Intelligent light control method and system

Abstract

The invention discloses an intelligent light control method and system, and the method comprises the steps: monitoring various environment data of the indoor environment of a user through the Internet of Things, and the position and behavior data of the indoor user; according to the environment data, utilizing a first light strategy prediction model to predict a first light control strategy required by the current indoor environment; according to the behavior data, utilizing a second light strategy prediction model to predict a second light control strategy required by the current user behavior; according to the first light control strategy and the second light control strategy, a comprehensive light control strategy needed by the current user indoors is determined; and determining an indoor function area corresponding to the position of the user, and performing real-time regulation and control on indoor light according to the indoor function area and the comprehensive light control strategy. According to the embodiment of the invention, the indoor environment and behavior data of the user can be monitored in real time, the comprehensive prediction and optimization of the light control strategy are realized in combination with artificial intelligence, and an intelligent, flexible and efficient light control solution is provided.

Description

Technical Field

[0001] The present invention belongs to the field of lighting technology, and in particular to an intelligent lighting control method and system. Background Art

[0002] In the rapid development of modern smart home systems, lighting control, as a basic intelligent application, has gradually become an indispensable part of homes, offices and public places. Traditional lighting control methods usually rely on manual switches or simple timers, which cannot adapt to the needs of users in different situations, resulting in energy waste and poor user experience. With the rise of the Internet of Things (IoT), big data and artificial intelligence (AI) technologies, smart lighting control systems targeting the environment and user behavior have gradually attracted attention. There are already some smart lighting control products on the market, but most of them still have various limitations, which is a problem that needs to be solved urgently. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent lighting control method and system to address the deficiencies in the prior art. The method and system can monitor the user's indoor environment and behavior data in real time through the Internet of Things technology, and combine with artificial intelligence models to achieve comprehensive prediction and optimization of lighting control strategies, thereby providing an intelligent, flexible and efficient lighting control solution.

[0004] An embodiment of the present application provides an intelligent lighting control method, the method comprising: Monitor various environmental data of the user's indoor environment through the Internet of Things, as well as the location and behavior data of indoor users; According to the multiple environmental data, using a pre-trained first lighting strategy prediction model, predicting a first lighting control strategy required for the current indoor environment; According to the behavior data, using a pre-trained second lighting strategy prediction model, predicting a second lighting control strategy required for the current user behavior; According to the first lighting control strategy and the second lighting control strategy, the comprehensive lighting control strategy required for the current user's room is determined; the indoor functional area corresponding to the user's position is determined, and the indoor lighting is adjusted in real time according to the indoor functional area and the comprehensive lighting control strategy.

[0005] Optionally, lighting lamps with different functions are provided in multiple functional areas indoors, and each functional area contains one or more lighting lamps.

[0006] Optionally, the determining of the indoor functional area corresponding to the user's position includes: Acquire all representative point data of the functional area, wherein the representative points are preset key points of the functional area; Select the split axis according to the variance of each dimension in all representative point data, recursively select the median point as the node split point, and build a balanced KD tree. When each node is split, the dimension with the largest variance in the current node data set is selected for splitting; Obtain user location data, use the constructed KD tree to perform nearest neighbor query, find the representative point closest to the user's location as the nearest neighbor point, and determine the functional area where the representative point is located.

[0007] Optionally, determining the comprehensive lighting control strategy required for the current user's room according to the first lighting control strategy and the second lighting control strategy includes: Obtaining parameter configurations in the first lighting control strategy and the second lighting control strategy; Performing a weighted summation on the first parameter value and the second parameter value of the same parameter in the parameter configuration to obtain a comprehensive parameter value of the same parameter, wherein the first parameter value corresponds to the environment weight and the second parameter value corresponds to the behavior weight; The comprehensive parameter value, the first lighting control strategy and other lighting configurations in the second lighting control strategy except for the same parameter configuration are combined into a comprehensive lighting control strategy.

[0008] Optionally, the real-time regulation of indoor lighting according to the indoor functional area and the comprehensive lighting control strategy includes: According to the comprehensive lighting control strategy, preliminarily adjust the indoor lighting; The indoor lighting after preliminary control is controlled in stages according to the indoor functional area corresponding to the user's location, the adjacent area of ​​the indoor functional area, and the remaining areas outside the adjacent area.

