A method and apparatus for communication transmission based on pseudo-random code

Abstract

This invention discloses a communication transmission method and apparatus based on pseudo-random codes. The method includes: acquiring information to be transmitted; randomly generating first seed information and second seed information; performing feedback iterative calculation processing on the first seed information and second seed information to obtain a pseudo-random code; encoding the information to be transmitted using the pseudo-random code to obtain information to be sent; and sending the information to be sent from a sending end to a receiving end. This invention, by performing linear feedback shift and fractal calculation processing on the random seed information, enables the generated pseudo-random code to possess chaotic characteristics, improving the randomness of the pseudo-random code, thereby achieving the rapid construction of pseudo-random codes with excellent randomness and statistical characteristics close to white noise.

Description

Technical Field

[0001] This invention relates to the field of communications, and more specifically to a communication transmission method and apparatus based on pseudo-random codes. Background Technology

[0002] With the rapid development of wireless communication technology, the security of wireless networks has received increasing attention. Encoding the information to be transmitted using pseudo-random numbers is one way to improve the security of wireless networks. Pseudo-random numbers are sequences of random numbers uniformly distributed in the [0,1] region, calculated using deterministic algorithms. They are not truly random, but possess statistical characteristics similar to random numbers. Pseudo-random sequences are deterministic sequences with certain random properties. Due to their excellent randomness and statistical characteristics close to white noise, they have wide applications in many scientific, technological, and engineering fields.

[0003] How to quickly construct pseudo-random codes with excellent randomness and statistical properties close to white noise, and use them for secure information transmission, is an urgent problem to be solved. Summary of the Invention

[0004] This invention primarily addresses the problem of how to quickly construct pseudo-random codes with excellent randomness and statistical characteristics close to white noise, and how to use them for information transmission. This invention discloses a communication transmission method and apparatus for pseudo-random codes.

[0005] In a first aspect, this application discloses a communication transmission method based on pseudo-random codes, comprising:

[0006] S1, Obtain the information to be transmitted;

[0007] S2, randomly generate first seed information and second seed information;

[0008] S3, perform feedback iterative calculation on the first seed information and the second seed information to obtain a pseudo-random code;

[0009] S4, using the pseudo-random code, the information to be transmitted is encoded to obtain the information to be sent;

[0010] S5, the information to be sent is sent from the sending end to the receiving end.

[0011] The step of performing feedback iterative calculation on the first seed information and the second seed information to obtain a pseudo-random code includes:

[0012] S31, Initialize the shift value;

[0013] S32, perform linear feedback shift processing on the first seed information to obtain a first state sequence; use the shift value to perform linear cyclic shift processing on the second seed information to obtain a second state sequence;

[0014] S33, Perform fractal calculation processing on the first state sequence to obtain the third state sequence;

[0015] S34, perform a summation operation on the second state sequence and the third state sequence to obtain the fourth state sequence;

[0016] S35, perform bitwise XOR operation on the fourth state sequence to obtain a pseudo-random code;

[0017] S36 updates the shift value using each value of the pseudo-random code.

[0018] The calculation expression for the linear feedback shift process is:

[0019]

[0020] Among them, f i Let a represent the i-th coefficient in the linear feedback shift process. i F represents the value of the i-th bit of the first seed information a. t F represents the value of the first state sequence at time t, which is also the output at time t of the linear feedback shift processing of the first seed information a. t-1 This represents the value of the first state sequence at time t-1.

[0021] The expression for the fractal calculation process is:

[0022]

[0023] Among them, z i+1 F represents the (i+1)th value of the third state sequence obtained through fractal computation. i Let z represent the value of the i-th position in the first state sequence. i Let be the i-th value of the third state sequence obtained by fractal computation, and μ and w be the preset multiplication factor and exponential factor of the fractal computation, respectively.

[0024] When performing fractal calculations, the z-axis during the first calculation... i The value is determined using the mean of the first state sequence.

[0025] The process of encoding the information to be transmitted using the pseudo-random code to obtain the information to be sent includes:

[0026] A pseudo-random code matrix is ​​constructed using pseudo-random codes obtained at several time points.

