Image data transmission and display method and system based on Android platform and related equipment

Abstract

The invention relates to the technical field of image data processing and transmission, and provides an image data transmission and display method and system based on an Android platform and related equipment. According to the method, image display data generated on an application layer of an Android platform is obtained, driving transformation is conducted on a Linux kernel layer, and a buffer area used for storing the image display data is generated and mapped to a user space where an application program runs; reading image display data from a buffer area of user space mapping, and performing compression processing on the image display data through an image preprocessing model and an attention mechanism obtained through deep learning training to generate an image data block; and packaging the image data block in the graphic hardware module to generate a display data packet, constructing a sending queue based on the display data packet, and sending the sending queue to an LED display receiving end for display. Efficient data transmission is achieved, the path of data from generation to display is optimized, and the efficiency and quality of image data transmission and display are improved.

Description

Image data transmission and display methods, systems, and related devices based on the Android platform Technical Field

[0001] This application relates to the field of image data processing and transmission technology, and more specifically, to an image data transmission and display method, system, and related equipment based on the Android platform. Background Technology

[0002] Currently, image data display systems based on the Android platform, especially in applications requiring real-time driving of large-screen remote light-emitting diodes (LEDs), typically involve a complex process: generating image data from the application layer, processing it through multiple layers of the operating system, and then transmitting it over the network to the receiving end for display. The industry generally pursues efficient, stable, and high-quality data transmission and display effects.

[0003] However, in actual implementation, existing technical solutions are prone to delays and redundant overhead when data is transmitted across layers within the system, affecting real-time performance and potentially causing unstable display frame rates or image quality loss, thus limiting the performance and application effects of the Android system in real-time display scenarios. Summary of the Invention

[0004] This application provides an image data transmission and display method, system, and related device based on the Android platform, which can at least partially solve the problem of low display effect of Android system in real-time display scenarios.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to one aspect of this application, an image data transmission and display method based on the Android platform is provided, comprising: acquiring image display data generated at the application layer of the Android platform; modifying the driver at the Linux kernel layer to generate a buffer for storing the image display data and mapping it to the user space where the application runs; reading the image display data from the buffer mapped from the user space; compressing the image display data using an image preprocessing model and attention mechanism trained by deep learning to generate image data blocks; encapsulating the image data blocks in the graphics hardware module of the Rockchip platform to generate display data packets; constructing a transmission queue based on the display data packets; and sending the display data packets in the transmission queue to an LED display receiver for display.

[0007] In this application, based on the aforementioned scheme, the step of modifying the driver at the Linux kernel level to generate a buffer for storing the image display data and mapping it to the user space where the application runs includes: in the Linux kernel level, sending a request to the kernel's memory management system through driver code to apply for an independent buffer with contiguous physical addresses; mapping the buffer to the address range of the user space where the application runs, establishing a mapping relationship between the user space and the buffer; and writing the display image data into the buffer based on the space address corresponding to the mapping relationship.

[0008] In this application, based on the aforementioned scheme, the step of reading the image display data from the user space mapping buffer, compressing the image display data using an image preprocessing model trained by deep learning and an attention mechanism to generate image data blocks includes: reading the image display data from the user space mapping buffer; extracting multi-scale image features from the image display data based on the image preprocessing model trained by deep learning; and compressing the image display data based on the visual parameters and the current network state to generate image data blocks.

[0009] In this application, based on the aforementioned scheme, the step of encapsulating the image data block in the graphics hardware module of the Rockchip platform to generate a display data packet, and constructing a transmission queue based on the display data packet, includes: performing an initialization operation on the graphics hardware module of the Rockchip platform; dividing and encapsulating the image data block in the graphics hardware module to generate a display data packet; and constructing a transmission queue based on the display data packet.

[0010] In this application, based on the aforementioned scheme, the initialization operation of the graphics hardware module of the Rockchip platform, and the segmentation and encapsulation of the image data block in the graphics hardware module to generate a display data packet, includes: initializing the graphics hardware module of the Rockchip platform and transmitting the image data block to the graphics hardware module; performing format conversion and segmentation processing on the image data block in the graphics hardware module based on a preset target format to generate multiple unit blocks; splitting the unit blocks to generate multiple data packets, encapsulating the data packets based on a data transmission protocol to generate a display data packet, and storing the display data packet in the buffer.

[0011] In this application, based on the aforementioned scheme, the step of constructing a sending queue based on the display data packet includes: generating a circular queue based on a descriptor of a preset size; writing the display data packet into the currently empty descriptor in the circular queue, and updating the queue pointer to point to the next empty descriptor.

[0012] In this application, based on the aforementioned scheme, the step of sending the display data packets in the sending queue to the LED display receiver for display includes: transmitting the display data packets in the sending queue to the hardware module of the network interface layer based on a preset timer; converting the display data packets into a preset signal format in the hardware module and transmitting them to the LED display receiver for display.

[0013] According to one aspect of this application, an image data transmission and display system based on the Android platform is provided, comprising: an acquisition module for acquiring image display data generated at the application layer of the Android platform; a mapping module for modifying the driver at the Linux kernel layer to generate a buffer for storing the image display data and mapping it to the user space where the application runs; a compression module for reading the image display data from the buffer mapped in the user space, compressing the image display data using an image preprocessing model and attention mechanism trained by deep learning to generate image data blocks; an encapsulation module for encapsulating the image data blocks in the graphics hardware module of the Rockchip platform to generate display data packets, and constructing a sending queue based on the display data packets; and a display module for sending the display data packets in the sending queue to an LED display receiver for display.

[0014] According to one aspect of this application, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the image data transmission and display method based on the Android platform as described in the above embodiments.

