3D game scene adaptation method and device based on arc-shaped screen

Abstract

The invention relates to the technical field of arc-shaped screens, and discloses a 3D game scene adaptation method and device based on an arc-shaped screen, and the method comprises the steps: carrying out the three-dimensional coordinate sampling of the surface of the arc-shaped screen, constructing an arc-shaped screen space model, and carrying out the dynamic tracking in a 3D game scene according to the arc-shaped screen space model, and obtaining scene mapping data; performing spatial distortion vector construction and compensation transformation analysis based on the scene mapping data to obtain target scene data; constructing a control grid node by using the curved surface feature tensor, applying a deformation constraint condition to the control grid node, and solving a deformation energy equation to obtain scene deformation control data; the rendering control instruction is created based on the scene deformation control data, and scene state acquisition and space adaptation calculation are executed according to the rendering control instruction to obtain the scene display data, so that accurate correction of scene display is realized, the image quality is remarkably improved, and the stability and continuity of scene display are ensured.

Description

Technical Field

[0001] The present application relates to the field of curved screen technology, and in particular to a 3D game scene adaptation method and device based on a curved screen. Background Art

[0002] With the rapid development of display technology, curved screens have been widely used in the gaming field due to their unique immersive visual experience. Compared with traditional flat displays, curved screens can provide a wider field of view and better spatial perception, and are particularly suitable for the presentation of 3D game scenes. However, due to the non-planar curved surface characteristics of curved screens, geometric distortion and visual distortion problems will occur when displaying 3D game scenes. Traditional scene rendering methods are difficult to ensure the realism and continuity of the picture.

[0003] At present, most rendering technologies for 3D game scenes are designed based on flat display. Directly applying these technologies to curved screens will lead to problems such as picture distortion, edge deformation, and visual discontinuity. Although some studies have proposed display methods based on geometric correction, these methods often only consider the adaptation of static scenes, which makes it difficult to cope with dynamic changes during the game, and have high computational complexity and poor real-time performance. In addition, during the display of curved screen games, due to the continuous change of viewing angle, scene rendering needs to consider both geometric deformation and visual perception. Existing technical solutions often separate these two aspects, lack a unified theoretical framework and systematic solutions, resulting in less than ideal rendering effects, especially when dealing with large scenes and high dynamic range game content, often resulting in technical problems such as screen tearing and unstable frame rate. Summary of the invention

[0004] The present application provides a 3D game scene adaptation method and device based on a curved screen, thereby achieving accurate correction of the scene display, significantly improving the picture quality, and ensuring the stability and continuity of the scene display.

[0005] In a first aspect, the present application provides a 3D game scene adaptation method based on a curved screen, and the 3D game scene adaptation method based on a curved screen includes: Sampling the three-dimensional coordinates of the curved screen surface, constructing a curved screen space model, and dynamically tracking the curved screen in a 3D game scene according to the curved screen space model to obtain scene mapping data; Based on the scene mapping data, a spatial distortion vector is constructed and a compensation transformation analysis is performed to obtain target scene data; Calculating a surface feature tensor according to the target scene data, constructing a control grid node using the surface feature tensor, applying deformation constraints to the control grid nodes and solving a deformation energy equation to obtain scene deformation control data; A rendering control instruction is created based on the scene deformation control data, and scene state acquisition and space adaptation calculation are performed according to the rendering control instruction to obtain scene display data.

[0006] The second aspect of the present application provides a 3D game scene adaptation device based on a curved screen, and the 3D game scene adaptation device based on a curved screen includes: A sampling module, used to perform three-dimensional coordinate sampling on the curved screen surface, construct a curved screen space model, and perform dynamic tracking in a 3D game scene according to the curved screen space model to obtain scene mapping data; An analysis module, used for constructing a spatial distortion vector and performing compensation transformation analysis based on the scene mapping data to obtain target scene data; A construction module, used to calculate a surface feature tensor according to the target scene data, construct a control grid node using the surface feature tensor, impose deformation constraints on the control grid nodes and solve the deformation energy equation to obtain scene deformation control data; A creation module is used to create a rendering control instruction based on the scene deformation control data, and perform scene state acquisition and space adaptation calculation according to the rendering control instruction to obtain scene display data.

[0007] Compared with the prior art, the present application has the following beneficial effects: by sampling the three-dimensional coordinates and constructing the spatial model of the curved screen surface, combined with the dynamic particle swarm tracking technology, the scene mapping data can be accurately obtained, effectively solving the problem of inaccurate scene spatial positioning in the traditional method. The spatial distortion vector construction and compensation transformation analysis method are adopted to process the geometric distortion and visual distortion separately. Through bilinear interpolation and boundary constraint optimization, the accurate correction of the scene display is achieved, and the picture quality is significantly improved. The surface feature tensor analysis and control grid node construction technology are introduced, and the precise deformation control of the scene is achieved by solving the deformation energy equation, which ensures the continuity and smoothness of the scene deformation process. The rendering resource allocation strategy based on priority distribution, combined with the cone model and shader optimization technology, realizes the efficient utilization of rendering resources and improves the real-time performance of the system. Through distributed state fusion and robustness control technology, the system delay and noise interference problems are effectively handled, ensuring the stability and continuity of the scene display. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0009] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0010] Figure 1 is a flow chart of a 3D game scene adaptation method based on a curved screen provided by an embodiment of the present invention; Figure 2 It is a schematic block diagram of the structure of a 3D game scene adaptation device based on a curved screen provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0013] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0014] It should be further understood that the term "and / or" used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. Figure 1 In the embodiment of the present application, an embodiment of a 3D game scene adaptation method based on a curved screen includes: Step 100: sampling the three-dimensional coordinates of the curved screen surface, constructing a curved screen space model, and performing dynamic tracking in a 3D game scene according to the curved screen space model to obtain scene mapping data; It is understandable that the execution subject of the present application can be a 3D game scene adaptation device based on a curved screen, or a terminal or a server, which is not limited here. The embodiment of the present application is described by taking a server as the execution subject as an example.

[0015] Specifically, the x-axis and y-axis are calibrated in the horizontal and vertical directions of the curved screen, respectively, and the z-axis is calibrated in the direction perpendicular to the screen reference plane to establish reference points for three-dimensional coordinate acquisition. These reference points are used to define the basic spatial range and reference coordinate system of the curved screen. According to the three-dimensional coordinate acquisition reference points, the surface of the curved screen is evenly divided into grid points according to certain rules to form a plurality of regularly arranged grid points. At each grid point, the corresponding spatial position coordinates are measured and recorded to obtain discrete spatial point data. The discrete spatial point data is divided into multiple data subsets according to the spatial distribution law, so that each subset can concentrate on describing the local spatial characteristics of the surface. After the data division is completed, the curvature of the adjacent point data of each data subset is calculated to extract the geometric characteristics of the local curved surface of the curved screen and obtain local curvature data. The local curvature data and the discrete spatial point data are fitted by cubic spline functions to construct a surface mathematical model to continuously describe the geometric shape of the curved screen. Based on the surface mathematical model, the specific distribution of the normal vector and the tangent vector is calculated at each spatial position to generate a curvature tensor matrix that reflects the change in the shape of the surface. The eigenvalue decomposition operation is performed on the curvature tensor matrix to obtain the curvature distribution vector, which reflects the curvature change characteristics of different areas on the surface of the curved screen. Based on the curvature distribution vector, the surface of the curved screen is divided into sub-regions, so that each sub-region can more accurately describe the local characteristics of the curved screen, while ensuring good continuity and coordination between different sub-regions, and obtaining regional segmentation data. An independent coordinate system is established for each sub-region in the regional segmentation data, and the spatial transformation relationship between the sub-regions is calculated. The spatial transformation relationship defines the geometric mapping rules between the various parts of the curved screen, thereby constructing the overall spatial model of the curved screen and describing the geometric characteristics and spatial structure of the curved screen. Using the constructed curved screen spatial model, an initial particle swarm is generated in the 3D game scene. These particle swarms, as the core of dynamic tracking, capture the dynamic changes in the scene in real time through interaction with the game scene content, and finally obtain the scene mapping data.

