Linear light color transformation control method, device, lamp and medium

Abstract

The present invention provides a method, device, lighting fixture and medium for controlling color transformation of a strip light, comprising the following steps: obtaining the ambient light intensity, color distribution, user position and action type in real time; converting them into color parameters by using a color mapping model trained by a neural network; generating a color transformation instruction based on the color parameters according to a color mixing and brightness compensation algorithm; sending the instruction to a driving circuit to control color transformation, and at the same time, according to the feedback of a temperature sensor in the strip light, when the temperature exceeds the threshold, switching the frequency and brightness value, and changing to a transition mode; gradually recovering after the temperature returns to normal to ensure safety and effect. The present invention accurately adjusts the color of the strip light according to the environment and user behavior. The color mixing and brightness compensation algorithm takes into account the human eye perception, dynamically adjusts the instruction according to the temperature, protects the lighting fixture, and the sensors of the lighting fixture cooperate with each other, and the control is realized by a computer program, improving the lighting experience.

Description

Technical Field

[0001] The present invention relates to the technical field of lamp color transformation control systems, and particularly to a method and device for controlling the color transformation of linear lights, a lamp, and a medium. Background Art

[0002] With the continuous development of lighting technology and the increasing demand for the quality of life of people, linear lights have been widely used in indoor and outdoor decoration, commercial display, smart home and other fields. Traditional linear lights usually only have a simple function of emitting light with a fixed color, or can only perform limited color switching through manual operation, which can no longer meet the needs of modern diverse scenarios.

[0003] In a smart home environment, people expect the lighting system to achieve intelligent interaction with the environment and user behavior. For example, under different ambient light conditions, such as when there is sufficient natural light during the day and in a dark environment at night, the color and brightness of the linear lights should be automatically adjusted to provide a comfortable and appropriate lighting atmosphere. Some existing methods for controlling linear lights lack effective perception and utilization of the ambient light intensity and color distribution, resulting in the inability to achieve adaptive lighting in complex environments.

[0004] At the same time, the activities of users in the space should also be an important factor affecting the color change of linear lights. When users are resting and watching TV in the living room, they may need soft and soothing light colors; while when users are engaged in entertainment activities, such as holding a party, they hope that the lights can become more lively and colorful. Most of the linear lights on the market at present cannot accurately capture the position and action type of users to correspondingly change the color, lacking sufficient intelligence and user-friendliness.

[0005] In the field of commercial display, linear lights are often used to highlight the display of goods or create a specific display atmosphere. Traditional linear lights cannot automatically adjust the color according to the changes in the display content and the surrounding environment, greatly reducing the display effect. For example, when displaying jewelry, the lights are required to highlight the luster and color of the jewelry, while when displaying clothing, different light colors are needed to set off the style of the clothing.

[0006] In addition, during the long-term use of existing linear lights, the color performance and service life may be affected due to temperature changes. However, there is currently a lack of an effective mechanism for dynamically adjusting the color transformation according to temperature feedback, which may lead to problems such as color deviation and damage of the linear lights due to overheating, affecting their stability and reliability.

[0007] From a technical perspective, although there are some current lighting control technologies, there are still deficiencies in the precise combination of environmental perception, user behavior analysis, and the color transformation of linear lights. Traditional color mapping methods are not precise and flexible enough, and do not make full use of advanced technologies such as neural networks to learn and analyze large amounts of data. Moreover, in terms of color mixing and brightness compensation algorithms, the visual perception characteristics of the human eye and complex environmental factors are not fully considered, resulting in unnatural and uncomfortable color transformations and unable to meet users' expectations for high-quality lighting experiences. Therefore, there is an urgent need for an innovative method, device, lighting fixture, and medium for controlling the color transformation of linear lights to solve the above problems. Summary of the Invention

[0008] The method, device, lighting fixture, and medium for controlling the color transformation of linear lights proposed by the present invention are used to solve the problems mentioned in the above prior art.

[0009] To achieve the above objectives, the present invention adopts the following technical solutions: A method for controlling the color transformation of linear lights includes the following steps:

[0010] S1. Real-time obtain environmental information and user behavior information, where the environmental information includes environmental light intensity and environmental color distribution, and the user behavior information includes user location and user action type;

[0011] S2. Based on a preset color mapping model, convert the environmental information and user behavior information into color parameters, where the color parameters include a target color set, color switching frequency, and color transition mode. The color mapping model is trained based on a neural network, and the training data includes color preference data under a large number of different environmental and user behavior scenarios. The output calculation formula of the neural network is: O = σ(W T ×X + b), where o is the output color parameter vector, X is the input vector composed of environmental information and user behavior information, W is the weight matrix, b is the bias vector, and σ is the activation function (such as ReLU, Sigmoid, etc.);

[0012] S3. Generate a color transformation instruction according to the color parameters. The color transformation instruction contains a color value sequence and a brightness value sequence for each light-emitting unit in the linear light. The color value sequence and the brightness value sequence are generated based on a color mixing algorithm and a brightness compensation algorithm. The color mixing algorithm considers the mixing effect of different color lights and the visual perception characteristics of the human eye, and the brightness compensation algorithm is used to compensate for the brightness difference caused by the influence of environmental light. The formula for calculating the color value of a light-emitting unit at a certain moment in the color mixing algorithm is: where n is the number of colors in the target color set, P i，t is the proportion of the th target color at time, C iis the original color value of the first target color (represented in RGB or other color spaces), K i is the sensitivity coefficient of the human eye to the light of the first color (obtained from a large amount of visual experiment data); the formula for calculating the brightness compensation value ΔL of the light-emitting unit in the brightness compensation algorithm is: ΔL = (L target -L env ) × α, where L target is the overall target brightness, L env is the ambient light intensity, and α is the brightness compensation ratio coefficient (determined according to the lamp characteristics and environmental characteristics);

