A method and system for one-way transmission of cross-network data based on image encoding

Abstract

This invention relates to the technical field of cross-network one-way data transmission, and in particular to a method and system for cross-network one-way data transmission based on image encoding. The method includes the following steps: packetizing the data to be transmitted to generate multiple data packets, and allocating memory space for each data packet; image encoding each data packet to convert it into an image frame; transmitting the encoded image frame to the receiving end using a discrete distributed multi-point transmission method; performing image recognition on the received image frame and extracting encoded information based on the data header and data blocks; verifying the extracted encoded information, and outputting the verified data to the data receiver. This application achieves highly secure one-way transmission of cross-network data through image encoding and intelligent transmission mechanisms, improving data transmission efficiency and real-time performance.

Description

Technical Field

[0001] This invention relates to the technical field of cross-network unidirectional data transmission, and in particular to a cross-network unidirectional data transmission method and system based on image encoding. Background Technology

[0002] With the escalating threats to cybersecurity, cross-network data security transmission is facing new challenges and opportunities. In sensitive sectors such as finance, military, and government, data transmission between different network domains (such as intranets and extranets) has become a core part of daily operations, and automated and intelligent data transfer technologies are gradually becoming the mainstream solution for cross-network data transmission. However, while the introduction of new technologies improves efficiency, it also brings new data security risks, such as man-in-the-middle attacks, data tampering, and data leakage. This makes the balance between data security and transmission efficiency particularly critical.

[0003] Traditional data transmission methods primarily rely on manual transfer, such as using physical media (like USB drives or optical discs) for data exchange. While this method offers some degree of isolation and security, its reliance on manual operation results in extremely low efficiency and makes it susceptible to data loss or delays due to human error. For example, in emergency response scenarios, traditional manual transfer may fail to meet real-time requirements, impacting decision-making efficiency. On the other hand, existing automated cross-network transmission technologies, such as protocol encapsulation or encrypted tunneling, while improving transmission speed, often have inherent drawbacks: First, these technologies typically depend on the continuity of network protocols, making them vulnerable to single points of failure in complex network topologies, leading to transmission interruptions. Second, encrypted transmission may introduce significant latency, making it difficult to adapt to high-concurrency or real-time applications, such as video surveillance or big data synchronization. Furthermore, existing technologies lack structured processing of data packets, failing to effectively address congestion issues caused by surging data volumes, especially in cross-network environments where the integrity and consistency of data transmission are difficult to guarantee.

[0004] Therefore, cross-network data transmission technology needs to improve transmission efficiency while ensuring security and adapting to constantly changing requirements. There is an urgent need for a data transmission method that can guarantee data security and be both efficient and convenient in cross-network data transmission scenarios, in order to improve data transmission timeliness and work efficiency, and reduce the cost of manual intervention. Summary of the Invention

[0005] To address the aforementioned technical issues, this application provides a cross-network unidirectional data transmission method and system based on image encoding.

[0006] The above-mentioned objective of this application is achieved through the following technical solution:

[0007] A cross-network one-way data transmission method based on image coding, the method comprising the following steps:

[0008] The sending end divides the data to be transmitted into packets, generates multiple data packets, and allocates memory space for each data packet;

[0009] Each data packet is image encoded to convert it into a frame image. The image encoding uses a combination of data header and data block. The data header is used to encode control information, and the data block is used to encode data content.

[0010] The encoded image frame is transmitted to the receiving end using a discrete distributed multi-point transmission method. Image recognition is performed on the received image frame, and the encoded information is extracted based on the data header and data block.

[0011] The extracted encoded information is verified, and the data that passes the verification is output to the data receiver.

[0012] By adopting the above technical solution, the sending end divides the data to be transmitted into packets, generating multiple data packets, and allocates memory space for each data packet. This step achieves intelligent segmentation based on a preset packet size threshold by dynamically analyzing the data size and transmission requirements, effectively reducing the amount of data transmitted in a single transmission and avoiding network congestion and delays caused by large data block transmissions, thereby optimizing the efficiency of subsequent image encoding and transmission. Image encoding is performed on each data packet, using a combination of data headers and data blocks to structurally map control information, such as keywords, frame numbers, and priorities, and data content into image frames. This leverages the intuitiveness and anti-interference characteristics of image pixels to enhance data recognizability. It exhibits robustness, particularly reducing bit error rate in high-noise networks. Employing a discrete, multi-point transmission method to transmit coded images to the receiving end, it dynamically selects multiple transmission points and performs concurrent transmission, diversifying and obfuscating data paths, thus reducing the risk of eavesdropping or single-point failure. Combined with image recognition and verification mechanisms, it ensures the accuracy and integrity of data extraction, thereby achieving highly secure unidirectional transmission of cross-network data. This significantly improves the reliability and real-time performance of data transmission, making it suitable for sensitive fields such as finance and the military. It reduces the cost and management risk of manual intervention, transforming the inefficiency of traditional manual transmission into efficient digital processing, and improving the accuracy of data integration.

[0013] In a preferred embodiment, this application can be further configured such that: the sending end divides the data to be transmitted into packets, generates multiple data packets, and allocates memory space for each data packet, specifically including:

[0014] The system acquires the raw data to be transmitted, analyzes the data size and transmission requirements, and divides the raw data into multiple data packets based on a preset packet size threshold. Each data packet contains a unique sequence identifier.

[0015] Assign a user ID and priority information to each data packet and store them in the sender's memory space;

[0016] Packet logs are generated based on the data packet sequence and used as a reference for subsequent image encoding.

[0017] By adopting the above technical solution, the raw data to be transmitted is acquired and its size and transmission requirements are analyzed. Based on a preset packet size threshold, the data is divided into multiple data packets containing unique sequence identifiers. This step, through an intelligent segmentation algorithm, ensures the order and traceability of the data packets, avoiding out-of-order or loss issues during transmission, thereby improving the accuracy of data reassembly. Each data packet is assigned a user number and priority information, which is stored in the sender's memory space, enabling classified management and priority scheduling of the data stream. For example, high-priority data can be processed first to reduce latency, optimizing resource utilization and transmission efficiency. Packet logs are generated based on the data packet sequence for subsequent image encoding reference, providing detailed metadata records, supporting error tracking and performance monitoring, facilitating debugging and optimization of transmission parameters. This enhances the maintainability and adaptability of the system, reduces transmission failures caused by improper data packet management, improves the overall reliability of data integration, upgrades traditional extensive transmission to a structured process, and significantly reduces operational complexity.

