Data encryption and decryption method and device, equipment and medium

Abstract

The invention discloses a data encryption and decryption method and device, equipment and a medium, relates to the technical field of computers, and is applied to a parallel computing system, a unit comprises a plurality of computing cores meeting synchronous computing requirements, and the method comprises the following steps: distributing different sound wave signal fragments in an initial sound wave signal into different computing cores, preprocessing the acquired sound wave signal segment by using a calculation core to obtain a target sound wave signal; extracting frequency characteristic information, an amplitude change rule and phase characteristic information of the target sound wave signal by using the calculation kernel, and mapping data constructed based on the frequency characteristic information, the amplitude change rule and the phase characteristic information to a target discrete data set; and obtaining a target key from the hash data obtained based on the target discrete data set by using the calculation core, and performing encryption and decryption operation on the corresponding to-be-processed data by using the target key. According to the application, the resource consumption and the labor cost of key management are greatly reduced, and meanwhile, the real-time requirement can be met.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a data encryption and decryption method, device, equipment and medium. Background Art

[0002] In the era of big data, the importance of data in various fields has become increasingly prominent, and the amount of data transmission and storage has exploded. Data security has become the key to ensuring the stable operation of information systems and user privacy, which has prompted encryption technology to develop in a more efficient direction. From the early simple substitution encryption and transposition encryption to the widely used symmetric encryption algorithms and asymmetric encryption algorithms today, data encryption technology has continued to evolve.

[0003] Although current data encryption technology has made significant progress, there are still some drawbacks. On the one hand, the key management of traditional encryption algorithms is relatively complex. Whether it is key distribution in symmetric encryption or key generation and storage in asymmetric encryption, it requires a lot of resources and energy. On the other hand, when faced with the encryption and decryption of large-scale data, the computational efficiency of existing algorithms is low. As the amount of data continues to increase, the time required for encryption and decryption has also increased significantly, which makes it difficult for some application scenarios with high real-time requirements, such as real-time video transmission, online transactions, etc., to meet their real-time requirements through existing encryption and decryption methods. For this reason, the above problems urgently need to be solved by technical personnel in this field. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a data encryption and decryption method, device, equipment and medium, which greatly reduces the resource consumption and manpower cost of key management and can meet the real-time requirements. The specific scheme is as follows:

[0005] In a first aspect, the present application discloses a data encryption and decryption method, which is applied to a parallel computing system, wherein the parallel computing system includes multiple computing cores that meet synchronous computing requirements, and the method includes:

[0006] Collecting an initial sound wave signal; the initial sound wave signal includes one or more sound wave signal segments;

[0007] Allocating different sound wave signal segments to different computing cores, and using the computing cores to preprocess the acquired sound wave signal segments to obtain target sound wave signals;

[0008] The calculation kernel is used to extract the frequency characteristic information, amplitude variation law and phase characteristic information of the target sound wave signal, and the data constructed based on the frequency characteristic information, the amplitude variation law and the phase characteristic information are mapped to a target discrete data set;

[0009] The computing core is used to obtain a target key from hash data obtained based on the target discrete data set, and the target key is used to perform encryption and decryption operations on corresponding data to be processed.

[0010] Optionally, preprocessing the acquired sound wave signal segment to obtain a target sound wave signal includes:

[0011] Determine a filtering method according to the noise characteristics of the sound wave signal segment, and perform noise reduction processing on the sound wave signal segment according to the filtering method to obtain a noise-reduced sound wave signal; wherein the noise characteristics include noise frequency characteristics, noise azimuth characteristics, and noise intensity characteristics;

[0012] The sampling rate of the noise-reduced sound wave signal is converted to the same value, and the amplitude of the noise-reduced sound wave signal after the sampling rate conversion is unified to obtain a target sound wave signal.

[0013] Optionally, determining a filtering method according to noise characteristics of the sound wave signal segment, and performing noise reduction processing on the sound wave signal segment according to the filtering method to obtain a noise-reduced sound wave signal includes:

[0014] Selecting a filter window with a target window length and a target sliding step length, determining an average value of the sound wave signal segment within the target window length, and sliding the filter window according to the target sliding step length to filter the high-frequency noise in the sound wave signal segment to obtain a denoised sound wave signal;

[0015] Or, if there are multiple sound wave acquisition devices, the direction of the noise in the sound wave signal segment is determined according to the time difference and amplitude difference of each sound wave acquisition device receiving the sound wave signal segment, and the noise in the direction is filtered to obtain a noise-reduced sound wave signal;

[0016] Or, in the process of filtering the sound wave signal segment using a filter, the signal error between the sound wave signal segment and the filtered signal is continuously compared, and the filtering parameters of the filter are adjusted using the signal error to obtain the sound wave signal after noise reduction; the signal error represents the noise intensity.

