Anti-monitoring method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of mobile communication, and discloses an anti-monitoring method and device, computer equipment and a storage medium, and the method comprises the steps: collecting audio parameters in a target space when an anti-monitoring function is triggered; the audio parameters comprise environment audio information and human voice audio information; analyzing the audio parameters, and generating adaptive interference noise according to an analysis result; the interference noise is used for interfering monitoring in the current target space by the monitoring equipment. By collecting and analyzing the audio parameters in the target space, the acoustic characteristics of the current space can be accurately grasped, and adaptive interference noise is generated based on the accurate analysis, so that the monitoring of the monitoring equipment on the target space is efficiently interfered, and the success rate and effectiveness of anti-monitoring are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to an anti-eavesdropping method, device, computer equipment and storage medium. Background Art

[0002] In today's highly information-sensitive social environment, the risks of surveillance are becoming increasingly severe, whether in the areas of personal privacy protection, commercial secrets, national security, etc.

[0003] As various types of monitoring equipment continue to develop and become more covert, traditional anti-monitoring methods often have limitations and are difficult to cope with complex and changing monitoring scenarios. For example, some fixed-frequency jammers are not effective when facing intelligent, adaptive frequency monitoring devices, and are prone to cause unnecessary interference to normal communications and the environment. They are unable to accurately target the actual audio conditions in the target space and provide effective protection, making it difficult to protect people from the threat of illegal monitoring in different scenarios.

[0004] The above information is presented as background information only to assist with understanding the present disclosure and no determination or admission is made as to whether any of the above may be used as prior art with respect to the present disclosure. Summary of the invention

[0005] The present invention provides an anti-eavesdropping method, device, computer equipment and storage medium to solve the problems existing in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An adaptive anti-monitoring method, comprising:

[0008] When the anti-monitoring function is triggered, the audio parameters in the target space are collected; the audio parameters include environmental audio information and human voice audio information;

[0009] The audio parameters are analyzed, and corresponding interference noise is generated according to the analysis results; the interference noise is used to interfere with the monitoring of the monitoring device in the current target space.

[0010] Optionally, the adaptive anti-eavesdropping method includes:

[0011] Collecting voice information in the target space and analyzing whether there is a preset monitoring keyword;

[0012] When the preset monitoring keywords are present, the anti-monitoring function is automatically triggered.

[0013] Optionally, the adaptive anti-eavesdropping method further includes:

[0014] Capture the image of the person in the target space in real time through the camera and compare it with the specific person image in the database;

[0015] When a matching specific person image is identified, the mouth movement of the corresponding specific person is identified to determine whether the specific person is speaking;

[0016] When it is recognized that the specific person is speaking, the anti-monitoring function is automatically triggered.

[0017] Optionally, the adaptive anti-eavesdropping method further includes:

[0018] Continuously monitor the sound signals in the target space, analyze the timbre of the sound signals, and compare them with the sound characteristics of specific people in the database;

[0019] When a matching specific person's voice characteristics are recognized, the anti-monitoring function is automatically triggered.

[0020] Optionally, analyzing the audio parameters includes:

[0021] The environmental audio information and human voice audio information are analyzed from three dimensions: sound frequency, volume and intensity, and the energy proportion, loudness value and intensity value of the environmental audio and human voice audio in each frequency band are obtained respectively.

[0022] Optionally, generating an adaptive interference noise according to the analysis result includes:

[0023] According to the analysis results, the energy proportion parameters, loudness parameters and intensity parameters of the interference noise in each frequency band are allocated to generate interference noise.

[0024] Optionally, the generating of the matching interference noise according to the analysis result further includes:

[0025] The interference noise is made to randomly jump within a predetermined frequency band.

[0026] Optionally, causing the interference noise to randomly jump within a predetermined frequency band includes:

[0027] When the ambient audio and / or human voice audio are distributed in the low frequency band, the interference noise is randomly jumped within the low frequency band;

[0028] When the ambient audio and / or human voice audio are distributed in the middle frequency band, the interference noise is randomly jumped in the middle frequency band and in the transition frequency bands between the middle frequency band and the high frequency band and the low frequency band;

[0029] When the environmental audio and / or human voice audio are distributed in the high frequency band, the interference noise is randomly jumped within the high frequency band.