[0013] S4. Send the color transformation instruction to the driving circuit of the strip light, so that the driving circuit controls the light-emitting unit of the strip light to achieve color transformation according to the color transformation instruction. At the same time, dynamically adjust the color transformation instruction according to the temperature feedback information of the strip light. The temperature feedback information is obtained through a temperature sensor set inside the strip light to ensure that the strip light works within a safe temperature range and the color transformation effect is not affected by temperature. The adjustment formula is: when T > T threshold (T is the temperature of the strip light, T threshold is the preset temperature threshold), M new = M slow , where F new is the new color switching frequency, F origial is the original color switching frequency, L new is the new brightness value, L original is the original brightness value, M new is the new color transition mode (assuming M slow is the slow transition mode), and T max is the maximum temperature allowed for the strip light.

[0014] Further, in the step of obtaining the environmental information and user behavior information in real time:

[0015] Use multiple ambient light sensors to obtain the ambient light intensity and ambient color distribution. The ambient light sensors are distributed at different positions in the space where the strip light is installed;

[0016] Obtain the user position and user action type through a depth camera and an action capture sensor set in the space. The depth camera and the action capture sensor work together to analyze the user behavior through image recognition and action analysis algorithms.

[0017] Further, in the step of converting the environmental information and user behavior information into color parameters based on the preset color mapping model:

[0018] The neural network structure of the color mapping model includes an input layer, a hidden layer, and an output layer. The input layer receives the vector representations of environmental information and user behavior information. The hidden layer uses multiple layers of neurons, and the connection weights between each layer of neurons are continuously adjusted through training. The output layer outputs color parameters;

[0019] When training the color mapping model, a cross-entropy loss function (where m is the number of training samples, c is the dimension of the output color parameters, y jk is the k-th true label of the j-th sample, is the predicted value) and an adaptive learning rate adjustment algorithm (such as Adam, Adagrad, etc.) are used to improve the accuracy of the model for color mapping in different scenarios.

[0020] Further, in the step of generating a color transformation instruction according to the color parameters:

[0021] The color mixing algorithm calculates the color value of each light-emitting unit according to the proportion of each color in the target color set at different times, in combination with the principle of color addition and the sensitivity of the human eye to different colors of light. The principle of color addition considers the relationship between the wavelength and energy of light;

[0022] The brightness compensation algorithm calculates the brightness compensation value of each light-emitting unit according to the ambient light intensity and the overall target brightness. The brightness compensation value is inversely proportional to the ambient light intensity and directly proportional to the overall target brightness.

[0023] Further, in the step of dynamically adjusting the color transformation instruction according to the temperature feedback information of the strip light:

[0024] When the temperature of the strip light rises above the preset threshold, reduce the color switching frequency and the brightness value of the light-emitting unit, and at the same time adjust the color transition mode to a slower transition method;

[0025] When the temperature of the strip light drops to the safe range, gradually restore the color switching frequency and the brightness value to the original set value. The restoration process uses a gradual change method to avoid color mutations.

[0026] Further, a strip light color transformation control device includes:

[0027] An information acquisition module for real-time acquisition of environmental information and user behavior information. The environmental information includes ambient light intensity and ambient color distribution. The user behavior information includes user location and user action type;

[0028] A color parameter generation module, which is used to convert the environmental information and user behavior information into color parameters based on a preset color mapping model. The color parameters include a target color set, a color switching frequency, and a color transition mode. The color mapping model is obtained by training based on a neural network, and the training data includes a large amount of color preference data under different environmental and user behavior scenarios;

[0029] An instruction generation module, which is used to generate a color transformation instruction according to the color parameters. The color transformation instruction contains a color value sequence and a brightness value sequence for each light-emitting unit in the linear light. The color value sequence and the brightness value sequence are generated based on a color mixing algorithm and a brightness compensation algorithm. The color mixing algorithm takes into account the mixing effect of different color lights and the human eye visual perception characteristics, and the brightness compensation algorithm is used to compensate for the brightness difference caused by the influence of ambient light;

[0030] An instruction sending and adjustment module, which is used to send the color transformation instruction to the driving circuit of the linear light, so that the driving circuit controls the light-emitting units of the linear light to achieve color transformation according to the color transformation instruction. At the same time, the color transformation instruction is dynamically adjusted according to the temperature feedback information of the linear light. The temperature feedback information is obtained by a temperature sensor set inside the linear light to ensure that the linear light works within a safe temperature range and the color transformation effect is not affected by temperature.

[0031] Furthermore, the information acquisition module includes:

[0032] An ambient light sensor group, which consists of multiple ambient light sensors distributed at different positions in the space where the linear light is installed, and is used to acquire the ambient light intensity and ambient color distribution;

[0033] A behavior monitoring unit, which includes a depth camera and an action capture sensor. By working together, it uses image recognition and action analysis algorithms to acquire the user's position and the type of user action.

