LED lamp and control method thereof

Abstract

The present invention relates to the field of electronic control technology, and in particular to an LED lamp and a control method thereof. The method comprises the following steps: obtaining LED lamp design data and performing material property analysis to obtain LED lamp material property data; performing optical field simulation based on LED lamp material property data and LED lamp structure data to obtain LED lamp spectral response curve; obtaining lighting area sensor data and performing LED lamp daytime brightness control strategy analysis to obtain LED lamp daytime brightness control strategy; constructing a user night behavior model based on lighting area sensor data; performing LED lamp night brightness control strategy analysis on the user night behavior model and LED lamp spectral response curve to obtain LED lamp night brightness control strategy; performing control strategy coupling on LED lamp daytime brightness control strategy and LED lamp night brightness control strategy to obtain LED lamp control strategy, and uploading to LED lamp management cloud platform. The present invention can further improve the applicability and user experience of LED lighting.

Description

Technical Field

[0001] The present invention relates to the field of electronic control technology, and in particular to an LED lamp and a control method thereof. Background Art

[0002] LED (light-emitting diode) lamps have become mainstream in the field of modern lighting. With its advantages of high efficiency, energy saving and environmental protection, it has gradually replaced traditional incandescent and fluorescent lamps. However, despite the excellent performance of LED lamps in energy efficiency and life, there are still challenges in lighting quality and control. In order to further improve the flexibility, adjustability and adaptability of LED lamps, researchers are committed to developing new LED lamps and their control methods to meet the needs of different application scenarios. Traditional LED lamp control methods usually have fixed brightness and spectrum, which are difficult to adapt to the needs of different environments and usage scenarios. For example, in home, commercial and industrial lighting, users may need light sources with different brightness and color temperature to create different atmospheres or provide suitable lighting conditions, which cannot meet the user's needs for different lighting scenarios. Therefore, a LED control method is needed to improve the applicability and user experience of LED lighting. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide an LED lamp and a control method thereof to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for controlling an LED lamp comprises the following steps:

[0005] Step S1: Acquire LED lamp design data, and perform feature extraction on the LED lamp design data, thereby obtaining LED lamp structure data and LED lamp material data; perform material property analysis on the LED lamp material data, thereby obtaining LED lamp material property data;

[0006] Step S2: performing optical field simulation according to the LED lamp material characteristic data and the LED lamp structure data, so as to obtain the LED lamp spectral response curve;

[0007] Step S3: Acquire the lighting area sensor data, perform regional lighting environment analysis based on the lighting area sensor data, and thus obtain regional lighting environment data; perform LED lamp daytime brightness control strategy analysis based on the LED spectral response curve and the regional lighting environment data, and thus obtain the LED lamp daytime brightness control strategy;

[0008] Step S4: Analyze the user's night lighting preference on the lighting area sensor data to obtain the user's night lighting preference data, and perform statistics on the user's night activity frequency based on the lighting area sensor data to obtain the user's night activity preference data; and construct a user's night behavior model based on the user's night lighting preference data and the user's night activity preference data;

[0009] Step S5: Analyze the LED light night brightness control strategy based on the user night behavior model and the LED light spectrum response curve, so as to obtain the LED light night brightness control strategy;

[0010] Step S6: coupling the LED light daytime brightness control strategy and the LED light nighttime brightness control strategy to obtain the LED light control strategy, and uploading it to the LED light management cloud platform to execute the LED light control task.

[0011] The present invention can deeply understand the structure and material properties of LED lamps by acquiring LED lamp design data and extracting features. This helps to determine the manufacturing process and material selection of LED lamps to improve the performance and efficiency of LED lamps. Obtaining the spectral response curve of LED lamps through optical field simulation helps to understand the luminous characteristics of LED lamps at different wavelengths. This provides key information for subsequent control strategies, so that LED lamps can adjust the spectrum according to environmental requirements to achieve more suitable lighting effects. By analyzing the sensor data of the lighting area, the real-time situation of the lighting environment can be understood. Combined with the spectral response curve of the LED lamp, a suitable daytime brightness control strategy can be formulated so that the LED lamp can automatically adjust the brightness according to environmental changes and provide a comfortable lighting environment. By analyzing the user's night lighting preferences and activity frequency, the user's needs for night lighting can be understood. Constructing a user's night behavior model can help design a more intelligent night brightness control strategy, so that the LED lamp can intelligently adjust the brightness and color temperature according to the user's actual needs and improve the user experience. Based on the user's night behavior model and the spectral response curve of the LED lamp, a night brightness control strategy for the LED lamp is formulated. This helps the LED lamp provide lighting suitable for user needs at night, for example, providing soft light before going to bed to promote sleep. The daytime and nighttime brightness control strategies are coupled to form a complete LED light control strategy, which is then uploaded to the management cloud platform. This enables remote management and intelligent control of LED lights, improves the flexibility and convenience of the lighting system, and reduces energy consumption and maintenance costs.

[0012] Optionally, step S1 specifically includes:

[0013] Step S11: acquiring LED lamp design data, and performing feature extraction on the LED lamp design data, thereby obtaining LED lamp structure data and LED lamp material data;

[0014] Step S12: constructing a three-dimensional model of the LED lamp according to the LED lamp structure data;

[0015] Step S13: meshing the LED lamp three-dimensional model according to the LED lamp design data, thereby obtaining an optical dense meshing model and a heat dissipation dense meshing model;

[0016] Step S14: performing optical characteristic analysis on the LED lamp material data according to the optical dense grid division model, thereby obtaining the LED lamp optical characteristic data;

[0017] Step S15: performing thermal characteristic analysis on the LED lamp material data according to the heat dissipation dense grid division model, thereby obtaining the LED lamp thermal characteristic data;

[0018] Step S16: merging the LED lamp optical characteristic data and the LED lamp thermal characteristic data to obtain the LED lamp material characteristic data.

[0019] The present invention obtains LED lamp design data and extracts features, which helps to deeply understand the structure and materials of LED lamps. These data are crucial for the performance analysis and optimization of LED lamps. Through feature extraction, key parameters such as LED chip type, heat dissipation structure, etc. can be identified, thereby providing basic data for subsequent steps. Building a three-dimensional model of an LED lamp allows the structure of the LED lamp to be visualized and analyzed in a computer. This enables designers and engineers to better understand the actual structure of the LED lamp, thereby optimizing and improving it. Through model meshing, the three-dimensional model of the LED lamp is divided into an optical dense grid and a heat dissipation dense grid. This allows optical and thermal characteristics to be analyzed more accurately in subsequent steps, providing more accurate data support for the optimization of the LED lamp. Optical characteristics analysis and thermal characteristics analysis respectively analyze the optical characteristics and thermal characteristics of the LED lamp in detail. Optical characteristics analysis can determine parameters such as optical efficiency and luminous intensity distribution of the LED lamp, while thermal characteristics analysis can determine information such as heat distribution and heat dissipation efficiency of the LED lamp during operation. The optical characteristics data and thermal characteristics data of the LED lamp are merged to obtain comprehensive material characteristics data of the LED lamp. This comprehensive data provides a comprehensive basis for the design optimization of LED lamps, allowing designers to strike a balance between optical efficiency and thermal management, thereby achieving more efficient and stable LED lamp designs.

[0020] Optionally, step S13 is specifically:

[0021] Step S131: classifying the LED lamp structure data by structure and use according to the LED lamp design data, thereby obtaining the LED lamp optical structure data and the LED lamp heat dissipation structure data;

[0022] Step S132: performing optical structure dense mesh division on the LED lamp three-dimensional model according to the LED lamp optical structure data, thereby obtaining an optical dense mesh division model;

[0023] Step S133: performing heat dissipation structure dense grid division on the LED lamp three-dimensional model according to the heat dissipation structure data of the LED lamp, thereby obtaining a heat dissipation dense grid division model.

[0024] The present invention can classify the structure of the LED lamp into two parts: optical structure and heat dissipation structure by classifying the structure data of the LED lamp according to the structure and purpose. Such classification is helpful for more refined optimization of different structural parts. The optical structure data includes optical components such as lenses and reflective covers of LED lamps, while the heat dissipation structure data covers heat dissipation components such as heat sinks and heat sinks of LED lamps. Through such classification, subsequent optical and thermal characteristics analysis and optimization can be carried out in a targeted manner. When the three-dimensional model is densely meshed for the optical structure according to the optical structure data of the LED lamp, the details of the optical structure can be captured more accurately. The optical dense meshing model can help simulate the propagation path and optical efficiency of light, thereby optimizing factors such as lens shape and surface quality, and improving the optical performance and brightness uniformity of the LED lamp. Similarly, when the three-dimensional model is densely meshed for the heat dissipation structure according to the heat dissipation structure data of the LED lamp, the morphology and details of the heat dissipation structure can be more finely portrayed. The heat dissipation dense meshing model can help simulate the conduction and heat dissipation path of heat, optimize the design of the heat dissipation structure, improve the heat dissipation efficiency of the LED lamp, and thus extend the service life and stability of the LED lamp.

[0025] Optionally, step S14 is specifically:

[0026] Step S141: extracting LED lamp optical structure material features from LED lamp material data according to the LED lamp optical structure data, thereby obtaining LED lamp optical structure material data;

[0027] Step S142: performing optical structure material mapping on the LED light optical structure material data and the optical dense mesh division model, thereby obtaining the LED light optical structure model;

[0028] Step S143: extracting the LED lamp design operating condition features according to the LED lamp design data, thereby obtaining the LED lamp design operating condition data;

[0029] Step S144: optically simulating the LED lamp design working condition data using the LED lamp optical structure model to obtain optical simulation data, and statistically analyzing the optical simulation data to obtain LED lamp optical characteristic data.