[0009] Another embodiment of the present application provides an intelligent lighting control system, the system comprising: A monitoring module, which is used to monitor various environmental data of the user's indoor environment through the Internet of Things, as well as the location and behavior data of indoor users; A first prediction module, configured to predict a first lighting control strategy required for the current indoor environment based on the multiple environmental data and using a pre-trained first lighting strategy prediction model; A second prediction module, configured to predict a second lighting control strategy required for the current user behavior based on the behavior data and using a pre-trained second lighting strategy prediction model; A determination module, used to determine the comprehensive lighting control strategy required for the current user's room according to the first lighting control strategy and the second lighting control strategy; The control module is used to determine the indoor functional area corresponding to the user's position, and to control the indoor lighting in real time according to the indoor functional area and the comprehensive lighting control strategy.

[0010] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0011] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0012] Compared with the prior art, the present invention provides an intelligent lighting control method, which monitors various environmental data of the user's indoor environment, as well as the location and behavior data of the indoor user through the Internet of Things; uses the various environmental data and a pre-trained first lighting strategy prediction model to predict the first lighting control strategy required for the current indoor environment; uses the behavior data and a pre-trained second lighting strategy prediction model to predict the second lighting control strategy required for the current user's behavior; determines the comprehensive lighting control strategy required for the current user's indoor environment based on the first lighting control strategy and the second lighting control strategy; determines the indoor functional area corresponding to the user's location, and performs real-time regulation of the indoor lighting based on the indoor functional area and the comprehensive lighting control strategy, so that the user's indoor environment and behavior data can be monitored in real time through the Internet of Things technology, and combined with the artificial intelligence model, the comprehensive prediction and optimization of the lighting control strategy can be achieved, providing an intelligent, flexible and efficient lighting control solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A hardware structure block diagram of a computer terminal for an intelligent lighting control method provided by an embodiment of the present invention; Figure 2 A schematic diagram of a flow chart of an intelligent lighting control method provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an intelligent lighting control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0015] The embodiment of the present invention firstly provides an intelligent lighting control method, which can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0016] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of an intelligent lighting control method provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0017] The memory 104 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the intelligent lighting control method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0018] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0019] See also Figure 2 , an embodiment of the present invention provides an intelligent lighting control method, which may include the following steps: S201, monitoring various environmental data of the user's indoor environment and the location and behavior data of the indoor user through the Internet of Things; Specifically, a variety of sensors can be placed in each room, including temperature and humidity sensors, light intensity sensors, sound level meters, and air quality sensors. These sensors are connected to the central control system via a wireless network and regularly collect environmental parameters in the room, such as indoor temperature, humidity, light intensity, noise level, and air quality. Each sensor is configured to send the collected data every few seconds or at a specific time interval to ensure real-time monitoring of environmental changes. These data are usually sent to the IoT platform in JSON format via MQTT or HTTP protocol.

[0020] Multiple Bluetooth beacons or Wi-Fi hotspots can be deployed indoors to track the user's location in real time. The user's mobile device (such as a smartphone or smart bracelet) regularly updates its location information by connecting to these beacons. Using triangulation or RSSI (received signal strength indication) technology, the system can calculate the user's exact location indoors based on the signal strength of multiple beacons, and send this location information to the IoT central control system via wireless signals.

[0021] Motion sensors and cameras are integrated in the user's main activity area. Motion sensors can detect the user's movement and trigger information feedback immediately once the user enters or leaves a certain area. Through image recognition technology, the camera can monitor the user's activities in real time and determine their behavior type (such as reading, working or resting). These recognition results are transmitted through video streams, and after preliminary processing, the behavior data is transmitted to the control system.

[0022] All monitored environmental data, user location and behavior data will be transmitted to the central control system through IoT network technology (such as LoRa, Zigbee or Wi-Fi network). In this process, ensure the efficiency and low latency of data transmission to support real-time response requirements. In order to maintain data security, the TLS / SSL protocol is used to encrypt the transmitted data to ensure the protection of user privacy and sensitive information. By integrating multiple sensors and devices, a unified IoT platform is formed to achieve efficient data collection. On this platform, various sensors can communicate with each other and coordinate work as needed.

