Intelligent lighting control method and system based on titanium material

Abstract

This invention discloses a smart lighting control method and system based on titanium materials, comprising the following steps: S1: Real-time monitoring of environmental parameters through integrated sensors and miniature cameras in the lighting device; S2: Inputting the environmental parameters collected by the integrated sensors and miniature cameras in the titanium material into a pre-trained transformer model, which outputs a lighting control strategy for the current environment; S3: Automatically and dynamically adjusting the brightness and color temperature of the LED light source in the titanium material smart lighting system according to the generated lighting control strategy; S4: Remotely controlling and monitoring the titanium material smart lighting system via a wireless communication module, including turning the lights on and off, adjusting brightness and color temperature, and setting timed tasks. This application utilizes an improved transformer model for real-time environmental parameter analysis to automatically and dynamically adjust the brightness and color temperature of the LED light source in the titanium material smart lighting system to adapt to the current environment and user needs, greatly improving the user experience.

Description

Technical Field

[0001] This invention relates to the field of smart home technology, specifically to a smart lighting control method and system based on titanium materials. Background Technology

[0002] In recent years, with the rapid development of smart home and IoT technologies, smart lighting systems have received widespread attention and application. These systems not only enable remote control and intelligent management through wireless communication, but also automatically adjust brightness and color temperature based on environmental conditions and user needs, providing more comfortable and energy-efficient lighting solutions. However, existing smart lighting systems still have many problems in material selection, environmental sensing, and intelligent control, requiring urgent improvement. Furthermore, most existing smart lighting systems use traditional materials such as aluminum alloys and plastics. While these materials are low-cost, they have shortcomings in durability, heat dissipation, and environmental adaptability. Especially under harsh environmental conditions, lighting equipment made of traditional materials is susceptible to corrosion and damage, affecting its lifespan and reliability. Titanium, due to its excellent corrosion resistance, high strength, and lightweight properties, has been widely used in aerospace and medical fields, but its application in smart lighting systems has not yet been fully explored.

[0003] Currently, the environmental perception capability of intelligent lighting systems directly impacts their intelligence level and user experience. Existing systems typically rely on independent light sensors, temperature sensors, and motion sensors for environmental data collection. However, the installation and maintenance of these sensors are complex, and they are susceptible to environmental interference, leading to insufficient data accuracy and reliability. Furthermore, traditional sensor technologies cannot effectively perceive multidimensional changes in the environment, such as light intensity, temperature, mechanical stress, and motion states, failing to provide comprehensive environmental data to support intelligent lighting control. In addition, regarding intelligent control, most current intelligent lighting systems employ simple rule-based control or pattern recognition algorithms based on historical data, making it difficult to achieve accurate responses to complex environmental changes and user behaviors. With the rapid development of machine learning and deep learning technologies, there is significant room for improvement in intelligent control algorithms, especially the transformer model. Due to its superior performance in processing time-series and multimodal data, it is expected to become the next generation of intelligent control algorithms for intelligent lighting systems.

[0004] While intelligent lighting control technology has achieved successful applications in many fields, current algorithm models lack strategies for dynamic adjustment based on a combination of light, temperature, and video images. Specifically, they fail to effectively integrate video image data for comprehensive analysis and do not account for damage caused by collisions between lights or damage to outdoor lights. For example, how to monitor collisions between groups of artistic chandeliers? When a group of aesthetically pleasing movable chandeliers is affected by strong winds or human intervention, collisions are unavoidable. Research on self-healing materials and stress-sensitive self-healing mechanisms is currently lacking, and these remain pressing technical challenges in the field of high-end intelligent lighting control. Furthermore, existing transformer models do not consider the combined effects of video images, collision forces, light, and temperature, leading to inaccurate judgments regarding light adjustment and self-healing. Therefore, a new solution is urgently needed to improve processing efficiency, judgment accuracy, and customer satisfaction. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention provides a smart lighting control method and system based on titanium materials. The method first monitors environmental parameters in real time using integrated sensors and miniature cameras within the lighting device. Second, the environmental parameters collected by the integrated sensors and miniature cameras in the titanium material are input into a pre-trained transformer model, which outputs a lighting control strategy for the current environment. Finally, based on the generated lighting control strategy, the brightness and color temperature of the LED light source in the titanium material smart lighting system are automatically and dynamically adjusted to adapt to the current environment and user needs. This application significantly improves the user experience by utilizing an improved transformer model for real-time environmental parameter analysis and automatically and dynamically adjusting the brightness and color temperature of the LED light source in the titanium material smart lighting system to adapt to the current environment and user needs.

[0006] This application provides a smart lighting control method based on titanium material, including the following steps:

[0007] S1: Environmental parameters are monitored in real time through integrated sensors and miniature cameras in the lighting device. The environmental parameters include light intensity, temperature, stress, and video images. The integrated sensors include a network of miniature sensors embedded in the titanium material for monitoring light intensity, temperature, and stress, which can sense changes in ambient light intensity, temperature, and mechanical stress through changes in surface resistance. The miniature cameras include cameras mounted on the surface of the titanium material.

[0008] S2: Environmental parameters collected by integrated sensors and miniature cameras in the titanium material are input into a pre-trained transformer model. The transformer model outputs a lighting control strategy for the current environment, including switching lights on and off, adjusting brightness, and adjusting color temperature. The transformer model uses an improved fusion attention mechanism as follows:

[0009]

[0010] Where y represents user preference, Q1, K1, and V1 are the query, key, and value matrices of sensor data, respectively; Q2, K2, and V2 are the query, key, and value matrices of camera data, respectively; W c The weight matrix for conditional encoding; d k is the dimension of the key matrix; T is the transpose of the matrix;

[0011] S3: Based on the generated lighting control strategy, automatically and dynamically adjust the brightness and color temperature of the LED light source in the titanium material intelligent lighting system to adapt to the current environment and user needs;

[0012] S4: Users can remotely control and monitor the titanium intelligent lighting system via the wireless communication module, including turning the lights on and off, adjusting brightness and color temperature, and setting timed tasks.

[0013] Preferably, the micro-sensor network includes a temperature sensor, a stress sensor, and a light sensor, wherein the light sensor can sense the intensity of light at different wavelengths to improve the accuracy of ambient light detection.

[0014] Preferably, the wireless communication module supports Wi-Fi, Bluetooth, and Zigbee protocols to enable seamless connection and data transmission between different smart devices.

[0015] Preferably, the titanium material shell is coated with a self-healing coating, the specific material composition of which includes microcapsules, a catalyst, and a matrix material, wherein: the microcapsules contain a repair agent polymer monomer or a low molecular weight compound; the catalyst is nano-titanium dioxide or vanadium oxide; and the matrix material is epoxy resin or polyurethane.

[0016] Preferably, the self-healing process of the self-healing coating is as follows: when the surface of the lighting device housing is slightly damaged, the microcapsules rupture and release the internal repair agent; the repair agent comes into contact with the catalyst in the coating, and a chemical reaction or molecular self-assembly occurs under the action of the catalyst; a new polymer or self-assembled structure is formed, filling and repairing the damaged area, restoring the integrity and function of the coating.

[0017] This application also provides an intelligent lighting control system based on titanium material, including:

[0018] Environmental parameter acquisition module: Real-time monitoring of environmental parameters, including light intensity, temperature, stress, and video images, through integrated sensors and miniature cameras in the lighting device. The integrated sensors include a network of miniature sensors embedded in titanium material for monitoring light intensity, temperature, and stress, which can sense changes in ambient light intensity, temperature, and mechanical stress through changes in surface resistance; the miniature camera includes a camera mounted on the surface of the titanium material.