[0027] The pseudo-random code matrix is ​​subjected to feature transformation processing to obtain the encoded sequence;

[0028] The encoded sequence is multiplied with the information to be transmitted to obtain the information to be sent.

[0029] The step of performing feature transformation processing on the pseudo-random code matrix to obtain the encoded sequence includes:

[0030] For each row vector of the pseudo-random code matrix, perform VMD transformation to obtain the corresponding transformation vector;

[0031] Using the transformation vectors as row vectors, a transformation matrix is ​​constructed.

[0032] The pseudo-random code matrix and the transformation matrix are multiplied by their rank transformations to obtain the random transformation matrix;

[0033] The singular values ​​of the random transformation matrix are solved to obtain a set of singular values ​​and a singular value vector.

[0034] Statistical calculations are performed on the singular value set and singular value vector to obtain the encoded sequence;

[0035] The expression for the statistical calculation is:

[0036]

[0037] Among them, c i For the i-th element of the encoded sequence, ρ i Let t be the i-th element of the singular value set. ij t is the j-th element of the i-th singular value vector. i and υt i Let be the mean and variance of the i-th singular value vector, respectively, and M be the number of elements in the singular value vector.

[0038] In a second aspect of this application, a communication transmission device based on pseudo-random codes is disclosed for implementing the communication transmission method based on pseudo-random codes, comprising: a source module, a pseudo-random code generation module, an encoding module, a sending module, and a receiving module;

[0039] The information source module is used to acquire the information to be transmitted;

[0040] The pseudo-random code generation module is used to randomly generate first seed information and second seed information;

[0041] The pseudo-random code generation module is used to perform feedback iterative calculation processing on the randomly generated first seed information and second seed information to obtain a pseudo-random code.

[0042] The encoding module is used to encode the information to be transmitted using pseudo-random codes to obtain the information to be sent.

[0043] The sending module is used to send the information to be sent to the receiving module;

[0044] The receiving module is used to receive the information to be sent.

[0045] The pseudo-random code generation module includes a random number submodule, a linear feedback shift register, a fractal function submodule, a shift register, a sum operation submodule, a register, an XOR operation submodule, and an output submodule.

[0046] The random number submodule is connected to the linear feedback shift register and the shift register respectively, and is used to randomly generate the first seed information and the second seed information;

[0047] The linear feedback shift register is connected to the fractal function submodule and is used to perform linear feedback shift processing on the first seed information to obtain the first state sequence;

[0048] The fractal function submodule is used to perform fractal calculation processing on the first state sequence to obtain the third state sequence;

[0049] The shift register is used to perform linear cyclic shifting on the second seed information using the shift value to obtain the second state sequence; the shift value is updated according to each value of the pseudo-random code output by the output submodule.

[0050] The sum operation submodule is connected to the fractal function submodule and the shift register respectively, and is used to perform a sum operation on the second state sequence and the third state sequence to obtain the fourth state sequence.

[0051] The register is connected to the sum operation submodule and the XOR operation submodule respectively, and is used to perform register processing on the fourth state sequence and send the fourth state sequence bit by bit to the XOR operation submodule;

[0052] The XOR operation submodule is connected to the shift register, register, and output submodule respectively, and is used to perform bit-by-bit XOR operation on the fourth state sequence to obtain a pseudo-random code, and send the pseudo-random code to the shift register and output submodule.

[0053] The output submodule is used to output pseudo-random codes.

[0054] A third aspect of this application discloses a communication transmission device based on a pseudo-random code, the device comprising:

[0055] Memory containing executable program code;

[0056] A processor coupled to the memory;

[0057] The processor calls the executable program code stored in the memory to execute the communication transmission method based on pseudo-random codes.

[0058] A fourth aspect of this application discloses a computer-storable medium storing computer instructions, which, when invoked by a computer, are used to execute the communication transmission method based on pseudo-random codes.

[0059] A fifth aspect of this application discloses an information data processing terminal, which is used to implement the communication transmission method based on pseudo-random codes.