[0034] Furthermore, the neural network structure of the color mapping model in the color parameter generation module includes an input layer, a hidden layer, and an output layer. The input layer receives the vector representation of the environmental information and user behavior information. The hidden layer uses multiple layers of neurons, and the connection weights between each layer of neurons are continuously adjusted through training. The output layer outputs color parameters. When training the color mapping model, a cross-entropy loss function and an adaptive learning rate adjustment algorithm are used;

[0035] The instruction generation module includes:

[0036] A color value calculation unit, configured to calculate the color value of each light-emitting unit according to a color mixing algorithm, where the color mixing algorithm calculates based on the proportion of each color in the target color set at different times, in combination with the principle of color addition and the sensitivity of the human eye to different colors of light;

[0037] A brightness value calculation unit, configured to calculate the brightness value of each light-emitting unit according to a brightness compensation algorithm, where the brightness compensation algorithm calculates based on the ambient light intensity and the target overall brightness;

[0038] When the instruction sending and adjustment module executes the operation of dynamically adjusting the color transformation instruction according to the temperature feedback information of the linear light:

[0039] When the temperature of the linear light rises above the preset threshold, reduce the color switching frequency and the brightness value of the light-emitting unit, and at the same time adjust the color transition mode to a slower transition method;

[0040] When the temperature of the linear light drops to the safe range, gradually restore the color switching frequency and the brightness value to the original set value, and the restoration process adopts a gradual change method.

[0041] Furthermore, a lighting fixture includes: a linear light body, and the linear light body includes a plurality of light-emitting units;

[0042] The linear light color transformation control device is connected to the driving circuit of the linear light body and is configured to control the color transformation of the linear light body;

[0043] A temperature sensor, arranged inside the linear light body, for obtaining the temperature feedback information of the linear light;

[0044] A plurality of ambient light sensors, distributed at different positions in the space where the lighting fixture is installed, for providing the ambient light intensity and ambient color distribution information for the linear light color transformation control device;

[0045] A depth camera and an action capture sensor, arranged in the space where the lighting fixture is installed, for providing the user position and user action type information for the linear light color transformation control device.

[0046] Furthermore, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the linear light color transformation control method are implemented.

[0047] Compared with the existing technology, the beneficial effects of the present invention are:

[0048] This patent realizes the automatic adjustment of the color of linear lights by obtaining environmental information (including light intensity and color distribution) and user behavior information (location and action type) in real time. This intelligent control enables the linear lights to automatically present appropriate colors in different environments, such as during the day and at night, and in different indoor lighting scenarios. At the same time, the light color is changed according to user behavior. For example, soft lighting is provided when the user is resting, and lively colors are switched to during entertainment, greatly enhancing the humanization and comfort of the user experience.

[0049] The color mapping model based on neural network training is trained using a large amount of data from different scenarios, making the determination of color parameters (target color set, color switching frequency, color transition mode) more accurate. Combining a color mixing algorithm and a brightness compensation algorithm that consider the characteristics of human visual perception, the color value sequence and brightness value sequence of the light-emitting unit are calculated. This ensures that the color transformation of the linear lights is natural and comfortable, achieving an ideal display effect in scenarios such as highlighting product features in commercial displays or creating a warm atmosphere at home.

[0050] Temperature feedback information is obtained through a temperature sensor set inside the linear light. When the temperature is too high, the color transformation instruction is dynamically adjusted, such as reducing the color switching frequency, brightness value, and adjusting the color transition mode. When the temperature returns to normal, it gradually returns. This mechanism effectively avoids problems such as color deviation and damage caused by overheating of the linear light, ensuring the stability and service life of the lamp, and reducing maintenance costs and replacement frequencies.

[0051] This patent can be applied to a variety of scenarios, including smart home, commercial display, indoor and outdoor decoration, etc. Whether it is the demand for personalized lighting from home users or the requirements for display effects in commercial venues, it can be well met, with high practical value and market prospects. Brief Description of the Drawings

[0052] Figure 1 It is a schematic block diagram of a method for controlling the color transformation of a linear light proposed by the present invention;

[0053] Figure 2 It is a schematic block diagram of a device for controlling the color transformation of a linear light proposed by the present invention. Detailed Embodiment

[0054] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0055] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention.

[0056] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, the meaning of "a plurality" is two or more unless otherwise specifically defined. In addition, the terms "mounted", "connected" and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the drawings.

[0057] Refer to Figure 1-2 : A method for controlling color transformation of a linear light, comprising the following steps:

[0058] S1. Obtain environmental information and user behavior information in real time. The environmental information includes environmental light intensity and environmental color distribution, and the user behavior information includes user position and user action type;

[0059] S2. Based on a preset color mapping model, convert the environmental information and user behavior information into color parameters. The color parameters include a target color set, a color switching frequency, and a color transition mode. The color mapping model is obtained by training based on a neural network. The training data includes a large amount of color preference data under different environmental and user behavior scenarios. The output calculation formula of the neural network is: O = σ(W T ×X + b), where o is the output color parameter vector, X is the input vector composed of environmental information and user behavior information, W is the weight matrix, b is the bias vector, and σ is the activation function (such as ReLU, Sigmoid, etc.);

