KR101769409B1 - Lighting control apparatus according to the bio-signals and environmental changes - Google Patents

Abstract

Based on the biometric information (brain wave, electrocardiogram, pulse, etc.) and environmental information (temperature, illumination, humidity, etc.), the emotional state of the user is analyzed and the brightness And a lighting control device according to a change in the environment. Biological and environmental information acquisition means for acquiring biometric information and environment information of a user; An information collection terminal for collecting biometric information and environment information in association with the biometric and environmental information acquisition means; The bio-information and the environmental information collected from the information collecting terminal are calculated and fused based on the bio-information and the environmental information, the personal bio-data pattern and the environmental pattern according to the movement route are analyzed, Intelligent lighting management middleware that analyzes the taste and creates a profile for LED lighting control to control lighting; And an LED lighting controller for controlling the brightness and color of the LED lighting based on the lighting control data output from the intelligent lighting management middleware, thereby realizing a lighting control device according to a living body signal and environment change.

Description

[0001] The present invention relates to a lighting control apparatus according to the present invention,

The present invention relates to a lighting control apparatus according to a biological signal and an environmental change, and more particularly to a lighting control apparatus for analyzing a user's emotional state based on biometric information (brain waves, electrocardiogram, pulse, etc.) and environmental information (temperature, illumination, humidity, And more particularly, to a lighting control apparatus according to a living body signal and an environment change in which brightness and color of an illumination (in particular, LED illumination) can be automatically adjusted based on the illumination.

Sensitivity ICT technology is a micro / ultra-precision sensor technology capable of sensing bio-signals, environment / situation signals, video signals, voice signals, and the like, which are manifested by the activity of the autonomic nervous system due to human emotional changes in everyday life, And environmental signals, and recognizes, verifies, and standardizes human emotions based on the information, and processes the information according to the usage situation to provide emotional customized products and services.

Among the various related fields currently being studied, LED emotional lighting can maximize the energy saving effect of LED lighting, and furthermore, it can provide the effect of reacting with human emotion. Reflecting this trend, foreign advanced companies are developing eco-friendly LEDs, and Korea is also responding quickly to changes in the market.

On the other hand, domestic lighting control technology has been going on system technology to centrally control lighting, technology to detect human body from sensor, on / off and dimming control technology, flicker control technology, It is insufficient.

Overseas, we are developing IoT technology that controls home appliances and lighting using Wi-Fi and low-power Bluetooth, and is developing technology that works with various wearable devices such as Google glass, watches and clothes.

As noted, previous lighting was the concept of manual lighting in which the user himself could decide on behaviors such as required brightness, on / off control, dimming, or could not change the default settings of the lighting fixtures that were installed . On the other hand, smart lighting based on ICT is the concept of active lighting. It detects the user's movement and environmental characteristics in the space requiring lighting, and automatically creates the lighting suitable for the situation and the event, so as to create various lighting environments and functions in addition to the functions unique to the lighting. Lighting system.

The development of the smart lighting system was made in earnest when a new light source of LED was applied to general lighting. LEDs can reduce dimming within 1% of maximum luminous intensity by LED, reduce energy consumption and shorten life of light source due to On / Off operation, The light source is advantageous in terms of control, and the lighting / light-off speed is very fast. In addition, since the size of the light source is very small, the degree of design freedom for the lighting apparatus is high, and there is no environmental pollution since gas and filament are not used.

Non-Patent Documents 1 to Non-Patent Documents 2 disclose the following prior arts for IT convergence smart illumination.

The prior art disclosed in Non-Patent Document 1 is based on knowledge of various factors such as environment, human behavior, sensibility, physiology, architecture, and illumination, and various technologies such as LED, IT, optical, To provide a lighting system that reflects emotions, pursues the convenience of living, and enables energy saving based on TPO (Time, Place, Occasion).

In addition, the prior art disclosed in Non-Patent Document 2 includes lighting such as fluorescent lamps, LED dimming lights, system lights and the like, lighting apparatuses providing beauty and safety, lighting control servers, lighting interfaces connecting sensors and wired / Terminal and the like, and provides an ICT-based automatic control lighting system that satisfies living convenience at the same time as energy saving of illumination electricity.

Kim Hoon, Lee Min Wook, IT Convergence Smart Lighting Technology, Journal of the Korean Institute of Communication Sciences, Vol. 28, No. 5 Pages 10-14, 2011. Information and Communications Industry Promotion Agency, Development direction of smart emotion lighting based on M2M technology, 2012.

However, in the conventional art as described above, a large amount of biometric data must be collected for training and parameters necessary for emotional recognition must be trained. Therefore, the reliability of the collected learning data greatly deteriorates the performance of the recognition system.

In addition, since the conventional emotional recognition system uses learning data expressed with exaggerated and directed emotions, it can not be regarded as a technique that best reflects emotions of a user. This is because it is easy to artificially acquire the learning data including emotion, and is one of the main factors that deteriorates the performance of the emotion recognition system in actual situations.

SUMMARY OF THE INVENTION The present invention has been made in order to solve all the problems occurring in the prior art as described above, and it is an object of the present invention to provide a method and apparatus for detecting a user's environment based on biometric information (brain waves, electrocardiogram, pulse, And an illumination control device according to a change in the environment of the living body signal, which is capable of automatically adjusting brightness and color of illumination (in particular, LED lighting) based on the analyzed emotion state.

It is another object of the present invention to provide a bio-signal and environment change detection system which avoids emotional recognition using only a single bio-signal and utilizes a plurality of bio-signals in combination or additionally uses emotion measurement indexes such as voice and facial expressions, And to provide a lighting control device according to the present invention.

Another object of the present invention is to provide an emotional lighting control middleware system in a smart building based on existing research results that the color and illuminance of the LED illumination affects the emotion and the state of the human being, And to provide a lighting control device according to a biological signal and an environment change in which brightness and color of illumination are adjusted.

According to an aspect of the present invention, there is provided an apparatus for controlling illumination according to a biological signal and an environment change, the apparatus comprising: biological and environmental information obtaining means for obtaining biological information and environmental information of a user; An information collection terminal for collecting biometric information and environment information in association with the biometric and environmental information acquisition means; The bio-information and the environmental information collected from the information collecting terminal are calculated and fused based on the bio-information and the environmental information, the personal bio-data pattern and the environmental pattern according to the movement route are analyzed, Intelligent lighting management middleware that analyzes the taste and creates a profile for LED lighting control to control lighting; And an LED lighting controller for controlling the brightness and color of the LED lighting based on the lighting control data output from the intelligent lighting management middleware.

The biological and environmental information acquiring means acquires biometric information of the user's brain waves, electrocardiogram and pulse, and acquires temperature / humidity / illuminance as environmental information.

