Indoor lighting color temperature intelligent adaptation system and method
By combining scene function analysis, sensor data acquisition, fuzzy control and machine learning optimization with reinforcement learning, personalized adaptation of indoor lighting systems has been achieved. This solves the problem that traditional systems cannot accurately match activity needs and user preferences, and improves user experience and device synergy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENZHEN DACHENG MICRO TECH CO LTD
- Filing Date
- 2026-05-08
- Publication Date
- 2026-07-03
AI Technical Summary
Traditional indoor lighting systems cannot accurately match the needs of different activities, lack environmental information perception and intelligent analysis capabilities, have poor equipment coordination, cannot be dynamically optimized based on user feedback, and lack personalized adaptation.
The system employs scene function analysis to classify indoor lighting modes, combines real-time data acquisition from sensors, optimizes color temperature adjustment based on fuzzy control and machine learning, achieves collaborative control of multiple devices, and incorporates user preferences through reinforcement learning to provide personalized lighting solutions.
It achieves precise matching of lighting modes, improves visual comfort and ease of operation, meets personalized needs, and enhances user experience and device synergy.
Smart Images
Figure CN122340683A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of indoor lighting technology, specifically relating to an intelligent color temperature adaptation system and method for indoor lighting. Background Technology
[0002] In the field of indoor lighting, as people's quality of life improves, the requirements for lighting effects are no longer limited to simply illuminating a space. Instead, the expectation is to create a comfortable, suitable, and personalized lighting environment based on different scenarios and needs. Traditional indoor lighting control methods are relatively simple, typically only allowing for simple on / off operations and brightness adjustments, unable to flexibly adjust color temperature according to actual scenarios and user needs. For example, a reading scenario might require higher color temperature, brighter, and more uniform light; while a resting scenario would be more suitable with lower color temperature and softer light. However, existing lighting systems struggle to accurately achieve this adaptation, resulting in users not getting the best visual experience during different activities. Furthermore, most existing lighting systems lack comprehensive environmental information perception and intelligent analysis capabilities. They cannot acquire real-time multi-dimensional data such as ambient light intensity, personnel location, time, and temperature and humidity, and therefore cannot make intelligent decisions based on this data to achieve automatic adjustment of color temperature and brightness. In addition, regarding device collaborative control, traditional lighting systems lack effective integration and linkage with curtains, temperature controllers, and other devices, making it difficult to achieve one-click switching of scene modes and provide users with a convenient experience. Moreover, traditional systems cannot dynamically adjust and optimize based on users' usage habits and comfort feedback to meet users' personalized needs, thus failing to satisfy the diverse preferences of different users for lighting environments.
[0003] Existing technologies suffer from several drawbacks: limited lighting modes that fail to accurately match different activity needs; incomplete environmental parameter collection that hinders intelligent adjustment; poor device coordination that prevents one-click scene switching; and an inability to dynamically optimize based on user feedback, resulting in a lack of personalized adaptation. Summary of the Invention
[0004] To address the aforementioned issues, this invention provides an intelligent color temperature adaptation system and method for indoor lighting. This system solves the problems of traditional indoor lighting mode segmentation failing to accurately adapt to different activity needs, incomplete environmental parameter collection limiting intelligent adjustment, poor inter-device coordination hindering one-click scene switching, and the inability to dynamically optimize and personalize based on user feedback. To achieve the above objectives, this invention adopts the following technical solution: The aforementioned intelligent color temperature adaptation method for indoor lighting includes the following steps: Using scene function analysis, combined with user behavior surveys and lighting design standards, indoor lighting is divided into three modes: basic, scene, and dynamic. An adaptive lighting mode classification table is obtained by matching different activity needs. By deploying sensors for illumination, human infrared, time, and temperature and humidity, ambient light intensity, color temperature, personnel location, and time information are collected in real time and wirelessly transmitted to the controller to obtain a multi-dimensional environmental parameter dataset. Based on fuzzy control theory, using time, illumination, location, and scene requirements as input variables, a multi-constraint optimization algorithm outputs color temperature and brightness adjustment commands. Machine learning is used for iterative optimization to obtain a smooth-transition color temperature adjustment algorithm module. A central controller integrates lighting fixtures, curtains, and temperature control modules, associating color temperature adjustment commands with device actions. An open protocol is used to ensure compatibility with mainstream platforms, enabling one-click switching of scene modes and obtaining a multi-device collaborative control scheme. A mobile app collects user comfort scores and usage habit data, combined with environmental parameters recorded by sensors. A reinforcement learning algorithm is used to dynamically adjust strategies, incorporating user preferences into the temperature control module to obtain a personalized adaptive optimization module.
