Thermal power data transmission method and system

Abstract

The invention relates to a thermal power data transmission method and system, the method is applied to an edge server, the edge server is used for communicating with a cloud server, and the method comprises the following steps: obtaining operation data and a carrier image of thermal power plant equipment, and carrying out edge detection on pixels in the carrier image according to a preset sequence; in response to the fact that the current pixel is an edge pixel, determining an embedding path according to Collatz conjecture, and embedding the operation data into the pixel of the carrier image based on the embedding path; in response to the fact that the current pixel is a non-edge pixel, embedding the operation data into a least significant bit of the non-edge pixel through an LSB algorithm; and in response to the operation data being completely embedded into the carrier image, completing embedding to obtain a steganographic image.

Description

Technical Field

[0001] This application relates to the field of data secure transmission, and particularly to a thermal power data transmission method and system. Background Art

[0002] In recent years, the energy industry has gradually developed towards digitalization and intelligentization under the promotion of the energy Internet. In the actual production process, as the main regulating power source, thermal power units will generate a large amount of data during operation, such as sensitive data like statistical data and fault records. Once these data are leaked, it may lead to production stagnation and information tampering, causing significant economic losses. Therefore, effective measures need to be taken to ensure the security of data during remote transmission and guarantee the safe and stable operation of the units.

[0003] Currently, data transmission security is mainly ensured through encryption technology and steganography technology. Encryption technology mainly uses specific algorithms to convert sensitive data into an encrypted form, and the encrypted sensitive data becomes incomprehensible to any third party (i.e., unreadable). Steganography technology is to embed sensitive information into a digital carrier, and use the redundant information of the carrier to generate a secret digital carrier containing hidden information. The steganographed secret data is imperceptible to any third party (i.e., invisible). Although data encryption can ensure that data is not easily cracked, the encrypted data is likely to arouse the vigilance of attackers. Data steganography technology hides secret information in seemingly ordinary carrier data, enabling it to be transmitted or stored without being noticed, improving the concealment of information.

[0004] Currently, existing data steganography methods have many deficiencies in terms of hiding capacity, invisibility, etc., and it is difficult to meet the growing security requirements of thermal power sensitive data. Summary of the Invention

[0005] The embodiments of this application provide a thermal power data transmission method and system to at least solve the problems of insufficient security and capacity existing in the existing transmission methods in related technologies.

[0006] In a first aspect, the embodiments of this application provide a thermal power data transmission method, which is applied to an edge server for communicating with a cloud server, and the method includes:

[0007] Obtain the operation data of the thermal power plant equipment and the carrier image, and perform edge detection on the pixels in the carrier image in a preset order;

[0008] In response to the current pixel being an edge pixel, determine an embedding path according to the Collatz conjecture, and embed the operation data into the pixels of the carrier image based on the embedding path;

[0009] In response to the current pixel being a non-edge pixel, the running data is embedded into the least significant bit of the non-edge pixel through the LSB algorithm;

[0010] In response to all the running data being embedded into the carrier image, the embedding is completed to obtain a stego image.

[0011] In one embodiment, before performing edge detection on the pixels in the carrier image in a preset order, the method further includes:

[0012] Convert the base of the running data to a first preset base;

[0013] Decompose the carrier image into three channels of R, G, and B, and convert the base of the pixel points in the carrier image after channel decomposition to the first preset base.

[0014] In one embodiment, the performing edge detection on the pixels in the carrier image in a preset order includes:

[0015] Clear the preset number of bits of each pixel in the carrier image in reverse order to obtain a first image, and perform edge detection on the first image in a preset order.

[0016] In one embodiment, the embedding the running data into the pixels of the carrier image based on the embedding path includes:

[0017] Determine a first data to be embedded from the unembedded running data based on the embedding path according to the preset reading order of the running data;

[0018] Embed the first data to be embedded into the pixels of the carrier image based on the embedding path, and update the unembedded data.