[0016] Based on the curved screen space model, the 3D game scene is evenly divided, and the scene space is logically partitioned based on the geometric characteristics of the curved screen to ensure that each partition can be reasonably mapped to the display area of ​​the curved screen. After completing the space division, the particle swarm is initialized to generate the initial particle distribution. The generation of the initial particle swarm needs to follow the surface characteristics and spatial distribution law of the curved screen model to ensure that the distribution density and position of the particle swarm match the geometric structure of the curved screen. The basic properties of each initial particle are set, including the position vector, velocity vector and acceleration vector, to form the basic state matrix of the particle, describing the dynamic characteristics of each particle in the 3D space. Based on the basic state matrix, the scene depth value, visual weight and deformation coefficient are configured for each particle, the description dimension of the particle is expanded, and the particle enhanced state data is generated. The depth value reflects the distance between the particle and the viewpoint, which is used to process the distance relationship of the space; the visual weight is used to measure the importance of the particle in the curved screen display; and the deformation coefficient is used to adapt to the surface characteristics of the curved screen to ensure that the particle can be mapped to the appropriate position. Based on the enhanced state data, the particle motion equation function is constructed to describe the dynamic changes of particles in the scene space. In order to accurately reflect the surface characteristics of the curved screen and its influence on the particle motion, the motion equation function is coupled with the curvature influence factor to generate a state transfer function. According to the state transfer function, the particle observation model is constructed by quantitatively analyzing the rendering parameters to generate observation correlation data. The observation model is used to describe the correlation between the display state of the particle group and the actual scene, and can provide necessary feedback data to correct the state of the particles. Based on the observation correlation data, the predicted value and the observed value of the particle are optimized by data fusion through the Kalman filter method to obtain the particle state optimization result. The Kalman filter can effectively combine the prior model of particle motion with the actual observation data, eliminate noise and optimize the state of the particle, so that the particle motion trajectory is smoother and meets the requirements of the curved screen display. According to the particle state optimization result, the interaction relationship of the local particle group is calculated to construct the group behavior model. The interaction relationship of the local particle group is established by analyzing the distance, direction and speed differences between particles, reflecting the synergy and overall behavior of the particle group in the scene space. Based on the group behavior model, the motion parameters of the particle swarm are dynamically updated to achieve dynamic adjustment of the particle swarm in the scene space. The optimized particle swarm motion parameters are mapped and transformed with the scene coordinate system to complete the final adaptation of the particles on the curved screen surface and generate scene mapping data that meets the display requirements of the curved screen surface.

[0017] Step 200: constructing a spatial distortion vector and performing compensation transformation analysis based on the scene mapping data to obtain target scene data; Specifically, the surface projection deformation amount is calculated for each rendering object in the scene mapping data. The deformation of each object when projected onto the curved screen is quantified by matching the curved surface characteristics of the curved screen with each rendering object in the mapping data. The quantification of the deformation is achieved through spatial coordinate transformation, in which the displacement between the source space point and the target space point of each object constitutes a spatial distortion vector, which describes the spatial distortion characteristics of the rendering object when mapped onto the curved screen surface. A geometric feature decomposition operation is performed on the spatial distortion vector, and it is decomposed into a geometric distortion component and a visual distortion component according to the surface projection rule. The geometric distortion component reflects the physical space offset caused by the change in the curvature of the curved screen, while the visual distortion component captures the influence of the viewing angle, the screen curvature, and the difference in the human eye's line of sight on the visual effect. After the decomposition is completed, a compensation transformation matrix is ​​constructed based on the geometric distortion component and the visual distortion component. The compensation transformation matrix maps the source space point to the target space to correct the distortion of the scene object on the curved screen display. The construction of the compensation transformation matrix needs to be combined with the position, size, shape of the objects in the scene and the curvature characteristics of the curved screen to ensure that the display effect of each object after mapping meets the requirements of realism and immersion. Through the mapping process, preliminary spatial transformation data is obtained. The geometric distortion component in the spatial transformation data is subjected to inverse projection transformation. By analyzing the curvature parameters of the curved screen, the projection compensation amount of each mapping point is calculated to obtain geometric correction data, accurately eliminating the deformation problem caused by the curved surface projection, so that the position and shape of the rendered object on the curved screen are more realistic. The rendering parameters are adjusted for the visual distortion component in the spatial transformation data. According to the law of viewing angle change, the compensation parameters of each pixel are calculated to generate visual optimization data. These compensation parameters can dynamically adjust the texture, lighting and other characteristics of the rendered object, so that the final display effect is more in line with the visual perception law of the audience. The geometric correction data and the visual optimization data are combined and calculated to generate compensation optimization data. In order to make the data distribution smoother and continuous, bilinear interpolation operations are performed on the compensation optimization data. By dividing the data into grids and smoothing the adjacent pixels, the boundary discontinuity problem caused by discrete calculation is eliminated, and continuous compensation data is obtained. The continuous compensation data is input into the boundary constraint processing unit to perform boundary calibration and gradient optimization. Boundary calibration is used to eliminate boundary distortion of the scene display area to ensure that the rendered object at the edge of the curved screen can maintain the same display effect as the center area. Gradient optimization improves the naturalness and consistency of the display by adjusting the color and brightness transition of adjacent areas. Through the above steps, the target scene data is finally obtained.

[0018] Step 300: Calculate the surface feature tensor according to the target scene data, construct the control grid nodes using the surface feature tensor, impose deformation constraints on the control grid nodes and solve the deformation energy equation to obtain the scene deformation control data; It should be noted that the target scene data is subjected to coordinate system standardization operation, and the coordinate system of the scene is integrated into the spatial model of the curved screen to ensure that the spatial relationship of all elements in the scene can adapt to the curvature characteristics of the curved screen. On the basis of standardization, a metric tensor is constructed according to the metric relationship of the local space. Through the geometric feature analysis of the metric tensor, the key surface information in the scene is extracted to obtain the surface feature tensor describing the geometric properties of the surface, including the local curvature, direction field and other geometric characteristics of the surface. The feature vector field is constructed based on the surface feature tensor. The spatial distribution law of the main direction, curvature line and geodesic of the surface is quantitatively calculated to obtain the surface feature vector. The surface feature vector is input into the meshing algorithm, and the spatial area is adaptively divided according to the topological structure of the scene to generate adaptive mesh node data. In the meshing, the complex scene geometry is effectively divided into refined mesh units under the guidance of the feature vector, so that each mesh unit can better reflect the local characteristics of the surface. For each generated mesh node, a local coordinate frame is established to analyze the geometric relationship between nodes in the local coordinate system. By calculating the associated transformation matrix between each node and its adjacent nodes, node transformation data is generated to describe the spatial relationship and transformation rules between grid nodes. A scene deformation model is established based on the node transformation data. By optimizing the node position parameters, the deformation variables between nodes are calculated to obtain spatial deformation data. The spatial deformation data directly reflects the geometric adjustment requirements of the scene when adapting to the curved screen. On this basis, geometric constraints and visual constraints are introduced for scene deformation. By constructing a geometric constraint function, the degree of preservation of the geometric shape after scene deformation is calculated to ensure that the deformation process does not destroy the basic structural characteristics of the scene. At the same time, by constructing a visual constraint function, the continuity of the visual effect after deformation is quantified to ensure that the display on the curved screen does not produce a fragmented or unnatural visual experience. Combining geometric constraints and visual constraints, a deformation energy function is constructed. Based on the deformation energy function, the optimal deformation parameters are obtained by numerical optimization method. These parameters can ensure that the scene deformation not only meets the spatial adaptation requirements of the curved screen, but also retains the original visual effects of the scene. After obtaining the optimal deformation parameters, these parameters are applied to the actual scene through scene deformation mapping operation to generate the final scene deformation control data.

