Aviation data bus signal encryption method and system based on FPGA

Abstract

The present invention relates to the field of signal encryption technology, specifically to an aviation data bus signal encryption method and system based on FPGA, comprising the following steps: based on the input three-dimensional tensor signal data, extracting slices at fixed intervals by the time index of the aviation data bus, and segmenting after the amplitude of the signal channel is sorted in ascending order. In the present invention, the processing logic of matrix decomposition and characteristic curve generation optimizes the feature extraction and dimensionality reduction process, improves the distinguishability and representation ability of feature information, and the key generation method combining dynamic perturbation factors with encryption parameters enhances the randomness and anti-cracking ability of the key, ensures the security of signal transmission, and combines amplitude superposition with frame-by-frame storage to realize the dynamic and efficient signal encryption process, reduce the risk of signal tampering and eavesdropping, and ensures the accurate restoration of signal amplitude through feature reconstruction and point-by-point matching processing in the decryption stage, and ensures data integrity and anti-interference ability.

Description

Technical Field

[0001] The present invention relates to the technical field of signal encryption, and in particular to an aviation data bus signal encryption method and system based on FPGA. Background Art

[0002] The field of signal encryption technology mainly involves the protection and encryption methods of communication signals. By encoding, encrypting and decrypting information during transmission, the confidentiality, integrity and anti-interference ability of data are ensured. This technology is usually used in military communications, aerospace, financial data transmission and other fields with high requirements for information security to prevent information from being stolen, tampered or monitored during transmission. The core of signal encryption technology is to realize real-time processing of signals through algorithms and hardware, thereby improving the security and reliability of the communication process.

[0003] The aviation data bus signal encryption method refers to a technical method for protecting the transmitted data by means of encryption for the data bus signals used in the aviation field. Its main purpose is to prevent the data from being stolen, tampered with or illegally accessed during the transmission process in the data communication between aircraft and related equipment, so as to ensure the safety and reliability of flight control, navigation and other key systems.

[0004] The existing technology relies on static key generation for signal encryption and lacks a dynamic adjustment mechanism. The key characteristics are single and easy to be cracked in a targeted manner. At the same time, the feature extraction method does not fully consider the complexity of multi-dimensional signal data, and the feature expression ability is insufficient, resulting in poor adaptability to high-complexity signals. The encryption method lacks dynamic signal amplitude adjustment means, and the anti-interference ability in noisy environments is limited, affecting the stability and security of data transmission. In the decryption process, no accurate feature reconstruction method is introduced, which is prone to restoration errors or information loss, affecting the reliability of signal decryption. These deficiencies limit the application effect of existing technologies in multi-dimensional and high-complexity signal processing, and cannot meet scenarios with high demands for data security and integrity. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an aviation data bus signal encryption method and system based on FPGA.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: an aviation data bus signal encryption method based on FPGA, comprising the following steps:

[0007] S1: Based on the input three-dimensional tensor signal data, slices are extracted at fixed intervals by the time index of the aviation data bus, the amplitude of the signal channel is sorted in ascending order and then segmented, and the signal amplitude is partitioned and reorganized into a matrix structure to generate a multi-dimensional signal slice matrix;

[0008] S2: extracting data frames of each aviation data bus from the multi-dimensional signal slice matrix, performing matrix decomposition on the multiple frames to calculate principal component values, processing the principal component values ​​according to signal amplitude integral to generate characteristic curve data, reducing the dimension of the characteristic curve and rearranging it according to mapping rules to generate a signal characteristic matrix;

[0009] S3: Based on the signal feature matrix, a dynamic disturbance factor is superimposed on the time index and column vector data of the aviation data bus, a disturbed feature vector is calculated, a modulus value of the feature vector is weighted and converted into a dynamic numerical sequence, and the numerical sequence is combined with an encryption parameter to generate a dynamic key sequence;

[0010] S4: using the dynamic key sequence, calling the amplitude sequence frame by frame for the signal channel of the aviation data bus, superimposing the key data and the signal amplitude to replace the original signal amplitude, archiving and storing the encrypted amplitude data by frame, and generating encrypted signal frame data;

[0011] S5: Based on the encrypted signal frame data, reconstruct the data frame of the aviation data bus into a matrix according to the encryption end slicing rule, calculate the principal component to establish the characteristic matrix, reconstruct the dynamic eigenvalue sequence, match the encrypted signal frame point by point to restore the signal amplitude, and generate a decrypted signal data stream.

[0012] The multidimensional signal slice matrix includes signal channel slices, amplitude ascending sorting data, and a reorganized matrix; the signal feature matrix includes feature curve data, feature data after dimensionality reduction, and a rearranged feature mapping matrix; the dynamic key sequence includes a dynamic perturbation factor feature vector, a weighted conversion numerical sequence, and a key parameter sequence; the encrypted signal frame data includes an encrypted amplitude sequence, frame archive storage data, and signal amplitudes replaced frame by frame; the decrypted signal data stream includes a dynamic feature value sequence, a restored signal amplitude, and a reconstructed signal data matrix.

[0013] As a further solution of the present invention, the step of acquiring the multidimensional signal slice matrix is ​​specifically:

[0014] S111: extracting signal amplitudes at fixed time intervals from the three-dimensional tensor signal data, structuring the signal at each time point, constructing a time series signal amplitude matrix, and obtaining a signal time series structure;

[0015] S112: sorting the data in the signal time series structure in ascending order of amplitude, dividing the signal into several intervals according to the amplitude, calculating the mean value and standard deviation of the signal amplitude for each interval, and defining a signal interval descriptor for each signal partition to obtain a signal amplitude interval description;

[0016] S113: According to the signal amplitude interval description, the signal is reorganized, the amplitude interval is remapped to the current position, and the signal amplitude matrix after reorganization is calculated by combining the signal density of the amplitude interval with the weighted average method, using the formula:

[0017]

[0018] Get the reconstructed signal amplitude matrix;

[0019] Among them, x k represents the signal amplitude in interval k, w k Represents the amplitude x k The corresponding signal density, n represents the number of signals, M ij Represents the signal amplitude of row i and column j;

[0020] S114: Convert the reorganized signal amplitude matrix into a three-dimensional structured slice matrix, where each slice reflects a physical dimension of the original signal data, to generate a multi-dimensional signal slice matrix.