[0017] Optionally, extracting frequency characteristic information, amplitude variation law, and phase characteristic information of the target sound wave signal includes:

[0018] Determine a sampling frequency according to the time domain characteristics of the target sound wave signal, determine a transformation window parameter according to the sampling frequency, perform signal transformation by using the transformation window parameter, and obtain frequency characteristic information;

[0019] The target sound wave signal is subjected to frame processing, the short-time energy of each frame signal is calculated according to the corresponding frame processing result, and the amplitude variation law is obtained by analyzing the numerical variation of the short-time energy of each frame signal;

[0020] The target sound wave signal is subjected to Hilbert transform to obtain an analytical signal, and the instantaneous phase is determined according to a phase angle calculated based on the analytical signal to obtain phase characteristic information.

[0021] Optionally, after obtaining the target key from the hash data obtained based on the target discrete data set, the method further includes:

[0022] Generate an index identifier of the target key using one or more key parameters in a key parameter set, and store the index identifier in an index register; wherein the key parameters in the key parameter set include key attribute parameters, key algorithm parameters and key usage parameters.

[0023] Optionally, before using the target key to perform encryption and decryption operations on the corresponding data to be processed, the method further includes:

[0024] Verifying whether the data to be processed meets a predefined format based on a regular expression or a parser;

[0025] Verifying the integrity of the data to be processed according to a hash value determined based on the data to be processed;

[0026] If the data to be processed meets the large data file determination condition, a block operation is performed on the data to be processed to obtain a plurality of block data, and a corresponding block sequence number is added to each of the block data.

[0027] Optionally, the using the target key to perform encryption and decryption operations on the corresponding data to be processed includes:

[0028] During the encryption or decryption process, if the data to be processed meets the large data file determination condition, multiple computing cores in the parallel computing system are used to synchronously process multiple block data.

[0029] In a second aspect, the present application discloses a data encryption and decryption device, which is applied to a parallel computing system, wherein the parallel computing system includes a plurality of computing cores that meet synchronous computing requirements, and the device includes:

[0030] A signal collection module, used to collect an initial sound wave signal; the initial sound wave signal includes one or more sound wave signal segments;

[0031] A preprocessing module, used for allocating different acoustic wave signal segments to different computing cores, and using the computing cores to preprocess the acquired acoustic wave signal segments to obtain target acoustic wave signals;

[0032] A feature extraction module, used to extract the frequency feature information, amplitude variation law and phase feature information of the target sound wave signal by using the calculation kernel, and map the data constructed based on the frequency feature information, the amplitude variation law and the phase feature information to a target discrete data set;

[0033] The encryption and decryption module is used to use the computing core to obtain a target key from hash data obtained based on the target discrete data set, and use the target key to perform encryption and decryption operations on corresponding data to be processed.

[0034] In a third aspect, the present application discloses an electronic device, comprising:

[0035] Memory, used to store computer programs;

[0036] The processor is used to execute the computer program to implement the aforementioned disclosed data encryption and decryption method.

[0037] In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the aforementioned disclosed data encryption and decryption method is implemented.

[0038] It can be seen that the present application proposes a data encryption and decryption method, which is applied to a parallel computing system, wherein the parallel computing system includes multiple computing cores that meet the requirements of synchronous computing, and the method includes: collecting an initial sound wave signal; the initial sound wave signal includes one or more sound wave signal segments; allocating different sound wave signal segments to different computing cores, and using the computing core to pre-process the acquired sound wave signal segments to obtain a target sound wave signal; using the computing core to extract the frequency characteristic information, amplitude variation law, and phase characteristic information of the target sound wave signal, and mapping the data constructed based on the frequency characteristic information, the amplitude variation law, and the phase characteristic information to a target discrete data set; using the computing core to obtain a target key from the hash data obtained based on the target discrete data set, and using the target key to perform encryption and decryption operations on the corresponding data to be processed. In summary, it can be seen that due to the randomness and uniqueness of the sound wave signal, the target key generated based on the sound wave signal is difficult to be predicted and tampered with, so that the present application improves the security of data encryption and decryption. At the same time, the acquisition of acoustic wave signals is relatively convenient. Compared with traditional encryption technology that requires a lot of manpower and material resources when generating keys, this application does not require complex and expensive equipment and a specific environment, nor does it require special complex key distribution, generation and storage operations, which greatly reduces the resource consumption and manpower costs of key management. At the same time, the parallel processing method of multiple computing cores can significantly shorten the processing time of each step, especially when facing large-scale data, which can greatly improve the overall computing efficiency of encryption and decryption. For application scenarios with high real-time requirements such as real-time video transmission and online transactions, this application can quickly complete data encryption and decryption operations, thereby meeting their real-time needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] 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 embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0040] Figure 1 A flow chart of a data encryption and decryption method disclosed in this application;

[0041] Figure 2 This is a schematic diagram of a newly added module disclosed in this application;

[0042] Figure 3 An architectural diagram of a parallel computing system disclosed in this application;

[0043] Figure 4 A flow chart of a specific data encryption and decryption method disclosed in this application;

[0044] Figure 5 This is a schematic diagram of the structure of a data encryption and decryption device disclosed in this application;

[0045] Figure 6 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Although current data encryption technology has made significant progress, there are still some drawbacks. On the one hand, the key management of traditional encryption algorithms is relatively complex. Whether it is key distribution in symmetric encryption or key generation and storage in asymmetric encryption, it requires a lot of resources and energy. On the other hand, when faced with the encryption and decryption of large-scale data, the computational efficiency of existing algorithms is low. As the amount of data continues to increase, the time required for encryption and decryption has also increased significantly, which makes it difficult for some application scenarios with high real-time requirements, such as real-time video transmission, online transactions, etc., to meet their real-time requirements through existing encryption and decryption methods. For this reason, the above problems urgently need to be solved by technical personnel in this field.