[0023] This step uses IoT technology to fully monitor the user's indoor environment, and can obtain real-time information about environmental changes, user location, and behavior. The effective implementation of this process provides a solid data foundation for subsequent lighting control strategies, enabling the intelligent lighting control system to respond promptly and accurately to changes in user needs and environmental conditions. At the same time, accurate monitoring data can also optimize the user's living experience, reduce energy consumption, and improve indoor comfort. In this way, the intelligent lighting control method can better adapt to the user's living habits and environmental characteristics, and provide personalized lighting solutions.

[0024] S202, predicting a first lighting control strategy required for the current indoor environment using a pre-trained first lighting strategy prediction model according to the multiple environmental data; Among them, various environmental data may include: Light intensity: The level of natural light in the front room will directly affect the demand for artificial lighting. For example, when the indoor light intensity is low, artificial lighting needs to be increased to achieve the required brightness; Temperature: Indoor temperature can affect the user's comfort and the performance of certain lights. Higher temperatures may make users want to use lights with cool color temperatures to enhance the coolness of the space. Humidity: Although humidity has little direct impact on lighting control, in certain circumstances, high humidity may cause changes in the user's sensitivity to the environment and affect the lighting mode they choose (such as preference for soft lighting); Noise level: Noise level can affect the user's mood and concentration. In a noisy environment, users may prefer softer or focused lighting modes to reduce distractions.

[0025] Specifically, various sensors placed indoors (such as light sensors, temperature and humidity sensors, air quality sensors, and noise sensors) can be used to monitor environmental data in real time. These sensors send data to the central control system via wireless protocols (such as Zigbee or Wi-Fi). The data from each sensor is integrated to form a unified environmental data structure, including light intensity, indoor temperature, humidity, and noise level. The timeliness and accuracy of the data are ensured during integration.

[0026] According to the needs of lighting control, key features are extracted from the integrated environmental data, such as current light intensity, rate of change, etc. By analyzing historical data, the features most relevant to lighting needs are identified. The extracted features are standardized using Z-score standardization or Min-Max normalization to eliminate the influence of different feature magnitudes and prepare data in a unified format for model input.

[0027] The processed features are combined into an input sample set. The sample set should cover different environmental scenarios so that the model can learn the optimization rules of the lighting control strategy. The sample set should include multi-dimensional data within the effective time period to reflect the dynamic characteristics of environmental changes. To enhance the prediction ability of the model, time series features (such as time period, day of the week) are used as additional inputs to capture the impact of time on lighting control.

[0028] The constructed input sample set is passed to the pre-trained first lighting strategy prediction model. The model can use machine learning algorithms, such as long short-term memory (LSTM) networks or random forests, to predict the lighting strategy based on the training results of the model. The model will generate the optimal lighting control strategy in the current environment based on the relationship between the environment and lighting control learned during the training process. The prediction results include, for example, the brightness, color temperature, switch status (on / off) of the light, and the appropriate lighting mode (such as reading mode, leisure mode, etc.).

[0029] This model focuses on various data of the indoor environment (such as light intensity, temperature, humidity, air quality, etc.), analyzes the impact of the environment on lighting requirements, and intelligently predicts the required lighting control strategy. The model makes predictions based on changes in the physical environment and is committed to ensuring that appropriate lighting is provided under different environmental conditions. This type of model often focuses on environmental characteristics, such as light changes, climate impacts, etc.

[0030] Through real-time monitoring and intelligent prediction, the automation and intelligence level of lighting control is effectively improved, ensuring that users get the best lighting experience under different environmental conditions. Accurate lighting control strategies can significantly improve living comfort, while reducing energy consumption and promoting the realization of sustainable development goals.

[0031] S203, predicting a second lighting control strategy required for the current user behavior using a pre-trained second lighting strategy prediction model according to the behavior data; Specifically, the user's indoor behavior data can be monitored in real time through IoT devices (such as smart cameras, motion sensors, smartphone positioning, etc.). Behavioral data includes the user's activity type (such as reading, working, entertainment, etc.), duration, frequency and pattern.

[0032] Integrate behavioral data obtained from various sensors to ensure the timeliness and integrity of the data and create a time-series record of user behavior. Use signal processing and pattern recognition techniques (such as hidden Markov models or convolutional neural networks) to identify specific user activity categories from raw behavioral data. For example, extract features from accelerometer data to distinguish between sitting, standing, or moving states.

