Intelligent manufacturing data security protection method based on post-quantum technology

Abstract

The invention belongs to the technical field of data security protection, and particularly relates to an intelligent manufacturing data security protection method based on a post-quantum technology, which comprises the following steps of: dividing a production monitoring image into a plurality of non-overlapping blocks with specified sizes through a block truncation algorithm based on an absolute distance, and obtaining a bitmap, a low quantization value and a high quantization value of each non-overlapping block, the specified size is the preselected size with the maximum comprehensive effect value; according to a preset key matrix and a weight matrix, embedding secret information into the bitmap of each non-overlapping block through a block hiding algorithm to obtain a ciphertext bitmap of each non-overlapping block; and coding the low quantization value, the high quantization value and the ciphertext bitmap of each non-overlapping block, and encrypting and storing a coding result based on an NIST anti-quantum cryptography standard algorithm. By means of the method, efficient integration of compression, encryption and watermark embedding can be achieved, and the overall performance can be improved to the maximum extent on the premise that the image quality is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of data security protection technology. More specifically, the present invention relates to a method for intelligent manufacturing data security protection based on post-quantum technology. Background Art

[0002] In the intelligent manufacturing process, production monitoring images are acquired in real time through various sensors, cameras and other data acquisition devices to monitor the operating status of production equipment, product quality and changes in the production environment.

[0003] With the rapid development of intelligent manufacturing, the role of production monitoring images in industrial processes is becoming increasingly important. They are not only an important basis for realizing intelligent production and decision-making, but also can provide key information for fault diagnosis, quality control and optimized scheduling.

[0004] However, since production monitoring images usually contain a large amount of sensitive data, how to ensure their security during transmission and storage becomes an urgent problem to be solved.

[0005] At the same time, considering the large amount of data in production monitoring images, direct storage or transmission will lead to excessive resource consumption, so they need to be compressed.

[0006] In addition, to prevent images from being tampered with or forged, secret information needs to be embedded to verify their integrity and authenticity.

[0007] Although traditional encryption, compression, and information hiding technologies perform well in their respective fields, they pose security risks when facing the threat of quantum computing. Existing information hiding technologies are often independent of the compression and encryption processes, making it difficult to simultaneously meet the requirements of efficient compression, security protection, and anti-tampering, and thus unable to achieve efficient integrated processing. Summary of the Invention

[0008] In order to solve the above technical problems, the present invention provides a smart manufacturing data security protection method based on post-quantum technology, comprising: dividing the production monitoring image into multiple non-overlapping blocks of specified sizes through a block truncation algorithm based on absolute distance, and obtaining the bitmap, low quantization value and high quantization value of each non-overlapping block; according to a preset key matrix and weight matrix, embedding secret information into the bitmap of each non-overlapping block through a block hiding algorithm, encoding the obtained ciphertext bitmap and low quantization value and high quantization value of each non-overlapping block, and encrypting and storing the encoding result based on the NIST anti-quantum cryptography standard algorithm; wherein, the specified size is a preselected size with the largest comprehensive effect value, and the comprehensive effect value of each preselected size is The method for obtaining is as follows: according to the number of non-overlapping blocks corresponding to the pre-selected size and the amount of data stored in each non-overlapping block, the image compression degree corresponding to the pre-selected size is calculated; according to the difference between the grayscale value of the pixel points and the low quantization value and the high quantization value in all non-overlapping blocks corresponding to the pre-selected size, the loss degree caused by the compression coding corresponding to the pre-selected size is calculated; according to the number of times the secret information is embedded in all non-overlapping blocks corresponding to the pre-selected size and the difference between the low quantization value and the high quantization value, the loss degree caused by the information hiding corresponding to the pre-selected size is calculated; the image compression degree, the loss degree caused by information hiding and the loss degree caused by the compression coding corresponding to each pre-selected size are weighted to obtain the comprehensive effect value of each pre-selected size.

[0009] The present invention performs lossy compression on production monitoring images through a block truncation algorithm based on absolute distance, effectively reducing the amount of data. At the same time, secret information is embedded in the bitmap of each non-overlapping block through a block hiding algorithm, ensuring the authenticity and integrity of the image. Furthermore, the NIST quantum-resistant cryptography standard algorithm is used to encrypt and store low quantization values, high quantization values, and ciphertext bitmaps after information hiding, greatly improving the security of image data and enabling it to remain resistant to attacks in a quantum computing environment. This not only achieves seamless integration of compression, encryption, and information hiding, but also takes into account compression efficiency, security protection, and anti-tampering requirements, significantly reducing storage and transmission costs, while ensuring the reliability and availability of production monitoring images in the intelligent manufacturing process.