[0005] Furthermore, the aforementioned scenario-based functional analysis method, combined with user behavior research and lighting design standards, divides indoor lighting into three categories: basic, scenario, and dynamic modes. By matching different activity needs, an adaptive lighting mode classification table is obtained, including the following steps: Using scenario-based functional analysis, the functions of indoor spaces are systematically reviewed to clarify daily activities, specific tasks, and diurnal rhythm changes; through user behavior research, lighting demand characteristics under different scenarios are extracted, including brightness preferences, color temperature range, and duration; combined with architectural lighting design standards, the demands are transformed into quantitative indicators; the functional classification and demand indicators are cross-matched to divide the lighting into basic modes covering daily activities, scenario modes adapting to specific tasks, and dynamic modes responding to diurnal changes, thus obtaining an adaptive lighting mode classification table.
[0006] Furthermore, the process involves deploying sensors for illumination, human infrared sensors, time sensors, and temperature and humidity sensors to collect ambient light intensity, color temperature, personnel location, and time information in real time. This data is then wirelessly transmitted to the controller to obtain a multi-dimensional environmental parameter dataset. The process includes the following steps: employing a distributed sensor layout strategy, deploying illumination sensors indoors to extract ambient light intensity and color temperature data in real time; utilizing human infrared sensors to acquire personnel location and activity status information; integrating a time module to record the current moment; configuring temperature and humidity sensors to capture changes in ambient temperature and humidity; and synchronously transmitting multi-source data to the central controller via a wireless communication protocol; and through data cleaning and fusion processing, extracting and integrating effective information to obtain a multi-dimensional environmental parameter dataset including illumination, personnel, time, and temperature and humidity parameters.
[0007] Furthermore, the aforementioned color temperature and brightness adjustment algorithm module, based on fuzzy control theory and using time, illumination, location, and scene requirements as input variables, outputs color temperature and brightness adjustment commands through a multi-constraint optimization algorithm. It then employs machine learning for iterative optimization to obtain a smooth-transition color temperature adjustment algorithm module. This module includes the following steps: Using a fuzzy control theory framework, time parameters, ambient light intensity, personnel location distribution, and preset scene requirements are used as core input variables. The fuzzy influence of these variables on color temperature and brightness is quantified using a membership function. A multi-constraint optimization algorithm is then used to generate an initial set of color temperature and brightness adjustment commands, based on historical adjustment data and user feedback, while meeting visual comfort, energy-saving requirements, and equipment performance limitations. Finally, the gradient descent method in machine learning is used to iteratively optimize the control parameters, reducing abrupt changes and oscillations during the adjustment process, resulting in a color temperature adjustment algorithm module with smooth transition characteristics.
