Multi-light-source illumination display method and device and electronic equipment

Abstract

The invention provides a multi-light-source illumination display method and device and electronic equipment, and relates to the field of data processing. According to the method, position data of a user is obtained, and the user wears AR glasses; according to the position data of the user, first feature data and a first preset room are determined, the first preset room is any one of a plurality of preset rooms, and any one preset room is provided with a plurality of light sources; receiving second feature data sent by the AR glasses for the first preset room; based on the first feature data and the second feature data, determining a target multi-light-source illumination display scheme corresponding to the first preset room; and displaying the target multi-light-source illumination display scheme through AR glasses. By implementing the technical scheme provided by the invention, the experience feeling of the user in the selection and decision-making process can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to a multi-light source lighting display method, device and electronic equipment. Background Art

[0002] In the existing home decoration and lighting design process, users usually face a complex problem after purchasing a new house: how to select and match various lamps to create an ideal lighting effect.

[0003] Currently, the traditional approach is to understand the layout and lighting effects of different lamps in a room by reviewing drawings or renderings provided by designers. However, this type of lighting display solution in the form of drawings has obvious limitations. Drawings can only show the layout of the lamps and the general lighting effect, but cannot fully reproduce the light and shadow changes and spatial perception of the actual room. Therefore, it is difficult for users to accurately judge the actual lighting effect of the lamps and their impact on the room atmosphere based on drawings, resulting in a poor user experience.

[0004] Therefore, there is an urgent need for a multi-light source lighting display method, device and electronic equipment. Summary of the Invention

[0005] The present application provides a multi-light source lighting display method, device and electronic equipment to improve the user experience in the selection and decision-making process.

[0006] In a first aspect of the present application, a multi-light source lighting display method is provided, the method comprising: obtaining location data of a user wearing AR glasses; determining first feature data and a first preset room based on the location data of the user, the first preset room being any one of a plurality of preset rooms, and any one of the preset rooms being provided with a plurality of light sources; receiving second feature data for the first preset room sent by the AR glasses; determining a target multi-light source lighting display scheme corresponding to the first preset room based on the first feature data and the second feature data; and displaying the target multi-light source lighting display scheme through the AR glasses.

[0007] By implementing the above technical solution, AR glasses allow users to visualize virtual lighting effects directly in their real-world environments. This intuitive display allows users to more accurately evaluate the actual effects of different lamps and lighting solutions. Compared to traditional blueprints or renderings, AR displays can more realistically reproduce the play of light and shadow and the sense of space in a room, giving users a more realistic visual experience. This method allows users to interact in real time through AR glasses, allowing them to view the lighting effects of different settings in real time. This interactivity not only increases user engagement and satisfaction, but also helps users quickly find the lighting solution that best suits their needs. Based on the user's location data, the system can intelligently identify the user's room and recommend a corresponding lighting solution. Furthermore, AR displays allow users to quickly try out multiple different lighting solutions, enabling them to make decisions more quickly. Compared to traditional trial-and-error methods, this AR-based display method significantly reduces the user's decision-making costs and time.

[0008] Optionally, obtaining the user's location data specifically includes: receiving positioning data sent by the AR glasses; determining a positioning coordinate point based on the positioning data; matching the positioning coordinate point with a preset electronic fence area to obtain a target preset electronic fence area to obtain the user's location data, wherein the preset electronic fence area is pre-set according to the physical space boundaries corresponding to multiple preset rooms.

[0009] By employing the above technical solution, the positioning data transmitted by AR glasses is typically highly accurate, accurately reflecting the user's position in physical space. This enables the system to more precisely determine the user's location in the area or room, providing an accurate basis for subsequent multi-light source lighting display solutions. By matching the positioning coordinates with pre-set geo-fenced areas, the system can intelligently identify the user's current location in a specific area, such as a pre-set room. This intelligent identification process is both fast and accurate, improving the user experience and system responsiveness. Pre-set geo-fenced areas can be pre-set based on the physical boundaries of multiple pre-set rooms to accommodate diverse and complex environments. Geometric fences can achieve precise zoning and positioning in both open and enclosed rooms. Because the system can accurately identify the user's location, it can provide users with more personalized and precise lighting display solutions. As users enter different rooms, the system can automatically adjust lighting settings to meet their specific needs, thereby enhancing the overall user experience.

[0010] Optionally, determining the first characteristic data and the first preset room based on the user's location data specifically includes: performing semantic recognition on the user's location data to obtain a target field; setting the target field to the first characteristic data, and the target field includes the room name of the first preset room.

