Railway high-precision space-time information security encryption method and system

Abstract

The application provides a railway high-precision space-time information security encryption method and system, and the steps of the method comprise: obtaining a first space-time image to be encrypted; generating two secret key matrices based on two pre-constructed chaotic sequences, calculating the first space-time image based on the secret key matrices to obtain a corresponding second space-time image; obtaining two first extraction sequences and a second extraction sequence based on the two chaotic sequences, and further obtaining two scrambling matrices, calculating a scrambled image based on the two scrambling matrices and the second space-time image; segmenting a pre-set carrier image, and performing filtering processing on each segmented sub-image, obtaining approximate image eigenvalues, longitudinal edge eigenvalues, transverse edge eigenvalues and diagonal eigenvalues for each sub-image; replacing any eigenvalue of each sub-image with a pixel value at a position in the scrambled image, and performing inverse discrete cosine transformation, and updating the carrier image to a carrier image.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information encryption technology, and in particular to a railway high-precision space-time information security encryption method and system. BACKGROUND

[0002] Information encryption technology is a technology that uses mathematical or physical means to protect electronic information during transmission and storage to prevent leaks. It is one of the most basic and important technologies to ensure information security, and is generally implemented using internationally recognized secure encryption algorithms. The information encryption process is implemented by various encryption algorithms, which can convert useful information into seemingly useless random codes, 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, space-time information data has become an indispensable component. With the development of technology, it has become easy to obtain these data. Currently, in order to protect these critical data, encryption algorithms are used to convert the original data into a disordered and unrecognizable form. However, this method has certain limitations in practical application, such as not fully considering the spatial structure characteristics of vector map data, and the permutation encryption based on one-to-one mapping may reduce the overall security of the algorithm, and in order to decrypt specific layer data, the entire encrypted map may need to be decrypted. In summary, the encryption effect of the data encryption of the prior art is poor, and it is difficult to meet the demand of data encryption. SUMMARY

[0004] In view of this, the embodiments of the present application provide a railway high-precision space-time information security encryption method and system to eliminate or improve one or more defects in the prior art.

[0005] One aspect of the present application provides a railway high-precision space-time information security encryption method, the steps of which include:

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

[0007] 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;

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

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

[0010] Two scrambling matrices are obtained based on the two first extraction sequences and the second extraction sequence corresponding to each other, and a scrambled image is calculated based on the two scrambling matrices and the second space-time image;

[0011] The preset carrier image is segmented, and each segmented sub-image is filtered, and approximate image feature values, longitudinal edge feature values, transverse edge feature values and diagonal feature values are obtained for each sub-image.

[0012] Any feature 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, and the carrier image is updated to a steganographic image.

[0013] With the above scheme, the scheme includes two parts, one is the hiding process: hiding the first space-time image to be encrypted, generating chaotic sequences based on the improved logistics mapping for diffusion and scrambling, then segmenting the carrier image and generating sub-images 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 performing inverse discrete cosine transformation to obtain a steganographic image of hidden space-time information, improving the encryption effect and meeting the data encryption demand.

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

[0015] In some embodiments of the present application, in the step of converting the remote sensing image based on latitude and longitude into a first space-time image based on a plane coordinate system using Mercator projection, the remote sensing image based on latitude and longitude is converted into a first space-time image based on a plane coordinate system using the following formula:

[0016]

[0017] wherein X and Y are the horizontal and vertical coordinates in the first space-time image, X lon and X lat The longitude and latitude are represented in the remote sensing image, and R represents the earth radius value.

[0018] In some embodiments of the present application, in the step of generating two secret matrixes based on two pre-constructed chaotic sequences, two chaotic sequences are generated using an improved logistic mapping, and the two chaotic sequences are sorted into matrixes to obtain two corresponding secret matrices.

[0019] In some embodiments of the present application, in the step of generating two chaotic sequences in the way of using improved Logistic mapping, the sequence value in the chaotic sequence is constructed by using the following formula:

[0020]

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

[0022] In some embodiments of the present application, in the step of obtaining two scrambling matrices based on two first extraction sequences and second extraction sequences corresponding to each other, the scrambling matrix is calculated based on the following formula:

[0023] Z=M*Z r ;

[0024] wherein, 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 application, in the step of calculating the scrambled image based on the two scrambling matrices and the second space-time image, the scrambled image is calculated by using the following formula:

[0026] S f =M1*S'*M2;

[0027] wherein, S f represents the scrambled image; M1 and M2 respectively represent the two scrambling matrices; and S' represents the second space-time image.