[0008] Furthermore, the integration of lighting, curtain, and temperature control modules through a central controller links color temperature adjustment commands with device actions. Adopting an open protocol compatible with mainstream platforms, it enables one-click switching of scene modes, resulting in a multi-device collaborative control scheme. This includes the following steps: A modular integration design is adopted, building a unified control interface in the central controller to connect and software-bind the color temperature adjustment modules of the lighting fixtures, the opening and closing control modules of the curtains, and the temperature adjustment module of the temperature control system; by parsing the target parameters in the color temperature adjustment commands, the corresponding device action requirements are extracted, and the commands are converted into control signals for changes in lighting brightness and color temperature, adjustments to the opening and closing angles of the curtains, and settings of the air conditioning temperature; the MQTT open communication protocol is used to ensure data interoperability with mainstream smart home platforms, triggering multi-device linkage through a single operating interface, resulting in a multi-device collaborative control scheme covering all scenarios.
[0009] Furthermore, the process of collecting user comfort scores and usage habit data via a mobile app, combining this with environmental parameters recorded by sensors, and employing a reinforcement learning algorithm to dynamically adjust strategies, integrates user preferences into the temperature control module to obtain a personalized adaptive optimization module. This includes the following steps: Using a data-driven optimization strategy, an interactive interface is designed in the mobile app to collect user feedback on the comfort level of the current environment, while simultaneously recording users' daily usage habits, including common scene modes and adjustment frequencies; Real-time acquisition of ambient light, temperature, and humidity data is achieved through sensors, and user feedback is correlated with environmental data to extract user preference features; A reinforcement learning algorithm is used, with user comfort as the reward function, to dynamically adjust the color temperature and brightness output strategies of the temperature control module. Through iterative training, personalized user needs are integrated into the control logic to obtain a personalized adaptive optimization module that can autonomously adapt to different user preferences.
[0010] The second aspect of this invention provides an intelligent color temperature adaptation system for indoor lighting. This system includes the following modules: a demand matching module, used to classify indoor lighting into three modes—basic, scenario, and dynamic—using scene function analysis combined with user behavior surveys and lighting design standards, and obtaining an adaptive lighting mode classification table by matching different activity needs; a data acquisition module, used to collect ambient light intensity, color temperature, personnel location, and time information in real time by deploying light, human infrared, time, and temperature / humidity sensors, and wirelessly transmit the data to the controller to obtain a multi-dimensional environmental parameter dataset; and a color temperature adjustment module, used to adjust the color temperature based on fuzzy control theory, using time, light intensity, location, and... The system uses scenario requirements as input variables. A multi-constraint optimization algorithm outputs color temperature and brightness adjustment commands. Machine learning is used for iterative optimization to obtain a smooth-transition color temperature adjustment algorithm module. A collaborative control module integrates lighting, curtain, and temperature control modules through a central controller, associating color temperature adjustment commands with device actions. An open protocol is used to ensure compatibility with mainstream platforms, enabling one-click switching of scene modes and resulting in a multi-device collaborative control solution. A feedback optimization module collects user comfort scores and usage habit data via a mobile app. Combined with environmental parameters recorded by sensors, a reinforcement learning algorithm is used to dynamically adjust strategies, incorporating user preferences into the temperature control module to obtain a personalized adaptive optimization module.
[0011] A third aspect of the present invention provides an intelligent indoor lighting color temperature adaptation device, the intelligent indoor lighting color temperature adaptation device comprising 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 indoor lighting color temperature adaptation device to perform the steps of the intelligent indoor lighting color temperature adaptation 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 indoor lighting color temperature intelligent adaptation method as described in any one of the preceding claims.
