阅读说明：本技术 照明亮度自适应调整系统及其调整方法 (Illumination brightness self-adaptive adjusting system and adjusting method thereof ) 是由 陈啸 于 2021-09-08 设计创作，主要内容包括：本发明公开了照明亮度自适应调整系统及其调整方法能够自主捕捉人脸图像,能针对用户的人脸情绪而开启不同的照明环境,智能地“识人”从而控制家庭不同位置灯具的亮暗,可根据所采集对象生理特征及生理信号特点自动调整信号采集电路的滤波及增益放大电路,实现对生理信号的准确获取,通过利用基于深度学习的神经网络识别人脸状态,通过对采集到的信息比拟出人脸心情从而调动照明模块对照明设备的亮灭状态。本发明通过识别人脸情绪状态,实现控制相适应照明模块的灯光亮暗,从而根据心情调控照明光线安抚心理,达到提高用户体验和舒适度的目的。(The invention discloses an illumination brightness self-adaptive adjusting system and an adjusting method thereof, which can automatically capture a face image, can open different illumination environments according to the face emotion of a user, intelligently identify people so as to control the brightness of lamps at different positions of a family, can automatically adjust a filtering and gain amplifying circuit of a signal acquisition circuit according to the physiological characteristics and the physiological signal characteristics of an acquired object, realize the accurate acquisition of physiological signals, recognize the face state by utilizing a neural network based on deep learning, and adjust the brightness and the extinction state of an illumination module to illumination equipment by simulating the face mood according to the acquired information. According to the invention, the light brightness of the adaptive illumination module is controlled by recognizing the emotional state of the face, so that the illumination light is regulated and controlled according to the mood to sooth the mind, and the purposes of improving the user experience and the comfort level are achieved.)

1. The illumination brightness self-adaptive adjusting method is characterized by comprising the following steps:

the method comprises the steps of collecting state information of a user in real time, preprocessing the collected state information, and sending the preprocessed state information to an analysis and evaluation module;

the analysis and evaluation module receives the preprocessed state information of the user, adopts the characteristic of the histogram of oriented gradients to input into the convolutional neural network to analyze and identify the information of the user, extracts the facial information of the user, judges the state information of the user under each condition and sends the judged state information to the monitoring feedback module;

receiving the state information, and generating light regulation output information according to the current emotional state of the user;

and receiving the output information of the control module, and controlling the lamp to emit corresponding lamp brightness and color according to a preset value.

2. The adaptive illumination brightness adjustment method of claim 1, wherein inputting the convolutional neural network with histogram of oriented gradients features comprises analyzing current state information of a user from the image signal with an emotion prediction neural network model and outputting the current state information.

3. The adaptive illumination brightness adjustment method according to claim 1, wherein the applying the emotion prediction neural network model comprises:

acquiring original modeling data, selecting various indexes of detected facial features, facial motion features and head motion features as input values of a model training stage, and selecting expression values corresponding to input quantities as expected output values of the model training stage;

constructing a BP neural network model according to the selected input quantity and the expected output quantity, wherein the BP neural network model comprises three layers of feedforward neural network structures, namely an input layer, a hidden layer and an output layer, the input index of the input layer is the selected input quantity, and the output index of the output layer is the expected output quantity; setting an expected error E according to the actual prediction precision requirement;

training the BP neural network model with current training data;

according to the currently measured data, applying a model, and confirming state information by using the BP neural network model;

the input layer, the hidden layer and the output layer respectively comprise nodes corresponding to expression types, when the expression types are M, the input layer comprises M +5 nodes, the output layer comprises M nodes, and the hidden layer comprises M +7 nodes;

the activation function of the hidden layer adopts a Relu function, and the activation function of the output layer adopts a linear function.

