Cross-platform content compatibility method and system based on virtual shooting LED display screen

Abstract

The invention provides a cross-platform content compatibility method and system based on a virtual shooting LED display screen. The method comprises the following steps: processing an irrelevant data content stream of a content stream of a heterogeneous content source to generate an intermediate data stream; constructing a virtual logic display space according to the resolution parameter of the intermediate data stream and the curvature data of the physical screen database; performing dynamic resource allocation based on the control point grid of the virtual logic display space, and generating a rendering task queue; loading a three-dimensional lookup table to perform forward color gamut conversion according to the rendering task queue and a physical screen color configuration file, and adjusting HSL offset according to collected real-time chroma data to generate frame cache data; and determining a delay compensation value based on the frame buffer data and the PTP clock signal, and inserting a corresponding time offset in an output queue through the delay compensation value to generate a multi-screen output signal. According to the scheme, the rendering accuracy, the color consistency and the display stability in the virtual shooting scene can be remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of LED display screens, and in particular to a cross-platform content compatibility method and system based on virtual shooting of LED display screens. Background Art

[0002] In virtual filming and extended reality production, LED displays serve as a key background imaging carrier and are widely used in high-end production scenarios such as filming, advertising, and live broadcasts. For example, when using Unreal Engine for virtual studio shooting, the background image is not synthesized in post-production but rendered in real time and projected directly onto a large LED wall, interacting with the real scene and actors to create an immersive effect. However, this type of production involves content stream input from multiple heterogeneous platforms (such as game engines, real-time rendering software, and video editing tools), and also requires adaptation to LED display hardware of varying specifications and forms (such as curved screens, spliced screens, and mobile screens). Content compatibility and display synchronization are becoming increasingly prominent issues.

[0003] Under the current technological system, the data formats and metadata of various content sources are not unified, and there is a lack of a cross-platform standardized middle layer. This results in the inefficient scheduling of rendering resources, and difficulty in accurately executing color management and multi-screen timing control. Especially after dynamic changes in LED screen parameters (such as module replacement and aging), traditional systems find it difficult to adjust output strategies in real time, and are prone to problems such as screen freezes, color deviations, and splicing misalignments, which seriously affect the quality of virtual shooting images and production efficiency. Summary of the Invention

[0004] The present application provides a cross-platform content compatibility method and system based on a virtual shooting LED display screen, which is used to solve the problem in related technologies that the data formats and metadata of multiple heterogeneous platforms are not unified, which seriously affects the quality of virtual shooting images and production efficiency.

[0005] In a first aspect, the present application provides a cross-platform content compatibility method based on virtual shooting of an LED display screen, the cross-platform content compatibility method based on virtual shooting of an LED display screen comprising: Obtaining content streams from heterogeneous content sources through a dynamic metadata injector, performing dynamic metadata processing on irrelevant data content streams of the content streams, and generating standardized intermediate data streams; Constructing a virtual logical display space according to the resolution parameters of the standardized intermediate data stream and the curvature data of the physical screen database; Based on the control point grid of the virtual logical display space, dynamic resource allocation is performed through a semantically driven hierarchical rendering pipeline to generate a rendering task queue; According to the rendering task queue and the physical screen color profile, a three-dimensional lookup table is loaded through a feedback color engine to perform forward color gamut conversion, and an HSL offset is adjusted according to the collected real-time chromaticity data to generate color-calibrated frame buffer data; Based on the frame buffer data and the PTP clock signal, the delay compensation value of each display screen is determined, and the corresponding time offset is inserted into the output queue according to the delay compensation value to generate a multi-screen output signal.

[0006] Optionally, in a first implementation of the first aspect of the present application, the steps of obtaining a content stream from a heterogeneous content source through a dynamic metadata injector, performing dynamic metadata processing on an irrelevant data content stream of the content stream, and generating a standardized intermediate data stream include: Performing frame buffer remapping on native data packets of content streams of heterogeneous content sources obtained by a dynamic metadata injector to generate a protocol-independent video frame sequence corresponding to the irrelevant data content stream; Based on the pixel format identification bit of the protocol-independent video frame sequence, a linear transformation is performed through a color gamut space conversion matrix to generate a color gamut reference frame; Extracting spatiotemporal features by performing semantic analysis on a metadata-free frame sequence of the protocol-independent video frame sequence to generate predicted metadata; The protocol-independent video frame sequence, the color gamut reference frame, and the prediction metadata are input into a data encapsulator to generate a standardized intermediate data stream.

[0007] Optionally, in a second implementation of the first aspect of the present application, the step of constructing a virtual logical display space according to the resolution parameter of the standardized intermediate data stream and the curvature data of the physical screen database includes: Extracting original resolution parameters of the standardized intermediate data stream through a resolution analyzer, determining display density in combination with pixel pitch and screen size in a physical screen database, and generating density correction parameters; generating a control point grid matching the screen topology according to the density correction parameter and the curvature radius data in the physical screen database; Dividing the projection area of each display screen in the virtual space based on the control point grid and the camera pose parameters; When it is detected that the projection areas of adjacent screens overlap, transition vertices are inserted into the control point grid according to the curvature difference values of the adjacent screens to generate a seamless virtual logical display space.

[0008] Optionally, in a third implementation of the first aspect of the present application, the step of dynamically allocating resources through a semantically driven hierarchical rendering pipeline based on the control point grid of the virtual logical display space to generate a rendering task queue includes: Analyzing the spatial topological structure of the control point grid through a semantic analysis engine to generate a scene depth distribution map and an object semantic label matrix; Dividing rendering priority areas through a dynamic grading strategy according to the gradient change rate of the scene depth distribution map and the category weights of the object semantic label matrix; Calculating the ray tracing sampling rate and texture mapping accuracy parameters of the rendering priority area through a resource allocator based on the rendering priority area and GPU memory status data; The ray tracing sampling rate, the texture mapping precision parameter and the corresponding control point coordinates are bound to generate a rendering task queue with a hierarchical identifier.

[0009] Optionally, in a fourth implementation of the first aspect of the present application, the step of loading a three-dimensional lookup table through a feedback color engine to perform forward color gamut conversion based on the rendering task queue and the physical screen color profile, and adjusting the HSL offset based on the collected real-time chromaticity data to generate color-calibrated frame buffer data includes: generating a hierarchical priority weight matrix according to the hierarchical identifier of the rendering task queue and the display unit color gamut parameter of the physical screen color profile; Loading a three-dimensional lookup table through a feedback color engine, performing forward color gamut conversion based on the hierarchical priority weight matrix on the frame data of each level in the rendering task queue, and generating a primary calibration frame sequence; Acquiring real-time chromaticity data of the primary calibration frame sequence after being displayed on the physical screen, and determining a color difference vector between a target color gamut and a measured color gamut based on the real-time chromaticity data; When it is detected that the color difference vector is greater than a preset threshold, the HSL offset is adjusted according to the direction component of the color difference vector, and secondary color gamut mapping is performed on the primary calibration frame sequence to generate color calibrated frame buffer data.

[0010] Optionally, in a fifth implementation of the first aspect of the present application, the step of determining a delay compensation value for each display screen based on the frame buffer data and the PTP clock signal, inserting a corresponding time offset into an output queue using the delay compensation value, and generating a multi-screen output signal includes: determining a basic delay value of transmission delay according to a data packet feature vector of the frame buffer data and a pixel response time parameter of the physical screen database; The clock deviation value of each display screen is obtained through the PTP clock signal analyzer, and the number of hops and distance parameters of the signal transmission path are determined based on the topological connection relationship of the physical screen database; monitoring network delay fluctuation data of the signal transmission path in real time, and generating a dynamic delay correction factor through a Kalman filter; Determining a delay compensation value for each display screen according to the basic delay value, the clock deviation value, the hop count and distance parameter, and the dynamic delay correction factor; A time offset is inserted into the output queue of each display screen using the delay compensation value to generate a synchronized multi-screen output signal.