[0013] In the technical solution provided by this invention, a scene function analysis method is adopted, combined with user behavior research and lighting design standards, to divide indoor lighting into three types of modes: basic, scene, and dynamic. By matching different activity needs, an adaptive lighting mode classification table is obtained. By deploying light, human infrared, time, and temperature and humidity sensors, ambient light intensity, color temperature, personnel location, and time information are collected in real time and wirelessly transmitted to the controller to obtain a multi-dimensional environmental parameter dataset. Based on fuzzy control theory, with time, light, location, and scene requirements as input variables, a multi-constraint optimization algorithm is used to output color temperature and brightness adjustment commands. Machine learning is used for iterative optimization to obtain a smooth transition color temperature adjustment algorithm module. The central controller integrates lighting fixtures, curtains, and temperature control modules, associating color temperature adjustment commands with device actions. An open protocol is used to be compatible with mainstream platforms, enabling one-click switching of scene modes and obtaining a multi-device collaborative control scheme. User comfort scores and usage habit data are collected through a mobile APP. Combined with environmental parameters recorded by sensors, a reinforcement learning algorithm is used to dynamically adjust the strategy, incorporating user preferences into the temperature control module to obtain a personalized adaptive optimization module. This invention solves the problems of traditional indoor lighting mode division failing to accurately adapt to different activity needs, incomplete environmental parameter collection leading to limited intelligent adjustment, poor inter-device coordination making it difficult to switch scenes with one click, and the inability to dynamically optimize and achieve personalized adaptation based on user feedback. 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 the first embodiment of an intelligent color temperature adaptation method for indoor lighting according to the present invention.
[0016] Figure 2 This is a schematic diagram of a second embodiment of an indoor lighting color temperature intelligent adaptation method according to an embodiment of the present invention.
[0017] Figure 3 This is a schematic diagram of a third embodiment of an indoor lighting color temperature intelligent adaptation method according to an embodiment of the present invention.
[0018] Figure 4 This is a schematic diagram of the fourth embodiment of an indoor lighting color temperature intelligent adaptation method according to the present invention.
[0019] Figure 5 This is a schematic diagram of the fifth embodiment of an indoor lighting color temperature intelligent adaptation 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 color temperature adaptation of indoor lighting, such as Figure 1 As shown, the process includes the following steps: Using scene function analysis, combined with user behavior research and lighting design standards, indoor lighting is divided into three modes: basic, scene, and dynamic. An adaptive lighting mode classification table is obtained by matching different activity needs. By deploying sensors for illumination, human infrared, time, and temperature and humidity, ambient light intensity, color temperature, personnel location, and time information are collected in real time and wirelessly transmitted to the controller, resulting in a multi-dimensional environmental parameter dataset. Based on fuzzy control theory, using time, illumination, location, and scene requirements as input variables, a multi-constraint optimization algorithm outputs color temperature and brightness adjustment commands. Machine learning is used for iterative optimization to obtain a smooth-transition color temperature adjustment algorithm module. The central controller integrates lighting fixtures, curtains, and temperature control modules, associating color temperature adjustment commands with device actions. An open protocol is used to ensure compatibility with mainstream platforms, enabling one-click switching of scene modes and obtaining a multi-device collaborative control scheme. A mobile app collects user comfort scores and usage habit data. Combined with environmental parameters recorded by sensors, a reinforcement learning algorithm is used to dynamically adjust strategies, incorporating user preferences into the temperature control module, resulting in a personalized adaptive optimization module.
[0023] like Figure 2 As shown, in this embodiment, the scene function analysis method is used to systematically sort out the functions of the indoor space, clarifying daily activities, specific tasks, and diurnal rhythm changes; through user behavior surveys, the lighting demand characteristics under different scenarios are extracted, and further lighting demand characteristics include brightness preference, color temperature range, and duration. Combined with architectural lighting design standards, the demand is transformed into quantitative indicators; the functional classification and demand indicators are cross-matched to divide the basic mode into daily activities, scene mode into specific tasks, and dynamic mode into diurnal changes, resulting in an adaptive lighting mode classification table.
[0024] By systematically analyzing the functions of indoor spaces using a scenario-based functional analysis method, and combining this with user behavior surveys to extract lighting needs and convert them into quantifiable indicators, three lighting modes were identified through cross-matching with functional categories, forming an adaptive lighting mode classification table. This approach makes the lighting mode classification more scientific and reasonable, accurately matching the lighting needs of different scenarios. Whether for daily activities, specific tasks, or changes in circadian rhythms, suitable lighting is provided, effectively improving users' visual comfort and experience in different indoor scenarios, laying a solid foundation for subsequent intelligent and personalized lighting adjustments.
