Computer intelligent information security processing system

Abstract

The invention relates to the technical field of information security, in particular to a computer intelligent information security processing system, which comprises a track generation module for acquiring initial parameters of a user as seed input for initializing a chaotic mapping function and generating a unique chaotic track; the dynamic structure module is used for dynamically constructing a nonlinear encryption structure function family according to the chaotic trajectory so as to generate target data; and the salt value injection module is used for carrying out content-aware partitioning on the target data and injecting a dynamic salt value generated based on a timestamp, a chaos value and a context. During use, by dynamically generating nonlinear encryption logic, security vulnerabilities caused by a fixed formula structure are avoided; extraction codes with high safety and unpredictability are extracted from the data subjected to salt value injection processing, high safety and time sensitivity of the extraction codes on the local data level are achieved, and the unpredictability of visual data protection is enhanced based on pseudo-random processing of the actual content of the image.

Description

Technical Field

[0001] This invention relates to the field of information security technology, and more specifically to a computer intelligent information security processing system. Background Technology

[0002] Computer intelligent information security refers to a series of measures and technologies to protect information systems from unauthorized access, use, disclosure, interruption, modification, or damage. With the development of information technology, especially the application of artificial intelligence (AI) and machine learning (ML) technologies, the field of information security is also constantly developing new methods to cope with the increasingly complex threat environment.

[0003] Patent publication number CN118400205A discloses a computer information security processing system and apparatus based on big data, comprising: a data transmission unit, a data processing unit, an extraction code setting module, an extraction formula setting module, an extraction code processing unit, a sub-image pool, and a data extraction unit. This invention relates to the field of information security technology. This computer information security processing system and apparatus based on big data significantly improves data security by converting data into image data, performing segmentation processing, and then generating a new extraction code by combining an initial extraction code with a mathematical reasoning formula. Even if data is intercepted during transmission and not correctly extracted... Even with the extraction code and reasoning formula, attackers find it difficult to recover the original data. The target user must set an initial extraction code and edit the mathematical reasoning formula. This step ensures that only users who know this key information can access and recover the data, effectively preventing unauthorized access. Although the above technology provides efficient multi-layered security protection through innovative data segmentation, dynamic extraction codes, and interference management mechanisms, making it difficult for attackers to reconstruct complete information even if they obtain partial sub-images due to the lack of extraction codes and sequential encoding, the security mechanism of the above technology lacks dynamic change capability. Attackers can reverse engineer by analyzing fixed patterns, which can easily expose patterns and lead to reverse engineering, affecting the security of computer intelligent information.

[0004] In conclusion, developing a computer-intelligent information security processing system remains a critical issue that urgently needs to be addressed in the field of information security technology. Summary of the Invention

[0005] The purpose of this invention is to address the problem that existing technologies, while providing efficient multi-layered security protection through innovative data segmentation, dynamic extraction codes, and interference management mechanisms, making it difficult for attackers to reconstruct complete information even if they obtain partial images due to the lack of extraction codes and sequential encoding, lack dynamic change capabilities in their security mechanisms. Attackers can perform reverse engineering by analyzing fixed patterns, easily exposing patterns and leading to reverse engineering and impacting the security of computer intelligent information.

[0006] To achieve the above objectives, the present invention provides a computer intelligent information security processing system, comprising: a trajectory generation module, which collects user initial parameters as seed inputs to initialize a chaotic mapping function and generate a unique chaotic trajectory; The dynamic structure module dynamically constructs a family of nonlinear encryption structure functions based on the chaotic trajectory, thereby generating the target data; The salt injection module performs content-aware segmentation on the target data and injects dynamic salt values ​​generated based on timestamps, chaos values, and context to obtain salt value data. The iterative generation module performs independent hashing on the salt value data and iterates through a chain structure to generate an extraction code. The random segmentation module extracts the content hash from the structured data in the extraction code, uses it as the seed for the pseudo-random number generator, and combines it with the edge detection algorithm to generate a dynamic segmentation path.

