Twin scene light dynamic adjustment method and device, computer device and storage medium

Abstract

The application discloses a twin scene light dynamic adjustment method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring data collected by a sensor in a target environment and user preference information for light effects in different scenes; creating a digital twin model corresponding to the target environment; mapping the data into the digital twin model; generating a light configuration scheme by using a light configuration model in combination with the data; and dynamically adjusting the light in the target environment according to the light configuration scheme. By implementing the method of the application, the problem that the existing light configuration mode often relies on manual setting, which not only leads to insufficient flexibility, but also affects real-time response capability, and is difficult to meet the diversified needs in dynamic scenes can be solved.

Description

Technical Field

[0001] This invention relates to digital twin systems, and more specifically to a method, apparatus, computer equipment, and storage medium for dynamically adjusting lighting in a twin scene. Background Technology

[0002] With the rapid development of technology, digital twin technology, as an emerging integrated technology, has become a key force driving the intelligent transformation of various industries. By creating virtual models of physical systems, digital twin technology achieves a seamless connection between the physical and digital worlds, making it possible to simulate, predict, and optimize the performance of physical systems. This technology has not only shown enormous potential in traditional industrial sectors such as manufacturing and aerospace, but has also found wide application scenarios in emerging fields such as architecture, entertainment, and virtual reality, especially in the intelligent configuration of lighting effects, where its importance is increasingly prominent.

[0003] Traditional lighting configuration primarily relies on manual settings, where technicians manually adjust parameters such as the position, angle, and color of the lights according to a predetermined design. While this approach met basic needs in the past, it has revealed significant limitations. First, manual settings lack flexibility; once the design is finalized, it's difficult to quickly adapt to new requirements or environmental changes. Second, slow response time is a significant issue; manual adjustments are clearly insufficient to respond promptly to unexpected events or situations requiring immediate changes to lighting effects. Furthermore, due to the lack of effective monitoring and feedback mechanisms, traditional methods struggle to ensure consistency between lighting effects and the intended goals, especially in large or complex scenarios, where such inconsistencies can severely degrade the user experience.

[0004] Therefore, it is necessary to design a new method to address the problem that existing lighting configuration methods often rely on manual settings, which not only leads to insufficient flexibility but also affects real-time response capabilities and makes it difficult to meet the diverse needs of dynamic scenarios. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, device, computer equipment and storage medium for dynamic adjustment of lighting in twin scenes.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for dynamic adjustment of lighting in a twin scene, comprising:

[0007] Acquire data collected by sensors within the target environment and user preferences for lighting effects in different scenarios;

[0008] Create a digital twin model corresponding to the target environment;

[0009] Map the data to the digital twin model;

[0010] A lighting configuration scheme is generated by combining the lighting configuration model with the aforementioned data.

[0011] The lighting in the target environment is dynamically adjusted according to the lighting configuration scheme.

[0012] A further technical solution is as follows: after dynamically adjusting the lighting in the target environment according to the lighting configuration scheme, it also includes:

[0013] Obtain user feedback information;

[0014] The lighting configuration model is updated based on the feedback information.

[0015] The further technical solution is as follows: the sensor includes a light intensity sensor.

[0016] The further technical solution is as follows: the creation of the digital twin model corresponding to the target environment includes:

[0017] A digital twin model is obtained by using CAD or BIM software to construct a three-dimensional model of the building and its interior space corresponding to the target environment.

[0018] The further technical solution is as follows: the lighting configuration model is obtained by collecting historical lighting usage data and user preference data, extracting key features through feature engineering, and then training a supervised learning model.

[0019] Its further technical solution is as follows: The lighting configuration model is obtained by collecting historical lighting usage data and user preference data, extracting key features through feature engineering, and then training a deep neural network, including:

[0020] Obtain historical lighting usage data and user preference data to obtain initial data;

[0021] The initial data is preprocessed to obtain the preprocessing result;

[0022] The preprocessing results are analyzed using an unsupervised learning model to obtain potential user preferences and behavioral patterns;

[0023] Feature engineering is used to extract key features from the preprocessed results and potential user preferences and behavioral patterns.

[0024] Construct a supervised learning model;

[0025] The supervised learning model is trained using the key features to obtain the lighting configuration model.

[0026] The further technical solution is as follows: the key features extracted from the preprocessed results and potential user preferences and behavioral patterns using feature engineering include:

[0027] Unsupervised learning methods are used to reduce the data dimensionality of the preprocessed results and potential user preferences and behavioral patterns, and important key features are extracted to obtain important key features.

