Intelligent induction illumination brightness adjusting system and method
By combining multiple sensors with reinforcement learning algorithms and geographic information models, an intelligent lighting system is constructed, which solves the problems of insufficient data collection and lack of dynamic control in traditional lighting systems. It achieves localized precise lighting and personalized management, improving lighting effects and energy utilization efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-24
- Publication Date
- 2026-04-14
AI Technical Summary
Traditional lighting systems struggle to collect scene data comprehensively and accurately, making it impossible to generate dynamic control schemes that fit the scene. They lack real-time optimization and management, and cannot achieve precise local lighting or meet personalized user needs, resulting in poor lighting effects and energy waste.
The system uses photosensitive, human infrared, and millimeter-wave radar sensors to collect ambient light intensity, human activity status, and location information in real time. It generates dynamic lighting control schemes through reinforcement learning algorithm models, divides independent control units by combining geographic and building information models, uses the Internet of Things for real-time optimization, and establishes user behavior profiles through long-term data collection to achieve personalized adaptation.
It achieves precise local lighting control, reduces energy consumption, enhances user experience, provides personalized intelligent lighting experience, and ensures intelligent and real-time optimized management of the lighting system.
Smart Images
Figure CN121865475A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of intelligent lighting control technology, specifically relating to an intelligent sensing lighting brightness adjustment system and method. Background Technology
[0002] In the lighting field, traditional lighting methods often employ fixed brightness or simple timer control, making it difficult to flexibly adjust to changes in the actual environment and the needs of users. For example, during the day when ambient light is strong, traditional lighting may still operate at high brightness, resulting in significant energy waste; while in areas with frequent human activity, insufficient lighting can cause inconvenience and negatively impact the user experience. With the development of sensor technology, although some lighting systems have begun to incorporate photosensitive and infrared sensors, most can only achieve simple, single-function control. For instance, they rely solely on light intensity or the presence of people to switch lights on or off, failing to consider multiple factors such as the location, activity status, and space occupancy of people, making precise lighting adjustment difficult. Furthermore, existing lighting control schemes are mostly unified overall control, lacking refined management of localized areas. In a large space, the lighting needs of different areas may vary greatly; unified control cannot meet the individualized needs of each area, leading to over- or under-lighting in some areas. In addition, in terms of lighting management, traditional methods lack real-time monitoring and analysis of equipment status, energy consumption, and other data, making it difficult to adjust control strategies promptly based on actual conditions to achieve energy-saving optimization. Moreover, traditional systems cannot deeply learn and adapt to users' personalized lighting needs, and cannot provide users with a seamless lighting experience that suits their own habits.
[0003] Existing technologies suffer from several drawbacks: they rely on a single or a few sensors to collect data, making it difficult to comprehensively acquire scene information; the control schemes lack dynamic adaptability; the lighting areas are mostly controlled as a whole, making it impossible to achieve precise local adjustments; and there is a lack of real-time data feedback optimization and personalized adaptation. Summary of the Invention
[0004] To address the aforementioned issues, this invention provides an intelligent sensor-based lighting brightness adjustment system and method. This system solves the problems of traditional lighting systems, such as difficulty in comprehensively and accurately collecting scene data, inability to generate dynamic control schemes tailored to the scene, inability to achieve precise localized lighting, lack of real-time optimization management, and inability to meet personalized user needs, resulting in poor lighting effects and energy waste. To achieve the above objectives, this invention adopts the following technical solution: The intelligent sensing lighting brightness adjustment method includes the following steps: Using photosensitive, human infrared, and millimeter-wave radar sensors, ambient light intensity, personnel activity status, and location information are collected in real time to extract scene data and obtain a scene basic dataset; using a reinforcement learning algorithm model, light intensity, personnel density, and space occupancy parameters from the scene basic dataset are extracted, and the input parameters are mapped into brightness adjustment commands to obtain a dynamic lighting control scheme that fits the scene requirements; using a geographic and building information model to construct a three-dimensional space, personnel positions are located using sensors, the lighting area is divided into independent control units, and on-demand activation data is extracted to obtain a localized precise lighting control effect; connecting edge nodes and a cloud platform through the Internet of Things, device status, energy consumption, and user feedback data are extracted, the analysis results are transformed into optimization strategies and remotely distributed to obtain a real-time updated intelligent lighting management scheme; using long-term data collection to establish user behavior profiles, and by analyzing daily lighting preference parameters, personalized needs are transformed into preset modes to obtain a seamless and personalized intelligent lighting experience.