[0011] Optionally, in a sixth implementation of the first aspect of the present application, the method further includes: The distributed current sensor array collects the working current waveform of each display screen and generates an abnormal diagnosis signal when it detects that the current fluctuation frequency exceeds the preset current threshold; Extracting the fault screen coordinate range of the abnormal diagnosis signal and the depth map information of the frame buffer data to generate a pixel migration boundary mask; Determine a migration path of the faulty pixel to an adjacent screen based on a topological relationship between the pixel migration boundary mask and a control point grid of the virtual logical display space, and generate a pixel migration task list; Based on the target screen coordinate range of the pixel migration task list, loading the scene structure features of the depth map information to generate filling screen data for the missing area; The filling screen data is fused with the multi-screen output signal at pixel level to reconstruct a frame data sequence of the fault screen corresponding to the abnormal diagnosis signal.

[0012] A second aspect of the present application provides a cross-platform content compatibility device based on a virtual shooting LED display screen, wherein the cross-platform content compatibility device based on a virtual shooting LED display screen is used to implement a cross-platform content compatibility method based on a virtual shooting LED display screen. The cross-platform content compatibility device based on a virtual shooting LED display screen includes: An acquisition module is used to acquire content streams from heterogeneous content sources through a dynamic metadata injector, perform dynamic metadata processing on irrelevant data content streams of the content streams, and generate a standardized intermediate data stream; A construction module, configured to construct a virtual logical display space according to the resolution parameters of the standardized intermediate data stream and the curvature data of the physical screen database; an allocation module for dynamically allocating resources through a semantically driven hierarchical rendering pipeline based on a control point grid of the virtual logical display space, and generating a rendering task queue; a generation module, configured to load a three-dimensional lookup table through a feedback color engine to perform forward color gamut conversion based on the rendering task queue and the physical screen color profile, and adjust the HSL offset according to the collected real-time chromaticity data to generate color-calibrated frame buffer data; The processing module is used to determine the delay compensation value of each display screen based on the frame buffer data and the PTP clock signal, insert the corresponding time offset into the output queue according to the delay compensation value, and generate a multi-screen output signal.

[0013] A third aspect of an embodiment of the present application provides an electronic device, including a memory and a processor, wherein the processor is used to execute a computer program stored on the memory. When the processor executes the computer program, it implements the steps of the cross-platform content compatibility method based on virtual shooting of LED display screens provided in the first aspect of the embodiment of the present application.

[0014] The fourth aspect of the embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the cross-platform content compatibility method based on virtual shooting of LED display screens provided in the first aspect of the embodiment of the present application are implemented.

[0015] In summary, according to the cross-platform content compatibility method and system based on virtual shooting of LED display screens provided by the present application scheme, content streams of heterogeneous content sources are obtained through a dynamic metadata injector, and dynamic metadata processing is performed on irrelevant data content streams of the content stream to generate a standardized intermediate data stream; a virtual logical display space is constructed according to the resolution parameters of the standardized intermediate data stream and the curvature data of the physical screen database; based on the control point grid of the virtual logical display space, dynamic resource allocation is performed through a semantically driven hierarchical rendering pipeline to generate a rendering task queue; according to the rendering task queue and the physical screen color profile, a three-dimensional lookup table is loaded through a feedback color engine to perform forward color gamut conversion, and the HSL offset is adjusted according to the collected real-time chromaticity data to generate color-calibrated frame buffer data; based on the frame buffer data and the PTP clock signal, the delay compensation value of each display screen is determined, and the corresponding time offset is inserted into the output queue through the delay compensation value to generate a multi-screen output signal. This application solution significantly improves the rendering accuracy, color consistency and display stability in virtual shooting scenes by constructing a standardized intermediate data stream, establishing a virtual logical display space, adopting a semantic-driven hierarchical rendering and feedback-type color calibration mechanism, and combining a delay compensation algorithm to achieve multi-screen synchronous output. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a cross-platform content compatibility method based on virtual shooting of an LED display screen provided in an embodiment of the present application; Figure 2 A schematic diagram of program modules of a cross-platform content compatible device based on virtual shooting of an LED display screen provided in an embodiment of the present application; Figure 3A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0018] In order to solve the problem in the related art that the data formats and meta information of multiple heterogeneous platforms are not unified, which seriously affects the quality of virtual shooting images and production efficiency, the embodiment of the present application provides a cross-platform content compatibility method based on virtual shooting LED display screen, such as Figure 1 The flowchart of the cross-platform content compatibility method based on virtual shooting of LED display screen provided in this embodiment is as follows: Step 110: Obtain content streams from heterogeneous content sources through a dynamic metadata injector, perform dynamic metadata processing on irrelevant data content streams of the content streams, and generate standardized intermediate data streams.

[0019] Specifically, in this embodiment, a dynamic metadata injector is used to acquire content streams from heterogeneous content sources. The technology employed includes protocol-agnostic parsing and metadata insertion mechanisms. First, content streams can originate from different rendering platforms or engines, potentially with varying formats, encodings, resolutions, and timestamp standards. To ensure unified management and processing, a dynamic parser is required to identify and classify the content of the input streams, extracting core video frames and auxiliary information. Then, based on the extracted structure, the injector inserts metadata in a unified format, such as frame identifiers, temporal information, content semantics, or physical attributes, providing a structured semantic description of the original data. This process enables subsequent systems to uniformly schedule, identify, and process data streams from diverse sources, thereby achieving cross-platform content access compatibility. Dynamic metadata processing is performed on irrelevant data within the content streams to generate standardized intermediate data streams. The data processing technology employed here is based on data screening and real-time data conversion mechanisms. The system automatically identifies data irrelevant to display rendering, such as control signals, redundant coded frames, or debug information, and removes it from the main data stream. Next, a unified format conversion engine is used to encapsulate different encoding standards (such as H.264, EXR, and OpenEXR) into a common intermediate format, such as structured video data based on JSON or protobuf. This standardization process also includes the alignment of attributes such as image resolution, frame rate, and color space, ensuring that all data passing through has a consistent processing foundation, facilitating the next stage of the spatial modeling and rendering process.

[0020] In an optional implementation of this embodiment, a content stream of a heterogeneous content source is obtained through a dynamic metadata injector, and dynamic metadata processing is performed on an irrelevant data content stream of the content stream to generate a standardized intermediate data stream. The steps include: performing frame cache remapping on native data packets of the content stream of the heterogeneous content source obtained by the dynamic metadata injector to generate a protocol-independent video frame sequence corresponding to the irrelevant data content stream; performing a linear transformation using a color gamut space conversion matrix based on pixel format identification bits of the protocol-independent video frame sequence to generate a color gamut reference frame; performing semantic analysis on a metadata-free frame sequence of the protocol-independent video frame sequence to extract spatiotemporal features to generate predicted metadata; and inputting the protocol-independent video frame sequence, the color gamut reference frame, and the predicted metadata into a data encapsulator to generate a standardized intermediate data stream.