[0060] The beneficial effects of this invention are as follows:

[0061] This invention uses linear feedback shift and fractal calculation to process random seed information, which gives the generated pseudo-random code chaotic characteristics and improves the randomness of the pseudo-random code. This enables the rapid construction of pseudo-random codes with excellent randomness and statistical characteristics close to white noise.

[0062] This application constructs a matrix by using pseudo-random codes obtained at multiple time points when encoding information, performs feature extraction on the matrix, and obtains the final encoded sequence, which effectively improves the stability and security of the encoding process and achieves efficient and secure information transmission. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention;

[0064] Figure 2 This is a schematic diagram of the pseudo-random code generation module of the present invention. Detailed Implementation

[0065] To better understand the content of this invention, an embodiment is provided here.

[0066] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention; Figure 2 This is a schematic diagram of the pseudo-random code generation module of the present invention.

[0067] In a first aspect, this application discloses a communication transmission method based on pseudo-random codes, comprising:

[0068] S1, Obtain the information to be transmitted;

[0069] S2, randomly generate first seed information and second seed information;

[0070] S3, perform feedback iterative calculation on the first seed information and the second seed information to obtain a pseudo-random code;

[0071] S4. Using pseudo-random codes, the information to be transmitted is encoded to obtain the information to be sent.

[0072] S5, the information to be sent is sent from the sending end to the receiving end to complete the communication transmission based on the pseudo-random code.

[0073] The step of performing feedback iterative calculation on the first seed information and the second seed information to obtain a pseudo-random code includes:

[0074] S31, Initialize the shift value;

[0075] S32, perform linear feedback shift processing on the first seed information to obtain a first state sequence; use the shift value to perform linear cyclic shift processing on the second seed information to obtain a second state sequence;

[0076] S33, Perform fractal calculation processing on the first state sequence to obtain the third state sequence;

[0077] S34, perform a summation operation on the second state sequence and the third state sequence to obtain the fourth state sequence;

[0078] S35, perform bitwise XOR operation on the fourth state sequence to obtain a pseudo-random code;

[0079] S36 updates the shift value using each value of the pseudo-random code.

[0080] The calculation expression for the linear feedback shift process is:

[0081]

[0082] Among them, f i Let a represent the i-th coefficient in the linear feedback shift process. i F represents the value of the i-th bit of the first seed information a. t F represents the value of the first state sequence at time t, which is also the output at time t of the linear feedback shift processing of the first seed information a. t-1 This represents the value of the first state sequence at time t-1. For the first state sequence at time t=0, it is represented using the first seed information.

[0083] The expression for the fractal calculation process is:

[0084] z i+1 =μz i (1–F i –zi ) w-1 ,

[0085] Among them, z i+1 F represents the (i+1)th value of the third state sequence obtained through fractal computation. i Let z represent the value of the i-th position in the first state sequence. i Let z be the i-th value of the third state sequence obtained by fractal computation, where μ and w are the preset multiplication factor and exponential factor of the fractal computation, respectively; and z is the value of the first calculation during fractal computation. i The value is determined using the mean of the first state sequence.

[0086] The process of encoding the information to be transmitted using pseudo-random codes to obtain the information to be sent includes:

[0087] A pseudo-random code matrix is ​​constructed using pseudo-random codes obtained at several time points; the row vectors of the pseudo-random code matrix are the pseudo-random codes at each time point.

[0088] The pseudo-random code matrix is ​​subjected to feature transformation processing to obtain the encoded sequence;

[0089] The encoded sequence is multiplied with the information to be transmitted to obtain the information to be sent.