[0048] To this end, an embodiment of the present application proposes a data encryption and decryption solution, which greatly reduces the resource consumption and manpower cost of key management, while being able to meet real-time requirements.

[0049] The present application embodiment discloses a data encryption and decryption method, which is applied to a parallel computing system. The parallel computing system includes multiple computing cores that meet the synchronous computing requirements. Figure 1 As shown, including:

[0050] Step S11: collecting an initial sound wave signal; the initial sound wave signal includes one or more sound wave signal segments.

[0051] In this embodiment, according to different collection requirements, professional audio collection equipment is selected, and the collection equipment is calibrated using a standard audio signal source to ensure that its sensitivity and frequency response meet the requirements. Then, the audio collection equipment is used to collect initial sound wave signals from a variety of audio sources (such as natural environment sounds, voice commands, music clips, etc.); the initial sound wave signal includes one or more sound wave signal segments.

[0052] Step S12: allocating different sound wave signal segments to different computing cores, and using the computing cores to pre-process the acquired sound wave signal segments to obtain target sound wave signals.

[0053] In this embodiment, the preprocessing includes noise reduction processing, sampling rate conversion and amplitude unification. Specifically, the filtering method is determined according to the noise characteristics of the sound wave signal segment, and the sound wave signal segment is subjected to noise reduction processing according to the filtering method to obtain the sound wave signal after noise reduction; the noise characteristics include noise frequency characteristics, noise azimuth characteristics and noise intensity characteristics; the sampling rate of the sound wave signal after noise reduction is converted to the same value, and the amplitude of the sound wave signal after noise reduction after sampling rate conversion is unified to obtain the target sound wave signal. In this way, the noise reduction processing determines the filtering method according to the frequency, azimuth and intensity characteristics of the noise, which can be highly adapted to the complex characteristics of different noises. The sampling rate conversion unifies the sampling rate of the sound wave signal after noise reduction, solves the problem of inconsistent sampling rates of different data sources or processing stages, and realizes data standardization. Amplitude unification processes the signal after sampling rate conversion, eliminates the amplitude differences caused by factors such as equipment sensitivity and transmission distance, and ensures data consistency, so as to more accurately learn and identify signal features.

[0054] In the first aspect, a filter window with a target window length and a target sliding step length is selected, the average value of the sound wave signal segment within the target window length is determined, and the filter window is slid according to the target sliding step length to filter the high-frequency noise in the sound wave signal segment to obtain the sound wave signal after noise reduction. Exemplarily, the present embodiment selects a filter window with a target window length of 100 signal sampling points and a target sliding step length of 10 signal sampling points. Further, the average value of the sound wave signal segment within the first target window length is determined. Then, the filter window is slid according to the target sliding step length, that is, the average value of the 11th-110th signal sampling points is calculated, and so on. By continuously repeating this process, filtering of the high-frequency noise in the sound wave signal segment can be achieved. It should be pointed out that since high-frequency noise often manifests itself as rapid fluctuations of the signal, calculating the average value can reduce this fluctuation, thereby obtaining the sound wave signal after noise reduction.

[0055] On the second aspect, if there are multiple sound wave collection devices, the direction of the noise in the sound wave signal segment is determined according to the time difference and amplitude difference of each sound wave collection device receiving the sound wave signal segment, and the noise in the direction is filtered to obtain the sound wave signal after noise reduction. The following is an example of collecting the speaker's voice signal at a large conference. At the conference site, three sound wave collection devices (microphones) are used and distributed at different locations in the venue. During the collection process, there is noise from different directions in the surrounding environment, such as the continuous hum of the air conditioner, the whispering of the audience in the back row, etc. In order to determine the direction of the noise, this embodiment records the time difference and amplitude difference of each sound wave collection device receiving the sound wave signal segment. For example, device A receives a noise signal 0.1 seconds earlier than device B, and the noise amplitude received by device A is larger than that of device B. Combined with the location information of these devices in the venue, this embodiment can roughly determine that the noise is coming from the direction close to device A. Once the direction of the noise is clear, the directional filtering technology can be used to attenuate the signal from that direction. Assuming that the noise is determined to come from the left side of the venue, the signal collected from the left side is filtered to reduce the impact of the noise on the overall signal, thereby obtaining a noise-reduced sound wave signal.