Wherein the information collecting terminal directly controls the LED lighting based on the lighting control signal transmitted from the LED lighting controller.

The information collecting terminal uses a smart phone that collects living body and environment information by using an application for collecting living body and environment information.

The information collecting terminal automatically controls the brightness and color of the LED lighting according to the living body and the environment information, and then transmits the evaluation information inputted from the user to the intelligent lighting management middleware.

The intelligent lighting management middleware includes a personal database for converting biometric information and environment information transmitted from the information collection terminal into a database; An index calculation and fusion unit for calculating a discomfort index and an emotion index for biometrics / environment data based on the biometric information and the environment information stored in the personal database and fusing the index; A biometric data pattern analyzing unit for analyzing a personal biometric data pattern based on the biometric information; An environment pattern analyzer for analyzing an environmental pattern according to the movement path based on the environmental information; The index calculation and fusion unit, the biometric data pattern analyzing unit, and the environment pattern analyzing unit analyze the personal taste based on the analyzed result information, generate the profile according to the analyzed personal taste, generate the lighting control data And a personal taste analysis and profile generating unit for outputting the personal taste analysis and profile.

According to the present invention, an emotion state of a user is accurately analyzed based on biometric information (brain wave, electrocardiogram, pulse, etc.) and environmental information (temperature, illumination, humidity, It has the advantage of automatically adjusting brightness and color.

According to the present invention, there is also an advantage that it is possible to precisely judge emotion by utilizing a plurality of bio-signals in combination, or further using emotion measurement indexes such as voice and facial expressions, avoiding emotion recognition using only a single bio-signal.

Also, according to the present invention, based on the existing research result that the color and the illuminance of the LED illumination affect the emotion and the state of the human being, the emotion lighting control middleware system in the smart building is provided, There is an advantage that the brightness and color of the light can be adjusted.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is an overall configuration diagram of a lighting control apparatus according to a biological signal and an environment change according to a preferred embodiment of the present invention;
Figure 2 shows the corresponding emotional color table,
FIG. 3 shows frequency bands and status tables of waveforms of brain waves,
4 is a feature extraction waveform chart of the electrocardiogram data,
5 is a table for emotional color response using a pulse,
FIG. 6 shows a corresponding color table using environmental information,
FIG. 7 is a conceptual diagram of a living body and environment information fusion algorithm and a lighting control method,
FIG. 8 is a schematic diagram of the emotion lighting control management middleware,
9 is a data acquisition and storage structure diagram in the emotion lighting control management middleware,
10 is a diagram illustrating a lighting control structure in the emotion lighting control management middleware,
11 is an example of a design screen of an application, showing an example of personal information management and a room setting screen,
Fig. 12 is an example of a design screen of an app,
Fig. 13 shows an example of a biometrics information graph, an example of a lighting control,
14 is a structural view of an LED control system;
15 is a block diagram of a high-
16 is a circuit diagram of a dimming signal controller,
17 is a circuit diagram of a DC dimmer switch type,
Figure 18 also shows the LED RGB controller specification,
19 is a basic configuration diagram for RGB LED lighting control,
20 is an RGB controller system configuration diagram,
21 is an exemplary view of an emotion lighting control middleware database,
FIG. 22 is a diagram showing an LED, a room, a sensor relationship diagram,
23 is a diagram showing a data communication relationship among emotional lighting control middleware databases,
24 is a data form of the SHT11 sensor,
25 is an example of a command code of the STH11 sensor,
26 is a diagram illustrating an example of the process of acquiring environmental sensor information through the ZigbeX motes,
Fig. 27 is a diagram showing a process of transmitting and receiving EEG data,
28 shows an ECG data transmission / reception process,
FIG. 29 is an exemplary view of sensor data reception and definition of an event;
30 is a table for emotional color response using a pulse,
FIG. 31 is a table of emotional index applied to a pulse,
32 is a diagram illustrating an example of a process of normalizing and optimizing the emotion index,
FIG. 33 shows a scale of emotion per color,
FIG. 34 is a diagram illustrating an emotional language versus a color emotion scale,
FIG. 35 is a diagram illustrating an emotion evaluation language for emotion definition,
FIG. 36 shows the color temperature, contrast,
37 shows the CIE 1931 chromaticity coordinate system,
38 is an exemplary RGB value according to emotion index,
FIG. 39 shows RGB values corresponding to the color temperature,
40 is an illustration of an experimental site for emotion lighting middleware experiment,
41 is an empirical illumination middleware experimental drawing and an example of attaching a sensor,
FIG. 42 is an example of connection establishment and node value confirmation of sensor nodes,
FIG. 43 is an exemplary illustration of an independent space setting of an experimental site,
FIG. 44 shows an emotional lighting middleware simulator,
45 is an exemplary view of sensor data browsing in the emotion lighting middleware simulator,
46 is an experimental result in the emotion lighting middleware simulator.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a lighting control apparatus according to a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.

1 is an overall configuration diagram of a lighting control apparatus according to a biological signal and an environmental change according to a preferred embodiment of the present invention.

The illumination control apparatus according to the present invention includes a living body and environment information acquisition unit 100, an information collection terminal 200, an intelligent lighting management middleware 300, and an LED lighting controller 400.

The living body and environment information acquisition means 100 acquires the biometric information and the environment information of the user. Preferably, the means 100 for acquiring biological and environmental information acquires biometric information of the user's brain waves, electrocardiogram and pulse, and acquires temperature / humidity / illuminance as environmental information.

The information collecting terminal 200 collects biometric information and environment information in association with the biometric and environmental information acquiring means 100. The information collecting terminal 200 may directly control the LED lighting based on the lighting control signal transmitted from the LED lighting controller 400. In this case, the information collecting terminal 200 plays the same role as the remote controller.

Preferably, the information collecting terminal 200 can use a smart phone that collects living body and environment information by using an application for collecting living body and environment information. In addition, the information collecting terminal 200 automatically controls the brightness and color of the LED lighting according to the living body and environment information, and then transmits the evaluation information input from the user to the intelligent lighting management middleware 300.

The intelligent lighting management middleware 300 calculates and fuses the biometric and environmental data indexes based on the biometric information and the environment information collected from the information collecting terminal 200, analyzes the personal biometric data pattern and the environmental pattern according to the movement route And analyzing the personal taste based on the analyzed result, thereby generating a profile for LED lighting control and controlling the lighting.