[0019] Step 400: Create a rendering control instruction based on the scene deformation control data, and perform scene state acquisition and space adaptation calculation according to the rendering control instruction to obtain scene display data.

[0020] Specifically, score calculation is performed on each scene element in the scene deformation control data, including evaluating the scene importance, dynamic characteristics and visual significance of each element. By combining these evaluation indicators, the importance of each scene element in the current scene is quantified, and priority distribution data is generated. The priority distribution data is input into the rendering resource allocation model, and computing resources are allocated according to the priority of each area in the scene. By dividing the computing resource levels of scene areas with different priorities, a resource allocation weight map is generated. The resource allocation weight map can guide the resource allocation strategy in the rendering process, so that computing resources can be concentrated on scene areas with higher priorities. At the same time, according to the resource allocation weight map, the rendering accuracy of scene elements is controlled at multiple levels. For areas with high priority, a higher rendering accuracy is adopted to ensure the presentation of picture details; while for areas with low priority, the rendering accuracy is appropriately reduced, thereby saving computing resources and improving overall rendering efficiency. Through this step, detail control data is generated to refine the rendering accuracy requirements of different areas. A frustum model is constructed based on the detail control data to perform visibility analysis and spatial occlusion judgment on scene elements. The frustum model simulates the player's viewing angle range, and can efficiently filter out scene objects visible from the current viewing angle and generate a list of visible objects. By eliminating scene elements outside the viewing angle or being blocked, the rendering workload is reduced and the rendering performance is improved. The list of visible objects is input into the shader control unit to optimize the viewing angle-related shading parameters and texture sampling methods. By dynamically adjusting the shader parameters and texture processing strategies, more efficient rendering can be achieved while ensuring that the display effect meets the curvature characteristics of the curved screen, and generating rendering state data. For the image content in the rendering state data, post-processing is performed to improve the visual quality. Anti-aliasing processing is performed on the image edges to eliminate the jagged phenomenon caused by resolution limitations. Image processing parameters are generated through pixel-level smooth interpolation operations to improve the smoothness and detail expression of the image. A rendering cache mechanism is established based on the generated image processing parameters. The core of this mechanism is to store and index the rendering results of static scene content so that these rendering results can be reused, avoiding multiple renderings of unchanged content and improving rendering efficiency. Through this step, cache management data is generated. The cache management data is integrated with dynamic scene elements and organized into a rendering scheduling sequence. The rendering scheduling sequence is the final rendering control instruction, which contains the rendering priority, precision control and state management information for all scene elements. When executing the rendering control instruction, the scene state is collected according to the rendering scheduling sequence, and the spatial adaptation calculation is combined with the geometric characteristics of the curved screen to generate the final scene display data. The scene display data contains all the necessary information for adapting to the curved screen display, including surface mapping, precision optimization and visual enhancement processing, so as to achieve high-quality 3D game scene rendering.

[0021] The camera position, view angle change and dynamic object state in the rendering control instructions are collected in real time. Through the synergy of sensors, rendering engines and real-time monitoring modules, the dynamic changes of these elements in the scene are obtained, and these real-time collection results are input into the state monitoring system for integration and processing. By cleaning, classifying and integrating the data, scene change data containing real-time changes of the scene is generated. The scene change data is input into the time-varying delay analysis unit to analyze the dynamic delay characteristics in the network transmission and processing process. By analyzing these delay characteristics, a delay distribution model is constructed to generate delay feature data. The construction of the delay distribution model needs to be combined with the actual network conditions and device performance. Especially in a multi-node rendering environment, the delay characteristics change over time, so the model needs to have a high degree of dynamic adaptability. In order to improve the reliability of the system, a composite noise analysis is performed on the delay feature data. In this process, the noise distribution data is generated by superimposing the signal interference model and the environmental noise model to quantify the degree of influence of different noise sources on the system. Based on the noise distribution data, a distributed consistency controller is constructed to generate a state synchronization strategy through distributed computing in a multi-node environment. The state synchronization strategy coordinates the working status of each node to ensure that the entire system can maintain consistency under the influence of noise interference and delay, and obtains consistent control data. After the consistent control data is input into the distributed state fusion unit, the state fusion operation is performed through multi-node collaborative calculation, the state data from different nodes are integrated, and the state optimization data is generated. In order to keep the system stable under uncertainty and disturbance, the state optimization data is input into the robust controller for calculation. The robust controller generates steady-state control data by suppressing system disturbances and external uncertainties to ensure the operation stability of the system in complex environments. The steady-state control data is input into the spatial mapping module to perform spatial mapping transformation. Combined with the geometric characteristics of the curved screen, the scene is adapted to the curved screen so that it can be presented on the curved screen without distortion. In the process of scene adaptation operation, the accuracy of spatial mapping is considered, and the change of viewing angle, the state of dynamic objects and the depth information of the scene are combined to ensure that the final display effect has a high sense of immersion and visual consistency. Through the above steps, the scene display data adapted to the curved screen is finally obtained.

[0022] In the embodiment of the present application, by sampling the three-dimensional coordinates of the curved screen surface and constructing a spatial model, combined with dynamic particle swarm tracking technology, the scene mapping data can be accurately obtained, effectively solving the problem of inaccurate scene space positioning in traditional methods. The spatial distortion vector construction and compensation transformation analysis method are adopted to process the geometric distortion and visual distortion separately. Through bilinear interpolation and boundary constraint optimization, accurate correction of scene display is achieved, and the picture quality is significantly improved. The surface feature tensor analysis and control grid node construction technology are introduced to achieve precise scene deformation control by solving the deformation energy equation, ensuring the continuity and smoothness of the scene deformation process. The rendering resource allocation strategy based on priority distribution, combined with the cone model and shader optimization technology, realizes the efficient utilization of rendering resources and improves the real-time performance of the system. Through distributed state fusion and robustness control technology, the system delay and noise interference problems are effectively handled, ensuring the stability and continuity of the scene display.

[0023] In a specific embodiment, the process of executing step 100 may specifically include the following steps: The x-axis and y-axis are calibrated in the horizontal and vertical directions of the curved screen respectively, and the z-axis is calibrated in the direction perpendicular to the screen reference plane to obtain the three-dimensional coordinate acquisition reference point; The curved screen surface is divided into uniform grid points according to the three-dimensional coordinate acquisition reference points to obtain multiple grid points, and the spatial position coordinates of each grid point are recorded to obtain discrete spatial point data; According to the spatial distribution law, the discrete spatial point data is divided into multiple data subsets, and the curvature of the adjacent point data of the data subset is calculated to obtain the local curvature data; Fitting the local curvature data and discrete spatial point data with a cubic spline function to obtain a surface mathematical model, and based on the surface mathematical model, calculating the spatial distribution of the normal vector and the tangent vector for each spatial position to obtain a curvature tensor matrix; Performing eigenvalue decomposition operation on the curvature tensor matrix to obtain a curvature distribution vector, and dividing the curved screen surface into sub-regions according to the curvature distribution vector to obtain region segmentation data; An independent coordinate system is established based on each sub-region in the region segmentation data, and the spatial transformation relationship between the sub-regions is calculated to obtain a curved screen space model; An initial particle swarm is generated in a 3D game scene according to the arc screen space model, and the initial particle swarm is dynamically tracked to obtain scene mapping data.