[0056] In the third aspect, in the process of filtering the sound wave signal segment using the filter, the signal error between the sound wave signal segment and the filtered signal is continuously compared, and the filtering parameters of the filter are adjusted using the signal error to obtain the sound wave signal after noise reduction; the signal error represents the noise intensity. Taking the audio signal of recording musical instrument performance as an example, during the recording process, there are some irregular noises in the environment. First, a filter set according to the initial parameters is used to filter the sound wave signal segment. During the filtering process, the signal error between the sound wave signal segment and the filtered signal is continuously compared. For example, the noise intensity can be represented by calculating the mean square error of the two. If the signal error is large, it means that the filtering effect of the current filter is not good, which means that the noise intensity is high. At this time, the filtering parameters of the filter are adjusted according to the above signal error, such as increasing the order of the filter or changing the filtering coefficient. Then use the filter with adjusted parameters to filter the signal again, and recalculate the signal error. Repeat this process until the signal error reaches an acceptable range, and finally obtain the sound wave signal after noise reduction.

[0057] Furthermore, the sampling rate of the noise-reduced sound wave signal is converted to the same value, and the amplitude of the noise-reduced sound wave signal after the sampling rate conversion is unified to obtain the target sound wave signal. It should be pointed out that in this embodiment, the sampling rate of the noise-reduced sound wave signal can be converted to the same value by upsampling or downsampling. In practical applications, the sampling rate value needs to be determined according to the specific application scenario and subsequent processing requirements. For example, in professional music production and high-quality audio playback scenarios, the common sampling rate is 44.1kHz or 48kHz. Subsequently, the amplitude of the noise-reduced sound wave signal after the sampling rate conversion is unified to eliminate the amplitude difference between different signals and ensure the consistency and accuracy of the data.

[0058] Step S13: using the computational kernel to extract the frequency characteristic information, amplitude variation law and phase characteristic information of the target sound wave signal, and mapping the data constructed based on the frequency characteristic information, the amplitude variation law and the phase characteristic information to a target discrete data set.

[0059] In this embodiment, the sampling frequency is determined according to the time domain characteristics of the target sound wave signal, and the transformation window parameters are determined according to the sampling frequency, and then the signal is transformed by the transformation window parameters to obtain frequency characteristic information. In addition, the target sound wave signal is framed, and the short-time energy of each frame signal is calculated according to the corresponding frame result, and then the amplitude variation law is obtained by analyzing the numerical change of the short-time energy of each frame signal. Finally, the target sound wave signal is Hilbert transformed to obtain an analytical signal, and the instantaneous phase is determined according to the phase angle calculated based on the analytical signal to obtain phase characteristic information.

[0060] Exemplarily, the sampling frequency needs to be determined first according to its time domain characteristics. For example, if the target sound wave signal contains higher frequency components, a higher sampling frequency needs to be selected to satisfy the Nyquist sampling theorem and avoid aliasing. After determining the sampling frequency, the transformation window parameters are determined according to the sampling frequency. The window parameters include the type of window function (such as Hanning window, Hamming window, etc.) and the window length. Appropriate window parameters help reduce spectral leakage and other problems. Then, multiply the target sound wave signal by the selected window function, and then perform signal transformation using the following formula to obtain frequency characteristic information:

[0061] ;

[0062] in, is the sampling value of the target sound wave signal at discrete time points, is a window function, k=0, 1,…, N-1, N is the number of sampling points of the target sound wave signal, and k is the frequency index.

[0063] Exemplarily, the short-term energy of each frame signal is calculated by the following formula:

[0064] ;

[0065] Among them, m is the sampling point index in the frame, M is the frame length, that is, the number of sampling points contained in each frame signal, and n indicates which frame signal is currently being calculated. is the sampling value of the target sound wave signal at a discrete time point (n+m). Furthermore, a curve is drawn with the frame number as the horizontal axis and the short-time energy as the vertical axis to present the trend of short-time energy changes, and the overall level and fluctuation degree of short-time energy are understood by calculating statistics such as the mean and variance. A small variance means a relatively stable amplitude change, and vice versa.

[0066] For example, for the signal , whose analytical signal It is expressed as: , yes The Hilbert transform of , j is the imaginary unit. The instantaneous phase It can be obtained by calculating the phase angle of the analytical signal: , arg function represents the argument principal value function.

[0067] Furthermore, mapping the data constructed based on the frequency characteristic information, the amplitude variation law, and the phase characteristic information to the target discrete data set {0, 1, 2, ..., L-1} can be achieved by the following formula: , where f is data constructed based on the frequency characteristic information, the amplitude variation law and the phase characteristic information (i.e., eigenvalue), L is the quantization level, min(f) and max(f) are the minimum and maximum eigenvalues, respectively, and round() represents the rounding function.

[0068] Step S14: using the computing core to obtain a target key from hash data obtained based on the target discrete data set, and using the target key to perform encryption and decryption operations on corresponding data to be processed.

[0069] In this embodiment, the target discrete data set is converted using the hash algorithm SHA-256 to obtain a 256-bit hash value. The first K bits of the hash value are used as the target key, and the target key is used to perform encryption and decryption operations on the corresponding data to be processed. The K value can be set to match the degree of data encryption.