The intelligent lighting management middleware 300 includes a personal database 310 for converting biometric information and environment information transmitted from the information collecting terminal 200 into a database; An exponent calculation and fusion unit 320 for calculating the unpleasantness and emotion indexes for the living body / environment data based on the biometric information and the environment information stored in the personal database 310 and fusing them; A biometric data pattern analysis unit 330 for analyzing the personal biometric data pattern based on the biometric information; An environment pattern analyzing unit 340 for analyzing an environmental pattern according to the movement path based on the environmental information; The index calculation and fusion unit 320, the biometric data pattern analysis unit 330, and the environment pattern analysis unit 340 analyze the personal taste based on the analyzed result information, and generate a profile according to the analyzed personal taste And a personal taste analysis and profile generation unit 350 that generates and outputs illumination control data.

The LED lighting controller 400 controls the brightness and color of the LED lighting based on the lighting control data output from the intelligent lighting management middleware 300.

The operation of the illumination control apparatus according to the biological signal and the environment change according to the present invention will be described in detail as follows.

The present invention automatically controls smart LED lighting based on the emotions of users residing in specific spaces. And automatically detects a user's biorhythm and environment change to provide a lighting system optimized for the user. It is a technology that can control more than 100 LED lights at the same time by the life log analysis. It is an IT technology fusion technology that provides emotional lighting based on living body and environmental information. It is a high technology that can improve user convenience and quality of life It is an effective technique.

To this end, the living body and environment information acquisition means 100 acquires the biometric information and the environment information of the user and delivers it to the information collection terminal 200.

Biometric information acquires EEG, electrocardiogram and pulse as an index for analyzing emotions of users. Electroencephalogram (EEG), electrocardiogram (ECG) and pulsation are preferably measured using various known measurement devices. In particular, QEEG and QECG products of "Laxa" can be used.

In addition, QEEG-8 can measure EEG at 8 sites at the same time, and it is possible to mark events during measurement, which can increase the reliability of measurement.

In addition, QECG-3 measures Lead I, II, and III electrocardiogram signals by standard limb induction method that attaches electrodes to both wrists and both ankles. It also has an advantage of measuring electrocardiogram have.

The environmental information acquires temperature / humidity / illuminance as environment information by using temperature / humidity / illuminance sensor. The environmental sensor can be used by adopting various known environmental sensors as it is, but the present inventor used HBE-ZigbeX II of "Hanbaek Electronics".

The biometric information and the environment information thus obtained are transmitted to the information collecting terminal 200.

Here, it is possible to transmit and receive data obtained through various communications between the living body and environment information acquisition means 100 and the information collection terminal 200.

For environmental sensors, temperature, humidity, and illumination data are acquired through ZigbeX equipment. ZigbeX consists of nine sink nodes, and each node consists of one root node and eight subnodes. In ZigbeX, the sensor for measuring temperature, humidity and illuminance is SHT11, which is directly connected to the CPU pin of the sensor motor. The SHT11 sensor itself includes an AD function that converts the measured value into a digital signal, and transmits the sensed data directly to the CPU.

24 is an example of the data format of the SHT11 sensor.

In order to read the sensing value measured from SHT11, a certain clock and command must be inputted through two lines connected to the CPU. 24 shows clock timing and command input method for receiving measured data from the sensor in the CPU. The CPU sends a bit-by-bit instruction to the SHT11 sensor via the data line and then obtains the actual sensing value. The data line reads the value only when the clock signal is HIGH, and the signal on the data line does not change. Before starting transmission, input a pulse to notify start of transmission, send address bit and command to SHT11, and read sensor data. The commands sent to the SHT11 chip are shown in Fig.

After sending the command, the CPU waits until the SHT11 sensor completes the humidity measurement. It takes about 55mx to obtain 12-bit humidity data. When the measurement is completed, SHT11 makes the data line LOW and transfers the data to the CPU. The CPU reads the 12-bit measured data through the data line, and then determines whether to transmit the ACK according to the content of the CRC error check field. If data can not be read at this time, the connection with SHT11 is updated through Reset. ZigbeX with SHT11 sensor transmits information about temperature, humidity, and illuminance measured at each sink node to 0 root node. The manner in which data is transmitted at each node is shown in FIG.

As a method of acquiring environmental sensor information through ZigbeX Mote, a network between the mote is constructed using the TreeRouting technique. The sink node (Mote 0) is specified as the root node, and the sink node collects the sensed data from each node (Mote). Through the serial communication, the sink node transmits the environmental sensor information to the server. Server can check environment data and set sensing period. Here, the server may be an information collecting terminal or an intelligent lighting management middleware.

Next, the EEG / ECG data transmission / reception method is as follows.

Biological signal data such as brain waves, electrocardiograms, and pulses are personalized data unlike environmental sensors and are connected to wearable devices via Bluetooth and received via a smart phone. Each device connected to the smartphone and Bluetooth transmits data to the smartphone by period and then transmitted to the server (intelligent lighting management middleware).

In the present invention, since QEEG-8 and QECG-3 measurement devices are used for EEG and electrocardiogram data measurement, they are connected to a smartphone through TCP / IP.

In the case of EEG, 42 bytes are transmitted per packet with 8 channel data acquired from 8 electrodes. The sampling frequency should be 256 Hz and transmit 256 packets per second. As shown in FIG. 27, the data structure includes a 6-byte header for classifying EEG / ECG and an index of 1 to 256, and transmits raw data of 4 bytes per channel.

The received raw data is stored in a data queue divided by 2 seconds for real-time processing and emotion information inference. The 2-second buffer size is the smallest unit that can collect meaningful data through FFT. The 2-second raw data stored in the data queue is changed to the frequency domain by FFT for each channel, and then stored as the power value of the frequency band corresponding to theta, alpha, beta, and gamma through the power spectrum.

In the case of an electrocardiogram, signals acquired from three channels are transmitted by being divided into 20 bytes per packet. As shown in Fig. 28, the structure of a packet to be transmitted is composed of a 6-byte header indicating that the ECG signal is an index, and data.

The index is 2-byte data containing 1 to 256 indexes so that it can be synchronized with the time data of the electrocardiogram signal sampled at 256 Hz, and the raw data is composed of 4 bytes each of 3 channels. The electrocardiogram also transmits 256 packets per second. Unlike electroencephalogram, electrocardiogram signals do not divide the interval but observe the ratio of sympathetic / parasympathetic nerves that occur at the measured time.

First of all, to detect the Q, R, S waveform, which is one of the characteristics of electrocardiogram, the moving average filter smoothes the signal height. In order to detect a QRS signal, various methods are used. In the present invention, positive peak and negative peak are detected through slope detection and detected as signals for Q and R S, respectively. The R-R interval indicating the index distance between the R waveforms is extracted from the detected QRS signal, and the HRV of the R-R interval that varies with time is calculated as shown in the lower left part of FIG. The heart beat variability is time series data of the heart rate of a user who changes over time, and information of sympathetic nerve and parasympathetic nerve can be acquired through FFT and spectrum analysis.

