Commodity display digital show window and digital travel scene AI fusion platform

Abstract

The invention discloses a commodity display digital show window and digital travel scene AI fusion platform, and relates to the technical field of digital travel, and the platform comprises a scenic spot collection module, a model construction module, a multi-person cooperation and interaction platform module, a digital commodity display module, a rendering and cloud computing module, and a data security protection module. The scenic spot acquisition module collects scenic spot data; through digital commodity display and digital travel scene construction, scenic spots and commodities can be displayed in a virtual space, the scenic spots and the commodities are displayed in a virtual display area through three-dimensional data and environment information collected by a scenic spot collection module in combination with a modeling technology, immersive and interactive shopping experience is provided for users, and the user experience is improved. The computing resources are dynamically adjusted through the rendering and cloud computing module, the rendering priority is optimized, the image quality and the rendering efficiency are improved through a modern graphic API, and the fluency and the visual effect of a virtual scene are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital culture and tourism, and specifically to an AI integration platform for digital display windows of goods and digital culture and tourism scenarios. Background Art

[0002] With the rapid development of digital technologies, traditional tourism and cultural experience methods have gradually been promoted by digital and intelligent technologies. The application of technologies such as virtual reality, augmented reality, big data, and cloud computing provides tourists with a brand-new interactive and immersive experience. Digitalization of culture and tourism has changed people's travel and experience methods, and also plays an important role in optimizing scenic area management, improving tourism service quality, and promoting cultural dissemination. Digital means provide a brand-new platform for the display and sales of cultural and creative products, greatly enhancing the efficiency and experience of the tourism industry;

[0003] Existing scenic area platforms can only display product pictures and scenic area pictures, lacking a sense of immersion. Users can only browse products and scenic areas, lacking interaction and feedback with scene elements, resulting in a relatively single and cold user experience;

[0004] In view of the above technical deficiencies, a solution is proposed. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides an AI integration platform for digital display windows of goods and digital culture and tourism scenarios.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An AI integration platform for digital display windows of goods and digital culture and tourism scenarios, including: a scenic area data collection module, a model construction module, a multi-person collaboration and interaction platform module, a digital product display module, a rendering and cloud computing module, and a data security protection module;

[0007] The scenic area data collection module collects scenic area data, obtains three-dimensional spatial data of the scenic area and products through laser scanning technology and generates point cloud data, uses drones in cooperation with high-resolution cameras for aerial photography, and combines GIS data to provide detailed geographical information of the scenic area. All data will be transmitted to the model construction module;

[0008] The model construction module processes the point cloud data into a visual three-dimensional model using Blender3D modeling software and plugins. The three-dimensional model of the scenic area is divided into different parts such as buildings, roads, vegetation, and scenic spots, and each part is given unique physical properties;

[0009] The multi-person collaboration and interaction platform module constructs a virtual scene that supports multi-user collaboration. Users can view the operations and edits of other users through the virtual scene sharing function. The module uses the WebRTC and WebSocket protocols for real-time synchronization of edit operations and performs version control through Git;

[0010] The digital commodity display module develops a commodity display area by importing the scenic area 3D model into the virtual exhibition area development module Unity. The virtual commodity data is loaded onto the user device through the RESTAPI interface and superimposed onto the user's real-time view using ARKit and ARCore technologies. The module also integrates a shopping interface to support the user in completing the purchase process;

[0011] The rendering and cloud computing module distributes rendering tasks to multiple computing nodes through the integration of AWS Lambda to improve rendering efficiency and reduce the load on a single device, dynamically adjusts computing resources to optimize the rendering priority according to user behavior and scene requirements, and enhances the rendering effect through the Vulkan and DirectX12 graphics APIs;

[0012] The data security protection module manages the transmitted data by encrypting it using the SSL / TLS protocol. For data storage, AES and RSA encryption algorithms are used, and it relies on the cloud module for data backup and recovery to ensure data security.

[0013] The scenic area collection module uses advanced technologies to collect scenic area data. It uses the Leica ScanStation P40 laser scanning technology to obtain the 3D spatial data of the scenic area and various commodities, and generates point cloud data with a precision set to the millimeter level. It uses the DJI Matrice 300 RTK drone in combination with a high-resolution camera for aerial photography, and combines GIS data to provide high-definition images of the scenic area for extracting detailed geographical information. The Bosch BME680 environmental sensor is deployed to collect environmental data such as temperature, humidity, and air pressure. Finally, the above data is transmitted to the model construction module.