[0021] Specifically, this embodiment parses and abstracts the raw data structure of heterogeneous content streams. These streams may originate from different video engines, sensor inputs, or file formats, such as H.264 video streams, EXR frame sequences, or real-time streaming based on the RTSP protocol. These streams may have different internal frame structures, timestamp management, and image encapsulation methods. Frame buffer remapping technology converts native data packets into a unified frame buffer index structure, enabling cross-protocol and cross-platform content alignment. Frame buffer remapping involves reorganizing the distribution of frames in memory based on the original order of image frames and the update frequency of the display buffer, so that all content can be uniformly scheduled according to the display control logic. For example, the video frame sequence of an RTSP stream is encoded based on a GOP (Group of Pictures) structure. Through remapping, the sequence of I-frames, P-frames, and B-frames can be flattened into a linear frame stream for subsequent unified analysis. The resulting protocol-independent video frame sequence contains image content in various formats. To ensure consistent color representation of these images, color gamut standardization based on their pixel format identifiers is required. The pixel format identifier is metadata within an image frame that describes the color encoding method, such as YUV420, RGB888, or HDR BT.2020. Different formats correspond to different color gamuts and luminance ranges. Accurate mapping can be achieved by introducing a color gamut conversion matrix, which uses a linear algebraic transformation matrix to project pixel values from one color space to a target color space. For example, if the source image is a YUV image in the BT.709 standard and the target display system uses an RGB format in the P3 color gamut, the YUV pixel values must first be converted to RGB, and then the color repositioning is performed using a BT.709 to P3 matrix. This linear transformation generates a color gamut reference frame, ensuring consistent and controllable color representation across different source images when displayed. To further impart interpretable semantics to unstructured image data, semantic analysis of image frames without metadata within protocol-independent video frame sequences is required. Semantic analysis relies on image recognition and feature extraction algorithms, such as convolutional neural networks (CNNs) or Transformer architecture models, to identify high-dimensional features such as object edges, motion trajectories, spatial depth, or texture distribution directly from image content without the aid of external descriptive information. This process is achieved through spatiotemporal feature extraction, which establishes temporal associations between image frames. For example, by analyzing the displacement vector, direction of change, or contour deformation of a moving object in consecutive frames, the system can predict the content category or the area of interest. For example, in a video containing a fast-moving car, the system can determine that it is a dynamic target based on the vehicle's movement trajectory and edge flow information, and generate predicted metadata including position, size, and speed. This predicted data can not only be used for rendering priority scheduling, but also facilitate dynamic content adjustment and scene recognition.Finally, the protocol-independent video frame sequence, color gamut reference frame, and predicted metadata are fed into a data encapsulator for integration. A data encapsulator is a structured data aggregation module whose core function is to encapsulate data of different dimensions and types into a common intermediate format. For example, it uses a data model based on protocol buffers (Protocol Buffers) or JSON Schema to package video frame content, color calibration parameters, and image semantic metadata. This generates a standardized intermediate data stream that is platform-independent and scalable, allowing it to be directly read and utilized by downstream rendering scheduling engines, color calibration modules, or multi-screen output management systems. For example, an encapsulated image frame can include pixel data, RGB values in the P3 color gamut, object location information, and timestamp information. This enables the system to allocate resources based on content and adjust strategies based on the scene during unified rendering, significantly improving the display consistency and interactive intelligence of multi-source heterogeneous content on large-scale display terminals.

[0022] Step 120: Construct a virtual logical display space according to the resolution parameters of the standardized intermediate data stream and the curvature data of the physical screen database.

[0023] Specifically, in this embodiment, a virtual logical display space is constructed based on the resolution parameters of the standardized intermediate data stream and the curvature data of the physical screen database. The key technologies involved include three-dimensional modeling and mapping matching algorithms. The system first reads the image resolution parameters embedded in the intermediate data stream and the spatial boundary information of the display content, and then calls the geometric layout, curvature radius and installation posture information of each LED screen recorded in the physical screen database. Combining these data, a virtual three-dimensional space model is generated using a spatial projection algorithm to accurately simulate the actual arrangement structure of the physical LED array. This spatial model provides a digital coordinate basis for subsequent image rendering, thereby logically completing the mapping between content and display entities, ensuring that the final display effect is consistent with the shooting perspective.

[0024] In an optional implementation of this embodiment, the step of constructing a virtual logical display space based on the resolution parameters of the standardized intermediate data stream and the curvature data of the physical screen database includes: extracting the original resolution parameters of the standardized intermediate data stream through a resolution parser, determining the display density in combination with the pixel pitch of the physical screen database and the screen size, and generating density correction parameters; generating a control point grid that matches the screen topology based on the density correction parameters and the curvature radius data of the physical screen database; dividing the projection area of each display screen in the virtual space based on the control point grid and the camera posture parameters; when it is detected that the projection areas of adjacent screens overlap, inserting transition vertices in the control point grid according to the curvature difference values of the adjacent screens to generate a seamless virtual logical display space.

[0025] Specifically, in this embodiment, the original resolution parameters of the standardized intermediate data stream are extracted using a resolution parser. The resolution parser is an image metadata reading unit that parses the frame header information in the data stream to determine the horizontal and vertical pixel counts of each frame, such as 1920×1080 or 3840×2160, to determine the original level of detail and display accuracy requirements of the image. By combining these parameters with the screen pixel pitch information (i.e., the distance occupied by each pixel in the actual physical space, typically in millimeters) and the physical screen dimensions (e.g., diagonal length or length and width) in the physical screen database, the physical display density of each display screen can be inferred—the number of pixels that can be displayed per unit area. This density information plays a key role in visual reproduction, so density correction parameters are required to accurately map the pixel distribution of the content space to the actual pixel distribution capabilities of the display screen. Once the density correction parameters are obtained, they are combined with the curvature radius data of each screen recorded in the physical screen database to generate a control point grid that matches the physical screen topology. The radius of curvature refers to the geometric characteristic of a screen's curvature in space, describing its degree of concavity or wraparound angle. For example, in a circular CAVE system or a curved LED array, the curvature of different screens determines how content is mapped. A control point grid is a spatial mapping framework consisting of a series of two- or three-dimensional spatial nodes used for projection control. These nodes are arranged based on the screen's actual curvature and pixel density, forming the basis for mapping. For example, for a curved screen with a radius of 1.5 meters, the control point distribution should exhibit a nonlinear, progressive relationship to ensure that the projected image maintains correct geometric proportions and perspective. The control point grid is a key data structure for achieving logical consistency between the screen's geometric characteristics and the content space. The virtual display space is constructed based on the generated control point grid and camera pose parameters. Camera pose parameters are spatial pose information used to simulate the observer's perspective. These parameters include position (i.e., 3D coordinates) and orientation (i.e., Euler angles or quaternions). They determine the virtual viewpoint's observation path and projection angle in space. Through the geometric mapping relationship between the camera viewpoint and the control point grid, the projection area of each display screen in the virtual three-dimensional space can be divided, so that the system can assign the corresponding content fragment to each display unit during the content rendering stage. For example, on a curved wall composed of multiple panels, the image blocks presented by different screens should be partitioned according to the projection of light emitted from the observer's perspective to ensure that there is no stretching or compression between the pictures and that continuous perspective lines are maintained. After the mapping is completed, in order to deal with the splicing gap problem caused by curvature differences or arrangement methods between multiple display screens, when it is detected that the projection areas of adjacent screens overlap in the virtual space, the system will further analyze their respective curvature difference values. The curvature difference value is the deviation in the curvature radius of two adjacent screens, which is used to determine the smoothness of the edge transition.Excessive differences in curvature will cause visible misalignment or brightness jumps in the stitched area, so transition vertices must be inserted into the control point grid. Transition vertices are interpolation nodes attached to the stitched edges. Using a three-dimensional interpolation algorithm (such as Bezier curves or Catmull-Rom splines), they flexibly interpolate the surface morphology between control points, creating a continuous deformation buffer zone for display areas that otherwise experience step changes. For example, by adding transition vertices between a screen with a curvature of 1.5 meters and one with a curvature of 1.0 meters, the system visually simulates a continuously curved surface, generating a seamless virtual logical display space. This achieves visual consistency and geometric smoothness when rendering content on physically discontinuous screens. Through this series of mapping, interpolation, and geometric modeling operations, the natural transition and unified integration of multi-source content on complex curved topology display systems can be ensured.

