Railway high-precision spatio-temporal information security encryption method and system

Abstract

The invention provides a railway high-precision spatio-temporal information security encryption method and system. The method comprises the following steps: acquiring a first spatio-temporal image to be encrypted; generating two secret key matrixes based on two pre-constructed chaotic sequences, and calculating the first space-time image based on the secret key matrixes to obtain a corresponding second space-time image; two first extraction sequences and a second extraction sequence are obtained based on the two chaotic sequences, two scrambling matrixes are further obtained, and a scrambling image is calculated based on the two scrambling matrixes and a second space-time image; the method comprises the following steps: segmenting a preset carrier image, filtering each segmented sub-image, and obtaining an approximate image feature value, a longitudinal edge feature value, a transverse edge feature value and a diagonal feature value for each sub-image; and replacing any characteristic value of each sub-image with a pixel value of one position in the scrambled image, carrying out inverse discrete cosine transform, and updating the carrier image into a secret-carrying image.

Description

Technical Field

[0001] The present invention relates to the technical field of information encryption, and in particular to a method and system for encrypting railway high-precision spatiotemporal information security. Background Art

[0002] Information encryption technology is a technology that uses mathematical or physical means to protect electronic information during transmission and storage to prevent leakage. It is one of the most basic and important technologies to ensure information security, and is generally implemented using internationally recognized security encryption algorithms. The information encryption process is implemented by a variety of encryption algorithms, which can turn useful information into seemingly useless garbled code, making it impossible for attackers to understand the content of the information, thereby achieving the purpose of protecting information.

[0003] In the process of digitalization and informatization of railway networks, spatiotemporal information data has become an indispensable component. With the development of technology, it has become easier to obtain these data. At present, in order to protect these key data, encryption algorithms are used to convert the original data into a disordered and unrecognizable form. Nevertheless, this method has certain limitations in practical applications. For example, the spatial structure characteristics of vector map data are not fully considered. The scrambled encryption based on one-to-one mapping may reduce the overall security of the algorithm. At the same time, in order to decrypt specific layer data, the entire encrypted map may need to be decrypted. In summary, the encryption effect of data encryption in the existing technology is poor, and it is difficult to meet the needs of data encryption. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a railway high-precision spatiotemporal information security encryption method and system to eliminate or improve one or more defects existing in the prior art.

[0005] One aspect of the present invention provides a method for securely encrypting railway high-precision spatiotemporal information, the method comprising the steps of:

[0006] Acquire a remote sensing image of the railway line to be encrypted, and perform coordinate transformation on the remote sensing image to obtain a corresponding first space-time image;

[0007] Generate two secret key matrices based on two pre-constructed chaotic sequences, and calculate the first space-time image based on the secret key matrices to obtain a corresponding second space-time image;

[0008] Extracting from the two chaotic sequences respectively to obtain two first extraction sequences corresponding to the two chaotic sequences;

[0009] sorting the two first extraction sequences respectively to obtain two second extraction sequences;

[0010] Obtaining two scrambled matrices based on two corresponding first extraction sequences and a second extraction sequence, and calculating a scrambled image based on the two scrambled matrices and the second spatiotemporal image;

[0011] Segmenting a preset carrier image, and filtering each segmented sub-image, and obtaining an approximate image eigenvalue, a longitudinal edge eigenvalue, a transverse edge eigenvalue, and a diagonal eigenvalue for each sub-image;

[0012] Any characteristic value of each sub-image is replaced by a pixel value of a position in the scrambled image, and an inverse discrete cosine transform is performed to update the carrier image to a scrambled image.

[0013] The above scheme is adopted. This scheme includes two parts. The first part is the hiding process: hiding the first space-time image that needs to be encrypted, generating a chaotic sequence based on the improved logistics mapping for diffusion and scrambling respectively, and then segmenting the carrier image to generate a sub-image containing edge feature information using discrete cosine transformation, and replacing the information of the second space-time image after diffusion and scrambling in the sub-image, and obtaining a secret image with hidden space-time information after inverse discrete cosine transformation, thereby improving the encryption effect and meeting the needs of data encryption.

