A digital sand table simulation method and system for smart parks

Abstract

This invention provides a digital sand table simulation method and system for smart parks, belonging to the field of sand table simulation technology. The method includes: adjusting the lighting in the display area using a dynamic lighting adjustment system based on calibrated real-time ambient light data to obtain adjusted lighting; constructing a rendering model based on the adjusted lighting by learning the light distribution and material reflection characteristics of the real world; rendering the digital sand table based on the rendering model to obtain a rendering result; optimizing the rendering result based on the rendering result by real-time monitoring of the lighting in the digital sand table display area and combining deep learning algorithms to obtain an optimized digital sand table display area; and automatically adjusting lighting parameters and rendering settings in the optimized digital sand table display area based on user feedback. This invention improves lighting issues and enhances the display effect of digital sand tables.

Description

Technical Field

[0001] This invention relates to the field of sand table simulation technology, and in particular to a digital sand table simulation method and system based on a smart park. Background Technology

[0002] As an important development direction for modern parks, smart parks aim to improve the management efficiency, operational level and service quality of parks through information technology and intelligent means. Digital sand table technology, as an advanced visualization simulation technology, can accurately reproduce and integrate real-world geographic information, building models, dynamic data and other data, providing strong support for the management and operation of smart parks. The production, operation and maintenance of digital sand tables rely on a variety of high-tech equipment and technical means, such as 3D modeling technology, virtual reality technology, big data and cloud computing technology.

[0003] Digital sand tables have high requirements for the lighting conditions of the display environment. Inappropriate lighting conditions may affect the display effect of the digital sand table and even cause visual discomfort. When using a digital sand table, it is necessary to strictly control and adjust the lighting conditions to ensure the best display effect.

[0004] In order to improve the lighting problem and enhance the display effect of digital sand table, this invention provides a digital sand table simulation method and system based on smart parks. Summary of the Invention

[0005] This invention provides a digital sand table simulation method and system based on smart parks, which improves lighting issues and enhances the display effect of digital sand tables.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0007] Firstly, a digital sand table simulation method based on a smart park, the method comprising:

[0008] The real-time ambient lighting conditions of the digital sand table display area are acquired and calibrated to obtain calibrated real-time ambient lighting data.

[0009] Based on the calibrated real-time ambient light data, the lighting in the display area is adjusted through the dynamic light adjustment system to obtain the adjusted lighting.

[0010] Based on the adjusted lighting, a rendering model is constructed by learning the light distribution and material reflection characteristics of the real world;

[0011] The digital sand table is rendered based on the rendering model to obtain the rendering result;

[0012] Based on the rendering results, the lighting in the digital sand table display area is monitored in real time, and the rendering results are optimized using deep learning algorithms to obtain an optimized digital sand table display area.

[0013] In the optimized digital sand table display area, lighting parameters and rendering settings are automatically adjusted based on user feedback.

[0014] Furthermore, the real-time ambient lighting conditions of the digital sand table display area are acquired and calibrated to obtain calibrated real-time ambient lighting data, including:

[0015] Use a light sensor to obtain the real-time ambient light conditions of the digital sand table display area;

[0016] The real-time ambient lighting conditions of the digital sand table display area were analyzed to obtain the analysis results;

[0017] The analysis results are constructed, and the real-time ambient light conditions are calibrated using a calibration algorithm to obtain calibrated real-time ambient light data.

[0018] Furthermore, based on the calibrated real-time ambient light data, the lighting in the display area is adjusted using a dynamic light adjustment system to obtain the adjusted lighting, including:

[0019] Based on the calibrated real-time ambient light data, the light parameters are obtained through analysis, including brightness, color temperature, and color distribution.

[0020] Based on the target of lighting adjustment in the digital sand table display area, a color balance algorithm is used to adjust the lighting parameters to obtain the adjusted lighting parameters;

[0021] Based on the adjusted lighting parameters, the dynamic light adjustment system is driven to adjust the lighting to obtain the adjusted lighting effect;

[0022] Monitor the adjusted lighting effects in real time and make fine adjustments based on the actual results.

[0023] Furthermore, based on the adjusted lighting, a rendering model is constructed by learning the real-world light distribution and material reflection characteristics, including:

[0024] To obtain the distribution of light in the real world, including the position, intensity, and color of the light source, as well as the reflection and refraction characteristics of light by different materials;

[0025] The reflection and refraction data of different materials are analyzed to determine the optical properties of the material surface, including reflectivity, refractive index, and diffuse reflection coefficient.