[0024] Specifically, in order to calibrate the three-dimensional coordinate system of the curved screen, axis) and vertical direction Axis) divides the screen evenly, and at the same time divides the screen perpendicular to the screen reference plane ( The physical width of the curved screen is , the physical height is , whose curvature is given by , which is defined as the curvature of the screen surface ( ,in is the radius of the surface). By dividing the screen horizontally into Segments, divided vertically into Segment, calculate the size of each partition unit: ; in, is the width of each grid cell in the horizontal direction, is the height of each grid cell in the vertical direction. For each point on the screen surface, its three-dimensional coordinates are defined as ,in, , indicating the The horizontal coordinates of the grid points; , indicating the The coordinates of the grid points in the vertical direction; , indicating the The coordinates of the grid points in the direction perpendicular to the screen plane. After the grid points are calibrated, the discrete space point data is obtained to describe the spatial geometric characteristics of the curved screen surface. According to the discrete space point data, it is divided into multiple data subsets to calculate the local curvature. For any two adjacent points and , the curvature between them Calculated by the following formula: ; in, Represents the modulus of the difference between the tangent vectors of two points; Represents the Euclidean distance between two points; , for point The tangent vector of the surface represents the rate of change of the surface direction at that point. These local curvature data reflect the geometric changes of the curved screen surface in different areas and provide a basis for surface fitting. Based on the local curvature data and discrete space point data, the cubic spline function is used for surface fitting to construct the overall surface mathematical model. Assume that the fitted surface is ,in and is a parameter variable (represented by normalized coordinates on the grid). The cubic spline function model is expressed as: ; in, is the weight coefficient of the control point, which indicates the influence of each control point on the surface shape; , is the cubic spline basis function, which is used to define the smoothness of the surface; It is a normalized coordinate, usually in the range of [0, 1]. The normal vector of each position is calculated by the surface mathematical model. and the tangent vector , respectively represent the surface normal direction and tangent direction of the point: ; Further analysis of the surface mathematical model is done by constructing the curvature tensor matrix To describe the geometric properties of the surface: ; in, is the principal curvature, which represents the maximum curvature of the surface along the first principal direction; is the secondary curvature, which represents the minimum curvature of the surface along the second principal direction. After performing eigenvalue decomposition, the curvature distribution vector is obtained, which is used to divide the screen surface into sub-areas. By establishing an independent coordinate system for each sub-area and calculating the spatial transformation relationship (such as rotation and translation) between adjacent areas, the overall spatial model of the curved screen is constructed. Based on this model, an initial particle swarm is generated in the 3D game scene. For example, assuming the particle position is , the speed is , through the dynamic tracking formula: ; Simulate the dynamic changes of particles in the scene. Combine geometric analysis with particle dynamics to generate scene mapping data that is suitable for the curved screen and achieve distortion-free scene display on the curved screen.

[0025] In a specific embodiment, the execution step generates an initial particle group in a 3D game scene according to the arc screen space model, and dynamically tracks the initial particle group to obtain scene mapping data, which may specifically include the following steps: Based on the arc screen space model, the 3D game scene is evenly divided and the particle swarm is initialized to generate the initial particle swarm; The position vector, velocity vector and acceleration vector are set for each particle in the initial particle group to obtain the particle basic state matrix, and the scene depth value, visual weight and deformation coefficient are configured for each particle according to the particle basic state matrix to obtain the particle enhancement state data; The motion equation function is constructed based on the particle enhancement state data, and the motion equation function is coupled with the curvature influence factor to obtain the state transfer function; Based on the state transfer function, a particle observation model is constructed through quantitative analysis of rendering parameters to obtain observation-related data. Kalman filter update operation is performed based on the observation-related data to perform data fusion optimization on the predicted value and the observed value to obtain the particle state optimization result. According to the particle state optimization results, the interaction relationship of the local particle group is calculated, the group behavior model is constructed, and it is dynamically updated to obtain the particle group motion parameters; The particle swarm motion parameters are mapped and transformed with the scene coordinate system to obtain the scene mapping data.

[0026] Specifically, the 3D game scene is evenly divided based on the spatial model of the curved screen. Based on the geometric characteristics of the curved screen, such as the width of the screen ,high , radius of curvature (with curvature Inversely proportional to the scene space), the scene space is divided into regular grids. Assume that the horizontal direction is divided into units, divided vertically into units, the resolution of the grid is: ; For the center point of each grid cell, its three-dimensional coordinates are expressed as ,in: ; These points constitute the spatial distribution of the initial particle swarm. Assume that the initial particle swarm contains particles, the position vector of each particle is expressed as Initialize the basic dynamic state for each particle, including the position vector , velocity vector , acceleration vector These states are combined into the basic state matrix of the particle : ; in, is the current position coordinate of the particle; is the velocity component of the particle; Is the acceleration component of the particle. Based on the basic state matrix, configure enhanced properties for each particle, including the scene depth value , visual weight and deformation coefficient .in, Indicates the depth from the particle to the observation point (or viewpoint), and the calculation formula is: ; in is the location of the observation point; Indicates the contribution of particles to the visual effect, which is determined by the curvature area weight of the particle; It is used to describe the degree of deformation of particles when adapting to the curved screen, which is calculated by curvature. Combined with these enhanced properties, the enhanced state data of the particles is formed. Based on the enhanced state data, the particle motion equation function is constructed In 3D space, the movement of particles is affected by position, velocity and acceleration, and the position update formula is: ; in is the time step. At the same time, the particle velocity is updated as: ; In order to reflect the influence of the curvature of the curved screen on the particle motion, the motion equation and the curvature influence factor are combined. Coupling, update acceleration: ; in is the normal vector of the particle point. Based on the motion equation, combined with rendering parameters (such as viewing angle, lighting and resolution), a quantitative analysis is performed to build a particle observation model. The observation model is expressed by the formula Describes the observed state of the particle, where is a nonlinear projection function used to map the enhanced state data to the screen display. In order to optimize the motion state of particles, data fusion is performed based on the Kalman filter method. The state update formula of the Kalman filter is: ; in, It is the optimized enhanced state data; is the Kalman gain, which is used to weigh the predicted value against the observed value; is the observation matrix, which represents the influence of particle state on the observation result. The particle state data obtained by filtering optimization is used to calculate the interaction relationship of the local particle group and build a group behavior model. By introducing the Boolean attraction-repulsion model calculation: ; in are the attraction and repulsion coefficients, is the distance threshold of attraction and repulsion. The particle swarm motion parameters are mapped to the scene coordinate system, and the coordinate transformation formula is used. The particle state is associated with the scene space, and finally the scene mapping data adapted to the curved screen is generated for dynamic rendering and display.

[0027] In a specific embodiment, the process of executing step 200 may specifically include the following steps: Calculate the surface projection deformation of each rendered object in the scene mapping data, and construct a spatial distortion vector through spatial coordinate transformation; Performing geometric feature decomposition operation on the spatial distortion vector, decomposing the spatial distortion vector into geometric distortion component and visual distortion component according to the surface projection rule; Construct a compensation transformation matrix according to the geometric distortion component and the visual distortion component, map the source space point to the target space, and obtain the spatial transformation data; Perform inverse projection transformation on the geometric distortion component in the spatial transformation data, calculate the projection compensation amount according to the curvature parameter of the curved screen, obtain geometric correction data, adjust the rendering parameters of the visual distortion component in the spatial transformation data, calculate the compensation parameters according to the law of viewing angle change, and obtain visual optimization data; The geometric correction data and the visual optimization data are combined and calculated to obtain the compensation optimization data, and a bilinear interpolation operation is performed on the compensation optimization data, and adjacent pixel points are smoothed according to the grid division to obtain continuous compensation data; The continuous compensation data is input into the boundary constraint processing unit for boundary calibration and gradient optimization to obtain the target scene data.