[0090] The step of performing feature transformation processing on the pseudo-random code matrix to obtain the encoded sequence includes:

[0091] The pseudo-random code matrix is ​​decomposed to obtain a left decomposition matrix, a feature matrix, and a right decomposition matrix;

[0092] The decomposition process is calculated using the following expression:

[0093] Y = UAV,

[0094] Where U is the left decomposition matrix, Y is the pseudo-random code matrix, A is the characteristic matrix, V is the right decomposition matrix, U and V are both orthogonal matrices, and A is a diagonal matrix;

[0095] Extract the diagonal elements of the feature matrix to obtain the feature vector;

[0096] Linear fitting is performed on the elements and element index values ​​of the feature vector to obtain the feature approximation polynomial;

[0097] For each row vector of the pseudo-random code matrix, the average value u0 of the row vector is calculated;

[0098] For each element in the row vector, calculate the absolute value of the difference between it and the average value u0, and determine the absolute value of the difference as the offset of the element;

[0099] For each row vector, find the element with the smallest offset, and determine the index value of the element in the row vector as the offset index value of the row vector;

[0100] By using the feature estimation polynomial, the deviation index value of each row vector is calculated and processed to obtain the feature value corresponding to each row vector;

[0101] The encoded sequence is constructed using the feature values ​​corresponding to all row vectors;

[0102] The decomposition process can be implemented using the singular value decomposition algorithm.

[0103] The linear fitting process involves using the characteristic vector element index value Ix as the known independent variable and the characteristic vector element value as the known dependent variable. The curve to be approximated is constructed using the known independent and dependent variables, and the curve to be approximated is fitted using the function approximation method to obtain the characteristic approximation polynomial.

[0104] The curve fitting of the curve to be approximated using the function approximation method can employ the best uniform linear approximation method.

[0105] The step of performing feature transformation processing on the pseudo-random code matrix to obtain the encoded sequence includes:

[0106] For each row vector of the pseudo-random code matrix, perform VMD transformation to obtain the corresponding transformation vector;

[0107] By using all the transformation vectors as row vectors, we can construct the transformation matrix.

[0108] The pseudo-random code matrix and the transformation matrix are multiplied by their rank transformations to obtain the random transformation matrix;

[0109] The singular values ​​of the random transformation matrix are solved to obtain a set of singular values ​​and a singular value vector.

[0110] Statistical calculations are performed on the singular value set and singular value vector to obtain the encoded sequence;

[0111] The expression for the statistical calculation is:

[0112]

[0113] Among them, c i For the i-th element of the encoded sequence, ρ i Let t be the i-th element of the singular value set. ij t is the j-th element of the i-th singular value vector. i and υt iLet be the mean and variance of the i-th singular value vector, respectively, and M be the number of elements in the singular value vector;

[0114] The first part of the statistical calculation expression uses the arcsine function to calculate the ratio of the difference and the sum of the two values. This fully explores the complex relationship between the singular value set and the singular value vector, making the generation of the encoded sequence no longer a simple linear relationship, but incorporating richer nonlinear factors. This enhances the randomness of the encoded sequence, making it more in line with the requirements of excellent randomness of pseudo-random codes, and helps to improve the security of information transmission.

[0115] The latter part of the statistical calculation expression utilizes the mean and variance of the i-th singular value vector. By calculating the norm of the difference between the elements of the singular value set and the mean of the singular value vector, and combining this with an exponential operation on the variance, the statistical properties of the singular value vector are incorporated into the generation of the encoded sequence. This statistically based calculation method makes the generated encoded sequence closer to the statistical properties of white noise. Because white noise has specific statistical regularities, using the mean and variance of the singular value vector can better simulate these statistical properties, thereby increasing the complexity of interference in information transmission and improving the security and reliability of information transmission.

[0116] The entire statistical calculation expression integrates information from multiple aspects, including the elements of the singular value set, the elements of the singular value vector, the mean, and the variance. Through nonlinear operations such as exponential and arcsine functions, it not only increases computational complexity, making it difficult for attackers to crack the encoded sequence through simple analysis, but also enables the effective utilization of various information obtained after transforming the pseudo-random code matrix features. This further optimizes the generation process of the encoded sequence, making it more suitable for secure information transmission and better meeting the current needs for improving wireless network security.

[0117] The VMD transformation is variational mode decomposition.

