Healthy lighting control method, device and equipment for dynamically adjusting classroom lighting environment

Abstract

The invention belongs to the technical field of healthy lighting, solves the problem that the lighting environment in a classroom cannot be dynamically adjusted in the prior art, and provides a healthy lighting control method, device and equipment for dynamically adjusting the lighting environment in the classroom. The method comprises the steps of obtaining a corresponding time schedule with a target illumination space as a classroom; querying the time schedule according to the current time to obtain teaching activity information corresponding to the current time; obtaining a parameter adjustment coefficient of an illumination adjustment parameter according to the teaching activity type; for each teaching activity type, adjusting an illumination control parameter by using an adjustment coefficient to obtain a target illumination parameter; according to the target illumination parameter, the historical illumination adjustment parameter and the teaching activity type and time period, obtaining an illumination adjustment rate; and controlling the light source to illuminate according to the target illumination parameter and the illumination adjustment rate. The illumination environment in the classroom can be dynamically adjusted, and the healthy illumination requirement of a user is met.

Description

[0001] This application is a divisional application of the invention patent application filed on January 4, 2024, with the invention name “Healthy lighting control method, device, equipment and medium based on human rhythm” and application number 202410027489.5. Technical Field

[0002] The present invention relates to the field of healthy lighting, and in particular to a healthy lighting control method, device and equipment for dynamically adjusting the lighting environment of a classroom. Background Art

[0003] In modern educational environments, classroom lighting regulation is essential to promote students' health and learning efficiency. Studies have shown that appropriate lighting conditions can significantly improve students' attention, memory and overall learning performance, while inappropriate lighting can cause problems such as visual fatigue, headaches and depression, affecting students' physical and mental health. By adopting an adjustable lighting system, teachers can flexibly adjust the light intensity and color temperature according to different teaching activities, seasonal changes and external light conditions to create a more comfortable learning environment. This healthy lighting can not only enhance the classroom atmosphere, but also effectively support students' vision protection and mental health, and promote their all-round development. Therefore, adjusting the lighting in the classroom to achieve healthy lighting is an important measure to improve the quality of education and student well-being.

[0004] Existing technologies have many limitations in the dynamic adjustment of classroom lighting environments, mainly reflected in the lack of intelligent, real-time response mechanisms and adaptive adjustment functions. Many traditional lighting systems rely on fixed light sources and manual switches, and cannot automatically adjust according to the number of students, activity types, or external lighting changes, resulting in lighting conditions that cannot adapt to actual needs in a timely manner. In addition, existing sensor technology is often insufficient to accurately detect and analyze light intensity, color temperature, and students' visual comfort, making it impossible to achieve precise dynamic adjustment. These deficiencies make classroom lighting unable to truly meet the requirements of flexibility and health in modern educational environments, limiting teaching effectiveness and students' learning experience.

[0005] Therefore, how to dynamically adjust the lighting environment in the classroom to meet the healthy lighting needs of students and teachers is an urgent problem to be solved. Summary of the invention

[0006] In view of this, the embodiments of the present invention provide a healthy lighting control method, device, equipment and storage medium for dynamically adjusting the lighting environment of a classroom, so as to solve the problem that the lighting environment in the classroom cannot be dynamically adjusted in the prior art.

[0007] In a first aspect, an embodiment of the present invention provides a healthy lighting control method for dynamically adjusting a classroom lighting environment, the method comprising:

[0008] Obtaining a corresponding time schedule for a classroom as the target lighting space, wherein the time schedule includes: a plurality of teaching activity information and corresponding time periods, the teaching activity information includes teaching activity types, the teaching activity types include courses, breaks, exams, and special activities, and the courses include theoretical courses, experimental courses, and computer courses;

[0009] Query the time schedule according to the current time to obtain the teaching activity information corresponding to the current time;

[0010] Through the activity type-adjustment coefficient mapping table, according to the teaching activity type, the parameter adjustment coefficient of the lighting adjustment parameter is obtained, wherein the lighting adjustment parameter is obtained according to the lighting demand data, the current time and the lighting control algorithm based on the human rhythm model, and the parameter adjustment coefficient includes a color temperature adjustment coefficient, a brightness adjustment coefficient and a blue light adjustment coefficient. The color temperature adjustment coefficient and / or brightness adjustment coefficient and / or blue light parameter of the parameter adjustment coefficient corresponding to different teaching activity types are different;

[0011] For each type of teaching activity, the corresponding color temperature adjustment coefficient, brightness adjustment coefficient and blue light adjustment coefficient are applied to adjust the lighting control parameters. The adjustment coefficient increases or decreases the value of the lighting control parameter obtained by the human rhythm model according to the needs of the specific activity to obtain the target lighting parameters.

[0012] Obtain the lighting adjustment rate according to the current target lighting parameters, the historical lighting adjustment parameters of the previous time period, the current teaching activity type and the corresponding time period;

[0013] The light source is controlled to perform illumination according to the target illumination parameter and the illumination adjustment rate.

[0014] Preferably, before obtaining the corresponding time schedule for the target lighting space being a classroom, the method further includes:

[0015] Acquire environmental parameters of the target lighting space, wherein the environmental parameters include illumination parameters and temperature and humidity parameters of the target lighting space, the illumination parameters are acquired by a light sensor, and the temperature and humidity parameters are acquired by a temperature sensor and a humidity sensor;

[0016] According to a preset visual algorithm, obtaining activity parameters of the user in the target lighting space, wherein the activity parameters include the movement intensity and residence time of the user in the target lighting space, and the preset visual algorithm includes but is not limited to an object tracking algorithm, a human posture recognition algorithm, a deep learning algorithm, and a behavior pattern recognition algorithm;

[0017] Comprehensively analyze environmental parameters and user activity parameters to obtain current lighting demand data;

[0018] Acquire lighting adjustment parameters according to the lighting demand data, the current time and a lighting control algorithm based on a human rhythm model, wherein the lighting adjustment parameters include brightness parameters and color temperature parameters;

[0019] According to the illumination adjustment parameters, the light source of the target illumination space is controlled to perform illumination.

[0020] Preferably, obtaining the activity parameters of the user in the target lighting space according to a preset visual algorithm includes:

[0021] Acquiring video data of the target lighting space;

[0022] Tracking the user in the video data according to the preset visual algorithm to obtain the user's residence time in the target lighting space and the user's movement distance and movement time within a preset time threshold, wherein the preset visual algorithm includes a YOLO algorithm and a multi-target tracking algorithm;

[0023] Calculate the user's moving distance and moving time within a preset time threshold to obtain the movement intensity;

[0024] The calculated movement intensity is combined with the user's residence time in the space to obtain activity parameters, which refer to data that comprehensively reflects the user's activity characteristics in the space, including movement intensity and residence time.

[0025] Preferably, the step of acquiring the lighting requirement data according to the environmental parameters and the activity parameters comprises:

[0026] According to the temperature and humidity parameters, obtaining the environmental comfort of the target lighting space;

[0027] According to the activity parameter, obtaining the activity intensity of the target lighting space;

[0028] According to the environmental comfort, activity intensity and the lighting parameters, the lighting demand data of the target lighting space is calculated by a preset lighting demand calculation formula, wherein the preset lighting demand calculation formula is as follows:

[0029] Ereq=α*Eenv+β*Pact+γ*Eclim

[0030] Wherein, Ereq is the lighting demand data, Eenv is the lighting intensity, Pact is the activity intensity, Eclim is the environmental comfort, α is the first adjustment coefficient, β is the second adjustment coefficient, and γ is the third adjustment coefficient.

[0031] Preferably, the temperature and humidity parameters include the ambient temperature and relative humidity of the target lighting space, and the step of acquiring the environmental comfort of the target lighting space according to the temperature and humidity parameters includes:

[0032] The environmental comfort of the target lighting space is obtained according to the ambient temperature, the relative humidity and a preset environmental comfort calculation formula, wherein the preset environmental comfort calculation formula is as follows:

[0033] Eclim=μ*T+(1-μ)*H

[0034] In the formula, Eclim is the environmental comfort, μ is the first preset weight, T is the ambient temperature, and H is the relative humidity.