[0018] In a preferred embodiment, this application can be further configured as follows: Image encoding is performed on each data packet to convert the data packet into a frame image, wherein the image encoding uses a combination of a data header and data blocks, the data header is used to encode control information, and the data blocks are used to encode data content, specifically including:

[0019] A data header is generated for each data packet. The data header contains at least one of the following information: keyword, frame number, frame rate, priority, user number, packet identifier, data length, and data mode.

[0020] Based on the content of the data packet, a data block image block is generated using a binary-to-image pixel mapping method, and image segmentation technology is used to map the data header and data block into a pixel array;

[0021] The data header and data block image blocks are combined to form a complete coded image frame, and the spatial positions of the data header and data block are ensured to conform to the preset matching rules.

[0022] By adopting the above technical solution, a data header is generated for each data packet, containing information such as keywords, frame number, frame rate, priority, user ID, packet identifier, data length, and data mode. This provides a complete control information framework, ensuring accurate identification of data attributes and order during decoding and reducing parsing errors caused by missing information. Data block image blocks are generated based on the data packet content using a binary-to-image pixel mapping method, for example, mapping binary bits to pixel brightness values. Image segmentation technology is then used to map the data header and data blocks to pixel arrays, achieving efficient visual encoding of the data. This leverages the maturity of image processing algorithms, improving encoding speed and compatibility. The data header and data block image blocks are combined to form a complete encoded image frame, ensuring that spatial positions conform to preset matching rules. This enhances the structure of the image, facilitating rapid location and extraction by the receiving end through template matching. This improves data transmission consistency and resistance to distortion, reduces decoding error rates, and is suitable for unstable network environments. By transforming abstract data into intuitive images, it enhances the applicability and efficiency of cross-network transmission.

[0023] In a preferred embodiment, this application can be further configured as follows: combining the data header and data block image blocks to form a complete encoded image frame, and ensuring that the spatial positions of the data header and data blocks conform to a preset matching rule, specifically includes:

[0024] Based on the frame number and packet identifier, the data header image block is positioned in the preset header region of the encoded image, and the data block image block is positioned in the data region of the encoded image, maintaining a continuous or discrete distribution with the data header region.

[0025] The consistency between the data header and the data block is verified by a preset algorithm, and a check code is generated and embedded in the data header. The combined image frame is then compressed and encoded to form a complete encoded image.

[0026] By adopting the above technical solution, the data header image block is positioned in the preset header region of the encoded image according to the frame number and packet identifier, and the data block image block is positioned in the data region, maintaining a continuous or discrete distribution with the data header region. This achieves precise management of the data space. Through preset rules, such as continuous distribution to optimize encoding speed and discrete distribution to enhance security, the layout efficiency of the image frame is optimized, improving the overall readability and anti-attack capability of the image. The consistency between the data header and data block is verified by a preset algorithm, and a checksum is generated and embedded in the data header. The combined image frame is compressed and encoded to form a complete encoded image, ensuring the integrity and tamper-proof nature of the data. For example, algorithms such as multi-point cross-checking, CRC check, and parity check are used to detect malicious modifications. At the same time, the compressed encoding reduces the image size, adapts to bandwidth-constrained networks, and improves transmission efficiency. By integrating checksum and compression mechanisms, the traditional simple encoding is upgraded to a security-enhanced processing, significantly reducing the risk of data leakage and supporting reliable transmission under high-load scenarios.

[0027] In a preferred embodiment, this application can be further configured such that: the transmission of the encoded image frame to the receiving end using a discrete distributed multi-point transmission method specifically includes:

[0028] Based on network topology and security requirements, multiple transmission points are dynamically selected, with each point corresponding to a different physical or logical transmission path.

[0029] Each frame of image data is divided into multiple sub-parts, and transmission point coordinates are assigned to each sub-part. The transmission order of the sub-parts is scheduled in a discrete sequence according to the network security policy.

[0030] Sub-parts are sent concurrently through multiple transmission channels, and timestamps are added during transmission. At the receiving end, the sub-parts are reassembled based on the point coordinates and timestamps to restore the complete image data.

[0031] By adopting the above technical solution, multiple transmission points are dynamically selected according to network topology and security requirements. Each point corresponds to a different physical or logical transmission path, realizing intelligent optimization and redundancy backup of transmission paths, enhancing the system's flexibility and resistance to single-point failures, and avoiding transmission interruptions caused by network fluctuations. Each frame of image data is divided into multiple sub-parts, and transmission point coordinates are assigned to each sub-part. The transmission order is scheduled according to a discrete sequence based on network security policies. A pseudo-random sequence is generated through encryption algorithms, obfuscating the data transmission mode, reducing the risk of eavesdropping or interception, and improving confidentiality. Sub-parts are sent concurrently through multiple transmission channels, and timestamps are added. At the receiving end, the sub-parts are reassembled based on the point coordinates and timestamps to restore the complete image data. High-concurrency processing and real-time synchronization are achieved, ensuring reliable data delivery in complex networks, significantly improving transmission efficiency and real-time performance. Through multi-point discrete transmission, the traditional centralized transmission is upgraded to a distributed architecture, which is suitable for scenarios with high security requirements.

[0032] In a preferred embodiment, this application can be further configured as follows: the image recognition of the received image frame, and the extraction of encoded information based on the data header and data block, specifically includes:

[0033] After receiving the image data, the receiving end preprocesses the image to eliminate noise and distortion, and uses a template matching algorithm to slide the data header and data block template in the image to calculate the region similarity.

[0034] Based on the similarity results, the positions of the data header and data block are located, and the encoding information is extracted. The extracted information is then preliminarily parsed to generate a data packet sequence and a content mapping table.

[0035] Decode the packet sequence and content mapping table into the original packet content, and verify the integrity of the header and data blocks.

[0036] By adopting the above technical solution, the receiving end preprocesses the image after receiving the image data to eliminate noise and distortion. Gaussian filtering and geometric correction algorithms are used to improve image quality, laying the foundation for subsequent recognition. A template matching algorithm is used to calculate the similarity of regions by sliding data header and data block templates in the image. Through normalized cross-correlation matching and other methods, high-precision positioning is achieved, enhancing the robustness of recognition. Based on the similarity results, the positions of data headers and data blocks are located and the encoded information is extracted. The extracted information is initially parsed to generate data packet sequences and content mapping tables, realizing efficient data decoding and structured processing. The data packet sequences and content mapping tables are decoded into the original data packet content, and the integrity of data headers and data blocks is verified. The data consistency is ensured through preset algorithm verification, thereby reducing the bit error rate and retransmission requirements, improving the reliability and security of cross-network data transmission, and making it suitable for critical applications such as real-time monitoring.