[0028] Clustering algorithms are applied to identify user behavior patterns within the aforementioned key features in order to obtain new features;

[0029] The important key features and the new features are combined to form key features.

[0030] This invention also provides a method for dynamically adjusting lighting in a twin scene, including:

[0031] The data acquisition unit is used to acquire data collected by sensors within the target environment.

[0032] A creation unit is used to create a digital twin model corresponding to the target environment;

[0033] A mapping unit is used to map the data onto the digital twin model;

[0034] The scheme generation unit is used to generate a lighting configuration scheme by combining the lighting configuration model with the data.

[0035] An adjustment unit is used to dynamically adjust the lighting in the target environment according to the lighting configuration scheme.

[0036] The present invention also provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the above-described method.

[0037] The present invention also provides a storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0038] The advantages of this invention compared to existing technologies are as follows: By acquiring sensor data and user lighting preference information, this invention first creates a digital twin model of the target environment to ensure a virtual representation of the environment. Then, these data are mapped into the digital twin model, providing a foundation for subsequent analysis. Using a lighting configuration model and combining the collected data, a lighting configuration scheme that meets the current requirements is automatically generated. By dynamically adjusting the lighting configuration scheme, real-time response and optimization of lighting effects are achieved. Ultimately, this intelligent configuration method not only improves flexibility but also addresses diverse dynamic scene requirements.

[0039] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A flowchart illustrating the method for dynamically adjusting lighting in a twin scene provided in an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of a sub-process of the method for dynamically adjusting lighting in a twin scene provided in an embodiment of the present invention;

[0043] Figure 3 This is a schematic block diagram of a twin scene lighting dynamic adjustment device provided in an embodiment of the present invention;

[0044] Figure 4 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0047] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0048] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0049] Please see Figure 1 , Figure 1 This is a schematic flowchart illustrating the dynamic lighting adjustment method for a digital twin scene provided in this invention. This method is applied to a server that interacts with a lighting controller. The intelligent lighting configuration based on digital twin technology offers significant advantages, reducing labor costs and improving resource utilization efficiency, thereby achieving cost reduction and efficiency improvement, and providing users with a better experience and more efficient management solutions. This method creates a virtual model highly consistent with the actual environment, enabling real-time adjustment and optimization of lighting configuration within the virtual environment. Specifically, environmental data and user preferences are collected through sensors and input into the digital twin model. The model then analyzes this data using machine learning algorithms to automatically generate the optimal lighting configuration. This intelligent configuration method reduces manual intervention, flexibly responds to environmental changes, and improves the accuracy of lighting management. Furthermore, digital twin technology supports comprehensive monitoring and feedback of lighting effects. The system can automatically identify and make corresponding adjustments when the environment changes or user needs are adjusted, ensuring that the lighting effect remains optimal while reducing energy consumption and promoting sustainable development. This intelligent management not only improves user experience but also reduces operating costs, thus gaining an advantage in a highly competitive market.

[0050] Figure 1 This is a flowchart illustrating the method for dynamically adjusting lighting in a twin scene provided in an embodiment of the present invention. Figure 1 As shown, the method includes the following steps S110 to S170.

[0051] S110. Acquire data collected by sensors within the target environment.

[0052] In this embodiment, the sensor includes a light intensity sensor.

[0053] Specifically, with the development of Internet of Things (IoT) technology, intelligent environmental monitoring has become possible. In specific target environments (such as smart homes, smart agriculture, and industrial production sites), various types of sensors are deployed to monitor various environmental parameters. These sensors are not limited to a single type, but include, but are not limited to, light intensity sensors, temperature and humidity sensors, motion sensors, and other sensors. They work together to achieve comprehensive, multi-dimensional monitoring of the environment.

[0054] Among them, light intensity sensors measure the intensity of light in the environment, which is particularly important for applications requiring precise control of lighting conditions, such as plant growth chambers and photography studios. Temperature and humidity sensors monitor both temperature and humidity, making them an indispensable part of many applications, especially in places where constant temperature and humidity need to be maintained, such as archives and laboratories. Motion sensors detect whether moving objects or people enter the monitored area and are widely used in security systems, smart homes, and other fields.