[0015] According to one aspect of this application, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the image data transmission and display method based on the Android platform as described in the above embodiments.

[0016] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the image data transmission and display method based on the Android platform provided in the various optional implementations described above.

[0017] The main differences and advantages of this technical solution compared to existing technologies are as follows: In terms of data transmission efficiency, existing technologies often require multiple copy operations when transmitting data between user space and kernel space. This not only consumes significant system resources but also increases data transmission latency. This technical solution modifies the Linux kernel layer to allocate an independent and physically contiguous buffer and map it to user space. Applications can directly access this buffer, achieving zero-copy writing of image data. This avoids multiple data copies between user space and kernel space, significantly reducing data transmission time and resource consumption, thereby greatly improving data transmission efficiency and enabling image data to be transmitted more quickly from the application layer to subsequent processing stages.

[0018] In terms of image processing quality, traditional methods typically employ fixed compression algorithms during image compression, leading to issues such as loss of image details, increased noise, or blurring when compressing complex images, thus affecting the final display quality. This application's technical solution introduces an image preprocessing model trained using deep learning for compression. Deep learning models possess powerful learning and adaptive capabilities, intelligently adjusting compression strategies based on different image features and content. While ensuring compression efficiency, it maximizes the preservation of key image information and minimizes image quality loss. After processing by the graphics hardware module, further optimization operations such as format conversion and image segmentation are performed to ensure the image is transmitted and displayed in its best condition, significantly improving image processing quality.

[0019] Regarding data transmission stability, existing technologies often rely on traditional network protocol stacks, whose scheduling mechanisms can lead to uncertainties in data transmission. Especially under high load, the processing capacity of the network protocol stack can become a bottleneck, affecting the stability and real-time performance of data transmission. This application's technical solution constructs an independent transmission queue, employing a descriptor circular queue to manage display data packets in an orderly manner. A timer triggers data packet transmission at a fixed frame rate, ensuring the regularity and real-time performance of data transmission. Simultaneously, the data transmission rate is dynamically adjusted based on status information fed back from the Field Programmable Gate Array (FPGA) terminal, enabling timely adaptation to changes in network conditions and system load, avoiding data congestion and overflow, effectively improving the stability and reliability of data transmission, and ensuring that the LED display receiver can continuously and stably receive the complete data stream and display it accurately.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0022] Figure 1 schematically illustrates a flowchart of an image data transmission and display method based on the Android platform in one embodiment of this application.

[0023] Figure 2 schematically illustrates a flowchart of generating a buffer in one embodiment of this application.

[0024] Figure 3 schematically illustrates a diagram of an image data transmission and display system based on the Android platform in one embodiment of this application.

[0025] Figure 4 shows a schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application. Detailed Implementation

[0026] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0027] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0028] It should be noted that the data acquisition or information collection in this embodiment is performed after authorization by the user or the object of collection, and its process and purpose strictly follow the relevant regulations.

[0029] The block diagrams shown in the attached figures are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more hardware modules composed of smart chips, smart integrated circuits, or application-specific integrated circuits (ASICs), or in different network and / or processor devices and / or microcontroller devices.

[0030] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0031] The implementation details of the technical solution of this application are described in detail below: Figure 1 shows a flowchart of an image data transmission and display method based on the Android platform according to an embodiment of this application. Referring to Figure 1, the image data transmission and display method based on the Android platform includes at least steps S110 to S150, which are described in detail below: S110, obtaining image display data generated in the application layer of the Android platform.

[0032] In practical applications, the application layer of the Android platform is the layer where applications run on top of the Android operating system and are directly interacted with and used by users. In this embodiment, image display data is generated at the Android application layer. Users set the display content through the application, and the application passes the corresponding image data to the lower layer for processing and transmission.

[0033] In this embodiment, the entity generating the image display data can be an Android application used to control the LED display screen, such as applications used in advertising displays, stage performance control, and traffic information displays. Users generate the content to be displayed on the LED display screen by operating the interface of these applications.

[0034] During the generation process, users perform various operations on the application's interface, such as selecting images to display, entering text information, and setting animation effects. For example, in an advertising display application, a user selects a product promotional image from a preset image library and sets the image's display duration and transition method; in a traffic information display application, a user may enter real-time traffic condition text information.

[0035] The application processes and integrates relevant materials based on user settings. If the user selects an image, the application performs operations such as format conversion and resolution adjustment to adapt it to the display requirements of the LED screen. For text information, the application converts it into a corresponding image representation, such as generating text images with specific fonts, colors, and sizes. Simultaneously, if the user sets animation effects, the application generates a series of consecutive image frames according to the set animation rules to achieve animated display.

[0036] Depending on the application scenario and user needs, the generated image display data has multiple formats, such as bitmap files (BMP), JPEG, PNG, etc., or a custom format optimized for LED displays to meet the requirements of efficient transmission and display.

[0037] It should be noted that, in this embodiment, the generated image display data is encapsulated at the Android application layer and then passed to the lower layer for processing through data interfaces and mechanisms. In this solution, the image display data is passed to the Linux kernel layer, where it undergoes optimization processes such as driver layer buffer reconstruction and zero-copy mapping. This is followed by a series of operations including image preprocessing, compression, hardware acceleration, data encapsulation, and scheduled, timed transmission, before finally being transmitted to the Field Programmable Gate Array (FPGA) to drive the LED display screen to show the corresponding content.

[0038] S120 modifies the driver at the Linux kernel level to generate a buffer for storing the image display data and maps it to the user space where the application runs.