[0030] Optionally, analyzing the audio parameters and generating corresponding interference noise according to the analysis results includes:

[0031] Identify the human voice audio information, and when a special timbre exists, identify a characteristic frequency band of the special timbre;

[0032] According to the characteristic frequency band of the special timbre, interference noise with an inverse spectrum is generated.

[0033] The present invention also provides an anti-eavesdropping device, which is used to implement the adaptive anti-eavesdropping method as described in any one of the above items, comprising:

[0034] A signal collection unit, used to collect audio parameters in the target space when the anti-monitoring function is triggered; the audio parameters include environmental audio information and human voice audio information;

[0035] The analysis and processing unit is used to analyze the audio parameters and generate corresponding interference noise according to the analysis results; the interference noise is used to interfere with the monitoring of the monitoring device in the current target space.

[0036] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the adaptive anti-eavesdropping method as described in any one of the above items when executing the computer program.

[0037] The present invention also provides a storage medium comprising computer executable instructions, wherein the computer executable instructions are executed by a computer processor to implement the adaptive anti-eavesdropping method as described in any one of the above items.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention provides an anti-eavesdropping method, device, computer equipment and storage medium, which can accurately grasp the acoustic characteristics of the current space by collecting and analyzing audio parameters in the target space, and generate adaptive interference noise based on this accurate analysis, thereby effectively interfering with the monitoring of the target space by the monitoring equipment, greatly improving the success rate and effectiveness of anti-eavesdropping.

[0040] The present invention has other features and advantages, which will be apparent from or will be described in detail in the accompanying drawings and the following detailed description incorporated herein, which together serve to explain the specific principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 is a flow chart of an anti-eavesdropping method provided in Embodiment 1 of the present invention;

[0043] Figure 2 is a flow chart of a triggering mechanism in an anti-eavesdropping method provided in Embodiment 1 of the present invention;

[0044] Figure 3 is another flow chart of a trigger mechanism in an anti-eavesdropping method provided in Embodiment 1 of the present invention;

[0045] Figure 4 is another flow chart of a trigger mechanism in an anti-eavesdropping method provided in Embodiment 1 of the present invention;

[0046] Figure 5 is a flowchart of step S2 in an anti-eavesdropping method provided in Embodiment 1 of the present invention;

[0047] Figure 6 is another flow chart of step S2 in an anti-eavesdropping method provided in Embodiment 1 of the present invention;

[0048] Figure 7 is another flow chart of step S2 in the anti-eavesdropping method provided in the first embodiment of the present invention;

[0049] Figure 8 It is a structural block diagram of an anti-eavesdropping device provided in Embodiment 1 of the present invention.

[0050] Reference numerals: 10, signal acquisition unit; 20, analysis and processing unit. DETAILED DESCRIPTION

[0051] In order to explain in detail the possible application scenarios, technical principles, specific schemes that can be implemented, and the purposes and effects that can be achieved, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0052] Reference to "embodiment" herein means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The term "embodiment" appearing in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or association with other embodiments. In principle, in the present application, as long as there is no technical contradiction or conflict, the various technical features mentioned in the embodiments can be combined in any way to form a corresponding implementable technical solution.

[0053] Unless otherwise defined, the technical terms used in this document have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms in this document is only for describing specific embodiments and is not intended to limit this application.

[0054] In the description of this application, the term "and / or" is an expression used to describe the logical relationship between objects, indicating that three relationships may exist, for example, A and / or B, which means: A exists, B exists, and A and B exist at the same time. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" logical relationship.

[0055] In the present application, terms such as “first” and “second” are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship of quantity, priority or sequence between these entities or operations.

[0056] Without further limitations, in this application, the words "include", "comprises", "has" or other similar expressions used in the sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those limited elements, but also other elements not explicitly listed, or also include elements inherent to such process, method or product.

[0057] Similar to the understanding in the Examination Guidelines, in this application, expressions such as "greater than", "less than", "exceed" and the like are understood to exclude the number itself; expressions such as "above", "below", "within" and the like are understood to include the number itself. In addition, in the description of the embodiments of this application, "multiple" means more than two (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups", "multiple times", etc., unless otherwise clearly and specifically limited.

[0058] In the description of the embodiments of the present application, space-related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or position relationship based on the orientation or position relationship shown in the specific embodiments or drawings, and are only for the convenience of describing the specific embodiments of the present application or facilitating the reader's understanding, and do not indicate or imply that the referred device or component must have a specific position, a specific orientation, or be constructed or operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0059] Unless otherwise expressly specified or limited, in the description of the embodiments of the present application, the terms such as "install", "connect", "connect", "fix", "set", etc. used should be understood in a broad sense. For example, the "connection" can be a fixed connection, a detachable connection, or an integrated setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For technicians in the technical field to which the present application belongs, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances. Embodiment 1

[0060] Please refer to Figure 1 , an embodiment of the present invention provides an adaptive anti-eavesdropping method, comprising:

[0061] S1. When the anti-monitoring function is triggered, the audio parameters in the target space are collected.

[0062] The audio parameters include environmental audio information and human voice audio information.

[0063] S2. Analyze audio parameters and generate appropriate interference noise according to the analysis results.

[0064] The interference noise is used to interfere with the monitoring of the monitoring device in the current target space.

[0065] In this embodiment, when the anti-monitoring function is triggered, the audio parameters in the target space are fully collected based on the audio collection device. The audio parameters include environmental audio information and human voice audio information, where the environmental audio information includes the natural environment sound, equipment operation sound and other background sounds in the target space, and the human voice audio information focuses on the voice content of the people in the space.

[0066] For example, in a conference room scenario, the ambient audio may include the sound of an air conditioner running, a projector fan, etc., while the human voice audio is the discussion and speeches of the participants.

[0067] In practical applications, when generating interference noise, a jammer based on digital signal processing can be used. This jammer can accurately generate interference noise with specific spectrum, amplitude and phase characteristics based on the input audio parameters, adapt to the target audio, and achieve efficient interference effect.

[0068] Please refer to Figure 2 In this embodiment, the adaptive anti-monitoring method further includes:

[0069] S011, collecting voice information in the target space, and analyzing whether there are preset monitoring keywords;

[0070] S012. When there is a preset monitoring keyword, the anti-monitoring function is automatically triggered.

[0071] By setting up a keyword monitoring mechanism, voice information in the target space is continuously collected. Once the preset monitoring keywords are detected, such as specific words involving commercial secrets, sensitive political topics, etc., the anti-monitoring function is automatically triggered, and the above-mentioned audio collection, analysis and interference noise generation and processing processes are fully initiated to ensure the security of information exchange in the target space, effectively resist various types of monitoring threats, and maintain the confidentiality and security of information.

[0072] In actual application scenarios, in addition to the existing keyword monitoring mechanism, the monitoring of words related to confidential content can be further refined and strengthened.

[0073] For example, we focus on strictly monitoring key sensitive words such as financial information (such as bank account balances, credit card security codes, online banking login verification codes), personal privacy identity information (such as ID numbers, passport numbers, home addresses, social security numbers), business secrets (such as undisclosed product development details, customer lists, marketing strategies, quotation bidding information, financial data), and national and government secrets (such as military deployment plans, intelligence sources and means, and national strategic plans). Once relevant keywords are detected, the anti-monitoring function will be automatically triggered immediately, and the audio collection, analysis, and interference noise generation and processing processes will be started to avoid serious consequences such as theft of funds, identity theft, loss of business advantages, and threats to national security, and to ensure the security and confidentiality of information exchanges.

[0074] Please refer to Figure 3 In this embodiment, the adaptive anti-monitoring method further includes:

[0075] S021, capturing the image of the person in the target space in real time through the camera, and comparing it with the specific person image in the database;

[0076] S022, when a matching specific person image is identified, identifying the mouth movement of the corresponding specific person, and determining whether the specific person is speaking;

[0077] S023. When a specific person is identified as speaking, the anti-monitoring function is automatically triggered.