[0011] By employing the above technical solution, semantic recognition technology accurately understands the meaning of user location data and extracts key information directly relevant to lighting display solutions. Using the room name as the target field ensures the system accurately matches the user's current preset room, providing an accurate basis for subsequent multi-light source lighting display solutions. By setting the target field as the first feature data, the system can quickly locate the user's preset room and quickly load and display the corresponding lighting display solution. This rapid response and location capability significantly improves user experience and system efficiency. Because the system can accurately identify the user's preset room, it can provide users with more personalized lighting display solutions. By automatically identifying and matching target fields such as room names, the system simplifies the user's operation process. Users no longer need to manually select a room or enter room information; the system automatically completes these operations, making it more convenient for users to use the lighting display function. By automatically identifying and matching target fields such as room names, the system can reduce the problems of mismatches and inaccurate displays caused by user errors or input errors. This helps improve system accuracy and reliability, providing users with a better lighting display experience.

[0012] Optionally, the receiving of the second feature data for the first preset room sent by the AR glasses specifically includes: using the AR glasses to scan the first preset room to obtain key feature data, the key feature data including the position and type corresponding to the furniture and the position and type corresponding to the multi-light source lighting fixture; determining the second feature data of the first preset room based on the position and type corresponding to the furniture and the position and type corresponding to the multi-light source lighting fixture.

[0013] By employing the above technical solution, AR glasses can scan a room in real time and capture the precise location and type of furniture and lighting fixtures. This real-time performance ensures the system can respond promptly to user needs and provide instant lighting solutions. Furthermore, the AR glasses' scanning technology ensures data accuracy, eliminating the potential errors associated with traditional manual measurement or input. The location and type of furniture and lighting fixtures are fundamental to developing personalized lighting solutions. Using this data, the system can more accurately understand the room's layout and style, providing users with lighting solutions that better meet their specific needs. Users no longer need to manually enter or measure furniture and lighting fixture information; they simply scan with AR glasses. This streamlined workflow reduces user involvement and improves the user experience. Furthermore, the system can quickly generate lighting solutions based on the captured data, allowing users to quickly see the results and further enhance user satisfaction. By understanding the precise location and type of furniture and lighting fixtures, the system can more effectively plan the layout and distribution of light sources, avoiding issues such as dead spots and over-lighting. This helps optimize lighting effects and enhance the comfort and aesthetics of the room. Traditionally, obtaining information about furniture and lighting fixtures in a room requires manual measurement and recording, which is time-consuming, labor-intensive, and error-prone. Using AR glasses to automatically scan and capture this information reduces manual intervention and costs, while improving efficiency and accuracy.

[0014] Optionally, determining the target multi-light source lighting display scheme corresponding to the first preset room based on the first feature data and the second feature data specifically includes: inputting the first feature data and the second feature data into a preset model to obtain a target feature data group, and the preset model is pre-trained by a neural network; using the preset model to match the target feature data group to obtain the target multi-light source lighting display scheme corresponding to the target feature data group, and the preset model pre-stores the correspondence between the target feature data group and the target multi-light source lighting display scheme.

[0015] By employing this technical solution, a preset model is trained through a neural network, enabling rapid processing and analysis of large amounts of feature data. This efficiency enables the system to generate lighting solutions for users in a short period of time, improving the user experience. Furthermore, the neural network's powerful learning capabilities ensure the accuracy of matching results, ensuring that the generated lighting solutions are more tailored to user needs and the actual room conditions. The preset model can perform personalized matching based on input first and second feature data (such as room name, furniture location and type, and lighting fixture location and type). This means the system can generate unique lighting solutions for each user or room, meeting their individual needs. The preset model can be continuously optimized and improved as new data and experience accumulate. This means the system can adapt to changing environments and user needs, maintaining the advancement and practicality of lighting solutions. Matching based on the preset model significantly reduces the need for manual intervention. Traditional lighting design often requires professionals to design and adjust according to room conditions and user needs. This approach automates this process, reducing both labor and time costs.

[0016] Optionally, the multi-light source lighting display solution includes a three-dimensional layout of the first preset room and light distribution, brightness, color, and shadow corresponding to each of the multiple light sources.

[0017] By implementing this technical solution, users can intuitively visualize the room's layout through a 3D layout display. This visual presentation helps users better understand the effects and layout of the lighting design. The layout includes information on the light distribution, brightness, color, and shadows of each light source, accurately simulating actual lighting effects. This helps users preview the actual lighting design effect in advance, avoiding unexpected results after installation. By adjusting parameters such as light distribution, brightness, and color of the light sources, a personalized lighting design can be achieved. Users can customize a lighting plan based on their preferences and needs. Considering shadow information in the layout is crucial for optimizing the lighting effect. By simulating the location and intensity of shadows, unnecessary shadows or dark areas can be avoided, making the room brighter and more comfortable. For non-expert users, the 3D layout and lighting simulation display methods are easier to understand and communicate. These intuitive visual elements allow users to quickly understand the intent and effects of the lighting design, facilitating effective communication and collaboration with the designer. By determining the lighting plan early in the installation process, unnecessary rework and adjustments can be avoided, saving costs and time. At the same time, accurate simulation effects can also reduce the workload of on-site debugging and further improve engineering efficiency.

