CN113038668A

CN113038668A - Lighting control system based on sleep efficiency factor

Info

Publication number: CN113038668A
Application number: CN202110304921.7A
Authority: CN
Inventors: 邹细勇; 胡晓静; 徐伟; 夏浩; 陈亮
Original assignee: China Jiliang University Shangyu Advanced Research Institute Co Ltd
Current assignee: China Jiliang University Shangyu Advanced Research Institute Co Ltd
Priority date: 2019-05-26
Filing date: 2019-05-26
Publication date: 2021-06-25
Also published as: CN113038667A; CN110113843A; CN110113843B

Abstract

The invention discloses an illumination control system based on a sleep efficiency factor, which comprises a sleep identification unit, a user interface unit, a dimmable lamp group and a control unit; the control unit takes the driving current and the irradiation angle of the LED strings in the lamp group as input quantities, and takes physical parameters such as the eye opening change rate, the eye closing duration change rate, the heart rate change rate, the body movement frequency change rate, the body temperature change rate and the like of a user as output quantities, and establishes a dynamic recursive Elman neural network to represent the mapping relation between the lamp control parameters and the user sleep efficiency; the trained network is used for predicting the sleep-onset efficiency parameters in the field light environment, the predicted values are used for calculating sleep-onset efficiency evaluation values in the process of optimizing the lighting parameters based on the multi-objective optimization algorithm, and the optimization results are directly transmitted to the lamp groups to perform dimming, so that lighting which is beneficial to a user to fall asleep in different environments is achieved.

Description

Lighting control system based on sleep efficiency factor

The application is divisional application with application number 201910442906.1, application date 2019, 05 and 26, and invention title "lighting control system and dimming mapping device based on sleep efficiency factor".

Technical Field

The invention relates to the field of intelligent lighting and sleep assistance, in particular to a lighting control system based on sleep efficiency factors.

Background

The human biological clock is a physiological and biochemical process, morphological structure and behavior, etc. which change periodically with time in the human body. The biological clock in human body is reflected on the periodic variation of various physiological indexes of human body, such as pulse, body temperature, blood pressure, physical strength, emotion and intelligence, etc. Taking a human body biological clock of 24 hours as an example, in 2 am, the sleep of a human body reaches the maximum depth, in 4 am, 30 min, the body temperature reaches the minimum, in 7 am, 30 min, the secretion of melatonin stops, in 10 am, the moment when the head of the human body is most clear, in 17 pm, the efficiency of cardiovascular work of the human body is the highest, the muscle strength is the maximum, in 18 pm, 30 min, the blood pressure of the human body reaches the peak of 1 day, in 19 pm, the body temperature reaches the peak, and in 21 pm, the secretion of melatonin starts.

Sufficient sleep is necessary to keep the human body energetic to work and learn during the twenty-four hour rhythmic activity of people. The sleep is periodic and can be divided into a waking period, a rapid eye movement period and a non-rapid eye movement period, wherein the latter period comprises a light sleep period and a deep sleep period. Like the effect of light on the human body's biological clock, light has a major influence on the human sleep. Typically, many devices use low color temperature lighting at night, such as iphone phones that turn the screen backlight to soft yellow light after switching to night mode, because blue light affects melatonin secretion, thereby preventing the night physiological switching mechanism from operating and making it more difficult for a person to fall asleep.

Proposals have been made to induce a human body to fall asleep, such as chinese patent No. 2013107527548, which induces sleep through audio. In order to detect the sleep quality or automatically turn off the hypnosis device after the user falls asleep, many schemes monitor and determine the sleep state, such as PCT patent No. 2015800746753 by apple, inc.

Although various schemes exist to detect sleep, or to help the user fall asleep by switching the light of the illumination lamp to soft yellow light; however, the sleep efficiency of different specific users can be quite different even under the same light environment, and the pertinence and the effectiveness of the traditional lighting scheme for simply providing one or a limited number of low-color-temperature low-illumination at night are very limited.

Therefore, there is a need for a lighting control system, which can optimally search the light emitted from the light source according to the detection of the influence of the light on the sleep efficiency of a specific user, so as to help the user to sleep.

Disclosure of Invention

In the prior art, no light adjusting scheme is specially used for the sleeping stage before sleeping, and people can turn off or turn on the light to sleep before sleeping. However, if the lighting control system can automatically adjust the brightness and the color temperature of the light before sleep according to the characteristics of the user, thereby helping the user fall asleep, the contradiction between turning off the light and turning on the light can be solved, and the automatic adjustment and control of the light can be realized.

For this purpose, the influence of the lighting conditions on the speed of falling asleep or the efficiency of falling asleep is specified, so that in the new environment an evaluation reference can be made for the optimal control of the lighting with regard to the influence of the different ambient lighting conditions on the user falling asleep. Before modeling the mapping relation between different lighting conditions and factors related to the sleep onset efficiency, the sleep onset behavior of the user needs to be detected and judged.

When a person falls asleep, it is difficult to determine by an accurate time value. However, several human body characteristics such as the eye opening and the change rate thereof during falling asleep, the duration of eye closure during blinking, the heart rate and the change rate thereof, and the body movement state can be objectively obtained by the detection module. Various attributes of the light environment, such as light intensity and light color, can have different effects on human sleep. In order to provide decision basis for searching the lighting condition control which is helpful for falling asleep, the mapping relation between the lighting condition and the human body characteristics related to falling asleep needs to be identified and expressed. Considering that falling asleep is a continuous dynamic process and there is close correlation between human body characteristics in adjacent time periods, the invention adopts a dynamic recurrent neural network to model the system.

Therefore, the method is based on a dynamic recursive Elman neural network, and models a complex nonlinear mapping relation between illumination conditions and sleep-falling efficiency factors, wherein the illumination conditions comprise illumination of a reading surface, color temperature and xyz color coordinate values of colors, and the sleep-falling efficiency factors are represented by 5 parameters of the eye opening change rate, the eye closing duration change rate, the heart rate change rate, the body movement frequency change rate and the body temperature change rate of a user.

The technical scheme of the invention is that the signals of several human body characteristics related to falling asleep are acquired, the trend of the signals is extracted, and accidental factors in various signals are eliminated by adopting a data fusion method, so that accurate falling asleep characteristic data is obtained. Then, based on the fitted function, the time difference between the two fixed dependent variables is calculated, and the time difference is used for defining each sleep-in efficiency physical sign parameter, so that the sleep-in efficiency evaluation function of each factor is defined according to the corresponding sleep-in time length. And finally, optimizing the lighting environment light color parameters of the user by an optimization algorithm based on the evaluation function, and sending the current value to a driver for dimming after the optimization result is mapped into the driving current, thereby finally realizing the lighting environment which is beneficial to the user to fall asleep.

Specifically, the invention provides a lighting control system based on sleep-in efficiency factor, which comprises the following structures: a sleep identification unit, an identity identification unit, a user interface unit, a light adjustable lamp group, and a control unit respectively connected with the sleep identification unit, the identity identification unit, the user interface unit and the lamp group,

the sleep-in recognition unit collects and recognizes physical parameters such as the opening degree value and the change rate of eyes of a user, the duration time of eye closure and the change rate of the eye closure, the heart rate and the change rate of the heart rate, the body motion frequency and the change rate of the body motion frequency, the body temperature and the change rate of the body motion frequency, and the like, and the control unit is configured to:

the processing module contained in the internal part reads the physical sign parameters from the sleep-in recognition unit through the input interface module,

the driving current of u LED strings in the lamp group and v irradiation angles are used, w illumination parameters are used as input quantities, 5 sleep onset efficiency sign parameters including the eye opening degree change rate, the eye closing duration change rate, the heart rate change rate, the body motion frequency change rate and the body temperature change rate of a user are used as output quantities, a dynamic recursive Elman neural network is established,

the dimming processing part sends dimming signals to the lamp group through the output module or the user interface unit, acquires a training sample set of the dynamic recursive Elman neural network in different light environments for a specific user based on the dimming signals and the sleep-in recognition unit, trains the neural network by using the sample set,

in the field environment, the lighting optimization processing part establishes a luminous environment evaluation function based on 5 sleep-in efficiency sign parameters, predicts sleep-in efficiency sign parameter values under different lighting parameter conditions by using a trained dynamic recursive Elman neural network corresponding to different users respectively, optimizes the driving current and the illumination angle of the LED string in a spatial range of the lighting parameter values of the field lamp group through a multi-objective optimization algorithm,

and transmitting the drive current and the irradiation angle obtained by optimization to a lamp group to perform dimming.