[0035] Preferably, the step of obtaining the activity intensity of the target lighting space according to the activity parameter comprises:

[0036] Normalizing the movement intensity and the residence time according to a maximum and minimum value normalization method to obtain a target movement intensity and a target residence time;

[0037] The activity intensity of the target lighting space is calculated according to the target movement intensity, the target residence time and a preset activity intensity calculation formula, wherein the preset activity intensity calculation formula is as follows:

[0038] Pact=λ*Mnorm+(1-λ)*Tnorm

[0039] Wherein, Pact is the activity intensity, Mnorm is the target movement intensity, Tnorm is the residence time, and λ is the second preset weight.

[0040] Preferably, the step of acquiring the lighting adjustment parameters according to the lighting demand data, the current time and the lighting control algorithm based on the human body rhythm model includes:

[0041] Acquire a reference data set, wherein the reference data set includes a plurality of reference data, each of the reference data includes temperature and humidity parameters;

[0042] According to the reference data set, construct a lighting parameter prediction model, wherein the lighting parameter prediction model is constructed based on a logistic regression algorithm, takes the lighting demand data and the current time as input, and takes brightness parameters and color temperature parameters as output;

[0043] Inputting the lighting demand data and the current time into the lighting parameter prediction model to obtain predicted lighting control parameters;

[0044] Control the light source to perform test lighting according to the predicted light control parameters to obtain test data and user feedback data;

[0045] According to the test data and the feedback data, the human body rhythm model is acquired, wherein the human body rhythm model includes a brightness parameter acquisition function and a color temperature parameter acquisition function;

[0046] The color temperature parameter acquisition function is as follows:

[0047] Tcolor=f1(Ereq,Time)

[0048] The brightness parameter acquisition function is as follows:

[0049] Lbrightness=f2(Ereq,Time)

[0050] Wherein, Tcolor is the color temperature parameter, Lbrightness is the brightness parameter, Ereq is the lighting demand data, and Time is the current time;

[0051] The lighting demand data and the current time are input into the human rhythm model to obtain lighting adjustment parameters.

[0052] In a second aspect, an embodiment of the present invention provides a healthy lighting control device for dynamically adjusting the lighting environment of a classroom, characterized in that the device comprises:

[0053] An environmental parameter acquisition module, used to acquire environmental parameters of a target lighting space, wherein the environmental parameters include illumination parameters and temperature and humidity parameters of the target lighting space;

[0054] An activity parameter acquisition module, used to acquire activity parameters of the user in the target lighting space according to a preset visual algorithm, wherein the activity parameters include the movement intensity and residence time of the user in the target lighting space;

[0055] A lighting demand acquisition module, used to acquire lighting demand data according to the environmental parameters and the activity parameters;

[0056] An adjustment parameter acquisition module is used to acquire the lighting adjustment parameters according to the lighting demand data, the current time and the lighting control algorithm based on the human rhythm model, wherein the lighting adjustment parameters include brightness parameters and color temperature parameters;

[0057] The lighting control module is used to control the light source of the target lighting space to illuminate according to the lighting adjustment parameters.

[0058] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method of the first aspect in the above-mentioned embodiment.

[0059] In a fourth aspect, an embodiment of the present invention provides a storage medium having computer program instructions stored thereon, which implements the method of the first aspect in the above-mentioned embodiment when the computer program instructions are executed by a processor.

[0060] In summary, the beneficial effects of the present invention are as follows:

[0061] The embodiments of the present invention provide a healthy lighting control method, device and equipment for dynamically adjusting the lighting environment of a classroom. The method comprises: obtaining a corresponding time schedule for a target lighting space being a classroom, wherein the time schedule comprises: multiple teaching activity information and corresponding time periods, wherein the teaching activity information comprises teaching activity types, wherein the teaching activity types comprise courses, breaks, examinations and special activities, wherein the courses comprise theoretical courses, experimental courses and computer courses; querying the time schedule according to the current time to obtain the teaching activity information corresponding to the current time; obtaining a parameter adjustment coefficient of a lighting adjustment parameter according to the teaching activity type through an activity type-adjustment coefficient mapping table, wherein the lighting adjustment parameter is adjusted according to lighting demand data, the current time and based on human body The lighting control algorithm of the rhythm model is obtained, and the parameter adjustment coefficient includes a color temperature adjustment coefficient, a brightness adjustment coefficient and a blue light adjustment coefficient. The color temperature adjustment coefficient and / or brightness adjustment coefficient and / or blue light parameter of the parameter adjustment coefficient corresponding to different teaching activity types are different; for each type of teaching activity, the corresponding color temperature adjustment coefficient, brightness adjustment coefficient and blue light adjustment coefficient are applied to adjust the lighting control parameter, and the adjustment coefficient increases or decreases the value of the lighting control parameter obtained by the human rhythm model according to the needs of the specific activity to obtain the target lighting parameter; according to the current target lighting parameter, the historical lighting adjustment parameter of the previous time period, and the current teaching activity type and the corresponding time period, the lighting adjustment rate is obtained; according to the target lighting parameter and the lighting adjustment rate, the light source is controlled for lighting. The present invention realizes flexible and efficient lighting management by dynamically adjusting the classroom lighting environment to meet the needs of different teaching activities, and significantly improves the teaching effect and the learning experience of students. First, the introduction of the time schedule enables the system to identify the current teaching activity type in real time to ensure that the lighting adjustment matches the specific needs. Through the activity type-adjustment coefficient mapping table, the corresponding lighting adjustment parameters, such as color temperature, brightness and blue light adjustment coefficient, can be automatically obtained according to different courses, breaks, exams or special activities, so as to optimize lighting conditions in different situations. This personalized adjustment not only takes into account the students' physiological rhythms and reduces visual fatigue, but also enhances attention and learning efficiency. In addition, by monitoring the historical lighting adjustment parameters and lighting adjustment rate, the system can smoothly adjust the light source to avoid the discomfort caused to students by sudden lighting changes, and create a more comfortable and healthy learning environment. In summary, this solution provides a scientific basis and practical support for the dynamic adjustment of classroom lighting. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings required for use in the embodiment of the present invention will be briefly introduced below. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work, and these are all within the protection scope of the present invention.

[0063] Figure 1 It is a flow chart of a healthy lighting control method for dynamically adjusting the classroom lighting environment according to an embodiment of the present invention.

[0064] Figure 2 It is a schematic diagram of a process of obtaining activity parameters according to an embodiment of the present invention.

[0065] Figure 3 It is a schematic diagram of the process of obtaining lighting requirement parameters according to an embodiment of the present invention.

[0066] Figure 4 It is a structural schematic diagram of a healthy lighting control device for dynamically adjusting the classroom lighting environment according to an embodiment of the present invention.

[0067] Figure 5 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The features and exemplary embodiments of various aspects of the present invention will be described in detail below. In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and Examples. It should be understood that the specific embodiments described herein are only configured to explain the present invention and are not configured to limit the present invention. For those skilled in the art, the present invention can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present invention by illustrating examples of the present invention.

[0069] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0070] Example 1

[0071] See also Figure 1 The embodiment of the present invention provides a healthy lighting control method for dynamically adjusting the lighting environment of a classroom, the method comprising:

[0072] S1. Acquire environmental parameters of a target lighting space, wherein the environmental parameters include illumination parameters and temperature and humidity parameters of the target lighting space;

[0073] In order to understand the current environmental state of the target lighting space, firstly, the environmental parameters of the target lighting space are obtained, wherein the environmental parameters include the illumination parameters and the temperature and humidity parameters of the target lighting space, so as to ensure that the adjustment of the lighting system is adapted to the actual environment and improve the efficiency and comfort of the lighting;

[0074] Specifically, the illumination parameters and the temperature and humidity parameters can be obtained through corresponding sensors. For example, the illumination parameters can be obtained through a light sensor, which can detect the illumination intensity and illumination range of natural light and artificial light in the room. The temperature and humidity parameters can be obtained through a temperature sensor and a humidity sensor to monitor the temperature and humidity levels in the space.