[0007] Furthermore, the operation process of the trajectory generation module includes: Collect the user's initial input parameter set, expressed as a formula: ,in It is a vector. This represents a single sub-parameter input by the user. This indicates the transpose operation. Represent a A real space of dimension 1, which will be a vector Mapping to interval The standard domain within the domain is expressed by the following formula: ,in It is the new vector after processing. Represents a vector Applying standardized functions, It is the input column vector. This means for each element Find the maximum value in its dimension. This means for each element Find the minimum value of its dimension. It is a standardized procedure. This means calculating the standardized value for each element to obtain the initial state seed vector. The initial chaotic mapping function employs a high-dimensional coupled Logistic-Chebyshev hybrid mapping to convert the initial state seed vector. The function used to initialize chaotic mappings is expressed as follows: ,in For coupling adjustment parameters, Indicates at time step The state vector at time, This indicates that the current state vector Mapping to the next state vector The process Indicates the current time step The state vector at time, Indicates the current state The Middle The value of each element, Represents the state vector The supplement, Indicates all Not equal to Summing is performed using the indices. Indicates to Perform a sine function transformation and multiply by , Logistic growth factor for each dimension, Represents the Chebyshev polynomial, the nth Rank , For the first The state vector of the next iteration generates the chaotic trajectory, expressed by the formula: ,in This represents a dataset of chaotic trajectories. Indicates at time step The next vector, This represents a one-dimensional real matrix.

[0008] Furthermore, the operation process of the dynamic structure module includes: The chaotic trajectory Center front The state vectors are transformed to generate a multi-core perturbation matrix. The nonlinear encryption structure function family based on chaotic trajectory mapping is expressed by the following formula: ,in It is a family of nonlinear encryption structure functions. Indicates the first The result of a dynamic transformation function Indicates the arctangent of the input value. It is a control item nonlinear intensity, It is an input variable. It is the amplitude coefficient. It is a cosine function. The frequency parameter controls the oscillation frequency of the cosine function. It is the phase offset. It is an index from arrive , It is the first A chaotic trajectory It is a chaotic trajectory The 2-norm, It is the sum of the squares of each element in the chaotic trajectory. It is the maximum value of all elements in the chaotic trajectory. It is the sum of the sine values ​​of each element in the chaotic trajectory.

[0009] Furthermore, the operation process of the dynamic structure module includes: Each function in the nonlinear encryption structure function family This will be used for nonlinear transformation encryption operations, setting the original data block in vector form. Based on the multi-core perturbation matrix and the aforementioned family of nonlinear encryption structure functions Construct a nonlinear transformation and express it as a formula: ,in This represents the original input data. It is a multi-core perturbation matrix. It is a bias term. This is the dynamic offset perturbation vector. It is a nonlinear stretching coefficient. It is a family of nonlinear encryption structure functions. Indicates at time Below is the initial data The function representation for encryption / transformation. At this moment Dynamic output data in the constructor The time constraint makes it a monotonic transformation, and the final output target data is: ,in It is the target data. Indicates the number from the 1st to the 2nd. The data results after the next iteration / block processing / perturbation.

[0010] Furthermore, the operation process of the salt injection module includes: target data Divide into multiple blocks, and set each data block as Next, a dynamic salt value is generated for each data block, with the timestamp set to [value missing]. The chaotic trajectory state is The context information is The formula for generating dynamic salt values ​​is as follows: ,in It consists of timestamps, chaotic trajectory states, and context information. The perturbation factor is generated based on the current state and context to enhance the randomness of the salt value. This is the calling format of the salt value generation function SaltGen. It is used to limit the salinity to a unit range. , It is a dynamic salinity value.

[0011] Furthermore, the operation process of the salt injection module includes: The enhancement process for each data block is expressed as follows: The dynamic salt value is injected into the data block. ,in It is the first A data block after salting. This represents the perturbation function. It is the first Content-aware chunking It is the first A dynamic salt value, It is the first One perturbation structure factor, It is a bitwise XOR operation. It is the first The sum of squares of the elements in each data block. It is the first Data blocks The first in One element, It is the total number of elements in the block. It is the first A chaotic trajectory The square of the L2 norm, ultimately, the entire salt value data consists of all data blocks that have undergone salt value injection, expressed as: ,in It's salinity data. It is a collection of data blocks.