[0026] Step 130 : Based on the control point grid of the virtual logical display space, dynamic resource allocation is performed through a semantically driven hierarchical rendering pipeline to generate a rendering task queue.

[0027] Specifically, in this embodiment, dynamic resource allocation is performed through a semantically driven hierarchical rendering pipeline based on the control point grid of the virtual logical display space, and the technologies used mainly include semantic recognition and GPU rendering scheduling mechanism. The system judges the display importance of each area, such as the center of the audience's sight, the moving target area or the focus range, based on the control point density and content complexity of different areas in the logical space. High-priority areas are identified through a pre-trained semantic model, and then higher computing resources are allocated to these areas in the rendering pipeline, such as higher texture resolution or more complex shader processing. A simplified rendering strategy is used for low-priority areas. The entire scheduling process is based on the dynamic allocation mechanism of the GPU resource pool, which can adjust the task execution priority according to the current computing power status, thereby improving rendering efficiency and ensuring image quality output in key areas.

[0028] In an optional implementation of this embodiment, based on the control point grid of the virtual logical display space, dynamic resource allocation is performed through a semantically driven hierarchical rendering pipeline to generate a rendering task queue, including: parsing the spatial topology of the control point grid through a semantic analysis engine to generate a scene depth distribution map and an object semantic label matrix; dividing the rendering priority area through a dynamic hierarchical strategy according to the gradient change rate of the scene depth distribution map and the category weight of the object semantic label matrix; calculating the ray tracing sampling rate and texture mapping accuracy parameters of the rendering priority area through a resource allocator based on the rendering priority area and the GPU memory status data; binding the ray tracing sampling rate, texture mapping accuracy parameters and the corresponding control point coordinates to generate a rendering task queue with a hierarchical identification.

[0029] Specifically, in this embodiment, the spatial topology of the control point grid is parsed using a semantic analysis engine. This is a parsing module that integrates pattern recognition and neural network analysis capabilities. Its primary function is to identify the geometric relationships, connections, and structural features of the scene represented by each control point in the grid in three-dimensional space. The control point grid is essentially a multidimensional array that records the spatial coordinates and arrangement of each display unit or pixel block in the virtual display space. Therefore, parsing this grid can obtain spatial depth information for each location in the scene, thereby generating a scene depth distribution map. The scene depth distribution map is a two- or three-dimensional matrix that records the distance value of each pixel or control point in the viewpoint direction. Its gradient represents the rate of depth change and is critical for determining near and far areas in the image. Simultaneously, the semantic analysis engine uses a trained convolutional neural network model to identify and label the content areas contained in the grid, thereby generating an object semantic label matrix. This matrix records the classification results of each identified object in the scene, such as "person," "building," "vehicle," and "background," along with the spatial location index of each category, providing semantic support for subsequent prioritization. A dynamic grading strategy is then implemented based on the gradient change rate of the scene depth distribution map and the category weights in the object semantic label matrix to effectively allocate rendering resources. The gradient change rate is the depth difference between adjacent pixels or control points in the depth map, reflecting the scene's three-dimensionality and spatial structural complexity. Areas with high gradient change rates often indicate distinct object edges or near-field abrupt changes, requiring higher sampling accuracy. The weights corresponding to each object category in the semantic label matrix are set based on the importance of the content. For example, faces are given a higher weight than the sky or ground textures because the former are more sensitive and have a greater impact on the viewer's visual perception. Therefore, by fusing the gradient change rate with the category weights, multiple rendering priority regions are dynamically determined. The scene is divided into high-priority areas (such as foreground figures and boundary outlines), medium-priority areas (such as the main object and the scene's core), and low-priority areas (such as blurred background areas). This allows subsequent computation to focus rendering resources on key areas. For example, in an interactive display system, foreground objects in the viewer's gaze area would be assigned high priority, while distant mountains or the sky would be assigned low priority. After the rendering priority areas are divided, the rendering system will further dynamically calculate the ray tracing sampling rate and texture mapping precision parameters for each area based on the designated rendering priority areas and GPU memory status data. The ray tracing sampling rate refers to the number of light paths calculated for each pixel or light intersection in the ray tracing rendering algorithm. The higher the sampling rate, the more realistic the rendered shadows, reflections, and refractions, but the greater the consumption of computing resources. The texture mapping precision parameter indicates the level of texture detail used when mapping the surface of a three-dimensional object. The higher the precision, the more refined the display effect.GPU memory status data records the current usage of various graphics card resources, such as memory capacity utilization, texture cache hit rate, and concurrent thread load. The resource allocator, the scheduling core, dynamically adjusts the sampling and texture precision of each priority zone based on the GPU's remaining computing power. For example, high-priority zones are configured with 32 ray paths per pixel and high-resolution textures, while low-priority zones are configured with only four ray paths and low-precision compressed textures, achieving a balance between performance and quality. The ray tracing sampling rate and texture mapping precision parameters calculated above are bound to coordinate nodes in the control point grid. Each control point carries a corresponding rendering level attribute, and these are packaged and organized into hierarchical rendering task queues based on priority. The rendering task queue is an ordered set of instructions for the GPU to execute rendering commands. It contains information such as geometry data, texture paths, sampling settings, and draw targets for each zone. Tasks in the queue are submitted to the GPU for execution in order of priority, ensuring that critical areas are rendered first and key content is rendered with high quality, thus achieving system-level hierarchical scheduling. For example, in a large panoramic projection system, this mechanism can ensure that the images in the area where the audience is looking are generated quickly and with high quality, while image delays or low-quality processing in the edges or secondary areas will not cause perceptible performance losses, thereby comprehensively improving the responsiveness and visual effects of the interactive display system.

[0030] Step 140: Based on the rendering task queue and the physical screen color profile, the feedback color engine loads a three-dimensional lookup table for forward color gamut conversion, and adjusts the HSL offset based on the collected real-time chromaticity data to generate color-calibrated frame buffer data.

[0031] Specifically, in this embodiment, based on the rendering task queue and the physical screen color profile, a three-dimensional lookup table is loaded through a feedback color engine to perform forward color gamut conversion, and the HSL offset is adjusted based on the collected real-time chromaticity data to generate color-calibrated frame buffer data. This process relies on color management technology and a real-time feedback correction mechanism. The system uses the color space information of each frame image in the rendering task queue to match the ICC or color profile of the physical screen, and performs color gamut mapping on the image color through a three-dimensional LUT to ensure color consistency between different display devices. At the same time, the supporting sensor equipment will collect parameters such as the color temperature and brightness of the LED screen in real time and feed them back to the color engine. The image's hue, saturation, and brightness value (HSL) will be dynamically adjusted based on the current deviation to achieve precise calibration, so that the final output image frame buffer has consistent color performance.

[0032] In an optional implementation of this embodiment, according to the rendering task queue and the physical screen color profile, a three-dimensional lookup table is loaded through a feedback color engine to perform forward color gamut conversion, and the HSL offset is adjusted according to the collected real-time chromaticity data to generate the color-calibrated frame buffer data. The steps include: generating a hierarchical priority weight matrix according to the hierarchical identifier of the rendering task queue and the display unit color gamut parameters of the physical screen color profile; loading a three-dimensional lookup table through the feedback color engine, performing forward color gamut conversion based on the hierarchical priority weight matrix on the frame data of each level in the rendering task queue, and generating a primary calibration frame sequence; obtaining real-time chromaticity data of the primary calibration frame sequence after display on the physical screen, and determining the color difference vector between the target color gamut and the measured color gamut based on the real-time chromaticity data; when it is detected that the color difference vector is greater than a preset threshold, adjusting the HSL offset according to the direction component of the color difference vector, and performing secondary color gamut mapping on the primary calibration frame sequence to generate color-calibrated frame buffer data.