[0019] In one embodiment, the embedding the running data into the least significant bit of the non-edge pixel through the LSB algorithm includes:

[0020] Determine a second data to be embedded from the unembedded running data according to the preset reading order of the running data;

[0021] Embed the second data to be embedded into the least significant bit of the non-edge pixel through the LSB algorithm, and update the unembedded data.

[0022] In a second aspect, an embodiment of the present application provides a thermal power data transmission method, which is applied to a cloud server for communicating with an edge server, and the method includes:

[0023] Receive the stego image sent by the edge server, and perform edge detection on the pixels in the stego image in a preset order;

[0024] In response to the current pixel being an edge pixel, determine an embedding path according to the Collatz conjecture, and extract first target data from the pixels of the stego-image based on the embedding path;

[0025] In response to the current pixel being a non-edge pixel, extract second target data from the least significant bit of the non-edge pixel according to the LSB algorithm;

[0026] Determine target data according to the first target data and the second target data, and restore the running data based on the target data.

[0027] In one embodiment, before performing edge detection on the pixels of the stego-image in a preset order, the method further includes:

[0028] Decompose the stego-image into three channels of R, G, and B, and convert the base of the pixel points in the stego-image after channel decomposition to a first preset base.

[0029] In one embodiment, the performing edge detection on the pixels of the stego-image in a preset order includes:

[0030] Clear a preset number of bits of each pixel in the stego-image in a reverse order to obtain a second image;

[0031] Extract the R channel in the second image, and perform edge detection on the pixels in the R channel in a preset order.

[0032] In one embodiment, the restoring the running data based on the target data includes:

[0033] Convert the base of the target data from the first preset base to a second preset base to obtain the running data.

[0034] In a third aspect, an embodiment of the present application provides a thermal power data transmission system, which is used for an edge server and cooperates with a cloud server. The system includes:

[0035] An acquisition module: used to acquire the running data of thermal power plant equipment and a carrier image, and perform edge detection on the pixels of the carrier image in a preset order;

[0036] A first embedding module: used to, in response to the current pixel being an edge pixel, determine an embedding path according to the Collatz conjecture, and embed the running data into the pixels of the carrier image based on the embedding path;

[0037] A second embedding module: used to, in response to the current pixel being a non-edge pixel, embed the running data into the least significant bit of the non-edge pixel through the LSB algorithm;

[0038] Result module: configured to complete embedding to obtain a stego-image in response to all of the operation data being embedded into the carrier image.

[0039] The thermal power data transmission method and system provided by the embodiments of the present application have at least the following technical effects.

[0040] In the present application, the edge processor in the cloud-edge collaboration architecture preprocesses the data, reducing the amount of data transmitted, thereby accelerating the data transmission speed. Edge detection is used to distinguish edge pixels from non-edge pixels, expanding the steganographic capacity. When embedding, according to the Collatz conjecture, the operation data is embedded into the carrier image along a random path, disrupting the original pattern of the data and making the data present in a disordered state, improving the confidentiality and integrity of the data during transmission and preventing the data from being illegally stolen or tampered with.

[0041] Details of one or more embodiments of the present application are set forth in the following drawings and description, so that other features, objects, and advantages of the present application will become more concise and understandable. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0043] Figure 1 is a flowchart of a thermal power data transmission method shown according to an embodiment of the present application;

[0044] Figure 2 is a flowchart of a data embedding process shown according to an embodiment of the present application;

[0045] Figure 3 is a flowchart of another thermal power data transmission method shown according to an exemplary embodiment;

[0046] Figure 4 is an overall flowchart of a thermal power data secure transmission shown according to an exemplary embodiment;

[0047] Figure 5 is a schematic diagram of an embedding result shown according to an exemplary embodiment;

[0048] Figure 6 is a schematic diagram of a data extraction result shown according to an exemplary embodiment;

[0049] Figure 7 is a schematic diagram of the gray value distributions of a carrier image and a stego-image shown according to an example embodiment;

[0050] Figure 8 It is a structural block diagram of a thermal power data transmission system shown according to an embodiment of the present application. Specific embodiments

[0051] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be described and explained below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments provided in the present application without creative efforts fall within the scope of protection of the present application.