[0012] Furthermore, the operation flow of the iterative generation module includes: Regarding the salinity data Each data block that has undergone salting It will undergo independent hashing, using a strong cryptographic hash function. Perform hash operations, expressed as the formula: ,in Represents data block The output after hashing It is the perturbation term after summing each data block. It is injected with salt value. One data block, It is a strong cryptographic hash function. It's salinity data.

[0013] Furthermore, the operation flow of the iterative generation module includes: The hash value is iteratively calculated using the chain structure to construct a chained hash structure. In each iteration, the digest from the previous round is added to the current digest. The formula is as follows: ,in It is the first A chain of hash nodes serves as a partial extraction code. It is a strong cryptographic hash function. It involves applying a left-handed perturbation to the previous node. It is the timestamp of the current block. It is a bitwise XOR. It is the first Each independent hash result It is the first output value in the chained hash, connecting all chained hash nodes and using a chaotic coding function. Extract the final extraction code, expressed as: ,in It is a chaotic perturbation factor. This represents a bit-by-bit multiplicative perturbation. Indicates each From 1 to hash value Perform with chaotic perturbation factor The bitwise multiplication operation. It is a chaotic coding function. It is after all disturbances XOR fusion of structured hash blocks It is the first A chain of hash nodes serves as a partial extraction code. This is the final generated extraction code.

[0014] Furthermore, the operation process of the random segmentation module includes: The final generated extraction code contains structured data, using a structure matrix. Extracting the content hash of the structured data yields the seed for the pseudo-random number generator, expressed as: ,in It is the seed of the pseudo-random number generator. It is a scalable hash function. It is a matrix flattening operation. It is the process of summing the cubes of all elements in a matrix. It is a concatenation operator. A timestamp represents the current time information. As a seed, initialize a high-dimensional chaotic pseudo-random function to generate a mixed perturbation matrix, expressed by the formula: ,in In position The pseudo-random perturbation weight matrix generated at that point, These are chaos control parameters. The position is obtained through iterative mapping. The perturbation distribution value at that location, The elements in a two-dimensional matrix represent positions. The result value at that point, It is about the angle The cosine operation represents the periodic changes related to the angle. It is a constant. It is the row index of the position in the matrix. and column indexes The sum of, It is a dynamically generated value. It is a hash function. It is the seed of the pseudo-random number generator. It is a modulo operation.

[0015] Furthermore, the operation process of the random segmentation module includes: Edge detection is performed on the structure matrix, and the hybrid perturbation matrix is ​​weighted, expressed by the following formula: ,in In position The local energy value at that location In position The pseudo-random perturbation weight matrix generated at that point, It is a location along The squared value of the gradient in the direction. It is the position of the matrix along Gradient of direction, This indicates that the gradient result is squared. It is a location along The squared value of the gradient in the direction. It is the position of the matrix along Gradient of direction, In position The value calculated at that point, It is the hyperbolic tangent function. It is a small constant. It is the weight value of all positions in the matrix. Finally, through piecewise integration, the path index is dynamically calculated to generate a dynamically segmented path set. Formula: ,in It is a dynamically segmented set of paths. A two-dimensional coordinate point represents an element in the set. From the origin Points to The regional integral, It is a two-dimensional function. It is the threshold of the judgment condition.