Preferably, the identification unit is configured to identify a user, the light group has at least one adjustable light property among brightness, color temperature, color, and illumination angle, and the control unit changes the light emitted from the LED light group in a step-by-step manner within a known dimming range of the LED light group through the output module.

Preferably, the output module comprises a display bar for indicating factor values of the sleep-falling efficiency of the current user in turn, and a communication interface, and outputs the detected or predicted factor values of the sleep-falling efficiency to the outside through the interface.

Preferably, the dynamic recursive Elman neural network outputs 5 sleep onset efficiency sign parameters k_iI is 1,2,3,4,5, and is obtained by processing as follows:

based on the sleep-in recognition unit, the change process data of the physical sign parameters in the sleep-in process under various illumination conditions are acquired and recorded, and after the data in the recorded physical sign parameter sequence in each sleep-in process are subjected to filtering and data fusion processing,

the duration of the user's closed eye, y1, pre-processed,

y1＝max(y1,4)，

then, an off-line data fitting is performed based on the following model,

y1＝g1(t)＝8·b/exp(4·c·(a-t))+1，

then calculating the change rate of the duration of the eye closure,

k_ec＝k₁t2-t1, wherein t1 is g1^-1(4e^-1),t2＝g1^-1(4-4e^-1)；

After normalization processing is carried out on each physical sign parameter in the eye opening degree, the heart rate, the body movement frequency and the body temperature of a user, off-line data fitting is carried out on the basis of the following models respectively,

y2＝g2(t)＝2·b/exp(4·c·(t-a))+1，

then the respective rates of change are calculated,

k_it2-t1, wherein t1 is g2^-1(1-e^-1),t2＝g2^-1(e^-1)，i＝2,3,4,5；

Wherein y1 and y2 are values obtained after sign parameter preprocessing or normalization, t is time, a, b and c are coefficients to be fitted, and k is_i(i-2, 3,4,5) corresponds to the eye opening change rate k_eoHeart rate change rate k_hRate of change of body motion frequency k_bBody temperature change rate k_p。

Preferably, the 5 sleep-onset efficiency sign parameters k are used as basis_iAnd the light environment evaluation function is as follows,

wherein f is_iRespectively corresponding to 5 factors of the eye opening degree, the eye closing duration, the heart rate, the body movement frequency and the body temperature of the user, namely an evaluation value of the sleep onset efficiency, w_iIs its corresponding weight, each f_iIs defined as follows:

wherein k is_eoTIs the eye opening change rate threshold, k_ecTFor duration of eye closure, the threshold value of the rate of change, k_hT1、k_hT2Two end point thresholds, k, for the interval of the heart rate change rate setting, respectively_hT3For a set heart rate interval width value, k_bTIs a body motion frequency rate of change threshold, k_pT1Is a threshold value of body temperature rate of change, k_pT2Setting a body temperature change rate interval width value;

the multi-objective optimization algorithm adopts evolution processing, calculates a total evaluation value F of each individual in an evolved group based on the luminous environment evaluation function, further performs inheritance, intersection and variation operations according to the total evaluation value F, updates the evolved group, then repeatedly evolves the group until the optimization is finished, and outputs the optimization result.

Preferably, the neural network may further add a time period parameter obtained from a real-time clock module as an input, wherein the time period is noon or evening;

the control unit can also be additionally provided with a temperature and humidity measurement module, and the neural network is additionally provided with two parameters of temperature and humidity acquired from the temperature and humidity measurement module as input.

The control unit may further add a noise measurement module, and the neural network adds a noise level parameter obtained from the noise measurement module as an input.

Preferably, the sleep-in recognition unit comprises an image acquisition module, a wearable module and a sleep-in judgment module,

the image processing part in the sleep judging module continuously detects the eye opening of the user, the heart rate calculating part, the body movement frequency calculating part and the body temperature calculating part calculate the heart rate, the body movement frequency and the body temperature based on the human body sensing signals acquired by the wearable module,

the data fusion processing part in the sleep judging module performs data fusion on the physical sign parameters output by the image processing part, the heart rate calculating part, the body movement frequency calculating part and the body temperature calculating part to eliminate inconsistent parts in a data set,

the image acquisition module employs a depth camera, the sleep onset recognition unit being further configured to:

and rotating a holder supporting the camera according to the processing result of the image processing part to align the camera with the face of the user.

Preferably, the lamp group is an LED lamp group, the driving current value of each LED lamp in the lamp group is adjusted by a driver, and the dimming signal and the driving current value are both PWM wave duty ratio values of the driving current of the LED lamp.

Preferably, the sleep duration parameter is added to the input quantity of the neural network, and the sleep sign parameter in the training sample is obtained according to the following processing procedure:

continuously detecting the eye opening of the user, when the eye opening value is continuously smaller than (1-delta%) times of the eye opening value in the initial stage of falling asleep within a set time length, taking the current time as the timing zero point of the falling asleep duration, and simultaneously discarding the sample record before the zero point time, wherein delta can be an integer between 5 and 10,

the user eye opening degree change rate k_eoRate of change k of duration of eye closure_ecHeart rate change rate k_hRate of change of body motion frequency k_bBody temperature change rate k_pThese 5 individual feature parameters were all calculated by a moving average filter, in which, for the eye opening change rate,

k_eo|_t＝u＝ave(dEO_u-2,dEO_u-1,dEO_u,dEO_u+1,dEO_u+2)，

where ave is the mean function, dEO_uIs the difference between the eye opening value at the time u and the eye opening value at the last time.

Preferably, the trained neural network predicts the sign parameters of a certain time point after the prediction, and the lighting is optimized and controlled periodically according to the duration in the sleep-in period.

Preferably, a button for falling asleep is added to the system, and when the button is pressed to fall asleep, a transition time period t for falling asleep is defined_slFor the time length from the time when the key is pressed to the time when the eyes keep closed for more than 1 minute,

the sleep transition duration parameter is added to the output quantity of the neural network, and a sleep transition duration evaluation value f is correspondingly added to the light environment evaluation function₆，

Wherein, t_slT1And t_slT2Respectively two thresholds for transition time to sleep.

Preferably, the model of the dynamic recursive Elman neural network is as follows:

xc_k(t)＝x_k(t-mod(k,q)-1)，

wherein mod is a remainder function, and f () is a sigmoid function; xc_k(t) is the carry layer output, x_j(t) is the hidden layer output, u_i(t-1) and y_h(t) input layer input and output layer output, w_j、w_jkAnd w_jiRespectively, the connection weight from the hidden layer to the output layer, the connection weight from the receiving layer to the hidden layer and the connection weight from the input layer to the hidden layer, theta_hAnd theta_jOutput layer and hidden layer thresholds, respectively; k is 1,2 … m, q is the selected regression delay scale, and is optimized according to the sampling period; j is 1,2 … m, i is 1,2, … n, the number m of hidden layer and accepting layer nodes can be selected from 12-25; h is 1,2 … 5;

the training uses a gradient descent method.