[0060] S3. Generate a color transformation instruction according to the color parameters. The color transformation instruction includes a color value sequence and a brightness value sequence for each light-emitting unit in the strip light. The color value sequence and the brightness value sequence are generated based on a color mixing algorithm and a brightness compensation algorithm. The color mixing algorithm takes into account the mixing effect of different color lights and the visual perception characteristics of the human eye. The brightness compensation algorithm is used to compensate for the brightness difference caused by the influence of ambient light. The formula for calculating the color value of a light-emitting unit at a certain moment in the color mixing algorithm is: where n is the number of colors in the target color set, P i，t is the proportion of the th target color at time, C i is the original color value of the th target color (represented in RGB or other color spaces), K i is the sensitivity coefficient of the human eye to the th color light (obtained from a large amount of visual experiment data); the formula for calculating the brightness compensation value ΔL of a light-emitting unit in the brightness compensation algorithm is: ΔL = (L target -L env ) × α, where L target is the target overall brightness, L env is the ambient light intensity, and α is the brightness compensation proportionality coefficient (determined according to the characteristics of the lamp and the environment);

[0061] S4. Send the color transformation instruction to the driving circuit of the strip light so that the driving circuit controls the light-emitting units of the strip light to achieve color transformation according to the color transformation instruction. At the same time, dynamically adjust the color transformation instruction according to the temperature feedback information of the strip light. The temperature feedback information is obtained through a temperature sensor set inside the strip light to ensure that the strip light works within a safe temperature range and the color transformation effect is not affected by temperature. The adjustment formula is: when T > T threshold (T is the temperature of the strip light, T threshold is the preset temperature threshold), M new = M slo w, where F new is the new color switching frequency, F original is the original color switching frequency, L new is the new brightness value, L original is the original brightness value, M new is the new color transition mode (assuming M slow is the slow transition mode), and T max is the maximum temperature allowed for the strip light.

[0062] In the present invention, in the step of obtaining environmental information and user behavior information in real time:

[0063] Obtain the ambient light intensity and ambient color distribution using multiple ambient light sensors, and the ambient light sensors are distributed at different positions in the space where the linear lights are installed;

[0064] Obtain the user's position and the type of user action through a depth camera and an action capture sensor set in the space. The depth camera and the action capture sensor work together, and analyze the user's behavior through image recognition and action analysis algorithms.

[0065] In the present invention, in the step of converting the ambient information and the user behavior information into color parameters based on a preset color mapping model:

[0066] The neural network structure of the color mapping model includes an input layer, a hidden layer, and an output layer. The input layer receives the vector representations of the ambient information and the user behavior information. The hidden layer uses multiple layers of neurons, and the connection weights between each layer of neurons are continuously adjusted through training. The output layer outputs the color parameters;

[0067] When training the color mapping model, use the cross-entropy loss function (where m is the number of training samples, c is the dimension of the output color parameters, y jk is the k-th true label of the j-th sample, is the predicted value) and an adaptive learning rate adjustment algorithm (such as Adam, Adagrad, etc.) to improve the accuracy of the model for color mapping in different scenarios.

[0068] In the present invention, in the step of generating a color transformation instruction according to the color parameters:

[0069] The color mixing algorithm is a highly accurate calculation method that conforms to the visual perception of the human eye. It determines the mixing ratio of colors based on the proportion of each color in the target color set at different times. Specifically, for each light-emitting unit, when calculating its color value, the principle of color addition is fully considered. This principle deeply involves the relationship between the wavelength and energy of light, because lights of different wavelengths have different energies, and their mixing is not a simple linear addition. For example, when red light and green light are mixed, due to the differences in their wavelengths and energies, the mixed color and brightness are not simply the superposition of their individual effects. At the same time, the algorithm also combines the key factor of the sensitivity of the human eye to different colors of light. The human eye may be more sensitive to certain colors (such as green), and appropriate weights will be given to these sensitive colors when calculating the color value, so as to ensure that the calculated color value appears natural and accurate to the human eye, making the light emitted by the linear lights present a more real and comfortable color effect.

[0070] The described brightness compensation algorithm aims to accurately calculate the brightness compensation value of each light-emitting unit according to the ambient light intensity and the overall target brightness. In the actual environment, the ambient light intensity is constantly changing, which has a significant impact on the visual brightness of the linear lights. When the ambient light intensity is high, in order to make the lighting effect of the linear lights reach the overall target brightness, it is necessary to reduce its brightness compensation value; conversely, when the ambient light intensity is low, it is necessary to increase the brightness compensation value. This relationship where the brightness compensation value is inversely proportional to the ambient light intensity and directly proportional to the overall target brightness can ensure that the linear lights maintain a stable and expected brightness level under various complex ambient light conditions, avoiding problems such as poor lighting effects caused by changes in ambient light.

[0071] In the present invention, in the step of dynamically adjusting the color transformation instruction according to the temperature feedback information of the linear lights:

[0072] When the temperature of the linear lights rises above the preset threshold during operation, a series of measures need to be taken to ensure its normal operation and color transformation effect. At this time, reducing the color switching frequency is to reduce the workload of the light-emitting units because frequent color switching will cause the light-emitting units to experience multiple current and energy changes in a short time, thus generating more heat. At the same time, reducing the brightness value of the light-emitting units also helps to reduce heat generation. In addition, adjusting the color transition mode to a slower transition method can make the light-emitting units more stable during the state change process, reducing the additional heat generated due to sharp changes. Such adjustments can effectively avoid problems such as color deviation and component damage of the linear lights due to overheating, ensuring its stability and reliability in high-temperature environments.