[0019] Transformer Model Calculation Module: Environmental parameters collected by integrated sensors and miniature cameras in the titanium material are input into a pre-trained transformer model. The transformer model outputs a lighting control strategy for the current environment, including switching lights on and off, adjusting brightness, and adjusting color temperature. The transformer model uses an improved fusion attention mechanism as shown below:

[0020]

[0021] Where y represents user preference, Q1, K1, and V1 are the query, key, and value matrices of sensor data, respectively; Q2, K2, and V2 are the query, key, and value matrices of camera data, respectively; W c The weight matrix for conditional encoding; d k is the dimension of the key matrix; T is the transpose of the matrix;

[0022] Control strategy adjustment module: Based on the generated lighting control strategy, automatically and dynamically adjust the brightness and color temperature of the LED light source in the titanium material intelligent lighting system to adapt to the current environment and user needs;

[0023] User-selectable control module: Users can remotely select and control the titanium material intelligent lighting system through the wireless communication module, including turning the lights on and off, adjusting brightness and color temperature, and setting timed tasks.

[0024] Preferably, the micro-sensor network includes a temperature sensor, a stress sensor, and a light sensor, wherein the light sensor can sense the intensity of light at different wavelengths to improve the accuracy of ambient light detection.

[0025] Preferably, the wireless communication module supports Wi-Fi, Bluetooth, and Zigbee protocols to enable seamless connection and data transmission between different smart devices.

[0026] Preferably, the titanium material shell is coated with a self-healing coating, the specific material composition of which includes microcapsules, a catalyst, and a matrix material, wherein: the microcapsules contain a repair agent polymer monomer or a low molecular weight compound; the catalyst is nano-titanium dioxide or vanadium oxide; and the matrix material is epoxy resin or polyurethane.

[0027] Preferably, the self-healing process of the self-healing coating is as follows: when the surface of the lighting device housing is slightly damaged, the microcapsules rupture and release the internal repair agent; the repair agent comes into contact with the catalyst in the coating, and a chemical reaction or molecular self-assembly occurs under the action of the catalyst; a new polymer or self-assembled structure is formed, filling and repairing the damaged area, restoring the integrity and function of the coating.

[0028] This invention provides an intelligent lighting control method and system based on titanium materials, which achieves the following beneficial technical effects:

[0029] 1. This invention first monitors environmental parameters in real time using integrated sensors and miniature cameras in the lighting device; secondly, the environmental parameters collected by the integrated sensors and miniature cameras in the titanium material are input into a pre-trained transformer model, which outputs a lighting control strategy for the current environment; finally, based on the generated lighting control strategy, the brightness and color temperature of the LED light source in the titanium material intelligent lighting system are automatically and dynamically adjusted to adapt to the current environment and user needs. This application, by utilizing an improved transformer model for real-time environmental parameter analysis, automatically and dynamically adjusts the brightness and color temperature of the LED light source in the titanium material intelligent lighting system to adapt to the current environment and user needs, greatly improving the user experience.

[0030] 2. This invention employs an integrated sensor and miniature camera in titanium material to collect environmental parameters, which are then input into a pre-trained transformer model. The transformer model outputs a lighting control strategy for the current environment, including switching lights on and off, adjusting brightness, and adjusting color temperature. The transformer model uses an improved fusion attention mechanism, as shown below:

[0031]

[0032] Where y represents user preference, Q1, K1, and V1 are the query, key, and value matrices of sensor data, respectively; Q2, K2, and V2 are the query, key, and value matrices of camera data, respectively. This application combines the query, key, and value matrices of sensor data with those of camera data for calculation through an improved fusion attention mechanism, which greatly improves the accuracy of lighting control strategies and enhances user experience.

[0033] 3. The titanium material shell of the present invention is coated with a self-healing coating. The specific material composition of the self-healing coating includes microcapsules, a catalyst, and a matrix material, wherein: the microcapsules contain a repair agent polymer monomer or a low molecular weight compound; the catalyst is nano-titanium dioxide or vanadium oxide; the matrix material is epoxy resin or polyurethane. When the surface of the lighting device shell is slightly damaged, the microcapsules rupture and release the internal repair agent; the repair agent comes into contact with the catalyst in the coating, and a chemical reaction or molecular self-assembly occurs under the action of the catalyst; a new polymer or self-assembled structure is formed, filling and repairing the damaged area, restoring the integrity and function of the coating. By adding titanium material to situations with high impact damage, the maintenance cost and efficiency of the lamps are greatly reduced, and the self-maintenance level of multifunctional lamps is effectively improved. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a schematic diagram of the steps of an intelligent lighting control method based on titanium material according to the present invention;

[0036] Figure 2 This is a schematic diagram of an intelligent lighting control system based on titanium material according to the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Example 1:

[0039] In view of the aforementioned problems mentioned in the prior art, and in order to solve the above technical problems, as shown in the appendix. Figure 1 As shown: This application provides a smart lighting control method based on titanium materials, including the following steps:

[0040] S1: Environmental parameters, including light intensity, temperature, stress, and video images, are monitored in real time via integrated sensors and miniature cameras within the lighting device. The integrated sensors include a network of miniature sensors embedded in titanium material for monitoring light intensity, temperature, and stress, capable of sensing changes in ambient light intensity, temperature, and mechanical stress through variations in surface resistance. The miniature camera includes a camera mounted on the surface of the titanium material. In some embodiments, ambient light intensity is monitored by a light sensor embedded in the titanium material, capable of sensing different wavelengths of light to achieve a comprehensive assessment of ambient lighting conditions. Ambient temperature changes are monitored by a temperature sensor embedded in the titanium material. The temperature sensor provides accurate temperature data by measuring changes in surface resistance. Changes in mechanical stress are monitored by a stress sensor embedded in the titanium material. Stress sensors can detect stress and strain on structures through the strain resistance effect of titanium materials. Miniature cameras mounted on the titanium surface capture environmental images and videos in real time, aiding in the analysis of light, temperature, and stress data and providing visual information for intelligent control. Sensor networks embedded in the titanium material ensure direct contact between the sensors and the environment, improving data accuracy and real-time performance. Using photoresistors (LDRs) or photodiodes, different wavelengths of light intensity can be detected. Thermistors or miniature thermocouples are used to sense temperature through changes in surface resistance. Strain gauges or piezoelectric sensors are used to sense mechanical stress by measuring strain changes on the titanium surface. High-definition miniature cameras mounted on the titanium surface can capture environmental video and images in real time, providing high-resolution visual data. An embedded image processing module analyzes the video images in real time to identify dynamic changes and specific events in the environment.

[0041] In one embodiment, a smart luminaire installed in the center of the living room in a home smart lighting system enables real-time monitoring and intelligent control of environmental parameters through a sensor network and miniature camera integrated into a titanium housing. A light sensor is installed on the top of the lamp to monitor the light intensity in the room in real time by sensing reflected light from the ceiling and the room. A temperature sensor is embedded inside the titanium housing to sense changes in room temperature through changes in surface resistance. A stress sensor is installed on the lamp bracket to monitor the mechanical stress on the lamp, ensuring its stability and safety. A miniature camera is installed on the edge of the lamp housing, covering the main activity areas of the room and capturing the activities in the room in real time. The system collects light intensity, temperature, and stress data in real time and comprehensively analyzes the environmental status through images and videos captured by the miniature camera. When the light sensor detects insufficient light in the room, it automatically increases the brightness of the lamp; when the temperature sensor detects temperature changes, it adjusts the color temperature of the light to improve comfort; when the stress sensor detects abnormal stress changes in the lamp, it sends an alarm message. The transformer model generates an optimized lighting control strategy based on sensor and camera data. Users can remotely control the lamp's on / off state, brightness, and color temperature through a mobile application and set timed tasks. At different times of the day, the lamp automatically adjusts its brightness and color temperature according to changes in natural light to provide the most comfortable lighting effect. When the camera detects that no one is in the room, the system automatically reduces the brightness or turns off the lamp to save energy.