[0010] Furthermore, by determining the specified size with the largest comprehensive effect value, the present invention can find the optimal balance between compression efficiency and image quality. It can not only achieve efficient integration of compression, encryption and watermark embedding, but also maximize the overall performance while ensuring image quality, providing a reliable solution for production monitoring image processing in intelligent manufacturing processes.

[0011] Preferably, the bitmap, low quantization value and high quantization value of each non-overlapping block are obtained, including: calculating the mean of the grayscale values ​​of all pixels in the non-overlapping block; dividing the pixels with grayscale values ​​greater than the mean into one category of pixels, and writing "1" at the corresponding position in the bitmap; dividing the pixels with grayscale values ​​not greater than the mean into two categories of pixels, and writing "0" at the corresponding position in the bitmap; thereby obtaining the bitmap of the non-overlapping block; calculating the mean of the grayscale values ​​of all pixels in the first category, and using the rounded result as the high quantization value of the non-overlapping block; calculating the mean of the grayscale values ​​of all pixels in the second category, and using the rounded result as the low quantization value of the non-overlapping block.

[0012] Preferably, the preset key matrix is ​​a matrix with a size equal to 4×4, and the elements in the matrix are 0 or 1.

[0013] Preferably, the weight matrix is ​​a matrix of size equal to 4×4, and the elements in the matrix are integers in the range of [1, 7], and the number of times each integer in the range of [1, 7] appears in the matrix is ​​equal to 2 or 3.

[0014] Preferably, the method of embedding secret information into the bitmap of each non-overlapping block by using a block hiding algorithm includes: dividing the bitmap of the non-overlapping block into a plurality of sub-blocks of a size of 4×4; for any sub-block , pair blocks Perform the transformation to obtain the transformed sub-block , requiring the transformed sub-block satisfy Where, 、 are the key matrix and weight matrix respectively, represents cumulative summation, Indicates the decimal number corresponding to the secret information, Represents the exclusive OR operation, represents bitwise multiplication, Represents a modulo operation; the pair of blocks Transformation means: At least one element in the array is swapped between 0 and 1.

[0015] Preferably, the preselected size is equal to ,in, 、 All are integers in the range [1,5].

[0016] Preferably, calculating the image compression degree corresponding to the preselected size includes: Where, To preselect the size, Indicates preselected size The corresponding image compression level, Indicates preselected size The number of corresponding non-overlapping blocks, the amount of data stored in each non-overlapping block is equal to , Monitor the image size for production.

[0017] In the present invention, the calculation of the comprehensive effect value includes the image compression degree indicator. Selecting the specified size with the largest comprehensive effect value can ensure that the highest possible compression ratio is achieved while meeting certain image quality requirements, thereby effectively reducing storage and transmission costs.

[0018] Preferably, the calculating of the loss degree caused by the compression encoding corresponding to the preselected size includes: Where, To preselect the size, Indicates preselected size The corresponding degree of loss caused by compression coding, Indicates preselected size The number of corresponding non-overlapping blocks, 、 Respectively The number of all pixels of type 1 and all pixels of type 2 in non-overlapping blocks, 、 Respectively In the non-overlapping blocks The first type of pixels and the Grayscale values ​​of the second-class pixels, 、 Respectively high quantization value and low quantization value of non-overlapping blocks, To produce the size of the monitoring image, Represents the absolute value function.

[0019] By analyzing the loss degree caused by compression coding, the present invention can fully reflect the impact of different block division sizes on image quality through a comprehensive effect value. Selecting the specified size with the largest comprehensive effect value can minimize image distortion and visual quality degradation while ensuring compression efficiency.

[0020] Preferably, the calculating of the degree of loss caused by information hiding corresponding to the preselected size includes: Where, To preselect the size, Indicates preselected size The corresponding degree of loss caused by information hiding, Indicates preselected size The number of non-overlapping blocks corresponding to the preselected size is equal to the number of times the secret information is embedded in each non-overlapping block. , 、 Preselected sizes The corresponding high quantization value and low quantization value of non-overlapping blocks, Monitor the image size for production.

[0021] In the present invention, the number of times secret information is embedded and the difference between the low quantization value and the high quantization value directly affect the performance of information hiding. By calculating the degree of loss caused by information hiding and optimizing the block division size, the impact of information hiding on image quality can be reduced while ensuring the robustness and invisibility of the watermark information, thereby better protecting the authenticity and integrity of the image data.

[0022] Preferably, the weighting of the image compression degree, the loss degree caused by information hiding, and the loss degree caused by compression coding corresponding to each preselected size to obtain a comprehensive effect value of each preselected size includes: Where, Indicates preselected size The comprehensive effect value, 、 、 Respectively represent the preselected size The corresponding image compression degree, the loss degree caused by compression coding and the loss degree caused by information hiding, represents the loss threshold, represents the natural exponential function, 、 are the first weight and the second weight respectively, 、 are greater than 0, and .