[0016] Beneficial effects Compared with known public technologies, the technical solution provided by this invention has the following beneficial effects: When in use, this invention avoids security vulnerabilities caused by fixed formula structures by dynamically generating nonlinear encryption logic. It extracts highly secure and unpredictable extraction codes from data that has undergone salt injection processing, achieving high security and time sensitivity of the extraction codes at the local data level. Pseudo-random processing based on the actual content of the image enhances the unpredictability of visual data protection. Attached Figure Description

[0017] Figure 1 This is a system diagram of a computer intelligent information security processing system according to the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0019] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] The present invention will now be described in further detail with reference to the accompanying drawings: Example: like Figure 1 As shown, the present invention provides a computer intelligent information security processing system, including: a trajectory generation module, which collects user initial parameters as seed input to initialize a chaotic mapping function and generate a unique chaotic trajectory; Furthermore, the operation process of the trajectory generation module includes: Collect the user's initial input parameter set, expressed as a formula: ,in It is a vector. This represents a single sub-parameter input by the user. This indicates the transpose operation. Represent a A real space of dimension 1, which will be a vector Mapping to interval The standard domain within the domain is expressed by the following formula: ,in It is the new vector after processing. Represents a vector Applying standardized functions, It is the input column vector. This means for each element Find the maximum value in its dimension. This means for each element Find the minimum value of its dimension. It is a standardized procedure. This means calculating the standardized value for each element to obtain the initial state seed vector. The initial chaotic mapping function employs a high-dimensional coupled Logistic-Chebyshev hybrid mapping to convert the initial state seed vector. The function used to initialize chaotic mappings is expressed as follows: ,in For coupling adjustment parameters, Indicates at time step The state vector at time, This indicates that the current state vector Mapping to the next state vector The process Indicates the current time step The state vector at time, Indicates the current state The Middle The value of each element, Represents the state vector The supplement, Indicates all Not equal to Summing is performed using the indices. Indicates to Perform a sine function transformation and multiply by , Logistic growth factor for each dimension, Represents the Chebyshev polynomial, the nth Rank , For the first The state vector of the next iteration generates the chaotic trajectory, expressed by the formula: ,in This represents a dataset of chaotic trajectories. Indicates at time step The next vector, Represents a one-dimensional real matrix; Specifically, taking a user-input-based image encryption system as an example, the system collects the user's device MAC address, timestamp, and browser information as initial vectors. After normalization, a chaotic seed is generated. The trajectory generated by the high-dimensional coupled Logistic-Chebyshev hybrid mapping is used to dynamically control the encryption order and perturbation intensity of image blocks, so that different chaotic trajectories are generated each time it is used. This is beneficial to improve the multidimensional nonlinear characteristics of the system, making the chaotic trajectory highly sensitive to the initial value. The chaotic trajectory can be generated in real time with changes in time, device status, etc., effectively enhancing the system's timeliness and defense capabilities.

[0021] The dynamic structure module dynamically constructs a family of nonlinear encryption structure functions based on the chaotic trajectory, thereby generating the target data; Furthermore, the operation process of the dynamic structure module includes: The chaotic trajectory Center front The state vectors are transformed to generate a multi-core perturbation matrix. The nonlinear encryption structure function family based on chaotic trajectory mapping is expressed by the following formula: ,in It is a family of nonlinear encryption structure functions. Indicates the first The result of a dynamic transformation function Indicates the arctangent of the input value. It is a control item nonlinear intensity, It is an input variable. It is the amplitude coefficient. It is a cosine function. The frequency parameter controls the oscillation frequency of the cosine function. It is the phase offset. It is an index from arrive , It is the first A chaotic trajectory It is a chaotic trajectory The 2-norm, It is the sum of the squares of each element in the chaotic trajectory. It is the maximum value of all elements in the chaotic trajectory. It is the sum of the sine values ​​of each element in the chaotic trajectory; Furthermore, the operation process of the dynamic structure module includes: Each function in the nonlinear encryption structure function family This will be used for nonlinear transformation encryption operations, setting the original data block in vector form. Based on the multi-core perturbation matrix and the aforementioned family of nonlinear encryption structure functions Construct a nonlinear transformation and express it as a formula: ,in This represents the original input data. It is a multi-core perturbation matrix. It is a bias term. This is the dynamic offset perturbation vector. It is a nonlinear stretching coefficient. It is a family of nonlinear encryption structure functions. Indicates at time Below is the initial data The function representation for encryption / transformation. At this moment Dynamic output data in the constructor The time constraint makes it a monotonic transformation, and the final output target data is: ,in It is the target data. Indicates the number from the 1st to the 2nd. Data results after the next iteration / block processing / perturbation; Specifically, taking an image encryption system as an example, the image is divided into multiple small blocks and chaotic trajectories are extracted. A family of functions based on the chaotic states of different image blocks is used to perform nonlinear encryption on pixel vectors. The output results exhibit extremely high perturbation. The dynamic construction method of the nonlinear encryption function family avoids the reversibility defect of traditional linear transformations and prevents linear analysis and algebraic attacks.

