CN118215176A

CN118215176A - LED lamp and control method thereof

Info

Publication number: CN118215176A
Application number: CN202410443906.4A
Authority: CN
Inventors: 姚国春; 易正; 方谨
Original assignee: Jiangmen Chihong Optoelectronic Technology Co ltd
Current assignee: Weng Xianyi
Priority date: 2024-04-14
Filing date: 2024-04-14
Publication date: 2024-06-18

Abstract

The invention relates to the technical field of electronic control, in particular to an LED lamp and a control method thereof. The method comprises the following steps: acquiring LED lamp design data and analyzing material characteristics to acquire LED lamp material characteristic data; performing optical field simulation according to the LED lamp material characteristic data and the LED lamp structure data to obtain an LED lamp spectrum response curve; acquiring sensing data of an illumination area and analyzing an LED lamp daytime brightness control strategy, so as to acquire the LED lamp daytime brightness control strategy; constructing a user night behavior model according to the illumination area sensing data; analyzing the night brightness control strategy of the LED lamp according to the night behavior model of the user and the response curve of the LED lamp spectrum, so as to obtain the night brightness control strategy of the LED lamp; and performing control strategy coupling on the LED lamp daytime brightness control strategy and the LED lamp night brightness control strategy, so as to obtain the LED lamp control strategy, and uploading the LED lamp control strategy to the LED lamp management cloud platform. The LED lighting system and the LED lighting method can further improve the applicability and the user experience of LED lighting.

Description

LED lamp and control method thereof

Technical Field

The invention relates to the technical field of electronic control, in particular to an LED lamp and a control method thereof.

Background

LED (light emitting diode) lamps have become mainstream in the field of modern lighting, and gradually replace conventional incandescent lamps and fluorescent lamps with the advantages of high efficiency, energy saving, environmental protection and the like. However, although the LED lamp is excellent in energy efficiency and lifetime, it still has challenges in terms of illumination quality and control. In order to further improve the flexibility, adjustability and adaptability of the LED lamp, researchers are devoted to developing new LED lamps and control methods thereof to meet the demands of different application scenarios. Conventional LED lamp control methods typically have fixed brightness and spectrum, which are difficult to accommodate for different environments and usage scenarios. For example, in home, commercial and industrial lighting, a user may need light sources of different brightness and color temperature to create different atmospheres or to provide suitable lighting conditions, failing to meet the user's needs for different lighting scenes. Therefore, an LED control method is needed to improve LED lighting applicability and user experience.

Disclosure of Invention

Accordingly, the present invention is directed to an LED lamp and a control method thereof, which solve at least one of the above-mentioned problems.

In order to achieve the above purpose, a method for controlling an LED lamp includes the following steps:

Step S1: acquiring LED lamp design data, and extracting characteristics of the LED lamp design data so as to acquire LED lamp structure data and LED lamp material data; carrying out material characteristic analysis on the LED lamp material data so as to obtain LED lamp material characteristic 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 an LED lamp spectrum response curve;

Step S3: acquiring illumination area sensing data, and analyzing the area illumination environment according to the illumination area sensing data so as to acquire area illumination environment data; analyzing the LED lamp daytime brightness control strategy according to the LED spectral response curve and the regional illumination environment data, so as to obtain the LED lamp daytime brightness control strategy;

Step S4: the illumination area sensing data is subjected to user night illumination preference analysis so as to obtain user night illumination preference data, and user night activity frequency statistics is carried out according to the illumination area sensing data so as to obtain user night activity preference data; constructing a user night behavior model according to the user night illumination preference data and the user night activity preference data;

Step S5: analyzing the night brightness control strategy of the LED lamp according to the night behavior model of the user and the response curve of the LED lamp spectrum, so as to obtain the night brightness control strategy of the LED lamp;

Step S6: and performing control strategy coupling on the LED lamp daytime brightness control strategy and the LED lamp night brightness control strategy so as to obtain an LED lamp control strategy, and uploading the LED lamp control strategy to an LED lamp management cloud platform so as to execute an LED lamp control task.

According to the invention, the structure and the material properties of the LED lamp can be deeply known by acquiring the design data of the LED lamp and extracting the characteristics. This helps to determine the manufacturing process and material selection of the LED lamp to improve the performance and efficiency of the LED lamp. The spectral response curve of the LED lamp is obtained through optical field simulation, so that the light-emitting characteristics of the LED lamp under different wavelengths can be known. The method provides key information for a subsequent control strategy, so that the LED lamp can adjust the spectrum according to the environmental requirement to achieve a more proper illumination effect. By analyzing the sensed data of the illumination area, the real-time situation of the illumination environment can be understood. By combining the spectral response curve of the LED lamp, a proper daytime brightness control strategy can be formulated, so that the LED lamp can automatically adjust brightness according to environmental changes, and a comfortable illumination environment is provided. By analyzing the user night lighting preferences and activity frequency, the user's need for night lighting can be known. The construction of the night behavior model of the user can help to design a more intelligent night brightness control strategy, so that the LED lamp can intelligently adjust brightness and color temperature according to the actual demands of the user, and user experience is improved. And (3) formulating a night brightness control strategy of the LED lamp based on the night behavior model of the user and the spectral response curve of the LED lamp. This helps the LED lamp to provide illumination suitable for the needs of the user at night, for example, to provide soft light before sleeping to promote sleep. And coupling daytime and night brightness control strategies to form a complete LED lamp control strategy, and uploading the complete LED lamp control strategy to the management cloud platform. The LED lamp can realize remote management and intelligent control, flexibility and convenience of the lighting system are improved, and energy consumption and maintenance cost are reduced.