[0023] The present invention introduces a weighting mechanism in the calculation of the comprehensive effect value. The weight parameters can be adjusted according to the specific application scenario, and the relationship between compression efficiency, image quality and information hiding performance can be flexibly weighed to meet the diverse needs of the intelligent manufacturing process.

[0024] The beneficial effects of the present invention are: The present invention not only realizes the seamless integration of compression, encryption and information hiding, but also takes into account the requirements of compression efficiency, security protection and anti-tampering, significantly reduces storage and transmission costs, and at the same time ensures the reliability and availability of production monitoring images in the intelligent manufacturing process; further, the present invention can find the best balance between compression efficiency and image quality by determining the specified size with the largest comprehensive effect value, which not only can realize the efficient integration of compression, encryption and watermark embedding, but also can maximize the overall performance while ensuring image quality, providing a reliable solution for production monitoring image processing in the intelligent manufacturing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 1 is a flow chart schematically illustrating a method for protecting intelligent manufacturing data security based on post-quantum technology in the present invention; Figure 2 is a flowchart schematically illustrating step S2; Figure 3 is a flow chart schematically illustrating a method for obtaining the comprehensive effect value of each preselected size in step S21; Figure 4 is a diagram schematically illustrating non-overlapping blocks; Figure 5 is a diagram schematically illustrating a bitmap of non-overlapping blocks; Figure 6 is a schematic diagram schematically illustrating a preset key matrix; Figure 7 is a schematic diagram schematically illustrating a preset weight matrix; Figure 8 is a diagram schematically illustrating transformed sub-blocks. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0027] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0028] The embodiment of the present invention discloses a method for protecting intelligent manufacturing data security based on post-quantum technology, referring to Figure 1 , including steps S1 to S2: S1. Collect production monitoring images in intelligent manufacturing.

[0029] It should be noted that intelligent manufacturing emphasizes automation, informatization and intelligence. Production monitoring images, as an important source of data, can reflect the operating status of equipment, product appearance quality and production environment conditions. Production monitoring images have the characteristics of large data volume, strong real-time performance and diversity.

[0030] Specifically, based on the needs of the production scenario, clarify the specific content that needs to be monitored, such as equipment operating status, product quality characteristics or production environment parameters; install the acquisition equipment at key locations on the production site to ensure coverage of all areas that need to be monitored; at the same time, configure the parameters of the acquisition system (such as exposure time, gain, frame rate, etc.) to adapt to different lighting conditions and production environments.

[0031] Furthermore, during the acquisition process, pre-processing operations such as denoising, contrast enhancement, and distortion correction are performed on the acquired production monitoring images to improve image quality.

[0032] S2. Divide the production monitoring image into multiple non-overlapping blocks of specified sizes through a block truncation algorithm based on absolute distance, and obtain the bitmap, low quantization value, and high quantization value of each non-overlapping block; embed the secret information into the bitmap of each non-overlapping block through a block hiding algorithm based on a preset key matrix and weight matrix, and obtain the ciphertext bitmap of each non-overlapping block; encode the low quantization value, high quantization value, and ciphertext bitmap of each non-overlapping block, and encrypt and store the encoded result based on the NIST quantum-resistant cryptography standard algorithm.

[0033] It should be noted that image data needs to be secure, reliable, and traceable to prevent data leakage, tampering, or loss. Therefore, production monitoring images collected during the intelligent manufacturing process face multiple technical challenges: First, due to the huge amount of image data, direct storage or transmission will result in huge resource consumption, and although lossless compression can retain all details, the compression efficiency is low; second, traditional encryption methods (such as AES, RSA, etc.) may no longer be safe under the threat of quantum computing and cannot fully protect the confidentiality of image data; finally, in order to prevent images from being tampered with or forged, secret information needs to be embedded to verify its authenticity, but existing information hiding technologies are often independent of the compression and encryption processes, making it difficult to achieve efficient integrated processing.

[0034] To address these issues, a comprehensive method is needed that can simultaneously achieve efficient lossy compression, post-quantum encryption, and information hiding to meet the security and practicality requirements of intelligent manufacturing for production monitoring images; therefore, this embodiment proposes a comprehensive solution that combines post-quantum encryption technology, lossy compression algorithm, and information hiding technology.

[0035] See the flowchart of step S2 Figure 2 , including steps S21 to S23, specifically: S21. Divide the production monitoring image into a plurality of non-overlapping blocks of a specified size by using a block truncation algorithm based on absolute distance, and obtain a bitmap, a low quantization value, and a high quantization value of each non-overlapping block.