[0018] Optionally, before inputting the first feature data and the second feature data into the preset model to obtain the target feature data group, the preset model is trained; the training of the preset model specifically includes: processing the first feature data and the second feature data to obtain multiple feature values; taking the feature values ​​greater than a preset threshold among the multiple feature values ​​as representative feature values; taking the feature data corresponding to the representative feature values ​​as training samples; inputting the training samples into the input layer of the neural network, and inputting the corresponding target feature data group into the output layer of the neural network for supervised training to obtain an initial model; performing accuracy testing on the initial model, and selecting a model whose accuracy meets preset conditions as the preset model.

[0019] By employing the above technical solution, the first and second feature data are processed to obtain multiple feature values. Feature values ​​greater than a preset threshold are selected as representative feature values, thereby screening out key features that have a significant impact on the model's prediction results. This allows the model to focus more on these important features during training, thereby improving prediction accuracy. The feature data corresponding to the representative feature values ​​are used as training samples, and these samples are used for supervised training of the neural network, allowing the model to learn the mapping relationship from input to output. As training progresses, the model continuously optimizes its internal parameters to more accurately predict the target feature data set, thereby improving model performance. During training, the initial model is accuracy-tested, and the model whose accuracy meets the preset criteria is selected as the preset model. This effectively avoids overfitting and underfitting. Overfitting may cause the model to perform well on the training set but degrade on the test set; underfitting may prevent the model from fully learning the data characteristics, resulting in inaccurate prediction results. Accuracy testing ensures that the resulting preset model has good generalization capabilities. Because the preset model is learned from a large number of training samples, it has strong adaptability. When encountering new first feature data and second feature data, the preset model can quickly process these data and output the corresponding target feature data group, thereby quickly generating a multi-light source lighting display plan. By training and optimizing the preset model, the degree of human intervention can be greatly reduced. After the model training is completed, the user only needs to input the first feature data and the second feature data to quickly obtain the corresponding multi-light source lighting display plan, without the need for tedious manual calculations and adjustments. The training and use of preset models can greatly improve the design efficiency of lighting display plans. The traditional lighting design process requires designers to make multiple attempts and adjustments based on experience and intuition, while the use of preset models can quickly generate multiple lighting plans and select the optimal plan based on actual needs, thereby greatly improving work efficiency.

[0020] In a second aspect of the present application, a multi-light source lighting display device is provided, which includes an acquisition module and a processing module, wherein the acquisition module is used to obtain the user's location data, and the user wears AR glasses; the processing module is used to determine first feature data and a first preset room based on the user's location data, and the first preset room is any one of a plurality of preset rooms, and any preset room is provided with multiple light sources; the acquisition module is also used to receive second feature data for the first preset room sent by the AR glasses; the processing module is also used to determine a target multi-light source lighting display scheme corresponding to the first preset room based on the first feature data and the second feature data; the processing module is also used to display the target multi-light source lighting display scheme through the AR glasses.

[0021] In a third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs the method described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, the method described above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: AR glasses allow users to visualize virtual lighting effects directly in their real-world environments. This intuitive display allows users to more accurately evaluate the actual effects of different lamps and lighting solutions. Compared to traditional blueprints or renderings, AR displays more realistically reproduce the play of light and shadow and the sense of space within a room, providing users with a more realistic visual experience. This method allows users to interact in real time through AR glasses, allowing them to view the lighting effects of different settings in real time. This interactivity not only increases user engagement and satisfaction, but also helps users quickly find the lighting solution that best suits their needs. Based on the user's location data, the system can intelligently identify the room the user is in and recommend a corresponding lighting solution. Furthermore, AR displays allow users to quickly try out multiple different lighting solutions, enabling them to make decisions more quickly. Compared to traditional trial-and-error methods, this AR-based display method significantly reduces the user's decision-making costs and time. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1A schematic flow chart of a multi-light source lighting display method provided in an embodiment of the present application.

[0025] Figure 2 Another schematic flow chart of a multi-light source lighting display method provided in an embodiment of the present application.

[0026] Figure 3 A schematic diagram of a module of a multi-light source lighting display device provided in an embodiment of the present application.

[0027] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0028] Explanation of the reference numerals: 31, acquisition module; 32, processing module; 41, processor; 42, communication bus; 43, user interface; 44, network interface; 45, memory. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0030] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0031] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0032] In the existing home decoration and lighting design process, users often face a challenge after purchasing a new house: how to accurately select and match lamps to create the ideal lighting effect.

[0033] Currently, the traditional method for previewing the layout of lamps in a room and the intended lighting effects is to review drawings or renderings provided by the designer. However, this type of lighting display in the form of drawings has inherent limitations. Specifically, drawings often only show the placement of lamps and a rough idea of ​​the lighting effects, but fail to fully and realistically reflect the interplay of light and shadow and spatial hierarchy in the actual room. As a result, it is difficult for users to accurately judge the actual lighting effects and the overall room atmosphere of the lamps based solely on drawings, which significantly reduces the user experience.