[0014] In some embodiments of the present invention, in the step of acquiring a remote sensing image of the railway line to be encrypted, performing coordinate transformation on the remote sensing image, and obtaining a corresponding first space-time image, the remote sensing image based on longitude and latitude is converted into a first space-time image based on a plane coordinate system using Mercator projection.

[0015] In some embodiments of the present invention, in the step of converting the longitude-latitude remote sensing image into the first spatiotemporal image based on the plane coordinate system using the Mercator projection, the following formula is used to convert the longitude-latitude remote sensing image into the first spatiotemporal image based on the plane coordinate system:

[0016]

[0017] Among them, X and Y are the horizontal and vertical coordinates in the first space-time image, X lon and X lat In remote sensing images, they represent longitude and latitude respectively, and R represents the radius of the earth.

[0018] In some embodiments of the present invention, in the step of generating two secret key matrices based on two pre-constructed chaotic sequences, two chaotic sequences are generated by using an improved Logistic mapping method, and the two chaotic sequences are sorted in matrices to obtain two corresponding secret matrices.

[0019] In some embodiments of the present invention, in the step of generating two chaotic sequences by using the improved Logistic mapping method, the following formula is used to construct the sequence value in the chaotic sequence:

[0020]

[0021] Among them, m n+1 Indicates the currently calculated sequence value; m n Indicates the previous sequence value of the currently calculated sequence value; m n-1 Indicates m n represents the previous sequence value of the previous sequence value of the currently calculated sequence value; α and μ both represent preset control parameters.

[0022] In some embodiments of the present invention, in the step of obtaining two scrambling matrices based on two corresponding first extraction sequences and second extraction sequences, the scrambling matrices are calculated based on the following formula:

[0023] Z=M*Z r ;

[0024] Among them, Z represents the first extraction sequence, Z r represents the second extraction sequence corresponding to the first extraction sequence Z, and M represents the scrambling matrix.

[0025] In some embodiments of the present invention, in the step of calculating the scrambled image based on the two scrambling matrices and the second spatiotemporal image, the scrambled image is calculated using the following formula:

[0026] S f =M 1 *S'*M 2 ;

[0027] Among them, S f represents the scrambled image; M 1 and M 2 Respectively represent two scrambled matrices; S' represents the second space-time image.

[0028] In some embodiments of the present invention, in the step of segmenting a preset carrier image, the carrier image is segmented into a plurality of sub-images of preset sizes.

[0029] In some embodiments of the present invention, in the step of filtering each segmented sub-image and obtaining approximate image eigenvalues, longitudinal edge eigenvalues, transverse edge eigenvalues ​​and diagonal eigenvalues ​​for each sub-image, each sub-image is subjected to discrete wavelet transform, and passes through a longitudinal low-pass filter and a high-pass filter, and then passes through a transverse low-pass filter and a high-pass filter, and finally obtains four sub-images with a length and width of 1*1, and the pixel values ​​of the four sub-images are the approximate image eigenvalues, longitudinal edge eigenvalues, transverse edge eigenvalues ​​and diagonal eigenvalues.

[0030] In some embodiments of the present invention, in the step of replacing any feature value of each sub-image with a pixel value at a position in the scrambled image, the longitudinal edge feature value of each sub-image is replaced with a pixel value at a position in the scrambled image.

[0031] The second aspect of the present invention also provides a railway high-precision spatiotemporal information security encryption system, the system includes a computer device, the computer device includes a processor and a memory, the memory stores computer instructions, the processor is used to execute the computer instructions stored in the memory, when the computer instructions are executed by the processor, the system implements the steps implemented by the method described above.

[0032] The third aspect of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps implemented by the aforementioned railway high-precision spatiotemporal information security encryption method.