[0022] The salt injection module performs content-aware segmentation on the target data and injects dynamic salt values ​​generated based on timestamps, chaos values, and context to obtain salt value data. Furthermore, the operation process of the salt injection module includes: target data Divide into multiple blocks, and set each data block as Next, a dynamic salt value is generated for each data block, with the timestamp set to [value missing]. The chaotic trajectory state is The context information is The formula for generating dynamic salt values ​​is as follows: ,in It consists of timestamps, chaotic trajectory states, and context information. The perturbation factor is generated based on the current state and context to enhance the randomness of the salt value. This is the calling format of the salt value generation function SaltGen. It is used to limit the salinity to a unit range. , It is a dynamic salinity; Furthermore, the operation process of the salt injection module includes: The enhancement process for each data block is expressed as follows: The dynamic salt value is injected into the data block. ,in It is the first A data block after salting. This represents the perturbation function. It is the first Content-aware chunking It is the first A dynamic salt value, It is the first One perturbation structure factor, It is a bitwise XOR operation. It is the first The sum of squares of the elements in each data block. It is the first Data blocks The first in One element, It is the total number of elements in the block. It is the first A chaotic trajectory The square of the L2 norm, ultimately, the entire salt value data consists of all data blocks that have undergone salt value injection, expressed as: ,in It's salinity data. It is a collection of data blocks; Specifically, content-aware segmentation makes the perturbations more semantically corresponding, which facilitates the improvement of the overall system's adaptability. Salt injection, through nonlinear perturbation and sum of squares enhancement, can effectively resist statistical analysis and differential attacks. The resulting salt data has stronger perturbation, concealment and verifiability, and is suitable for subsequent hash digest and extraction code generation processes.

[0023] The iterative generation module performs independent hashing on the salt value data and iterates through a chain structure to generate an extraction code. Furthermore, the operation flow of the iterative generation module includes: Regarding the salinity data Each data block that has undergone salting It will undergo independent hashing, using a strong cryptographic hash function. Perform hash operations, expressed as the formula: ,in Represents data block The output after hashing It is the perturbation term after summing each data block. It is injected with salt value. One data block, It is a strong cryptographic hash function. It's salinity data; Furthermore, the operation flow of the iterative generation module includes: The hash value is iteratively calculated using the chain structure to construct a chained hash structure. In each iteration, the digest from the previous round is added to the current digest. The formula is as follows: ,in It is the first A chain of hash nodes serves as a partial extraction code. It is a strong cryptographic hash function. It involves applying a left-handed perturbation to the previous node. It is the timestamp of the current block. It is a bitwise XOR. It is the first Each independent hash result It is the first output value in the chained hash, connecting all chained hash nodes and using a chaotic coding function. Extract the final extraction code, expressed as: ,in It is a chaotic perturbation factor. This represents a bit-by-bit multiplicative perturbation. Indicates each From 1 to hash value Perform with chaotic perturbation factor The bitwise multiplication operation. It is a chaotic coding function. It is after all disturbances XOR fusion of structured hash blocks It is the first A chain of hash nodes serves as a partial extraction code. This is the final generated extraction code; Specifically, an extraction code with high security and unpredictability is extracted from the salt-injected data through an iterative generation module. In one example of file extraction, the file is divided into multiple content-aware blocks, which are hashed after being injected with dynamic salt values ​​to form a basic digest. The digest is iterated in a chain, and perturbed by combining timestamps and content structure. The digest is then encoded and fused through a chaotic perturbation function to generate a unique extraction code, which serves as the complete identity identifier of the file. This achieves a dual security mechanism of multi-dimensional perturbation + hash chain, which helps to improve the complexity and security of the data digest.