Compared with the prior art, the scheme of the invention has the following advantages: the method characterizes the illumination conditions by the illumination of a reading surface, color temperature and other photochromic parameters, characterizes the sleep onset efficiency by adopting user eye opening degree change rate, eye closing duration change rate, heart rate change rate, body movement frequency change rate, body temperature change rate and other characteristic parameters acquired by data fusion and data fitting, constructs and models the influence relationship between the environmental illumination conditions and the user sleep onset efficiency factors by adopting nonlinear mapping in a control unit after the signals of the parameters are acquired and processed by a photochromic recognition unit and a sleep onset recognition unit respectively, and can predict the sleep onset efficiency of the user in different light environments by the trained or fitted mapping, thereby providing a basis for the recommendation and evaluation of the illumination conditions with high sleep onset efficiency in various sleep onset environments. And searching out the light color parameters with high sleep-in efficiency evaluation values based on a multi-objective optimization algorithm, and mapping the optimized light color parameters into the driving current of the lamp group based on a lookup table or a conversion polynomial or a nonlinear mapping network, thereby realizing the illumination condition which is beneficial to the user to quickly fall asleep.

It should be understood that all combinations of the foregoing concepts, as well as additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent), are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing in the presently disclosed aspects can be contemplated as being part of the inventive subject matter disclosed herein.

Drawings

FIG. 1 is a schematic diagram of human body's biological clock rhythm;

FIG. 2 is a block diagram of the components of a lighting control system based on sleep-enabling efficiency factors;

FIG. 3 is a view showing a constitution of a control unit;

FIG. 4 is a structural diagram of a photochromic identification unit;

FIG. 5 is a view showing a constitution of a sleep-in recognition unit;

FIG. 6 is a structural diagram of a dimmable lamp set;

FIG. 7 is a schematic diagram of an Elman neural network structure;

FIG. 8 is a schematic diagram of a module layout structure;

FIG. 9 is a schematic view of the pan/tilt head rotation of the image capture module;

FIG. 10 is a rotation diagram of the light color obtaining module;

FIG. 11 is a schematic structural diagram of a light color obtaining module rotating platform;

FIG. 12 is a schematic view of a lighting environment based on sleep onset efficiency factors;

FIG. 13 is a block diagram of a dimming mapping apparatus based on sleep efficiency;

fig. 14 is a graph showing a sequence of eye opening detection.

Wherein:

100 a lighting control system based on the sleep-in efficiency factor, 110 a light color identification unit, 120 a sleep-in identification unit, 130 an identity identification unit, 140 a control unit, 150 a user interface unit, 160 a dimmable lamp set, 170 a dimming mapping unit,

a 111 light color obtaining module, a 112 light color judging module, a 113 rotating platform,

121 image acquisition module, 122 wearable module, 123 sleep-falling judgment module, 1231 image processing part, 1232 heart rate calculating part, 1233 body motion frequency calculating part, 1234 body temperature calculating part, 1235 data fusion processing part,

141 an input interface module, 142 a processing module, 143Elman neural network, 144 an iterative learning module, 145 a memory, 146 a first connection array, 147 a second connection array, 148 an output module,

the number of drivers 161, LED strings 162,

101 a base, 102 a support, 103 a depth camera, 104 a pan-tilt, 105 a display bar, 106 a light color sensing block, 107 a key block, 108 a dimming panel,

1061 first connecting piece, 1062 roll shaft, 1063 roll plate, 1064 pitch shaft, 1065 pitch plate, 1066 photo-color sensor, 1067 second connecting piece,

300 a dimming mapping device based on sleep-in efficiency factor, 310 a mapping input module, 311 a light color parameter input part, 312 a driving current parameter input part, 320 a mapping planning module, 330 a mapping determination module, 340 a mapping storage module, 350 a mapping output module,

400 external control device.

Detailed Description

Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention is not limited to only these embodiments. The invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention.

In the following description of the preferred embodiments of the present invention, specific details are set forth in order to provide a thorough understanding of the present invention, and it will be apparent to those skilled in the art that the present invention may be practiced without these specific details.

The invention is described in more detail in the following paragraphs by way of example with reference to the accompanying drawings. It should be noted that the drawings are in simplified form and are not to precise scale, which is only used for convenience and clarity to assist in describing the embodiments of the present invention.

Example 1:

the human biological clock is the phenomena of physiological and biochemical processes, morphological structures, behaviors and the like which change periodically with time in a human body. The biological clocks in human body are various, and various physiological indexes of human body, such as pulse, body temperature, blood pressure, physical strength, emotion, intelligence and the like, can change periodically along with day and night changes.

As shown in FIG. 1, in 2 am, the sleep of the person reaches the maximum depth, in 4 am, 30 min, the body temperature reaches the minimum, in 6 am, the blood pressure rises most rapidly at 45 min, in 7 am, the secretion of melatonin stops at 30 min, in 8 am, 30 min, the intestinal peristalsis occurs frequently, the testosterone secretion reaches the highest level at 9 hours, and is the most clear-headed time at 10 am, the limbs of the person are movably matched to the optimal condition in 30 minutes at 14 pm, 30 minutes at 15 pm is the most sensitive time of the reaction of the person, and by 17 pm, the efficiency of cardiovascular work of a human body is highest, the muscle strength is maximum, 30 minutes in the afternoon at 18 hours, the blood pressure of the human body reaches the peak of 1 day, immediately at 19 hours in the evening, the body temperature reaches a peak, melatonin secretion starts at 21 pm, and by 30 minutes at 22 pm, human intestinal peristalsis is inhibited, which is one day of a typical human body's biological clock.

According to the periodic change of physiological and biochemical activities of people, people can reasonably arrange activities in one day, so that the working efficiency and the resting efficiency are the highest, and the physical and psychological health states of people are the best. Among them, it is necessary for people to keep energy to arrange and guide sleep according to a biological clock.

When a human body falls into sleep from a waking state, the heartbeat is slowed down, the body temperature is lowered, the breathing is slowed down, muscles are relaxed, and a change process of relaxation, loss, fatigue, sleepiness and falling asleep in spirit is adapted to the muscles. Comparative studies with electroencephalography have shown that the longer the duration of eye closure, the more severe the fatigue. Therefore, the fatigue degree can be determined by measuring the opening degree of eyes and the duration of closing, thereby providing a detection means for the falling asleep process.

In the sleep stage, the human body shows a tendency change such as increased fatigue, eyelid sagging, intermittent blinking until complete eye closure, slow body movement, pulse and reduced body temperature, which can be detected by means of sensors. The detection of the eye state of the face, particularly the change of the opening degree, can be based on technologies such as machine vision, image processing and the like, the heart rate, the body movement and the body temperature can be detected by wearable modules such as a bracelet, and the detection means are applied to traffic driving or sleep monitoring.

The light has direct and important influence on the sleep of the human body, and in order to help find the illumination which is beneficial to the faster sleep, the invention firstly carries out detection and prejudgment on the sleep efficiency characteristics of the user in different light color environments through nonlinear system modeling.

As shown in fig. 2, the lighting control system 100 based on the sleep-in efficiency factor of the present invention includes a light color identification unit 110, a sleep-in identification unit 120, an identity identification unit 130, a control unit 140, a user interface unit 150, a dimming mapping unit 170, and a dimmable light group 160. The identification unit 130 employs a fingerprint recognizer, biometric or other feature recognizer, and the biometric features may employ iris features or facial measurement data features such as the distance between the eyes, nose and mouth of the user, etc.

As shown in fig. 2, 5 and 8, the sleep onset identifying unit 120 includes an image capturing module 121, a wearable module 122 and a sleep onset determining module 123, wherein the image capturing module 121 is supported by the cradle head 104. The camera 103 of the image acquisition module, together with the cradle head 104, is fixed on a support 102 placed near the user in a sleeping scene, and the bottom of the support 102 is supported by a base 101.

The image acquisition module 121 acquires continuous images of a face and a reading object in a scene of falling asleep, the image processing unit processes the acquired images, periodically monitors the eye opening of the user, and acquires the eye opening value of the user and the change rate thereof, the eye closing duration and the change rate thereof. The image processing part also identifies the orientation of the reading object relative to the bracket in order to match the light color identification unit to identify the light color of the reading surface.