[0026] Based on the distribution of light and the optical properties of material surfaces in the real world, a scene from the real world is constructed in a virtual environment to obtain a virtual scene;

[0027] In a virtual environment, using L e (x)=∫ Ω ρ(x,ω i ,ω o )L i (x,ω i )(ω i ·n x )dω i The light intensity emitted by the light source on each surface in the scene is calculated, where L e (x) represents the intensity of the emitted light, ρ(x,ω) i ,ω o ) is the distance from the incident direction ω at the surface point x. i To the direction of launch ω o Bidirectional reflection distribution, L i (x,ω i ) is from the direction ω i The intensity of the ray incident on point x, n is the surface normal at point x, Ω represents all possible incident directions of the ray, (ω i ·n x dω is the dot product of the incident ray direction and the normal direction at point x on the surface. i This indicates integration over the incident direction;

[0028] Based on the optical properties of the material surface, use The reflectivity of the reflected light is calculated for each material, where θ i It is the angle of incidence, θ t It is the angle of refraction, R F (θ i () represents the reflectivity of light when it travels from one medium to another.

[0029] Based on the light intensity emitted by the light source on each surface and the reflectivity of the reflected light from each material, L(x,ω) is used. o ) = L e (x,ω o )+∫ Ω f r (x,ω i ,ω o )L i (x,ω i )(ω i ·n x )dω i The rendering model is obtained by calculating the rate of change of light intensity along the light path after multiple reflections and refractions, where L(x,ω)° ) is from point x along direction ω o Observed light intensity, f r (x,ω i ,ω o ( ) represents the surface reflection distribution. L e (x,ω o ) is from point x along direction ω o The intensity of the emitted light, L i (x,ω i ) is from the direction ω i The intensity of the light ray incident on point x, (ω i ·n x dω is the dot product of the incident ray direction and the normal direction at point x on the surface. i This indicates that the integral is performed over the incident direction.

[0030] Furthermore, based on the rendering model, the digital sand table is rendered to obtain the rendering results, including:

[0031] Adjust the light sources in the scene according to the lighting settings in the rendering model. The light sources in the scene include the main light source, auxiliary light source, and ambient light.

[0032] Based on the lighting settings in the rendered model, assign appropriate materials to each 3D model;

[0033] Based on the light sources and materials in the scene, a rendering model is used to simulate the multiple reflections, refractions, and shadow effects of light in the sandbox to obtain the rendering result.

[0034] Furthermore, based on the rendering results, the lighting in the digital sand table display area is monitored in real time. Combined with deep learning algorithms, the rendering results are optimized to obtain an optimized digital sand table display area, including:

[0035] Based on the rendering results, light sensors within the digital sand table display area are used to monitor ambient light data in real time.

[0036] The rendering results are correlated with the real-time ambient light monitoring data to form a training dataset;

[0037] Based on the training dataset, the deep learning model is trained to obtain a trained deep learning model.

[0038] Using a trained deep learning model, the system monitors lighting data in real time and generates an optimized rendering result image based on the input lighting data and rendering results. This optimized digital sand table display area is the result of this process.

[0039] Furthermore, in the optimized digital sand table display area, lighting parameters and rendering settings are automatically adjusted based on user feedback, including:

[0040] In the optimized digital sand table display area, we collect user opinions and suggestions on the display effect of the digital sand table in order to obtain feedback information;

[0041] The collected feedback information is organized and analyzed to obtain users' needs and preferences for lighting and rendering effects, and to obtain the analysis results of user feedback;

[0042] Based on the analysis results of user feedback, we determined the lighting parameters that needed adjustment and the rendering settings that needed optimization.

[0043] Secondly, a digital sand table simulation system based on a smart park includes:

[0044] The acquisition module is used to acquire the real-time ambient light conditions of the digital sand table display area and perform calibration processing to obtain calibrated real-time ambient light data;

[0045] The processing module adjusts the lighting in the display area using a dynamic lighting adjustment system based on calibrated real-time ambient light data. It then builds a rendering model by learning the light distribution and material reflection characteristics of the real world. Based on this model, it renders the digital sand table to obtain the rendering result. The module monitors the lighting in the digital sand table display area in real time and optimizes the result using deep learning algorithms to obtain an optimized digital sand table display area. Finally, it automatically adjusts lighting parameters and rendering settings based on user feedback within the optimized digital sand table display area.

