Rhythm lighting control method and system

Abstract

The invention relates to the technical field of LED lighting, in particular to a rhythm lighting control method and system. The method comprises the following steps: acquiring operation behavior data of a user on a wall switch of an illumination system, wherein the operation behavior data comprises lamp turning-on and turning-off time and illumination parameter adjustment information; based on the operation behavior data, adaptive learning processing is executed, and user illumination habit feature data is generated; according to the illumination habit characteristic data of the user, a personalized rhythm illumination light formula curve is constructed, and the personalized rhythm illumination light formula curve comprises color temperature and brightness parameters corresponding to different time points; and according to the personalized rhythm illumination light formula curve, an illumination system is controlled to carry out illumination adjustment according to the color temperature and brightness parameters of the corresponding time points. The personalized rhythm illumination light formula curve can be adjusted based on the environment illumination intensity data and the environment temperature data, the rhythm illumination use threshold is lowered, non-inductive personalized configuration is achieved, the user privacy is effectively protected, the illumination system upgrading cost is lowered, and the user sleep quality is improved through a natural awakening and sleep mode.

Description

Technical Field

[0001] The present invention relates to the technical field of LED lighting, and in particular to a rhythmic lighting control method and system thereof. Background Art

[0002] Rhythmic lighting is an intelligent lighting technology based on the human circadian rhythm. By dynamically adjusting the color temperature and brightness of LED light sources, it simulates the changes in natural light and creates a healthy lighting environment that suits the human body's physiological needs. With people's increasing emphasis on healthy living, rhythmic lighting has become a key development trend in LED lighting.

[0003] Currently, the most common way to implement rhythmic lighting is to pre-set the time and color temperature and brightness parameters corresponding to each time period in the LED lighting. Users need to use an app or WeChat mini-program on their smartphone to set these parameters. This approach has several problems: first, it is complex to operate and has a high barrier to entry, especially for the elderly and children; second, it is difficult for users to understand and set appropriate color temperature and brightness variation parameters; third, the preset typical rhythmic parameters are difficult to adapt to the different rhythms of different users; in addition, rhythmic lighting control achieved through mobile apps may lead to the leakage of user privacy data; finally, rhythmic lighting fixtures usually require Bluetooth or WiFi communication capabilities, which makes upgrade costs high. Summary of the Invention

[0004] The purpose of the present invention is to provide a rhythmic lighting control method and system, aiming to solve the problems of complex operation, low personalization, high privacy risk and high upgrade cost in the existing technology.

[0005] The present invention proposes a rhythmic lighting control method, comprising:

[0006] Acquire user operation behavior data on a lighting system wall switch, the operation behavior data including light on / off time and lighting parameter adjustment information;

[0007] Based on the operation behavior data, performing adaptive learning processing to generate user lighting habit feature data;

[0008] Constructing a personalized rhythmic lighting light recipe curve based on the user's lighting habit characteristic data, wherein the personalized rhythmic lighting light recipe curve includes color temperature and brightness parameters corresponding to different time points; and

[0009] According to the personalized rhythmic lighting light recipe curve, the lighting system is controlled to perform lighting adjustment according to the color temperature and brightness parameters at the corresponding time point.

[0010] Preferably, the obtaining of the user's operation behavior data on the lighting system wall switch includes:

[0011] Record the time when the user turns on and off the light;

[0012] Record the user's adjustment values ​​for the color temperature and brightness of the lighting system; and

[0013] Record the user's operation frequency within a specific time period.

[0014] Preferably, the performing of the adaptive learning process comprises:

[0015] Use each time a user turns on the light as the starting point of the learning cycle to establish a 24-hour dynamic learning window;

[0016] Record and analyze user operation behavior data within the preset learning cycle; and

[0017] The user's lighting parameter preferences in different time periods are extracted to form user lighting habit feature data.

[0018] Preferably, the preset learning period is 7 days, including:

[0019] The first three days serve as the initial learning phase to establish a basic rhythm model; and

[0020] The last 4 days served as the optimization phase to fine-tune the basic rhythm model.