[0030] The optical structure material feature extraction of LED lamps of the present invention is to extract the characteristics of the materials used for the optical elements (such as lenses, reflectors, etc.) of LED lamps. These characteristics may include refractive index, transmittance, reflectivity, etc. The material characteristics can be matched with the optical structure of the LED lamp to provide accurate material data for subsequent optical simulation. In the optical structure material mapping step, the optical structure material data of the LED lamp is matched with the optical dense meshing model. The advantage of this is that the material characteristics can be accurately mapped to the optical model to ensure the accuracy and reliability of the optical simulation. The design working condition feature extraction of LED lamps is to extract various working conditions and environmental factors that may be encountered by LED lamps in actual use. These characteristics may include operating temperature, operating current, ambient light, etc. By extracting these characteristics, real-world working conditions can be provided for optical simulation, thereby more accurately evaluating the performance of LED lamps. Optical simulation of the design working condition data of LED lamps can be performed through the LED lamp optical structure model to simulate the optical performance of LED lamps under different working conditions. Statistical analysis of optical simulation data can help evaluate the optical characteristics of LED lamps such as brightness, color, uniformity, etc., and provide guidance and basis for the design and optimization of LED lamps.

[0031] Optionally, step S15 is specifically:

[0032] Step S151: extracting the characteristics of the LED lamp heat dissipation structure material from the LED lamp material data according to the LED lamp heat dissipation structure data, thereby obtaining the LED lamp heat dissipation structure material data;

[0033] Step S152: performing heat dissipation structure material mapping on the LED lamp heat dissipation structure material data and the optical dense mesh division model, thereby obtaining the LED lamp heat dissipation structure model;

[0034] Step S153: thermally simulate the optical simulation data and the LED lamp design operating condition data using the LED lamp heat dissipation structure model to obtain thermal simulation data, and perform statistical analysis on the thermal simulation data to obtain thermal characteristic data of the LED lamp.

[0035] The LED lamp heat dissipation structure material feature extraction of the present invention allows the characteristics of the materials used for the heat dissipation components of the LED lamp (such as heat sinks, radiators, etc.) to be extracted. These characteristics may include thermal conductivity, thermal conductivity coefficient, etc. This can correspond the material characteristics to the heat dissipation structure of the LED lamp, and provide accurate material data for subsequent thermal energy simulation. In the heat dissipation structure material mapping step, the LED lamp heat dissipation structure material data is corresponded to the optical dense meshing model. The advantage of this is that the material characteristics can be accurately mapped to the heat dissipation structure model to ensure the accuracy and reliability of thermal energy simulation. Thermal energy simulation is performed on the optical simulation data and the LED lamp design working condition data through the LED lamp heat dissipation structure model, and the thermal characteristics of the LED lamp under different working conditions can be simulated. Statistical analysis of thermal energy simulation data can help evaluate the heat dissipation performance of LED lamps, including temperature distribution, heat dissipation efficiency, etc., thereby providing a basis for the design and optimization of LED lamps.

[0036] Optionally, step S3 specifically includes:

[0037] Step S31: acquiring lighting area sensor data, and performing illumination feature extraction and camera image feature extraction on the lighting area sensor data, thereby obtaining regional illumination sensor data and regional camera images;

[0038] Step S32: time-series division of the regional light sensing data, thereby obtaining regional daytime light sensing data and regional nighttime light sensing data;

[0039] Step S33: performing high-frequency light intensity statistics on the regional daytime light sensor data, thereby obtaining high-frequency daytime light intensity data; performing high-frequency light intensity statistics on the regional nighttime light sensor data, thereby obtaining high-frequency nighttime light intensity data;

[0040] Step S34: performing illumination distribution analysis based on the regional camera image to obtain illumination distribution data;

[0041] Step S35: performing temporal-spatial fusion of the high-frequency daytime light intensity data and the high-frequency nighttime light intensity data according to the light distribution data, thereby obtaining regional light environment data;

[0042] Step S36: Spectral matching is performed according to the regional lighting environment data and the LED spectral response curve to obtain the regional lighting demand data, and daylight brightness control strategy analysis is performed according to the regional lighting demand data and the LED lamp material characteristic data to obtain the LED lamp daylight brightness control strategy.

[0043] The present invention obtains the sensor data of the lighting area and performs illumination feature extraction and camera image feature extraction, which can help understand the illumination and visual environment of the lighting area. These data provide information about the light intensity, light distribution and environmental characteristics, and provide basic data for the subsequent optimization of the lighting system. The regional illumination sensor data is divided into two time periods: daytime and nighttime. This helps to distinguish the illumination characteristics and requirements in different time periods, and provides a time series basis for subsequent analysis. By performing high-frequency illumination intensity statistics on the illumination sensor data of the day and night, more detailed illumination data can be obtained, including the frequency and amplitude of illumination changes. This is of great significance for understanding the dynamic characteristics of illumination changes and corresponding countermeasures. Using regional camera images to perform illumination distribution analysis, the illumination distribution of the lighting area can be more accurately understood, including the spatial distribution and change trend of the illumination intensity. This helps to better understand the characteristics of the lighting environment. By performing time-series spatial fusion on the illumination distribution data, the illumination conditions of different time periods and spatial positions can be comprehensively considered to obtain more comprehensive and comprehensive regional illumination environment data. This helps to design and optimize the lighting system more effectively. Through spectrum matching and lighting demand analysis, combined with LED lamp material characteristic data, the daytime brightness control strategy of LED lamps can be determined. This helps to reasonably adjust the brightness of LED lamps according to the actual lighting environment and needs, and improve energy efficiency and comfort.

[0044] Optionally, step S34 is specifically:

[0045] Step S341: grayscale conversion is performed on the regional camera image to obtain a regional grayscale image, and high-frequency pixel statistics are performed on the regional grayscale image to obtain high-frequency pixel data;

[0046] Step S342: performing pixel fluctuation calculation according to the high-frequency pixel data to obtain image pixel fluctuation data, and performing pixel fluctuation classification on the image pixel fluctuation data to obtain high-value pixel fluctuation data and low-value pixel fluctuation data;

[0047] Step S343: dividing the regional grayscale image into illumination ranges according to the high-value pixel fluctuation data, thereby obtaining an illumination range image;

[0048] Step S344: dividing the regional grayscale image into backlight ranges according to the low-value pixel fluctuation data, thereby obtaining a backlight range image;

[0049] Step S345: performing regional illumination distribution statistics on the regional grayscale image according to the illumination range image and the backlight range image, thereby obtaining illumination distribution data.

[0050] The present invention converts the regional camera image into a grayscale image, which helps to simplify the image processing process while retaining the illumination information in the image. Then, high-frequency pixel statistics are performed on the grayscale image to capture the pixels that change frequently in the image. These pixels often correspond to areas with high illumination change frequencies, thereby providing information about the dynamics of illumination. Through pixel fluctuation calculation, the image pixel fluctuation data is divided into high-amount and low-amount pixel fluctuation data. High-amount pixel fluctuation data usually corresponds to areas with large or frequent illumination changes, while low-amount pixel fluctuation data may correspond to areas with stable illumination. This classification helps to more finely distinguish areas with different illumination characteristics in the image. The illumination range of the regional grayscale image is divided using the high-amount pixel fluctuation data to obtain an illumination range image. This can help determine areas with large illumination changes, provide a visual representation of illumination distribution, and help further analyze illumination characteristics. The backlight range of the regional grayscale image is divided according to the low-amount pixel fluctuation data to obtain a backlight range image. For areas with relatively stable illumination, it helps to further distinguish the background illumination in the image and provide a more accurate data basis for illumination distribution statistics. The illumination distribution statistics of the regional grayscale image are calculated based on the illumination range image and the backlight range image. This helps to fully understand the different light intensities and distributions in the area, and provides an important reference for the optimization of the lighting system, such as providing guidance when designing the layout of lamps or adjusting the brightness.

[0051] Optionally, step S4 specifically includes:

[0052] Step S41: extracting night time-series sensing features from the sensor data of the lighting area, thereby obtaining night sensing data of the lighting area, and extracting night camera image features from the night sensor data of the area, thereby obtaining night camera images;

[0053] Step S42: performing low-frequency pixel statistics on the night camera image to obtain low-frequency pixel data, and performing color feature extraction on the low-frequency pixel data to obtain low-frequency pixel color data;

[0054] Step S43: performing spectral energy distribution conversion on the low-frequency pixel color data, thereby obtaining night spectral energy distribution data;

[0055] Step S44: Calculate the illumination intensity of the night spectral energy distribution data according to the LED spectral response curve, thereby obtaining the user's night lighting preference data;

[0056] Step S45: Counting the frequency of nighttime activities of users according to the lighting area sensing data, thereby obtaining the nighttime activity preference data of users;

[0057] Step S46: constructing a user's night behavior model based on the user's night lighting preference data and the user's night activity preference data.

[0058] The present invention can obtain the perception data and visual information of the night environment by extracting the night sensing features and night camera image features of the lighting area, providing a basis for subsequent analysis and processing. By low-frequency pixel statistics and color feature extraction, the basic color information in the night camera image can be obtained, which is helpful to understand the visual features and color composition of the night environment. By converting the spectral energy distribution of the low-frequency pixel color data, the distribution of the night spectral energy can be further analyzed to provide data support for the subsequent calculation of the light intensity. According to the LED spectral response curve, the light intensity is calculated, and the user's lighting preference can be inferred based on the night spectral energy distribution data, so as to better meet the user's demand for night lighting. By counting the user's night activity frequency through the lighting area sensor data, the user's activity habits and behavior patterns at night can be understood, providing a basis for personalized lighting services. By combining the user's night lighting preference data and the night activity preference data to construct a user's night behavior model, the user's lighting needs at night can be more accurately predicted and responded to, providing a more intelligent and personalized lighting solution.

[0059] Optionally, step S45 is specifically:

[0060] Step S451: extracting audio features from the nighttime sensor data of the lighting area, thereby obtaining the nighttime audio data of the lighting area;

[0061] Step S452: performing spectrum transformation on the audio data of the lighting area at night, thereby obtaining the audio spectrum of the lighting area at night;

[0062] Step S453: performing spectrum fluctuation statistics on the audio spectrum of the lighting area at night, thereby obtaining spectrum fluctuation data;

[0063] Step S454: extracting time series features from the spectrum fluctuation data to obtain fluctuation time series data, and classifying and calculating the night audio spectrum of the lighting area according to the fluctuation time series data to obtain night fluctuation spectrum and night flat spectrum;

[0064] Step S455: performing an inverse Fourier transform on the night fluctuation spectrum to obtain night activity data; performing an inverse Fourier transform on the night smooth spectrum to obtain night smooth data;

[0065] Step S456: Time-series merge the night calm data and night activity data to obtain the user's night activity preference data.

[0066] The audio feature extraction in the present invention can extract audio information from the night sensor data of the lighting area, such as the sound intensity and frequency distribution in the environment. This information can reflect the sound characteristics of nighttime activities and provide basic data for subsequent analysis. Spectral transformation can convert the nighttime audio data of the lighting area into a representation in the frequency domain, that is, an audio spectrum, which helps to better understand the distribution of different frequency components in the nighttime environment. Spectral fluctuation statistics can analyze the dynamic changes of the audio spectrum to obtain spectrum fluctuation data, which reflects the changing trend of the sound in the nighttime environment and provides important information for subsequent behavior analysis. By extracting the time series features of the spectrum fluctuation data, the fluctuation time series data can be obtained, and the audio spectrum is classified and calculated based on these data, thereby obtaining the night fluctuation spectrum and the night smooth spectrum, which helps to distinguish different types of sound activities at night. Inverse Fourier transform is used to convert the night fluctuation spectrum and the night smooth spectrum back to the time domain, thereby obtaining night activity data and night smooth data, which reflect the time series characteristics of different types of activities at night. By performing time series merging of night smooth data and night activity data, the user's night activity preference data, that is, the type and frequency of night activities, can be obtained, which provides a basis for personalized lighting services.

[0067] Optionally, the present specification further provides an LED lamp, which includes an LED lamp main body, a power supply unit and an electrical control unit, wherein the power supply unit is installed inside the LED lamp main body, the electrical control unit is electrically connected to the power supply unit, and the electrical control unit is used to charge the LED lamp main body and control the LED lamp main body, and the electrical control unit includes:

[0068] The material property analysis module is used to obtain the LED lamp design data and perform feature extraction on the LED lamp design data, thereby obtaining the LED lamp structure data and the LED lamp material data; perform material property analysis on the LED lamp material data, thereby obtaining the LED lamp material property data;

[0069] An optical field simulation module is used to perform optical field simulation according to the material characteristic data of the LED lamp and the structure data of the LED lamp, so as to obtain the spectral response curve of the LED lamp;

[0070] The daylight brightness control strategy analysis module is used to obtain the lighting area sensor data, perform regional lighting environment analysis based on the lighting area sensor data, and thus obtain the regional lighting environment data; perform LED light daylight brightness control strategy analysis based on the LED spectral response curve and regional lighting environment data, and thus obtain the LED light daylight brightness control strategy;

[0071] A user night behavior model building module is used to analyze the user's night lighting preference based on the lighting area sensor data, thereby obtaining the user's night lighting preference data, and to count the user's night activity frequency based on the lighting area sensor data, thereby obtaining the user's night activity preference data; and to build a user's night behavior model based on the user's night lighting preference data and the user's night activity preference data;

[0072] Night brightness control strategy analysis module, used to analyze the night brightness control strategy of LED lights based on the user's night behavior model and the LED light spectrum response curve, so as to obtain the night brightness control strategy of LED lights;

[0073] The control strategy coupling module is used to couple the control strategy of the LED light daytime brightness control strategy and the LED light night brightness control strategy, so as to obtain the LED light control strategy and upload it to the LED light management cloud platform to execute the LED light control task.

[0074] The LED lamp of the present invention can implement any LED lamp control method of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the LED lamp control method. The internal modules of the device cooperate with each other, thereby realizing precise adjustment and control of parameters such as light intensity, color temperature, and spectrum, meeting the needs of users in different lighting scenarios, and further improving the applicability and user experience of LED lighting. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0076] Figure 1 It is a schematic diagram of the steps of the LED lamp control method of the present invention;

[0077] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0078] Figure 3 Detailed step flow diagram of step S3 in the present invention;

[0079] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0080] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0081] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0082] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0083] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for controlling an LED lamp, the method comprising the following steps:

[0084] Step S1: Acquire LED lamp design data, and perform feature extraction on the LED lamp design data, thereby obtaining LED lamp structure data and LED lamp material data; perform material property analysis on the LED lamp material data, thereby obtaining LED lamp material property data;

[0085] In this embodiment, the LED lamp design data can be obtained by cooperating with the LED chip manufacturer or directly obtaining the design documents and technical specifications from the LED lamp manufacturer. In the feature extraction stage, computer-aided design software, such as CAD software, can be used to perform three-dimensional modeling of the structure of the LED lamp, and extract features such as size, shape, and optical properties from it. The analysis of LED lamp material data may involve knowledge in the fields of material science and optical engineering. For example, an optical spectrometer is used to perform spectral analysis on LED lamp materials to obtain characteristic data such as the refractive index and transmittance of the material.

[0086] Step S2: performing optical field simulation according to the LED lamp material characteristic data and the LED lamp structure data, so as to obtain the LED lamp spectral response curve;

[0087] In this embodiment, in the optical field simulation, optical simulation software can be used to establish an optical model of the LED lamp based on the material characteristic data and the structure data of the LED lamp, including the LED chip, heat dissipation structure, etc., and set the light source parameters, material properties, etc. By tracing the light and simulating the optical effect, the spectral response curve of the LED lamp under different working conditions can be generated, thereby accurately evaluating the optical performance of the LED lamp.

[0088] Step S3: Acquire the lighting area sensor data, perform regional lighting environment analysis based on the lighting area sensor data, and thus obtain regional lighting environment data; perform LED lamp daytime brightness control strategy analysis based on the LED spectral response curve and the regional lighting environment data, and thus obtain the LED lamp daytime brightness control strategy;

[0089] In this embodiment, obtaining the sensor data of the lighting area involves installing devices such as light sensors and temperature sensors, and transmitting the data to a computer for processing through a data acquisition system. The analysis of the regional lighting environment data can use statistical methods, spatial analysis methods, etc. to obtain the spatial characteristics and time variation laws of the lighting distribution. According to the LED spectral response curve and the regional lighting environment data, optical simulation software can be used to simulate the light field, evaluate the lighting effect of the LED lamp, and design a daytime brightness control strategy. For example, the brightness and color temperature of the LED lamp can be adjusted to adapt to different lighting environments.

[0090] Step S4: Analyze the user's night lighting preference on the lighting area sensor data to obtain the user's night lighting preference data, and perform statistics on the user's night activity frequency based on the lighting area sensor data to obtain the user's night activity preference data; and construct a user's night behavior model based on the user's night lighting preference data and the user's night activity preference data;

[0091] In this embodiment, by performing preference analysis on the lighting area sensor data through statistical analysis and other methods, the user's lighting preference and activity frequency at night can be understood. For example, the user may prefer to reduce the lighting intensity during a specific time period to improve comfort or save energy. The user's night activity frequency is analyzed using statistical methods to understand the user's activity characteristics in different time periods. Building a user night behavior model may involve data mining, machine learning and other technologies to discover the rules and patterns of user behavior from a large amount of sensor data.

[0092] Step S5: Analyze the LED light night brightness control strategy based on the user night behavior model and the LED light spectrum response curve, so as to obtain the LED light night brightness control strategy;

[0093] In this embodiment, by establishing a mathematical model or simulating the user's night behavior model and the spectral response curve of the LED lamp, the LED lamp night brightness control strategy analysis is performed. For example, during the time period when the user's activity frequency is low, the brightness of the LED lamp can be reduced to save energy; during the time period when the user's activity frequency is high, the brightness of the LED lamp can be increased to provide a better lighting effect.

[0094] Step S6: coupling the LED light daytime brightness control strategy and the LED light nighttime brightness control strategy to obtain the LED light control strategy, and uploading it to the LED light management cloud platform to execute the LED light control task.

[0095] In this embodiment, the daytime brightness control strategy and the nighttime brightness control strategy of the LED lamp are coupled. First, the time range of daytime and nighttime needs to be determined. For example, from 6 am to 10 pm is daytime, and the rest of the time is nighttime. The brightness during the daytime can be set to 100%, and the brightness at night is automatically adjusted according to the light intensity. For example, when the light intensity is lower than a certain threshold, the brightness is adjusted to 30% to save energy. For example, according to the time period of daytime and nighttime, the control mode of the LED lamp is automatically switched. During the daytime, the daytime brightness control strategy is adopted to adjust the brightness according to the lighting environment and user activities; at night, the night brightness control strategy is switched to adjust the brightness according to the user behavior model and the ambient lighting conditions. According to the real-time needs and preferences of the user, the user's lighting needs are given priority in different time periods. For example, during the night time when the user is active frequently, the night brightness control strategy is preferred; and during the daytime when the user is less active, the daytime brightness control strategy is mainly used. The LED lamp control program is written in Python, and the date and time library is used to determine whether the current moment belongs to daytime or night. Conditional statements are implemented in the program to set the brightness of the LED lamp according to the daytime or night. For night brightness control, sensors can be used to obtain the ambient light intensity and adjust it according to the preset algorithm. Different time periods and lighting conditions are simulated in a laboratory environment to verify the accuracy and stability of the LED light control program. By simulating different light intensities and times, it is ensured that the LED light can adjust the brightness as expected. Using the MTT protocol in this embodiment, the written LED control program is connected to the LED light management cloud platform. Configure the MTT client in this embodiment, set the subject and client ID of the cloud platform, and perform identity authentication to ensure communication security. Upload the written LED control program to the LED light management cloud platform, create a corresponding task on the cloud platform, and bind the LED control program to the LED light device. Set the execution time and repetition interval of the task to ensure that the LED light control strategy can be executed on time.

[0096] The present invention can deeply understand the structure and material properties of LED lamps by acquiring LED lamp design data and extracting features. This helps to determine the manufacturing process and material selection of LED lamps to improve the performance and efficiency of LED lamps. Obtaining the spectral response curve of LED lamps through optical field simulation helps to understand the luminous characteristics of LED lamps at different wavelengths. This provides key information for subsequent control strategies, so that LED lamps can adjust the spectrum according to environmental requirements to achieve more suitable lighting effects. By analyzing the sensor data of the lighting area, the real-time situation of the lighting environment can be understood. Combined with the spectral response curve of the LED lamp, a suitable daytime brightness control strategy can be formulated so that the LED lamp can automatically adjust the brightness according to environmental changes and provide a comfortable lighting environment. By analyzing the user's night lighting preferences and activity frequency, the user's needs for night lighting can be understood. Constructing a user's night behavior model can help design a more intelligent night brightness control strategy, so that the LED lamp can intelligently adjust the brightness and color temperature according to the user's actual needs and improve the user experience. Based on the user's night behavior model and the spectral response curve of the LED lamp, a night brightness control strategy for the LED lamp is formulated. This helps the LED lamp provide lighting suitable for user needs at night, for example, providing soft light before going to bed to promote sleep. The daytime and nighttime brightness control strategies are coupled to form a complete LED light control strategy, which is then uploaded to the management cloud platform. This enables remote management and intelligent control of LED lights, improves the flexibility and convenience of the lighting system, and reduces energy consumption and maintenance costs.

[0097] Optionally, step S1 specifically includes:

[0098] Step S11: acquiring LED lamp design data, and performing feature extraction on the LED lamp design data, thereby obtaining LED lamp structure data and LED lamp material data;

[0099] In this embodiment, the design file of the LED lamp is obtained from the LED lamp design team, which includes the structural design, size parameters, material list and other information of the LED lamp. Computer-aided design software, such as AutoCAD or SolidWorks, is used to open the design file and perform feature extraction to extract the structural data of the LED lamp, such as the shell shape, internal component layout, etc., and to extract the material information used by the LED lamp, such as metal, plastic, etc.

[0100] Step S12: constructing a three-dimensional model of the LED lamp according to the LED lamp structure data;

[0101] In this embodiment, based on the acquired LED lamp structure data, a 3D model of the LED lamp is constructed using 3D modeling software, such as Blender or Rhino. According to the design size and appearance characteristics of the LED lamp, the appearance structure of the LED lamp is accurately established, including the housing, the light-transmitting part, and the layout of electronic components.

[0102] Step S13: meshing the LED lamp three-dimensional model according to the LED lamp design data, thereby obtaining an optical dense meshing model and a heat dissipation dense meshing model;

[0103] In this embodiment, the three-dimensional model of the LED lamp is divided into two parts: an optical dense grid and a heat dissipation dense grid by using a meshing technique. The optical dense grid is used for optical property analysis, and the heat dissipation dense grid is used for thermal property analysis. The LED lamp model is meshed using mesh generation software, such as ANSYS or COMSOL Multiphysics, to ensure the accuracy and efficiency of the model.

[0104] Step S14: performing optical characteristic analysis on the LED lamp material data according to the optical dense grid division model, thereby obtaining the LED lamp optical characteristic data;

[0105] In this embodiment, based on the optical dense meshing model, optical simulation software such as Zemax or LightTools is used to analyze the optical characteristics of the LED lamp material data. By simulating the propagation and reflection of light, the optical characteristic data of the LED lamp, such as beam angle, light intensity distribution, etc., are obtained.

[0106] Step S15: performing thermal characteristic analysis on the LED lamp material data according to the heat dissipation dense grid division model, thereby obtaining the LED lamp thermal characteristic data;

[0107] In this embodiment, according to the heat dissipation dense grid division model, thermal simulation software such as ANSYS Fluent or SolidWorks Flow Simulation is used to perform thermal characteristic analysis on the material data of the LED lamp. By simulating the conduction and heat dissipation of heat, the thermal characteristic data of the LED lamp, such as temperature distribution, thermal conductivity, etc., are obtained.

[0108] Step S16: merging the LED lamp optical characteristic data and the LED lamp thermal characteristic data to obtain the LED lamp material characteristic data.

[0109] In this embodiment, the optical characteristic data and thermal characteristic data of the LED lamp are merged to form the material characteristic data of the LED lamp. According to the design requirements and performance indicators, the merged data is analyzed and processed to guide the optimal design and performance improvement of the LED lamp.

[0110] The present invention obtains LED lamp design data and extracts features, which helps to deeply understand the structure and materials of LED lamps. These data are crucial for the performance analysis and optimization of LED lamps. Through feature extraction, key parameters such as LED chip type, heat dissipation structure, etc. can be identified, thereby providing basic data for subsequent steps. Building a three-dimensional model of an LED lamp allows the structure of the LED lamp to be visualized and analyzed in a computer. This enables designers and engineers to better understand the actual structure of the LED lamp, thereby optimizing and improving it. Through model meshing, the three-dimensional model of the LED lamp is divided into an optical dense grid and a heat dissipation dense grid. This allows optical and thermal characteristics to be analyzed more accurately in subsequent steps, providing more accurate data support for the optimization of the LED lamp. Optical characteristics analysis and thermal characteristics analysis respectively analyze the optical characteristics and thermal characteristics of the LED lamp in detail. Optical characteristics analysis can determine parameters such as optical efficiency and luminous intensity distribution of the LED lamp, while thermal characteristics analysis can determine information such as heat distribution and heat dissipation efficiency of the LED lamp during operation. The optical characteristics data and thermal characteristics data of the LED lamp are merged to obtain comprehensive material characteristics data of the LED lamp. This comprehensive data provides a comprehensive basis for the design optimization of LED lamps, allowing designers to strike a balance between optical efficiency and thermal management, thereby achieving more efficient and stable LED lamp designs.

[0111] Optionally, step S13 is specifically:

[0112] Step S131: classifying the LED lamp structure data by structure and use according to the LED lamp design data, thereby obtaining the LED lamp optical structure data and the LED lamp heat dissipation structure data;

[0113] In this embodiment, according to the LED lamp design data, the structural data of the LED lamp is classified, including the optical structure and the heat dissipation structure. The optical structure data involves the layout of the optical components and the design of the light-transmitting part of the LED lamp, while the heat dissipation structure data includes the design of the heat sink and the layout of the heat dissipation channel. By classifying the structural data, the optical structure data and the heat dissipation structure data of the LED lamp are obtained for subsequent model division and analysis.

[0114] Step S132: performing optical structure dense mesh division on the LED lamp three-dimensional model according to the LED lamp optical structure data, thereby obtaining an optical dense mesh division model;

[0115] In this embodiment, optical structure dense meshing is performed on the three-dimensional model of the LED lamp according to the optical structure data of the LED lamp. The optical component part of the LED lamp is meshed using meshing software, such as ANSYS Meshing or HyperMesh, to obtain an optical dense meshing model. When meshing, the shape, surface characteristics and optical transmission path of the optical component need to be considered to ensure the fineness and accuracy of the mesh.

[0116] Step S133: performing heat dissipation structure dense grid division on the LED lamp three-dimensional model according to the heat dissipation structure data of the LED lamp, thereby obtaining a heat dissipation dense grid division model.

[0117] In this embodiment, based on the heat dissipation structure data of the LED lamp, the three-dimensional model of the LED lamp is subjected to dense mesh division of the heat dissipation structure. Professional meshing software, such as ANSYS Meshing or HyperMesh, is used to mesh the heat dissipation part of the LED lamp to obtain a heat dissipation dense mesh division model. When meshing, the shape of the radiator, the vent design, and the heat conduction path need to be considered to ensure the density and accuracy of the mesh, and to provide an accurate model basis for subsequent thermal property analysis.

[0118] The present invention can classify the structure of the LED lamp into two parts: optical structure and heat dissipation structure by classifying the structure data of the LED lamp according to the structure and purpose. Such classification is helpful for more refined optimization of different structural parts. The optical structure data includes optical components such as lenses and reflective covers of LED lamps, while the heat dissipation structure data covers heat dissipation components such as heat sinks and heat sinks of LED lamps. Through such classification, subsequent optical and thermal characteristics analysis and optimization can be carried out in a targeted manner. When the three-dimensional model is densely meshed for the optical structure according to the optical structure data of the LED lamp, the details of the optical structure can be captured more accurately. The optical dense meshing model can help simulate the propagation path and optical efficiency of light, thereby optimizing factors such as lens shape and surface quality, and improving the optical performance and brightness uniformity of the LED lamp. Similarly, when the three-dimensional model is densely meshed for the heat dissipation structure according to the heat dissipation structure data of the LED lamp, the morphology and details of the heat dissipation structure can be more finely portrayed. The heat dissipation dense meshing model can help simulate the conduction and heat dissipation path of heat, optimize the design of the heat dissipation structure, improve the heat dissipation efficiency of the LED lamp, and thus extend the service life and stability of the LED lamp.

[0119] Optionally, step S14 is specifically:

[0120] Step S141: extracting LED lamp optical structure material features from LED lamp material data according to the LED lamp optical structure data, thereby obtaining LED lamp optical structure material data;

[0121] In this embodiment, various materials involved in the LED lamp, such as LED chips, lenses, reflectors, etc., are first identified based on the optical structure data of the LED lamp. Then, for each material, its optical characteristic parameters are extracted, including refractive index, absorption coefficient, scattering coefficient, etc. These characteristic parameters can be obtained through experimental measurement or from a material database. Through the extraction of characteristic parameters, the optical structure material data of the LED lamp is obtained to provide input for subsequent optical simulation.

[0122] Step S142: performing optical structure material mapping on the LED light optical structure material data and the optical dense mesh division model, thereby obtaining the LED light optical structure model;

[0123] In this embodiment, the optical structure material data of the LED lamp is mapped to the optical dense meshing model to establish the optical structure model of the LED lamp. During the mapping process, the optical property parameters of each material are assigned to the corresponding mesh unit to ensure that the material information in the model is consistent with the actual LED lamp structure. The key to this step is to accurately match the material properties with the mesh unit to ensure the accuracy and reliability of the subsequent optical simulation.

[0124] Step S143: extracting the LED lamp design operating condition features according to the LED lamp design data, thereby obtaining the LED lamp design operating condition data;

[0125] In this embodiment, according to the design data of the LED lamp, the design operating characteristics of the LED lamp are extracted, including operating current, operating temperature, light output power, etc. These design operating characteristics have an important influence on the optical performance and thermal performance of the LED lamp, so it is necessary to accurately obtain these data as input parameters when performing optical simulation.

[0126] Step S144: optically simulating the LED lamp design working condition data using the LED lamp optical structure model to obtain optical simulation data, and statistically analyzing the optical simulation data to obtain LED lamp optical characteristic data.

[0127] In this embodiment, the optical structure model of the LED lamp is used to perform optical simulation on the design working condition data of the LED lamp. The optical performance of the LED lamp under different working conditions, such as light distribution and beam angle, is simulated by simulation software, such as LightTools or Zemax. Then, the simulation data is statistically analyzed, including indicators such as average light intensity and beam uniformity, to obtain the optical characteristic data of the LED lamp.

[0128] The optical structure material feature extraction of LED lamps of the present invention is to extract the characteristics of the materials used for the optical elements (such as lenses, reflectors, etc.) of LED lamps. These characteristics may include refractive index, transmittance, reflectivity, etc. The material characteristics can be matched with the optical structure of the LED lamp to provide accurate material data for subsequent optical simulation. In the optical structure material mapping step, the optical structure material data of the LED lamp is matched with the optical dense meshing model. The advantage of this is that the material characteristics can be accurately mapped to the optical model to ensure the accuracy and reliability of the optical simulation. The design working condition feature extraction of LED lamps is to extract various working conditions and environmental factors that may be encountered by LED lamps in actual use. These characteristics may include operating temperature, operating current, ambient light, etc. By extracting these characteristics, real-world working conditions can be provided for optical simulation, thereby more accurately evaluating the performance of LED lamps. Optical simulation of the design working condition data of LED lamps can be performed through the LED lamp optical structure model to simulate the optical performance of LED lamps under different working conditions. Statistical analysis of optical simulation data can help evaluate the optical characteristics of LED lamps such as brightness, color, uniformity, etc., and provide guidance and basis for the design and optimization of LED lamps.

[0129] Optionally, step S15 is specifically:

[0130] Step S151: extracting the characteristics of the LED lamp heat dissipation structure material from the LED lamp material data according to the LED lamp heat dissipation structure data, thereby obtaining the LED lamp heat dissipation structure material data;

[0131] In this embodiment, the heat dissipation materials used in the LED lamp, such as aluminum substrates, heat sinks, etc., are identified based on the heat dissipation structure data of the LED lamp. Then, for each heat dissipation material, its thermal characteristic parameters are extracted, including thermal conductivity, specific heat capacity, thermal conductivity, etc. These parameters can be measured experimentally or obtained from a material database. By extracting the characteristic parameters, the heat dissipation structure material data of the LED lamp is obtained to provide input for subsequent thermal energy simulation.

[0132] Step S152: performing heat dissipation structure material mapping on the LED lamp heat dissipation structure material data and the optical dense mesh division model, thereby obtaining the LED lamp heat dissipation structure model;

[0133] In this embodiment, the LED lamp heat dissipation structure material data is mapped to the optical dense meshing model to establish the LED lamp heat dissipation structure model. During the mapping process, the thermal characteristic parameters of each heat dissipation material are assigned to the corresponding mesh unit to ensure that the heat dissipation material information in the model is consistent with the actual LED lamp structure. The key to this step is to accurately match the material properties with the mesh unit to ensure the accuracy and reliability of the subsequent thermal energy simulation.

[0134] Step S153: thermally simulate the optical simulation data and the LED lamp design operating condition data using the LED lamp heat dissipation structure model to obtain thermal simulation data, and perform statistical analysis on the thermal simulation data to obtain thermal characteristic data of the LED lamp.

[0135] In this embodiment, the heat dissipation structure model of the LED lamp is used to perform thermal simulation on the optical simulation data and the design working condition data of the LED lamp. Professional thermal simulation software, such as ANSYS or COMSOL Multiphysics, is used to simulate the heat dissipation performance of the LED lamp under different working conditions, including temperature distribution, heat conduction path, etc. Then, the thermal simulation data is statistically analyzed, including indicators such as the maximum temperature and temperature gradient, to obtain the thermal characteristic data of the LED lamp and evaluate the heat dissipation effect of the LED lamp.

[0136] The LED lamp heat dissipation structure material feature extraction of the present invention allows the characteristics of the materials used for the heat dissipation components of the LED lamp (such as heat sinks, radiators, etc.) to be extracted. These characteristics may include thermal conductivity, thermal conductivity coefficient, etc. This can correspond the material characteristics to the heat dissipation structure of the LED lamp, and provide accurate material data for subsequent thermal energy simulation. In the heat dissipation structure material mapping step, the LED lamp heat dissipation structure material data is corresponded to the optical dense meshing model. The advantage of this is that the material characteristics can be accurately mapped to the heat dissipation structure model to ensure the accuracy and reliability of thermal energy simulation. Thermal energy simulation is performed on the optical simulation data and the LED lamp design working condition data through the LED lamp heat dissipation structure model, and the thermal characteristics of the LED lamp under different working conditions can be simulated. Statistical analysis of thermal energy simulation data can help evaluate the heat dissipation performance of LED lamps, including temperature distribution, heat dissipation efficiency, etc., thereby providing a basis for the design and optimization of LED lamps.

[0137] Optionally, step S3 specifically includes:

[0138] Step S31: acquiring lighting area sensor data, and performing illumination feature extraction and camera image feature extraction on the lighting area sensor data, thereby obtaining regional illumination sensor data and regional camera images;

[0139] In this embodiment, real-time environmental data such as light, temperature, and humidity are obtained by installing sensor devices in the lighting area. Then, light feature extraction is performed on the light data, including light intensity, light distribution, light change trend, etc. At the same time, feature extraction is performed on the camera image to identify information such as light distribution and light source position in the image. Through these processes, regional light sensing data and regional camera images are obtained.

[0140] Step S32: time-series division of the regional light sensing data, thereby obtaining regional daytime light sensing data and regional nighttime light sensing data;

[0141] In this embodiment, the regional light sensor data is divided according to time to distinguish the light conditions during the day and at night. This can be determined by indicators such as timestamps or sunrise and sunset times. After the division, regional daylight sensor data and regional nightlight sensor data are obtained.

[0142] Step S33: performing high-frequency light intensity statistics on the regional daytime light sensor data, thereby obtaining high-frequency daytime light intensity data; performing high-frequency light intensity statistics on the regional nighttime light sensor data, thereby obtaining high-frequency nighttime light intensity data;

[0143] In this embodiment, a statistical analysis of high-frequency light intensity is performed on the light sensor data of daytime and nighttime. This includes statistical analysis of the light intensity distribution in each time period, and the frequency of light intensity changes. Through these statistical data, high-frequency daytime light intensity data and high-frequency nighttime light intensity data are obtained.

[0144] Step S34: performing illumination distribution analysis based on the regional camera image to obtain illumination distribution data;

[0145] In this embodiment, the regional camera images are used to perform a detailed analysis of the illumination distribution, including identifying the light source position, light scattering, shadow distribution, etc. Accurate illumination distribution data is obtained through image processing technology.

[0146] Step S35: performing temporal-spatial fusion of the high-frequency daytime light intensity data and the high-frequency nighttime light intensity data according to the light distribution data, thereby obtaining regional light environment data;

[0147] In this embodiment, the light sensing data and the light distribution data are combined to perform temporal spatial fusion to obtain more comprehensive regional light environment data. This may include matching the light intensity data with the light distribution data to obtain more accurate light environment information.

[0148] Step S36: Spectral matching is performed according to the regional lighting environment data and the LED spectral response curve to obtain the regional lighting demand data, and daylight brightness control strategy analysis is performed according to the regional lighting demand data and the LED lamp material characteristic data to obtain the LED lamp daylight brightness control strategy.

[0149] In this embodiment, spectrum matching is performed based on the regional lighting environment data and the LED spectral response curve to determine the LED lighting requirements. For example, the luminous wavelength in the LED spectral response curve is compared with the degree of overlap of the spectrum in the regional lighting environment data to determine the optimal luminous wavelength and spectral distribution of the LED lamp to improve the lighting effect and comfort. Then, the most suitable daytime brightness control strategy is analyzed in combination with the material properties and power characteristics of the LED lamp to achieve the best balance between energy saving and lighting effect. For example, a daytime brightness control strategy is formulated based on the light intensity data of the office lighting area during the day. On sunny days, the brightness of the LED lamp can be reduced to save energy and reduce glare. On cloudy days or when there is insufficient light, the brightness of the LED lamp can be appropriately increased to ensure the brightness and comfort of the office.

[0150] The present invention obtains the sensor data of the lighting area and performs illumination feature extraction and camera image feature extraction, which can help understand the illumination and visual environment of the lighting area. These data provide information about the light intensity, light distribution and environmental characteristics, and provide basic data for the subsequent optimization of the lighting system. The regional illumination sensor data is divided into two time periods: daytime and nighttime. This helps to distinguish the illumination characteristics and requirements in different time periods, and provides a time series basis for subsequent analysis. By performing high-frequency illumination intensity statistics on the illumination sensor data of the day and night, more detailed illumination data can be obtained, including the frequency and amplitude of illumination changes. This is of great significance for understanding the dynamic characteristics of illumination changes and corresponding countermeasures. Using regional camera images to perform illumination distribution analysis, the illumination distribution of the lighting area can be more accurately understood, including the spatial distribution and change trend of the illumination intensity. This helps to better understand the characteristics of the lighting environment. By performing time-series spatial fusion on the illumination distribution data, the illumination conditions of different time periods and spatial positions can be comprehensively considered to obtain more comprehensive and comprehensive regional illumination environment data. This helps to design and optimize the lighting system more effectively. Through spectrum matching and lighting demand analysis, combined with LED lamp material characteristic data, the daytime brightness control strategy of LED lamps can be determined. This helps to reasonably adjust the brightness of LED lamps according to the actual lighting environment and needs, and improve energy efficiency and comfort.

[0151] Optionally, step S34 is specifically:

[0152] Step S341: grayscale conversion is performed on the regional camera image to obtain a regional grayscale image, and high-frequency pixel statistics are performed on the regional grayscale image to obtain high-frequency pixel data;

[0153] In this embodiment, the area camera image is converted into a grayscale image, which involves converting the RGB value of each pixel in the color image into a single grayscale value. Then, high-frequency pixel statistics are performed on the grayscale image, which includes analyzing pixels that change frequently in the image, which usually correspond to edges, textures or other details in the image. In this way, high-frequency pixel data is obtained, which can be used for pixel fluctuation calculation in subsequent steps.

[0154] Step S342: performing pixel fluctuation calculation according to the high-frequency pixel data to obtain image pixel fluctuation data, and performing pixel fluctuation classification on the image pixel fluctuation data to obtain high-value pixel fluctuation data and low-value pixel fluctuation data;

[0155] In this embodiment, high-frequency pixel data is used to perform pixel fluctuation calculation, which involves analyzing the changes between pixel values, usually by calculating the gradient or difference between pixels. Then, the pixel fluctuation data is classified into two categories: high-value pixel fluctuation and low-value pixel fluctuation. High-value pixel fluctuation usually indicates high-frequency details or edges in the image, while low-value pixel fluctuation may represent flat areas or low-frequency details in the image.

[0156] Step S343: dividing the regional grayscale image into illumination ranges according to the high-value pixel fluctuation data, thereby obtaining an illumination range image;

[0157] In this embodiment, the illumination range of the regional grayscale image is divided based on the high-value pixel fluctuation data. This means that the areas with large pixel fluctuations in the image are marked as illumination ranges, which may correspond to bright areas in the image or areas with high illumination intensity. In this way, an illumination range image is obtained, which can be used for subsequent illumination distribution statistics.

[0158] Step S344: dividing the regional grayscale image into backlight ranges according to the low-value pixel fluctuation data, thereby obtaining a backlight range image;

[0159] In this embodiment, the regional grayscale image is divided into backlight ranges according to the low-value pixel fluctuation data. This means that the areas with smaller pixel fluctuations in the image are marked as backlight ranges, which may correspond to dark areas in the image or areas with lower illumination intensity. In this way, a backlight range image is obtained, which can be used for subsequent illumination distribution statistics.

[0160] Step S345: performing regional illumination distribution statistics on the regional grayscale image according to the illumination range image and the backlight range image, thereby obtaining illumination distribution data.

[0161] In this embodiment, based on the illumination range image and the backlight range image, the illumination distribution statistics are performed on the regional grayscale image. This includes analyzing the illumination intensity distribution of different regions in the image, which is usually achieved by calculating the average grayscale value or illumination intensity of the pixels in each region. Detailed data on regional illumination distribution can be obtained, thereby providing a basis for subsequent image processing or analysis.

[0162] The present invention converts the regional camera image into a grayscale image, which helps to simplify the image processing process while retaining the illumination information in the image. Then, high-frequency pixel statistics are performed on the grayscale image to capture the pixels that change frequently in the image. These pixels often correspond to areas with high illumination change frequencies, thereby providing information about the dynamics of illumination. Through pixel fluctuation calculation, the image pixel fluctuation data is divided into high-amount and low-amount pixel fluctuation data. High-amount pixel fluctuation data usually corresponds to areas with large or frequent illumination changes, while low-amount pixel fluctuation data may correspond to areas with stable illumination. This classification helps to more finely distinguish areas with different illumination characteristics in the image. The illumination range of the regional grayscale image is divided using the high-amount pixel fluctuation data to obtain an illumination range image. This can help determine areas with large illumination changes, provide a visual representation of illumination distribution, and help further analyze illumination characteristics. The backlight range of the regional grayscale image is divided according to the low-amount pixel fluctuation data to obtain a backlight range image. For areas with relatively stable illumination, it helps to further distinguish the background illumination in the image and provide a more accurate data basis for illumination distribution statistics. The illumination distribution statistics of the regional grayscale image are calculated based on the illumination range image and the backlight range image. This helps to fully understand the different light intensities and distributions in the area, and provides an important reference for the optimization of the lighting system, such as providing guidance when designing the layout of lamps or adjusting the brightness.

[0163] Optionally, step S4 is specifically:

[0164] Step S41: extracting night time-series sensing features from the sensor data of the lighting area, thereby obtaining night sensing data of the lighting area, and extracting night camera image features from the night sensor data of the area, thereby obtaining night camera images;

[0165] In this embodiment, the sensor data of the lighting area is analyzed by time series to extract the nighttime sensor features, such as the changing trend of the nighttime light intensity, the average temperature, etc. Computer vision technology is used to extract features from the nighttime camera images, such as extracting lights, vehicles, and pedestrians in the images.

[0166] Step S42: performing low-frequency pixel statistics on the night camera image to obtain low-frequency pixel data, and performing color feature extraction on the low-frequency pixel data to obtain low-frequency pixel color data;

[0167] In this embodiment, low-frequency pixel statistics are performed for night-time camera images, that is, pixels that change slowly in the image are analyzed, such as background, large areas, etc. Then, color feature extraction is performed on these low-frequency pixel data to capture color information in the image, such as extracting background color, subject color, etc., thereby obtaining low-frequency pixel color data.

[0168] Step S43: performing spectral energy distribution conversion on the low-frequency pixel color data, thereby obtaining night spectral energy distribution data;

[0169] In this embodiment, the spectral energy distribution conversion is performed on the low-frequency pixel color data, and the color data is converted into spectral energy distribution data to better describe the energy distribution of different colors in the image, thereby obtaining the night spectral energy distribution data. For example, the color data is converted into RGB or HSV color space. The spectrum analysis technology is used to calculate the energy distribution of different colors on the spectrum. The energy distribution data is normalized to obtain the night spectral energy distribution data.

[0170] Step S44: Calculate the illumination intensity of the night spectral energy distribution data according to the LED spectral response curve, thereby obtaining the user's night lighting preference data;

[0171] In this embodiment, the light intensity calculation of the night spectral energy distribution data according to the LED spectral response curve is to better understand the user's preference for night lighting. The LED spectral response curve reflects the luminous efficiency of the LED lamp under different wavelengths of light, while the light intensity is the light intensity of the LED lamp in the lighting area. By calculating the light intensity of the night spectral energy distribution data under the LED spectral response curve, the light intensity within different wavelength ranges can be known, so as to understand the user's lighting preference for specific wavelengths of light at night. Then, the night spectral energy distribution data is convolved with the LED spectral response curve to obtain the light intensity data. Finally, by calculating the area of ​​the convolution result as the light intensity data, the user's night lighting preference data is obtained.

[0172] Step S45: Counting the frequency of nighttime activities of users according to the lighting area sensing data, thereby obtaining the nighttime activity preference data of users;

[0173] In this embodiment, the frequency of nighttime activities of users is counted based on the sensor data of the lighting area, and the time period is divided into fixed time periods, such as every hour or every half hour, and then the frequency of human activities in each time period is counted, including walking, resting, using equipment, etc. By analyzing the statistical data, the user's nighttime activity preference data can be obtained.

[0174] Step S46: constructing a user's night behavior model based on the user's night lighting preference data and the user's night activity preference data.

[0175] In this embodiment, a user's nighttime behavior model is constructed based on the user's nighttime lighting preference data and the user's nighttime activity preference data, and the lighting preference data and the activity preference data are integrated into a user's nighttime behavior data set. Then, machine learning or statistical models, such as decision trees and cluster analysis, are used to model the user's nighttime behavior data. Finally, the model is verified and optimized to ensure its prediction accuracy and applicability.

[0176] The present invention can obtain the perception data and visual information of the night environment by extracting the night sensing features and night camera image features of the lighting area, providing a basis for subsequent analysis and processing. By low-frequency pixel statistics and color feature extraction, the basic color information in the night camera image can be obtained, which is helpful to understand the visual features and color composition of the night environment. By converting the spectral energy distribution of the low-frequency pixel color data, the distribution of the night spectral energy can be further analyzed to provide data support for the subsequent calculation of the light intensity. According to the LED spectral response curve, the light intensity is calculated, and the user's lighting preference can be inferred based on the night spectral energy distribution data, so as to better meet the user's demand for night lighting. By counting the user's night activity frequency through the lighting area sensor data, the user's activity habits and behavior patterns at night can be understood, providing a basis for personalized lighting services. By combining the user's night lighting preference data and the night activity preference data to construct a user's night behavior model, the user's lighting needs at night can be more accurately predicted and responded to, providing a more intelligent and personalized lighting solution.

[0177] Optionally, step S45 is specifically:

[0178] Step S451: extracting audio features from the nighttime sensor data of the lighting area, thereby obtaining the nighttime audio data of the lighting area;

[0179] In this embodiment, when extracting audio features from the sensor data of the lighting area at night, common audio signal processing techniques such as short-time Fourier transform (STFT) or wavelet transform can be used. These techniques can convert the sensor data into a spectrum diagram, extract features such as frequency and amplitude, and thus obtain audio data of the lighting area at night.

[0180] Step S452: performing spectrum transformation on the audio data of the lighting area at night, thereby obtaining the audio spectrum of the lighting area at night;

[0181] In this embodiment, when performing spectrum transformation on the audio data of the lighting area at night, a fast Fourier transform (FFT) algorithm can be used to convert the time domain signal into a frequency domain signal to obtain the audio spectrum of the lighting area at night. This step can be used to analyze the spectrum characteristics of the audio signal, such as frequency distribution, energy distribution, etc.

[0182] Step S453: performing spectrum fluctuation statistics on the audio spectrum of the lighting area at night, thereby obtaining spectrum fluctuation data;

[0183] In this embodiment, when performing spectrum fluctuation statistics on the audio spectrum of the lighting area at night, the variation of the spectrum data in different frequency ranges, such as the mean, variance, kurtosis, skewness and other statistics of the spectrum, can be calculated to obtain spectrum fluctuation data. These statistics can reflect the stability and degree of variation of the audio spectrum.

[0184] Step S454: extracting time series features from the spectrum fluctuation data to obtain fluctuation time series data, and classifying and calculating the night audio spectrum of the lighting area according to the fluctuation time series data to obtain night fluctuation spectrum and night flat spectrum;

[0185] In this embodiment, when extracting time series features from spectrum fluctuation data, time domain analysis methods such as autoregressive model (AR), moving average model (MA), etc. can be used to extract the time series features of spectrum fluctuation. Then, the audio spectrum of the lighting area at night is classified and calculated based on the fluctuation time series data to obtain the night fluctuation spectrum and the night flat spectrum, which can be classified using a clustering algorithm or a classifier.

[0186] Step S455: performing an inverse Fourier transform on the night fluctuation spectrum to obtain night activity data; performing an inverse Fourier transform on the night smooth spectrum to obtain night smooth data;

[0187] In this embodiment, when performing an inverse Fourier transform on the night fluctuation spectrum, an inverse FFT algorithm can be used to convert the spectrum data back to time domain data to obtain night activity data. Similarly, performing an inverse Fourier transform on the night smooth spectrum can obtain night smooth data, which reflects the different activity levels in the night lighting area.

[0188] Step S456: Time-series merge the night calm data and night activity data to obtain the user's night activity preference data.

[0189] In this embodiment, when the night calm data and the night activity data are time-series merged, a time series analysis method, such as a sliding window method or a time series model, can be used according to specific needs to merge the two types of data, thereby obtaining the user's night activity preference data. For example, the activity levels in different time periods can be counted to further analyze the user's night activity preferences.

[0190] The audio feature extraction in the present invention can extract audio information from the night sensor data of the lighting area, such as the sound intensity and frequency distribution in the environment. This information can reflect the sound characteristics of nighttime activities and provide basic data for subsequent analysis. Spectral transformation can convert the nighttime audio data of the lighting area into a representation in the frequency domain, that is, an audio spectrum, which helps to better understand the distribution of different frequency components in the nighttime environment. Spectral fluctuation statistics can analyze the dynamic changes of the audio spectrum to obtain spectrum fluctuation data, which reflects the changing trend of the sound in the nighttime environment and provides important information for subsequent behavior analysis. By extracting the time series features of the spectrum fluctuation data, the fluctuation time series data can be obtained, and the audio spectrum is classified and calculated based on these data, so as to obtain the night fluctuation spectrum and the night smooth spectrum, which helps to distinguish different types of sound activities at night. Inverse Fourier transform is used to convert the night fluctuation spectrum and the night smooth spectrum back to the time domain, so as to obtain night activity data and night smooth data, which reflect the time series characteristics of different types of activities at night. By performing time series merging of the night smooth data and the night activity data, the user's night activity preference data, that is, the type and frequency of night activities, can be obtained, which provides a basis for personalized lighting services.

[0191] Optionally, the present specification further provides an LED lamp, which includes an LED lamp main body, a power supply unit and an electrical control unit, wherein the power supply unit is installed inside the LED lamp main body, the electrical control unit is electrically connected to the power supply unit, and the electrical control unit is used to charge the LED lamp main body and control the LED lamp main body, and the electrical control unit includes:

[0192] The material property analysis module is used to obtain the LED lamp design data and perform feature extraction on the LED lamp design data, thereby obtaining the LED lamp structure data and the LED lamp material data; perform material property analysis on the LED lamp material data, thereby obtaining the LED lamp material property data;

[0193] An optical field simulation module is used to perform optical field simulation according to the material characteristic data of the LED lamp and the structure data of the LED lamp, so as to obtain the spectral response curve of the LED lamp;

[0194] The daylight brightness control strategy analysis module is used to obtain the lighting area sensor data, perform regional lighting environment analysis based on the lighting area sensor data, and thus obtain the regional lighting environment data; perform LED light daylight brightness control strategy analysis based on the LED spectral response curve and regional lighting environment data, and thus obtain the LED light daylight brightness control strategy;

[0195] A user night behavior model building module is used to analyze the user's night lighting preference based on the lighting area sensor data, thereby obtaining the user's night lighting preference data, and to count the user's night activity frequency based on the lighting area sensor data, thereby obtaining the user's night activity preference data; and to build a user's night behavior model based on the user's night lighting preference data and the user's night activity preference data;

[0196] Night brightness control strategy analysis module, used to analyze the night brightness control strategy of LED lights based on the user's night behavior model and the LED light spectrum response curve, so as to obtain the night brightness control strategy of LED lights;

[0197] The control strategy coupling module is used to couple the control strategy of the LED light daytime brightness control strategy and the LED light night brightness control strategy, so as to obtain the LED light control strategy and upload it to the LED light management cloud platform to execute the LED light control task.

[0198] The LED lamp of the present invention can implement any LED lamp control method of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the LED lamp control method. The internal modules of the device cooperate with each other, thereby realizing precise adjustment and control of parameters such as light intensity, color temperature, and spectrum, meeting the needs of users in different lighting scenarios, and further improving the applicability and user experience of LED lighting.