[0033] Convert user activity information into feature vectors. The feature vector contains the coded value of the activity type, the duration of the activity, and the statistics of historical behavior patterns (such as common activities in the past hour). Use the extracted features to build a sample data set to ensure that the sample is representative, including the diversity of different time periods and different user behavior scenarios. For example, build a sample set including multiple behavior scenarios such as reading, working, and entertainment. Standardize the feature vectors, using Z-score normalization or Min-Max normalization to ensure that the model can converge quickly during training and prediction and improve prediction accuracy.

[0034] Input the prepared feature vector into the pre-trained second light strategy prediction model. The model can be trained based on a variety of machine learning algorithms (such as decision trees, deep learning networks, etc.) to learn the relationship between user behavior and lighting needs. The model outputs the current optimal second light control strategy based on the input user behavior characteristics to form a set of lighting configuration solutions suitable for the user's current behavior. This solution may include light brightness, color temperature, switch status and mode (such as reading mode, party mode, entertainment mode, etc.).

[0035] The model focuses on user behavior data and intelligently predicts lighting control strategies that meet current user needs by analyzing user activity patterns in the room (such as reading, working, and entertainment). The model focuses on user habits and behavioral changes and aims to adjust lighting configurations in real time to suit user activity needs. For example, a user may need stronger light when reading, but may prefer softer light when relaxing.

[0036] Intelligent processing of lighting control through user behavior data can significantly improve the user's lighting experience in different activity states. Through precise lighting control strategies, users can obtain the best lighting conditions when performing various activities, thereby improving work efficiency, reading comfort and overall quality of life. This intelligent lighting control method not only creates a personalized user experience, but also promotes energy conservation and emission reduction, helping to achieve a more sustainable lifestyle.

[0037] S204, determining a comprehensive lighting control strategy required for the current user's room according to the first lighting control strategy and the second lighting control strategy; This process first obtains the first lighting control strategy (a control strategy based on environmental data prediction) and the second lighting control strategy (a control strategy based on user behavior data prediction). Then, the relevant parameters in the two strategies are integrated to form a comprehensive lighting control strategy. The specific process includes comparison and analysis of parameter configurations to ensure that the values ​​of the same parameters can be reasonably merged. The formulation of a comprehensive lighting control strategy ensures the comprehensiveness and adaptability of lighting control. Not only does it consider the impact of environmental factors on lighting needs, but it also fully integrates the user's real-time behavioral preferences. Through this dual integration, the system can achieve precise lighting control in a complex and dynamic indoor environment to meet the user's comfort and practicality needs, thereby improving the overall experience of the living or working environment.

[0038] Specifically, the parameter configurations in the first lighting control strategy and the second lighting control strategy may be obtained; This step involves extracting all relevant parameter configurations in the first and second lighting control strategies. These parameters can include light brightness, color temperature, on and off times, and other characteristic values ​​that affect lighting settings. The system organizes the parameters in the two strategies for subsequent weighted summation. By systematically acquiring and analyzing the parameters in the lighting control strategies, we can better understand the relationship between environmental changes and user behavior, and provide a solid data foundation for the formulation of comprehensive lighting strategies. This systematic analysis helps ensure that the final lighting control strategy is efficient and reasonable, so as to meet the actual needs of users.

[0039] Performing a weighted summation on the first parameter value and the second parameter value of the same parameter in the parameter configuration to obtain a comprehensive parameter value of the same parameter, wherein the first parameter value corresponds to the environment weight and the second parameter value corresponds to the behavior weight; The system will analyze the same items of parameters in the first and second lighting control strategies and assign weights to them. The setting of environmental weights and behavioral weights is based on their respective influence or user preference, and a comprehensive parameter value is calculated through weighted summation, which represents the optimal setting of relevant lighting parameters in the current context. This step ensures that the comprehensive lighting control strategy can fully reflect the changes in the environment and the user's behavioral preferences. Through a scientific weighting mechanism, the strategy is more personalized and the intelligent level of lighting control is improved. In addition, the dynamic adjustment of weights can make the lighting control system perform well in different environments and usage scenarios, improving the user experience.

[0040] The comprehensive parameter value, the first lighting control strategy and other lighting configurations in the second lighting control strategy except for the same parameter configuration are combined into a comprehensive lighting control strategy.