[0025] Specific scene function analysis is a scientific and practical analytical method commonly used in fields such as indoor lighting. It first systematically analyzes the function of a specific space, clarifying the needs of various activities in different scenarios, such as daily living, work / study, and leisure / entertainment. Next, it delves into the specific lighting requirements of each scenario, such as brightness, color temperature, and uniformity. Through this method, the inherent relationship between different scenarios and lighting needs can be accurately grasped, providing a solid basis for the subsequent rational division of lighting modes and design of lighting schemes, thereby creating a more practical, comfortable, and efficient lighting environment.
[0026] like Figure 3 As shown, in this embodiment, a distributed sensor layout strategy is adopted. Light sensors are deployed indoors to extract ambient light intensity and color temperature data in real time. Human infrared sensors are used to obtain information on the location and activity status of personnel. An integrated time module records the current moment, and temperature and humidity sensors are configured to capture changes in ambient temperature and humidity. Multi-source data is synchronously transmitted to the central controller through a wireless communication protocol. Through data cleaning and fusion processing, effective information is extracted and integrated to obtain a multi-dimensional environmental parameter dataset including light, personnel, time, and temperature and humidity parameters.
[0027] Employing a distributed sensor deployment strategy, different types of sensors are deployed in multiple locations indoors to comprehensively and accurately collect multi-source data such as ambient light, personnel location and activity, time, temperature, and humidity. Synchronous transmission using wireless communication protocols ensures the timeliness and integrity of the data. After data cleaning and fusion processing, invalid information is effectively eliminated, and a multi-dimensional environmental parameter dataset is integrated. This dataset provides rich and accurate data support for subsequent intelligent color temperature adaptation of lighting, enabling the system to make scientific and reasonable adjustment decisions based on real-time environmental conditions, greatly improving the accuracy and reliability of intelligent indoor lighting control.
[0028] like Figure 4As shown, in this embodiment, the fuzzy control theory framework is adopted. The time parameter, ambient light intensity, personnel position distribution, and preset scenario requirements are used as the core input variables. The membership function is used to quantify the fuzzy influence of the variables on the color temperature and brightness. The multi-constraint optimization algorithm is used to generate the initial color temperature and brightness adjustment instruction set under the conditions of meeting visual comfort, energy-saving requirements, and equipment performance limitations. Based on historical adjustment data and user feedback, the gradient descent method in machine learning is used to iteratively optimize the control parameters, reduce the mutations and oscillations in the adjustment process, and obtain the color temperature adjustment algorithm module with smooth transition characteristics.
[0029] With the fuzzy control theory as the framework, selecting multi-core input variables and quantifying their fuzzy influence can comprehensively consider the combined effects of various factors on color temperature and brightness. Using the multi-constraint optimization algorithm to generate the initial adjustment instruction set takes into account multiple requirements such as visual comfort, energy conservation, and equipment performance. And using the gradient descent method to iteratively optimize the control parameters effectively reduces the mutations and oscillations in the adjustment, making the color temperature adjustment have smooth transition characteristics. This not only improves the accuracy and stability of lighting adjustment but also creates a more comfortable and natural light environment for users, greatly enhancing the practicality and user experience of intelligent adaptation of indoor lighting color temperature.
[0030] As Figure 5 shown, in this embodiment, modular integrated design is adopted. A unified control interface is built in the central controller, and the hardware connection and software binding of the color temperature adjustment module of the lamp, the opening and closing control module of the curtain, and the temperature adjustment module of the temperature control system are carried out. By parsing the target parameters in the color temperature adjustment instruction, the corresponding device action requirements are extracted, and the instruction is converted into control signals for the change of the lamp brightness and color temperature, the adjustment of the curtain opening and closing angle, and the setting of the air conditioner temperature. The MQTT open communication protocol is used to ensure data interconnection with mainstream smart home platforms, and multi-device linkage is triggered through a single operation interface to obtain a multi-device collaborative control solution covering the entire scene.