4. The adaptive illumination brightness adjustment method according to claim 1, wherein training the BP neural network model comprises:

taking a sample P from the index_i、Q_jA 1 is to P_iInputting a network;

calculating an error measure E_iAnd the actual output O_i；

Repeatedly adjusting the weight until sigma E_i＜ε；

Calculating the actual output O_pAnd an ideal output Q_iA difference of (d);

adjusting the output layer weight matrix by the error of the output layer;

estimating the error of a leading layer of the output layer through the error of the output layer so as to obtain the error estimation of other layers;

modifying the weight matrix through error estimation;

wherein the error calculation formula is

5. The adaptive illumination brightness adjustment method according to claim 1, wherein the controlling the lamp to emit the corresponding illumination brightness according to the preset value comprises adjusting the illumination brightness of the mobile phone:

the driving unit generates a pulse modulation signal to drive the lighting unit to be lightened;

increasing or decreasing a duty cycle of the pulse modulated signal to adjust a brightness of the lighting unit;

when the duty ratio of the pulse modulation signal is increased to less than 100%, the duty ratio of the pulse modulation signal is continuously increased, so that the lighting time of the lighting unit is continuously increased, and the brightness is continuously enhanced;

when the duty ratio of the pulse modulation signal is increased to 100%, the duty ratio of the pulse modulation signal is continuously increased, so that the lighting time of the lighting unit is not increased, and the brightness is not enhanced;

when the duty ratio of the pulse modulation signal is lower than a set lower limit value, the lighting unit starts to alternate bright and dark, and twinkles;

when the duty ratio of the pulse modulation signal is reduced to 0, the lighting time of the lighting unit is 0, and the lighting unit is turned off.

6. The illumination brightness adaptive adjustment system comprises the illumination brightness adaptive adjustment method of the claims 1-5, and is characterized by further comprising an information acquisition module, an analysis and evaluation module, a transmission module, a control module and an illumination unit;

the information acquisition module is used for acquiring information of a user in an environment in real time, preprocessing the acquired information and sending the preprocessed information to the analysis and evaluation module;

the analysis and evaluation module is used for receiving the preprocessed information of the user, adopting the characteristic input convolutional neural network of the histogram of oriented gradients to analyze and identify the information of the user, extracting the facial information of the user, and judging the state information of the user under each condition, wherein the state information is the emotion state of the user and/or represents that the user is in a waking state or a sleeping state;

the transmission module is used for transmitting the state information judged by the analysis and evaluation module to the control module;

the control module is used for receiving the state information sent by the analysis and evaluation module and generating light regulation output information according to the current emotional state of the user;

and the lighting unit is used for receiving the output information of the control module and controlling the lamp to emit corresponding lamp brightness and color according to a preset value.

7. The adaptive illumination brightness adjustment system according to claim 6, wherein the analysis and evaluation module is disposed at a local end connected to the information acquisition module via a local area network; or the analysis and evaluation module is arranged at a server side which is remotely connected with the information acquisition module.

8. The adaptive illumination brightness adjustment system according to claim 6, wherein the information acquisition module comprises a camera unit, and the camera unit is configured to acquire and obtain an image signal of a face of a user.

9. The adaptive illumination brightness adjustment system according to claim 5, wherein the analysis and evaluation module comprises an image processing unit for analyzing the current status information of the user from the image signal by using an emotion prediction neural network model and outputting the current status information.

10. The adaptive illumination brightness adjustment system according to claim 9, comprising a driving unit in signal connection with the illumination unit, wherein the driving unit comprises:

the waveform generation module is used for generating pulse modulation signals;

and the waveform adjusting module is in signal connection with the waveform generating module and is used for adjusting the duty ratio of the pulse modulation signal.

Technical Field

The invention relates to the technical field of lighting equipment, in particular to a lighting brightness self-adaptive adjusting system and an adjusting method thereof.

Background

Most of domestic lighting control depends on manual control of personnel, although a plurality of projects consider grouping time-sharing control during the design period, even some projects are provided with multiple control modes such as induction control and the like, daily management is actually controlled manually during the period of putting the projects into use after the projects are built, and in order to save the running cost, a plurality of companies have few managers and lack technical function training (do not know operation or are lazy and the like), or directly turn off lighting sources for saving electric charges and lamp replacement periods, only use 25% of the lighting sources to maintain the basic illumination requirement, or do not consider the power supply due to lazy. Therefore, the problem of insufficient illumination or excessive illumination waste is caused, the sight line and the recognition degree of people are influenced, the condition that the illumination is uneven and a piece of light and a piece of dark appears, and the comfort is very poor.

