtering or particle filtering) for data processing, combine and denoise the sound wave signals from different sensors to improve signal quality and reliability. Through the analysis of the fusion data, a three-dimensional sound wave environment model is constructed, laying a solid foundation for subsequent sound wave feature entropy calculation.

[0046] Through the above steps, a systematic acoustic monitoring network can be formed to collect and analyze sound wave signals in the target area in real time, laying a foundation for subsequent judgment and interference signal generation. This complex implementation not only improves the intelligence level of the system, but also enhances the reliability and effectiveness of the anti-eavesdropping effect.

[0047] S202, calculating the sound wave feature entropy according to the acoustic data matrix, and judging whether there is an eavesdropping signal based on the sound wave feature entropy;

[0048] After obtaining the acoustic data matrix, the next step is to analyze these data in depth. Sound wave feature entropy is an index that measures the complexity and uncertainty of signals. By calculating the distribution of sound wave signals in different frequency intervals, a quantitative description of the overall variation of sound wave signals can be obtained. The lower the feature entropy, the higher the concentration of signal frequency components, which may indicate the presence of a single source of interference signal, such as an eavesdropping device.

[0049] By calculating the sound wave feature entropy, it can be quickly judged whether the sound wave signal in the environment is normal, and then the potential eavesdropping behavior is identified. If the feature entropy value changes abnormally, it may indicate the presence of unauthorized sound wave signals, which can take timely protective measures to enhance information security and provide necessary support for protecting user privacy and important information.

[0050] Specifically, the frequency characteristics of the sound wave signal can be extracted from the acoustic data matrix, and the amplitude spectrum of different frequency components can be calculated.

[0051] This step aims to analyze the sound wave signal components in the acoustic data matrix. Through spectral analysis methods such as Fourier transform, the frequency characteristics and corresponding amplitude spectrum information of the signal are extracted. By calculating the amplitude spectrum, the intensity distribution of the sound wave signal in each frequency interval can be clearly understood, which helps to identify the possible eavesdropping signal frequency.

[0052] The calculated amplitude spectrum frequency range is divided into several frequency intervals, and the amplitude value in each frequency interval is statistically analyzed.

[0053] This step subdivides the amplitude spectrum into multiple frequency intervals to facilitate more in-depth statistical analysis. For example, the frequency range from 0Hz to 1000Hz is divided into 10 equal intervals. The amplitude within each frequency interval is statistically analyzed to form a probability distribution of frequency characteristics. This division of frequency intervals provides the foundational data for subsequent acoustic wave characteristic entropy calculations, helping to capture subtle changes in the acoustic signal and enhancing the detection capability of eavesdropping signals.

[0054] The probability distribution for each frequency interval is calculated as follows:

[0055] Among them, the For the i-th frequency interval The probability distribution, the The sum of amplitude values ​​falling within the i-th frequency interval, the This is the sum of the amplitude values ​​across all frequency ranges.

[0056] This step calculates the probability distribution of each frequency interval by statistically analyzing the amplitude values ​​within that interval. This probability distribution provides the necessary input data for calculating the acoustic wave characteristic entropy, and helps identify signal complexity and anomalies in subsequent steps.

[0057] Based on the probability distribution of each frequency interval, the characteristic entropy of the sound wave is calculated as follows:

[0058] Here, n represents the number of frequency intervals. By calculating the probability distribution of each frequency interval, the value of the acoustic wave characteristic entropy is obtained using the formula for information entropy. The calculation of acoustic wave characteristic entropy can quantify the complexity of the signal, providing an important basis for subsequent eavesdropping signal identification.

[0059] Specifically, it can be determined whether the sound wave feature entropy is less than a preset threshold. If it is less than the preset threshold, it is determined that there is a potential eavesdropping signal.

[0060] After calculating the acoustic wave characteristic entropy, this step compares it with a preset threshold to determine whether the acoustic signal contains potential eavesdropping activity. The set threshold is usually based on empirical values ​​or historical data; an entropy value below this threshold may indicate that the acoustic signal has become too concentrated, suggesting the presence of potential interference or eavesdropping signals.

[0061] A low value of acoustic wave characteristic entropy usually means that the frequency distribution of the signal is relatively concentrated, which may indicate the existence of a single source of the signal, such as audio signals from eavesdropping devices. This logic is based on the basic principle of information theory: if the complexity of the signal decreases, it means that it is likely emitted by a specific device.