[0037] In a preferred embodiment, this application can be further configured as follows: the template matching algorithm is used to slide data header and data block templates in the image and calculate region similarity, specifically including:

[0038] Initialize the sliding window, using the header template and block template as the kernels of the sliding window respectively, and set the initial sliding step size;

[0039] The sliding window is controlled to slide pixel by pixel on the received full frame image with the sliding step size. Each time it slides to a new position, the similarity value between the image region in the current window and the template is calculated.

[0040] Record the similarity value corresponding to each sliding position and generate a similarity distribution matrix for the entire image. The value of each point in the matrix represents the degree of matching between the window region with that point as the top left vertex and the template.

[0041] Analyze the similarity distribution matrix and locate the coordinates of the extreme points in the matrix. These coordinates are the candidate points for the best matching position of the template in the image.

[0042] By adopting the above technical solution, the sliding window is initialized and the data header template and data block template are used as the core of the sliding window. An initial sliding step size is set, and by adjusting the step size, such as using a 1-pixel step size in high-precision mode, the matching speed and accuracy are balanced, adapting to the needs of images with different resolutions. The sliding window is controlled to slide pixel by pixel on the entire frame image with the sliding step size, and the similarity value between each position and the template is calculated, generating a similarity distribution matrix for the entire image. Through matrix analysis, comprehensive coverage of template matching is achieved, improving the comprehensiveness and reliability of matching. The coordinates of extreme points are located by analyzing the similarity distribution matrix as the optimal matching position of the template. Through threshold filtering and local maximum detection, the accuracy and anti-interference ability of matching are improved, the influence of environmental noise is reduced, and the stability of data extraction is ensured, making it suitable for high-deformation or low-quality image transmission scenarios.

[0043] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0044] A cross-network one-way data transmission system based on image coding, the image coding-based cross-network one-way data transmission system comprising:

[0045] The data packet generation module is used by the sending end to divide the data to be transmitted into packets, generate multiple data packets, and allocate memory space for each data packet;

[0046] The image encoding module is used to encode each data packet into an image frame. The image encoding uses a combination of data header and data block. The data header is used to encode control information, and the data block is used to encode data content.

[0047] The data image recognition module is used to transmit the encoded image frame to the receiving end using a discrete distributed multi-point transmission method, perform image recognition on the received image frame, and extract the encoded information based on the data header and data block;

[0048] The data verification module is used to verify the extracted encoded information and filter out the data that passes the verification to output to the data receiver.

[0049] By adopting the above technical solution, the sending end divides the data to be transmitted into packets, generating multiple data packets, and allocates memory space for each data packet. This step achieves intelligent segmentation based on a preset packet size threshold by dynamically analyzing the data size and transmission requirements, effectively reducing the amount of data transmitted in a single transmission and avoiding network congestion and delays caused by large data block transmissions, thereby optimizing the efficiency of subsequent image encoding and transmission. Image encoding is performed on each data packet, using a combination of data headers and data blocks to structurally map control information, such as keywords, frame numbers, and priorities, and data content into image frames. This leverages the intuitiveness and anti-interference characteristics of image pixels to enhance data recognizability. It exhibits robustness, particularly reducing bit error rate in high-noise networks. Employing a discrete, multi-point transmission method to transmit coded images to the receiving end, it dynamically selects multiple transmission points and performs concurrent transmission, diversifying and obfuscating data paths, thus reducing the risk of eavesdropping or single-point failure. Combined with image recognition and verification mechanisms, it ensures the accuracy and integrity of data extraction, thereby achieving highly secure unidirectional transmission of cross-network data. This significantly improves the reliability and real-time performance of data transmission, making it suitable for sensitive fields such as finance and the military. It reduces the cost and management risk of manual intervention, transforming the inefficiency of traditional manual transmission into efficient digital processing, and improving the accuracy of data integration.

[0050] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0051] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described image-encoded cross-network unidirectional data transmission method.

[0052] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0053] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described image-encoded cross-network unidirectional data transmission method.

[0054] In summary, this application includes at least one of the following beneficial technical effects:

[0055] 1. The sending end divides the data to be transmitted into packets, generating multiple data packets and allocating memory space for each packet. This step dynamically analyzes the data size and transmission requirements, and intelligently segments the data based on a preset packet size threshold, effectively reducing the amount of data transmitted in a single transmission and avoiding network congestion and delays caused by large data block transmissions, thereby optimizing the efficiency of subsequent image encoding and transmission. Each data packet is then image encoded using a combination of data headers and data blocks. Control information, such as keywords, frame numbers, and priorities, is structurally mapped to image frames along with the data content. This leverages the intuitiveness and anti-interference characteristics of image pixels to enhance data recognizability and robustness. Especially in high-noise networks, it can reduce the bit error rate; by adopting a discrete distributed multi-point transmission method to transmit the coded image to the receiving end, and by dynamically selecting multiple transmission points and sending concurrently, it realizes the diversification and obfuscation of data paths, reducing the risk of eavesdropping or single point failure. At the same time, combined with image recognition and verification mechanisms, it ensures the accuracy and integrity of data extraction, thereby realizing highly secure one-way transmission of cross-network data, significantly improving the reliability and real-time performance of data transmission. It is suitable for sensitive fields such as finance and military, reducing the cost of manual intervention and management risks, transforming the inefficiency of traditional manual transmission into efficient digital processing, and improving the accuracy of data integration.

[0056] 2. A data header is generated for each data packet, containing information such as keywords, frame number, frame rate, priority, user ID, packet identifier, data length, and data mode. This provides a complete control information framework, ensuring accurate identification of data attributes and order during decoding and reducing parsing errors caused by missing information. Data blocks and image blocks are generated based on the data packet content using a binary-to-image pixel mapping method. For example, binary bits are mapped to pixel brightness values, and image segmentation technology is used to map the data header and data blocks to pixel arrays. This achieves efficient and visual encoding of data, leveraging the maturity of image processing algorithms to improve encoding speed and compatibility. The data header and data block image blocks are combined to form a complete encoded image frame, ensuring that the spatial position conforms to preset matching rules. This enhances the structure of the image, facilitating rapid location and extraction by the receiving end through template matching. This improves the consistency and anti-distortion capability of data transmission, reduces the decoding error rate, and is suitable for unstable network environments. It transforms abstract data into intuitive images, improving the applicability and efficiency of cross-network transmission.

[0057] 3. Multiple transmission points are dynamically selected based on network topology and security requirements. Each point corresponds to a different physical or logical transmission path, achieving intelligent optimization and redundancy backup of transmission paths. This enhances system flexibility and resistance to single-point failures, avoiding transmission interruptions caused by network fluctuations. Each frame of image data is divided into multiple sub-parts, and transmission point coordinates are assigned to each sub-part. The transmission order is scheduled according to a discrete sequence based on network security policies. A pseudo-random sequence is generated using an encryption algorithm, obfuscating the data transmission mode, reducing the risk of eavesdropping or interception, and improving confidentiality. Sub-parts are sent concurrently through multiple transmission channels, with timestamps added. At the receiving end, the sub-parts are reassembled based on the point coordinates and timestamps to restore the complete image data. This achieves high-concurrency processing and real-time synchronization, ensuring reliable data delivery in complex networks and significantly improving transmission efficiency and real-time performance. Through multi-point discrete transmission, the traditional centralized transmission is upgraded to a distributed architecture, suitable for scenarios with high security requirements.