[0034] In order to solve the above technical problems, this application provides a multi-light source lighting display method, referring to Figure 1 , Figure 1 This is a flow chart of a multi-light source lighting display method provided in an embodiment of the present application. The multi-light source lighting display method is applied to a server and includes steps S110 to S150, which are as follows: S110: Acquire user location data, where the user is wearing AR glasses.

[0035] Specifically, AR glasses are equipped with various sensors, including GPS, Wi-Fi positioning, Bluetooth beacon positioning, and an inertial measurement unit (IMU). These sensors can capture information such as the user's location, orientation, and movement speed. In embodiments of the present application, to further improve positioning accuracy, more precise location information can be provided through external positioning systems, such as indoor Bluetooth beacon networks or UWB (ultra-wideband) systems. Once the AR glasses capture location data, this data is typically sent to a server. This can be achieved through wireless communication technologies such as Wi-Fi, 5G / 4G, etc. After receiving the location data, the server performs a series of processing, including location verification, data cleaning, and mapping with virtual content. The processed virtual lighting effects are then sent back to the AR glasses, where the user can view these virtual effects in the real environment. A server is a device or program used to process requests, store data, provide resources, or perform other computing tasks.

[0036] For example, imagine a home designer using AR glasses to demonstrate the lighting effects of different lamps in a client's living room. As the client enters the living room wearing the AR glasses, the glasses' built-in sensors begin capturing their location and orientation. The AR glasses transmit this location data via Wi-Fi to a server. The server then combines this location data with a 3D model of the living room and the lamp parameters to calculate the virtual lighting effects the client should see from their current perspective. The server then sends the generated virtual lighting effects back to the AR glasses, allowing the designer to visualize the virtual lighting effects in the real living room, including light reflections and projections on walls and furniture.

[0037] In one possible implementation, obtaining the user's location data specifically includes: receiving positioning data sent by the AR glasses; determining a positioning coordinate point based on the positioning data; matching the positioning coordinate point with a preset electronic fence area to obtain a target preset electronic fence area to obtain the user's location data, where the preset electronic fence area is pre-set according to the physical space boundaries corresponding to multiple preset rooms.

[0038] Specifically, when a user wears AR glasses and moves, sensors on the AR glasses, such as GPS, Wi-Fi, Bluetooth, and an inertial measurement unit, collect information about the user's location and send this data to a server. After receiving the positioning data from the AR glasses, the server uses this data to calculate the user's precise location, which can be represented as a coordinate point, such as longitude and latitude, XY coordinates, etc. This process may involve data processing, conversion, and / or fusion of data from different sensors to improve accuracy. Preset electronic fence areas are predefined spatial ranges that represent a room, corridor, or other area in the real environment. These areas are set based on the physical boundaries of a building, for example, using a building's floor plan or CAD file, and defined through means such as Bluetooth beacons. The server matches the user's location coordinates with these electronic fence areas to determine the specific area the user is currently in. Once the user's coordinate point is successfully matched to a preset electronic fence area, the server considers the user to be currently located in that area and uses that area as the user's location data.

[0039] S120. Determine first characteristic data and a first preset room according to the user's location data. The first preset room is any one of a plurality of preset rooms, and any one of the preset rooms is provided with a plurality of light sources.

[0040] Specifically, as previously mentioned, the server receives positioning data sent by the AR glasses and, after a series of processing, ultimately determines the user's specific location. This location data is typically represented as a coordinate point or area, representing the user's precise or approximate location in physical space. Feature data is information or attributes associated with a specific location or environment. "First feature data" relates to the room or area the user is currently in, such as the room's location, name, decor, and functional attributes. This data is stored in the server's database and associated with each preset room. A "first preset room" refers to one of multiple preset rooms determined by the server based on the user's location data. These preset rooms are pre-defined based on the building's actual layout and usage. Each preset room is equipped with multiple light sources, meaning each room has a custom lighting design, including different types of lamps (such as chandeliers, wall lamps, and table lamps) and different lighting modes (such as daily lighting, spotlighting, and ambient lighting).

[0041] In one possible implementation, the first characteristic data and the first preset room are determined based on the user's location data, specifically including: performing semantic recognition on the user's location data to obtain a target field; setting the target field to the first characteristic data, and the target field including the room name of the first preset room.

[0042] Specifically, semantic recognition is a technology used to extract and understand meaning from text, data or other information sources. In an embodiment of the present application, semantic recognition is used to process the user's location data to identify and extract key information related to the preset room. The target field is specific information extracted from the user's location data after semantic recognition, which matches a certain attribute or feature of the preset room. In this scenario, the target field includes the room name of the "first preset room". Once the target field (i.e., the room name) is extracted from the user's location data, this field is set to "first feature data", which means that the room name is regarded as a key feature for subsequent operations or decisions. In this scenario, since the target field contains the room name, the "first preset room" can be directly determined.

[0043] S130: Receive second feature data for the first preset room sent by the AR glasses.