[0039] In this embodiment, a request is made at the Linux kernel level to the kernel's memory management system to allocate a dedicated buffer with independent and contiguous physical addresses for storing image display data generated by the Android platform application layer. Subsequently, using a specific mapping mechanism, this buffer, which resides in kernel space, is mapped to the address range of user space required for application execution, establishing a clear and explicit mapping relationship between user space and the buffer. This allows the application to directly access and process the image display data in the buffer.

[0040] In one embodiment of this application, driver modification is performed at the Linux kernel level to generate a buffer for storing the image display data and map it to the user space where the application runs. This includes: in the Linux kernel level, sending a request to the kernel's memory management system through driver code to apply for an independent buffer with contiguous physical addresses; mapping the buffer to the address range of the user space where the application runs, establishing a mapping relationship between the user space and the buffer; and writing the display image data into the buffer based on the space address corresponding to the mapping relationship.

[0041] In practical applications, the Linux kernel layer is the core of the Linux operating system, responsible for managing system hardware resources and providing basic system services such as process scheduling, memory management, and device drivers. In this solution, the modification of the stmmac driver is performed at the Linux kernel layer. By allocating independent buffers and other operations, the management and optimization of network devices (related to LED display data transmission) are achieved, providing stable and efficient hardware operation support for upper-layer applications.

[0042] In practical applications, the stmmac driver is a device driver for Ethernet controllers. In this solution, it is used to manage network hardware devices related to LED display data transmission. By modifying the stmmac driver, an independent buffer is allocated to achieve efficient data transmission. Furthermore, by mapping the buffer to user space, the Android application layer can directly access the data, thus improving data transmission efficiency.

[0043] In this embodiment, when the system starts or the relevant driver is loaded, the modification operation of the stmmac driver begins. In the Linux kernel layer, the driver code sends a request to the kernel's memory management system to allocate an independent and physically contiguous buffer. A buffer is a memory area in a computer system used for direct data transfer between the device and memory. The memory management system searches for a contiguous space in physical memory that meets the requirements. Once found, it marks the memory block as allocated and records its relevant information in the memory management structure. This buffer is specifically used as a transmission channel for LED display data, isolated from standard network data channels. This avoids data interference between the two and ensures the independence and stability of LED display data transmission.

[0044] After the buffer allocation is completed, the buffer, modified by the stmmac driver, is directly mapped to the address range of the process's user space. User space is the space where user applications run in the operating system. The Android application layer in user space can directly read and write to the buffer like accessing ordinary memory. This allows the Android application layer to directly access the image display data in the buffer, achieving zero-copy writing of image data. It eliminates the need for data copying between user space and kernel space via traditional read / write system calls, avoiding multiple copies of data between user space and kernel space, improving data access efficiency, and ensuring the high efficiency and real-time performance of image display data transmission.

[0045] Specifically, when an Android application layer initiates an access request for image display data, it establishes a mapping relationship in the process address space of user space based on the physical address of the previously allocated buffer and other relevant information. The kernel allocates a segment of virtual address space in the process's virtual address space and establishes a mapping relationship between this virtual address (space address) and the physical address of the buffer. In this way, when the Android application layer accesses this segment of virtual address space, it is actually accessing the corresponding buffer, realizing direct access from user space to kernel space buffers.

[0046] Because a mapping relationship has been established between user space and the buffer, image display data generated by the Android application layer can be directly written to the mapped address space. During the writing process, there is no need for multiple copy operations from user space to kernel space. After the application layer program prepares the data, it directly stores the data in the corresponding virtual address location through pointer operations. The kernel can directly identify this data as the LED display data stored in the buffer through the mapping relationship. This method greatly reduces the system overhead caused by data copying, enabling reading and writing of image display data in the buffer as if accessing local memory, improving data transmission efficiency, and allowing image display data to enter subsequent transmission and processing flows more quickly.

[0047] The above process allocates an independent and physically contiguous buffer at the Linux kernel level to provide dedicated storage space for image data, preventing it from being mixed with other data and ensuring the stability and security of data storage. This buffer is then mapped to the user space where the application runs, establishing a mapping relationship that allows the application to directly access data in the kernel-level buffer. This enables efficient interaction between user-space and kernel-space data, reducing the performance overhead of data copying. Based on this mapping relationship, the image data is written to the buffer, ensuring accurate data storage and providing a data foundation for subsequent reading and processing.

[0048] S130, read the image display data from the user space mapping buffer, compress the image display data using the image preprocessing model and attention mechanism trained by deep learning, and generate image data blocks.

[0049] In this embodiment, based on the established mapping relationship, the buffer location mapped to the user space is located, and image display data is read from it. Then, an image preprocessing model trained based on deep learning is invoked to comprehensively process the read raw image data. While preserving as much key image information and quality as possible, redundant information is removed, compression is performed, and finally, image data blocks suitable for subsequent transmission and processing are generated.

[0050] In one embodiment of this application, reading the image display data from the user space mapping buffer, and compressing the image display data using an image preprocessing model trained by deep learning and an attention mechanism to generate image data blocks includes: reading the image display data from the user space mapping buffer; extracting multi-scale image features from the image display data using an image preprocessing model trained by deep learning, and generating visual parameters using an attention mechanism; and compressing the image display data based on the visual parameters and the current network state to generate image data blocks.

[0051] First, image display data is read from the user-space mapped buffer. This data is raw, unprocessed image information, stored in memory with a specific data structure, awaiting subsequent processing. The reading process directly retrieves data from the corresponding memory region through a pre-established mapping relationship, ensuring efficient data retrieval.