[0028] Specifically, the initial spatial position of each rendered object is in three-dimensional coordinates express is the position of the object in the plane, The depth information of the object. Due to the curved surface characteristics of the curved screen, all rendered objects are projected onto the curved surface to fit the screen surface. By calculating the deformation of the object's curved projection, the position offset of each object in the mapping from the plane to the curved surface is quantified. The radius of curvature of the screen is In the case of The calculation formula is: ; in, are coordinates in the source space; is the coordinate in the target surface space. Through this transformation, we get the spatial distortion vector of each rendered object. ,in are the offsets of the object in three dimensions respectively. Perform geometric feature decomposition on the spatial distortion vector. Each distortion vector D is decomposed into geometric distortion components according to the surface projection rule. and visual distortion component . Geometric distortion component Reflects the physical displacement of the object under the change of the curved screen geometry, visual distortion component It describes the distortion caused by the change of viewing angle or the perception characteristics of the human eye. The decomposition formula is: ; in, is the normal vector at the object's location, defined as Projection along the normal vector direction indicates geometric changes; Is the residual component, representing the visual change. Based on the decomposed geometric distortion component and visual distortion component, construct the compensation transformation matrix , used to convert the source space point Mapping to target space point The general expression of the compensation transformation matrix is: ; in is the identity matrix, and They are the geometric compensation term and the visual compensation term. For the geometric distortion component in the constructed spatial transformation data, a reverse projection transformation is performed to calculate the projection compensation amount. By analyzing the curvature parameters of the curved screen To correct the geometric offset of the object so that it fits the screen surface better. The inverse projection transformation formula is: ; in is the geometric position after projection compensation, is the curvature parameter, is the normal vector. At the same time, for the visual distortion component in the spatial transformation data, the rendering parameters such as lighting, texture and resolution are adjusted according to the law of viewing angle change. Assume that the viewing angle parameter is (the angle between the viewing direction and the normal), then the visual compensation parameter Calculated by the following formula: ; in is the visual weight of the object. Applied to the rendering pipeline, it optimizes the visual display effect and generates visual optimization data. Combine and calculate the geometric correction data and the visual optimization data to generate compensation optimization data. Smooth the compensation optimization data through bilinear interpolation to eliminate pixel-level jumps caused by discrete grids. Input the generated continuous compensation data into the boundary constraint processing unit for boundary calibration and gradient optimization. In boundary calibration, the pixel value of the object beyond the screen area is limited to ensure that the rendered object is fully adapted to the curved screen display area. In gradient optimization, the display effect is made more natural and smooth by minimizing the pixel gradient changes. Generate target scene data adapted to the curved screen.

[0029] In a specific embodiment, the process of executing step 300 may specifically include the following steps: The target scene data is subjected to coordinate system standardization operations, a metric tensor is constructed based on the local spatial metric relationship, and the metric tensor is subjected to geometric feature analysis to obtain a surface feature tensor; Based on the surface feature tensor, the feature vector field is constructed, and the spatial distribution law of the main direction, curvature line and geodesic line of the surface is quantified and calculated to obtain the surface feature vector; The surface feature vector is input into the meshing algorithm, the space area is adaptively divided according to the scene topology structure, the mesh node data is obtained, and a local coordinate frame is established for each node in the mesh node data, and the associated transformation matrix between adjacent nodes is calculated to obtain the node transformation data; A scene deformation model is established based on the node transformation data, and the deformation variables are calculated by optimizing and adjusting the node position parameters to obtain the spatial deformation data. A geometric constraint function is constructed for the spatial deformation data to calculate the degree of geometric shape preservation, and a visual constraint function is constructed to calculate the continuity of the visual effect to obtain a deformation energy function; Based on the deformation energy function, a numerical optimization solution is performed to obtain the optimal deformation parameters, and a scene deformation mapping operation is performed on the optimal deformation parameters to obtain the scene deformation control data.

[0030] Specifically, the target scene data contains all objects that need to be rendered and their positions, shapes, and texture information in three-dimensional space. These data are originally defined in the global coordinate system, but in order to adapt to the geometric characteristics of the curved screen, the coordinate system is standardized. Assume that the point position in the global coordinate system is , through the standardization transformation, it is mapped to the unitized local coordinate system so that the coordinate range is normalized to [0,1]. The standardization formula is: ; in, is the standardized coordinate; are the minimum and maximum boundary points of the scene data respectively. Based on the coordinate standardization, the metric tensor is constructed according to the local spatial metric relationship of the scene. The metric tensor describes the geometric properties of the local surface and is defined as: ; in, , indicating that along Change of direction; ,express and The coupling changes between , indicating that along Change of direction; is the dot product operation. The constructed metric tensor is subjected to geometric feature analysis, and the surface feature tensor is obtained by calculating its eigenvalues ​​and eigenvectors. . Eigenvalue The principal curvature of the corresponding surface, the eigenvector Represent the main directions of the surface respectively. Based on the surface eigentensor, the eigenvector field is constructed to quantitatively calculate the spatial distribution of the main directions, curvature lines and geodesics of the surface. Assume that the eigenvector field is , expressed by the following formula: ; in, is the weight coefficient, which is used to adjust the contribution of different eigenvectors to the field. The constructed eigenvector field is input into the meshing algorithm, and adaptive meshing is performed according to the topological structure of the scene to generate mesh node data. Assume that the mesh node set is , the position of each node is . Establish a local coordinate frame for each node and define the associated transformation matrix between adjacent nodes : ; in, is the rotation matrix, describing the angle change between nodes; is the translation vector, describing the displacement between nodes. Based on the node transformation data, a scene deformation model is established. Assume that the initial position of the node is , calculate the deformation by optimizing the node position parameters , get the node position after deformation: ; Deformation Determined by optimization calculation to minimize the deformation energy function. During the calculation process, the geometric constraint function is constructed To measure the degree of preservation of geometry: ; in and are the transformation matrices before and after deformation. At the same time, construct the visual constraint function To assess visual continuity: ; in is the visual weight, is the current visual state, is the target visual state. The total deformation energy function is: ; in is the trade-off coefficient. The deformation energy function is numerically optimized and the optimal deformation parameters are obtained by gradient descent or finite element method. These parameters are applied to the mesh nodes through the scene deformation mapping operation to generate scene deformation control data.

[0031] In a specific embodiment, the execution step creates a rendering control instruction based on the scene deformation control data, and performs scene state acquisition and space adaptation calculation according to the rendering control instruction to obtain the scene display data, which may specifically include the following steps: The importance, dynamic characteristics and visual significance of scene elements in the scene deformation control data are scored and calculated to obtain priority distribution data; The priority distribution data is input into the rendering resource allocation model, and the scene areas with different priorities are divided into computing resource levels to obtain a resource allocation weight map. The rendering accuracy of the scene elements is controlled at multiple levels according to the resource allocation weight map to obtain detail control data. Construct a view cone model based on the detail control data, perform visibility analysis and spatial occlusion judgment on scene elements, obtain a list of visible objects, and input the list of visible objects into the shader control unit, optimize and calculate the viewing angle-related shading parameters and texture sampling methods, and obtain rendering status data; Anti-aliasing is performed on the image edges in the rendering state data, and image processing parameters are obtained through smooth interpolation operations at the pixel level; A rendering cache mechanism is established based on image processing parameters to store and index the rendering results of static scene content to obtain cache management data, and the cache management data and dynamic scene elements are organized into a rendering scheduling sequence to obtain rendering control instructions; According to the rendering control instructions, scene state acquisition and space adaptation calculation are performed to obtain scene display data.

[0032] Specifically, the importance, dynamic characteristics and visual significance of the scene elements in the scene deformation control data are scored and calculated. elements, each element The score is composed of three key indicators: Importance score , Dynamic Characteristics Scoring and visual saliency score These scores are linearly combined to calculate the total priority score : ; in, is the weight coefficient, which is used to adjust the impact of each score on the overall priority; Reflects the importance of scene elements in the current rendering task, such as the main target or key background that the player focuses on; Indicates the dynamics of scene elements through their velocity or acceleration calculate: ; in is the vector modulus, is the acceleration weight; Describes the visual prominence of an element, such as color contrast or texture complexity. The priority score of each element is calculated using the above formula to obtain the priority distribution data. The priority distribution data is input into the rendering resource allocation model to divide the resource levels of different areas in the scene. Assuming that the total amount of rendering resources is , the amount of resources allocated to each element is , resources are allocated proportionally according to the priority scores of the elements: ; Through this calculation, a resource allocation weight map is generated to guide the rendering accuracy control of each scene element. For example, for elements with high importance, higher rendering resources are allocated to improve their resolution and detail fidelity. Based on the resource allocation weight map, multiple levels of rendering accuracy control are performed on each scene element to generate detail control data. ,in Representation Elements Based on the detail control data, a frustum model is constructed to analyze the visibility and determine the spatial occlusion of scene elements. The frustum model represents the visible range of the player's perspective, which is composed of the viewpoint , sight direction and viewing angle range Definition, where and The horizontal and vertical viewing angles are respectively. To determine whether a scene element is within the viewing cone, the following formula is used: ; in, is the center point position of the scene element; It is a dot product operation. Through the frustum analysis, the visible objects within the viewing range are screened out to generate a list of visible objects. The visible object list is input into the shader control unit to optimize the viewing parameters (such as lighting direction, shadow intensity) and texture sampling method related to the viewing angle. Assume that the optimization parameters of texture sampling are , calculated by the following formula: ; in, is the texture weight; is the rendering accuracy level; is the depth value of the element from the viewpoint. The optimized result generates rendering state data, including texture, lighting and shadow parameters. In order to improve the visual quality of the rendered image, anti-aliasing is performed on the image edges in the rendering state data. Through pixel-level smooth interpolation operations, the aliasing phenomenon is effectively eliminated and smoother edges are generated. Based on the generated image processing parameters, a rendering cache mechanism is established. This mechanism reduces the overhead of repeated rendering by storing the rendering results of static scene content (such as background elements). Assume that the cache index of the static scene is , the update frequency of dynamic elements is , then the rendering priorities of static and dynamic content are coordinated through the rendering scheduling sequence, and finally the rendering control instructions are generated. When executing the rendering control instructions, according to the current state of the scene, the dynamic changes are collected and the spatial adaptation calculation is performed, and the final scene display data is generated in combination with the rendering scheduling sequence.

[0033] In a specific embodiment, the process of executing step 400 may specifically include the following steps: The camera position, viewing angle change and dynamic object status in the rendering control instruction are collected in real time to obtain real-time collection results, and the real-time collection results are input into the status monitoring system for data integration to obtain scene change data; Input the scene change data into the time-varying delay analysis unit, build a delay distribution model according to the time-varying characteristics of network transmission delay and processing delay, and obtain delay feature data; Perform composite noise analysis on the delay characteristic data, obtain noise distribution data through superposition calculation of signal interference model and environmental noise model, and build a distributed consistency controller based on the noise distribution data. Generate state synchronization strategy through multi-node collaborative calculation to obtain consistency control data. Perform distributed state fusion operations on the consistency control data to obtain state optimization data, and input the state optimization data into the robust controller to suppress system disturbances and uncertainties to obtain steady-state control data; A spatial mapping transformation is performed on the steady-state control data, and a scene adaptation operation is performed according to the display characteristics of the curved screen to obtain scene display data.

[0034] Specifically, in the rendering control process, the camera position, perspective changes, and the state of dynamic objects are the core data sources for real-time adjustment of scene display effects. Assume that the camera position is , the viewing direction is given by the unit vector Definition: The state of a dynamic object is determined by its position ,speed , acceleration And the rotation angle In the real-time collection stage, the data is continuously acquired through sensors and input devices, and organized into a real-time collection result matrix. :

[0035] Each row represents the real-time status of a dynamic object or camera. The real-time acquisition results are input into the status monitoring system, and after data cleaning, classification and integration, scene change data is generated. Scene change data matrix The dynamic changes of the scene at a specific time point are recorded, and the basis for delay analysis and subsequent optimization is provided. The scene change data is input into the time-varying delay analysis unit to build a delay distribution model. The delay distribution model takes into account the network transmission delay. and processing delays , which together constitute the total delay : ; Assume that network delay follows a normal distribution , processing delay follows an exponential distribution , the probability density function of the total delay is expressed as a convolution form: ; in and are the probability density functions of network and processing delays respectively. Through this delay distribution model, we can get the delay characteristic data , including the mean, variance and time-varying characteristics of the delay. These data can quantify the delay impact of different scene elements. Composite noise analysis is performed on the delay feature data, considering the superposition effect of signal interference and environmental noise. Assume that the signal interference follows a normal distribution with a mean of zero. , the environmental noise is white noise, and its power spectrum density is . Composite noise It is expressed as: ; in, , is signal interference; , is the environmental noise. By superposition calculation, the noise distribution data matrix is ​​obtained , and build a distributed consistency controller based on this. The consistency controller generates a state synchronization strategy through multi-node collaborative calculation to optimize the dynamic consistency in the scene. Assume that the state of each node is , the consistency control law is: ; in, For Node The set of neighbor nodes of is the connection weight between nodes. By solving this differential equation group, we can get the consistency control data , used to maintain the dynamic consistency of the scene. Perform distributed state fusion operations on the consistency control data, combine the local states of each node, and generate globally optimized state data To improve the stability of the system, the optimization data is input into the robust controller, which generates steady-state control data by suppressing disturbances and uncertainties. The design of the robust controller is based on the Lyapunov stability criterion, which aims to find a Lyapunov function , so that: ; The meaning of each variable is as follows: is the Lyapunov function, a non-negative scalar function used to describe the "energy" or "degree of deviation" of the system state. It is usually defined as a quadratic form, for example: ; in, is the state vector of the system, such as the position, velocity, acceleration and other state information of a dynamic object in the scene. ; is a symmetric positive definite matrix used to weigh the impact of different state variables on system stability. The time derivative of the Lyapunov function is used to determine whether the system state tends to be stable as it changes over time. , indicating that the system energy does not increase and tends to be stable. is the Lyapunov function for the state vector The gradient of is defined as: ; is the time derivative of the state vector, which represents the dynamic change of the system state. For example, the position change of a dynamic object is expressed in the following form: ; in, is the system dynamic matrix, describing the linear relationship between system states; is the control input matrix, which is used to describe the influence of external control on the system state; is the control input vector, which represents the input applied by the controller, such as the command to adjust the speed and position of the dynamic object; is an external disturbance or uncertainty, such as network delay, noise and other nonlinear effects. The design goal of a robust controller is to find a control input , so that in the presence of external disturbances Under this condition, the system can still maintain stability, that is, it satisfies: ; is the control gain, which is used to control the convergence speed; is the modulus of the state vector, indicating the degree to which the system deviates from the equilibrium state. Control input The design is based on the following formula: ; in, is the feedback gain matrix, which is obtained through optimization calculation and is used to adjust the response strength of the controller. Substitute into the dynamic equation , to ensure the stability of the system, that is . Perform spatial mapping transformation on the steady-state control data and perform scene adaptation calculation according to the display characteristics of the curved screen. Assume that the curvature radius of the curved screen is , the target position of each scene element is . Through the surface mapping formula: ; The depth information of the element is adjusted to adapt to the curved surface characteristics of the screen to generate the final scene display data.

[0036] In this embodiment, after the scene display data is generated, it also includes a display enhancement control step: constructing a feature space mapping network, separating and extracting the spatial distribution features and visual perception features in the scene display data, and obtaining feature decomposition data; performing extreme value distribution analysis on the spatial distribution features in the feature decomposition data, modeling the feature boundaries through marginal distribution functions, and obtaining boundary feature data; inputting the boundary feature data into a multivariate extreme value model, constructing a joint distribution function between feature variables, and performing probability density estimation on the feature space to obtain density distribution data; establishing a display area adaptive weight model based on the density distribution data, and performing importance evaluation and weighting on different display areas. allocation to obtain regional control data; perform fusion optimization calculation on the regional control data and visual perception features to generate display enhancement control instructions, and adjust the display parameters in real time according to the display enhancement control instructions to obtain parameter optimization data; perform display effect evaluation based on the parameter optimization data, score the display results through visual quality evaluation indicators to obtain quality evaluation data; input the quality evaluation data into the feedback controller to generate a parameter adjustment strategy, and dynamically optimize the display effect according to the parameter adjustment strategy to obtain display optimization data; perform fine-tuning and enhanced control on the scene display effect according to the display optimization data to achieve continuous improvement of the scene display quality.

[0037] Among them, when fine-tuning and enhancing the control of the scene display effect according to the display optimization data, it includes: constructing a pre-training model for the scene rendering stage, extracting the feature information of different display areas of the curved screen in the continuous rendering stage through a deep neural network, adaptively segmenting the display area, and establishing a mapping relationship between areas, and quantitatively analyzing the rendering parameters of each area to obtain regional feature data; inputting the regional feature data into the instance-level contrast learning network, using the dynamic threshold method to select anchor samples, constructing a local rendering quality evaluation matrix, extracting the stable rendering features of each display area, and filtering the interference features that do not match the current rendering state through a bidirectional attention mechanism to obtain instance feature data; performing category-level contrast learning operations on the instance feature data, constructing a multi-scale feature pyramid, enhancing the contrast between the main body and the background of the scene, using an adaptive weight strategy to balance the contribution of features of different scales, optimizing the feature expression through a cross-entropy loss function, and obtaining contrast learning data; constructing a rendering optimization model based on the contrast learning data, performing dimensionality reduction analysis on the rendering parameter space, and establishing a parameter space. The algorithm uses a number of sensitivity evaluation indicators, adopts a gradient-guided parameter search strategy, realizes local fine adjustment of rendering parameters, and obtains rendering control data; inputs the rendering control data into the feature fusion network, builds a spatial adaptive convolution layer based on the geometric characteristics of the curved screen, performs adaptive weighted fusion on the features of different curvature areas, maintains detail information through residual connection, and obtains fused optimization data; establishes a regional adaptive controller based on the fused optimization data, adopts a multi-task learning framework to simultaneously optimize the rendering quality and computational efficiency, builds a quality evaluation index system, realizes real-time monitoring and evaluation of rendering effects, and obtains regional control data; performs parameter correction calculation based on the regional control data, establishes a mapping model between parameters and rendering quality, adopts a reinforcement learning method to optimize the parameter adjustment strategy, solves the optimal parameter combination through a dynamic programming algorithm, and obtains parameter correction data; integrates the parameter correction data into the rendering pipeline, establishes a hierarchical rendering control mechanism, prioritizes scene elements, realizes the reasonable allocation of rendering resources, ensures the display quality of key areas, and completes the precise control of the rendering effect of the curved screen.

[0038] The above describes the 3D game scene adaptation method based on the curved screen in the embodiment of the present application. The following describes the 3D game scene adaptation device 10 based on the curved screen in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of a 3D game scene adaptation device 10 based on a curved screen includes: The sampling module 11 is used to perform three-dimensional coordinate sampling on the curved screen surface, construct a curved screen space model, and perform dynamic tracking in the 3D game scene according to the curved screen space model to obtain scene mapping data; An analysis module 12 is used to construct a spatial distortion vector and perform compensation transformation analysis based on the scene mapping data to obtain target scene data; A construction module 13 is used to calculate a surface feature tensor according to target scene data, construct a control grid node using the surface feature tensor, impose deformation constraints on the control grid nodes and solve the deformation energy equation to obtain scene deformation control data; The creation module 14 is used to create a rendering control instruction based on the scene deformation control data, and perform scene state acquisition and space adaptation calculation according to the rendering control instruction to obtain scene display data.

[0039] Through the collaboration of the above components, by sampling the three-dimensional coordinates of the curved screen surface and constructing a spatial model, combined with dynamic particle swarm tracking technology, the scene mapping data can be accurately obtained, effectively solving the problem of inaccurate scene spatial positioning in traditional methods. The spatial distortion vector construction and compensation transformation analysis method are used to process geometric distortion and visual distortion separately. Through bilinear interpolation and boundary constraint optimization, accurate correction of scene display is achieved, significantly improving the picture quality. The surface feature tensor analysis and control grid node construction technology are introduced to achieve accurate scene deformation control by solving the deformation energy equation, ensuring the continuity and smoothness of the scene deformation process. The rendering resource allocation strategy based on priority distribution, combined with the cone model and shader optimization technology, realizes efficient utilization of rendering resources and improves the real-time performance of the system. Through distributed state fusion and robustness control technology, the system delay and noise interference problems are effectively handled to ensure the stability and continuity of scene display.

[0040] Those skilled in the art can 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.

[0041] 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 is essentially 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, including several instructions to enable an electronic device (which can be a personal computer, server, or network device, etc.) to perform 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, etc., various media that can store program codes.

[0042] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned 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 3D game scene adaptation method based on a curved screen, characterized in that: The method comprises: Sampling the three-dimensional coordinates of the curved screen surface, constructing a curved screen space model, and dynamically tracking the curved screen in a 3D game scene according to the curved screen space model to obtain scene mapping data; Based on the scene mapping data, a spatial distortion vector is constructed and a compensation transformation analysis is performed to obtain target scene data; Calculating a surface feature tensor according to the target scene data, constructing a control grid node using the surface feature tensor, applying deformation constraints to the control grid nodes and solving a deformation energy equation to obtain scene deformation control data; A rendering control instruction is created based on the scene deformation control data, and scene state acquisition and space adaptation calculation are performed according to the rendering control instruction to obtain scene display data.

2. The 3D game scene adaptation method based on a curved screen according to claim 1, characterized in that: The three-dimensional coordinate sampling of the curved screen surface is performed to construct a curved screen space model, and dynamic tracking is performed in a 3D game scene according to the curved screen space model to obtain scene mapping data, including: The x-axis and y-axis are calibrated in the horizontal and vertical directions of the curved screen respectively, and the z-axis is calibrated in the direction perpendicular to the screen reference plane to obtain the three-dimensional coordinate acquisition reference point; The curved screen surface is divided into uniform grid points according to the three-dimensional coordinate acquisition reference points to obtain a plurality of grid points, and the spatial position coordinates of each grid point are recorded to obtain discrete spatial point data; Dividing the discrete spatial point data into multiple data subsets according to the spatial distribution law, and performing curvature calculation on the adjacent point data of the data subsets to obtain local curvature data; Fitting the local curvature data and the discrete space point data with a cubic spline function to obtain a surface mathematical model, and based on the surface mathematical model, calculating the spatial distribution of the normal vector and the tangent vector for each spatial position to obtain a curvature tensor matrix; Performing an eigenvalue decomposition operation on the curvature tensor matrix to obtain a curvature distribution vector, and dividing the curved screen surface into sub-regions according to the curvature distribution vector to obtain region segmentation data; Establishing an independent coordinate system based on each sub-region in the region segmentation data, calculating the spatial transformation relationship between the sub-regions, and obtaining a curved screen space model; An initial particle group is generated in a 3D game scene according to the arc-shaped screen space model, and the initial particle group is dynamically tracked to obtain scene mapping data.