[0036] It should be noted that Absolute Moment Block Truncation Coding (AMBTC) is an image compression technology. Its core idea is to divide the image into several non-overlapping small blocks and achieve image compression by quantizing and simplifying the grayscale value distribution of pixels in each block.

[0037] Specifically, the production monitoring image is divided into multiple non-overlapping blocks of specified sizes through a block truncation algorithm based on absolute distance, and the bitmap, low quantization value and high quantization value of each non-overlapping block are obtained. The specific process is as follows: 1. Calculate the mean grayscale value of all pixels in non-overlapping blocks.

[0038] 2. Pixels with grayscale values ​​greater than the mean are divided into one category, and "1" is written to the corresponding position in the bitmap; pixels with grayscale values ​​not greater than the mean are divided into two categories, and "0" is written to the corresponding position in the bitmap; thus, a bitmap with non-overlapping blocks is obtained.

[0039] 3. Calculate the mean of the grayscale values ​​of all the pixels in the first category, and use the rounded result as the high quantization value of the non-overlapping blocks; calculate the mean of the grayscale values ​​of all the pixels in the second category, and use the rounded result as the low quantization value of the non-overlapping blocks.

[0040] For example, for Figure 4 The non-overlapping blocks shown in the figure have a mean grayscale value of 160.625 for all pixels in the non-overlapping blocks. There are 10 pixels with grayscale values ​​greater than the mean, which are classified as type 1 pixels and "1" is written to the corresponding positions in the bitmap. There are 6 pixels with grayscale values ​​not greater than the mean, which are classified as type 2 pixels and "0" is written to the corresponding positions in the bitmap. Figure 4 The bitmap of non-overlapping blocks is shown as Figure 5 shown.

[0041] S22. Embed the secret information into the bitmaps of each non-overlapping block using a block hiding algorithm according to a preset key matrix and weight matrix to obtain the ciphertext bitmaps of each non-overlapping block.

[0042] It should be noted that the block-based hiding algorithm is an information hiding algorithm for binary matrices. The algorithm divides the binary image into image blocks of the same size, introduces a key matrix and a weight matrix of the same size, and uses the block-based hiding algorithm to determine the difference based on the image block, key matrix, weight matrix and secret information. The information hiding method is determined based on the difference, and information is hidden in the image block according to the information hiding method, so that information hiding is performed once in each image block.

[0043] Specifically, according to the preset key matrix and weight matrix, the secret information is embedded into the bitmap of each non-overlapping block through the block hiding algorithm to obtain the ciphertext bitmap of each non-overlapping block. The specific process is: 1. Divide the bitmap of non-overlapping blocks into multiple sub-blocks of size equal to 4×4.

[0044] 2. For any sub-block , pair blocks The transformed sub-block is obtained , requiring the transformed sub-block Satisfies the following relationship: ; Where, 、 are the key matrix and weight matrix respectively, represents cumulative summation, Indicates the decimal number corresponding to the secret information, Represents the exclusive OR operation, represents bitwise multiplication, Represents the modulo operation.

[0045] Among them, the pair block Transformation means: At least one element in is swapped between 0 and 1, where swapping between 0 and 1 means that when the element is equal to 1, it is set to 0; when the element is equal to 0, it is set to 1.

[0046] It should be noted that if the sub-block satisfy , then there is no need for sub-blocks Transform, that is, the sub-block It is itself the transformed sub-block .

[0047] In addition, when there are multiple transformation methods, the transformation method with the least number of transformations is selected and used for the sub-block Transform the elements in to obtain the transformed sub-block ; When there are multiple transformations with the least number of transformations, randomly select a transformation from them to transform the sub-block Transform the elements in to obtain the transformed sub-block ; The transformed sub-block obtained at this time Secret information has been embedded in it.

[0048] The preset key matrix is ​​a matrix of size 4×4, and the elements in the matrix are 0 or 1. For example, the schematic diagram of the key matrix is ​​as follows: Figure 6 shown.

[0049] The preset weight matrix is ​​a 4×4 matrix, and the elements in the matrix are integers in the range of [1,7], and the number of times each integer in the range of [1,7] appears in the matrix is ​​equal to 2 or 3; for example, the schematic diagram of the weight matrix is ​​as follows Figure 7 shown.

[0050] The secret information is intercepted from the secret information sequence, and the length of the intercepted secret information is equal to 3; the secret information sequence is a binary sequence consisting of 0 and 1; in one embodiment, the secret information sequence can be set randomly; in another embodiment, the encoding result of the key information in intelligent manufacturing can be used as the secret information sequence.

[0051] 3. The image block composed of all transformed sub-blocks in sequence is the ciphertext bitmap of the non-overlapping blocks. At this point, the secret information is embedded in the bitmap of the non-overlapping blocks.

