LED lamp self-adaptive control method and device for LED backlight bathroom mirror

Abstract

The invention relates to the field of bathroom mirror LED lamp control, in particular to an LED lamp self-adaptive control method and device of an LED backlight bathroom mirror. The method comprises the following steps: acquiring a monitoring log of a bathroom mirror LED lamp, performing use time frequency distribution statistics and use behavior prediction, and generating a use behavior prediction time point; performing real-time brightness demand prediction according to the use behavior prediction time point, and performing light intensity output parameter calculation to obtain an initial light intensity output parameter; performing instant bathroom mirror illumination control according to the initial light intensity output parameter, performing mirror surface fuzzy situation analysis according to the environment humidity monitoring parameter, and constructing a mirror surface fuzzy situation evolution graph; and performing mirror surface heat effect demisting analysis and dynamic heating temperature self-adaptive adjustment according to the mirror surface fuzzy situation evolution graph to generate a self-adaptive water mist fuzzy adjustment strategy. By dynamically adjusting the illumination parameters, the clear visualization effect of the bathroom mirror under the fuzzy water mist condition is improved, and the visual effect of a user is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of LED lamp control for bathroom mirrors, and particularly to an LED lamp adaptive control method and device for an LED-backlit bathroom mirror. Background Art

[0002] With the continuous development of smart home technology, the LED-backlit bathroom mirror, as a product integrating modern technology and home life, has gradually become a standard configuration in many households and commercial places. Compared with traditional bathroom mirrors, the LED-backlit bathroom mirror can not only provide a more uniform lighting effect, improve the clarity of the mirror surface, but also has high energy efficiency and a long service life. Especially in daily activities such as beauty care, shaving, and makeup, the lighting effect of the bathroom mirror is crucial to the user experience. Therefore, the LED-backlit bathroom mirror has been widely used in the market. However, with the continuous penetration of intelligent technology, how to improve the usability, comfort, and energy efficiency of the LED-backlit bathroom mirror has become an urgent problem to be solved.

[0003] Most of the lighting control methods of traditional LED-backlit bathroom mirrors rely on manual adjustment switches or simple brightness adjustment buttons, and users need to manually adjust the lighting intensity according to different needs. However, manual control not only increases the operation burden of users, but also often cannot accurately meet the lighting brightness requirements at different times and under different environmental conditions. In addition, due to the high humidity in the bathroom environment, water mist is easily generated on the mirror surface, affecting the visual effect of the mirror, which makes the traditional LED lighting control method unable to effectively adapt to this special use environment. Therefore, with the continuous development of smart home technology and adaptive control theory, there is an urgent need for a more intelligent, real-time, and adaptive LED-backlit bathroom mirror control method. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes an LED lamp adaptive control method and device for an LED-backlit bathroom mirror to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides an LED lamp adaptive control method for an LED-backlit bathroom mirror, including the following steps:

[0006] Step S1: Obtain the monitoring log of the LED lamp of the bathroom mirror, perform statistics on the usage time frequency distribution and predict the usage behavior, and generate the predicted time points of the usage behavior;

[0007] Step S2: Predict the real-time brightness requirement according to the predicted time points of the usage behavior, and calculate the light intensity output parameter to obtain the initial light intensity output parameter;

[0008] Step S3: Perform instant bathroom mirror lighting control according to the initial light intensity output parameters to obtain environmental humidity monitoring parameters; conduct mirror surface blur situation analysis based on the environmental humidity monitoring parameters and construct a mirror surface blur situation evolution diagram;

[0009] Step S4: Conduct mirror surface heat effect defogging analysis and perform dynamic heating temperature adaptive adjustment according to the mirror surface blur situation evolution diagram to generate an adaptive water mist blur adjustment strategy;

[0010] Step S6: Conduct dynamic brightness compensation optimization according to the mirror surface blur situation evolution diagram, perform adaptive light color temperature adaptation, and construct a dynamic lighting adjustment strategy;

[0011] Step S6: Perform dynamic bathroom mirror LED lamp control based on the adaptive water mist blur adjustment strategy and the dynamic lighting adjustment strategy, and conduct intelligent reinforcement transfer learning to construct an intelligent bathroom lamp control engine.

[0012] By long-term collecting the time data of users enabling the LED lights of the bathroom mirror, the present invention can establish a daily behavior pattern model of users. Users have a relatively high usage frequency at 6:45 in the morning on weekdays, and this time point can be identified and predicted as a high-probability usage period. Generating predicted time points based on the usage frequency distribution helps to pre-start relevant modules (such as lighting, heating, defogging, etc.) in advance, achieving "senseless pre-start" and avoiding a poor user experience due to waiting for the function response. Through user behavior clustering, the usage habits of different family members can be distinguished, and multi-time period prediction can be carried out to improve the applicability and intelligence. Only prepare in advance during the predicted high-probability usage periods, reducing the time of ineffective operation, especially during the less frequently used night or daytime periods, so that the device can maintain a low-power standby state. Combining the predicted time points with the natural light brightness of the external environment (such as early morning, dusk, night), automatically judge whether additional lighting is needed and calculate appropriate light intensity output parameters. For example, in the early morning, the vision is more sensitive, so the brightness is automatically reduced to avoid glare; the brightness is increased at night or when the bathroom has poor lighting to meet the visual needs. Automatically calculating the light intensity output parameters, users do not need to manually adjust the brightness, improving convenience. Reasonably controlling the LED output power through light intensity demand prediction can minimize energy consumption while ensuring the usage experience. Since the initial light intensity parameters have been pre-calculated, the lighting state can be adjusted immediately before the user approaches or moves in front of the mirror, achieving the experience of "lighting up immediately when in use". Quickly lighting up the LED based on the light intensity parameters improves the usage response speed and automatically matches the currently required lighting intensity. By integrating a humidity sensor, the humidity level of the bathroom is collected in real time, the fogging risk is identified, providing basic data for defogging. Associating the humidity change trend with the historical mirror blur data to establish a "blur situation evolution map" for analyzing the correlation between different humidity levels and the degree of mirror blur. Compared with the traditional "timed heating" method, it can judge whether defogging treatment is needed according to the situation map, providing a precise response rather than blind heating. Due to the obvious differences in different regions, seasons, and family usage habits, the blur situation evolution map can dynamically adjust the model parameters to ensure the universality and accuracy of defogging judgment. Judging the defogging intensity requirement through the blur situation evolution map to achieve dynamic adjustment of the heating power, no longer using the "fixed time / fixed power" mode, saving energy consumption. Precisely controlling the heating temperature according to the real-time situation to make the mirror reach the state of "just not blurred", rather than overheating, ensuring comfort and safety. Personalized heating strategies can be formed for different family users and climates (such as different heating logics in humid southern and dry northern environments). The clarity of the mirror after defogging is significantly improved, not affecting the user's operations such as looking in the mirror and applying makeup. When the mirror blur is still in the residual stage, visual compensation can be carried out by increasing the brightness or adjusting the color temperature to enhance the overall mirror-looking experience. Dynamically adjusting the color temperature can improve the perception of skin tone, making tasks such as applying makeup, skin care, and shaving more accurate. Integrating environmental perception, user behavior analysis, and light and heat adjustment strategies to achieve a "smart mirror" in the true sense.The control model is continuously iteratively upgraded through reinforcement learning and transfer learning to adapt to the usage habits of different users or family members. Compared with traditional bathroom mirrors, it has stronger intelligence, energy-saving performance, and user-friendliness, forming a differential advantage.

[0013] In this specification, an LED lamp adaptive control device for an LED-backlit bathroom mirror is provided, which is used to execute the LED lamp adaptive control method for the LED-backlit bathroom mirror as described above, including:

[0014] A behavior prediction module, which is used to obtain the monitoring logs of the LED lamp of the bathroom mirror, perform statistics on the usage time frequency distribution and predict the usage behavior, and generate predicted usage behavior time points;

[0015] A brightness demand prediction module, which is used to perform real-time brightness demand prediction based on the predicted usage behavior time points and calculate the light intensity output parameters to obtain the initial light intensity output parameters;

[0016] A mirror surface blur analysis module, which is used to perform immediate bathroom mirror lighting control based on the initial light intensity output parameters and obtain the environmental humidity monitoring parameters; perform mirror surface blur situation analysis based on the environmental humidity monitoring parameters and construct a mirror surface blur situation evolution diagram;

[0017] A heating temperature adjustment module, which is used to perform mirror surface heat effect defogging analysis and perform dynamic heating temperature adaptive adjustment based on the mirror surface blur situation evolution diagram to generate an adaptive water mist blur adjustment strategy;

[0018] A dynamic lighting adjustment module, which is used to perform dynamic brightness compensation optimization based on the mirror surface blur situation evolution diagram and perform adaptive lighting color temperature adaptation to construct a dynamic lighting adjustment strategy;

[0019] An intelligent control module, which is used to perform dynamic bathroom mirror LED lamp control and perform intelligent reinforcement transfer learning based on the adaptive water mist blur adjustment strategy and the dynamic lighting adjustment strategy to construct an intelligent bathroom lamp control engine.