[0052] In this embodiment, the image preprocessing model is a lightweight neural network model based on a convolutional autoencoder. Its structure mainly includes an encoder, a quantization layer, and a decoder, trained through reinforcement learning. The encoder consists of several convolutional and activation layers, used to progressively extract and compress image features. The quantization layer discretizes the features to further reduce the data volume. The decoder reconstructs a compressed image that retains key visual information through deconvolutional layers. The training method for this model is as follows: using a large-scale image dataset containing various scenes, the original image is used as input, and the image processed by a traditional lossy compression algorithm is used as the reference label. By minimizing the perceptual loss function and mean squared error loss between the reconstructed image and the label, end-to-end supervised training and reinforcement learning are performed using gradient descent algorithm. The final result is a preprocessing model that can adapt to image content and achieve efficient compression while ensuring visual quality.

[0053] During preprocessing, an image preprocessing model trained based on deep learning is invoked to process the read raw image data. The model first performs denoising on the raw image data, analyzing the relationship between each pixel and its surrounding pixels, identifying and removing noise points that do not conform to normal image feature patterns, thus making the image cleaner.

[0054] Subsequently, the model enhances the denoised image, adjusting parameters such as contrast and brightness based on the overall features and local details to highlight important information and make details more clearly visible. Simultaneously, it extracts key image features—representative and discriminative information—providing a more targeted data foundation for subsequent compression processing, thereby improving compression efficiency and display quality.

[0055] After preprocessing, an adaptive image compression algorithm is executed. This algorithm considers both the characteristics of the image content and the available network bandwidth. For the image content, its complexity is analyzed, such as the amount of detail and color variations. If the image content is complex and contains a large amount of detail, a lower compression level is preferred to preserve more image detail and ensure image quality. Conversely, if the image content is relatively simple, a higher compression level is applied to reduce the amount of data.

[0056] Optionally, in this embodiment, a visual saliency detection and content-aware quantization mechanism is introduced on the basis of the existing deep learning preprocessing model to construct a hybrid compression model with a multi-layer coding structure. This model can not only adjust the compression intensity according to the complexity of the image content, but also combine the characteristics of the human visual system to adopt differentiated compression strategies for different regions in the image.

[0057] Specifically, lightweight neural networks are used to extract multi-scale features from images, and an attention mechanism is used to generate visual parameters for image display data. for: in, This represents the k-th layer feature map; multiple feature maps together constitute the multi-scale features of the image. Indicates learnable weights, The function represents the normalization function, k and K represent the identifier and total number of layers in the neural network, respectively, and (x,y) represents the pixel coordinates in the image. In this embodiment, the visual parameters are used to quantify the importance of different regions in the image to the human eye. Based on computer vision theory, the algorithm performs image recognition and classification on key regions (such as faces, text, and moving objects), prioritizing or processing them with high fidelity, while performing higher-level image compression, extraction, or simplification on insignificant regions.

[0058] Then, based on visual parameters and the local complexity of image display data, such as image gradient and texture entropy, the image content complexity is dynamically determined. for: in, Indicates adjustable parameters; image gradient It reflects the drastic changes in pixel values ​​in an image; the larger the gradient magnitude, the richer the image details; texture entropy. This measure assesses the degree of clutter in an image; the higher the entropy, the more information the image contains. Combining these two metrics allows for an accurate evaluation of the complexity of the image content. A higher calculated image content complexity value indicates that the image needs to retain more details for subsequent feature analysis.

[0059] In addition, it can monitor the current network bandwidth status in real time. If available bandwidth is limited, the amount of data transmitted is reduced to ensure smooth data transmission and avoid transmission congestion or packet loss due to excessive data volume. Conversely, when available bandwidth is sufficient, the amount of data retained can be adaptively increased to ensure data quality and integrity. By dynamically adjusting the compression strategy based on image content and bandwidth, the goal of maintaining image quality while minimizing data transmission volume is achieved.

[0060] Specifically, in this embodiment, the image factor is used to characterize the amount of data that needs to be retained during image compression. Based on the compression principles described above, the image factor R is generated as follows: in, These represent the algorithm parameters, Negative values ​​are allowed; these parameters are obtained through extensive training data and experiments. During training, by adjusting the values ​​of these parameters, the algorithm can achieve good compression results under different image content and bandwidth conditions; L represents the image content complexity, which is obtained based on the gradient magnitude and entropy of the image. B represents the maximum available bandwidth, which is a pre-set system parameter that depends on the actual network transmission environment and limits the maximum data transmission rate; B represents the current available bandwidth, which is obtained by the system monitoring the network status in real time and changes dynamically with changes in the network status.

[0061] Based on the image factors calculated using the above process, appropriate compression techniques are employed to compress the preprocessed image data. When the image content is relatively simple and bandwidth is limited, the calculated image factors are automatically reduced, thereby minimizing the amount of data retained after compression and minimizing data transmission while ensuring image quality. Conversely, when the image content is complex and bandwidth is sufficient, the image factors are appropriately increased to ensure data integrity. In other cases of complex image content and bandwidth, the image factors are determined in conjunction with actual real-time data to comprehensively determine the degree of compression.

[0062] After adaptive compression, the original image data is compressed into smaller data blocks. These compressed image data blocks are stored in a designated memory area, awaiting subsequent transmission and processing steps. During storage, relevant data information, such as compression method and compression parameters, is recorded to ensure proper decompression at the receiving end, recovering the original image information for LED display.

[0063] The above process, through deep learning-based image preprocessing, effectively removes noise and interference from the image, enhances important image features, and provides a higher-quality image foundation for subsequent compression and display. Furthermore, the adaptive image compression algorithm dynamically adjusts image factors based on image content and bandwidth, minimizing data transmission volume while ensuring image quality, thus improving data transmission efficiency and reducing network bandwidth consumption. For systems transmitting LED display data via Gigabit Ethernet, this significantly improves overall transmission performance and display effects, ensuring stable and high-quality LED displays in various network environments.

[0064] S140, the image data block is encapsulated in the graphics hardware module of the Rockchip platform to generate a display data packet, and a sending queue is built based on the display data packet.

[0065] In this embodiment, the graphics hardware module of the Rockchip platform is first initialized by transmitting the previously generated image data blocks. With its powerful functions, the data blocks are divided and processed according to preset requirements. Then, these processed contents are encapsulated into display data packets. Subsequently, a circular queue is built using descriptors of a specific size. The encapsulated display data packets are written sequentially into the currently free descriptors in the circular queue, while the queue pointer is continuously updated to point to the next free descriptor, thus constructing the sending queue.

[0066] In one embodiment of this application, the image data block is encapsulated in the graphics hardware module of the Rockchip platform to generate a display data packet, and a sending queue is constructed based on the display data packet. This includes: initializing the graphics hardware module of the Rockchip platform; dividing and encapsulating the image data block in the graphics hardware module to generate a display data packet; and constructing a sending queue based on the display data packet.

[0067] Specifically, in one embodiment of this application, the graphics hardware module of the Rockchip platform is initialized, and the image data block is segmented and encapsulated in the graphics hardware module to generate a display data packet. This includes: initializing the graphics hardware module of the Rockchip platform and transmitting the image data block to the graphics hardware module; performing format conversion and segmentation processing on the image data block in the graphics hardware module based on a preset target format to generate multiple unit blocks; splitting the unit blocks to generate multiple data packets, encapsulating the data packets based on a data transmission protocol to generate a display data packet, and storing the display data packet in the buffer.

[0068] In practical applications, the Rockchip platform is a SoC (System-on-a-Chip) solution designed for mid-to-high-end smart devices. It is primarily used for the development and application of smart terminal devices, with a high-performance, low-power AIoT (Artificial Intelligence of Things) processor at its core. It provides complete support from IoT chip level to IoT operating system level, and is widely used in fields such as artificial intelligence, IoT, and multimedia processing. Its core advantages lie in the balance between high performance and low power consumption, as well as rich peripheral interfaces, making it widely applicable in consumer electronics, industrial control, security, education, and healthcare scenarios. The graphics hardware module is a dedicated 2D graphics accelerator integrated into the Rockchip SoC.

[0069] In this embodiment, the graphics hardware module of the Rockchip platform is first initialized, including checking the working status of the hardware module, ensuring that its power supply is normal and the clock signal is stable, configuring the relevant registers of the hardware module, setting its working mode, processing parameters, etc., so that the graphics hardware module enters a standby state and is ready to receive and process image data at any time.

[0070] Specifically, for the graphics hardware module of the Rockchip platform, its registers are initialized and configured, its operating mode and data format are set, and the preprocessed image data blocks are directly transferred from system memory to the local memory of the graphics hardware module via the DMA engine. Inside the graphics hardware module, according to preset segmentation rules and target display format, the hardware circuitry performs format conversion, pixel rearrangement, and logical segmentation operations in parallel, automatically adding address identifiers and checksums to each data unit. Finally, it encapsulates the data into a data packet conforming to the link layer transport protocol and writes it back to the memory location specified by the send descriptor ring, completing the hardware-accelerated data preparation process. Once the graphics hardware module is initialized, the preprocessed image data blocks are transferred to the graphics hardware module. Leveraging its hardware circuitry specifically designed for image processing and its parallel processing architecture, the graphics hardware module begins efficient processing of the image data.

[0071] First, image format conversion is performed. The graphics hardware module has internal logic circuits for various image format conversions. Based on the preset target format, the pixel arrangement, color space, and other aspects of the input image data are converted. For example, if the target display device supports a specific color format, the graphics hardware module can quickly convert the input RGB format image data to the corresponding format to meet display requirements.

[0072] The image data is then segmented. Based on the characteristics of LED displays and the needs of subsequent processing, the graphics hardware module divides the entire image data into multiple smaller blocks. According to preset rules, such as the row and column structure of the image or the division method of the hardware processing units, the image data is divided into appropriately sized unit blocks, and each block will be used as an independent processing unit for subsequent operations.

[0073] Within the segmented unit blocks, control information is added via a graphics hardware module as data tags for the image blocks. This control information may include row and column address information, checksums, etc. Specifically, the row and column address information indicates the exact display position of the unit block on the LED screen, ensuring accurate image stitching; the checksum is used for error detection during data transmission, allowing the receiving end to determine the accuracy of the received data.

[0074] Next, the unit blocks obtained after processing by the graphics hardware module are split into multiple data packets according to the requirements of the LED display. The splitting process takes into account factors such as the LED screen's refresh mechanism and data transmission bandwidth limitations, and reasonably determines the size and data content of each data packet to ensure smooth data transmission and display stability.

[0075] Simultaneously, the fragmented data packets are encapsulated using protocols. Following the defined data transmission protocol, each data packet is appended with a unique and sequential frame number, as well as information such as synchronization frames and command frames.

[0076] Specifically, the frame sequence number increments sequentially according to the order in which the data packets are generated, and each data packet is given a timestamp or sequence tag. Synchronization frames coordinate the working rhythm of the sending and receiving ends, ensuring that data is received and processed at the correct time. Command frames can include various control instructions, such as starting and stopping the display and adjusting brightness, enabling the receiving end to control the LED screen as required. This method optimizes the data packet structure, improving the efficiency and reliability of data transmission.

[0077] After data packet splitting and protocol encapsulation, the encapsulated display data packets are stored in a specific buffer. This buffer is connected to subsequent data transmission channels, waiting for the appropriate time to send the data packets to the LED display receiver, thus preparing the data for the final LED display.