Optionally, step S1 specifically includes:

step S11: acquiring LED lamp design data, and extracting characteristics of the LED lamp design data so as to acquire LED lamp structure data and LED lamp material data;

Step S12: constructing a three-dimensional model of the LED lamp according to the structural data of the LED lamp;

Step S13: performing model mesh division on the LED lamp three-dimensional model according to the LED lamp design data, so as to obtain an optical dense mesh division model and a heat dissipation dense mesh division model;

step S14: carrying out optical characteristic analysis on the LED lamp material data according to the optical dense grid division model so as to obtain LED lamp light characteristic data;

Step S15: carrying out thermal characteristic analysis on the LED lamp material data according to the heat dissipation dense grid division model, so as to obtain LED lamp thermal characteristic data;

step S16: and combining the LED lamp light characteristic data and the LED lamp thermal characteristic data, thereby obtaining the LED lamp material characteristic data.

The LED lamp design data are obtained, the characteristics are extracted, and the LED lamp structure and the LED lamp materials are deeply known. These data are critical to the performance analysis and optimization of LED lamps. Through feature extraction, key parameters such as LED chip type, heat dissipation structure and the like can be identified, so that basic data is provided for subsequent steps. Constructing a three-dimensional model of the LED lamp allows the structure of the LED lamp to be visualized and analyzed in a computer. This allows designers and engineers to better understand the actual structure of the LED lamp for optimization and improvement. And dividing the three-dimensional model of the LED lamp into an optical dense grid and a heat dissipation dense grid through model grid division. This allows for a more accurate analysis of the optical and thermal properties in subsequent steps, providing more accurate data support for the optimization of the LED lamp. The optical characteristic analysis and the thermal characteristic analysis are respectively performed in detail for the optical characteristic and the thermal characteristic of the LED lamp. The optical characteristic analysis can determine parameters such as optical efficiency, luminous intensity distribution and the like of the LED lamp, and the thermal characteristic analysis can determine information such as heat distribution, heat dissipation efficiency and the like of the LED lamp in the operation process. And combining the optical characteristic data and the thermal characteristic data of the LED lamp to obtain the comprehensive material characteristic data of the LED lamp. The comprehensive data provides a comprehensive basis for the design optimization of the LED lamp, so that a designer can balance between optical efficiency and thermal management, and therefore the LED lamp design is more efficient and stable.

Optionally, step S13 specifically includes:

Step S131: classifying the structural use of the LED lamp structural data according to the LED lamp design data, so as to obtain the LED lamp structural data and the LED lamp heat dissipation structural data;

Step S132: performing optical structure dense meshing on the LED lamp three-dimensional model according to the LED lamp light structure data, so as to obtain an optical dense meshing model;

Step S133: and carrying out heat dissipation structure dense grid division on the LED lamp three-dimensional model according to the LED lamp heat dissipation structure data, so as to obtain a heat dissipation dense grid division model.

The LED lamp structure can be divided into an optical structure and a heat dissipation structure by classifying the structural use of the LED lamp structure data. Such classification facilitates more refined optimization for different structural parts. The optical structure data includes optical components such as lenses and reflectors of the LED lamp, and the heat dissipation structure data covers heat dissipation elements such as radiators and cooling fins of the LED lamp. By such classification, subsequent analysis and optimization of optical and thermal properties can be performed in a targeted manner. When the three-dimensional model is subjected to optical structure dense grid division according to the LED lamp optical structure data, the details of the optical structure can be more accurately captured. The optical dense grid division model can help simulate the propagation path and optical efficiency of light, so that factors such as lens shape, surface quality and the like are optimized, and the optical performance and brightness uniformity of the LED lamp are improved. Similarly, when the three-dimensional model is subjected to heat dissipation structure dense grid division according to LED lamp heat dissipation structure data, the form and detail of the heat dissipation structure can be more finely characterized. The heat dissipation dense grid division model can help simulate heat conduction and heat dissipation paths, optimize the design of a heat dissipation structure, improve the heat dissipation efficiency of the LED lamp, and prolong the service life and stability of the LED lamp.

Optionally, step S14 specifically includes:

step S141: extracting the characteristics of the LED lamp light structural material according to the LED lamp light structural data, so as to obtain the LED lamp light structural material data;

Step S142: performing optical structure material mapping on the LED lamp optical structure material data and the optical dense grid division model, thereby obtaining an LED lamp optical structure model;

step S143: extracting the characteristics of the LED lamp design working conditions according to the LED lamp design data, so as to obtain the LED lamp design working condition data;

Step S144: and carrying out optical simulation on the LED lamp design working condition data through the LED lamp light structure model so as to obtain optical simulation data, and carrying out statistical analysis on the optical simulation data so as to obtain LED lamp light characteristic data.

The invention discloses a characteristic extraction method of LED lamp light structural materials, which aims at extracting characteristics of materials used by optical elements (such as lenses, reflecting covers and the like) of an LED lamp. These characteristics may include refractive index, transmittance, reflectance, etc. The material characteristics can be corresponding to the optical structure of the LED lamp, and accurate material data can be provided for subsequent optical simulation. In the optical structural material mapping step, the LED light optical structural material data is correlated with the optical dense meshing model. This has the advantage that the material properties can be mapped accurately into the optical model, ensuring the accuracy and reliability of the optical simulation. The LED lamp design working condition feature extraction is to extract various working conditions and environmental factors possibly encountered by the LED lamp in actual use. These characteristics may include operating temperature, operating current, ambient light, etc. By extracting these features, real world operating conditions can be provided for optical simulation to more accurately evaluate the performance of the LED lamp. The LED lamp design working condition data is subjected to optical simulation through the LED lamp light structure model, so that the optical performance of the LED lamp under different working conditions can be simulated. The statistical analysis of the optical simulation data can help to evaluate the optical characteristics of the LED lamp such as brightness, color, uniformity and the like, and provide guidance and basis for the design and optimization of the LED lamp.