[0033] Specifically, in this embodiment, to achieve a high-precision color consistency display system, a layer priority weight matrix is generated based on the layer identifiers in the rendering task queue and the display unit color gamut parameters in the physical screen color profile. The layer identifiers in the rendering task queue are used to distinguish the rendering priority of different areas or objects. Higher layers typically correspond to semantically important areas such as foreground characters or interactive elements, while lower layers correspond to backgrounds or secondary layers. The physical screen color profile records the hardware color gamut parameters of each display unit, including color gamut coverage (such as sRGB, DCI-P3, or Rec.2020) and technical indicators such as the dominant wavelengths and white point coordinates of the red, green, and blue primaries. By performing a matrix-based matching of the layer identifiers in the task queue with the screen color gamut capabilities, a layer priority weight matrix is established. Each matrix element represents the importance and adaptability of color expression for a given layer of rendered content on a given display unit, with higher weights indicating higher color accuracy requirements for that area. A feedback color engine loads a three-dimensional lookup table and performs forward color gamut conversion based on the layer priority weight matrix on the frame data of each layer in the rendering task queue, generating a sequence of primary calibration frames. A three-dimensional lookup table is a color mapping structure that maps input RGB values to output RGB values. It is constructed using a three-dimensional grid, so that each color input can obtain the corresponding output color through interpolation in three-dimensional space, enabling high-precision color correction. A feedback color engine is a color processing module combined with a real-time display monitoring mechanism. It can record the relationship between input and output data during color conversion and generate feedback adjustments for subsequent results. During the forward color gamut conversion process, the system adjusts the search strategy of the three-dimensional lookup table based on the values of each level area in the weight matrix. For example, it uses dense search and high-bit depth interpolation for high-weight areas, and only coarse search and low-precision interpolation for low-weight areas. This improves overall computational efficiency while ensuring color accuracy in key areas. The primary calibration frame sequence refers to the frame image sequence after the first color mapping is completed. At this stage, the colors have basically matched the standard color gamut of the target display device, but the color deviation in the actual display has not yet been considered. A colorimetric sensor embedded in the display device or a synchronized colorimetric acquisition system then acquires real-time colorimetric data after the primary calibration frame sequence is displayed on the physical screen. This measured colorimetric data is then compared with the standard color values that should be present for the same image in the target color gamut, and a color difference vector is calculated between the two. A color difference vector is a three-dimensional vector representing the difference between the target color and the actual color in color space (a mathematical model used to describe and represent color, which defines the range and representation of color). The direction of the vector indicates the direction of the color shift, and the length indicates the severity of the shift. For example, if a pixel in the standard sRGB color gamut should be neutral gray (R=128, G=128, B=128), but the measured color is bluish (R=110, G=125, B=145), the color difference vector will point toward the blue channel.When the system detects that the length of the color difference vector in a certain area exceeds a preset threshold, it indicates that the color presentation in that area deviates significantly from the intended color. This triggers secondary color gamut mapping for correction. The system analyzes the color shift trend based on the directional components of the color difference vectors and dynamically adjusts the HSL offset parameters accordingly. HSL stands for hue, saturation, and lightness, and is a color model that better aligns with human perception. By applying targeted offsets to deviating color areas in HSL space—for example, shifting the hue of bluish areas toward yellow-green or reducing the saturation of oversaturated areas—this allows for more precise control of the perceived color correction effect. The corrected data is then processed again through the color mapping model to generate a color-calibrated frame buffer. This frame buffer is ultimately fed into the display interface or multi-screen compositing module, ensuring that the image output on each display unit is highly consistent with the design target, achieving closed-loop color control from rendering to display. In high-end digital exhibition halls, for example, this mechanism can significantly reduce the discontinuity caused by color errors between adjacent screens, thereby enhancing visual continuity and immersion.

[0034] Step 150: Determine the delay compensation value of each display screen based on the frame buffer data and the PTP clock signal, insert the corresponding time offset into the output queue according to the delay compensation value, and generate a multi-screen output signal.

[0035] Specifically, in this embodiment, based on the frame buffer data and the PTP clock signal, the exclusive delay compensation value of each display screen is determined, and the corresponding time offset is inserted into the output queue through the delay compensation value to generate a multi-screen output signal. The core technologies used here include the PTP (Precision Time Protocol) synchronization mechanism and the delay correction algorithm. The system synchronizes the clocks of all rendering nodes and display terminals through the PTP protocol to ensure that each device has a consistent reference time. Subsequently, the offset value relative to the reference clock is calculated based on the signal transmission path, processing time and response delay of each screen. According to the offset value, a specific delay offset is set when outputting the frame buffer, so that all output signals are aligned in time when finally presented, avoiding screen tearing or frame jumping, thereby achieving precise synchronous display under multi-screen linkage.

[0036] In an optional implementation of this embodiment, based on the frame buffer data and the PTP clock signal, the delay compensation value of each display screen is determined, and the corresponding time offset is inserted into the output queue through the delay compensation value to generate a multi-screen output signal. The steps include: determining the basic delay value of the transmission delay according to the data packet feature vector of the frame buffer data and the pixel response time parameter of the physical screen database; obtaining the clock deviation value of each display screen through the PTP clock signal parser, and determining the number of hops and distance parameters of the signal transmission path based on the topological connection relationship of the physical screen database; monitoring the network delay fluctuation data of the signal transmission path in real time, and generating a dynamic delay correction factor through a Kalman filter; determining the delay compensation value of each display screen according to the basic delay value, clock deviation value, number of hops and distance parameters and the dynamic delay correction factor; inserting the time offset into the output queue of each display screen through the delay compensation value to generate a synchronized multi-screen output signal.

[0037] Specifically, in this embodiment, to achieve high-precision time synchronization output in a multi-screen display system, a basic transmission delay value is determined based on the packet feature vectors of the frame buffer data and the pixel response time parameters in the physical screen database. Frame buffer data is packaged into packets before being output to the physical display device. The packet feature vectors contain information such as frame size, frame rate, compression ratio, and bit rate. These characteristics directly affect the time overhead of data transmission. For example, a larger frame size or higher bit rate increases the amount of data required to be transmitted per unit time, which in turn increases network transmission time. Conversely, the pixel response time parameters in the physical screen database record the time it takes for the screen to receive a pixel signal and actually display that pixel. For example, in an LCD, this time is primarily determined by the rotation speed of the liquid crystal molecules. By jointly modeling the data transmission characteristics and response delay, a basic delay value can be obtained from the buffered frame to screen display, providing a reference for subsequent time compensation. The clock offset value of each display is then obtained using a PTP clock signal parser. The number of hops and distance parameters of the signal transmission path are calculated based on the topological connectivity recorded in the physical screen database. PTP (Precision Time Protocol) is a high-precision clock synchronization protocol for distributed systems. Its resolver detects deviations in the system clock of each display node to determine the time difference relative to a master or reference clock. For example, if a display node's local clock is 1.3 milliseconds ahead of the master clock, the deviation is +1.3ms. The system also determines the topological position of each display based on the physical connection structure, including the number of switching nodes (hops) and physical distance. For example, the distance from the master node to screen A may be only one hop, 20 meters, while screen B requires four hops, for a total distance of 60 meters. Signal transmission over long links increases propagation time, so hop count and distance are key factors in determining link latency variation. After obtaining physical link structure information, the system also monitors network latency fluctuations along the signal transmission path in real time and uses a Kalman filter to generate dynamic latency correction factors. Network latency fluctuation refers to the non-stationary transmission time differences caused by factors such as link load, device queuing, and protocol congestion during data packet transmission. This fluctuation exhibits nonlinear and random characteristics. The Kalman filter is an optimal estimation algorithm suitable for noise filtering and state prediction in dynamic systems. In this scenario, it is used to smooth delay fluctuations and generate a real-time correction factor to reflect the short-term trend of the current link. For example, when instantaneous network congestion causes a spike in the delay of some data packets, the Kalman filter can use a weighted calculation of past delay sequences to determine whether this is a short-term anomaly rather than a systematic deviation, thus avoiding overcompensation due to short-term fluctuations.By inputting the aforementioned base delay value, clock offset, hop count, and distance parameters, along with a dynamic delay correction factor, into the delay calculation model, the delay compensation value applied to each display in the current time period is determined. This compensation value, expressed as a time offset accurate to milliseconds or even microseconds, is used to correct for the time differences between frame data received and displayed by each display node in the multi-screen system. By inserting this compensation value into the output queue of the corresponding display, the system can control the timing of the output signal. Specifically, the rendered frame is delayed or advanced in the queue based on the compensation value before being output to the screen, ensuring that all screens achieve visual synchronization after physical and network delays. For example, in an immersive multi-channel projection system, color images between projectors must be synchronized within milliseconds. Otherwise, the color images in the tiled area will jump or misalign. This compensation mechanism effectively avoids such issues, ensuring the consistency and stability of the scene content. Ultimately, through the fusion of multiple parameters and real-time dynamic adjustment, a synchronized multi-screen output signal with strict timing control is generated, achieving a unified time domain representation from a logical perspective for physically distributed display devices.

