Intangible cultural heritage digital interactive experience management method and system

Abstract

The invention relates to the technical field of digital interactive experience management, in particular to an intangible cultural heritage digital interactive experience management method and system. The method comprises the following steps of: extracting action and background elements in a video and performing time sequence splitting; performing action rendering deformation analysis and flat analysis of a background light and shadow rendering space based on the split data; performing visual focus error deviation simulation through the action rendering deformation data and the light and shadow data to obtain visual focus error deviation data; the light and shadow intensity is adjusted based on the visual focus error deviation data, and the digitized interactive experience management logic is designed by using the adjusted data, so that the interactive effect and the visual performance are improved. According to the invention, the digital interactive experience management technology is optimized, so that the digital interactive experience management technology is more perfect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital interactive experience management, and in particular to a non-material cultural heritage digital interactive experience management method and system. BACKGROUND

[0002] Non-material cultural heritage is an important part of human history and culture, carrying rich traditional skills, knowledge systems, customs and artistic forms. However, over time, many traditional cultures are facing the crisis of gradual disappearance in the process of modernization. Especially in the background of globalization, urbanization and technological progress, the inheritance of non-material cultural heritage is facing great challenges. In order to protect and inherit these valuable cultural resources, digital technology provides a new solution. Digitalization not only can effectively record and save various information of these cultural heritage, but also can make more people, especially the younger generation, participate in it, feel its charm and promote the understanding and inheritance of traditional culture through interactive experience. Traditional non-material cultural heritage display methods usually rely on physical display, such as museums, cultural heritage sites, etc., but these methods cannot fully present the details in the process of skills, and are limited by space and time. With the rapid development of virtual reality (VR), augmented reality (AR), 3D scanning and other technologies, digital interactive experience has become a new effective display method. This way not only can truly reproduce each detail of non-material cultural heritage, but also can let the audience participate in it in a more immersive way, providing a richer and more flexible way of learning and experience. However, the traditional non-material cultural heritage digital interactive experience management method has the problem of inaccurate analysis of interactive rendering performance bottleneck, resulting in large visual perception error of interactive experience. SUMMARY

[0003] Therefore, it is necessary to provide a non-material cultural heritage digital interactive experience management method and system to solve at least one of the above technical problems.

[0004] To achieve the above purpose, the non-material cultural heritage digital interactive experience management method comprises the following steps: Step S1: obtaining a non-material cultural heritage skill video; performing action / background element splitting on the non-material cultural heritage skill video to obtain action / background element time sequence splitting data; Step S2: performing action rendering deformation analysis based on the action / background element time sequence splitting data to obtain action rendering deformation data; and performing background light and shadow rendering space flattening analysis based on the action / background element time sequence splitting data to obtain light and shadow rendering space flattening data; Step S3: performing visual focus error deviation simulation based on the action rendering deformation data and the light and shadow rendering space flattening data to obtain visual focus error deviation data; Step S4: adjusting the light and shadow intensity of the light and shadow rendering space flat data based on the visual focus error deviation normalized data to obtain visual error light and shadow intensity adjustment data; and performing digital interactive experience management logic design according to the visual error light and shadow intensity adjustment data to obtain the interactive experience management logic.

[0005] Preferably, step S1 comprises the following steps: Step S11: acquiring the non-material cultural heritage skill video; Step S12: performing skill performance flow disassembly on the non-material cultural heritage skill video to obtain skill performance disassembly flow; Step S13: performing action / background element disassembly on the skill performance disassembly flow to obtain action / background element disassembly data; Step S14: performing timing change marking on the action / background element disassembly data to obtain action / background element timing disassembly data.

[0006] Preferably, step S2 comprises the following steps: Step S21: acquiring interactive device configuration data; Step S22: performing three-dimensional rendering limitation simulation according to the interactive device configuration data to obtain three-dimensional rendering limitation data; Step S23: performing action rendering deformation analysis on the three-dimensional rendering limitation data based on the action / background element timing disassembly data to obtain action rendering deformation data; Step S24: performing background light and shadow rendering space flat analysis on the three-dimensional rendering limitation data according to the action / background element timing disassembly data to obtain light and shadow rendering space flat data.

[0007] Preferably, step S23 comprises the following steps: Step S231: performing rendering space resolution output limitation analysis on the three-dimensional rendering limitation data to obtain rendering resolution output limitation data; Step S232: performing action space pose deflection identification on the action / background element timing disassembly data to obtain space action pose deflection data; Step S233: performing continuity action connection amplitude analysis on the action / background element timing disassembly data according to the space action pose deflection data to obtain continuity action connection amplitude; Step S234: performing action connection resolution frame numerical interval quantization on the rendering resolution output limitation data based on the continuity action connection amplitude to obtain connection resolution frame numerical interval; Step S235: performing action rendering deformation analysis based on the connection resolution frame numerical interval to obtain action rendering deformation data.

[0008] Preferably, step S24 comprises the following steps: Step S241: Perform background composition layout analysis on the temporal split data of action / background elements to obtain background element composition layout data; Step S242: Analyze the dynamic depth change of the background element composition layout data based on the action / background element time sequence split data to obtain the dynamic depth change data of the composition layout. Step S243: Fit the dynamic depth change data of the composition layout to the performance requirements of the lighting and shadow rendering, and obtain the performance requirements data of the composition lighting and shadow rendering. Step S244: Quantify the rendering performance bottleneck value of the 3D rendering limitation data according to the rendering performance requirements data of composition and lighting, and obtain the rendering performance bottleneck quantification value. Step S245: Based on the rendering performance bottleneck quantization value, estimate the probability of the jagged edge distribution of the dynamic depth change data of the composition layout to obtain the jagged edge distribution data. Step S246: Perform background light and shadow rendering space flattening analysis based on the jagged edge distribution data to obtain light and shadow rendering space flattening data.

[0009] Preferably, step S3 includes the following steps: Step S31: Perform light and shadow visual overlap analysis based on motion rendering deformation data and light and shadow rendering space flatness data to obtain light and shadow visual overlap data; Step S32: Based on the light and shadow visual overlap data, motion rendering deformation data and light and shadow rendering space flattening data, perform visual focus error deviation simulation to obtain visual focus error deviation data.

[0010] Preferably, step S4 includes the following steps: Step S41: Normalize the visual focus error deviation data to obtain normalized visual focus error deviation data; Step S42: Adjust the light and shadow intensity of the light and shadow rendering space flat data based on the visual focus error deviation normalization data to obtain visual error light and shadow intensity adjustment data; Step S43: Based on the visual focus error deviation normalization data and the visual error light and shadow intensity adjustment data, perform motion smoothness frame number matching on the motion rendering deformation data to obtain motion smoothness frame number matching data; Step S44: Perform adaptive adjustment of motion scaling based on motion smoothness frame number matching data to obtain motion scaling adaptive adjustment data; Step S45: Design the digital interactive experience management logic based on the visual error light and shadow intensity adjustment data, motion smoothness frame number matching data, and motion scaling adaptive adjustment data to obtain the interactive experience management logic.

[0011] Preferably, the present invention also provides a digital interactive experience management system for intangible cultural heritage, used to execute the digital interactive experience management method for intangible cultural heritage as described above, the digital interactive experience management system for intangible cultural heritage comprising: The action / background element splitting module is used to acquire videos of intangible cultural heritage skills; it splits the action / background elements of the videos of intangible cultural heritage skills to obtain action / background element time sequence splitting data. The element analysis module is used to perform motion rendering deformation analysis based on the temporal split data of motion / background elements to obtain motion rendering deformation data; and to perform background lighting and shadow rendering space flattening analysis based on the temporal split data of motion / background elements to obtain lighting and shadow rendering space flattening data. The visual focus deviation analysis module is used to simulate visual focus error deviation based on motion rendering deformation data and light and shadow rendering space flat data, and obtain visual focus error deviation data. The interactive experience management logic design module is used to adjust the light and shadow intensity of the light and shadow rendering space flat data based on the visual focus error deviation normalization data to obtain visual error light and shadow intensity adjustment data; and to design digital interactive experience management logic based on the visual error light and shadow intensity adjustment data to obtain the interactive experience management logic.