Optionally, step S15 specifically includes:

Step S151: extracting the characteristics of the LED lamp heat dissipation structure material data according to the LED lamp heat dissipation structure data, so as to obtain the LED lamp heat dissipation structure material data;

Step S152: carrying out heat radiation structure material mapping on the LED lamp heat radiation structure material data and the optical dense grid division model, thereby obtaining an LED lamp heat radiation structure model;

Step S153: and carrying out thermal energy simulation on the optical simulation data and the LED lamp design working condition data through the LED lamp radiating structure model so as to obtain thermal energy simulation data, and carrying out statistical analysis on the thermal energy simulation data so as to obtain the LED lamp thermal characteristic data.

The extraction of the characteristics of the material of the heat dissipation structure of the LED lamp allows the characteristics of the material used for the heat dissipation part (such as a heat dissipation sheet, a heat sink and the like) of the LED lamp to be extracted. These characteristics may include thermal conductivity, thermal conductivity coefficient, and the like. The material characteristics can be corresponding to the heat dissipation structure of the LED lamp, and accurate material data can be provided for subsequent thermal energy simulation. In the heat radiation structure material mapping step, the heat radiation structure material data of the LED lamp is corresponding to the optical dense grid division model. The method has the advantages that the material characteristics can be accurately mapped into the heat dissipation structure model, and the accuracy and the reliability of heat energy simulation are ensured. The heat energy simulation is carried out on the optical simulation data and the LED lamp design working condition data through the LED lamp heat radiation structure model, so that the thermal characteristics of the LED lamp under different working conditions can be simulated. The statistical analysis of the thermal energy simulation data can help to evaluate the heat radiation performance of the LED lamp, including temperature distribution, heat radiation efficiency and the like, so that a basis is provided for the design and optimization of the LED lamp.

Optionally, step S3 specifically includes:

Step S31: acquiring illumination area sensing data, and extracting illumination characteristics and image pickup image characteristics of the illumination area sensing data so as to acquire area illumination sensing data and area image pickup images;

step S32: carrying out time sequence division on the regional illumination sensing data so as to obtain regional daytime illumination sensing data and regional night illumination sensing data;

Step S33: carrying out high-frequency illumination intensity statistics on the regional daytime illumination sensing data so as to obtain high-frequency daytime illumination intensity data; carrying out high-frequency illumination intensity statistics on the regional night illumination sensing data so as to obtain high-frequency night illumination intensity data;

Step S34: carrying out illumination distribution analysis according to the regional camera image so as to obtain illumination distribution data;

Step S35: carrying out time sequence space fusion on the high-frequency daytime illumination intensity data and the high-frequency night illumination intensity data according to the illumination distribution data so as to obtain regional illumination environment data;

step S36: and performing spectrum matching according to the regional illumination environment data and the LED spectrum response curve to obtain regional illumination demand data, and performing daytime brightness control strategy analysis according to the regional illumination demand data and the LED lamp material characteristic data to obtain an LED lamp daytime brightness control strategy.

The invention acquires the sensing data of the illumination area, extracts the illumination characteristics and the image pickup image characteristics, and can help to know the illumination and visual environment of the illumination area. These data provide information about the illumination intensity, the illumination distribution, and the environmental characteristics, providing the basis data for subsequent lighting system optimization. The area illumination sensing data is time-sequentially divided into two periods of daytime and nighttime. This helps to distinguish between illumination characteristics and needs over different time periods, providing a timing basis for subsequent analysis. By performing high frequency illumination intensity statistics on daytime and night illumination sensing data, finer illumination data, including frequency and amplitude of changes in illumination, can be obtained. This is of great importance for understanding the dynamics of illumination changes and corresponding countermeasures. The illumination distribution analysis is carried out by utilizing the region camera image, so that the illumination distribution condition of the illumination region, including the spatial distribution and the change trend of the illumination intensity, can be known more accurately. This helps to better understand the characteristics of the lighting environment. By means of time sequence space fusion of the illumination distribution data, illumination conditions of different time periods and spatial positions can be comprehensively considered, and more comprehensive regional illumination environment data can be obtained. This helps to more effectively design and optimize the lighting system. By means of spectrum matching and illumination requirement analysis, the daytime brightness control strategy of the LED lamp can be determined by combining the LED lamp material characteristic data. The LED lamp brightness adjusting device is beneficial to reasonably adjusting the brightness of the LED lamp according to actual illumination environment and requirements, and improves energy efficiency and comfort.

Optionally, step S34 specifically includes:

step S341: performing gray level conversion on the region photographic image to obtain a region gray level image, and performing high-frequency pixel statistics on the region gray level 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-volume pixel fluctuation data and low-volume pixel fluctuation data;

step S343: dividing the illumination range of the regional gray level image according to the high-volume pixel fluctuation data, so as to obtain an illumination range image;

Step S344: performing backlight range division on the regional gray scale image according to the low-volume pixel fluctuation data, so as to obtain a backlight range image;

Step S345: and carrying out regional illumination distribution statistics on the regional gray scale image according to the illumination range image and the backlight range image, thereby obtaining illumination distribution data.