[0020] The present invention constructs a user usage behavior profile based on log data such as the on - time, frequency, and usage duration of LED lights, realizing the transformation from "passive response" to "active prediction". It avoids the unnecessary lighting of LED lights during periods of no use, effectively reducing energy consumption and extending the service life of the lamps. It can integrate sensor (such as human body sensing) data for multi - user behavior separation and personalized analysis, enhancing the degree of intelligence. It can automatically adjust the light intensity according to historical preferences and environmental brightness before the user is about to use the bathroom mirror, improving the response speed and usage experience. According to the lighting demand preferences of different users (for example, men need high - brightness light for shaving and women need soft light for makeup), it realizes personalized initial light settings. By establishing a mirror fogging model through real - time environmental humidity data (linked with temperature and usage period), it can predict changes in the fogging degree. It quantifies the fogging state and constructs an evolution map of "clear - slightly fogged - severely fogged" to provide a visual basis for defogging decisions. It identifies actual foggy or about - to - fog scenes, effectively avoiding false triggering of heating and saving energy. The fogging state of the mirror affects the light reflection effect, and it can be linked with light changes to judge the fogging level, improving the analysis accuracy. It matches a reasonable heating power and duration according to the fogging degree, avoiding increased energy consumption due to over - heating or incomplete defogging due to insufficient heating. It can adjust the temperature control algorithm for different environments (winter / summer, humid / dry) and mirror fogging levels to enhance adaptability. It avoids the mirror repeatedly fogging or clearing during the defogging process, always maintaining visual clarity and enhancing the usage comfort in high - humidity environments. When the mirror is not fogged or only slightly fogged, it adopts a low - power mode to achieve "fogging removal on demand". In the case of slightly fogged mirror, it enhances the reflection clarity through brightness compensation without immediately starting the defogging device. For example, it can automatically adjust the color temperature to match the scene, such as warm light in the early morning to help wake up, cold light at night to help sleep, and high - color - rendering - index light source for makeup. By intelligently adjusting the lighting angle and brightness gradient, it alleviates visual problems such as glare and light spots caused by mirror fog. Dynamic lighting enhances visual safety, especially providing a clearer view in high - humidity scenes after hot showers and reducing usage risks. It adjusts the control strategy through user feedback (such as the frequency of manual intervention and usage satisfaction) to improve long - term performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic flow chart of the steps of an LED adaptive control method for an LED - backlit bathroom mirror according to the present invention;

[0022] Figure 2 It is a schematic detailed implementation step flow chart of step S1;

[0023] Figure 3 It is a schematic detailed implementation step flow chart of step S2;

[0024] Figure 4 It is a schematic detailed implementation step flow chart of step S3. Specific Embodiments

[0025] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] An example of this application provides an LED light adaptive control method and device for an LED backlit bathroom mirror. The execution subject of the LED light adaptive control method and device for the LED backlit bathroom mirror includes but is not limited to: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that can be regarded as general computing nodes of this application. The data processing platform includes but is not limited to at least one of: an audio and image management system, an information management system, and a cloud data management system.

[0027] Please refer to Figures 1 to 4 , the present invention provides an LED light adaptive control method for an LED backlit bathroom mirror. The LED light adaptive control method for the LED backlit bathroom mirror includes the following steps:

[0028] Step S1: Obtain the monitoring log of the LED lights of the bathroom mirror, perform statistics on the usage time frequency distribution and predict the usage behavior to generate predicted usage behavior time points;

[0029] Step S2: Predict the real-time brightness requirement according to the predicted usage behavior time point and calculate the light intensity output parameter to obtain the initial light intensity output parameter;

[0030] Step S3: Perform immediate bathroom mirror lighting control according to the initial light intensity output parameter, and obtain the environmental humidity monitoring parameter; perform mirror surface blur situation analysis according to the environmental humidity monitoring parameter and construct a mirror surface blur situation evolution map;

[0031] Step S4: Perform mirror surface heat effect defogging analysis and dynamic heating temperature adaptive adjustment according to the mirror surface blur situation evolution map to generate an adaptive water mist blur adjustment strategy;

[0032] Step S5: Perform dynamic brightness compensation optimization according to the mirror surface blur situation evolution map and perform adaptive lighting color temperature adaptation to construct a dynamic lighting adjustment strategy;

[0033] Step S6: Perform dynamic bathroom mirror LED light control and perform intelligent reinforcement transfer learning based on the adaptive water mist blur adjustment strategy and the dynamic lighting adjustment strategy to construct an intelligent bathroom light control engine.

[0034] By long-term collecting the time data of users enabling the LED lights of the bathroom mirror, the present invention can establish a daily behavior pattern model of users. Users have a relatively high usage frequency at 6:45 in the morning on weekdays, and this time point can be identified and predicted as a high-probability usage period. Generating predicted time points based on the usage frequency distribution helps to start relevant modules (such as lighting, heating, defogging, etc.) in advance, achieving "seamless pre-start", and avoiding a poor user experience due to waiting for the function response. Through user behavior clustering, the usage habits of different family members can be distinguished, and multi-time period prediction can be carried out to improve applicability and intelligence. Only prepare in advance during the predicted high-probability usage periods, reducing the time of ineffective operation. Especially during the less frequently used night or daytime periods, the device can be kept in a low-power standby state. Combining the predicted time points with the natural light brightness of the external environment (such as early morning, dusk, night), it can automatically judge whether to enhance the lighting and calculate appropriate light intensity output parameters. For example, in the early morning, the vision is more sensitive, so the brightness is automatically reduced to avoid glare; the brightness is increased at night or when the bathroom lighting is poor to meet the visual needs. Automatically calculating the light intensity output parameters, users do not need to manually adjust the brightness, improving convenience. Reasonably controlling the LED output power through light intensity demand prediction can minimize energy consumption on the premise of ensuring the usage experience. Since the initial light intensity parameters have been pre-calculated, the lighting state can be adjusted immediately before the user approaches or moves in front of the mirror, achieving the experience of "lighting up immediately when in use". Lighting up the LED quickly based on the light intensity parameters improves the usage response speed and automatically matches the current required lighting intensity. By integrating a humidity sensor, the humidity level of the bathroom can be collected in real time, identifying the risk of fogging and providing basic data for defogging. Associating the humidity change trend with the historical mirror blurring data to establish a "blurring situation evolution map" for analyzing the correlation between different humidity levels and the degree of mirror blurring. Compared with the traditional "timed heating" method, it can judge whether defogging treatment is needed according to the situation map, providing a precise response rather than blind heating. Due to obvious differences in different regions, seasons, and family usage habits, the blurring situation evolution map can dynamically adjust the model parameters to ensure the universality and accuracy of defogging judgment. Judging the defogging intensity demand through the blurring situation evolution map to achieve dynamic adjustment of the heating power, no longer adopting the "fixed time / fixed power" mode, saving energy consumption. Precisely controlling the heating temperature according to the real-time situation to make the mirror reach the state of "just not blurring", rather than overheating, ensuring comfort and safety. Personalized heating strategies can be formed for different family users and climates (such as different heating logics in humid southern and dry northern environments). The clarity of the mirror after defogging is significantly improved, without affecting the user's operations such as looking in the mirror and applying makeup. When the mirror blurring is still in the residual stage, visual compensation can be carried out by increasing the brightness or adjusting the color temperature to enhance the overall mirror-looking experience. Dynamically adjusting the color temperature can improve the perception of skin tone, making tasks such as applying makeup, skin care, and shaving more accurate. Integrating environmental perception, user behavior analysis, and light and heat adjustment strategies to achieve a "smart mirror" in the true sense.Continuously iterate and upgrade the control model through reinforcement learning and transfer learning to adapt to the usage habits of different users or family members. Compared with traditional bathroom mirrors, it has stronger intelligence, energy efficiency and user-friendliness, forming a differential advantage.

[0035] In the embodiments of the present invention, refer to Figure 1 , which is a schematic flow chart of the steps of an LED adaptive control method for an LED backlight bathroom mirror of the present invention. In this example, the steps of the LED adaptive control method for the LED backlight bathroom mirror include:

[0036] Step S1: Obtain the monitoring log of the bathroom mirror LED lamp, conduct statistics on the usage time frequency distribution and predict the usage behavior to generate the predicted time points of the usage behavior;

[0037] In this embodiment, the usage logs of the LED lights on the bathroom mirror are recorded regularly. These logs should include information such as the timestamp of each use, the duration of use, the brightness setting, and user feedback. This data can be automatically collected through built-in sensors and control systems and stored in a database. It is set to record data automatically each time it is used, in the format: Timestamp: 2023-04-01 08:00:00, Duration: 15 minutes, Brightness: 400 lux, User feedback: Satisfied. In this way, the accuracy and integrity of the data can be ensured, providing a basis for subsequent analysis. The collected monitoring log data is sorted and cleaned to remove duplicate records and outliers to ensure the reliability of the data. A time window can be set to merge the records within the same time period to form a structured dataset. If a user turns on the LED lights multiple times within the same time period, these records are merged into one, recording the total usage time and average brightness. This step is the key to ensuring the effectiveness of subsequent statistical analysis. Using the sorted data, a statistical analysis of the usage time frequency distribution is carried out. The usage frequency of users in different time periods can be statistically analyzed in the form of a histogram or frequency table. The time interval is set to hours, and the number of uses and total duration per hour are statistically analyzed. The analysis results show that the number of uses between 08:00-09:00 is 20 times, and the total usage duration is 300 minutes; the number of uses between 09:00-10:00 is 15 times, and the total usage duration is 200 minutes. These statistical data will help identify the peak usage periods of users. Based on the usage time frequency distribution data, a usage behavior prediction model is constructed. Time series analysis or machine learning algorithms (such as ARIMA model or LSTM model) can be used to predict future usage time points. The goal of this step is to identify the usage habits of users and predict the time when they may use the bathroom mirror in the future. The model is trained with historical data and it is found that the usage frequency of users is relatively high between 6 am and 8 am. The model predicts that in the next few days, the probability of the user using the bathroom mirror at 7 am is 80%. According to the output of the prediction model, specific usage behavior prediction time points are generated. These time points will be used to dynamically adjust the lighting parameters of the LED lights to meet the usage needs of users. The predicted time points can be recorded in the system for subsequent use. If the model predicts that the user will use the bathroom mirror at time points such as 2023-04-02 07:00:00, 2023-04-02 08:00:00, etc., the system will adjust the lighting settings in advance to ensure the best usage effect.