[0024] The random segmentation module extracts the content hash from the structured data in the extraction code, uses it as the seed for the pseudo-random number generator, and combines it with the edge detection algorithm to generate a dynamic segmentation path; Furthermore, the operation process of the random segmentation module includes: The final generated extraction code contains structured data, using a structure matrix. Extracting the content hash of the structured data yields the seed for the pseudo-random number generator, expressed as: ,in It is the seed of the pseudo-random number generator. It is a scalable hash function. It is a matrix flattening operation. It is the process of summing the cubes of all elements in a matrix. It is a concatenation operator. A timestamp represents the current time information. As a seed, initialize a high-dimensional chaotic pseudo-random function to generate a mixed perturbation matrix, expressed by the formula: ,in In position The pseudo-random perturbation weight matrix generated at that point, These are chaos control parameters. The position is obtained through iterative mapping. The perturbation distribution value at that location, The elements in a two-dimensional matrix represent positions. The result value at that point, It is about the angle The cosine operation represents the periodic changes related to the angle. It is a constant. It is the row index of the position in the matrix. and column indexes The sum of, It is a dynamically generated value. It is a hash function. It is the seed of the pseudo-random number generator. It is a modulo operation; Furthermore, the operation process of the random segmentation module includes: Edge detection is performed on the structure matrix, and the hybrid perturbation matrix is ​​weighted, expressed by the following formula: ,in In position The local energy value at that location In position The pseudo-random perturbation weight matrix generated at that point, It is a location along The squared value of the gradient in the direction. It is the position of the matrix along Gradient of direction, This indicates that the gradient result is squared. It is a location along The squared value of the gradient in the direction. It is the position of the matrix along Gradient of direction, In position The value calculated at that point, It is the hyperbolic tangent function. It is a small constant. It is the weight value of all positions in the matrix. Finally, through piecewise integration, the path index is dynamically calculated to generate a dynamically segmented path set. Formula: ,in It is a dynamically segmented set of paths. A two-dimensional coordinate point represents an element in the set. From the origin Points to The regional integral, It is a two-dimensional function. It is the threshold of the judgment condition; Specifically, the random segmentation module performs deep processing on the structured data in the extraction code to achieve a dynamic path segmentation mechanism driven by content awareness and hybrid perturbation. In an image encryption embodiment, the structured data represented by the extraction code is the statistical description information of the image region. Each unit in the structure matrix represents the pixel distribution characteristics within that region. This system drives the perturbation matrix generation algorithm by extracting a pseudo-random seed composed of the content hash and the current timestamp, forming an asymmetric perturbation at the image edge, and finally dynamically constructing a segmentation path related to the image content. This helps to improve the randomness and unpredictability of the encryption path, thereby enhancing the security of the system. At the same time, the integration of edge detection and chaotic perturbation enables the segmentation path to adapt to content changes, exhibiting good perceptual relevance. Multi-source information fusion enhances the robustness and anti-attack capability of the system.

[0025] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A computer intelligent information security processing system, characterized in that, include: The trajectory generation module collects the user's initial parameters as seed input to initialize the chaotic mapping function and generate a unique chaotic trajectory. The dynamic structure module dynamically constructs a family of nonlinear encryption structure functions based on the chaotic trajectory, thereby generating the target data; The salt injection module performs content-aware segmentation on the target data and injects dynamic salt values ​​generated based on timestamps, chaos values, and context to obtain salt value data. The iterative generation module performs independent hashing on the salt value data and iterates through a chain structure to generate an extraction code. The random segmentation module extracts the content hash from the structured data in the extraction code, uses it as the seed for the pseudo-random number generator, and combines it with the edge detection algorithm to generate a dynamic segmentation path.