The invention converts the regional camera image into the gray image, which is helpful to simplify the image processing process, and at the same time, the illumination information in the image is reserved. High frequency pixel statistics of the gray scale image can then be performed to capture frequently varying pixels in the image that tend to correspond to areas of higher frequency of illumination variation, thereby providing information about illumination dynamics. The image pixel fluctuation data is divided into high-and low-volume pixel fluctuation data by pixel fluctuation calculation. High pixel fluctuation data generally corresponds to regions where illumination changes are large or frequent, while low pixel fluctuation data may correspond to regions where illumination is stable. Such classification helps to more finely distinguish areas of different illumination features in an image. And carrying out illumination range division on the regional gray level image by using the high-volume pixel fluctuation data, thereby obtaining an illumination range image. This may help to determine areas of greater illumination variation, providing a visual representation of the illumination distribution, facilitating further analysis of the illumination characteristics. And carrying out backlight range division on the regional gray scale image according to the low-amount pixel fluctuation data, thereby obtaining a backlight range image. Aiming at the area with relatively stable illumination, the background illumination in the image is further differentiated, and a more accurate data base is provided for illumination distribution statistics. And carrying out illumination distribution statistics on the regional gray scale image according to the illumination range image and the backlight range image. This helps to fully understand the different illumination intensities and distributions within the area, providing an important reference for optimization of the lighting system, for example providing guidance in designing a luminaire layout or adjusting the brightness.

Optionally, step S4 specifically includes:

Step S41: night time sequence sensing characteristic extraction is carried out on the illumination area sensing data, so that illumination area night sensing data are obtained, and night shooting image characteristic extraction is carried out on the area night sensing data, so that night shooting images are obtained;

Step S42: performing low-frequency pixel statistics on the night shooting 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: calculating illumination intensity of the night spectrum energy distribution data according to the LED spectrum response curve, so as to obtain night illumination preference data of a user;

step S45: counting the night activity frequency of the user according to the sensing data of the illumination area, so as to obtain night activity preference data of the user;

Step S46: and constructing a user night behavior model according to the user night illumination preference data and the user night activity preference data.

According to the night sensing method, the night sensing characteristics and the night shooting image characteristics of the illumination area are extracted, so that the sensing data and the visual information of the night environment can be obtained, and a foundation is provided for subsequent analysis and processing. Basic color information in night photographic images can be acquired through low-frequency pixel statistics and color feature extraction, and visual features and color composition of night environments can be understood. The distribution condition of the spectrum energy at night can be further analyzed by carrying out spectrum energy distribution conversion on the low-frequency pixel color data, and data support is provided for subsequent illumination intensity calculation. The illumination intensity is calculated according to the LED spectral response curve, and the illumination preference of the user can be deduced based on the night spectral energy distribution data, so that the requirement of the user on night illumination is better met. The night activity frequency statistics of the user is carried out through the illumination area sensing data, so that the activity habit and the behavior pattern of the user at night can be known, and a basis is provided for personalized illumination service. By combining the user night lighting preference data and the night activity preference data to construct a user night behavior model, the lighting needs of the user at night can be predicted and responded more accurately, and a more intelligent and personalized lighting solution is provided.

Optionally, step S45 specifically includes:

step S451: extracting audio characteristics of night sensing data of the illumination area, so as to obtain night audio data of the illumination area;

step S452: performing frequency spectrum transformation on the night audio data of the illumination area so as to obtain the night audio frequency spectrum of the illumination area;

Step S453: carrying out frequency spectrum fluctuation statistics on the night audio frequency spectrum of the illumination area so as to obtain frequency spectrum fluctuation data;

Step S454: extracting time sequence characteristics of the frequency spectrum fluctuation data to obtain fluctuation time sequence data, and classifying and calculating night audio frequency spectrum of the illumination area according to the fluctuation time sequence data to obtain night fluctuation frequency spectrum and night gentle frequency spectrum;

Step S455: performing inverse Fourier transform on the night fluctuation frequency spectrum, so as to obtain night activity data; performing inverse Fourier transform on the night mild spectrum, so as to obtain night mild data;

Step S456: and carrying out time sequence combination on the night gentle data and the night activity data, so as to obtain night activity preference data of the user.

The audio feature extraction in the invention can extract audio information from night sensing data of an illumination area, such as sound intensity, frequency distribution and the like in the environment, and the information can reflect the sound features of night activities and provide basic data for subsequent analysis. The spectral transformation may convert the illumination area night audio data into a representation in the frequency domain, i.e. an audio spectrum, which helps to better understand the distribution of the different frequency components in the night environment. The frequency spectrum fluctuation statistics can analyze the dynamic change of the audio frequency spectrum so as to obtain frequency spectrum fluctuation data, the data reflect the change trend of sound in the night environment, and important information is provided for subsequent behavior analysis. By extracting time sequence characteristics of the frequency spectrum fluctuation data, fluctuation time sequence data can be obtained, and the audio frequency spectrum is classified and calculated according to the data, so that a night fluctuation frequency spectrum and a night gentle frequency spectrum are obtained, and the method is helpful for distinguishing different types of sound activities at night. The inverse fourier transform is used to convert the night fluctuation spectrum and the night flattening spectrum back to the time domain, thereby obtaining night activity data and night flattening data reflecting the timing characteristics of different types of activities at night. By combining the time sequence of the night gentle data and the night activity data, night activity preference data of the user, namely information such as the type and the frequency of night activity, can be obtained, and a basis is provided for personalized lighting service.