[0052] Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application. For those of ordinary skill in the art, without creative efforts, the present application can also be applied to other similar scenarios based on these drawings. In addition, it can also be understood that although the efforts made in such a development process may be complex and lengthy, for those of ordinary skill in the art related to the content disclosed in the present application, some design, manufacturing or production changes made on the basis of the technical content disclosed in the present application are only conventional technical means and should not be understood that the content disclosed in the present application is insufficient.

[0053] Referring to "embodiment" in the present application means that the specific features, structures or characteristics described in connection with the embodiment may be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those of ordinary skill in the art explicitly and implicitly understand that the embodiments described in the present application can be combined with other embodiments without conflict.

[0054] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the ordinary meanings understood by those with ordinary skills in the technical field to which this application belongs. The words such as "a", "an", "one", "the" and the like involved in this application do not indicate a limitation in quantity and may represent a singular or plural number. The terms "comprising", "including", "having" and any variations thereof involved in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may further include unlisted steps or units, or may further include other steps or units inherent to these processes, methods, products or devices. The words such as "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "plurality" involved in this application means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after. The terms "first", "second", "third" and the like involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0055] In a first aspect, an embodiment of the present application provides a thermal power data transmission method, which is applied to an edge server for communicating with a cloud server.

[0056] Optionally, the thermal power data transmission method provided by this application is based on a collaborative architecture of a cloud server and an edge server to uniformly control the edge server, transmission data, and cloud server, realizing unified management and rapid processing of data, and improving data management efficiency and security.

[0057] Figure 1 is a flowchart of a thermal power data transmission method shown according to an embodiment of the present application, as Figure 1 shown, the method includes:

[0058] Step S101, obtain the operation data and carrier image of the thermal power plant equipment, and perform edge detection on the pixels in the carrier image in a preset order.

[0059] Optionally, select appropriate carrier data to carry the operation data. The operation data of the thermal power plant equipment is collected in real time through sensors and controllers, where the operation data includes the operation status of the equipment and production process data. The edge server performs preprocessing such as data cleaning and format conversion on the operation data to reduce the bandwidth requirement and storage cost of data transmission.

[0060] In one example, before performing edge detection on the pixels in the carrier image in step S101 according to a preset order, the method further includes:

[0061] Step S1011, converting the base of the operation data into a first preset base.

[0062] Step S1012, decomposing the carrier image into three channels of R, G, and B, and converting the base of the pixel points in the carrier image after channel decomposition into the first preset base.

[0063] Optionally, the operation data collected by the sensor and the controller is generally decimal data, and the decimal operation data is converted into eight-bit binary data. The pixel point data in the image after channel decomposition is also converted into binary data. Unifying the data base of the carrier image and the operation data facilitates the subsequent steganography of the operation data. It should be noted that the first preset base is not limited to the binary defined in this application and can be defined according to the actual application scenario.

[0064] In one example, step S101 includes: clearing the preset number of bits of each pixel in the carrier image in the reverse order from the back to the front to obtain a first image, and performing edge detection on the first image in a preset order.

[0065] Optionally, to facilitate extraction and expansion of the edge area, a first image is generated by clearing the last n bits of each pixel in the carrier image channel. Where n is a positive integer, and in this example, n = 5. For any pixel R(m, n) of the new image, edge detection is performed on the pixel according to a preset order through edge detection algorithms such as Sobel, Laplacian, and Canny, and then data embedding is performed. The preset order includes a traversal order from left to right and from top to bottom. Preferably, the Canny algorithm is used for edge detection. The Canny algorithm has a good effect, especially in the case of more noise, and is suitable for detecting fine edges.

[0066] Step S102, in response to the current pixel being an edge pixel, determining an embedding path according to the Collatz conjecture, and embedding the operation data into the pixels of the carrier image based on the embedding path.