[0070] Furthermore, in order to facilitate subsequent search and use, a unique index identifier is generated for each key and stored in the index register built into the module. Specifically, one or more key parameters in the key parameter set are used to generate the index identifier of the target key, and the index identifier is stored in the index register; wherein the key parameters in the key parameter set include key attribute parameters, key algorithm parameters and key usage parameters. The key attribute parameters reflect the inherent characteristics of the key, such as key length, key type, etc. The common key lengths are 128 bits, 192 bits and 256 bits, and the key types are mainly divided into symmetric keys and asymmetric keys; key algorithm parameters include AES (Advanced Encryption Standard) algorithm, RSA (Rivest-Shamir-Adleman) algorithm, etc., and key usage parameters include usage scope, usage frequency and access rights, etc. For example, key length: 256 bits, meeting high security requirements; key type: asymmetric key, conducive to data transmission and remote control security; key algorithm parameters: RSA algorithm, mature and suitable for encryption of acoustic wave monitoring systems; scope of use: limited to the encryption of communication between acoustic wave acquisition equipment and data processing centers in a specific urban area of ​​a city; frequency of use: limited to a maximum of 30 times per minute; access rights: only authorized professional technicians and system administrators can operate. Generate index identifier: ASY_256_RSA_URB30, ASY (Asymmetric Key) represents asymmetric key, 256 is the key length, RSA is the algorithm name, URB (Urban) refers to the scope of use in a specific urban area, and 30 is the upper limit of use per minute. This index facilitates quick search and management of keys in the index register.

[0071] It should be pointed out that before encrypting the data to be processed based on the target key, the data to be processed needs to be verified to ensure that the format of the data to be processed is correct, the data is complete and suitable for encryption. Specifically, the data to be processed is verified based on a regular expression or a parser to see if it meets the predefined format, and the integrity of the data to be processed is verified based on a hash value determined based on the data to be processed. If the data to be processed meets the big data file judgment criteria, the data to be processed is divided into blocks to obtain multiple block data, and a corresponding block number is added to each block data. For data that is less than a block size, the (Public-Key Cryptography Standards #7, Public Key Cryptography Standards #7) padding scheme, to dynamically add padding bytes according to the size of the block. During the encryption or decryption process, if the data to be processed meets the large data file determination condition, multiple computing cores in the parallel computing system are used to synchronously process multiple block data.

[0072] When encrypting the data to be processed based on the target key, select a suitable algorithm from the encryption algorithm library, such as AES, RSA, and ECC (Elliptic Curve Cryptography), according to the sensitivity and security requirements of the data. For symmetric encryption algorithms such as AES, the same key is used for encryption and decryption; for asymmetric encryption algorithms such as RSA, public key encryption and private key decryption are used. After the data is encrypted, a compression algorithm is used to compress the data to reduce the storage space and transmission bandwidth requirements. Note that the compression algorithm should be an algorithm that can be reversely decompressed for subsequent data decryption. Parallel computing systems can use parallel computing frameworks to realize the parallelization of encryption algorithms. The block-based encrypted data will be distributed to multiple computing cores for encryption processing at the same time, making full use of the high throughput and parallel computing capabilities of the parallel computing system to greatly improve the encryption speed and data compression speed.

[0073] When decrypting data, use the decompression algorithm corresponding to the compression to decompress the data and restore it to its original size before encryption. After decrypting the encrypted data, use the decryption algorithm corresponding to the encryption to decrypt the data: for symmetric encryption, use the same key as the encryption key for decryption; for asymmetric encryption, use the private key for decryption. After decryption, check the integrity of the data by recalculating the hash value and comparing it with the original hash value, and handle the padding in the last block (such as Padding) to restore the original data format, ensuring that the decrypted data is intact and consistent with the original data.

[0074] This application aims to build an efficient and secure encrypted data processing system by leveraging the powerful parallel computing capabilities of parallel computing systems and combining the uniqueness, randomness, and easy-to-access characteristics of sound waves. This system can effectively solve the problems existing in traditional encryption technology and meet modern data security needs. To achieve this goal, this application provides a schematic diagram of the newly added module (see Figure 2 ) and the overall framework diagram (see Figure 3 ).

[0075] See also Figure 2 As shown, Figure 2 The newly added modules in the system include preprocessing module, key generation module, key storage module, data preparation module, encryption execution module and decryption execution module.

[0076] See also Figure 3 As shown, Figure 3It contains multiple computing cores (such as computing core 1-computing core 8, etc.). The internal structure of each computing core is complex. Take computing core 0 as an example: (1) Instruction processing part: The instruction fetch module obtains instructions from the instruction cache, and stores them in the instruction buffer after being parsed by the decoding module. The emission module emits instructions under the control of the scoreboard, and the operand collector is responsible for collecting the operands required for the instructions. The SIMT (Single Instruction Multiple Threads) stack in the scheduling module is used for single instruction multiple thread execution. (2) Operation part: It contains arithmetic logic unit (ALU), floating-point unit (FPU) and load storage unit (LSU), which are used to perform various calculations and data access operations. The special function unit (SFU) has a preprocessing module, a key generation module, etc., which can complete encryption and decryption related operations. In addition, the write-back module is responsible for writing back the operation results. The entire architecture also includes tensor cores for tensor operations in deep learning; L2 cache as a secondary cache to speed up data access; and a key storage module for storing encryption-related keys.