[0052] For example, for Figure 5 The bitmap shown in the figure has only one sub-block. When the secret information is "101", the decimal number corresponding to the secret information is =5, pair block To transform, one transformation method is: The elements in row 1 and column 1 equal to 0 are set to 1, and the transformed sub-block is obtained. like Figure 8 As shown in (1), at this time, the transformed sub-block Satisfies the relationship: ; Another transformation method is to convert the sub-block The elements in the 2nd row and 3rd column that are equal to 1 are set to 0, and the transformed sub-block is obtained. like Figure 8 As shown in (2), at this time, the transformed sub-block It also satisfies the relationship: In summary, for Figure 5 There are two transformation modes for the bitmap shown in the figure. The transformation times of these two transformation modes are the same and both equal to 1, so one transformation mode can be randomly selected from them to be used for the sub-blocks. Transform the elements in to obtain the transformed sub-block , the transformed sub-block obtained at this time Secret information has been embedded in it.

[0053] S23. Encode the low quantization value, high quantization value, and ciphertext bitmap of each non-overlapping block, and encrypt and store the encoding result based on the NIST quantum-resistant cryptography standard algorithm.

[0054] Specifically, the low quantization value, the high quantization value and the ciphertext bitmap of each non-overlapping block are encoded to obtain an encoding result of the production monitoring image.

[0055] Among them, for the low quantization value and high quantization value of non-overlapping blocks: Since the low quantization value and the high quantization value are obtained by averaging and rounding the grayscale values ​​of the pixels, the range of the low quantization value and the high quantization value is [0,255], a total of 256 values, so when the low quantization value and the high quantization value are encoded with a fixed length, the length of the encoding result is fixed equal to Specifically, the binary numbers with a length of 8 corresponding to the low quantization value and the high quantization value are respectively used as the encoding results of the low quantization value and the high quantization value, and the binary numbers are composed of 0 and 1.

[0056] Among them, for the ciphertext bitmap of non-overlapping blocks, since the ciphertext bitmap is essentially a matrix composed of 0s and 1s, it can be directly encrypted without encoding.

[0057] It should be noted that with the development of quantum computing technology, traditional public key cryptographic algorithms such as RSA, Diffie-Hellman, and elliptic curves face the risk of being cracked by quantum computers. Post-quantum cryptography technology is a new generation of cryptographic algorithms that can resist attacks by quantum computers on existing cryptographic algorithms. Post-quantum cryptography technology is built on mathematical problems, and its core lies in using the computational complexity of certain mathematical problems to resist attacks by quantum computers.

[0058] Furthermore, encrypting the encoded results based on the NIST quantum-resistant cryptography standard algorithm can effectively resist quantum computing attacks and ensure the long-term security of data.

[0059] Optionally, the data encryption link uses the NIST-standardized CRYSTALS-Kyber algorithm (one of the algorithms included in the NIST quantum-resistant cryptography standard algorithms) to encapsulate the shared key, and uses the shared key to symmetrically encrypt the low quantization value, high quantization value, and ciphertext bitmap encoding results of the non-overlapping blocks obtained after image segmentation, to ensure the quantum-resistant security of production monitoring images throughout the entire storage cycle.

[0060] Optionally, a hash algorithm (such as SM3) can be used to generate a summary of the image data, and the CRYSTALS-Dilithium algorithm (another algorithm included in the NIST quantum-resistant cryptography standard algorithm) can be used to digitally sign the summary to achieve quantum-resistant integrity verification and watermark source tracing.

[0061] Among them, the NIST-standardized CRYSTALS-Kyber algorithm and CRYSTALS-Dilithium algorithm are well-known technologies and will not be described in detail here.

[0062] Regarding the specified size in step S21 , in one embodiment, the specified size may be any preselected size.

[0063] Where the preselected size is equal to ,in, 、 are all integers in the range [1,5], so 、 Under different values, different pre-selected sizes will be obtained.

[0064] It should be noted that in order to minimize the loss of image quality and information hiding performance while meeting the requirements of compression efficiency, security protection and anti-tampering, it is necessary to select an optimal block division size; by calculating the comprehensive effect value of each pre-selected size, and selecting the size with the largest comprehensive effect value as the specified size.

[0065] In another embodiment, the image compression degree, the loss degree caused by information hiding, and the loss degree caused by compression encoding corresponding to each preselected size are weighted to obtain a comprehensive effect value of each preselected size; and the preselected size with the largest comprehensive effect value is used as the designated size.

[0066] The method for obtaining the comprehensive effect value of each pre-selected size in step S21 is as follows: Figure 3 , including steps S211 to S214: S211 , calculating the image compression degree corresponding to the preselected size according to the number of non-overlapping blocks corresponding to the preselected size and the amount of data stored in each non-overlapping block.