[0042] In some embodiments, titanium materials include titanium alloys and pure titanium. The first titanium alloy composition is Ti-6Al-4V (6% aluminum, 4% vanadium, balance titanium), one of the most commonly used titanium alloys, possessing high strength, good corrosion resistance, and excellent machinability. The second is Ti-3Al-2.5V (3% aluminum, 2.5% vanadium, balance titanium), a titanium alloy with moderate strength and high ductility, suitable for applications requiring higher toughness and durability. Pure titanium includes two grades: Grade I and Grade II. Grade I pure titanium has excellent ductility and corrosion resistance, suitable for applications that do not require high strength but require good corrosion resistance. Grade II pure titanium has good overall performance, including moderate strength, good ductility, and excellent corrosion resistance, and is the most widely used grade of pure titanium.

[0043] In some embodiments, the lighting equipment housing adopts an integrated design, avoiding weaknesses in splicing and welding areas, improving the overall strength and reliability of the structure. The housing surface is precision-machined to ensure the installation accuracy of sensors and cameras, improving the accuracy of environmental data acquisition. The housing surface is anodized or coated with a nano-coating to improve surface hardness and wear resistance, while enhancing aesthetics. A self-healing coating technology is employed, allowing the coating to automatically repair itself through molecular self-assembly or chemical reaction when the surface is slightly damaged, maintaining the integrity and function of the housing. Miniature sensors are embedded in key locations within the titanium housing, ensuring direct contact between the sensors and the environment, improving the real-time nature and accuracy of the data. The sensors detect ambient light intensity, temperature, and mechanical stress through changes in surface resistance; the integrated network of miniature sensors within the housing enables comprehensive environmental monitoring. High-definition miniature cameras are installed in appropriate locations within the titanium housing, ensuring the camera's field of view covers the main monitoring area of ​​the device; the camera is tightly integrated with the titanium housing, employing a waterproof and dustproof design to ensure long-term stable operation under various environmental conditions.

[0044] S2: Environmental parameters collected by integrated sensors and miniature cameras in the titanium material are input into a pre-trained transformer model. The transformer model outputs a lighting control strategy for the current environment, including switching lights on and off, adjusting brightness, and adjusting color temperature. The transformer model uses an improved fusion attention mechanism as follows:

[0045]

[0046] Where y represents user preference, Q1, K1, and V1 are the query, key, and value matrices of sensor data, respectively; Q2, K2, and V2 are the query, key, and value matrices of camera data, respectively; W c The weight matrix for conditional encoding; d k is the dimension of the key matrix; T is the transpose of the matrix;

[0047] In some embodiments, a micro-sensor network in the titanium material monitors changes in light intensity, temperature, and stress in real time, outputting this data in numerical form; changes in ambient light intensity provide information about current lighting conditions; current ambient temperature data reflects real-time changes in ambient temperature; mechanical stress data on the titanium material structure monitors the stress on the shell; micro-cameras mounted on the titanium material surface capture real-time video images of the environment, which are converted into visual data to assist other sensor data in more accurate environmental analysis; sensor and camera data are cleaned to remove noise and outliers, ensuring data accuracy and reliability; data from different sensors are standardized to the same scale for easier subsequent processing and analysis; key features, such as light change trends, temperature fluctuation patterns, and stress change characteristics, are extracted from sensor and camera data; preprocessed sensor and camera data are fused together to form a comprehensive environmental data input; conditional coding will be used... User preferences, as conditional variables, are input into the transformer model along with environmental data to ensure that the output lighting control strategy meets the user's personalized needs. An improved fusion attention mechanism processes both sensor and camera data simultaneously, assigning different weights to different data features to ensure the model focuses on the most important environmental changes and user preferences. The model determines whether to turn lights on or off based on ambient light intensity and user activity. It dynamically adjusts the brightness of the lights based on changes in ambient light and user preferences to ensure optimal lighting effects in different time periods and activity scenarios. It adjusts the color temperature of the lights based on changes in ambient temperature and user comfort needs, simulating changes in natural light to improve user comfort. The transformer model generates lighting control strategies in real time and dynamically adjusts the brightness and color temperature of the LED light source through the intelligent lighting system, ensuring the lighting system can quickly respond to environmental changes. Through a wireless communication module, users can remotely control the lighting system via smart devices and view and adjust lighting settings in real time.

[0048] In one embodiment, in a smart office lighting system, ceiling-mounted smart luminaires utilize a sensor network and miniature cameras integrated into a titanium housing to achieve real-time monitoring and intelligent control of office environmental parameters. A light sensor, mounted on the top of the smart luminaire in the ceiling, monitors the office's light intensity in real time by sensing changes in natural and artificial light. A temperature sensor, embedded inside the titanium housing, senses indoor temperature through changes in surface resistance, ensuring real-time accuracy of temperature data. Stress sensors are installed at the luminaire brackets and connections to monitor mechanical stress, ensuring the safety and stability of the luminaire installation. A miniature camera, mounted on the edge of the luminaire housing, covers the office area and captures video images of employee activities and environmental changes in real time. The system collects light intensity, temperature, and stress data in real time and performs comprehensive analysis on the images captured by the camera. The collected data is cleaned and standardized to extract key features, ensuring data accuracy and consistency. Through pre-trained variable... The system analyzes environmental data and user preferences using a pressure sensor model to generate optimal lighting control strategies. When a light sensor detects insufficient indoor light, the system automatically increases the brightness of the lights. When a temperature sensor detects a temperature change, the system adjusts the color temperature of the lights. When a camera detects employee activity, the system automatically adjusts the light brightness to provide a suitable working environment. The intelligent lights can automatically adjust brightness and color temperature according to different times of day and changes in natural light to provide the most comfortable lighting effect. When a camera detects that no one is in the office, the system automatically reduces brightness or turns off the lights to save energy. When a stress sensor detects abnormal stress changes in the light fixture bracket, the system sends an early warning message via a wireless communication module to prompt maintenance personnel to inspect and repair, ensuring the safe operation of the equipment.

[0049] The Transformer model is a deep learning model based on an attention mechanism, widely used in natural language processing and sequence data processing tasks. Its core lies in using an attention mechanism to weight input data, thereby capturing complex relationships between data points. In intelligent lighting control systems, the Transformer model can process multimodal data (such as sensor data and video images) to generate precise lighting control strategies.

[0050] In some embodiments, the transformer model's operating mechanism first involves preprocessing sensor data (such as light intensity, temperature, and stress) and camera image data into a vector form suitable for the transformer model's processing. Positional encoding is applied to the data to introduce sequence information, enabling the model to capture the temporal or spatial order of the data. The encoder consists of multiple identical stacked layers, each layer comprising two sub-layers: a multi-head self-attention mechanism and a feedforward neural network. The multi-head self-attention mechanism processes the input data in parallel through multiple attention heads, each calculating a different weighted sum to capture different relationships within the data; the feedforward neural network performs a non-linear transformation on the output of the attention mechanism, improving the model's expressive power. The decoder has a similar structure to the encoder, but adds a sub-layer in each layer to receive the encoder's output. The decoder progressively generates lighting control strategies, including switching lights on and off, adjusting brightness, and adjusting color temperature; the final decoder output at the output layer undergoes a linear transformation and a softmax function to generate specific values ​​for the lighting control strategy.