[0038] Step S2: Perform real-time brightness requirement prediction based on the predicted usage behavior time points and calculate the light intensity output parameters to obtain the initial light intensity output parameters;

[0039] In this embodiment, after the usage behavior prediction is completed, the system will generate a series of potential usage time points. These time points will serve as the basis for subsequent brightness requirement prediction. First, the system should sort and label these time points for subsequent real-time monitoring and analysis. Suppose the predicted time points are 2023-04-02 07:00:00 and 2023-04-02 08:00:00. The system takes these time points as the critical moments when the user may use the bathroom mirror in the future. Before the predicted time points arrive, the system needs to monitor the environmental conditions in real time to accurately predict the brightness requirements. This includes parameters such as environmental light intensity, humidity, and temperature. These data can be collected in real time through built-in sensors. Suppose at 2023-04-02 06:59:00, the system detects that the environmental light intensity is 200 lux and the humidity is 70%. These environmental data will provide an important basis for subsequent brightness requirement calculations. Based on historical usage data and real-time environmental data, a brightness requirement prediction model is constructed. Linear regression, decision tree, or machine learning models can be used to predict the user's brightness requirements under specific environmental conditions. By analyzing past data, it is found that when the environmental light intensity is 200 lux, the user usually prefers a brightness of 400 lux. The model can predict that under this environmental condition, the user's brightness requirement is 400 lux. According to the real-time brightness requirement prediction and environmental light intensity, the required light intensity output parameter is calculated. I(output) = I(demand) - I(ambient), where I(output) is the light intensity to be output, I(demand) is the predicted brightness requirement, and I(ambient) is the current environmental light intensity. If the predicted brightness requirement is 400 lux and the environmental light intensity is 200 lux, the required light intensity output parameter is: II(output) = 400 lux - 200 lux = 200 lux. The calculated initial light intensity output parameters are recorded in the system for adjustment when the user actually uses it. These parameters should be associated with the time points and relevant environmental data to ensure data traceability.

[0040] Step S3: Perform immediate bathroom mirror lighting control according to the initial light intensity output parameters, and obtain environmental humidity monitoring parameters; perform mirror surface blur situation analysis according to the environmental humidity monitoring parameters, and construct a mirror surface blur situation evolution diagram;

[0041] In this embodiment, according to the calculated initial light intensity output parameters, the system will automatically control the brightness output of the LED lamp. This step involves adjusting the power of the LED lamp in real time to ensure that the mirror reaches the required brightness level. If the initial light intensity output parameter is 200 lux, the system will adjust the output power of the LED lamp to ensure that the mirror brightness is stable at 400 lux (when the ambient light intensity is 200 lux). The PWM (Pulse Width Modulation) technology can be used to precisely control the brightness and ensure a smooth transition of the light. While controlling the light, the system will continuously monitor the ambient humidity in the bathroom through the installed humidity sensors. These sensors should have high precision and fast response capabilities to obtain humidity data in a timely manner. Suppose the system detects that the ambient humidity is 75% at 2023-04-02 07:01:00. This data is the basis for analyzing the mirror fogging situation and will be used for subsequent analysis of the impact of humidity on fogginess. The obtained ambient humidity data will be recorded in the system's database and associated with the current timestamp and light conditions. Ensure that the data is complete and traceable to provide support for subsequent analysis. The recording format is: timestamp: 2023-04-02 07:01:00, ambient humidity: 75%, LED brightness: 400 lux. These records will be used to construct an evolution graph of the mirror fogging situation. According to the ambient humidity monitoring parameters, analyze the mirror fogging situation. The impact of fogginess on mirror clarity can be evaluated by calculating the concentration of water vapor on the mirror and its relationship with the ambient humidity. If the humidity is 75%, the current water vapor concentration can be calculated using the previous water vapor concentration formula, and its impact on mirror fogginess can be determined. Suppose after analysis, it is found that when the humidity reaches 75%, the fogginess of the mirror increases by 30%. Visualize the relationship between humidity and fogginess to construct an evolution graph of the mirror fogging situation. This graph should show the trends of water vapor concentration, humidity, and fogginess over time to help users understand the changes in mirror clarity.

[0042] Step S4: Perform mirror thermal effect defogging analysis and dynamic heating temperature adaptive adjustment according to the evolution graph of the mirror fogging situation to generate an adaptive water mist fogging adjustment strategy;

[0043] In this embodiment, the generated mirror surface blur situation evolution diagram is utilized to analyze the changing trend of the mirror surface blur degree under different humidity conditions. By observing the relationship between the water vapor concentration and the blur degree in the diagram, the key humidity threshold and the law of blur degree change are identified. Suppose the blur situation evolution diagram shows that when the humidity exceeds 70%, the mirror surface blur degree increases sharply, reaching a blur degree of 90%. This discovery will provide an important basis for subsequent thermal effect analysis, helping to determine when to activate the heating and defogging function. According to the mirror surface blur situation evolution diagram, a thermal effect model is constructed to analyze the influence of mirror surface heating on defogging. This model should consider factors such as the thermal conductivity of the mirror surface material, ambient temperature, humidity, and water vapor concentration to evaluate the required heating temperature. After analyzing the thermal effect, the system will dynamically adjust the heating temperature of the mirror surface according to the real-time humidity and blur degree values. When it is detected that the humidity reaches the set threshold (such as 70%), the system will automatically activate the heating function and adjust the heating intensity and time according to the current blur degree. If the current blur degree is 85%, the system may heat the mirror surface to 60°C and set the heating time to 5 minutes to achieve the best defogging effect. The system should monitor the temperature change of the mirror surface in real time and adjust the heating strategy according to the feedback of the blur degree. During the dynamic heating process, the system will generate an adaptive water mist blur adjustment strategy, which can automatically adjust the heating temperature and time according to the real-time environmental changes to ensure that the mirror surface remains clear in a high-humidity environment. If it is found during the heating process that the blur degree drops to 70%, the system will automatically record this effect and adjust the strategy to adapt to future similar environmental conditions. Strategy rules can be set, for example, when the humidity exceeds 75%, automatically heat to 65°C for 6 minutes. The generated adaptive adjustment strategy and its implementation results are recorded in the system for subsequent analysis and optimization. The record should include information such as the temperature, duration, blur degree change, and user feedback of each heating. The record format is: Timestamp: 2023-04-02 07:10:00, Heating temperature: 60°C, Heating time: 5 minutes, Blur degree before change: 85%, Blur degree after change: 70%, User feedback: Satisfied. These data will provide a basis for subsequent system optimization and improvement of user experience.

[0044] Step S5: Perform dynamic brightness compensation optimization according to the mirror surface blur situation evolution diagram, and perform adaptive light color temperature adaptation to construct a dynamic light adjustment strategy;

[0045] In this embodiment, according to the evolution diagram of the mirror surface blur situation, the lighting requirements under different humidity and blur conditions are analyzed. By comparing historical usage data and the current environmental state, the brightness level preferred by the user and the influence of blur on brightness are determined. Suppose the analysis data shows that when the blur reaches 80%, the user usually expects a brightness of 450 lux to maintain clarity. The system will utilize this data to establish a dynamic brightness compensation model to ensure that the brightness of the LED lights can be adjusted in a timely manner under high blur conditions. Using the real-time monitored blur and ambient light intensity, the required dynamic compensation parameters are calculated. On the basis of dynamic brightness compensation, the system will perform adaptive adjustment of the lighting color temperature according to the humidity of the environment and the user's preference. The color temperature adjustment not only affects the visual effect of the mirror surface but also improves the overall user experience. When the humidity reaches 75% and the blur is 80%, the system may adjust the color temperature to a warm color tone (about 3000K) to reduce visual fatigue and enhance comfort. Through the user feedback data, the optimal color temperature settings under different humidity conditions are determined. Combining brightness compensation and color temperature adaptation, a comprehensive dynamic lighting adjustment strategy is constructed. The system should have real-time response capabilities so that the lighting parameters can be automatically adjusted during user use to achieve the best effect. Set the rule: if the humidity exceeds 70% and the blur reaches 80%, automatically set the brightness of the LED lights to 450 lux and adjust the color temperature to 3000K; if the blur drops below 70%, restore to the standard color temperature (about 4000K). This strategy ensures that users always obtain a comfortable usage experience under different environmental conditions. Record the implementation results of the dynamic lighting adjustment strategy in the system and conduct performance verification. The record should include the specific parameters of each adjustment and its impact on the user experience for subsequent analysis and optimization.

[0046] Step S6: Based on the adaptive water mist blur adjustment strategy and the dynamic lighting adjustment strategy, perform dynamic control of the bathroom mirror LED lights and conduct intelligent reinforcement transfer learning to construct an intelligent bathroom light control engine.

[0047] In this embodiment, based on the aforementioned adaptive water mist blur adjustment strategy and dynamic light adjustment strategy, the system will initiate the dynamic control of the LED lights. This control will automatically adjust the brightness and color temperature of the LED lights according to real-time environmental data (such as humidity, blur degree, brightness requirements, etc.) to ensure the clarity of the mirror and the comfort of the user. When the system detects that the humidity is 75% and the blur degree is 80%, the LED lights will automatically adjust to a brightness of 450 lux and a color temperature of 3000K. This process relies on real-time monitoring and feedback, enabling the system to quickly respond to the user's needs. During the dynamic control process, the system will continuously monitor the environmental status, including humidity, temperature, light intensity, etc. These data will provide the basis for subsequent intelligent learning to ensure the effectiveness of the control strategy. Suppose during the control process, the system records the change in humidity in real time and finds that the humidity drops from 75% to 65%. This change will trigger the corresponding adjustment strategy, which may lead to adjustments in brightness and color temperature to adapt to the new environmental conditions. To improve the intelligence level of the system, a reinforcement learning framework is constructed. The framework should include a state space (such as environmental humidity, blur degree, user feedback, etc.), an action space (such as the adjustment of the brightness and color temperature of the LED lights), and a reward function (based on user satisfaction and clarity change). The model can be trained through historical data and user feedback so that it can quickly adapt and optimize the control strategy under similar environmental conditions. The system will utilize the existing usage data to accelerate the learning process, thereby improving the accuracy and efficiency of the control. In the intelligent reinforcement learning framework, transfer learning technology is adopted to utilize the existing user feedback and environmental data to accelerate the learning process in new situations. By transferring the knowledge learned in the early stage to new situations, the system can immediately provide optimized lighting settings when the user uses it for the first time. If the system records that the user has a high satisfaction level at a certain specific humidity and blur degree, then under similar conditions, the system will preferentially select the corresponding brightness and color temperature settings to reduce the user's adaptation time. After the intelligent control engine is put into actual use, performance verification is carried out, and user feedback and system operation data are collected. According to the user's satisfaction and actual usage situation, the control strategy and model parameters are continuously optimized to ensure that the system always provides the best user experience. Record the data of user feedback. If most users rate the clarity satisfaction as 4 points (out of 5) when the humidity is 75%, the system may further adjust the brightness and color temperature settings at this humidity to improve the overall user satisfaction.

[0048] In this embodiment, refer to Figure 2 , which is a schematic diagram of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of the said step S1 include:

[0049] Obtain the monitoring logs of the LED lights of the bathroom mirror; conduct user usage behavior analysis on the monitoring logs and extract all user usage behavior information;

[0050] Calculate the usage time periods for the user's usage behavior information and mark multiple usage time periods;

[0051] Conduct statistics on the user's usage time frequency distribution based on multiple usage time periods to obtain a usage time frequency distribution diagram;

[0052] Analyze the usage time demand for the usage time frequency distribution diagram to obtain the historical usage time demand characteristics;

[0053] Predict the usage behavior based on the historical usage time demand characteristics and generate the predicted time points for usage behavior.

[0054] In this embodiment, ensure that the monitoring system of the LED lamp is correctly installed and can record usage data in real time. The monitoring system should include sensors and a data recording module to capture the on / off state, brightness changes of the light, and the user's usage time in real time. Set the sensor to record the state changes once per second, and the recorded data includes timestamps, on / off states (on / off), brightness values, etc., for subsequent analysis. Store the monitored log data in a database to ensure the integrity and traceability of the data. The data should be stored in a structured format, including fields such as timestamps, user IDs (if applicable), status information, etc. The record format is: Timestamp: 2023-04-01 08:00:00, Status: on, Brightness: 300 lumens. This data will provide a basis for subsequent analysis. Regularly export the monitoring logs to an analyzable file format (such as CSV or Excel) for subsequent data analysis and processing. Ensure the timeliness of the exported data to promptly reflect the user's usage behavior. Export the data once a day, and the file contains all the monitoring records of the past 24 hours for subsequent usage behavior analysis. Perform data cleaning and preprocessing on the exported monitoring logs, including removing invalid data, filling in missing values, etc. Ensure that the data for analysis is accurate and valid. Check the format of the timestamps to ensure their uniformity. If abnormal records are found (such as multiple records with the same timestamp), they need to be screened. Based on the monitoring logs, identify the user's usage behavior, including the time periods of turning on and off the lights. Usage behavior criteria can be set, and continuous lighting for more than 5 minutes is considered a "usage behavior". Analyze the records. If the status is "on" between 2023-04-01 08:00:00 and 2023-04-01 08:05:00, mark this as the user's usage behavior. Extract the information of all user usage behaviors to form a usage behavior list, recording the specific time and usage duration of each user. According to the extracted user usage behavior information, classify the usage behaviors by time period. Set the size of the time period, divided by hour or by 30 minutes for more detailed analysis. Divide a day into 24 hours, and count the usage times and durations within each hour. Mark the usage behavior of each user and record the time period corresponding to each behavior. Establish the association between the time period and the usage behavior for subsequent statistical analysis. If the user uses it 3 times between 8:00 and 9:00, mark it as: Time period: 08:00-09:00, Usage times: 3 times. Organize the usage situations of all time periods, summarize the user usage frequency and duration of each time period, and form a time period usage statistics table. The statistical result is: 08:00-09:00, Usage times: 5 times, Average usage duration: 4 minutes. Based on the usage situations of the time periods, calculate the user usage frequency in each time period. Frequency distribution data can be formed by counting the usage times of each time period.Assume it is used 5 times from 08:00 to 09:00 and 3 times from 09:00 to 10:00. Then the frequency distribution is as follows: 08:00 - 09:00: 5 times, 09:00 - 10:00: 3 times. Use statistical software or tools to generate a usage time frequency distribution graph to visually display the user's usage behavior pattern. This can help identify high-frequency and low-frequency usage time periods. Generate a bar chart with the time period on the X-axis and the number of usage times on the Y-axis to visually show the usage situation in each time period. Record the frequency distribution statistical results and conduct a preliminary analysis to identify the user's high-frequency usage time periods and their characteristics. This will provide a basis for subsequent requirement analysis. If it is found that 08:00 - 09:00 is the peak usage period, record the usage characteristics of this time period for subsequent analysis. Conduct an in-depth analysis of the data in the frequency distribution graph to extract the historical usage time requirement characteristics of the user. The characteristics can be summarized by calculating the average usage duration, number of usage times, etc. for each time period. Calculate that the average usage duration from 08:00 to 09:00 is 4 minutes, and the average usage duration from 09:00 to 10:00 is 3 minutes. Analyze the usage demand trends in different time periods to identify the user's usage preferences and their changes in different time periods. In-depth mining can be carried out through time series analysis methods. If it is found that the usage frequency in the morning increases year by year while the usage frequency in the evening decreases, record this trend change for subsequent strategy adjustment. Record the extracted historical time requirement characteristics in the system to form a detailed user usage behavior profile for subsequent usage behavior prediction. The record format is: time period: 08:00 - 09:00, average usage duration: 4 minutes, usage frequency: 5 times, trend: rising. Use measurement, regression analysis, or machine learning methods to predict future usage behavior. Use a linear regression model to predict the usage frequency for each future time period based on past usage data. Use the constructed prediction model to predict future usage behavior and generate future usage time points and corresponding usage frequencies. The model predicts that there will be 6 usages from 08:00 to 09:00 next Monday, and 4 usages from 09:00 to 10:00. Record the predicted usage behavior in the system and conduct an analysis to identify future usage trends. Ensure that each prediction has a detailed record for subsequent strategy adjustment. The record format is: prediction time: 2023-04-10, time period: 08:00 - 09:00, predicted number of usage times: 6 times, status: expected to increase.

[0055] In this embodiment, refer to Figure 3 , which is a schematic flow diagram of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of the said step S2 include:

[0056] Obtain the real-time bathroom environment light parameters according to the usage behavior prediction time point;

[0057] Calculate the light intensity parameter of the said real-time bathroom environment light parameters;

[0058] Perform light intensity temporal fluctuation evolution on the light intensity parameter to obtain the temporal fluctuation characteristics of the bathroom light intensity;

[0059] Based on the real-time bathroom environmental light parameters, perform light distribution recognition to obtain the spatial distribution data of the bathroom light;

[0060] According to the spatial distribution data of the bathroom light, perform spatio-temporal light distribution fitting on the temporal fluctuation characteristics of the bathroom light intensity to construct a real-time bathroom light distribution network;

[0061] Based on all user usage behavior information, perform real-time brightness demand prediction to generate a brightness demand prediction value;

[0062] According to the brightness demand prediction value and the real-time bathroom light distribution network, calculate the light intensity output parameter to obtain the initial light intensity output parameter.

[0063] In this embodiment, it is set that the sensor records the light intensity once per second, and the monitoring data includes the timestamp and the corresponding light intensity value for subsequent analysis. The collected light data is stored in a database to ensure data integrity and traceability. The data should be stored in a structured format for easy subsequent analysis and processing. The recording format is: Timestamp: 2023-04-01 08:00:00, Light Intensity: 300 lux. These data will provide a basis for subsequent analysis. At the predicted time point of user usage behavior, the latest real-time environmental light parameters are obtained. These parameters will be used to calculate the light intensity and analyze the lighting conditions in the bathroom. The light data is obtained 5 minutes before the expected user usage time to prepare for subsequent light intensity calculations. Based on the real-time light data, the light intensity parameters are calculated. The lux value directly collected by the sensor can be used as the main parameter of the light intensity. If the light intensity recorded by the light sensor at a certain moment is 350 lux, then this value is directly used as the light intensity parameter of the current environment. The calculated light intensity parameters are recorded to form a light intensity data set for subsequent analysis and comparison. The recording format is: Timestamp: 2023-04-01 08:00:05, Light Intensity: 350 lux. These records will be used to analyze the temporal fluctuation characteristics of the light intensity. The light intensity parameters are visually displayed to intuitively understand the light changes. A line chart can be used to show the change trend of the light intensity at different time points. A chart is generated with the X-axis as time and the Y-axis as light intensity, showing the light intensity changes in the past hour. The real-time light intensity parameters are organized into temporal data to form a time series for fluctuation analysis. Ensure the temporality of the data for convenient subsequent analysis. The light intensity data in the past hour is recorded to form a time series list: [(t0, 300 lux), (t1, 350 lux), (t2, 320 lux)]. The temporal data of the light intensity is subjected to fluctuation analysis, and the standard deviation and mean of the light intensity are calculated to obtain the fluctuation characteristics. The short-term fluctuations can be analyzed by the sliding window method. If the light intensity fluctuation range in the past 10 minutes is 300 - 350 lux, then its mean is calculated as 325 lux and the standard deviation is 15 lux. Based on the real-time light parameters, the light distribution is identified. By arranging sensors at different positions in the bathroom, the light intensity data of each area can be obtained to form the light spatial distribution data. Multiple sensors are installed in different areas of the bathroom (such as above the mirror, beside the washbasin, etc.) to record the light intensity at each position to form a light distribution map. The light intensity data at different positions is organized into spatial distribution data for convenient subsequent light analysis. Ensure that the data includes the position coordinates and the corresponding light intensity. The recording format is: Position: (x1, y1), Light Intensity: 300 lux; Position: (x2, y2), Light Intensity: 350 lux. The light spatial distribution data is visualized to generate a light distribution map.Show the light intensity of different areas through a heat map to help users understand the light distribution. Generate a heat map with colors changing from blue (low light) to red (high light) to clearly display the light distribution in each area of the bathroom. Based on the light spatial distribution data and the characteristics of the temporal fluctuations of light intensity, construct a spatio-temporal light distribution model. This model should be able to reflect the changes in light intensity over time and space. Use a multiple linear regression model, input the light intensity and spatial position data, construct a light distribution model, and predict the light distribution at different time points. Fit the model, use historical data to optimize the model parameters to ensure the accuracy and effectiveness of the model. The performance of the model can be evaluated by the cross-validation method. Use historical light data for model training, adjust the model parameters to ensure that the difference between the predicted light intensity and the actual value is minimized. Verify the constructed spatio-temporal light distribution model to ensure its effectiveness in the real environment. Record the prediction results of the model and its accuracy for subsequent adjustment. Integrate all user usage behavior information and analyze the brightness requirements of users at different time periods. Identify the brightness preferences of users through historical usage behavior data. If the user has a high usage frequency in the morning and mostly selects a higher brightness, record this brightness requirement characteristic. Construct a brightness requirement prediction model, based on user usage frequency and light intensity data, to predict future brightness requirements. Time series analysis or machine learning methods can be used. Use the ARIMA model to predict the future brightness requirements of users and analyze the trend of brightness changes in historical data. Through the prediction model, generate the predicted value of the brightness requirement at the current time point. This value will provide a basis for calculating the subsequent light intensity output parameters. The model predicts that the user's brightness requirement in the next time period is 400 lux, which will be used as the target brightness value for subsequent adjustment. According to the predicted value of the brightness requirement and the real-time bathroom light distribution network, calculate the light intensity output parameters. These parameters should be able to ensure the realization of the user's brightness requirement. If the brightness requirement is 400 lux and the current light intensity is 300 lux, then the output needs to be increased by 100 lux. Adjust the brightness output of the LED lights according to the calculated light intensity output parameters to meet the user's needs. Ensure that the adjustment process is smooth and avoid sudden changes. Smoothly increase the brightness output of the LED lights through PWM (pulse width modulation) technology to achieve the required light intensity. Record the light intensity output parameters and their adjustment results for subsequent optimization and adjustment. Establish a feedback mechanism to ensure that the light intensity output can be monitored and adjusted in real time during user usage.

[0064] In this embodiment, the specific steps for performing real-time brightness requirement prediction based on all user usage behavior information to generate a predicted value of the brightness requirement are as follows:

[0065] Extract the historical usage brightness of the user according to all user usage behavior information;

[0066] Calculate the usage frequency of the historical usage brightness of the user;

[0067] Analyze the distribution of brightness values for the usage frequency to generate a brightness value distribution map;

[0068] Calculate the usage brightness with the highest frequency based on the brightness value distribution map;

[0069] Identify the usage environment brightness based on the usage brightness with the highest frequency, and extract the corresponding usage environment brightness;

[0070] Mine the user's brightness preference based on the corresponding usage environment brightness and the usage brightness with the highest frequency, so as to generate user brightness preference features;

[0071] Predict the real-time brightness demand based on the user brightness preference features, so as to generate a brightness demand prediction value.

[0072] In this embodiment, all usage behavior information of the user is collected, which should include the brightness value at each use. The brightness parameters when the user turns on the LED lamp each time are recorded by the monitoring system, and these data are sorted to form a historical usage brightness data set. Assume that the usage records of the user at different time periods are: Usage time 1: 2023-04-01 08:00:00, brightness: 300 lux; Usage time 2: 2023-04-01 08:05:00, brightness: 400 lux; Usage time 3: 2023-04-01 08:10:00, brightness: 350 lux. Clean the collected brightness data, remove outliers and duplicate records to ensure the accuracy and integrity of the data. A brightness range (such as 100 - 800 lux) can be set to filter out records that do not meet the range. If the brightness of a certain record is 900 lux, it is regarded as an outlier and deleted.

[0073] Organize the cleaned brightness data into a list or data frame for subsequent analysis. Each record should contain information such as user ID, usage time, brightness value, etc. The record format is: user ID: 001, brightness record: [(time1, 300 lux), (time2, 400 lux), (time3, 350 lux)]. Based on the historical usage brightness data, count the usage frequency of each brightness value. The histogram method can be used to divide the brightness values into several intervals (e.g., every 50 lux as an interval) and count the number of times used in each interval. The statistical results may show that the usage frequency in the brightness interval of 300 - 350 lux is 10 times, and the usage frequency in the interval of 350 - 400 lux is 15 times. Organize the statistically obtained usage frequency data into a frequency table, recording each brightness value and its corresponding usage frequency. This will provide the basic data for subsequent analysis of the brightness value distribution. Record the frequency statistics results and visualize them to generate a brightness value distribution graph for intuitively showing the distribution of users' historical usage brightness. Analyze the generated brightness value frequency table and calculate statistical indicators such as the mean and standard deviation of the brightness values to deeply understand users' brightness usage habits. If the calculated mean brightness value is 365 lux and the standard deviation is 25 lux, it can be inferred that users' brightness preferences are relatively concentrated. Extract the brightness value with the highest frequency from the frequency table to identify the brightness most frequently used by users. The representative value of the interval can be determined by finding the brightness interval with the highest frequency. If the usage frequency in the interval of 350 - 400 lux is the highest (15 times), then the brightness with the highest frequency of use can be considered as 375 lux (the median of the interval). Record the analysis results, including the brightness value with the highest frequency and its corresponding usage frequency, and generate corresponding charts for visualization. The record format is: highest frequency brightness value: 375 lux, usage frequency: 15 times, and generate a pie chart to show the proportion of each brightness frequency.

[0074] During user usage, the light data of the bathroom environment is monitored in real time. A light sensor is used to obtain the current ambient light intensity for environmental brightness recognition. The sensor records an ambient light intensity of 200 lux when the user is using. The collected ambient light data is compared with the user's highest frequency brightness value to identify the brightness characteristics of the current usage environment. The recording format is: current ambient light intensity: 200 lux, highest frequency brightness value: 375 lux. Record the relationship between the current ambient brightness and the user's preferred brightness value for subsequent analysis of the user's brightness preference characteristics. Record the difference between the ambient brightness and the preferred brightness as 175 lux and analyze the impact of this difference on the user experience. Based on the current ambient brightness and the user's highest frequency brightness value, calculate the user's brightness preference characteristics. The preference characteristics can be set as the user's brightness requirements under different ambient light conditions. If the user prefers 375 lux when the ambient light is 200 lux, this preference characteristic can be recorded. Classify the user's brightness preference characteristics to form a data file of the user's brightness preferences. This will provide a basis for subsequent brightness requirement prediction. The recording format is: user ID: 001, ambient light: 200 lux, brightness preference: 375 lux, preference characteristic: [difference between ambient light and preferred brightness 175 lux]. Analyze the mined brightness preference characteristics to identify the brightness requirement characteristics of different users in specific environments for personalized brightness adjustment. The analysis finds that users tend to prefer higher brightness settings in low light environments and lower brightness in high light environments. Based on the user's brightness preference characteristics, construct a real-time brightness requirement prediction model. Machine learning algorithms such as linear regression or decision tree can be used to predict future brightness requirements. Collect the brightness requirement data of users under different ambient light conditions and train the model to identify potential brightness requirement patterns. Use the constructed prediction model to predict the brightness requirement in the current ambient light condition in real time. Input the current ambient light intensity and the user preference characteristics to generate a predicted brightness requirement value. If the current ambient light is 200 lux, the model predicts a brightness requirement of 350 lux. Record the predicted brightness requirement value and analyze it to identify its impact on the user experience. This will provide a basis for subsequent LED light adjustment. The recording format is: prediction time: 2023-04-01 08:30:00, predicted brightness requirement: 350 lux, status: qualified.

[0075] In this embodiment, refer to Figure 4 , which is a schematic diagram of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0076] When it is detected that the user is using the bathroom mirror, perform immediate bathroom mirror lighting control according to the initial light intensity output parameters, and obtain the environmental humidity monitoring parameters based on the sensor.

[0077] Calculate the air water vapor concentration based on the environmental humidity monitoring parameters to obtain the bathroom water vapor concentration value;

[0078] Analyze the concentration trend change of the bathroom water vapor concentration value to obtain the water vapor concentration trend change characteristics;

[0079] Conduct a quantitative correlation analysis of the mirror surface water mist blur based on the bathroom water vapor concentration value to construct a quantitative relationship of the mirror surface water mist blur;

[0080] Based on the quantitative relationship of the mirror surface water mist blur, conduct an evolution of the mirror surface blur situation for the water vapor concentration trend change characteristics, and construct a mirror surface blur situation evolution diagram.

[0081] In this embodiment, when the system detects that the user uses the bathroom mirror (through a motion sensor or an infrared sensor), the LED light is immediately turned on, and the lighting control is performed according to the previously calculated initial light intensity output parameters. This process ensures that the user can obtain the required brightness when using the bathroom mirror. Assume that the initial light intensity output parameter is 400 lux. The system will immediately adjust the brightness of the LED light to ensure that this brightness level is reached when the user uses it. By adjusting the power of the LED light or using pulse width modulation (PWM) technology, the light intensity output is precisely controlled. This technology allows for a smooth transition between different brightness levels and avoids sudden brightness changes. If the current illumination is 200 lux and the target is 400 lux, the system will gradually increase the brightness, for example, increasing by 50 lux per second until the target value is reached. The system should monitor the lighting effect in real time to ensure that the actual output is consistent with the target. A light sensor can be used to feedback the current illumination intensity. If a deviation is found, the system should automatically adjust. If the sensor detects that the actual illumination is 380 lux, the system will further increase the brightness until it reaches 400 lux. Install a humidity sensor in the bathroom to ensure that it can monitor the environmental humidity change in real time. The sensor should have high precision and fast response capabilities to obtain humidity data in a timely manner. Set the sensor to record the humidity value once a minute, and the monitoring data includes the timestamp and the current humidity percentage. Store the humidity monitoring data in the system database for subsequent analysis. The data format should be structured for easy extraction and processing. The recording format is: timestamp: 2023-04-01 08:00:00, humidity: 70%. These data will provide a basis for subsequent water vapor concentration calculations. During the user's use of the bathroom mirror, continuously obtain the environmental humidity monitoring parameters to ensure the real-time and accuracy of the data. This will help evaluate the water vapor concentration in the bathroom. Record the humidity value in real time every time the user uses it, and ensure that the data is continuously updated during the user's use. Calculate the water vapor concentration in the air according to the environmental humidity monitoring parameters. The following formula can be used for the calculation: where, C v is the water vapor concentration (g / m 3 ), H is the relative humidity (%), P sis the saturated water vapor pressure (Pa), R is the gas constant, and T is the absolute temperature (K). Assuming a relative humidity of 70% and a temperature of 25°C (298K), the saturated water vapor pressure can be obtained from a table as 3168 Pa. Substitute it into the formula to calculate the water vapor concentration. Record the calculated water vapor concentration value in the system for subsequent analysis. Ensure that each calculation result is associated with a timestamp for easy tracking. The recording format is: timestamp: 2023-04-01 08:00:05, water vapor concentration: 14.7 g / m 3 . Organize the water vapor concentration data to form a clear dataset. The concentration change can be visualized through a chart to help analyze its trend. Generate a line chart with the X-axis as time and the Y-axis as the water vapor concentration to show the trend of concentration change over time. Conduct a trend analysis on the collected water vapor concentration data, calculate the average value, standard deviation, and change rate of the concentration to identify trend characteristics. If within the past hour, the water vapor concentration increased from 10 g / m 3 to 15 g / m 3 , calculate the average change rate as 0.083 g / m 3 / min. Extract the characteristics of the water vapor concentration change, such as the rising rate, stable period, and falling period. The stage of concentration change can be judged by setting thresholds. If the concentration continuously rises by more than 3 g / m 3 within 15 minutes, it can be marked as the "rapid rising stage". Record the characteristics of the concentration trend change and visualize them through a chart for easy intuitive understanding of the concentration change. Define the blurriness index of the mirror fog. Usually, physical properties such as light transmittance and reflectance are used to quantify the influence degree of the fog. Set the blurriness as the percentage decrease in light transmittance. If the light transmittance decreases from 90% to 70%, the blurriness is 22.2%. Analyze the relationship between the water vapor concentration and the mirror blurriness, and establish a quantitative model. The correlation between the two can be determined through regression analysis. If through data analysis, it is found that for every 1 g / m 3 increase in the water vapor concentration, the blurriness increases by 2%, record this relationship. Record the analysis results of the fuzzy quantitative relationship in the system and visualize them through a chart to help understand the quantitative relationship between the water vapor concentration and the mirror blurriness. Generate a scatter plot with the X-axis as the water vapor concentration and the Y-axis as the blurriness to show the linear relationship between the two. Based on the water vapor concentration change trend and the mirror fuzzy quantitative relationship, analyze the evolution of the mirror blurriness situation. A dynamic model can be used to simulate the influence of the water vapor concentration change on the mirror blurriness. If the concentration rises rapidly in a short period, it can be speculated that the mirror blurriness increases sharply in a short period. According to the analysis results, construct an evolution diagram of the mirror blurriness situation to show how the water vapor concentration change affects the trend of the mirror blurriness. The diagram should include a time axis and a blurriness change curve. Generate a dynamic change diagram with the X-axis as time and the Y-axis as the blurriness to show the influence of the water vapor concentration on the mirror blurriness at different time points.

[0082] In this embodiment, step S4 includes the following steps:

[0083] Identify the water mist accumulation and flow direction on the mirror surface according to the mirror surface blur situation evolution diagram;

[0084] Conduct mirror surface heat effect defogging analysis based on the water mist accumulation and flow direction to obtain the mirror surface heat effect defogging rule;

[0085] Conduct mirror surface heating treatment based on the mirror surface heat effect defogging rule and analyze the real-time blur situation of the mirror surface;

[0086] Speculate on the mirror surface clarity of the real-time blur situation to obtain the real-time clarity evaluation value under heat effect defogging;

[0087] Conduct dynamic heating temperature adaptive adjustment on the real-time clarity evaluation value to generate an adaptive water mist blur adjustment strategy.

[0088] In this embodiment, based on the mirror surface blur situation evolution diagram, image processing technology is used to analyze the water mist distribution on the mirror surface. Methods such as edge detection and threshold segmentation can be used to identify the boundary of the water mist from the image. By analyzing the blur situation diagram, the edge area of the water mist is identified, and the area and distribution density of the water mist are calculated. These data will help determine the water mist accumulation area and flow direction. By comparing consecutive frame images, the movement trajectory and flow direction of the water mist are calculated. Optical flow method or motion vector analysis can be used to track the changes of the water mist on the mirror surface. If the center position of the water mist changes between two consecutive time points, its displacement is calculated to determine the flow direction. Record the angle and speed of the flow direction for subsequent analysis. Record the analysis results of the water mist accumulation area and flow direction in the system and visualize them through charts to help users intuitively understand the dynamic characteristics of the water mist. Generate a graph showing the hot spots of water mist accumulation and flow direction arrows, clearly identifying the distribution of the water mist. Based on the water mist accumulation and flow direction, a heat effect model is constructed. This model should consider factors such as the thermal conductivity of the mirror material, environmental temperature, and water vapor concentration to analyze the influence of the heat effect on mirror surface defogging. Set the heat effect model formula as: Q = k·A·(T m -T e ), where Q is the heat, k is the thermal conductivity, A is the mirror surface area, T m is the mirror surface temperature, T eis the ambient temperature. Through experimental data collection, analyze the dissipation speed and clarity change of mirror surface water mist under different temperature conditions. Record the relationship between the time when the water mist disappears and the mirror surface clarity at different heating temperatures. If the water mist disappears in 30 seconds at 60°C and in 50 seconds at 50°C, the relationship between temperature and defogging efficiency can be inferred. According to the heat effect defogging law, configure a mirror surface heating system. The system should have a heating element with adjustable temperature and be able to monitor the mirror surface temperature in real time. Set the power of the heating element to 100W to quickly increase the mirror surface temperature in a short time. While performing the mirror surface heating treatment, monitor the blurring trend of the mirror surface in real time. Through a light sensor or an image sensor, obtain the data of the mirror surface clarity change. During the heating process, record the clarity value of the mirror surface every 1 second and compare it with the previous blurring trend. Record the change of the blurring trend after the mirror surface heating treatment and analyze the influence of heating on the mirror surface clarity. The recording format is: heating time: 30 seconds, mirror surface temperature: 60°C, clarity evaluation value: 80% (20% higher than the previous value). Set the evaluation criteria for the mirror surface clarity. Quantifiable indicators such as light transmittance or blurriness index can be used. Set the clarity as a percentage of the light transmittance. If the light transmittance is 90%, the clarity is evaluated as 90%, and if it drops to 70%, it is evaluated as blurry. During the mirror surface heating treatment, infer the change of the mirror surface clarity in combination with the real-time monitoring data. According to the current temperature and water mist concentration, infer the improvement of the mirror surface clarity. If the clarity increases from 70% to 85% when heated to 55°C, record this change. Record the real-time clarity evaluation value in the system and establish a feedback mechanism to adjust the heating strategy when the clarity does not meet the expectation. The recording format is: time: 2023-04-01 08:15:00, real-time clarity: 85%, status: qualified; if the clarity fails to reach the target, the system will automatically increase the heating temperature. Based on the real-time clarity evaluation value, design a dynamic heating temperature adaptive adjustment strategy. The strategy should be able to automatically adjust the heating temperature according to the clarity change to achieve the best defogging effect. Set the rule: if the real-time clarity is lower than 75%, the heating temperature increases by 5°C; if it is higher than 85%, the temperature is reduced to avoid overheating. Implement the dynamic adjustment strategy and adjust the heating temperature in real time. The system should have a feedback function and automatically adjust the temperature according to the current clarity and environmental changes. When the clarity evaluation value is 70%, the system will automatically adjust the heating temperature from 55°C to 60°C.

[0089] In this embodiment, step S5 includes the following steps:

[0090] Conduct an analysis of the mirror surface water vapor distribution on the mirror surface blurring trend evolution diagram to obtain the mirror surface water vapor distribution characteristics;

[0091] Based on the mirror surface water vapor distribution characteristics, calculate the mirror surface brightness attenuation trend and generate the mirror surface brightness attenuation trend;

[0092] Based on the trend of mirror brightness attenuation, perform dynamic brightness compensation optimization for the LED lamp to generate dynamic brightness compensation parameters;

[0093] Calculate the illumination color temperature parameters of the mirror according to the mirror blur situation evolution diagram;

[0094] Perform adaptive illumination color temperature adaptation on the trend change characteristics of the water vapor concentration and the illumination color temperature parameters to generate color temperature adaptation parameters;

[0095] According to the dynamic brightness compensation parameters and the color temperature adaptation parameters, perform dynamic illumination parameter adjustment on the initial light intensity output parameters to construct a dynamic illumination adjustment strategy.

[0096] In this embodiment, water vapor distribution data is extracted from the mirror blur situation evolution diagram. This can be achieved by analyzing the density and distribution of the blurred area using image processing techniques. Methods such as threshold segmentation and region analysis are used to identify the distribution characteristics of water vapor. A blur threshold of 50% is set, the pixels of the blurred area are extracted, and the proportion of the total mirror area they occupy is calculated to obtain the distribution characteristics of water vapor. By analyzing the distribution characteristics of water vapor, relevant parameters such as the concentration, coverage area, and maximum concentration of the distribution are extracted. These characteristics will help understand the impact of water vapor on the mirror clarity. If the extraction result shows that the water vapor is concentrated in the center of the mirror, the coverage area is 60% and the concentration is 15 g / m 3, then record these characteristic data. Record the water vapor distribution characteristics in the system and generate a visualization chart to facilitate the user's intuitive understanding of the dynamic distribution of water vapor. Generate a heat map to show the distribution of water vapor on the mirror surface, with colors representing different concentration levels, to help analyze its impact on the mirror clarity. Collect the brightness values of the mirror at different time points and record the brightness changes after each user use. The brightness of the mirror can be monitored in real time through a light sensor. Set the monitoring time period to once per minute and record the brightness values: Timestamp 1: 2023-04-01 08:00:00, Brightness: 400 lux; Timestamp 2: 2023-04-01 08:01:00, Brightness: 380 lux. Use linear regression or time series analysis methods to calculate the attenuation trend of the mirror brightness. Analyze the change of brightness over time and identify the brightness attenuation rate. If the brightness drops from 400 lux to 360 lux within 10 minutes, the attenuation rate is 4 lux / min, and record this rate. Based on the mirror brightness attenuation trend, set up a calculation model for dynamic compensation parameters. This model should consider the brightness attenuation rate and the user's usage habits to dynamically adjust the brightness output of the LED lights. If it is set that when the attenuation rate is 4 lux / min, the LED lights should compensate 5 lux of brightness in advance, then calculate the compensation parameter as 5 lux. Design a dynamic brightness compensation strategy so that the brightness of the LED lights can be automatically adjusted during user use to offset the attenuation of the mirror brightness. This strategy should have real-time response capabilities. If the user's usage time is 10 minutes, the system will monitor the brightness every minute and correspondingly increase the brightness of the LED lights to maintain a stable 400 lux. Record the dynamic brightness compensation parameters in the system and conduct real-time verification to ensure the effectiveness of the compensation strategy. Record each adjusted parameter and its impact on the mirror brightness. The recording format is: Compensation Time: 2023-04-01 08:10:00, Compensation Parameter: 5 lux, Adjusted Brightness: 400 lux, Status: Qualified. Monitor the illumination color temperature of the LED lights in real time through a color temperature sensor to obtain the color temperature value of the current light source. The color temperature is usually expressed in Kelvin (K) and reflects the hue of the light source. Assume that the current color temperature of the LED lights is monitored to be 5000 K, which will be used as the basic data for subsequent analysis. Record the obtained color temperature data in the system for subsequent analysis. Timed monitoring can be set to record the color temperature changes in different time periods. The recording format is: Timestamp: 2023-04-01 08:00:00, Color Temperature: 5000 K. These data will provide a basis for subsequent color temperature adjustment. Based on the trend change characteristics of the water vapor concentration and the illumination color temperature parameters, construct an adaptive illumination color temperature model. This model should consider the environmental humidity and the mirror clarity to optimize the color temperature output. If the increase in water vapor concentration leads to a decrease in clarity, automatically adjust the color temperature to a warm color tone (such as 3000 K) to improve the user's visual comfort.Design a color temperature adaptation strategy so that the color temperature of the LED light can be automatically adjusted under different environmental conditions to achieve the best usage effect. Set the rules: when the environmental humidity exceeds 70%, the color temperature is automatically adjusted to the warm color temperature of 3000K; when the humidity is below 50%, the color temperature returns to the standard color temperature of 5000K. Construct a comprehensive dynamic lighting adjustment strategy based on the dynamic brightness compensation parameter and the color temperature adaptation parameter. This strategy should be able to respond to user needs and environmental changes in real time to optimize the lighting effect. Set that when the user is using it, if a decrease in clarity is detected, the system will automatically increase the brightness and adjust the color temperature to maintain the best visual effect. Implement the dynamic lighting adjustment strategy, monitor the environment and user status in real time, and automatically adjust the lighting parameters to meet the usage requirements. After the user enters the bathroom, the system will detect the environmental humidity and mirror clarity, dynamically adjust the brightness of the LED light to 400 lux, and adjust the color temperature according to the humidity change. Record the implementation results of the dynamic lighting adjustment strategy in the system and establish a feedback mechanism to ensure that the lighting effect can be optimized in real time during the user's use. The recording format is: adjustment time: 2023-04-01 08:30:00, brightness: 400 lux, color temperature: 3000K, user satisfaction: high.

[0097] In this embodiment, step S6 includes the following steps:

[0098] Perform dynamic control of the LED light of the bathroom mirror based on the adaptive water mist blur adjustment strategy and the dynamic lighting adjustment strategy, and obtain the feedback information of the user;

[0099] Deeply mine multi-scenario requirements based on the feedback information of the user to obtain the multi-scenario requirement characteristics of the user;

[0100] Perform intelligent reinforcement transfer learning according to the multi-scenario requirement characteristics of the user to construct an intelligent bathroom light control engine.

[0101] In this embodiment, based on the previously constructed adaptive water mist blur adjustment strategy and dynamic lighting adjustment strategy, the LED lamp control system is activated. The system should automatically adjust the brightness and color temperature of the LED lamp according to real-time environmental parameters (such as humidity, temperature, and user behavior) to provide the best user experience. When the user enters the bathroom, the system automatically detects that the humidity is 70%, adjusts the light brightness to 400 lux, and at the same time adjusts the color temperature to a warm tone (about 3000 K) to reduce the impact of water mist blur on the line of sight. At an appropriate time after the user uses the bathroom mirror, actively collect the user's feedback information. The user can provide satisfaction feedback on the lighting effect, clarity, and overall user experience through methods such as touch screens, mobile applications, or voice assistants. Set up a feedback questionnaire, which includes questions such as "Your satisfaction with the current lighting brightness (1-5 points)" and "Whether the mirror clarity meets the expectations (yes / no)". Record the user's feedback data for subsequent analysis. Organize and store the user's feedback information in the database, ensuring that each piece of feedback data is associated with a timestamp and a user ID for subsequent analysis and tracking. The record format is: timestamp: 2023-04-01 08:30:00, user ID: 001, lighting satisfaction: 4, clarity satisfaction: yes. Ensure the integrity and accuracy of the data to provide a basis for subsequent requirement mining. Conduct in-depth analysis of the collected user feedback information to identify the demand characteristics of users in different usage scenarios. This can be achieved through data mining techniques such as clustering analysis and association rule mining. Use the K-means clustering method to divide the user's feedback data into several main groups, and identify the commonalities and differences between highly satisfied and low-satisfied users. Based on the results of the demand analysis, extract the demand characteristics of users in different scenarios, such as lighting requirements and mirror clarity requirements in the morning, evening, or during bathing. Record these characteristics for subsequent modeling. The analysis results may show that in the morning, users prefer a brightness of 400 lux and a color temperature of 3000 K; while in the evening, they prefer a brightness of 350 lux and a color temperature of 4500 K. Record the extracted multi-scenario demand characteristics of users in the system, and display the demand differences in different scenarios through visual charts to help understand the changes in user preferences. Generate a radar chart to show the lighting and color temperature preferences in different time periods, making the user demand characteristics more intuitive. Based on the multi-scenario demand characteristics of users, design a reinforcement learning model to optimize the control strategy of the LED lamp. The model should be able to learn the user's usage habits and feedback, and dynamically adjust the control strategy to adapt to the changes in user needs. Use the Q-learning algorithm, set the state space as environmental humidity, lighting brightness, user feedback, etc., the action space as the adjustment of the brightness and color temperature of the LED lamp, and the reward function is set according to the satisfaction of the user feedback. Utilize the existing user feedback data and scenario demand characteristics to implement transfer learning and optimize the learning process. Accelerate the learning in new scenarios through a pre-trained model to achieve faster adaptation.If there is a successful experience of existing users under usage conditions, apply it to the scenarios of new users to improve the learning efficiency of the system in the initial stage. Integrate the control strategy optimized by reinforcement learning into the intelligent bathroom light control engine. Conduct system tests to verify the effectiveness of the control engine under different usage scenarios. The test results show that in a high-humidity environment, when the user feedback satisfaction increases by 20%, the control engine can automatically adjust the brightness and color temperature to ensure the best usage experience.

[0102] In this embodiment, an LED lamp adaptive control device for an LED backlight bathroom mirror is provided, including:

[0103] A behavior prediction module, configured to obtain the monitoring log of the LED lamp of the bathroom mirror, perform statistics on the usage time frequency distribution and predict the usage behavior, and generate usage behavior prediction time points;

[0104] A brightness demand prediction module, configured to perform real-time brightness demand prediction according to the usage behavior prediction time points, and calculate the light intensity output parameters to obtain the initial light intensity output parameters;

[0105] A mirror surface blur analysis module, configured to perform immediate bathroom mirror light control according to the initial light intensity output parameters, obtain the environmental humidity monitoring parameters; perform mirror surface blur situation analysis according to the environmental humidity monitoring parameters, and construct a mirror surface blur situation evolution diagram;

[0106] A heating temperature adjustment module, configured to perform mirror surface heat effect defogging analysis and perform dynamic heating temperature adaptive adjustment according to the mirror surface blur situation evolution diagram to generate an adaptive water mist blur adjustment strategy;

[0107] A dynamic light adjustment module, configured to perform dynamic brightness compensation optimization according to the mirror surface blur situation evolution diagram, and perform adaptive light color temperature adaptation to construct a dynamic light adjustment strategy;

[0108] An intelligent control module, configured to perform dynamic bathroom mirror LED lamp control and perform intelligent reinforcement transfer learning based on the adaptive water mist blur adjustment strategy and the dynamic light adjustment strategy to construct an intelligent bathroom light control engine.

[0109] The present invention constructs a user usage behavior portrait based on log data such as the on-time, frequency, and usage duration of LED lights, realizing the transformation from "passive response" to "active prediction". It avoids the unnecessary lighting of LED lights during periods of no use, effectively reducing energy consumption and extending the service life of the lamps. It can integrate sensor (such as human presence detection) data for multi-user behavior separation and personalized analysis, enhancing the degree of intelligence. It can automatically adjust the light intensity according to historical preferences and ambient brightness before the user is about to use the bathroom mirror, improving the response speed and usage experience. According to the lighting demand preferences of different users (such as high brightness for men's shaving and soft light for women's makeup), it realizes personalized initial light settings. By establishing a mirror fogging model with real-time ambient humidity data (linked to temperature and usage time), it can predict changes in the fogging degree. It quantifies the fogging state and constructs an evolution map of "clear - slightly fogged - severely fogged" to provide a visual basis for defogging decisions. It identifies actual fogged or about-to-fog scenarios, effectively avoiding false triggering of heating and saving energy. The fogging state of the mirror affects the light reflection effect, and it can be linked with the lighting change to judge the fogging level, improving the analysis accuracy. It matches a reasonable heating power and duration according to the fogging degree, avoiding increased energy consumption due to overheating or incomplete defogging due to insufficient heating. It can adjust the temperature control algorithm for different environments (winter / summer, humid / dry) and mirror fogging levels to enhance adaptability. It avoids repeated fogging or clearing of the mirror during the defogging process, always maintaining visual clarity and improving the usage comfort in high-humidity environments. When the mirror is not fogged or only slightly fogged, it adopts a low-power mode to achieve "defogging on demand". In the case of slightly fogged mirror, it improves the reflection clarity through brightness compensation without immediately starting the defogging device. For example, it can automatically adjust the color temperature to match the scene, such as warm light in the early morning to help wake up, cold light at night to help sleep, and high color rendering index light source for makeup. By intelligently adjusting the lighting angle and brightness gradient, it alleviates visual problems such as glare and light spots caused by mirror fog. Dynamic lighting enhances visual safety, especially providing a clearer view in high-humidity scenarios after hot showers and reducing usage risks. It adjusts the control strategy through user feedback (such as the frequency of manual intervention and usage satisfaction) to improve long-term performance.

[0110] Therefore, in every aspect, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed by the present invention.

[0111] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. An LED lamp adaptive control method for an LED backlight bathroom mirror, characterized in that, It includes the following steps: Step S1: Obtain the monitoring log of the bathroom mirror LED light, conduct statistics on the usage time frequency distribution and predict the usage behavior, and generate the predicted time points of the usage behavior; Step S2: Predict the real-time brightness requirement according to the predicted time points of the usage behavior, and calculate the light intensity output parameters to obtain the initial light intensity output parameters; Step S3: Conduct immediate lighting control of the bathroom mirror according to the initial light intensity output parameters, and obtain the environmental humidity monitoring parameters; conduct an analysis of the mirror surface blur situation based on the environmental humidity monitoring parameters, and construct an evolution graph of the mirror surface blur situation; Step S4: Conduct an analysis of defogging the mirror surface thermal effect and perform adaptive adjustment of the dynamic heating temperature according to the evolution graph of the mirror surface blur situation to generate an adaptive water mist blur adjustment strategy; Step S5: Conduct dynamic brightness compensation optimization according to the evolution graph of the mirror surface blur situation, and perform adaptive lighting color temperature adaptation to construct a dynamic lighting adjustment strategy; Step S6: Based on the adaptive water mist blur adjustment strategy and the dynamic lighting adjustment strategy, conduct dynamic control of the bathroom mirror LED light and perform intelligent reinforcement transfer learning to construct an intelligent bathroom light control engine.

2. The LED lamp adaptive control method of the LED backlight bathroom mirror according to claim 1, characterized in that, The specific steps of Step S1 are as follows: Obtain the monitoring log of the bathroom mirror LED light; conduct an analysis of the user usage behavior on the monitoring log, and extract all user usage behavior information; Calculate the usage time period for the user usage behavior information and mark multiple usage time periods; Conduct statistics on the user usage time frequency distribution according to multiple usage time periods to obtain a usage time frequency distribution graph; Conduct an analysis of the usage time requirement on the usage time frequency distribution graph to obtain the historical usage time requirement characteristics; Based on the historical usage time requirement characteristics, conduct a prediction of the usage behavior to generate the predicted time points of the usage behavior.

3. The LED lamp adaptive control method for the LED backlight bathroom mirror according to claim 1, characterized in that, The specific steps of Step S2 are as follows: Obtain the real-time bathroom environment lighting parameters according to the predicted time points of the usage behavior; Calculate the lighting intensity parameter of the real-time bathroom environment lighting parameters; Conduct an evolution of the light intensity time series fluctuation of the lighting intensity parameter to obtain the bathroom light intensity time series fluctuation characteristics; Based on the real-time bathroom environment lighting parameters, conduct an identification of the light distribution to obtain the bathroom light spatial distribution data; According to the bathroom light spatial distribution data, conduct a spatio-temporal light distribution fitting on the bathroom light intensity time series fluctuation characteristics to construct a real-time bathroom light distribution network; Predict the real-time brightness requirement according to all the user usage behavior information to generate a brightness requirement prediction value; Calculate the light intensity output parameters according to the brightness requirement prediction value and the real-time bathroom light distribution network to obtain the initial light intensity output parameters.

4. The LED lamp adaptive control method of the LED backlight bathroom mirror according to claim 1, characterized in that The specific steps of predicting the real-time brightness requirement according to all the user usage behavior information to generate a brightness requirement prediction value are as follows: Extract the user's historical usage brightness according to all the user usage behavior information; Calculate the usage frequency of the user's historical usage brightness; Conduct an analysis of the brightness value distribution of the usage frequency to generate a brightness value distribution graph; Calculate the usage brightness with the highest frequency according to the brightness value distribution graph; Conduct an identification of the usage environment brightness according to the usage brightness with the highest frequency, and extract the corresponding usage environment brightness; Mining user brightness preferences based on the corresponding usage environment brightness and the highest frequency usage brightness, thereby generating user brightness preference features; Based on the user brightness preference features, predicting the real-time brightness requirements, thereby generating a brightness requirement prediction value.

5. The LED lamp adaptive control method of the LED backlight bathroom mirror according to claim 1, wherein The specific steps of step S3 are as follows: When it is detected that the user uses the bathroom mirror, perform immediate bathroom mirror lighting control according to the initial light intensity output parameters, and obtain the environmental humidity monitoring parameters based on the sensor; Calculate the air water vapor concentration according to the environmental humidity monitoring parameters to obtain the bathroom water vapor concentration value; Perform a concentration trend change analysis on the bathroom water vapor concentration value to obtain the water vapor concentration trend change characteristics; Perform a quantitative correlation analysis of mirror surface water mist blur according to the bathroom water vapor concentration value to construct a quantitative relationship of mirror surface water mist blur; Based on the quantitative relationship of mirror surface water mist blur, perform an evolution of the mirror surface blur situation on the water vapor concentration trend change characteristics, and construct a mirror surface blur situation evolution diagram.

6. The LED lamp adaptive control method of the LED backlight bathroom mirror according to claim 1, characterized in that, The specific steps of step S4 are as follows: Identify the water mist accumulation and flow direction of the mirror surface according to the mirror surface blur situation evolution diagram; Perform a mirror surface heat effect defogging analysis based on the water mist accumulation and flow direction to obtain the mirror surface heat effect defogging law; Perform a mirror surface heating treatment based on the mirror surface heat effect defogging law, and analyze the real-time blur situation of the mirror surface; Speculate on the clarity of the mirror surface for the real-time blur situation to obtain a real-time clarity evaluation value under the heat effect defogging; Perform a dynamic heating temperature adaptive adjustment on the real-time clarity evaluation value to generate an adaptive water mist blur adjustment strategy.

7. The LED lamp adaptive control method of the LED backlight bathroom mirror according to claim 1, characterized in that The specific steps of step S5 are as follows: Perform a mirror surface water vapor distribution analysis on the mirror surface blur situation evolution diagram to obtain the mirror surface water vapor distribution characteristics; Calculate the mirror surface brightness attenuation trend based on the mirror surface water vapor distribution characteristics to generate a mirror surface brightness attenuation trend; Perform an optimization of the dynamic brightness compensation of the LED lamp based on the mirror surface brightness attenuation trend to generate dynamic brightness compensation parameters; Calculate the illumination color temperature parameters of the mirror surface according to the mirror surface blur situation evolution diagram; Perform an adaptive illumination color temperature adaptation on the water vapor concentration trend change characteristics and the illumination color temperature parameters to generate color temperature adaptation parameters; Adjust the initial light intensity output parameters according to the dynamic brightness compensation parameters and the color temperature adaptation parameters to construct a dynamic illumination adjustment strategy.

8. The LED lamp adaptive control method of the LED backlight bathroom mirror according to claim 1, characterized in that, The specific steps of step S6 are as follows: Perform a dynamic bathroom mirror LED lamp control based on the adaptive water mist blur adjustment strategy and the dynamic illumination adjustment strategy, and obtain the feedback information of the user; Deeply mine the multi-situation requirements based on the feedback information of the user to obtain the user multi-situation requirement characteristics; Perform intelligent reinforcement transfer learning according to the user multi-situation requirement characteristics to construct an intelligent bathroom lamp control engine.

9. An LED lamp adaptive control device for an LED backlight bathroom mirror, characterized in that, For executing the LED lamp adaptive control method of the LED backlight bathroom mirror as described in claim 1, including: A behavior prediction module, configured to obtain the monitoring log of the bathroom mirror LED lamp, perform a statistical analysis of the usage time frequency distribution and usage behavior prediction, and generate a usage behavior prediction time point; A brightness requirement prediction module, configured to predict the real-time brightness requirement according to the usage behavior prediction time point, and perform a calculation of the light intensity output parameters to obtain the initial light intensity output parameters; A mirror surface blur analysis module, which is used to perform instant bathroom mirror lighting control according to the initial light intensity output parameters and obtain environmental humidity monitoring parameters; perform mirror surface blur situation analysis according to the environmental humidity monitoring parameters and construct a mirror surface blur situation evolution diagram; A heating temperature adjustment module, which is used to perform mirror surface thermal effect defogging analysis and dynamic heating temperature adaptive adjustment according to the mirror surface blur situation evolution diagram to generate an adaptive water mist blur adjustment strategy; A dynamic lighting adjustment module, which is used to perform dynamic brightness compensation optimization according to the mirror surface blur situation evolution diagram, perform adaptive lighting color temperature adaptation, and construct a dynamic lighting adjustment strategy; An intelligent control module, which is used to perform dynamic bathroom mirror LED lamp control and perform intelligent enhanced transfer learning based on the adaptive water mist blur adjustment strategy and the dynamic lighting adjustment strategy to construct an intelligent bathroom lamp control engine.

Images