Optionally, this specification still provides a LED lamp, and this LED lamp includes LED lamp main part, power supply portion and electrical control portion, and power supply portion installs inside LED lamp main part, and electrical control portion and power supply portion electric connection, electrical control portion are used for charging and controlling LED lamp main part for LED lamp main part, and electrical control portion includes:

The material characteristic analysis module is used for acquiring LED lamp design data and extracting characteristics of the LED lamp design data so as to acquire LED lamp structure data and LED lamp material data; carrying out material characteristic analysis on the LED lamp material data so as to obtain LED lamp material characteristic data;

The optical field simulation module is used for performing optical field simulation according to the LED lamp material characteristic data and the LED lamp structure data so as to obtain an LED lamp spectrum response curve;

The daytime brightness control strategy analysis module is used for acquiring illumination area sensing data, and carrying out area illumination environment analysis according to the illumination area sensing data so as to acquire area illumination environment data; analyzing the LED lamp daytime brightness control strategy according to the LED spectral response curve and the regional illumination environment data, so as to obtain the LED lamp daytime brightness control strategy;

The user night behavior model construction module is used for carrying out user night illumination preference analysis on the illumination area sensing data so as to obtain user night illumination preference data, and carrying out user night activity frequency statistics according to the illumination area sensing data so as to obtain user night activity preference data; constructing a user night behavior model according to the user night illumination preference data and the user night activity preference data;

The night brightness control strategy analysis module is used for analyzing the night brightness control strategy of the LED lamp on the night behavior model of the user and the response curve of the LED lamp spectrum so as to obtain the night brightness control strategy of the LED lamp;

The control strategy coupling module is used for carrying out control strategy coupling on the LED lamp daytime brightness control strategy and the LED lamp night brightness control strategy so as to obtain the LED lamp control strategy, and uploading the LED lamp control strategy to the LED lamp management cloud platform so as to execute an LED lamp control task.

The LED lamp can realize any LED lamp control method, is used for combining the operation and signal transmission media among the modules to finish the LED lamp control method, and the internal modules of the device cooperate with each other, so that the accurate adjustment and control of parameters such as illumination intensity, color temperature, spectrum and the like are realized, the requirements of users in different illumination scenes are met, and the applicability and user experience of LED illumination are further improved.

Drawings

Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:

FIG. 1 is a schematic flow chart of the steps of the LED lamp control method of the present invention;

FIG. 2 is a detailed step flow chart of step S1 of the present invention;

FIG. 3 is a detailed step flow chart of step S3 of the present invention;

the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

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

In order to achieve the above objective, referring to fig. 1 to 3, the present invention provides a method for controlling an LED lamp, the method comprising the following steps:

In this embodiment, the LED lamp design data may be obtained by cooperation with the LED chip manufacturer or directly obtaining the design file and technical specification from the LED lamp manufacturer. In the feature extraction stage, computer aided design software, such as CAD software, can be utilized to perform three-dimensional modeling on the structure of the LED lamp, and the features of size, shape, optical characteristics and the like can be extracted from the three-dimensional modeling. Analysis of LED lamp material data may involve knowledge in the fields of material science and optical engineering, for example, spectral analysis of LED lamp material by using an optical spectrometer to obtain characteristic data such as refractive index, transmittance, etc. of the material.

In this embodiment, in the optical field simulation, the optical simulation software may be used to build an optical model of the LED lamp, including an LED chip, a heat dissipation structure, and the like, for the LED lamp material characteristic data and the LED lamp structure data, and set light source parameters, material properties, and the like. Through the tracing of light rays and the simulation of optical effects, a spectrum response curve of the LED lamp under different working conditions can be generated, so that the optical performance of the LED lamp can be accurately estimated.

In this embodiment, acquiring the sensing data of the illumination area involves installing a light sensor, a temperature sensor, and the like, and transmitting the data to a computer for processing through a data acquisition system. The analysis of the regional illumination environment data can utilize a statistical method, a spatial analysis method and the like to acquire the spatial characteristics and the time change rule of illumination distribution. According to the LED spectral response curve and regional illumination environment data, optical simulation software can be utilized to perform light field simulation, the illumination effect of the LED lamp is estimated, and a daytime brightness control strategy is designed. For example, the brightness and color temperature of the LED lamp are adjusted to accommodate different lighting environments.

in this embodiment, preference analysis is performed on the illumination area sensing data by a method such as statistical analysis, so that illumination preference and activity frequency of a user at night can be known. For example, a user may prefer to decrease the intensity of illumination for a particular period of time to increase comfort or save energy. The night activity frequency of the user is analyzed by using a statistical method so as to know the activity characteristics of the user in different time periods. Constructing a model of user night behavior may involve techniques such as data mining, machine learning, etc., to discover rules and patterns of user behavior from a large amount of sensory data.

In this embodiment, the night brightness control strategy analysis of the LED lamp is performed by establishing a mathematical model or simulating a spectral response curve of the LED lamp to the user night behavior model. For example, during periods of time when the user activity frequency is low, the brightness of the LED lamp may be reduced to save energy; in the time period with higher user activity frequency, the brightness of the LED lamp can be increased to provide better illumination effect.

In this embodiment, the daytime brightness control strategy and the nighttime brightness control strategy of the LED lamp are coupled, and it is first required to determine the time ranges of the daytime and the nighttime, for example, from 6 a.m. to 10 a.m. for daytime and the rest for nighttime. The brightness in the daytime can be set to be 100%, and the brightness is automatically adjusted at night according to the light intensity, for example, when the light intensity is lower than a certain threshold value, the brightness is adjusted to be 30% so as to save energy. For example, the control mode of the LED lamp is automatically switched according to the time periods of day and night. During daytime, a daytime brightness control strategy is adopted, and brightness is adjusted according to the illumination environment and the activity condition of a user; and when the user is at night, switching to a night brightness control strategy, and adjusting the brightness according to the user behavior model and the ambient lighting condition. The lighting needs of the user are prioritized over different time periods according to the real-time needs and preferences of the user. For example, during night hours when the user is active frequently, the night brightness control strategy is adopted preferentially; and during daytime hours when the user is less active, the daytime brightness control strategy is dominant. And (3) programming an LED lamp control program by using Python, and determining whether the current moment belongs to the day or the night by using a date and time base. And realizing conditional sentences in the program, and setting the brightness of the LED lamp according to different days or nights. For night brightness control, the ambient light intensity can be acquired by a sensor and adjusted according to a preset algorithm. Different time periods and illumination conditions are simulated in a laboratory environment, and the accuracy and stability of the LED lamp control program are verified. By simulating different light intensities and times, it is ensured that the LED lamp can adjust the brightness as desired. In the embodiment, the TT protocol is used for connecting the written LED control program with the LED lamp management cloud platform. In the embodiment of the configuration M, the TT client sets the theme of the cloud platform and the ID of the client, and performs identity verification to ensure the communication security. Uploading the written LED control program to an LED lamp management cloud platform, creating a corresponding task on the cloud platform, and binding the LED control program with the LED lamp equipment. Setting execution time and repetition interval of tasks, and ensuring that the LED lamp control strategy can be executed on time.