[0051] This invention fuses sensor data and camera image data, utilizing a multimodal attention mechanism to calculate attention weights for both sensor and image data separately, enhancing the model's comprehensive processing capabilities for multimodal data. User preferences are introduced as conditional variables and fused with input data through conditional encoding, enabling the model to generate lighting control strategies that meet individual user needs. A hybrid attention mechanism combines global and local attention; global attention focuses on the overall relationships within the data, while local attention focuses on subtle changes in neighboring data points, improving the model's ability to handle both long-term and short-term dependencies. This improved multimodal attention mechanism allows the transformer model to effectively fuse sensor and camera image data, enhancing the accuracy and comprehensiveness of environmental perception. By incorporating user preferences through conditional encoding, the model can generate more personalized lighting control strategies that better meet user needs, improving the user experience. More precise environmental adaptability is achieved through the hybrid attention mechanism's enhanced perception of data changes across different time and spatial ranges, resulting in more accurate and real-time control strategies that adapt to complex and changing environmental conditions.

[0052] S3: Based on the generated lighting control strategy, automatically and dynamically adjust the brightness and color temperature of the LED light source in the titanium material intelligent lighting system to adapt to the current environment and user needs; based on the generated lighting control strategy, automatically and dynamically adjust the brightness and color temperature of the LED light source in the titanium material intelligent lighting system to adapt to the current environment and user needs. In some embodiments, by analyzing sensor and camera data through a transformer model and combining it with user preferences, an optimal lighting control strategy for the current environment is generated; the control strategy includes specific brightness adjustment values ​​and color temperature adjustment values ​​to ensure that the lighting effect meets environmental conditions and user needs. The control strategy is translated into specific control signals by the control module inside the intelligent lighting system. These control signals include PWM (Pulse Width Modulation) signals to adjust the brightness of the LED light source and control commands to adjust the color temperature. The LED driver receives the PWM signals and dynamically adjusts the current of the LED light source, changing its brightness. Based on ambient light intensity and user activity, the brightness is adjusted in real time to ensure optimal lighting effects in different time periods and activity scenarios. When natural light is abundant or there is no activity, the brightness is reduced or the lights are turned off to save energy. The control module adjusts the current ratio of LEDs with different color temperatures according to the control strategy, changing the color temperature of the LED light source. The color temperature is dynamically adjusted based on ambient temperature and user comfort requirements, for example, using warm light in cold environments and cool light in hot environments. The color temperature is adjusted according to different situations (such as work, rest, and entertainment) to create a suitable atmosphere. Sensors and cameras monitor the adjusted effects in real time, feeding the data back to the transformer model to optimize the control strategy. Users can provide feedback on the adjustment effects through smart devices, further optimizing the model and control strategy.

[0053] In some embodiments, in a smart bedroom lighting system, smart luminaires installed on the ceiling achieve real-time monitoring and intelligent control of environmental parameters through a sensor network and miniature cameras within a titanium housing. A light sensor is installed on the top of the luminaire to monitor indoor light intensity in real time, ensuring sufficient illumination. A temperature sensor is embedded inside the titanium housing to sense changes in indoor temperature, ensuring accurate and real-time temperature data. A stress sensor is installed at the luminaire bracket to monitor mechanical stress, ensuring the safety and stability of the luminaire installation. A miniature camera is installed on the edge of the luminaire to capture images and videos of indoor activities in real time. The system collects light intensity, temperature, and stress data in real time and performs comprehensive analysis on the images captured by the camera. The collected data is cleaned and standardized to extract key features, ensuring data accuracy and consistency. An environmental data and user preferences are analyzed using a pre-trained transformer model to generate an optimal lighting control strategy. The smart luminaire dynamically adjusts the brightness and color temperature of the LED light source according to the control strategy output by the model. In the morning, the luminaire gradually increases brightness to simulate changes in natural light, helping users wake up naturally; in the evening, the luminaire gradually decreases brightness to create a comfortable resting environment. The color temperature is dynamically adjusted according to indoor temperature and user activity. For example, warm-colored light is used on winter mornings to make the room feel cozy; cool-colored light is used on summer evenings to provide a refreshing feeling; when the light sensor detects sufficient natural light, the system automatically reduces the brightness of the lights to save energy. When the camera detects that no one is in the room, the system automatically turns off the lights to avoid unnecessary energy consumption; users can adjust the lighting settings and provide feedback on the lighting effects through a smartphone app; based on user feedback and sensor data, the system optimizes the transformer model and control strategy to provide a lighting experience that better meets user needs.

[0054] S4: Users can remotely control and monitor the titanium-based smart lighting system via the wireless communication module, including turning the lights on and off, adjusting brightness and color temperature, and setting timed tasks. In some embodiments, the wireless communication module supports multiple protocols such as Wi-Fi, Bluetooth, and Zigbee, enabling seamless connection and data transmission between different smart devices. User commands and system status data are transmitted via the wireless network, ensuring real-time response and control. The control interface provides a user-friendly smartphone application or web interface, allowing users to remotely control and monitor the lighting system. Users can view the current status of the lights in real time, including brightness, color temperature, and on / off status, and monitor environmental parameters such as light intensity and temperature. Users can remotely control the lights at any time via the application. For example, turning on the lights before arriving home or turning them off after leaving. Users can adjust the brightness and color temperature of the lights according to their needs via the application. For example, increasing brightness while reading and decreasing brightness while resting, adjusting the color temperature to suit different activity scenarios. Users can preset timed tasks to automatically control the on / off state, brightness, and color temperature of the lights. For example, the system can be set to automatically turn on the lights at 6 a.m. every morning, gradually increasing the brightness to help users wake up naturally; and automatically dim the lights at 10 p.m. to create a comfortable sleep environment. Users can provide feedback through the application, and the system will optimize the lighting control strategy based on user opinions and suggestions. The system will also collect user usage habits and preference data, and optimize the transformer model through big data analysis and machine learning to improve the accuracy of intelligent control and user experience.

[0055] In a smart home lighting system, smart lights installed in the living room, bedroom, and kitchen utilize a sensor network and miniature cameras within a titanium housing to achieve real-time monitoring and intelligent control of environmental parameters. A light sensor mounted on the top of the light fixture monitors indoor light intensity in real time; a temperature sensor embedded inside the titanium housing senses changes in indoor temperature. A stress sensor is installed at the fixture's bracket to monitor mechanical stress; miniature cameras are installed on the edge of the fixture to capture images and videos of indoor activities in real time. The system collects light intensity, temperature, and stress data in real time and performs comprehensive analysis using the images captured by the cameras. The collected data is cleaned and standardized to extract key features, ensuring accuracy and consistency. A pre-trained transformer model analyzes environmental data and user preferences to generate optimal lighting control strategies. The smart lights dynamically adjust the brightness and color temperature of the LED light source according to the control strategy output by the model. Users can control and monitor the lighting status anytime, anywhere via a smartphone app. Users can view the lighting status of each room, including brightness, color temperature, and on / off status. Users can turn on the living room lights before arriving home to ensure adequate illumination upon entering; after leaving, they can turn off all lights via the app to save energy. Users can adjust the brightness and color temperature of the lights according to their needs, such as increasing brightness while reading and dimming the lights and using a warmer color temperature when resting. Users can set scheduled tasks, such as automatically turning on the bedroom lights at 6 AM and automatically turning off all lights at 10 PM. The system can integrate with other smart home devices, such as smart locks and smart curtains, to achieve whole-house smart control. For example, when a user enters through a smart lock, the system automatically turns on the living room lights; when the smart curtains close, the system automatically adjusts the light brightness based on the light intensity. Users can provide feedback through the app, such as whether the light brightness is appropriate and whether the color temperature is comfortable. The system collects user data and optimizes the transformer model through big data analysis and machine learning to continuously improve the accuracy of smart control and user satisfaction.