[0055] First, strategically deploy the aforementioned sensors within the target environment. Select the appropriate sensor type and quantity based on specific needs, and determine the optimal installation location to ensure accurate capture of the required environmental information. The sensors transmit the collected data to the gateway device in real time via built-in wireless communication modules (such as Wi-Fi, Bluetooth, Zigbee, etc.) or wired connections. The gateway device acts as an intermediary, responsible for uploading the received sensor data to a cloud server via the internet. This process may utilize lightweight messaging protocols such as MQTT to improve data transmission efficiency and reduce network latency. In the cloud, the received raw data is stored and preprocessed as necessary, such as data cleaning and format conversion, to facilitate subsequent in-depth analysis and application development.

[0056] The system collects user preferences for lighting effects in different scenarios through the user interface and combines this information with environmental data to provide a basis for subsequent intelligent configuration.

[0057] S120. Create a digital twin model corresponding to the target environment.

[0058] In this embodiment, CAD or BIM software is used to construct a 3D model of the building and its interior space corresponding to the target environment to obtain a digital twin model. This ensures that the model is highly consistent with the actual environment.

[0059] Specifically, creating a digital twin model of the target environment is a crucial component of the entire system design. A digital twin model is a virtual copy of the target environment constructed using computer-aided design (CAD) or building information modeling (BIM) software. This copy includes not only a three-dimensional model of the physical structure but also attribute information of various elements within the environment and their interrelationships. During the creation process, the first step is to collect relevant information about the target environment, including architectural design drawings, environmental layout diagrams, and material lists. This information will provide the foundation for building an accurate digital twin model. Suitable CAD or BIM software, such as Autodesk Revit, SketchUp, and AutoCAD, is selected. These software programs offer a wealth of modeling tools and functions to meet modeling needs with varying levels of precision. Based on the architectural design drawings, the selected software is used to construct the building's external structural model, including elements such as walls, roofs, windows, and doors. The interior space design is refined, dividing the space into different rooms, corridors, staircases, and other areas according to the environmental layout diagram, ensuring the functionality and accessibility of each space. Other important elements from the environment, such as furniture, decorations, and mechanical equipment, are added to the model to make it richer and more realistic. Detailed attribute information is defined for each object in the model, including but not limited to size, material, color, brand, and model number. For variable attributes (such as temperature, humidity, and light intensity), the corresponding values ​​in the model can be updated through real-time synchronization with sensor data to maintain consistency between the model and the actual situation. The constructed digital twin model is then compared and verified with the actual environment to ensure its accuracy. This step may require multiple iterations until the model closely matches the real-world environment. Based on actual usage, the model should be continuously adjusted and optimized to improve its performance, such as increasing loading speed and enhancing the user experience.

[0060] S130. Map the data into the digital twin model.

[0061] In this embodiment, the data stream from the sensor is connected to the digital twin model to enable real-time data visualization and analysis.

[0062] S140. A lighting configuration scheme is generated by combining the lighting configuration model with the data.

[0063] In this embodiment, the lighting configuration scheme refers to a lighting setup plan customized according to the user's specific needs and preferences. This scheme typically considers multiple factors, such as time, activity type, and ambient light intensity, aiming to create the most suitable lighting environment for the current scenario by intelligently adjusting factors such as brightness, color temperature, and color of the lights. An effective lighting configuration scheme can improve the comfort of living or working, enhance visual experience, and even affect a person's mood and health.

[0064] In a smart home environment, lighting configuration schemes can be executed automatically by the smart system or manually selected through the user interface. The system dynamically adjusts the lighting settings based on the user's preference history, current activity type (such as reading, resting, or socializing), and external conditions (such as changes in natural light). For example:

[0065] Reading mode: The brightness may be increased and a cool white light option may be selected to reduce eye strain.

[0066] Sleep mode: Reduce brightness and choose warm colors to create a relaxing atmosphere and help you fall asleep.

[0067] Entertainment Mode: Automatically adjusts the color and brightness of the lights according to the content being played, enhancing the viewing experience.

[0068] The process of generating lighting configuration schemes involves multiple steps, including data collection, user preference analysis, and model training. The goal is to transform lighting from merely fulfilling basic illumination needs into a vital element that enhances quality of life and user experience. Through the application of intelligent algorithms, lighting configuration schemes can be personalized to meet the unique needs of different users in various scenarios.

[0069] The lighting configuration model is obtained by collecting historical lighting usage data and user preference data, extracting key features through feature engineering, and then training a supervised learning model.

[0070] In one embodiment, please refer to Figure 2 The lighting configuration model is obtained by collecting historical lighting usage data and user preference data, extracting key features through feature engineering, and then training a supervised learning model, which may include steps S141 to S146.