[0021] As a further solution of the present invention, the step of acquiring the signal feature matrix is ​​specifically as follows:

[0022] S211: extracting the aviation data bus signal data frames in the multidimensional signal slice matrix, performing structured sorting on the data frames through time series analysis to form a serialized signal frame matrix, and performing preliminary amplitude distribution analysis based on signal strength to generate a preliminary signal frame description matrix;

[0023] S212: applying matrix decomposition technology to the preliminary signal frame description matrix, extracting key signal features as principal components, and determining their contribution to obtain a principal component matrix;

[0024] S213: Calculate the time series characteristic curve of the signal from the principal component matrix using the formula:

[0025]

[0026] Perform integration processing on the signal amplitude to generate the characteristic curve of the signal;

[0027] Among them, I t represents the integral value of time t, P t is the amplitude of the principal component, λ represents the coefficient of amplitude decay over time, and μ is the amplitude enhancement coefficient, which enhances the signal characteristics;

[0028] S214: Performing dimensionality reduction processing on the characteristic curve of the signal, rearranging the data after dimensionality reduction using a target mapping rule, integrating all processed data, and obtaining a signal characteristic matrix.

[0029] As a further solution of the present invention, the step of obtaining the dynamic key sequence is specifically as follows:

[0030] S311: Based on the signal characteristic matrix, each column vector of the matrix is ​​superimposed according to the time index of the aviation data bus, and a dynamic disturbance factor is inserted, and the amplitude, direction and timing parameters of the disturbance factor are adjusted at the same time, and the column vector of the superimposed matrix is ​​recalculated to obtain a disturbed characteristic vector matrix;

[0031] S312: Calculate the vector modulus column by column for the disturbed eigenvector matrix, and transform the modulus into a weighted dynamic value of the target by normalizing the modulus and the corresponding dynamic weight factor, using the formula:

[0032]

[0033] Calculate the weighted dynamic value to obtain a dynamic numerical sequence;

[0034] Among them, v ij represents the element in the i-th row and j-th column of the perturbed eigenvector matrix, |v ij | represents element v ij The absolute value of represents the modulus of the i-th row vector, v kj represents the element in the kth row and jth column of the perturbed eigenvector matrix, |v kj | represents element v kj The absolute value of v kj Represents the specific element value of the kth row and jth column in the eigenvector matrix, which is used to calculate the modulus value of the kth row vector. represents the modulus of the k-th row vector, w i represents the dynamic weight factor corresponding to the i-th column, w k represents the dynamic weight factor corresponding to the kth column, It means to sum all the columns of a row in the eigenvector matrix, n is the number of columns in the eigenvector matrix, is the weighted modulus sum of all rows for normalization, m is the number of rows in the eigenvector matrix, v i represents the dynamic weight value of the ith row after weighted normalization, represents the product of the weighting factor and the modulus value;

[0035] S313: Perform a nonlinear combination operation on the dynamic numerical sequence and the input encryption parameter, combine each item in the numerical sequence with the encryption parameter, generate a new numerical value through recursive calculation, apply a nonlinear mapping relationship to update and sort the combined sequence, and generate a dynamic key sequence.

[0036] As a further solution of the present invention, the step of obtaining the encrypted signal frame data is specifically as follows:

[0037] S411: using the dynamic key sequence to call the signal channel amplitude sequence of the aviation data bus frame by frame, matching and binding the corresponding part of the dynamic key according to the frame index of the amplitude sequence, performing element-level superposition operation on the key data and the signal amplitude sequence, and generating a signal amplitude sequence after dynamic superposition;

[0038] S412: The signal amplitude sequence after dynamic superposition is replaced frame by frame according to the signal frame structure, using the formula:

[0039]

[0040] Calculate and generate the encrypted signal amplitude;

[0041] Among them, V′ ij Represents the encrypted signal amplitude, V ij Represents the original signal amplitude, K ij represents the corresponding element value in the dynamic key matrix, |V ij | represents the original signal amplitude V ij The absolute value of Represents the square of the k-th amplitude element in the i-th frame in the original signal amplitude matrix, represents the sum of the squares of all amplitude elements in the i-th frame, Represents the square root of the sum of the squares of all amplitude elements in the i-th frame, that is, the modulus value, represents the sum of all elements of the amplitude sequence in the i-th frame, n represents the length of the amplitude sequence in each frame, · represents the product of the dynamic key value and the amplitude change factor, The normalization coefficient representing the amplitude change is used to adjust the dynamic change amplitude of the signal amplitude. i represents the number of the signal frame, j represents the position of the amplitude sequence in the signal frame, and k represents the element index of the amplitude sequence in the normalization.

[0042] S413: Archiving and storing the encrypted signal amplitude according to the signal frame structure of the aviation data bus, and reconstructing the encrypted signal frame data according to the frame index.

[0043] As a further solution of the present invention, the step of acquiring the decrypted signal data stream is specifically as follows:

[0044] S511: Based on the encrypted signal frame data, segment the data frame according to the slicing rule defined by the encryption end, reconstruct the column vector of each data frame into a matrix form according to the time index order, extract the key eigenvalue by calculating the column vector covariance of the matrix, and generate an initial feature matrix;

[0045] S512: Based on the initial feature matrix, the column vectors of the feature matrix are decomposed column by column, and the dynamic feature value sequence is reconstructed through the time index and feature amplitude normalization rule, using the formula:

[0046]

[0047] Generate a normalized dynamic eigenvalue sequence;

[0048] Among them, F t represents the dynamic eigenvalue at time t, T i represents the eigenvalue of the i-th column of the feature matrix, w i represents the normalized weighting factor, γ represents the dynamic adjustment parameter, n represents the number of elements in the eigenvalue column vector, Represents the sum of all items from i 1 to n, the denominator Represents the weighting factor w j The square sum of and traverse j from 1 to n, the formula Represents T i The absolute value of the square root of Represents the dynamic feature contribution value of each column of eigenvalues ​​after amplitude normalization and weight adjustment;

[0049] S513: Match the encrypted signal frame data point by point using the normalized dynamic eigenvalue sequence, infer the original signal amplitude through the dynamic eigenvalue, perform decryption and reconstruction operations point by point, integrate all frame signal amplitudes according to the serialization rules, and generate a decrypted signal data stream.