Optionally, step S1 specifically includes:

In this embodiment, a design file of an LED lamp is obtained from an LED lamp design team, where the design file includes information such as structural design, dimensional parameters, and bill of materials of the LED lamp. The design file is opened and the feature extraction is performed by using computer aided design software such as AutoCAD or SolidWorks, the structural data of the LED lamp such as the shape of the shell, the layout of the internal element and the like are extracted, and the material information such as metal, plastic and the like used by the LED lamp is extracted.

In this embodiment, based on the obtained structural data of the LED lamp, a three-dimensional model of the LED lamp is constructed using three-dimensional 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, and the LED lamp comprises a shell, a light-transmitting part, an electronic element layout and the like.

In this embodiment, for the three-dimensional model of the LED lamp, a meshing technique is used to divide the three-dimensional model into two parts, namely an optical dense mesh and a heat dissipation dense mesh. The optical dense grid is used for optical characteristic analysis, and the heat dissipation dense grid is used for thermal characteristic analysis. And grid generation software such as ANSYS or COMSOL Multiphysics is utilized to carry out grid division on the LED lamp model, so that the accuracy and the high efficiency of the model are ensured.

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 material data of the LED lamp. Optical characteristic data of the LED lamp, such as beam angle, light intensity distribution and the like, are obtained by simulating the propagation and reflection of light.

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. Thermal characteristic data, such as temperature distribution, thermal conductivity coefficient and the like, of the LED lamp are obtained by simulating heat conduction and heat dissipation.

In this embodiment, the optical characteristic data and the thermal characteristic data of the LED lamp are combined to form the material characteristic data of the LED lamp. And analyzing and processing the combined data according to the design requirements and the performance indexes to guide the optimal design and the performance improvement of the LED lamp.

Optionally, step S13 specifically includes:

In this embodiment, according to the design data of the LED lamp, the structural data of the LED lamp is classified, including an optical structure and a heat dissipation structure. The optical structure data relates to the layout of optical elements, the design of light-transmitting parts and the like of the LED lamp, and the heat dissipation structure data comprises the design of a heat sink, the layout of heat dissipation channels and the like. By classifying the structural data, the optical structural data and the heat dissipation structural data of the LED lamp are obtained so as to facilitate subsequent model division and analysis.

In this embodiment, according to the optical structure data of the LED lamp, optical structure dense meshing is performed for the three-dimensional model of the LED lamp. The optical element portion of the LED lamp is gridded using gridding software, such as ANSYS MESHING or HYPERMESH, to obtain an optically dense gridding model. When the grids are divided, the shape, the surface characteristics and the optical transmission path of the optical element are considered, so that the fineness and the accuracy of the grids are ensured.

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 grid division of the heat dissipation structure. The heat sink portion of the LED lamp is grid partitioned using specialized grid partitioning software, such as ANSYS MESHING or HYPERMESH, to obtain a heat sink dense grid partition model. When grid division is carried out, the shape of the radiator, the design of the ventilation holes and the heat conduction path are required to be considered, so that the density and the accuracy of grids are ensured, and an accurate model basis is provided for subsequent thermal characteristic analysis.

Optionally, step S14 specifically includes:

In this embodiment, according to the optical structure data of the LED lamp, various materials related to the LED lamp, such as an LED chip, a lens, a reflector, and the like, are first identified. Then, for each material, its optical characteristic parameters including refractive index, absorption coefficient, scattering coefficient, and the like are extracted. These characteristic parameters may be measured experimentally or obtained from a database of materials. And extracting characteristic parameters to obtain the material data of the LED lamp light structure, and providing input for subsequent optical simulation.

In this embodiment, the LED lamp light structural material data is mapped with the optical dense meshing model to build an optical structural model of the LED lamp. In the mapping process, optical characteristic parameters of each material are distributed to corresponding grid cells, and material information in the model is ensured to be consistent with an actual LED lamp structure. The key to this step is to accurately match the material properties to the grid cells to ensure the accuracy and reliability of the subsequent optical simulation.

In this embodiment, according to design data of the LED lamp, design working condition characteristics of the LED lamp are extracted, including working current, working temperature, light output power, and the like. These design operating characteristics have an important impact on the optical and thermal performance of the LED lamp, so that accurate acquisition of these data as input parameters is required when optical simulations are performed.

In this embodiment, an optical structural model of an LED lamp is used to perform optical simulation on design condition data of the LED lamp. The optical performance of the LED lamp under different working conditions, such as illumination distribution, beam angles and the like, is simulated through simulation software, such as LightTools or Zemax. And then, carrying out statistical analysis on the simulation data, including indexes such as average illumination intensity, beam uniformity and the like, so as to obtain the optical characteristic data of the LED lamp.

