Data Encryption Transmission Method and System

Abstract

The present application relates to the field of data encryption technology, and provides a data encryption transmission method and system, including: dividing the obtained voice signal into multiple frequency band signals, extracting and encrypting the frequency domain features of the multiple frequency band signals to obtain frequency domain encrypted signals, extracting and encrypting the time domain features of the voice signal to obtain time domain encrypted signals, extracting and encrypting the dynamic features of the multiple frequency band signals to obtain dynamic encrypted signals, and generating data to be transmitted based on the voice signal, the frequency domain encrypted signal, the time domain encrypted signal, and the dynamic encrypted signal, thereby improving the security and integrity of the data to be transmitted.

Description

Technical Field

[0001] This application belongs to the technical field of data encryption, and particularly relates to a data encryption transmission method and system. Background Art

[0002] Existing data encryption technologies (such as voice data encryption technology) usually use symmetric key or asymmetric key encryption algorithms to encrypt voice data. They may not be able to resist new attack methods and have insufficient security. Moreover, data may be lost or damaged during the encryption process, resulting in incomplete data. Summary of the Invention

[0003] Embodiments of this application provide a data encryption transmission method and system, which can solve the technical problems of existing data encryption methods that cannot resist new attack methods, have insufficient security, and data may be lost or damaged during the encryption process, resulting in incomplete data.

[0004] In a first aspect, embodiments of this application provide a data encryption transmission method, including:

[0005] Performing frequency band division on the obtained voice signal to obtain multiple frequency band signals ;

[0006] Performing frequency domain feature extraction and encryption on the multiple frequency band signals to obtain a frequency domain encrypted signal ;

[0007] Performing time domain feature extraction and encryption on the voice signal to obtain a time domain encrypted signal ;

[0008] Performing dynamic feature extraction and encryption on the multiple frequency band signals to obtain a dynamic encrypted signal ;

[0009] Generating data to be transmitted based on the voice signal , frequency domain encrypted signal , time domain encrypted signal , and dynamic encrypted signal .

[0010] In a possible implementation manner of the first aspect, the expression of the frequency band signal becomes: ;

[0011] where is a filter introducing the sinc function , ; is an adaptive frequency band filter , is an adjustment parameter, is the dynamically calculated center of the frequency band, ; .

[0012] In a possible implementation of the first aspect, the performing frequency-domain feature extraction and encryption on multiple frequency-band signals to obtain a frequency-domain encrypted signal , includes:

[0013] Performing feature extraction on multiple frequency-band signals to obtain a compressed representation of the frequency-domain features ;

[0014] Performing an encryption operation on the compressed representation of the frequency-domain features to obtain a frequency-domain encrypted signal ;

[0015] The performing time-domain feature extraction and encryption on the speech signal to obtain a time-domain encrypted signal , includes:

[0016] Performing feature extraction on the speech signal to obtain a compressed representation of the time-domain features ;

[0017] Performing an encryption operation on the compressed representation of the time-domain features to obtain a time-domain encrypted signal ;

[0018] The performing dynamic feature extraction and encryption on multiple frequency-band signals to obtain a dynamic encrypted signal , includes:

[0019] Performing dynamic feature extraction on multiple frequency-band signals to obtain a compressed representation of the dynamic features ;

[0020] Performing an encryption operation on the compressed representation of the dynamic features to obtain a dynamic encrypted signal .

[0021] In a possible implementation of the first aspect, the generating the data to be transmitted based on the speech signal , frequency-domain encrypted signal , time-domain encrypted signal , dynamic encrypted signal , includes:

[0022] Transmitting the frequency-domain encrypted signal , time-domain encrypted signal , dynamic encrypted signal are fused to obtain an encrypted signal ;

[0023] Perform steganography embedding and blockchain verification on the encrypted signal to generate the transmission data.

[0024] In a possible implementation manner of the first aspect, the performing steganography embedding and blockchain verification on the encrypted signal to generate the transmission data includes:

[0025] Perform steganography embedding on the voice signal and the encrypted signal to generate a hidden message ;

[0026] Use blockchain technology to verify and encrypt the hidden message to obtain the data to be transmitted.