[0077] See also Figure 4As shown, first, the sound wave acquisition module collects sound wave signals (which can be voice commands, music melodies, or natural environmental sounds, etc.), converts them into digital signals and sends them to the preprocessing module. In the preprocessing module, the digital signal is subjected to noise reduction processing to remove the interference of environmental noise and improve the clarity of the signal. Next, the signal is sampled and converted and unified to ensure that all signals have the same sampling rate and amplitude range, providing a consistent data format for subsequent key generation and data encryption. Subsequently, the preprocessed data is sent to the key generation module. This module generates the key required for encryption based on the characteristic information of the sound wave signal, such as frequency characteristics, amplitude change law and phase information. These characteristic information are extracted by Fourier transform and other methods, and finally converted into encryption keys by hash algorithm. The generated key is then stored in the key storage module. This module is responsible for securely storing and managing the key, generating a unique index identifier for each key, and storing it in the index register built into the module. At the same time, a redundant storage strategy is adopted to back up the key to multiple storage locations to prevent data loss and improve the stability of the system. (ii) Data encryption, the data preparation module further preprocesses and formats the collected digital signal. Through format verification, integrity check and data segmentation, ensure that the data is in the correct format, complete and suitable for encryption before encryption. The encryption execution module receives the standard data block and encryption key from the data preparation module, and selects the appropriate encryption algorithm (such as AES, RSA, ECC, etc.) to encrypt the data. The encrypted data is compressed to reduce the storage space and transmission bandwidth requirements to generate the final encrypted data. (III) Data decryption, when the encrypted data needs to be decrypted, the data to be decrypted is sent to the decryption execution module. This module first decompresses the compressed encrypted data, and then uses the decryption algorithm corresponding to the encryption to decrypt the data. The decrypted data is processed by data recovery to ensure that it is consistent with the original data, completing the entire encryption-decryption process. Among them, the parallel computing system plays a key acceleration role in the entire data processing and encryption process. By processing a large amount of data in parallel, the parallel computing system significantly improves the processing speed of each module, thereby shortening the overall processing time and improving system performance. In addition, the application of the parallel computing system also enhances the security and stability of the system, especially in key generation and storage, ensuring the high requirements of the data encryption system for key management.

[0078] This application forms an efficient, secure and reliable data encryption system by integrating multiple modules such as sound wave acquisition, preprocessing, key generation, data preparation, encryption execution, decryption execution and key storage. By utilizing the parallel computing capabilities of the parallel computing system, this system significantly improves the processing speed of each module, including data preprocessing, key generation, data encryption and decryption, thereby shortening the overall processing time and improving system performance. In addition, by adopting advanced encryption algorithms and compression technologies, this system effectively reduces the demand for data storage space and transmission bandwidth while ensuring data security.

[0079] The present application also enhances the security of keys and the stability of the system through redundant storage strategies and efficient key management mechanisms, prevents the risk of key loss or overwriting, and ensures the long-term security of data. In addition, the system strictly verifies and processes the integrity and consistency of data, ensuring the accuracy and reliability of data during encryption and decryption. Through these advantages, the present invention provides a powerful solution for application scenarios that require high security and high-efficiency data processing.

[0080] In addition, the present application can further expand the integration of other modal data, such as the intensity of light signals, frequency changes, temperature fluctuations and other information, together with the sound wave signal, through the parallel processing of multiple computing cores of the parallel computing system. First, the different modal data are pre-processed separately to remove noise, unify the format, etc., and then the characteristics of each modal data are comprehensively extracted. For example, a more complex and unique data set is constructed by combining the frequency characteristics of sound waves, the spectral characteristics of light signals, the trend characteristics of temperature changes, etc. The hash algorithm is then used to convert the fused data set into an encryption key, so that the generated key contains richer information and is difficult to crack.

[0081] It can be seen that the present application proposes a data encryption and decryption method, which is applied to a parallel computing system, wherein the parallel computing system includes multiple computing cores that meet the requirements of synchronous computing, and the method includes: collecting an initial sound wave signal; the initial sound wave signal includes one or more sound wave signal segments; allocating different sound wave signal segments to different computing cores, and using the computing core to pre-process the acquired sound wave signal segments to obtain a target sound wave signal; using the computing core to extract the frequency characteristic information, amplitude variation law, and phase characteristic information of the target sound wave signal, and mapping the data constructed based on the frequency characteristic information, the amplitude variation law, and the phase characteristic information to a target discrete data set; using the computing core to obtain a target key from the hash data obtained based on the target discrete data set, and using the target key to perform encryption and decryption operations on the corresponding data to be processed. In summary, it can be seen that due to the randomness and uniqueness of the sound wave signal, the target key generated based on the sound wave signal is difficult to be predicted and tampered with, so that the present application improves the security of data encryption and decryption. At the same time, the acquisition of acoustic wave signals is relatively convenient. Compared with traditional encryption technology that requires a lot of manpower and material resources when generating keys, this application does not require complex and expensive equipment and a specific environment, nor does it require special complex key distribution, generation and storage operations, which greatly reduces the resource consumption and manpower costs of key management. At the same time, the parallel processing method of multiple computing cores can significantly shorten the processing time of each step, especially when facing large-scale data, which can greatly improve the overall computing efficiency of encryption and decryption. For application scenarios with high real-time requirements such as real-time video transmission and online transactions, this application can quickly complete data encryption and decryption operations, thereby meeting their real-time needs.