[0062] The main role of this judgment process is to identify potential eavesdropping signals in a timely manner, thereby providing immediate warnings and protective measures for users. In this way, the information security protection capability can be effectively improved, and the privacy and important information of users can be protected from being violated.

[0063] S203, if there is an eavesdropping signal, performing frequency spectrum analysis on the sound wave signal to extract the potential eavesdropping signal frequency;

[0064] In the case of judging that there is an eavesdropping signal, the purpose of frequency spectrum analysis is to further analyze the frequency components of the sound wave signal to determine the possible eavesdropping signal source. The frequency spectrum analysis technique can effectively identify the components of different frequencies in the signal and extract their amplitude information, which is crucial for locating potential eavesdropping signals. By using frequency spectrum analysis, complex sound wave signals can be converted into clear frequency information, so that the frequency of potential eavesdropping signals can be identified and extracted. Significance:

[0065] By deeply analyzing the frequency components of the sound wave signal, the frequency of the potential eavesdropping signal can be accurately identified, thereby providing specific frequency reference for subsequent anti-interference signal generation. This identification can help the security system take more effective action, enhance information protection, and ensure that the privacy of users is not violated. One implementation can include:

[0066] Step 1: Preprocessing sound wave signal Before performing frequency spectrum analysis, the collected sound wave signal needs to be preprocessed. This includes steps such as noise removal, 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.

[0067] Step 2: Fast Fourier Transform (FFT) Apply Fast Fourier Transform (FFT) to the preprocessed sound wave signal to convert time-domain signal to frequency-domain signal. FFT can effectively and quickly calculate the frequency spectrum of the signal, generating amplitude spectrum and phase spectrum of the frequency components. This conversion makes the amplitude information of different frequency components visible.

[0068] Step 3: Generate frequency distribution chart Convert the output of FFT to a frequency distribution chart to identify each frequency component in the signal and its corresponding amplitude. By analyzing the frequency distribution chart, it can be determined which frequency components in the signal have high intensity and may indicate the presence of eavesdropping devices.

[0069] Step 4: Set frequency analysis threshold Based on the characteristics of normal sound wave signals, set a dynamic frequency analysis threshold. This threshold can be set through historical data or machine learning algorithms (such as clustering analysis) to ensure that frequency components with intensity exceeding the threshold in the frequency spectrum are identified as potential eavesdropping signals.

[0070] Step 5: Frequency component sorting and selection Sort the frequency components in the frequency distribution whose amplitude values are higher than the threshold value. Select the top N frequencies with the highest amplitude values as potential eavesdropping signal frequencies. This selection can be further confirmed by calculating the energy spectral density to determine its credibility.

[0071] Step 6: Signal feature verification By verifying the features of the selected potential signal frequencies, check their similarity with the time-domain waveform of known eavesdropping device signals. This can be achieved through matching algorithms such as correlation coefficient calculation to ensure that the extracted frequencies indeed correspond to possible eavesdropping sources.

[0072] Step 7: Signal recording and feedback Finally, record the extracted potential eavesdropping signal frequencies in the system database and feed them back to the anti-eavesdropping system for subsequent generation and transmission of anti-interference signals. This recording mechanism also facilitates subsequent data analysis and model optimization.

[0073] Through the above steps, potential eavesdropping signal frequencies can be effectively extracted, providing strong data support and technical foundation for active anti-eavesdropping. This implementation not only improves the accuracy of eavesdropping signal detection, but also provides a practical method for information security protection.

[0074] S204, for each potential eavesdropping signal frequency, generate and transmit corresponding anti-interference signals to form multi-band anti-interference signals, achieving comprehensive coverage of active anti-eavesdropping.

[0075] In the case of confirming the existence of eavesdropping signals, in order to effectively counter these signals, anti-interference signals need to be generated and transmitted for each extracted potential eavesdropping signal frequency. Anti-interference signals are generated through specific mathematical models and physical methods, corresponding to potential eavesdropping signal frequencies. These anti-interference signals aim to interfere or mask the signals received by eavesdropping devices by superimposing their frequencies, reducing their effectiveness or rendering them ineffective.