[0041] The final step is to combine the obtained comprehensive parameter values ​​with the non-repeated lighting configurations in the first and second lighting control strategies to form the final comprehensive lighting control strategy. At this point, all relevant lighting settings are integrated to ensure that users get the best lighting experience at a specific time and environment. Through this merging process, the comprehensive lighting control strategy not only covers the dual impact of the environment and user behavior on the lighting, but also considers other factors that may affect the lighting settings. This comprehensive and detailed strategy merging helps ensure that the lighting settings are flexible and adaptable while meeting the needs of use, thereby improving the comfort of the living and working environment.

[0042] S205, determining the indoor functional area corresponding to the user's position, and adjusting the indoor lighting in real time according to the indoor functional area and the comprehensive lighting control strategy.

[0043] First, the user's current location data is obtained through the IoT device. Then, the location data is matched with the pre-defined indoor functional areas to determine the specific functional area (such as living room, study, bedroom, etc.) where the user is currently located. Then, the system uses the previously calculated comprehensive lighting control strategy to adjust the lighting settings of the functional area in real time to ensure that the lighting configuration matches the user's needs and environmental behavior. This process includes adjusting the brightness, color temperature, and switch status of the light.

[0044] Among them, lighting lamps with different functions are set in multiple functional areas in the room, and each functional area contains one or more lighting lamps. This design ensures that flexible and effective lighting solutions can be provided under different environments and needs. The lighting settings of each functional area can be customized according to the characteristics of the environment and function, so as to achieve precise lighting. For example, the living room area may have softer lighting settings to create a warm atmosphere, while the kitchen may need brighter lights for cooking operations. At the same time, the lamps in different functional areas can be centrally managed through the intelligent control system to ensure the coordination and uniformity of the lighting. In practical applications, this area division and lighting design enhances the flexibility and operability of the entire smart home system, allowing users to easily adjust according to their own needs.

[0045] Specifically, to determine the indoor functional area corresponding to the user's position, all representative point data of the functional area can be obtained, wherein the representative point is a preset key point of the functional area; each functional area can be represented by one or more key points, which can be the center point, corner point or boundary point of the area. These points are used to construct a KD tree to represent the position of each functional area in space.

[0046] First, define and collect representative point data for each functional area (such as living room, study, kitchen, etc.). These representative points can be the center point, corner point or boundary point of the area. The selection of these key positions is to facilitate subsequent spatial positioning and user behavior analysis. By presetting these key points, the system can more accurately reflect the indoor layout and ensure that when tracking the user's position, it can quickly locate which functional area. Obtaining representative point data is the basis for achieving accurate indoor positioning. Through such key point calibration, the system can quickly determine the specific area where the user is located when the user moves, thereby improving the accuracy and response speed of intelligent lighting control. This not only optimizes the user experience, but also improves the intelligence level of lighting control, so that the lighting settings can adapt to the user's activity needs and environmental changes in real time.

[0047] Select the split axis according to the variance of each dimension in all representative point data, recursively select the median point as the node split point, and build a balanced KD tree. When each node is split, the dimension with the largest variance in the current node data set is selected for splitting; In this step, by analyzing all representative point data, the system calculates the variance of each dimension (such as X and Y coordinates). Variance is a measure of data distribution. Selecting the dimension with the largest variance as the split axis can effectively improve the balance of the KD tree. The system recursively selects the median point as the node split point to build a balanced KD tree, making subsequent spatial queries and user positioning more efficient. By building a balanced KD tree, the system can efficiently store and retrieve spatial data. The balance of the KD tree helps to divide the space more evenly, which can significantly reduce the search time and improve the accuracy when performing nearest neighbor queries. This process ensures the integrity and efficiency of the positioning data, thereby providing a more accurate information basis for real-time lighting control. Obtain user location data, use the constructed KD tree to perform nearest neighbor queries, find the representative point closest to the user's location as the nearest neighbor point, and determine the functional area where the representative point is located.