Pulse information is transmitted from a smart band to a smartphone connected via Bluetooth. The smart band used in the present invention is a Samsung Galaxy Gear, and communication between the smart band based on the Tezen OS and the smartphone based on the Android is required. The user's pulse information measured in the smart band is transmitted to the smartphone in real time through the Tethered Bluetooth API. The pulse information received by the smartphone is stored in the server through XML parsing. If you parse it according to the post form, the server will check the format and enter it into the DB.

The information collecting terminal 200 transmits the biometric information and the environment information acquired by the biometric and environmental information acquiring means 100 to the intelligent lighting management middleware 300 serving as a server. Here, the time at which the bio-information and environmental information acquisition and transmission of the bio-information and environment information is performed by the bio-environment information acquisition means 100 and the acquisition of the acquisition information to the intelligent lighting management middleware 300 at the information collection terminal 200 can be performed in real time or at a predetermined cycle . Since data communication occurs too frequently in real-time, it can be said that there are many ways to set up a period that can transmit sensibility sufficiently and occur periodically. However, when operating in a method of transmitting acquisition information periodically, it is preferable to acquire information in real time, but to transmit information at the time of occurrence of an event when an event occurs (when biometric information or environment information has changed in comparison with previous ones).

Next, the intelligent lighting management middleware 300 registers the biometric information and environment information transmitted from the information collecting terminal 200 in the personal database 310. Here, it is assumed that the information collecting terminal 200 is provided with unique information for identifying the terminal.

Therefore, when registering the biometric information and the environment information in the personal database 310, it is possible to calculate the emotion index for each user by registering the unique information on the basis of the unique information.

The exponent calculation and fusion unit 320 of the intelligent light management middleware 300 calculates the discomfort index and emotion index for the living body / environment data based on the biometric information and the environment information stored in the personal database 310, do.

That is, the exponent calculation and fusion unit 320 calculates the emotion index through the collection of the acquired biometric information and the environment information, and outputs the illumination control data for controlling the LED illumination with the color and brightness corresponding to the calculated emotion index .

For the emotion classification, representative color elements are selected based on the 20 color emotion models set in "The Meaning of Color" of HP (Hewlett-Packard), and five emotion categories are classified according to the biorhythm index shown in FIG. 2 And apply it to the emotion index calculation algorithm.

In addition, EEG and electrocardiogram data are calculated as bio-rhythm index by analyzing complex bio-signals.

In the case of brain waves, the ratio of each waveform corresponding to delta, alpha, beta, and gamma is referred to as relative power and is used as an index for determining the emotional state of the user. 3 shows frequency bands and states of the respective waveforms.

In the case of an electrocardiogram, waveforms having characteristics corresponding to P, Q, R, S, and T are repeated. Here, R means the highest peak value of the electrocardiogram waveform, and the period of the R peak means the pulse of the user. 4 shows the electrocardiogram data of the user and the cycle of the R peak.

The R peak period of an electrocardiogram can be expressed by time series data called HRV (Heart Rate Variability). HRV of 0.004-0.15 Hz is an index of activity of sympathetic nerves, and 0.15-0.4 Hz is an index of activity of parasympathetic nerves.

In the present invention, the ratio of the sympathetic nerve to the parasympathetic nerve in the electrocardiogram is used as the characteristic data for analyzing the complex bio-signal together with the EEG relative power. In addition, the pulse detected by the electrocardiogram can be used as an auxiliary index for calculating the vital rhythm index, thereby improving the reliability of the combined bio signal. FIG. 5 is a table for emotional color response using a pulse.

The information for extracting the emotion index consists of a bio signal 1 (brain wave / electrocardiogram), a bio signal 2 (pulse), and environmental information (temperature / humidity / illuminance). It compiles the three kinds of information, deduces the emotion index of the user, and provides the necessary lighting for the user.

The reception daemon of the intelligent light management middleware 300 receives the biometric signal 1, the biometric signal 2, and the environment sensor data from time to time. If environmental sensor data with the most frequent acquisition period is received, a new event is generated. FIG. 29 is a definition relationship diagram of sensor data reception and events. FIG. One Event is a basic unit for inferring user's emotion. Like the database structure described later, when the environmental sensor data is acquired, the sensor event inspects the received data. Assigns the data of the sensor input to each sensor tuple stored in Boo format as True. When the environmental data sensor is acquired for the first time, new event numbers are added with the illumination (e_ILLU), temperature (e_Humi), humidity (e_Tempo)

(E_EEG), an electrocardiogram (e_ECG), a heartbeat (e_ECG) corresponding to the current event number in the SensorEvent, and a heartbeat signal corresponding to the current event number in the SensorEvent when the biosignal data such as ECG, EEG, (e_pulse) Input signal of tuple is changed to True.

The reason for distinguishing and inspecting the data coming in through the event number is to extract the emotion and control the lighting even if there is a missing signal among the three pieces of information. For example, if you have four environmental sensors in Room 1 and you do not have any users in them, or if you are wearing only a pulse sensor, check the event number and cycle to provide the appropriate lighting control.

The bio-signals input to the reception daemon are refined as information for emotional index extraction through a preprocessing process corresponding to each signal.

For example, the bio-signal 1 (brain wave / electrocardiogram) data generation is as follows.

In the case of EEG, the power spectrum of the theta (4 ~ 8Hz), SMR (12 ~ 15Hz) and mid beta (15 ~ 20Hz) are extracted through the FFT . Generally, theta is high in activities such as sleeping and meditation, and SMR and mid-beta waves are high in excitement and arousal. Therefore, in the present invention, the awakening state of the brain wave is analyzed through the following expression (1).

As can be seen from the above equation (1), the degree of awakening is calculated by calculating the ratios of the non-arousal state and the awake state waveform of the EEG.

In case of electrocardiogram, Q, R and S characteristics of electrocardiogram are detected by Moving Average Filter and QRS Detection in received data. Peak value detection for Q, R, S feature detection is calculated using the following equation (2).

R peak is converted into time series information HRV composed of time difference and accumulation time through the following Equation (3).

HRV is converted to power values of sympathetic nerve (LF: 0.04 to 0.15 Hz) and parasympathetic nerve (HF: 0.15 to 0.4 Hz) by FFT and power spectrum analysis like brain waves.

The sympathetic nerves and parasympathetic nerves normally appear to be slightly active at a ratio of 6: 4 to the parasympathetic nerves during normal activities.

Therefore, in the present invention, the emotional state of the user is classified as pleasant or unpleasant through the autonomic nervous system ratio (LF / HF). In the general experiment, the ratio of autonomic nervous system was about 1.5, and it was 2.5 in fear, angry, and dislike.