[0046] Thirdly, a computing device includes:

[0047] One or more processors;

[0048] A storage system for storing one or more programs that, when executed by one or more processors, cause the one or more processors to perform the methods described above.

[0049] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the above-described method.

[0050] The above-described solution of the present invention has at least the following beneficial effects:

[0051] The present invention achieves real-time monitoring, calibration, adjustment, and optimization of ambient light in the digital sand table display area, thereby significantly improving the display effect of the digital sand table. By dynamically adjusting the lighting and constructing a rendering model, the rendering result of the digital sand table becomes more realistic and vivid. Combining deep learning algorithms to optimize the rendering result further enhances the visual effect of the display area. Automatically adjusting lighting parameters and rendering settings based on user feedback enables personalized display and meets the needs of different users. It not only improves the display quality of the digital sand table but also enhances the user experience. Through precise control of lighting conditions, the digital sand table can better simulate the real-world environment, providing users with a more realistic visual experience. At the same time, automatic adjustment based on user feedback makes the digital sand table more interactive and intelligent. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating the digital sand table simulation method for smart parks provided in an embodiment of the present invention.

[0053] Figure 2 This is a schematic diagram of a digital sand table simulation system based on a smart park, provided by an embodiment of the present invention. Detailed Implementation

[0054] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0055] like Figure 1 As shown, an embodiment of the present invention proposes a digital sand table simulation method based on a smart park, the method comprising:

[0056] 11. Obtain the real-time ambient light conditions of the digital sand table display area and perform calibration processing to obtain calibrated real-time ambient light data;

[0057] 12. Based on the calibrated real-time ambient light data, adjust the lighting in the display area using the dynamic light adjustment system to obtain the adjusted lighting;

[0058] 13. Based on the adjusted lighting, construct a rendering model by learning the light distribution and material reflection characteristics of the real world;

[0059] 14. Render the digital sand table according to the rendering model to obtain the rendering result;

[0060] 15. Based on the rendering results, monitor the lighting in the digital sand table display area in real time, and optimize the rendering results using deep learning algorithms to obtain an optimized digital sand table display area.

[0061] 16. In the optimized digital sand table display area, lighting parameters and rendering settings are automatically adjusted based on user feedback.

[0062] In this embodiment of the invention, a digital sand table simulation method based on a smart park is proposed. This method comprehensively applies technologies such as ambient light monitoring, dynamic light adjustment, real-world light learning, rendering model construction, deep learning optimization, and user feedback adjustments. The aim is to achieve high-fidelity simulation and personalized display effects for the digital sand table display area. By acquiring and calibrating the real-time ambient light conditions of the digital sand table display area, this method ensures the accuracy and reliability of the acquired ambient light data, providing precise basic data for subsequent light adjustment and rendering, thus improving the simulation and realism of the digital sand table. Based on the calibrated real-time ambient light data, the lighting in the display area is adjusted using a dynamic light adjustment system. This dynamic adjustment ensures that the lighting effects of the digital sand table remain highly consistent with actual ambient lighting conditions, enhancing the immersive and lifelike experience of the sand table. The system can also automatically adjust lighting parameters based on changes in ambient light, ensuring the sand table presents optimal visual effects under any lighting conditions. By deeply learning the light distribution and material reflection characteristics of the real world, this method can construct a highly realistic rendering model. It considers not only the position, intensity, and color of the light source but also the reflection, refraction, and scattering characteristics of different materials, enabling the digital sand table to more realistically simulate the lighting effects in actual scenes during rendering, thereby greatly improving the simulation accuracy and visual quality of the sand table. During the rendering process, this... The method can monitor the lighting effects of the digital sand table display area in real time and optimize the rendering results by combining deep learning algorithms. Through continuous iteration and adjustment, the system can automatically find the best rendering parameters and lighting settings, so that the display effect of the sand table reaches the optimal state and ensures that the digital sand table can present a high-quality visual effect under any circumstances. Finally, the method can also automatically adjust the lighting parameters and rendering settings based on user feedback. By collecting and analyzing user evaluations and suggestions on the sand table display effect, the system can understand user needs and preferences and make personalized adjustments and optimizations accordingly. This not only improves the interactivity and user experience of the sand table, but also enables the sand table to better meet the needs and expectations of different users.