[0005] Furthermore, the method employs photosensitive, human infrared, and millimeter-wave radar sensors to extract scene data and obtain a basic scene dataset by real-time acquisition of ambient light intensity, personnel activity status, and location information. This includes the following steps: constructing a multi-dimensional perception network using photosensitive, human infrared, and millimeter-wave radar sensors; the photosensitive sensor is responsible for real-time acquisition of ambient light intensity data, accurately capturing changes in natural and artificial light; the human infrared sensor detects infrared radiation emitted by the human body to obtain personnel activity status and determine the presence of people in the area; and the millimeter-wave radar utilizes the principle of electromagnetic wave reflection to accurately determine personnel location information; and by synchronously integrating the data collected by the three sensors, light, activity, and location information are extracted to obtain the basic scene dataset.
[0006] Furthermore, the step of extracting illumination, personnel density, and space occupancy parameters from the scene's basic dataset using a reinforcement learning algorithm model, and mapping the input parameters to brightness adjustment commands to obtain a dynamic lighting control scheme that meets the scene's needs, includes the following steps: Using a reinforcement learning algorithm model with self-learning capabilities, the model performs deep analysis of the scene's basic dataset to extract illumination intensity values, personnel distribution density information, and actual space occupancy parameters; using the extracted illumination intensity values, personnel distribution density information, and actual space occupancy parameters as model input, the model performs intelligent computation and decision-making reasoning based on a reward mechanism and optimization objective; and accurately mapping the input parameters to specific brightness adjustment commands to obtain a dynamic lighting control scheme that meets the scene's needs.
[0007] Furthermore, the method of constructing a three-dimensional space using Geographic and Building Information Modeling (GIM), locating personnel positions through sensors, dividing the lighting area into independent control units, and extracting on-demand activation data to obtain precise local lighting control effects includes the following steps: Using GIM technology, a three-dimensional spatial architecture of the lighting area is constructed, clearly presenting the building structure and spatial layout; Personnel positioning sensors are deployed to capture personnel location information in real time, determining their specific coordinates in the three-dimensional space; based on personnel positions and building functional zoning, the lighting area is rationally divided into multiple independent control units; Based on personnel distribution and activity, on-demand activation data is extracted, including the personnel's location and dwell time; the activation data is applied to lighting control to obtain precise local lighting control effects for each independent unit.
[0008] Furthermore, the process of connecting edge nodes and the cloud platform via the Internet of Things (IoT) to extract device status, energy consumption, and user feedback data, transforming the analysis results into optimization strategies, and remotely distributing them to obtain a real-time updated intelligent lighting management solution includes the following steps: Using IoT communication technology, a data transmission bridge is built between the edge nodes and the cloud platform to ensure high-speed interconnection; sensors and monitoring modules deployed on the lighting equipment collect real-time data on device operating status, energy consumption, and user feedback, which is then uploaded to the cloud platform; the cloud platform uses big data analytics tools to obtain analysis results on device health, energy consumption trends, and user demand preferences; the analysis results are transformed into feasible optimization strategies and remotely distributed to the edge nodes via the IoT, resulting in an intelligent lighting management solution that can be updated in real-time according to actual conditions.
[0009] Furthermore, the method of establishing user behavior profiles through long-term data collection, and transforming personalized needs into preset modes by analyzing daily lighting preference parameters to obtain a seamlessly adapted personalized smart lighting experience, includes the following steps: A continuous and stable data collection module is used to collect raw data from users when using lighting devices. This raw data includes parameters such as light intensity selection, color temperature preference, and usage duration at different times of the day; Data analysis algorithms are used to deeply mine this raw data, extracting information reflecting users' lighting habits and preferences to obtain a complete user behavior profile; Based on the profile content, the user's personalized needs are accurately transformed into preset modes for the lighting devices. When a user enters a corresponding scene, the device automatically matches the preset mode, resulting in a highly adapted personalized smart lighting experience that is almost imperceptible to the user.