[0050] An aviation data bus signal encryption system based on FPGA, the aviation data bus signal encryption system based on FPGA is used to execute the above-mentioned aviation data bus signal encryption method based on FPGA, and the system includes:

[0051] The signal slice construction module extracts slices at fixed intervals based on the input three-dimensional tensor signal data, sorts the signal channel amplitudes and then segments them, recombines the segmented signal amplitudes into a matrix structure, and generates a multi-dimensional signal slice matrix;

[0052] The feature matrix generation module extracts the aviation data bus signal data frame based on the multi-dimensional signal slice matrix, decomposes the multi-data frame matrix row by row to obtain key component values, processes the key component values ​​according to amplitude integration to generate amplitude integration data, reduces the dimension of the feature curve and rearranges it according to the mapping rule to generate a signal feature matrix;

[0053] The dynamic key generation module, based on the signal characteristic matrix, superimposes the disturbance factor on the aviation data bus time index and the characteristic matrix column vector, calculates the modulus value according to the disturbance sequence vector, performs weighted conversion on the modulus value to generate a dynamic numerical sequence, and generates a dynamic key sequence in combination with the encryption parameters;

[0054] The encrypted signal construction module calls the aviation data bus signal channel amplitude sequence frame by frame based on the dynamic key sequence, superimposes the dynamic key sequence and the amplitude point by point to replace the signal amplitude, archives and stores the replaced signal amplitude frame by frame, and generates encrypted signal frame data;

[0055] The signal data decryption module reconstructs the aviation data bus signal frame matrix based on the encrypted signal frame data and the encryption end slicing rule, recalculates the key component values ​​of the matrix to establish a feature matrix, constructs a dynamic eigenvalue sequence for the feature matrix, matches the encrypted signal frame data point by point to restore the signal amplitude, and generates a decrypted signal data stream.

[0056] Compared with the prior art, the advantages and positive effects of the present invention are:

[0057] In the present invention, by performing time index slicing and amplitude partitioning reorganization on the input signal, the structured expression of the signal is strengthened and the processing accuracy of complex multidimensional signals is improved. The processing logic based on matrix decomposition and characteristic curve generation optimizes the feature extraction and dimensionality reduction process, and improves the distinguishability and representation ability of feature information. The key generation method combining dynamic perturbation factors with encryption parameters enhances the randomness and anti-cracking ability of the key and ensures the security of signal transmission. The combination of amplitude superposition and frame-by-frame storage realizes the dynamic and efficient signal encryption process and reduces the risk of signal tampering and eavesdropping. Through feature reconstruction and point-by-point matching processing in the decryption stage, the accurate restoration of the signal amplitude is ensured, the data integrity and anti-interference ability are guaranteed, and it is suitable for a variety of complex communication environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0059] Figure 2 A flowchart of the steps of obtaining a multi-dimensional signal slice matrix of the present invention;

[0060] Figure 3 Flow chart of the steps of obtaining the signal feature matrix of the present invention;

[0061] Figure 4 A flowchart of the steps for obtaining a dynamic key sequence of the present invention;

[0062] Figure 5 A flowchart of the steps for obtaining encrypted signal frame data of the present invention;

[0063] Figure 6The present invention is a flowchart of the steps for obtaining the decryption signal data stream. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0065] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0066] Embodiment 1

[0067] See also Figure 1 The present invention provides a technical solution: an aviation data bus signal encryption method based on FPGA, comprising the following steps:

[0068] S1: Based on the input three-dimensional tensor signal data, slices are extracted at fixed intervals by the time index of the aviation data bus, the amplitude of the signal channel is sorted in ascending order and then segmented, and the signal amplitude is partitioned and reorganized into a matrix structure to generate a multi-dimensional signal slice matrix;

[0069] S2: extract the data frame of each aviation data bus from the multi-dimensional signal slice matrix, perform matrix decomposition on the multiple frames to calculate the principal component value, process the principal component value according to the signal amplitude integral to generate characteristic curve data, reduce the dimension of the characteristic curve and rearrange it according to the mapping rule to generate a signal feature matrix;

[0070] S3: Based on the signal feature matrix, the dynamic disturbance factor is superimposed on the time index and column vector data of the aviation data bus, the characteristic vector after disturbance is calculated, the characteristic vector modulus value is weighted and converted into a dynamic numerical sequence, and the numerical sequence is combined with the encryption parameter to generate a dynamic key sequence;

[0071] S4: Using the dynamic key sequence, the amplitude sequence is called frame by frame for the signal channel of the aviation data bus, the key data is superimposed with the signal amplitude to replace the original signal amplitude, the encrypted amplitude data is archived and stored frame by frame, and the encrypted signal frame data is generated;

[0072] S5: Based on the encrypted signal frame data, reconstruct the data frame of the aviation data bus into a matrix according to the encryption end slicing rule, calculate the principal component to establish the characteristic matrix, reconstruct the dynamic eigenvalue sequence, match it point by point with the encrypted signal frame to restore the signal amplitude, and generate a decrypted signal data stream.

[0073] The multidimensional signal slice matrix includes signal channel slices, amplitude ascending sorting data, and a reorganized matrix. The signal feature matrix includes feature curve data, feature data after dimensionality reduction, and a rearranged feature mapping matrix. The dynamic key sequence includes a dynamic perturbation factor feature vector, a weighted conversion numerical sequence, and a key parameter sequence. The encrypted signal frame data includes an encrypted amplitude sequence, frame archive storage data, and signal amplitudes replaced frame by frame. The decrypted signal data stream includes a dynamic feature value sequence, a restored signal amplitude, and a reconstructed signal data matrix.

[0074] See also Figure 2 , the steps for obtaining the multidimensional signal slice matrix are as follows:

[0075] S111: extracting signal amplitudes at fixed time intervals from the three-dimensional tensor signal data, structuring the signal at each time point, constructing a time series signal amplitude matrix, and obtaining a signal time series structure;

[0076] This process involves extracting the signal at each time point one by one, ensuring that the construction of each time series signal amplitude matrix accurately reflects the original data structure. This method can ensure that the time correspondence and integrity of the signal data are maintained, and the use of data structured processing enhances the operability of signal processing and the ease of handling of subsequent analysis. The execution of this step plays a key role in the efficiency of the entire data processing process and the accuracy of the results. The signal time series structure is established, providing basic data support for subsequent signal analysis.

[0077] S112: sorting the data in the signal time series structure in ascending order of amplitude, dividing the signal into several intervals according to the amplitude, calculating the mean value and standard deviation of the signal amplitude for each interval, and defining a signal interval descriptor for each signal partition to obtain a signal amplitude interval description;

[0078] This process involves calculating the mean and standard deviation of the signal amplitude in each interval. This refinement not only improves the accuracy of signal processing, but also provides a scientific basis for signal classification and identification. The description of the signal amplitude interval accurately reflects the statistical characteristics of the signal, which is helpful for the subsequent signal reconstruction and analysis work. The signal amplitude interval description is defined, which effectively converts the continuous signal into a discrete interval that is easy to process.

[0079] S113: According to the description of the signal amplitude interval, the signal is reorganized, the amplitude interval is remapped to the current position, and the signal amplitude matrix after reorganization is calculated by combining the signal density of the amplitude interval with the weighted average method, using the formula:

[0080]

[0081] Get the reconstructed signal amplitude matrix;

[0082] Among them, x k represents the signal amplitude in interval k, w k Represents the amplitude x k The corresponding signal density, n represents the number of signals, M ij Represents the signal amplitude of row i and column j;

[0083] formula:

[0084]

[0085] The benefit of the formula is that it takes into account the signal density of each signal amplitude through the weighted average method, effectively reflects the importance and frequency of the signal in the interval, and makes the reconstructed signal closer to the real physical phenomenon.

[0086] Detailed explanation of the formula and the process of formula calculation and derivation:

[0087] Assume that there are three signal amplitudes in a signal interval, x1=10, x2=20, x3=30, and the corresponding signal density weights are w1=0.2, w2=0.3, w3=0.5, then the weighted average calculation process of the signal amplitude is as follows:

[0088]

[0089] The result shows that the amplitude of this position in the recombinant signal amplitude matrix is ​​23, which reflects the statistical characteristics and practical importance of the original signal in this interval. Further sorting out the specific values ​​of the recombinant signal amplitude matrix in the step results.

[0090] S114: Convert the reorganized signal amplitude matrix into a three-dimensional structured slice matrix, where each slice reflects a physical dimension of the original signal data, to generate a multi-dimensional signal slice matrix.

[0091] This step involves remapping the multidimensional data to match the physical dimensions of the original signal. This process ensures the accurate reorganization of the data and the correct expression of each dimension. In this way, each slice can effectively reflect a physical dimension of the signal, maintain the integrity and functionality of the data, and ensure that each dimension of the signal is accurately structured. The resulting multidimensional signal slice matrix can be used for deeper signal processing and analysis tasks, achieving the goal of data structuring. The generation of the multidimensional signal slice matrix provides an important data foundation for subsequent applications.

[0092] See also Figure 3 , the steps to obtain the signal feature matrix are as follows:

[0093] S211: extracting the aviation data bus signal data frames in the multi-dimensional signal slice matrix, performing structured sorting on the data frames through time series analysis to form a serialized signal frame matrix, and performing preliminary amplitude distribution analysis based on signal strength to generate a preliminary signal frame description matrix;

[0094] When extracting the signal data frames of the aviation data bus, the time series of the signal needs to be analyzed, which involves extracting data frames in time order from the multidimensional signal slice matrix and classifying these data frames according to signal strength. Once the signal data frames are extracted and classified, they are used to construct a structured signal frame matrix. This matrix is ​​constructed by analyzing the signal strength in each data frame and sorting them by strength. Each data frame consists of multiple signals, and the strength of each signal is recorded and analyzed to determine its importance in the entire communication process, thus forming a preliminary signal frame description matrix. This matrix not only reflects the time series distribution of the signal, but also contains preliminary information on the signal amplitude distribution, which is essential for subsequent signal processing and feature extraction.

[0095] S212: applying matrix decomposition technology to the preliminary signal frame description matrix, extracting key signal features as principal components, and determining their contribution to obtain a principal component matrix;

[0096] By applying matrix decomposition technology to the preliminary signal frame description matrix, important signal features such as principal components can be effectively extracted. These principal components are obtained by applying decomposition methods such as singular value decomposition (SVD) to the signal frame matrix. They express the most significant statistical features in the data. Each principal component corresponds to a specific signal direction, which captures the main variability in the data. The statistical significance of these principal components is determined by calculating the contribution of each component. The degree of contribution reflects the importance of the component in the signal. The result of this step is the principal component matrix, which provides a basis for further signal feature analysis.

[0097] S213: Calculate the time series characteristic curve of the signal from the principal component matrix using the formula:

[0098]

[0099] Perform integration processing on the signal amplitude to generate the characteristic curve of the signal;

[0100] Among them, I t represents the integral value of time t, P t is the amplitude of the principal component, λ represents the coefficient of amplitude decay over time, and μ is the amplitude enhancement coefficient, which enhances the signal characteristics;

[0101] formula:

[0102]

[0103] The benefit of the formula is that it enhances the signal characteristics by combining linear and nonlinear terms. It not only considers the initial strength P of the signal t The effect of time attenuation is also introduced by the square term of signal strength To strengthen the main features, thereby improving the sensitivity and accuracy of feature extraction.

[0104] Detailed explanation of the formula and the process of formula calculation and derivation:

[0105] Assume P t =50 unit amplitude, attenuation factor λ=0.1, signal enhancement factor μ=0.05, start time t0=0 and end time t=10 seconds. The calculation formula becomes:

[0106]

[0107]

[0108] The result shows that the integral value of the signal characteristic curve is 1566.06, which means that from t = 0 to t = 10 seconds, the signal characteristic value has accumulated to this level, reflecting the overall strength and change trend of the signal characteristics during this period, and is a quantitative description of the dynamic behavior of the signal.

[0109] S214: Perform dimensionality reduction processing on the characteristic curve of the signal, rearrange the data after dimensionality reduction using the target mapping rule, integrate all processed data, and obtain a signal feature matrix.

[0110] Through the application of dimensionality reduction processing and mapping rules, the high-dimensional signal characteristic curve is converted into a more concise and effective expression form. Dimensionality reduction processing usually uses methods such as principal component analysis or linear discriminant analysis. These methods can effectively extract the most important features from complex data and convert these features into low-dimensional space representations. Through mapping rules, these low-dimensional features are rearranged and organized to optimize subsequent analysis and processing processes. The rearranged signal feature matrix is ​​constructed, which arranges each important signal feature in a certain logical order to ensure the integrity and interpretability of the data. The final signal feature matrix provides an important basis for subsequent data analysis and decision-making.

[0111] See also Figure 4 , the steps to obtain the dynamic key sequence are as follows:

[0112] S311: Based on the signal characteristic matrix, each column vector of the matrix is ​​superimposed according to the time index of the aviation data bus, and a dynamic disturbance factor is inserted, and the amplitude, direction and timing parameters of the disturbance factor are adjusted at the same time, and the column vector of the superimposed matrix is ​​recalculated to obtain the characteristic vector matrix after disturbance;

[0113] First, the superposition of each column vector and the aviation data bus time index is calculated. Dynamic disturbance factors are introduced in the superposition. The dynamic disturbance factors include the amplitude and direction of random fluctuations. These factors are obtained based on the statistics of past data fluctuations. The amplitude, direction and timing parameters of the disturbance factors are adjusted to ensure the realism of the results. Then, a new disturbance eigenvector matrix is ​​recalculated to ensure that the data maintains a certain degree of dynamics and unpredictability, thereby improving the safety of the system.

[0114] S312: Calculate the vector modulus column by column for the disturbed eigenvector matrix, and transform the modulus into a weighted dynamic value of the target by normalizing the modulus and the corresponding dynamic weight factor, using the formula:

[0115]

[0116] Calculate the weighted dynamic value to obtain a dynamic numerical sequence;

[0117] Among them, v ij represents the element in the i-th row and j-th column of the perturbed eigenvector matrix, |v ij | represents element v ij The absolute value of represents the modulus of the i-th row vector, v kj represents the element in the kth row and jth column of the perturbed eigenvector matrix, |v kj | represents element v kj The absolute value of v kjRepresents the specific element value of the kth row and jth column in the eigenvector matrix, which is used to calculate the modulus value of the kth row vector. represents the modulus of the k-th row vector, w i represents the dynamic weight factor corresponding to the i-th column, w k represents the dynamic weight factor corresponding to the kth column, It means to sum all the columns of a row in the eigenvector matrix, n is the number of columns in the eigenvector matrix, is the weighted modulus sum of all rows for normalization, m is the number of rows in the eigenvector matrix, v i represents the dynamic weight value of the ith row after weighted normalization, represents the product of the weighting factor and the modulus value;

[0118] formula:

[0119]

[0120] Detailed explanation of the formula and the process of formula calculation and derivation:

[0121] Assume a simplified example, the eigenvector matrix V is as follows: The corresponding weight factor is w = {2, 3}.

[0122] Calculate v 11 and v 12 The modulus value is:

[0123]

[0124] Calculate v 21 and v 22 The modulus value is:

[0125]

[0126] Compute the sum of the weighted modulo values ​​of the denominators:

[0127]

[0128] The final value of v1 is:

[0129]

[0130] This result shows that the value of v1 reflects the relative importance of the first row of features under the weight and modulus adjustment. This value is associated with the result of the dynamic value sequence generation step, indicating the normalized expression of the feature under the adjusted weight, which is helpful for further data analysis or model training.

[0131] S313: Perform a nonlinear combination operation on the dynamic numerical sequence and the input encryption parameter, combine each item in the numerical sequence with the encryption parameter, generate a new numerical value through recursive calculation, apply a nonlinear mapping relationship to update and sort the combined sequence, and generate a dynamic key sequence.

[0132] First, the numerical sequence is mapped to specific encryption parameters. This mapping relationship is adjusted based on encryption performance data to ensure that the key sequence has high security and randomness. Non-linear combination includes using polynomials or logarithmic functions to adjust the sequence values. The combined sequence is then updated through a recursive algorithm. The recursive update includes predicting and adjusting the current value based on the previous sequence value to ensure that the output of each step depends on the output of the previous step, increasing the difficulty of cracking. The generated dynamic key sequence has strong security and can effectively defend against external decryption attempts to ensure the security of communications.

[0133] See also Figure 5 , the specific steps for obtaining the encrypted signal frame data are:

[0134] S411: Using the dynamic key sequence to call the signal channel amplitude sequence of the aviation data bus frame by frame, matching and binding the corresponding part of the dynamic key according to the frame index of the amplitude sequence, performing element-level superposition operation on the key data and the signal amplitude sequence, and generating a signal amplitude sequence after dynamic superposition;

[0135] This process involves the dynamic combination of key data and signal amplitude to ensure the security and integrity of communications. The generation of dynamic key sequences depends on the aforementioned key generation algorithm, which uses a highly secure random number generator and a time synchronization mechanism to ensure that each frame of the signal can match a unique key. Furthermore, the signal amplitude of each frame is superimposed with the corresponding key data. This superposition process not only takes into account the original amplitude of the signal, but also adds the changing factor of the dynamic key to ensure the diversity and security of encryption. The signal amplitude processed by the algorithm replaces the original amplitude and is archived for storage. The archive not only records the encrypted amplitude data, but also retains the comparison data before and after encryption for subsequent security inspections and data recovery. This process ensures the security of data during transmission and prevents the risk of eavesdropping or tampering that may occur during data transmission.

[0136] S412: Replace the signal amplitude sequence after dynamic superposition frame by frame according to the signal frame structure, using the formula:

[0137]

[0138] Calculate and generate the encrypted signal amplitude;

[0139] Among them, V′ ijRepresents the encrypted signal amplitude, V ij Represents the original signal amplitude, K ij represents the corresponding element value in the dynamic key matrix, |V ij | represents the original signal amplitude V ij The absolute value of Represents the square of the k-th amplitude element in the i-th frame in the original signal amplitude matrix, represents the sum of the squares of all amplitude elements in the i-th frame, Represents the square root of the sum of the squares of all amplitude elements in the i-th frame, that is, the modulus value, represents the sum of all elements of the amplitude sequence in the i-th frame, n represents the length of the amplitude sequence in each frame, · represents the product of the dynamic key value and the amplitude change factor, The normalization coefficient representing the amplitude change is used to adjust the dynamic change amplitude of the signal amplitude. i represents the number of the signal frame, j represents the position of the amplitude sequence in the signal frame, and k represents the element index of the amplitude sequence in the normalization.