[0021] Preferably, the process of constructing a personalized rhythmic lighting light recipe curve includes:

[0022] Generate a reference brightness curve, wherein the reference brightness curve represents a brightness change trend within 24 hours;

[0023] Establishing a dynamic color temperature mapping table, wherein the dynamic color temperature mapping table represents color temperature values ​​corresponding to different time points; and

[0024] A sensitive period is identified, where the sensitive period is a time period during which users frequently adjust lighting parameters.

[0025] Preferably, the controlling the lighting system to adjust the lighting according to the color temperature and brightness parameters at the corresponding time point includes:

[0026] In a preset time period before detecting that the user is about to turn on the lights, the natural wake-up mode is activated, gradually increasing the brightness and adjusting the color temperature to the user's preferred value; and

[0027] When it is detected that the user is approaching the preset time period before turning off the lights, the natural sleep mode is activated, gradually reducing the brightness and adjusting the color temperature to a low color temperature value suitable for sleep.

[0028] Preferably, the preset time period is 15 to 30 minutes.

[0029] As an option, it also includes:

[0030] Get ambient light intensity data;

[0031] Adjusting the brightness parameters in the personalized rhythmic lighting recipe curve based on the ambient light intensity data;

[0032] Obtaining ambient temperature data; and

[0033] Based on the ambient temperature data, the color temperature parameters in the personalized rhythmic lighting light recipe curve are adjusted.

[0034] As an option, it also includes:

[0035] Detect the user's real-time operation of the wall switch;

[0036] In response to detecting a real-time operation by a user, temporarily replacing control of the personalized rhythmic lighting recipe curve; and

[0037] The real-time operation data is fed back into the adaptive learning process to update the personalized rhythmic lighting light recipe curve.

[0038] A rhythmic lighting control system, comprising:

[0039] A wall switch, for receiving a user's lighting control operation;

[0040] a rhythm control module, electrically connected to the wall switch, configured to obtain user operation behavior data of the wall switch, perform adaptive learning processing to generate user lighting habit feature data, and construct a personalized rhythmic lighting light recipe curve based on the user lighting habit feature data;

[0041] a power supply and driving module, electrically connected to the rhythm control module, and configured to receive a control signal sent by the rhythm control module; and

[0042] an LED light source, electrically connected to the power supply and the driving module, for adjusting color temperature and brightness parameters according to the control signal;

[0043] The rhythm control module is further configured to control the LED light source to adjust the lighting according to the color temperature and brightness parameters at the corresponding time point based on the personalized rhythm lighting light formula curve.

[0044] The beneficial effects of the present invention are:

[0045] 1. It lowers the threshold for using rhythmic lighting, especially for users such as the elderly and children who are not convenient to use smartphones;

[0046] 2. It realizes non-intuitive personalized configuration. Users do not need to understand complex rhythmic lighting parameters. The system automatically learns user habits and generates personalized rhythmic lighting configurations.

[0047] 3. Effectively protect user privacy, all data is processed locally to prevent the leakage of personal schedule data;

[0048] 4. Significantly reduces the cost of upgrading from traditional lighting to rhythmic lighting, without the need to replace lamps or add communication modules;

[0049] 5. Improve users' sleep quality and quality of life through natural wake-up and sleep patterns. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a structural diagram of a rhythmic lighting control system of the present invention;

[0051] Figure 2 It is a flow chart of a rhythmic lighting control method of the present invention;

[0052] Figure 3 is a schematic flow chart of the adaptive learning process of the present invention;

[0053] Figure 4 This is an example diagram of the personalized rhythmic lighting light recipe curve of the present invention. DETAILED DESCRIPTION

[0054] Please refer to the attached Figure 1-4 , the present invention is further described in detail below with reference to the accompanying drawings.

[0055] like Figure 1 As shown, the rhythmic lighting control system of the present invention includes: a wall switch 1, a rhythmic control module 2, a power supply and drive module 3, an LED light source 4, and an optional environmental sensor 5.