[0073] When the temperature of the linear lights drops to the safe range, in order to restore the normal lighting effect, it is necessary to gradually restore the color switching frequency and brightness value to the original set values. It is crucial that this restoration process uses a gradual change method. If it is suddenly restored, it may cause color mutations, bringing visual discomfort to users and even potentially causing adverse effects such as instantaneous current shocks to the light-emitting units. By using a gradual change method, the changes in color and brightness can be smooth and natural, making it almost imperceptible to users during the adjustment process, ensuring the continuity and comfort of the lighting experience.

[0074] In the present invention, a color transformation control device for linear lights includes:

[0075] An information acquisition module, whose function is to acquire environmental information and user behavior information in real time, providing key data support for the entire color transformation control process. The environmental information here includes ambient light intensity and ambient color distribution, and these two types of information are crucial for understanding the lighting environment where the linear lights are located. The ambient light intensity determines the brightness of the surrounding environment, while the ambient color distribution reflects the proportion of various color components in the ambient light. The user behavior information includes user location and user action type. By tracking the user's location, the relative position relationship between the user and the linear lights can be determined, and then the lighting effect can be adjusted according to different positions. For example, when the user approaches the linear lights, the color and brightness can be appropriately adjusted to provide a better lighting experience. The user action type can reflect the user's current activity state. When the user is in different actions such as stationary, walking, or jumping, the color mode of the linear lights can be changed accordingly to create a lighting environment that better fits the user's activity atmosphere.

[0076] A color parameter generation module, which undertakes the important task of converting the acquired environmental information and user behavior information into color parameters based on a preset color mapping model. The color parameters include a target color set, color switching frequency, color transition mode, etc. The color mapping model here is obtained through neural network training, and the training data covers a large amount of color preference data in different environmental and user behavior scenarios. This model trained based on big data can accurately capture the user's color expectations in different situations, thereby generating color parameters that better meet the actual needs. For example, in a warm family gathering scene, according to the warm-toned light in the environment and people's lively actions, the model can generate color parameters including warm-colored tones, appropriate color switching frequency, and smooth transition mode, enabling the lighting of the linear lights to better set off the atmosphere.

[0077] An instruction generation module, whose core function is to generate color transformation instructions according to the color parameters. These color transformation instructions contain color value sequences and brightness value sequences for each light-emitting unit in the linear lights. The generation of the color value sequences and brightness value sequences is based on color mixing algorithms and brightness compensation algorithms. The color mixing algorithm fully considers the mixing effect of different color lights and the human eye's visual perception characteristics, and can accurately calculate the color values of each light-emitting unit at different times, making the light emitted by the linear lights rich and natural in color. The brightness compensation algorithm is used to compensate for the brightness difference caused by the influence of ambient light, ensuring that the linear lights can maintain an appropriate brightness under various ambient light conditions and meet the user's visual needs.

[0078] Instruction Sending and Adjusting Module: It is responsible for sending color transformation instructions to the driving circuit of the strip lights, enabling the driving circuit to control the light-emitting units of the strip lights according to these instructions to achieve color transformation. At the same time, this module can also dynamically adjust the color transformation instructions based on the temperature feedback information of the strip lights. The temperature feedback information is obtained through a temperature sensor set inside the main body of the strip lights. This dynamic adjustment mechanism can ensure that the strip lights operate within a safe temperature range and guarantee that the color transformation effect is not affected by temperature changes. For example, in a high-temperature environment, the instructions are adjusted to reduce the working intensity of the strip lights to avoid overheating damage; after the temperature returns to normal, the instructions can be adjusted in a timely manner to make the strip lights return to the optimal lighting state.

[0079] In the present invention, the information acquisition module includes:

[0080] Ambient Light Sensor Group: It is composed of multiple ambient light sensors, which are distributed at different positions in the space where the strip lights are installed. Each sensor can independently detect the light intensity and color information of the surrounding environment. Through the collaborative work of multiple sensors, the ambient light intensity and ambient color distribution in the entire space can be comprehensively and accurately obtained. This distributed design can overcome the detection blind spot problem that may exist in a single sensor, and no matter from which direction the light shines, it can be accurately sensed. For example, in an irregularly shaped indoor space, the ambient light sensors at each corner and different height positions can jointly collect light information, providing comprehensive data basis for subsequent color adjustment.

[0081] Behavior Monitoring Unit: It mainly includes a depth camera and an action capture sensor. The depth camera can obtain three-dimensional image information in the space, not only being able to identify the position of the user in the space, but also providing depth information such as the distance and shape of objects. The action capture sensor focuses on analyzing the type of user actions. By capturing and analyzing the subtle changes in the user's limb movements, it can determine whether the user is stationary, walking, running, or performing other specific actions. These two devices work together, using advanced image recognition and action analysis algorithms, to accurately obtain the user's position and the type of user actions. For example, in an intelligent conference room, when the user gets up and walks towards the podium, the behavior monitoring unit can timely detect this action change and transmit the relevant information to other modules, so that the strip lights can adjust the lighting according to the user's behavior, providing better visual guidance for the user.