[0010] The second aspect of this invention provides an intelligent sensing lighting brightness adjustment system, which includes the following modules: a data acquisition module, used to collect ambient light intensity, personnel activity status and location information in real time using photosensitive, human infrared and millimeter-wave radar sensors, extract scene data, and obtain a scene basic dataset; an adjustment command module, used to extract the light intensity, personnel density and space occupancy parameters in the scene basic dataset through a reinforcement learning algorithm model, map the input parameters into brightness adjustment commands, and obtain a dynamic lighting control scheme that fits the scene requirements; a lighting control module, used to construct a three-dimensional space using a geographic and building information model, locate personnel positions through sensors, divide the lighting area into independent control units, extract on-demand activation data, and obtain a local precise lighting control effect; an optimization strategy module, used to connect edge nodes and a cloud platform through the Internet of Things, extract device status, energy consumption and user feedback data, convert the analysis results into optimization strategies and remotely distribute them, and obtain a real-time updated intelligent lighting management scheme; and a personalized adaptation module, used to establish user behavior profiles through long-term data collection, analyze daily lighting preference parameters, convert personalized needs into preset modes, and obtain a seamless personalized intelligent lighting experience.
[0011] A third aspect of the present invention provides an intelligent sensing lighting brightness adjustment, the intelligent sensing lighting brightness adjustment including a memory and at least one processor, the memory storing instructions; the at least one processor calling the instructions in the memory to cause the intelligent sensing lighting brightness adjustment to perform the steps of the intelligent sensing lighting brightness adjustment method as described in any of the preceding claims.
[0012] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions, characterized in that, when executed by a processor, the instructions implement the steps of the intelligent sensing lighting brightness adjustment method as described in any one of the preceding claims.
[0013] The technical solution provided by this invention employs photosensitive, human infrared, and millimeter-wave radar sensors to collect ambient light intensity, personnel activity status, and location information in real time, extracting scene data to obtain a basic scene dataset. Through a reinforcement learning algorithm model, it extracts light intensity, personnel density, and space occupancy parameters from the basic scene dataset, mapping the input parameters to brightness adjustment commands to obtain a dynamic lighting control scheme tailored to the scene's needs. A three-dimensional space is constructed using geographic and building information models, and personnel positions are located using sensors. The lighting area is divided into independent control units, and on-demand activation data is extracted to achieve precise local lighting control. By connecting edge nodes and a cloud platform through the Internet of Things, device status, energy consumption, and user feedback data are extracted. The analysis results are transformed into optimization strategies and remotely distributed to obtain a real-time updated intelligent lighting management scheme. Long-term data collection establishes user behavior profiles, and by analyzing daily lighting preference parameters, personalized needs are transformed into preset modes, resulting in a seamlessly adapted personalized intelligent lighting experience. This invention solves the problems of traditional lighting, such as difficulty in comprehensively and accurately collecting scene data, inability to generate dynamic control schemes tailored to specific scenes, inability to achieve precise local lighting, lack of real-time optimization management, and inability to meet personalized user needs, leading to poor lighting effects and energy waste. Attached Figure Description
[0014] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention.
[0015] Figure 1 This is a schematic diagram of a first embodiment of an intelligent sensing lighting brightness adjustment method according to an embodiment of the present invention.
[0016] Figure 2 This is a schematic diagram of a second embodiment of an intelligent sensing lighting brightness adjustment method according to an embodiment of the present invention.
[0017] Figure 3 This is a schematic diagram of a third embodiment of an intelligent sensing lighting brightness adjustment method according to the present invention.
[0018] Figure 4 This is a schematic diagram of the fourth embodiment of an intelligent sensing lighting brightness adjustment method according to the present invention.
[0019] Figure 5 This is a schematic diagram of the fifth embodiment of an intelligent sensing lighting brightness adjustment method according to the present invention. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0021] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.