[0033] Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention can be specifically pointed out and obtained in the specification and the accompanying drawings.

[0034] Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of this application, and do not constitute a limitation of the present invention.

[0036] Figure 1 This is a schematic diagram of an implementation method of the railway high-precision spatiotemporal information security encryption method of this scheme;

[0037] Figure 2 This is a schematic diagram of the processing architecture of this solution;

[0038] Figure 3 This is a schematic diagram of the overall architecture of this solution. DETAILED DESCRIPTION

[0039] 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 embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0040] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0041] like Figure 1 , 2 As shown in 3, the present invention proposes a railway high-precision spatiotemporal information security encryption method, the method comprising the steps of:

[0042] Step S100, obtaining a remote sensing image of the railway line to be encrypted, and performing coordinate transformation on the remote sensing image to obtain a corresponding first space-time image;

[0043] In the specific implementation process, the acquisition of the remote sensing image mainly depends on different types of remote sensing platforms and sensors. Common remote sensing platforms include satellites, aircraft, drones, etc., while sensors include optical cameras, multispectral cameras, infrared cameras, radars, etc. These sensors can capture different information on the earth's surface and convert it into image data.

[0044] In a specific implementation process, in the step of performing coordinate transformation on the remote sensing image to obtain the corresponding first space-time image, the coordinate transformation is performed by using Mercator projection.

[0045] In some embodiments of the present invention, the size of the first spatiotemporal image is N×N.

[0046] Step S200, generating two secret key matrices based on two pre-constructed chaotic sequences, and calculating the first space-time image based on the secret key matrices to obtain a corresponding second space-time image;

[0047] In some embodiments of the present invention, two chaotic sequences are generated by using an improved Logistic mapping method, and the improved Logistic mapping method creates a first sequence value of the chaotic sequence, and the value range of the first sequence value is (0, 1);

[0048] Specifically, two chaotic sequences can be constructed using the following formulas:

[0049]

[0050] Among them, m n+1 Indicates the currently calculated sequence value; m n Indicates the previous sequence value of the currently calculated sequence value; m n-1 Indicates m n represents the previous sequence value of the previous sequence value of the currently calculated sequence value; α and μ both represent preset control parameters.

[0051] In the specific implementation process, two lengths N are generated based on the improved Logistic mapping. 2 Chaos series.

[0052] In some embodiments of the present invention, in the step of generating two key matrices based on two pre-constructed chaotic sequences:

[0053] The two chaotic sequences are mapped to integers on [0,255] by taking a modulo method. First, the sequence value of (0,1) is enlarged by 1000 times, and the sequence is mapped to [0,255] by taking a modulo method, thereby obtaining two integer sequences.

[0054] Set the length to N 2 sequence of integers W 1 and W 2 Arrange the rows in the original order to obtain two secret key matrices of size N*N.

[0055] In some embodiments of the present invention, in the step of calculating the first space-time image based on the secret key matrix to obtain the corresponding second space-time image, the second space-time image is calculated using the following formula:

[0056]

[0057] Where S' represents the second space-time image; S represents the first space-time image; K 1 and K 2 Represent two secret key matrices respectively; represents an XOR operation; mod256 represents the modulus of (S+K 1 ) is mapped to integers in the range [0,255].

[0058] Step S300, extracting from the two chaotic sequences respectively to obtain two first extraction sequences corresponding to the two chaotic sequences;

[0059] In some embodiments of the present invention, in the step of extracting from the two chaotic sequences respectively to obtain two first extraction sequences corresponding to the two chaotic sequences, a preset number of sequence values ​​are extracted from the two chaotic sequences respectively to obtain two first extraction sequences corresponding to the two chaotic sequences respectively.

[0060] In some embodiments of the present invention, from two lengths N 2 N values ​​are extracted from each chaotic series to obtain two first extraction sequences corresponding to the two chaotic sequences.