[0140] formula:

[0141]

[0142] The benefit of the formula is that it combines the original amplitude of the signal with the dynamic key factor, and enhances the security of the signal by making personalized weighted adjustments to each signal amplitude, while retaining the basic characteristics of the signal, so that the effective information of the signal is not lost while ensuring security.

[0143] Detailed explanation of the formula and the process of formula calculation and derivation:

[0144] Consider the signal amplitude V ij is 50, the corresponding key value K ij is 5, the number of elements in the signal frame n is 10, and the amplitude of other signals V ik is [50,45,55,60,40,65,70,45,55,50], and the calculation process is as follows:

[0145] 1. Calculate the denominator

[0146]

[0147] 2. Calculate the key weighted part:

[0148]

[0149] 3. Calculate the encrypted amplitude V′ ij :

[0150] 50+5·1.265=56.325;

[0151] The results show that by properly weighting the dynamic key, the amplitude of the original signal is adjusted from 50 to 56.325, which improves the security of the signal while ensuring the validity and recognition of the signal amplitude. This adjustment is crucial to maintaining security during data transmission and ensures that the data is not easily tampered with or cracked.

[0152] S413: Archiving and storing the encrypted signal amplitude according to the signal frame structure of the aviation data bus, and reconstructing the encrypted signal frame data according to the frame index.

[0153] First, the security of stored data is ensured through the advanced encryption standard. Secondly, the encrypted signal of each frame is checked and calculated to verify that the data has not been tampered with during transmission. Then, a distributed storage system is used to store the encrypted signal in multiple servers. This increases the reliability of data recovery and reduces the risk of single point failure. In addition, regular integrity checks are performed on the encrypted signal frame data to ensure that the data remains untampered after long-term storage. Through these detailed execution processes, not only the security level of data processing is improved, but also the data management efficiency is optimized, so that the encrypted signal can be quickly and accurately retrieved and used when needed, ensuring the efficient operation of the aviation data communication system.

[0154] See also Figure 6 , the specific steps for obtaining the decrypted signal data stream are:

[0155] S511: Based on the encrypted signal frame data, the data frame is segmented according to the slicing rule defined by the encryption end, the column vector of each data frame is reconstructed into a matrix form according to the time index order, and the key eigenvalues ​​are extracted by calculating the column vector covariance of the matrix to generate an initial feature matrix;

[0156] First, it is necessary to analyze the structure of the data frame in detail and determine the key parameters of each frame of data, such as timestamps and index labels. These parameters will determine how the data frame is segmented and how to reconstruct the matrix afterwards. Next, matrix processing is applied to each slice. The matrix operations involved include row-column conversion and data type conversion to ensure that the slice data can adapt to the subsequent processing flow. The integrity and consistency of the data also need to be considered in this process to prevent data loss during the conversion process. The data is reconstructed according to the results of principal component analysis. This step is to calculate the covariance matrix of each data point and then extract the main components. This requires a lot of computing resources and precise algorithm implementation to ensure the accuracy of the feature matrix.

[0157] S512: Based on the initial feature matrix, the column vectors of the feature matrix are decomposed column by column, and the dynamic feature value sequence is reconstructed through the time index and feature amplitude normalization rules, using the formula:

[0158]

[0159] Generate a normalized dynamic eigenvalue sequence;

[0160] Among them, F t represents the dynamic eigenvalue at time t, T i represents the eigenvalue of the i-th column of the feature matrix, w i represents the normalized weighting factor, γ represents the dynamic adjustment parameter, n represents the number of elements in the eigenvalue column vector, Represents the sum of all items from i 1 to n, the denominator Represents the weighting factor w j The square sum of and traverse j from 1 to n, the formula Represents T i The absolute value of the square root of Represents the dynamic feature contribution value of each column of eigenvalues ​​after amplitude normalization and weight adjustment;

[0161] formula:

[0162]

[0163] The benefit of the formula is that it provides a weighting method to highlight those eigenvalues ​​that are more important in statistical analysis, thereby reconstructing the signal more accurately, which is especially important when dealing with encrypted data because the encryption process may have changed some of the basic characteristics of the data. i and dynamically adjust the parameter γ, the formula can flexibly adapt to different signal characteristics and optimize the decryption process.

[0164] Detailed explanation of the formula and the process of formula calculation and derivation:

[0165] Assume T i is 1.5, w i is 0.5, there are 5 data points, γ is set to 0.05, and the calculation process is as follows:

[0166] 1. Calculate the sum of the squares of the weights:

[0167] 2. Calculate the contribution of each term:

[0168] 3. Add all the terms together and add γ: 5 × 0.81 + 0.05 = 4.1;

[0169] This result shows that under given parameters and weights, the reconstructed dynamic eigenvalue F t It is approximately 4.1, which means that the signal reconstructed by this formula can reflect the dynamic characteristics of the original signal to a certain extent, providing an effective numerical basis for decryption.

[0170] S513: Use the normalized dynamic eigenvalue sequence to match the encrypted signal frame data point by point, reverse the original signal amplitude through the dynamic eigenvalue, perform decryption and reconstruction operations point by point, integrate all frame signal amplitudes according to the serialization rules, and generate a decrypted signal data stream.

[0171] First, it is necessary to index and synchronize the dynamic eigenvalue sequence and the encrypted signal frame data to ensure that the time axis of the sequence corresponds to the signal frame time axis one by one. Then, the signal amplitude information of the corresponding eigenvalue in the encrypted signal frame is extracted through the point-to-point matching method. In this process, the matching value needs to be inversely operated, and the original signal amplitude is restored point by point by reversing the perturbation coefficient and dynamic key in the encryption process. The restored signal amplitude is then reconstructed in frame order, and the reconstructed signal data of each frame is serialized, sorted, and finally a decrypted signal data stream is generated.