The electroencephalogram (ECG) complex electrocardiogram is calculated as one of the three emotion indexes, and since the reliability of the sympathetic nerve and the parasympathetic nerve ratio of the electrocardiogram is higher in a state of awakening, Likewise, define ECG and EEG signals.

In the case of pulse data, the emotion information of the user is inferred through the average value of the rate of change in the event number cycle. 30 shows the bio-signal index for the pulse average value and the user's reasoning sensitivity.

Finally, the data input from the environmental sensor is an index that can calculate the discomfort index of the current space.

The discomfort index is a combination of temperature and humidity that expresses the temperature felt by humans. It is also referred to as the Temperature and Humidity Index (THI), and the degree of discomfort depending on the discomfort index value varies slightly depending on the individual However, since the degree of comfort of the indoor environment can be known, it affects the emotion of the user.

Environmental sensor can acquire data of temperature, humidity, and illuminance, but discomfort index uses sensor temperature and humidity information. In case of illuminance, it is used to check illuminance and color temperature of output LED illumination. The discomfort index is calculated by the following equation (5).

Where T is the temperature (° C) and RH is the humidity (%).

The range corresponding to the value of the discomfort index and the comfortable state are shown in Fig. 31 below.

The three indices are normalized and expressed as a percentage to provide the user with appropriate illumination through the bio-signal 1, bio-signal 2, and environmental data. For normalization, each data should be given a coefficient and emotion index should be calculated. At this time, the coefficient for each signal is consistently indexed and adjusted by user's evaluation and feedback, and as the training is repeated, a customized profile suitable for the user is generated. The normalization and optimization process of the emotion index is shown in FIG.

Next, the biometric data pattern analyzing unit 330 analyzes the biometric data pattern based on the obtained individual biometric information. The biometric data pattern is a pattern for determining the biorhythm. For example, we analyze how the biorhythms change. The analyzed biometric data pattern analysis information is transmitted to the personal taste analysis and profile generation unit 350.

In addition, the environment pattern analyzer 340 analyzes the pattern of the environment information based on the obtained environment information of the corresponding space. For example, the type of the environment information is changed into a pattern. The analyzed environment pattern analysis information is also transmitted to the personal taste analysis and profile generation unit 350.

The personal taste analyzing and profile generating unit 350 analyzes the emotion index and the discomfort index, the biometric data pattern analyzing unit 330, and the environment pattern analyzing unit 340 acquired by the exponent calculating and converging unit 320 Personal taste is analyzed on the basis of the biometric pattern and the environment pattern information, and a profile is generated according to the personal taste analyzed, and then illumination control data is generated and output.

Here, when creating a profile according to personal preference, the LED color temperature database linked with the emotion index and the discomfort index of the user by sex / age group is constructed, and the optimal environment information is automatically derived by using the mining technique for the accumulated data , And calculates LED lighting data corresponding thereto and transmits it to the LED lighting controller 400.

6 is an example of a corresponding color using environmental information.

For example, the personal taste analysis and profile generation unit 350 generates the personal taste analysis and profile generation unit 350 based on the three types of user-related data, i.e., biometrics data 1 (brain wave, electrocardiogram) / biometrics data 2 (pulse) / environment data (temperature, humidity, Based on which the biorhythm and environmental indexes are measured.

7, the emotion index based on the biological and environmental data is derived, and the LED color is controlled through the emotion index.

The emotion index deduced from the three kinds of data can be defined as the color of the illumination required by the user.

First, emotional evaluation should be performed to define the lighting color by emotional index. Using the color image scale and the adjective image scale, which are studied in the field of sensibility engineering, we can see how emotions and colors match the general user.

There are image scales that use XY coordinates to measure and display a specific image of each color. In order to determine what factors are in the X and Y axes of the coordinates for measuring the degree of image, it is first necessary to identify the factors that are most important to the image evaluation.

Figure 33 shows a static-dynamic image representing the X-axis and a Y-axis representing a soft-rigid image. This image scale is a metric concept made on the assumption that the entire interior of a color image can be seen in its internal space.

Fig. 34 is a scale of the emotional language listed for each color sensitivity scale. X-axis Dynamic - In static speech, Y-axis can simulate colors that match human emotions through soft, hard image languages.

If you match two scales, you can see that the overall color is mostly on the soft side and the one color on the hard side. However, it can be seen that the tone is a soft, static image with a light tone, a dark tone with a dynamic and rigid image, a quiet tone with a static and rigid image, and a brilliant tone with a dynamic and smooth image.

In other words, it can be seen that tone rather than color is a more important variable in changing the detailed sensitivity. Accordingly, the present invention proposes an algorithm for defining the color temperature to be compared with each emotion language and converting it into an RGB value so that it can be reflected in the LED illumination, in order to increase the reliability of the color sensibility and the matching color of the emotional language.

In the present invention, the emotional language used to define the emotion of the user deletes the redundant language among the languages defined in FIG. 34, and selects the language as generally shown in FIG. 35 as the user can feel.

In addition, selected emotional languages can be classified into activity, stability, and competence. For example, a language such as refreshing - unpleasant, vivid - blurred can be defined as a factor of activity and is warm - cool, comfortable - anxious language is a factor for stability - Languages such as silver, glittering, and brilliant can be defined as factors.

The color temperature for evaluating the selected languages is 8300 [K]> 5800 [K]> 3800 [K] for the active factors, which were previously studied with illumination of 3800 [K], 5800 [ , 8300 [K] in the order of 3800 [K], 5800 [K] and 8300 [K] in the stable factors.

Accordingly, in the present invention, an appropriate color temperature is defined as shown in FIG. 36 based on the user's active emotions and depressed emotions by applying the color temperature and emotional evaluation results that have been studied previously. For example, we define a way to provide low and cool 3800 [K] lighting for an awakened, overactive sensation, and 8300 [K] for a bright and shiny sensation.

The color temperature is a numerical value of the color of a light source, which means the color represented by a black body when heat is applied to an ideal black body. Therefore, in order to be applied to LED lighting, the color temperature must be converted into the RGB value.

First, it is converted into the x, y coordinates of the CIE 1931 chromaticity coordinate system as shown in FIG. 37 corresponding to the color temperature.

(X, y) of the chromaticity coordinate system into the CIE XYZ color space through the following expression (6).

The CIE XYZ color space is converted into the CIE RGB color space using Equation (7) below.

The RGB values of 8300 [K] to 3800 [K] color temperature defined through substitution in the CIE 1931 chromaticity coordinate system are shown in FIG. 38, and FIG. 39 is a distribution diagram of RGB values corresponding to the color temperature.