[0022] A method for intelligent sensing and adjusting lighting brightness, such as Figure 1 As shown, the process includes the following steps: Using photosensitive, infrared, and millimeter-wave radar sensors, ambient light intensity, human activity status, and location information are collected in real time to extract scene data and obtain a basic scene dataset; Using a reinforcement learning algorithm model, light intensity, human density, and space occupancy parameters from the basic scene dataset are extracted, and the input parameters are mapped to brightness adjustment commands to obtain a dynamic lighting control scheme that fits the scene requirements; A three-dimensional space is constructed using a geographic and building information model, and human positions are located using sensors. The lighting area is divided into independent control units, and on-demand activation data is extracted to obtain a precise local lighting control effect; By connecting edge nodes and the cloud platform through the Internet of Things, device status, energy consumption, and user feedback data are extracted, and the analysis results are transformed into optimization strategies and remotely distributed to obtain a real-time updated intelligent lighting management scheme; Long-term data collection is used to establish user behavior profiles, and by analyzing daily lighting preference parameters, personalized needs are transformed into preset modes to obtain a seamless and personalized intelligent lighting experience.
[0023] like Figure 2 As shown, in this embodiment, a multi-dimensional perception network is constructed using three types of sensors: photosensitive, human infrared, and millimeter-wave radar. The photosensitive sensor is responsible for collecting ambient light intensity data in real time, accurately capturing changes in natural and artificial light. The human infrared sensor detects infrared radiation emitted by the human body to obtain the activity status of people and determine whether there are people in the area. The millimeter-wave radar uses the principle of electromagnetic wave reflection to accurately determine the location information of people. By synchronously integrating the data collected by the three sensors, the illumination, activity, and location information are extracted to obtain the basic scene dataset.
[0024] A multi-dimensional sensing network constructed using three sensors—photosensitive, infrared, and millimeter-wave radar—has yielded significant and crucial results. Photosensitive sensors accurately capture changes in illumination, providing a fundamental basis for lighting adjustments and preventing energy waste caused by excessive lighting. Infrared sensors effectively determine the presence of people, ensuring timely lighting when people are present. Millimeter-wave radar precisely determines the location of people, achieving accurate positioning. The synchronized integration of data from these three sensors comprehensively extracts information on illumination, activity, and location, forming a complete scene-based dataset. This lays a solid foundation for subsequently generating dynamic lighting control schemes tailored to specific needs, greatly enhancing the intelligence and precision of the lighting system.
[0025] like Figure 3 As shown, in this embodiment, a reinforcement learning algorithm model with self-learning capabilities is used to perform deep analysis on the scene's basic dataset, extracting light intensity values, personnel distribution density information, and actual space occupancy parameters. These extracted light intensity values, personnel distribution density information, and actual space occupancy parameters are then used as model inputs. Based on the reward mechanism and optimization objectives, the model performs intelligent computation and decision-making reasoning. The input parameters are then precisely mapped to specific brightness adjustment commands, resulting in a dynamic lighting control scheme that meets the needs of the scene.
[0026] Employing a reinforcement learning algorithm model with self-learning capabilities, the model has yielded remarkable results and played a crucial role. This model can deeply analyze the scene's basic dataset, accurately extracting key parameters such as illumination, personnel density, and space occupancy, providing comprehensive and detailed data support for lighting adjustment. Through reward mechanisms and optimization objectives, the model can intelligently perform computation and decision-making reasoning, accurately translating complex parameters into brightness adjustment commands and generating dynamic lighting control schemes tailored to the scene's needs. This not only achieves intelligent and automated lighting adjustment but also enables real-time optimization based on scene changes, effectively improving lighting quality, reducing energy consumption, and enhancing the user experience.
[0027] Specific learning algorithms are the core of artificial intelligence, enabling automatic optimization of decisions through data training. Common examples include neural networks, which simulate the structure of neurons in the human brain, learning complex mappings between inputs and outputs through massive amounts of data, and excelling in areas such as image and speech recognition. Decision tree algorithms, like a tree, start from the root node and branch continuously based on data features to arrive at a decision result; they are simple, intuitive, and easy to interpret. Reinforcement learning models, through agent-environment interaction, continuously adjust strategies based on reward mechanisms to maximize long-term gains, and are widely used in games, robot control, and other fields. These models each have their own characteristics, providing powerful tools for solving different problems.