[0082] In the present invention, the neural network structure of the color mapping model in the color parameter generation module includes an input layer, a hidden layer, and an output layer. The input layer receives vector representations of environmental information and user behavior information, which are a mathematical abstraction of complex environmental and behavioral data. For example, environmental light intensity and color distribution information can be transformed into vectors through a specific coding method, and user position and action type information can also be represented in vector form. The hidden layer employs multiple layers of neurons, and the connection weights between each layer of neurons are continuously adjusted through a large amount of training. This multi-layer structure and adjustable weights enable the neural network to learn the complex non-linear relationships between the environment, user behavior, and color parameters. For example, under different combinations of environmental light colors and user action patterns, the neural network can accurately generate color parameters such as the corresponding target color set, color switching frequency, and color transition mode by adjusting the weights. The output layer is responsible for outputting the color parameters processed by the neural network, and these parameters will be directly used in the subsequent instruction generation process. When training the color mapping model, a cross-entropy loss function and an adaptive learning rate adjustment algorithm are adopted. The cross-entropy loss function can measure the degree of difference between the predicted color parameters and the actual expected color parameters. By minimizing this loss function, the prediction results of the model can be made more accurate. The adaptive learning rate adjustment algorithm can automatically adjust the learning rate according to the situation during the training process, enabling the model to converge quickly in the initial stage of training and more finely adjust the weights when approaching the optimal solution, thereby improving the accuracy of color mapping of the model in different scenarios and ensuring that the generated color parameters can meet diverse lighting requirements.

[0083] The instruction generation module includes:

[0084] A color value calculation unit, whose main function is to calculate the color value of each light-emitting unit according to a color mixing algorithm. This color mixing algorithm is calculated based on the proportion of each color in the target color set at different times, combined with the principle of color addition and the sensitivity of the human eye to different colors of light. During the calculation process, for each color in the target color set, its proportion at the current moment needs to be considered. For example, if the target color set includes red, green, and blue, and at a certain moment, the proportion of red is 30%, green is 50%, and blue is 20%, the color value calculation unit will determine the color value of the light-emitting unit according to this proportion, the principle of color addition, and the sensitivity of the human eye to these three colors. This calculation method fully considers the interaction between different colors and the visual characteristics of the human eye, making the calculated color value present a natural and comfortable visual effect in the view of the human eye and meeting the requirements of users for high-quality lighting colors.

[0085] The brightness value calculation unit is a key part that calculates the brightness value of each light-emitting unit according to the brightness compensation algorithm. The brightness compensation algorithm determines the brightness compensation value of each light-emitting unit based on the ambient light intensity and the overall target brightness. In practical applications, the brightness value calculation unit will obtain the ambient light intensity information in real time and perform calculations in combination with the preset overall target brightness value. For example, when the ambient light intensity is high, the brightness compensation value will be correspondingly reduced to ensure that the strip light does not appear too dazzling in a strong light environment; when the ambient light intensity is low, the brightness compensation value will increase, enabling the strip light to provide sufficient illumination brightness. This calculation method of dynamically adjusting the brightness value according to environmental changes ensures that the strip light can provide a stable and appropriate brightness level under various ambient light conditions, enhancing the user's lighting experience.

[0086] When the instruction sending and adjustment module executes the operation of dynamically adjusting the color transformation instruction according to the temperature feedback information of the strip light:

[0087] When the temperature of the strip light rises above the preset threshold, in order to protect the strip light and maintain a stable lighting effect, it is necessary to adjust the color transformation instruction. Reducing the color switching frequency can reduce the heat generated by the frequent color switching of the light-emitting units, because each color switch is accompanied by changes in current and energy, and reducing the frequency can effectively alleviate the heating problem. At the same time, reducing the brightness value of the light-emitting units can also reduce their energy consumption, thereby reducing heat generation. In addition, adjusting the color transition mode to a slower transition method makes the light-emitting units more stable during the color change process, avoiding additional heat generated due to sudden color changes. For example, in a high-temperature environment, adjusting the original fast color-switching mode to a slow fade-in transition mode can significantly reduce the heating speed of the strip light and extend its service life.

[0088] When the temperature of the strip light drops to the safe range, gradually restoring the color switching frequency and brightness value to the original set values is a process that requires fine control. This restoration process uses a gradual change method to avoid the visual discomfort caused by sudden color changes to the user. During the restoration process, the color switching frequency and brightness value are not restored instantaneously, but are gradually adjusted according to a certain gradual change rule. For example, a gradual change time parameter can be set, and within this time, the color switching frequency and brightness value are gradually increased, enabling the lighting effect of the strip light to smoothly return to the normal state, ensuring that users can enjoy a comfortable lighting experience throughout the process, and at the same time ensuring the stability and reliability of the strip light during the temperature change process.

[0089] In the present invention, a lighting fixture includes:

[0090] The linear light body, which is the core part of the lamp, consists of multiple light-emitting units. These light-emitting units are the key components for generating light, and their coordinated operation determines the lighting effect of the linear light. Each light-emitting unit has independent light-emitting characteristics and can emit light of different colors and brightness levels according to the received control instructions. For example, in some commercial display scenarios, the light-emitting units of the linear light body can be precisely controlled to form various colorful lighting effects to highlight the characteristics of the displayed products.

[0091] The linear light color transformation control device is connected to the drive circuit of the linear light body and is used to control the color transformation of the linear light body. The linear light color transformation control device generates and sends color transformation instructions to command the drive circuit to precisely control the light-emitting units. This connection method realizes the information transmission from the control device to the light-emitting units, enabling the linear light to achieve intelligent color transformation according to different environments and user needs. For example, in a smart home environment, when the user is watching a movie, the control device can send instructions to the drive circuit to adjust the linear light body to a soft low-brightness color to create a comfortable movie-watching atmosphere.