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

[0029] In some embodiments of the present application, in the step of obtaining the approximate image feature value, the longitudinal edge feature value, the transverse edge feature value and the diagonal feature value for each segmented sub-image by performing filtering processing on each segmented sub-image, discrete wavelet transform is performed on each sub-image, and then a longitudinal low-pass filter and a longitudinal high-pass filter are used, followed by a transverse low-pass filter and a transverse high-pass filter, and finally four sub-images with a size of 1*1 are obtained, and the pixel values of the four sub-images are the approximate image feature value, the longitudinal edge feature value, the transverse edge feature value and the diagonal feature value.

[0030] In some embodiments of the present application, in the step of replacing each sub-image's feature value of any kind with the pixel value of a position in the scrambled image, the vertical edge feature value of each sub-image is replaced with the pixel value of a position in the scrambled image.

[0031] The second aspect of the present application also provides a railway high-precision space-time information security encryption system, comprising a computer device, the computer device comprising a processor and a memory, the memory storing computer instructions, and the processor being configured to execute the computer instructions stored in the memory, so as to realize the steps of the method as described above.

[0032] The third aspect of the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps of the railway high-precision space-time information security encryption method as described above.

[0033] Additional advantages, objects, and features of the application will be set forth in part by the description that follows, and will become apparent to those skilled in the art upon examination of the following detailed description and drawings in which

[0034] Those skilled in the art will appreciate that the objects and advantages of the application can be realized and attained by means summarized fully in the detailed description that follows, particularly when considered in light of the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0035] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the application and serve to explain the principles of the application.

[0036] Figure 1 A schematic diagram of an embodiment of the railway high-precision space-time information security encryption method of the present application;

[0037] Figure 2 A schematic diagram of the processing architecture of the present application;

[0038] Figure 3 A schematic diagram of the overall architecture of the present application. DETAILED DESCRIPTION

[0039] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the embodiments and drawings. Herein, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but not as a limitation of the present application.

[0040] It should be noted that, in order not to obscure the present application with unnecessary details, only the structures and / or processing steps closely related to the solutions according to the present application are shown in the drawings, and other details not closely related to the present application are omitted.

[0041] As shown in FIGS. Figure 1 , 2 and 3, the present application proposes a high-precision space-time information security encryption method for railways, the steps of which include:

[0042] Step S100: Obtain a remote sensing image of a railway line to be encrypted, and perform coordinate conversion 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, airplanes, drones, etc., and sensors include optical cameras, multispectral cameras, infrared cameras, radars, etc. These sensors can capture different information on the earth's surface and convert them into image data.

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

[0045] In some embodiments of the present application, the size of the first space-time image is N x N.

[0046] Step S200: Generate two secret key matrices based on two pre-constructed chaotic sequences, and perform calculation on 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 application, the two chaotic sequences are generated in the way of improved Logistic mapping, which creates a first sequence value of the chaotic sequence, and the value range of the first sequence value is (0, 1);

[0048] Specifically, the two chaotic sequences can be constructed by the following formula:

[0049]

[0050] wherein, m n+1 represents the current calculated sequence value; m n represents the previous sequence value of the current calculated sequence value; m n-1 represents m n represents the previous sequence value of the previous sequence value of the current calculated sequence value; and both α and μ represent pre-set control parameters.

[0051] In the implementation process, two chaotic sequences with a length of N 2 are generated based on an improved Logistic mapping.

[0052] In some embodiments of the present application, 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 the modulus, first expanding the sequence value of (0, 1) by 1000 times, and then mapping the sequence to [0, 255] by taking the modulus, thereby obtaining two integer sequences;

[0054] The integer sequences W1 and W2 with a length of N 2 are arranged in the original order to obtain two key matrices with a size of N*N.

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

[0056]

[0057] Wherein, S' represents the second space-time image; S represents the first space-time image; K1 and K2 represent two key matrices respectively; represents the XOR operation; mod256 represents the mapping of the value of (S+K1) to an integer on [0, 255] by taking the modulus.

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

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

[0060] In some embodiments of the present application, N values are extracted from two chaotic sequences with a length of N 2 respectively to obtain two first extraction sequences corresponding to two chaotic sequences.

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

[0062] Step S500, obtaining two permutation matrices based on two first extraction sequences and second extraction sequences corresponding to each other, and calculating a permutation image based on two permutation matrices and a second space-time image.

[0063] In step S600, the pre-set carrier image is segmented, and each segmented sub-image is filtered to obtain 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 pre-set carrier image is segmented into N 2 4*4 size sub-images.

[0065] In the specific implementation process, the approximate image feature value refers to the numerical value corresponding to the characteristics in the image that have visual similarity or repetition. These characteristics may include texture, color, shape, etc. in the image. In image processing, various algorithms are often used to extract these feature values for image recognition, classification, retrieval, etc. For example, in the feature similarity algorithm (such as FSIM), the phase congruency (PC) and gradient magnitude (GM) of the image are considered to evaluate the similarity between images.