[0048] The user's location information is obtained in real time, and then the KD tree constructed previously is used for nearest neighbor query. By calculating the distance between the user's current location and each representative point in the KD tree, the system can quickly find the representative point closest to the user and determine the functional area to which the point belongs. This process can efficiently and accurately identify the user's current spatial location. Performing nearest neighbor query is the core step in achieving indoor positioning, which directly affects the timeliness and correctness of the lighting control response. By quickly locating the functional area, the system can adjust the lighting settings in real time according to the comprehensive lighting control strategy of the user's area, thereby improving the user's comfort and environmental adaptability. Accurate area recognition not only improves the level of home intelligence, but also provides users with a personalized lighting experience.

[0049] Specifically, the indoor lighting is regulated in real time according to the indoor functional area and the comprehensive lighting control strategy, and the indoor lighting can be preliminarily regulated according to the comprehensive lighting control strategy; Adjust the basic configuration of indoor lighting according to the comprehensive lighting control strategy. This includes adjusting the brightness, color temperature, and light switch status, so that the basic lighting settings of the room meet the current environmental conditions and user needs. The goal of preliminary regulation is to provide an appropriate light environment to lay the foundation for the user's subsequent activities.

[0050] In this step, the intelligent system will make preliminary adjustments to the indoor lighting based on the comprehensive lighting control strategy. This adjustment not only takes into account the current environmental data (such as brightness, light source quality, etc.), but also combines the user's specific behavior patterns (such as reading, entertainment or meetings, etc.) to set the initial light brightness, color temperature and switch status. The purpose is to provide a lighting environment that meets the user's expectations and activity needs the moment the user enters the room. Preliminary adjustment can provide an ideal lighting foundation for the user's daily activities and enhance the user's comfort and work efficiency in the environment. This step is the key to the entire lighting control process because it directly affects the user's first impression and overall experience. Appropriate preliminary adjustment helps improve the response speed and user satisfaction of the smart home system.

[0051] The indoor lighting after preliminary control is controlled in stages according to the indoor functional area corresponding to the user's location, the adjacent area of ​​the indoor functional area, and the remaining areas outside the adjacent area.

[0052] Based on the functional area determined by the user's current location, the light after preliminary adjustment is adjusted in a more detailed hierarchical manner. First, the system identifies the specific functional area where the user is located (such as study, living room, etc.), and then considers the adjacent areas of the area and the areas farther away, so as to achieve step-by-step adjustment of the light. Such hierarchical adjustment can ensure that the light in the user's area meets their needs while avoiding light conflicts in other areas. Hierarchical adjustment makes the lighting setting not limited to simple brightness adjustment, but provides the best lighting conditions for each area. This approach not only reduces the redundancy of light, but also improves the comfort and functionality of the overall indoor environment and promotes visual harmony between areas. For example, in a study where a person is working, stronger focused light can be provided, while softer light can be used in the adjacent rest area to create a relaxing atmosphere.

[0053] For example, by establishing a hierarchical relationship between indoor functional areas and lighting configurations, dynamic adjustments can be made using graded priorities. Specifically, the system will first evaluate the lighting needs of the user's area, and then gradually reduce the lighting in adjacent areas based on the user's distance and functional role, and then further reduce the lighting in farther areas. For example, a maximum lighting value can be set, and the lighting intensity can be gradually reduced based on the user's distance and activity type. At the same time, the system can automatically adjust the lighting balance between areas based on environmental changes (such as fluctuations in the intensity of natural light outside the window), making the lighting control process more intelligent and flexible.

[0054] It can be seen that the Internet of Things monitors various environmental data of the user's indoor environment, as well as the location and behavior data of the indoor user; utilizes various environmental data and a pre-trained first lighting strategy prediction model to predict the first lighting control strategy required for the current indoor environment; utilizes behavioral data and a pre-trained second lighting strategy prediction model to predict the second lighting control strategy required for the current user behavior; determines the comprehensive lighting control strategy required for the current user's indoor environment based on the first lighting control strategy and the second lighting control strategy; determines the indoor functional area corresponding to the user's location, and adjusts the indoor lighting in real time based on the indoor functional area and the comprehensive lighting control strategy, so that the user's indoor environment and behavior data can be monitored in real time through the Internet of Things technology, and combined with the artificial intelligence model, the comprehensive prediction and optimization of the lighting control strategy can be achieved, providing an intelligent, flexible and efficient lighting control solution.