As shown in fig. 8 and 9, the image acquisition module adopts a depth camera, and images are captured by a color camera and a group of depth-of-field infrared cameras, the color camera is used for capturing images, the infrared cameras are used for generating a pixel depth matrix, and depth information of a target is generated through operation, so that human eyes at various angles are tracked and detected. In the process of tracking and detecting the human eyes, the holder supporting the camera is rotated according to the processing result of the image processing part, so that the camera is aligned to the face of the user, and imaging and processing are facilitated. The motion of the pan-tilt makes the camera perform pitching rotation around the Y axis and perform deflection rotation around the Z axis respectively.

The wearable module 122 includes information acquisition modules such as a pulse sensor, an acceleration sensor, and a body temperature sensor, and signals acquired by these sensors are processed by the heart rate calculating part 1232, the body movement frequency calculating part 1233, and the body temperature calculating part 1234 in the sleep determination module 123, respectively, to obtain the heart rate, the body movement frequency, the body temperature, and their respective unit time change rates of the user.

Based on the image of the scene of falling asleep collected by the depth camera, the image processing part 1231 firstly performs smoothing processing and threshold segmentation, removes noise, positions the face and the eye region of the user, and extracts characteristic information such as the aspect ratio of human eyes; and secondly, performing geometric correction based on the depth information, performing three-dimensional reconstruction on the eye region, obtaining three-dimensional world coordinates of the eye region, and obtaining actual eye opening values at different angles and distances.

The eye opening value can be calculated based on the periodically acquired human eye height-width ratio, and the eye closing duration can be acquired in the process of periodically sampling images. The closed eye state is defined as the area of the eyelid covering the pupil exceeding 80%, and in the image sampling process, if the eye images acquired in two consecutive times are both in the closed state, the two acquisition time intervals are considered as the closed eye duration. An eye-open-eye-closed-eye-open sequence is continuously acquired, and the difference between the two eye-open times is the eye-closed duration.

The image-based sleep characteristic processing process comprises the following steps: after the face of the image is positioned, the left eye region and the right eye region are segmented, and the eye opening and the eye closing duration are recognized for two eyes respectively.

The amplitude and frequency of the body motion are gradually reduced in the sleeping process, so that the method can be used for auxiliary detection of sleeping. The current state is characterized by the statistical period of physical activity, such as wrist activity energy and frequency, over half a minute. By adopting zero-crossing detection, if the acceleration value is compared with a reference value slightly larger than zero, the counting is carried out once every time the reference value passes. The following formula is adopted to represent the body motion frequency characteristics:

wherein A is_iThe number of times of wrist movement in the i-th cycle, R, is obtained according to the acceleration value_iIs a time sequence coefficient, η_j(j ═ 1,2,3,4) is an item coefficient, Q_iSD is a function for solving standard deviation, and the number of the periods of measuring the cycle time and the number of the periods of 2 periods before and after the cycle time is higher than a set threshold value such as 5. In the formula, each coefficient can be a value between 0 and 1, and d can also be_iAnd comparing with other physiological indexes such as myoelectricity recorded at the same time to carry out fitting calibration.

The pulse sensor is used for measuring the heart rate based on the principle that substances absorb light, and the pulse sensor irradiates blood vessels through a green light LED and is matched with a photosensitive photodiode to measure reflected light. Because the blood is red, the blood can reflect red light and absorb green light, and when the heart beats, the blood flow is increased, and the absorption amount of the green light is increased; the blood flow decreases in the beating gap of the heart, and the green light absorption decreases accordingly. Thus, heart rate can be measured from the absorbance of blood.

The pulse sensor converts the absorption of blood flow to light into a fluctuation signal, the signal is a mixture of a direct current signal and an alternating current signal, the alternating current signal reflecting the blood flow characteristics is extracted through band-pass filtering between 0.8Hz and 2.5Hz, then the maximum value point of the amplitude is extracted by adopting Fourier transform, the frequency value corresponding to the point is obtained, and the frequency value is multiplied by 60 times to obtain the actual heart rate value.

The body temperature calculating part carries out filtering processing on the signals collected by the body temperature sensor and calculates a body temperature value.

After basic data such as eye opening, eye closing duration, heart rate, body movement frequency, body temperature and the like are obtained, a data fusion processing part in the sleep judging module carries out data fusion on the physical sign parameters so as to eliminate inconsistent parts in a data set.

The data fusion adopts an evidence reasoning method and is based on the set heuristic rule. Rules include both single-factor and multi-factor categories. Take the single factor rule as an example: for the eye opening degree, if it is detected that one eye is closed while the other eye is open, the current state is determined as open. Other signs, such as occasional large amplitude increases in body temperature during the fall, occasional rather than sustained reverse increases following heart rate fall, all require evidential reasoning to exclude individual data.

In the multi-factor rule inference, the opposite change trend of the individual sign data is excluded according to the consistent change trend of most feature data. When data fitting is performed on the duration of eye closure by using a curve such as exponential distribution, a plurality of short duration of eye closure data which are mixed in the data sequence are gradually increased for the duration of eye closure, if other sign data show that drowsiness is gradually deepened, the plurality of short duration of eye closure data are excluded, which may be anti-fatigue actions which are actively generated by people consciously adjusting the state of the people during falling asleep, and at the moment, the eyes are quickly blinked for a plurality of times. Similarly, if other vital sign data do not change much, i.e., exhibit fatigue, but the duration of eye closure is much longer than normal, the data should be excluded, which may be the presence of a foreign object in the eye. For another example, when the body tends to be calm, the acceleration sensor detects a sudden touch of the body, if the change of other sign data is not large, the touch may be caused by dozing during falling asleep, and the touch data should be deleted when the body movement frequency trend is calculated.

Based on various physical sign data sequences after data fusion processing, the sleep onset judging module expresses the data sequences by adopting a data fitting method. Fig. 14 shows a detection sequence of eye opening during reading before sleep, in which a sampling sequence of normalized eye opening de is first subjected to a pre-filtering process, and then subjected to data fusion to further remove the influence of accidental factors. In the first stage, the eye opening de does not change much, and basically changes within a range from the average value to the next value in the normal state; in the second stage, as sleepiness approaches, the eye opening gradually decreases until finally being detected as substantially closed.

As can be seen from fig. 14, the turning point of the human eye opening is hard to predict during the transition period of falling asleep, and from the turning point, it is closed gradually in a short time; meanwhile, the gradual change duration is greatly different at different times. In order to fit the sampling sequence, the method is different from the common trend functions such as Sigmoid, tanh and the like, and the following fitting functions are designed:

y2＝g2(t)＝2·b/exp(4·c·(t-a))+1，

where b is a scaling factor, which can be 0.5 for normalized data, and a and c are parameters related to the sample. The data sequence with various turning point positions and different change rates and descending trend can be fitted only by properly changing the values of a and c.

Correspondingly, the y2 function can be adopted to perform data fitting on the sign data sequence which tends to be stable after the heart rate, the body motion frequency, the body temperature and the like are reduced. For the duration of the closed eye, accordingly, another fitting function is designed:

y1＝g1(t)＝8·b/exp(4·c·(a-t))+1。

also, for the duration of the eye-closing, if it reaches 4 seconds, it is generally judged that the person has entered the sleep state. Thus, the duration of the eye closure is pre-processed:

y1＝max(y1,4)，

otherwise, the duration of the closed eye can take many values, and the sample loses the meaning of the characterization.

According to the method, on the basis of data fitting of the sign data sequence, the time difference, which is the independent variable corresponding to two determined dependent variables, is calculated according to the fitted trend function to represent the sign change rate. For example, for eye opening, its rate of change keo is calculated:

k_eot2-t1, wherein t1 is g2^-1(1-e^-1),t2＝g2^-1(e^-1)。

Similarly, the rate of change of other signs can be calculated. Through the data processing, various physical signs and the change rate thereof can show consistent evaluation standards; for example, a smaller defined rate of change of the physical sign indicates a shorter transition time to sleep. Meanwhile, compared with single-factor evaluation such as eye opening evaluation, the multi-factor sign evaluation can reflect the sleep-in efficiency or speed characteristics of different people, so that a foundation is provided for subsequent illumination influence modeling and illumination optimization control.