[0071] S141. Obtain historical lighting usage data and user preference data to obtain initial data.

[0072] In this embodiment, historical lighting usage data includes information such as the time, brightness, color, and scene in which the user used the lights. User preference data includes user feedback and selection records for different lighting settings.

[0073] This data can be collected automatically through smart home systems or obtained through user surveys and other methods.

[0074] S142. The initial data is preprocessed to obtain the preprocessing result.

[0075] In this embodiment, the goal of preprocessing is to improve the quality of the data, preparing it for subsequent feature engineering and model training. This step includes:

[0076] Data cleaning and data transformation are two main processes. Data cleaning refers to removing or imputing missing values ​​and removing outliers. Data transformation refers to encoding categorical variables (such as one-hot encoding) and standardizing or normalizing continuous variables.

[0077] S143. Analyze the preprocessing results using an unsupervised learning model to obtain potential user preferences and behavioral patterns.

[0078] In this embodiment, unsupervised learning methods are used to analyze preprocessed data in order to discover potential user preferences and behavioral patterns. Commonly used methods include Principal Component Analysis (PCA) and clustering algorithms (such as K-Means). PCA is used for dimensionality reduction to help identify major trends in the data. Clustering algorithms (such as K-Means) are used to identify natural groupings in the data, which helps to understand the differences between different user groups.

[0079] S144. Use feature engineering to extract key features from the preprocessed results and potential user preferences and behavioral patterns.

[0080] In one embodiment, step S144 described above may include steps S1441 to S1443.

[0081] S1441. Use unsupervised learning methods to reduce the data dimensionality of the preprocessed results and potential user preferences and behavior patterns, and extract important key features to obtain important key features.

[0082] In this embodiment, unsupervised learning methods are first used to process the preprocessed data. Unsupervised learning refers to allowing machines to discover and learn patterns or regularities in data without pre-defined labels. In this application scenario, the main purpose is to reduce data dimensionality while retaining the features that best reflect the data's characteristics. Commonly used data dimensionality reduction techniques include Principal Component Analysis (PCA), Independent Component Analysis (ICA), and t-distributed random neighborhood embedding (t-SNE). These methods can extract important key features from the original data, which are crucial for subsequent user behavior pattern recognition.

[0083] By applying unsupervised learning methods to reduce the dimensionality of the preprocessed data and potential user preferences and behavioral patterns, features crucial for identifying user behavior patterns can be identified. These features help reduce computational complexity while maintaining or improving model performance.

[0084] S1442. Apply clustering algorithms to identify user behavior patterns within the important key features to obtain new features;

[0085] In this embodiment, the new features are user behavior patterns identified through algorithms such as clustering, based on further analysis of important key features. These features are derived from existing data and represent a certain pattern or trend of user behavior, which may not appear directly in the original data.

[0086] New features identified through clustering algorithms can reveal the segmented structure within user groups, helping to understand the similarities and differences between different users. For example, in recommender systems, new features can help more accurately locate user groups and achieve more personalized services; in anomaly detection scenarios, new features may help identify user activities that do not conform to normal behavioral patterns, thereby providing timely warnings of potential risks.

[0087] Specifically, after obtaining the key features, the next step is to apply clustering algorithms to these features. Clustering is a common unsupervised learning method that attempts to divide samples in a dataset into several groups (or clusters), maximizing the similarity of samples within the same group and minimizing the similarity between samples in different groups. Through clustering algorithms, commonalities in user behavior patterns can be identified, i.e., new features. Common clustering algorithms include K-means, hierarchical clustering, and DBSCAN. This step helps to gain a deeper understanding of user preferences and behavioral patterns, providing a basis for further optimizing lighting configuration schemes.

[0088] S1443. Combine the important key features and the new features to form key features.

[0089] In this embodiment, the key features extracted from S1441 are finally combined with the new features identified by the clustering algorithm in S1442 to form the final set of key features. This set not only contains important information from the original data but also incorporates behavioral pattern features derived from cluster analysis, enabling a more comprehensive and accurate description of user behavior habits and preferences. These key features will serve as the foundation for constructing personalized lighting configuration schemes, helping the system better understand and predict user needs, thereby providing more personalized services.

[0090] By following these three steps, valuable user preferences and behavioral patterns can be effectively extracted from large amounts of data, providing strong support for subsequent intelligent decision-making.

[0091] S145. Construct a supervised learning model.