[0038] In an optional implementation of the present embodiment, the operating current waveform of each display screen is collected through a distributed current sensor array, and when it is detected that the current fluctuation frequency exceeds a preset current threshold, an abnormal diagnosis signal is generated; the fault screen coordinate range of the abnormal diagnosis signal and the depth map information of the frame buffer data are extracted to generate a pixel migration boundary mask; based on the pixel migration boundary mask and the control point grid topological relationship of the virtual logical display space, the migration path of the faulty pixel to the adjacent screen is determined, and a pixel migration task list is generated; based on the target screen coordinate range of the pixel migration task list, the scene structure features of the depth map information are loaded to generate filling screen data for the missing area; the filling screen data is fused with the multi-screen output signal at the pixel level to reconstruct the frame data sequence of the faulty screen corresponding to the abnormal diagnosis signal.

[0039] Specifically, in this embodiment, the operating current waveform of each display screen is collected using a distributed current sensor array. The distributed current sensor array is a cluster of sensor devices deployed on the power supply module of each display unit. Its function is to record the instantaneous current change curve of the driving circuit at a high sampling frequency. Under normal conditions, the current waveform of a display screen exhibits stable periodic characteristics, and the amplitude variation is within a controllable range. However, when a display unit suffers a fault such as backlight damage, unstable power supply, or a short circuit in the driving circuit, its current waveform may undergo a sudden change in frequency or amplitude. Therefore, when the system detects that the current fluctuation frequency in a certain area exceeds the set current threshold, it determines that there is a potential hardware anomaly in that area and promptly generates an anomaly diagnostic signal indicating the specific range of the failed unit and the time when the anomaly was triggered. The corresponding coordinate range of the faulty screen is extracted from this anomaly diagnostic signal, and the pixel migration boundary mask is generated by combining it with the depth map information in the frame buffer data. The depth map information is a two-dimensional matrix data describing the distance relationship between each pixel in the scene and the observation viewpoint, which is used to express the three-dimensional structural information of the image. The pixel migration boundary mask is a binary mask image delineated on the depth map based on the spatial coordinates of the faulty area. It marks the boundary pixels between the normal display area and the faulty area, serving as a reference boundary for subsequent image reconstruction and task scheduling. The migration path of the faulty pixels to adjacent screens is then determined based on the topological relationship between the pixel migration boundary mask and the control point grid in the virtual logical display space, thereby generating a pixel migration task list. The control point grid is structural data that describes the projection form and connectivity of each display unit in a multi-screen display space in three-dimensional space. The logical grid, generated by parameters such as curvature, density, and boundaries, determines the spatial adjacency and geometric mapping between displays. The pixel migration task list is a pixel replacement plan generated based on the mapping relationship between the faulty screen and its adjacent units, recording the coordinates of the adjacent screen to which each faulty pixel should be migrated. For example, in a curved video wall, if a display anomaly occurs on the central screen, its pixels can be migrated through the curved areas of the left and right screens using boundary blending to maintain visual continuity and integrity. After determining the pixel migration path, the system needs to load the corresponding depth map information based on the coordinate range of each target screen in the task list, so as to extract the scene structure features of the target area to generate the filling screen data of the missing area. The scene structure features include spatial information such as surface normals, edge gradients, occlusion relationships, etc., which are used to refer to the three-dimensional form of the real scene for content interpolation and texture restoration when reconstructing the picture. The generation process of the filling screen data is usually based on multi-view image synthesis or neural network reasoning. For example, a depth-guided image restoration algorithm is used to complete the texture and spatial compensation of the missing area, so that the reconstructed picture conforms to the original visual logic in terms of geometry and color. Finally, the filling screen data is fused with the multi-screen output signal at the pixel level to reconstruct the frame data sequence of the faulty screen corresponding to the original abnormal diagnosis signal.Pixel-level fusion refers to aligning the generated fill content with the original picture pixel by pixel in the boundary area, using weighted averaging, gradient fusion, or optical flow compensation to achieve a smooth transition and avoid visual discontinuity or color jumps. For example, when constructing a fused frame, the brightness gradient of adjacent areas can be used to adjust the fuzzy weights of edge pixels so that the fill content naturally blends into the overall picture. Through this mechanism, even if a display unit completely fails, the system can dynamically migrate its picture data to adjacent units and perform intelligent completion, thereby achieving self-healing of the picture in a multi-screen environment and improving the system's robustness and stability.

[0040] According to the cross-platform content compatibility method based on virtual shooting LED display screen provided by the present application, the content stream of heterogeneous content sources is obtained through a dynamic metadata injector, and the irrelevant data content stream of the content stream is dynamically processed to generate a standardized intermediate data stream; a virtual logical display space is constructed according to the resolution parameters of the standardized intermediate data stream and the curvature data of the physical screen database; based on the control point grid of the virtual logical display space, dynamic resource allocation is performed through a semantically driven hierarchical rendering pipeline to generate a rendering task queue; according to the rendering task queue and the physical screen color profile, a three-dimensional lookup table is loaded through a feedback color engine for forward color gamut conversion, and the HSL offset is adjusted according to the collected real-time chromaticity data to generate color-calibrated frame buffer data; based on the frame buffer data and the PTP clock signal, the delay compensation value of each display screen is determined, and the corresponding time offset is inserted into the output queue through the delay compensation value to generate a multi-screen output signal. The present application scheme significantly improves the rendering accuracy, color consistency and display stability in the virtual shooting scene by constructing a standardized intermediate data stream, establishing a virtual logical display space, adopting a semantically driven hierarchical rendering and feedback color calibration mechanism, and combining a delay compensation algorithm to achieve multi-screen synchronous output.

[0041] Figure 2 The embodiment of the present application provides a cross-platform content compatibility device based on a virtual shooting LED display screen. The cross-platform content compatibility device based on a virtual shooting LED display screen can be used to implement the cross-platform content compatibility method based on a virtual shooting LED display screen in the aforementioned embodiment. Figure 2 As shown, the cross-platform content compatible device based on virtual shooting LED display mainly includes: The acquisition module 10 is used to acquire content streams from heterogeneous content sources through a dynamic metadata injector, perform dynamic metadata processing on irrelevant data content streams of the content streams, and generate standardized intermediate data streams; A construction module 20 is used to construct a virtual logical display space according to the resolution parameters of the standardized intermediate data stream and the curvature data of the physical screen database; An allocation module 30 is configured to dynamically allocate resources based on the control point grid of the virtual logical display space through a semantically driven hierarchical rendering pipeline and generate a rendering task queue; A generation module 40 is configured to load a three-dimensional lookup table through a feedback color engine to perform forward color gamut conversion based on the rendering task queue and the physical screen color profile, and adjust the HSL offset based on the collected real-time chromaticity data to generate color-calibrated frame buffer data; The processing module 50 is used to determine the delay compensation value of each display screen based on the frame buffer data and the PTP clock signal, insert the corresponding time offset into the output queue according to the delay compensation value, and generate a multi-screen output signal.