Current light is adjusted and is only adjusted to the light under the specific environment, more application is at the acoustic control lamp, there is certain technical difficulty when meetting multiple application scene, for example PPT explains light and adjusts the scene, light adjusts the scene during the meeting, light adjusts the scene when cleaning the health, this kind of light under the multi-scene is adjusted, often adopt the manual work to control, lead to the flexibility ratio very low, and current light is adjusted and is related to image processing and speech recognition very seldom and carry out light adjustment, carry out intelligent recognition through the artifical interactive scene of image and wait to strengthen urgently.

In the existing research, the research on office area lighting mainly lies in the aspects of structural design and lighting investigation. The light environment has significant influence on the physiology and the psychology of people. First, light is a necessary condition for normal people to produce vision. If sufficient light is not available, people cannot exert visual effects and cannot recognize the environment, so that judgment and behaviors of the people are influenced. This indicates that the light environment conditions are poor, which can lead to visual errors and affect normal learning or operation. Therefore, the light environment has a direct relationship with the survival and personal safety of people. In the luminous environment, the light stimulation also has great influence on the blood pressure, pulse, respiration, muscle tension and the like of the human body, and can cause the activity of the autonomic nervous system and cerebral cortex.

Secondly, on the basis that the light stimulation influences the physiological activities of the human body, the light color also influences the physiological feelings of the human body by combining with the visual experience and the life experience of the human body. For example, soft and warm light makes people feel calm, while cold and blue light makes people think intensively and think actively.

Disclosure of Invention

The invention aims to provide a lighting brightness self-adaptive adjusting system and a lighting brightness self-adaptive adjusting method, which can realize the purpose of emotion recognition by recognizing the emotional state of a human face, realize the function of brightness adjustment, ensure that the output of an output light source is rich enough to respond to different emotions, and achieve the purposes of improving user experience and comfort.

The illumination brightness self-adaptive adjusting method comprises the following steps:

the method comprises the steps of collecting state information of a user in real time, preprocessing the collected state information, and sending the preprocessed state information to an analysis and evaluation module;

the analysis and evaluation module receives the preprocessed state information of the user, adopts the characteristic of the histogram of oriented gradients to input into the convolutional neural network to analyze and identify the information of the user, extracts the facial information of the user, judges the state information of the user under each condition and sends the judged state information to the monitoring feedback module;

receiving the state information, and generating light regulation output information according to the current emotional state of the user;

and receiving the output information of the control module, and controlling the lamp to emit corresponding lamp brightness and color according to a preset value.

In one embodiment, inputting the convolutional neural network by using the histogram of oriented gradients feature comprises analyzing current state information of the user from the image signal by using an emotion prediction neural network model and outputting the current state information in the state information.

In one embodiment, the emotion prediction neural network model is adopted and comprises the following steps:

acquiring original modeling data, selecting various indexes of detected facial features, facial motion features and head motion features as input values of a model training stage, and selecting expression values corresponding to input quantities as expected output values of the model training stage;

constructing a BP neural network model according to the selected input quantity and the expected output quantity, wherein the BP neural network model comprises three layers of feedforward neural network structures, namely an input layer, a hidden layer and an output layer, the input index of the input layer is the selected input quantity, and the output index of the output layer is the expected output quantity; setting an expected error E according to the actual prediction precision requirement;

training the BP neural network model with current training data;

according to the currently measured data, applying a model, and confirming state information by using the BP neural network model;

when the expression types are M, the input layer is M +3 nodes, the output layer is M nodes, and the hidden layer is M +7 nodes;

the activation function of the hidden layer adopts a Relu function, and the activation function of the output layer adopts a linear function.

In one embodiment, training the BP neural network model comprises:

taking a sample P from the index_i、Q_jA 1 is to P_iInputting a network;

calculating an error measure E_iAnd the actual output O_i；

Repeatedly adjusting the weight until sigma E_i＜ε；

Calculating the actual output O_pAnd an ideal output Q_iA difference of (d);

adjusting the output layer weight matrix by the error of the output layer;

estimating the error of a leading layer of the output layer through the error of the output layer so as to obtain the error estimation of other layers;

modifying the weight matrix through error estimation;

wherein the error calculation formula is

In one embodiment, the controlling the light to emit corresponding light brightness according to the preset value includes adjusting the mobile phone lighting brightness:

the driving unit generates a pulse modulation signal to drive the lighting unit to be lightened;

increasing or decreasing a duty cycle of the pulse modulated signal to adjust a brightness of the lighting unit;

when the duty ratio of the pulse modulation signal is increased to less than 100%, the duty ratio of the pulse modulation signal is continuously increased, so that the lighting time of the lighting unit is continuously increased, and the brightness is continuously enhanced;

when the duty ratio of the pulse modulation signal is increased to 100%, the duty ratio of the pulse modulation signal is continuously increased, so that the lighting time of the lighting unit is not increased, and the brightness is not enhanced

When the duty ratio of the pulse modulation signal is lower than a set lower limit value, the lighting unit starts to alternate bright and dark, and twinkles;

when the duty ratio of the pulse modulation signal is reduced to 0, the lighting time of the lighting unit is 0, and the lighting unit is turned off.

The illumination brightness self-adaptive adjusting system comprises the illumination brightness self-adaptive adjusting method, and further comprises an information acquisition module, an analysis and evaluation module, a transmission module, a control module and an illumination unit;

the information acquisition module is used for acquiring information of a user in an environment in real time, preprocessing the acquired information and sending the preprocessed information to the analysis and evaluation module;

the analysis and evaluation module is used for receiving the preprocessed information of the user, adopting the characteristic input convolutional neural network of the histogram of oriented gradients to analyze and identify the information of the user, extracting the facial information of the user, and judging the state information of the user under each condition, wherein the state information is the emotion state of the user and/or represents that the user is in a waking state or a sleeping state;

the transmission module is used for transmitting the state information judged by the analysis and evaluation module to the control module;

the control module is used for receiving the state information sent by the analysis and evaluation module and generating light regulation output information according to the current emotional state of the user;

and the lighting unit is used for receiving the output information of the control module and controlling the lamp to emit corresponding lamp brightness and color according to a preset value.

In one embodiment, the analysis and evaluation module is arranged at a local end connected with the information acquisition module through a local area network; or the analysis and evaluation module is arranged at a server side which is remotely connected with the information acquisition module.

In one embodiment, the information acquisition module comprises a camera unit, and the camera unit is used for acquiring an image signal of the face of the user.

In one embodiment, the analysis and evaluation module comprises an image processing unit for analyzing the current state information of the user from the image signal by using an emotion prediction neural network model and outputting the current state information in the state information.

In one embodiment, the lighting device comprises a driving unit, wherein the driving unit is in signal connection with the lighting unit, and the driving unit comprises:

the waveform generation module is used for generating pulse modulation signals;

and the waveform adjusting module is in signal connection with the waveform generating module and is used for adjusting the duty ratio of the pulse modulation signal.

An electronic device, comprising: a memory and one or more processors;

wherein the memory is communicatively coupled to the one or more processors and stores instructions executable by the one or more processors, and when the instructions are executed by the one or more processors, the electronic device is configured to implement the method of any of the above embodiments.

A computer-readable storage medium having stored thereon computer-executable instructions operable, when executed by a computing device, to implement the method of any of the above embodiments.

A computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are operable to carry out the method of any of the above embodiments.

The technical scheme has the following advantages or beneficial effects:

the illumination brightness self-adaptive adjusting system and the adjusting method thereof can automatically capture face images, can open different illumination environments according to the face emotion of a user, intelligently identify people so as to control the brightness of lamps at different positions of a family, can automatically adjust the filtering and gain amplifying circuit of the signal acquisition circuit according to the physiological characteristics and the physiological signal characteristics of an acquired object, realize the accurate acquisition of physiological signals, recognize the face state by utilizing a neural network based on deep learning, and adjust the brightness and the extinction state of an illumination module to illumination equipment by simulating the face mood according to the acquired information. According to the invention, the light brightness of the adaptive illumination module is controlled by recognizing the emotional state of the face, so that the illumination light is regulated and controlled according to the mood to sooth the mind, and the purposes of improving the user experience and the comfort level are achieved.

Drawings

FIG. 1 is a flow chart of an adaptive illumination brightness adjustment method according to the present invention;

fig. 2 is a schematic structural diagram of an illumination brightness adaptive adjustment system according to the present invention.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.