[0061] Step S400, sorting the two first extraction sequences respectively to obtain two second extraction sequences;

[0062] Step S500, obtaining two scrambling matrices based on two corresponding first extraction sequences and second extraction sequences, and calculating a scrambled image based on the two scrambling matrices and the second spatiotemporal image;

[0063] Step S600, segmenting a preset carrier image, and filtering each segmented sub-image, obtaining an approximate image feature value, a longitudinal edge feature value, a transverse edge feature value, and a diagonal feature value for each sub-image;

[0064] In the specific implementation process, the preset carrier image is segmented into N 2 A 4*4 size sub-image.

[0065] In the specific implementation process, the approximate image feature values ​​refer to the values ​​corresponding to those features in the image that are visually similar or repetitive. These features may include texture, color, shape, etc. in the image. In image processing, various algorithms are often used to extract these feature values ​​for tasks such as image recognition, classification, and retrieval. For example, in feature similarity algorithms (such as FSIM), features such as phase congruency (PC) and gradient magnitude (GM) of the image are considered to evaluate the similarity between images.

[0066] The vertical edge eigenvalue refers to the value corresponding to the edge information in the vertical direction (i.e., vertical direction) of the image. Edges are places where the pixel values ​​in the image change significantly, and they are crucial for the recognition of the image's contour and structure. In image processing, various edge detection operators (such as the Sobel operator, the Prewitt operator, etc.) can be used to extract vertical edge eigenvalues. These operators detect edges by calculating the first-order or second-order derivatives of the image pixel values ​​and give the strength and direction information of the edge.

[0067] The horizontal edge eigenvalue refers to the value corresponding to the edge information along the horizontal direction (i.e., horizontal direction) in the image. Similar to the vertical edge eigenvalue, the horizontal edge eigenvalue is also extracted by the edge detection operator. These eigenvalues ​​help to identify horizontal structures in the image, such as the lines of buildings, the horizon, etc.

[0068] Diagonal eigenvalues ​​refer to the numerical values ​​corresponding to the feature information along the diagonal direction in the image. Although traditional edge detection operators (such as Sobel operator, Prewitt operator, etc.) mainly focus on edges in the horizontal and vertical directions, there are also some operators (such as Roberts operator) that can detect diagonal edges. Diagonal eigenvalues ​​are important for identifying inclined structures or diagonal elements in images. However, it should be noted that the extraction of diagonal eigenvalues ​​may be more complicated than the extraction of horizontal and vertical edge eigenvalues, because diagonal edges may not be as obvious and stable as horizontal and vertical edges.

[0069] In a specific implementation process, the size of the carrier image is 4N×4N.

[0070] Step S700: replace any characteristic value of each sub-image with a pixel value at a position in the scrambled image, and perform an inverse discrete cosine transform to update the carrier image to a scrambled image.

[0071] In a specific implementation process, the inverse discrete cosine transform is an inverse transform of a two-dimensional discrete cosine transform.

[0072] The above scheme is adopted. This scheme includes two parts. The first part is the hiding process: hiding the first space-time image that needs to be encrypted, generating a chaotic sequence based on the improved logistics mapping for diffusion and scrambling respectively, and then segmenting the carrier image to generate a sub-image containing edge feature information using discrete cosine transformation, and replacing the information of the second space-time image after diffusion and scrambling in the sub-image, and obtaining a secret image with hidden space-time information after inverse discrete cosine transformation, thereby improving the encryption effect and meeting the needs of data encryption.

[0073] In some embodiments of the present invention, in the step of acquiring a remote sensing image of the railway line to be encrypted, performing coordinate transformation on the remote sensing image, and obtaining a corresponding first space-time image, the remote sensing image based on longitude and latitude is converted into a first space-time image based on a plane coordinate system using Mercator projection.

[0074] In some embodiments of the present invention, in the step of converting the longitude-latitude remote sensing image into the first spatiotemporal image based on the plane coordinate system using the Mercator projection, the following formula is used to convert the longitude-latitude remote sensing image into the first spatiotemporal image based on the plane coordinate system:

[0075]

[0076] Among them, X and Y are the horizontal and vertical coordinates in the first space-time image, X lon and X lat In remote sensing images, they represent longitude and latitude respectively, and R represents the radius of the earth.

[0077] In some embodiments of the present invention, in the step of generating two secret key matrices based on two pre-constructed chaotic sequences, two chaotic sequences are generated by using an improved Logistic mapping method, and the two chaotic sequences are sorted in matrices to obtain two corresponding secret matrices.

[0078] In some embodiments of the present invention, in the step of generating two chaotic sequences by using the improved Logistic mapping method, the following formula is used to construct the sequence value in the chaotic sequence:

[0079]

[0080] Among them, m n+1 Indicates the currently calculated sequence value; m n Indicates the previous sequence value of the currently calculated sequence value; m n-1 Indicates m n represents the previous sequence value of the previous sequence value of the currently calculated sequence value; α and μ both represent preset control parameters.

[0081] Specifically, Indicates rounding down. The improved Logistic mapping creates the first and second sequence values ​​of the chaotic sequence. The range of the sequence values ​​is (0,1).

[0082] Using the above scheme, two lengths of N are generated through improved Logistic mapping. 2 The advantages of the improved Logistic mapping for chaotic series are: it has two parameters, μ and α, and their value ranges are arbitrary; the mapping range of m is arbitrary, and it is a full mapping within any parameter range, which greatly improves the security of the sequence and greatly improves the distribution of iterative values.

[0083] In some embodiments of the present invention, in the step of obtaining two scrambling matrices based on two corresponding first extraction sequences and second extraction sequences, the scrambling matrices are calculated based on the following formula:

[0084] Z=M*Z r ;

[0085] Among them, Z represents the first extraction sequence, Z r represents the second extraction sequence corresponding to the first extraction sequence Z, and M represents the scrambling matrix.

[0086] In some embodiments of the present invention, in the step of calculating the scrambled image based on the two scrambling matrices and the second spatiotemporal image, the scrambled image is calculated using the following formula:

[0087] S f =M 1 *S'*M 2 ;

[0088] Among them, S f represents the scrambled image; M 1 and M 2 Respectively represent two scrambled matrices; S' represents the second space-time image.

[0089] In some embodiments of the present invention, in the step of segmenting a preset carrier image, the carrier image is segmented into a plurality of sub-images of preset sizes.

[0090] In some embodiments of the present invention, in the step of filtering each segmented sub-image and obtaining approximate image eigenvalues, longitudinal edge eigenvalues, transverse edge eigenvalues ​​and diagonal eigenvalues ​​for each sub-image, each sub-image is subjected to discrete wavelet transform, and passes through a longitudinal low-pass filter and a high-pass filter, and then passes through a transverse low-pass filter and a high-pass filter, and finally obtains four sub-images with a length and width of 1*1, and the pixel values ​​of the four sub-images are the approximate image eigenvalues, longitudinal edge eigenvalues, transverse edge eigenvalues ​​and diagonal eigenvalues.

[0091] In some embodiments of the present invention, in the step of replacing any feature value of each sub-image with a pixel value at a position in the scrambled image, the longitudinal edge feature value of each sub-image is replaced with a pixel value at a position in the scrambled image.

[0092] Using the above scheme, for many signals, their low-frequency components are often hidden in the basic characteristics of the signal, while the high-frequency signal only gives the detailed information of the signal. For example, the low-frequency signal of the image signal contains the basic contour information of the image, while the high-frequency signal preserves the edge contour information of the image. The loss of the high-frequency signal has little effect on the visual effect of the image. The pixels of the secret image after the vertical edge feature sub-image of one of the four sub-images generated after the carrier image is transformed by wavelet are replaced by the pixels of the vertical edge feature sub-image, that is, the pixel value of a position in the scrambled image, and the pixel values ​​of each position in the scrambled image are inserted into the carrier image, thereby obtaining a secret image containing secret information.