[0082] Accordingly, the embodiment of the present application also discloses a data encryption and decryption device, which is applied to a parallel computing system, wherein the parallel computing system includes multiple computing cores that meet the synchronous computing requirements, see Figure 5 As shown, the device comprises:

[0083] The signal collection module 11 is used to collect an initial sound wave signal; the initial sound wave signal includes one or more sound wave signal segments;

[0084] A preprocessing module 12, used for allocating different acoustic wave signal segments to different computing cores, and using the computing cores to preprocess the acquired acoustic wave signal segments to obtain target acoustic wave signals;

[0085] A feature extraction module 13 is used to extract the frequency feature information, amplitude variation law and phase feature information of the target sound wave signal by using the calculation kernel, and map the data constructed based on the frequency feature information, the amplitude variation law and the phase feature information to a target discrete data set;

[0086] The encryption and decryption module 14 is used to use the computing core to obtain a target key from hash data obtained based on the target discrete data set, and use the target key to perform encryption and decryption operations on corresponding data to be processed.

[0087] Among them, for more specific working processes of the above-mentioned modules, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0088] It can be seen that the present application proposes a data encryption and decryption method, which is applied to a parallel computing system, wherein the parallel computing system includes multiple computing cores that meet the requirements of synchronous computing, and the method includes: collecting an initial sound wave signal; the initial sound wave signal includes one or more sound wave signal segments; allocating different sound wave signal segments to different computing cores, and using the computing core to pre-process the acquired sound wave signal segments to obtain a target sound wave signal; using the computing core to extract the frequency characteristic information, amplitude variation law, and phase characteristic information of the target sound wave signal, and mapping the data constructed based on the frequency characteristic information, the amplitude variation law, and the phase characteristic information to a target discrete data set; using the computing core to obtain a target key from the hash data obtained based on the target discrete data set, and using the target key to perform encryption and decryption operations on the corresponding data to be processed. In summary, it can be seen that due to the randomness and uniqueness of the sound wave signal, the target key generated based on the sound wave signal is difficult to be predicted and tampered with, so that the present application improves the security of data encryption and decryption. At the same time, the acquisition of acoustic wave signals is relatively convenient. Compared with traditional encryption technology that requires a lot of manpower and material resources when generating keys, this application does not require complex and expensive equipment and a specific environment, nor does it require special complex key distribution, generation and storage operations, which greatly reduces the resource consumption and manpower costs of key management. At the same time, the parallel processing method of multiple computing cores can significantly shorten the processing time of each step, especially when facing large-scale data, which can greatly improve the overall computing efficiency of encryption and decryption. For application scenarios with high real-time requirements such as real-time video transmission and online transactions, this application can quickly complete data encryption and decryption operations, thereby meeting their real-time needs.

[0089] Furthermore, an embodiment of the present application also provides an electronic device. Figure 6 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of use of the present application.

[0090] Figure 6The present invention provides a schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a display screen 23, an input / output interface 24, a communication interface 25, a power supply 26, and a communication bus 27. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the data encryption / decryption method disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0091] In this embodiment, the power supply 26 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 25 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 24 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0092] In addition, the memory 22, as a carrier for storing resources, may be a read-only memory, a random access memory, a disk or an optical disk, etc., and the resources stored thereon may include a computer program 221, and the storage method may be temporary storage or permanent storage. In addition to including a computer program that can be used to complete the data encryption and decryption method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 221 may further include a computer program that can be used to complete other specific tasks.

[0093] Furthermore, an embodiment of the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the aforementioned disclosed data encryption and decryption method is implemented.

[0094] For the specific steps of the method, reference may be made to the corresponding contents disclosed in the aforementioned embodiments, which will not be described in detail here.

[0095] The various embodiments in this application are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0096] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0097] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0098] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0099] The above is a detailed introduction to a data encryption and decryption method, device, equipment, and storage medium provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A data encryption and decryption method, characterized in that: Applied to a parallel computing system, the parallel computing system includes a plurality of computing cores that meet synchronous computing requirements, the method includes: Collecting an initial sound wave signal; the initial sound wave signal includes one or more sound wave signal segments; Allocating different sound wave signal segments to different computing cores, and using the computing cores to preprocess the acquired sound wave signal segments to obtain target sound wave signals; The calculation kernel is used to extract the frequency characteristic information, amplitude variation law and phase characteristic information of the target sound wave signal, and the data constructed based on the frequency characteristic information, the amplitude variation law and the phase characteristic information are mapped to a target discrete data set; The computing core is used to obtain a target key from hash data obtained based on the target discrete data set, and the target key is used to perform encryption and decryption operations on corresponding data to be processed.