3. The 3D game scene adaptation method based on a curved screen according to claim 2, characterized in that: The generating an initial particle group in the 3D game scene according to the arc screen space model, and dynamically tracking the initial particle group to obtain scene mapping data, includes: Uniformly divide the 3D game scene and initialize the particle swarm based on the arc-shaped screen space model to generate an initial particle swarm; Setting a position vector, a velocity vector and an acceleration vector for each particle in the initial particle group to obtain a particle basic state matrix, and configuring a scene depth value, a visual weight and a deformation coefficient for each particle according to the particle basic state matrix to obtain particle enhancement state data; Constructing a motion equation function based on the particle enhancement state data, and coupling the motion equation function with the curvature influence factor to obtain a state transfer function; Based on the state transfer function, a particle observation model is constructed through quantitative analysis of rendering parameters to obtain observation-related data, and a Kalman filter update operation is performed based on the observation-related data to perform data fusion optimization on the predicted value and the observed value to obtain a particle state optimization result; Calculating the interaction relationship of the local particle group according to the particle state optimization result, constructing a group behavior model, and dynamically updating it to obtain the particle group motion parameters; The particle swarm motion parameters are mapped and transformed into the scene coordinate system to obtain scene mapping data.

4. The 3D game scene adaptation method based on a curved screen according to claim 1, characterized in that: The step of constructing a spatial distortion vector and performing a compensation transformation analysis based on the scene mapping data to obtain target scene data includes: Calculating the surface projection deformation of each rendering object in the scene mapping data, and constructing a spatial distortion vector through spatial coordinate transformation; Performing a geometric feature decomposition operation on the spatial distortion vector to decompose the spatial distortion vector into a geometric distortion component and a visual distortion component according to a surface projection rule; Constructing a compensation transformation matrix according to the geometric distortion component and the visual distortion component, mapping the source space point to the target space, and obtaining spatial transformation data; Performing a reverse projection transformation on the geometric distortion component in the spatial transformation data, calculating a projection compensation amount according to the curved screen curvature parameter to obtain geometric correction data, and adjusting rendering parameters on the visual distortion component in the spatial transformation data, calculating compensation parameters according to a law of viewing angle change, and obtaining visual optimization data; The geometric correction data and the visual optimization data are combined and calculated to obtain compensation optimization data, and a bilinear interpolation operation is performed on the compensation optimization data, and adjacent pixel points are smoothed according to grid division to obtain continuous compensation data; The continuous compensation data is input into the boundary constraint processing unit for boundary calibration and gradient optimization to obtain target scene data.

5. The 3D game scene adaptation method based on a curved screen according to claim 1, characterized in that: The step of calculating a surface feature tensor according to the target scene data, constructing a control grid node using the surface feature tensor, applying deformation constraints to the control grid node and solving a deformation energy equation to obtain scene deformation control data includes: Performing coordinate system normalization operation on the target scene data, constructing a metric tensor according to a local space metric relationship, and performing geometric feature analysis on the metric tensor to obtain a surface feature tensor; Based on the surface feature tensor, a feature vector field is constructed, and the spatial distribution law of the main direction, curvature line and geodesic line of the surface is quantified and calculated to obtain the surface feature vector; Input the surface feature vector into a meshing algorithm, adaptively mesh the space region according to the scene topology structure, obtain mesh node data, establish a local coordinate frame for each node in the mesh node data, calculate the associated transformation matrix between adjacent nodes, and obtain node transformation data; A scene deformation model is established according to the node transformation data, and the deformation variables are calculated by optimizing and adjusting the node position parameters to obtain the spatial deformation data; Constructing a geometric constraint function for the spatial deformation data, calculating the degree of geometric shape preservation, and constructing a visual constraint function, calculating the continuity of the visual effect, and obtaining a deformation energy function; A numerical optimization solution is performed based on the deformation energy function to obtain an optimal deformation parameter, and a scene deformation mapping operation is performed on the optimal deformation parameter to obtain scene deformation control data.

6. The 3D game scene adaptation method based on a curved screen according to claim 5, characterized in that: The creating a rendering control instruction based on the scene deformation control data, and performing scene state acquisition and space adaptation calculation according to the rendering control instruction to obtain scene display data includes: Score and calculate the importance, dynamic characteristics, and visual significance of scene elements in the scene deformation control data to obtain priority distribution data; Inputting the priority distribution data into a rendering resource allocation model, dividing the scene areas with different priorities into computing resource levels, obtaining a resource allocation weight map, and performing multi-level control on the rendering accuracy of scene elements according to the resource allocation weight map to obtain detail control data; Constructing a view cone model based on the detail control data, performing visibility analysis and spatial occlusion determination on scene elements to obtain a visible object list, and inputting the visible object list into a shader control unit, optimizing and calculating view-related shading parameters and texture sampling methods to obtain rendering state data; Performing anti-aliasing processing on the image edge in the rendering state data, and obtaining image processing parameters through pixel-level smoothing interpolation operation; Establishing a rendering cache mechanism based on the image processing parameters, storing rendering results and indexing static scene content to obtain cache management data, and organizing the cache management data and dynamic scene elements into a rendering scheduling sequence to obtain rendering control instructions; Scene state acquisition and space adaptation calculation are performed according to the rendering control instruction to obtain scene display data.

7. The 3D game scene adaptation method based on a curved screen according to claim 6, characterized in that: The performing scene state acquisition and space adaptation calculation according to the rendering control instruction to obtain scene display data includes: The camera position, viewing angle change and dynamic object state in the rendering control instruction are collected in real time to obtain real-time collection results, and the real-time collection results are input into the state monitoring system for data integration to obtain scene change data; Inputting the scene change data into a time-varying delay analysis unit, constructing a delay distribution model according to the time-varying characteristics of network transmission delay and processing delay, and obtaining delay feature data; Performing composite noise analysis on the delay characteristic data, obtaining noise distribution data by superimposing a signal interference model and an environmental noise model, and constructing a distributed consistency controller based on the noise distribution data, generating a state synchronization strategy through multi-node collaborative calculation, and obtaining consistency control data; Performing distributed state fusion operation on the consistency control data to obtain state optimization data, and inputting the state optimization data into the robustness controller to perform suppression calculation on system disturbance and uncertainty to obtain steady-state control data; A space mapping transformation is performed on the steady-state control data, and a scene adaptation operation is performed according to the display characteristics of the curved screen to obtain scene display data.

8. A 3D game scene adaptation device based on a curved screen, characterized in that: Used to execute the 3D game scene adaptation method based on a curved screen according to any one of claims 1 to 7, the device comprising: A sampling module is used to perform three-dimensional coordinate sampling on the curved screen surface, construct a curved screen space model, and perform dynamic tracking in a 3D game scene according to the curved screen space model to obtain scene mapping data; An analysis module, used for constructing a spatial distortion vector and performing compensation transformation analysis based on the scene mapping data to obtain target scene data; A construction module, used to calculate a surface feature tensor according to the target scene data, construct a control grid node using the surface feature tensor, impose deformation constraints on the control grid nodes and solve the deformation energy equation to obtain scene deformation control data; A creation module is used to create a rendering control instruction based on the scene deformation control data, and perform scene state acquisition and space adaptation calculation according to the rendering control instruction to obtain scene display data.

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