[0028] like Figure 4As shown, in this embodiment, geographic and building information modeling (BIM) technology is used to construct a three-dimensional spatial architecture of the lighting area, clearly presenting the building structure and spatial layout. By deploying personnel positioning sensors, the location information of personnel is captured in real time, and their specific coordinates in three-dimensional space are determined. Based on the personnel location and building functional zoning, the lighting area is reasonably divided into multiple independent control units. According to the personnel distribution and activity, activation data is extracted as needed, including the personnel's location and dwell time. The activation data is applied to lighting control to obtain a localized and precise lighting control effect for each independent unit.
[0029] The application of Geographic Information Modeling (GIS) technology yields outstanding results and plays a crucial role. The constructed three-dimensional spatial architecture clearly presents the building structure and layout, providing an intuitive spatial reference for lighting control. By using personnel positioning sensors to capture location information and determine coordinates in real time, lighting zones can be rationally divided into multiple independent control units based on personnel location and building function, enabling refined management. On-demand activation data extracted based on personnel distribution and activities can be precisely applied to lighting control, allowing each independent unit to adjust its brightness according to actual needs. In this way, energy waste caused by general lighting is avoided, while providing just the right amount of lighting for different areas.
[0030] like Figure 5 As shown, in this embodiment, IoT communication technology is used to build a data transmission bridge between edge nodes and the cloud platform, ensuring high-speed interconnection between the two. Through sensors and monitoring modules deployed on the lighting equipment, the operating status of the equipment, energy consumption values, and user feedback information are collected in real time and uploaded to the cloud platform. The cloud platform uses big data analysis tools to obtain analysis results on the health status of the equipment, energy consumption trends, and user demand preferences. The analysis results are transformed into practical optimization strategies and remotely distributed to the edge nodes via IoT to obtain a set of intelligent lighting management solutions that can be updated in real time according to the actual situation.
[0031] This embodiment utilizes IoT communication technology, yielding significant results and playing a crucial role. The established data transmission bridge ensures high-speed interconnection between edge nodes and the cloud platform, laying the foundation for data flow. Sensors and monitoring modules deployed on the lighting equipment can collect real-time data on equipment status, energy consumption, and user feedback, uploading this information to the cloud platform. The cloud platform employs big data analytics tools to accurately obtain analysis results on equipment health, energy consumption trends, and user demand preferences. These results are then transformed into optimization strategies and remotely deployed, resulting in a real-time updated intelligent lighting management solution. This enables intelligent and refined management of the lighting system, effectively improving equipment operating efficiency and reducing energy consumption.
[0032] In this embodiment, a continuous and stable data acquisition module is used to collect raw data from users when using lighting equipment. The raw data includes light intensity selection, color temperature preference, and usage duration parameters at different times of the day. Through data analysis algorithms, this raw data is deeply mined to extract information reflecting the user's lighting habits and preferences, obtaining a complete user behavior profile. Based on the profile, the user's personalized needs are accurately converted into preset modes of the lighting equipment. When the user enters the corresponding scene, the equipment automatically matches the preset mode, resulting in a highly personalized and intelligent lighting experience that is almost imperceptible to the user.
[0033] Utilizing a continuously stable data acquisition module and data analysis algorithms, the system delivers outstanding results and plays a crucial role. The data acquisition module comprehensively collects raw data on user usage of lighting equipment, covering light intensity, color temperature preferences, and usage duration, providing rich material for precise analysis. Through in-depth data mining, information reflecting user habits and preferences can be extracted, constructing a complete user behavior profile. Based on this profile, personalized needs are transformed into preset modes. When a user enters a corresponding scene, the device automatically matches, achieving a seamless yet highly adaptive lighting experience.