[0058] 4. After receiving image data, the receiving end preprocesses the image to eliminate noise and distortion. Gaussian filtering and geometric correction algorithms are used to improve image quality, laying the foundation for subsequent recognition. A template matching algorithm is used to calculate the similarity of regions by sliding data header and data block templates in the image. Through normalized cross-correlation matching and other methods, high-precision positioning is achieved, enhancing the robustness of recognition. Based on the similarity results, the positions of data headers and data blocks are located and the encoded information is extracted. The extracted information is initially parsed to generate data packet sequences and content mapping tables, realizing efficient data decoding and structured processing. The data packet sequences and content mapping tables are decoded into the original data packet content, and the integrity of data headers and data blocks is verified. Pre-set algorithms are used to verify the authenticity and consistency of the data, thereby reducing the bit error rate and retransmission requirements, improving the reliability and security of cross-network data transmission, and making it suitable for critical applications such as real-time monitoring. Attached Figure Description

[0059] Figure 1 This is a flowchart of a cross-network one-way data transmission method based on image encoding in one embodiment of this application;

[0060] Figure 2 This is a flowchart illustrating the implementation of step S10 in a cross-network one-way data transmission method based on image encoding in one embodiment of this application.

[0061] Figure 3 This is a flowchart illustrating the implementation of step S20 in a cross-network unidirectional data transmission method based on image encoding in one embodiment of this application.

[0062] Figure 4 This is a flowchart illustrating the implementation of step S23 in a cross-network unidirectional data transmission method based on image encoding in one embodiment of this application.

[0063] Figure 5 This is a flowchart illustrating the implementation of step S30 in a cross-network unidirectional data transmission method based on image encoding in one embodiment of this application.

[0064] Figure 6 This is another implementation flowchart of step S30 in the cross-network unidirectional data transmission method based on image encoding in one embodiment of this application;

[0065] Figure 7 This is a flowchart illustrating the implementation of step S34 in a cross-network unidirectional data transmission method based on image encoding in one embodiment of this application.

[0066] Figure 8 This is a principle block diagram of a cross-network unidirectional data transmission system based on image encoding in one embodiment of this application;

[0067] Figure 9 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0068] The following is in conjunction with the appendix Figure 1-9 This application will be described in further detail.

[0069] In one embodiment, such as Figure 1 As shown, this application discloses a cross-network unidirectional data transmission method based on image coding, which specifically includes the following steps:

[0070] S10: The sending end will process the data to be transmitted into packets, generate multiple data packets, and allocate memory space for each data packet.

[0071] In this embodiment, data packet processing refers to dividing the original data to be transmitted into multiple smaller data packets to optimize transmission efficiency and reliability. Packetization reduces the amount of data transmitted in a single transmission, facilitating subsequent image encoding and discrete transmission, while sequence identifiers ensure the integrity of the data order.

[0072] Specifically, the sending end first acquires the raw data to be transmitted and analyzes the data size and transmission requirements. Based on a preset packet size threshold, the raw data is divided into multiple data packets, each containing a unique sequence identifier for data reassembly at the receiving end. Subsequently, each data packet is assigned a user number and priority information, which are stored in the sending end's memory space. A packet log is generated based on the data packet sequence, recording packet size, sequence number, and timestamp, for reference in subsequent image encoding and error tracking.

[0073] S20: Perform image encoding on each data packet, converting the data packet into a frame image. The image encoding uses a combination of data header and data block. The data header is used to encode control information, and the data block is used to encode data content.

[0074] In this embodiment, image encoding is the process of converting data packets into image frames, achieving structured encoding through a combination of data headers and data blocks. The data header encapsulates control information to ensure decoding accuracy; the data blocks carry the actual data content, using image pixels to represent binary data.

[0075] Specifically, a header is generated for each data packet. The header contains keywords (such as a start identifier), frame number (sequence number), frame rate (transmission rate), priority, user number, packet identifier, data length, and data mode (such as compression flags). The data block portion generates image blocks through a binary-to-image pixel mapping (e.g., each bit of data is mapped to the brightness value of one pixel). Image segmentation techniques (such as block segmentation) are used to map the header and data blocks to pixel arrays, which are then combined into a single image frame, ensuring that the header is located in the top region of the image and the data blocks are located in the bottom region, conforming to preset spatial matching rules.

[0076] S30: The encoded image frame is transmitted to the receiving end using a discrete distributed multi-point transmission method. Image recognition is performed on the received image frame, and the encoded information is extracted based on the data header and data block.

[0077] In this embodiment, discrete distributed multi-point transmission enhances security and anti-interference capabilities by sending image sub-parts concurrently via multiple paths. Image recognition utilizes a template matching algorithm to extract coded information from noise.

[0078] Specifically, based on network topology and security requirements, multiple transmission points (such as different IP addresses or physical ports) are dynamically selected. Each image frame is segmented into multiple sub-parts (e.g., based on grid segmentation), and transmission point coordinates are assigned to each sub-part, with the transmission order scheduled according to a discrete sequence. The sub-parts are sent concurrently through multiple transmission channels (e.g., UDP streams), with timestamps added to ensure timing. The receiving end reassembles the sub-parts based on the point coordinates and timestamps to restore the complete image data. Subsequently, the image is preprocessed (e.g., noise reduction and geometric correction), and a template matching algorithm (e.g., normalized cross-correlation matching) is used to slide the data header and data block templates, calculate region similarity, locate, and extract encoded information.

[0079] S40: Verify the extracted encoded information, and output the data that passes the verification to the data receiver.

[0080] In this embodiment, the verification uses a preset algorithm to detect errors and filters to ensure that only valid data is output.

[0081] Specifically, a checksum is calculated on the extracted encoded information, such as multi-point cross-checking, CRC check, parity check, etc., and compared with the checksum in the data header. If they match, the data packet is marked as valid and output to the data receiver, such as an application or storage system; otherwise, an error is recorded and a retransmission mechanism is triggered. The output process includes data format conversion, such as binary to application format, and a notification mechanism.