[0012] The beneficial effects of this invention lie in the temporal decomposition of actions and background elements in videos of intangible cultural heritage skills, thereby extracting specific actions and background content from the video. By analyzing the temporal data of character actions, environmental changes, and background elements in the video, each action and its corresponding background scene can be clearly identified. This decomposition provides accurate data support for subsequent analysis and rendering, and helps maintain the independence and coordination of actions and backgrounds in different technical processing processes, making the overall digital processing effect more accurate and efficient. Based on the decomposed temporal data of actions and background elements, action rendering deformation analysis is performed. This means that by calculating the changes and forms of actions and their impact on background elements, the details of the actions and how they interact with the background are simulated. In addition, background lighting and shadow rendering space flattening analysis helps to understand the lighting and shadow change patterns of the background and how they affect the visual effects in the scene, ultimately forming an accurate lighting and shadow rendering space model. This analysis provides a foundation for subsequent visual rendering and interactive experience optimization, making the digitally reconstructed skill video not only vivid in action but also have layered lighting and shadow effects. Based on action rendering deformation data and lighting and shadow rendering space flattening data, visual focus error is simulated and analyzed. By analyzing the shift in focus during the viewing process, a realistic visual experience can be simulated, such as the impact of focus deviation on the viewing effect. In this process, the system detects whether the visual focus in the video deviates from the viewer's line of sight, and then quantifies the deviation. The core value of this module lies in providing data support for subsequent visual optimization. By correcting the deviated focus, it ensures that viewers always focus on the most important parts when watching intangible cultural heritage techniques, thereby improving the quality of the viewing experience. Based on the visual focus error deviation data, the interactive experience management logic design module adjusts the intensity of light and shadow. Adjusting the intensity of light and shadow reduces the impact of visual errors on the viewer experience, thus providing more accurate visual feedback and ensuring the synchronization and accuracy of light and shadow effects with actions in the picture. Furthermore, the interactive experience management logic design module also combines light and shadow adjustment data to design and optimize the user interaction experience. Ultimately, through this process, the system can design an interactive experience that can adapt to the needs of different viewers, ensuring that the digital display of intangible cultural heritage techniques is both aesthetically pleasing and conforms to the natural response of human vision, enhancing the viewer's immersion and participation. Therefore, this invention is an optimization of a traditional method for managing digital interactive experiences of intangible cultural heritage. It solves the problem that the traditional method for managing digital interactive experiences of intangible cultural heritage has inaccurate analysis of interactive rendering performance bottlenecks, resulting in large visual perception errors in the interactive experience. This invention improves the accuracy of the analysis of interactive rendering performance bottlenecks and reduces the visual perception errors in the interactive experience. Attached Figure Description

[0013] Figure 1A flowchart illustrating the steps of a digital interactive experience management method for intangible cultural heritage. Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2. Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3. Detailed Implementation

[0014] Please see Figures 1 to 3 A method for managing the digital interactive experience of intangible cultural heritage, the method comprising the following steps: Step S1: Obtain videos of intangible cultural heritage skills; decompose the videos of intangible cultural heritage skills into action / background elements to obtain temporal decomposition data of action / background elements; Step S2: Perform motion rendering deformation analysis based on the temporal split data of motion / background elements to obtain motion rendering deformation data; perform background lighting and shadow rendering space flattening analysis based on the temporal split data of motion / background elements to obtain lighting and shadow rendering space flattening data. Step S3: Simulate visual focus error deviation based on motion rendering deformation data and light and shadow rendering space flat data to obtain visual focus error deviation data; Step S4: Adjust the light and shadow intensity of the flat data of the light and shadow rendering space based on the visual focus error deviation normalization data to obtain visual error light and shadow intensity adjustment data; design the digital interactive experience management logic based on the visual error light and shadow intensity adjustment data to obtain the interactive experience management logic.

[0015] In this embodiment of the invention, reference Figure 1 The above is a flowchart illustrating the steps of a digital interactive experience management method for intangible cultural heritage according to the present invention. In this example, the digital interactive experience management method for intangible cultural heritage includes the following steps: Step S1: Obtain videos of intangible cultural heritage skills; decompose the videos of intangible cultural heritage skills into action / background elements to obtain temporal decomposition data of action / background elements; In this embodiment of the invention, after acquiring the video of intangible cultural heritage skills, it is necessary to analyze the image content of the video to separate the human movements during the performance from the background environment. A time-series-based video frame decomposition method is used to analyze the video frame by frame. Optical flow is used to calculate pixel motion information between adjacent frames, and a background modeling algorithm is combined to extract static background elements. During the optical flow calculation, the motion vector of each pixel between consecutive frames is calculated to generate complete motion field data. Foreground detection techniques, such as frame difference and Gaussian mixture models, are used to further remove noise areas to ensure the accuracy of background extraction. For background interference in complex scenes, a spatiotemporal information fusion method can be used to improve the stability of background modeling by constructing a time-series distribution pattern of pixels. The extraction of action elements combines edge detection, morphological processing, and deep learning-based target detection methods, such as convolutional neural networks for human pose estimation, to accurately separate the subject from the background and record its position and shape changes at different time points. Based on the above methods, the contour information of action elements and the static features of background elements are extracted, and these data are time-series encoded to ensure the integrity of temporal information in subsequent analysis.

[0016] Step S2: Perform motion rendering deformation analysis based on the temporal split data of motion / background elements to obtain motion rendering deformation data; perform background lighting and shadow rendering space flattening analysis based on the temporal split data of motion / background elements to obtain lighting and shadow rendering space flattening data. In this embodiment of the invention, based on the separated action and background element data, further analysis of the deformation characteristics of the action in the video is required. First, a keypoint tracking algorithm is used to detect the joint positions of the performer, and the angle changes of various body parts are calculated based on the joint topology. Bezier curve interpolation is used to smooth the motion trajectory, thereby eliminating jitter noise and maintaining motion continuity. For high-speed motion sections, Fourier transform is used to analyze the motion frequency components, filtering out high-frequency components to reduce abrupt deformation. A deep learning skeleton detection algorithm is combined to perform three-dimensional reconstruction of the human skeleton to analyze the changes in motion amplitude between joints. For background lighting and shadow rendering analysis, image segmentation is first performed to identify illuminated areas, and histogram equalization is used to enhance lighting and shadow contrast, then the brightness gradient of different background areas is calculated. Texture features are extracted from lighting changes using local binary mode to quantify the lighting and shadow distribution of the background. For spatial flatness analysis of lighting and shadow, a gradient divergence-based calculation method is used to analyze the diffusion trend of light on the background and quantify the uniformity of lighting and shadow in different spatial regions.

[0017] Step S3: Simulate visual focus error deviation based on motion rendering deformation data and light and shadow rendering space flat data to obtain visual focus error deviation data; In this embodiment of the invention, after obtaining motion deformation data and lighting rendering space data, further simulation of visual focus error deviation is required. First, eye-tracking technology combined with computer vision's point of interest detection method is used to determine the visual attention area in the performance. The SIFT feature matching algorithm is used to extract feature points in the high-attention area, and their distribution density relative to the overall image is calculated. For dynamic elements in the image, optical flow tracking technology is used to analyze the drift of the visual focus during movement, and Kalman filtering is used to smooth the focus trajectory to reduce jitter error. Combining a human retinal perception model, the response capability of the human eye to the focus area under different lighting conditions is simulated. Gaussian blur filtering is used to calculate the attenuation effect around the visual focus and quantify the degree of visual error deviation.