[0034] This invention also provides an intelligent sensing lighting brightness adjustment system, comprising the following modules: a data acquisition module, used to acquire ambient light intensity, personnel activity status and location information in real time using photosensitive, human infrared and millimeter-wave radar sensors, extract scene data, and obtain a scene basic dataset; an adjustment command module, used to extract the light, personnel density and space occupancy parameters from the scene basic dataset through a reinforcement learning algorithm model, map the input parameters into brightness adjustment commands, and obtain a dynamic lighting control scheme that fits the scene requirements; a lighting control module, used to construct a three-dimensional space using a geographic and building information model, locate personnel positions through sensors, divide the lighting area into independent control units, extract on-demand activation data, and obtain a localized precise lighting control effect; an optimization strategy module, used to connect edge nodes and a cloud platform through the Internet of Things, extract device status, energy consumption and user feedback data, convert the analysis results into optimization strategies and remotely distribute them, and obtain a real-time updated intelligent lighting management scheme; and a personalized adaptation module, used to establish user behavior profiles through long-term data collection, analyze daily lighting preference parameters, convert personalized needs into preset modes, and obtain a seamlessly adapted personalized intelligent lighting experience.
[0035] This invention also provides an intelligent sensor-activated lighting brightness adjustment system. This system may further include one or more power supplies, one or more wired or wireless network interfaces, one or more input / output interfaces, and / or one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that the intelligent sensor-activated lighting brightness adjustment structure does not constitute a limitation on the computer device provided by this invention, and may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements.
[0036] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the various steps of the intelligent sensing lighting brightness adjustment method provided in the above embodiments.
[0037] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.
Claims
1. A method for intelligent sensing and adjusting lighting brightness, characterized in that, The intelligent sensor-based lighting brightness adjustment method includes the following steps: Using photosensitive, human infrared and millimeter-wave radar sensors, the scene data is extracted by real-time collection of ambient light intensity, human activity status and location information to obtain the scene basic dataset; By using reinforcement learning algorithm models, the illumination, personnel density and space occupancy parameters in the scene's basic dataset are extracted, and the input parameters are mapped into brightness adjustment commands to obtain a dynamic lighting control scheme that fits the scene's needs. A three-dimensional space is constructed using geographic and building information models. The location of people is located by sensors, the lighting area is divided into independent control units, and on-demand activation data is extracted to obtain a precise local lighting control effect. By connecting edge nodes and cloud platforms through the Internet of Things, data on device status, energy consumption, and user feedback are extracted. The analysis results are then transformed into optimization strategies and distributed remotely to obtain a real-time updated smart lighting management solution. By collecting long-term data to create user behavior profiles, and analyzing daily lighting preference parameters, personalized needs are transformed into preset modes, resulting in a seamless and personalized smart lighting experience.
2. The intelligent sensing lighting brightness adjustment method according to claim 1, characterized in that, The process employs photosensitive, infrared, and millimeter-wave radar sensors to collect real-time ambient light intensity, personnel activity status, and location information, extracting scene data to obtain a basic scene dataset, including the following steps: A multi-dimensional perception network is constructed using three types of sensors: photosensitive, human infrared, and millimeter-wave radar. The photosensitive sensor is responsible for collecting ambient light intensity data in real time and accurately capturing changes in natural light and artificial light. Human infrared sensors detect the infrared radiation emitted by the human body to obtain the activity status of people and determine whether there are people in the area, while millimeter-wave radar uses the principle of electromagnetic wave reflection to accurately determine the location information of people. By synchronizing and integrating data collected by three sensors, illumination, activity, and location information are extracted to obtain a basic scene dataset.
3. The intelligent sensing lighting brightness adjustment method according to claim 1, characterized in that, The process involves extracting illumination, personnel density, and space occupancy parameters from the scene's basic dataset using a reinforcement learning algorithm model, mapping the input parameters to brightness adjustment commands, and obtaining a dynamic lighting control scheme that meets the scene's requirements. This includes the following steps: A reinforcement learning algorithm model with self-learning capability is adopted to perform in-depth analysis on the basic dataset of the scene, and extract parameters such as light intensity values, personnel distribution density information, and actual occupancy status of spatial areas. The model inputs are extracted light intensity values, population distribution density information, and actual spatial occupancy parameters. Based on the reward mechanism and optimization objectives, the model performs intelligent calculations and decision-making reasoning. By precisely mapping the input parameters to specific brightness adjustment commands, a dynamic lighting control solution tailored to the needs of the scene can be obtained.