[0082] In this embodiment, the sending end divides the data to be transmitted into packets, generating multiple data packets and allocating memory space for each packet. This step achieves intelligent segmentation based on a preset packet size threshold by dynamically analyzing data size and transmission requirements, effectively reducing the amount of data transmitted in a single transmission and avoiding network congestion and delays caused by large block transmissions, thereby optimizing the efficiency of subsequent image encoding and transmission. Each data packet is image encoded using a combination of data headers and data blocks, structurally mapping control information, such as keywords, frame numbers, and priorities, to data content into image frames. This leverages the intuitiveness and anti-interference characteristics of image pixels, enhancing data recognizability and robustness. It exhibits robust performance, particularly in high-noise networks, reducing bit error rates. Employing a discrete, multi-point transmission method to transmit coded images to the receiving end, it dynamically selects multiple transmission points and performs concurrent transmission, diversifying and obfuscating data paths, thus reducing the risk of eavesdropping or single-point failures. Combined with image recognition and verification mechanisms, it ensures the accuracy and integrity of data extraction, thereby achieving highly secure unidirectional transmission of cross-network data. This significantly improves the reliability and real-time performance of data transmission, making it suitable for sensitive fields such as finance and the military. It reduces the cost and management risks of manual intervention, transforming the inefficiency of traditional manual transmission into efficient digital processing, and improving the accuracy of data integration.

[0083] In one embodiment, such as Figure 2 As shown, in step S10, the sending end processes the data to be transmitted into packets, generating multiple data packets, and allocates memory space for each data packet. Specifically, this includes:

[0084] S11: Obtain the raw data to be transmitted, analyze the data size and transmission requirements, and divide the raw data into multiple data packets according to the preset packet size threshold. Each data packet contains a unique sequence identifier.

[0085] In this embodiment, the original data segmentation is dynamically adjusted based on data size and transmission requirements to ensure that the segmented data packets conform to the size limitations of image encoding. A unique sequence identifier is used to maintain the order and traceability of data packets during transmission.

[0086] Specifically, the sending end obtains the raw data through a file system interface or network stream, calculates the total data size, and performs analysis such as checksum or metadata analysis. Based on a preset packet size threshold, for example, set according to network bandwidth and encoding efficiency, it uses a data segmentation algorithm, such as fixed-size or dynamic segmentation, to divide the data into multiple packets. Each packet is assigned a unique sequence identifier, which is written into the packet header so that the receiving end can reassemble them in order.

[0087] S12: Assign a user number and priority information to each data packet and store them in the sender's memory space.

[0088] In this embodiment, user ID and priority information are used to distinguish data streams from different users or applications and to optimize transmission scheduling. High-priority data packets (such as emergency commands) can be processed first to reduce latency.

[0089] Specifically, based on the data source or application context (such as user ID or business type), each data packet is assigned a user number (such as an integer code) and a priority level (such as levels 1-5, with 1 being the highest). This information, along with the data packet, is stored in the sender's memory buffer, and efficient access is ensured through a memory management unit (such as a dynamic allocator). Priority information can be used for subsequent transmission scheduling, such as prioritizing the transmission of high-priority packets under high load.

[0090] S13: Generate packet logs based on the data packet sequence for subsequent image encoding reference.

[0091] In this embodiment, the packet sub-log records metadata of the packet sub-process, which is used for monitoring, debugging, and adjusting image encoding parameters.

[0092] Specifically, packet logs are stored in a structured format, including packet sequence numbers, sizes, timestamps, user IDs, and priorities. Log files are periodically persisted to disk or sent to a monitoring system for analyzing transmission performance. During the image encoding phase, logs can be used to adjust frame rates or encoding parameters, such as optimizing image dimensions based on packet size.

[0093] In one embodiment, such as Figure 3 As shown, in step S20, each data packet is image encoded, converting the data packet into a frame image. The image encoding uses a combination of a data header and data blocks. The data header encodes control information, and the data blocks encode the data content, specifically including:

[0094] S21: Generate a data header for each data packet, the data header containing at least one of the following information: keyword, frame number, frame rate, priority, user number, packet identifier, data length, and data mode.

[0095] In this embodiment, the data header serves as the metadata area of ​​the encoded image, providing the control information required for decoding. Keywords are used to identify the start of a frame, frame numbers are used for sequence control, and data modes indicate the encoding method.

[0096] Specifically, header generation is based on packet attributes: the keyword is set to a fixed value (e.g., 0x55AA) to identify the frame header; the frame number is obtained from the packet log; the frame rate is dynamically set according to transmission requirements (e.g., 30fps); priority and user number are inherited from S12; the packet identifier uses a sequence number; the data length records the packet size; and the data mode indicates compression or encryption status. The header information is encoded as a binary sequence and then mapped to image pixels (e.g., each byte corresponds to the RGB value of one pixel).

[0097] S22: Based on the content of the data packet, generate data block image blocks using a binary-to-image pixel mapping method, and use image segmentation technology to map the data header and data blocks into pixel arrays.

[0098] In this embodiment, binary-to-pixel mapping converts data into a visual image, facilitating transmission and recognition. Image segmentation techniques ensure the precise positioning of data headers and data blocks within the image.

[0099] Specifically, after the data packet content is converted into a binary stream, data block image blocks are generated using a mapping algorithm (e.g., each bit of data is mapped to a specific color channel of a pixel). For example, a binary "0" is mapped to a black pixel (RGB 0,0,0), and a "1" is mapped to a white pixel (RGB 255,255,255). The data header and data blocks are each segmented into independent image regions (e.g., the data header occupies 20% of the image height, and the data blocks occupy 80%), and arranged as a continuous pixel array. Segmentation parameters (e.g., block size) are adaptively adjusted according to the image resolution.

[0100] S23: Combine the data header and data block image blocks to form a complete encoded image frame, and ensure that the spatial position of the data header and data block conforms to the preset matching rules.

[0101] In this embodiment, image combination achieves coordination between data headers and data blocks through spatial positioning, and matching rules ensure accurate information extraction during decoding.

[0102] Specifically, based on the frame number and packet identifier, the data header image block is positioned in a predetermined header region of the encoded image, such as the upper left corner, while the data block image block is positioned in the data region, such as below the header, maintaining a continuous distribution to avoid gaps. Using predetermined algorithms, such as multi-point cross-checking, CRC check, and parity check, the consistency between the data header and data blocks is verified, and a checksum is generated and embedded in the data header. The combined image frames are then compressed and encoded to form the final encoded image, with its size optimized according to network conditions.

[0103] In one embodiment, such as Figure 4 As shown, in step S23, the data header and data block image blocks are combined to form a complete encoded image frame, and the spatial positions of the data header and data blocks are ensured to conform to the preset matching rules. Specifically, this includes:

[0104] S231: Based on the frame number and packet identifier, the data header image block is positioned in the preset header region of the encoded image, and the data block image block is positioned in the data region of the encoded image, maintaining a continuous or discrete distribution with the data header region.