[0031] The modular integrated design with a unified control interface realizes the hardware connection and software binding of multiple devices such as lamps, curtains, and temperature control systems, with a clear structure and convenient management and maintenance. Parsing the color temperature adjustment instruction and converting it into accurate control signals can flexibly adjust the lamps, curtains, and air conditioners according to requirements to meet diverse scenario needs. Using the MQTT open communication protocol ensures data interconnection with mainstream smart home platforms and breaks down the barriers between devices of different brands. Triggering multi-device linkage through a single operation interface greatly improves the operation convenience.
[0032] In this embodiment, a data-driven optimization strategy is adopted. An interactive interface is designed in a mobile APP to collect user feedback on the comfort level of the current environment, and to record users' daily usage habits, including commonly used scene modes and adjustment frequencies. Ambient light, temperature and humidity are acquired in real time through sensors, and user feedback is correlated with environmental data to extract user preference features. A reinforcement learning algorithm is used, with user comfort as the reward function, to dynamically adjust the color temperature and brightness output strategy of the temperature control module. Through iterative training, personalized user needs are integrated into the control logic to obtain a personalized adaptive optimization module that can autonomously adapt to different user preferences.
[0033] By collecting user comfort ratings and daily usage habits through a mobile app interface and combining this with environmental data acquired from sensors, the system can accurately extract user preference features, providing a basis for personalized services. Employing a reinforcement learning algorithm, the system dynamically adjusts the output strategy of the temperature control module using user comfort as the reward function, and through iterative training, incorporates personalized needs into the control logic. This not only enables the lighting system to autonomously adapt to different user preferences, providing a lighting environment more tailored to user needs, but also enhances the user experience and increases user satisfaction and reliance on the smart lighting system.
[0034] This invention also provides an intelligent color temperature adaptation system for indoor lighting, comprising the following modules: a demand matching module, used to divide indoor lighting into three modes—basic, scene, and dynamic—using scene function analysis combined with user behavior surveys and lighting design standards, and obtaining an adaptive lighting mode classification table by matching different activity needs; a data acquisition module, used to collect ambient light intensity, color temperature, personnel location, and time information in real time by deploying light, human infrared, time, and temperature and humidity sensors, and wirelessly transmit the data to the controller to obtain a multi-dimensional environmental parameter dataset; and a color temperature adjustment module, used to adjust the color temperature based on fuzzy control theory, taking into account time, light, location, and scene needs. The system takes the input variable "Q" and outputs color temperature and brightness adjustment commands through a multi-constraint optimization algorithm. It then employs machine learning for iterative optimization to obtain a smooth-transition color temperature adjustment algorithm module. A collaborative control module integrates lighting, curtain, and temperature control modules through a central controller, associating color temperature adjustment commands with device actions. It uses an open protocol compatible with mainstream platforms to achieve one-click switching of scene modes, resulting in a multi-device collaborative control scheme. A feedback optimization module collects user comfort scores and usage habit data via a mobile app. Combined with environmental parameters recorded by sensors, it uses a reinforcement learning algorithm to dynamically adjust strategies, incorporating user preferences into the temperature control module to obtain a personalized adaptive optimization module.