[0078] In the above steps, the camera captures the image of the person and compares it with the specific person image pre-stored in the database to achieve monitoring of the specific person. When the specific person appears in the target space, its mouth movements are further tracked and analyzed. Once the specific person speaks, the anti-monitoring function is automatically triggered to prevent the specific person's speech from being monitored, thereby effectively protecting confidential content from being leaked.

[0079] Please refer to Figure 4 In this embodiment, the adaptive anti-monitoring method further includes:

[0080] S031. Continuously monitor the sound signals in the target space, perform timbre analysis on the sound signals, and compare them with the sound characteristics of specific people in the database;

[0081] S032. When a matching voice feature of a specific person is identified, the anti-monitoring function is automatically triggered.

[0082] In this embodiment, an enhanced prevention mechanism is set up for specific persons. The speech of specific persons is monitored from the dimension of sound, and the anti-eavesdropping function is automatically activated when the specific person speaks, so as to improve the protection against leakage of confidential content.

[0083] Furthermore, in step S2, analyzing audio parameters includes:

[0084] The environmental audio information and human voice audio information are analyzed from three dimensions: sound frequency, volume and intensity, and the energy proportion, loudness value and intensity value of the environmental audio and human voice audio in each frequency band are obtained respectively.

[0085] Please refer to Figure 5 Further, in step S2, generating an adaptive interference noise according to the analysis result includes:

[0086] S21. According to the analysis result, energy proportion parameters, loudness parameters and intensity parameters of the interference noise in each frequency band are allocated to generate interference noise.

[0087] In this embodiment, when analyzing audio parameters, the ambient audio information and human voice audio information are analyzed from three key dimensions: frequency, volume, and intensity. In the frequency dimension, the energy proportion of ambient audio and human voice audio in each frequency band, such as low frequency band, mid-frequency band, and high frequency band, is accurately calculated; the loudness value is obtained from the volume dimension to clarify the size of the sound; in addition, the intensity value is determined from the intensity dimension to reflect the energy strength of the sound.

[0088] Based on the above precise analysis results, the energy proportion, loudness and intensity parameters of each frequency band are reasonably allocated to build an adaptive interference noise model, and then generate anti-monitoring interference noise that is adapted to the current audio. For example, if the low-frequency energy proportion of the ambient audio is high, the energy distribution of the generated interference noise in the low-frequency band will also be increased accordingly to ensure the pertinence and effectiveness of the interference effect.

[0089] For example, when performing frequency analysis, the fast Fourier transform (FFT) can be used to divide the low frequency band (20Hz-200Hz), mid-frequency band (200Hz-2kHz) and high frequency band (2kHz-20kHz), and calculate the energy proportion of each frequency band. For volume analysis, the root mean square (RMS) algorithm is combined with dynamic range compression technology to accurately obtain the loudness value, which is suitable for precise monitoring in different volume scenarios. Intensity analysis is based on the amplitude and power characteristics of the audio signal, comprehensively considering factors such as environmental attenuation, and determining the intensity value, providing a key basis for the subsequent generation of interference noise.

[0090] Then, after obtaining the analysis results, an interference noise model is constructed based on the existing intelligent generation algorithm. For example, if the low-frequency energy of the ambient audio accounts for 40%, the generated interference noise is dynamically adjusted between 35% and 45% in the low-frequency energy distribution setting. At the same time, the loudness and intensity parameters are adapted with reference to this method to generate an interference noise signal with a specific spectrum shape, loudness change curve and intensity fluctuation pattern, ensuring that the interference effect covers both the key frequency band and avoids excessive interference with normal audio perception, achieving the purpose of accurately interfering with monitoring equipment.

[0091] Please refer to Figure 6 , further, generating an adaptive interference noise according to the analysis result, further comprising:

[0092] S22. Make the interference noise jump randomly within a predetermined frequency band.

[0093] In this embodiment, the interference noise is randomly jumped within a predetermined frequency band to enhance the randomness and unpredictability of the interference, and the interference noise is randomly jumped within a predetermined frequency band.

[0094] For example, when the ambient audio and / or human voice audio are distributed in the low frequency band, the interference noise is locked in the low frequency band and randomly switches the frequency, loudness and intensity, continuously interfering with the monitoring equipment's capture and analysis of this frequency band; if the audio is concentrated in the mid-frequency band, the interference noise flexibly jumps in the mid-frequency band and the transition area between the high and low frequency bands, fully covering the frequency range that may be monitored; when the audio is in the high frequency band, the interference noise changes randomly in the high frequency band to prevent information leakage in the high frequency band.

[0095] Furthermore, in step S22, the interference noise is randomly jumped within a predetermined frequency band, including:

[0096] When the ambient audio and / or human voice audio are distributed in the low frequency band, the interference noise is randomly jumped within the low frequency band;

[0097] When the ambient audio and / or human voice audio are distributed in the middle frequency band, the interference noise is randomly jumped in the middle frequency band and in the transition frequency bands between the middle frequency band and the high frequency band and the low frequency band;

[0098] When the environmental audio and / or human voice audio are distributed in the high frequency band, the interference noise is randomly jumped within the high frequency band.

[0099] Please refer to Figure 7 Further, in step S2, the audio parameters are analyzed, and the corresponding interference noise is generated according to the analysis result, which also includes:

[0100] S231, identifying human voice audio information, and when a special timbre exists, identifying a characteristic frequency band of the special timbre;

[0101] S232. Generate interference noise of a reverse spectrum according to a characteristic frequency band of a special timbre.

[0102] It is understandable that during the audio analysis process, if a special timbre is detected in the human voice audio information, its characteristic frequency band is immediately locked, and then interference noise with the opposite spectrum is generated. Taking a voice with a high-frequency sharp timbre as an example, the generated interference noise presents the opposite spectrum characteristics in the corresponding high-frequency band, which can offset the monitoring device's ability to receive and recognize the special timbre voice to the greatest extent. Embodiment 2

[0103] Please refer to Figure 8 Based on the above embodiments, an embodiment of the present invention provides an anti-eavesdropping device, which is used to implement any of the above adaptive anti-eavesdropping methods, including:

[0104] The signal collection unit 10 is used to collect audio parameters in the target space when the anti-monitoring function is triggered; the audio parameters include environmental audio information and human voice audio information;

[0105] The analysis and processing unit 20 is used to analyze audio parameters and generate matching interference noise according to the analysis result; the interference noise is used to interfere with the monitoring of the monitoring device in the current target space.

[0106] Specifically, the signal acquisition unit 10 includes an audio sensor array for accurately capturing subtle audio fluctuations in the target space to ensure the comprehensiveness and accuracy of audio parameters.

[0107] When the anti-monitoring function is triggered, the signal acquisition unit 10 responds quickly and synchronously acquires the ambient audio information and the human voice audio information according to the preset sampling frequency and accuracy.

[0108] After receiving the audio data, the analysis and processing unit 20 conducts an in-depth analysis of the audio parameters based on advanced digital signal processing algorithms and deep learning models.

[0109] For example, when performing frequency analysis, fast Fourier transform can be used to divide the low frequency band (20Hz-200Hz), mid-frequency band (200Hz-2kHz) and high frequency band (2kHz-20kHz), and the energy proportion of each frequency band can be calculated. For volume analysis, the root mean square algorithm is combined with dynamic range compression technology to accurately obtain the loudness value, which is suitable for precise monitoring in different volume scenarios. Intensity analysis is based on the amplitude and power characteristics of the audio signal, comprehensively considering factors such as environmental attenuation, and determining the intensity value, providing a key basis for the subsequent generation of interference noise.

[0110] Then, after obtaining the analysis results, an interference noise model is constructed based on the existing intelligent generation algorithm. For example, if the low-frequency energy of the ambient audio accounts for 40%, the generated interference noise is dynamically adjusted between 35% and 45% in the low-frequency energy distribution setting. At the same time, the loudness and intensity parameters are adapted with reference to this method to generate an interference noise signal with a specific spectrum shape, loudness change curve and intensity fluctuation pattern, ensuring that the interference effect covers both the key frequency band and avoids excessive interference with normal audio perception, achieving the purpose of accurately interfering with monitoring equipment. Embodiment 3

[0111] Based on the foregoing embodiments, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the adaptive anti-eavesdropping method in Embodiment 1 is implemented. Embodiment 4

[0112] Based on the foregoing embodiments, the present invention provides a storage medium including computer executable instructions, and the computer executable instructions are executed by a computer processor to implement the adaptive anti-eavesdropping method in Embodiment 1.