[0067] Optionally, if the current pixel R(m, n) is identified as an edge pixel, then according to the Collatz conjecture, the secret data is embedded into the edge area of the carrier image along a random path. In this way, the original law of the data is disrupted, making the data present a disordered state, increasing the difficulty of external cracking, and improving the security of data transmission.

[0068] In one example, step S102 includes: determining first data to be embedded from the unembedded running data based on the embedding path according to the preset reading order of the running data. Embedding the first data to be embedded into the pixels of the carrier image based on the embedding path, and updating the unembedded data.

[0069] Optionally, during each embedding, determine the data to be embedded from the unembedded running data in sequence, and embed the data to be embedded into the carrier image based on the embedding path.

[0070] Step S103, in response to the current pixel being a non-edge pixel, embed the running data into the least significant bit of the non-edge pixel through the LSB algorithm.

[0071] Optionally, if the current pixel R(m,n) is classified as a non-edge pixel, that is, the smooth area of the carrier image, then through the LSB algorithm, embed the unembedded running data into the two least significant bits of the B channel of the pixel.

[0072] In one example, step S103 includes: determining second data to be embedded from the unembedded running data according to the preset reading order of the running data. Embedding the second data to be embedded into the least significant bit of the non-edge pixel through the LSB algorithm, and updating the unembedded data.

[0073] Optionally, during each embedding, determine the data to be embedded from the unembedded running data in sequence, and embed the data to be embedded into the carrier image based on the embedding path.

[0074] Through steps S101 - S103, use the Canny detection algorithm to distinguish the edge area and the smooth area, expand the steganography capacity, and adaptively embed sensitive data based on the edge features of the image. Utilize the characteristic that the human eye is more sensitive to changes in the smooth area than in the sharp area, disrupt the original statistical law of the data, make the data present a disordered state, increase the difficulty of external cracking, and improve the security of data transmission.

[0075] Step S104, in response to all the running data being embedded into the carrier image, complete the embedding to obtain the stego image.

[0076] Optionally, Figure 2 is a flowchart of a data embedding process shown according to an embodiment of the present application. As Figure 2 shown, when all the running data is embedded into the carrier image, stop the edge detection and complete the embedding. When all the running data is not fully embedded into the carrier image, continue to perform edge detection on the pixels in the carrier image, and embed the running data into the carrier image based on the detection result until all the running data is embedded to obtain the stego image.

[0077] In summary, in the present application, the edge processor in the cloud-edge collaborative architecture preprocesses data to reduce the amount of data transmitted, thereby accelerating the data transmission speed. The Canny detection algorithm is used to distinguish the edge area and the smooth area, expanding the steganography capacity, and adaptively embedding sensitive data based on the edge features of the image. When embedding, according to the Collatz conjecture, the running data is embedded into the carrier image along a random path. In this way, the original law of the data is disrupted, making the data present in a disordered state, improving the confidentiality and integrity of the data during transmission, and preventing the data from being illegally stolen or tampered with. Moreover, a better balance is achieved between the steganography quality and the data embedding capacity.

[0078] In a second aspect, an embodiment of the present application provides a thermal power data transmission method, which is applied to a cloud server for communicating with an edge server. Figure 3 is a flowchart of another thermal power data transmission method shown according to an exemplary embodiment, as Figure 3 shown, the method includes:

[0079] Step S201, receiving the stego image sent by the edge server, and performing edge detection on the pixels in the stego image in a preset order. Optionally, the preset order includes a traversal order from left to right and from top to bottom.

[0080] In one example, before performing edge detection on the pixels in the stego image in the preset order in step S201, the method further includes: decomposing the stego image into three channels of R, G, and B, and converting the base of the pixel points in the stego image after channel decomposition to a first preset base.

[0081] Optionally, converting the base of the pixel points in the stego image after channel decomposition to binary. It should be noted that the first preset base is not limited to the binary defined in the present application and can be defined according to the actual application scenario.