[0056] In some embodiments, the micro-sensor network includes temperature sensors, stress sensors, and light sensors. The light sensors can sense the intensity of light at different wavelengths to improve the accuracy of ambient light detection. Temperature sensors monitor changes in ambient temperature, providing real-time temperature data to help adjust the color temperature and brightness of the lighting. Stress sensors monitor mechanical stress on the luminaire or bracket to ensure the safety and stability of the equipment. Light sensors can sense the intensity of light at different wavelengths, providing accurate ambient light data for intelligent adjustment of light brightness and color temperature. Common temperature sensors include thermistors (NTC or PTC), thermocouples, and miniature resistance temperature detectors (RTDs), embedded inside a titanium housing, directly contacting the ambient air to provide fast-responding temperature change data. They measure temperature by measuring changes in resistance or voltage caused by temperature changes. Common stress sensors include strain gauge sensors and piezoelectric sensors, installed on the luminaire bracket or critical structural parts to monitor mechanical stress and strain. Strain gauge sensors measure stress by measuring resistance changes caused by strain, while piezoelectric sensors measure stress by generating electrical signals through pressure changes. Light sensors, including light-sensitive resistors (LDRs), photodiodes, phototransistors, and multi-channel spectral sensors, are mounted on the top or side of light fixtures to directly receive ambient light. LDRs and photodiodes measure light intensity by detecting changes in resistance or current caused by light. Multi-channel spectral sensors can sense the intensity of light at different wavelengths, providing spectral information. Multi-channel spectral sensors can simultaneously measure the intensity of multiple wavelengths of light, such as the red, green, and blue (RGB) primary color bands of visible light, and even include ultraviolet and infrared bands. By sensing the intensity of light at different wavelengths, the composition and changes in ambient light can be detected more accurately, thereby optimizing lighting control strategies. In environments with complex and variable lighting, such as offices, shopping malls, and homes, they can provide accurate light data to ensure optimal lighting effects.

[0057] In a smart conference room lighting system, ceiling-mounted smart luminaires utilize a network of miniature sensors and cameras within a titanium housing to achieve real-time monitoring and intelligent control of environmental parameters. Temperature sensors, embedded within the titanium housing, detect temperature changes within the conference room through variations in surface resistance. Stress sensors, installed at the luminaire brackets and connections, monitor mechanical stress to ensure the luminaire's safety and stability. Light sensors, mounted on the top of the luminaire, use a multi-channel spectral sensor to monitor the intensity of different wavelengths of light within the conference room in real time. The multi-channel spectral sensor detects the intensity of red, green, and blue (RGB) light, providing spectral information. The system comprehensively analyzes the light intensity data for different wavelengths to determine the composition and changes in ambient light. Ambient light adjustment: Based on the light intensity data from the light sensors, the smart luminaires automatically adjust brightness and color temperature to provide the most suitable lighting effect. When the temperature sensor detects temperature changes, the system adjusts the light color temperature to improve comfort. For example, cooler light is used at higher temperatures, and warmer light at lower temperatures. The stress sensor monitors the mechanical stress on the luminaire brackets to ensure safe installation. If abnormal stress changes are detected, the system will send an alarm to prompt maintenance. When the meeting starts, the system will automatically adjust the brightness and color temperature of the lights according to the indoor light intensity to provide the best meeting lighting environment. Users can remotely adjust the brightness and color temperature of the lights through a smartphone application, or set timed tasks to achieve personalized control. When the light sensor detects that there is sufficient outdoor light, the system will automatically reduce the brightness of the lights to save energy.

[0058] In some embodiments, the wireless communication module supports Wi-Fi, Bluetooth, and Zigbee protocols to achieve seamless connectivity and data transmission between different smart devices. The wireless communication module is used to transmit data and control signals between the smart lighting system and other smart devices. It supports multiple communication protocols, enabling the system to flexibly adapt to different network environments and user needs. Wi-Fi: Provides high-speed data transmission and wide coverage, suitable for applications with high bandwidth requirements, such as video transmission and remote control. Bluetooth: Low power consumption, short-range transmission, suitable for point-to-point or small-area device connections, such as direct control between smartphones and lighting fixtures. Zigbee: Low power consumption, low bandwidth wireless communication protocol, suitable for interconnection of smart home devices, with good networking capabilities and stability. Wi-Fi protocol: High-speed data transmission, wide coverage, supports large-scale device access; used for remote control and monitoring, users can control their home's smart lighting system from anywhere with internet access via a smartphone application; smart lighting fixtures connect to the internet through a home router, and the user's smartphone application sends control commands to the lighting fixtures via the internet. Bluetooth protocol: Low power consumption, fast pairing, point-to-point transmission; used for short-range control, such as users controlling lights directly via their smartphones at home; smart lights have a built-in Bluetooth module that pairs with the user's smartphone, allowing the user to send control commands through an app, which the Bluetooth module receives and executes. Zigbee protocol: Low power consumption, low bandwidth, strong networking capabilities, high stability. Used for interconnectivity of smart home devices, such as communication between lights, sensors, and smart sockets; smart lights have a built-in Zigbee module that connects to the home network via a Zigbee gateway, allowing users to control and manage all devices centrally through the smart home system.

[0059] In some embodiments, the titanium material shell is coated with a self-healing coating. The specific material composition of the self-healing coating includes microcapsules, a catalyst, and a matrix material, wherein: the microcapsules contain a repair agent polymer monomer or a low molecular weight compound; the catalyst is nano-titanium dioxide or vanadium oxide; and the matrix material is epoxy resin or polyurethane. The self-healing coating is used to automatically repair coating damage when the surface of the titanium material shell is slightly damaged, maintaining the integrity and function of the shell and extending the service life of the device. The self-healing coating consists of three parts: microcapsules, a catalyst, and a matrix material. Microcapsules are tiny particles with a core-shell structure, containing a repair agent inside, and the shell is made of a durable polymer material. The microcapsules are filled with a repair agent, such as a polymer monomer or a low molecular weight compound (e.g., epoxy resin monomer, acrylate, etc.). When the coating is damaged, the microcapsules rupture, releasing the internal repair agent to fill and repair the damaged area. Commonly used catalysts include nano-titanium dioxide or vanadium oxide. The catalyst can promote the polymerization reaction or molecular self-assembly of the repair agent, accelerating the repair process. The catalyst is uniformly distributed in the matrix material and immediately exerts its catalytic effect upon contact with the repair agent, promoting the repair reaction. Commonly used matrix materials include epoxy resin and polyurethane, which possess good mechanical properties and durability. As the main component of the coating, the matrix material provides the necessary mechanical strength and adhesion, while also supporting the microcapsules and catalyst. The matrix material's excellent adhesion ensures the coating firmly adheres to the titanium shell surface. When the coating suffers minor damage (such as scratches or cracks), the microcapsules rupture, releasing the internal repair agent. The repair agent comes into contact with the catalyst in the coating, and under the catalyst's action, the repair agent undergoes a chemical reaction or molecular self-assembly, forming a new polymer or self-assembled structure. The newly formed polymer or self-assembled structure fills the damaged area, restoring the integrity and function of the coating. In a smart street light system, a titanium shell is used with a self-healing coating to improve the equipment's durability and lifespan. The titanium shell is made of Ti-6Al-4V titanium alloy, which has high strength and excellent corrosion resistance. The microcapsules are selected to contain epoxy resin monomers, and the shell is made of a durable polymer material. The catalyst is nano-titanium dioxide (TiO2), which can catalyze the polymerization reaction of epoxy resin under light irradiation. Epoxy resin is used as the matrix material, providing good adhesion and mechanical strength. The coating is prepared by mixing microcapsules, catalyst, and matrix material in a specific ratio to create a self-healing coating material. This self-healing coating is then uniformly applied to the surface of the titanium shell, forming a protective film with self-healing capabilities.