[0076] Specifically, the generated anti-interference signals can be:

[0077] wherein the is the anti-interference signal corresponding to the jth potential eavesdropping signal frequency, the is a dynamically adjustable amplitude, and the amplitude of the anti-interference signal can be dynamically adjusted to ensure that the coverage range and strength are sufficient to interfere with the reception of eavesdropping signals. Proper amplitude design can optimize the signal countermeasure effect and prevent signal loss in the target area. The For the jth potential eavesdropping signal frequency, each anti-interference signal corresponds to a specific eavesdropping signal frequency, and can accurately counter the eavesdropping signal of a specific frequency. The selection of the frequency is based on the potential eavesdropping signal frequency extracted in the analysis, so that the anti-interference signal can have the maximum effect in the corresponding frequency band. The For the phase, by adjusting the phase, the waveform of the anti-interference signal can be controlled to achieve the best interference effect under certain receiving conditions. The adjustment of the phase helps to optimize the interference effect between the interference signal and the eavesdropping signal. The For the random noise, 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 enhance the effectiveness of the anti-interference signal, preventing it from being identified or analyzed by the eavesdropping device, thereby further enhancing information protection. The For the time. Through the reasonable design and adjustment of these parameters, the anti-interference signal can form an effective barrier to potential eavesdropping signals, ensuring the safety of information in the target area.

[0078] The design of the anti-interference signal aims to generate a complex waveform by matching the potential eavesdropping signal frequency, 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, increasing the difficulty of analyzing the signal by the eavesdropping equipment. By introducing changes in phase, amplitude and noise, the eavesdropping device can be effectively confused, thereby achieving the purpose of protecting information.

[0079] The principle of the anti-interference signal lies in the use of interference effect and wave fluctuation principle. When the anti-interference signal and the eavesdropping signal overlap in space, the waveforms of the two will form interference, which can cancel each other out or significantly reduce the effect of one side. By creating a strong interference field relative to the eavesdropping signal frequency, the eavesdropping device cannot effectively analyze the clear signal, thereby improving the effectiveness of protection. This strategy makes it possible for the system to maintain the privacy of information even in an environment with multiple eavesdropping devices.

[0080] By generating and transmitting multi-frequency anti-interference signals, comprehensive coverage of eavesdropping signals in the entire target area can be achieved. This not only effectively prevents eavesdropping behavior, but also provides an active information protection mechanism to ensure the privacy and sensitive information of users are not leaked. In addition, the ability to dynamically generate anti-interference signals enables the system to adapt to different eavesdropping environments and signal interference conditions, improving the flexibility and effectiveness of the anti-eavesdropping system.

[0081] It can be seen that multiple acoustic sensors are deployed in the target area, real-time acoustic wave signals are collected, and an acoustic data matrix is generated; the acoustic wave feature entropy is calculated according to the acoustic data matrix, and it is judged whether there is an eavesdropping signal based on the acoustic wave feature entropy; if there is an eavesdropping signal, the frequency spectrum of the acoustic wave signal is analyzed, and the potential eavesdropping signal frequency is extracted; for each potential eavesdropping signal frequency, a corresponding anti-interference signal is generated and emitted to form a multi-band anti-interference signal, and comprehensive coverage of active anti-eavesdropping is realized, so that real-time monitoring of acoustic wave signals and generation of multi-band anti-interference signals can be realized to quickly identify and effectively protect eavesdropping behavior, thereby significantly improving information security and privacy protection capabilities.

[0082] Another embodiment of the application provides an active anti-eavesdropping system, see Figure 3 , which can include:

[0083] The collection module 301 is configured to deploy multiple acoustic sensors in the target area, collect real-time acoustic wave signals, and generate an acoustic data matrix;

[0084] The judgment module 302 is configured to calculate the acoustic wave feature entropy according to the acoustic data matrix, and judge whether there is an eavesdropping signal based on the acoustic wave feature entropy;

[0085] The extraction module 303 is configured to analyze the frequency spectrum of the acoustic wave signal if there is an eavesdropping signal, and extract the potential eavesdropping signal frequency;

[0086] The interference module 304 is configured to generate and emit a corresponding anti-interference signal for each potential eavesdropping signal frequency to form a multi-band anti-interference signal, and realize comprehensive coverage of active anti-eavesdropping.

