Interactive four-time scene change clear landscape painting dynamic simulation system

Abstract

The invention discloses an interactive four-season scene change clear landscape painting dynamic simulation system, which relates to the technical field of digital media, and comprises a data acquisition and style analysis module for acquiring a clear landscape painting and separating elements to count four-season colors and establish a parameter set; the four-season landscape parameter modeling module is used for constructing a four-season dynamic model and forming a parameter sequence according to a time axis; the dynamic rendering engine module carries an ink renderer, simulates cloud and mist water surface dynamic states and calls elements according to parameters of four seasons; the interaction processing module is provided with touch control, voice and gesture interaction interfaces; the user feedback optimization module is used for recording an operation generation preference report and optimizing parameters; the system resource management module is used for monitoring resources and performing hierarchical cache preloading; and the style consistency verification module is used for comparing the picture with the style library and adjusting parameters to keep the style. The method fits the style of the clear landscape painting, and the dynamic change is natural; the interaction modes are rich, and the immersion is strong; traditional painting digital inheritance is assisted, and user experience and practical value are improved.

Description

An interactive dynamic simulation system for depicting the changing scenery of early Qing Dynasty landscape paintings throughout the four seasons. Technical Field

[0001] This invention relates to the field of digital media technology, and in particular to an interactive dynamic simulation system for early Qing Dynasty landscape paintings depicting the changing scenery of the four seasons. Background Technology

[0002] Early Qing dynasty landscape painting, as an important school of traditional Chinese painting, possesses both artistic and cultural value. Its depiction of rocks, trees, and the seasonal moods exhibits distinct stylistic and technical characteristics characteristic of the era. With the development of digital technology, the preservation and dissemination of early Qing landscape paintings currently relies heavily on static scanning and high-definition photography to convert the original works into digital images for storage or display. While these methods preserve the static details of the paintings, they fail to capture the implied seasonal changes and moods inherent in the landscapes. For example, in Wang Shimin's "Farewell at Yushan," the tender green vegetation of spring and the red and yellow leaves of autumn are presented statically only as a single season, failing to allow viewers to intuitively experience the artistic expression of "different scenes in four seasons," and also failing to reflect the early Qing painters' delicate observation and technical interpretation of the changing seasons.

[0003] Existing technical solutions attempting dynamic simulation of landscape paintings suffer from significant stylistic and technical deficiencies. On the one hand, these solutions often employ generic dynamic models of natural landscapes without delving into the unique techniques of early Qing dynasty landscape paintings. For instance, they replace the morphological changes of "deer antler branches" and "crab claw branches" in early Qing paintings with animations of ordinary tree growth, and replace the ink wash effect of "coloring according to type" with conventional color gradients. This results in a significant deviation between the simulated image and the original style, even creating a sense of incongruity, like "modern landscapes wearing traditional paintwork." On the other hand, the dynamic transitions are abrupt, with seasonal color changes often being direct replacements lacking natural temporal connections. Dynamic elements such as flowing clouds and changing water bodies do not conform to the laws of fluid mechanics or the expressive intent of traditional paintings. For example, the winter snow cover is simply overlaid with a white mask without considering the subtle appearance of the mountain and rock textures under the snow, thus destroying the original's sense of layering and technical details.

[0004] Furthermore, existing solutions lack interactivity and adaptability, failing to meet users' needs for personalized experiences and multi-scenario applications. Most systems only support simple seasonal switching button operations, preventing users from adjusting details such as the position of landscape elements and the angle of light and shadow according to their preferences. They also cannot simulate more culturally immersive interactive methods such as calligraphy inscriptions and AR virtual-real integration. At the same time, the systems have weak resource management capabilities, easily experiencing rendering stutters and poor resolution adaptation on terminals with different performance levels. Moreover, they lack the ability to learn and optimize user interaction habits, and cannot dynamically adjust simulation parameters according to user operation preferences. This results in insufficient user experience smoothness and cultural perception depth, making it difficult to truly achieve the goal of digitally inheriting early Qing Dynasty landscape paintings as "observable, tangible, and interactive." Summary of the Invention

[0005] This invention proposes an interactive dynamic simulation system for early Qing Dynasty landscape paintings depicting the changing scenery of the four seasons, in order to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an interactive dynamic simulation system for early Qing Dynasty landscape paintings depicting seasonal changes, comprising the following modules: a data acquisition and style analysis module: acquiring images of original early Qing Dynasty landscape paintings, extracting visual features of rock textures, tree forms, and water ripples, and establishing a style feature library; separating mountain, vegetation, water, and architectural elements through image segmentation technology, statistically analyzing the color distribution of each element in the spring, summer, autumn, and winter paintings, and storing it as a set of seasonal feature parameters; a four-season landscape parameter modeling module: based on the style analysis results, constructing a dynamic change model of the four-season landscape, establishing a time axis mapping relationship, dividing one year into 365 time nodes, each node corresponding to a set of landscape parameters, forming a continuously changing parameter sequence; a dynamic rendering engine module: equipped with an ink wash renderer, using a particle system to simulate cloud and fog flow, and realizing water surface dynamics through texture animation; calling elements from the style feature library according to the four-season landscape parameters; and interactive processing. Modules: This module includes three interaction interfaces: touch, voice, and gesture. The voice interface recognizes commands for "spring," "summer," "autumn," and "winter," and associates them with corresponding seasonal parameters. The gesture interface uses a camera to capture waving gestures and control cloud and fog density. User feedback optimization module: This module records user interaction data and generates a weekly user preference report. When the frequency of seasonal switching exceeds 40% of the total operations, it optimizes the rendering details for that season. If a user adjusts the position of the same element three times consecutively, it automatically sets that position as the default parameter. System resource management module: This module monitors CPU usage and memory usage. When resource usage exceeds limits, it automatically reduces the number of particles and texture resolution. It employs a tiered caching mechanism to preload frequently accessed seasonal parameter sets, shortening rendering startup time. Style consistency verification module: This module compares the matching degree between the dynamic simulation image and the style feature library of early Qing Dynasty landscape paintings, calculating the similarity of mountain and rock textures and color deviation values. When the matching degree is below a threshold, it automatically adjusts rendering parameters.

[0007] Furthermore, it also includes a module for optimizing the smoothness of color transitions throughout the four seasons, using a formula. Calculate the color parameters at any time t, where Let be the color value at time t. The initial seasonal color values, The target seasonal color value is defined by k, the transition coefficient is defined by r, the attenuation coefficient is defined by t, and the transition time is defined by t. This calculation achieves a natural color gradient across the four seasons while dynamically adjusting the color saturation based on the material reflection characteristics of rocks, vegetation, and water bodies.

[0008] Furthermore, it also includes a user interaction intent prediction module, which collects 5-10 consecutive user interaction operations, constructs an operation sequence feature vector, and uses a formula... Calculate the intention prediction probability, where P is the prediction probability and n is the length of the operation sequence. Let be the weight of the i-th operation. To determine the similarity between the i-th operation and the preset intent, when P≥0.75, the landscape parameters corresponding to the predicted intent are preloaded, and the parameter priority is adjusted in conjunction with the user's historical preferences.

[0009] Furthermore, it also includes a dynamic generation module for mountain and rock texture strokes. Based on the line characteristics of axe-cut and hemp-fiber texture strokes in early Qing Dynasty landscape paintings, a texture stroke generation rule library is established. A deep learning model is introduced to generate new texture stroke lines that conform to stylistic characteristics by analyzing texture stroke samples from more than 200 original works. The texture stroke type is automatically selected according to the slope of the mountain, and the ink density of the lines is adjusted according to seasonal changes. Users can adjust the texture stroke density by sliding, and a style switching option for famous artists' texture strokes is provided. After switching, the line thickness and ink smudging degree are automatically matched to the characteristics of the corresponding painter to generate a mountain and rock effect that matches personal preferences.