Preferably, the credibility of physical parameters such as the opening degree of eyes, the duration of eye closure, the heart rate, the body movement frequency, the body temperature and the like of the user is calculated according to a plurality of front and rear terms of the time data sequence of the user, and a Bayesian data fusion method is used for fusing a plurality of physical parameters into one output.

As shown in fig. 2, 4, 10 and 11, the light color identification unit 110 includes a light color obtaining module 111 disposed on the rotary platform 113 and a light color determining module 112 for processing and calculating the obtained light sensing signal. The rotary platform and the light color obtaining module form a light color sensing block 106, which is connected to the bracket 102.

The light color sensor 1066 in the light color obtaining module is connected to the bracket 102 sequentially through the pitching plate 1065, the rolling plate 1063 and the first connecting member 1061. The pitching plate 1065 is connected to the roll plate 1063 through a pitching rotating shaft 1064, and drives the photochromic sensor 1066 to pitch around the Y axis; the roll plate 1062 is connected to the first connecting member 1061 via a roll shaft 1062, and drives the pitching plate 1065 and the light color sensor 1066 to roll around the X axis. The roll shaft 1062 and the pitch shaft 1064 are driven to rotate by a motor, and they are powered by a first connecting piece 1061 and a second connecting piece 1067, respectively, and the control of the motor is realized by a light color judging module or a control unit located in the base. The first connecting part 1061 is a hard connection and provides an electrical connection channel in addition to supporting and fixing, and the second connecting part 1067 is a soft connection and only provides an electrical connection channel.

The photochromic sensor comprises an illuminance sensor, a color temperature sensor and a color sensor, wherein the color temperature and the color can be acquired by the same RGB or xyz color sensing module. Preferably, the color sensing module may be a TCS3430 sensor, the filter of TCS3430 having five channels including X, Y, Z channel and two Infrared (IR) channels, which may be used to infer the light source type. The TCS3430 sensor collects the light color signal of the reading surface in real time, and the xyz color coordinate value and the color temperature of the color are respectively obtained after signal processing and conversion by the processing module in the control unit.

The user may move about the table before falling asleep, such as for a work schedule or schedule for the next day or for a short reading, while the reading surface is substantially stationary and light detection may be performed in the horizontal plane. However, sometimes, the reading surface of the user is not horizontal, for example, the user leans on a couch, a sofa or a bed head to read, at this time, based on the recognition of the reading surface orientation by the image processing unit, there are two methods to detect the illumination, especially the illuminance, of the reading surface, one is to convert the illuminance detected by the photochromic sensor 1066 to the reading surface according to the spatial distribution characteristics of the light source, and the other is to convert the photochromic sensor to the orientation parallel to the reading surface by the rotating platform, so that the illuminance of the reading surface is obtained by the photochromic calculation module. The former method requires modeling of the spatial distribution of the light source, and has a small application range, and for this reason, the second method is adopted.

In different light environments, the orientation of the surface of the light color sensor is changed by respectively rotating the pitching rotating shaft and the rolling rotating shaft, light color parameter values such as the illumination, the color temperature, the color xyz color coordinate value and the like of the oriented surface are calculated by the light color judging module after light is sampled, the pitch angle alpha and the roll angle beta corresponding to each orientation are recorded, and a mapping table combining alpha and beta to each light color parameter value is established.

In order to generalize the mapping table to any specific orientation, when the combination of the pitch angle and the roll angle of the orientation is not in the mapping table, the corresponding photochromic parameter value is obtained through distance weighted interpolation calculation in the angle combination space based on the mapping table, and the process is as follows.

For simplicity, without loss of generality, only 2 parameters of the light color parameters, i.e., the reading surface illuminance and the color temperature, are taken as examples, and more light color parameters can be processed similarly.

Mapping tables for each photochromic parameter value based on pitch angle alpha and roll angle beta combinations for a specific angle combination (alpha)₀，β₀) And the values of the illumination and the color temperature are obtained by interpolation in the mapping table.

First, find P (alpha) in the angle space₀，β₀) Four points around: a (alpha)₁，β₁)，B(α₂，β₁)，C(α₁，β₂) And D (alpha)₂，β₂) In which α is₁≤α₀≤α₂，β₁≤β₀≤β₂，

Illumination and color temperature value (E)₀，K₀) The distance is used as a weighted value for interpolation,

wherein d is₁Represents the shortest distance of P to four points, d₂The point is the second shortest, and so on; e₁And K₁Respectively the illumination and color temperature values of the shortest distance point; and respectively adding different weights to four points closest to the P point to be searched according to different distances, wherein the four points are the shortest and the heaviest.

Preferably, in establishing the mapping of the angle combinations to the light color parameter values, the light color sensor surface is arranged as close as possible to the reading surface, so that the difference in illumination on the two planes is not small enough to affect the efficiency of falling asleep. This is easily met when the light source is at a distance from the reading surface.

Referring to fig. 1 and 6, the dimmable LED light set 160 is a dimmable LED light set, in which the driving current value of each LED string 162 in the light set is adjusted by a driver, the driver 161 is a driver capable of changing the output current, and the driver performs light output adjustment by changing the PWM duty cycle of the driving current of each channel of the LED string. By changing the driving current, the dimmable light set 160 can adjust at least one of the light properties such as brightness, color temperature, color, and illumination angle.

As shown in fig. 12, for a specific individual, their sleep onset efficiency performance under various light color conditions is collected, and a first mapping is established between the light color parameters and the sleep onset efficiency parameters. And establishing a sleep-in efficiency evaluation function for the sleep-in efficiency parameters of the user, and optimizing the photochromic parameters based on a multi-objective optimization algorithm such as a multi-objective genetic algorithm (MOGA) because the evaluation indexes comprise a plurality of indexes. In the optimization process, aiming at each photochromic parameter combination in the search space, the sleep-enabling efficiency parameter corresponding to the parameter combination is predicted based on generalization of the first mapping, so that the sleep-enabling efficiency evaluation corresponding to the combination can be calculated according to the predicted sleep-enabling efficiency parameter.

The result of the optimization is a combination of the light color parameters in the search space that needs to be converted into the actual drive currents for the lamp set, for which a second mapping of the light color parameters to the lamp set drive currents is established. And based on the second mapping, converting the optimization result into a driving current value of the lamp group, transmitting a dimming command to a driver of the lamp group for execution, outputting corresponding current to each channel, and then adjusting the light emitted by the LED strings to realize the light environment illumination corresponding to the optimized sleep efficiency value.

As shown in fig. 2 and fig. 3, the control unit includes an input interface module 140, a processing module 142, an Elman neural network 143, an iterative learning module 144, a memory 145, a first connection array 146, a second connection array 147, and an output module 148.

And constructing and modeling the mapping relation between the illumination condition of the environment and the sleep efficiency factor of the user by adopting a neural network. Specifically, the Elman neural network shown in fig. 7 is established in the control unit, and the network takes the light color parameters such as the illuminance of the reading surface, the color temperature and the like as input quantities, and takes 5 individual characteristic parameters including the opening change rate, the eye closing duration change rate, the heart rate change rate, the body movement frequency change rate and the body temperature change rate of the user's eyes as output quantities. Among these, it is preferable to add an xyz color coordinate value parameter of the color to the input amount.

Compared with a BP (back propagation) neural network, the Elman neural network has a recursive structure and also comprises a receiving layer besides an input layer, a hidden layer and an output layer, wherein the receiving layer is used for feedback connection among layers, so that the receiving layer can express time delay and parameter time sequence characteristics between input and output, and the network has a memory function. Referring to fig. 7, the built neural network has 2 units in the input layer, m nodes in the hidden layer and the receiving layer, and 5 units in the output layer.