[0092] The temperature sensor is arranged inside the linear light body and is specifically used to obtain the temperature feedback information of the linear light. The temperature sensor can monitor the temperature change of the linear light in real time during operation, which is crucial for ensuring the safe operation of the linear light. When the temperature is too high, it may have an adverse impact on the performance and lifespan of the light-emitting units and even cause safety problems. Through the monitoring of the temperature sensor, abnormal temperature conditions can be detected in a timely manner, and the information can be fed back to the control device for corresponding adjustment measures. For example, when the linear light is used for a long time in a high-temperature environment, the temperature sensor can detect the rising trend of the temperature in a timely manner and trigger the control device to adjust the color transformation instructions to prevent the linear light from being damaged due to overheating.

[0093] Multiple ambient light sensors are distributed at different positions in the space where the lamp is installed and are used to provide the linear light color transformation control device with ambient light intensity and ambient color distribution information. These ambient light sensors can comprehensively sense the light conditions of the surrounding environment and can accurately detect whether the light is natural light or generated by other artificial light sources. After the information they collect is transmitted to the color transformation control device, the linear light can better adapt to the ambient light change. For example, when there is sufficient sunlight during the day, the ambient light sensors can detect the strong light environment, and the control device adjusts the brightness and color of the linear light according to this information, enabling it to still play a good lighting and decorative role against the strong light background.

[0094] A depth camera and an action capture sensor, which are arranged in the space where the lamp is installed, are used to provide the user position and user action type information for the color transformation control device of the linear lamp. The depth camera can obtain three-dimensional information in the space to accurately determine the position coordinates of the user, while the action capture sensor focuses on analyzing the details of the user's actions. Through the collaborative work of the two, the control device can adjust the lighting effect of the linear lamp in real time according to the user's behavior. For example, when the user is dancing in the living room, the action capture sensor can recognize the user's dance movements, and the control device adjusts the color and flashing frequency of the linear lamp accordingly to create a more dynamic atmosphere for the user.

[0095] In the present invention, a computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the steps of the color transformation control method of the linear lamp are implemented. Such a computer-readable storage medium can be various storage devices such as a hard disk and a flash memory. The computer program contains the code logic for implementing a series of steps of the color transformation control method of the linear lamp, such as color parameter generation, instruction generation, and temperature feedback adjustment. When the processor executes these programs, it can simulate the entire color transformation control process. For example, in an intelligent lighting system, the processor can read the program in the storage medium, calculate color parameters according to the environmental and user information, generate color transformation instructions, and perform dynamic adjustment according to the temperature of the linear lamp, so as to realize the intelligent control of the linear lamp and provide a more comfortable and personalized lighting experience for the user.

[0096] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered within the protection scope of the present invention.

Claims

1. A color change control method for a linear light, characterized in that: The following steps are involved: S1. Acquire environmental information and user behavior information in real time, wherein the environmental information includes ambient light intensity and ambient color distribution, and the user behavior information includes user location and user action type; S2. Based on a preset color mapping model, the environment information and user behavior information are converted into color parameters, wherein the color parameters include a target color set, a color switching frequency, and a color transition mode. The color mapping model is obtained based on neural network training, and the training data includes color preference data under different environments and user behavior scenarios. The output calculation formula of the neural network is: O = σ(W T ×X+b), where O is the output color parameter vector, X is the input vector composed of environment information and user behavior information, W is the weight matrix, b is the bias vector, and σ is the activation function; S3. Generate a color change instruction according to the color parameter, wherein the color change instruction includes a color value sequence and a brightness value sequence for each light-emitting unit in the linear light, wherein the color value sequence and the brightness value sequence are generated based on a color mixing algorithm and a brightness compensation algorithm, wherein the color mixing algorithm takes into account the mixing effect of different colors of light and the visual perception characteristics of the human eye, and the brightness compensation algorithm is used to compensate for the brightness difference caused by the influence of ambient light. The formula for calculating the color value of the light-emitting unit at a certain moment in the color mixing algorithm is: Where n is the number of colors in the target color set, P i，t is the proportion of the i-th target color at time t, C i is the original color value of the i-th target color, K i is the sensitivity coefficient of human eyes to the i-th color light; brightness The formula for calculating the brightness compensation value ΔL of the light-emitting unit in the compensation algorithm is: ΔL=(L target -L env )×α, where L target is the overall brightness of the target, L env is the ambient light intensity, α is the brightness compensation proportional coefficient; S4, sending the color change instruction to the driving circuit of the linear light, the driving circuit controlling the light-emitting unit of the linear light to achieve color change according to the color change instruction, and dynamically adjusting the color change instruction according to the temperature feedback information of the linear light, the temperature feedback information is obtained by the temperature sensor set inside the linear light, and the adjustment formula is: when T>T threshold hour, M new =M slow , where T is the temperature of the line lamp, T threshold is the preset temperature threshold, F new is the new color switching frequency, F original is the original color switching frequency, L new is the new brightness value, L original is the original brightness value, M new For the new color transition mode, M slow For slow transition mode, T max The maximum temperature allowed for the linear light; In the step of obtaining environment information and user behavior information in real time: A plurality of ambient light sensors are used to obtain the ambient light intensity and the ambient color distribution, wherein the ambient light sensors are distributed at different positions in the space where the linear lights are installed; The user's position and the type of user's action are obtained by using a depth camera and a motion capture sensor set in the space, wherein the depth camera and the motion capture sensor work together to analyze the user's behavior through image recognition and motion analysis algorithms; In the step of converting the environmental information and user behavior information into color parameters based on a preset color mapping model: The neural network structure of the color mapping model includes an input layer, a hidden layer and an output layer, wherein the input layer receives a vector representation of environmental information and user behavior information, the hidden layer uses multiple layers of neurons, the connection weights between neurons in each layer are adjusted through training, and the output layer outputs color parameters; When training the color mapping model, the cross entropy loss function is used and adaptive learning rate adjustment algorithm, where m is the number of training samples, c is the output color parameter dimension, and y jk is the k-th dimension true label of the j-th sample, is the predicted value, which is used to improve the accuracy of the model's color mapping in different scenarios; In the step of generating a color conversion instruction according to the color parameters: The color mixing algorithm calculates the color value of each light-emitting unit according to the proportion of each color in the target color set at different times, combined with the color addition principle and the sensitivity of the human eye to different colors of light. The color addition principle takes into account the relationship between the wavelength and energy of light; The brightness compensation algorithm calculates the brightness compensation value of each light-emitting unit according to the ambient light intensity and the overall brightness of the target, and the brightness compensation value is inversely proportional to the ambient light intensity and directly proportional to the overall brightness of the target; In the step of dynamically adjusting the color change instruction according to the temperature feedback information of the linear light: When the temperature of the line light rises above a preset threshold, the color switching frequency and the brightness value of the light-emitting unit are reduced, and the color transition mode is adjusted to a slower transition mode; When the temperature of the linear lamp drops to a safe range, the color switching frequency and brightness value are gradually restored to the original set values. The restoration process adopts a gradual manner to avoid sudden color changes.