[0118] A second aspect of the present invention discloses a communication transmission method based on pseudo-random codes, comprising: a source module, a pseudo-random code generation module, an encoding module, a sending module, and a receiving module;

[0119] The information source module is used to acquire the information to be transmitted; the pseudo-random code generation module is used to randomly generate the first seed information and the second seed information.

[0120] The pseudo-random code generation module is used to perform feedback iterative calculation processing on the randomly generated first seed information and second seed information to obtain a pseudo-random code.

[0121] The encoding module is used to encode the information to be transmitted using pseudo-random codes to obtain the information to be sent.

[0122] The sending module is used to send the information to be sent to the receiving module;

[0123] The receiving module is used to receive the information to be sent;

[0124] The pseudo-random code generation module includes a random number submodule, a linear feedback shift register, a fractal function submodule, a shift register, a sum operation submodule, a register, an XOR operation submodule, and an output submodule.

[0125] The random number submodule is connected to the linear feedback shift register and the shift register respectively, and is used to randomly generate the first seed information and the second seed information;

[0126] The linear feedback shift register is connected to the fractal function submodule and is used to perform linear feedback shift processing on the first seed information to obtain the first state sequence;

[0127] The fractal function submodule is used to perform fractal calculation processing on the first state sequence to obtain the third state sequence;

[0128] The shift register is used to perform linear cyclic shifting on the second seed information using the shift value to obtain the second state sequence; the shift value is updated according to each value of the pseudo-random code output by the output submodule.

[0129] The sum operation submodule is connected to the fractal function submodule and the shift register respectively, and is used to perform a sum operation on the second state sequence and the third state sequence to obtain the fourth state sequence.

[0130] The register is connected to the sum operation submodule and the XOR operation submodule respectively, and is used to perform register processing on the fourth state sequence and send the fourth state sequence bit by bit to the XOR operation submodule;

[0131] The XOR operation submodule is connected to the shift register, register, and output submodule respectively, and is used to perform bit-by-bit XOR operation on the fourth state sequence to obtain a pseudo-random code, and send the pseudo-random code to the shift register and output submodule.

[0132] The output submodule is used to output pseudo-random codes.

[0133] Figure 2 This is a schematic diagram of the pseudo-random code generation module. Figure 2In this context, mLFSR represents the pseudo-random code generation module, lamdafractal function represents the fractal function submodule, Shift Register represents the shift register module, XOR represents the XOR operation submodule, Register represents the register module, Output represents the output submodule, and Seed2 and Seed1 represent the first seed information and the second seed information, respectively.

[0134] The calculation expression for the linear feedback shift process is:

[0135]

[0136] Among them, f i Let a represent the i-th coefficient in the linear feedback shift process. i F represents the value of the i-th bit of the first seed information a. t F represents the value of the first state sequence at time t, which is also the output at time t of the linear feedback shift processing of the first seed information a. t-1 This represents the value of the first state sequence at time t-1. For the first state sequence at time t=0, it is represented using the first seed information.

[0137] The expression for the fractal calculation process is:

[0138] z i+1 =μz i (1–F i –z i ) w-1 ,

[0139] Among them, z i+1 F represents the (i+1)th value of the third state sequence obtained through fractal computation. i Let z represent the value of the i-th position in the first state sequence. i Let z be the i-th value of the third state sequence obtained by fractal computation, where μ and w are the preset multiplication factor and exponential factor of the fractal computation, respectively; and z is the value of the first calculation during fractal computation. i The value is determined using the mean of the first state sequence.

[0140] The encoding module is used to construct a pseudo-random code matrix using pseudo-random codes obtained at several time points; the row vectors of the pseudo-random code matrix are the pseudo-random codes at each time point; the pseudo-random code matrix is ​​subjected to feature transformation processing to obtain an encoding sequence; the encoding sequence is multiplied with the information to be transmitted to obtain the information to be sent.