[0044] Specifically, AR glasses serve as the interface between the user and the AR system. They not only receive data from the server, such as virtual content and instructions, but also collect and send data to the server. This data includes information about the room's environmental layout. The first feature data relates to the basic attributes or settings of the room itself (such as the room name and lighting mode), while the second feature data focuses more on the specific furnishing design and item placement within the room.

[0045] In a possible implementation, receiving the second feature data for the first preset room sent by the AR glasses specifically includes: using the AR glasses to scan the first preset room to obtain key feature data, the key feature data including the position and type corresponding to the furniture and the position and type corresponding to the multi-light source lighting fixture; determining the second feature data of the first preset room based on the position and type corresponding to the furniture and the position and type corresponding to the multi-light source lighting fixture.

[0046] Specifically, when a user enters a room wearing AR glasses, the glasses activate a scanning function to comprehensively capture and analyze the room's environment. This scanning is based on image recognition, deep learning, or other advanced computer vision technologies. During the scanning process, the AR glasses identify and extract key feature data from the room. This key feature data primarily includes the location and type of furniture, as well as the location and type of multi-light source lighting fixtures. Furniture and lighting fixtures are important components of a room, and their location and type directly reflect the room's function and style. Once these key feature data are obtained, the AR glasses send this data to a server. Upon receiving this data, the server performs further processing and analysis. Based on the location and type of furniture and lighting fixtures, the server can infer the room's usage, layout style, lighting requirements, and other aspects. Once this information is integrated and summarized, it forms "secondary feature data."

[0047] For example, consider a smart home system scenario where a user wears AR glasses and enters a bedroom, activating the scanning function. The AR glasses perform a comprehensive scan of the bedroom environment, capturing every detail. During the scan, the AR glasses identify key features of the bedroom. For example, they identify the location and type of the bed (single or double), the location and type of the wardrobe (sliding door or swing door), and the location and type of multi-light source lighting fixtures such as bedside lamps and pendant lights. The AR glasses then send this key feature data to a server. This data includes the specific locations (e.g., coordinates) of the bed, wardrobe, and lighting fixtures, as well as their types (e.g., brand and model).

[0048] S140: Determine a target multi-light source lighting display solution corresponding to the first preset room based on the first feature data and the second feature data.

[0049] Specifically, after obtaining the first and second feature data, the server will analyze and calculate these two sets of data to select an optimal lighting display solution and display it directly or in a rolling display to the user. This solution takes into account various factors such as room size, usage, furniture layout, lighting fixture type and location, etc. to ensure that the lighting effect not only matches the overall style of the room but also meets the actual needs of the user.

[0050] For example, when a user enters a living room, considering the living room is 30 square meters and used for family leisure, entertainment, and social gatherings, they scan the living room using AR glasses and discover that it contains a sofa, a coffee table, a TV cabinet, and several different types of lighting fixtures (such as downlights, spotlights, and floor lamps). The sofa and coffee table are near the window, while the TV cabinet is located on the other side of the living room. The lighting fixtures are distributed in different locations in the living room, including near the sofa and along the TV wall. Based on the first and second feature data, the server begins selecting the optimal lighting solution. Taking into account the living room's use and size, the system selects "Party Mode" as the basic lighting mode to ensure sufficient and soft overall lighting. For the sofa area, the server adds several downlights or spotlights to provide a localized, concentrated light effect, making it convenient for reading or chatting. Furthermore, to avoid direct light in the eyes, the angle and brightness of these lamps can be fine-tuned using AR glasses. The TV wall area can be illuminated indirectly using light strips or downlights hidden above the TV cabinet to provide a uniform background light, reducing screen glare and enhancing the viewing experience.

[0051] In one possible embodiment, based on the first feature data and the second feature data, a target multi-light source lighting display scheme corresponding to the first preset room is determined, specifically including: inputting the first feature data and the second feature data into a preset model to obtain a target feature data group, the preset model being pre-trained through a neural network; using the preset model to match the target feature data group to obtain a target multi-light source lighting display scheme corresponding to the target feature data group, the preset model pre-stores the correspondence between the target feature data group and the target multi-light source lighting display scheme.

[0052] Specifically, the preset model is a machine learning model that has been pre-trained through a neural network. The model can predict or generate a target feature data set based on the input feature data. The training process of the preset model involves a large amount of historical data and label data, which are used to guide the model to learn how to predict or generate target data based on the input feature data. The target feature data set is an intermediate result generated by the preset model based on the input first feature data and second feature data. This data set contains the model's understanding and prediction of the room lighting requirements and is the key basis for determining the final lighting display plan. In the preset model, the correspondence between the target feature data set and the target multi-light source lighting display plan is pre-stored. This means that the model has learned how to map different feature data combinations to the most suitable lighting display plan. After the model generates the target feature data set, it will match the corresponding target multi-light source lighting display plan based on the correspondence of this data set in the model.

[0053] In one possible implementation, the multi-light source lighting display solution includes a three-dimensional layout of a first preset room and light distribution, brightness, color, and shadow corresponding to each of the multiple light sources.