[0018] Step S4: Adjust the light and shadow intensity of the flat data of the light and shadow rendering space based on the visual focus error deviation normalization data to obtain visual error light and shadow intensity adjustment data; design the digital interactive experience management logic based on the visual error light and shadow intensity adjustment data to obtain the interactive experience management logic.

[0019] In this embodiment of the invention, based on visual focus error deviation data, the intensity of light and shadow needs to be adjusted to optimize the interactive experience. First, the changes in illumination intensity in the focus-offset area are analyzed, the local contrast of pixels is calculated, and the distribution of light and shadow is adjusted using an adaptive brightness adjustment algorithm to enhance the contrast of the visual focus area. Dynamic range compression technology is used to compress the brightness of bright areas to reduce visual deviations caused by light and shadow overflow. In low-brightness areas, Gamma correction is used to enhance dark details, making the layering of background elements clearer. For moving performances, the inter-frame change rate is calculated, and the frame rate is optimized through interpolation to make the movement smoother. Combining motion vector analysis, time series prediction methods are used to calculate the motion trend of future frames, and the lighting and shadow rendering parameters are dynamically adjusted to improve the matching degree between light and shadow and movement. After obtaining the optimized light and shadow intensity data, the key movements of the performance are further marked using interactive management logic, and an interactive feedback mechanism is designed to provide viewers with more accurate visual perception feedback during the digital experience.

[0020] Step S1 includes the following steps: Step S11: Obtain videos of intangible cultural heritage techniques; Step S12: Deconstruct the performance process of the intangible cultural heritage skills video to obtain the skill performance deconstruction process; Step S13: Decompose the skill performance process into action / background elements to obtain action / background element decomposition data; Step S14: Mark the temporal changes in the action / background element split data to obtain the action / background element temporal split data.

[0021] In this embodiment of the invention, when acquiring videos of intangible cultural heritage skills, high-definition video capture equipment is required to ensure that the image quality meets the needs of subsequent processing. The frame rate of the capture equipment needs to be set to more than 60 frames per second to ensure the accuracy of capturing motion details. The resolution should be maintained at more than 4,000 pixels to ensure the clarity of background elements and skill movements in the picture. During the recording process, it is necessary to switch between fixed camera positions and multi-angle camera positions so that complete spatial information can be obtained in subsequent analysis. Light control is a key factor, and shooting should be carried out in a uniform light source environment to avoid visual information distortion caused by changes in lighting. During the filming process, the background should be as monochromatic as possible to facilitate accurate segmentation later. Audio data acquisition needs to be carried out simultaneously so that the accuracy of skill process decomposition can be improved by using audio-visual alignment methods later. After the video recording is completed, the original data is standardized using a format conversion tool to convert different format image files into a lossless compressed storage format to ensure the integrity of the video data. The skill performance process decomposition requires the use of video frame analysis methods to divide the entire skill performance process according to the time sequence. First, a keyframe extraction algorithm is used to analyze the motion change characteristics of the video, selecting the key time points where the performance actions change most significantly, and using these as the basis for decomposing the skill sequence. For the transitions between keyframes, optical flow analysis is used to calculate the motion trajectory, and the inflection point of the motion trajectory change is used as the boundary for skill decomposition. For more complex skill actions, an edge detection algorithm is used to extract the performer's contour, and the rate of motion change is calculated by combining the differences between consecutive frames to determine the start and end times of the skill action. For skill performances involving props, target detection analysis is required, and the motion trajectory of the props is calculated based on an object tracking algorithm and aligned with the skill action to ensure the integrity of the skill sequence. Audio data also plays an important role in the skill performance decomposition process. Speech recognition methods are used to analyze the performer's language cues, and the time is aligned with the video data to improve the accuracy of the decomposition. For skill sequences involving multiple performers, multi-target detection technology is used to identify the motion trajectories of different performers and perform individual-level motion segmentation to avoid interference between the actions of different performers. The separation of action and background elements requires processing based on video image segmentation technology. First, a background modeling algorithm is used to extract the static parts of the image. A Gaussian mixture model is used to establish a background model, and the differences between consecutive frames are calculated to remove moving objects and obtain stable background information. For images with complex backgrounds, a temporal filtering-based background extraction method is used to identify pixels that remain static for a long time as background parts and to construct a background depth map for subsequent processing. Motion element extraction relies on foreground detection algorithms; inter-frame differencing is used to detect moving regions, combined with morphological processing to remove noise.For scenes with significant lighting variations, an adaptive histogram equalization method is needed to enhance the contrast of action edges and improve segmentation accuracy. Human pose recognition technology plays a crucial role in action extraction, using pose estimation algorithms to detect key joints of the human body and separating actions from the background based on the skeletal structure. For subtle movements involved in performances, such as minute changes in fingers and feet, optical flow tracking technology is required to calculate local motion vectors to accurately identify small-scale action details. In cases where props interact with performance actions, additional target tracking algorithms are needed to analyze the prop's movement path and, combined with human pose information, ensure the matching relationship between props and performance actions. When marking temporal changes in the action and background element segmentation data, time series analysis methods are required to ensure data temporal consistency. First, a timestamp sequence is calculated based on the action trajectory, assigning a time index to each frame's action element. For continuous actions in performances, a dynamic time warping algorithm is used to calculate the similarity of actions between different frames and assign the same action label to consecutive frames with high similarity. For abrupt changes in motion, such as high-speed movements like jumping and spinning, acceleration information needs to be calculated, and time points of drastic motion changes need to be marked based on motion peak detection methods. In the temporal variation analysis of background elements, a region growing algorithm is used to detect changing regions in the background, and background stability is calculated based on the inter-frame change rate. For scenes with significant changes in lighting and shadow, local brightness variation analysis methods are used to detect temporal patterns in lighting, and time points with prominent lighting and shadow changes are marked. Finally, the temporal data of the motion and background are aligned to construct a complete temporal index table for subsequent analysis.

[0022] Step S2 includes the following steps: Step S21: Obtain interactive device configuration data; Step S22: Perform a 3D rendering limitation simulation based on the interactive device configuration data to obtain 3D rendering limitation data; Step S23: Perform motion rendering deformation analysis on the 3D rendering limitation data based on the temporal split data of motion / background elements to obtain motion rendering deformation data; Step S24: Based on the temporal split data of the action / background elements, perform background lighting and shadow rendering space flattening analysis on the 3D rendering limitation data to obtain lighting and shadow rendering space flattening data.

[0023] As an example of the present invention, reference is made to... Figure 2 As shown, step S2 in this example includes: Step S21: Obtain interactive device configuration data; In this embodiment of the invention, when acquiring configuration data for interactive devices, it is necessary to collect data from multiple dimensions, including hardware parameters, rendering capabilities, and input / output latency, and to construct a device performance index library. Hardware parameter acquisition includes key data such as processor model, graphics card specifications, memory capacity, storage space, screen resolution, and refresh rate. The graphics card specifications need further refinement, including parameters such as core frequency, memory bandwidth, number of raster units, and number of texture units, to ensure the accuracy of rendering capability assessment. Rendering capability is measured using a rendering benchmark testing method. Standardized rendering tasks are executed, and performance indicators such as frame rate, latency, and jitter are recorded, and the rendering stability of the device under different loads is analyzed. Input / output latency is measured based on synchronization signal analysis. A high frame rate camera captures the response time of input devices (such as gamepads, touchscreens, and motion-sensing cameras), and the time difference between user interaction and screen feedback is calculated. All collected data is stored in a unified format and an index is established for easy retrieval during subsequent 3D rendering limitation simulation.