[0063] like Figure 1 As shown in Figure 11, the real-time ambient lighting conditions of the digital sand table display area are acquired and calibrated to obtain calibrated real-time ambient lighting data, including:

[0064] Use a light sensor to obtain the real-time ambient light conditions of the digital sand table display area;

[0065] The real-time ambient lighting conditions of the digital sand table display area were analyzed to obtain the analysis results;

[0066] The analysis results are constructed, and the real-time ambient light conditions are calibrated using a calibration algorithm to obtain calibrated real-time ambient light data.

[0067] In this embodiment of the invention, in order to achieve a more realistic and dynamic visual effect in the digital sand table display area, the system needs to accurately capture and calibrate the ambient light. By deploying highly sensitive light sensors in the digital sand table display area, the system can capture the ambient light conditions in the display area in real time. The light conditions include various parameters such as light intensity, color temperature, and illumination angle, which are the basic data for subsequent calibration processing. The captured real-time ambient light data is transmitted to the processing unit for in-depth analysis. The analysis process involves the interpretation of light data, feature extraction, and correlation analysis with environmental factors. Through this series of analysis steps, the system can accurately understand the specific conditions of the current ambient light and its possible impact on the digital sand table display effect. Based on the analysis results, the system employs an advanced calibration algorithm to calibrate the real-time ambient lighting conditions. This algorithm comprehensively considers the visual requirements of the digital sand table display content, the physical characteristics of the display equipment, and the optical principles of human eye perception. Its aim is to adjust and optimize lighting parameters, making the digital sand table display effect more closely resemble the real environment while improving viewer visual comfort. The real-time ambient lighting data processed by the calibration algorithm is used to dynamically adjust the digital sand table's display parameters. This not only ensures the consistency of the digital sand table's display under various lighting conditions but also enhances the interactivity and immersion of the displayed content. In this way, the digital sand table can adapt to changes in lighting at different times and in different scenarios, providing viewers with a more natural and realistic visual experience.

[0068] like Figure 1 As shown in Figure 12, based on the calibrated real-time ambient light data, the lighting in the display area is adjusted using a dynamic light adjustment system to obtain the adjusted lighting, including:

[0069] Based on the calibrated real-time ambient light data, the light parameters are obtained through analysis, including brightness, color temperature, and color distribution.

[0070] Based on the target of lighting adjustment in the digital sand table display area, a color balance algorithm is used to adjust the lighting parameters to obtain the adjusted lighting parameters;

[0071] Based on the adjusted lighting parameters, the dynamic light adjustment system is driven to adjust the lighting to obtain the adjusted lighting effect;

[0072] Monitor the adjusted lighting effects in real time and make fine adjustments based on the actual results.

[0073] In this embodiment of the invention, the digital sand table display system utilizes calibrated real-time ambient light data to dynamically adjust the lighting in the display area, thereby ensuring optimal display effects. First, the system receives and analyzes the calibrated real-time ambient light data, which reflects the current actual lighting conditions of the display area. Through analysis, the system extracts key lighting parameters, including brightness, color temperature, and color distribution. These parameters form the basis for subsequent lighting adjustments. Based on the needs and expected effects of the digital sand table display, the system sets specific goals for lighting adjustments, such as improving contrast, maintaining color consistency, or creating a specific atmosphere. Subsequently, a color balance algorithm is used to fine-tune the extracted lighting parameters to ensure that the adjusted lighting parameters meet these goals. The color balance algorithm comprehensively considers factors such as light source characteristics, display content, and human visual perception. Once the adjusted lighting parameters are obtained, the system transmits these parameters to the dynamic light adjustment system. Based on the received instructions, the system adjusts the lighting equipment in the display area in real time, including changing the brightness, color temperature, and color distribution of the lights, thereby achieving a lighting effect that matches the current ambient light conditions. During the lighting adjustment process, the system monitors the adjusted lighting effect in real time through sensors and visual feedback mechanisms. If a deviation is found between the actual effect and the expectation, the system will immediately make fine adjustments to ensure that the lighting can continuously and stably meet the display requirements. The real-time monitoring and fine-tuning mechanism helps the system maintain a high degree of adaptability and flexibility under constantly changing environmental conditions.