[0078] The above process initializes the graphics hardware module of the Rockchip platform, creating conditions for its normal operation and transmitting image data blocks to the module to leverage its hardware processing advantages. By converting and segmenting the data according to a preset target format, the data is adapted to different display requirements, generating multiple unit blocks for easier subsequent processing and transmission. By splitting the unit blocks and encapsulating them into display data packets based on a data transmission protocol, the integrity and standardization of the data during transmission are ensured, and the data is stored in a buffer awaiting transmission.

[0079] In one embodiment of this application, constructing a sending queue based on the display data packet includes: generating a circular queue based on a descriptor of a preset size; writing the display data packet into a currently empty descriptor in the circular queue; and updating the queue pointer to point to the next empty descriptor.

[0080] In this embodiment, a circular queue is created as an independent sending queue based on the descriptor. The circular queue is laid out in memory in a circular structure and consists of a series of descriptors of fixed size. Each descriptor is used to store relevant information of the corresponding display data packet, such as the address, length, and status flag of the display data packet in memory. These descriptors are arranged in a circular order to form a circular storage structure.

[0081] When a data packet needs to be sent, the packet is displayed and filled into the currently available descriptor in the circular queue, and the queue pointer is updated to point to the next available (empty) descriptor. The data packet is then read from memory based on the information in the descriptor and sent. After sending, the corresponding descriptor's status flag is updated, and subsequent data packets continue to use the descriptors in the queue in sequence, thus achieving ordered management and efficient transmission of data packets. This circular structure efficiently manages data packets in an ordered manner, ensuring that subsequent data packets are arranged in the correct pre-set order, laying the foundation for ordered transmission.

[0082] After the sending queue is set up, the encapsulated display data packets are sequentially placed into this independent sending queue for management. Each data packet entering the queue is assigned a specific location and identifier for accurate tracking and processing.

[0083] The above process generates a circular queue based on a preset-size descriptor, providing an ordered storage structure for data packets and making efficient use of memory space. Display data packets are written to empty descriptors and pointers are updated to ensure ordered arrangement of data packets, guaranteeing the accuracy of the sending order and improving data transmission efficiency.

[0084] S150, the display data packets in the sending queue are sent to the LED display receiver for display.

[0085] In this embodiment, display data packets in the transmission queue are sequentially transmitted to the hardware module of the network interface layer according to a pre-set timer. The hardware module converts the received display data packets into a preset signal format that the LED display receiver can recognize and process, and then transmits these signals stably and continuously to the LED display receiver, ultimately presenting a clear and accurate image display effect at the receiver.

[0086] In one embodiment of this application, sending the display data packets in the sending queue to the LED display receiver for display includes: transmitting the display data packets in the sending queue to a hardware module of the network interface layer based on a preset timer; converting the display data packets into a preset signal format in the hardware module and transmitting them to the LED display receiver for display.

[0087] Optionally, a high-precision timer within the kernel can be used to control the timing of data packet transmission. The timer's trigger frame rate is set to a fixed value, such as 60fps, which is determined based on the LED display requirements and the system's processing capabilities. The timer periodically triggers an interrupt signal according to the set frame rate. Each time the timer triggers an interrupt, a data packet is retrieved sequentially from the transmission queue and sent out. This timer-controlled method ensures the real-time performance and stability of data packet transmission, avoiding latency uncertainties and packet loss issues that may occur during traditional network protocol stack scheduling, allowing LED display data to be transmitted stably at a fixed rhythm.

[0088] The display data packets in the transmission queue are passed to the relevant hardware modules at the network interface layer. These hardware modules convert the data packets into a signal format suitable for transmission over the physical link according to the physical layer protocol of the network, and then send the packets out via Gigabit Ethernet. Finally, the data is transmitted to the LED display receiver for data integration and display, completing one data transmission and display operation. Subsequently, the system continues to wait for the next timer interrupt, repeatedly executing the above data packet management and transmission process to continuously and stably provide data support for the LED display.

[0089] Optionally, after the hardware module (FPGA side) receives these data packets, it checks the frame sequence number in each data packet. The FPGA has corresponding logic circuits or programs to record the sequence number of the next data packet expected to be received. If the received data packet sequence number matches the expected sequence number, the data transmission is normal; if the received sequence numbers are not consecutive—for example, if the expected data packet with sequence number N is received, but the data packet with sequence number N+2 is received—the FPGA can determine that the data packet with sequence number N+1 is lost. In this case, the FPGA can use a specific feedback mechanism, such as an interrupt signal or status register setting, to report this incomplete or out-of-order data situation. Based on this feedback information, corresponding measures can be taken, such as retransmitting the lost data packet, thereby ensuring synchronous data transmission and guaranteeing the accuracy and continuity of the LED display content.

[0090] Optionally, continuous monitoring of status information fed back from the FPGA can be performed. This status information includes the FPGA's temperature, bit error rate, and whether the FIFO (First-In-First-Out) queue has overflowed. This feedback information can be obtained by communicating with the FPGA through specific hardware interfaces, such as General Purpose Input / Output (GPIO), Serial Peripheral Interface (SPI), or Integrated Circuit Bus (I2C).

[0091] Optionally, the driver periodically queries the interface registers communicating with the FPGA to obtain the FPGA's temperature, bit error rate, and FIFO status flags. Based on this feedback, the current network conditions and system load are assessed. If the FPGA's temperature is too high, it means the FPGA's workload is too heavy, which may affect data reception. A high bit error rate indicates significant interference during data transmission and poor transmission quality. FIFO overflow directly indicates that the FPGA's data reception speed cannot keep up with the computer's data transmission speed, resulting in data accumulation within the FPGA.