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

[0067] The transverse edge feature value refers to the numerical value corresponding to the edge information along the horizontal direction (i.e. the transverse direction) in the image. Similar to the longitudinal edge feature value, the transverse edge feature value is also extracted by edge detection operators. These feature values help identify horizontal structures in the image, such as lines of buildings, horizons, etc.

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

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

[0070] In step S700, each feature 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 steganographic image.

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

[0072] With the above scheme, the present scheme includes two parts, one is the hiding process: hiding the first space-time image that needs to be encrypted, generating chaotic sequences based on an improved logistics mapping for diffusion and scrambling, then segmenting the carrier image to generate sub-images containing edge feature information using discrete cosine transform, and replacing the information of the second space-time image after diffusion and scrambling in the sub-image, and performing inverse discrete cosine transform to obtain a steganographic image of hidden space-time information, improving the encryption effect and meeting the data encryption demand.

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

[0074] In some embodiments of the present application, in the step of converting the remote sensing image based on latitude and longitude into a first space-time image based on a plane coordinate system using Mercator projection, the remote sensing image based on latitude and longitude is converted into a first space-time image based on a plane coordinate system using the following formula:

[0075]

[0076] wherein X and Y are the horizontal and vertical coordinates in the first space-time image, X lon and X lat In the remote sensing image, longitude and latitude are represented respectively, and R represents the value of the earth's radius.

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

[0078] In some embodiments of the present application, in the step of generating two chaotic sequences using an improved Logistic mapping method, the sequence values in the chaotic sequences are constructed using the following formula:

[0079]

[0080] wherein, m n+1 denotes the current calculated sequence value; m n denotes the previous sequence value of the current calculated sequence value; m n-1 denotes m n denotes the previous sequence value of the previous sequence value of the current calculated sequence value; and both α and μ denote preset control parameters.

[0081] Specifically, denotes the floor function, the improved Logistic mapping creates the first and second sequence values of the chaotic sequence, and the sequence value ranges from 0 to 1.

[0082] By using the above scheme, two chaotic sequences with a length of N 2 are generated by using the improved Logistic mapping, and the improved Logistic mapping has the following advantages: the improved Logistic mapping has two parameters μ and α, and the values of the two parameters are arbitrary; the mapping range of m is arbitrary, and the mapping is full in any parameter range, which greatly improves the security of the sequence and greatly improves the distribution of the iteration values.

[0083] In some embodiments of the present application, in the step of obtaining two scrambling matrices based on two first extraction sequences and second extraction sequences corresponding to each other, the scrambling matrix is calculated based on the following formula:

[0084] Z = M * Z r ;

[0085] wherein, Z denotes the first extraction sequence, Z r denotes the second extraction sequence corresponding to the first extraction sequence Z, and M denotes the scrambling matrix.

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

[0087] S f = M1 * S' * M2;

[0088] wherein, S f denotes the scrambled image; M1 and M2 respectively denote the two scrambling matrices; and S' denotes the second space-time image.

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

[0090] In some embodiments of the present application, in the step of obtaining the approximation image feature value, the vertical edge feature value, the horizontal edge feature value and the diagonal feature value for each sub-image after filtering each segmented sub-image, discrete wavelet transform is performed on each sub-image, and then a low-pass filter and a high-pass filter in the vertical direction are applied, followed by a low-pass filter and a high-pass filter in the horizontal direction, finally obtaining four sub-images with a size of 1*1, and the pixel values of the four sub-images are the approximation image feature value, the vertical edge feature value, the horizontal edge feature value and the diagonal feature value.

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

[0092] With the above scheme, for many signals, the low-frequency components often contain the basic features of the signal, and the high-frequency signals only give the detailed information of the signal. For example, the low-frequency signal of an image signal contains the basic outline information of the image, and the high-frequency signal preserves the image edge outline information. The loss of high-frequency signal has little effect on the visual effect of the image. The pixel value of the scrambled image at a position is obtained by replacing the pixel of the vertical edge feature sub-image of one of the four sub-images generated after wavelet transform of the carrier image with the secret image pixel, and each pixel value of a position in the scrambled image is inserted into the carrier image, thereby obtaining the stego image containing secret information.

[0093] The beneficial effects of the present scheme include:

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

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

[0096] 3. High-efficiency encryption and decryption process: This technology uses a bidirectional 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 applications that require data integrity.

[0097] 4. Multi-level encryption strategy with wide applicability: For different levels of time-space information security, the present technology provides a multi-level encryption scheme. This flexible strategy adapts to various encryption needs, ensuring appropriate protection levels from general security information to high-level security information.