The neural network model is:

xc_k(t)＝x_k(t-mod(k,q)-1)，

wherein mod is a remainder function, and f () is a sigmoid function; xc_k(t) is the carry layer output, x_j(t) is the hidden layer output, u_i(t-1) and y_h(t) input layer input and output layer output, w_j、w_jkAnd w_jiRespectively, the connection weight from the hidden layer to the output layer, the connection weight from the receiving layer to the hidden layer and the connection weight from the input layer to the hidden layer, theta_hAnd theta_jOutput layer and hidden layer thresholds, respectively; k is 1,2 … m, q is the selected regression extensionTime scale is optimized according to sampling period; j is 1,2 … m, i is 1,2, the number m of nodes of the hidden layer and the accepting layer can be selected from 12-25; h is 1,2 … 5.

In order to improve the generalization capability of the neural network, enough training samples are collected. The control unit sends out dimming signals to the lamp group through the output module or the user interface unit, obtains a training sample set of the neural network based on the light color identification unit and the sleep-in identification unit in different light environments aiming at specific users, and records output values y of all samples_hDesired value y of_hd。

Through the processing modules in the sleep-in recognition unit and the control unit, the 5 characteristic parameters of the neural network output quantity are obtained by processing in the following way:

based on the sleep-in recognition unit, the change process of the physical sign parameters in the sleep-in process under various illumination conditions is obtained and recorded, and for the recorded data in the physical sign parameter sequence in each sleep-in process,

the duration of the user's closed eye, y1, pre-processed,

y1＝max(y1,4)，

then, an off-line data fitting is performed based on the following model,

y1＝g1(t)＝8·b/exp(4·c·(a-t))+1，

then calculating the change rate of the duration of the eye closure,

k_ec＝k₁t2-t1, wherein t1 is g1^-1(4e^-1),t2＝g1^-1(4-4e^-1)；

y2＝g2(t)＝2·b/exp(4·c·(t-a))+1，

then the respective rates of change are calculated,

k_it2-t1, wherein t1 is g2^-1(1-e^-1),t2＝g2^-1(e^-1)，i＝2,3,4,5；

Wherein y1 and y2 are sign parameter preprocessing or normalizationThe value of t is time, a, b and c are fitting coefficients, k_i(i-2, 3,4,5) corresponds to the eye opening change rate k_eoHeart rate change rate k_hRate of change of body motion frequency k_bBody temperature change rate k_p。

In the process of sampling the sleep onset process, when the change rates of a plurality of physical sign parameters are detected to be smaller than a set threshold value in a plurality of continuous periods, the user is considered to be asleep, and the sleep onset sampling is stopped.

The neural network training adopts a gradient descent method, and the weight and threshold value adjusting method in the training is as follows.

Assuming a total of P training samples, let the error function be:

then the adjustment of the weight from the hidden layer to the output layer is shown as follows:

w_hj(t+1)＝w_hj(t)+Δw_hj(t+1)，

wherein the content of the first and second substances,

δ_yh＝-(y_hd-y_h)·y_h·(1-y_h)，

the adjustment formula of the output layer threshold is as follows:

θ(t+1)＝θ(t)+Δθ(t+1)，

wherein the content of the first and second substances,

similarly, the input layer to hidden layer connection weights, hidden layer thresholds, and the accept layer to hidden layer connection weights are adjusted.

The initial value range of each weight is an interval of (-0.1, 0.1), the learning rate eta is a decimal less than 1, and the learning rate eta can be dynamically adjusted by adopting a fixed rate or according to the total error of the current network output. The training end condition may be set to a total error or a variation thereof smaller than a set value or a number of times of training up to a certain amount.

Before network training, normalization preprocessing can be performed on input quantity and output quantity:

r'＝r-r_min/r_max-r_min，

wherein r is an unprocessed physical quantity, r' is a normalized physical quantity, r_maxAnd r_minRespectively the maximum and minimum values of the sample data set.

When calculating the predicted value, the network output quantity is converted back to the output quantity value by the following formula:

r＝r_min+r'·(r_max-r_min)。

when the online prediction is applied, the first connection array is disconnected, the neural network predicts each output quantity and outputs the output quantity to the processing module through the second connection array, and the output quantity is displayed and output through the output module and is sent to the outside in a signal form after the output quantity is processed and analyzed by the processing module.

As shown in fig. 1 and fig. 6, preferably, the LED string is a dimming lamp including RGB three-primary-color current channels, and at this time, the light color of the lamp can be changed by changing the driving current value of one of the channels. When the three channel currents are increased or decreased in synchronization from a certain state, the lamp exhibits approximately the same color and gradually bright or dim brightness.

Preferably, the control unit changes the light emission of the LED lamp set in a stepwise manner within a known dimming range of the LED lamp set through the output module. For example, a variable current-light color mapping table is established by combining the value of each channel current of the LED string with the corresponding illuminance, color temperature and color collected on the reading surface, only one of the variables such as the illuminance is changed and the other variables such as the color temperature and the color are kept unchanged in the value space of the illumination vector space composed of the illuminance, the color temperature and the color, the current-light color mapping table is reversely searched to find the current value of each channel of the LED string corresponding to the current illumination vector, and the control unit sends the PWM wave duty ratio of each channel current to the driver in the form of a signal through the output module. The control unit obtains enough training samples of the neural network after multiple sleep-in detections by continuously changing working points of an illumination vector space, wherein sampling points can be sparse in end value areas of various light color variables, and the sampling points are denser in low color temperature areas such as areas near color temperature 3000k and illumination of 100 lx-300 lx. The collected sample is stored in a memory.

The parameters such as preset values required for processing by the control unit are input through keys in the user interface unit. The trained neural network can predict and judge the sleep efficiency of the user under the current illumination condition in a new light environment based on the generalization capability of the neural network, and display or output the predicted result through an output module.

Specifically, as shown in fig. 1 and 8, on the base 101, the keys of the user interface unit are disposed in the area of the key block 107, and on the other side opposite to the key block, the user interface unit may further be disposed with a dimming panel 108 for manually adjusting the light emission of the light set.

Preferably, the output module 148 includes a display bar 105 for indicating the values of the factors of the sleep efficiency of the current user in turn. Preferably, the output module further comprises a communication interface, and the detected or predicted values of the factors of the sleep onset efficiency are output to the outside through the interface module.

Since the drowsiness or fatigue level in preparation for falling asleep varies, it is preferable that a key indicating the current fatigue level is provided in the user interface unit while the neural network increases an input amount of a fatigue index, which may be an integer between 1 and 5.

When the user has difficulty falling asleep due to emotions and the like, the collected samples have larger deviation from the samples under normal conditions, and although the neural network has better fault tolerance, the accuracy of the network is affected when too many samples are available. For this purpose, a cancel sampling key is preferably provided in the user interface unit, and the control unit suspends data sampling and sample recording after detecting that this key is pressed.

After the mapping from the light color parameters to the sleep-in efficiency parameters is established, based on the generalization ability of the network, the light environment which can improve the sleep-in efficiency of a specific individual can be searched through an optimization algorithm, and the on-site light environment is configured according to the optimization result through a dimming means. For this purpose, a light environment evaluation function is established on the basis of 5 sleep-in efficiency parameters:

wherein f is_iThe evaluation values w of 5 parameters of sleep onset efficiency defined according to time difference after data fitting is carried out on each physical sign parameter in the eye opening degree, the eye closing duration, the heart rate, the body movement frequency and the body temperature of the user_iIs its corresponding weight, each f_iIs defined as follows:

in the formula, k_eoIs the rate of change of eye opening, k_eoTIs an eye opening change rate threshold; k is a radical of_ecFor the duration rate of change of the eye closure duration, k_ecTIs a closed eye duration change rate threshold; k is a radical of_hIs the rate of change of heart rate, k_hT1、k_hT2Two end point thresholds, k, for the interval of the heart rate change rate setting, respectively_hT3Setting a heart rate interval width value; k is a radical of_bIs the rate of change of body motion frequency, k_bTIs a body motion frequency change rate threshold; k is a radical of_pIs the rate of change of body temperature, k_pT1Is a threshold value of body temperature rate of change, k_pT2Is the set value of the width of the body temperature change rate interval.