[0074] like Figure 1 As shown in Figure 13, based on the adjusted lighting, a rendering model is constructed by learning the light distribution and material reflection characteristics of the real world, including:

[0075] To obtain the distribution of light in the real world, including the position, intensity, and color of the light source, as well as the reflection and refraction characteristics of light by different materials;

[0076] The reflection and refraction data of different materials are analyzed to determine the optical properties of the material surface, including reflectivity, refractive index, and diffuse reflection coefficient.

[0077] Based on the distribution of light and the optical properties of material surfaces in the real world, a scene from the real world is constructed in a virtual environment to obtain a virtual scene;

[0078] In a virtual environment, using L e (x)=∫ Ω ρ(x,ω i ,ω o )L i (x,ωi )(ω i ·n x )dω i The light intensity emitted by the light source on each surface in the scene is calculated, where L e (x) represents the intensity of the emitted light, ρ(x,ω) i ,ω o (is the surface point x from the incident direction ω) i To the direction of launch ω o Bidirectional reflection distribution, L i (x,ω i ) is from the direction ω i The intensity of the ray incident on point x, n is the surface normal at point x, and Ω represents all possible incident directions of the ray. i ·n x dω is the dot product of the incident ray direction and the normal direction at point x on the surface. i This indicates integration over the incident direction;

[0079] Based on the optical properties of the material surface, use The reflectivity of the reflected light is calculated for each material, where θ i It is the angle of incidence, θ t It is the angle of refraction, R F (θ i () represents the reflectivity of light when it travels from one medium to another.

[0080] Based on the light intensity emitted by the light source on each surface and the reflectivity of the reflected light from each material, L(x,ω) is used. o ) = L e (x,ω o )+∫ Ω f r (x,ω i ,ω o )L i (x,ω i )(ω i ·n x )dω i The rendering model is obtained by calculating the rate of change of light intensity along the light path after multiple reflections and refractions, where L(x,ω) ° ) is from point x along direction ω o Observed light intensity, f r (x,ω i ,ω o ( ) represents the surface reflection distribution. L e (x,ω o ) is from point x along direction ω o The intensity of the emitted light, L i(x,ω i ) is from the direction ω i The intensity of the light ray incident on point x, (ω i ·n x dω is the dot product of the incident ray direction and the normal direction at point x on the surface. i This indicates that the integral is performed over the incident direction.

[0081] In this embodiment of the invention, during the advanced rendering process of the digital sandbox, a highly realistic rendering model is constructed by learning the light distribution and material reflection characteristics of the real world. The system first collects light distribution data from the real world, including the position, intensity, and color of light sources, as well as detailed characteristics of how different materials reflect and refract light, ensuring that the light representation in the virtual environment is consistent with the real world. In-depth analysis of the reflection and refraction data of different material surfaces determines the optical properties of each material surface, such as reflectivity, refractive index, and diffuse reflection coefficient, which is crucial for accurately simulating the behavior of light in the virtual environment. Using the collected real-world light distribution data and material optical properties, the system reconstructs a scene in the virtual environment that corresponds to the real world, including not only the geometric structure of the real world but also accurately simulating the interaction of light on various material surfaces. In the simulated scene, the system calculates the light intensity received and emitted by each surface point from the light source based on the position, intensity, and color of the light source, as well as the bidirectional reflectance distribution function (BRDF) of the scene surface. It considers the entire path of light from incident to outgoing, ensuring the physical correctness of the rendering results. Based on the optical properties of each material, the system further calculates the reflectivity of light when it propagates between different media, involving precise measurements of the angle of incidence and the angle of refraction, as well as the energy distribution of light when it passes through different material interfaces. The system integrates the light intensity emitted by the light source on each surface and the reflectivity of the reflected light from each material, and simulates the intensity changes of light after multiple reflections and refractions in the virtual scene through complex integral calculations. It considers all possible interactions between light and the scene surface, thereby generating a highly realistic rendering model that can accurately predict and display the lighting conditions at any point in the virtual environment.

[0082] like Figure 1 As shown in Figure 14, the digital sand table is rendered according to the rendering model to obtain the rendering result, including:

[0083] Adjust the light sources in the scene according to the lighting settings in the rendering model. The light sources in the scene include the main light source, auxiliary light source, and ambient light.

[0084] Based on the lighting settings in the rendered model, assign appropriate materials to each 3D model;

[0085] Based on the light sources and materials in the scene, a rendering model is used to simulate the multiple reflections, refractions, and shadow effects of light in the sandbox to obtain the rendering result.