[0092] Optionally, based on a comprehensive assessment of the above situations, the data transmission rate can be dynamically adjusted. When the evaluation results show that the FPGA has strong receiving capabilities and the network conditions are good, the data transmission rate can be appropriately increased to fully utilize system resources and improve data transmission efficiency. Conversely, when it is found that the FPGA is having difficulty receiving data or the network conditions are poor, the data transmission rate can be reduced to avoid further data accumulation at the FPGA end leading to overflow, or more bit errors and packet loss due to network congestion. This dynamic adjustment process is continuous, constantly adjusting the transmission rate in real time based on feedback information from the FPGA to ensure that data transmission remains stable and efficient at all times.

[0093] The above process transmits display data packets in the sending queue to the network interface layer hardware module according to a preset timer rhythm, ensuring the real-time performance and stability of data transmission and avoiding data congestion or delay. The network interface layer hardware module then converts the display data packets into a preset signal format and transmits them to the LED display receiver, adapting the data to the receiver's requirements and ultimately achieving accurate image display on the LED screen.

[0094] This application's technical solution involves: acquiring image display data generated at the application layer of the Android platform; modifying the driver at the Linux kernel layer to generate a buffer for storing the image display data and mapping it to the user space where the application runs; reading the image display data from the buffer mapped to the user space; compressing the image display data using an image preprocessing model and attention mechanism trained through deep learning to generate image data blocks; encapsulating the image data blocks in the graphics hardware module of the Rockchip platform to generate display data packets; constructing a sending queue based on the display data packets; and sending the display data packets in the sending queue to the LED display receiver for display. By modifying the driver and mapping the buffer, efficient data transmission is achieved; compressing the read image data to generate data blocks reduces redundancy; encapsulating the data blocks to build a sending queue ensures orderly data management; and finally, sending the data packets to the LED display. The overall process optimizes the data path from generation to display, improving transmission and display efficiency and quality.

[0095] The following describes embodiments of the image data transmission and display system based on the Android platform of this application, which can be used to execute the image data transmission and display method based on the Android platform in the above embodiments of this application. It is understood that the image data transmission and display system based on the Android platform can be a computer program (including program code) running on a computer device. For example, the image data transmission and display system based on the Android platform can install application software or control management software to realize the transmission and display of big data through a cloud platform. The image data transmission and display system based on the Android platform can be used to execute the corresponding steps in the method provided in the embodiments of this application. For details not disclosed in the embodiments of the image data transmission and display system based on the Android platform of this application, please refer to the embodiments of the image data transmission and display method based on the Android platform described above.

[0096] Figure 3 shows a block diagram of an image data transmission and display system based on the Android platform according to an embodiment of this application.

[0097] Referring to Figure 3, an image data transmission and display system based on the Android platform according to an embodiment of this application includes: an acquisition module 310, used to acquire image display data generated at the application layer of the Android platform; a mapping module 320, used to modify the driver at the Linux kernel layer to generate a buffer for storing the image display data and map it to the user space where the application runs; a compression module 330, used to read the image display data from the buffer mapped to the user space, compress the image display data through an image preprocessing model and attention mechanism trained by deep learning, and generate image data blocks; an encapsulation module 340, used to encapsulate the image data blocks in the graphics hardware module of the Rockchip platform to generate display data packets, and construct a sending queue based on the display data packets; and a display module 350, used to send the display data packets in the sending queue to an LED display receiver for display.

[0098] In this application, based on the aforementioned scheme, the step of modifying the driver at the Linux kernel level to generate a buffer for storing the image display data and mapping it to the user space where the application runs includes: in the Linux kernel level, sending a request to the kernel's memory management system through driver code to apply for an independent buffer with contiguous physical addresses; mapping the buffer to the address range of the user space where the application runs, establishing a mapping relationship between the user space and the buffer; and writing the display image data into the buffer based on the space address corresponding to the mapping relationship.

[0099] In this application, based on the aforementioned scheme, the step of reading the image display data from the user space mapping buffer, compressing the image display data using an image preprocessing model trained by deep learning and an attention mechanism to generate image data blocks includes: reading the image display data from the user space mapping buffer; extracting multi-scale image features from the image display data based on the image preprocessing model trained by deep learning; and compressing the image display data based on the visual parameters and the current network state to generate image data blocks.

[0100] In this application, based on the aforementioned scheme, the step of encapsulating the image data block in the graphics hardware module of the Rockchip platform to generate a display data packet, and constructing a transmission queue based on the display data packet, includes: performing an initialization operation on the graphics hardware module of the Rockchip platform; dividing and encapsulating the image data block in the graphics hardware module to generate a display data packet; and constructing a transmission queue based on the display data packet.

[0101] In this application, based on the aforementioned scheme, the initialization operation of the graphics hardware module of the Rockchip platform, and the segmentation and encapsulation of the image data block in the graphics hardware module to generate a display data packet, includes: initializing the graphics hardware module of the Rockchip platform and transmitting the image data block to the graphics hardware module; performing format conversion and segmentation processing on the image data block in the graphics hardware module based on a preset target format to generate multiple unit blocks; splitting the unit blocks to generate multiple data packets, encapsulating the data packets based on a data transmission protocol to generate a display data packet, and storing the display data packet in the buffer.

[0102] In this application, based on the aforementioned scheme, the step of constructing a sending queue based on the display data packet includes: generating a circular queue based on a descriptor of a preset size; writing the display data packet into the currently empty descriptor in the circular queue, and updating the queue pointer to point to the next empty descriptor.

[0103] In this application, based on the aforementioned scheme, the step of sending the display data packets in the sending queue to the LED display receiver for display includes: transmitting the display data packets in the sending queue to the hardware module of the network interface layer based on a preset timer; converting the display data packets into a preset signal format in the hardware module and transmitting them to the LED display receiver for display.