[0141] The step of performing feature transformation processing on the pseudo-random code matrix to obtain the encoded sequence includes:

[0142] The pseudo-random code matrix is ​​decomposed to obtain a left decomposition matrix, a feature matrix, and a right decomposition matrix;

[0143] The decomposition process is calculated using the following expression:

[0144] Y = UAV,

[0145] Where U is the left decomposition matrix, Y is the pseudo-random code matrix, A is the characteristic matrix, V is the right decomposition matrix, U and V are both orthogonal matrices, and A is a diagonal matrix;

[0146] Extract the diagonal elements of the feature matrix to obtain the feature vector;

[0147] Linear fitting is performed on the elements and element index values ​​of the feature vector to obtain the feature approximation polynomial;

[0148] For each row vector of the pseudo-random code matrix, the average value u0 of the row vector is calculated;

[0149] For each element in the row vector, calculate the absolute value of the difference between it and the average value u0, and determine the absolute value of the difference as the offset of the element;

[0150] For each row vector, find the element with the smallest offset, and determine the index value of the element in the row vector as the offset index value of the row vector;

[0151] By using the feature estimation polynomial, the deviation index value of each row vector is calculated and processed to obtain the feature value corresponding to each row vector;

[0152] The encoded sequence is constructed using the feature values ​​corresponding to all row vectors;

[0153] The decomposition process can be implemented using the singular value decomposition algorithm.

[0154] The linear fitting process involves using the characteristic vector element index value Ix as the known independent variable and the characteristic vector element value as the known dependent variable. The curve to be approximated is constructed using the known independent and dependent variables, and the curve to be approximated is fitted using the function approximation method to obtain the characteristic approximation polynomial.

[0155] The curve fitting of the curve to be approximated using the function approximation method can employ the best uniform linear approximation method.

[0156] The sending module, after obtaining the encoded sequence but before sending the information to be sent, sends the encoded sequence to the receiving module; after receiving the encoded sequence, the receiving module performs an inverse operation on the encoded sequence to obtain the decoded sequence; after receiving the information to be sent, it uses the decoded sequence to decode the information to be sent to obtain the received information.

[0157] The inversion process involves solving for a sequence that, when multiplied by the encoded sequence, yields a unit sequence, which is then determined to be the decoded sequence.

[0158] A third aspect of this application discloses a communication transmission device based on a pseudo-random code, the device comprising:

[0159] Memory containing executable program code;

[0160] A processor coupled to the memory;

[0161] The processor calls the executable program code stored in the memory to execute the communication transmission method based on pseudo-random codes.

[0162] A fourth aspect of this application discloses a computer-storable medium storing computer instructions, which, when invoked by a computer, are used to execute the communication transmission method based on pseudo-random codes.

[0163] A fifth aspect of this application discloses an information data processing terminal, which is used to implement the communication transmission method based on pseudo-random codes.

[0164] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A communication transmission method based on pseudo-random codes, characterized in that, include: S1, Obtain the information to be transmitted; S2, randomly generate first seed information and second seed information; S3, perform feedback iterative calculation on the first seed information and the second seed information to obtain a pseudo-random code; S4, using the pseudo-random code, the information to be transmitted is encoded to obtain the information to be sent; S5, the information to be sent is sent from the sending end to the receiving end; The step of performing feedback iterative calculation on the first seed information and the second seed information to obtain a pseudo-random code includes: S31, Initialize the shift value; S32, perform linear feedback shift processing on the first seed information to obtain a first state sequence; use the shift value to perform linear cyclic shift processing on the second seed information to obtain a second state sequence; S33, Perform fractal calculation processing on the first state sequence to obtain the third state sequence; S34, perform a summation operation on the second state sequence and the third state sequence to obtain the fourth state sequence; S35, perform bitwise XOR operation on the fourth state sequence to obtain a pseudo-random code; S36 updates the shift value using each value of the pseudo-random code.

2. The communication transmission method based on pseudo-random codes as described in claim 1, characterized in that, The expression for the fractal calculation process is: z i+1 =μz i (1–F i –z i ) w-1 , Among them, z i+1 F represents the (i+1)th value of the third state sequence obtained through fractal computation. i Let z represent the value of the i-th position in the first state sequence. i Let be the i-th value of the third state sequence obtained by fractal computation, and μ and w be the preset multiplication factor and exponential factor of the fractal computation, respectively. When performing fractal calculations, the z-axis during the first calculation... i The value is determined using the mean of the first state sequence.