[0082] In one example, step S201 includes: clearing the preset number of bits of each pixel in the stego image in the order from back to front to obtain a second image. Extracting the R channel in the second image, and performing edge detection on the pixels in the R channel in a preset order.

[0083] Optionally, clearing the last n bits of each pixel in the stego image channel to generate a second image. Where n is a positive integer, and in this example, n = 5. Traversing the pixels in the R channel of the second image in the order from left to right and from top to bottom, for any pixel R(m, n), performing edge detection on the pixel through edge detection algorithms such as Sobel, Laplacian, and Canny in a preset order. Preferably, the Canny algorithm is used for edge detection, and the Canny algorithm has a good effect, especially in the case of more noise, and is suitable for detecting fine edges.

[0084] Step S202: In response to the current pixel being an edge pixel, determine an embedding path according to the Collatz conjecture, and extract first target data from the pixels of the stego-image based on the embedding path.

[0085] Step S203: In response to the current pixel being a non-edge pixel, extract second target data from the least significant bit of the non-edge pixel according to the LSB algorithm. Optionally, if the current pixel R’(m,n) is a non-edge pixel, that is, a smooth area of the image, the LSB algorithm is applied to extract data from the two least significant bits of the B channel within the smooth area of the image.

[0086] Step S204: Determine target data according to the first target data and the second target data, and restore the running data based on the target data. Optionally, all the extracted first target data and second target data are reorganized in the extraction order to obtain the target data.

[0087] In one example, Step S204 includes: converting the base of the target data from a first preset base to a second preset base to obtain the running data. Optionally, the running data is generally decimal data. By converting the extracted eight-bit binary data to decimal, the running data can be obtained. Restore the carrier image according to the extracted stego-image.

[0088] Figure 4 is an overall flowchart of secure transmission of thermal power data shown according to an exemplary embodiment. As Figure 4 shown, the edge server steganographically embeds the running data into the carrier image; the cloud server restores the received stego-image to obtain the running data and the carrier image.

[0089] As an example, an experimental evaluation is conducted on the effectiveness of the thermal power data transmission proposed in this application. The data involved includes: appropriate color images as carriers, and three types of carrier data are selected according to the pixel size for experiments: 128*128*3, 256*256*3, 512*512*3.

[0090] Figure 5 is a schematic diagram of an embedding result shown according to an exemplary embodiment. Figure 6 is a schematic diagram of a data extraction result shown according to an exemplary embodiment. As Figure 5 and Figure 6 shown, it can be intuitively seen from the experimental results that there is no obvious visual difference between the carrier image and the stego-image, and there is also no difference between the embedded data and the extracted data. It shows that the stego-image obtained by the thermal power data transmission method proposed in this application can maintain good visual quality and can meet the security requirements of data transmission visually. Figure 7It is a schematic diagram showing the grayscale value distributions of a carrier image and a stego image according to an exemplary embodiment. As Figure 7 shown, compared with the carrier image, the histogram of the stego image has no obvious change, indicating that the steganography method used in this application can well maintain the correlation between adjacent pixels.

[0091] During the information transmission process, it may be interfered by hardware or attackers, resulting in image damage. The robustness of the data transmission method is reflected in the ability to recover important information from the damaged image. Two quantitative metrics, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM), are used to evaluate the robustness of the stego image. PSNR is usually used to measure the reference value of image quality between the maximum signal and background noise. And SSIM characterizes the similarity between two images. The similarity between the stego image and the stego image uses the mean as the luminance estimate, the standard deviation as the contrast estimate, and the covariance as the similarity measure. The larger the PSNR, the smaller the distortion degree of the image, and the smaller the PSNR, the larger the distortion degree of the image. If the PSNR value reaches above 40 dB, it indicates that the stego image has high quality. In addition, the value range of SSIM is [0, 1], and the larger the value, the higher the similarity between the two images.