[0093] The beneficial effects of this program include:

[0094] 1. High-level data security protection: Through improved logistics mapping, this technology introduces multiple variables and parameter controls when generating chaotic sequences. This method not only increases the complexity of the encryption process, but also improves the security of high-level spatiotemporal information, effectively preventing unauthorized access and decryption attempts.

[0095] 2. Optimized visual fidelity and information hiding technology: This technology cleverly hides the encrypted information in the edge feature sub-image of the carrier image through discrete cosine transform. This method makes the encrypted image visually almost the same as the original image, while ensuring the undetectability of the encrypted information, improving concealment and confidentiality.

[0096] 3. Highly efficient encryption and decryption process: This technology adopts a two-way symmetric encryption and decryption process, which not only makes the decryption process simple and efficient, but also ensures that the information can be restored with high fidelity after decryption, with almost no information loss, which is particularly critical for application scenarios that need to maintain data integrity.

[0097] 4. Multi-level encryption strategy with wide applicability: This technology provides a multi-level encryption scheme for spatiotemporal information with different confidentiality levels. This flexible strategy adapts to various encryption requirements and ensures that the appropriate level of protection can be obtained from general confidential information to high-level confidential information.

[0098] 5. The application of improved logistics mapping in this technology provides more complex and unpredictable chaotic sequences, which is crucial for enhancing the security of encryption algorithms.

[0099] 6. Through discrete cosine transform, this technology can effectively hide encrypted information without affecting the visual quality of the original image. This is an innovative information hiding method.

[0100] 7. Encryption technology for hierarchical spatiotemporal information: Based on different levels of confidentiality requirements, this technology provides targeted encryption solutions. This hierarchical encryption method is extremely valuable in a variety of application scenarios.

[0101] 8. By normalizing, sorting and mapping the chaotic sequence, the randomness and security of the encryption process are significantly improved.

[0102] 9. The scrambling matrix design in this technology plays a key role in the protection of encrypted data and improves the unpredictability and complexity of the data encryption process.

[0103] 10. This technology uses discrete wavelet transform to process the carrier image. This method effectively hides information while maintaining image quality.

[0104] 11. This technology uses multi-level data encryption, which encrypts and protects spatiotemporal information data of different levels by classifying the data.

[0105] An embodiment of the present invention also provides a railway high-precision spatiotemporal information security encryption system, the system includes a computer device, the computer device includes a processor and a memory, the memory stores computer instructions, the processor is used to execute the computer instructions stored in the memory, when the computer instructions are executed by the processor, the system implements the steps implemented by the method described above.

[0106] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps implemented by the above-mentioned railway high-precision spatiotemporal information security encryption method are implemented. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0107] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0108] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0109] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A railway high-precision spatiotemporal information security encryption method, characterized in that: The steps of the method include: Acquire a remote sensing image of the railway line to be encrypted, and perform coordinate transformation on the remote sensing image to obtain a corresponding first space-time image; Generate two secret key matrices based on two pre-constructed chaotic sequences, and calculate the first space-time image based on the secret key matrices to obtain a corresponding second space-time image; Extracting from the two chaotic sequences respectively to obtain two first extraction sequences corresponding to the two chaotic sequences; sorting the two first extraction sequences respectively to obtain two second extraction sequences; Obtaining two scrambled matrices based on two corresponding first extraction sequences and a second extraction sequence, and calculating a scrambled image based on the two scrambled matrices and the second spatiotemporal image; Segmenting a preset carrier image, and filtering each segmented sub-image, and obtaining an approximate image eigenvalue, a longitudinal edge eigenvalue, a transverse edge eigenvalue, and a diagonal eigenvalue for each sub-image; Any characteristic value of each sub-image is replaced by a pixel value of a position in the scrambled image, and an inverse discrete cosine transform is performed to update the carrier image to a scrambled image.