2. The computer intelligent information security processing system according to claim 1, characterized in that, The operation process of the trajectory generation module includes: Collect the user's initial input parameter set, expressed as a formula: ,in It is a vector. This represents a single sub-parameter input by the user. This indicates the transpose operation. Represent a A real space of dimension 1, which will be a vector Mapping to interval The standard domain within the domain is expressed by the following formula: ,in It is the new vector after processing. Represents a vector Apply the standardized function, It is the input column vector. This means for each element Find the maximum value in its dimension. This means for each element Find the minimum value of its dimension. It is a standardized procedure. This means calculating the standardized value for each element to obtain the initial state seed vector. The initial chaotic mapping function employs a high-dimensional coupled Logistic-Chebyshev hybrid mapping to convert the initial state seed vector. The function used to initialize chaotic mappings is expressed as follows: ,in For coupling adjustment parameters, Indicates at time step The state vector at time, This indicates that the current state vector Mapping to the next state vector The process Indicates the current time step The state vector at time, Indicates the current state The Middle The value of each element, Represents the state vector The supplement, Indicates all Not equal to Summing is performed using the indices. Indicates to Perform a sine function transformation and multiply by , Logistic growth factor for each dimension, Represents the Chebyshev polynomial, the nth Rank , For the first The state vector of the next iteration generates the chaotic trajectory, expressed by the formula: ,in This represents a dataset of chaotic trajectories. Indicates at time step The next vector, This represents a one-dimensional real matrix.

3. The computer intelligent information security processing system according to claim 2, characterized in that, The operation process of the dynamic structure module includes: The chaotic trajectory Center front The state vectors are transformed to generate a multi-core perturbation matrix. The nonlinear encryption structure function family based on chaotic trajectory mapping is expressed by the following formula: ,in It is a family of nonlinear encryption structure functions. Indicates the first The result of a dynamic transformation function Indicates the arctangent of the input value. It is a control item nonlinear intensity, It is an input variable. It is the amplitude coefficient. It is a cosine function. The frequency parameter controls the oscillation frequency of the cosine function. It is the phase offset. It is an index from arrive , It is the first A chaotic trajectory It is a chaotic trajectory The 2-norm, It is the sum of the squares of each element in the chaotic trajectory. It is the maximum value of all elements in the chaotic trajectory. It is the sum of the sine values ​​of each element in the chaotic trajectory.

4. The computer intelligent information security processing system according to claim 3, characterized in that, The operation process of the dynamic structure module includes: Each function in the nonlinear encryption structure function family This will be used for nonlinear transformation encryption operations, setting the original data block in vector form. Based on the multi-core perturbation matrix and the aforementioned family of nonlinear encryption structure functions Construct a nonlinear transformation and express it as a formula: ,in This represents the original input data. It is a multi-core perturbation matrix. It is a bias term. This is the dynamic offset perturbation vector. It is a nonlinear stretching coefficient. It is a family of nonlinear encryption structure functions. Indicates at time Below is the initial data The function representation for encryption / transformation. At this moment Dynamic output data in the constructor The time constraint makes it a monotonic transformation, and the final output target data is: ,in It is the target data. Indicates the number from the 1st to the 2nd. The data results after the next iteration / block processing / perturbation.

5. A computer intelligent information security processing system according to claim 4, characterized in that, The operation process of the salt injection module includes: target data Divide into multiple blocks, and set each data block as Next, a dynamic salt value is generated for each data block, with the timestamp set to [value missing]. The chaotic trajectory state is The context information is The formula for generating dynamic salt values ​​is as follows: ,in It consists of timestamps, chaotic trajectory states, and context information. The perturbation factor is generated based on the current state and context to enhance the randomness of the salt value. This is the calling format of the salt value generation function SaltGen. It is used to limit the salinity to a unit range. , It is a dynamic salinity value.