[0067] It's important to note that different block sizes result in different levels of image compression and loss. Larger block sizes generally improve compression ratios but can result in greater image distortion. Smaller block sizes preserve more detail but reduce compression efficiency. By determining the size that maximizes the overall effect, you can find the optimal balance between compression efficiency and image quality.

[0068] When encoding and compressing production monitoring images using a block truncation algorithm based on absolute distance, for each non-overlapping block corresponding to a preselected size, the information that needs to be stored includes the low quantization value, high quantization value, and bitmap of the non-overlapping block; wherein, since the low quantization value and the high quantization value are obtained by averaging and rounding the grayscale values ​​of the pixels, and the grayscale value range is [0, 255], the low quantization value and the high quantization value range are [0, 255], with a total of 256 values. When the low quantization value and the high quantization value are encoded with a fixed length, the length of the encoding result is fixed equal to , that is, the amount of data storing the low quantization value of each non-overlapping block is equal to 8, and the amount of data storing the high quantization value of each non-overlapping block is equal to 8; since the bitmap is essentially a matrix composed of 0 and 1, it can be stored directly without encoding. The amount of data storing the bitmap of each non-overlapping block is equal to the number of elements in the bitmap, that is, the amount of data storing the bitmap of each non-overlapping block is equal to ; In summary, the amount of data stored in each non-overlapping block is equal to , preselect size The corresponding number of non-overlapping blocks is , when the production monitoring image is encoded and compressed by the block truncation algorithm based on absolute distance, the amount of data stored in the encoding of the entire production monitoring image is equal to .

[0069] If the production monitoring image is directly coded and compressed, the grayscale value of each pixel in the production monitoring image needs to be coded and compressed. Since the grayscale value range is [0, 255], with a total of 256 values, when the grayscale value is coded with a fixed length, the length of the coded result is fixed equal to , that is, the amount of data storing the grayscale value of each pixel is equal to 8, and the number of all pixels in the production monitoring image is equal to In summary, when the production monitoring image is directly encoded and compressed, the amount of data stored in the encoding of the entire production monitoring image is equal to .

[0070] The calculation formula for the image compression degree corresponding to the preselected size is: ; Where, To preselect the size, Indicates preselected size The corresponding image compression level, Indicates preselected size The number of corresponding non-overlapping blocks, the amount of data stored in each non-overlapping block is equal to , Monitor the image size for production.

[0071] It should be noted that when encoding and compressing production monitoring images using the block truncation algorithm based on absolute distance, the smaller the amount of data stored in the encoding of the entire production monitoring image, the better the compression effect, and accordingly, the greater the degree of image compression corresponding to the preselected size.

[0072] S212 , calculating the degree of loss caused by compression coding corresponding to the preselected size based on the difference between the grayscale values ​​of the pixels in all non-overlapping blocks corresponding to the preselected size and the low quantization value and the high quantization value.

[0073] When encoding and compressing production monitoring images using a block truncation algorithm based on absolute distance, for each non-overlapping block corresponding to a preselected size, the decoding process is to replace the elements of the bitmap of the non-overlapping block with a high quantization value, and replace the elements of the bitmap of the non-overlapping block with a low quantization value. Therefore, the high quantization value and the low quantization value determine the degree of image quality loss of the decoded production monitoring image compared to the original production monitoring image: for pixels in non-overlapping blocks, if the element at the corresponding position in the bitmap is equal to "1", that is, for a type of pixel in the non-overlapping block, the degree of loss is equal to the difference between the grayscale value of the type one pixel and the high quantization value; for pixels in non-overlapping blocks, if the element at the corresponding position in the bitmap is equal to "0", that is, for a type two pixel in the non-overlapping block, the degree of loss caused by compression encoding is equal to the difference between the grayscale value of the type two pixel and the low quantization value.

[0074] The calculation formula for the loss caused by compression coding is: ; Where, To preselect the size, Indicates preselected size The corresponding degree of loss caused by compression coding, Indicates preselected size The number of corresponding non-overlapping blocks, 、 Respectively The number of all pixels of type 1 and all pixels of type 2 in non-overlapping blocks, 、 Respectively In the non-overlapping blocks The first type of pixels and the Grayscale values ​​of the second-class pixels, 、 Respectively high quantization value and low quantization value of non-overlapping blocks, To produce the size of the monitoring image, Represents the absolute value function.

[0075] It should be noted that Indicates the In the non-overlapping blocks The difference between the gray value of a class of pixels and the high quantization value, Indicates the In the non-overlapping blocks The difference between the grayscale value of the second-class pixel and the low quantization value.

[0076] S213 , calculating the degree of loss caused by information hiding corresponding to the preselected size based on the number of secret information embedding times of all non-overlapping blocks corresponding to the preselected size, and the difference between the low quantization value and the high quantization value.