[0172] The aviation data bus signal encryption system based on FPGA is used to execute the aviation data bus signal encryption method based on FPGA. The system includes:

[0173] The signal slice construction module extracts slices at fixed intervals based on the input three-dimensional tensor signal data, sorts the signal channel amplitudes and then segments them, recombines the segmented signal amplitudes into a matrix structure, and generates a multi-dimensional signal slice matrix;

[0174] The feature matrix generation module extracts the aviation data bus signal data frame based on the multi-dimensional signal slice matrix, decomposes the multi-data frame matrix row by row to obtain the key component values, processes the key component values ​​by amplitude integration to generate amplitude integration data, reduces the dimension of the feature curve and rearranges it according to the mapping rules to generate the signal feature matrix;

[0175] The dynamic key generation module is based on the signal feature matrix. It adds disturbance factors to the aviation data bus time index and the feature matrix column vector, calculates the modulus value according to the disturbance sequence vector, performs weighted transformation on the modulus value to generate a dynamic numerical sequence, and generates a dynamic key sequence in combination with encryption parameters.

[0176] The encrypted signal construction module calls the amplitude sequence of the aviation data bus signal channel frame by frame based on the dynamic key sequence, superimposes the dynamic key sequence and the amplitude point by point to replace the signal amplitude, archives and stores the replaced signal amplitude frame by frame, and generates encrypted signal frame data;

[0177] The signal data decryption module reconstructs the aviation data bus signal frame matrix based on the encrypted signal frame data and the encryption end slicing rules, recalculates the key component values ​​of the matrix to establish the characteristic matrix, constructs a dynamic eigenvalue sequence for the characteristic matrix, matches the encrypted signal frame data point by point to restore the signal amplitude, and generates a decrypted signal data stream.

[0178] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. The aviation data bus signal encryption method based on FPGA is characterized by: The following steps are involved: Based on the input three-dimensional tensor signal data, slices are extracted at fixed intervals by the time index of the aviation data bus, the amplitude of the signal channel is sorted in ascending order and then segmented, and the signal amplitude is partitioned and reorganized into a matrix structure to generate a multi-dimensional signal slice matrix; Extract the data frame of each aviation data bus from the multi-dimensional signal slice matrix, perform matrix decomposition on the multiple frames to calculate the principal component value, perform signal amplitude integral processing on the principal component value to generate characteristic curve data, reduce the dimension of the characteristic curve and rearrange it according to the mapping rule to generate a signal characteristic matrix; Based on the signal characteristic matrix, a dynamic disturbance factor is superimposed on the time index and column vector data of the aviation data bus, a characteristic vector after disturbance is calculated, a modulus value of the characteristic vector is weighted and converted into a dynamic numerical sequence, and the numerical sequence is combined with an encryption parameter to generate a dynamic key sequence; Using the dynamic key sequence, the amplitude sequence is called frame by frame for the signal channel of the aviation data bus, the key data is superimposed with the signal amplitude to replace the original signal amplitude, the encrypted amplitude data is archived and stored frame by frame, and the encrypted signal frame data is generated; Based on the encrypted signal frame data, the data frame of the aviation data bus is reconstructed into a matrix according to the encryption end slicing rule, the principal component is calculated to establish a characteristic matrix, the dynamic eigenvalue sequence is reconstructed, and the signal amplitude is restored by point-by-point matching with the encrypted signal frame to generate a decrypted signal data stream; The multidimensional signal slice matrix includes signal channel slices, amplitude ascending sorting data, and a reorganized matrix; the signal feature matrix includes feature curve data, feature data after dimensionality reduction, and a rearranged feature mapping matrix; the dynamic key sequence includes a dynamic perturbation factor feature vector, a weighted conversion numerical sequence, and a key parameter sequence; the encrypted signal frame data includes an encrypted amplitude sequence, frame archive storage data, and signal amplitudes replaced frame by frame; the decrypted signal data stream includes a dynamic feature value sequence, a restored signal amplitude, and a reconstructed signal data matrix.

2. The aviation data bus signal encryption method based on FPGA according to claim 1, characterized in that: The steps of obtaining the multidimensional signal slice matrix are specifically as follows: From the three-dimensional tensor signal data, the signal amplitude is extracted at fixed time intervals, the signal at each time point is structured, the time series signal amplitude matrix is ​​constructed, and the signal time series structure is obtained; The data in the signal time series structure are sorted in ascending order of amplitude, the signal is divided into a number of intervals according to the amplitude, the mean value and the standard deviation of the signal amplitude are calculated for each interval, and a signal interval descriptor is defined for each signal partition to obtain a signal amplitude interval description; According to the signal amplitude interval description, the signal is reorganized, the amplitude interval is remapped to the current position, and the signal amplitude matrix after reorganization is calculated by combining the signal density of the amplitude interval with the weighted average method, using the formula: Get the reconstructed signal amplitude matrix; Among them, x k represents the signal amplitude in interval k, w k Represents the amplitude x k The corresponding signal density, n represents the number of signals, M ij Represents the signal amplitude of row i and column j; The reorganized signal amplitude matrix is ​​converted into a three-dimensional structured slice matrix, each slice reflects a physical dimension of the original signal data, and a multi-dimensional signal slice matrix is ​​generated.

3. The aviation data bus signal encryption method based on FPGA according to claim 2 is characterized in that: The steps of obtaining the signal feature matrix are specifically as follows: Extracting the aviation data bus signal data frames in the multidimensional signal slice matrix, performing structured sorting on the data frames through time series analysis to form a serialized signal frame matrix, and performing preliminary amplitude distribution analysis based on signal strength to generate a preliminary signal frame description matrix; Applying matrix decomposition technology to the preliminary signal frame description matrix, extracting key signal features as principal components, and determining their contributions to obtain a principal component matrix; The time series characteristic curve of the signal is calculated from the principal component matrix using the formula: Perform integration processing on the signal amplitude to generate the characteristic curve of the signal; Among them, I t represents the integral value of time t, P t is the amplitude of the principal component, λ represents the coefficient of amplitude decay over time, and μ is the amplitude enhancement coefficient, which enhances the signal characteristics; The characteristic curve of the signal is subjected to dimensionality reduction processing, the data after dimensionality reduction is rearranged using a target mapping rule, and all processed data are integrated to obtain a signal characteristic matrix.