[0092] In this embodiment, a suitable supervised learning model is selected based on the requirements of the task, such as random forest, support vector machine (SVM), deep learning model, etc. The choice of model depends on the characteristics of the data and the business objectives.

[0093] S146. Train the supervised learning model using the key features to obtain the lighting configuration model.

[0094] In this embodiment, the selected supervised learning model is trained using the key feature set determined in the previous step. During training, it is important to note the following: find the optimal model parameters using methods such as cross-validation; evaluate model performance using metrics such as accuracy, recall, and F1-score to ensure the model performs well even on unseen data.

[0095] Once the model is trained and optimized, it can be used to generate personalized lighting configuration suggestions based on the user's current environment (such as time and activity type) and personal historical preferences. Furthermore, a real-time feedback mechanism should be established to allow users to evaluate the recommended lighting configurations, enabling continuous model optimization and improved user experience.

[0096] This approach not only generates lighting configurations that match individual user preferences, but also evolves as user habits change, providing more intelligent services.

[0097] S150. Dynamically adjust the lighting in the target environment according to the lighting configuration scheme.

[0098] In this embodiment, the lighting settings in the target environment are automatically adjusted according to a predetermined lighting configuration scheme to meet the current environmental conditions and the expected user experience standards.

[0099] Ambient light is monitored in real time using light sensors to capture ambient light intensity and other relevant parameters, such as temperature and humidity, to gain a comprehensive understanding of the environment. Historical data and machine learning models are used to predict potential user needs, such as the preferred light brightness and color temperature during specific time periods. Based on the monitored environmental data and predicted user needs, the brightness, color, and mode of the lights are automatically adjusted. For example, when natural light intensity increases, the brightness of artificial lights is appropriately reduced; or when a user is detected entering the room, the lights are automatically turned on and adjusted to the user's most frequently used settings.

[0100] S160. Obtain user feedback information.

[0101] In this embodiment, user satisfaction feedback on the current lighting settings is collected as an important basis for optimizing the lighting configuration.

[0102] Provide a user-friendly interface that allows users to quickly express their opinions on the current lighting effects via buttons, touchscreens, etc., including satisfaction, dissatisfaction, or specific suggestions for improvement. Introduce a quantitative rating system (e.g., 1 to 5 points) to allow users to express their satisfaction levels more specifically, facilitating quantitative analysis by the system. All user feedback will be recorded, along with a timestamp and the environmental conditions at the time, for subsequent data analysis and model training.

[0103] S170. Update the lighting configuration model based on the feedback information.

[0104] In this embodiment, the lighting configuration model is continuously optimized and updated based on user feedback to better meet user needs and improve user experience.

[0105] Statistical methods and machine learning techniques are used to conduct in-depth analysis of collected user feedback data, identifying key factors and patterns affecting user satisfaction. Based on the analysis results, existing lighting configuration model parameters are adjusted, such as improving user behavior prediction algorithms and optimizing the automatic adjustment logic of light brightness and color temperature. For frequent system users, personalized lighting profiles can be created based on their historical feedback and personal preferences, achieving more precise service. After updating the model, simulation tests and field trials are conducted to verify the effectiveness of model optimization, ensuring the stability and reliability of the system under different environmental conditions. A continuous feedback loop is established to encourage users to provide more feedback, supporting continuous iteration and optimization of the model, ultimately achieving the best user experience.

[0106] Through the above steps, the system can not only achieve intelligent management of lighting in the target environment, but also provide a lighting experience that better meets user expectations through continuous learning and optimization.

[0107] In this embodiment, an aesthetically pleasing and practical user interface is designed to enable users to easily interact with the intelligent lighting system while providing necessary information feedback to enhance the user experience.

[0108] Adopting a modern, minimalist style, the interface minimizes unnecessary icons and text descriptions, ensuring users can easily understand how to operate the system. It offers multiple preset modes, such as reading, relaxation, and party modes, each with specific preset brightness, color temperature, and light distribution to meet the needs of different scenarios. Users can manually adjust parameters such as brightness and color temperature to personalize their setup. Controls such as sliders and knobs make the adjustment process intuitive and simple. A dedicated area on the interface displays the current lighting status (e.g., brightness, color temperature) and environmental parameters (e.g., light intensity, temperature, humidity), allowing users to stay informed about changes in their surroundings. An integrated user feedback function allows users to submit opinions or suggestions on the current lighting settings directly from the interface, promoting continuous system optimization.