[0092] Table 1 shows the evaluation results of the mean-square error (MSE) value, PSNR value, SSIM value, and Maximum Embedded Capacity (MEC) value of images of different sizes.

[0093] Table 1

[0094]

[0095] As shown in Table 1, through data analysis, it can be seen that the stego image has high quality and capacity, and there is basically no difference between the carrier image and the stego image.

[0096] For the same image, under the same embedding rate, Table 2 is a comparison table of the PSNR values of the thermal power data transmission method of this application and the PSNR values of existing schemes.

[0097] Table 2

[0098]

[0099] As shown in Table 2, compared with the existing schemes, under the same embedding rate, the PSNR value of the thermal power data transmission method of this application is higher, and the distortion rate of the image is lower.

[0100] In summary, in the present application, the edge processor in the cloud-edge collaboration architecture preprocesses data to reduce the amount of data transmitted, thereby accelerating the data transmission speed. The Canny detection algorithm is used to distinguish the edge region and the smooth region, expanding the steganography capacity, and adaptively embedding sensitive data based on the edge features of the image. When embedding, according to the Collatz conjecture, the running data is embedded into the carrier image along a random path. In this way, the original law of the data is disrupted, making the data present in a disordered state, improving the confidentiality and integrity of the data during transmission, and preventing the data from being illegally stolen or tampered with. Moreover, a better balance is achieved between the steganography quality and the data embedding capacity.

[0101] In a third aspect, an embodiment of the present application provides a thermal power data transmission system, which is used for an edge server and cooperates with a cloud server. Figure 8 It is a structural block diagram of a thermal power data transmission system shown according to an embodiment of the present application, as Figure 8 shown, the system includes:

[0102] An acquisition module 100: configured to acquire the running data and the carrier image of the thermal power plant equipment, and perform edge detection on the pixels in the carrier image in a preset order.

[0103] A first embedding module 200: configured to, in response to the current pixel being an edge pixel, determine an embedding path according to the Collatz conjecture, and embed the running data into the pixels of the carrier image based on the embedding path.

[0104] A second embedding module 300: configured to, in response to the current pixel being a non-edge pixel, embed the running data into the least significant bit of the non-edge pixel through the LSB algorithm.

[0105] A result module 400: configured to, in response to all the running data being embedded into the carrier image, complete the embedding to obtain a stego image.

[0106] In an example, before the acquisition module 100 performs edge detection on the pixels in the carrier image in a preset order, the system further includes:

[0107] It is configured to convert the base of the running data into a first preset base. The carrier image is decomposed into three channels of R, G, and B, and the base of the pixel points in the carrier image after channel decomposition is converted into the first preset base.

[0108] In an example, the acquisition module 100 includes: configured to clear the preset number of bits of each pixel in the carrier image in reverse order to obtain a first image, and perform edge detection on the first image in a preset order.

[0109] In one example, the first embedding module 200 includes: determining first data to be embedded from unembedded running data based on an embedding path according to a preset reading order of the running data; embedding the first data to be embedded into pixels of a carrier image based on the embedding path, and updating the unembedded data.

[0110] In one example, the second embedding module 300 includes: determining second data to be embedded from unembedded running data; embedding the second data to be embedded into the least significant bit of non-edge pixels by means of the LSB algorithm, and updating the unembedded data.

[0111] In summary, in this application, the edge processor in the cloud-edge collaboration architecture preprocesses data, reduces the amount of data transmitted, and speeds up data transmission. The edge area and the smooth area are distinguished by a detection algorithm to expand the steganography capacity, and sensitive data is adaptively embedded based on the edge features of the image. When embedding, according to the Collatz conjecture, the running data is embedded into the carrier image along a random path. In this way, the original rule of the data is disrupted, making the data present a disordered state, improving the confidentiality and integrity of the data during transmission, and preventing the data from being illegally stolen or tampered with. Moreover, a better balance is achieved between the steganography quality and the data embedding capacity.

[0112] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0113] The above-described embodiments merely represent several implementation manners of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of the patent of this application shall be subject to the appended claims.