In the evaluation function，f₃Is at k_hT1、k_hT2And the evaluation value is a grading function of the end points distributed according to the trapezoid, when the heart rate change rate is within a set end point interval range, the evaluation value is the highest, otherwise, the evaluation value is lower as the distance from the end points is farther. f. of₅Then is k_pT1A triangular distribution of the scoring function as the center, k_pT1May be obtained by probabilistically counting the samples that are artificially labeled as being the fastest to fall asleep. Preferably, a similar f can also be used₃The trapezoidal distribution scoring function of (a).

The established evaluation function F has higher score when the user falls asleep for a short time, namely the efficiency of falling asleep is high, otherwise, the score is reduced. The evaluation standards of the evaluation values of all factors in the evaluation function are consistent. The change rates defined according to the time difference and corresponding to the eye opening degree, the eye closing duration, the heart rate, the body motion frequency and the body temperature of the user are smaller, which indicates that the faster the user falls asleep, the higher the evaluation value is.

Since the optimization of the sleep onset efficiency involves a plurality of factors, the optimization problem is a multi-objective optimization problem, and for the multi-objective optimization problem, an optimization solution is called a Pareto solution. The problem is solved by a multi-objective genetic optimization algorithm, i.e. MOGA.

The genetic algorithm is used as a method for simulating a biological genetic process of natural selection, high-quality and low-quality selection and survival of suitable persons, and an effective solving way is provided for a target optimization problem. Due to the superior characteristics of robustness, global convergence and the like, the method is widely applied to many disciplines such as production scheduling, communication, circuit design, robot path planning and the like.

In MOGA solving, firstly, determining a strategy for coding 5 light color parameters including the illumination of a reading surface, the color temperature and the xyz color coordinate value of the color, and determining respective value intervals of the light color parameters; in evolution iteration, for individuals in a group, using the trained Elman neural network to predict the sleep onset efficiency parameters of the corresponding photochromic parameters of each individual, and obtaining 5 predicted values of the eye opening change rate, the eye closing duration change rate, the heart rate change rate, the body movement frequency change rate and the body temperature change rate of a user; based on the predicted value, calculating an evaluation value of the predicted value according to an evaluation function F, performing intersection and variation operations according to the evaluation value, and updating an evolutionary population; and repeating the iteration until the optimization is finished, and outputting a Pareto optimization solution.

And obtaining the photochromic parameters with high evaluation on the sleep efficiency after optimization and solution. Then, the dimming mapping unit 170 maps the optimized light color parameter to a driving current value of each driving current channel of the lamp set 160, and transmits the driving current value to the driver in the lamp set, so as to obtain an illumination environment that is helpful for the user to fall asleep.

The dimming mapping unit converts the light color parameters into a mapping of the lamp group driving current, which may be based on various means. For example, the light source may be based on a look-up table of light color space to driving current space generated in advance.

For the sake of simplicity, without loss of generality, the color parameters in the above-mentioned light color parameters are removed, and only 2 parameters of the reading surface illuminance and the color temperature are considered.

As a commonly used dimmable light set, it is assumed that the light set includes two LED strings of high color temperature and low color temperature, and each LED string corresponds to one driving current channel. The light modulation mapping unit comprises a lookup table from a light color space consisting of reading surface illumination and color temperature to a dual-channel drive current space, and the optimization result (E) is obtained₀，K₀) The dual channel drive current values are obtained by interpolation in a look-up table.

First, find P (E) in the photochromic space₀，K₀) Four points around: a (E)₁，K₁)，B(E₂，K₁)，C(E₁，K₂) And D (E)₂，K₂) In which E₁≤E₀≤E₂，K₁≤K₀≤K₂，

Two-channel current value (i)₀₁，i₀₂) The distance is used as a weighted value for interpolation,

wherein d is₁Represents the shortest distance of P to four points, d₂The second shortest point, and so on, d_TIs the sum of all distances; i.e. i₁₁And i₂₁The current values of the two channels with the shortest distance are respectively; and respectively adding different weights to four points closest to the P point to be searched according to different distances, wherein the four points are the shortest and the heaviest.

Preferably, the dimming map after the optimization search may be further based on a BP neural network, where the BP neural network takes 5 light color parameters in total, such as the illuminance of the reading surface, the color temperature, and the xyz color coordinate value of the color, as input quantities, and takes the current values of all the driving current channels of the lamp set as output quantities.

To increase the applicability of the Elman neural network, the control unit may preferably further include a real-time clock module, and the neural network may further include a seasonal parameter obtained from the real-time clock module as an input.

Preferably, the neural network may further add as an input a time period parameter obtained from the real-time clock module, the time period being noon or night respectively.

Preferably, the control unit can be additionally provided with a temperature and humidity measurement module, and the neural network is used for adding two parameters of temperature and humidity acquired from the temperature and humidity measurement module as input.

Preferably, the control unit may further include a noise measurement module, and the neural network may further include a noise level parameter obtained from the noise measurement module as an input.

Example 2

Different from embodiment 1, in this embodiment, a sleep duration parameter is added to the input quantity of the Elman neural network, and the sleep sign parameter in the training sample is obtained according to the following processing procedure:

when the eye opening degree of the user is continuously detected, and the eye opening degree value is continuously smaller than (1-delta%) times of the eye opening degree value in the initial stage of falling asleep within a set time length, the current time is taken as a timing zero point of the falling asleep duration, and simultaneously, a sample record before the zero point moment is abandoned, wherein delta can be an integer between 5 and 10.

Rate of change k to eye opening of user_eoRate of change k of duration of eye closure_ecHeart rate change rate k_hRate of change of body motion frequency k_bBody temperature change rate k_pThese 5 individual characteristic parameters are all calculated by means of a moving average filter, e.g. k for the eye opening change rate_eo|_t＝u＝ave(dEO_u-2,dEO_u-1,dEO_u,dEO_u+1,dEO_u+2)，

As shown in fig. 14, since the turning point of falling asleep of the user cannot be predicted, in the present embodiment, by continuously monitoring the eye opening, when it is significantly deviated from the normal range, the data sequence after sampling and recording is started.

Compared with the embodiment 1, because the time length from the turning point of falling asleep is introduced into the input of the neural network, the physical sign parameters at a certain time point after the turning point of falling asleep can be predicted by the trained neural network, so that the illumination can be periodically optimized and controlled according to time or other set conditions during the falling asleep period, and dynamic optimization is realized.

Example 3

Different from the embodiment 1, in this embodiment, the system further includes a button for falling asleep, and when the user is ready to fall asleep, the button is pressed,

defining a sleep transition time t_slFor the time length from the time when the key is pressed to the time when the eyes keep closed for more than 1 minute,

adding a sleep transition duration parameter in the output quantity of the Elman neural network, and correspondingly adding a sleep transition duration evaluation value f in the light environment evaluation function₆，

Wherein the content of the first and second substances,

in the formula, t_slT1And t_slT2Respectively two thresholds for transition time to sleep.

f₆Is given by t_slT1The score function of the endpoints distributed according to a semi-trapezoid is that the score value is lower when the sleep transition duration is longer.

Example 4

In still another embodiment of the present invention, a dimming mapping apparatus based on the sleep efficiency factor is also provided. Referring to fig. 13, the dimming mapping apparatus 300 based on the sleep-enabling efficiency factor includes a mapping input module 310, a mapping planning module 320, a mapping determining module 330, a mapping storage module 340, and a mapping output module 350, where the mapping input module 310 further includes a light color parameter input 311 and a driving current parameter input 312.