[0054] Specifically, a room's three-dimensional spatial structure includes its length, width, and height, as well as the position, size, and orientation of objects within it, such as furniture, doors, windows, and walls. A three-dimensional layout provides a clear understanding of the room's spatial structure, providing a foundation for subsequent light source placement and lighting simulation. In lighting design, the position and number of light sources directly influence light distribution. Light distribution refers to the propagation and distribution of light within a space, including its direction, range, and intensity. Light sources in different locations produce different light distributions, thus affecting the room's lighting effect. Brightness is a measure of the intensity or brightness of the light emitted by a light source. In a lighting display solution, the brightness settings of different light sources directly affect the room's brightness and visual experience. Based on the room's function and user needs, light source brightness can be appropriately set to achieve a comfortable lighting effect. Light source color is also a crucial factor in a lighting display solution. Different light sources can create different atmospheres and moods. For example, warm-toned light sources can create a warm and cozy feeling, while cool-toned light sources can appear fresh and bright. The appropriate light source color can be selected based on the room's function and user preferences. Shadows are dark areas created by light blocked by objects. In lighting design, shadows can enhance the three-dimensionality and layering of a space. However, excessive shadows can also affect the visual quality and comfort of a room. Therefore, the generation and distribution of shadows must be properly controlled in lighting display solutions.

[0055] Therefore, through the 3D layout display, users can intuitively visualize the room's layout structure. This visual presentation helps them better understand the effects and layout of the lighting design. The plan includes information on the light distribution, brightness, color, and shadows of each light source, accurately simulating actual lighting effects. This helps users preview the actual lighting design effect in advance, avoiding unexpected results after installation. By adjusting parameters such as the light distribution, brightness, and color of the light sources, a personalized lighting design can be achieved. Users can tailor a lighting plan to their preferences and needs. Considering shadow information in the plan is crucial for optimizing the lighting effect. By simulating the location and intensity of shadows, unnecessary shadows or dark areas can be avoided, making the room brighter and more comfortable. For non-expert users, the 3D layout and lighting simulation display methods are easier to understand and communicate. These intuitive visual elements allow users to quickly understand the intent and effects of the lighting design, facilitating effective communication and collaboration with designers. By determining the lighting plan at an early stage, unnecessary rework and adjustments can be avoided during the actual installation process, saving costs and time. Furthermore, accurate simulations can reduce on-site commissioning workload, further improving project efficiency.

[0056] S150. Display the target multi-light source lighting display solution through AR glasses.

[0057] Specifically, once the server determines the target multi-light source lighting display scheme, it converts it into a format understandable by the AR glasses (such as a 3D model, light simulation data, etc.). The server then sends this data to the AR glasses. After receiving the data, the AR glasses, using their built-in rendering engine and display technology, overlay these virtual lighting effects on the user's real environment. This allows the user to see an augmented reality scene incorporating virtual lighting effects through the AR glasses. Furthermore, when the user puts on the AR glasses, they no longer see a simple three-dimensional layout of the living room; instead, they see an augmented reality scene overlaid with virtual lighting effects. They can observe how light from light sources in different locations illuminates the entire living room, as well as the shadows and reflections these lights create on objects such as walls and furniture.

[0058] AR glasses allow users to visualize virtual lighting effects directly in their real-world environments. This intuitive display allows users to more accurately evaluate the actual effects of different lamps and lighting solutions. Compared to traditional blueprints or renderings, AR displays can more realistically reproduce the play of light and shadow and the sense of space within a room, giving users a more realistic visual experience. This method allows users to interact in real time through AR glasses, allowing them to view the lighting effects of different settings in real time. This interactivity not only increases user engagement and satisfaction, but also helps users quickly find the lighting solution that best suits their needs. Based on the user's location data, the system can intelligently identify the room the user is in and recommend a corresponding lighting solution. Furthermore, AR displays allow users to quickly try out multiple different lighting solutions, enabling them to make decisions more quickly. Compared to traditional trial-and-error methods, this AR-based display method significantly reduces the user's decision-making costs and time.

[0059] In one possible implementation, refer to Figure 2 , Figure 2 Another flow chart of a multi-light source lighting display method provided in an embodiment of the present application. Before inputting the first feature data and the second feature data into a preset model to obtain a target feature data group, the preset model is trained; training the preset model specifically includes steps S210 to S250, which are as follows: S210, processing the first feature data and the second feature data to obtain multiple feature values; S220, taking a feature value greater than a preset threshold among the multiple feature values ​​as a representative feature value; S230, taking the feature data corresponding to the representative feature value as a training sample; S240, inputting the training sample into the input layer of the neural network, and inputting the corresponding target feature data group into the output layer of the neural network for supervised training to obtain an initial model; S250, performing an accuracy test on the initial model, and selecting a model whose accuracy meets the preset conditions as the preset model.