2. A linear light color change control device, used to implement the linear light color change control method according to claim 1, characterized in that: include: An information acquisition module, used to acquire environmental information and user behavior information in real time, wherein the environmental information includes ambient light intensity and ambient color distribution, and the user behavior information includes user location and user action type; A color parameter generation module, which is used to convert the environment information and user behavior information into color parameters based on a preset color mapping model, wherein the color parameters include a target color set, a color switching frequency, and a color transition mode. The color mapping model is obtained based on neural network training, and the training data includes a large amount of color preference data under different environments and user behavior scenarios; An instruction generation module, configured to generate a color change instruction according to the color parameter, wherein the color change instruction includes a color value sequence and a brightness value sequence for each light-emitting unit in the linear light, wherein the color value sequence and the brightness value sequence are generated based on a color mixing algorithm and a brightness compensation algorithm, wherein the color mixing algorithm takes into account the mixing effect of different color lights and the visual perception characteristics of the human eye, and the brightness compensation algorithm is used to compensate for the brightness difference caused by the influence of ambient light; The instruction sending and adjustment module is used to send the color change instruction to the driving circuit of the linear light, so that the driving circuit controls the light-emitting unit of the linear light to achieve color change according to the color change instruction. At the same time, the color change instruction is dynamically adjusted according to the temperature feedback information of the linear light. The temperature feedback information is obtained by a temperature sensor arranged inside the linear light to ensure that the linear light operates within a safe temperature range and the color change effect is not affected by temperature.

3. The color change control device of the linear light according to claim 2, characterized in that: The information acquisition module includes: An ambient light sensor group, consisting of a plurality of ambient light sensors distributed at different positions in the space where the linear lights are installed, for obtaining ambient light intensity and ambient color distribution; The behavior monitoring unit, including a depth camera and a motion capture sensor, works together to obtain the user's location and the type of user movement using image recognition and motion analysis algorithms.

4. The color change control device of the linear light according to claim 2, characterized in that: The neural network structure of the color mapping model in the color parameter generation module includes an input layer, a hidden layer and an output layer, wherein the input layer receives a vector representation of environmental information and user behavior information, the hidden layer uses multiple layers of neurons, and the connection weights between neurons in each layer are continuously adjusted through training, and the output layer outputs color parameters. When training the color mapping model, a cross entropy loss function and an adaptive learning rate adjustment algorithm are used; The instruction generation module comprises: A color value calculation unit, used to calculate the color value of each light-emitting unit according to a color mixing algorithm, wherein the color mixing algorithm is calculated based on the proportion of each color in the target color set at different times, combined with the color addition principle and the sensitivity of the human eye to different colors of light; A brightness value calculation unit, used to calculate the brightness value of each light-emitting unit according to a brightness compensation algorithm, wherein the brightness compensation algorithm is calculated according to the ambient light intensity and the overall brightness of the target; When the instruction sending and adjusting module performs the operation of dynamically adjusting the color change instruction according to the temperature feedback information of the line light: When the temperature of the line light rises above a preset threshold, the color switching frequency and the brightness value of the light-emitting unit are reduced, and the color transition mode is adjusted to a slower transition mode; When the temperature of the linear lamp drops to a safe range, the color switching frequency and the brightness value are gradually restored to the original set values, and the restoration process adopts a gradual manner.

5. A lamp, characterized in that: include: A linear light body, the linear light body comprising a plurality of light-emitting units; The linear light color change control device according to any one of claims 2 to 4, wherein the linear light color change control device is connected to a driving circuit of the linear light body, and is used to control the color change of the linear light body; A temperature sensor is arranged inside the linear light body and is used to obtain temperature feedback information of the linear light; A plurality of ambient light sensors are distributed at different positions in the space where the lamp is installed, and are used to provide ambient light intensity and ambient color distribution information to the linear light color change control device; The depth camera and the motion capture sensor are arranged in the space where the lamp is installed, and are used to provide the linear lamp color change control device with user position and user motion type information.

6. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the linear light color change control method as claimed in claim 1 are implemented.

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