[0014] The model construction module processes the point cloud data, including the scenic area 3D model data and the commodity 3D model data. It uses the Blender3D modeling software in combination with Python scripts and the BlenderGIS plugin for modeling to generate the scenic area 3D model and the commodity 3D model, and refines the texture, shape, and color of the scene elements. The scenic area 3D model is divided into multiple types such as buildings, roads, vegetation, and scenic spots, with each part managed independently. It analyzes the high-definition image data of the scenic area to assign unique physical attributes to each part, including temperature, humidity, and air pressure. The module uses the Level of Detail technology to provide model displays at different precision levels under different network conditions.

[0015] The multi - person collaboration and interaction platform module builds a virtual environment on the basis of the scenic area 3D model using Unity's Multiplayer Networking, supports multiple users to enter the complex scene for interaction. Through the virtual scene sharing function, users can view the operations and edits of other users in real - time. The WebRTC and WebSocket protocols are used for real - time synchronization of editing operations, and the version control tool Git is used to ensure the traceability and recoverability of modifications. The multi - person collaboration and interaction platform module integrates voice, video, text communication tools and a shared whiteboard function to support real - time discussion, modification and annotation.

[0016] The digital commodity display module imports the scenic area 3D model into the virtual exhibition area development module Unity. The scenic area 3D model data includes buildings, roads, vegetation, and scenic spots. The virtual exhibition area can reflect the spatial layout of the real scenic area. According to the exhibition requirements, the layout of the commodity display area is carried out through the Scene Editor of Unity, a virtual space construction tool. Each commodity display area will be classified and arranged according to the commodity type, and the virtual commodity model is added to the corresponding display area. Users can enter the virtual exhibition area through a mobile device such as an iPhone or an Android phone to browse virtual commodities.

[0017] The virtual commodity data is loaded from the server through the RESTAPI interface and displayed on the user device screen. The commodity model is 3D - modeled and converted into a format suitable for AR applications. Through the rendering engines of ARKit and ARCore, these commodities will be superimposed on the user's real - time view. The virtual exhibition area provides a shopping interface to guide users to complete the purchase process, integrating two payment methods: Alipay and WeChat Pay. After the payment is completed, the digital commodity display module generates order information and sends it to the warehouse administrator for distribution, and provides an order tracking function. The digital commodity display module also pushes personalized commodity recommendations based on the user's shopping history and preferences.

[0018] The rendering and cloud computing module integrates AWS Lambda to distribute rendering tasks to multiple computing nodes, improving the rendering efficiency and reducing the load on a single device. By monitoring and analyzing the rendering requirements of the scene in real - time, the rendering and cloud computing module can dynamically adjust computing resources. The rendering and cloud computing module analyzes user behavior and scene usage through TensorFlow and Keras, predicts user operations, and optimizes the rendering priority according to the scene characteristics to ensure that key areas and elements are efficiently rendered. It supports seamless switching between PC, mobile, and VR devices, automatically optimizing the rendered content according to the device performance, thus ensuring a smooth user experience. By using modern graphics APIs such as Vulkan and DirectX12, the rendering and cloud computing module further optimizes the rendering efficiency and enhances the lighting effects and texture details.

[0019] The data security protection module is responsible for ensuring the security and privacy of user data. All transmitted data is encrypted using the SSL / TLS protocol. In terms of data storage, the module uses AES and RSA encryption algorithms to perform symmetric encryption and public-key encryption on the data, and physically isolates the private key through a Hardware Security Module (HSM). For identity authentication, the OAuth2.0 and JWT mechanisms are used for user identity verification and permission management. The module performs fine-grained permission management based on user roles to ensure that different users can only access authorized data and perform corresponding operations. Operation logs encrypted for storage are generated for auditing and traceability. The module relies on the cloud module for data backup and recovery, and performs automatic backups regularly.

[0020] The present invention provides an AI integration platform for digital display windows of commodities and digital cultural and tourism scenarios. Compared with the prior art, it has the following beneficial effects:

[0021] Through the construction of digital commodity display and digital cultural and tourism scenarios, the present invention can display scenic spots and commodities in a virtual space. By combining the three-dimensional data and environmental information collected by the scenic spot collection module with modeling technology, the scenic spots and commodities are displayed in the virtual exhibition area, providing users with an immersive and interactive shopping experience. By the rendering and cloud computing module, computing resources are dynamically adjusted, the rendering priority is optimized, and modern graphics APIs are used to improve image quality and rendering efficiency, enhancing the smoothness and visual effect of the virtual scene;

[0022] By integrating data protection measures, the present invention improves the shopping experience of users in the virtual exhibition area, strengthens data security and privacy protection. The real-time synchronous editing function implemented through the WebRTC and WebSocket protocols, as well as the identity authentication through the OAuth2.0 and JWT mechanisms, ensure the efficiency and data security of multi-person collaboration. The application of these technologies significantly improves the stability and reliability of the system, reducing the risks brought by system failures or data leaks. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0025] Please refer to Figure 1, this application provides an AI integration platform for digital display windows of commodities and digital cultural and tourism scenarios, including a scenic area data collection module, a model construction module, a multi-person collaboration and interaction platform module, a digital commodity display module, a rendering and cloud computing module, and a data security protection module;

[0026] The scenic area data collection module collects scenic area data, obtains three-dimensional spatial data of the scenic area and commodities through laser scanning technology and generates point cloud data, conducts aerial photography using a drone in cooperation with a high-resolution camera, and provides detailed geographical information of the scenic area in combination with GIS data. All data will be transmitted to the model construction module;

[0027] The model construction module processes the point cloud data into a visual three-dimensional model using Blender3D modeling software and plugins. The three-dimensional model of the scenic area is divided into different parts such as buildings, roads, vegetation, and scenic spots, and each part is given unique physical properties;

[0028] The multi-person collaboration and interaction platform module constructs a virtual scene that supports multi-user collaboration. Users can view the operations and edits of other users through the virtual scene sharing function. The module uses the WebRTC and WebSocket protocols to synchronize editing operations in real time and performs version control through Git;

[0029] The digital commodity display module constructs a commodity display area by importing the three-dimensional model of the scenic area into the virtual exhibition area development module Unity. The virtual commodity data is loaded onto the user device through the RESTAPI interface and superimposed onto the user's real-time view using ARKit and ARCore technologies. The module also integrates a shopping interface to support users to complete the purchase process;

[0030] The rendering and cloud computing module distributes rendering tasks to multiple computing nodes through the integration of AWS Lambda to improve rendering efficiency and reduce the load on a single device, dynamically adjusts computing resources to optimize the rendering priority according to user behavior and scene requirements, and enhances the rendering effect through the Vulkan and DirectX12 graphics APIs;

[0031] The data security protection module manages the transmitted data and encrypts it using the SSL / TLS protocol. In terms of data storage, it uses AES and RSA encryption algorithms and relies on the cloud module for data backup and recovery to ensure data security.

[0032] The scenic area collection module uses advanced technologies to collect scenic area data. It uses the laser scanning technology Leica ScanStation P40 to obtain the three-dimensional spatial data of the scenic area and various commodities, and generates point cloud data with the precision set to the millimeter level. Through the cooperation of the drone DJI Matrice 300 RTK and a high-resolution camera for aerial photography, combined with GIS data, it provides high-definition images of the scenic area for extracting detailed geographical information. The Bosch BME680 environmental sensor is deployed to collect environmental data such as temperature, humidity, and air pressure. Finally, the above data is transmitted to the model construction module.

[0033] The laser scanning technology (Leica ScanStation P40) is used to obtain the three-dimensional spatial data of the scenic area and commodities. The laser scanning technology generates high-precision point cloud data, which is usually applied to occasions such as buildings and scenic areas that require high-precision spatial measurement. Processing point cloud data requires powerful computing capabilities. Commonly used computing tools include CloudCompare and MeshLab for point cloud processing, registration, and noise reduction.

[0034] The model construction module processes point cloud data, including the three-dimensional model data of the scenic area and the three-dimensional model data of commodities. It uses the Blender 3D modeling software in combination with Python scripts and the BlenderGIS plugin for modeling to generate the three-dimensional model of the scenic area and the three-dimensional model of commodities, and refines the texture, shape, and color of the scene elements. The three-dimensional model of the scenic area is divided into various types of buildings, roads, vegetation, and scenic spots, with each part managed independently. The high-definition image data of the scenic area is analyzed to endow each part with unique physical attributes, including temperature, humidity, and air pressure. The module adopts the Level of Detail technology to provide model displays with different precision levels under different network conditions.

[0035] Blender is open-source three-dimensional modeling and rendering software used to create high-precision digital twin models. In this module, Blender 3D will be used to extract and generate the three-dimensional models of the scenic area and commodities from the point cloud data, process the data and refine it. The point cloud data collected using LiDAR or other scanning technologies contains high-precision terrain and object structure information. The point cloud data is directly loaded from an external source through Python scripts. Through Blender's sculpting tools and mesh optimization tools, the point cloud data is accurately modeled to generate the three-dimensional skeleton of the scene, and the details are further refined according to requirements;

[0036] Python scripts are used in Blender to automate the model creation and modification process. The Python API provides access to Blender, allowing users to create, edit, and analyze models. Through Python scripts, operations such as texture, color, and shape refinement can be performed. The scripts automate the management of multiple elements, including assigning different objects to different layers for easier subsequent management and rendering. Python is used to generate parametric models of elements such as buildings and scenic spots through mathematical algorithms. The BlenderGIS plugin provides powerful Geographic Information System (GIS) functionality, suitable for processing geographical data and height maps of scenic areas to generate high-precision terrain models, generate terrain models based on GIS data (DEM), and refine the three-dimensional form of scenic areas;

[0037] UV unwrapping and texture mapping techniques are used in Blender to add textures, making buildings, roads, vegetation, etc. look more realistic. The UVMapping tool in Blender is used to automatically unwrap the surface of 3D models and correctly map texture images onto the models. With the help of Blender's built-in image editing tools, the model surface is refined and drawn. During modeling, Blender's sculpting function and polygon modeling tools are used to refine the shape of each scene element, such as the windows of buildings, the curvature of roads, and the branches and leaves of trees. The color, gloss, and other properties of the model are adjusted through Blender's material editor to simulate different lighting and environmental effects.

[0038] The multi-person collaboration and interaction platform module uses Unity's Multiplayer Networking to build a virtual environment based on the 3D model of the scenic area, supporting multiple users to enter the complex scene for interaction. Through the virtual scene sharing function, users can view the operations and edits of other users in real time. The WebRTC and WebSocket protocols are used for real-time synchronization of edit operations, and the version control tool Git is used to ensure the traceability and recoverability of modifications. The multi-person collaboration and interaction platform module integrates voice, video, text communication tools, and a shared whiteboard function to support real-time discussion, modification, and annotation.

[0039] The digital commodity display module imports the 3D model of the scenic area into the virtual exhibition area development module Unity. The 3D model data of the scenic area includes buildings, roads, vegetation, and scenic spots. The virtual exhibition area can reflect the spatial layout of the real scenic area. According to the exhibition requirements, the layout of the commodity display area is carried out through the SceneEditor of Unity, a virtual space construction tool. Each commodity display area will be classified and arranged according to the commodity type, and virtual commodity models are added to the corresponding display areas. Users can enter the virtual exhibition area through mobile devices such as iPhones or Android phones to browse virtual commodities.

[0040] Unity is used to build a virtual exhibition area, and it can import the 3D model of the real scenic area into the virtual environment. SceneEditor is a core workspace in the Unity editor, mainly used for building and editing 3D scenes. In SceneEditor, first create global illumination to increase the scene brightness, then create display stands and arrange them into the required display structure. Drag the product models from the Assets window into the scene, adjust the position and rotation angle to place them correctly on the display stands, and use the alignment tool to improve the layout neatness. Finally, select the main camera and adjust the position and angle to make the product display clearly visible.

[0041] Virtual product data is loaded from the server through the RESTAPI interface and displayed on the user device screen. The product models are 3D modeled and converted into a format suitable for AR applications. Through the rendering engines of ARKit and ARCore, these products will be superimposed on the user's live view. The virtual exhibition area provides a shopping interface to guide users to complete the purchase process, integrating two payment methods: Alipay and WeChat Pay. After the payment is completed, the digital product display module generates order information and sends it to the warehouse administrator for distribution, and provides an order tracking function. The digital product display module also pushes personalized product recommendations based on the user's shopping history and preferences.

[0042] The rendering and cloud computing module integrates AWS Lambda to distribute rendering tasks to multiple computing nodes, improving the rendering efficiency and reducing the load on a single device. By monitoring and analyzing the rendering requirements of the scene in real time, the rendering and cloud computing module can dynamically adjust computing resources. The rendering and cloud computing module analyzes user behavior and scene usage through TensorFlow and Keras, predicts user operations, and optimizes the rendering priority according to scene characteristics to ensure that key areas and elements are efficiently rendered. It supports seamless switching between PC, mobile, and VR devices, and automatically optimizes the rendered content according to device performance, thus ensuring a smooth user experience. By using modern graphics APIs such as Vulkan and DirectX12, the rendering and cloud computing module further optimizes the rendering efficiency and enhances the lighting effects and texture details.

[0043] AWS Lambda is a serverless computing service. AWS Lambda dynamically allocates computing resources according to the requirements of rendering tasks. Through Lambda, rendering tasks are distributed to multiple computing nodes, thereby improving rendering efficiency and reducing the load on a single device. The rendering tasks are split through the AWS cloud infrastructure and multiple virtual machine instances are used for parallel computing. The combination of Blender and AWS reduces the rendering time. The two modern graphics APIs, Vulkan and DirectX 12, provide developers with more refined control, allowing for efficient interaction directly with the hardware, enhancing the rendering efficiency, especially on multi-core processors and GPU architectures. They support more efficient resource management, rendering of lighting effects, and texture detail performance.

[0044] The data security protection module is responsible for ensuring the security and privacy of user data. All transmitted data is encrypted using the SSL / TLS protocol. In terms of data storage, the module uses AES and RSA encryption algorithms to perform symmetric encryption and public key encryption on the data, and physically isolates the private key through the Hardware Security Module (HSM). Identity authentication uses the OAuth2.0 and JWT mechanisms for user identity verification and permission management. The module performs fine-grained permission management based on user roles to ensure that different users can only access authorized data and perform corresponding operations. Operation logs encrypted for storage are generated for auditing and traceability. The module relies on the cloud module for data backup and recovery, and performs automatic backups regularly.

[0045] SSL (Secure Sockets Layer) and TLS (Transport Layer Security) are standard protocols for encrypting network data transmission, ensuring that all transmitted data is in an encrypted state, avoiding man-in-the-middle attacks and data eavesdropping. OpenSSL is used to implement the encryption and decryption of the SSL / TLS protocol, supporting certificate generation, key exchange, and the implementation of encryption algorithms.

[0046] Furthermore, through the digital commodity display and digital cultural and tourism scenario construction, the present invention can display scenic spots and commodities in a virtual space. By combining the three-dimensional data and environmental information collected by the scenic spot collection module with modeling techniques, the scenic spots and commodities are displayed in the virtual exhibition area, providing users with an immersive and interactive shopping experience. By dynamically adjusting computing resources through the rendering and cloud computing module, optimizing the rendering priority, and using modern graphics APIs to improve the image quality and rendering efficiency, the smoothness and visual effects of the virtual scene are enhanced.

[0047] Some of the data in the above formula are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0048] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The digital showcase for product display and the AI ​​fusion platform for digital cultural tourism scenes are characterized by: include: Scenic spot collection module, model building module, multi-person collaboration and interaction platform module, digital product display module, rendering and cloud computing module and data security protection module; The scenic spot acquisition module collects scenic spot data, obtains the three-dimensional spatial data of the scenic spot and commodities through laser scanning technology and generates point cloud data, uses drones with high-resolution cameras for aerial photography, and combines GIS data to provide detailed geographic information of the scenic spot. All data will be transmitted to the model building module; The model building module processes the point cloud data into a visual 3D model using Blender 3D modeling software and plug-ins. The 3D model of the scenic area is divided into different parts such as buildings, roads, vegetation, and scenic spots, and each part is given unique physical properties. The multi-user collaboration and interaction platform module builds a virtual scene that supports multi-user collaboration. Users can view other users' operations and edits through the virtual scene sharing function. The module uses WebRTC and WebSocket protocols for real-time synchronization of editing operations and uses Git for version control. The digital product display module imports the 3D model of the scenic spot into the virtual exhibition area development module Unity to build the product display area. The virtual product data is loaded onto the user's device through the REST API interface and superimposed onto the user's real-time view using ARKit and ARCore technology. The module also integrates a shopping interface to support users in completing the purchase process. The rendering and cloud computing module distributes rendering tasks to multiple computing nodes through the integration of AWS Lambda to improve rendering efficiency and reduce the load on a single device. It dynamically adjusts computing resources to optimize rendering priorities based on user behavior and scene requirements, and improves rendering effects through Vulkan and DirectX 12 graphics APIs. The data security protection module manages the transmission of data using SSL / TLS protocol encryption, uses AES and RSA encryption algorithms for data storage, and relies on the cloud module for data backup and recovery to ensure data security.

2. The AI ​​fusion platform for digital product display windows and digital cultural tourism scenes according to claim 1 is characterized in that: The scenic spot acquisition module uses advanced technology to collect scenic spot data, uses a laser scanner to obtain three-dimensional spatial data of the scenic spot and various commodities, and generates point cloud data with an accuracy set to the millimeter level. It uses drones with high-resolution cameras for aerial photography and combines GIS data to provide high-definition images of the scenic spot for extracting detailed geographic information. It deploys Bosch BME680 environmental sensors to collect environmental data such as temperature, humidity, and air pressure, and finally transmits the above data to the model construction module.

3. The product display digital showcase and digital cultural tourism scene AI fusion platform according to claim 1 is characterized in that: The model building module processes point cloud data, including scenic area 3D model data and commodity 3D model data, and uses Blender3D modeling software combined with Python scripts and BlenderGIS plug-ins for modeling, generates scenic area 3D models and commodity 3D models, and refines the texture, shape and color of scene elements. The scenic area 3D model is divided into various types of buildings, roads, vegetation and attractions, each part is managed independently, and the high-definition image data of the scenic area is analyzed to give each part unique physical properties, including temperature, humidity, and air pressure; the module uses Level of Detail technology to provide model displays of different accuracy levels under different network conditions.

4. The product display digital showcase and digital cultural tourism scene AI fusion platform according to claim 1 is characterized in that: The multiplayer collaboration and interaction platform module uses Unity's Multiplayer Networking to build a virtual environment based on the three-dimensional model of the scenic area, supports multiple users to enter complex scenes for interaction, and through the virtual scene sharing function, users can view other users' operations and edits in real time. It uses WebRTC and WebSocket protocols for real-time synchronous editing operations, and uses the version control tool Git to ensure the traceability and recoverability of modifications. The multiplayer collaboration and interaction platform module integrates voice, video, text communication tools and shared whiteboard functions to support real-time discussion, modification and annotation.

5. The product display digital showcase and digital cultural tourism scene AI fusion platform according to claim 1 is characterized in that: The digital product display module imports the three-dimensional model of the scenic area into the virtual exhibition area development module Unity. The three-dimensional model data of the scenic area includes buildings, roads, vegetation, and scenic spots. The virtual exhibition area can reflect the spatial layout of the real scenic area. According to the exhibition needs, the layout of the product display area is carried out through the Scene Editor of the virtual space construction tool Unity. Each product display area will be classified and arranged according to the type of product, and the virtual product model is added to the corresponding display area. Users enter the virtual exhibition area through mobile devices such as iPhone or Android phones to browse virtual products.

6. The product display digital showcase and digital cultural tourism scene AI fusion platform according to claim 1 is characterized in that: Virtual product data is loaded from the server through the REST API interface and displayed on the user's device screen. The product model is 3D modeled and converted into a format suitable for AR applications. Through the rendering engines of ARKit and ARCore, these products will be superimposed on the user's real-time view. The virtual exhibition area provides a shopping interface to guide users to complete the purchase process and integrates two payment methods: Alipay and WeChat Pay. After payment is completed, the digital product display module generates order information and sends it to the warehouse manager for delivery. It also provides order tracking function. The digital product display module also pushes personalized product recommendations based on user shopping history and preferences.

7. The AI ​​fusion platform for digital product display windows and digital cultural tourism scenes according to claim 1 is characterized in that: The rendering and cloud computing module integrates AWS Lambda to distribute rendering tasks to multiple computing nodes, improve rendering efficiency and reduce the load on a single device. By real-time monitoring and analyzing the rendering requirements of the scene, the rendering and cloud computing module can dynamically adjust computing resources. The rendering and cloud computing module analyzes user behavior and scene usage through TensorFlow and Keras, predicts user operations, and optimizes rendering priorities based on scene characteristics to ensure that key areas and elements are efficiently rendered. It supports seamless switching between PCs, mobile terminals, and VR devices, and automatically optimizes rendering content based on device performance, thereby ensuring a smooth user experience. By using Vulkan and DirectX 12 modern graphics APIs, the rendering and cloud computing module further optimizes rendering efficiency and improves light and shadow effects and texture details.

8. The product display digital showcase and digital cultural tourism scene AI fusion platform according to claim 1 is characterized in that: The data security protection module is responsible for ensuring the security and privacy of user data. All transmitted data is encrypted using the SSL / TLS protocol. In terms of data storage, the system uses AES and RSA encryption algorithms to perform symmetric encryption and public key encryption on the data, and physically isolates the private key through the hardware security module HSM. Identity authentication uses OAuth2.0 and JWT mechanisms for user identity authentication and permission management. The module performs fine-grained permission management based on user roles to ensure that different users can only access authorized data and perform corresponding operations, and generates encrypted and stored operation logs for auditing and tracing. The system relies on the cloud module for data backup and recovery, and performs automatic backups regularly.

Images