[0104] This application's technical solution involves: acquiring image display data generated at the application layer of the Android platform; modifying the driver at the Linux kernel layer to generate a buffer for storing the image display data and mapping it to the user space where the application runs; reading the image display data from the buffer mapped to the user space; compressing the image display data using an image preprocessing model and attention mechanism trained through deep learning to generate image data blocks; encapsulating the image data blocks in the graphics hardware module of the Rockchip platform to generate display data packets; constructing a sending queue based on the display data packets; and sending the display data packets in the sending queue to the LED display receiver for display. By modifying the driver and mapping the buffer, efficient data transmission is achieved; compressing the read image data to generate data blocks reduces redundancy; encapsulating the data blocks to build a sending queue ensures orderly data management; and finally, sending the data packets to the LED display. The overall process optimizes the data path from generation to display, improving transmission and display efficiency and quality.

[0105] Figure 4 shows a schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application.

[0106] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not impose any limitations on the function and scope of use of the embodiments of this application.

[0107] In this embodiment, the computer system includes a central processing unit 401, which can perform various appropriate actions and processes based on programs stored in read-only memory 402 or programs loaded from storage section 408 into random access memory 403, such as executing the image data transmission and display method based on the Android platform described in the above embodiment. The random access memory 403 also stores various programs and data required for system operation, thereby realizing big data storage and big data management. The central processing unit 401, read-only memory 402, and random access memory 403 are interconnected via bus 404. Input / output interface 405 is also connected to bus 404.

[0108] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0109] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit 401, it performs various functions defined in the system of this application.

[0110] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0111] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0112] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0113] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0114] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the image data transmission and display method based on the Android platform described in the above embodiments.

[0115] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0116] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the methods according to the embodiments of this application.

[0117] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0118] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for transmitting and displaying image data based on the Android platform, characterized in that, include: Obtain image display data generated at the application layer on the Android platform; The driver is modified at the Linux kernel level to generate a buffer for storing the image display data and map it to the user space where the application runs; the image display data is read from the buffer mapped from the user space, and the image display data is compressed using an image preprocessing model and attention mechanism trained by deep learning to generate image data blocks; The image data block is encapsulated in the graphics hardware module of the Rockchip platform to generate a display data packet, and a sending queue is constructed based on the display data packet; the display data packet in the sending queue is sent to the LED display receiver for display.

2. The image data transmission and display method based on the Android platform according to claim 1, characterized in that, The driver is modified at the Linux kernel level to generate a buffer for storing the image display data and map it to the user space where the application runs. This includes: in the Linux kernel level, the driver code sends a request to the kernel's memory management system to apply for an independent buffer with contiguous physical addresses; mapping the buffer to the address range of the user space where the application runs, establishing a mapping relationship between the user space and the buffer; and writing the display image data into the buffer based on the space address corresponding to the mapping relationship.

3. The image data transmission and display method based on the Android platform according to claim 1, characterized in that, The process of reading image display data from the user space mapping buffer, compressing the image display data using an image preprocessing model trained by deep learning and an attention mechanism to generate image data blocks includes: reading the image display data from the user space mapping buffer; extracting multi-scale features of the image display data using an image preprocessing model trained by deep learning, generating visual parameters using an attention mechanism; and compressing the image display data based on the visual parameters and the current network state to generate image data blocks.

4. The image data transmission and display method based on the Android platform according to claim 1, characterized in that, In the graphics hardware module of the Rockchip platform, the image data blocks are encapsulated to generate display data packets, and a sending queue is constructed based on the display data packets. This includes: initializing the graphics hardware module of the Rockchip platform; dividing and encapsulating the image data blocks in the graphics hardware module to generate display data packets; and constructing a sending queue based on the display data packets.

5. The image data transmission and display method based on the Android platform according to claim 4, characterized in that, The process includes: initializing the graphics hardware module of the Rockchip platform and transmitting the image data block to the graphics hardware module; performing format conversion and segmentation on the image data block in the graphics hardware module based on a preset target format to generate multiple unit blocks; splitting the unit blocks to generate multiple data packets, encapsulating the data packets based on a data transmission protocol to generate display data packets, and storing the display data packets in the buffer.

6. The image data transmission and display method based on the Android platform according to claim 4, characterized in that, Constructing a sending queue based on the display data packet includes: generating a circular queue based on a descriptor of a preset size; writing the display data packet into the currently empty descriptor in the circular queue, and updating the queue pointer to point to the next empty descriptor.

7. The image data transmission and display method based on the Android platform according to claim 1, characterized in that, Sending the display data packets in the sending queue to the LED display receiver for display includes: based on a preset timer, transmitting the display data packets in the sending queue to a hardware module at the network interface layer; in the hardware module, converting the display data packets into a preset signal format and transmitting them to the LED display receiver for display.

8. An image data transmission and display system based on the Android platform, characterized in that, include: The acquisition module is used to acquire image display data generated at the application layer on the Android platform; The mapping module is used to modify the driver at the Linux kernel level, generate a buffer to store the image display data, and map it to the user space where the application runs; The compression module is used to read the image display data from the user space mapped buffer, and compress the image display data through an image preprocessing model and attention mechanism trained by deep learning to generate image data blocks; The encapsulation module is used to encapsulate the image data block in the graphics hardware module of the Rockchip platform to generate a display data packet, and to build a transmission queue based on the display data packet; the display module is used to send the display data packet in the transmission queue to the LED display receiver for display.

9. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the image data transmission and display method based on the Android platform as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the image data transmission and display method based on the Android platform as described in any one of claims 1 to 7.

Images