[0105] In this embodiment, the purpose of this step is to ensure the orderly arrangement of the data header and data blocks in the encoded image through spatial positioning, facilitating accurate decoding at the receiving end. The frame number and packet identifier serve as key indexes to determine the relative positions of the data header and data blocks. The preset header region and data region are defined using an image coordinate system, and their continuous or discrete distribution is dynamically adjusted according to transmission security requirements to balance coding efficiency and anti-interference capability.

[0106] Specifically, a location mapping table is generated based on the frame number and packet identifier. This table defines the header region where the data header should be placed and the data region where the data blocks should be placed. During localization, an image processing library is used to place the data header image blocks in the header region using pixel copying or interpolation algorithms, and the data block image blocks are similarly placed in the data region. The distribution method is selected based on the security policy: continuous distribution means that the data header and data blocks are arranged adjacently in the image to reduce transmission overhead; discrete distribution disperses the data blocks in different locations in the image using a pseudo-random sequence to enhance security. For example, in discrete distribution mode, the data block may be divided into multiple sub-blocks and randomly placed in the data region, with random coordinates generated using the frame number as a seed. The frame number and packet identifier serve as spatial anchors to ensure fast localization through reverse mapping during decoding; the preset region is adaptively adjusted based on the image resolution, such as setting the header region height to 108 pixels for a 1080p image; continuous distribution optimizes encoding speed, while discrete distribution improves resistance to attacks by obfuscating the data layout.

[0107] S232: Verify the consistency between the data header and the data block using a preset algorithm, generate a checksum and embed it into the data header, compress and encode the combined image frame to form a complete encoded image frame.

[0108] In this embodiment, a preset algorithm is used to detect whether the data header and data block have been tampered with during transmission. The check code is embedded in the data header as a digital signature, and the compression encoding reduces the image size to adapt to network bandwidth limitations.

[0109] Specifically, using preset algorithms such as multi-point cross-checking, CRC check, and parity check, a consistency value is calculated between the data header and the data block content. This consistency value serves as the consistency check code. After the check code is generated, it is embedded into a reserved field in the data header. Before embedding, the check code may be encrypted using an encryption algorithm to enhance security. Subsequently, the combined image frames are compressed and encoded: lossless or lossy compression algorithms are used, and compression parameters are dynamically set according to network conditions. After compression, a frame tail identifier is added to the image frames for decoding synchronization.

[0110] In one embodiment, such as Figure 5 As shown, in step S30, the encoded image frame is transmitted to the receiving end using a discrete distributed multi-point transmission method, specifically including:

[0111] S31: Based on network topology and security requirements, dynamically select multiple transmission points, each corresponding to a different physical or logical transmission path.

[0112] In this embodiment, dynamic location selection adapts to network changes, reducing the risk of single-point failures through multi-pathing. Physical path refers to the actual network link, while logical path refers to the virtual channel.

[0113] Specifically, based on network scan results (such as routing tables or SDN controllers), multiple endpoints (such as server nodes or cloud instances) are selected, each associated with an independent path. Factors considered in the selection algorithm include latency, bandwidth, and security level (such as encryption requirements). Node coordinates (such as IP addresses and ports) are stored in a configuration database and selected during transmission based on a strategy, either round-robin or randomly.

[0114] S32: Divide each frame of image data into multiple sub-parts, assign transmission point coordinates to each sub-part, and schedule the transmission order of the sub-parts in a discrete sequence according to the network security policy.

[0115] In this embodiment, image segmentation and discrete scheduling obfuscate the transmission mode to prevent eavesdropping or tampering. Network security policies define sequence rules.

[0116] Specifically, each frame of the image is divided into sub-parts (e.g., 16x16 blocks) according to a pixel grid, and each sub-part is assigned a point coordinate. The discrete sequence is scheduled for transmission order based on an encryption algorithm (e.g., AES to generate pseudo-random numbers) to avoid sending adjacent sub-parts consecutively. The scheduler maintains a sequence table to ensure that the receiving end can reassemble them in order.

[0117] S33: Sub-parts are sent concurrently through multiple transmission channels, and timestamps are added during transmission. At the receiving end, the sub-parts are reassembled based on the point coordinates and timestamps to restore the complete image data.

[0118] In this embodiment, concurrent transmission improves efficiency, and timestamps ensure the accuracy of reassembly. The reassembly algorithm is based on coordinate and temporal matching sub-parts.

[0119] Specifically, multi-threading or asynchronous I / O is used to send sub-parts concurrently through multiple channels. Each sub-part is appended with a high-precision timestamp. The receiving end buffers the sub-parts, maps them to image locations based on their coordinates, and reassembles them by timestamp. After reassembly, image integrity is verified; if verification fails, a retransmission is requested.

[0120] In one embodiment, such as Figure 6 As shown, in step S30, image recognition is performed on the received image frame, and encoded information is extracted based on the data header and data block. Specifically, this includes:

[0121] S34: After receiving image data, the receiving end preprocesses the image to eliminate noise and distortion, and uses a template matching algorithm to slide the data header and data block template in the image to calculate the region similarity.

[0122] In this embodiment, image preprocessing improves recognition accuracy, and template matching locates the encoded region through similarity calculation. Noise cancellation uses a filtering algorithm, and deformation correction is based on geometric transformation.

[0123] Specifically, the receiving end first applies Gaussian filtering to eliminate noise and then corrects image distortion through affine transformation. A template matching algorithm initializes the sliding window, using the data header and data block template as the kernel and setting the sliding step size. The window slides pixel by pixel, calculating the similarity between each position and the template. The similarity values ​​are recorded as a matrix for subsequent localization to find the optimal matching position.

[0124] S35: Based on the similarity results, locate the data header and data block positions, extract the encoding information, perform preliminary parsing of the extracted information, and generate data packet sequences and content mapping tables.

[0125] In this embodiment, the location is based on similarity extreme points, and the encoded information is parsed and converted into structured data. A content mapping table associates data packets with image regions.

[0126] Specifically, the similarity matrix is ​​analyzed to find the coordinates of extreme points (such as the maximum value) as template matching positions. Data headers and data block pixels are extracted from these positions and decoded into binary information. Preliminary parsing and verification of the data header keywords and checksums are performed to generate a data packet sequence (sorted by frame number) and a content mapping table (recording the coordinates and size of data blocks in the image).

[0127] S36: Decode the packet sequence and content mapping table into the original packet content, and verify the integrity of the data header and data blocks.

[0128] In this embodiment, the decoded inverse-mapped pixels are binary data, and integrity verification ensures that the data is lossless.