[0086] In this embodiment of the invention, during the rendering process of the digital sand table, a previously constructed rendering model is used to perform high-quality rendering of the digital sand table, thereby generating realistic visual effects. Based on the lighting settings defined in the rendering model, the system first adjusts various light sources in the scene, including the main light source, auxiliary light sources, and ambient light. The main light source is responsible for providing the main illumination in the scene, the auxiliary light source is used to enhance details and create a sense of depth, and the ambient light simulates a global diffuse reflection effect, providing a uniform base brightness for the scene. According to the specifications in the rendering model, corresponding materials are assigned to each 3D model, determining how the model's surface reacts to light, including characteristics such as reflection, refraction, and diffuse reflection. Correct materials... Allocation is a key factor in ensuring the realism of the rendering results. Utilizing the pre-set light sources and allocated materials in the scene, the system begins to use rendering models to simulate the multiple reflections, refractions, and shadow effects of light in the digital sandbox, including the interaction between light and object surfaces, light path tracing, energy attenuation and distribution, etc., which can generate highly realistic images containing rich light and shadow variations and delicate material details. The rendering results not only accurately reflect the geometric structure and material characteristics in the scene, but also vividly demonstrate the propagation and interaction effects of light in the scene, providing viewers with an immersive visual experience, making them feel as if they are in a real and vibrant virtual world.

[0087] like Figure 1 As shown in Figure 15, based on the rendering results, the lighting in the digital sand table display area is monitored in real time. Combined with deep learning algorithms, the rendering results are optimized to obtain an optimized digital sand table display area, including:

[0088] Based on the rendering results, light sensors within the digital sand table display area are used to monitor ambient light data in real time.

[0089] The rendering results are correlated with the real-time ambient light monitoring data to form a training dataset;

[0090] Based on the training dataset, the deep learning model is trained to obtain a trained deep learning model.

[0091] Using a trained deep learning model, the system monitors lighting data in real time and generates an optimized rendering result image based on the input lighting data and rendering results. This optimized digital sand table display area is the result of this process.

[0092] In this embodiment of the invention, real-time monitored ambient light data and deep learning algorithms are used to optimize the rendering results of the digital sand table, thereby improving the visual effect of the display area. Light sensors are arranged in the display area of ​​the digital sand table to monitor changes in ambient light in real time. The sensors can capture key parameters such as light intensity, color temperature, and direction, providing real-time data support for the subsequent optimization process. The system matches the real-time monitored ambient light data with the corresponding rendering results to form a series of training data pairs. These data reflect the rendering effects of the digital sand table under different lighting conditions, forming the training foundation for the deep learning model. Using the constructed training dataset, the deep learning model is trained to predict and optimize rendering results based on the input lighting data. Through numerous training iterations, the model gradually learns how to adjust rendering parameters according to real-time lighting conditions to achieve the best visual effect. During the digital sand table display, the system uses the trained deep learning model to process the real-time monitored lighting data. The model generates optimized rendering images based on the current lighting conditions, adjusting aspects such as color, brightness, and contrast to adapt to the current ambient light, thus providing a more natural and comfortable viewing experience. The optimized rendering images are displayed on the digital sand table in real time, allowing viewers to observe a more realistic and vivid display effect, ensuring that the digital sand table maintains high-quality visual effects under various lighting conditions.

[0093] like Figure 1 As shown in Figure 16, in the optimized digital sand table display area, lighting parameters and rendering settings are automatically adjusted based on user feedback, including:

[0094] In the optimized digital sand table display area, we collect user opinions and suggestions on the display effect of the digital sand table in order to obtain feedback information;

[0095] The collected feedback information is organized and analyzed to obtain users' needs and preferences for lighting and rendering effects, and to obtain the analysis results of user feedback;

[0096] Based on the analysis results of user feedback, we determined the lighting parameters that needed adjustment and the rendering settings that needed optimization.

[0097] In this embodiment of the invention, in the optimized digital sand table display area, the lighting parameters and rendering settings are automatically adjusted based on actual user feedback to enhance the user experience. The system provides a feedback mechanism allowing users to express their opinions and suggestions on the digital sand table display effect. Feedback information includes user views on lighting brightness, color, shadows, etc., as well as overall satisfaction with the rendering effect. The collected user feedback is organized and analyzed to identify users' specific needs and preferences regarding lighting and rendering effects. For example, users may express a need for softer lighting or suggestions for improving the rendering effect of specific materials. In-depth analysis of this feedback yields a clear profile of user expectations and preferences. Based on the analysis results of user feedback, the system determines the lighting parameters that need adjustment, such as light source intensity, color temperature, and light projection angle, as well as the rendering settings that need optimization, such as material reflection coefficient and shadow detail level. These adjustments aim to better align with users' visual habits and aesthetic preferences, thereby improving the display effect of the digital sand table and user satisfaction.