[0027] In a second aspect, an embodiment of the present application provides a data encryption transmission system, including:

[0028] A frequency band division module, configured to perform frequency band division on the obtained voice signal to obtain a plurality of frequency band signals ;

[0029] A frequency domain encrypted signal generation module, configured to perform frequency domain feature extraction and encryption on the plurality of frequency band signals to obtain a frequency domain encrypted signal ;

[0030] A time domain encrypted signal generation module, configured to perform time domain feature extraction and encryption on the voice signal to obtain a time domain encrypted signal ;

[0031] A dynamic encrypted signal generation module, configured to perform dynamic feature extraction and encryption on the plurality of frequency band signals to obtain a dynamic encrypted signal ;

[0032] A transmission data generation module, configured to generate data to be transmitted based on the voice signal , frequency domain encrypted signal , time domain encrypted signal , dynamic encrypted signal .

[0033] In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the data encryption transmission method described in any one of the above first aspects is implemented.

[0034] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the data encryption transmission method described in any one of the above first aspects is implemented.

[0035] In a fifth aspect, an embodiment of the present application provides a computer program product, and when the computer program product runs on a computer device, the computer device is caused to execute the data encryption transmission method described in any one of the above first aspects.

[0036] In the embodiment of the present application, the obtained voice signal is divided into frequency bands to obtain a plurality of band signals ; frequency domain features of the plurality of band signals are extracted and encrypted to obtain frequency domain encrypted signals ; time domain features of the voice signal are extracted and encrypted to obtain time domain encrypted signals ; dynamic features of the plurality of band signals are extracted and encrypted to obtain dynamic encrypted signals ; based on the voice signal , frequency domain encrypted signals , time domain encrypted signals , and dynamic encrypted signals , data to be transmitted is generated. Through frequency domain feature extraction, the frequency components of the voice signal can be captured, and encrypting these features increases the security of the data. Time domain features reflect the changes of the voice signal over time, and encrypting these features can protect the dynamic change information of the voice. Dynamic features describe the dynamic changes of the voice signal over time, and encrypting these features can further enhance the security and concealment of the data. Based on the voice signal, frequency domain encrypted signals, time domain encrypted signals, and dynamic encrypted signals, data to be transmitted is generated, ensuring the integrity and security of the data.

[0037] Through multiple encryptions of frequency domain, time domain, and dynamic features, the security of the data is improved, making it more difficult to crack the data during transmission. And even if part of the data is tampered with, other features can still ensure the integrity of the data, ensuring the integrity of the data.

[0038] It can be understood that the beneficial effects of the above second aspect to fifth aspect can be referred to the relevant descriptions in the above first aspect, and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0040] Figure 1 is a schematic flowchart of a data encryption and transmission method provided by an embodiment of the present application;

[0041] Figure 2 is a schematic structural diagram of a data encryption and transmission system provided by an embodiment of the present application;

[0042] Figure 3 is a schematic structural diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0044] It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0045] It should also be understood that the term "and / or" used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0046] As used in the specification of the present application and the appended claims, the term "if" can be interpreted as "when...", "once", "in response to determining", or "in response to detecting" according to the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]" according to the context.

[0047] In addition, in the description of the specification and the appended claims of this application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0048] Reference to "one embodiment" or "some embodiments" etc. described in the specification of this application means that a specific feature, structure or characteristic described in connection with that embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0049] Figure 1 The schematic flowchart of the data encryption transmission method provided by one embodiment of this application is shown.

[0050] S101. Perform frequency band division on the obtained voice signal to obtain a plurality of frequency band signals .

[0051] Among them, obtain the voice signal . Perform frequency band division on the voice signal to obtain a plurality of frequency band signals .

[0052] Among them, the expression of the frequency band signal becomes: ;

[0053] Among them, is a filter introducing the sinc function , ; is an adaptive frequency band filter, , is an adjustment parameter, a parameter for adjusting the steepness of the filter, is the angular frequency, is the dynamically calculated frequency band center, ; .

[0054] Among them, This formula calculates the frequency corresponding to the maximum value of the autocorrelation function of the signal in the frequency domain, that is, the main frequency component of the signal. This frequency component is used as the center frequency of the adaptive frequency band filter.

[0055] In an adaptive band filter, and jointly define the filter . Among them, is the representation of the sinc function in the time domain, which is used to enhance the time-frequency localization characteristics.

[0056] In the prior art, traditional fixed-band filters may not be able to adapt to the dynamic changes of signals, resulting in inaccurate analysis results.