With reference to fig. 1 and fig. 2, the method for adaptively adjusting the illumination brightness includes:

the method comprises the steps of collecting state information of a user in real time, preprocessing the collected state information, and sending the preprocessed state information to an analysis and evaluation module;

the analysis and evaluation module receives the preprocessed state information of the user, adopts the characteristic of the histogram of oriented gradients to input into the convolutional neural network to analyze and identify the information of the user, extracts the facial information of the user, judges the state information of the user under each condition and sends the judged state information to the monitoring feedback module;

receiving the state information, and generating light regulation output information according to the current emotional state of the user;

and receiving the output information of the control module, and controlling the lamp to emit corresponding lamp brightness and color according to a preset value.

Further, in a preferred embodiment of the adaptive illumination brightness adjustment method of the present invention, inputting the histogram of oriented gradients into the convolutional neural network includes analyzing the image signal by using an emotion prediction neural network model to obtain current state information of the user and outputting the current state information in the state information.

Further, in a preferred embodiment of the adaptive illumination brightness adjustment method of the present invention, the applying the emotion prediction neural network model includes:

acquiring original modeling data, selecting various indexes of detected facial features, facial motion features and head motion features as input values of a model training stage, and selecting expression values corresponding to input quantities as expected output values of the model training stage;

constructing a BP neural network model according to the selected input quantity and the expected output quantity, wherein the BP neural network model comprises three layers of feedforward neural network structures, namely an input layer, a hidden layer and an output layer, the input index of the input layer is the selected input quantity, and the output index of the output layer is the expected output quantity; setting an expected error E according to the actual prediction precision requirement;

training the BP neural network model with current training data;

according to the currently measured data, applying a model, and confirming state information by using the BP neural network model;

the input layer, the hidden layer and the output layer respectively comprise nodes corresponding to expression types, when the expression types are M, the input layer comprises M +5 nodes, the output layer comprises M nodes, and the hidden layer comprises M +7 nodes;

the activation function of the hidden layer adopts a Relu function, and the activation function of the output layer adopts a linear function.

In the aspect of expression prediction, a BP neural network with 5 nodes in an input layer, 1 node in an output layer and 15 nodes in a middle hidden layer can be constructed in a specific embodiment. The P1 to P5 of the input layer are replaced with the following numerical values in this respect:

p1 type of detected expression;

p2 time when expression is detected;

p3, characteristics of five sense organs;

p4 facial motion features;

p5 head movement characteristics;

and output O₁The predicted kind of expression in the nth time period can be replaced, the Relu function is adopted as the activation function of the hidden layer in the model, and the linear function is adopted as the activation function of the output layer.

Further, in a preferred embodiment of the adaptive illumination brightness adjusting method of the present invention, the training of the BP neural network model includes:

taking a sample P from the index_i、Q_jA 1 is to P_iInputting a network;

calculating an error measure E_iAnd the actual output O_i；

Repeatedly adjusting the weight until sigma E_i＜ε；

Calculating the actual output O_pAnd an ideal output Q_iA difference of (d);

adjusting the output layer weight matrix by the error of the output layer;

estimating the error of a leading layer of the output layer through the error of the output layer so as to obtain the error estimation of other layers;

modifying the weight matrix through error estimation to transmit the error of the output end to the output end step by step along the direction opposite to the output signal;

wherein the error calculation formula is

The BP neural network adopted by the project obtains a proper linear or nonlinear relation between input and output through the event of 'training'. The "training" process can be divided into two stages, forward transmission and backward transmission. .

Further, in a preferred embodiment of the adaptive illumination brightness adjusting method of the present invention, the controlling the lamp to emit the corresponding illumination brightness according to the preset value includes adjusting the illumination brightness of the mobile phone:

the driving unit generates a pulse modulation signal to drive the lighting unit to be lightened;

increasing or decreasing a duty cycle of the pulse modulated signal to adjust a brightness of the lighting unit;

when the duty ratio of the pulse modulation signal is increased to less than 100%, the duty ratio of the pulse modulation signal is continuously increased, so that the lighting time of the lighting unit is continuously increased, and the brightness is continuously enhanced;

when the duty ratio of the pulse modulation signal is increased to 100%, the duty ratio of the pulse modulation signal is continuously increased, so that the lighting time of the lighting unit is not increased, and the brightness is not enhanced