[0060] In some embodiments, scratches cause microcapsules to rupture, releasing the internal epoxy resin monomers. These monomers then polymerize upon contact with nano-titanium dioxide under light. The newly generated epoxy resin fills the scratches, restoring the integrity of the coating. Microscopic observation and mechanical property testing verify that the repaired coating has restored its initial mechanical strength and appearance integrity. The smart street light coating can be used for extended periods in outdoor environments, resisting weathering and mechanical damage. The self-healing coating reduces the frequency and cost of equipment maintenance, improving the overall reliability and durability of the smart street light system. The street light system integrates an intelligent control module for remote monitoring and control, and combined with the self-healing coating, provides an efficient and reliable smart lighting solution. Through the above embodiments, the smart street light system of the present invention not only improves the durability and lifespan of the equipment but also achieves automatic repair through self-healing coating technology, reducing maintenance costs and enhancing the overall performance and reliability of the system. This technology can be widely applied in smart homes, smart cities, and other fields, improving the efficiency of smart devices and the user experience.

[0061] In some embodiments, the self-healing process of the self-healing coating is as follows: When the surface of the lighting device housing is slightly damaged, the microcapsules rupture, releasing the internal repair agent; the repair agent comes into contact with the catalyst in the coating, and a chemical reaction or molecular self-assembly occurs under the action of the catalyst; a new polymer or self-assembled structure is formed, filling and repairing the damaged area, restoring the integrity and function of the coating. Microcapsules are tiny particles with a core-shell structure, containing a repair agent inside, and the outer shell is made of a durable polymer material. Common repair agents include epoxy resin monomers, acrylates, etc. The catalyst is uniformly distributed in the matrix material; commonly used catalysts include nano-titanium dioxide or vanadium oxide. These catalysts can promote the polymerization reaction or molecular self-assembly process of the repair agent. The matrix material is usually epoxy resin or polyurethane, which provides good mechanical strength and durability and can support the microcapsules and catalyst. When the coating surface is slightly damaged (such as scratches or cracks), the damage causes some microcapsules to rupture. After the microcapsules rupture, the internal repair agent (such as epoxy resin monomers) is released into the damaged area. The repair agent comes into contact with the catalyst distributed in the matrix material; under the action of the catalyst, the repair agent undergoes a chemical reaction (such as the polymerization reaction of epoxy resin monomers) or molecular self-assembly (such as the self-assembly reaction of acrylates); the newly formed polymer or self-assembled structure fills the damaged area, forming a new protective layer; after the repair process is completed, the coating restores its integrity and function, and continues to provide protection for the shell.

[0062] In one embodiment, an outdoor smart lighting device utilizes a titanium housing coated with a self-healing coating to enhance the device's durability and lifespan. The titanium housing is made of Ti-6Al-4V titanium alloy, which possesses high strength and excellent corrosion resistance. The microcapsules are selected to contain epoxy resin monomers, and the housing is made of a durable polymer material. The catalyst is nano-titanium dioxide (TiO2), capable of catalyzing the polymerization reaction of epoxy resin under light irradiation. Epoxy resin is used as the matrix material, providing good adhesion and mechanical strength. The microcapsules, catalyst, and matrix material are mixed in a specific ratio to prepare a self-healing coating material. The self-healing coating is uniformly applied to the surface of the titanium housing, forming a protective film with self-healing capabilities. Scratches cause microcapsules to rupture, releasing the epoxy resin monomers inside. These monomers then polymerize upon contact with nano-titanium dioxide under light, filling the scratches and restoring the coating's integrity. Microscopic observation and mechanical performance testing verify that the repaired coating regains its initial mechanical strength and appearance integrity. The intelligent lighting device can then be used long-term in outdoor environments, resisting wind, rain, and mechanical damage. The self-healing coating reduces maintenance frequency and costs, improving the overall reliability and durability of the intelligent lighting system. The integrated intelligent control module enables remote monitoring and control, and combined with the self-healing coating, provides an efficient and reliable intelligent lighting solution.

[0063] In one application of a smart garden light, the titanium housing is coated with a self-healing coating to enhance durability and aesthetics. The garden light is installed outdoors and exposed to various weather conditions, such as sunlight, rain, and sandstorms. Microcapsules contain epoxy resin monomers, with a durable polymer shell. The catalyst is nano-titanium dioxide, which catalyzes the reaction of the repair agent under light. The matrix material is epoxy resin, providing the coating's mechanical strength and adhesion. For example, if the garden light is scratched by a tree branch, causing minor scratches on the coating surface, the scratches cause the microcapsules to rupture, releasing the internal epoxy resin monomers. The repair agent comes into contact with the nano-titanium dioxide and polymerizes under sunlight, forming a new polymer. The newly formed polymer fills the scratch, restoring the coating's integrity. Once the scratch disappears, the coating regains its original smoothness and protective function. The self-healing coating significantly extends the lifespan of the garden light, reduces maintenance costs, and maintains the device's aesthetics and functionality.

[0064] This application also provides an intelligent lighting control system based on titanium materials, such as... Figure 2As shown, the system comprises multiple hardware components and their interconnections to achieve efficient, intelligent, and reliable lighting control. The titanium housing, made of Ti-6Al-4V titanium alloy, provides high-strength, corrosion-resistant protection; it supports and protects the internal electronic components and sensors, and features a self-healing coating. The LED light source uses energy-efficient LED beads that support adjustable brightness and color temperature; it is fixed inside the titanium housing and maintains stable operation through a heat dissipation structure. A multi-channel spectral sensor detects ambient light intensity and spectral information. A thermistor or miniature thermocouple monitors the ambient temperature in real time. A strain gauge or piezoelectric sensor monitors the mechanical stress on the titanium housing. A miniature camera captures real-time environmental images and video, aiding in the analysis of light, temperature, and stress data. Integrated on the surface of the titanium housing, it provides a wide-angle field of view. A high-performance embedded processor runs intelligent control algorithms and transformer models. Memory stores control programs and sensor data. The wireless communication module supports protocols: Wi-Fi, Bluetooth, and Zigbee, enabling seamless connectivity and data transmission between the intelligent lighting system and other intelligent devices. The power supply connects to AC power or a solar panel, providing a stable power supply. Power management includes functions such as voltage regulation and overload protection to ensure safe system operation. Smartphone applications connect via a wireless communication module for remote control and monitoring. Smart home integration allows integration with smart home systems (such as Amazon Alexa and Google Home) for voice control and scene-based interaction. Light, temperature, and stress sensors connect to the control module via I2C or SPI interfaces to transmit environmental data in real time. Miniature cameras connect to the control module via MIPI or USB interfaces to provide image and video data. The control module controls the LED driver via PWM (Pulse Width Modulation) signals to adjust the brightness and color temperature of the LED light source. The LED light source connects to the power management module to provide stable power. The control module connects to the wireless communication module via UART, I2C, or SPI interfaces for data transmission and remote control. Smartphone applications connect to the wireless communication module via Wi-Fi or Bluetooth for remote control and monitoring. Smart home systems connect to the wireless communication module via Zigbee or Wi-Fi for scene-based interaction and voice control.

[0065] In a smart home environment, a titanium-based smart lighting control system is installed in key areas such as the living room, bedroom, and kitchen, achieving intelligent control through multiple hardware components. Living room smart lights: Titanium housing, built-in LED light source, sensor network, and miniature camera. Bedroom smart lights: Titanium housing, built-in LED light source, sensor network, and miniature camera. Kitchen smart lights: Titanium housing, built-in LED light source, sensor network, and miniature camera. Sensors and control module: All sensors (light, temperature, stress) and miniature cameras are connected to the control module of each smart light fixture via I2C and MIPI interfaces. Control module and LED light source: The control module adjusts the brightness and color temperature of the LED light source via PWM signals. Control module and wireless communication module: The control module connects to the wireless communication module via a UART interface, enabling connectivity with smartphones and the smart home system. The smartphone application connects via Wi-Fi to remotely control and monitor the status of the smart lights, while the smart home system connects via Zigbee for scene linkage and voice control. Real-time monitoring of light intensity, temperature, and mechanical stress provides accurate environmental data. Based on sensor data and user preferences, an optimal lighting control strategy is generated through a transformer model, automatically adjusting the light brightness and color temperature. Users can remotely turn lights on and off, adjust brightness and color temperature, and set timed tasks via a smartphone app. Integration with smart home systems enables automated scene control, such as automatically turning on bedroom lights in the morning and turning off all lights at night. Users can set personalized lighting preferences and timed tasks through the app, and through the smart home system's energy consumption statistics, users can understand the power consumption of their lights, optimize usage habits, and achieve energy savings.