[0042] In an optional implementation of this embodiment, the acquisition module is specifically used to: perform frame cache remapping on native data packets of the content stream of the heterogeneous content source obtained by the dynamic metadata injector to generate a protocol-independent video frame sequence corresponding to the irrelevant data content stream; perform linear transformation based on the pixel format identification bits of the protocol-independent video frame sequence through the color gamut space conversion matrix to generate a color gamut reference frame; extract spatiotemporal features by performing semantic analysis on the metadata-free frame sequence of the protocol-independent video frame sequence to generate predicted metadata; and input the protocol-independent video frame sequence, the color gamut reference frame, and the predicted metadata into the data encapsulator to generate a standardized intermediate data stream.

[0043] In an optional implementation of this embodiment, the construction module is specifically used to: extract the original resolution parameters of the standardized intermediate data stream through a resolution parser, determine the display density in combination with the pixel pitch and screen size of the physical screen database, and generate density correction parameters; generate a control point grid that matches the screen topology based on the density correction parameters and the curvature radius data of the physical screen database; divide the projection area of each display screen in the virtual space based on the control point grid and the camera posture parameters; when it is detected that the projection areas of adjacent screens overlap, insert transition vertices in the control point grid according to the curvature difference values of the adjacent screens to generate a seamless virtual logical display space.

[0044] In an optional implementation of this embodiment, the allocation module is specifically used to: parse the spatial topological structure of the control point grid through a semantic analysis engine to generate a scene depth distribution map and an object semantic label matrix; divide the rendering priority area through a dynamic grading strategy according to the gradient change rate of the scene depth distribution map and the category weight of the object semantic label matrix; calculate the ray tracing sampling rate and texture mapping accuracy parameters of the rendering priority area through a resource allocator based on the rendering priority area and GPU memory status data; bind the ray tracing sampling rate, texture mapping accuracy parameters and corresponding control point coordinates to generate a rendering task queue with a hierarchical identification.

[0045] In an optional implementation of this embodiment, the generation module is specifically used to: generate a hierarchical priority weight matrix based on the hierarchical identifier of the rendering task queue and the display unit color gamut parameters of the physical screen color profile; load a three-dimensional lookup table through a feedback color engine, perform a forward color gamut conversion based on the hierarchical priority weight matrix on the frame data of each level in the rendering task queue, and generate a primary calibration frame sequence; obtain real-time chromaticity data of the primary calibration frame sequence after being displayed on the physical screen, and determine the color difference vector between the target color gamut and the measured color gamut based on the real-time chromaticity data; when it is detected that the color difference vector is greater than a preset threshold, adjust the HSL offset according to the direction component of the color difference vector, and perform secondary color gamut mapping on the primary calibration frame sequence to generate frame buffer data after color calibration.

[0046] In an optional implementation of this embodiment, the processing module is specifically used to: determine the basic delay value of the transmission delay based on the data packet feature vector of the frame buffer data and the pixel response time parameter of the physical screen database; obtain the clock deviation value of each display screen through the PTP clock signal parser, and determine the number of hops and distance parameters of the signal transmission path based on the topological connection relationship of the physical screen database; monitor the network delay fluctuation data of the signal transmission path in real time, and generate a dynamic delay correction factor through the Kalman filter; determine the delay compensation value of each display screen based on the basic delay value, clock deviation value, number of hops and distance parameters and dynamic delay correction factor; insert a time offset into the output queue of each display screen through the delay compensation value to generate a synchronized multi-screen output signal.

[0047] In an optional implementation of this embodiment, the processing module is further used to: collect the working current waveform of each display screen through a distributed current sensor array, and generate an abnormal diagnosis signal when it is detected that the current fluctuation frequency exceeds a preset current threshold; extract the fault screen coordinate range of the abnormal diagnosis signal and the depth map information of the frame buffer data to generate a pixel migration boundary mask; determine the migration path of the faulty pixel to the adjacent screen based on the pixel migration boundary mask and the control point grid topology relationship of the virtual logical display space, and generate a pixel migration task list; based on the target screen coordinate range of the pixel migration task list, load the scene structure features of the depth map information to generate filling screen data for the missing area; perform pixel-level fusion of the filling screen data with the multi-screen output signal to reconstruct the frame data sequence of the faulty screen corresponding to the abnormal diagnosis signal.

[0048] According to the solution of the present application, a cross-platform content compatibility device based on a virtual shooting LED display screen is provided. The content stream of a heterogeneous content source is obtained through a dynamic metadata injector, and dynamic metadata processing is performed on the irrelevant data content stream of the content stream to generate a standardized intermediate data stream; a virtual logical display space is constructed according to the resolution parameters of the standardized intermediate data stream and the curvature data of the physical screen database; based on the control point grid of the virtual logical display space, dynamic resource allocation is performed through a semantically driven hierarchical rendering pipeline to generate a rendering task queue; according to the rendering task queue and the physical screen color profile, a three-dimensional lookup table is loaded through a feedback color engine to perform forward color gamut conversion, and the HSL offset is adjusted according to the collected real-time chromaticity data to generate color-calibrated frame buffer data; based on the frame buffer data and the PTP clock signal, the delay compensation value of each display screen is determined, and the corresponding time offset is inserted into the output queue through the delay compensation value to generate a multi-screen output signal. This application solution significantly improves the rendering accuracy, color consistency and display stability in virtual shooting scenes by constructing a standardized intermediate data stream, establishing a virtual logical display space, adopting a semantic-driven hierarchical rendering and feedback-type color calibration mechanism, and combining a delay compensation algorithm to achieve multi-screen synchronous output.

[0049] According to the application plan Figure 3 An electronic device provided in an embodiment of the present application can be used to implement the cross-platform content compatibility method based on virtual shooting of LED display screens in the aforementioned embodiment, mainly comprising: Memory 301, processor 302, and computer program 303 stored on memory 301 and executable on processor 302. Memory 301 and processor 302 are connected via communication. When processor 302 executes computer program 303, the cross-platform content compatibility method based on virtual shooting of LED display screens described in the aforementioned embodiment is implemented. The number of processors can be one or more.

[0050] The memory 301 can be a high-speed random access memory (RAM) memory or a non-volatile memory such as a disk drive. The memory 301 is used to store executable program code. The processor 302 is coupled to the memory 301 .

[0051] Furthermore, the embodiment of the present application also provides a computer-readable storage medium, which can be provided in the electronic device in the above embodiments. The computer-readable storage medium can be the above Figure 3 Memory in the illustrated embodiment.

[0052] The computer-readable storage medium stores a computer program that, when executed by a processor, implements the cross-platform content compatibility method for virtual shooting of an LED display screen described in the aforementioned embodiment. Furthermore, the computer-readable storage medium may be a USB flash drive, a mobile hard drive, a read-only memory (ROM), RAM, a magnetic disk, or an optical disk, among other media capable of storing program code.