The intelligent lighting management middleware for calculating the above illumination data (illumination brightness and color) is shown in FIG.

The intelligent lighting management middleware 300 shown in FIG. 8 is developed for personalization and emotional lighting control of each user in a multi-environment environment. As the input data of the middleware, a sensor device for acquiring emotion information (biometric information) and environmental information of the user is used as well known.

First, emotion information (biometric information) includes an EEG device and a bio-signal based sensor device, and environmental information includes environmental sensors such as temperature, humidity, and illumination. The middleware collects biometric information and environmental information to calculate the user's emotional index based on the biorhythm index / discomfort index, and ultimately realizes the illumination as a color corresponding to the emotional response. The data flow of the middleware for emotional lighting control is shown in Figs. 9 and 10. Fig.

In other words, biometric and environmental stream data acquired in real time is refined through Metadata Extractor, and the refined data is stored in User Metadata Registry and Emotion Data Warehouse. Then, the user's emotional state and environment are sorted by multi-level reasoning based on the bio-signals and the personal IDs separated by the wearable device, and a personalization profile is constructed based on the extracted information, Thereby automatically determining the color temperature and the illuminance. Then, the optimal color temperature / illumination information automatically determined by the middleware is transmitted to the LED lighting controller 400 via the wireless to control the indoor / outdoor illumination.

On the other hand, even when most wearable devices provide only Bluetooth communication or wearable devices equipped with some WCDMA chips, the storage space is extremely narrow. Therefore, biometric signal data acquired in real time can be stored in a smart phone owned by the user There is a need.

For this, biometric information acquired from the wearable device via Bluetooth / Wi-Fi / WCDMA and environment information acquired through Bluetooth / Wi-Fi are collected / refilled / temporarily stored, and biometric / environmental information is transmitted to the intelligent lighting management middleware 300 ). Accordingly, it is possible to integrate the "user personal information setting and management application" in the intelligent information collecting terminal 200 to provide the user with information related to the lighting, including the emotion index / lighting status, (300). Here, the user's personal information setting and management application is responsible for storing / managing the user's movement path and sensor information embedded in the smart phone in the form of a life log and transmitting the information to the intelligent lighting management middleware. It is also desirable to implement a function that allows the user to manage and verify his / her personal information.

In addition, the user can directly control the lighting system in consideration of the user's situation and convenience. That is, the user can easily control the lighting through the smart device application. In this case, the information collecting terminal 200, which is a smart phone, acts as a remote controller, and controls the lighting directly using the information collecting terminal 200.

11 is an illustration of a main design screen of the personal information setting and management application. Users view and modify the installation location and personal information of the lighting through the personal information configuration and management app.

12 is an exemplary diagram for explaining the change of illumination information based on the position of the user. When the user selects a specific position (e.g., Room 1), the sensor information corresponding to the user's position is integrated to control the illumination. The user can browse the information of the current sensors through the personal information setting and management application.

Here, the function of reading sensor information is as follows.

Current environmental / biometric information, current environment / biometric information display, final measurement date and time display, confirmation of past environmental information (click on environment information item -> graph by item), confirmation of past biometric information (click on biometric information item -> item Graph).

The user controls the graph and illumination of the bio-signal through the personal information setting and management application. FIG. 13 is an illustration of a relationship between a biometric information graph and a lighting control. Since the biometric information is received through the application and transmitted to the intelligent lighting management middleware 300, it is possible to check and monitor the signals acquired in real time.

If the user does not like the currently controlled lighting condition or desires different lighting, the user can change to the new lighting through the personal information setting and management application. At this time, the changed illumination acts as a variable to change the index of emotion index calculation by the feedback of the user.

FIG. 14 is a block diagram of an embodiment of the LED lighting controller 400. FIG.

14, the LED lighting controller 400 includes an AC rectifier 401, a power factor corrector (PFC) 402, a DC / DC converter 403 And an LED string controller (404).

As shown in Figure 14, a special power electronic microcontroller is used to reduce the cost of lighting power supply components through higher integration. A single microcontroller with sufficient performance, power optimization and communication ports can potentially control all three major components of the lighting system. This high-level, integrated lighting system allows the central programmable platform to control all aspects of the intelligent lighting system in a coordinated way, as well as potentially control over multiple components.

Here, the LED lighting controller 400 realizes high-speed dimming control, which is a limitation of a high-output LED driver. 15 is a circuit diagram showing an embodiment of a high-power LED driver for high-speed dimming control. The PWM dimming control algorithm based on the inductor current line drive method was used, and a dimming signal delay circuit was further used.

In order to apply the inductor current line driving method to the conventional LED driver, a dimming signal controller which generates delayed dimming signals is added. The dimming signal controller is implemented by combining a rising edge trigger D-flip flop and a logic gate in Fig.

As shown in FIG. 17, the dimming signal controller 410 is designed as a converter-integrated type, and a dimming controller employing a switch for driving an LED module operating at DC 12V is designed so that the LED module 412 is connected between the driving circuit 411 and the LED module 412 .

Meanwhile, the present invention enables the color control of the LED. For the RGB LED dimmer, the existing MCU module was used. 18 is a specification of the LED RGB controller.

19, the basic configuration for controlling the RGB LED lighting includes a user interface 421, a network interface 422, a lighting controller 423, a color mixer 422, A color mixer & converter 424, a PWM signal generator 425, an LED driver interface 426, an LED driver 427, (LED Sensing & Compensation) 428, a power controller 429, and an LED

The user interface 421 generates numerical information for expressing the light of the LED lighting and generates information such as a color temperature, a brightness, a CRI (color rendering index) (Lighting Controller) 423 via a network interface (Network Interface)

The network interface 422 is divided into a wired network and a wireless network. The wired network system selectively applies UART, USB, IEEE1394, Ethernet, DALI, DMX512, etc. and the wireless network includes ZigBee, Bluetooth, IrDA, VLC, LAN, and the like.

The lighting controller 423 is connected to the user interface 421 or another lighting module through a network interface 422 to transmit and receive data. The lighting controller 423 is connected to the user interface 421 or other lighting modules Transmits control signals to a color mixer and converter 424 and transmits the data collected from the color mixer and converter 424 and the LED sensing and compensator 428 to a user interface 421) or other lighting module to control the entire flow of data for smooth operation of the LED lighting.

In addition, the color mixer and converter 424 are configured to receive control signals of the LED lighting from the lighting controller 423 to determine a suitable combination of colors, and to adapt the values given by various color and white representation methods to RGB LEDs Control signal data, and transmits the converted control signal data to the RGB PWM signal generator 425.