[0109] By accurately monitoring and intelligently controlling energy consumption, unnecessary energy consumption is reduced while maintaining good lighting effects, achieving the goal of energy conservation and environmental protection. The system has a built-in energy consumption monitoring function that records the power consumption of each light fixture in real time, helping managers understand energy usage. Based on factors such as ambient light intensity and user activity patterns, the system automatically adjusts the operating status of the lights, such as reducing the output of artificial light sources when natural light is abundant, or automatically turning off lights in unoccupied areas. An energy-saving mode option is provided, which users can choose to activate during specific periods to further reduce energy consumption, suitable for nighttime or low-activity periods.

[0110] Intelligent management and energy-saving measures not only improve user experience but also reduce operating costs, enhance competitiveness, and contribute to environmental protection. Intelligent lighting control creates comfortable living and working environments, improving users' quality of life and work efficiency. Optimized energy use significantly reduces electricity bills and lowers long-term operating costs, bringing economic benefits to businesses and households. Reducing energy waste and carbon emissions helps address climate change and promotes sustainable development. In a society increasingly focused on environmental protection and energy conservation, products with these characteristics are more likely to gain consumer favor and help brands stand out in the market.

[0111] In conclusion, the combination of a user-friendly interface and energy-saving and sustainable development characteristics can not only provide a superior user experience but also bring economic benefits and social value, making it an important consideration in the design of intelligent lighting systems.

[0112] The aforementioned method for dynamically adjusting lighting in a digital twin environment first creates a digital twin model of the target environment by acquiring sensor data and user lighting preference information, ensuring a virtual representation of the environment. Then, this data is mapped into the digital twin model, providing a foundation for subsequent analysis. Using a lighting configuration model and the collected data, a lighting configuration scheme tailored to the current needs is automatically generated. By dynamically adjusting the lighting configuration scheme, real-time response and optimization of lighting effects are achieved. Ultimately, this intelligent configuration method not only improves flexibility but also addresses diverse dynamic scene requirements.

[0113] Figure 3 This is a schematic block diagram of a twin scene lighting dynamic adjustment device 300 provided in an embodiment of the present invention. Figure 3 As shown, corresponding to the above-described method for dynamically adjusting lighting in a twin scene, the present invention also provides a device 300 for dynamically adjusting lighting in a twin scene. This device 300 includes a unit for executing the above-described method for dynamically adjusting lighting in a twin scene, and the device can be configured in a server. Specifically, please refer to... Figure 3 The twin scene lighting dynamic adjustment device 300 includes a data acquisition unit 301, a creation unit 302, a mapping unit 303, a scheme generation unit 304, and an adjustment unit 305.

[0114] The data acquisition unit 301 is used to acquire data collected by sensors in the target environment; the creation unit 302 is used to create a digital twin model corresponding to the target environment; the mapping unit 303 is used to map the data to the digital twin model; the scheme generation unit 304 is used to generate a lighting configuration scheme by combining the data with a lighting configuration model; and the adjustment unit 305 is used to dynamically adjust the lighting in the target environment according to the lighting configuration scheme.

[0115] Also includes:

[0116] Information acquisition unit 306 is used to acquire user feedback information;

[0117] The update unit 307 is used to update the lighting configuration model based on the feedback information.

[0118] In one embodiment, the creation unit 302 is used to construct a three-dimensional model of the building and its interior space corresponding to the target environment using CAD or BIM software, so as to obtain a digital twin model.

[0119] In one embodiment, a model training unit is also included, which is used to train a deep neural network by collecting historical lighting usage data and user preference data, extracting key features through feature engineering, and then obtaining a lighting configuration model.

[0120] In one embodiment, the model training unit includes:

[0121] The initial data acquisition subunit is used to acquire historical lighting usage data and user preference data to obtain initial data;

[0122] A preprocessing subunit is used to preprocess the initial data to obtain a preprocessing result;

[0123] The analysis subunit is used to analyze the preprocessing results using an unsupervised learning model to obtain potential user preferences and behavioral patterns;

[0124] An extraction subunit is used to extract key features from the preprocessed results and potential user preferences and behavioral patterns using feature engineering.

[0125] Construct sub-units for building supervised learning models;

[0126] The training subunit is used to train the supervised learning model using the key features to obtain the lighting configuration model.

[0127] In one embodiment, the extraction subunit is configured to use an unsupervised learning method to reduce the data dimensionality of the preprocessed results and potential user preferences and behavior patterns, and extract important key features to obtain important key features; apply a clustering algorithm to identify user behavior patterns within the important key features to obtain new features; and combine the important key features and the new features to form key features.