[0055] Another embodiment of the present invention provides an intelligent lighting control system. Figure 3 , the system may include: The monitoring module 301 is used to monitor various environmental data of the user's indoor environment and the location and behavior data of the indoor user through the Internet of Things; A first prediction module 302 is used to predict the first lighting control strategy required for the current indoor environment based on the multiple environmental data using a pre-trained first lighting strategy prediction model; The second prediction module 303 is used to predict the second lighting control strategy required for the current user behavior based on the behavior data using a pre-trained second lighting strategy prediction model; A determination module 304, configured to determine a comprehensive lighting control strategy required for the current user's room according to the first lighting control strategy and the second lighting control strategy; The control module 305 is used to determine the indoor functional area corresponding to the user's position, and to control the indoor lighting in real time according to the indoor functional area and the comprehensive lighting control strategy.

[0056] It can be seen that the Internet of Things monitors various environmental data of the user's indoor environment, as well as the location and behavior data of the indoor user; utilizes various environmental data and a pre-trained first lighting strategy prediction model to predict the first lighting control strategy required for the current indoor environment; utilizes behavioral data and a pre-trained second lighting strategy prediction model to predict the second lighting control strategy required for the current user behavior; determines the comprehensive lighting control strategy required for the current user's indoor environment based on the first lighting control strategy and the second lighting control strategy; determines the indoor functional area corresponding to the user's location, and adjusts the indoor lighting in real time based on the indoor functional area and the comprehensive lighting control strategy, so that the user's indoor environment and behavior data can be monitored in real time through the Internet of Things technology, and combined with the artificial intelligence model, the comprehensive prediction and optimization of the lighting control strategy can be achieved, providing an intelligent, flexible and efficient lighting control solution.

[0057] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0058] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, monitoring various environmental data of the user's indoor environment and the location and behavior data of the indoor user through the Internet of Things; S202, predicting a first lighting control strategy required for the current indoor environment using a pre-trained first lighting strategy prediction model according to the multiple environmental data; S203, predicting a second lighting control strategy required for the current user behavior using a pre-trained second lighting strategy prediction model according to the behavior data; S204, determining a comprehensive lighting control strategy required for the current user's room according to the first lighting control strategy and the second lighting control strategy; S205, determining the indoor functional area corresponding to the user's position, and adjusting the indoor lighting in real time according to the indoor functional area and the comprehensive lighting control strategy.

[0059] It can be seen that the Internet of Things monitors various environmental data of the user's indoor environment, as well as the location and behavior data of the indoor user; utilizes various environmental data and a pre-trained first lighting strategy prediction model to predict the first lighting control strategy required for the current indoor environment; utilizes behavioral data and a pre-trained second lighting strategy prediction model to predict the second lighting control strategy required for the current user behavior; determines the comprehensive lighting control strategy required for the current user's indoor environment based on the first lighting control strategy and the second lighting control strategy; determines the indoor functional area corresponding to the user's location, and adjusts the indoor lighting in real time based on the indoor functional area and the comprehensive lighting control strategy, so that the user's indoor environment and behavior data can be monitored in real time through the Internet of Things technology, and combined with the artificial intelligence model, the comprehensive prediction and optimization of the lighting control strategy can be achieved, providing an intelligent, flexible and efficient lighting control solution.

[0060] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0061] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0062] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, monitoring various environmental data of the user's indoor environment and the location and behavior data of the indoor user through the Internet of Things; S202, predicting a first lighting control strategy required for the current indoor environment using a pre-trained first lighting strategy prediction model according to the multiple environmental data; S203, predicting a second lighting control strategy required for the current user behavior using a pre-trained second lighting strategy prediction model according to the behavior data; S204, determining a comprehensive lighting control strategy required for the current user's room according to the first lighting control strategy and the second lighting control strategy; S205, determining the indoor functional area corresponding to the user's position, and adjusting the indoor lighting in real time according to the indoor functional area and the comprehensive lighting control strategy.

[0063] It can be seen that the Internet of Things monitors various environmental data of the user's indoor environment, as well as the location and behavior data of the indoor user; utilizes various environmental data and a pre-trained first lighting strategy prediction model to predict the first lighting control strategy required for the current indoor environment; utilizes behavioral data and a pre-trained second lighting strategy prediction model to predict the second lighting control strategy required for the current user behavior; determines the comprehensive lighting control strategy required for the current user's indoor environment based on the first lighting control strategy and the second lighting control strategy; determines the indoor functional area corresponding to the user's location, and adjusts the indoor lighting in real time based on the indoor functional area and the comprehensive lighting control strategy, so that the user's indoor environment and behavior data can be monitored in real time through the Internet of Things technology, and combined with the artificial intelligence model, the comprehensive prediction and optimization of the lighting control strategy can be achieved, providing an intelligent, flexible and efficient lighting control solution.