3. The communication transmission method based on pseudo-random codes as described in claim 1, characterized in that, The process of encoding the information to be transmitted using the pseudo-random code to obtain the information to be sent includes: A pseudo-random code matrix is ​​constructed using pseudo-random codes obtained at several time points. The pseudo-random code matrix is ​​subjected to feature transformation processing to obtain the encoded sequence; The encoded sequence is multiplied with the information to be transmitted to obtain the information to be sent; The step of performing feature transformation processing on the pseudo-random code matrix to obtain the encoded sequence includes: For each row vector of the pseudo-random code matrix, perform VMD transformation to obtain the corresponding transformation vector; Using the transformation vectors as row vectors, a transformation matrix is ​​constructed. The pseudo-random code matrix and the transformation matrix are multiplied by their rank transformations to obtain the random transformation matrix; The singular values ​​of the random transformation matrix are solved to obtain a set of singular values ​​and a singular value vector. Statistical calculations are performed on the singular value set and singular value vector to obtain the encoded sequence; The expression for the statistical calculation is: Among them, c i For the i-th element of the encoded sequence, ρ i Let t be the i-th element of the singular value set. ij t is the j-th element of the i-th singular value vector. i and vt i Let be the mean and variance of the i-th singular value vector, respectively, and M be the number of elements in the singular value vector.

4. A communication transmission device based on pseudo-random codes, characterized in that, The method for implementing the communication transmission method based on pseudo-random codes as described in any one of claims 1 to 3 includes: a source module, a pseudo-random code generation module, an encoding module, a sending module, and a receiving module; The information source module is used to acquire the information to be transmitted; The pseudo-random code generation module is used to randomly generate first seed information and second seed information; The pseudo-random code generation module is used to perform feedback iterative calculation processing on the randomly generated first seed information and second seed information to obtain a pseudo-random code. The encoding module is used to encode the information to be transmitted using pseudo-random codes to obtain the information to be sent. The sending module is used to send the information to be sent to the receiving module; The receiving module is used to receive the information to be sent.

5. The communication transmission device based on pseudo-random codes as described in claim 4, characterized in that, The pseudo-random code generation module includes a random number submodule, a linear feedback shift register, a fractal function submodule, a shift register, a sum operation submodule, a register, an XOR operation submodule, and an output submodule. The random number submodule is connected to the linear feedback shift register and the shift register respectively, and is used to randomly generate the first seed information and the second seed information; The linear feedback shift register is connected to the fractal function submodule and is used to perform linear feedback shift processing on the first seed information to obtain the first state sequence; The fractal function submodule is used to perform fractal calculation processing on the first state sequence to obtain the third state sequence; The shift register is used to perform linear cyclic shifting on the second seed information using the shift value to obtain the second state sequence; the shift value is updated according to each value of the pseudo-random code output by the output submodule. The sum operation submodule is connected to the fractal function submodule and the shift register respectively, and is used to perform a sum operation on the second state sequence and the third state sequence to obtain the fourth state sequence. The register is connected to the sum operation submodule and the XOR operation submodule respectively, and is used to perform register processing on the fourth state sequence and send the fourth state sequence bit by bit to the XOR operation submodule; The XOR operation submodule is connected to the shift register, register, and output submodule respectively, and is used to perform bit-by-bit XOR operation on the fourth state sequence to obtain a pseudo-random code, and send the pseudo-random code to the shift register and output submodule. The output submodule is used to output pseudo-random codes.

6. A communication transmission device based on pseudo-random codes, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the communication transmission method based on pseudo-random codes as described in any one of claims 1 to 3.

7. A computer-storable medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked by the computer, are used to execute the communication transmission method based on pseudo-random codes as described in any one of claims 1 to 3.

8. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the communication transmission method based on pseudo-random codes as described in any one of claims 1 to 3.

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