[0129] Specifically, based on the content mapping table, the data block pixels are decoded into a binary stream and reassembled into the original data packet. The consistency between the data header and the data block is verified; if this fails, the data is discarded or retransmitted. The decoded data packet is temporarily stored in a buffer for subsequent verification.

[0130] In one embodiment, such as Figure 7 As shown, in step S34, a template matching algorithm is used to slide the data header and data block templates in the image and calculate the region similarity, specifically including:

[0131] S341: Initialize the sliding window, using the header template and data block template as the kernels of the sliding window respectively, and set the initial sliding step size.

[0132] In this embodiment, the sliding window serves as the scanning unit, the template serves as the matching reference, and the sliding step size controls the scanning accuracy, affecting the matching speed and accuracy.

[0133] Specifically, header and block templates are loaded from a predefined library. The templates are of fixed size (e.g., 64x64 pixels for header templates and 128x128 pixels for block templates). These templates are generated based on training data and contain standardized encoding patterns. During sliding window initialization, the window size is set to the same as the template, and the kernel is the pixel region within the window. The initial sliding step size is set according to image resolution and real-time requirements: a step size of 1 pixel for high-precision mode, suitable for high-resolution images; and a step size of 4-8 pixels for low-precision mode to improve speed. For example, for a 1024x768 image, the step size is set to 2 pixels to balance efficiency and accuracy.

[0134] S342: Control the sliding window to slide pixel by pixel on the received full frame image with the sliding step size. Each time it slides to a new position, calculate the similarity value between the image region in the current window and the template.

[0135] In this embodiment, local similarity is calculated using a sliding window to locate the template's position in the image. The similarity value quantifies the degree of matching between the template and the image region, and the algorithm selection affects robustness and computational cost.

[0136] Specifically, the sliding window starts from the top-left corner (0,0) of the image and moves pixel by pixel to the right and down with a step size s (e.g., 2 pixels). At each new position (u,v), the image region I(x+u,y+v) within the current window is extracted and its similarity is calculated with the template T(x,y). Various algorithms can be used for similarity calculation, selected based on the application scenario.

[0137] Furthermore, a suitable motherboard matching algorithm can be selected based on different scenarios:

[0138] Method 1: Squared difference matching: Calculation formula: SSD(u,v) = Σ[T(x,y) - I ] 2 This method is sensitive to changes in brightness and is suitable for situations where lighting conditions are stable, the deformation of the motherboard and the target is small, and real-time requirements are not high.

[0139] Method 2: Normalized Squared Difference Matching: Calculation formula: NSSD(u,v) =Σ[T(x,y) - I ] 2 / sqrt Based on squared difference matching, it has a certain robustness to brightness changes and is suitable for occasions with stable lighting conditions, small deformation of template and target, and low real-time requirements.

[0140] Method 3: Cross-correlation matching: Calculation formula: CC(u,v) =Σ[T(x,y) * I It has a certain tolerance for changes in brightness, but may be biased towards areas with higher brightness, making it suitable for situations in the real world where light may change.

[0141] Method 4: Normalized cross-correlation matching: Calculation formula: NCC(u,v) = Σ[T(x,y) * I ] / sqrt This method is invariant to linear changes in illumination, but it requires more computation and is suitable for situations in the real world where light may change.

[0142] Method 5: Cross-correlation coefficient matching: Calculation formula: CCorr(u,v) = This method, which subtracts the mean, is more robust to changes in lighting, but it is computationally intensive. It is suitable for real-world scenarios where lighting conditions may vary and does not require real-time performance.

[0143] Method 6: Absolute difference matching: Calculation formula: SAD(u,v) = Σ[T(x,y) - I This method has a fast calculation speed and is relatively robust to changes in brightness, but it is sensitive to changes in illumination. It is suitable for use in controlled environments, where illumination, contrast, and sensor parameters are completely fixed, and where high real-time performance is required.

[0144] S343: Record the similarity value corresponding to each sliding position and generate a similarity distribution matrix for the entire image. The value of each point in the matrix represents the degree of matching between the window region with that point as the top left vertex and the template.

[0145] In this embodiment, the distribution matrix stores the full-image matching results in a two-dimensional form. Each matrix point corresponds to a sliding position, and the value represents the matching score, which facilitates visualization and analysis.

[0146] Specifically, during the sliding process, the similarity value at each position (u,v) is recorded in a two-dimensional matrix M. The size of the matrix is ​​determined by the image size and stride. For example, if the image size is W x H and the stride is s, then the matrix size is (W / s) x (H / s). Each element M[i][j] of matrix M stores the similarity value at position (is, js), where i and j are indices. After the matrix is ​​generated, post-processing may be performed, such as Gaussian smoothing, to reduce noise.

[0147] S344: Analyze the similarity distribution matrix and locate the coordinates of the extreme points in the matrix. These coordinates are the candidate points for the best matching position of the template in the image.

[0148] In this embodiment, the extreme point indicates the most likely location of the template, and the analysis algorithm needs to consider the multi-peak situation to avoid misjudgment.

[0149] Specifically, extreme value detection is performed on the similarity distribution matrix using algorithms such as local maximum / minimum search (e.g., sliding window detection of extreme values ​​within the neighborhood). For multi-template cases (e.g., data headers and data blocks), multiple candidate points may exist, requiring threshold filtering (e.g., points with similarity values ​​greater than 0.8 are considered candidates). Coordinate positions are mapped back to image coordinates from matrix indices.

[0150] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0151] In one embodiment, an image-encoded cross-network one-way data transmission system is provided, which corresponds one-to-one with the image-encoded cross-network one-way data transmission method described in the above embodiments. For example... Figure 8 As shown, this image-encoding-based cross-network one-way data transmission system includes a data packet generation module, an image encoding module, a data image recognition module, and a data verification module. Detailed descriptions of each functional module are as follows:

[0152] The data packet generation module is used by the sending end to divide the data to be transmitted into packets, generate multiple data packets, and allocate memory space for each data packet;

[0153] The image encoding module is used to encode each data packet into an image frame. The image encoding uses a combination of data header and data block. The data header is used to encode control information, and the data block is used to encode data content.

[0154] The data image recognition module is used to transmit the encoded image frame to the receiving end using a discrete distributed multi-point transmission method, perform image recognition on the received image frame, and extract the encoded information based on the data header and data block;

[0155] The data verification module is used to verify the extracted encoded information and filter out the data that passes the verification to output to the data receiver.

[0156] Specific limitations regarding the image coding-based cross-network one-way data transmission system can be found in the limitations of the image coding-based cross-network one-way data transmission method described above, and will not be repeated here. Each module in the aforementioned image coding-based cross-network one-way data transmission system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the electronic device, or stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of each module.