[0077] It should be noted that factors such as the number of times the secret information is embedded, the difference between the low quantization value and the high quantization value will affect the degree of loss caused by information hiding; choosing an appropriate block size can reduce the impact of information hiding on image quality while ensuring the robustness and invisibility of the watermark information.

[0078] The secret information is embedded in the bitmap of each non-overlapping block through the block hiding algorithm. When obtaining the ciphertext bitmap of each non-overlapping block: first divide the bitmap of the non-overlapping block into multiple sub-blocks of size equal to 4×4, then each sub-block of size equal to The bitmap of non-overlapping blocks is divided into sub-blocks; by setting some elements in the sub-block that are equal to 1 to 0 and some elements that are equal to 0 to 1, each sub-block is transformed to obtain the transformed sub-block, which will result in the elements originally replaced by high quantization values ​​being replaced by low quantization values ​​during decoding, or the elements originally replaced by low quantization values ​​being replaced by high quantization values. In either case, the decoded production monitoring image will suffer from image quality loss compared to the original production monitoring image, and the degree of loss of each sub-block is equal to the difference between the high quantization value and the low quantization value. , then the loss of each non-overlapping block caused by information hiding is equal to .

[0079] The calculation formula for the degree of loss caused by information hiding is: ; Where, To preselect the size, Indicates preselected size The corresponding degree of loss caused by information hiding, Indicates preselected size The number of non-overlapping blocks corresponding to the preselected size is equal to the number of times the secret information is embedded in each non-overlapping block. , 、 Preselected sizes The corresponding high quantization value and low quantization value of non-overlapping blocks, Monitor the image size for production.

[0080] S214 , weighting the image compression degree, the loss degree caused by information hiding, and the loss degree caused by compression coding corresponding to each preselected size to obtain a comprehensive effect value of each preselected size.

[0081] It should be noted that the overall performance value takes into account multiple factors, including the degree of image compression, the degree of loss caused by compression encoding, and the degree of loss caused by information hiding, and is calculated through weighted calculation. Therefore, choosing the specified size with the largest overall performance value ensures that the overall method achieves optimal performance in terms of compression, encryption, and watermark embedding.

[0082] The calculation formula for the comprehensive effect value of each pre-selected size is: ; Where, Indicates preselected size The comprehensive effect value, Indicates preselected size The corresponding image compression level, Indicates preselected size The corresponding degree of loss caused by compression coding, Indicates preselected size The corresponding degree of loss caused by information hiding, represents the loss threshold, represents the natural exponential function.

[0083] in, 、 are the first weight and the second weight respectively, 、 are greater than 0, and ; The specific values ​​of the first weight and the second weight can be set according to the actual application scenario and requirements. The present invention sets the preset first weight to 0.4 and the second weight to 0.6.

[0084] The specific value of the loss threshold can be set according to actual application scenarios and requirements, and the value range of the loss threshold is [5, 20]. The present invention sets the loss threshold to 13.

[0085] It should be noted that the calculation of the comprehensive effect value includes the image compression degree. Selecting the specified size with the largest comprehensive effect value can ensure that the highest possible compression ratio is achieved while meeting certain image quality requirements, thereby effectively reducing storage and transmission costs. In addition, by analyzing the degree of loss caused by compression encoding and the degree of loss caused by information hiding, the comprehensive effect value can fully reflect the impact of different block division sizes on image quality. Selecting the specified size with the largest comprehensive effect value can minimize image distortion and visual quality degradation while ensuring compression efficiency.

Claims

1. The intelligent manufacturing data security protection method based on post-quantum technology is characterized by: include: The production monitoring image is divided into multiple non-overlapping blocks of a specified size using a block truncation algorithm based on absolute distance. The bitmap, low quantization value, and high quantization value of each non-overlapping block are obtained. Based on a preset key matrix and weight matrix, the secret information is embedded in the bitmap of each non-overlapping block using a block hiding algorithm. The ciphertext bitmap, low quantization value, and high quantization value of each non-overlapping block are encoded, and the encoded result is encrypted and stored based on the NIST quantum-resistant cryptography standard algorithm. The specified size is the pre-selected size with the largest comprehensive effect value. The method for obtaining the comprehensive effect value of each pre-selected size is as follows: The image compression degree corresponding to the preselected size is calculated based on the number of non-overlapping blocks corresponding to the preselected size and the amount of data stored in each non-overlapping block; the loss degree caused by the compression coding corresponding to the preselected size is calculated based on the difference between the grayscale value of the pixel points in all non-overlapping blocks corresponding to the preselected size and the low quantization value and the high quantization value; the loss degree caused by the information hiding corresponding to the preselected size is calculated based on the number of secret information embedding times, the low quantization value and the high quantization value of all non-overlapping blocks corresponding to the preselected size; the image compression degree corresponding to each preselected size, the loss degree caused by information hiding and the loss degree caused by compression coding are weighted to obtain a comprehensive effect value of each preselected size.