4. The intelligent sensing lighting brightness adjustment method according to claim 1, characterized in that, The process involves constructing a three-dimensional space using geographic and building information models, locating personnel positions through sensors, dividing the lighting area into independent control units, extracting on-demand activation data, and obtaining precise local lighting control effects. This includes the following steps: Using geographic and building information modeling technology, a three-dimensional spatial architecture of the lighting area is constructed, clearly presenting the building structure and spatial layout; By deploying personnel positioning sensors, the location information of personnel is captured in real time, and their specific coordinates in three-dimensional space are determined. Based on the personnel location and building functional zoning, the lighting area is reasonably divided into multiple independent control units. Based on the distribution and activities of personnel, activation data is extracted for on-demand activation. The activation includes the area where the personnel are located and the time spent there. The activation data is applied to lighting control to obtain a localized and precise lighting control effect for each independent unit.
5. The intelligent sensing lighting brightness adjustment method according to claim 1, characterized in that, The process of connecting edge nodes and the cloud platform via the Internet of Things (IoT) to extract device status, energy consumption, and user feedback data, transforming the analysis results into optimization strategies, and remotely distributing them to obtain a real-time updated intelligent lighting management solution includes the following steps: By employing IoT communication technology, a data transmission bridge is built between edge nodes and the cloud platform to ensure high-speed interconnection between the two. By deploying sensors and monitoring modules on lighting equipment, the system collects real-time data on equipment operation status, energy consumption, and user feedback, and uploads this data to the cloud platform. The cloud platform then uses big data analytics tools to obtain analysis results on equipment health, energy consumption trends, and user demand preferences. The analysis results are transformed into practical optimization strategies, which are then remotely distributed to edge nodes via the Internet of Things to obtain a smart lighting management solution that can be updated in real time according to actual conditions.
6. The intelligent sensing lighting brightness adjustment method according to claim 1, characterized in that, The process of establishing user behavior profiles through long-term data collection, analyzing daily lighting preference parameters, and transforming personalized needs into preset modes to obtain a seamlessly adapted personalized smart lighting experience includes the following steps: A continuous and stable data acquisition module is used to collect raw data when users use lighting equipment. The raw data includes light intensity selection, color temperature preference, and usage duration parameters at different times of the day. By using data analysis algorithms, these raw data are deeply mined to extract information reflecting users' lighting habits and preferences, and to obtain a complete user behavior profile. Based on the user profile, the user's personalized needs are accurately translated into preset modes for lighting devices. When the user enters the corresponding scene, the device automatically matches the preset mode, resulting in a highly personalized and intelligent lighting experience that is almost imperceptible to the user.
7. An intelligent sensor-activated lighting brightness adjustment system, characterized in that, The intelligent sensor-activated lighting brightness adjustment system includes the following modules: The data acquisition module is used to extract scene data and obtain a basic scene dataset by using photosensitive, human infrared and millimeter-wave radar sensors to collect ambient light intensity, personnel activity status and location information in real time; The adjustment command module is used to extract the illumination, personnel density and space occupancy parameters from the scene's basic dataset through a reinforcement learning algorithm model, and map the input parameters into brightness adjustment commands to obtain a dynamic lighting control scheme that fits the scene's needs. The lighting control module is used to construct a three-dimensional space using geographic and building information models, locate personnel positions through sensors, divide the lighting area into independent control units, extract on-demand activation data, and obtain localized and precise lighting control effects. The optimization strategy module is used to connect edge nodes and cloud platforms through the Internet of Things to extract device status, energy consumption and user feedback data, transform the analysis results into optimization strategies and remotely distribute them to obtain a real-time updated smart lighting management solution. The personalized adaptation module is used to build user behavior profiles through long-term data collection. By analyzing daily lighting preference parameters, personalized needs are transformed into preset modes to obtain a seamless personalized smart lighting experience.
8. An intelligent sensor-controlled lighting brightness adjustment, characterized in that, The intelligent sensing lighting brightness adjustment includes a memory and at least one processor. The memory stores instructions, and the at least one processor invokes the instructions in the memory to cause the intelligent sensing lighting brightness adjustment to perform the steps of the intelligent sensing lighting brightness adjustment method as described in any one of claims 1-6.
9. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement each step of the intelligent sensing lighting brightness adjustment method as described in any one of claims 1-6.