[0128] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned twin scene lighting dynamic adjustment device 300 and each unit can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0129] The aforementioned twin scene lighting dynamic adjustment device 300 can be implemented as a computer program, which can, for example... Figure 4 It runs on the computer device shown.

[0130] Please see Figure 4 , Figure 4 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 can be a server, wherein the server can be a standalone server or a server cluster composed of multiple servers.

[0131] See Figure 4 The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0132] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a twin scene lighting dynamic adjustment method.

[0133] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0134] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a twin scene lighting dynamic adjustment method.

[0135] This network interface 505 is used for network communication with other devices. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0136] The processor 502 is used to run a computer program 5032 stored in the memory to perform the following steps:

[0137] Acquire data collected by sensors within the target environment and user preferences for lighting effects in different scenarios; create a digital twin model corresponding to the target environment; map the data into the digital twin model; generate a lighting configuration scheme by combining the data with a lighting configuration model; and dynamically adjust the lighting within the target environment according to the lighting configuration scheme.

[0138] The sensor includes a light intensity sensor.

[0139] The lighting configuration model is obtained by collecting historical lighting usage data and user preference data, extracting key features through feature engineering, and then training a supervised learning model.

[0140] In one embodiment, after implementing the step of dynamically adjusting the lighting in the target environment according to the lighting configuration scheme, the processor 502 further implements the following steps:

[0141] Obtain user feedback; update the lighting configuration model based on the feedback.

[0142] In one embodiment, when implementing the step of creating a digital twin model corresponding to the target environment, the processor 502 specifically implements the following steps:

[0143] A digital twin model is obtained by using CAD or BIM software to construct a three-dimensional model of the building and its interior space corresponding to the target environment.

[0144] In one embodiment, when the processor 502 implements the step of collecting historical lighting usage data and user preference data, extracting key features through feature engineering, and then training a deep neural network, the specific steps are as follows:

[0145] Historical lighting usage data and user preference data are acquired to obtain initial data; the initial data is preprocessed to obtain preprocessing results; the preprocessing results are analyzed using an unsupervised learning model to obtain potential user preferences and behavioral patterns; key features in the preprocessing results and potential user preferences and behavioral patterns are extracted using feature engineering; a supervised learning model is constructed; the supervised learning model is trained using the key features to obtain a lighting configuration model.

[0146] In one embodiment, when implementing the step of extracting key features from the preprocessed results and potential user preferences and behavioral patterns using feature engineering, the processor 502 specifically implements the following steps:

[0147] Unsupervised learning methods are used to reduce the data dimensionality of the preprocessed results and potential user preferences and behavior patterns, and important key features are extracted to obtain important key features; clustering algorithms are applied to identify user behavior patterns within the important key features to obtain new features; the important key features and the new features are combined to form key features.

[0148] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0149] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0150] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein when executed by a processor, the computer program causes the processor to perform the following steps:

[0151] Acquire data collected by sensors within the target environment and user preferences for lighting effects in different scenarios; create a digital twin model corresponding to the target environment; map the data into the digital twin model; generate a lighting configuration scheme by combining the data with a lighting configuration model; and dynamically adjust the lighting within the target environment according to the lighting configuration scheme.

[0152] The sensor includes a light intensity sensor.

[0153] The lighting configuration model is obtained by collecting historical lighting usage data and user preference data, extracting key features through feature engineering, and then training a supervised learning model.

[0154] In one embodiment, after executing the computer program to implement the step of dynamically adjusting the lighting in the target environment according to the lighting configuration scheme, the processor further implements the following steps:

[0155] Obtain user feedback; update the lighting configuration model based on the feedback.

[0156] In one embodiment, when the processor executes the computer program to implement the step of creating a digital twin model corresponding to the target environment, it specifically implements the following steps:

[0157] A digital twin model is obtained by using CAD or BIM software to construct a three-dimensional model of the building and its interior space corresponding to the target environment.

[0158] In one embodiment, when the processor executes the computer program to implement the lighting configuration model, which is obtained by collecting historical lighting usage data and user preference data, extracting key features through feature engineering, and then training a deep neural network, the specific steps are as follows:

[0159] Historical lighting usage data and user preference data are acquired to obtain initial data; the initial data is preprocessed to obtain preprocessing results; the preprocessing results are analyzed using an unsupervised learning model to obtain potential user preferences and behavioral patterns; key features in the preprocessing results and potential user preferences and behavioral patterns are extracted using feature engineering; a supervised learning model is constructed; the supervised learning model is trained using the key features to obtain a lighting configuration model.