Optionally, step S15 specifically includes:

In this embodiment, according to the heat dissipation structure data of the LED lamp, the heat dissipation material used by the LED lamp, such as an aluminum substrate, a heat sink, and the like, is identified. Then, for each heat dissipating material, its thermal characteristic parameters including thermal conductivity, specific heat capacity, thermal conductivity, etc. are extracted. These parameters may be measured experimentally or obtained from a materials database. And through characteristic parameter extraction, the material data of the heat dissipation structure of the LED lamp is obtained, and input is provided for subsequent thermal energy simulation.

In this embodiment, the heat dissipation structure material data of the LED lamp is mapped with the optical dense mesh division model to establish a heat dissipation structure model of the LED lamp. In the mapping process, the thermal characteristic parameters of each heat dissipation material are distributed to the corresponding grid cells, so that the heat dissipation material information in the model is ensured to be consistent with the actual LED lamp structure. The key of this step is to accurately match the material characteristics to the grid cells to ensure the accuracy and reliability of the subsequent thermal simulation.

In this embodiment, the heat energy simulation is performed on the optical simulation data and the design condition data of the LED lamp by using the heat dissipation structure model of the LED lamp. And through professional thermal simulation software, such as ANSYS or COMSOL Multiphysics, the heat dissipation performance of the LED lamp under different working conditions is simulated, including temperature distribution, heat conduction paths and the like. Then, statistical analysis is carried out on the thermal energy simulation data, wherein the statistical analysis comprises indexes such as the highest temperature, the temperature gradient and the like, so that thermal property data of the LED lamp are obtained, and the heat dissipation effect of the LED lamp is evaluated.

Optionally, step S3 specifically includes:

In this embodiment, environmental data such as illumination, temperature, humidity, etc. in real time is acquired by a sensor device installed in the illumination area. Then, aiming at illumination data, illumination characteristic extraction is carried out, wherein the illumination characteristic extraction comprises illumination intensity, illumination distribution, illumination change trend and the like. And simultaneously, extracting the characteristics of the photographed image, and identifying information such as illumination distribution, light source position and the like in the image. By these processes, regional illumination sensing data and regional imaging images are obtained.

In this embodiment, the regional illumination sensing data is divided according to time to distinguish the illumination conditions of daytime and nighttime. This can be determined by an index such as a time stamp or sunrise and sunset time. After division, regional daytime illumination sensing data and regional night illumination sensing data are obtained.

in this embodiment, statistical analysis of the high-frequency illumination intensity is performed for illumination sensing data of daytime and nighttime. This includes counting the distribution of the illumination intensity in each period, the frequency of the variation of the illumination intensity, and the like. Through the statistical data, high-frequency daytime illumination intensity data and high-frequency night illumination intensity data are obtained.

In this embodiment, the illumination distribution is analyzed in detail by using the region-captured image. This includes identifying light source location, light scattering conditions, shadow distribution, etc. Accurate illumination distribution data is obtained through an image processing technology.

in this embodiment, time sequence spatial fusion is performed by combining the illumination sensing data and the illumination distribution data, so as to obtain more comprehensive regional illumination environment data. This may include matching the illumination intensity data with the illumination distribution data to obtain more accurate illumination environment information.

In this embodiment, spectrum matching is performed according to the regional illumination environment data and the LED spectral response curve, so as to determine the LED illumination requirement. For example, the light emission wavelength in the LED spectral response curve is compared with the overlapping degree of the spectrum in the regional illumination environment data, and the optimal light emission wavelength and spectrum distribution of the LED lamp are determined, so as to improve the lighting effect and comfort. Then, the most suitable daytime brightness control strategy is analyzed by combining the material characteristics and the power characteristics of the LED lamp so as to realize the best balance of energy conservation and illumination effect. For example, daytime brightness control strategies are formulated based on the daytime illumination intensity data of the office lighting area. During sunny days, the brightness of the LED lamp may be reduced to save energy and reduce glare. Under the condition of overcast days or insufficient illumination, the brightness of the LED lamp can be properly improved so as to ensure the brightness and the comfort level of an office.

Optionally, step S34 specifically includes:

In this embodiment, for a region-captured image, it is converted into a grayscale image, which involves converting the RGB value of each pixel in a color image into a single grayscale value. The gray scale image is then subjected to high frequency pixel statistics, which include analyzing frequently changing pixels in the image, which typically correspond to edges, textures, or other details in the image. High-frequency pixel data is obtained in this way, which can be used for pixel fluctuation calculation in the subsequent step.

In this embodiment, pixel fluctuation calculation is performed using high-frequency pixel data, which involves analyzing the variation between pixel values, typically by calculating gradients or differences between pixels. Then, the pixel fluctuation data is classified into two types of high-order pixel fluctuation and low-order pixel fluctuation. High pixel fluctuations typically represent high frequency details or edges in the image, while low pixel fluctuations may represent flat areas or low frequency details in the image.

in this embodiment, the illumination range of the area gray-scale image is divided based on the high-volume pixel fluctuation data. This means that areas in the image where the pixels fluctuate more are marked as illumination ranges, which areas may correspond to bright areas or areas of higher illumination intensity in the image. Thus, an illumination range image is obtained, which can be used for subsequent illumination distribution statistics.

in this embodiment, the area gray scale image is backlight-range-divided according to the low-pixel fluctuation data. This means that areas in the image where the pixel fluctuations are small, which areas may correspond to dark areas or areas of lower illumination intensity in the image, are marked as backlight areas. In this way, a backlight range image is obtained, which can be used for subsequent illumination distribution statistics.

In this embodiment, illumination distribution statistics is performed on the area grayscale image based on the illumination range image and the backlight range image. This involves analyzing the illumination intensity distribution of different areas in the image, typically by calculating the average gray value or illumination intensity of the pixels in each area. Detailed data about the area illumination distribution may be obtained, providing a basis for subsequent image processing or analysis.