[0060] Specifically, the first and second feature data are first processed to obtain multiple feature values. This processing may include steps such as feature extraction, feature selection, feature encoding, or normalization to facilitate subsequent model training. Among the multiple feature values, those that exceed a preset threshold are selected as representative feature values. This step aims to identify features that have a significant impact on model training, thereby improving model training efficiency and performance. The feature data corresponding to these representative feature values ​​are used as training samples. These training samples are then used for subsequent neural network training. The training samples are input into the input layer of the neural network, and the corresponding target feature data set (i.e., the desired results that the model is expected to learn) is input into the output layer of the neural network. This process is supervised training, meaning that the model continuously adjusts its internal parameters during training to ensure that its output is as close as possible to the target feature data set. After a certain number of rounds of training, the neural network generates an initial model. This initial model already has a certain level of predictive ability, but may require further optimization. The initial model is then accuracy-tested, using an independent set of test data to evaluate its predictive ability. If the model's accuracy meets the preset conditions (such as accuracy, recall rate, F1 score, etc. reaching a certain level), the model will be used as the default model; otherwise, it may be necessary to adjust the model parameters, change the network structure, or increase the training data to further optimize the model performance.

[0061] This application also provides a multi-light source lighting display device, referring to Figure 3 , Figure 3 A schematic diagram of a module of a multi-light source lighting display device provided in an embodiment of the present application. The multi-light source lighting display device is a server, comprising an acquisition module 31 and a processing module 32. The acquisition module 31 acquires the location data of a user wearing AR glasses; the processing module 32 determines first feature data and a first preset room based on the user's location data. The first preset room is any one of a plurality of preset rooms, each of which is provided with multiple light sources; the acquisition module 31 receives second feature data for the first preset room sent by the AR glasses; the processing module 32 determines a target multi-light source lighting display solution corresponding to the first preset room based on the first feature data and the second feature data; and the processing module 32 displays the target multi-light source lighting display solution through the AR glasses.

[0062] In one possible implementation, obtaining the user's location data specifically includes: an acquisition module 31 receives positioning data sent by the AR glasses; a processing module 32 determines a positioning coordinate point based on the positioning data; the processing module 32 matches the positioning coordinate point with a preset electronic fence area to obtain a target preset electronic fence area to obtain the user's location data, where the preset electronic fence area is pre-set according to the physical space boundaries corresponding to multiple preset rooms.

[0063] In one possible implementation, the processing module 32 determines the first characteristic data and the first preset room based on the user's location data, specifically including: the processing module 32 performs semantic recognition on the user's location data to obtain a target field; the processing module 32 sets the target field to the first characteristic data, and the target field includes the room name of the first preset room.

[0064] In a possible implementation, the acquisition module 31 receives the second feature data for the first preset room sent by the AR glasses, specifically including: the processing module 32 uses the AR glasses to scan the first preset room to obtain key feature data, the key feature data including the position and type corresponding to the furniture and the position and type corresponding to the multi-light source lighting fixture; the processing module 32 determines the second feature data of the first preset room based on the position and type corresponding to the furniture and the position and type corresponding to the multi-light source lighting fixture.

[0065] In one possible embodiment, the processing module 32 determines the target multi-light source lighting display scheme corresponding to the first preset room based on the first feature data and the second feature data, specifically including: the processing module 32 inputs the first feature data and the second feature data into the preset model to obtain a target feature data group, and the preset model is pre-trained by a neural network; the processing module 32 uses the preset model to match the target feature data group to obtain a target multi-light source lighting display scheme corresponding to the target feature data group, and the preset model pre-stores the correspondence between the target feature data group and the target multi-light source lighting display scheme.

[0066] In one possible implementation, the multi-light source lighting display solution includes a three-dimensional layout of a first preset room and light distribution, brightness, color, and shadow corresponding to each of the multiple light sources.

[0067] In one possible embodiment, the processing module 32 trains the preset model before inputting the first feature data and the second feature data into the preset model to obtain the target feature data group; the processing module 32 trains the preset model, specifically including: the processing module 32 processes the first feature data and the second feature data to obtain multiple feature values; the processing module 32 uses the feature values ​​greater than the preset threshold among the multiple feature values ​​as representative feature values; the processing module 32 uses the feature data corresponding to the representative feature values ​​as training samples; the processing module 32 inputs the training samples into the input layer of the neural network, and inputs the corresponding target feature data group into the output layer of the neural network for supervised training to obtain an initial model; the processing module 32 performs accuracy detection on the initial model, and selects a model whose accuracy meets the preset conditions as the preset model.

[0068] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0069] This application also provides an electronic device, referring to Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include: at least one processor 41, at least one network interface 44, a user interface 43, a memory 45, and at least one communication bus 42.

[0070] The communication bus 42 is used to realize the connection and communication between these components.

[0071] The user interface 43 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 43 may also include a standard wired interface and a wireless interface.

[0072] The network interface 44 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0073] The processor 41 may include one or more processing cores. Using various interfaces and circuits, the processor 41 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 45, as well as accesses data stored in the memory 45, to perform various server functions and process data. Optionally, the processor 41 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 41 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may also be implemented as a separate chip, rather than integrated into the processor 41.

[0074] Among them, the memory 45 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 45 includes a non-transitory computer-readable storage medium. The memory 45 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 45 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 45 may also be optionally at least one storage device located away from the aforementioned processor 41. As Figure 4 As shown, the memory 45 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a multi-light source lighting display method.

[0075] exist Figure 4 In the electronic device shown, the user interface 43 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 41 can be used to call an application program for a multi-light source lighting display method stored in the memory 45. When executed by one or more processors, the electronic device executes one or more methods as in the above embodiments.

[0076] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0077] The present application also provides a computer-readable storage medium storing instructions, which, when executed by one or more processors, enable an electronic device to execute one or more of the methods described in the above embodiments.

[0078] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0080] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0081] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0082] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0083] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A multi-light source lighting display method, characterized in that: The method comprises: Obtaining location data of a user, wherein the user is wearing AR glasses; Determine first characteristic data and a first preset room according to the location data of the user, wherein the first preset room is any one of a plurality of preset rooms, and any one of the preset rooms is provided with a plurality of light sources; Receiving second characteristic data for the first preset room sent by the AR glasses; Determine a target multi-light source lighting display solution corresponding to the first preset room based on the first feature data and the second feature data; The target multi-light source lighting display solution is displayed through the AR glasses.

2. The multi-light source lighting display method according to claim 1, characterized in that: The obtaining of the user's location data specifically includes: Receiving positioning data sent by the AR glasses; Determine the positioning coordinate point according to the positioning data; The positioning coordinate point is matched with a preset electronic fence area to obtain a target preset electronic fence area to obtain the location data of the user, wherein the preset electronic fence area is pre-set according to the physical space boundaries corresponding to a plurality of preset rooms.

3. The multi-light source lighting display method according to claim 1, characterized in that: The determining the first characteristic data and the first preset room according to the location data of the user specifically includes: Performing semantic recognition on the user's location data to obtain a target field; The target field is set to the first characteristic data, and the target field includes a room name of the first preset room.

4. The multi-light source lighting display method according to claim 1, characterized in that: The receiving the second characteristic data for the first preset room sent by the AR glasses specifically includes: Scanning the first preset room using AR glasses to obtain key feature data, where the key feature data includes positions and types corresponding to furniture and positions and types corresponding to multi-light source lighting fixtures; According to the position and type corresponding to the furniture and the position and type corresponding to the multi-light source lighting fixture, the second characteristic data of the first preset room is determined and obtained.

5. The multi-light source lighting display method according to claim 1, characterized in that: The determining, based on the first feature data and the second feature data, a target multi-light source lighting display scheme corresponding to the first preset room specifically includes: Inputting the first feature data and the second feature data into a preset model to obtain a target feature data group, wherein the preset model is obtained in advance through neural network training; The preset model is used to match the target feature data group to obtain the target multi-light source lighting display scheme corresponding to the target feature data group, and the preset model pre-stores the corresponding relationship between the target feature data group and the target multi-light source lighting display scheme.

6. The multi-light source lighting display method according to claim 1, characterized in that: The multi-light source lighting display solution includes the three-dimensional layout of the first preset room and the light distribution, brightness, color, and shadow corresponding to each of the multiple light sources.

7. The multi-light source lighting display method according to claim 5, characterized in that: Before inputting the first feature data and the second feature data into a preset model to obtain a target feature data group, training the preset model; the training of the preset model specifically includes: Processing the first feature data and the second feature data to obtain a plurality of feature values; Taking a characteristic value greater than a preset threshold value among the plurality of characteristic values ​​as a representative characteristic value; Taking the feature data corresponding to the representative feature value as a training sample; Inputting the training samples into the input layer of the neural network, and inputting the corresponding target feature data group into the output layer of the neural network for supervised training to obtain an initial model; The initial model is tested for accuracy, and a model whose accuracy meets a preset condition is selected as the preset model.

8. A multi-light source lighting display device, characterized in that: The multi-light source lighting display device comprises an acquisition module (31) and a processing module (32), wherein: The acquisition module (31) is used to acquire the location data of a user wearing AR glasses; The processing module (32) is used to determine first characteristic data and a first preset room according to the location data of the user, wherein the first preset room is any one of a plurality of preset rooms, and any one of the preset rooms is provided with a plurality of light sources; The acquisition module (31) is further used to receive second characteristic data for the first preset room sent by the AR glasses; The processing module (32) is further used to determine a target multi-light source lighting display scheme corresponding to the first preset room based on the first characteristic data and the second characteristic data; The processing module (32) is also used to display the target multi-light source lighting display solution through the AR glasses.

9. An electronic device, characterized in that: The electronic device comprises a processor (41), a memory (45), a user interface (43) and a network interface (44), wherein the memory (45) is used to store instructions, the user interface (43) and the network interface (44) are both used to communicate with other devices, and the processor (41) is used to execute the instructions stored in the memory (45) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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