When the duty ratio of the pulse modulation signal is lower than a set lower limit value, the lighting unit starts to alternate bright and dark and flicker, and the flicker can be obtained through testing;

when the duty ratio of the pulse modulation signal is reduced to 0, the lighting time of the lighting unit is 0, and the lighting unit is turned off;

when the duty ratio of the generated pulse modulation signal is 100%, the lighting time is maximized and the brightness is increased to the maximum, and the duty ratio of the generated pulse modulation signal is increased/decreased by 20% per adjustment. When the duty ratio of the pulse modulation signal is increased/decreased by more than 20%, the brightness adjustment cannot be performed accurately, and flicker is likely to occur. The setting can ensure that each adjustment can reach the optimal change value of the lighting time, so that the adjustment of the brightness reaches the most obvious change.

The illumination brightness self-adaptive adjusting system comprises the illumination brightness self-adaptive adjusting method, and further comprises an information acquisition module 1, an analysis and evaluation module 2, a transmission module 3, a control module 4 and an illumination unit 5;

the information acquisition module 1 is used for acquiring information of a user in an environment in real time, preprocessing the acquired information and sending the preprocessed information to the analysis and evaluation module 2;

the analysis and evaluation module 2 is used for receiving the preprocessed information of the user, analyzing and identifying the information of the user by inputting the characteristic of the histogram of oriented gradients into the convolutional neural network, extracting the facial information of the user, and judging the state information of the user under each condition, wherein the state information is the emotion state of the user and/or represents that the user is in a waking state or a sleeping state;

the transmission module 3 is configured to send the state information determined by the analysis and evaluation module 2 to the control module 4, where the transmission module 3 includes any one or more combinations of WIFI, Zigbee, NB-loT, eMTC, Z-wave, LoRa, SigFox, RF radio frequency, and bluetooth, and specifically, the WIFI and the mobile data have different signal frequency bands, and may be switched between the WIFI and the mobile data at will according to different environmental signals where a user is located, and sometimes, after a WIFI network is unstable or disconnected, if the mobile network is previously turned on, the mobile network is switched to use by default;

the control module 4 is used for receiving the state information sent by the analysis and evaluation module 2 and generating light regulation output information according to the current emotional state of the user;

the lighting unit 5 is used for receiving the output information of the control module and controlling the lamp to emit corresponding light brightness and color according to a preset value, the lighting unit 5 comprises an LED lamp, the LED lamp does not have stroboflash and works in a pure direct current mode, visual fatigue caused by traditional light source stroboflash is eliminated, and meanwhile, the work of the LED lamp is conveniently controlled through a pulse modulation signal with an adjustable duty ratio.

Further, in a preferred embodiment of the illumination brightness adaptive adjustment system of the present invention, the analysis and evaluation module 2 is disposed at a local end that is connected to the information acquisition module 1 through a local area network; or the analysis and evaluation module 2 is arranged at a server side which is remotely connected with the information acquisition module 1.

Further, in a preferred embodiment of the illumination brightness adaptive adjustment system of the present invention, the information collecting module 1 includes a camera unit 11, the camera unit 11 is configured to collect and obtain an image signal of a face of a user, the camera unit 11 is configured to collect and obtain an image signal of the user, specifically, the camera unit 11 is an array camera, a sensing distance of the array camera is 1-7m, a vertical sensing angle is 30 °, a horizontal sensing angle is 120 °, and thus, when measuring a physiological signal state of the user, the array camera can basically and completely cover the whole room without rotating to perform measurement.

Further, in a preferred embodiment of the illumination brightness adaptive adjustment system of the present invention, the analysis and evaluation module 2 includes an image processing unit 21, configured to analyze the current state information of the user from the image signal by using an emotion prediction neural network model and output the current state information in the state information.