[0064] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. An intelligent lighting control method, characterized in that: The method comprises: Monitor various environmental data of the user's indoor environment through the Internet of Things, as well as the location and behavior data of indoor users; According to the multiple environmental data, using a pre-trained first lighting strategy prediction model, predicting a first lighting control strategy required for the current indoor environment; According to the behavior data, using a pre-trained second lighting strategy prediction model, predicting a second lighting control strategy required for the current user behavior; Determining a comprehensive lighting control strategy required for the current user's room according to the first lighting control strategy and the second lighting control strategy; The indoor functional area corresponding to the user's position is determined, and the indoor lighting is controlled in real time according to the indoor functional area and the comprehensive lighting control strategy.

2. The method according to claim 1, characterized in that in, Lighting lamps with different functions are arranged in multiple functional areas indoors, and each functional area contains one or more lighting lamps.

3. The method according to claim 2, characterized in that The determining of the indoor functional area corresponding to the user's position includes: Acquire all representative point data of the functional area, wherein the representative points are preset key points of the functional area; Select the split axis according to the variance of each dimension in all representative point data, recursively select the median point as the node split point, and build a balanced KD tree. When each node is split, the dimension with the largest variance in the current node data set is selected for splitting; Obtain user location data, use the constructed KD tree to perform nearest neighbor query, find the representative point closest to the user's location as the nearest neighbor point, and determine the functional area where the representative point is located.

4. The method according to claim 3, characterized in that The step of determining the comprehensive lighting control strategy required for the current user's room according to the first lighting control strategy and the second lighting control strategy includes: Obtaining parameter configurations in the first lighting control strategy and the second lighting control strategy; Performing a weighted summation on the first parameter value and the second parameter value of the same parameter in the parameter configuration to obtain a comprehensive parameter value of the same parameter, wherein the first parameter value corresponds to the environment weight and the second parameter value corresponds to the behavior weight; The comprehensive parameter value, the first lighting control strategy and other lighting configurations in the second lighting control strategy except for the same parameter configuration are combined into a comprehensive lighting control strategy.

5. The method according to claim 4, characterized in that The real-time regulation of indoor lighting according to the indoor functional area and the comprehensive lighting control strategy includes: According to the comprehensive lighting control strategy, preliminarily adjust the indoor lighting; The indoor lighting after preliminary control is controlled in stages according to the indoor functional area corresponding to the user's location, the adjacent area of ​​the indoor functional area, and the remaining areas outside the adjacent area.

6. An intelligent lighting control system, characterized in that: The system comprises: A monitoring module, which is used to monitor various environmental data of the user's indoor environment through the Internet of Things, as well as the location and behavior data of indoor users; A first prediction module, configured to predict a first lighting control strategy required for the current indoor environment based on the multiple environmental data and using a pre-trained first lighting strategy prediction model; A second prediction module, configured to predict a second lighting control strategy required for the current user behavior based on the behavior data and using a pre-trained second lighting strategy prediction model; A determination module, used to determine the comprehensive lighting control strategy required for the current user's room according to the first lighting control strategy and the second lighting control strategy; The control module is used to determine the indoor functional area corresponding to the user's position, and to control the indoor lighting in real time according to the indoor functional area and the comprehensive lighting control strategy.

7. The system according to claim 6, characterized in that in, Lighting lamps with different functions are arranged in multiple functional areas indoors, and each functional area contains one or more lighting lamps.

8. The system according to claim 7, characterized in that The control module is specifically used for: Acquire all representative point data of the functional area, wherein the representative points are preset key points of the functional area; Select the split axis according to the variance of each dimension in all representative point data, recursively select the median point as the node split point, and build a balanced KD tree. When each node is split, the dimension with the largest variance in the current node data set is selected for splitting; Obtain user location data, use the constructed KD tree to perform nearest neighbor query, find the representative point closest to the user's location as the nearest neighbor point, and determine the functional area where the representative point is located.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 5 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 5.

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