[0087] It can be seen that multiple acoustic sensors are deployed in the target area, real-time acoustic wave signals are collected, and an acoustic data matrix is generated; the acoustic wave feature entropy is calculated according to the acoustic data matrix, and it is judged whether there is an eavesdropping signal based on the acoustic wave feature entropy; if there is an eavesdropping signal, the frequency spectrum of the acoustic wave signal is analyzed, and the potential eavesdropping signal frequency is extracted; for each potential eavesdropping signal frequency, a corresponding anti-interference signal is generated and emitted to form a multi-band anti-interference signal, and comprehensive coverage of active anti-eavesdropping is realized, so that real-time monitoring of acoustic wave signals and generation of multi-band anti-interference signals can be realized to quickly identify and effectively protect eavesdropping behavior, thereby significantly improving information security and privacy protection capabilities.

[0088] The embodiment of the application also provides a storage medium, and the storage medium stores a computer program, wherein the computer program is set to execute the steps in any one of the method embodiments.

[0089] Specifically, in the present embodiment, the storage medium described above can be configured to store a computer program for executing the following steps:

[0090] S201, deploying a plurality of acoustic sensors in a target area, collecting acoustic signals in real time and generating an acoustic data matrix;

[0091] S202, calculating acoustic feature entropy according to the acoustic data matrix, and judging whether there is an eavesdropping signal based on the acoustic feature entropy;

[0092] S203, if there is an eavesdropping signal, performing spectral analysis on the acoustic signal, and extracting a potential eavesdropping signal frequency;

[0093] S204, for each potential eavesdropping signal frequency, generating and transmitting a corresponding anti-interference signal to form a multi-band anti-interference signal, and realizing comprehensive coverage of active anti-eavesdropping.

[0094] It can be seen that, by deploying a plurality of acoustic sensors in a target area, collecting acoustic signals in real time and generating an acoustic data matrix, calculating acoustic feature entropy according to the acoustic data matrix, judging whether there is an eavesdropping signal based on the acoustic feature entropy, if there is an eavesdropping signal, performing spectral analysis on the acoustic signal, and extracting a potential eavesdropping signal frequency, for each potential eavesdropping signal frequency, generating and transmitting a corresponding anti-interference signal to form a multi-band anti-interference signal, and realizing comprehensive coverage of active anti-eavesdropping, the eavesdropping behavior can be quickly identified and effectively protected by monitoring the acoustic signal in real time and generating a multi-band anti-interference signal, thereby significantly improving the information security and privacy protection capability.

[0095] The embodiment of the present application also provides an electronic device comprising a memory and a processor, the memory storing a computer program, and the processor being configured to run the computer program to execute the steps in any one of the method embodiments.

[0096] Specifically, the electronic device described above can further comprise 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.

[0097] Specifically, in the present embodiment, the processor described above can be configured to execute the following steps through the computer program:

[0098] S201, deploying a plurality of acoustic sensors in a target area, collecting acoustic signals in real time and generating an acoustic data matrix;

[0099] S202, calculating acoustic feature entropy according to the acoustic data matrix, and judging whether there is an eavesdropping signal based on the acoustic feature entropy;

[0100] S203, if there is an eavesdropping signal, performing spectrum analysis on the sound wave signal to extract potential eavesdropping signal frequency;

[0101] S204, for each potential eavesdropping signal frequency, generating and transmitting a corresponding anti-interference signal to constitute a multi-band anti-interference signal, realizing comprehensive coverage of active anti-eavesdropping.

[0102] It can be seen that by deploying multiple acoustic sensors in the target area, real-time collection of sound wave signals and generation of acoustic data matrix are realized; sound wave feature entropy is calculated according to the acoustic data matrix, and whether there is an eavesdropping signal is judged based on the sound wave feature entropy; if there is an eavesdropping signal, spectrum analysis is performed on the sound wave signal to extract potential eavesdropping signal frequency; for each potential eavesdropping signal frequency, a corresponding anti-interference signal is generated and transmitted to constitute a multi-band anti-interference signal, realizing comprehensive coverage of active anti-eavesdropping, so that through real-time monitoring of sound wave signals and generation of multi-band anti-interference signals, rapid identification and effective protection of eavesdropping behavior are realized, thereby significantly improving information security and privacy protection capability.