Further, in a preferred embodiment of the illumination brightness adaptive adjustment system of the present invention, the illumination brightness adaptive adjustment system includes a driving unit 6, the driving unit 6 is in signal connection with the illumination unit 5, and the driving unit 6 includes:

a waveform generating module 61 for generating a pulse modulation signal;

the waveform adjusting module 62 is in signal connection with the waveform generating module 61 and is used for adjusting the duty ratio of the pulse modulation signal;

when the brightness of the lighting unit 5 is adjusted, the signal frequency of the pulse modulation signal is unchanged, the time of the high level of the pulse modulation signal pulse, namely the on time of the lighting unit 5 is changed, the signal adjustment brightness is equivalent to the adjustment of the average current of the lighting unit 5, so that the current can be changed, and the duty ratio change of the pulse modulation signal can change the power of the lighting unit 5;

the waveform generating module 61 generates a pulse width modulation signal. PWM is pulse width modulation, i.e. a pulse shape with a variable duty cycle. The PWM waveform controls the turn-on and turn-off of the semiconductor switching device so that the output receives a series of pulses of equal amplitude and unequal width that are used to replace a sine wave or other desired waveform. The width of each pulse is modulated according to a certain rule, so that the magnitude of the output voltage of the inverter circuit can be changed, and the output frequency can also be changed. The current tracking type PWM converter circuit adopts current tracking control to the converter circuit. That is, the current desired to be output is used as a command signal and the actual current is used as a feedback signal without modulating a carrier wave with a signal wave, and the on/off of each power device of the inverter circuit is determined by comparing instantaneous values of the command signal and the actual current, so that the actual output tracks the change of the current. The PWM waveform frequency is not changed, and the width of the pulse is directly changed, namely the conducting time of the switching element is controlled; for example, when the current voltage is high level conduction, the larger the square wave A, the smaller the square wave B, and the longer the conduction time; otherwise the shorter.

An electronic device, comprising: a memory and one or more processors;

wherein the memory is communicatively coupled to the one or more processors and has stored therein instructions executable by the one or more processors, the electronic device operable to implement the method as any one of the above when the instructions are executed by the one or more processors.

In particular, the processor and the memory may be connected by a bus or other means, such as by a bus connection. The processor may be a Central Processing Unit (CPU). The Processor may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or a combination thereof.

The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor executes various functional applications and data processing of the processor by executing non-transitory software programs/instructions and functional modules stored in the memory.

The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor, and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and such remote memory may be coupled to the processor via a network, such as through a communications interface. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

A computer-readable storage medium having stored thereon computer-executable instructions operable, when executed by a computing device, to implement a method as in any above.

The foregoing computer-readable storage media include physical volatile and nonvolatile, removable and non-removable media implemented in any manner or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. The computer-readable storage medium specifically includes, but is not limited to, a USB flash drive, a removable hard drive, a Read-Only Memory (ROM), a Random Access Memory (RAM), an erasable programmable Read-Only Memory (EPROM), an electrically erasable programmable Read-Only Memory (EEPROM), flash Memory or other solid state Memory technology, a CD-ROM, a Digital Versatile Disk (DVD), an HD-DVD, a Blue-Ray or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer.

While the subject matter described herein is provided in the general context of execution in conjunction with the execution of an operating system and application programs on a computer system, those skilled in the art will recognize that other implementations may also be performed in combination with other types of program modules. Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. Those skilled in the art will appreciate that the subject matter described herein may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like, as well as distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Those of ordinary skill in the art will appreciate that the various illustrative elements and method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application.

In summary, the illumination brightness adaptive adjustment system and the adjustment method thereof of the invention can capture face images autonomously, open different illumination environments according to the face emotion of a user, intelligently identify people to control the brightness of lamps at different positions of a family, automatically adjust the filtering and gain amplifying circuit of the signal acquisition circuit according to the physiological characteristics and physiological signal characteristics of an acquired object, realize accurate acquisition of physiological signals, recognize the face state by utilizing a neural network based on deep learning, and simulate the face mood by comparing the acquired information to transfer the brightness and darkness of the illumination device by the illumination module. According to the invention, the light brightness of the adaptive illumination module is controlled by recognizing the emotional state of the face, so that the illumination light is regulated and controlled according to the mood to sooth the mind, and the purposes of improving the user experience and the comfort level are achieved.

While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.

In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", and the like, which indicate orientations or positional relationships, are based on the orientations or positional relationships shown in the drawings, are only for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the referred devices or elements must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.