6. A computer intelligent information security processing system according to claim 5, characterized in that, The operation process of the salt injection module includes: The enhancement process for each data block is expressed as follows: The dynamic salt value is injected into the data block. ,in It is the first A data block after salting. This represents the perturbation function. It is the first Content-aware chunking It is the first A dynamic salt value, It is the first One perturbation structure factor, It is a bitwise XOR operation. It is the first The sum of squares of the elements in each data block. It is the first Data blocks The first in One element, It is the total number of elements in the block. It is the first A chaotic trajectory The square of the L2 norm, ultimately, the entire salt value data consists of all data blocks that have undergone salt value injection, expressed as: ,in It's salinity data. It is a collection of data blocks.

7. A computer intelligent information security processing system according to claim 6, characterized in that, The operation process of the iterative generation module includes: Regarding the salinity data Each data block that has undergone salting It will undergo independent hashing, using a strong cryptographic hash function. Perform hash operations, expressed as the formula: ,in Represents data block The output after hashing It is the perturbation term after summing each data block. It is injected with salt value. One data block, It is a strong cryptographic hash function. It's salinity data.

8. A computer intelligent information security processing system according to claim 7, characterized in that, The operation process of the iterative generation module includes: The hash value is iteratively calculated using the chain structure to construct a chained hash structure. In each iteration, the digest from the previous round is added to the current digest. The formula is as follows: ,in It is the first A chain of hash nodes serves as a partial extraction code. It is a strong cryptographic hash function. It involves applying a left-handed perturbation to the previous node. It is the timestamp of the current block. It is a bitwise XOR. It is the first Each independent hash result It is the first output value in the chained hash, connecting all chained hash nodes and using a chaotic coding function. Extract the final extraction code, expressed as: ,in It is a chaotic perturbation factor. This represents a bit-by-bit multiplicative perturbation. Indicates each From 1 to hash value Perform with chaotic perturbation factor The bitwise multiplication operation. It is a chaotic coding function. It is after all disturbances XOR fusion of structured hash blocks It is the first A chain of hash nodes serves as a partial extraction code. This is the final generated extraction code.

9. A computer intelligent information security processing system according to claim 8, characterized in that, The operation process of the random segmentation module includes: The final generated extraction code contains structured data, using a structure matrix. Extracting the content hash of the structured data yields the seed for the pseudo-random number generator, expressed as: ,in It is the seed of the pseudo-random number generator. It is a scalable hash function. It is a matrix flattening operation. It is the process of summing the cubes of all elements in a matrix. It is a concatenation operator. A timestamp represents the current time information. As a seed, initialize a high-dimensional chaotic pseudo-random function to generate a mixed perturbation matrix, expressed by the formula: ,in In position The pseudo-random perturbation weight matrix generated at that point, These are chaos control parameters. The position is obtained through iterative mapping. The perturbation distribution value at that location, The elements in a two-dimensional matrix represent positions. The result value at that point, It is about the angle The cosine operation represents the periodic changes related to the angle. It is a constant. It is the row index of the position in the matrix. and column indexes The sum of, It is a dynamically generated value. It is a hash function. It is the seed of the pseudo-random number generator. It is a modulo operation.

10. A computer intelligent information security processing system according to claim 9, characterized in that, The operation process of the random segmentation module includes: Edge detection is performed on the structure matrix, and the hybrid perturbation matrix is ​​weighted, expressed by the following formula: ,in In position The local energy value at that location In position The pseudo-random perturbation weight matrix generated at that point, It is a location along The squared value of the gradient in the direction. It is the position of the matrix along Gradient of direction, This indicates that the gradient result is squared. It is a location along The squared value of the gradient in the direction. It is the position of the matrix along Gradient of direction, In position The value calculated at that point, It is the hyperbolic tangent function. It is a small constant. It is the weight value of all positions in the matrix. Finally, through piecewise integration, the path index is dynamically calculated to generate a dynamically segmented path set. Formula: ,in It is a dynamically segmented set of paths. A two-dimensional coordinate point represents an element in the set. From the origin Points to The regional integral, It is a two-dimensional function. It is the threshold of the judgment condition.

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