[0035] This invention also provides an intelligent indoor lighting color temperature adaptation device. This device 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 structure of the intelligent indoor lighting color temperature adaptation device 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 each step of the indoor lighting color temperature intelligent adaptation 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 color temperature adaptation of indoor lighting, characterized in that, The intelligent color temperature adaptation method for indoor lighting includes the following steps: Using the scenario function analysis method, combined with user behavior research and lighting design standards, indoor lighting is divided into three types of modes: basic, scenario, and dynamic. By matching different activity needs, an adaptive lighting mode classification table is obtained. By deploying light, human infrared, time, and temperature and humidity sensors, the system collects ambient light intensity, color temperature, personnel location, and time information in real time, and wirelessly transmits the data to the controller to obtain a multi-dimensional environmental parameter dataset. Based on fuzzy control theory, with time, illumination, position and scene requirements as input variables, a multi-constraint optimization algorithm is used to output color temperature and brightness adjustment commands. Machine learning is used for iterative optimization to obtain a color temperature adjustment algorithm module with smooth transition. By integrating lighting, curtain and temperature control modules through a central controller, color temperature adjustment commands are associated with device actions. An open protocol is adopted to be compatible with mainstream platforms, enabling one-click switching of scene modes and obtaining a multi-device collaborative control solution. By collecting user comfort ratings and usage habit data through a mobile app, and combining them with environmental parameters recorded by sensors, a reinforcement learning algorithm is used to dynamically adjust the strategy, incorporating user preferences into the temperature control module to obtain a personalized adaptive optimization module.
2. The method for intelligent color temperature adaptation of indoor lighting according to claim 1, characterized in that, The method employs scene function analysis, combined with user behavior research and lighting design standards, to classify indoor lighting into three modes: basic, scene, and dynamic. By matching different activity needs, an adaptive lighting mode classification table is obtained, including the following steps: Using the scenario function analysis method, the functions of the interior space are systematically sorted out to clarify daily activities, specific tasks and diurnal rhythm changes; Through user behavior research, the characteristics of lighting needs in different scenarios are extracted. These characteristics include brightness preference, color temperature range, and duration. Combined with architectural lighting design standards, the needs are transformed into quantitative indicators. By cross-matching functional categories with demand indicators, we can divide the lighting into basic modes that cover daily activities, scene modes that adapt to specific tasks, and dynamic modes that respond to day and night changes, thus obtaining an adaptive lighting mode classification table.
3. The method for intelligent color temperature adaptation of indoor lighting according to claim 1, characterized in that, The process involves deploying sensors for illumination, human infrared sensors, time sensors, and temperature and humidity sensors to collect real-time information on ambient light intensity, color temperature, personnel location, and time. This data is then wirelessly transmitted to the controller to obtain a multi-dimensional environmental parameter dataset. The process includes the following steps: A distributed sensor deployment strategy is adopted, with indoor light sensors deployed to extract ambient light intensity and color temperature data in real time, and human infrared sensors used to obtain information on personnel location and activity status. The integrated time module records the current moment, and the configured temperature and humidity sensors capture changes in ambient temperature and humidity. Multi-source data is synchronously transmitted to the central controller via a wireless communication protocol. Through data cleaning and fusion processing, effective information is extracted and integrated to obtain a multi-dimensional environmental parameter dataset including parameters such as illumination, personnel, time, and temperature and humidity.
4. The method for intelligent color temperature adaptation of indoor lighting according to claim 1, characterized in that, The aforementioned color temperature adjustment algorithm module, based on fuzzy control theory and using time, illumination, location, and scene requirements as input variables, outputs color temperature and brightness adjustment commands through a multi-constraint optimization algorithm. It then employs machine learning for iterative optimization to obtain a smooth-transition color temperature adjustment algorithm module, comprising the following steps: Using the fuzzy control theory framework, time parameters, ambient light intensity, personnel location distribution, and preset scene requirements are taken as core input variables. The fuzzy influence of the variables on color temperature and brightness is quantified through membership functions. Using a multi-constraint optimization algorithm, an initial color temperature and brightness adjustment instruction set is generated under the conditions of meeting visual comfort, energy saving requirements and equipment performance limitations, based on historical adjustment data and user feedback; The gradient descent method in machine learning is used to iteratively optimize the control parameters, reducing abrupt changes and oscillations during the adjustment process, resulting in a color temperature adjustment algorithm module with smooth transition characteristics.
5. The method for intelligent color temperature adaptation of indoor lighting according to claim 1, characterized in that, The process involves integrating lighting, curtain, and temperature control modules through a central controller, associating color temperature adjustment commands with device actions, adopting an open protocol compatible with mainstream platforms, and achieving one-click switching of scene modes to obtain a multi-device collaborative control solution. This includes the following steps: The modular integrated design is adopted, and a unified control interface is built in the central controller to connect the color temperature adjustment module of the lamps, the opening and closing control module of the curtains, and the temperature adjustment module of the temperature control system in hardware and software. By analyzing the target parameters in the color temperature adjustment command, the corresponding equipment action requirements are extracted and the command is converted into control signals for changes in lamp brightness and color temperature, adjustment of curtain opening and closing angle, and setting of air conditioner temperature. The MQTT open communication protocol is used to ensure data interoperability with mainstream smart home platforms. Multiple devices can be linked through a single operation interface to achieve a multi-device collaborative control solution covering all scenarios.
6. The method for intelligent color temperature adaptation of indoor lighting according to claim 1, characterized in that, The process of collecting user comfort scores and usage habit data via a mobile app, combining this with environmental parameters recorded by sensors, and using a reinforcement learning algorithm to dynamically adjust strategies, incorporating user preferences into the temperature control module, results in a personalized adaptive optimization module. This includes the following steps: A data-driven optimization strategy is adopted, and an interactive interface is designed in the mobile APP to collect user feedback on the comfort level of the current environment, while recording users' daily usage habits, including common scenario modes and adjustment frequency. By acquiring ambient light, temperature and humidity data in real time through sensors, user feedback is correlated with environmental data for analysis, and user preference features are extracted. By employing a reinforcement learning algorithm and using user comfort as the reward function, the color temperature and brightness output strategies of the temperature control module are dynamically adjusted. Through iterative training, personalized user needs are integrated into the control logic, resulting in a personalized adaptive optimization module that can autonomously adapt to different user preferences.
7. An intelligent color temperature adaptation system for indoor lighting, characterized in that, The indoor lighting color temperature intelligent adaptation system includes the following modules: The demand matching module is used to divide indoor lighting into three types: basic, scene, and dynamic modes by using the scenario function analysis method, combined with user behavior research and lighting design standards. By matching different activity needs, an adaptive lighting mode classification table is obtained. The data acquisition module is used to collect ambient light intensity, color temperature, personnel location and time information in real time by deploying light, human infrared, time and temperature and humidity sensors, and wirelessly transmit the data to the controller to obtain a multi-dimensional environmental parameter dataset. The color temperature adjustment module is based on fuzzy control theory, taking time, illumination, position and scene requirements as input variables, and outputs color temperature and brightness adjustment commands through a multi-constraint optimization algorithm. It also uses machine learning to iteratively optimize and obtain a color temperature adjustment algorithm module with smooth transition. The collaborative control module is used to integrate lighting, curtain and temperature control modules through the central controller, associate color temperature adjustment commands with device actions, adopt open protocol to be compatible with mainstream platforms, realize one-click switching of scene mode, and obtain multi-device collaborative control solution; The feedback optimization module collects user comfort scores and usage habit data through a mobile app. Combined with environmental parameters recorded by sensors, it uses reinforcement learning algorithms to dynamically adjust strategies, incorporating user preferences into the temperature control module to obtain a personalized adaptive optimization module.
8. An intelligent color temperature adaptation device for indoor lighting, characterized in that, The indoor lighting color temperature intelligent adaptation device includes a memory and at least one processor. The memory stores instructions, and the at least one processor calls the instructions in the memory to cause the indoor lighting color temperature intelligent adaptation device to perform the steps of the indoor lighting color temperature intelligent adaptation 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 indoor lighting color temperature intelligent adaptation method as described in any one of claims 1-6.