[0199] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0200] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for controlling an LED lamp, characterized in that: The following steps are involved: Step S1: acquiring LED lamp design data, and performing feature extraction on the LED lamp design data, thereby obtaining LED lamp structure data and LED lamp material data; Perform material property analysis on LED lamp material data to obtain LED lamp material property data; Step S2: performing optical field simulation according to the LED lamp material characteristic data and the LED lamp structure data, so as to obtain the LED spectral response curve; Step S3: Acquire lighting area sensor data, and perform regional lighting environment analysis based on the lighting area sensor data to obtain regional lighting environment data; The LED lamp daytime brightness control strategy is analyzed according to the LED spectral response curve and the regional lighting environment data, so as to obtain the LED lamp daytime brightness control strategy; wherein step S3 includes: Step S31: acquiring lighting area sensor data, and performing illumination feature extraction and camera image feature extraction on the lighting area sensor data, thereby obtaining regional illumination sensor data and regional camera images; Step S32: time-series division of the regional light sensing data, thereby obtaining regional daytime light sensing data and regional nighttime light sensing data; Step S33: performing high-frequency light intensity statistics on the regional daytime light sensor data, thereby obtaining high-frequency daytime light intensity data; performing high-frequency light intensity statistics on the regional nighttime light sensor data, thereby obtaining high-frequency nighttime light intensity data; Step S34: performing illumination distribution analysis based on the regional camera image to obtain illumination distribution data; Wherein, step S34 comprises: Step S341: grayscale conversion is performed on the regional camera image to obtain a regional grayscale image, and high-frequency pixel statistics are performed on the regional grayscale image to obtain high-frequency pixel data; Step S342: performing pixel fluctuation calculation according to the high-frequency pixel data to obtain image pixel fluctuation data, and performing pixel fluctuation classification on the image pixel fluctuation data to obtain high-value pixel fluctuation data and low-value pixel fluctuation data; Step S343: dividing the regional grayscale image into illumination ranges according to the high-value pixel fluctuation data, thereby obtaining an illumination range image; Step S344: dividing the regional grayscale image into backlight ranges according to the low-value pixel fluctuation data, thereby obtaining a backlight range image; Step S345: performing regional illumination distribution statistics on the regional grayscale image according to the illumination range image and the backlight range image, thereby obtaining illumination distribution data; Step S35: performing temporal-spatial fusion of the high-frequency daytime light intensity data and the high-frequency nighttime light intensity data according to the light distribution data, thereby obtaining regional light environment data; Step S36: performing spectrum matching according to the regional lighting environment data and the LED spectrum response curve to obtain the regional lighting demand data, and performing daytime brightness control strategy analysis according to the regional lighting demand data and the LED lamp material characteristic data to obtain the LED lamp daytime brightness control strategy; Step S4: Analyze the user's night lighting preference on the lighting area sensor data to obtain the user's night lighting preference data, and perform statistics on the user's night activity frequency based on the lighting area sensor data to obtain the user's night activity preference data; and construct a user's night behavior model based on the user's night lighting preference data and the user's night activity preference data; Step S5: Analyze the LED light brightness control strategy at night based on the user's night behavior model and the LED spectrum response curve, so as to obtain the LED light brightness control strategy at night; Step S6: coupling the LED light daytime brightness control strategy and the LED light nighttime brightness control strategy to obtain the LED light control strategy, and uploading it to the LED light management cloud platform to execute the LED light control task.

2. The LED lamp control method according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: acquiring LED lamp design data, and performing feature extraction on the LED lamp design data, thereby obtaining LED lamp structure data and LED lamp material data; Step S12: constructing a three-dimensional model of the LED lamp according to the LED lamp structure data; Step S13: meshing the LED lamp three-dimensional model according to the LED lamp design data, thereby obtaining an optical dense meshing model and a heat dissipation dense meshing model; Step S14: performing optical characteristic analysis on the LED lamp material data according to the optical dense grid division model, thereby obtaining the LED lamp optical characteristic data; Step S15: performing thermal characteristic analysis on the LED lamp material data according to the heat dissipation dense grid division model, thereby obtaining the LED lamp thermal characteristic data; Step S16: merging the LED lamp optical characteristic data and the LED lamp thermal characteristic data to obtain the LED lamp material characteristic data.

3. The LED lamp control method according to claim 2, characterized in that: Step S13 is specifically as follows: Step S131: classifying the LED lamp structure data by structure and use according to the LED lamp design data, thereby obtaining the LED lamp optical structure data and the LED lamp heat dissipation structure data; Step S132: performing optical structure dense mesh division on the LED lamp three-dimensional model according to the LED lamp optical structure data, thereby obtaining an optical dense mesh division model; Step S133: performing heat dissipation structure dense grid division on the LED lamp three-dimensional model according to the heat dissipation structure data of the LED lamp, thereby obtaining a heat dissipation dense grid division model.

4. The LED lamp control method according to claim 2, characterized in that: Step S14 is specifically as follows: Step S141: extracting LED lamp optical structure material features from LED lamp material data according to the LED lamp optical structure data, thereby obtaining LED lamp optical structure material data; Step S142: performing optical structure material mapping on the LED light optical structure material data and the optical dense mesh division model, thereby obtaining the LED light optical structure model; Step S143: extracting the LED lamp design operating condition features according to the LED lamp design data, thereby obtaining the LED lamp design operating condition data; Step S144: optically simulating the LED lamp design working condition data using the LED lamp optical structure model to obtain optical simulation data, and statistically analyzing the optical simulation data to obtain LED lamp optical characteristic data.

5. The LED lamp control method according to claim 2, characterized in that: Step S15 is specifically as follows: Step S151: extracting the characteristics of the LED lamp heat dissipation structure material from the LED lamp material data according to the LED lamp heat dissipation structure data, thereby obtaining the LED lamp heat dissipation structure material data; Step S152: performing heat dissipation structure material mapping on the LED lamp heat dissipation structure material data and the optical dense meshing model, thereby obtaining the LED lamp heat dissipation structure model; Step S153: thermally simulate the optical simulation data and the LED lamp design operating condition data using the LED lamp heat dissipation structure model to obtain thermal simulation data, and perform statistical analysis on the thermal simulation data to obtain thermal characteristic data of the LED lamp.

6. The LED lamp control method according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: extracting night time-series sensing features from the sensor data of the lighting area, thereby obtaining night sensing data of the lighting area, and extracting night camera image features from the night sensor data of the area, thereby obtaining night camera images; Step S42: performing low-frequency pixel statistics on the night camera image to obtain low-frequency pixel data, and performing color feature extraction on the low-frequency pixel data to obtain low-frequency pixel color data; Step S43: performing spectral energy distribution conversion on the low-frequency pixel color data, thereby obtaining night spectral energy distribution data; Step S44: Calculate the illumination intensity of the night spectral energy distribution data according to the LED spectral response curve, thereby obtaining the user's night lighting preference data; Step S45: Counting the frequency of nighttime activities of users according to the lighting area sensing data, thereby obtaining the nighttime activity preference data of users; Step S46: constructing a user's night behavior model based on the user's night lighting preference data and the user's night activity preference data.

7. The LED lamp control method according to claim 6, characterized in that: Step S45 is specifically as follows: Step S451: extracting audio features from the nighttime sensor data of the lighting area, thereby obtaining the nighttime audio data of the lighting area; Step S452: performing spectrum transformation on the audio data of the lighting area at night, thereby obtaining the audio spectrum of the lighting area at night; Step S453: performing spectrum fluctuation statistics on the audio spectrum of the lighting area at night, thereby obtaining spectrum fluctuation data; Step S454: extracting time series features from the spectrum fluctuation data to obtain fluctuation time series data, and classifying and calculating the night audio spectrum of the lighting area according to the fluctuation time series data to obtain night fluctuation spectrum and night flat spectrum; Step S455: performing an inverse Fourier transform on the night fluctuation spectrum to obtain night activity data; Performing inverse Fourier transform on the night calm spectrum to obtain night calm data; Step S456: Time-series merge the night calm data and night activity data to obtain the user's night activity preference data.

8. An LED lamp, used to execute the LED lamp control method according to any one of claims 1 to 7, characterized in that: The LED lamp comprises a main body, a power supply and an electrical control unit. The power supply is installed inside the main body of the LED lamp. The electrical control unit is electrically connected to the power supply. The electrical control unit is used to charge the main body of the LED lamp and control the main body of the LED lamp. The electrical control unit comprises: The material property analysis module is used to obtain the LED lamp design data and perform feature extraction on the LED lamp design data, thereby obtaining the LED lamp structure data and the LED lamp material data; perform material property analysis on the LED lamp material data, thereby obtaining the LED lamp material property data; An optical field simulation module is used to perform optical field simulation according to the material characteristic data and structure data of the LED lamp, so as to obtain the LED spectral response curve; The daylight brightness control strategy analysis module is used to obtain the lighting area sensor data, perform regional lighting environment analysis based on the lighting area sensor data, and thus obtain the regional lighting environment data; perform LED light daylight brightness control strategy analysis based on the LED spectral response curve and regional lighting environment data, and thus obtain the LED light daylight brightness control strategy; A user night behavior model building module is used to analyze the user's night lighting preference based on the lighting area sensor data, thereby obtaining the user's night lighting preference data, and to count the user's night activity frequency based on the lighting area sensor data, thereby obtaining the user's night activity preference data; and to build a user's night behavior model based on the user's night lighting preference data and the user's night activity preference data; Night brightness control strategy analysis module, used to analyze the night brightness control strategy of LED lights based on the user's night behavior model and LED spectrum response curve, so as to obtain the night brightness control strategy of LED lights; The control strategy coupling module is used to couple the control strategy of the LED light daytime brightness control strategy and the LED light night brightness control strategy, so as to obtain the LED light control strategy and upload it to the LED light management cloud platform to execute the LED light control task.

Images