2. The intelligent manufacturing data security protection method based on post-quantum technology according to claim 1 is characterized in that: The obtaining of the bitmap, the low quantization value and the high quantization value of each non-overlapping block includes: Calculate the mean grayscale value of all pixels in non-overlapping blocks; Pixels with grayscale values ​​greater than the mean are classified as type 1 pixels, and "1" is written to the corresponding positions in the bitmap; pixels with grayscale values ​​not greater than the mean are classified as type 2 pixels, and "0" is written to the corresponding positions in the bitmap. In this way, a bitmap with non-overlapping blocks is obtained. Calculate the mean of the grayscale values ​​of all the first-class pixels and use the rounded result as the high quantization value of the non-overlapping blocks; calculate the mean of the grayscale values ​​of all the second-class pixels and use the rounded result as the low quantization value of the non-overlapping blocks.

3. The intelligent manufacturing data security protection method based on post-quantum technology according to claim 1 is characterized in that: The preset key matrix is ​​a matrix with a size of 4×4, and the elements in the matrix are 0 or 1.

4. The intelligent manufacturing data security protection method based on post-quantum technology according to claim 1 is characterized in that: The weight matrix is ​​a matrix of size equal to 4×4, and the elements in the matrix are integers in the range of [1, 7], and the number of times each integer in the range of [1, 7] appears in the matrix is ​​equal to 2 or 3.

5. The intelligent manufacturing data security protection method based on post-quantum technology according to claim 1 is characterized in that: The method of embedding secret information into the bitmap of each non-overlapping block by using a block hiding algorithm includes: Divide the bitmap of non-overlapping blocks into multiple sub-blocks of size equal to 4×4; for any sub-block , pair blocks Perform the transformation to obtain the transformed sub-block , requiring the transformed sub-block satisfy Where, 、 are the key matrix and weight matrix respectively, represents cumulative summation, Indicates the decimal number corresponding to the secret information, Represents the exclusive OR operation, represents bitwise multiplication, Represents the modulo operation; The pair of blocks Transformation means: At least one element in the array is swapped between 0 and 1.

6. The intelligent manufacturing data security protection method based on post-quantum technology according to claim 1 is characterized in that: The preselected size is equal to ,in, 、 All are integers in the range [1,5].

7. The intelligent manufacturing data security protection method based on post-quantum technology according to claim 6 is characterized in that: Calculating the image compression degree corresponding to the preselected size includes: ； Where, To preselect the size, Indicates preselected size The corresponding image compression level, Indicates preselected size The number of corresponding non-overlapping blocks, the amount of data stored in each non-overlapping block is equal to , Monitor the image size for production.

8. The intelligent manufacturing data security protection method based on post-quantum technology according to claim 6 is characterized in that: The calculating of the loss degree caused by the compression encoding corresponding to the preselected size includes: ； Where, To preselect the size, Indicates preselected size The corresponding degree of loss caused by compression coding, Indicates preselected size The number of corresponding non-overlapping blocks, 、 Respectively The number of all pixels of type 1 and all pixels of type 2 in non-overlapping blocks, 、 Respectively In the non-overlapping blocks The first type of pixels and the Grayscale values ​​of the second-class pixels, 、 Respectively high quantization value and low quantization value of non-overlapping blocks, To produce the size of the monitoring image, Represents the absolute value function.

9. The intelligent manufacturing data security protection method based on post-quantum technology according to claim 6 is characterized in that: The calculating of the degree of loss caused by information hiding corresponding to the preselected size includes: ； Where, To preselect the size, Indicates preselected size The corresponding degree of loss caused by information hiding, Indicates preselected size The number of non-overlapping blocks corresponding to the preselected size is equal to the number of times the secret information is embedded in each non-overlapping block. , 、 Preselected sizes The corresponding high quantization value and low quantization value of non-overlapping blocks, Monitor the image size for production.

10. The intelligent manufacturing data security protection method based on post-quantum technology according to claim 6 is characterized in that: The weighting of the image compression degree, the loss degree caused by information hiding, and the loss degree caused by compression coding corresponding to each preselected size to obtain a comprehensive effect value of each preselected size includes: ； Where, Indicates preselected size The comprehensive effect value, 、 、 Respectively represent the preselected size The corresponding image compression degree, the loss degree caused by compression coding and the loss degree caused by information hiding, represents the loss threshold, represents the natural exponential function, 、 are the first weight and the second weight respectively, 、 are greater than 0, and .

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