[0160] In one embodiment, when the processor executes the computer program to implement the step of extracting key features from the preprocessed results and potential user preferences and behavioral patterns using feature engineering, it specifically implements the following steps:

[0161] Unsupervised learning methods are used to reduce the data dimensionality of the preprocessed results and potential user preferences and behavior patterns, and important key features are extracted to obtain important key features; clustering algorithms are applied to identify user behavior patterns within the important key features to obtain new features; the important key features and the new features are combined to form key features.

[0162] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0163] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0164] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0165] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0166] 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 storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part 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 storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0167] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for dynamically adjusting lighting in a twin scene, characterized in that, include: Acquire data collected by sensors within the target environment and user preferences for lighting effects in different scenarios; Create a digital twin model corresponding to the target environment; Map the data to the digital twin model; A lighting configuration scheme is generated by combining the lighting configuration model with the aforementioned data. The lighting in the target environment is dynamically adjusted according to the lighting configuration scheme. The lighting configuration model is obtained by collecting historical lighting usage data and user preference data, extracting key features through feature engineering, and then training a supervised learning model. The lighting configuration model is obtained by collecting historical lighting usage data and user preference data, extracting key features through feature engineering, and then training a deep neural network, including: Obtain historical lighting usage data and user preference data to obtain initial data; The initial data is preprocessed to obtain the preprocessing result; The preprocessing results are analyzed using an unsupervised learning model to obtain potential user preferences and behavioral patterns; Feature engineering is used to extract key features from the preprocessed results and potential user preferences and behavioral patterns. Construct a supervised learning model; The supervised learning model is trained using the key features to obtain the lighting configuration model; The feature engineering process extracts key features from the preprocessed results and potential user preferences and behavioral patterns, including: Unsupervised learning methods are used to reduce the data dimensionality of the preprocessed results and potential user preferences and behavioral patterns, and important key features are extracted to obtain important key features. Clustering algorithms are applied to identify user behavior patterns within the aforementioned key features in order to obtain new features; The important key features and the new features are combined to form key features.

2. The method for dynamically adjusting lighting in a twin scene according to claim 1, characterized in that, After dynamically adjusting the lighting in the target environment according to the lighting configuration scheme, the method further includes: Obtain user feedback information; The lighting configuration model is updated based on the feedback information.

3. The method for dynamically adjusting lighting in a twin scene according to claim 1, characterized in that, The sensor includes a light intensity sensor.

4. The method for dynamically adjusting lighting in a twin scene according to claim 1, characterized in that, The creation of the digital twin model corresponding to the target environment includes: A digital twin model is obtained by using CAD or BIM software to construct a three-dimensional model of the building and its interior space corresponding to the target environment.

5. A method for dynamically adjusting lighting in a twin scene, characterized in that, include: The data acquisition unit is used to acquire data collected by sensors within the target environment. A creation unit is used to create a digital twin model corresponding to the target environment; A mapping unit is used to map the data onto the digital twin model; The scheme generation unit is used to generate a lighting configuration scheme by combining the lighting configuration model with the data. The adjustment unit is used to dynamically adjust the lighting in the target environment according to the lighting configuration scheme; The lighting configuration model is obtained by collecting historical lighting usage data and user preference data, extracting key features through feature engineering, and then training a supervised learning model. The lighting configuration model is obtained by collecting historical lighting usage data and user preference data, extracting key features through feature engineering, and then training a deep neural network, including: Obtain historical lighting usage data and user preference data to obtain initial data; The initial data is preprocessed to obtain the preprocessing result; The preprocessing results are analyzed using an unsupervised learning model to obtain potential user preferences and behavioral patterns; Feature engineering is used to extract key features from the preprocessed results and potential user preferences and behavioral patterns. Construct a supervised learning model; The supervised learning model is trained using the key features to obtain the lighting configuration model; The feature engineering process extracts key features from the preprocessed results and potential user preferences and behavioral patterns, including: Unsupervised learning methods are used to reduce the data dimensionality of the preprocessed results and potential user preferences and behavioral patterns, and important key features are extracted to obtain important key features. Clustering algorithms are applied to identify user behavior patterns within the aforementioned key features in order to obtain new features; The important key features and the new features are combined to form key features.

6. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 4.

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