[0075] S2. acquiring, according to a preset visual algorithm, activity parameters of the user in the target lighting space, wherein the activity parameters include movement intensity and residence time of the user in the target lighting space;

[0076] This step monitors and analyzes the activity patterns of users in the target space, and uses a preset visual algorithm to track the user's movement intensity and residence time through a camera or other visual sensor installed in the target lighting space. In one embodiment, the preset visual algorithm includes but is not limited to an object tracking algorithm, a human posture recognition algorithm, a deep learning algorithm, a behavior pattern recognition algorithm, etc., wherein the object tracking algorithm can identify and track people in the space, for example, using the multi-target tracking technology in the OpenCV library. It can continuously track the user's movement path in the space and provide more accurate activity intensity and residence time data; the human posture recognition algorithm can identify and analyze the posture of the human body, such as standing, sitting or walking. Using tools such as OpenPose, the user's activity status in the space can be more finely understood; deep learning algorithms such as using convolutional neural networks can analyze images captured by the camera, identify the number of people in the space, the type of activity, etc., and can extract useful information from complex environments to better adjust lighting settings. The behavior pattern recognition algorithm can learn the user's regular behavior patterns, such as often staying in a certain area within a certain period of time, so as to predict future behavior and lighting needs. The above algorithms are all prior art and will not be elaborated here;

[0077] As an optional embodiment of the present invention, see Figure 2, the step of obtaining the activity parameters of the user in the target lighting space according to the preset visual algorithm includes:

[0078] S21, acquiring video data of the target lighting space;

[0079] Specifically, first collect real-time video data in the lighting space as the basis for analyzing user behavior. Install visual sensors such as cameras in the lighting space to record video in real time. These visual sensors should be able to cover the entire space to ensure that user activities are captured without blind spots. Video data refers to the continuous image data captured by the camera system, which is used for subsequent analysis and processing, thereby providing a comprehensive perspective to observe and analyze user behavior in the space, providing a data basis for more accurate activity analysis.

[0080] S22, tracking the user in the video data according to the preset visual algorithm to obtain the user's residence time in the target lighting space and the user's movement distance and movement time within a preset time threshold, wherein the preset visual algorithm includes a YOLO algorithm and a multi-target tracking algorithm;

[0081] To accurately identify and track users in video data and obtain their activity data, computer vision algorithms such as YOLO (You Only Look Once) for real-time object detection and multi-target tracking algorithms are used to track each user in the video. The YOLO algorithm quickly and accurately identifies people in the video, while the multi-target tracking algorithm continuously tracks the movement of these people.

[0082] YOLO is a popular object detection algorithm that can quickly identify different objects (such as people) in a video. The multi-target tracking algorithm DeepSORT (Deep Simple Online and Realtime Tracking) is an efficient multi-target tracking algorithm that combines traditional tracking algorithms with deep learning to provide an accurate and fast multi-target tracking solution. It is mainly used for real-time tracking of multiple objects in a video sequence in video streams.

[0083] Through YOLO and multi-target tracking algorithms, the user's residence time in the target lighting space and the user's moving distance and moving time within a preset time threshold can be obtained;

[0084] S23, obtaining the movement intensity according to the movement distance and the movement time;

[0085] This step quantifies the user's movement intensity. Movement intensity is a quantitative indicator to measure the user's activity level. It reflects the speed and activity of the user's movement. By analyzing the tracking data, the user's movement distance and movement time within the preset time threshold are calculated to obtain the movement intensity. Movement intensity can be calculated by dividing the movement distance by the movement time, thus providing a quantitative way to evaluate the user's activity level in the space, which is crucial for adjusting the lighting settings.

[0086] S24. Obtain the activity parameter according to the movement intensity and the dwell time.

[0087] Specifically, the calculated movement intensity is combined with the user's residence time in the space to obtain more comprehensive activity parameters. Activity parameters refer to data that comprehensively reflects the characteristics of users' activities in the space, including movement intensity and residence time. By analyzing these activity parameters, the lighting system can be adjusted more accurately to meet the actual needs of users, thereby improving lighting effects and user experience.

[0088] S3. Acquire lighting demand data according to the environmental parameters and the activity parameters;

[0089] This step combines the environment and user activity data to determine the current lighting demand, and comprehensively analyzes the environmental parameters (such as light intensity, temperature and humidity) and user activity parameters (such as movement intensity and residence time) to obtain the current lighting demand data. The lighting demand data is a key information used to guide the intelligent lighting system on how to adjust its output to meet the needs of specific environments and users. It is based on the comprehensive results of environmental parameters and user activity parameters, and aims to create a lighting environment that is both comfortable and efficient.

[0090] Specifically, the lighting demand data can be analyzed by integrating the data of environmental parameters and activity parameters, including the intensity of user activities, the usage pattern of the space, and the current light level using specific algorithms. For example, the brightness can be increased in areas with low light and frequent user activities, or reduced in areas with sufficient light but no one is using them.

[0091] As an optional embodiment of the present invention, see Figure 3 The step of obtaining the lighting demand data according to the environmental parameters and the activity parameters comprises:

[0092] S31. Acquire the environmental comfort of the target lighting space according to the temperature and humidity parameters;

[0093] This step evaluates the environmental comfort of the target lighting space through temperature and humidity parameters. The temperature and humidity data can be compared with the comfort standard to determine whether the environment is within the comfortable range. Environmental comfort has an important impact on human feelings and behaviors, and it can affect people's perception of lighting needs;

[0094] As an optional embodiment of the present invention, the temperature and humidity parameters include the ambient temperature and relative humidity of the target lighting space, and the step of obtaining the environmental comfort of the target lighting space according to the temperature and humidity parameters includes:

[0095] S311. Obtain the environmental comfort of the target lighting space according to the ambient temperature, the relative humidity and a preset environmental comfort calculation formula, wherein the preset environmental comfort calculation formula is as follows:

[0096] Eclim=μ*T+(1-μ)*H

[0097] In the formula, Eclim is the environmental comfort, μ is the first preset weight, T is the ambient temperature, and H is the relative humidity.

[0098] Specifically, this step quantifies and evaluates the environmental comfort of the target lighting space, and uses the formula Eclim = μ*T + (1-μ) * H to calculate the environmental comfort. Here, T represents the ambient temperature, H represents the relative humidity, and μ is a preset weight used to balance the effects of temperature and humidity on comfort. Temperature has a significant impact on human comfort. Generally speaking, a suitable temperature range can make people feel more comfortable. Humidity also affects comfort. Too high or too low humidity may cause discomfort. The relative importance of temperature and humidity in the comfort calculation is determined by the first preset weight, which is used to balance the effects of temperature and humidity on the comfort index. Different spaces and usage scenarios may require different weight settings. According to the value of Eclim, the environmental comfort can be classified into three levels: unsuitable, suitable, and very suitable.

[0099] S32, acquiring the activity intensity of the target lighting space according to the activity parameter;

[0100] This step quantifies the activity intensity of the target lighting space through the activity parameters. The activity intensity reflects the user's activity level in the space, which directly affects the demand for lighting intensity and type. The activity intensity is determined by analyzing the user's movement intensity and residence time, such as the motion tracking and behavior analysis data described above.

[0101] As an optional embodiment of the present invention, the step of obtaining the activity intensity of the target lighting space according to the activity parameter includes:

[0102] S321, normalizing the movement intensity and the residence time according to a maximum and minimum value normalization method to obtain a target movement intensity and a target residence time;

[0103] In order to convert the mobility intensity and residence time into a unified measure for comparison and calculation, the data was first processed using the maximum-minimum normalization method. This method converts all data to a range of 0 to 1, so that different data types (mobility intensity and residence time) can be compared on the same scale. After normalization, the minimum value in the data becomes 0, the maximum value becomes 1, and other values ​​are adjusted according to their relative position between the minimum and maximum values. Normalization helps eliminate differences between different data types or measures, making comparisons and subsequent calculations more reasonable.