[0057] In the embodiments of the present application, the sinc function has good localization characteristics in both the time domain and the frequency domain, can accurately represent the frequency components of signals, and is suitable for time-frequency analysis of signals. The adaptive band filter can dynamically adjust the frequency band according to the autocorrelation characteristics of the signal, which makes the processing method more flexible and can adapt to the characteristics of different signals. Combining the sinc function and the adaptive band can improve the resolution of time-frequency analysis. The sinc function provides good frequency-domain localization, while the adaptive band provides the ability of dynamic adjustment in the time domain.

[0058] By combining the sinc function and the adaptive band, more refined time-frequency localization can be achieved, so as to more accurately capture the local characteristics of the signal. Accurate time-frequency analysis helps to improve the accuracy of signal reconstruction, especially in complex environments. This method can adapt to different types and characteristics of speech signals, improving the versatility and flexibility of the system. In encrypted transmission, accurate time-frequency analysis can be used to generate more complex keys, thus enhancing the security of signal transmission.

[0059] S102, perform frequency-domain feature extraction and encryption on multiple band signals to obtain a frequency-domain encrypted signal .

[0060] Specifically: perform feature extraction on multiple band signals to obtain a compressed representation of the frequency-domain features ; perform an encryption operation on the compressed representation of the frequency-domain features to obtain a frequency-domain encrypted signal .

[0061] Among them, the frequency-domain features include: Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Cepstral Coefficients (LPCC), Spectral Centroid, Spectral Spread, and Formant Frequencies.

[0062] Among them, the calculation formula of the frequency-domain encrypted signal is:

[0063] Among them, is the weight coefficient of the th feature, is the encrypted description label of the th feature, is the encryption operation. This operation can be XOR or other encryption operations. The specific operation to be selected depends on the encryption requirements and security level.

[0064] Among them, it is preferably the XOR operation. The reasons for choosing the XOR operation as the encryption operation include its simplicity, efficiency, irreversibility, security, and flexibility. These characteristics make the XOR operation an ideal choice in applications that require fast, secure, and efficient encryption.

[0065] Among them, the encrypted description label , is used to indicate that the frequency domain features are being processed. This label can be the output of a hash function , combined with the key and the frequency domain feature description . . Among them, is the description information containing the frequency domain features, which can be any descriptive information about the features, such as feature type, source, number of features, statistical information of the features (such as mean, variance), etc.

[0066] S103. Extract and encrypt the time domain features of the speech signal to obtain the time domain encrypted signal .

[0067] Specifically: Extract the features of the said speech signal to obtain the compressed representation of the time domain features ; Perform an encryption operation on the compressed representation of the time domain features to obtain the time domain encrypted signal .

[0068] Among them, the time domain features include: fundamental frequency (F0), sound intensity (Energy), zero crossing rate (ZCR), speech rate (SpeechRate), silent segment detection (Silent Segment Detection).

[0069] Among them, the calculation formula of the time domain encrypted signal is:

[0070] . Among them, is the weight coefficient of the th feature, is the encrypted description label of the th feature, For encryption operations (such as XOR), the relevant explanations are the same as above.

[0071] Among them, the encryption description tag , which is used to indicate that the time-domain features are being processed. This tag can be the output of a hash function , combined with the secret key and the time-domain feature description . . Among them, is the description information of the th feature, including the description information of the time-domain features.

[0072] S104, perform dynamic feature extraction and encryption on multiple band signals to obtain the dynamically encrypted signal .

[0073] Among them, the dynamic features include: Mel-frequency cepstral contrast (MFCC Delta), linear prediction residual (LPR).

[0074] Among them, the calculation formula for the dynamically encrypted signal is:

[0075] . Among them, is the weight coefficient of the th feature, is the encryption description tag of the th feature, is the encryption operation (such as XOR), and the relevant explanations are the same as above.

[0076] Among them, the encryption description tag , which is used to indicate that the dynamic features are being processed. This tag can be the output of a hash function , combined with the secret key and the dynamic feature description . . is the description information of the th feature, including the description information of the dynamic features.

[0077] Among them, for the secret key in the above text: . Among them, use the quantum random number generator to generate the secret key , combined with the quantum entanglement-based random variable , the scene perception matrix and the chaos parameter . Provides a highly random and unpredictable secret key for encryption.

[0078] Among them, is the description information containing frequency-domain features, is the description information of the th feature, is the description information of the th feature, which may include metadata such as the source, type, content summary, creation time, author, etc. of the data. In the context of encryption and data processing, the above description information can provide additional information about the data, which is very useful for understanding the context of the data, managing data access permissions, or performing data tracking and verification. It can be used for subsequent deep learning steganography embedding, enhancing the security and traceability of the data.

[0079] Among them, the scene awareness matrix may contain the following information:

[0080] User behavior: The behavior patterns or preferences of the user, such as the usage habits of the user's voice signal, which can be used to adjust the key generation process to make it more personalized and secure.

[0081] Device status: The status information of the device generating the key, such as the physical location and network connection status of the device, which can be used to ensure that the key generation is associated with the device status, increasing security.

[0082] Time information: The current timestamp or time series, used to ensure that the key generation is associated with a specific time point, increasing the security in the time dimension.

[0083] By integrating these scene awareness information into the key generation process, it can be ensured that the generated key is not only highly random but also closely related to a specific scene, thus improving the security and adaptability of the encryption system. This method is particularly suitable for scenarios that require high security and dynamic adaptability, such as financial transactions, etc.

[0084] In the embodiment of the present application, by dividing the obtained voice signal into multiple frequency band signals, it is ensured that each frequency band component of the voice signal is independently processed and encrypted. This method helps that even if part of the data is lost or damaged during data transmission, the data in other frequency bands can still be used as a reference.

[0085] In the embodiment of the present application, not only different types of features are classified and specially encrypted, but also the encrypted description tags are classified ( , and ) to adapt to different encryption requirements. This method improves the security of the data, and at the same time optimizes the accuracy and efficiency of signal extraction, and is applicable to application scenarios that require high security and high-quality signal processing. For the convenience of understanding, an example is given here.

[0086] For , assume the secret key is a 256-bit number. For MFCC features, the description may include information such as the type of feature (MFCC), the number of features (e.g., 13 coefficients), the sampling rate (e.g., 16 kHz), etc. Then . denotes a concatenation operation used to combine the secret key and the above-described information.

[0087] For , assume that the fundamental frequency (F0) feature is being processed, which is a time-domain feature. The secret key is the same as above. For F0 features,[[]] may include information such as the type of feature (F0), the sampling rate (e.g., 16 kHz), the frame size (e.g., 20 ms), etc. Then .

[0088] For , assume that the Mel-frequency cepstrum contrast (MFCC Delta) feature is being processed, which is a dynamic feature. The secret key is the same as above. For MFCC Delta features,[[]] may include information such as the type of feature (MFCC Delta), the number of features (e.g., 13 coefficients), the sampling rate (e.g., 16 kHz), etc. Then .

[0089] In this way, each type of encrypted description tag contains sufficient information to indicate the type of feature being processed, while incorporating the secret key to ensure security. These tags are then used to guide the corresponding encryption process to ensure the security of data during transmission and storage.

[0090] S105, generate the data to be transmitted based on the speech signal , the frequency-domain encrypted signal , the time-domain encrypted signal , and the dynamic encrypted signal .

[0091] Specifically: fuse the frequency-domain encrypted signal , the time-domain encrypted signal , and the dynamic encrypted signal to obtain an encrypted signal ; perform steganographic embedding and blockchain verification on the encrypted signal to generate the transmission data.

[0092] Among them, fuse the encryption results of all features to obtain the final encrypted signal :[[]] Perform steganography embedding on the voice signal and the encrypted signal to generate the stego information : Verify and encrypt the stego information using blockchain technology to obtain the data to be transmitted.

[0093] Among them, verify and encrypt the generated stego information using blockchain technology. The immutability and transparency of the blockchain can ensure the integrity and security of the data. Store the hash value of the stego information on the blockchain to verify the integrity of the data.

[0094] In the prior art, the generated data is highly similar to the original data but contains hidden information. However, in this application, perform steganography embedding on the voice signal and the encrypted signal to generate the stego information : Ensure that the generated data is not only highly similar to the original data but also highly similar to the encrypted data (i.e., the encrypted signal ). Specifically: Use a generative adversarial network to generate data that is highly similar to the encrypted signal but contains hidden information. The generative adversarial network consists of a generator and a discriminator. The generator is responsible for generating data, and the discriminator is responsible for distinguishing the generated data from the real data (here it is the encrypted signal ). Embed the features into the generated data to form the stego information .

[0095] Among them, when training the generative adversarial network , we need to ensure that the generator G can generate data that is both similar to and similar to . This can be achieved through the following loss function:

[0096] . Among them, is the similarity loss between the generated data and the original data . is the similarity loss between the generated data and . and is a weight parameter used to balance the importance of the two loss terms. By optimizing the above loss function, the generator can be trained to generate data that is highly similar to both the original data and the encrypted signal with high similarity. To achieve the purpose of steganography and encryption.

[0097] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0098] Corresponding to the data encryption transmission method described in the above embodiments, Figure 2 the structural block diagram of the data encryption transmission system provided by the embodiments of the present application is shown. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown.

[0099] Referring to Figure 2 , the data encryption transmission system includes:

[0100] A frequency band division module for dividing the acquired voice signal into multiple frequency band signals ;

[0101] A frequency domain encrypted signal generation module for performing frequency domain feature extraction and encryption on the multiple frequency band signals to obtain a frequency domain encrypted signal ;

[0102] A time domain encrypted signal generation module for performing time domain feature extraction and encryption on the voice signal to obtain a time domain encrypted signal ;

[0103] A dynamic encrypted signal generation module for performing dynamic feature extraction and encryption on the multiple frequency band signals to obtain a dynamic encrypted signal ;

[0104] A transmission data generation module for generating data to be transmitted based on the voice signal , the frequency domain encrypted signal , the time domain encrypted signal , and the dynamic encrypted signal .

[0105] In a possible implementation manner, the expression of the frequency band signal becomes: ;

[0106] Wherein, is the introduced sinc function The filter ; is an adaptive band filter , is the adjustment parameter is the dynamically calculated band center ; .

[0107] In a possible implementation, the frequency-domain encrypted signal generation module is used for:

[0108] Extract features from multiple band signals to obtain a compressed representation of the frequency-domain features ;

[0109] Perform an encryption operation on the compressed representation of the frequency-domain features to obtain a frequency-domain encrypted signal .

[0110] In a possible implementation, the time-domain encrypted signal generation module is used for:

[0111] Extract features from the speech signal to obtain a compressed representation of the time-domain features ;

[0112] Perform an encryption operation on the compressed representation of the time-domain features to obtain a time-domain encrypted signal .

[0113] In a possible implementation, the dynamic encrypted signal generation module is used for:

[0114] Extract dynamic features from multiple band signals to obtain a compressed representation of the dynamic features ;

[0115] Perform an encryption operation on the compressed representation of the dynamic features to obtain a dynamic encrypted signal .

[0116] In a possible implementation, the transmission data generation module is used for:

[0117] Fuse the frequency-domain encrypted signal , the time-domain encrypted signal , and the dynamic encrypted signal to obtain an encrypted signal ;

[0118] Perform steganography embedding and blockchain verification on the encrypted signal to generate the transmission data.

[0119] In a possible implementation, a transmission data generation module is configured to:

[0120] perform steganography embedding on the voice signal and the encrypted signal to generate hidden information ;

[0121] Verify and encrypt the hidden information using blockchain technology to obtain the data to be transmitted.

[0122] It should be noted that, regarding the information interaction, execution process, etc. between the above modules, since they are based on the same concept as the method embodiments of this application, for their specific functions and the technical effects brought about, reference can be specifically made to the method embodiment part, and details will not be elaborated here.

[0123] Those skilled in the art can clearly understand that, for the sake of convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0124] An embodiment of this application also provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor. When the processor executes the computer program, the steps in any of the above method embodiments are implemented.

[0125] An embodiment of this application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in each of the above method embodiments can be implemented.

[0126] An embodiment of this application provides a computer program product. When the computer program product runs on a computer device, the computer device is caused to execute the steps in each of the above method embodiments.

[0127] Figure 3 A schematic structural diagram of a computer device provided by an embodiment of the present application. As Figure 3 shown, the computer device of this embodiment includes: at least one processor 20 ( Figure 3 only one is shown in the figure), a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor 20. When the processor 20 executes the computer program 22, the steps in any of the above-mentioned embodiments of the data encryption transmission method are implemented.

[0128] The computer device may include, but is not limited to, a processor 20 and a memory 21. Those skilled in the art can understand that Figure 3 merely an example of a computer device, which does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine some components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0129] The processor 20 may be a central processing unit (CPU), and the processor 20 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0130] In some embodiments, the memory 21 may be an internal storage unit of the computer device, such as the hard disk or memory of the computer device. In other embodiments, the memory 21 may also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device. Further, the memory 21 may also include both the internal storage unit and the external storage device of the computer device. The memory 21 is used to store an operating system, application programs, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 21 may also be used to temporarily store data that has been output or will be output.

[0131] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of this application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the device / computer equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a portable hard drive, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0132] In the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0133] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0134] In the embodiments provided in this application, it should be understood that the disclosed device / computer equipment and method can be implemented in other ways. For example, the device / computer equipment embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical, or other form.

[0135] The unit described as a separating component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A data encryption and transmission method, characterized in that, including: For the obtained voice signal perform frequency band division to obtain multiple frequency band signals ; For multiple band signals perform frequency domain feature extraction and encryption to obtain a frequency domain encrypted signal ; Perform time-domain feature extraction and encryption on the voice signal to obtain a time-domain encrypted signal ; For multiple band signals perform dynamic feature extraction and encryption to obtain a dynamically encrypted signal ; Based on the voice signal , frequency-domain encrypted signal , time-domain encrypted signal , dynamic encrypted signal , generate data to be transmitted; Among them, the band signal has the following expression: ; Among them, is a filter for introducing the sinc function ; ; is an adaptive band filter , is a tuning parameter is the dynamically calculated band center ; .

2. The data encryption transmission method according to claim 1, characterized in that The multiple-band signals are subjected to frequency-domain feature extraction and encryption to obtain frequency-domain encrypted signals , including: Extract features from multiple band signals to obtain a compressed representation of the frequency domain features ; Compressed representation of frequency-domain features Perform an encryption operation to obtain a frequency-domain encrypted signal ; The speech signal is subjected to time-domain feature extraction and encryption to obtain a time-domain encrypted signal , including: Perform feature extraction on the speech signal to obtain a compressed representation of the time-domain features ; Compressed representation of time-domain features Perform an encryption operation to obtain a time-domain encrypted signal ; The multiple-band signals are subjected to dynamic feature extraction and encryption to obtain dynamic encrypted signals , including: For multiple band signals perform dynamic feature extraction to obtain a compressed representation of the dynamic features ; Compressed representation of dynamic features Perform an encryption operation to obtain a dynamic encrypted signal .

3. The data encryption and transmission method according to claim 2, wherein Based on the speech signal , frequency-domain encrypted signal , time-domain encrypted signal , dynamic encrypted signal , generate data to be transmitted, including: Fuse the frequency-domain encrypted signal , time-domain encrypted signal , and dynamic encrypted signal to obtain an encrypted signal ; Perform steganography embedding and blockchain verification on the encrypted signal to generate the transmission data.

4. The data encryption and transmission method according to claim 3, characterized in that, The encrypted signal is subjected to steganography embedding and blockchain verification to generate the transmission data, including: For the said speech signal and the said encrypted signal perform steganography embedding to generate the hidden information ; where ; where is a random variable based on quantum entanglement; is a scene perception matrix; represents the feature to be embedded; Verify and encrypt the confidential information using blockchain technology to obtain the data to be transmitted; Among them, the loss function for training the generative adversarial network is as follows: ; wherein, is the similarity loss between the generated data and the original data ; is the similarity loss between the generated data and ; and are weight parameters.

5. A data encryption and transmission system, characterized in that, including: A frequency band division module, which is used for the acquired voice signal to perform frequency band division to obtain multiple frequency band signals ; Frequency-domain encryption signal generation module, used for multiple band signals to perform frequency-domain feature extraction and encryption to obtain a frequency-domain encrypted signal ; Time-domain encryption signal generation module, used for voice signals to perform time-domain feature extraction and encryption to obtain a time-domain encrypted signal ; A dynamic encryption signal generation module for multiple band signals to perform dynamic feature extraction and encryption to obtain a dynamically encrypted signal ; A transmission data generation module, for generating data to be transmitted based on the speech signal , a frequency-domain encrypted signal , a time-domain encrypted signal , a dynamic encrypted signal , and generating data to be transmitted; Among them, the band signal has the following expression: ; Among them, is a filter for introducing the sinc function ; ; is an adaptive band filter , is an adjustment parameter is the dynamically calculated band center ; .

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the method described in any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the method described in any one of claims 1 to 4 is implemented.

8. A computer program product, characterized in that, When the computer program product runs on a computer device, the computer device is caused to execute the method described in any one of claims 1 to 4.

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