The PWM signal generator 425 is configured to generate a PWM signal for controlling the individual LEDs. The PWM signal generator 425 generates a PWM signal for controlling the LED array 426 by transmitting a PWM signal generated through the LED driver interface 426, .

In addition, the LED driver interface 426 transmits the PWM signal generated from the PWM signal generator 425 to the LED driver 427. At this time, the PWM signal transmission method can use three methods (direct connection method, I2C (Inter-IC), SPI (Serial Peripheral Interface)).

The driver IC 427 is configured such that the driver IC is driven by applying voltage and current to the LED module 430. However, since there are various types, the driver IC suitable for the selected RGB LED is preferably selected.

In addition, the LED sensing and compensating device 428 compensates for the characteristics of the LED due to the characteristics of the ambient environment, the lapse of time, and the characteristics of the LED itself. Thus, So as to sense the change of the LED. After comparing the sensed data with the reference data, the correction data is transmitted to the lighting controller 423 so that the LED module 430 is controlled with the corrected data.

The power controller 429 includes a constant current circuit for driving RGB LEDs and includes a current sensing circuit for constant current driving to supply smooth power to the LED module 430.

At this time, brightness control of LED lighting basically controls Red LED, Blue LED and Green LED to produce various colors. In addition, Warm White LED, Cool White LED and Amber LED are added to adjust the brightness of the whole lighting . Each LED is individually controllable and can have different resolution depending on the characteristics of each LED. In order to eliminate flickering, the repetition period of the PWM signal applied to the illumination can be adjusted, and the repetition period of the PWM signal applied to the illumination can be adjusted in consideration of different characteristics of each LED chip. The brightness is controlled in consideration of the nonlinear characteristics of the forward voltage and the forward current of the LED. The brightness can be adjusted by modulating the voltage on / off while keeping the current supply of the current source constant. It is also possible to adjust the brightness by the current while keeping the voltage supply of the voltage source constant.

The overall configuration of this RGB LED controller is composed of two types of "MAIN" and "SUB (Sub)" in consideration of expandability, and these components (user interface, network interface, lighting controller, color mixer and converter , A PWM signal generator, an LED driver interface, an LED driver, an LED sensing and compensator, a power controller and an LED module) are appropriately implemented.

20 is an overall configuration diagram of an RGB controller system.

When human and environmental information is transmitted to PC, PC transmits human body and environmental information data to smartphone and control box (using UTP cable or wireless LAN). The smartphone accesses the PC using the app, receives the human body and environment information transmitted from the PC, and controls the control box (using Bluetooth). The PC or mobile switch is a device (PC, Mobile) selection switch that controls the main control box. The initial setting is RS-485. It is preferable to implement the selection in the master MCU of the main control box. Mobile control is not possible when selecting a PC, and PC control when a mobile is selected. The AC / DC converter supplies power to the main control box and the power supply of the main control box. The master MCU collects the data transmitted from the PC and mobile and transmits color and dimming signals to each slave MCU. The slave MCU and LED drive use the color and dimming signals received from the master MCU to control the LED drive circuit. LED Lighting is an LED lighting fixture that performs RGB, Warm, and Cool lighting.

21 is a middleware database structure for emotional lighting control. Means the structure of the intelligent lighting management middleware 300 of FIG.

In FIG. 21, a client may be divided into an administrator / user / auxiliary user according to the authority. The administrator can create a room according to the user's request. Room refers to office space, residential space, including one controller, which controls multiple LEDs and sensors. The user can control the LED. The controllable range is on, off, color change, and leaves Control_History every time it is controlled. The administrator can create LEDs and sensors in the room. The LED and sensor use the CTR_ID of the controller as a foreign key, and the controller can use the R_ID of the room as a foreign key to check the status of the controller installed in the room. An event occurs when data is periodically stored in the sensor data. The event number is mapped to sensor data and stored.

22 is an example of definitions of LED, Room, and Sensor in the emotion lighting control middleware database. The user can ask the administrator to manage LEDs and sensors in his / her office, residential area. The administrator defines the Room at the request of the user. Room is defined by primary key R_ID and stores address, user name, and so on. Controller is installed in user's room and controls LED and sensor. The Controller and the Room are connected through the R_ID of the Room. The controller controls multiple sensors and LEDs. LED and sensor are connected through CRT_NUM of controller. LED and Sensor store each setting value, on / off status, and current operation status and store the location in the residential space in X, Y, Z coordinates. In web pages, this X, Y, Z coordinate allows you to define the installed location.

23 is an example of data communication of the emotion lighting control middleware database. User and Event are composed of 1: different. The sensor communicates with the server every predetermined period to transmit sensor data. At this time, every cycle is defined as Event. The server (intelligent light management middleware) configures a data receiving daemon to communicate with the sensor. It is possible to set the period for acquiring data from the sensor, and to set the interval of data for analyzing the state of the user.

Meanwhile, emotion lighting control simulation and experiments were conducted to show that the proposed emotional lighting control middleware can infer emotions of actual users and control the appropriate lighting. Figure 40 is a photograph of the laboratory used for the experiment.

Experiments were carried out using 8 environmental sensors in a laboratory of 15 m width, 8 m width and 120 m 2 (about 36 pyeong). For the experiment, five 20 year old male and female subjects measured EEG, ECG, and pulse, respectively. The simulator deduces the emotion index through the user's biosignal 1, biosignal 2, and environmental sensor data, defines the corresponding illumination, and displays it on the simulator.

41 is a drawing for an emotional illumination middleware experiment and an example of sensor attachment. For the experiment, the sensor was installed on the column of the experiment site. The distance between the poles was about 5 meters, and the distance of the sensor was slightly modified to facilitate communication between the sensors. The node 0 is located at the center of the experiment site and allows the sensor to transmit data when it finds node 0 during communication.

FIG. 42 is a view showing connection settings of the respective sensors connected to the test site and inputting temperature, illuminance, and humidity values.

The experiment room was divided into three and three rooms were set in one space. This is to ensure that lighting and environmental sensors are applied differently in independent spaces separated from one large residential space by a wall.

One room is recognized through each of four sensors, and simulation is performed using sensors corresponding to each room as shown in FIG.

For the experiment of the middleware proposed in the present invention, a simulator as shown in FIG. 44 which operates in the same manner as the receiving server has been developed.

Eight environmental sensors installed in the experimental site transmit information to the simulator (receiving server) every 3 minutes. The PC of the simulator includes the emotion inference module and the web server, and it analyzes real-time EEG, electrocardiogram and pulse as well as received environmental sensor data and stores it in the database. The simulator deduces the emotion through the analysis result stored in the database and displays the analysis result to the user.