[0113] Finally, it should be noted that although the above embodiments have been described in the specification and drawings of this application, this does not limit the scope of patent protection of this application. All technical solutions generated by replacing or modifying equivalent structures or equivalent processes based on the essential concept of this application using the contents recorded in the specification and drawings of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the scope of patent protection of this application.

Claims

1. An adaptive anti-eavesdropping method, characterized in that: include: When the anti-monitoring function is triggered, the audio parameters in the target space are collected; the audio parameters include environmental audio information and human voice audio information; The audio parameters are analyzed, and corresponding interference noise is generated according to the analysis results; the interference noise is used to interfere with the monitoring of the monitoring device in the current target space.

2. The adaptive anti-eavesdropping method according to claim 1, characterized in that: include: Collecting voice information in the target space and analyzing whether there is a preset monitoring keyword; When the preset monitoring keywords are present, the anti-monitoring function is automatically triggered.

3. The adaptive anti-eavesdropping method according to claim 1, characterized in that: Also includes: Capture the image of the person in the target space in real time through the camera and compare it with the specific person image in the database; When a matching specific person image is identified, the mouth movement of the corresponding specific person is identified to determine whether the specific person is speaking; When it is recognized that the specific person is speaking, the anti-monitoring function is automatically triggered.

4. The adaptive anti-eavesdropping method according to claim 1, characterized in that: Also includes: Continuously monitor the sound signals in the target space, analyze the timbre of the sound signals, and compare them with the sound characteristics of specific people in the database; When a matching specific person's voice characteristics are recognized, the anti-monitoring function is automatically triggered.

5. The adaptive anti-eavesdropping method according to claim 1, characterized in that: The analyzing the audio parameters comprises: The environmental audio information and human voice audio information are analyzed from three dimensions: sound frequency, volume and intensity, and the energy proportion, loudness value and intensity value of the environmental audio and human voice audio in each frequency band are obtained respectively.

6. The adaptive anti-eavesdropping method according to claim 5, characterized in that: The generating of the matching interference noise according to the analysis result includes: According to the analysis results, the energy proportion parameters, loudness parameters and intensity parameters of the interference noise in each frequency band are allocated to generate interference noise.

7. The adaptive anti-eavesdropping method according to claim 1, characterized in that: The generating of the matching interference noise according to the analysis result also includes: The interference noise is made to randomly jump within a predetermined frequency band.

8. The adaptive anti-eavesdropping method according to claim 7, characterized in that: The step of causing the interference noise to randomly jump within a predetermined frequency band includes: When the ambient audio and / or human voice audio are distributed in the low frequency band, the interference noise is randomly jumped within the low frequency band; When the ambient audio and / or human voice audio are distributed in the middle frequency band, the interference noise is randomly jumped in the middle frequency band and in the transition frequency bands between the middle frequency band and the high frequency band and the low frequency band; When the environmental audio and / or human voice audio are distributed in the high frequency band, the interference noise is randomly jumped within the high frequency band.

9. The adaptive anti-eavesdropping method according to claim 1, characterized in that: The step of analyzing the audio parameters and generating corresponding interference noise according to the analysis results includes: Identify the human voice audio information, and when a special timbre exists, identify a characteristic frequency band of the special timbre; According to the characteristic frequency band of the special timbre, interference noise with an inverse spectrum is generated.

10. An anti-eavesdropping device, characterized in that: The method for implementing the adaptive anti-eavesdropping method according to any one of claims 1 to 9 comprises: A signal collection unit, used to collect audio parameters in the target space when the anti-monitoring function is triggered; the audio parameters include environmental audio information and human voice audio information; The analysis and processing unit is used to analyze the audio parameters and generate corresponding interference noise according to the analysis results; the interference noise is used to interfere with the monitoring of the monitoring device in the current target space.

11. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the adaptive anti-eavesdropping method according to any one of claims 1 to 9 is implemented.

12. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are executed by a computer processor to implement the adaptive anti-eavesdropping method according to any one of claims 1 to 9.

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