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

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

[0100] 7. Encryption technology for hierarchical time-space information: According to different levels of security requirements, the present technology provides targeted encryption schemes. This hierarchical encryption method is highly valuable in various 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 design of the scrambling matrix in the present technology plays a crucial role in protecting encrypted data, improving the unpredictability and complexity of the data encryption process.

[0103] 10. The present technology uses discrete wavelet transform to process the carrier image, which effectively hides information while maintaining image quality.

[0104] 11. The present technology uses multi-level data encryption, which encrypts and protects different levels of time-space information data by classifying data.

[0105] The present embodiment also provides a railway high-precision time-space information security encryption system, which includes a computer device, the computer device includes a processor and a memory, the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory, when the computer instructions are executed by the processor, the system realizes the steps as implemented by the method described above.

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

[0107] Those skilled in the art will understand 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 both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the desired tasks. The programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave.

[0108] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are 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. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0109] In this 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 or in place of features of other embodiments.

[0110] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations of the embodiments of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A high-precision spatiotemporal information security encryption method for railways, characterized in that, The steps of this method include: Obtain remote sensing images of the railway line to be encrypted, and perform coordinate transformation on the remote sensing images to obtain the corresponding first spatiotemporal image; Two key matrices are generated based on two pre-constructed chaotic sequences. The first spatiotemporal image is then calculated based on the key matrices to obtain the corresponding second spatiotemporal image. Extracting from the two chaotic sequences respectively, we obtain two first extraction sequences corresponding to the two chaotic sequences; Sort the two first extraction sequences respectively to obtain two second extraction sequences; Two scrambling matrices are obtained based on two corresponding first extraction sequences and second extraction sequences, and a scrambling image is calculated based on the two scrambling matrices and the second spatiotemporal image. The preset carrier image is segmented, and each segmented sub-image is filtered to obtain approximate image feature values, vertical edge feature values, horizontal edge feature values, and diagonal feature values ​​for each sub-image. Replace any feature value of each sub-image with a pixel value at a location in the scrambled image, and perform an inverse discrete cosine transform to update the carrier image into a scrambled image.

2. The high-precision spatiotemporal information security encryption method for railways according to claim 1, characterized in that, In the steps of acquiring remote sensing images of the railway line to be encrypted and performing coordinate transformation on the remote sensing images to obtain the corresponding first spatiotemporal image, Mercator projection is used to convert the latitude and longitude-based remote sensing images into the first spatiotemporal image based on a plane coordinate system.

3. The high-precision spatiotemporal information security encryption method for railways according to claim 2, characterized in that, In the step of converting latitude-longitude-based remote sensing images into a first spatiotemporal image based on a planar coordinate system using Mercator projection, the following formula is used to convert the latitude-longitude-based remote sensing images into a first spatiotemporal image based on a planar coordinate system: Where X and Y are the x and y coordinates in the first spatiotemporal image, X lon and X lat In remote sensing images, longitude and latitude are represented respectively, and R represents the Earth's radius.

4. The high-precision spatiotemporal information security encryption method for railways according to claim 1, characterized in that, In the step of generating two key matrices based on two pre-constructed chaotic sequences, an improved Logistic mapping method is used to generate two chaotic sequences. The two chaotic sequences are then sorted into matrices to obtain the corresponding two secret matrices.

5. The high-precision spatiotemporal information security encryption method for railways according to claim 4, characterized in that, In the step of generating two chaotic sequences using the improved Logistic mapping, the sequence values ​​in the chaotic sequences are constructed using the following formula: Where, m n+1 Indicates the currently calculated sequence value; m n This represents the previous sequence value in the currently calculated sequence; m n-1 m n This represents the value preceding the current 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 and second extraction sequences, the scrambling matrix is ​​calculated based on the following formula: Z=M*Z r ; Where Z represents the first extraction sequence, Z r Let Z represent the second extraction sequence corresponding to the first extraction sequence Z, and M represent the scrambling matrix.

7. The high-precision spatiotemporal information security encryption method for railways according to claim 6, characterized in that, 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: S f =M1*S'*M2; Among them, S f M1 and M2 represent two scrambling matrices, respectively; S' represents the second spatiotemporal image.

8. The high-precision spatiotemporal information security encryption method for railways according to claim 1 or 7, characterized in that, In the step of filtering each segmented sub-image to obtain approximate image feature values, vertical edge feature values, horizontal edge feature values, and diagonal feature values ​​for each sub-image, a discrete wavelet transform is performed on each sub-image, and then it is passed through a vertical low-pass filter and a high-pass filter, and then through a horizontal low-pass filter and a high-pass filter, finally resulting in four sub-images with a length and width of 1*1. The pixel values ​​of the four sub-images are the approximate image feature values, vertical edge feature values, horizontal edge feature values, and diagonal feature values.

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

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

Images