Optionally, step S4 specifically includes:

In this embodiment, by a time series analysis method, sensing characteristics of the night, such as a variation trend of illumination intensity at night, an average value of temperature, and the like, are extracted from sensing data of a lighting area. And (3) extracting features of the night camera image by using a computer vision technology, such as extracting lights, vehicles, pedestrians and the like in the image.

in this embodiment, for night-shot images, low-frequency pixel statistics is performed, i.e., pixels with slow changes in the image, such as background, large areas, etc., are analyzed. Then, color feature extraction is performed on these low-frequency pixel data to capture color information in the image, such as extracting background color, body color, and the like, thereby obtaining low-frequency pixel color data.

In this embodiment, spectral energy distribution conversion is performed on the low-frequency pixel color data, and the color data is converted into spectral energy distribution data, so as to better describe the energy distribution conditions of different colors in the image, thereby obtaining night spectral energy distribution data. For example, the color data is converted into RGB or HSV color space. Using spectral analysis techniques, the energy distribution of the different colors over the spectrum is calculated. Normalizing the energy distribution data to obtain night spectrum energy distribution data

In this embodiment, the illumination intensity calculation of the night spectrum energy distribution data according to the LED spectrum response curve is to better understand the preference of the user for night illumination. The LED spectral response curve reflects the luminous efficiency of the LED lamp under different wavelength light, and the illumination intensity is the light intensity of the LED lamp in the illumination area. By calculating the illumination intensity of the night spectrum energy distribution data under the LED spectrum response curve, the light intensity in different wavelength ranges can be known, so that the illumination preference of a user for light rays with specific wavelengths at night is known. And performing convolution operation on the night spectrum energy distribution data and the LED spectrum response curve to obtain illumination intensity data. Finally, the area of the convolution result is calculated to be used as illumination intensity data, so that night illumination preference data of the user are obtained.

In this embodiment, the night activity frequency statistics of the user is performed according to the sensing data of the illumination area, the time period is divided into fixed time periods, for example, each hour or each half hour, and then the activity frequency of the personnel in each time period is counted, including walking, resting, using equipment, and the like. Through analysis of the statistical data, user night activity preference data can be obtained.

In this embodiment, a user night behavior model is constructed according to the user night illumination preference data and the user night activity preference data, and the illumination preference data and the activity preference data are integrated into a user night behavior data set. The user night behavior data is then modeled using machine learning or statistical models, such as decision trees, cluster analysis, and the like. Finally, the model is verified and optimized to ensure its prediction accuracy and applicability.

Optionally, step S45 specifically includes:

in this embodiment, when audio feature extraction is performed on night sensor data in an illumination area, a common audio signal processing technique, such as short-time fourier transform (STFT) or wavelet transform, may be used. The technologies can convert the sensor data into spectrograms, extract the characteristics of frequency, amplitude and the like, and further obtain night audio data of an illumination area.

In this embodiment, when the frequency spectrum of the night audio data in the illumination area is transformed, a Fast Fourier Transform (FFT) algorithm may be used to convert the time domain signal into a frequency domain signal, so as to obtain the night audio frequency spectrum in the illumination area. This step may be used to analyze spectral characteristics of the audio signal, such as frequency distribution, energy distribution, etc.

In this embodiment, when the spectrum fluctuation statistics is performed on the night audio spectrum in the illumination area, the variation condition of the spectrum data in different frequency ranges, such as the statistics of the mean, variance, kurtosis, skewness, etc., of the spectrum can be calculated, so as to obtain the spectrum fluctuation data. These statistics may reflect the stability and degree of variation of the audio spectrum.

In this embodiment, when extracting the time sequence features of the spectrum fluctuation data, a time domain analysis method, such as an autoregressive model (AR) and a moving average Model (MA), may be used to extract the time sequence features of the spectrum fluctuation. Then, classifying and calculating the night audio frequency spectrum of the illumination area according to the fluctuation time sequence data to obtain a night fluctuation frequency spectrum and a night gentle frequency spectrum, wherein the night fluctuation frequency spectrum and the night gentle frequency spectrum can be classified by adopting a clustering algorithm or a classifier.

In this embodiment, when the inverse fourier transform is performed on the night fluctuation spectrum, the inverse FFT algorithm may be used to convert the spectrum data back into time domain data, so as to obtain night activity data. Similarly, inverse fourier transforming the night-time flat spectrum may result in night-time flat data reflecting different degrees of activity of the night-time illumination area.

In this embodiment, when the night mild data and the night activity data are time-sequentially combined, a time sequence analysis method, such as a sliding window method or a time sequence model, may be adopted according to specific requirements, so as to combine the two types of data, thereby obtaining night activity preference data of the user. For example, activity levels over different time periods may be counted, thereby analyzing the user's activity preferences at night.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The LED lamp control method is characterized by comprising the following steps of:

2. The method of controlling an LED lamp according to claim 1, wherein step S1 specifically comprises:

3. The method of controlling an LED lamp according to claim 2, wherein step S13 specifically comprises:

4. The method of controlling an LED lamp according to claim 2, wherein step S14 is specifically:

5. The method of controlling an LED lamp according to claim 2, wherein step S15 specifically comprises:

6. The method of controlling an LED lamp according to claim 1, wherein step S3 specifically comprises:

7. The method of claim 6, wherein step S34 is specifically:

8. The method of controlling an LED lamp according to claim 1, wherein step S4 specifically comprises:

9. The method of controlling an LED lamp according to claim 8, wherein step S45 is specifically:

10. The utility model provides a LED lamp, its characterized in that includes LED lamp main part, power supply portion and electrical control portion, and power supply portion installs inside LED lamp main part, and electrical control portion and power supply portion electric connection, electrical control portion are used for charging and controlling LED lamp main part for LED lamp main part, and electrical control portion includes: