A LED backlight intelligent dimming method and system

Abstract

The present invention discloses an LED backlight intelligent dimming method and system, and relates to the field of lighting technology. When the system is running, indoor and outdoor environment information and user vital sign information are obtained through an environment acquisition module and a user status module, and processed and integrated through a data processing module to obtain a first data set and a second data set. A user environment user association analysis model is established through a control strategy module, and after training and analysis, an intelligent control coefficient Tkxs and a user environment association control backlight scheme, as well as a behavior habit analysis factor Xwyz and a user preset habit analysis control correction scheme are obtained. The intelligent control coefficient Tkxs and a user environment association control backlight scheme, and a behavior habit analysis factor Xwyz and a user preset habit analysis control correction scheme are specifically executed and corrected through a decision module, so as to achieve dynamic adjustment according to real-time feedback from users, reduce manual intervention of users, and enable the lighting system to not only respond to current needs, but also adapt to long-term usage habits, optimize energy use, and ultimately achieve the dual goals of energy saving and improving the comfort of living and working environments.

Description

Technical Field

[0001] The present invention relates to the field of lighting technology, and in particular to an intelligent dimming method and system for an LED backlight source. Background Art

[0002] With the rapid development of science and technology, people have higher requirements for the quality of lighting in their living and working environments. Traditional lighting systems usually only provide basic brightness adjustment and cannot meet the personalized needs of users, including adjusting the light according to different activities, time and psychological state. In addition, the stress and irregular work and rest in modern life have an impact on people's health. Studies have shown that appropriate lighting can significantly improve emotional and physical health.

[0003] In recent years, physical and emotional health have gradually been regarded as an important part of the quality of daily life. Inappropriate lighting conditions, such as too strong or too weak light, and wrong spectrum combinations, may have a negative impact on the user's vision, sleep quality, emotional state and overall physical health. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an LED backlight intelligent dimming method and system, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an LED backlight intelligent dimming system, including an environment acquisition module, a user status module, a data processing module, a control strategy module and a decision module;

[0006] The environment acquisition module acquires indoor and outdoor environment information through environment sensors and Internet crawler technology to form an environment information group;

[0007] The user status module obtains user physical sign information and user emotion information through the user's physical sign detection sensor and the face recognition sensor to form a user physical sign emotion information group;

[0008] The data processing module pre-processes the environment information group and the user's vital signs and emotions information group, and then integrates the processed environment information group into a first data set, and integrates the processed user's vital signs and emotions information group into a second data set;

[0009] The control strategy module establishes a user environment user association analysis model for the first data set and the second data set, and after training and fitting the user environment user association analysis model, obtains: an intelligent control coefficient Tkxs, and matches it with a preset user environment association control matching threshold P, obtains a user environment association control backlight scheme, and simultaneously matches and analyzes the intelligent control coefficient Tkxs with the behavior habit analysis factor Xwyz, obtains a user preset habit analysis control correction scheme, and corrects the user environment association control backlight scheme according to the user preset habit analysis control correction scheme;

[0010] The decision module is specifically executed according to the content of the backlight source control scheme associated with the user environment, and at the same time amends the backlight source control scheme associated with the user environment according to the content of the control correction scheme analyzed according to the user's preset habits.

[0011] Preferably, the environment acquisition module includes an environment acquisition unit and a network acquisition unit;

[0012] The environment collection unit collects indoor and outdoor environment data in real time by using a variety of environment sensors, including light sensors, temperature and humidity sensors, air pressure sensors, and air quality sensors;

[0013] The network acquisition unit obtains outdoor environment data related to the current positioning area from the meteorological website and the Environmental Protection Bureau website through Internet crawler technology, including wind speed changes, temperature changes, humidity changes, air quality index AQI and ultraviolet index related information;

[0014] The environmental information group is obtained by aggregating and integrating the data acquired by the environmental acquisition unit and the network acquisition unit.

[0015] Preferably, the user status module includes a user vital sign collection unit and a user emotion collection unit;

[0016] The user vital signs collection unit monitors the user's physiological parameters in real time through the smart bracelet carried by the user, including heart rate monitoring, blood pressure monitoring, blood oxygen saturation monitoring, body temperature monitoring and step number monitoring;

[0017] The user emotion acquisition unit acquires the user's facial expression captured by the camera by using facial recognition technology, and analyzes the expression by using image processing and machine learning technology to identify the user's emotional state, wherein the emotion recognition includes preset facial expression emotions related to happiness, sadness, anger and surprise;

[0018] The user vital sign emotion information group is integrated into the user vital sign emotion information group after statistics are collected from the data acquired by the user vital sign acquisition unit and the user emotion acquisition unit.

[0019] Preferably, the data processing module includes a processing unit and an integration unit;

[0020] The processing unit first preprocesses the raw data received from the environment acquisition module and the user status module, and the preprocessing includes data cleaning processing, data normalization processing, missing value processing, data denoising processing and normalization processing;

[0021] The integration unit integrates the processed environment information group into a first data set, and integrates the processed user physical sign emotion information group into a second data set;

[0022] The first data set includes: outdoor temperature value Wwd, indoor temperature value Nwd, indoor and outdoor humidity change value Sdz and outdoor wind speed change value Fsz;

[0023] The second data set includes: heart rate value Xlz, blood pressure value Xyz, body temperature value Twz, body temperature rise rate value Slz, step value Bsz and recognized emotion quantity value Qxz.

[0024] Preferably, the control strategy module includes a modeling unit and a scheme matching unit;

[0025] The modeling unit establishes a user environment and user behavior association analysis model by using machine learning algorithm technology, and trains by using data from the first data set and the second data set to obtain the relationship between user behavior and environmental factors: intelligent control coefficient Tkxs, and matches and analyzes it with the behavior habit analysis factor Xwyz;

[0026] Among them, the user's preset backlight-related preset environment habit behaviors include: activity type sequence value Hdxl, emotional state sequence value Qxxl, time period sequence value Sjxl, sequence matching brightness preference value Xlld and sequence matching color temperature preference value Xlsw;

[0027] The scheme matching unit matches the preset user environment associated control matching threshold P with the intelligent control coefficient Tkxs to obtain the user environment associated control backlight scheme, and simultaneously matches the intelligent control coefficient Tkxs with the behavior habit analysis factor Xwyz to obtain the correction factor, and obtains the user preset habit analysis and control correction scheme.

[0028] Preferably, the intelligent control coefficient Tkxs is obtained by the following calculation formula:

[0029] ;

[0030] Wherein, Tkxs represents the intelligent control coefficient, Hjyz represents the indoor and outdoor environment fluctuation factor, Tzyz represents the user's physical sign fluctuation factor, Bsz represents the step value, and Qxz represents the recognized emotion quantity value; by taking the geometric mean of the indoor and outdoor environment fluctuation factor Hjyz and the user's physical sign fluctuation factor Tzyz, the step value Bsz is combined with the square root function to represent its direct impact on the physiological state and the logarithmic function log to identify the nonlinear impact of the emotion quantity value Qxz, so as to obtain the nonlinear relationship between the simulated environment and the user's physical sign: the intelligent control coefficient Tkxs;

[0031] The indoor and outdoor environment fluctuation factor Hjyz is obtained by the following calculation formula:

[0032] ;

[0033] In the formula, Hjyz represents the indoor and outdoor environmental fluctuation factor, Wwd represents the outdoor temperature value, Nwd represents the indoor temperature value, Sdz represents the indoor and outdoor humidity change value, and Fsz represents the outdoor wind speed change value; the exponential function exp and the logarithmic function log are used to measure the impact of the indoor and outdoor temperature difference on the outdoor temperature value Wwd and the indoor temperature value Nwd using a logarithmic ratio to achieve the purpose of reducing the sensitivity to extreme values, and the hyperbolic tangent function tanh is used to smooth the impact of the humidity difference on the indoor and outdoor humidity change value Sdz, and the impact on the outdoor wind speed change value Fsz is enhanced by the square term of the square root;

[0034] The user's physical sign fluctuation factor Tzyz is obtained by the following calculation formula:

[0035] ;

[0036] Wherein, Tzyz represents the fluctuation factor of the user's physical signs, Xlz represents the heart rate value, Xyz represents the blood pressure value, Twz represents the body temperature value, and Slz represents the body temperature rise rate value; the heart rate value Xlz and the blood pressure value Xyz are combined through the sine function sin and the cosine function cos to represent the periodicity of the physiological response, and the exponential function exp negative impact of the body temperature value Twz is combined with the logarithmic function log gain of the body temperature rise rate value Slz to represent the body temperature regulation state information.

[0037] Preferably, the behavior habit analysis factor Xwyz is obtained by the following calculation formula:

[0038] ;

[0039] In the formula, Xwyz represents the behavior habit analysis factor, Hdxl represents the activity type sequence value, Qxxl represents the emotional state sequence value, Sjxl represents the time period sequence value, Xlld represents the sequence matching brightness preference value, and Xlsw represents the sequence matching color temperature preference value; the nonlinear influence of the activity type sequence value Hdxl is enhanced by performing logarithmic function log transformation to reflect the influence of the diversity of activity types on lighting requirements; the emotional state sequence value Qxxl is processed by using the sine function sin number to show periodicity and variability, and express the influence of emotional changes on lighting requirements; the time period sequence value Sjxl is adjusted by using the hyperbolic cosine function cosh to show the sensitivity and changes of lighting requirements in different time periods; the influence of color temperature is enhanced by directly adding the sequence matching brightness preference value Xlld and the sequence matching color temperature preference value Xlsw and taking the square root of the sequence matching color temperature preference value Xlsw.

[0040] Preferably, the user environment-related backlight source control scheme is obtained by the following matching method:

[0041] The intelligent control coefficient Tkxs is less than the user environment associated control matching threshold P, and the result of the non-controlled LED backlight solution is obtained. The current LED backlight operation state meets the current user demand state;

[0042] The intelligent control coefficient Tkxs ≥ the user environment associated control matching threshold P, and the control LED backlight solution result is obtained. The current LED backlight operation state does not meet the current user demand state. The control includes using gradual dimming to smoothly transition to high brightness, using warm color temperature to promote sleep and rest, and using cold color temperature to promote concentration and improve mental state.

[0043] The user preset habit analysis regulation and correction scheme is obtained by the following matching method:

[0044] When the user's preset habit is analyzed and regulated, the first correction scheme is obtained, and the control result of the content of the user environment-related control backlight scheme is corrected to 90%;

[0045] , obtaining a second correction scheme after analyzing and adjusting the user's preset habits, and correcting the control result of the user environment-related backlight control scheme content to 70%;

[0046] When the user's preset habit is analyzed and adjusted, a third correction scheme is obtained to correct the control result of the backlight source control scheme content associated with the user environment to 50%.

[0047] Preferably, the decision module includes an execution unit and a correction unit;

[0048] The execution unit specifically executes the backlight source control scheme content according to the user environment association, including converting the backlight source control scheme content into specific operation instructions according to the user environment association, and adjusting the brightness and color temperature of the LED backlight source in real time;

[0049] The correction unit corrects the execution result of the user environment associated backlight source control scheme according to the user's preset habit analysis and control correction scheme content, including adjusting the brightness or color temperature according to the user's behavioral habit feedback percentage.

[0050] An LED backlight intelligent dimming method comprises the following steps:

[0051] Step 1: The environment collection module obtains indoor and outdoor environment information through environmental sensors and Internet crawler technology to form an environmental information group;

[0052] Step 2: The user status module obtains the user's physical sign information and user's emotional information through the user's physical sign detection sensor and the facial recognition sensor to form a user's physical sign emotional information group;

[0053] Step 3: The data processing module pre-processes the environment information group and the user's vital signs and emotions information group, and then integrates the processed environment information group into a first data set, and integrates the processed user's vital signs and emotions information group into a second data set;

[0054] Step 4: The control strategy module establishes a user environment user association analysis model for the first data set and the second data set. After training and fitting the user environment user association analysis model, the intelligent control coefficient Tkxs is obtained, and matched with the preset user environment association control matching threshold P, the user environment association control backlight scheme is obtained, and the intelligent control coefficient Tkxs is matched with the behavior habit analysis factor Xwyz to obtain the user preset habit analysis control correction scheme, and the user environment association control backlight scheme is corrected according to the user preset habit analysis control correction scheme;

[0055] Step 5: The decision module performs specific execution according to the content of the backlight source control scheme associated with the user environment, and at the same time corrects the user environment-related backlight source control scheme according to the user's preset habits by analyzing the control correction scheme content.

[0056] The present invention provides an LED backlight intelligent dimming method and system, which has the following beneficial effects:

[0057] (1) When the system is running, the environment acquisition module and the user status module obtain indoor and outdoor environment information and user vital signs information, which are processed and integrated by the data processing module to obtain the first data set and the second data set. The control strategy module establishes a user environment user association analysis model, and after training and analysis, the intelligent control coefficient Tkxs and the user environment association control backlight scheme, as well as the behavior habit analysis factor Xwyz and the user preset habit analysis control correction scheme are obtained. The decision module machine performs specific execution and correction, so as to achieve dynamic adjustment based on the user's real-time feedback, reduce the user's manual intervention, optimize energy use, and finally achieve the dual goals of energy saving and improving the comfort of living and working environment.

[0058] (2) Calculation of the intelligent control coefficient Tkxs and the behavior habit analysis factor Xwyz, acquisition of the user environment-related control backlight scheme and the user preset habit analysis control correction scheme, so as to learn the user's behavior habits, predict future demand changes, and further make the lighting adjustment more precise.

[0059] (3) Through steps 1 and 2, environmental and personal data are systematically collected, including indoor and outdoor environmental information and the user's physiological and emotional state. Step 3 performs data preprocessing and integration operations. Step 4 performs modeling analysis to reflect the current user's needs and can also predict and adapt to the user's habits and preferences. Through step 5, the final light plan content is executed and corrected to reflect the optimal dimming decision in real time, matching personal preferences and needs, greatly improving the comfort of the living or working environment and the overall satisfaction of the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a schematic diagram of a flow chart of an LED backlight intelligent dimming system according to the present invention;

[0061] Figure 2 The present invention is a schematic diagram of the steps of an LED backlight intelligent dimming method. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0063] Example 1

[0064] The present invention provides an LED backlight intelligent dimming system, please refer to Figure 1, including environment acquisition module, user status module, data processing module, control strategy module and decision-making module;

[0065] The environment acquisition module acquires indoor and outdoor environment information through environment sensors and Internet crawler technology to form an environment information group;

[0066] The user status module obtains user physical sign information and user emotion information through the user's physical sign detection sensor and the face recognition sensor to form a user physical sign emotion information group;

[0067] The data processing module pre-processes the environment information group and the user's vital signs and emotions information group, and then integrates the processed environment information group into a first data set, and integrates the processed user's vital signs and emotions information group into a second data set;

[0068] The control strategy module establishes a user environment user association analysis model for the first data set and the second data set, and after training and fitting the user environment user association analysis model, obtains: an intelligent control coefficient Tkxs, and matches it with a preset user environment association control matching threshold P, obtains a user environment association control backlight scheme, and simultaneously matches and analyzes the intelligent control coefficient Tkxs with the behavior habit analysis factor Xwyz, obtains a user preset habit analysis control correction scheme, and corrects the user environment association control backlight scheme according to the user preset habit analysis control correction scheme;

[0069] The decision module is specifically executed according to the content of the backlight source control scheme associated with the user environment, and at the same time amends the backlight source control scheme associated with the user environment according to the content of the control correction scheme analyzed according to the user's preset habits.

[0070] In this embodiment, indoor and outdoor environmental information and user vital signs information are obtained through the environment acquisition module and the user status module, processed and integrated through the data processing module to obtain the first data set and the second data set, and the user environment user association analysis model is established through the control strategy module. After training and analysis, the intelligent control coefficient Tkxs and the user environment association control backlight scheme, as well as the behavior habit analysis factor Xwyz and the user preset habit analysis control correction scheme are obtained, which are specifically executed and corrected through the decision-making module to achieve dynamic adjustment based on the user's real-time feedback and reduce the user's manual intervention, so that the lighting system not only responds to current needs, but also adapts to long-term usage habits, optimizes energy use, and ultimately achieves the dual goals of energy saving and improving the comfort of living and working environments.

[0071] Example 2

[0072] This embodiment is explained in Example 1, please refer to Figure 1,Specifically: the environment acquisition module includes an environment acquisition unit and a network acquisition unit;

[0073] The environment collection unit collects indoor and outdoor environment data in real time by using a variety of environment sensors, including light sensors, temperature and humidity sensors, air pressure sensors, and air quality sensors;

[0074] The network acquisition unit obtains outdoor environment data related to the current positioning area from the meteorological website and the Environmental Protection Bureau website through Internet crawler technology, including wind speed changes, temperature changes, humidity changes, air quality index AQI and ultraviolet index related information;

[0075] The environmental information group is obtained by aggregating and integrating the data acquired by the environmental acquisition unit and the network acquisition unit.

[0076] The user status module includes a user vital sign collection unit and a user emotion collection unit;

[0077] The user vital sign collection unit monitors the user's physiological parameters in real time through a smart bracelet or related sensors carried by the user, including heart rate monitoring, blood pressure monitoring, blood oxygen saturation monitoring, body temperature monitoring and step count monitoring;

[0078] The user emotion acquisition unit acquires the user's facial expression captured by the camera by using facial recognition technology, and analyzes the expression by using image processing and machine learning technology to identify the user's emotional state, wherein the emotion recognition includes preset facial expression emotions related to happiness, sadness, anger and surprise;

[0079] The user vital sign emotion information group is integrated into the user vital sign emotion information group after statistics are collected from the data acquired by the user vital sign acquisition unit and the user emotion acquisition unit.

[0080] The data processing module includes a processing unit and an integration unit;

[0081] The processing unit first preprocesses the raw data received from the environment acquisition module and the user status module, and the preprocessing includes data cleaning processing, data normalization processing, missing value processing, data denoising processing and normalization processing;

[0082] The integration unit integrates the processed environment information group into a first data set, and integrates the processed user physical sign emotion information group into a second data set;

[0083] The first data set includes: outdoor temperature value Wwd, indoor temperature value Nwd, indoor and outdoor humidity change value Sdz and outdoor wind speed change value Fsz;

[0084] The second data set includes: heart rate value Xlz, blood pressure value Xyz, body temperature value Twz, body temperature rise rate value Slz, step value Bsz and recognized emotion quantity value Qxz.

[0085] Example 3

[0086] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the control strategy module includes a modeling unit and a scheme matching unit;

[0087] The modeling unit establishes a user environment and user behavior association analysis model by using machine learning algorithm technology, and is trained by using data from the first data set and the second data set to obtain the relationship between user behavior and environmental factors: the intelligent control coefficient Tkxs is matched and analyzed with the behavior habit analysis factor Xwyz;

[0088] Among them, the user's preset backlight-related preset environment habit behaviors include: activity type sequence value Hdxl, emotional state sequence value Qxxl, time period sequence value Sjxl, sequence matching brightness preference value Xlld and sequence matching color temperature preference value Xlsw;

[0089] The scheme matching unit matches the preset user environment associated control matching threshold P with the intelligent control coefficient Tkxs to obtain the user environment associated control backlight scheme, and simultaneously matches the intelligent control coefficient Tkxs with the behavior habit analysis factor Xwyz to obtain the correction factor, and obtains the user preset habit analysis and control correction scheme.

[0090] The intelligent control coefficient Tkxs is obtained by the following calculation formula:

[0091] ;

[0092] Wherein, Tkxs represents the intelligent control coefficient, Hjyz represents the indoor and outdoor environment fluctuation factor, Tzyz represents the user's physical sign fluctuation factor, Bsz represents the step value, and Qxz represents the recognized emotion quantity value; by taking the geometric mean of the indoor and outdoor environment fluctuation factor Hjyz and the user's physical sign fluctuation factor Tzyz, the step value Bsz is combined with the square root function to represent its direct impact on the physiological state and the logarithmic function log to identify the nonlinear impact of the emotion quantity value Qxz, so as to obtain the nonlinear relationship between the simulated environment and the user's physical sign: the intelligent control coefficient Tkxs;

[0093] The indoor and outdoor environment fluctuation factor Hjyz is obtained by the following calculation formula:

[0094] ;

[0095] In the formula, Hjyz represents the indoor and outdoor environmental fluctuation factor, Wwd represents the outdoor temperature value, Nwd represents the indoor temperature value, Sdz represents the indoor and outdoor humidity change value, and Fsz represents the outdoor wind speed change value; the exponential function exp and the logarithmic function log are used to measure the impact of the indoor and outdoor temperature difference on the outdoor temperature value Wwd and the indoor temperature value Nwd using a logarithmic ratio to achieve the purpose of reducing the sensitivity to extreme values, and the hyperbolic tangent function tanh is used to smooth the impact of the humidity difference on the indoor and outdoor humidity change value Sdz, and the impact on the outdoor wind speed change value Fsz is enhanced by the square term of the square root;

[0096] The user's physical sign fluctuation factor Tzyz is obtained by the following calculation formula:

[0097] ;

[0098] Wherein, Tzyz represents the fluctuation factor of the user's physical signs, Xlz represents the heart rate value, Xyz represents the blood pressure value, Twz represents the body temperature value, and Slz represents the body temperature rise rate value; the heart rate value Xlz and the blood pressure value Xyz are combined through the sine function sin and the cosine function cos to represent the periodicity of the physiological response, and the exponential function exp negative impact of the body temperature value Twz is combined with the logarithmic function log gain of the body temperature rise rate value Slz to represent the body temperature regulation state information.

[0099] The behavior habit analysis factor Xwyz is obtained by the following calculation formula:

[0100] ;

[0101] In the formula, Xwyz represents the behavior habit analysis factor, Hdxl represents the activity type sequence value, Qxxl represents the emotional state sequence value, Sjxl represents the time period sequence value, Xlld represents the sequence matching brightness preference value, and Xlsw represents the sequence matching color temperature preference value; the nonlinear influence of the activity type sequence value Hdxl is enhanced by performing logarithmic function log transformation to reflect the influence of the diversity of activity types on lighting requirements; the emotional state sequence value Qxxl is processed by using the sine function sin number to show periodicity and variability, and express the influence of emotional changes on lighting requirements; the time period sequence value Sjxl is adjusted by using the hyperbolic cosine function cosh to show the sensitivity and changes of lighting requirements in different time periods; the influence of color temperature is enhanced by directly adding the sequence matching brightness preference value Xlld and the sequence matching color temperature preference value Xlsw and taking the square root of the sequence matching color temperature preference value Xlsw.

[0102] The user environment-related backlight source control solution is obtained by the following matching method:

[0103] The intelligent control coefficient Tkxs is less than the user environment associated control matching threshold P, and the result of the non-controlled LED backlight solution is obtained. The current LED backlight operation state meets the current user demand state;

[0104] The intelligent control coefficient Tkxs ≥ the user environment associated control matching threshold P, and the control LED backlight solution result is obtained. The current LED backlight operation state does not meet the current user demand state. The control includes using gradual dimming to smoothly transition to high brightness, using warm color temperature to promote sleep and rest, and using cold color temperature to promote concentration and improve mental state.

[0105] The user preset habit analysis regulation and correction scheme is obtained by the following matching method:

[0106] When the user's preset habit is analyzed and regulated, the first correction scheme is obtained, and the control result of the content of the user environment-related control backlight scheme is corrected to 90%;

[0107] , obtaining a second correction scheme after analyzing and adjusting the user's preset habits, and correcting the control result of the user environment-related backlight control scheme content to 70%;

[0108] When the user's preset habit is analyzed and adjusted, a third correction scheme is obtained to correct the control result of the backlight source control scheme content associated with the user environment to 50%.

[0109] The decision module includes an execution unit and a correction unit;

[0110] The execution unit specifically executes the backlight source control scheme content according to the user environment association, including converting the backlight source control scheme content into specific operation instructions according to the user environment association, and adjusting the brightness and color temperature of the LED backlight source in real time;

[0111] The correction unit corrects the execution result of the user environment associated backlight source control scheme according to the user's preset habit analysis and control correction scheme content, including adjusting the brightness or color temperature according to the user's behavioral habit feedback percentage.

[0112] In this embodiment, the intelligent control coefficient Tkxs and the behavior habit analysis factor Xwyz are calculated, the user environment is associated with the backlight source control scheme, and the user preset habit analysis control correction scheme is obtained, so as to learn the user's behavior habits, predict future changes in demand, and further make the lighting adjustment more refined.

[0113] Example 4

[0114] A LED backlight intelligent dimming method, please refer to Figure 2, specifically: including the following steps:

[0115] Step 1: The environment collection module obtains indoor and outdoor environment information through environmental sensors and Internet crawler technology to form an environmental information group;

[0116] Step 2: The user status module obtains the user's physical sign information and user's emotional information through the user's physical sign detection sensor and the facial recognition sensor to form a user's physical sign emotional information group;

[0117] Step 3: The data processing module pre-processes the environment information group and the user's vital signs and emotions information group, and then integrates the processed environment information group into a first data set, and integrates the processed user's vital signs and emotions information group into a second data set;

[0118] Step 4: The control strategy module establishes a user environment user association analysis model for the first data set and the second data set. After training and fitting the user environment user association analysis model, the intelligent control coefficient Tkxs is obtained, and matched with the preset user environment association control matching threshold P, the user environment association control backlight scheme is obtained, and the intelligent control coefficient Tkxs is matched with the behavior habit analysis factor Xwyz to obtain the user preset habit analysis control correction scheme, and the user environment association control backlight scheme is corrected according to the user preset habit analysis control correction scheme;

[0119] Step 5: The decision module performs specific execution according to the content of the backlight source control scheme associated with the user environment, and at the same time corrects the user environment-related backlight source control scheme according to the user's preset habits by analyzing the control correction scheme content.

[0120] In this embodiment, environmental and personal data are systematically collected through steps one and two, including indoor and outdoor environmental information and the user's physiological and emotional state. Step three performs data preprocessing and integration operations. Step four performs modeling analysis to reflect the current user's needs and can also predict and adapt to the user's habits and preferences. Step five executes and corrects the final light plan content to reflect the optimal dimming decision in real time, match personal preferences and needs, and greatly improve the comfort of the living or working environment and the overall satisfaction of the user.

[0121] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An LED backlight intelligent dimming system, characterized in that: It includes environment acquisition module, user status module, data processing module, control strategy module and decision-making module; The environment acquisition module acquires indoor and outdoor environment information through environment sensors and Internet crawler technology to form an environment information group; The user status module obtains user physical sign information and user emotion information through the user's physical sign detection sensor and the face recognition sensor to form a user physical sign emotion information group; The data processing module pre-processes the environment information group and the user's vital signs and emotions information group, and then integrates the processed environment information group into a first data set, and integrates the processed user's vital signs and emotions information group into a second data set; The control strategy module establishes a user environment user association analysis model for the first data set and the second data set, and after training and fitting the user environment user association analysis model, obtains: an intelligent control coefficient Tkxs, and matches it with a preset user environment association control matching threshold P, obtains a user environment association control backlight scheme, and simultaneously matches and analyzes the intelligent control coefficient Tkxs with the behavior habit analysis factor Xwyz, obtains a user preset habit analysis control correction scheme, and corrects the user environment association control backlight scheme according to the user preset habit analysis control correction scheme; The control strategy module includes a modeling unit and a scheme matching unit; The modeling unit establishes a user environment and user behavior association analysis model by using a machine learning algorithm technology, and is trained by using data from the first data set and the second data set to obtain the relationship between the user behavior and the environmental factors: the intelligent control coefficient Tkxs; Among them, the user's preset backlight-related preset environment habit behaviors include: activity type sequence value Hdxl, emotional state sequence value Qxxl, time period sequence value Sjxl, sequence matching brightness preference value Xlld and sequence matching color temperature preference value Xlsw; The scheme matching unit matches the preset user environment associated control matching threshold P with the intelligent control coefficient Tkxs to obtain the user environment associated control backlight scheme, and simultaneously matches the intelligent control coefficient Tkxs with the behavior habit analysis factor Xwyz to obtain the correction factor, and obtains the user preset habit analysis control correction scheme; The intelligent control coefficient Tkxs is obtained by the following calculation formula: ； In the formula, Tkxs represents the intelligent control coefficient, Hjyz represents the indoor and outdoor environment fluctuation factor, Tzyz represents the user's physical sign fluctuation factor, Bsz represents the step value, and Qxz represents the number of recognized emotions; The indoor and outdoor environment fluctuation factor Hjyz is obtained by the following calculation formula: ; In the formula, Hjyz represents the indoor and outdoor environmental fluctuation factor, Wwd represents the outdoor temperature value, Nwd represents the indoor temperature value, Sdz represents the indoor and outdoor humidity change value, and Fsz represents the outdoor wind speed change value; The user's physical sign fluctuation factor Tzyz is obtained by the following calculation formula: ； In the formula, Tzyz represents the user's physical sign fluctuation factor, Xlz represents the heart rate value, Xyz represents the blood pressure value, Twz represents the body temperature value, and Slz represents the body temperature rise rate value; The behavior habit analysis factor Xwyz is obtained by the following calculation formula: ； In the formula, Xwyz represents the behavior habit analysis factor, Hdxl represents the activity type sequence value, Qxxl represents the emotional state sequence value, Sjxl represents the time period sequence value, Xlld represents the sequence matching brightness preference value, and Xlsw represents the sequence matching color temperature preference value; The decision module is specifically executed according to the content of the backlight source control scheme associated with the user environment, and at the same time amends the backlight source control scheme associated with the user environment according to the content of the control correction scheme analyzed according to the user's preset habits.

2. The LED backlight intelligent dimming system according to claim 1, characterized in that: The environment acquisition module includes an environment acquisition unit and a network acquisition unit; The environment collection unit collects indoor and outdoor environment data in real time by using a variety of environment sensors, including light sensors, temperature and humidity sensors, air pressure sensors, and air quality sensors; The network acquisition unit obtains outdoor environment data related to the current positioning area from the meteorological website and the Environmental Protection Bureau website through Internet crawler technology, including wind speed changes, temperature changes, humidity changes, air quality index AQI and ultraviolet index related information; The environmental information group is obtained by aggregating and integrating the data acquired by the environmental acquisition unit and the network acquisition unit.

3. The LED backlight intelligent dimming system according to claim 1, characterized in that: The user status module includes a user vital sign collection unit and a user emotion collection unit; The user vital signs collection unit monitors the user's physiological parameters in real time through the smart bracelet carried by the user, including heart rate monitoring, blood pressure monitoring, blood oxygen saturation monitoring, body temperature monitoring and step number monitoring; The user emotion acquisition unit acquires the user's facial expression captured by the camera by using facial recognition technology, and analyzes the expression by using image processing and machine learning technology to identify the user's emotional state, wherein the emotion recognition includes preset facial expression emotions related to happiness, sadness, anger and surprise; The user vital sign emotion information group is integrated into the user vital sign emotion information group after statistics are collected from the data acquired by the user vital sign acquisition unit and the user emotion acquisition unit.

4. The LED backlight intelligent dimming system according to claim 1, characterized in that: The data processing module includes a processing unit and an integration unit; The processing unit first preprocesses the raw data received from the environment acquisition module and the user status module, and the preprocessing includes data cleaning processing, data normalization processing, missing value processing, data denoising processing and normalization processing; The integration unit integrates the processed environment information group into a first data set, and integrates the processed user physical sign emotion information group into a second data set; The first data set includes: outdoor temperature value Wwd, indoor temperature value Nwd, indoor and outdoor humidity change value Sdz and outdoor wind speed change value Fsz; The second data set includes: heart rate value Xlz, blood pressure value Xyz, body temperature value Twz, body temperature rise rate value Slz, step value Bsz and recognized emotion quantity value Qxz.

5. The LED backlight intelligent dimming system according to claim 1, characterized in that: The user environment-related backlight source control solution is obtained by the following matching method: The intelligent control coefficient Tkxs is less than the user environment associated control matching threshold P, and the result of the non-controlled LED backlight solution is obtained. The current LED backlight operation state meets the current user demand state; The intelligent control coefficient Tkxs ≥ the user environment associated control matching threshold P, and the control LED backlight solution result is obtained. The current LED backlight operation state does not meet the current user demand state. The control includes using gradual dimming to smoothly transition to high brightness, using warm color temperature to promote sleep and rest, and using cold color temperature to promote concentration and improve mental state. The user preset habit analysis regulation and correction scheme is obtained by the following matching method: When the user's preset habit is analyzed and regulated, the first correction scheme is obtained, and the control result of the content of the user environment-related control backlight scheme is corrected to 90%; , obtaining a second correction scheme after analyzing and adjusting the user's preset habits, and correcting the control result of the user environment-related backlight control scheme content to 70%; When the user's preset habit is analyzed and adjusted, a third correction scheme is obtained to correct the control result of the backlight source control scheme content associated with the user environment to 50%.

6. The LED backlight intelligent dimming system according to claim 1, characterized in that: The decision module includes an execution unit and a correction unit; The execution unit specifically executes the backlight source control scheme content according to the user environment association, including converting the backlight source control scheme content into specific operation instructions according to the user environment association, and adjusting the brightness and color temperature of the LED backlight source in real time; The correction unit corrects the execution result of the user environment associated backlight source control scheme according to the user's preset habit analysis and control correction scheme content, including adjusting the brightness or color temperature according to the user's behavioral habit feedback percentage.

7. An LED backlight intelligent dimming method, applied to an LED backlight intelligent dimming system according to any one of claims 1 to 6, characterized in that: The following steps are involved: Step 1: The environment collection module obtains indoor and outdoor environment information through environmental sensors and Internet crawler technology to form an environmental information group; Step 2: The user status module obtains the user's physical sign information and user's emotional information through the user's physical sign detection sensor and the facial recognition sensor to form a user's physical sign emotional information group; Step 3: The data processing module pre-processes the environment information group and the user's vital signs and emotions information group, and then integrates the processed environment information group into a first data set, and integrates the processed user's vital signs and emotions information group into a second data set; Step 4: The control strategy module establishes a user environment user association analysis model for the first data set and the second data set. After training and fitting the user environment user association analysis model, the intelligent control coefficient Tkxs is obtained, and matched with the preset user environment association control matching threshold P, the user environment association control backlight scheme is obtained, and the intelligent control coefficient Tkxs is matched with the behavior habit analysis factor Xwyz to obtain the user preset habit analysis control correction scheme, and the user environment association control backlight scheme is corrected according to the user preset habit analysis control correction scheme; Step 5: The decision module performs specific execution according to the content of the backlight source control scheme associated with the user environment, and at the same time corrects the user environment-related backlight source control scheme according to the user's preset habits by analyzing the control correction scheme content.

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