[0056] The wall switch 1 is installed on the wall and is an ordinary lighting control switch used to receive user lighting control operations, such as turning on and off the lights, adjusting the color temperature and brightness, etc. The rhythm control module 2 is electrically connected to the wall switch 1 and is used to obtain the user's operating behavior data through the wall switch 1, perform adaptive learning processing and generate user lighting habit feature data, and then construct a personalized rhythm lighting light formula curve. The power supply and drive module 3 is electrically connected to the rhythm control module 2, receives the control signal sent by the rhythm control module 2, provides power to the system and drives the LED light source 4. The LED light source 4 is electrically connected to the power supply and drive module 3, adjusts the color temperature and brightness parameters according to the control signal, and is composed of multiple LED lamp beads, which can be in the form of a light panel or a light strip. The environmental sensor 5 is optionally connected to the rhythm control module 2 to collect data such as ambient light intensity and temperature.

[0057] Reference Figure 2 The rhythmic lighting control method of the present invention comprises the following steps:

[0058] The present invention first obtains user operational data on the lighting system wall switch 1, including the time at which the light is turned on and off and lighting parameter adjustment information. Specifically, the data records the time at which the user turns the light on and off; the user's adjustment values ​​for the lighting system's color temperature and brightness; and the user's operation frequency within a specific time period.

[0059] In one embodiment of the present invention, the system records light on / off times accurate to the minute, for example, turning on the light at 07:30 and turning off the light at 22:45. The color temperature adjustment range is 2700K to 6500K, and the brightness adjustment range is 1% to 100%. Operation frequency data is used to identify time periods when users are sensitive to lighting adjustments, such as frequent brightness and color temperature adjustments during evening reading time.

[0060] Preferably, the system not only records the absolute time point of the operation, but also calculates relative time intervals, such as the time interval from turning on the light to the first adjustment, the time interval between consecutive adjustments, etc. These data help to understand user habits more accurately.

[0061] Based on the acquired operational behavior data, the system performs adaptive learning processing to generate user lighting habit feature data. Figure 3 As shown in the figure, this process includes: establishing a 24-hour dynamic learning window with each time the user turns on the light as the starting point of the learning cycle; recording and analyzing the user's operating behavior data within the preset learning cycle; and extracting the user's lighting parameter preferences in different time periods to form user lighting habit feature data.

[0062] The learning cycle is preferably set to 7 days, with the first 3 days serving as the primary learning phase to establish a basic rhythm model, and the last 4 days as the optimization phase to fine-tune the basic rhythm model. This phased learning approach allows for rapid establishment of a basic model and gradual optimization, balancing learning speed and accuracy requirements.

[0063] In a specific embodiment of the present invention, the system uses a weighted data processing method to assign different weights to user operation data according to time. Recent data has a higher weight, and the weight decreases over time, which can be expressed as:

[0064] ,

[0065] in: is the weight of the data from the nth day ago, where n starts counting from 0 (today is day 0). For example, for a user who turns off the lights at 9 pm every night, if they turn off the lights at 10 pm today, 9 pm yesterday, and 9 pm the day before yesterday, the final predicted light-off time will be more inclined to 9 pm than 10 pm, because the cumulative weight of yesterday's and the day before yesterday's data (0.9 + 0.81 = 1.71) is greater than today's weight (1). This weight distribution ensures that the system can filter out sporadic behavior while maintaining sensitivity to changes in user habits.

[0066] In addition, the system identifies and filters abnormal operating data. For example, a user's occasional high brightness adjustment late at night may be a temporary need rather than a regular habit. The system uses the 3σ principle to identify and filter such data. Specifically, when an operating parameter deviates from the historical average value for that period by more than 3 standard deviations, it is considered abnormal data. The calculation formula is:

[0067] ,

[0068] in: is the current operating parameter value (such as the brightness value at a certain moment), is the average value of historical parameters during this period, For example, if the average brightness setting between 8:00 PM and 9:00 PM is 60% and the standard deviation is 5%, then if a user sets the brightness to 80% during this time (a deviation of 4 standard deviations from the average), the system will mark this data as an outlier and will not include it in the habit learning data.

[0069] According to the user's lighting habit characteristic data, the system builds a personalized rhythmic lighting recipe curve, which includes the color temperature and brightness parameters corresponding to different time points. Figure 4 As shown, the construction process includes: generating a baseline brightness curve to represent the brightness change trend within 24 hours; establishing a dynamic color temperature mapping table to represent the color temperature values ​​corresponding to different time points; and identifying sensitive periods, that is, the time period when users frequently adjust lighting parameters.

[0070] In a preferred embodiment of the present invention, the baseline brightness curve uses minute-level sampling to generate a sequence of brightness values ​​at 1440 time points. The system first identifies key time points, including morning awakening (when the lights are first turned on), bedtime (when the lights are last turned off), and periods of high-frequency user activity. It then assigns user-preferred brightness values ​​to these key time points and generates a smooth transition curve using cubic spline interpolation.

[0071] The color temperature mapping table also uses minute-by-minute granularity, recording color temperature values ​​at each point in time. Generally speaking, color temperatures tend to be higher in the morning and daytime (5000K to 6500K), promoting alertness and productivity. Color temperatures gradually cooler in the evening and at night (2700K to 3500K), helping the body relax and prepare for sleep. The system adjusts these values ​​based on user preferences.

[0072] Sensitive time periods correspond to time periods when users frequently adjust lighting parameters, such as early morning wake-up time (e.g., 6:30-7:30) and evening reading time (e.g., 8:00-22:00). During these periods, the system will adjust lighting parameters more finely to ensure that user needs are met.

[0073] Finally, based on the personalized rhythmic lighting recipe curve, the system controls the lighting system to adjust the color temperature and brightness parameters according to the corresponding time point. Specific implementation methods include: activating the natural wake-up mode within a preset period of time before the user's usual light-on time is detected, gradually increasing the brightness and adjusting the color temperature to the user's preferred value; and activating the natural sleep mode within a preset period of time before the user's usual light-off time is detected, gradually reducing the brightness and adjusting the color temperature to a low color temperature suitable for sleep.

[0074] Preferably, the preset time period is 15 to 30 minutes. For example, if the user is accustomed to turning on the lights at 7:00, the system will start the natural wake-up mode from 6:30 to 6:45, with the light slowly increasing from 0% brightness and the color temperature gradually transitioning from warm yellow (approximately 2700K) to the user's preferred morning color temperature (such as 5000K). If the user is accustomed to turning off the lights at 22:30, the system will start the natural sleep mode from 22:00 to 22:15, with the color temperature gradually decreasing from the current value to approximately 2700K, and the brightness will also gradually decrease until it turns off.

[0075] In the natural wake-up and sleep modes, the changes in brightness and color temperature use nonlinear curves to simulate the gradual effect of natural sunrise and sunset. For example, the brightness change can use an S-shaped curve, and the change formula is:

[0076] ,

[0077] in: is the brightness value at time t, in percentage (%); is the minimum brightness (usually 0%), in percentage (%); The target maximum brightness (user preferred value, such as 70%), in percentage (%); is the middle time point, i.e. the moment in the middle of the process, in minutes; is the current time in minutes; This parameter is used to adjust the steepness of the curve. It is usually set between 0.2 and 0.5 and has no unit. The base of natural logarithms, approximately equal to 2.718.

[0078] For example, suppose the user is used to turning on the lights at 7:00 in the morning, and the system sets the natural wake-up time to 20 minutes. 10 minutes after the start of natural awakening (i.e. 6:50), 0%, 70% of the user's preferred brightness in the morning. Take 0.3. When you start to wake up naturally at 6:40, Assuming it is 0 minutes, we can calculate it by substituting it into the formula. , the lights began to dimly light up; by 6:50, For 10 minutes, substitute into the formula to get , the brightness reaches a medium level; by 7:00, For 20 minutes, we can substitute into the formula , the brightness is close to the target value. This gradual change method can provide a more natural and comfortable light change experience, helping users transition comfortably from sleep to wakefulness.

[0079] In one embodiment of the present invention, the system also includes an environmental adaptation control function. Specifically, the system acquires ambient light intensity data; adjusts the brightness parameters in the personalized rhythmic lighting recipe curve based on the ambient light intensity data; acquires ambient temperature data; and adjusts the color temperature parameters in the personalized rhythmic lighting recipe curve based on the ambient temperature data.

[0080] Preferably, the ambient light intensity is collected by the light sensor 5. When the ambient light intensity is higher than a preset threshold (e.g., 500 lux), the system appropriately reduces the LED brightness to achieve energy saving. When the ambient light intensity is weaker than the preset threshold, the system increases the LED brightness to ensure a good lighting effect. The brightness adjustment adopts the following formula:

[0081] ,

[0082] in: is the brightness value after adjustment, in percentage (%); is the original brightness value, in percentage (%); is the adjustment factor (usually 0.2-0.5), unitless; is the ambient light intensity, in lux; is the preset threshold, the unit is lux.

[0083] For example, on a bright afternoon, the ambient light intensity is 600 lux, the preset threshold is 500 lux, the original brightness value is 80%, and the adjustment factor is If 0.3 is selected, the adjusted brightness is: In this way, the system appropriately reduces the LED brightness when the ambient light is sufficient, saving energy while avoiding discomfort caused by excessive brightness.

[0084] Similarly, the ambient temperature is collected by the temperature sensor, and the system adjusts the color temperature based on the temperature data to provide a more comfortable lighting environment. When the ambient temperature is lower than the comfortable temperature (such as 18°C), the system adjusts the color temperature to a lower level (warmer colors) to enhance the sense of warmth; when the ambient temperature is higher than the comfortable temperature (such as 26°C), the system adjusts the color temperature to a higher level (cooler colors) to enhance the sense of coolness. The color temperature adjustment formula is:

[0085] ,

[0086] in: is the adjusted color temperature value in Kelvin (K); is the original color temperature value, in Kelvin (K); is the adjustment factor (usually 50-100K / ℃), the unit is Kelvin per degree Celsius (K / ℃); is the ambient temperature in degrees Celsius (℃); The comfortable temperature is usually 22°C in degrees Celsius (°C).

[0087] For example, on a cold winter morning, the indoor temperature is 15℃, the original color temperature is 4000K, and the adjustment factor is Taking 75K / ℃ and the comfortable temperature as 22℃, the adjusted color temperature is: In this way, the system automatically provides warmer light in cold environments, enhancing the user's sense of warmth and improving living comfort.

[0088] To balance automatic control with real-time user needs, the present invention also includes real-time user operation processing. Specifically, the system detects real-time user operation of the wall switch 1; in response to the detected real-time user operation, it temporarily overrides the control of the personalized rhythmic lighting recipe curve; and feeds the real-time operation data into the adaptive learning process to update the personalized rhythmic lighting recipe curve.

[0089] In a preferred embodiment of the present invention, when a user manually operates the wall switch 1, the system immediately responds to the user's request by suspending automatic rhythmic lighting control. The system records the user's operating parameters and time, and if no further operation has occurred, it resumes automatic control after a preset time (e.g., 30 minutes). Simultaneously, this real-time operating data is fed back to the adaptive learning module as new learning samples, used to continuously optimize the personalized rhythmic lighting recipe curve.

[0090] To determine whether the light recipe curve should be updated, the system will evaluate the degree of deviation between the user's real-time operation and the current light recipe curve. If the deviation exceeds a preset threshold (such as a color temperature deviation exceeding 500K or a brightness deviation exceeding 20%), it is considered that the current light recipe may no longer meet the user's needs and needs to be adjusted. The deviation calculation formula is:

[0091] ,

[0092] ,

[0093] in: It is the color temperature deviation percentage, no unit; It is the brightness deviation percentage, without unit; Color temperature value manually set by the user, in Kelvin (K); The brightness value manually set by the user, in percentage (%); is the color temperature value automatically controlled by the system, in Kelvin (K); The brightness value automatically controlled by the system, in percentage (%); Color temperature adjustable range (such as 3800K, i.e. 6500K ~ 2700K), unit is Kelvin The brightness adjustment range (such as 99%, i.e. 100% to 1%), the unit is percentage (%).

[0094] For example, suppose at 8 pm, the system automatically sets the color temperature to 3500K and the brightness to 60% based on the light recipe curve; and the user manually adjusts the color temperature to 3000K and the brightness to 80%. Then:

[0095] ;

[0096] ;

[0097] If the color temperature deviation threshold set by the system is 15% and the brightness deviation threshold is 20%, then the manually adjusted brightness deviation exceeds the threshold. The system will focus on learning this operation data and may update the light recipe curve parameters for this time period.

[0098] As previously mentioned, the rhythmic lighting control system of the present invention includes a wall switch 1, a rhythmic control module 2, a power supply and driver module 3, an LED light source 4, and an optional environmental sensor 5. These components cooperate with each other to implement the aforementioned rhythmic lighting control method.

[0099] Wall switch 1 provides a simple and intuitive user interface and can be a standard light switch or one with dimming and color adjustment capabilities. The rhythm control module 2 is the core of the system, comprising components such as a microcontroller, memory, and a real-time clock. It is responsible for data acquisition, algorithm processing, and control signal generation. The power supply and driver module 3 is responsible for power conversion and LED driving, using PWM (pulse width modulation) technology to adjust brightness and color temperature by adjusting the output ratio of the two groups of cooling and heating LEDs. The LED light source 4 can be a light panel or light strip, containing LED beads with adjustable color temperature. Environmental sensors 5, which may include light sensors and temperature sensors, are used to collect environmental data.

[0100] Preferably, the rhythm control module 2 adopts a low-power design to keep key data and clock running even in the event of a power outage. At the same time, the rhythm control module 2 reserves multiple communication interfaces to support future functional expansion, such as optional connection to a remote control and support for offline voice recognition.

[0101] In order to achieve precise color temperature control, this system uses dual color temperature LED mixing technology to achieve full color temperature range adjustment by adjusting the mixing ratio of cold light LED (about 6500K) and warm light LED (about 2700K). The color temperature adjustment formula is:

[0102] ,

[0103] ,

[0104] in: The output ratio of the warm light LED is unitless and the value range is 0-1; The output ratio of the cold light LED is unitless and the value range is 0-1; is the target color temperature, in Kelvin (K); is the color temperature of warm light LED (about 2700K), in Kelvin (K); It is the color temperature of cold light LED (about 6500K), and the unit is Kelvin (K).

[0105] For example, to achieve a neutral color temperature of 4000K, the calculation process is: That is, the output ratio of the warm LED is 66%, and the output ratio of the cold LED is 34%. The system calculates the corresponding ratio based on this formula and then controls the output power of the two groups of LEDs through PWM signals to achieve precise color temperature adjustment.

[0106] In practice, a typical bedroom rhythmic lighting scenario is as follows: the user is accustomed to waking up at 7:00 AM and going to bed at 11:00 PM. After learning this pattern, the system begins natural wake-up mode at 6:40 AM each day, gradually transitioning from 0% brightness and 2700K color temperature to 70% brightness and 5000K color temperature at 7:00 AM, simulating a natural sunrise. Natural sleep mode begins at 10:30 PM, gradually decreasing the color temperature from 4000K to 2700K and the brightness from 50% to 0%, completely shutting off the lights at 11:00 PM. The system also adjusts parameters based on ambient light and temperature, automatically increasing brightness on cloudy days and lowering color temperature on cold days, providing users with a more comfortable and healthy lighting environment.

[0107] The rhythmic lighting control system of the present invention preferably adopts a standard power supply interface and a drive signal interface to ensure compatibility with existing lighting systems, thereby significantly reducing the cost and difficulty of upgrading from traditional lighting to rhythmic lighting.

[0108] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A rhythmic lighting control method, characterized in that: include: Acquire user operation behavior data on a lighting system wall switch, the operation behavior data including light on / off time and lighting parameter adjustment information; Based on the operation behavior data, performing adaptive learning processing to generate user lighting habit feature data; Constructing a personalized rhythmic lighting light recipe curve based on the user's lighting habit characteristic data, wherein the personalized rhythmic lighting light recipe curve includes color temperature and brightness parameters corresponding to different time points; as well as According to the personalized rhythmic lighting light recipe curve, the lighting system is controlled to adjust the lighting according to the color temperature and brightness parameters at the corresponding time point.

2. The rhythmic lighting control method according to claim 1, characterized in that: The obtaining of the user's operation behavior data on the lighting system wall switch includes: Record the time when the user turns on and off the light; Record the user's adjustment values ​​for the color temperature and brightness of the lighting system; and Record the user's operation frequency within a specific time period.

3. The rhythmic lighting control method according to claim 1, characterized in that: The performing of the adaptive learning process includes: Use each time a user turns on the light as the starting point of the learning cycle to establish a 24-hour dynamic learning window; Record and analyze user operation behavior data within the preset learning cycle; and The user's lighting parameter preferences in different time periods are extracted to form user lighting habit feature data.

4. The rhythmic lighting control method according to claim 3, characterized in that: The preset learning period is 7 days, including: The first three days serve as the initial learning phase to establish a basic rhythm model; and The last 4 days served as the optimization phase to fine-tune the basic rhythm model.

5. The rhythmic lighting control method according to claim 1, characterized in that: The process of constructing a personalized rhythmic lighting recipe curve includes: Generate a reference brightness curve, wherein the reference brightness curve represents a brightness change trend within 24 hours; Establishing a dynamic color temperature mapping table, wherein the dynamic color temperature mapping table represents color temperature values ​​corresponding to different time points; and A sensitive period is identified, where the sensitive period is a time period during which users frequently adjust lighting parameters.

6. The rhythmic lighting control method according to claim 1, characterized in that: The control lighting system performs lighting adjustment according to the color temperature and brightness parameters at the corresponding time point, including: In a preset time period before detecting that the user is about to turn on the lights, the natural wake-up mode is activated, gradually increasing the brightness and adjusting the color temperature to the user's preferred value; and When it is detected that the user is approaching the preset time period before turning off the lights, the natural sleep mode is activated, gradually reducing the brightness and adjusting the color temperature to a low color temperature value suitable for sleep.

7. The rhythmic lighting control method according to claim 6, characterized in that: The preset time period is 15 to 30 minutes.

8. The rhythmic lighting control method according to claim 1, characterized in that: Also includes: Get ambient light intensity data; Adjusting the brightness parameters in the personalized rhythmic lighting recipe curve based on the ambient light intensity data; Get ambient temperature data; as well as Based on the ambient temperature data, the color temperature parameters in the personalized rhythmic lighting recipe curve are adjusted.

9. The rhythmic lighting control method according to claim 1, characterized in that: Also includes: Detect the user's real-time operation of the wall switch; In response to detecting a real-time operation of a user, temporarily replacing control of the personalized rhythmic lighting recipe curve; as well as The real-time operation data is fed back into the adaptive learning process to update the personalized rhythmic lighting light recipe curve.

10. A rhythmic lighting control system, characterized in that: include: A wall switch, for receiving a user's lighting control operation; a rhythm control module, electrically connected to the wall switch, configured to obtain user operation behavior data through the wall switch, perform adaptive learning processing to generate user lighting habit feature data, and construct a personalized rhythmic lighting light recipe curve based on the user lighting habit feature data; a power supply and driving module electrically connected to the rhythm control module and configured to receive a control signal sent by the rhythm control module; and an LED light source electrically connected to the power supply and driving module and configured to adjust color temperature and brightness parameters according to the control signal; The rhythm control module is further configured to control the LED light source to adjust the lighting according to the color temperature and brightness parameters at the corresponding time point based on the personalized rhythm lighting light formula curve.

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