[0024] Step S22: Perform a 3D rendering limitation simulation based on the interactive device configuration data to obtain 3D rendering limitation data; In this embodiment of the invention, when simulating the limitations of 3D rendering based on the configuration data of the interactive device, it is necessary to analyze the performance boundaries of the device under different rendering complexities based on rasterization rendering and real-time lighting calculation methods. First, a test scene containing various rendering characteristics is constructed, including rendering requirements such as high polygon count, complex lighting interactions, and large-scale texture mapping, and run on the target device to record rendering performance bottlenecks. For scenes with significant frame rate drops, a stepwise downsampling analysis method is used to reduce the number of polygon faces, reduce texture resolution, and reduce lighting calculations, observing the performance change curves to determine the main sources of rendering pressure. For real-time lighting calculation capabilities, a light source complexity incrementing method is used to gradually increase the number of dynamic light sources in the scene, recording the device's computational load to analyze the maximum number of handleable light sources and shadow rendering capabilities. For the device's latency characteristics, a time-segment tracking method is used to record input response times under different rendering loads and analyze the input latency change trends under different rendering modes. Finally, all test data are summarized and organized to form 3D rendering limitation data, including key information such as frame rate, lighting calculation load, input / output latency, and maximum handleable texture resolution under different rendering scenarios, providing a basis for subsequent rendering optimization.

[0025] Step S23: Perform motion rendering deformation analysis on the 3D rendering limitation data based on the temporal split data of motion / background elements to obtain motion rendering deformation data; In this embodiment of the invention, when performing motion rendering deformation analysis on 3D rendering limitation data based on temporal split data of motion and background elements, it is necessary to combine motion trajectory analysis and deformation compensation calculation to ensure that the realism of the skill motion can still be maintained under the rendering limitations of different interactive devices. First, using time series analysis methods, the motion trajectory of the skill motion between consecutive frames is extracted, and the motion vectors of key joints are calculated to obtain the complete dynamic features of the skill motion. For the rendering limitation data of interactive devices, an motion deformation mapping method is used to project the original skill motion onto the rendering space supported by the device and calculate the deformation error. For polygon deformation, a vertex offset compensation method is used to adjust the vertex distribution of the motion model based on the maximum number of polygons that the device can render, ensuring that the model will not be distorted due to rendering limitations on different devices. For texture deformation, texture adaptation calculation is used to resample the detailed texture of the skill motion based on the maximum processable texture resolution of the device, so that it can still maintain clarity under different rendering capabilities. For motion error, an interpolation optimization method is used to perform interpolation calculation for low frame rate motion caused by device performance limitations, supplementing the transition data between key frames to reduce the sense of motion stuttering. All calculation results are stored as motion rendering deformation data, which is then used for subsequent visual error analysis and optimization.

[0026] Step S24: Based on the temporal split data of the action / background elements, perform background lighting and shadow rendering space flattening analysis on the 3D rendering limitation data to obtain lighting and shadow rendering space flattening data.

[0027] In this embodiment of the invention, when performing background lighting and shadow rendering space flattening analysis on 3D rendering limitation data based on the temporal split data of action and background elements, it is necessary to combine simplified calculations of the lighting model and background layer decomposition methods to ensure that the background lighting and shadows maintain realism under the rendering capability limitations of different devices. First, based on the lighting characteristics of the skill performance scene, an ambient light contribution analysis method is used to calculate the main lighting sources in the background area and to divide the lighting influence range into regions. For devices with low rendering capabilities, a lighting baking method is used to convert some dynamic light sources into static light and shadow maps to reduce real-time calculation pressure. For background layer analysis, a depth mapping analysis method is used to calculate the depth distribution of each background layer, and the background layers are normalized according to the maximum depth buffer accuracy supported by the device to adapt to devices with different rendering capabilities. For lighting and shadow flattening calculation, a lighting gradient mapping method is used to analyze the changes in light intensity in different areas of the scene, and complex lighting and shadow effects are simulated through gradient color transitions on devices with low rendering capabilities to reduce the burden of real-time shadow calculation. All calculation results are stored as lighting and shadow rendering space flattening data to support subsequent visual focus error analysis and optimization.

[0028] Step S23 includes the following steps: Step S231: Perform rendering space resolution output limitation analysis on the 3D rendering limitation data to obtain rendering resolution output limitation data; Step S232: Perform motion spatial pose deflection recognition on the temporal split data of action / background elements to obtain spatial motion pose deflection data; Step S233: Analyze the continuity of motion transitions based on the spatial motion pose deflection data to obtain the continuity of motion transitions. Step S234: Based on the continuity of motion connection amplitude, quantize the motion connection resolution stuttering value range of the rendering resolution output limit data to obtain the connection resolution stuttering value range. Step S235: Perform motion rendering deformation analysis based on the range of stuttering values ​​at the connection resolution to obtain motion rendering deformation data.

[0029] In this embodiment of the invention, when analyzing the rendering space resolution output limitations of 3D rendering constraint data, it is necessary to calculate key parameters such as the screen resolution, video memory bandwidth, and rendering fill rate of the interactive device, and determine the upper limit of the device's resolution output under different rendering loads based on the pixel fill rate and frame buffer occupancy. First, the maximum number of pixels that can be transmitted is determined based on the device's video memory bandwidth, and the maximum supported resolution frame rate combination is calculated in combination with the screen refresh rate. For fill rate analysis, the rendering pipeline tracing method is used to measure the rendering time of the device at different resolutions and calculate the impact of different resolutions on the frame rate. For frame buffer analysis, the video memory occupancy evaluation method is used to calculate the frame data occupancy at different resolutions, and the maximum accommodating rendering resolution is analyzed in combination with the device's total video memory capacity. All analysis data is stored as rendering resolution output constraint data, providing constraint parameters for subsequent motion rendering deformation calculations. When performing motion space pose deflection recognition on the temporal split data of motion and background elements, the skeletal point cloud alignment analysis method is required to calculate the pose offset of the skill motion in space, and vector difference analysis is performed on the angle changes between different keyframes. First, skeletal points are extracted from key action frames in the performance, and the spatial distribution of joints is calculated based on Euclidean coordinates. For continuous action frames, a pose vector calculation method is used to calculate the motion trajectory of the skeletal joints, and the motion direction between different frames is differentially calculated to obtain the deflection angle of the action pose. For pose data with large deflections, a joint rotation matrix mapping method is used to compare the joint rotation angle with standard skill movements and calculate the deflection error. All calculated data is stored as spatial action pose deflection data, providing input parameters for subsequent calculation of the continuity of action transitions. When performing continuity of action transitions analysis on the temporal split data of action and background elements based on the spatial action pose deflection data, a time series smoothing calculation method is required to analyze the smoothness of the transition between different frames of the skill movement, and to analyze the changing trend of the transition amplitude based on acceleration vectors. First, based on the time series interpolation calculation method, keyframe interpolation is performed on continuous action frames, and the impact of different interpolation methods on the smoothness of the action is calculated. For motion amplitude analysis, a vector acceleration calculation method is used to calculate the acceleration changes between consecutive frames, and abrupt changes are filtered to eliminate abnormal jitter caused by frame rate instability. For motion transition detection, a transition interval analysis method is used to match the start and end frames of different motion segments and calculate the smooth transition parameters of motion amplitude. All calculated data is stored as continuous motion transition amplitude, providing input data for subsequent resolution stuttering analysis. When quantifying the resolution stuttering value range of motion transitions based on the rendering resolution output limit data based on continuous motion transition amplitude, a stuttering threshold detection method is required to analyze the smoothness of motion transitions at different resolutions and calculate the stuttering threshold range caused by frame rate drops.First, based on the device's maximum supported frame rate, the frame interval at different resolutions is calculated, and the impact of frame rate drops on motion smoothness is analyzed. For abnormal frame rate changes, a stuttering detection method is used to statistically analyze frame rate fluctuations in the time series and calculate the frame time jitter amplitude. For motion smoothness analysis, a motion time synchronization detection method is used to calculate motion synchronization errors at different resolutions and quantify the impact of resolution on motion smoothness. All calculated data is stored as the stuttering value range at the transition resolution, providing parameter support for subsequent motion rendering deformation analysis. When performing motion rendering deformation analysis based on the stuttering value range at the transition resolution, a deformation compensation calculation method is needed to analyze the motion deformation error caused by resolution limitations and adjust the motion trajectory based on joint angle compensation calculation. First, based on the deformation error calculation method, the bone point offset error at different resolutions is calculated, and the deformation trend of the motion model at different resolutions is analyzed. For joint angle adjustment, an inverse kinematics calculation method is used to calculate the bone angle deviation caused by resolution drops and adjust the joints based on the principle of minimum angle offset. For motion smoothness optimization, a time interpolation smoothing method is used to correct motion discontinuities caused by frame rate drops and calculate the optimal interpolation parameters. All computational data is stored as motion rendering deformation data, providing foundational data for subsequent visual focus error analysis.