2. The data encryption and decryption method according to claim 1, characterized in that: Preprocessing the acquired sound wave signal fragment to obtain a target sound wave signal includes: Determine a filtering method according to the noise characteristics of the sound wave signal segment, and perform noise reduction processing on the sound wave signal segment according to the filtering method to obtain a noise-reduced sound wave signal; wherein the noise characteristics include noise frequency characteristics, noise azimuth characteristics, and noise intensity characteristics; The sampling rate of the noise-reduced sound wave signal is converted to the same value, and the amplitude of the noise-reduced sound wave signal after the sampling rate conversion is unified to obtain a target sound wave signal.

3. The data encryption and decryption method according to claim 2, characterized in that: The step of determining a filtering method according to the noise characteristics of the sound wave signal segment, and performing noise reduction processing on the sound wave signal segment according to the filtering method to obtain a noise-reduced sound wave signal includes: Selecting a filter window with a target window length and a target sliding step length, determining an average value of the sound wave signal segment within the target window length, and sliding the filter window according to the target sliding step length to filter the high-frequency noise in the sound wave signal segment to obtain a denoised sound wave signal; Or, if there are multiple sound wave acquisition devices, the direction of the noise in the sound wave signal segment is determined according to the time difference and amplitude difference of each sound wave acquisition device receiving the sound wave signal segment, and the noise in the direction is filtered to obtain a noise-reduced sound wave signal; Or, in the process of filtering the sound wave signal segment using a filter, the signal error between the sound wave signal segment and the filtered signal is continuously compared, and the filtering parameters of the filter are adjusted using the signal error to obtain the sound wave signal after noise reduction; the signal error represents the noise intensity.

4. The data encryption and decryption method according to claim 1, characterized in that: Extracting the frequency characteristic information, amplitude variation law and phase characteristic information of the target sound wave signal includes: Determine a sampling frequency according to the time domain characteristics of the target sound wave signal, determine a transformation window parameter according to the sampling frequency, perform signal transformation by using the transformation window parameter, and obtain frequency characteristic information; The target sound wave signal is subjected to frame processing, the short-time energy of each frame signal is calculated according to the corresponding frame processing result, and the amplitude variation law is obtained by analyzing the numerical variation of the short-time energy of each frame signal; The target sound wave signal is subjected to Hilbert transform to obtain an analytical signal, and the instantaneous phase is determined according to a phase angle calculated based on the analytical signal to obtain phase characteristic information.

5. The data encryption and decryption method according to claim 1, characterized in that: After obtaining the target key from the hash data obtained based on the target discrete data set, the method further includes: Generate an index identifier of the target key using one or more key parameters in a key parameter set, and store the index identifier in an index register; wherein the key parameters in the key parameter set include key attribute parameters, key algorithm parameters and key usage parameters.

6. The data encryption and decryption method according to any one of claims 1 to 5, characterized in that: Before using the target key to perform encryption and decryption operations on the corresponding data to be processed, the method further includes: Verifying whether the data to be processed meets a predefined format based on a regular expression or a parser; Verifying the integrity of the data to be processed according to a hash value determined based on the data to be processed; If the data to be processed meets the large data file determination condition, a block operation is performed on the data to be processed to obtain a plurality of block data, and a corresponding block sequence number is added to each of the block data.

7. The data encryption and decryption method according to claim 6, characterized in that: The using the target key to perform encryption and decryption operations on the corresponding data to be processed includes: During the encryption or decryption process, if the data to be processed meets the large data file determination condition, multiple computing cores in the parallel computing system are used to synchronously process multiple block data.

8. A data encryption and decryption device, characterized in that: Applied to a parallel computing system, the parallel computing system includes multiple computing cores that meet synchronous computing requirements, and the device includes: A signal collection module, used to collect an initial sound wave signal; the initial sound wave signal includes one or more sound wave signal segments; A preprocessing module, used for allocating different acoustic wave signal segments to different computing cores, and using the computing cores to preprocess the acquired acoustic wave signal segments to obtain target acoustic wave signals; A feature extraction module, used to extract the frequency feature information, amplitude variation law and phase feature information of the target acoustic wave signal by using the calculation kernel, and map the data constructed based on the frequency feature information, the amplitude variation law and the phase feature information to a target discrete data set; The encryption and decryption module is used to use the computing core to obtain a target key from hash data obtained based on the target discrete data set, and use the target key to perform encryption and decryption operations on corresponding data to be processed.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the data encryption and decryption method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program; wherein, when the computer program is executed by a processor, the data encryption and decryption method according to any one of claims 1 to 7 is implemented.

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