[0104] S322, calculating the activity intensity of the target lighting space according to the target movement intensity, the target residence time and a preset activity intensity calculation formula, wherein the preset activity intensity calculation formula is as follows:

[0105] Pact=λ*Mnorm+(1-λ)*Tnorm

[0106] Wherein, Pact is the activity intensity, Mnorm is the target movement intensity, Tnorm is the residence time, and λ is the second preset weight.

[0107] Specifically, in order to comprehensively consider the user's movement intensity and residence time, a comprehensive indicator representing the activity intensity is obtained, and the activity intensity is calculated using the formula Pact = λ*Mnorm+(1-λ)*Tnorm. Here, Mnorm is the normalized movement intensity, which indicates the user's activity level in the space, Tnorm is the normalized residence time, which reflects the user's stay or stillness in a certain area, and λ is a preset weight that determines the relative importance of movement intensity and residence time when calculating the activity intensity, and is used to balance the impact of movement intensity and residence time;

[0108] By accurately reflecting the user's activity status, ineffective or excessive lighting can be avoided, energy can be saved, and reasonable lighting settings can provide a more comfortable and healthy light environment based on the user's actual behavior in the space.

[0109] S33. Acquire lighting demand data of the target lighting space according to the environmental comfort, activity intensity and the lighting parameters.

[0110] This step comprehensively considers environmental comfort, activity intensity and current light levels to determine the most appropriate lighting requirements. Algorithms or rules can be used to comprehensively analyze these parameters and determine the optimal lighting requirements for the target lighting space. For example, if the environmental comfort is low and the activity intensity is high, it may be necessary to increase the light intensity to provide more energy and improve the environment. Specifically, the environmental comfort can be classified into unsuitable, suitable, and very suitable levels according to the environmental comfort and the preset comfort threshold. The activity intensity can be classified into three levels: low, medium, and high according to the value of the activity intensity and the preset activity intensity threshold. For example, if Eclim is less than the preset comfort threshold, it is considered that the environment is too cold or too dry, and the light needs to be increased. If Eclim is greater than the preset comfort threshold, it is considered that the environment is too hot or too humid, and the light may need to be reduced. If Pact is greater than the preset activity intensity threshold, it is considered that the area is frequently active and the light needs to be increased. If Pact is less than the preset activity intensity threshold, it is considered that the area is relatively static and the light can be reduced. The adjustment range is determined according to the gap between the current light intensity and the ideal light intensity. If the indicators of the environmental comfort and the activity intensity both point to increasing the light, a light increase operation is performed. If one of the two indicators points to reducing the light, and the other is within the ideal range, a fine-tuning operation is performed. If both indicators point to reducing the light, a light reduction operation is performed.

[0111] As an optional embodiment of the present invention, the lighting parameter includes lighting intensity, and the step of acquiring the lighting demand data of the target lighting space according to the environmental comfort, activity intensity and the lighting parameter includes:

[0112] S331. Calculate the lighting demand data of the target lighting space according to the environmental comfort, activity intensity and lighting parameters using a preset lighting demand calculation formula, wherein the preset lighting demand calculation formula is as follows:

[0113] Ereq=α*Eenv+β*Pact+γ*Eclim

[0114] Wherein, Ereq is the lighting demand data, Eenv is the lighting intensity, Pact is the activity intensity, Eclim is the environmental comfort, α is the first adjustment coefficient, β is the second adjustment coefficient, and γ is the third adjustment coefficient;

[0115] Specifically, in this embodiment, the lighting demand is calculated using a preset lighting demand calculation formula. The formula combines the ambient light intensity, user activity intensity, and environmental comfort, providing a comprehensive perspective for determining lighting needs. The coefficient can be adjusted according to different space types and usage scenarios to make the lighting settings more adaptable to the needs of specific environments.

[0116] The first adjustment coefficient α, i.e., the light intensity coefficient, can be set according to the purpose of the space and the availability of natural light. For example, for a space that relies on natural light, the value of α can be reduced.

[0117] The second adjustment factor β, i.e., the activity intensity factor, can be set according to the purpose of the space. If the space is often used for activity-intensive work or study, β can be set higher to ensure sufficient lighting;

[0118] The third adjustment coefficient γ, i.e., the environmental comfort coefficient, can be adjusted according to the user's sensitivity to temperature and humidity. For example, for a living space that pays more attention to temperature and humidity comfort, γ can be set higher.

[0119] In specific applications, the setting of these coefficients should be based on user feedback, environmental monitoring data and analysis of actual usage. Adjustment and optimization through experiments or field tests may be required to ensure that they accurately reflect the needs of different spaces and user groups.

[0120] S4. Acquire lighting adjustment parameters according to the lighting demand data, the current time, and a lighting control algorithm based on a human rhythm model, wherein the lighting adjustment parameters include brightness parameters and color temperature parameters;

[0121] This step aims to combine the calculated light demand data with the current time, and use a human rhythm-based model to determine the most suitable brightness and color temperature settings. By considering the response of the human body clock to light, the lighting is adjusted to improve human comfort and health. The human rhythm model usually refers to the model of the human body clock, which explains how the human body naturally regulates its physiological and behavioral processes. These physiological and behavioral processes follow a cycle of approximately 24 hours, which is called the circadian rhythm. The circadian rhythm is the body's internal clock system that controls a variety of physiological processes such as sleep-wake cycles, hormone secretion, and body temperature regulation. These rhythms are regulated by a small area in the brain (called the suprachiasmatic nucleus). Natural light and artificial light have a significant effect on the human body clock. Light is the main external factor in regulating biological rhythms, especially blue light, which is very important for regulating sleep-wake cycles;

[0122] Specifically, first, analyze the calculated lighting demand data, which may include an assessment of the type of light intensity and color temperature required for a specific area, taking into account the functionality of the space and the type of activity of the user, for example, reading and office areas may require higher light intensity and cooler color temperature; then, adjust the lighting settings according to the time of day (morning, noon, evening) to simulate the daily changes in natural light, for example, warm light may be used in the morning to simulate sunrise, while cooler light may be used in the evening to simulate sunset; then, adjust the lighting settings based on the human rhythm model, taking into account the body's natural response to light, such as melatonin secretion and alertness cycles, for example, reducing blue light exposure at night to promote melatonin production, which helps improve sleep quality; combining all of the above factors, use an algorithm to determine the optimal settings for the brightness (such as the number of lumens) and color temperature (such as the number of Kelvin) of the light source; finally, combining all of the above factors, use an algorithm to determine the optimal settings for the brightness and color temperature of the light source to ensure that the lighting settings meet the actual needs and biological clock of the user, thereby improving the overall user experience;

[0123] As an optional embodiment of the present invention, the step of obtaining the lighting adjustment parameters according to the lighting demand data, the current time and the lighting control algorithm based on the human body rhythm model includes:

[0124] S41, obtaining a reference data set, wherein the reference data set includes a plurality of reference data, each of which includes temperature and humidity parameters;

[0125] Specifically, we first collect multiple reference data including temperature and humidity parameters to provide basic data for building a lighting parameter prediction model. We collect data from different lighting environments, including temperature, humidity and other lighting-related environmental parameters. These data sets will help determine the ideal lighting settings under different environmental conditions.

[0126] S42. Constructing a lighting parameter prediction model according to the reference data set, wherein the lighting parameter prediction model is constructed based on a logistic regression algorithm, with the lighting demand data and the current time as input, and with brightness parameters and color temperature parameters as output;

[0127] This step builds a model based on the logistic regression algorithm to predict the ideal brightness and color temperature under specific lighting requirements and time. The reference data set is analyzed using the logistic regression algorithm to learn the relationship between lighting requirement data and time and brightness and color temperature. This model will be able to predict the best lighting settings under any given conditions.