[0030] Step S234 includes the following steps: Simulate the frame rate fluctuation required for multi-part motion coordination to measure the amplitude of continuous motion transitions, and obtain the frame rate fluctuation required for motion coordination. Based on the fluctuating frame rate required for motion coordination, the inter-frame rendering delay gradient is calculated using the rendering resolution output limit data to obtain the inter-frame rendering delay gradient data. Perform inter-frame motion break perception quantization on the inter-frame rendering delay gradient data to obtain inter-frame motion break perception quantization data. Inter-frame breakage numerical integration is performed on the inter-frame motion breakage perception quantization data to obtain the inter-frame breakage integral value. The motion transition resolution stuttering value range is obtained by quantizing the motion transition resolution stuttering value range based on the inter-frame rendering delay gradient data and the inter-frame break integral value.

[0031] In this embodiment of the invention, when simulating the frame rate fluctuation required for multi-part action coordination in continuous action transitions, it is necessary to calculate the frame rate requirements for the joint movements of multiple parts of different skill movements and analyze the impact of frame rate fluctuations on action synchronization. First, based on human kinematics analysis methods, the main joint position information of key skill movements is extracted, and the frame rate range required for different parts during action transitions is calculated. For multi-part coordination analysis, a joint synchronization detection method is used to calculate the motion phase difference of joints in different parts and analyze the impact of frame rate fluctuations on the phase difference. For frame rate requirement calculation, a time step adjustment method is used to calculate the changing trend of action continuity under different frame rates and quantify the impact of frame rate fluctuations on action stability. All calculated data is stored as the required fluctuating frame rate for action coordination, providing input parameters for subsequent inter-frame rendering delay gradient calculation. When calculating the inter-frame rendering delay gradient based on the required fluctuating frame rate for action coordination and the rendering resolution output limit data, a rendering pipeline delay analysis method is needed to calculate the rendering delay change trend under different resolutions and analyze the impact of inter-frame rendering delay on action transitions. First, based on frame rendering time measurement methods, the average rendering time under different resolutions is calculated, and the impact of rendering load changes on the frame rate is analyzed. For inter-frame latency analysis, a time offset calculation method is used to calculate the rendering time difference between adjacent frames and analyze the range of latency gradient variation at different resolutions. For rendering synchronization calculation, an inter-frame temporal stability detection method is used to calculate the standard deviation of frame time and quantify the impact of frame rate fluctuations on motion smoothness. All computational data is stored as inter-frame rendering latency gradient data, providing input data for subsequent inter-frame motion fragmentation perception quantization. When performing inter-frame motion fragmentation perception quantization on the inter-frame rendering latency gradient data, a motion smoothness analysis method is needed to calculate the impact of rendering latency on motion continuity and quantify the degree of visual fragmentation caused by frame rate fluctuations. First, based on the acceleration change rate calculation method, the motion acceleration difference between adjacent frames is calculated, and the impact of rendering latency on motion acceleration is analyzed. For visual fragmentation perception calculation, a time difference analysis method is used to calculate the motion trajectory changes between adjacent frames and mark trajectory abrupt change points. For fragmentation amplitude measurement, a pixel offset calculation method is used to calculate the displacement changes of key points between frames and quantify the degree of fragmentation under different rendering latencies. All computational data is stored as inter-frame motion fragmentation perception quantization data, providing input parameters for subsequent inter-frame fragmentation numerical integration. When performing inter-frame fragmentation numerical integration on the perceptual quantization data of inter-frame motion fragmentation, a time-cumulative error calculation method is required to calculate the total motion fragmentation error between different frames and analyze the cumulative trend of motion fragmentation error over time. First, based on the error integration calculation method, the fragmentation error between adjacent frames is integrated over time, and the total fragmentation error value within different time periods is calculated. For cumulative error analysis, a time window shifting calculation method is used to calculate the error changes within different time windows and analyze the impact of error accumulation on motion continuity.For adjusting the fracture values, an error normalization method is used to convert the fracture values ​​at different resolutions into standardized intervals to ensure the comparability of the calculation results. All computational data is stored as inter-frame fracture integral values, providing input data for subsequent quantization of motion transition resolution stuttering value intervals. When quantizing the motion transition resolution stuttering value intervals based on inter-frame rendering delay gradient data and inter-frame fracture integral values, a resolution adaptability analysis method is required to calculate the motion transition smoothness at different resolutions and quantify the impact of frame rate fluctuations on motion stuttering. First, based on the stuttering threshold calculation method, the minimum acceptable frame rate at different resolutions is calculated, and the motion stuttering phenomenon when the frame rate is below this value is analyzed. For resolution adaptability analysis, a frame rate stability measurement method is used to calculate the frame rate fluctuation range at different resolutions and analyze the impact of frame rate changes on motion continuity. For stuttering value interval quantization, a hierarchical analysis method is used to divide the stuttering degree within different frame rate ranges into multiple levels and calculate the stuttering value range at different resolutions. All computational data is stored as transition resolution stuttering value intervals, providing input data for subsequent motion rendering deformation analysis.

[0032] Step S24 includes the following steps: Step S241: Perform background composition layout analysis on the temporal split data of action / background elements to obtain background element composition layout data; Step S242: Analyze the dynamic depth change of the background element composition layout data based on the action / background element time sequence split data to obtain the dynamic depth change data of the composition layout. Step S243: Fit the dynamic depth change data of the composition layout to the performance requirements of the lighting and shadow rendering, and obtain the performance requirements data of the composition lighting and shadow rendering. Step S244: Quantify the rendering performance bottleneck value of the 3D rendering limitation data according to the rendering performance requirements data of composition and lighting, and obtain the rendering performance bottleneck quantification value. Step S245: Based on the rendering performance bottleneck quantization value, estimate the probability of the jagged edge distribution of the dynamic depth change data of the composition layout to obtain the jagged edge distribution data. Step S246: Perform background light and shadow rendering space flattening analysis based on the jagged edge distribution data to obtain light and shadow rendering space flattening data.