[0010] Furthermore, it also includes a water dynamic simulation enhancement module, which distinguishes between three types of water bodies: rivers, lakes, and waterfalls. Rivers are set with flow direction vectors, lakes with ripple diffusion coefficients, and waterfalls with particle falling parameters. Combined with acoustic simulation technology, seasonal sound effects are added to different water bodies. During the four seasons, rivers partially freeze in winter, and the frozen areas show ice crack textures. The waterfall's water volume decreases and is accompanied by icicle hanging effects. When the user clicks on a water body area, a ripple effect is triggered, and the sound effects change accordingly.

[0011] Furthermore, it includes a refined vegetation growth cycle module, which divides trees into evergreen and deciduous trees. The leaf density of evergreen trees varies by 10%-20% throughout the four seasons, with new leaves accounting for 30%-40% in spring and old leaves accounting for 60%-70% in winter. For deciduous trees, the leaf density increases from 30% to 80% in spring and decreases from 80% to 20% in autumn. The trajectory of falling leaves simulates the effects of gravity and wind. Tree age parameters are set, with young trees reaching 60%-70% of the height of middle-aged trees, and old trees having crack textures added to their trunks and tilted at 5°-10°. Users can trigger a growth animation by double-clicking a tree, which synchronously displays the annual ring changes of the tree throughout the four seasons, intuitively presenting the morphological changes of the tree in the four seasons.

[0012] Furthermore, it includes a dynamic light and shadow tracking module to simulate the sun's rising and setting trajectory and calculate the angle of light and shadow projection at different times of day; it introduces an atmospheric scattering model, adding a mist scattering effect in spring to make the light appear soft and diffused, and enhancing air transparency in autumn to make the edges of light and shadow clearer; summer has the longest daylight hours and winter has the shortest, with light and shadow intensity changing with the seasons, and the shadow hue being more bluish in winter; users can drag the sun icon to adjust the angle of illumination, and the system automatically updates the length of the mountain shadow, while simultaneously calculating the reflective area of ​​the water surface in real time to achieve personalized light and shadow effects.

[0013] Furthermore, it also includes a module for seasonal adaptation of architectural elements. For the three types of buildings—pavilions, bridges, and houses—found in early Qing Dynasty landscape paintings, it extracts architectural features and creates 3D models. In spring, it adds floral decorations around the buildings; in summer, it increases the length of the eaves shadows and hangs vines; in autumn, it adorns the roofs with fallen leaves; and in winter, it covers them with a thin layer of snow. The reflectivity of building materials is adjusted according to the season. When users click on a building, it displays a comparison image of the building's state in the four seasons, and at the same time, it pops up a description of the building's form, enhancing the dual perception of seasonal changes and architectural styles. Clicking on a house in winter will also display a candlelight effect inside the window, echoing the literati painting sentiment of "a guest arrives on a cold night, and tea is served as wine."

[0014] Furthermore, it includes a cloud and fog flow path planning module, which generates the main flow line of clouds and fog based on the mountain outline and water body location, and sets the flow velocity gradient; it introduces wind parameters, so that the clouds and fog flow slowly along the main line at level 1 wind and produce a vortex effect at level 3 wind; the cloud and fog concentration is highest in spring and is milky white, while it is lowest in winter and is slightly bluish-gray; through formulas... Calculate the acceleration of cloud and fog particles, where Let 'a' be the acceleration and 'a' be an adjustment coefficient. Let v be the target speed, v be the current speed, and b be the pressure coefficient. Using pressure gradients, this calculation makes the flow of clouds and fog more consistent with the laws of fluid mechanics. At the same time, combined with the relationship of mountain and rock obstruction, clouds and fog will flow around the mountain when they encounter it, avoiding irregular drifting.

[0015] Furthermore, it includes a multi-dimensional interactive expansion module. Building upon existing touch, voice, and gesture interactions, it adds a pen-touch interaction interface, allowing users to simulate brushstrokes with a pressure-sensitive pen and add personalized inscriptions to the image. These inscriptions will display different ink effects depending on the season. It also introduces an AR interaction mode, using a camera to recognize physical picture frames and project dynamic simulation images into them, achieving a combined virtual and real viewing effect. In AR mode, users can move around the picture frame to observe the three-dimensional perspective changes of the image. Users can also save custom landscapes as personalized scene templates, each containing complete seasonal parameter configurations for one-click loading upon next use. The templates also offer scene sharing functionality for cross-user creative exchange.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: In terms of style fidelity, this invention deeply analyzes the technical characteristics of early Qing Dynasty landscape paintings, constructs a unique style feature library and seasonal parameter models, and strictly matches the original work's rock texture, tree shape, and color rendering effects during dynamic simulation. For example, it automatically selects axe-cut texture and hemp-fiber texture according to the slope of the mountain, and adjusts the ink density and line thickness according to the season to ensure that the simulated picture conforms to the artistic style of early Qing Dynasty paintings, avoids style deviations caused by general models, and allows users to still feel the essence of traditional painting techniques and cultural connotations in the dynamic experience, thus helping to accurately inherit the techniques and artistic conception of early Qing Dynasty landscape paintings.

[0017] In terms of dynamic naturalness, this invention achieves a natural connection and artistic expression of scenery changes through refined modeling of seasonal changes and scientific dynamic calculations. Color transitions are achieved using smooth integral calculations to avoid abrupt changes, while saturation and hue are adjusted in conjunction with the reflective properties of materials to conform to the natural temporal order. Dynamic elements such as flowing clouds and changing water bodies take into account the laws of fluid mechanics and the artistic conception of traditional paintings. For example, clouds and mist flowing around mountains and ice cracks in winter are linked with sound effects, making the dynamic effects both realistic and full of the literati painting charm of "mountain light and water color", far exceeding the simple animation effects of existing solutions and enhancing the user's intuitive perception of the changing artistic conception of the four seasons.

[0018] In terms of interactive immersion, this invention breaks through the limitations of traditional simple button operations and constructs a multi-dimensional interactive system. In addition to touch, voice, and gesture interaction, it adds interactive methods such as brushstroke inscriptions and AR virtual-real combination. Users can simulate writing with a brush and observe the three-dimensional perspective effect around the painting frame. At the same time, the system can learn user operation preferences, preload predicted parameters, and automatically adjust default settings, allowing users to transform from "passive viewing" to "active participation," deeply immersing themselves in the cultural atmosphere of early Qing Dynasty landscape paintings and enhancing the richness of cultural perception and artistic experience.

[0019] In terms of adaptability and practicality, this invention has excellent resource management and multi-terminal adaptation capabilities. It can dynamically allocate rendering resources according to terminal performance, avoiding problems such as lag and poor resolution adaptation, and ensuring a smooth experience on different devices such as mobile phones and computers. At the same time, it supports the saving and sharing of personalized scene templates, allowing users to create their own seasonal landscape scenes and share them. This not only meets the needs of personal appreciation, teaching demonstrations and other scenarios, but also provides a new path for the digital innovation and dissemination of early Qing Dynasty landscape paintings, combining cultural preservation value with practical promotion value. Attached Figure Description

[0020] Figure 1 is a schematic block diagram of the interactive dynamic simulation system for the changing scenery of early Qing Dynasty landscape paintings in four seasons proposed in this invention; Figure 2 is a bar chart comparing user participation in different interaction methods; Figure 3 is a radar chart comparing the core parameters of the brushstroke techniques of famous early Qing Dynasty painters. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0024] Referring to Figures 1 to 3: An interactive dynamic simulation system for early Qing Dynasty landscape paintings depicting seasonal changes includes the following modules: Data acquisition and style analysis module: Acquires 30-50 original images of early Qing Dynasty landscape paintings, extracting visual features of rock textures, tree forms, and water ripples. Rock textures include axe-cut textures and hemp-fiber textures; tree forms include antler branches and crab claw branches; and water ripples include fish scale patterns and net-like patterns. Based on this, a style feature database is established. Using image segmentation technology, elements such as mountains, vegetation, water, and buildings are separated. The color distribution of each element in the spring, summer, autumn, and winter paintings is statistically analyzed. For example, in spring, lush green vegetation accounts for 35%-45%, and in autumn, red leaves account for 25%-35%. These color distribution data are stored as seasonal feature parameters. Data set; Four-season landscape parameter modeling module: Based on the style analysis results obtained from the data acquisition and style analysis module, a dynamic change model of the landscape in all four seasons is constructed. Three types of core parameters are set: vegetation growth cycle parameters, light and shadow angle parameters, and water body state parameters. Among them, the vegetation growth cycle parameters cover four stages: budding, flourishing, leaf fall, and withering. In the light and shadow angle parameters, the solar altitude angle is 30°-50° in spring, 60°-80° in summer, 30°-50° in autumn, and 10°-30° in winter. In the water body state parameters, the ripple frequency in spring is 1-2. The system provides rendering speeds of 60%-80% per second and winter icing transparency. It establishes a timeline mapping, dividing the year into 365 time nodes, matching each node with a corresponding set of landscape parameters to form a continuously changing parameter sequence. The dynamic rendering engine module features an ink-wash renderer, employing a particle system to simulate cloud and fog flow (500-2000 particles, 0.5-2m / s movement speed), and achieving dynamic water surface movement through texture animation (24-30fps). Based on the seasonal landscape parameters, it calls elements from the style feature library: overlaying tender green vegetation textures in spring, enhancing light and shadow contrast in summer (brightness increase of 15%-25%), replacing leaf textures with a red-yellow gradient in autumn, and covering snow masks in winter (coverage of 30%-60%). It supports 1080P / 4K resolution output with rendering latency controlled within 50-100ms. The interactive processing module includes touch, voice, and gesture interfaces.The touch interface supports clicking to switch seasons and dragging to adjust the position of landscape elements. The response time for clicking to switch seasons is <100ms, and the accuracy for dragging to adjust the position of landscape elements is ±5 pixels. The voice interface can recognize "spring," "summer," "autumn," and "winter" commands with an accuracy of ≥95%, and will associate the corresponding seasonal parameters after recognition. The gesture interface captures waving gestures through the camera, with a recognition distance of 0.5-3m. After capturing the gesture, the cloud and fog concentration can be controlled, and the larger the wave, the higher the cloud and fog concentration. The user feedback optimization module records user interaction data, including the frequency of season switching, the number of times elements are adjusted, and the dwell time. A user preference report is generated weekly based on this data. When the frequency of switching a certain season exceeds 40% of the total number of operations, the rendering details of that season are optimized accordingly, such as adding special vegetation types. When it is detected that a user adjusts the position of the same element 3 times consecutively, the position is automatically set as the default parameter to improve the interaction adaptability. The system resource management module monitors CPU utilization and memory usage, with the CPU utilization threshold set to 80% and the memory usage threshold set to 75%. When the occupancy rate or memory usage exceeds the corresponding threshold, the particle count and texture resolution are automatically reduced, with a minimum particle count of 300 and a minimum texture resolution of 720P. This module adopts a hierarchical caching mechanism to preload frequently accessed seasonal parameter sets, thereby shortening the rendering startup time and ensuring that the rendering startup time is ≤2s. Style consistency verification module: compares the matching degree between the dynamic simulation image and the style feature library of early Qing Dynasty landscape paintings (threshold 0.85), calculates the similarity of mountain and rock texture (such as the angle error of axe-cut texture lines ≤5°) and color deviation value (ΔE≤3). When the matching degree is lower than the threshold, the rendering parameters are automatically adjusted (such as increasing the ink density and correcting the line thickness) to ensure that the simulation effect conforms to the style of the original work.

[0025] This invention also includes a four-season color transition smoothness optimization module, which uses a formula... Calculate the color parameters at any time t, where The color value at time t (RGB components 0-255). The initial seasonal color values, The target seasonal color value is defined by k (0.01-0.05), the attenuation coefficient is defined by r (0.02-0.06), and the transition time is defined by t (in seconds). This calculation achieves a natural color gradient across the four seasons while dynamically adjusting the color saturation based on the material reflection characteristics of rocks, vegetation, and water. For example, the color saturation of vegetation under sunlight in spring is increased by 10%-15% compared to the shaded areas. In autumn, red leaves are offset with an orange tint in the evening light (offset value 5-10) to avoid the abruptness caused by direct switching. This ensures that the color changes conform to natural laws and fit the brushwork characteristics of "coloring according to type" in early Qing Dynasty landscape paintings. When transitioning from emerald green in summer to red and yellow in autumn, the leaf color will gradually change according to the integral curve. At the 10th second, a yellow-green fusion effect is shown, and at the 30th second, it completely turns red and yellow, while the veins retain the ink outline, enhancing the sense of layering in the picture.

[0026] This invention also includes a user interaction intent prediction module, which collects 5-10 consecutive user interaction operations (such as adjusting tree positions and then changing seasons), constructs an operation sequence feature vector, and uses a formula... Calculate the intention prediction probability, where P is the prediction probability (ranging from 0 to 1), and n is the length of the operation sequence. This is the weight of the i-th operation (ranging from 0.1 to 0.3, with more recent operations having higher weights). The similarity between the i-th operation and the preset intention (value 0-1) is used. When P≥0.75, the landscape parameters corresponding to the predicted intention are preloaded. At the same time, the priority of parameters is adjusted based on the user's historical preferences. For example, for users who frequently adjust the cloud and fog concentration, cloud and fog particle parameter sets of different concentrations are cached first. For users who prefer to adjust the light and shadow, shadow projection data at different times are pre-calculated to shorten the interaction response delay and improve the smoothness of operation. If it is predicted that the user may switch from summer to autumn, the system not only preloads the autumn leaf texture, but also pre-calculates the mountain shadow parameters under the typical light and shadow angles of autumn, so that the screen instantly presents the complete seasonal characteristics after the switch.

[0027] This invention also includes a dynamic generation module for mountain and rock texture strokes. Based on the line characteristics of axe-cut and hemp-fiber texture strokes in early Qing Dynasty landscape paintings, a texture stroke generation rule library is established. Axe-cut texture strokes are short and straight with a spacing of 5-10 pixels, while hemp-fiber texture strokes are curved and long with a spacing of 8-15 pixels. A deep learning model is introduced to analyze texture stroke samples from over 200 original works, generating new texture stroke lines that conform to stylistic characteristics. The line curvature error is ≤3°. The module automatically selects the texture stroke type based on the mountain slope (0°-90°), prioritizing axe-cut texture strokes for steep slopes and hemp-fiber texture strokes for gentle slopes. The ink density of the lines is adjusted according to seasonal changes, with the ink density increased by 10%-20% in winter to highlight the mountain outline under snow. This module allows users to adjust the texture stroke density through sliding operations; the sliding distance is positively correlated with the density. It also provides "imitating Shi Tao" and "imitating Gong Xian" styles. The option to switch between different styles of brushstrokes by famous artists allows you to automatically match the thickness of the lines and the degree of ink blending to the characteristics of the corresponding painter, generating a mountain and rock effect that suits your personal preferences. For example, when you select the "Imitation of Gong Xian" style, the brushstroke lines are layered with 3-5 layers of ink to create an "accumulated ink" effect, which enhances the sense of weight and solidity of the mountains.

[0028] This invention also includes a water body dynamic simulation enhancement module. This module distinguishes between three types of water bodies: rivers, lakes, and waterfalls, and sets parameters for each: a flow direction vector is set for rivers, with a flow velocity of 0.3-0.8 m / s; a ripple diffusion coefficient is set for lakes, with a diffusion velocity of 0.1-0.3 m / s; and particle falling parameters are set for waterfalls, with a particle size of 2-5 pixels and a falling velocity of 1-3 m / s. The module combines acoustic simulation technology to add seasonal sound effects to different water bodies. In spring, the frequency of the babbling river sound is 200-500 Hz, and in winter, the frequency of the ice crack sound is 800-1200 Hz. During the changing seasons, in winter, the river partially freezes, with the frozen length accounting for 30%-50%, and the frozen area displays ice crack textures with a crack density of 2-5 cracks / 100 pixels. In winter, the waterfall's water volume decreases, the number of particles decreases by 40%-60%, and an ice floe hanging effect is added, with an ice floe length of 5-15 pixels. When a user clicks on a water area, a ripple effect is triggered. The ripple radius is 50-100 pixels and the duration is 2-3 seconds. At the same time, the sound effect changes, and the volume at the click point is instantly increased by 10%-20% to enhance the interactive immersion. For example, clicking on a frozen lake surface in winter will cause ice surface vibration ripples accompanied by a "click" sound, simulating the feedback of a real ice surface.

[0029] This invention also includes a refined vegetation growth cycle module, which categorizes trees into evergreen and deciduous trees. Evergreen trees include pines and cypresses, while deciduous trees include maples and willows. The leaf density variation of evergreen trees is controlled within 10%-20% throughout the four seasons. In spring, new leaves account for 30%-40% and are light green, while in winter, old leaves account for 60%-70% and are dark green. For deciduous trees, the leaf density increases from 30% to 80% in spring, with new leaves curling at 20°-30°. In autumn, the leaf density decreases from 80% to 20%, with leaves curling at 40°-60°. The falling leaf trajectory simulates the effects of gravity and wind, with a horizontal drift distance of 10-30 pixels. The module sets tree age parameters, divided into three stages: juvenile, middle-aged, and old. Juvenile trees are 60%-70% the height of middle-aged trees, and their branches are 0.5-0.7 slender. Old trees have crack textures added to their trunks, with 5-10 cracks, 2-3 pixels deep, and the trunk tilted at 5°-10°. Users can trigger a growth animation by double-clicking the tree. The animation lasts 3-5 seconds and simultaneously displays the tree's annual ring changes throughout the four seasons. Each annual ring is 1-2 pixels wide, visually representing the tree's morphological changes in the four seasons. The module also allows users to select growth modes such as "withering and flourishing" and "prosperous" to adjust the tree's life cycle speed, which can be accelerated by 1-5 times.

[0030] This invention also includes a dynamic light and shadow tracking module to simulate the sun's rising and setting trajectory, calculating the light and shadow projection angles (30°-45° in the morning, 60°-75° in the afternoon, and 30°-45° in the evening) at different times (6:00-8:00 AM, 12:00-2:00 PM, and 4:00-6:00 PM). An atmospheric scattering model is introduced, adding a hazy scattering effect in spring (scattering coefficient 0.1-0.2) to create soft, diffused light, and enhancing air transparency in autumn (scattering coefficient 0.05-0.1) to make the edges of light and shadow clearer. Summer has the longest daylight hours (10-12 hours). The light and shadow intensity varies with the seasons (180-220 in summer, 100-140 in winter), and the shadow hue is more bluish in winter (the blue component increases by 5-10). Users can manually adjust the lighting angle by dragging the sun icon, and the system automatically updates the length of the mountain shadow (the shadow length is inversely proportional to the sun's altitude angle). At the same time, it calculates the reflective area of ​​the water surface in real time (the reflective area is positively correlated with the lighting angle) to achieve personalized lighting effects. For example, when the lighting is adjusted to the twilight angle, an orange-red reflective band (20-50 pixels wide) will appear on the water surface, which fits the artistic conception of "mountain light and water color" in early Qing Dynasty landscape paintings.

[0031] This invention also includes a module for seasonal adaptation of architectural elements. For pavilions, bridges, and houses depicted in early Qing Dynasty landscape paintings, it extracts architectural features, including the pyramidal roof of pavilions and the arch curvature of bridges, and creates 3D models. In spring, it adds floral decorations around the buildings, with 10-20 plants matching the spring flowers of Jiangnan, such as azaleas and forsythia. In summer, it increases the length of the eaves shadow, with a shadow coefficient of 1.2-1.5, and hangs vines with a droop angle of 30°-45°. In autumn, it adorns the roof with fallen leaves, with a density of 5-10 leaves / ㎡, and a ratio of maple to ginkgo leaves of 7:3. In winter, it covers the roof with a thin layer of snow, 2-5 pixels thick, with the snow thickness at the roof edges increased by 1-2 pixels. The reflectivity of building materials is adjusted seasonally, with the reflectivity of snow surfaces increased by 20%-30% in winter and the reflectivity of wood decreased by 5%-10% in summer to reflect a sense of dampness. Clicking on a building displays a comparison image of its appearance in the four seasons, shown in four panels. At the same time, a description of the building's form pops up, such as "Qing Dynasty pavilions and terraces often used a pyramidal roof with four corners, paired with a railing for women to lean on," enhancing the dual perception of seasonal changes and architectural style. Clicking on a house in winter will also display a candlelight effect inside the window, with a halo radius of 10-15 pixels, echoing the literati painting imagery of "a guest arriving on a cold night, tea served as wine."

[0032] This invention also includes a cloud and fog flow path planning module, which generates a main flow line for clouds and fog based on the mountain outline and water position (from valley to mountaintop or from water to land), sets a flow speed gradient (the speed in the edge area is 60%-80% of that in the center area); introduces wind parameters (wind speed 0-3), with clouds and fog flowing slowly along the main line at level 1 wind and producing a vortex effect at level 3 wind (vortex diameter 50-100 pixels); the cloud and fog concentration is highest in spring (transparency 40%-60%), appearing milky white, and lowest in winter (transparency 70%-90%), slightly bluish-gray; and uses a formula... Calculate the acceleration of cloud and fog particles, where Acceleration (unit: pixels per second) 2 ), where 'a' is an adjustment factor (ranging from 0.02 to 0.05). Target speed (unit: pixels / second), v is current speed (unit: pixels / second), and b is pressure coefficient (value: 0.01-0.03). Pressure gradient (unit: pixels) 1 This calculation makes the flow of clouds and fog more in line with the laws of fluid mechanics. At the same time, combined with the relationship of the mountains and rocks, the clouds and fog will flow around the mountains (the flow angle is 10°-30°) when they encounter the mountains, avoiding irregular drifting and enhancing the realism of the scene. For example, the clouds and fog in the valley in spring will flow along the direction of the hemp fiber texture lines, echoing the texture of the mountains and conforming to the composition principle of "clouds following the shape of mountains" in landscape painting.

[0033] This invention also includes a multi-dimensional interactive extension module. Based on the existing touch, voice, and gesture interactions, this module adds two new interaction methods: First, it adds a pen-touch interaction interface. Users can simulate brushstrokes using a pressure-sensitive pen. The pressure of the pen tip is positively correlated with the line thickness, with a pressure range of 0-1024 levels. Personalized inscriptions can be added to the screen, supporting early Qing Dynasty calligraphic styles such as "Dong Qichang style" and "Bada Shanren style," and the ink color will change with the seasons, being lighter in summer and darker in winter. Second, it introduces an AR interaction mode. A camera recognizes a physical picture frame (30-50cm in size), and after recognition, a dynamic simulation image is projected into the frame, achieving a combined virtual and real viewing effect. In AR mode, users can move around the frame at angles of 0°-360° to observe the three-dimensional perspective changes of the image, and the perspective effect conforms to the early Qing Dynasty's "scattered perspective" principle. In addition, the module allows users to save custom landscapes as personalized scene templates, with a maximum of 10 templates that can be saved. Each template contains complete seasonal parameter configurations and can be loaded with one click the next time it is called. It also provides a scene sharing function, which generates a QR code with parameters. Others can scan the code to reproduce the same landscape, enabling cross-user creative communication. For example, a user-created "Spring Mountain Dwelling" can be shared via QR code. After scanning the code, the recipient can directly view the seasonal changes of the scene and make further adjustments, thus expanding the artistic creation and dissemination value of the system.

[0034] The following two examples further illustrate the specific implementation of this system: Example 1: Dynamic simulation of Wang Shimin's "Four Seasons Mountain Dwelling" in the style of early Qing Dynasty in a museum exhibition scenario (Application scenario: "Early Qing Dynasty Landscape Art Exhibition" in a provincial museum, showcasing the changing artistic conception of Wang Shimin's school of landscape painting in the four seasons to the general public, deployed on a 55-inch touch screen (4K resolution) and AR frame (40cm×60cm), equipped with voice interaction devices and a camera, the system is connected to the museum's local area network (latency ≤30ms), and needs to meet an average daily visitor count of 100. (For 0+ viewers' smooth interaction needs) I. Data Collection and Style Analysis Module Implementation: Collected 35 original images of Wang Shimin's works (including "Floating Mist and Warm Greenery" and "Farewell at Yushan"). Extracted stylistic features using AI image analysis technology: The rocks are mainly characterized by hemp fiber texture strokes, with long and curved lines, a spacing of 8-12 pixels, and 3-5 levels of ink color; the trees are mostly characterized by antler-shaped branches, with the angle of new branches in spring being 30°-40° and the angle of dead branches in winter being 60°-70°; the water surface uses fish scale patterns, with a ripple density of 2-3 lines / 10 pixels in summer and an irregular net-like texture when the ice forms in winter. The semantic segmentation algorithm was used to separate the mountain (45%), vegetation (30%), water (15%), and mountain dwellings (10%). The colors of the four seasons were statistically analyzed: lush green vegetation (RGB120,200,100) accounted for 42% in spring, dark green (RGB80,180,80) accounted for 48% in summer, red leaves (RGB200,80,80) accounted for 32% in autumn, and withered brown (RGB150,120,80) accounted for 35% in winter. These were stored as a set of seasonal feature parameters, with each element of the parameter set associated with the original source to ensure style traceability.

[0035] II. Implementation of the Four Seasons Landscape Parameter Modeling Module: Constructing a Four Seasons Model in the Style of Wang Shimin: During the vegetation growth cycle, the proportion of new pine leaves in spring is 35% (leaf length 2-3 pixels), and the leaf drop rate of maple trees in autumn is 70% (falling speed 1.2m / s); Light and shadow angles: spring solar altitude angle 40° (morning light), summer 75° (noon light), autumn 45° (twilight), winter 20° (noon light); Water body status: spring ripple frequency 1.5 times / second (amplitude 5-8 pixels), winter ice transparency 70% (ice crack density 3 lines / 100 pixels). The year is divided into 365 time nodes. The parameters for the node on March 20 (Spring Equinox) are set as "40% of vegetation is green, 45° of light and shadow, and 1.2 ripples per second in the water". The parameters for the node on December 22 (Winter Solstice) are set as "38% of vegetation is withered and brown, 15° of light and shadow, and 65% of the transparency of the frozen water". This forms a continuous parameter sequence. Every 10 nodes in the sequence correspond to a partial feature of one of Wang Shimin's original works, ensuring that the dynamic changes fit the style and logic of the original work.

[0036] III. The dynamic rendering engine module is equipped with a customized ink wash renderer, simulating the "clear and moist ink" characteristics of Wang Shimin's paintings: In spring, a light green vegetation texture is overlaid (85% transparency); in summer, the contrast of light and shadow is enhanced (brightness increased by 22%, and the ink color in dark areas deepened by 5%); in autumn, the leaf texture is replaced with a red-yellow gradient (transitioning from RGB200,80,80 to RGB180,50,50); and in winter, a snow mask is applied (55% coverage at the top of the mountain, 30% at the foot, with blurred edges). The cloud and fog particle system uses 1500 particles with a movement speed of 0.8-1.5 m / s. In spring, the particle transparency is 50% (milky white), and in winter, it is 75% (with a bluish-gray tone). The water surface texture animation has a frame rate of 28 fps. In summer, the fish scale texture dynamically shifts with the ripples, and in winter, the ice crack texture slightly shrinks with temperature changes (by 1-2 pixels). At 4K resolution, the rendering latency is stable at 75ms, and the frame rate remains above 28 fps during continuous audience interaction.

[0037] IV. Interactive Processing and Multi-Dimensional Interactive Module Implementation: The touchscreen supports clicking to switch seasons (response time 80ms). When the viewer clicks the "Spring" button, the system loads spring parameters within 100ms, and the scene gradually changes from a barren winter mountain to a green spring mountain. Dragging adjusts the position of mountain dwellings (accuracy ±3 pixels), and the system automatically adjusts the surrounding vegetation distribution after dragging (e.g., moving a building 50 pixels to the left adds 3 forsythia bushes on the left). The voice interface recognizes commands such as "switch to summer" and "add fog" (accuracy 96%), executing the operation within 150ms after recognition. The gesture interface captures waving gestures via camera (recognition distance 0.8-2.5m), increasing fog density by 10% for a 10cm wave and by 30% for a 30cm wave. In AR interactive mode, the simulated image is projected onto the physical frame. Viewers can move around the frame (0°-360°) and observe the three-dimensional perspective changes of the mountain dwelling (in accordance with Wang Shimin's "scattered perspective", with the door facing right when viewed from the front and facing the front when viewed from the side). The projection delay is ≤50ms and the error in the fit between the image and the edge of the frame is ≤2mm.

[0038] V. Formula Application and Implementation: Optimization of Color Transition Smoothness in Four Seasons: Transitioning from winter (RGB150, 120, 80) to spring (RGB120, 200, 100), let... =RGB(150,120,80), =RGB(120,200,100), k=0.03, r=0.04, t=10 seconds, substitute into the formula The R channel is calculated as follows: G channel ≈ 120 + 0.03 × 80 × 17.5 ≈ 162, B ​​channel ≈ 80 + 0.03 × 20 × 17.5 ≈ 90.5, that is, C(10) = RGB(134, 162, 91), the picture presents the early spring effect of yellow and green blending, and the G channel is reduced by 8% in the shadow of vegetation, which is in line with Wang Shimin's technique of "yin and yang of ink color".

[0039] Cloud / fog flow path planning: Assume the current velocity of the cloud / fog is v = 1.0 pixels / second, the target velocity is vtarget = 1.2 pixels / second, a = 0.03, b = 0.02, and the pressure gradient is ∇p = 0.5 pixels⁻ 1 Substitute into the formula The calculated acceleration is 0.03 × (1.2 - 1.0) + 0.02 × 0.5 = 0.006 + 0.01 = 0.016 pixels per second. 2 The speed of the clouds increases by 0.016 pixels per second, reaching the target speed after 12 seconds. When encountering the mountain, the clouds flow around at a 20° angle, flowing along the direction of the hemp fiber texture strokes, conforming to the composition of "clouds following the shape of mountains".

[0040] VI. Other Module Implementation and User Feedback Optimization: The system records viewer actions: Summer switching frequency reached 45% (over 40%), so the system added summer "Mountain Pavilion" details (adding bamboo chair textures inside the pavilion); it detected 20% of viewers continuously adjusting the fog concentration, so the default fog concentration was reduced from 50% to 45%. The system resource management module monitors CPU usage; at peak viewer numbers (50 people interacting simultaneously), CPU usage reached 75%, maintaining a particle count of 1500; memory usage was 72%, so no resolution reduction was needed. The style consistency verification module compares the image with the feature library in real time; the autumn red leaf color deviation ΔE=2.3 (≤3), the hemp-fiber texture line angle error was 3° (≤5°), and the matching degree was 0.88 (≥0.85), so no parameter adjustments were needed.

[0041] Table 1: Evaluation Indicators for Comparison of Traditional Static Display and This System in Museum Scene, Example 1 Traditional Static Display (Replica of Wang Shimin's Original Work) This System's Dynamic Simulation Effect Difference Style Fidelity 95% (Static detail reproduction, no dynamic) 93% (Dynamically matches the original technique, small error) Maintains high style consistency even under dynamic conditions Interaction Method Richness 0 types (Viewing only) 4 types (Touch / Voice / Gesture / AR) Significantly expanded interaction dimensions Average audience dwell time 120 seconds 380 seconds Dwell time increased by more than 2 times Seasonal Atmosphere Perception 60% (Requires text explanation to distinguish seasons) 92% (Dynamic changes are intuitively perceived) Significantly reduced threshold for understanding artistic conception Table 1 shows that while traditional static displays offer high fidelity, they lack interactivity and require textual aids to understand the seasonal moods, resulting in short visitor dwell time. This system, through dynamic simulation that aligns with the original techniques, offers four interactive methods to encourage active visitor participation, extending the dwell time to 380 seconds, with 92% of visitors able to intuitively perceive the differences in seasonal moods. In particular, AR interaction allows visitors to observe the images from multiple perspectives, overcoming the limitation of traditional static displays' "single perspective," while maintaining 93% style fidelity even in dynamic settings. This satisfies the museum's requirement for "stylish precision" while enhancing the exhibition's appeal and cultural dissemination effect, aligning with the museum's goal of "balancing preservation and dissemination."

[0042] Example 2: Dynamic Simulation of the Comparison of Landscape Painting Techniques of Famous Early Qing Dynasty Masters in College Art Teaching Scenarios (Application Scenario: A course on "Landscape Painting Techniques of the Early Qing Dynasty" at a certain art college, aimed at 20 third-year students, used to explain the differences in the application of the "hemp fiber texture stroke" (Wang Shimin) and "axe-cut texture stroke" (Dai Benxiao) in the four seasons. Deployed on a 65-inch touch screen (4K) for the teacher and a 15.6-inch laptop (1080P) for the students, with a pressure-sensitive pen (1024 pressure levels). The system is connected to the campus network (latency ≤20ms) and needs to support teacher-student interaction and detailed analysis of techniques.) I. Data Collection and Style Analysis Module Implementation: Collect 40 original works by famous early Qing Dynasty masters, divided into two groups: Wang Shimin group (20 works, hemp fiber texture stroke) and Dai Benxiao group (20 works, axe-cut texture stroke). Analysis of features: Wang Shimin's hemp-fiber texture strokes are long and curved (15-25 pixels) with light and moist ink (grayscale value 180-220); Dai Benxiao's axe-cut texture strokes are short and straight (5-10 pixels) with heavy ink (grayscale value 120-160). Separate element statistics: In Wang Shimin's group, spring vegetation accounts for 35%, while in Dai Benxiao's group, winter mountains account for 55%; in terms of color, Wang Shimin's style is more elegant (spring RGB 130, 210, 110), while Dai Benxiao's style is more vigorous (winter RGB 100, 80, 60). A three-dimensional feature library of "Master - Technique - Seasons" is established, with each feature labeled with a technique description (e.g., "Wang Shimin's hemp-fiber texture strokes are suitable for gentle slopes, expressing the warmth and moisture of spring"), for retrieval during teaching.

[0043] II. The four-season landscape parameter modeling module constructs a dual-style four-season model: Wang Shimin style vegetation (pine trees) with 40% new leaves in spring, and Dai Benxiao style vegetation (withered pines) with 10% new leaves in winter; the light and shadow angles are: Wang Shimin style 45° (soft diffused light) in spring, and Dai Benxiao style 25° (bold direct light) in winter; the water body ripple frequency is: Wang Shimin style ripples 1.2 times / second in spring, and Dai Benxiao style ice transparency is 60% in winter (ice cracks are coarser, 5 lines / 100 pixels). The timeline nodes are associated with changes in technique: in March, Wang Shimin's hemp-fiber texture strokes are lightened by 10%, and in September, Dai Benxiao's axe-cut texture strokes are deepened by 15%; a "technique comparison node" is set (e.g., June 15th), with the same image showing Wang Shimin style (hemp-fiber texture strokes + tender green vegetation) on the left and Dai Benxiao style (axe-cut texture strokes + dark green vegetation) on the right, facilitating student comparison.

[0044] III. Dynamic Rendering Engine Module Implementation for Teacher-Side 4K Screen Rendering: Wang Shimin style spring overlay with tender green texture (90% transparency), hemp-fiber texture lines with 200 grayscale; Dai Benxiao style winter overlay with snow mask (50% mountain coverage), axe-cut texture lines with 140 grayscale. Cloud and Fog Particle System: 1200 particles for Wang Shimin group (velocity 0.6-1.2m / s, transparency 55%), 800 particles for Dai Benxiao group (velocity 1.0-1.8m / s, transparency 70%), reflecting the difference between "Wang Shimin's soft clouds and Dai Benxiao's powerful clouds." Student-Side Laptop Rendering: Automatically downgrades to 1080P resolution, with 800 particles for Wang Shimin group and 500 for Dai Benxiao group, rendering latency ≤80ms, ensuring students can observe synchronously without lag.

[0045] IV. Interactive Processing and Brushstroke Interaction Module Implementation: The teacher's touchscreen supports "split-screen comparison" (clicking the split-screen button splits the left and right styles within 150ms), and dragging the slider adjusts the comparison ratio (moving the slider to the left increases the proportion of Wang Shimin's style); the voice command "explain the hemp-fiber texture stroke" triggers a technique pop-up window (displaying line parameters and the original author's source), with a recognition accuracy of 97%. Student-side pressure-sensitive pen interaction: selecting the "Wang Shimin style" inscription, the line thickness is 2 pixels at pressure level 512 (simulating the center of the brush), and 3 pixels at pressure level 1024 (simulating the side of the brush). The ink color of the inscription changes with the season (light gray RGB220, 220, 220 in spring, dark gray RGB160, 160, 160 in winter); when the student double-clicks on a rock, the system displays an animation of the texture stroke generation process (from the beginning to the end of the stroke, lasting 3 seconds), annotating key parameters (such as "light beginning, slow stroke, light ending" for the hemp-fiber texture stroke).

[0046] V. Formula Application and User Interaction Intent Prediction: Students continuously perform the operations "Switch to Wang Shimin's Winter Style → Adjust the density of the hemp-fiber texture strokes → Click the technique pop-up" (n=3). Let w1=0.1 (first operation), w2=0.2 (second operation), w3=0.3 (third operation), s1=0.8 (similarity to the intent of "learning hemp-fiber texture strokes"), s2=0.9, s3=0.95, and substitute them into the formula. The calculated P = 0.1 × 0.8 + 0.2 × 0.9 + 0.3 × 0.95 = 0.08 + 0.18 + 0.285 = 0.545 (< 0.75, not reaching the prediction threshold); when the student operates "play the texture animation" for the fourth time (w4 = 0.4, s4 = 0.98), P = 0.545 + 0.4 × 0.98 = 0.545 + 0.392 = 0.937 (≥ 0.75), the system preloads Wang Shimin's winter hemp-fiber texture teaching video (2 minutes long) for the student to watch later.

[0047] Dynamic tracking of light and shadow: The teacher adjusts the winter light and shadow angle of Dai Benxiao's style to 25° (midday light), sets the sun altitude angle θ=25°, the mountain height h=100 pixels, and the shadow length L=h / tanθ=100 / tan25°≈214 pixels. The system automatically generates a shadow of 214 pixels long (dark gray 120), and adds "blurring" (3 pixels wide) to the edge of the shadow, which fits Dai Benxiao's style of "shadows that are strong but not harsh". At the same time, the area of ​​the water surface reflection area = 0.3× the total area of ​​the water surface (positively correlated with θ), presenting the effect of "weak reflection of water surface in backlight in winter".

[0048] VI. Other Modules Implementing a Refined Vegetation Growth Cycle: The teacher demonstrates the growth of a pine tree in Wang Shimin's style. Double-clicking the pine tree triggers an animation (3 seconds), showing the sprouting of new branches in spring (growing from 2 pixels to 5 pixels) and the yellowing of leaves in autumn (RGB transitioning from 120,200,100 to 200,150,80), labeled "New branch angle 35° (a typical characteristic of Wang Shimin)." Building Adaptation Module: Clicking on a mountain dwelling in Wang Shimin's style displays a seasonal comparison image (15 plants around the pavilion in spring, 4 pixels of snow on the roof in winter), accompanied by the text "Early Qing Dynasty mountain dwellings often used pyramidal roofs; the spacing between the pavilion pillars in Wang Shimin's painting is 10 pixels." System Resource Management Module: The teacher's CPU usage is 70% (with 20 students connected simultaneously), and memory usage is 68%, maintaining 4K rendering; the student's CPU usage is 65%, and memory usage is 60%, running smoothly.

[0049] Table 2: Evaluation Indicators of the Comparison between Traditional Teaching Methods and This System in a Higher Education Teaching Scenario Traditional Teaching (Original Images + PPT Explanation) This System's Dynamic Simulation Teaching Effect Difference Technique Comprehension Accuracy Rate 65% (Relying on Students' Imagination of Dynamic Changes) 94% (Intuitive Observation of Technique Application in All Seasons) Comprehension accuracy rate increased by nearly 30% Student Participation 30% (Only 5-6 people actively asked questions) 95% (19 people participated in interactive operations) Participation increased by more than 2 times Teaching Content Richness 2 categories (Static Images + Text) 6 categories (Dynamic Simulation / Technique Animation / AR / Inscription) Content dimensions significantly expanded Teaching Efficiency 45 minutes to explain 1 type of brushstroke technique 20 minutes to explain 2 types of brushstroke technique Comparative efficiency increased by more than 1 time Table 2 shows that traditional teaching relies on static materials, making it difficult for students to understand the seasonal differences in the application of techniques, resulting in low participation and slow efficiency. This system, through dynamic simulation, intuitively demonstrates the seasonal changes of Wang Shimin's and Dai Benxiao's brushstroke techniques. Combined with brushstroke interaction and technique animation, students can personally simulate inscriptions and observe the generation of brushstrokes, achieving a participation rate of 95% and increasing the accuracy of technique comprehension to 94%. Simultaneously, teaching efficiency is significantly improved; a comparative lesson on two brushstroke techniques can be completed in 20 minutes, addressing the pain points of traditional teaching—"abstract and difficult to understand, poor interactivity"—and helping students quickly grasp the essence of early Qing Dynasty landscape painting techniques, aligning with the need for "theory and practice integration" in higher education art education.

[0050] Referring to Figure 2, the AR and brushstroke interaction engagement exceeds 90%, significantly higher than traditional touch and voice interactions, validating the immersive experience of innovative interactions such as "virtual-real integration" and "brush inscription." AR interaction allows users to observe three-dimensional perspective through a physical frame, overcoming the limitations of traditional two-dimensional displays; brushstroke interaction supports inscriptions in the style of early Qing Dynasty calligraphy, satisfying users' need for "participation in creation." Both align with the invention's design goal of "enhancing cultural immersion." Compared to existing systems that only support 2-3 basic interactions, this system covers exhibition, teaching, and other scenarios through multi-dimensional interactions.

[0051] Referring to Figure 3, this figure comprehensively demonstrates the technical details of adapting texturing techniques to cloud and mist effects. Wang Shimin's "hemp fiber" texture strokes, characterized by long lines, wide spacing, and light ink, are well-suited to the soft cloud and mist of spring, aligning with his "clear and moist" style. Dai Benxiao's "axe-cut" texture strokes, with short lines, narrow spacing, and dark ink, are well-suited to the rugged cloud and mist of winter, matching his "vigorous" characteristics. The parameter differences verify the system's ability to "dynamically adjust techniques according to style." For example, in terms of seasonal variations, Dai Benxiao's axe-cut texture strokes show a 25% variation (with more pronounced ink deepening in winter), while Wang Shimin's "hemp fiber" texture strokes show a 15% variation (with gradual ink changes throughout the seasons), both conforming to the creative patterns of renowned artists. Compared to the shortcomings of existing systems that "uniform texture stroke parameters," this system achieves style fidelity through parametric modeling, providing data support for technique comparisons in teaching scenarios.

[0052] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An interactive dynamic simulation system for early Qing Dynasty landscape paintings depicting the changing scenery of the four seasons, characterized in that: The module includes the following components: Data acquisition and style analysis module: collects images of original landscape paintings from the early Qing Dynasty, extracts visual features of rock texture, tree shapes, and water ripples, and establishes a style feature library; separates mountain, vegetation, water, and architectural elements through image segmentation technology, and statistically analyzes the color distribution of each element in the paintings of spring, summer, autumn, and winter, storing it as a set of seasonal feature parameters; The four-season landscape parameter modeling module: Based on style analysis results, it constructs a dynamic change model of the four seasons landscape, establishes a time axis mapping relationship, divides one year into 365 time nodes, and each node corresponds to a set of landscape parameters, forming a continuously changing parameter sequence; Dynamic rendering engine module: Equipped with an ink painting renderer, it uses a particle system to simulate the flow of clouds and fog, and realizes the dynamics of the water surface through texture animation; It calls elements from the style feature library according to the four-season landscape parameters; Interaction processing module: Configures three interaction interfaces: touch, voice, and gesture. The voice interface recognizes "spring," "summer," "autumn," and "winter" commands and associates them with the corresponding seasonal parameters; The gesture interface captures waving actions through the camera to control the cloud and fog concentration; User feedback optimization module: Records user interaction operation data and generates a user preference report every week; When the frequency of switching seasons exceeds 40% of the total operation volume, it optimizes the rendering details of that season; If the system detects that the user has adjusted the position of the same element three times in a row, it will automatically set that position as the default parameter; the system resource management module monitors CPU utilization and memory usage, and automatically reduces the number of particles and texture resolution when resource usage exceeds the limit; a hierarchical caching mechanism is adopted to preload the seasonal parameter set that users frequently access, thereby shortening the rendering startup time; Style consistency verification module: compares the matching degree between the dynamic simulation image and the style feature library of early Qing Dynasty landscape paintings, and calculates the similarity of mountain and rock texture and color deviation value; When the matching degree is lower than the threshold, the rendering parameters are automatically adjusted.

2. The interactive dynamic simulation system for early Qing Dynasty landscape paintings depicting the changing scenery of the four seasons, as described in claim 1, is characterized in that... It also includes a module for optimizing the smoothness of color transitions throughout the four seasons, using a formula. Calculate the color parameters at any time t, where Let be the color value at time t. The initial seasonal color values, The target seasonal color value is defined by k, the transition coefficient is defined by r, the attenuation coefficient is defined by t, and the transition time is defined by t. This calculation achieves a natural color gradient across the four seasons while dynamically adjusting the color saturation based on the material reflection characteristics of rocks, vegetation, and water bodies.

3. The interactive dynamic simulation system for early Qing Dynasty landscape paintings depicting the changing scenery of the four seasons, as described in claim 1, is characterized in that... It also includes a user interaction intent prediction module, which collects 5-10 consecutive user interaction operations, constructs an operation sequence feature vector, and uses a formula... Calculate the intention prediction probability, where P is the prediction probability and n is the length of the operation sequence. Let be the weight of the i-th operation. To determine the similarity between the i-th operation and the preset intent, when P≥0.75, the landscape parameters corresponding to the predicted intent are preloaded, and the parameter priority is adjusted in conjunction with the user's historical preferences.

4. The interactive dynamic simulation system for early Qing Dynasty landscape paintings depicting the changing scenery of the four seasons, as described in claim 1, is characterized in that... It also includes a dynamic generation module for mountain and rock texture strokes. Based on the line characteristics of axe-cut and hemp-fiber texture strokes in early Qing Dynasty landscape paintings, a texture stroke generation rule library is established. A deep learning model is introduced to generate new texture stroke lines that conform to stylistic characteristics by analyzing texture stroke samples from more than 200 original works. The texture stroke type is automatically selected according to the slope of the mountain, and the ink density of the lines is adjusted according to seasonal changes. Users can adjust the texture stroke density by sliding, and a style switching option for famous artists' texture strokes is provided. After switching, the line thickness and ink smudging degree are automatically matched with the characteristics of the corresponding painter to generate a mountain and rock effect that matches personal preferences.

5. The interactive dynamic simulation system for early Qing Dynasty landscape paintings depicting the changing scenery of the four seasons, as described in claim 1, is characterized in that... It also includes a water dynamics simulation enhancement module, which distinguishes between three types of water bodies: rivers, lakes, and waterfalls. Rivers are set with flow direction vectors, lakes with ripple diffusion coefficients, and waterfalls with particle falling parameters. Combined with acoustic simulation technology, seasonal sound effects are added to different water bodies. During the four seasons, rivers partially freeze in winter, and the frozen areas show ice crack textures. The waterfalls have reduced water volume and are accompanied by icicle hanging effects. When users click on a water body area, a ripple effect is triggered, and the sound effects change accordingly.

6. The interactive dynamic simulation system for early Qing Dynasty landscape paintings depicting the changing scenery of the four seasons, as described in claim 1, is characterized in that... It also includes a refined vegetation growth cycle module, which divides trees into evergreen trees and deciduous trees. The leaf density of evergreen trees changes by 10%-20% throughout the four seasons, with new leaves accounting for 30%-40% in spring and old leaves accounting for 60%-70% in winter. For deciduous trees, the leaf density increases from 30% to 80% in spring and decreases from 80% to 20% in autumn. The trajectory of falling leaves simulates the effects of gravity and wind. Tree age parameters are set, with young trees being 60%-70% the height of middle-aged trees, and crack textures added to the trunks of old trees and tilted at 5°-10°. Users can trigger a growth animation by double-clicking a tree, which synchronously displays the annual ring changes of the tree throughout the four seasons, intuitively presenting the morphological changes of the tree in the four seasons.

7. The interactive dynamic simulation system for early Qing Dynasty landscape paintings depicting the changing scenery of the four seasons, as described in claim 1, is characterized in that... It also includes a dynamic light and shadow tracking module to simulate the sun's rising and setting trajectory and calculate the angle of light and shadow projection at different times; it introduces an atmospheric scattering model, adds a fog scattering effect in spring to make the light appear soft and diffused, and enhances air transparency in autumn to make the edges of light and shadow clearer; summer has the longest daylight hours and winter has the shortest, the intensity of light and shadow changes with the seasons, and the shadow hue is more bluish in winter. Users drag the sun icon to adjust the angle of the light, and the system automatically updates the length of the mountain's shadow while simultaneously calculating the reflective area of ​​the water surface in real time to achieve personalized lighting effects.

8. The interactive dynamic simulation system for early Qing Dynasty landscape paintings depicting the changing scenery of the four seasons, as described in claim 1, is characterized in that... It also includes a module for seasonal adaptation of architectural elements. For the three types of buildings—pavilions, bridges, and houses—in early Qing Dynasty landscape paintings, it extracts architectural features and creates 3D models. In spring, it adds floral decorations around the buildings; in summer, it increases the length of the eaves shadows and hangs vines; in autumn, it adorns the roofs with fallen leaves; and in winter, it covers them with a thin layer of snow. The reflectivity of building materials is adjusted according to the season. When users click on a building, it displays a comparison image of the building's state in the four seasons, and at the same time, it pops up a description of the building's form, enhancing the dual perception of seasonal changes and architectural styles. Clicking on a house in winter will also display a candlelight effect inside the window, echoing the literati painting sentiment of "a guest arrives on a cold night, and tea is served as wine." 9. The interactive dynamic simulation system for early Qing Dynasty landscape paintings depicting the changing scenery of the four seasons, as described in claim 1, is characterized in that... It also includes a cloud and fog flow path planning module, which generates the main flow line of clouds and fog based on the mountain outline and water body location, and sets the flow velocity gradient; it introduces wind parameters, so that the clouds and fog flow slowly along the main line at level 1 wind and produce a vortex effect at level 3 wind; the cloud and fog concentration is highest in spring and is milky white, while it is lowest in winter and is slightly bluish-gray; through formulas Calculate the acceleration of cloud and fog particles, where Let 'a' be the acceleration and 'a' be an adjustment coefficient. Let v be the target speed, v be the current speed, and b be the pressure coefficient. Using pressure gradients, this calculation makes the flow of clouds and fog more consistent with the laws of fluid mechanics. At the same time, combined with the relationship of mountain and rock obstruction, clouds and fog will flow around mountains.

10. The interactive dynamic simulation system for early Qing Dynasty landscape paintings depicting the changing scenery of the four seasons, as described in claim 1, is characterized in that... It also includes a multi-dimensional interactive extension module, adding a pen-touch interaction interface to the existing touch, voice, and gesture interactions. Users can simulate brushstrokes with a pressure-sensitive pen to add personalized inscriptions to the image, which will show different ink effects depending on the season. It also introduces an AR interaction mode, which uses a camera to recognize physical picture frames and projects dynamic simulated images into the frames to achieve a viewing effect that combines virtual and real elements. In AR mode, users can move around the picture frame to observe the three-dimensional perspective changes of the image. It also supports users to save custom landscapes as personalized scene templates, which contain complete seasonal parameter configurations and can be loaded with one click the next time they are called. It also provides a scene sharing function for cross-user creative communication.

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