Claims

1. A thermal power data transmission method, characterized in that, The method is applied to an edge server, which is used to communicate with a cloud server. The method includes: Obtain the operation data and carrier images of the thermal power plant equipment, and perform edge detection on the pixels in the carrier images in a preset order; In response to the current pixel being an edge pixel, determine the embedding path according to the Collatz conjecture, and embed the operation data into the pixels of the carrier image based on the embedding path; In response to the current pixel being a non-edge pixel, embed the operation data into the least significant bit of the non-edge pixel through the LSB algorithm; In response to all the operation data being embedded into the carrier image, complete the embedding to obtain a stego image.

2. The method for transmitting thermal power data according to claim 1, characterized in that, Before performing edge detection on the pixels in the carrier image in a preset order, the method further includes: Convert the base of the operation data to a first preset base; Decompose the carrier image into three channels of R, G, and B, and convert the base of the pixel points in the carrier image after channel decomposition to the first preset base.

3. A thermal power data transmission method according to claim 2, characterized in that, The performing edge detection on the pixels in the carrier image in a preset order includes: Perform a clearing process on the preset number of bits of each pixel in the carrier image in a reverse order to obtain a first image, and perform edge detection on the first image in a preset order.

4. A thermal power data transmission method according to claim 3, characterized in that, The embedding the operation data into the pixels of the carrier image based on the embedding path includes: Based on the preset reading order of the operation data, determine the first data to be embedded from the unembedded operation data based on the embedding path; Embed the first data to be embedded into the pixels of the carrier image based on the embedding path, and update the unembedded data.

5. The method for transmitting thermal power data according to claim 3, wherein The embedding the operation data into the least significant bit of the non-edge pixel through the LSB algorithm includes: Based on the preset reading order of the operation data, determine the second data to be embedded from the unembedded operation data; Embed the second data to be embedded into the least significant bit of the non-edge pixel through the LSB algorithm, and update the unembedded data.

6. A thermal power data transmission method, characterized in that The method is applied to a cloud server, which is used to communicate with an edge server. The method includes: Receive the stego image sent by the edge server, and perform edge detection on the pixels in the stego image in a preset order; In response to the current pixel being an edge pixel, determine the embedding path according to the Collatz conjecture, and extract the first target data from the pixels of the stego image based on the embedding path; In response to the current pixel being a non-edge pixel, extract the second target data from the least significant bit of the non-edge pixel according to the LSB algorithm; Determine the target data according to the first target data and the second target data, and restore the operation data based on the target data.

7. A thermal power data transmission method according to claim 6, characterized in that Before performing edge detection on the pixels in the stego image in a preset order, the method further includes: Decompose the stego image into three channels of R, G, and B, and convert the base of the pixel points in the stego image after channel decomposition to the first preset base.

8. A thermal power data transmission method according to claim 7, characterized in that, The performing edge detection on the pixels in the stego image in a preset order includes: Clear the preset number of bits of each pixel in the steganographic image in the reverse order to obtain a second image; Extract the R channel in the second image, and perform edge detection on the pixels in the R channel in a preset order.

9. The method for transmitting thermal power data according to claim 8, wherein, The restoring the operating data based on the target data includes: Convert the base of the target data from the first preset base to a second preset base to obtain the operating data.

10. A thermal power data transmission system, characterized in that, The system is used for an edge server and cooperates with a cloud server. The system includes: An acquisition module: used to acquire the operating data and a carrier image of a thermal power plant device, and perform edge detection on the pixels in the carrier image in a preset order; A first embedding module: used to determine an embedding path according to the Collatz conjecture in response to the current pixel being an edge pixel, and embed the operating data into the pixels of the carrier image based on the embedding path; A second embedding module: used to embed the operating data into the least significant bit of the non-edge pixel by the LSB algorithm in response to the current pixel being a non-edge pixel; A result module: used to complete the embedding to obtain a steganographic image in response to all the operating data being embedded into the carrier image.

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