[0128] Specifically, the step of constructing a lighting parameter prediction model according to the reference data set includes:

[0129] S421, preprocessing the reference data set according to a preset data preprocessing method to obtain a target data set;

[0130] Specifically, in order to convert the original reference data set into a format suitable for model training and ensure the quality and consistency of the data, a preset data preprocessing method, such as removing missing values, outlier processing, feature standardization or normalization, is applied to preprocess the reference data set to obtain a clean and formatted target data set suitable for further analysis and model training;

[0131] S422, dividing the target data set into a training set and a test set according to a preset ratio;

[0132] To divide the target dataset into training and test sets, so as to evaluate the performance and generalization ability of the model when training it, the dataset is divided according to the preset ratio. The setting of the preset ratio is very critical in the process of model training and testing, because it determines how much data is used to train the model and how much data is used to evaluate the generalization ability of the model. The correct ratio setting is very important to avoid overfitting.

[0133] The preset ratio can be 70% training set / 30% test set, which is the most common division ratio and is suitable for most cases, especially when the data set is large enough; the preset ratio can also be 80% training set / 20% test set: when the amount of data is larger, you can choose to set aside more data for training, thereby improving the learning ability of the model; the preset ratio can also be 60% training set / 40% test set: when the data set is relatively small, or a more rigorous evaluation of model performance is required, the proportion of the test set can be increased.

[0134] In practical applications, the best practice is to try different split ratios and observe the changes in model performance. Sometimes, cross-validation (such as K-fold cross-validation) can be used instead of a fixed split ratio, which divides the dataset into multiple small parts to repeatedly train and test the model, thereby more comprehensively evaluating the performance of the model.

[0135] S423, training the initial prediction model constructed based on the logistic regression algorithm according to the training set to obtain an intermediate prediction model;

[0136] In this step, the logistic regression algorithm is applied, and the model is trained using the training set. Features are selected from the training set as model input. These features should be key factors affecting lighting parameters (brightness and color temperature), such as ambient temperature and humidity, lighting demand data, current time, etc. Then, the logistic regression model is initialized, and the basic parameters of the model, such as regularization coefficient, learning rate, etc., are set. The prepared training data is input into the model, and the model weights are adjusted through an iterative process (such as gradient descent algorithm) to minimize the loss function. At the same time, performance indicators such as loss value and accuracy are monitored during the training process to ensure that the model learns correctly. After training and verification, the obtained model is the intermediate prediction model, which can make reasonable predictions on the training data.

[0137] S423. Adjust the intermediate prediction model according to the test set to obtain a lighting parameter prediction model.

[0138] In this step, the performance of the intermediate prediction model is evaluated using the test set, focusing on the performance of the model on unseen data. The test set data is input into the model, and the accuracy, error rate or other relevant performance indicators of the model on the test set are calculated; by comparing the performance indicators of the training set and the test set, the type and pattern of the error are analyzed. For example, high deviation may indicate underfitting, and high variance may indicate overfitting; then, according to the results of the previous step analysis, the model parameters are adjusted to improve its performance on the test set, such as adjusting the learning rate, regularization parameters or the complexity of the model; the model is retrained using the adjusted parameters, using the same training set data but applying updated model parameters, and the adjusted model is evaluated again using the test set to ensure that the improved model can achieve better results on the test set. Through the above steps, the final model obtained is the illumination parameter prediction model.

[0139] S43, inputting the lighting demand data and the current time into the lighting parameter prediction model to obtain predicted lighting control parameters;

[0140] Taking the current lighting demand data and time as input, the lighting parameter prediction model is used to obtain the predicted lighting settings. The lighting parameter prediction model based on logistic regression is more personalized. It takes into account specific data in a specific environment, such as the activity pattern of a specific user, the ambient light intensity, and the indoor temperature and humidity. By analyzing a large amount of data to optimize and personalize the lighting settings, the specific needs of users can be met more accurately. This model may be more suitable for complex or changing environments and can be adjusted in real time to respond to changes in the environment.

[0141] S44, controlling the light source to perform test lighting according to the predicted light control parameters, and obtaining test data and user feedback data;

[0142] Specifically, in order to evaluate the effectiveness of the prediction model in a controlled environment, first, a variety of light sources are set up in a laboratory or simulation environment with adjustable brightness and color temperature, and the parameters of these light sources are set according to the output of the prediction model. In each test scene, the light source is adjusted according to the prediction of the model, and the actual settings of the light source and environmental conditions are recorded. User feedback is collected through questionnaires, interviews or real-time observations, and users' subjective feelings and reactions to different lighting settings are collected. The impact of lighting settings on user comfort and satisfaction is analyzed, and user feedback under different lighting settings is compared. Through this process, it can be ensured that the lighting prediction model can accurately meet the actual needs of users and improve the performance of the intelligent lighting system and user satisfaction.

[0143] S45, acquiring the human rhythm model according to the test data and the feedback data, wherein the human rhythm model includes a brightness parameter acquisition function and a color temperature parameter acquisition function;

[0144] The color temperature parameter acquisition function is as follows:

[0145] Tcolor=f1(Ereq,Time)

[0146] The brightness parameter acquisition function is as follows:

[0147] Lbrightness=f2(Ereq,Time)

[0148] Wherein, Tcolor is the color temperature parameter, Lbrightness is the brightness parameter, Ereq is the lighting demand data, and Time is the current time;

[0149] Specifically, in order to extract valuable information from actual tests and user feedback to guide the establishment of the human rhythm model, first analyze the lighting settings (such as brightness and color temperature) collected during the test and the user's response to these settings, identify the user's preferred lighting mode at different time points and under different lighting needs, use the data analysis results to define the form of the f1 function, which may include linear or nonlinear relationships, to create a function that determines the optimal color temperature based on lighting needs and the current time, and determine the structure of the f2 function based on the test data and user feedback, considering how to best reflect the relationship between brightness preference and time and lighting needs;

[0150] By constructing the brightness parameter acquisition function and the color temperature parameter acquisition function, a human rhythm model can be obtained;

[0151] S46: Input the lighting demand data and the current time into the human rhythm model to obtain lighting adjustment parameters.

[0152] Finally, the lighting demand data and the current time are input into the human body rhythm model to obtain the lighting adjustment parameters. The human body rhythm model focuses more on simulating the natural biological rhythm of the human body, especially the rhythm related to the sleep-wake cycle. It is usually based on generalized human biological clock data and may take into account the physiological demand for light at different times of the day. The human body biological rhythm model is mainly used to simulate the impact of the natural environment on human rhythms, such as the changes in light at sunrise and sunset. Lighting control based on human body rhythms and actual data improves the intelligence and automation level of the lighting system.

[0153] In this solution, both the human biorhythm model and the prediction model based on logistic regression are used, although they both use the same input (time and light requirements) to predict color temperature and brightness. In fact, although these two models have similarities, their applications and focuses are different:

[0154] Specifically, if the biorhythm model is used alone, it only provides a baseline that simulates the changes in light in the natural environment, while the logistic regression model adds personalized adjustments to this baseline. The combination of the two can ensure that the lighting system not only meets the universal physiological needs of humans, but also adapts to the specific needs of individual users or specific environments;

[0155] That is, the biorhythm model may not be sufficient to address all individual differences or the needs of a specific environment. The logistic regression model provides greater flexibility and can make more precise adjustments based on real-time data. In complex or changing environments, the logistic regression model based on real-time data may be more effective because it can reflect and adapt to environmental changes in a timely manner. In summary, although the two models overlap in terms of input and goals, their focus and role in practical applications are different. Combining the two models can provide a more comprehensive, flexible and personalized lighting solution.

[0156] S5. Control the light source of the target lighting space to illuminate according to the lighting adjustment parameter.

[0157] Finally, the light source is actually adjusted according to the lighting adjustment parameters to meet the lighting parameters obtained through analysis, so as to create the lighting conditions that best suit the current environment and user needs, provide the best visual experience and physiological comfort, and improve energy efficiency and reduce energy consumption.