4. The aviation data bus signal encryption method based on FPGA according to claim 3 is characterized in that: The steps of obtaining the dynamic key sequence are specifically as follows: Based on the signal characteristic matrix, each column vector of the matrix is ​​superimposed according to the time index of the aviation data bus, and a dynamic disturbance factor is inserted, and the amplitude, direction and timing parameters of the disturbance factor are adjusted at the same time, and the column vector of the superimposed matrix is ​​recalculated to obtain a disturbed characteristic vector matrix; The vector modulus is calculated column by column for the disturbed eigenvector matrix, and the modulus is transformed into a weighted dynamic value of the target by normalizing the modulus and the corresponding dynamic weight factor, using the formula: Calculate the weighted dynamic value to obtain a dynamic numerical sequence; Among them, v ij represents the element in the i-th row and j-th column of the perturbed eigenvector matrix, |v ij | represents element v ij The absolute value of represents the modulus of the i-th row vector, v kj represents the element in the kth row and jth column of the perturbed eigenvector matrix, |v kj | represents element v kj The absolute value of v kj Represents the specific element value of the kth row and jth column in the eigenvector matrix, which is used to calculate the modulus value of the kth row vector. represents the modulus of the k-th row vector, w i represents the dynamic weight factor corresponding to the i-th column, w k represents the dynamic weight factor corresponding to the kth column, It means to sum all the columns of a row in the eigenvector matrix, n is the number of columns in the eigenvector matrix, is the weighted modulus sum of all rows for normalization, m is the number of rows in the eigenvector matrix, v i represents the dynamic weight value of the ith row after weighted normalization, represents the product of the weighting factor and the modulus value; A nonlinear combination operation is performed on the dynamic numerical sequence and the input encryption parameter. After each item in the numerical sequence is combined with the encryption parameter, a new numerical value is generated by recursive calculation. The combined sequence is updated and sorted using a nonlinear mapping relationship to generate a dynamic key sequence.

5. The aviation data bus signal encryption method based on FPGA according to claim 4 is characterized in that: The steps for obtaining the encrypted signal frame data are specifically as follows: The signal channel amplitude sequence of the aviation data bus is called frame by frame using the dynamic key sequence, the corresponding part of the dynamic key is matched and bound according to the frame index of the amplitude sequence, the key data and the signal amplitude sequence are superimposed element by element, and the signal amplitude sequence after dynamic superposition is generated; The signal amplitude sequence after dynamic superposition is replaced frame by frame according to the signal frame structure, using the formula: Calculate and generate the encrypted signal amplitude; Among them, V′ ij Represents the encrypted signal amplitude, V ij Represents the original signal amplitude, K ij represents the corresponding element value in the dynamic key matrix, |V ij | represents the original signal amplitude V ij The absolute value of V i 2 k Represents the square of the k-th amplitude element in the i-th frame in the original signal amplitude matrix, represents the sum of the squares of all amplitude elements in the i-th frame, Represents the square root of the sum of the squares of all amplitude elements in the i-th frame, that is, the modulus value, represents the sum of all elements of the amplitude sequence in the i-th frame, n represents the length of the amplitude sequence in each frame, and represents the product of the dynamic key value and the amplitude change factor. The normalization coefficient representing the amplitude change is used to adjust the dynamic change amplitude of the signal amplitude. i represents the number of the signal frame, j represents the position of the amplitude sequence in the signal frame, and k represents the element index of the amplitude sequence in the normalization. The encrypted signal amplitude is archived and stored according to the signal frame structure of the aviation data bus, and the encrypted signal frame data is reconstructed according to the frame index.

6. The aviation data bus signal encryption method based on FPGA according to claim 5, characterized in that: The steps of obtaining the decrypted signal data stream are specifically as follows: Based on the encrypted signal frame data, the data frame is segmented according to the slicing rule defined by the encryption end, the column vectors of each data frame are reconstructed into a matrix form according to the time index order, and the key eigenvalues ​​are extracted by calculating the column vector covariance of the matrix to generate an initial feature matrix; Based on the initial feature matrix, the column vector of the feature matrix is ​​decomposed column by column, and its dynamic feature value sequence is reconstructed through the time index and feature amplitude normalization rules, using the formula: Generate a normalized dynamic eigenvalue sequence; Among them, F t represents the dynamic eigenvalue at time t, T i represents the eigenvalue of the i-th column of the feature matrix, w i represents the normalized weighting factor, γ represents the dynamic adjustment parameter, n represents the number of elements in the eigenvalue column vector, Represents the sum of all items from i 1 to n, the denominator Represents the weighting factor w j The square sum of and traverse j from 1 to n, the formula Represents T i The absolute value of the square root of Represents the dynamic feature contribution value of each column of eigenvalues ​​after amplitude normalization and weight adjustment; The encrypted signal frame data is matched point by point using a normalized dynamic eigenvalue sequence, the original signal amplitude is inferred through the dynamic eigenvalue, the decryption and reconstruction operation is performed point by point, all frame signal amplitudes are integrated according to the serialization rules, and a decrypted signal data stream is generated.

7. The aviation data bus signal encryption system based on FPGA is characterized by: According to the FPGA-based aviation data bus signal encryption method according to any one of claims 1 to 6, the system comprises: The signal slice construction module extracts slices at fixed intervals based on the input three-dimensional tensor signal data, sorts the signal channel amplitudes and then segments them, recombines the segmented signal amplitudes into a matrix structure, and generates a multi-dimensional signal slice matrix; The feature matrix generation module extracts the aviation data bus signal data frame based on the multi-dimensional signal slice matrix, decomposes the multi-data frame matrix row by row to obtain key component values, processes the key component values ​​according to amplitude integration to generate amplitude integration data, reduces the dimension of the feature curve and rearranges it according to the mapping rule to generate a signal feature matrix; The dynamic key generation module, based on the signal characteristic matrix, superimposes the disturbance factor on the aviation data bus time index and the characteristic matrix column vector, calculates the modulus value according to the disturbance sequence vector, performs weighted conversion on the modulus value to generate a dynamic numerical sequence, and generates a dynamic key sequence in combination with the encryption parameters; The encrypted signal construction module calls the aviation data bus signal channel amplitude sequence frame by frame based on the dynamic key sequence, superimposes the dynamic key sequence and the amplitude point by point to replace the signal amplitude, archives and stores the replaced signal amplitude frame by frame, and generates encrypted signal frame data; The signal data decryption module reconstructs the aviation data bus signal frame matrix based on the encrypted signal frame data and the encryption end slicing rule, recalculates the key component values ​​of the matrix to establish a feature matrix, constructs a dynamic eigenvalue sequence for the feature matrix, matches the encrypted signal frame data point by point to restore the signal amplitude, and generates a decrypted signal data stream.

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