[0066] Environmental parameter acquisition module: Real-time monitoring of environmental parameters, including light intensity, temperature, stress, and video images, through integrated sensors and miniature cameras in the lighting device. The integrated sensors include a network of miniature sensors embedded in titanium material for monitoring light intensity, temperature, and stress, which can sense changes in ambient light intensity, temperature, and mechanical stress through changes in surface resistance; the miniature camera includes a camera mounted on the surface of the titanium material.

[0067] Transformer Model Calculation Module: Environmental parameters collected by integrated sensors and miniature cameras in the titanium material are input into a pre-trained transformer model. The transformer model outputs a lighting control strategy for the current environment, including switching lights on and off, adjusting brightness, and adjusting color temperature. The transformer model uses an improved fusion attention mechanism as shown below:

[0068]

[0069] Where y represents user preference, Q1, K1, and V1 are the query, key, and value matrices of sensor data, respectively; Q2, K2, and V2 are the query, key, and value matrices of camera data, respectively; W c The weight matrix for conditional encoding; d k is the dimension of the key matrix; T is the transpose of the matrix;

[0070] Control strategy adjustment module: Based on the generated lighting control strategy, automatically and dynamically adjust the brightness and color temperature of the LED light source in the titanium material intelligent lighting system to adapt to the current environment and user needs;

[0071] User-selectable control module: Users can remotely select and control the titanium material intelligent lighting system through the wireless communication module, including turning the lights on and off, adjusting brightness and color temperature, and setting timed tasks.

[0072] In one embodiment, light intensity data is light intensity and spectral information (e.g., the light intensity of the three primary colors, red, green, and blue) detected by a multi-channel spectral sensor. Temperature data is ambient temperature data monitored by a temperature sensor. Stress data is mechanical stress data monitored by a stress sensor. Video image data is real-time environmental images or video data captured by a miniature camera. User preference data, such as ideal brightness and color temperature, lighting requirements for a specific time period, etc., are input via a smartphone application. Light on / off commands: determine whether the light needs to be turned on or off. Brightness adjustment value: a specific brightness percentage (e.g., from 0% to 100%). Color temperature adjustment value: a specific color temperature value (e.g., 2700K to 6500K).

[0073] In one embodiment, a light sensor collects ambient light intensity data in real time and outputs spectral information; a temperature sensor collects ambient temperature data in real time; a stress sensor monitors mechanical stress data to ensure device safety; and a miniature camera captures real-time image or video data. The sensor and camera data are preprocessed, including noise reduction, standardization, and feature extraction. Light data is converted into standardized light intensity values ​​and spectral information; temperature and stress data are standardized; and video data undergoes frame extraction and image feature extraction. The preprocessed sensor data and video image features are fused to form a comprehensive environmental data input. User preference data is used as a conditional variable and input into the transformer model along with the environmental data. The multi-head self-attention mechanism transformer model processes the input data, calculating the importance and correlation of different parts of the input data. Different attention heads can capture different features of the environmental data, such as light change trends, temperature fluctuation patterns, and image features. Encoder and Decoder: The encoder encodes the input data into a high-dimensional feature vector. The decoder generates the output, i.e., the lighting control strategy, based on the encoded feature vector and user preferences. Based on the light intensity data and user preferences, the model determines whether to turn the lights on or off. For example, when ambient light is insufficient and the user needs illumination, a light-on command is output; when ambient light is sufficient or illumination is not needed, a light-off command is output. Based on ambient light intensity and user preference, the model calculates the required brightness adjustment value. For example, a higher brightness value is output in dim light, and a lower brightness value is output in bright light. Based on ambient temperature and user preference, the model calculates the required color temperature adjustment value. For example, a higher color temperature value (cool light) is output in low ambient temperature, and a lower color temperature value (warm light) is output in high ambient temperature. The control module receives the light-on / off command and controls the light switching via relays or electronic switches. The control module adjusts the output current of the LED driver using PWM (Pulse Width Modulation) signals to dynamically adjust the light brightness. The control module also dynamically adjusts the color temperature by adjusting the current ratio of LEDs with different color temperatures (such as warm white LEDs and cool white LEDs). Sensors monitor the adjusted environmental parameters in real time and feed the data back to the transformer model to optimize the control strategy. Users can provide feedback through a smartphone application to further optimize the model and control strategy.

[0074] In a smart home lighting system, a titanium-based intelligent lighting control system is installed in the living room, achieving efficient and intelligent lighting control through a transformer model. A light sensor detects the light intensity and spectral information in the living room, a temperature sensor detects the current ambient temperature, and a miniature camera captures real-time video images. Sensor and camera data are preprocessed, including noise reduction and normalization. The user inputs preference data, such as desired brightness and color temperature, via a smartphone application. The preprocessed light intensity, temperature, and image feature data are fused with the user preference data and input into the transformer model. The transformer model processes the input data using a multi-head self-attention mechanism to generate a high-dimensional feature vector. The encoder encodes the input data into a high-dimensional feature vector. The decoder generates outputs based on the encoded feature vector and user preferences, including on / off commands, brightness adjustment values, and color temperature adjustment values. The control module receives the control strategy output by the transformer model. If the model outputs an on command, the control module turns on the light via a relay; if it outputs an off command, the light is turned off. The control module adjusts the output current of the LED driver via a PWM signal to achieve brightness adjustment, and adjusts the current ratio of LEDs with different color temperatures to achieve color temperature adjustment. Sensors monitor the adjusted light intensity and temperature data in real time, feeding the data back to the transformer model for optimization. Users provide feedback via a smartphone app, and the system further optimizes the control strategy based on this feedback.

[0075] In some embodiments, the micro-sensor network includes a temperature sensor, a stress sensor, and a light sensor, wherein the light sensor is capable of sensing the intensity of light at different wavelengths to improve the accuracy of ambient light detection.

[0076] In some embodiments, the wireless communication module supports Wi-Fi, Bluetooth, and Zigbee protocols to enable seamless connectivity and data transmission between different smart devices.

[0077] In some embodiments, the titanium material shell is coated with a self-healing coating, the specific material composition of which includes microcapsules, a catalyst, and a matrix material, wherein: the microcapsules contain a repair agent polymer monomer or a low molecular weight compound; the catalyst is nano-titanium dioxide or vanadium oxide; and the matrix material is epoxy resin or polyurethane.

[0078] In some embodiments, the self-healing process of the self-healing coating is as follows: when the surface of the lighting device housing is slightly damaged, the microcapsules rupture and release the internal repair agent; the repair agent comes into contact with the catalyst in the coating, and a chemical reaction or molecular self-assembly occurs under the action of the catalyst; a new polymer or self-assembled structure is formed, filling and repairing the damaged area, restoring the integrity and function of the coating.