[0157] In one embodiment, an electronic device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, this electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The database stores data to be transmitted and image-encoded data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a cross-network unidirectional data transmission method based on image encoding.

[0158] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0159] The sending end divides the data to be transmitted into packets, generates multiple data packets, and allocates memory space for each data packet;

[0160] Each data packet is image encoded to convert it into a frame image. The image encoding uses a combination of data header and data block. The data header is used to encode control information, and the data block is used to encode data content.

[0161] The encoded image frame is transmitted to the receiving end using a discrete distributed multi-point transmission method. Image recognition is performed on the received image frame, and the encoded information is extracted based on the data header and data block.

[0162] The extracted encoded information is verified, and the data that passes the verification is output to the data receiver.

[0163] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0164] The sending end divides the data to be transmitted into packets, generates multiple data packets, and allocates memory space for each data packet;

[0165] Each data packet is image encoded to convert it into a frame image. The image encoding uses a combination of data header and data block. The data header is used to encode control information, and the data block is used to encode data content.

[0166] The encoded image frame is transmitted to the receiving end using a discrete distributed multi-point transmission method. Image recognition is performed on the received image frame, and the encoded information is extracted based on the data header and data block.

[0167] The extracted encoded information is verified, and the data that passes the verification is output to the data receiver.

[0168] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0169] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0170] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A cross-network unidirectional data transmission method based on image coding, characterized in that, The image coding-based cross-network one-way data transmission method includes the following steps: The sending end divides the data to be transmitted into packets, generates multiple data packets, and allocates memory space for each data packet; Each data packet is image encoded to convert it into a frame image. The image encoding uses a combination of data header and data block. The data header is used to encode control information, and the data block is used to encode data content. The encoded image frame is transmitted to the receiving end using a discrete distributed multi-point transmission method. Image recognition is performed on the received image frame, and the encoded information is extracted based on the data header and data block. The extracted encoded information is verified, and the data that passes the verification is output to the data receiver.

2. The cross-network unidirectional data transmission method based on image coding according to claim 1, characterized in that, The sending end divides the data to be transmitted into packets, generating multiple data packets, and allocates memory space for each data packet, specifically including: The system acquires the raw data to be transmitted, analyzes the data size and transmission requirements, and divides the raw data into multiple data packets based on a preset packet size threshold. Each data packet contains a unique sequence identifier. Assign a user ID and priority information to each data packet and store them in the sender's memory space; Packet logs are generated based on the data packet sequence and used as a reference for subsequent image encoding.

3. The cross-network unidirectional data transmission method based on image coding according to claim 1, characterized in that, The process of image encoding each data packet to convert it into a frame image involves using a combination of a data header and data blocks. The data header encodes control information, while the data blocks encode the data content. Specifically, this includes: A data header is generated for each data packet. The data header contains at least one of the following information: keyword, frame number, frame rate, priority, user number, packet identifier, data length, and data mode. Based on the content of the data packet, a data block image block is generated using a binary-to-image pixel mapping method, and image segmentation technology is used to map the data header and data block into a pixel array; The data header and data block image blocks are combined to form a complete coded image frame, and the spatial positions of the data header and data block are ensured to conform to the preset matching rules.

4. The cross-network unidirectional data transmission method based on image coding according to claim 3, characterized in that, The process of combining the data header and data block image blocks to form a complete encoded image frame, and ensuring that the spatial positions of the data header and data blocks conform to preset matching rules, specifically includes: Based on the frame number and packet identifier, the data header image block is positioned in the preset header region of the encoded image, and the data block image block is positioned in the data region of the encoded image, maintaining a continuous or discrete distribution with the data header region. The consistency between the data header and the data block is verified by a preset algorithm, and a check code is generated and embedded in the data header. The combined image frame is then compressed and encoded to form a complete encoded image.

5. The cross-network unidirectional data transmission method based on image coding according to claim 1, characterized in that, The method of transmitting the encoded image frame to the receiving end using a discrete distributed multi-point transmission method specifically includes: Based on network topology and security requirements, multiple transmission points are dynamically selected, with each point corresponding to a different physical or logical transmission path. Each frame of image data is divided into multiple sub-parts, and transmission point coordinates are assigned to each sub-part. The transmission order of the sub-parts is scheduled in a discrete sequence according to the network security policy. Sub-parts are sent concurrently through multiple transmission channels, and timestamps are added during transmission. At the receiving end, the sub-parts are reassembled based on the point coordinates and timestamps to restore the complete image data.

6. The cross-network unidirectional data transmission method based on image coding according to claim 5, characterized in that, The step of performing image recognition on the received image frames, and extracting encoded information based on the data header and data blocks, specifically includes: After receiving the image data, the receiving end preprocesses the image to eliminate noise and distortion, and uses a template matching algorithm to slide the data header and data block template in the image to calculate the region similarity. Based on the similarity results, the positions of the data header and data block are located, and the encoding information is extracted. The extracted information is then preliminarily parsed to generate a data packet sequence and a content mapping table. Decode the packet sequence and content mapping table into the original packet content, and verify the integrity of the header and data blocks.

7. The cross-network unidirectional data transmission method based on image coding according to claim 6, characterized in that, The template matching algorithm is used to slide data header and data block templates in the image and calculate region similarity, specifically including: Initialize the sliding window, using the header template and block template as the kernels of the sliding window respectively, and set the initial sliding step size; The sliding window is controlled to slide pixel by pixel on the received full frame image with the sliding step size. Each time it slides to a new position, the similarity value between the image region in the current window and the template is calculated. Record the similarity value corresponding to each sliding position and generate a similarity distribution matrix for the entire image. The value of each point in the matrix represents the degree of matching between the window region with that point as the top left vertex and the template. Analyze the similarity distribution matrix and locate the coordinates of the extreme points in the matrix. These coordinates are the candidate points for the best matching position of the template in the image.

8. A cross-network unidirectional data transmission system based on image coding, characterized in that, The image-encoded cross-network unidirectional data transmission system includes: The data packet generation module is used by the sending end to divide the data to be transmitted into packets, generate multiple data packets, and allocate memory space for each data packet; The image encoding module is used to encode each data packet into an image frame. The image encoding uses a combination of data header and data block. The data header is used to encode control information, and the data block is used to encode data content. The data image recognition module is used to transmit the encoded image frame to the receiving end using a discrete distributed multi-point transmission method, perform image recognition on the received image frame, and extract the encoded information based on the data header and data block; The data verification module is used to verify the extracted encoded information and filter out the data that passes the verification to output to the data receiver.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the cross-network unidirectional data transmission method based on image coding as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the cross-network unidirectional data transmission method based on image coding as described in any one of claims 1 to 7.

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