[0053] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0054] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0055] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A cross-platform content compatibility method based on virtual shooting of LED display screen, characterized in that: include: Obtaining content streams from heterogeneous content sources through a dynamic metadata injector, performing dynamic metadata processing on irrelevant data content streams of the content streams, and generating standardized intermediate data streams; Constructing a virtual logical display space according to the resolution parameters of the standardized intermediate data stream and the curvature data of the physical screen database; Based on the control point grid of the virtual logical display space, dynamic resource allocation is performed through a semantically driven hierarchical rendering pipeline to generate a rendering task queue; According to the rendering task queue and the physical screen color profile, a three-dimensional lookup table is loaded through a feedback color engine to perform forward color gamut conversion, and an HSL offset is adjusted according to the collected real-time chromaticity data to generate color-calibrated frame buffer data; Based on the frame buffer data and the PTP clock signal, the delay compensation value of each display screen is determined, and the corresponding time offset is inserted into the output queue according to the delay compensation value to generate a multi-screen output signal.

2. The cross-platform content compatibility method based on virtual shooting of LED display screen according to claim 1 is characterized in that: The step of obtaining content streams from heterogeneous content sources through a dynamic metadata injector, performing dynamic metadata processing on irrelevant data content streams of the content streams, and generating a standardized intermediate data stream includes: Performing frame buffer remapping on native data packets of content streams of heterogeneous content sources obtained by a dynamic metadata injector to generate a protocol-independent video frame sequence corresponding to the irrelevant data content stream; Based on the pixel format identification bit of the protocol-independent video frame sequence, a linear transformation is performed through a color gamut space conversion matrix to generate a color gamut reference frame; Extracting spatiotemporal features by performing semantic analysis on a metadata-free frame sequence of the protocol-independent video frame sequence to generate predicted metadata; The protocol-independent video frame sequence, the color gamut reference frame, and the prediction metadata are input into a data encapsulator to generate a standardized intermediate data stream.

3. The cross-platform content compatibility method based on virtual shooting of LED display screen according to claim 1 is characterized in that: The step of constructing a virtual logical display space according to the resolution parameters of the standardized intermediate data stream and the curvature data of the physical screen database includes: Extracting original resolution parameters of the standardized intermediate data stream through a resolution analyzer, determining display density in combination with pixel pitch and screen size in a physical screen database, and generating density correction parameters; generating a control point grid matching the screen topology according to the density correction parameter and the curvature radius data in the physical screen database; Dividing the projection area of each display screen in the virtual space based on the control point grid and the camera pose parameters; When it is detected that the projection areas of adjacent screens overlap, transition vertices are inserted into the control point grid according to the curvature difference values of the adjacent screens to generate a seamless virtual logical display space.

4. The cross-platform content compatibility method based on virtual shooting of LED display screen according to claim 3 is characterized in that: The step of dynamically allocating resources based on the control point grid of the virtual logical display space through a semantically driven hierarchical rendering pipeline to generate a rendering task queue includes: Analyzing the spatial topological structure of the control point grid through a semantic analysis engine to generate a scene depth distribution map and an object semantic label matrix; Dividing rendering priority areas through a dynamic grading strategy according to the gradient change rate of the scene depth distribution map and the category weights of the object semantic label matrix; Calculating the ray tracing sampling rate and texture mapping accuracy parameters of the rendering priority area through a resource allocator based on the rendering priority area and GPU memory status data; The ray tracing sampling rate, the texture mapping accuracy parameter and the corresponding control point coordinates are bound to generate a rendering task queue with a hierarchical identifier.

5. The cross-platform content compatibility method based on virtual shooting of LED display screen according to claim 1 is characterized in that: The step of loading a three-dimensional lookup table through a feedback color engine to perform forward color gamut conversion based on the rendering task queue and the physical screen color profile, and adjusting the HSL offset according to the collected real-time chromaticity data to generate color-calibrated frame buffer data includes: generating a hierarchical priority weight matrix according to the hierarchical identifier of the rendering task queue and the display unit color gamut parameter of the physical screen color profile; Loading a three-dimensional lookup table through a feedback color engine, performing forward color gamut conversion based on the hierarchical priority weight matrix on the frame data of each level in the rendering task queue, and generating a primary calibration frame sequence; Acquiring real-time chromaticity data of the primary calibration frame sequence after being displayed on the physical screen, and determining a color difference vector between a target color gamut and a measured color gamut based on the real-time chromaticity data; When it is detected that the color difference vector is greater than a preset threshold, the HSL offset is adjusted according to the direction component of the color difference vector, and secondary color gamut mapping is performed on the primary calibration frame sequence to generate color calibrated frame buffer data.

6. The cross-platform content compatibility method based on virtual shooting of LED display screen according to claim 1 is characterized in that: The step of determining the delay compensation value of each display screen based on the frame buffer data and the PTP clock signal, inserting the corresponding time offset into the output queue according to the delay compensation value, and generating a multi-screen output signal includes: determining a basic delay value of transmission delay according to a data packet feature vector of the frame buffer data and a pixel response time parameter of the physical screen database; The clock deviation value of each display screen is obtained through the PTP clock signal analyzer, and the number of hops and distance parameters of the signal transmission path are determined based on the topological connection relationship of the physical screen database; monitoring network delay fluctuation data of the signal transmission path in real time, and generating a dynamic delay correction factor through a Kalman filter; Determining a delay compensation value for each display screen according to the basic delay value, the clock deviation value, the hop count and distance parameter, and the dynamic delay correction factor; A time offset is inserted into the output queue of each display screen using the delay compensation value to generate a synchronized multi-screen output signal.

7. The cross-platform content compatibility method based on virtual shooting of LED display screen according to claim 1 is characterized in that: The method further comprises: The distributed current sensor array collects the working current waveform of each display screen and generates an abnormal diagnosis signal when it detects that the current fluctuation frequency exceeds the preset current threshold; Extracting the fault screen coordinate range of the abnormal diagnosis signal and the depth map information of the frame buffer data to generate a pixel migration boundary mask; Determine a migration path of the faulty pixel to an adjacent screen based on a topological relationship between the pixel migration boundary mask and a control point grid of the virtual logical display space, and generate a pixel migration task list; Based on the target screen coordinate range of the pixel migration task list, loading the scene structure features of the depth map information to generate filling screen data for the missing area; The filling screen data is fused with the multi-screen output signal at pixel level to reconstruct a frame data sequence of the fault screen corresponding to the abnormal diagnosis signal.

8. A cross-platform content compatible device based on virtual shooting LED display screen, characterized in that: The cross-platform content compatibility device based on virtual shooting of LED display screen is used to implement the cross-platform content compatibility method based on virtual shooting of LED display screen according to claim 1, and the cross-platform content compatibility device based on virtual shooting of LED display screen comprises: An acquisition module is used to acquire content streams from heterogeneous content sources through a dynamic metadata injector, perform dynamic metadata processing on irrelevant data content streams of the content streams, and generate a standardized intermediate data stream; A construction module, configured to construct a virtual logical display space according to the resolution parameters of the standardized intermediate data stream and the curvature data of the physical screen database; an allocation module for dynamically allocating resources through a semantically driven hierarchical rendering pipeline based on a control point grid of the virtual logical display space, and generating a rendering task queue; a generation module, configured to load a three-dimensional lookup table through a feedback color engine to perform forward color gamut conversion based on the rendering task queue and the physical screen color profile, and adjust the HSL offset according to the collected real-time chromaticity data to generate color-calibrated frame buffer data; The processing module is used to determine the delay compensation value of each display screen based on the frame buffer data and the PTP clock signal, insert the corresponding time offset into the output queue according to the delay compensation value, and generate a multi-screen output signal.

9. An electronic device, characterized in that: Comprising a memory and a processor, wherein: The processor is configured to execute a computer program stored in the memory; When the processor executes the computer program, the steps of the cross-platform content compatibility method based on virtual shooting of LED display screens described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the cross-platform content compatibility method based on virtual shooting of LED display screens described in any one of claims 1 to 7 are implemented.

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