[0158] As an optional embodiment of the present invention, the target lighting space is a classroom, and the lighting adjustment parameters further include blue light parameters, and the blue light parameters include blue light wavelength and blue light intensity;

[0159] Specifically, in the present embodiment, the target lighting space is a classroom. In a lighting space such as a classroom, blue light parameters are included, and the blue light parameters include blue light wavelength and blue light intensity. Blue light has a significant effect on the human body's biological clock. In a teaching environment, a suitable blue light intensity can help regulate students' biorhythms and improve their attention and vitality in class. For example, stronger blue light (especially long-wave blue light) can stimulate vitality and increase alertness in the morning; appropriate blue light intensity helps improve cognitive function and learning efficiency. In teaching activities that require high concentration (such as exams), appropriately increasing blue light intensity may help improve students' performance; long-term exposure to high-intensity short-wave blue light may have adverse effects on the eyes, especially for young students. Therefore, it is very important to control the wavelength and intensity of blue light to ensure that they are within a safe and healthy range;

[0160] In non-classroom scenarios, such as offices and homes, blue light parameters can be set to short-wave blue light and low intensity by default to help reduce the risk of eye fatigue and disrupting sleep cycles;

[0161] In this embodiment, the step of controlling the light source of the target lighting space to illuminate according to the lighting adjustment parameter includes:

[0162] S51, obtaining a time schedule corresponding to the classroom, wherein the time schedule includes: a plurality of teaching activity information and corresponding time periods, the teaching activity information includes teaching activity types, the teaching activity types include courses, breaks, exams, and special activities, and the courses include theoretical courses, experimental courses, and computer courses;

[0163] Specifically, to ensure that the lighting system can anticipate and adapt to upcoming teaching activities and thus adjust the lighting settings in advance, this step collects the classroom schedule, including various teaching activities and their time periods;

[0164] To ensure that the lighting settings can be precisely adapted to the needs of various teaching environments. Different types of teaching activities have significantly different lighting requirements, so distinguishing these types of activities is crucial to achieve the best lighting effect.

[0165] The courses include theoretical courses, experimental courses and computer courses. Theoretical courses usually require uniform and soft lighting to reduce eye fatigue and promote learning concentration. Experimental courses require higher brightness to ensure the accuracy of experimental operations and may require specific color temperatures to reduce interference with experimental observations. Computer courses require lowering the brightness and adjusting the blue light intensity to reduce screen reflections and relieve eye fatigue.

[0166] During breaks, lowering the brightness or adjusting the color temperature can provide a relaxing environment for students, helping them to recover before the next class. During exams, higher brightness and reduced shadows are usually required so that students can see the test papers clearly and concentrate. For special events held by the school, such as assemblies or celebrations, special lighting settings may be required to create the right atmosphere.

[0167] By identifying and adjusting lighting settings according to these different types of teaching activities, intelligent lighting systems can not only improve students' learning efficiency and comfort, but also enhance teachers' teaching experience, while effectively saving energy.

[0168] S52, querying the time schedule according to the current time to obtain teaching activity information corresponding to the current time;

[0169] Specifically, this step extracts relevant teaching activity information from the timetable according to the current time, so that the lighting system can respond in real time and adjust the lighting according to the current activity.

[0170] S53, according to the type of the teaching activity, obtaining the parameter adjustment coefficient of the illumination adjustment parameter, wherein the parameter adjustment coefficient includes a color temperature adjustment coefficient, a brightness adjustment coefficient and a blue light adjustment coefficient, and the color temperature adjustment coefficient and / or brightness adjustment coefficient and / or blue light parameter of the parameter adjustment coefficient corresponding to different teaching activity types are different;

[0171] This step determines the adjustment coefficients of color temperature, brightness and blue light parameters according to the type of teaching activities (such as theoretical courses, experimental courses, etc.), and realizes customized lighting settings for specific teaching activities to improve teaching and learning effects.

[0172] In a specific embodiment, according to the teaching activity type, obtaining the parameter adjustment coefficient of the illumination adjustment parameter can be achieved through an activity type-adjustment coefficient mapping table. To construct the above mapping table, it is first necessary to define the illumination requirements;

[0173] The lighting requirements for various types of teaching activities are as follows:

[0174] Theoretical course: Uniform and soft lighting is required.

[0175] Experimental courses: High brightness and appropriate color temperature are required to ensure accurate visual observation.

[0176] Computer courses: Brightness and blue light intensity need to be reduced to reduce screen reflections and visual fatigue.

[0177] Recess: Medium brightness and warm color temperature are required to create a relaxing environment.

[0178] Examination: Requires high brightness and neutral color temperature to improve concentration and reduce shadows.

[0179] Special events: Depending on the nature of the event, you may need adjustable color temperature and brightness, or use long-wave blue light with high intensity to stimulate vitality.

[0180] When defining these lighting requirements, it is necessary to take into account the user's comfort, the nature of the activity, and the impact of light on human biorhythms. The right lighting settings can improve students' learning efficiency, reduce eyestrain, and create a suitable learning environment.

[0181] According to the above lighting requirements, the color temperature adjustment coefficient, brightness adjustment coefficient and blue light adjustment coefficient are determined. The color temperature adjustment coefficient is determined according to the color temperature range (warm light, neutral light or cold light) required by the activity, and the brightness adjustment coefficient is determined according to the brightness level required by the activity. For example, the brightness required for experimental courses may be higher than that for theoretical courses. The blue light parameters are adjusted according to the needs for students' attention and visual comfort. For example, the blue light intensity is reduced in computer courses.

[0182] After clarifying the lighting requirements and establishing an activity type-adjustment coefficient mapping table, the activity type-adjustment coefficient mapping table is shown in Table 1 below, which is an exemplary table of the activity type-adjustment coefficient mapping table:

[0183] Table 1 Example of activity type-adjustment coefficient mapping table

[0184]

[0185]

[0186] In practical applications, these coefficients will be used to adjust the basic lighting settings derived from the human rhythm model. For example, if the basic settings recommend medium brightness and neutral color temperature, then for the "experimental course", the lighting system will be adjusted to a higher brightness and maintain a neutral color temperature. In this way, the lighting system can more accurately adapt to the specific needs of different teaching activities. It should be noted that the above adjustment coefficients are only examples and not limitations.

[0187] S54, adjusting the illumination adjustment parameter according to the parameter adjustment coefficient to obtain a target illumination parameter;

[0188] Specifically, for each type of teaching activity, the corresponding color temperature adjustment coefficient, brightness adjustment coefficient and blue light adjustment coefficient are applied to adjust the lighting control parameters obtained by the human rhythm model. The adjustment coefficient will increase or decrease the value of the lighting control parameter obtained by the human rhythm model according to the needs of the specific activity.

[0189] This light adjustment method ensures that the lighting system in the classroom can flexibly adapt to different teaching needs, providing students and teachers with the best learning and teaching environment.

[0190] S55, obtaining a lighting adjustment rate according to the current target lighting parameter, the historical lighting adjustment parameter of the previous time period, the current teaching activity type and the corresponding time period;

[0191] Specifically, this step obtains the current target illumination parameters and the illumination parameters of the previous time period, determines the difference between the target illumination parameters and the illumination parameters of the previous time period, and calculates the adjustment rate required to achieve the target illumination state based on the activity type and time period, as well as the illumination change amount;

[0192] Different teaching activities have different lighting requirements. By adjusting the lighting rate to meet these needs, the efficiency and comfort of teaching activities can be improved. Specifically, smoothly adjusting the lighting can avoid sudden changes, thereby reducing interference to students and teachers. Especially in an environment that requires concentration, the appropriate adjustment rate helps maintain visual comfort in the classroom and avoid visual discomfort caused by too fast lighting changes.

[0193] Specifically, the light adjustment rate is calculated by the following formula:

[0194]

[0195] Where R is the light regulation rate, W B , W C and W G They represent the weights of brightness parameter, color temperature brightness and blue light parameter in the light adjustment rate, ΔB, ΔC and ΔG are the change rates of the brightness parameter, color temperature brightness and blue light parameter, respectively. activity is the length of the current teaching activity time period, p is the adjustment time window factor, F activity is a rate adjustment factor obtained based on the type of teaching activity;

[0196] Among them, W B , W C and W G Respectively represent the weights of brightness parameter, color temperature brightness and blue light parameter in the lighting adjustment rate. The weight setting should be based on the importance of each lighting parameter on the teaching activities. For example, for experimental courses that require high visual accuracy, brightness may be more important than color temperature or blue light. This setting can ensure that the lighting adjustment is more refined and focused on the most critical lighting parameters, improving the pertinence and effectiveness of the lighting effect;

[0197] The function of the activity type adjustment factor Factivity is to adjust the overall rate of lighting regulation according to the characteristics of different teaching activities to ensure that the lighting changes match the nature of the activity. It is set according to the different sensitivity and needs of different activity types to lighting. Its value indicates the sensitivity of a specific teaching activity to lighting conditions. A higher value means that the activity is more sensitive to lighting changes or requires more specific lighting conditions.