[0079] This invention provides an intelligent lighting control method and system based on titanium materials, which achieves the following beneficial technical effects:

[0080] 1. This invention first monitors environmental parameters in real time using integrated sensors and miniature cameras in the lighting device; secondly, the environmental parameters collected by the integrated sensors and miniature cameras in the titanium material are input into a pre-trained transformer model, which outputs a lighting control strategy for the current environment; finally, based on the generated lighting control strategy, the brightness and color temperature of the LED light source in the titanium material intelligent lighting system are automatically and dynamically adjusted to adapt to the current environment and user needs. This application, by utilizing an improved transformer model for real-time environmental parameter analysis, automatically and dynamically adjusts the brightness and color temperature of the LED light source in the titanium material intelligent lighting system to adapt to the current environment and user needs, greatly improving the user experience.

[0081] 2. This invention employs an integrated sensor and miniature camera in titanium material to collect environmental parameters, which are then input into a pre-trained transformer model. The transformer model outputs a lighting control strategy for the current environment, including switching lights on and off, adjusting brightness, and adjusting color temperature. The transformer model uses an improved fusion attention mechanism, as shown below:

[0082]

[0083] Where y represents user preference, Q1, K1, and V1 are the query, key, and value matrices of sensor data, respectively; Q2, K2, and V2 are the query, key, and value matrices of camera data, respectively. This application combines the query, key, and value matrices of sensor data with those of camera data for calculation through an improved fusion attention mechanism, which greatly improves the accuracy of lighting control strategies and enhances user experience.

[0084] 3. The titanium material shell of the present invention is coated with a self-healing coating. The specific material composition of the self-healing coating includes microcapsules, a catalyst, and a matrix material, wherein: the microcapsules contain a repair agent polymer monomer or a low molecular weight compound; the catalyst is nano-titanium dioxide or vanadium oxide; the matrix material is epoxy resin or polyurethane. When the surface of the lighting device shell is slightly damaged, the microcapsules rupture and release the internal repair agent; the repair agent comes into contact with the catalyst in the coating, and a chemical reaction or molecular self-assembly occurs under the action of the catalyst; a new polymer or self-assembled structure is formed, filling and repairing the damaged area, restoring the integrity and function of the coating. By adding titanium material to situations with high impact damage, the maintenance cost and efficiency of the lamps are greatly reduced, and the self-maintenance level of multifunctional lamps is effectively improved.

[0085] The above provides a detailed description of an intelligent lighting control method and system based on titanium materials. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas and methods of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A smart lighting control method based on titanium materials, characterized in that, Including the following steps: S1: Environmental parameters are monitored in real time through integrated sensors and miniature cameras in the lighting device. The environmental parameters include light intensity, temperature, stress, and video images. The integrated sensors include a network of miniature sensors embedded in the titanium material for monitoring light intensity, temperature, and stress, which can sense changes in ambient light intensity, temperature, and mechanical stress through changes in surface resistance. The miniature cameras include cameras mounted on the surface of the titanium material. S2: Environmental parameters collected by integrated sensors and miniature cameras in the titanium material are input into a pre-trained transformer model. The transformer model outputs a lighting control strategy for the current environment, including switching lights on and off, adjusting brightness, and adjusting color temperature. The transformer model uses an improved fusion attention mechanism as follows: Where y represents user preference, Q1, K1, and V1 are the query, key, and value matrices of sensor data, respectively; Q2, K2, and V2 are the query, key, and value matrices of camera data, respectively; W c The weight matrix for conditional encoding; d k is the dimension of the key matrix; T is the transpose of the matrix; S3: Based on the generated lighting control strategy, automatically and dynamically adjust the brightness and color temperature of the LED light source in the titanium material intelligent lighting system to adapt to the current environment and user needs; S4: Users can remotely control and monitor the titanium intelligent lighting system via the wireless communication module, including turning the lights on and off, adjusting brightness and color temperature, and setting timed tasks.

2. The intelligent lighting control method based on titanium material as described in claim 1, characterized in that, The micro-sensor network includes a temperature sensor, a stress sensor, and a light sensor, wherein the light sensor can sense the intensity of light at different wavelengths to improve the accuracy of ambient light detection.

3. The intelligent lighting control method based on titanium material as described in claim 1, characterized in that, The wireless communication module supports Wi-Fi, Bluetooth, and Zigbee protocols to enable seamless connection and data transmission between different smart devices.

4. The intelligent lighting control method based on titanium material as described in claim 1, characterized in that, The titanium material shell is coated with a self-healing coating. The specific material composition of the self-healing coating includes microcapsules, a catalyst, and a matrix material, wherein: the microcapsules contain a repair agent polymer monomer or a low molecular weight compound; the catalyst is nano-titanium dioxide or vanadium oxide; and the matrix material is epoxy resin or polyurethane.

5. The intelligent lighting control method based on titanium material as described in claim 4, characterized in that, The self-healing process of the self-healing coating is as follows: when the surface of the lighting device housing is slightly damaged, the microcapsules rupture and release the internal repair agent; the repair agent comes into contact with the catalyst in the coating, and a chemical reaction or molecular self-assembly occurs under the action of the catalyst; a new polymer or self-assembled structure is formed, which fills and repairs the damaged area, restoring the integrity and function of the coating.

6. A smart lighting control system based on titanium material, characterized in that, include: Environmental parameter acquisition module: Real-time monitoring of environmental parameters, including light intensity, temperature, stress, and video images, through integrated sensors and miniature cameras in the lighting device. The integrated sensors include a network of miniature sensors embedded in titanium material for monitoring light intensity, temperature, and stress, which can sense changes in ambient light intensity, temperature, and mechanical stress through changes in surface resistance; the miniature camera includes a camera mounted on the surface of the titanium material. Transformer Model Calculation Module: Environmental parameters collected by integrated sensors and miniature cameras in the titanium material are input into a pre-trained transformer model. The transformer model outputs a lighting control strategy for the current environment, including switching lights on and off, adjusting brightness, and adjusting color temperature. The transformer model uses an improved fusion attention mechanism as shown below: Where y represents user preference, Q1, K1, and V1 are the query, key, and value matrices of sensor data, respectively; Q2, K2, and V2 are the query, key, and value matrices of camera data, respectively; W c The weight matrix for conditional encoding; d k is the dimension of the key matrix; T is the transpose of the matrix; Control strategy adjustment module: Based on the generated lighting control strategy, automatically and dynamically adjust the brightness and color temperature of the LED light source in the titanium material intelligent lighting system to adapt to the current environment and user needs; User-selectable control module: Users can remotely select and control the titanium material intelligent lighting system through the wireless communication module, including turning the lights on and off, adjusting brightness and color temperature, and setting timed tasks.

7. The intelligent lighting control system based on titanium material as described in claim 6, characterized in that, The micro-sensor network includes a temperature sensor, a stress sensor, and a light sensor, wherein the light sensor can sense the intensity of light at different wavelengths to improve the accuracy of ambient light detection.

8. The intelligent lighting control system based on titanium material as described in claim 6, characterized in that, The wireless communication module supports Wi-Fi, Bluetooth, and Zigbee protocols to enable seamless connection and data transmission between different smart devices.

9. The intelligent lighting control system based on titanium material as described in claim 6, characterized in that, The titanium material shell is coated with a self-healing coating. The specific material composition of the self-healing coating includes microcapsules, a catalyst, and a matrix material, wherein: the microcapsules contain a repair agent polymer monomer or a low molecular weight compound; the catalyst is nano-titanium dioxide or vanadium oxide; and the matrix material is epoxy resin or polyurethane.

10. A smart lighting control system based on titanium material as described in claim 9, characterized in that, The self-healing process of the self-healing coating is as follows: when the surface of the lighting device housing is slightly damaged, the microcapsules rupture and release the internal repair agent; the repair agent comes into contact with the catalyst in the coating, and a chemical reaction or molecular self-assembly occurs under the action of the catalyst; a new polymer or self-assembled structure is formed, which fills and repairs the damaged area, restoring the integrity and function of the coating.

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