2. The railway high-precision spatiotemporal information security encryption method according to claim 1 is characterized in that: In the step of acquiring a remote sensing image of the railway line to be encrypted, performing coordinate conversion on the remote sensing image, and obtaining a corresponding first space-time image, the remote sensing image based on longitude and latitude is converted into a first space-time image based on a plane coordinate system using Mercator projection.

3. The railway high-precision spatiotemporal information security encryption method according to claim 2 is characterized in that: In the step of converting the longitude-latitude remote sensing image into the first spatiotemporal image based on the plane coordinate system by using the Mercator projection, the following formula is used to convert the longitude-latitude remote sensing image into the first spatiotemporal image based on the plane coordinate system: Among them, X and Y are the horizontal and vertical coordinates in the first space-time image, X lon and X lat In remote sensing images, they represent longitude and latitude respectively, and R represents the radius of the earth.

4. The railway high-precision spatiotemporal information security encryption method according to claim 1 is characterized in that: In the step of generating two secret key matrices based on two pre-constructed chaotic sequences, two chaotic sequences are generated by adopting an improved Logistic mapping method, and the two chaotic sequences are sorted in matrices to obtain two corresponding secret matrices.

5. The railway high-precision spatiotemporal information security encryption method according to claim 4 is characterized in that: In the step of generating two chaotic sequences by using the improved Logistic mapping method, the following formula is used to construct the sequence value in the chaotic sequence: Among them, m n+1 Indicates the currently calculated sequence value; m n Indicates the previous sequence value of the currently calculated sequence value; m n-1 Indicates m n represents the previous sequence value of the previous sequence value of the currently calculated sequence value; α and μ both represent preset control parameters.

6. The railway high-precision spatiotemporal information security encryption method according to any one of claims 1 to 5, characterized in that: In the step of obtaining two scrambling matrices based on two corresponding first extraction sequences and second extraction sequences, the scrambling matrix is ​​calculated based on the following formula: Z=M*Z r ; Among them, Z represents the first extraction sequence, Z r represents the second extraction sequence corresponding to the first extraction sequence Z, and M represents the scrambling matrix.

7. The railway high-precision spatiotemporal information security encryption method according to claim 6 is characterized in that: In the step of calculating the scrambled image based on the two scrambled matrices and the second spatiotemporal image, the scrambled image is calculated using the following formula: S f =M1*S'*M2; Among them, S f represents a scrambled image; M1 and M2 represent two scrambled matrices respectively; S' represents the second spatiotemporal image.

8. The railway high-precision spatiotemporal information security encryption method according to claim 1 or 7, characterized in that: In the step of filtering each segmented sub-image and obtaining the approximate image eigenvalue, longitudinal edge eigenvalue, transverse edge eigenvalue and diagonal eigenvalue for each sub-image, a discrete wavelet transform is performed on each sub-image, and it passes through a longitudinal low-pass filter and a high-pass filter, and then through a transverse low-pass filter and a high-pass filter, and finally four sub-images with a length and width of 1*1 are obtained, and the pixel values ​​of the four sub-images are the approximate image eigenvalue, longitudinal edge eigenvalue, transverse edge eigenvalue and diagonal eigenvalue.

9. The railway high-precision spatiotemporal information security encryption method according to claim 8 is characterized in that: In the step of replacing any one feature value of each sub-image with a pixel value at a position in the scrambled image, a longitudinal edge feature value of each sub-image is replaced with a pixel value at a position in the scrambled image.

10. A railway high-precision spatiotemporal information security encryption system, characterized in that: The system includes a computer device, which includes a processor and a memory. The memory stores computer instructions. The processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps implemented by the method as described in any one of claims 1 to 9.

Images