The left side of the simulator displays information corresponding to the processes analyzed by the user sensitivity, UserData, Emotion Result, and SensorData menu. The SensorData group retrieves the information value of the selected sensor, and the Emotion Result group calculates the emotion index including the user profile and the environmental sensor value of the corresponding area when the user selects the current position through the app. Finally, the user emotion group shows emotional level and lighting color of the user through calculated emotion index. 45 is an example of the sensor data browsing in the emotion lighting middleware simulator.

For the experiment of the present invention, eight sensors were installed and 24 hours data was accumulated. If the sensor icon is clicked in the drawing included in the simulator, the current temperature, humidity, illuminance, and the discomfort index calculated through the sensor can be confirmed as shown in FIG.

In this experiment, the user separately measured the EEG and electrocardiogram using QEEG-8 and QECG-3 equipment and transmitted them to the service management app. FIG. 46 shows experimental results generated when an electroencephalogram, an electrocardiogram, pulse data are inserted, and a user is located in Room 1.

Electroencephalogram (EEG) was calculated by arousal index and ECG was calculated by autonomic nervous system ratio. In the case of pulse, the mean value of the measurement time was recorded as 102.4 and defined as 60 by the emotion index adaptation table. The last environmental sensor was the average value of sensor 1, 2, 3, 4, and was defined as temperature 27.3, humidity 38%, and discomfort index 85.755.

The emotion index was calculated as the initial exponent value of 0.3: 0.3: 0.4 and the emotion index was calculated as 69.282 for the bio signal 1 = 56.6, the bio signal 2 = 60, and the environmental sensor = 85.755. The emotion index was defined as active in the index correspondence table and the lighting was set to 4700 [K] (R: 244, G: 210, B: 186) .

Although the present invention has been described in detail with reference to the above embodiments, it is needless to say that the present invention is not limited to the above-described embodiments, and various modifications may be made without departing from the spirit of the present invention.

The present invention deduces emotions using biometric information and environmental information, and is applied to the brightness and color control technology of LED lighting based on this.

100: Means for acquiring biological and environmental information
200: information collecting terminal
300: Intelligent lighting management middleware
310: Personal Database
320: Index calculation and fusion unit
330: biometric data pattern analysis unit
340: Environmental pattern analysis unit
350: personal taste analysis and profile generation unit
400: LED lighting controller

Claims (10)

An apparatus for automatically adjusting brightness and color of illumination according to a biological signal and an environmental change,
Biological and environmental information acquisition means for acquiring biometric information and environment information of a user;
An information collection terminal for collecting biometric information and environment information in association with the biometric and environmental information acquisition means;
The bio-information and the environmental information collected from the information collecting terminal are calculated and fused based on the bio-information and the environmental information, the personal bio-data pattern and the environmental pattern according to the movement route are analyzed, Intelligent lighting management middleware that analyzes the taste and creates a profile for LED lighting control to control lighting; And
And an LED lighting controller for controlling the brightness and color of the LED lighting based on the lighting control data output from the intelligent lighting management middleware,
Wherein the means for acquiring biological and environmental information acquires the user's brain waves and electrocardiogram as first bio-information, acquires the user's pulse as second bio-information, acquires temperature / humidity / illuminance as environmental information,
Wherein the intelligent lighting management middleware comprises: a personal database for converting the first biometric information, the second biometric information, and the environment information transmitted from the information collecting terminal into a database; An index calculation and fusion unit for calculating an unpleasantness index and an emotion index for living body / environment data based on the first biometric information, the second biometric information, and the environment information stored in the personal database and fusing them; A biometric data pattern analyzer for analyzing a personal biometric data pattern based on the first biometric information and the second biometric information; And an environment pattern analyzing unit for analyzing an environmental pattern according to the movement route based on the environmental information,
Wherein the exponent calculation and convergence unit derives an electroencephalogram (EEG) index from the brain wave information of the first biometric information, acquires sympathetic and parasympathetic information from the electrocardiographic information of the first biometric information through FFT and spectral analysis, Calculating an emotion index for the first biometric information by combining the acquired EEG and parasympathetic information,
The information collecting terminal directly controls the LED lighting based on the lighting control signal transmitted from the LED lighting controller,
The information collecting terminal automatically controls the brightness and color of the LED lighting according to the living body and the environment information, and then transmits the evaluation information inputted from the user to the intelligent lighting management middleware,
The intelligent lighting management middleware analyzes individual preferences based on the result information analyzed by the exponent calculation and fusion unit, the biometric data pattern analysis unit, and the environmental pattern analysis unit, generates a profile according to the personal taste analyzed, And a personal taste analysis and profile generation unit for generating and outputting illumination control data,
Wherein the exponent calculation and fusion unit normalizes the first biometric information, the second biometric information, and the environment information to represent the first biometric information, the second biometric information, and the environment information as a percentage, and for each of the first biometric information, the second biometric information, The second bio-information, and the environment information, and when receiving the evaluation information from the information collection terminal, adjusts the coefficient according to the evaluation information, And emits the emotion index for each of the first biometric information, the second biometric information, and the environment information.
delete delete The illumination control apparatus according to claim 1, wherein the information collecting terminal uses a smart phone for collecting living body and environment information by using an application for collecting living body and environment information.
delete delete delete The personal taste analysis and profile generation unit may generate illumination control data for automatically adjusting the brightness and color of the LED illumination based on the sensibility index extracted according to the biometric information and the environment information. Lighting control device according to environment change.
The LED lighting controller generates numerical information for expressing the light of the LED lighting and generates information of Color Temperature, Brightness, and CRI (Color Rendering Index) A user interface for transmitting data to a lighting controller through an interface; A lighting controller connected to the user interface or another lighting module via a network to transmit and receive data, and transmitting data received from a user interface or another lighting module to a subsequent Color Mixer &Converter; And receives a control signal of the LED illumination from the lighting controller to determine a combination of colors according to the received control signal, and converts the given value into control signal data applicable to the RGB LED by a method of representing multiple colors and white colors A color mixer and a converter for transmitting the converted control signal data to a PWM signal generator; A PWM signal generator for generating a PWM signal for controlling individual LEDs based on the control signal data output from the color mixer and the converter and transmitting the PWM signal generated through the LED driver interface to control the LED arrays; An LED driver interface for transmitting the PWM signal generated from the PWM signal generator to the LED driver; And an LED driver for applying voltage and current to the LED module based on the PWM signal transmitted from the LED driver interface.
The LED lighting controller of claim 9, wherein the LED lighting controller senses a change in LED using a color sensor, a temperature sensor, and a humidity sensor, compares the sensed data with reference data, and stores the resultant data in a surrounding environment, And an LED sensing and compensating unit for transmitting the correction data to the lighting controller for correcting the LED characteristic conversion according to a characteristic change of the light emitted by the light source.

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