The photochromic parameter input portion 311 receives 5 photochromic parameters in total, including the reading surface illuminance, the color temperature, and the xyz color coordinate value of the color, from the external control device 400; the drive current parameter input 312 receives current parameters for the w drive current channels of the externally dimmable lamp set from the external control device 400.

A BP neural network is established in the mapping determining module 330, and the input quantity of the BP neural network is the 5 photochromic parameter values, and the output quantity is the current parameters of the w driving current channels.

The mapping planning module 320 is configured to collect training sample sets of the neural network in different light environments through the light color parameter input unit 311 and the driving current parameter input unit 312 after the external control device 400 sends the dimming signal to the lamp group; and training the neural network off-line by using the training sample set, adjusting the connection weight of the neural network, keeping the connection weight in the mapping storage module 340,

in the field environment, the photochromic parameter input unit 311 receives the photochromic parameters with high sleep efficiency, which are optimally searched by the external control device, from the external control device 400, maps the photochromic parameter values to the driving current values of the driving current channels of the lamp set by the trained BP neural network in the mapping determination module, and outputs the driving current values through the mapping output module 350.

The model of the BP neural network is as follows:

the output of the jth node of the hidden layer by layer is

The p-th node of the output layer outputs

Wherein the f () function is taken as sigmoid function, w_ijAnd v_jpRespectively the connection weight from the input layer to the hidden layer and the connection weight from the hidden layer to the output layer, a_jAnd b_pRespectively representing the threshold values of a hidden layer and an output layer, and k representing the number of nodes of the hidden layer, and performing network training by adopting a gradient descent method.

The optimization is performed by adopting the multi-objective genetic optimization algorithm in the embodiment 1, in the multi-objective genetic optimization algorithm, a corresponding sleep onset efficiency parameter predicted value is obtained for each photochromic parameter combination condition in the optimization search based on the first mapping, then, the grading is performed based on the predicted values, and then, the photochromic parameter combination with high grading value is searched.

Example 5

If a user frequently sleeps in a fixed indoor environment and has the same lighting fixture environment within that environment. For such a situation, in order to realize illumination with a high sleep efficiency factor, a light color conversion link in the process of converting the driving current into the sleep efficiency expression can be omitted, and the driving current value is directly mapped to the sleep efficiency parameter value.

The third mapping is implemented in this embodiment with a dynamic recursive Elman neural network.

In yet another embodiment of the present invention, as shown in fig. 12, there is provided another lighting control system based on sleep-enabling efficiency factor, which includes:

an identification unit for identifying the user,

the user interface unit that enters the parameters and initiates the operation,

having a lamp group adjustable in at least one of brightness, color temperature, color and illumination angle,

a sleep-in recognition unit for collecting and recognizing physical parameters such as the opening value and the change rate of eyes of a user, the duration of eye closure and the change rate thereof, the heart rate and the change rate thereof, the body motion frequency and the change rate thereof, the body temperature and the change rate thereof, and the like,

a control unit respectively connected with the sleep identification unit, the identity identification unit, the user interface unit and the lamp group,

wherein the control unit is configured to:

The sleep onset efficiency sign parameters in this embodiment are obtained by the sampling and data fitting method in embodiment 1.

It can be understood that, in the solution of the present invention, the illumination in the color parameters of the reading surface is for the reading object without an active light source, and for the reading object with a backlight source, such as a mobile phone, a tablet, an electronic book, etc., in the mapping from the color condition to the sleep-enabling efficiency related factor, a backlight source brightness item is supplemented to the parameter set of the color condition.

In addition, all models related to the sleep efficiency factor are based on specific individuals, so that related data in the process of generating network training samples, mapping tables and the like are based on users with the same identity; for multiple users, one data set should be created and saved for each user independently.

The invention is applied to detect and prejudge each factor of the sleep-in efficiency under different light environments, after collecting samples with abundant changes, due to infinite combinations in light color change domains, the invention can be used for predicting sleep-in efficiency parameters under illumination conditions in various field environments, including eye opening change rate, heart rate change rate and the like, the predicted values are used in the sleep-in efficiency evaluation calculation of searched light color conditions in the process of optimizing the light color parameters based on a multi-objective optimization algorithm, the optimizing results are mapped to the driving current values of the lamp group, and the lamp group driver drives the LED strings according to the current values, thereby realizing the illumination control which is beneficial to the sleep of users.

While the embodiments of the present invention have been described above, these embodiments are presented as examples and do not limit the scope of the invention. These embodiments may be implemented in other various ways, and various omissions, substitutions, and changes may be made without departing from the spirit of the invention. These embodiments and modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalent scope thereof.

Claims

1. A lighting control system based on sleep-enabling efficiency factors, comprising: a sleep identification unit, an identity identification unit, a user interface unit, a light adjustable lamp group, and a control unit respectively connected with the sleep identification unit, the identity identification unit, the user interface unit and the lamp group,

2. The sleep-enabling efficiency factor-based lighting control system of claim 1, wherein the identification unit is configured to identify a user, the light set has at least one adjustable light property selected from the group consisting of brightness, color temperature, color and illumination angle, and the control unit changes the light output of the LED light set in a step-by-step manner through the output module within a known dimming range of the LED light set.

3. The fall-asleep efficiency factor-based lighting control system according to claim 1, wherein the output module comprises a display bar for alternately indicating values of the factors of the current user fall-asleep efficiency, and a communication interface, and outputs the detected or predicted values of the factors of the fall-asleep efficiency to the outside through the interface.

4. The sleep-onset efficiency factor-based lighting control system of claim 1,

5 sleep onset efficiency sign parameters k of the dynamic recursive Elman neural network output quantity_iI is 1,2,3,4,5, and is obtained by processing as follows:

the duration of the user's closed eye, y1, pre-processed,

y1＝max(y1,4)，

then, an off-line data fitting is performed based on the following model,

y1＝g1(t)＝8·b/exp(4·c·(a-t))+1，

then calculating the change rate of the duration of the eye closure,

k_ec＝k₁t2-t1, wherein t1 is g1^-1(4e^-1),t2＝g1^-1(4-4e^-1)；

y2＝g2(t)＝2·b/exp(4·c·(t-a))+1，

then the respective rates of change are calculated,

k_it2-t1, wherein t1 is g2^-1(1-e^-1),t2＝g2^-1(e^-1)，i＝2,3,4,5；

5. The fall-asleep efficiency factor-based lighting control system according to claim 4, wherein k is based on the 5 fall-asleep efficiency sign parameters_iAnd the light environment evaluation function is as follows,

6. The sleep-onset efficiency factor based lighting control system of claim 1 wherein the neural network further adds as input a time period parameter obtained from a real time clock module, the time period being noon or evening respectively;

7. The sleep-onset efficiency factor-based lighting control system of claim 1 wherein the sleep-onset identification unit comprises an image acquisition module, a wearable module, and a sleep-onset determination module,

8. The sleep-onset efficiency factor-based illumination control system as claimed in claim 1, wherein the lamp set is an LED lamp set, the driving current value of each LED lamp in the lamp set is adjusted by a driver, and the dimming signal and the driving current value are PWM wave duty ratio values of the driving current of the LED lamp.

9. The lighting control system based on the sleep-onset efficiency factor as claimed in claim 1, wherein the sleep-onset duration parameter is added to the input volume of the neural network, and the sleep-onset sign parameter in the training sample is obtained according to the following processing procedure:

the user eye opening degree change rate k_eoRate of change k of duration of eye closure_ecHeart rate change rate k_hRate of change of body motion frequency k_bBody temperature change rate k_pThese 5 individual feature parameters were all calculated by a moving average filter, wherein,as for the rate of change of the eye opening,

k_eo|_t＝u＝ave(dEO_u-2,dEO_u-1,dEO_u,dEO_u+1,dEO_u+2)，

10. The sleep-onset efficiency factor based illumination control system as claimed in claim 9, wherein the trained neural network predicts the sign parameters of a certain time point after the prediction, and the illumination is optimized and controlled periodically according to the duration during the sleep onset period.