[0103] The above describes the structure, features and effects of the present application in detail according to the embodiments shown in the drawings. The above description is only the preferred embodiments of the present application, but the present application is not limited to the embodiments shown in the drawings. Any changes or modifications made in accordance with the concept of the present application, or equivalent embodiments with equivalent changes, are still within the scope of the present application.

Claims

1. An active eavesdropping prevention method characterized by comprising: The method comprises: deploying a plurality of acoustic sensors in a target area, collecting acoustic wave signals in real time and generating an acoustic data matrix; calculating acoustic wave feature entropy according to the acoustic data matrix, and judging whether there is eavesdropping signal based on the acoustic wave feature entropy; wherein the calculation of the acoustic wave feature entropy according to the acoustic data matrix comprises: extracting the frequency characteristics of the acoustic wave signals from the acoustic data matrix, calculating the amplitude spectrum of different frequency components; dividing the frequency range of the calculated amplitude spectrum into a plurality of frequency intervals, and counting the amplitude values in each frequency interval; calculating the probability distribution of each frequency interval as: ； Among them, the For the i-th frequency interval The probability distribution, the The sum of amplitude values ​​falling within the i-th frequency interval, the The sum of amplitude values ​​across all frequency intervals; based on the probability distribution of each frequency interval, the characteristic entropy of the sound wave is calculated as follows: ； wherein n is the number of frequency intervals; the judgment of whether there is eavesdropping signal based on the acoustic wave feature entropy comprises: judging whether the acoustic wave feature entropy is less than a preset threshold value, and if it is less than the preset threshold value, judging that there is a potential eavesdropping signal; if there is an eavesdropping signal, performing spectral analysis on the acoustic wave signals to extract the potential eavesdropping signal frequency; for each potential eavesdropping signal frequency, generating and transmitting a corresponding anti-interference signal to form a multi-band anti-interference signal, so as to realize comprehensive coverage of active anti-eavesdropping, wherein the anti-interference signal is: ； Wherein, the is the anti-jamming signal corresponding to the jth potential eavesdropping signal frequency, the is a dynamically adjustable amplitude, the is the jth potential eavesdropping signal frequency, the is a phase, the is random noise, the is time.

2. An active anti-eavesdropping system, characterized by The system comprises: a collection module for deploying a plurality of acoustic sensors in a target area, collecting acoustic wave signals in real time and generating an acoustic data matrix; a judgment module for calculating acoustic wave feature entropy according to the acoustic data matrix, and judging whether there is eavesdropping signal based on the acoustic wave feature entropy; wherein the calculation of the acoustic wave feature entropy according to the acoustic data matrix comprises: extracting the frequency characteristics of the acoustic wave signals from the acoustic data matrix, calculating the amplitude spectrum of different frequency components; dividing the frequency range of the calculated amplitude spectrum into a plurality of frequency intervals, and counting the amplitude values in each frequency interval; calculating the probability distribution of each frequency interval as: ； Among them, the For the i-th frequency interval The probability distribution, the The sum of amplitude values ​​falling within the i-th frequency interval, the The sum of amplitude values ​​across all frequency intervals; based on the probability distribution of each frequency interval, the characteristic entropy of the sound wave is calculated as follows: ； wherein n is the number of frequency intervals; the judgment of whether there is eavesdropping signal based on the acoustic wave feature entropy comprises: judging whether the acoustic wave feature entropy is less than a preset threshold value, and if it is less than the preset threshold value, judging that there is a potential eavesdropping signal; an extraction module for performing spectral analysis on the acoustic wave signals to extract the potential eavesdropping signal frequency if there is an eavesdropping signal; an interference module for generating and transmitting a corresponding anti-interference signal for each potential eavesdropping signal frequency to form a multi-band anti-interference signal, so as to realize comprehensive coverage of active anti-eavesdropping, wherein the anti-interference signal is: ； Wherein, the is the anti-jamming signal corresponding to the jth potential eavesdropping signal frequency, the is a dynamically adjustable amplitude, the is the jth potential eavesdropping signal frequency, the is a phase, the is random noise, the is time.

3. A storage medium, characterized by The storage medium has a computer program stored therein, wherein the computer program is configured to execute the method of claim 1 when running.

4. An electronic device comprising a memory and a processor, characterized in that, The memory has a computer program stored therein, and the processor is configured to execute the computer program to execute the method of claim 1.

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