[0098] like Figure 2 As shown, embodiments of the present invention also provide a digital sand table simulation system 20 based on a smart park, comprising:

[0099] The acquisition module 21 is used to acquire the real-time ambient light conditions of the digital sand table display area and perform calibration processing to obtain calibrated real-time ambient light data;

[0100] The processing module 22 is used to adjust the lighting in the display area through the dynamic lighting adjustment system based on the calibrated real-time ambient light data to obtain the adjusted lighting. Based on the adjusted lighting, a rendering model is constructed by learning the light distribution and material reflection characteristics of the real world. The digital sand table is rendered based on the rendering model to obtain the rendering result. Based on the rendering result, the lighting in the display area of ​​the digital sand table is monitored in real time, and the rendering result is optimized by combining deep learning algorithms to obtain the optimized digital sand table display area. In the optimized digital sand table display area, the lighting parameters and rendering settings are automatically adjusted based on user feedback.

[0101] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0102] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0103] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0104] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0105] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, units, and processes described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0106] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0107] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0109] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0110] Furthermore, it should be noted that in the system and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent solutions of the present invention. Moreover, the steps performing the above series of processes can naturally be executed in the order described, but are not necessarily required to be executed in chronological order; some steps can be executed in parallel or independently of each other. Those skilled in the art will understand that all or any step or component of the method and system of the present invention can be implemented in any computing system (including processors, storage media, etc.) or network of computing systems, in hardware, firmware, software, or a combination thereof, which can be achieved by those skilled in the art using their basic programming skills after reading the description of the present invention.

[0111] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing system. The computing system can be a known general-purpose system. Therefore, the object of the present invention can also be achieved simply by providing a program product containing program code implementing the method or system. That is, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any known storage medium or any storage medium developed in the future. It should also be noted that in the system and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent to the present invention. Furthermore, the steps performing the above series of processes can naturally be performed in the order described, but are not necessarily required to be performed in chronological order. Some steps can be performed in parallel or independently of each other.

[0112] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A digital sand table simulation method based on a smart park, characterized in that, The method comprises: acquiring real-time ambient light conditions of a digital sand table display area and performing calibration processing to obtain calibrated real-time ambient light data; adjusting the light rays of the display area through a dynamic light adjustment system according to the calibrated real-time ambient light data to obtain adjusted light rays; specifically, light ray parameters including brightness, color temperature and color distribution are obtained by analyzing the calibrated real-time ambient light data; the light ray parameters are adjusted using a color balance algorithm according to the target of light adjustment of the digital sand table display area to obtain adjusted light ray parameters; the dynamic light adjustment system is driven to adjust the light according to the adjusted light ray parameters to obtain adjusted light rays; the adjusted light effect is monitored in real time, and fine tuning is performed according to the actual effect; a rendering model is constructed by learning the light distribution and material reflection characteristics of the real world according to the adjusted light rays; specifically, the light distribution in the real world is acquired, including the position, intensity and color of the light source, and the reflection and refraction characteristics of different materials to light; the reflection and refraction data of different materials are analyzed to determine the optical properties of the material surface, including reflectivity, refractivity and diffuse reflection coefficient; a virtual scene is constructed in a virtual environment according to the light distribution in the real world and the optical properties of the material surface to obtain a virtual scene; the digital sand table is rendered according to the rendering model to obtain a rendering result; the light of the digital sand table display area is monitored in real time according to the rendering result, and the rendering result is optimized using a deep learning algorithm to obtain an optimized digital sand table display area; in the optimized digital sand table display area, the light ray parameters and rendering settings are automatically adjusted according to user feedback.

2. The smart park based digital sand table simulation method according to claim 1, wherein, acquiring real-time ambient light conditions of a digital sand table display area and performing calibration processing to obtain calibrated real-time ambient light data comprises: using a light sensor to acquire real-time ambient light conditions of a digital sand table display area; analyzing the real-time ambient light conditions of the digital sand table display area to obtain an analysis result; constructing the analysis result and calibrating the real-time ambient light conditions using a calibration algorithm to obtain calibrated real-time ambient light data.