[0033] In this embodiment of the invention, when performing background composition layout analysis on the temporal segmentation data of action / background elements, it is necessary to perform quantitative analysis of the composition layout of background elements at different time points based on visual feature extraction methods, and calculate the spatial distribution pattern of background elements. First, an edge detection algorithm is used to extract the contour information of background elements and calculate their proportion in the image. For the composition analysis of background elements, an image partitioning method is used to divide the image into multiple regions and calculate the density distribution of background elements in each region. For layout feature extraction, a morphological analysis method is used to calculate the arrangement of background elements and extract their geometric structural features. All calculated data is stored as background element composition layout data, providing input data for subsequent dynamic depth change analysis of composition layout. When performing dynamic depth change analysis of background element composition layout data based on the temporal segmentation data of action / background elements, it is necessary to calculate the depth information of background elements at different time points based on depth estimation methods and analyze the dynamic depth change trend of background elements. First, an optical flow analysis method is used to calculate the displacement of background elements between adjacent frames and extract the motion direction information of background elements. For depth variation calculation, a binocular parallax analysis method is used to calculate the disparity value of background elements and quantify their depth variation range. For dynamic trend analysis, a time series modeling method is used to calculate the temporal gradient of background element depth variation and extract its variation pattern. All computational data is stored as dynamic depth variation data of the composition layout, providing input data for subsequent lighting and shadow rendering performance requirement fitting. When fitting lighting and shadow rendering performance requirements to the dynamic depth variation data of the composition layout, it is necessary to calculate the rendering performance requirements under different lighting conditions based on rendering computation analysis methods and establish a fitting relationship between rendering performance requirements and composition depth variation. First, a lighting calculation method is used to calculate the lighting intensity at different background element depths and analyze the impact of lighting changes on rendering computation. For rendering load calculation, a pixel fill rate analysis method is used to calculate the pixel rendering requirements at different depth layers and quantify the impact of pixel fill rate on rendering performance. For performance requirement fitting, a multivariate regression analysis method is used to calculate the relationship between rendering computation and background element depth variation and establish a mathematical model of lighting and shadow rendering performance requirements. All computational data is stored as rendering performance requirement data for composition, lighting, and shading, providing input data for subsequent numerical quantification of rendering performance bottlenecks. When quantifying rendering performance bottlenecks based on the rendering performance requirement data for 3D rendering limitations, it is necessary to use rendering resource analysis methods to calculate rendering resource consumption under different lighting conditions and quantify the numerical range of rendering performance bottlenecks. First, a rendering time measurement method is used to calculate the average rendering time under different background element compositions and analyze the trend of rendering time changes with lighting conditions. For resource usage analysis, a memory bandwidth calculation method is used to calculate the memory usage rate under different rendering loads and analyze the impact of memory bandwidth on rendering efficiency.For bottleneck quantification, a computational resource utilization analysis method is used to calculate GPU and CPU utilization under different lighting conditions and identify key factors affecting rendering performance bottlenecks. All computational data is stored as quantified rendering performance bottleneck values, providing input data for subsequent estimation of the probability distribution of jagged edges in the composition. When estimating the probability distribution of jagged edges in the dynamic depth variation data of the composition layout based on the quantified rendering performance bottleneck values, an edge smoothness analysis method is needed to calculate the jagged effect at different background element depth layers and estimate the probability distribution of jagged edges. First, an edge sharpness measurement method is used to calculate the edge gradient intensity under different background element compositions and quantify the impact of edge sharpness on the jagged effect. For jagged edge distribution estimation, a pixel alignment deviation calculation method is used to calculate the pixel error at different background element depths and establish the relationship between pixel deviation and jagged edge distribution probability. For probability distribution calculation, a statistical regression analysis method is used to calculate the probability of jagged edges occurring under different rendering conditions and establish a jagged edge distribution probability model. All computational data is stored as composition jagged edge distribution data, providing input data for subsequent background lighting and shadow rendering space flattening analysis. When performing background lighting and shadow rendering space flattening analysis based on jagged edge distribution data, it is necessary to calculate the lighting and shadow rendering errors at different background element depth layers and analyze the flattening characteristics of the lighting and shadow rendering space based on spatial depth flattening analysis methods. First, a lighting contrast measurement method is used to calculate the lighting contrast under different background element compositions and analyze the impact of lighting and shadow flattening on the image's sense of depth. For lighting and shadow flattening error calculation, a lighting model error analysis method is used to calculate the lighting errors at different background element depth layers and quantify the impact of lighting calculation errors on spatial flattening. For spatial flattening feature analysis, a three-dimensional depth mapping analysis method is used to calculate the background depth distribution under different lighting conditions and establish a flattening feature model of the lighting and shadow rendering space. All computational data is stored as lighting and shadow rendering space flattening data, providing input data for subsequent visual focus error deviation simulation.

[0034] Step S3 includes the following steps: Step S31: Perform light and shadow visual overlap analysis based on motion rendering deformation data and light and shadow rendering space flatness data to obtain light and shadow visual overlap data; Step S32: Based on the light and shadow visual overlap data, motion rendering deformation data and light and shadow rendering space flattening data, perform visual focus error deviation simulation to obtain visual focus error deviation data.

[0035] As an example of the present invention, reference is made to... Figure 3 As shown, step S3 in this example includes: Step S31: Perform light and shadow visual overlap analysis based on motion rendering deformation data and light and shadow rendering space flatness data to obtain light and shadow visual overlap data; In this embodiment of the invention, when performing light and shadow visual overlap analysis based on motion rendering deformation data and light and shadow rendering spatial flattening data, it is necessary to quantify the spatial overlap relationship between light and shadow and motion in the image based on a visual perception model, and calculate the visual interaction influence between light and shadow and motion. First, an edge detection algorithm is used to extract the boundary information of motion elements and light and shadow changes in the image, and the edge gradient change rate is calculated. For calculating the light and shadow overlap area, a pixel contrast analysis method is used to calculate the brightness difference between motion elements and light and shadow areas, and visual interference areas are extracted. For spatial overlap quantification, a two-dimensional histogram statistical method is used to calculate the probability of overlap between motion edges and bright light and shadow areas, and a light and shadow visual overlap distribution model is established. All calculated data is stored as light and shadow visual overlap data, providing input data for subsequent visual focus error deviation simulation.

[0036] Step S32: Based on the light and shadow visual overlap data, motion rendering deformation data and light and shadow rendering space flattening data, perform visual focus error deviation simulation to obtain visual focus error deviation data.

[0037] In this embodiment of the invention, when simulating visual focus error deviation based on light and shadow visual overlap data, motion rendering deformation data, and light and shadow rendering spatial flattening data, it is necessary to dynamically simulate the visual focus offset under different light and shadow and motion interaction conditions based on a visual focus tracking algorithm, and calculate the spatial distribution of the visual focus error. First, a gaze tracking method is used to calculate the user's gaze point in different light and shadow and motion interaction scenarios, and visual focus trajectory data is extracted. For focus error calculation, a Gaussian distribution fitting method is used to calculate the probability distribution of visual focus deviation and analyze the impact of light and shadow visual interference on focus stability. For error deviation quantification, a cumulative deviation integral method is used to calculate the cumulative offset of the visual focus at different time points, and a visual focus error deviation model is established. All calculated data is stored as visual focus error deviation data, providing input data for subsequent interaction optimization.

[0038] Step S32 includes: Step S321: Segment the visual focus region of the light and shadow visual overlap data to obtain the visual focus region data; Step S322: Perform layered light and shadow attenuation analysis of the focal area based on the visual focal area data and the flat data of the light and shadow rendering space to obtain the layered light and shadow attenuation data of the focal area; Step S323: Calculate the disparity offset based on the layered light and shadow attenuation data of the focal area to obtain the disparity offset data; Step S324: Based on the layered light and shadow attenuation data and parallax offset data of the focal region, perform visual focus error deviation simulation to obtain visual focus error deviation data.

[0039] In this embodiment of the invention, when segmenting the visual focus region of the light and shadow visual overlap data, the image is first divided into regions based on an image segmentation algorithm. A superpixel segmentation method is used to subdivide the light and shadow overlap region into multiple continuous visual sub-regions. A Gaussian mixture model is then used to calculate the region probability distribution to determine the region with the most significant light and shadow interference. By calculating the brightness gradient change and edge contrast of each sub-region, key regions with a significant impact on the visual focus are selected, and the boundaries of the focus region are set based on an adaptive threshold method. Finally, the visual focus region data is obtained, serving as input for subsequent focus region light and shadow analysis. When performing layered light and shadow attenuation analysis of the focus region based on the visual focus region data and the flattened light and shadow rendering space data, the light and shadow attenuation characteristics of the focus region are processed layer by layer based on a multi-layer illumination model. First, a light propagation calculation method is used to calculate the spatial distribution of illumination intensity based on the depth information of the focus region, and the attenuation degree of illumination with distance is calculated using Lambert's law. For the light and shadow influence of different depth layers in the focus region, radiometric methods are applied to calculate the attenuation factor of each layer of illumination, and Bessel interpolation is used to smooth the illumination transition region, eliminating visual discomfort caused by abrupt changes in light and shadow. Finally, layered light and shadow attenuation data for the focal region are obtained, providing fundamental data for disparity offset calculation. When calculating disparity offset based on motion rendering deformation data using layered light and shadow attenuation data for the focal region, a disparity estimation algorithm is used to analyze the impact of light and shadow attenuation on the spatial position of motion elements. First, the depth offset of motion elements after light and shadow attenuation is calculated using a binocular stereo matching method, and the disparity offset trajectory over time is calculated using an optical flow estimation method. Combining the layered light and shadow attenuation data for the focal region, a weighted average method is used to calculate the contribution ratio of different light and shadow layers to disparity offset, and the calculation results are optimized using the least squares method to ensure the accuracy of the disparity offset. Finally, disparity offset data is obtained, providing input for visual focus error deviation simulation. When simulating visual focus error deviation based on layered light and shadow attenuation data and disparity offset data for the focal region, focus tracking technology is first used to record the impact of different light and shadow layers on the visual focus position, and a visual focus change model is established based on the disparity offset data. A Markov process is used to simulate the dynamic offset trajectory of the focus under different lighting conditions, and a cumulative error analysis method is applied to calculate the temporal cumulative deviation of the focus deviation. By combining light and shadow visual overlap data, a probability distribution fitting method was used to calculate the focus deviation probability distribution under different lighting conditions, and a visual focus error variation curve was established using a curve fitting method. Ultimately, visual focus error deviation data was obtained, providing a basis for subsequent interaction optimization.

[0040] Step S4 includes the following steps: Step S41: Normalize the visual focus error deviation data to obtain normalized visual focus error deviation data; Step S42: Adjust the light and shadow intensity of the light and shadow rendering space flat data based on the visual focus error deviation normalization data to obtain visual error light and shadow intensity adjustment data; Step S43: Based on the visual focus error deviation normalization data and the visual error light and shadow intensity adjustment data, perform motion smoothness frame number matching on the motion rendering deformation data to obtain motion smoothness frame number matching data; Step S44: Perform adaptive adjustment of motion scaling based on motion smoothness frame number matching data to obtain motion scaling adaptive adjustment data; Step S45: Design the digital interactive experience management logic based on the visual error light and shadow intensity adjustment data, motion smoothness frame number matching data, and motion scaling adaptive adjustment data to obtain the interactive experience management logic.

[0041] In this embodiment of the invention, when normalizing the visual focus error deviation data, the global distribution characteristics of the statistical data are first analyzed, including the maximum, minimum, mean, and variance of the error deviation values. Then, the Min-Max normalization method is used to map the data to the [0,1] interval, allowing the impact of different error values ​​to be uniformly measured. For error values ​​with abnormal fluctuations, median filtering is used for smoothing to ensure the stability of the normalized data. During the normalization process, quantile transformation is used to standardize the data for skewed distributions of error values, ensuring a uniform distribution. Finally, the normalized visual focus error deviation data is obtained and used as input for subsequent light and shadow intensity adjustment. When adjusting the light and shadow intensity of the flattened light and shadow rendering space based on the normalized visual focus error deviation data, the brightness gradient changes of each pixel in the flattened light and shadow rendering space are first analyzed, and their correlation with the visual focus error is calculated. A brightness error compensation algorithm is used to dynamically adjust the light and shadow intensity in high-error areas. Specifically, the brightness of areas with large error values ​​is enhanced or weakened using bilinear interpolation, and the light and shadow hierarchy is optimized using a local contrast adjustment algorithm to ensure that the adjusted light and shadow distribution effectively reduces visual errors. During the adjustment process, the light and shadow transition is optimized using a local adaptive histogram equalization method based on the flattening degree of light and shadow in different areas, making the light and shadow performance in the visual focus area more natural. Finally, visual error light and shadow intensity adjustment data is obtained, providing optimized light and shadow environment data for subsequent motion smoothness matching. When matching the motion rendering deformation data to the motion smoothness frame rate based on the visual focus error deviation normalization data and the visual error light and shadow intensity adjustment data, the key frame sequence in the motion rendering deformation data is first extracted, and the deformation difference between each key frame is calculated. Subsequently, the time interval between key frames is optimized based on the temporal interpolation method to adapt to the changing trend of light and shadow errors. For time periods with large errors, a non-uniform temporal sampling method is used to increase the frame rate and improve the smoothness of motion transitions; for time periods with small errors, the frame rate is appropriately reduced to optimize the allocation of computational resources. By combining visual error light and shadow intensity adjustment data, a dynamic time warping algorithm is used to match the motion frame sequence, ensuring that motion changes are synchronized with light and shadow adjustments. Ultimately, motion smoothness frame number matching data is obtained, providing a foundation for subsequent adaptive motion scaling adjustments. When performing adaptive motion scaling adjustments based on motion smoothness frame number matching data, the motion trajectory at different frame rates is first calculated, and the optimal scaling ratio is determined based on scale transformation analysis. A local affine transformation method is used to dynamically adjust the motion form to ensure motion consistency across different frame rates. For fast-moving motion segments, optical flow tracking is used to calculate the scaling ratio of the motion direction, and smooth transformations are inserted between keyframes to reduce visual abrupt changes during scaling.During the scaling adjustment process, a depth-aware scaling method is employed for action elements at different spatial depths to ensure that the distance and layering of actions conform to visual perspective. Ultimately, adaptive scaling adjustment data for actions is obtained, providing optimized action performance data for the design of the interactive experience management logic. When designing the digital interactive experience management logic based on visual error light and shadow intensity adjustment data, motion smoothness frame rate matching data, and adaptive scaling adjustment data, a multi-parameter fusion-based interactive experience optimization model is first established, including parameters such as light and shadow error compensation, motion smoothness control, and motion scaling adaptation. A weighted decision method is used to calculate the impact weights of different optimization strategies on the interactive experience, and a heuristic search method is used to optimize the adaptation rules of the interactive logic. During the interactive experience logic design process, a finite state machine method is used to establish interactive state transition rules for the user's operation path, ensuring smooth logical connections between different actions and light and shadow adjustments. Furthermore, to ensure real-time interactive response, an event-driven processing mechanism is adopted, enabling the interactive logic to dynamically adjust according to user operations. Finally, the interactive experience management logic is obtained, providing an optimization solution for the digital interactive experience of intangible cultural heritage.

[0042] The present invention also provides a digital interactive experience management system for intangible cultural heritage, used to execute the digital interactive experience management method for intangible cultural heritage as described above, the digital interactive experience management system for intangible cultural heritage comprising: The action / background element splitting module is used to acquire videos of intangible cultural heritage skills; it splits the action / background elements of the videos of intangible cultural heritage skills to obtain action / background element time sequence splitting data. The element analysis module is used to perform motion rendering deformation analysis based on the temporal split data of motion / background elements to obtain motion rendering deformation data; and to perform background lighting and shadow rendering space flattening analysis based on the temporal split data of motion / background elements to obtain lighting and shadow rendering space flattening data. The visual focus deviation analysis module is used to simulate visual focus error deviation based on motion rendering deformation data and light and shadow rendering space flat data, and obtain visual focus error deviation data. The interactive experience management logic design module is used to adjust the light and shadow intensity of the light and shadow rendering space flat data based on the visual focus error deviation normalization data to obtain visual error light and shadow intensity adjustment data; and to design digital interactive experience management logic based on the visual error light and shadow intensity adjustment data to obtain the interactive experience management logic.

[0043] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for managing the digital interactive experience of intangible cultural heritage, characterized in that, Includes the following steps: Step S1: Obtain videos of intangible cultural heritage skills; decompose the videos of intangible cultural heritage skills into action / background elements to obtain temporal decomposition data of action / background elements; Step S2: Perform motion rendering deformation analysis based on the temporal split data of motion / background elements to obtain motion rendering deformation data; Based on the temporal splitting data of action / background elements, the background lighting and shadow rendering space flattening is analyzed to obtain the lighting and shadow rendering space flattening data; Step S3: Simulate visual focus error deviation based on motion rendering deformation data and light and shadow rendering space flat data to obtain visual focus error deviation data; Step S4: Adjust the light and shadow intensity of the light and shadow rendering space flat data based on the visual focus error deviation normalization data to obtain visual error light and shadow intensity adjustment data; The digital interactive experience management logic is designed based on the visual error light and shadow intensity adjustment data, resulting in the interactive experience management logic.

2. The method for managing the digital interactive experience of intangible cultural heritage according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain videos of intangible cultural heritage techniques; Step S12: Deconstruct the performance process of the intangible cultural heritage skills video to obtain the skill performance deconstruction process; Step S13: Decompose the skill performance process into action / background elements to obtain action / background element decomposition data; Step S14: Mark the temporal changes in the action / background element split data to obtain the action / background element temporal split data.

3. The method for managing the digital interactive experience of intangible cultural heritage according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Obtain interactive device configuration data; Step S22: Perform a 3D rendering limitation simulation based on the interactive device configuration data to obtain 3D rendering limitation data; Step S23: Perform motion rendering deformation analysis on the 3D rendering limitation data based on the temporal split data of motion / background elements to obtain motion rendering deformation data; Step S24: Based on the temporal split data of the action / background elements, perform background lighting and shadow rendering space flattening analysis on the 3D rendering limitation data to obtain lighting and shadow rendering space flattening data.

4. The method for managing the digital interactive experience of intangible cultural heritage according to claim 3, characterized in that, Step S23 includes the following steps: Step S231: Perform rendering space resolution output limitation analysis on the 3D rendering limitation data to obtain rendering resolution output limitation data; Step S232: Perform motion spatial pose deflection recognition on the temporal split data of the action / background elements to obtain spatial motion pose deflection data; Step S233: Analyze the continuity of motion transitions based on the spatial motion pose deflection data to obtain the continuity of motion transitions. Step S234: Based on the continuity of motion connection amplitude, quantize the motion connection resolution stuttering value range of the rendering resolution output limit data to obtain the connection resolution stuttering value range. Step S235: Perform motion rendering deformation analysis based on the range of stuttering values ​​at the connection resolution to obtain motion rendering deformation data.

5. The method for managing the digital interactive experience of intangible cultural heritage according to claim 3, characterized in that, Step S24 includes the following steps: Step S241: Perform background composition layout analysis on the temporal split data of action / background elements to obtain background element composition layout data; Step S242: Analyze the dynamic depth change of the background element composition layout data based on the action / background element time sequence split data to obtain the dynamic depth change data of the composition layout. Step S243: Fit the dynamic depth change data of the composition layout to the performance requirements of the lighting and shadow rendering, and obtain the performance requirements data of the composition lighting and shadow rendering. Step S244: Quantify the rendering performance bottleneck value of the 3D rendering limitation data according to the rendering performance requirements data of composition and lighting, and obtain the rendering performance bottleneck quantification value. Step S245: Based on the rendering performance bottleneck quantization value, estimate the probability of the jagged edge distribution of the dynamic depth change data of the composition layout to obtain the jagged edge distribution data. Step S246: Perform background light and shadow rendering space flattening analysis based on the jagged edge distribution data to obtain light and shadow rendering space flattening data.

6. The method for managing the digital interactive experience of intangible cultural heritage according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Perform light and shadow visual overlap analysis based on motion rendering deformation data and light and shadow rendering space flatness data to obtain light and shadow visual overlap data; Step S32: Based on the light and shadow visual overlap data, motion rendering deformation data and light and shadow rendering space flattening data, perform visual focus error deviation simulation to obtain visual focus error deviation data.

7. The method for managing the digital interactive experience of intangible cultural heritage according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Normalize the visual focus error deviation data to obtain normalized visual focus error deviation data; Step S42: Adjust the light and shadow intensity of the light and shadow rendering space flat data based on the visual focus error deviation normalization data to obtain visual error light and shadow intensity adjustment data; Step S43: Based on the visual focus error deviation normalization data and the visual error light and shadow intensity adjustment data, perform motion smoothness frame number matching on the motion rendering deformation data to obtain motion smoothness frame number matching data; Step S44: Perform adaptive adjustment of motion scaling based on motion smoothness frame number matching data to obtain motion scaling adaptive adjustment data; Step S45: Design the digital interactive experience management logic based on the visual error light and shadow intensity adjustment data, motion smoothness frame number matching data, and motion scaling adaptive adjustment data to obtain the interactive experience management logic.

8. A digital interactive experience management system for intangible cultural heritage, characterized in that, For executing the digital interactive experience management method for intangible cultural heritage as described in claim 1, the digital interactive experience management system for intangible cultural heritage includes: The action / background element splitting module is used to acquire videos of intangible cultural heritage skills; it splits the action / background elements of the videos of intangible cultural heritage skills to obtain action / background element time sequence splitting data. The element analysis module is used to perform motion rendering deformation analysis based on the temporal split data of motion / background elements to obtain motion rendering deformation data; and to perform background lighting and shadow rendering space flattening analysis based on the temporal split data of motion / background elements to obtain lighting and shadow rendering space flattening data. The visual focus deviation analysis module is used to simulate visual focus error deviation based on motion rendering deformation data and light and shadow rendering space flat data, and obtain visual focus error deviation data. The interactive experience management logic design module is used to adjust the light and shadow intensity of the light and shadow rendering space flat data based on the visual focus error deviation normalization data to obtain visual error light and shadow intensity adjustment data; and to design digital interactive experience management logic based on the visual error light and shadow intensity adjustment data to obtain the interactive experience management logic.

Images