[0198] For some teaching activities, such as exams or special demonstrations, it may be necessary to achieve ideal lighting conditions more quickly to minimize distractions and provide necessary visual support. activity May be set higher to speed up the adjustment rate, such as 1.2.

[0199] For regular teaching activities or breaks, light adjustment can be more gentle and gradual. activity It will be set lower to reduce the adjustment rate such as 0.8;

[0200] The adjustment time window factor is set based on the urgency of the activity and the sensitivity to light changes. Rapid changes may be suitable for short and urgent activities, while slower changes are suitable for long activities. For example, if you want to complete the adjustment within 1 / 4 of the time before the start of the activity, then p = 1 / 4. By controlling the adjustment rate, you can avoid the interference of sudden light changes on teaching activities, while providing enough time to adapt to the new lighting environment.

[0201] In summary, by determining the lighting adjustment rate by considering current and historical lighting parameters and the characteristics of teaching activities, this setting method not only improves the intelligence and adaptability of the classroom lighting system, but also helps to create an environment more suitable for learning and teaching.

[0202] S56: Control the light source to perform illumination according to the target illumination parameter and the illumination adjustment rate.

[0203] Finally, according to the calculated target lighting parameters and adjustment rate, the light source is controlled for lighting, achieving smooth and precise lighting adjustment to provide a continuous and comfortable teaching environment. The lighting can be dynamically adjusted according to the actual usage and needs of the classroom to create an optimal teaching and learning environment. This system not only improves the teaching quality and learning efficiency, but also improves the comfort of students and teachers.

[0204] Example 2

[0205] See also Figure 4 The embodiment of the present invention provides a healthy lighting control device for dynamically adjusting the lighting environment of a classroom, the device comprising:

[0206] An environmental parameter acquisition module, used to acquire environmental parameters of a target lighting space, wherein the environmental parameters include illumination parameters and temperature and humidity parameters of the target lighting space;

[0207] An activity parameter acquisition module, used to acquire activity parameters of the user in the target lighting space according to a preset visual algorithm, wherein the activity parameters include the movement intensity and residence time of the user in the target lighting space;

[0208] A lighting demand acquisition module, used to acquire lighting demand data according to the environmental parameters and the activity parameters;

[0209] An adjustment parameter acquisition module is used to acquire the lighting adjustment parameters according to the lighting demand data, the current time and the lighting control algorithm based on the human rhythm model, wherein the lighting adjustment parameters include brightness parameters and color temperature parameters;

[0210] The lighting control module is used to control the light source of the target lighting space to illuminate according to the lighting adjustment parameters.

[0211] It should be noted that the modules and units in the healthy lighting control device for dynamically adjusting the classroom lighting environment in this embodiment correspond one-to-one to the steps in the healthy lighting control method for dynamically adjusting the classroom lighting environment in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned healthy lighting control method for dynamically adjusting the classroom lighting environment, which will not be repeated here.

[0212] Example 3

[0213] In addition, combined Figure 1 The healthy lighting control method for dynamically adjusting the classroom lighting environment in the described embodiment of the present invention can be implemented by an electronic device. Figure 5 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention is shown.

[0214] The electronic device may include a processor and a memory storing computer program instructions.

[0215] Specifically, the processor may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present invention.

[0216] The memory may include a large capacity memory for data or instructions. By way of example and not limitation, the memory may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory may include a removable or non-removable (or fixed) medium. Where appropriate, the memory may be inside or outside a data processing device. In a particular embodiment, the memory is a non-volatile solid-state memory. In a particular embodiment, the memory includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM) or a flash memory or a combination of two or more of these.

[0217] The processor reads and executes computer program instructions stored in the memory to implement any one of the healthy lighting control methods for dynamically adjusting the classroom lighting environment in the above embodiments.

[0218] In one example, the electronic device may further include a communication interface and a bus. Figure 5 As shown, the processor 401, the memory 402, and the communication interface 403 are connected via a bus 410 and communicate with each other.

[0219] The communication interface is mainly used to implement communication between the modules, devices, units and / or equipment in the embodiments of the present invention.

[0220] Bus includes hardware, software or both, and the parts of electronic equipment are coupled to each other.For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In suitable cases, bus may include one or more buses. Although the embodiment of the present invention describes and shows a specific bus, the present invention considers any suitable bus or interconnection.

[0221] Example 4

[0222] In addition, in combination with the healthy lighting control method for dynamically adjusting the classroom lighting environment in the above embodiment, the embodiment of the present invention can provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the healthy lighting control methods for dynamically adjusting the classroom lighting environment in the above embodiment is implemented.

[0223] In summary, the healthy lighting control method, device and equipment for dynamically adjusting the classroom lighting environment provided by the embodiments of the present invention obtains the environmental parameters of the target lighting space, wherein the environmental parameters include the lighting parameters and temperature and humidity parameters of the target lighting space, and by understanding the current environmental conditions, it can ensure that the adjustment of the lighting system is adapted to the actual environment, thereby improving the efficiency and comfort of the lighting; according to a preset visual algorithm, the activity parameters of the user in the target lighting space are obtained, wherein the activity parameters include the movement intensity and residence time of the user in the target lighting space, and the lighting can be adjusted according to the actual activities of the user in the space to achieve personalized lighting settings, which not only increases comfort, but also may improve the productivity and safety of the user; according to the environmental parameters and the activity parameters, the lighting demand data is obtained to ensure that the lighting settings not only meet the environmental conditions, but also meet the actual conditions of the user. demand, thereby improving energy efficiency and user satisfaction; obtaining lighting adjustment parameters according to the lighting demand data, the current time and a lighting control algorithm based on a human rhythm model, wherein the lighting adjustment parameters include brightness parameters and color temperature parameters, and determining the optimal settings of brightness and color temperature by comprehensively analyzing the lighting demand, the current time and the human biorhythm model, helps to create a lighting environment synchronized with the natural rhythm of the human body, which can improve sleep quality, reduce eyestrain, and even enhance overall health and mood; controlling the light source of the target lighting space for lighting according to the lighting adjustment parameters, and finally adjusting the brightness and color temperature of the light source to meet the optimal lighting parameters obtained from the above analysis, and by precisely controlling the lighting parameters, creating the lighting conditions that best suit the current environment and user needs, thereby improving energy efficiency, reducing energy consumption, and providing the best visual experience and physiological comfort.

[0224] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0225] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0226] It should also be noted that the exemplary embodiments mentioned in the present invention describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiments, or in a different order from the embodiments, or several steps can be performed simultaneously.

[0227] The above is only a specific implementation of the present invention. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the system, module and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be covered within the protection scope of the present invention.

Claims

1. A healthy lighting control method for dynamically adjusting the classroom lighting environment, characterized in that: The method comprises: Obtaining a corresponding time schedule for a classroom as the target lighting space, wherein the time schedule includes: a plurality of teaching activity information and corresponding time periods, the teaching activity information includes teaching activity types, the teaching activity types include courses, breaks, exams, and special activities, and the courses include theoretical courses, experimental courses, and computer courses; Query the time schedule according to the current time to obtain the teaching activity information corresponding to the current time; Through the activity type-adjustment coefficient mapping table, according to the teaching activity type, the parameter adjustment coefficient of the lighting adjustment parameter is obtained, wherein the lighting adjustment parameter is obtained according to the lighting demand data, the current time and the lighting control algorithm based on the human rhythm model, and the parameter adjustment coefficient includes a color temperature adjustment coefficient, a brightness adjustment coefficient and a blue light adjustment coefficient. The color temperature adjustment coefficient and / or brightness adjustment coefficient and / or blue light parameter of the parameter adjustment coefficient corresponding to different teaching activity types are different; For each type of teaching activity, the corresponding color temperature adjustment coefficient, brightness adjustment coefficient and blue light adjustment coefficient are applied to adjust the lighting control parameters. The adjustment coefficient increases or decreases the value of the lighting control parameter obtained by the human rhythm model according to the needs of the specific activity to obtain the target lighting parameters. Obtain the lighting adjustment rate according to the current target lighting parameters, the historical lighting adjustment parameters of the previous time period, the current teaching activity type and the corresponding time period; The light source is controlled to perform illumination according to the target illumination parameter and the illumination adjustment rate.

2. The healthy lighting control method for dynamically adjusting the classroom lighting environment according to claim 1 is characterized in that: Before obtaining the corresponding time schedule for the target lighting space being a classroom, the method further includes: Acquire environmental parameters of the target lighting space, wherein the environmental parameters include illumination parameters and temperature and humidity parameters of the target lighting space, the illumination parameters are acquired by a light sensor, and the temperature and humidity parameters are acquired by a temperature sensor and a humidity sensor; According to a preset visual algorithm, obtaining activity parameters of the user in the target lighting space, wherein the activity parameters include the movement intensity and residence time of the user in the target lighting space, and the preset visual algorithm includes but is not limited to an object tracking algorithm, a human posture recognition algorithm, a deep learning algorithm, and a behavior pattern recognition algorithm; Comprehensively analyze environmental parameters and user activity parameters to obtain current lighting demand data; Acquire lighting adjustment parameters according to the lighting demand data, the current time and a lighting control algorithm based on a human rhythm model, wherein the lighting adjustment parameters include brightness parameters and color temperature parameters; According to the illumination adjustment parameters, the light source of the target illumination space is controlled to perform illumination.

3. The healthy lighting control method for dynamically adjusting the classroom lighting environment according to claim 2 is characterized in that: The obtaining of the activity parameters of the user in the target lighting space according to the preset visual algorithm includes: Acquiring video data of the target lighting space; Tracking the user in the video data according to the preset visual algorithm to obtain the user's residence time in the target lighting space and the user's movement distance and movement time within a preset time threshold, wherein the preset visual algorithm includes a YOLO algorithm and a multi-target tracking algorithm; Calculate the user's moving distance and moving time within a preset time threshold to obtain the movement intensity; The calculated movement intensity is combined with the user's residence time in the space to obtain activity parameters, which refer to data that comprehensively reflects the user's activity characteristics in the space, including movement intensity and residence time.

4. The healthy lighting control method for dynamically adjusting the classroom lighting environment according to claim 3 is characterized in that: The step of acquiring lighting demand data according to the environmental parameters and the activity parameters comprises: According to the temperature and humidity parameters, obtaining the environmental comfort of the target lighting space; According to the activity parameter, obtaining the activity intensity of the target lighting space; According to the environmental comfort, activity intensity and the lighting parameters, the lighting demand data of the target lighting space is calculated by a preset lighting demand calculation formula, wherein the preset lighting demand calculation formula is as follows: Ereq=α*Eenv+β*Pact+γ*Eclim Wherein, Ereq is the lighting demand data, Eenv is the lighting intensity, Pact is the activity intensity, Eclim is the environmental comfort, α is the first adjustment coefficient, β is the second adjustment coefficient, and γ is the third adjustment coefficient.

5. The healthy lighting control method for dynamically adjusting the classroom lighting environment according to claim 4 is characterized in that: The temperature and humidity parameters include the ambient temperature and relative humidity of the target lighting space. The step of obtaining the environmental comfort of the target lighting space according to the temperature and humidity parameters includes: The environmental comfort of the target lighting space is obtained according to the ambient temperature, the relative humidity and a preset environmental comfort calculation formula, wherein the preset environmental comfort calculation formula is as follows: Eclim=μ*T+(1-μ)*H In the formula, Eclim is the environmental comfort, μ is the first preset weight, T is the ambient temperature, and H is the relative humidity.

6. The healthy lighting control method for dynamically adjusting the classroom lighting environment according to claim 4 is characterized in that: The step of obtaining the activity intensity of the target lighting space according to the activity parameter comprises: Normalizing the movement intensity and the residence time according to a maximum and minimum value normalization method to obtain a target movement intensity and a target residence time; The activity intensity of the target lighting space is calculated according to the target movement intensity, the target residence time and a preset activity intensity calculation formula, wherein the preset activity intensity calculation formula is as follows: Pact=λ*Mnorm+(1-λ)*Tnorm Wherein, Pact is the activity intensity, Mnorm is the target movement intensity, Tnorm is the residence time, and λ is the second preset weight.

7. The healthy lighting control method for dynamically adjusting the classroom lighting environment according to claim 6 is characterized in that: The step of obtaining the lighting adjustment parameters according to the lighting demand data, the current time and the lighting control algorithm based on the human body rhythm model comprises: Acquire a reference data set, wherein the reference data set includes a plurality of reference data, each of the reference data includes temperature and humidity parameters; According to the reference data set, construct a lighting parameter prediction model, wherein the lighting parameter prediction model is constructed based on a logistic regression algorithm, takes the lighting demand data and the current time as input, and takes brightness parameters and color temperature parameters as output; Inputting the lighting demand data and the current time into the lighting parameter prediction model to obtain predicted lighting control parameters; Control the light source to perform test lighting according to the predicted light control parameters to obtain test data and user feedback data; According to the test data and the feedback data, the human body rhythm model is acquired, wherein the human body rhythm model includes a brightness parameter acquisition function and a color temperature parameter acquisition function; The color temperature parameter acquisition function is as follows: Tcolor=f1(Ereq,Time) The brightness parameter acquisition function is as follows: Lbrightness=f2(Ereq,Time) Wherein, Tcolor is the color temperature parameter, Lbrightness is the brightness parameter, Ereq is the lighting demand data, and Time is the current time; The lighting demand data and the current time are input into the human rhythm model to obtain lighting adjustment parameters.

8. A healthy lighting control device for dynamically adjusting the classroom lighting environment, characterized in that: The device comprises: A time schedule acquisition module is used to acquire a corresponding time schedule when the target lighting space is a classroom, wherein the time schedule includes: a plurality of teaching activity information and corresponding time periods, the teaching activity information includes teaching activity types, the teaching activity types include courses, breaks, exams, and special activities, and the courses include theoretical courses, experimental courses, and computer courses; A teaching activity information query module, used to query the time schedule according to the current time and obtain the teaching activity information corresponding to the current time; A parameter adjustment coefficient determination module, used to obtain the parameter adjustment coefficient of the illumination adjustment parameter according to the teaching activity type through the activity type-adjustment coefficient mapping table, wherein the illumination adjustment parameter is obtained according to the illumination demand data, the current time and the illumination control algorithm based on the human rhythm model, and the parameter adjustment coefficient includes a color temperature adjustment coefficient, a brightness adjustment coefficient and a blue light adjustment coefficient, and the color temperature adjustment coefficient and / or brightness adjustment coefficient and / or blue light parameter of the parameter adjustment coefficient corresponding to different teaching activity types are different; The lighting control parameter adjustment module is used to adjust the lighting control parameters by applying the corresponding color temperature adjustment coefficient, brightness adjustment coefficient and blue light adjustment coefficient for each type of teaching activity. The adjustment coefficient increases or decreases the value of the lighting control parameter obtained by the human rhythm model according to the needs of the specific activity to obtain the target lighting parameter; The light adjustment rate acquisition module is used to obtain the light adjustment rate according to the current target light parameters, the historical light adjustment parameters of the previous time period, the current teaching activity type and the corresponding time period; The lighting control module is used to control the light source to perform lighting according to the target lighting parameters and the lighting adjustment rate.

9. An electronic device, characterized in that: include: At least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, implement the method according to any one of claims 1 to 7.

10. A storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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