3. The digital sand table simulation method based on the smart park according to claim 1, wherein In the virtual scene, the light intensity emitted by the light source is calculated using L e (x) = ∫ Ω ρ(x, ω i , ω o )L i (x, ω i )(ω i · n x )dω i , where L e (x) is the emitted light intensity, ρ(x, ω i , ω o ) is the bidirectional reflectance distribution of a surface point x from an incident direction ω i to an outgoing direction ω o , L i (x, ω i ) is the light intensity incident on point x from direction ω i , n x is the surface normal at point x, Ω represents all possible light incident directions, (ω i · n x ) is the dot product of the incident light direction and the normal direction at surface point x, and dω i represents the integration over the incident direction. According to the optical properties of the surface of the material, use The reflectivity of the reflected light of each material is calculated, where θ i is the angle of incidence, θ t is the angle of refraction, R F (θ i ) is the reflectivity of the light when it is reflected from one medium to another. Based on the light intensity emitted by the light source on each surface and the reflectivity of the reflected light from each material, L(x,ω) is used. o ) = L e (x,ω0)+∫ Ω f r (x,ω i ,ω o )L i (x,ω i (ω) i ·n x )dω i The rendering model is obtained by calculating the rate of change of light intensity along the light path after multiple reflections and refractions, where L(x,ω) o ) is from point x along direction ω o Observed light intensity, f r (x,ω i ,ω o ) represents the surface reflection distribution, L e (x,ω o ) is from point x along direction ω o The intensity of the emitted light, L i (x,ω i ) is from the direction ω i The intensity of the light ray incident on point x.

4. The smart park based digital sand table simulation method of claim 1, wherein, rendering the digital sand table according to the rendering model to obtain a rendering result comprises: adjusting the light sources in the scene according to the light settings in the rendering model, including the main light source, the auxiliary light source and the ambient light; assigning appropriate materials to each 3D model according to the light settings in the rendering model; using the rendering model to simulate multiple reflections, refractions and shadow effects of light in the sand table according to the light sources and materials in the scene to obtain a rendering result.

5. The smart park based digital sand table simulation method of claim 1, wherein, monitoring the light of the digital sand table display area in real time according to the rendering result, and optimizing the rendering result using a deep learning algorithm to obtain an optimized digital sand table display area, comprises: monitoring the data of the ambient light in real time using the light sensor in the digital sand table display area according to the rendering result; The rendering result and the data of real-time monitoring of ambient light are corresponded to form a training data set; According to the training data set, the deep learning model is trained to obtain a trained deep learning model; Using the trained deep learning model, real-time monitoring of light data is performed, and according to the input light data and the rendering result, an optimized rendering result image, i.e. an optimized digital sand table display area, is generated.

6. The smart park based digital sand table simulation method of claim 1, wherein, In the optimized digital sand table display area, the light parameters and rendering settings are automatically adjusted according to user feedback, including: In the optimized digital sand table display area, user opinions and suggestions on the digital sand table display effect are collected to obtain feedback information; The collected feedback information is sorted and analyzed to obtain user requirements and preferences for light and rendering effects, and an analysis result of user feedback is obtained; According to the analysis result of user feedback, the light parameters to be adjusted and the rendering settings to be optimized are determined.

7. A digital sand table simulation system based on a smart park, characterized in that, The method for simulating and simulating a digital sand table based on a smart park according to claim 1 comprises: An acquisition module is configured to acquire real-time ambient light conditions of a digital sand table display area and perform calibration processing to obtain calibrated real-time ambient light data; A processing module is configured to adjust the light of the display area through a dynamic light adjustment system according to the calibrated real-time ambient light data, to obtain adjusted light, to construct a rendering model by learning the light distribution and material reflection characteristics of the real world according to the adjusted light, to render the digital sand table according to the rendering model to obtain a rendering result, to monitor the light of the digital sand table display area in real time according to the rendering result, to optimize the rendering result by combining a deep learning algorithm, and to obtain an optimized digital sand table display area, and to automatically adjust the light parameters and rendering settings according to user feedback in the optimized digital sand table display area.

8. A computing device, comprising: It comprises: One or more processors; A storage system for storing one or more programs, when the one or more programs are executed by the one or more processors, so that the one or more processors implement the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a program which is executed by the processor to implement the method of any one of claims 1-6.

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